diff --git a/jupyter_notebooks/classifier_tests.ipynb b/jupyter_notebooks/classifier_tests.ipynb
index 25ac170..6861194 100644
--- a/jupyter_notebooks/classifier_tests.ipynb
+++ b/jupyter_notebooks/classifier_tests.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "a20b8991-06b8-4686-b296-aeb0d177125b",
    "metadata": {},
    "outputs": [],
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "32a509a0-ff60-4744-b0a6-7077c4a15bec",
    "metadata": {},
    "outputs": [
@@ -35,9 +35,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_11621/2846935186.py:45: UserWarning: Unknown pitch for 'cal-open'\n",
+      "/tmp/ipykernel_8132/2846935186.py:45: UserWarning: Unknown pitch for 'cal-open'\n",
       "  warnings.warn(f'Unknown pitch for {meta!r}')\n",
-      "/tmp/ipykernel_11621/2846935186.py:45: UserWarning: Unknown pitch for 'baseline-5-open'\n",
+      "/tmp/ipykernel_8132/2846935186.py:45: UserWarning: Unknown pitch for 'baseline-5-open'\n",
       "  warnings.warn(f'Unknown pitch for {meta!r}')\n"
      ]
     }
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "26ba6967-a405-49d5-b835-c3cb9276bc08",
    "metadata": {
     "scrolled": true
@@ -124,7 +124,7 @@
        "1264"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "62650fb7-6230-4883-ba9a-aad9e0c4b69e",
    "metadata": {
     "scrolled": true
@@ -148,10 +148,10 @@
        "         'attack-repeat-25-07-09-01-p0.3': 100,\n",
        "         'copy-interleave-25-07-11-01-p0.3': 63,\n",
        "         'short-within-trace-interleave-reference-25-07-11-04-p0.3': 42,\n",
+       "         'patch-interleave-25-07-11-02-p0.3': 40,\n",
+       "         'short-within-trace-interleave-modified-25-07-11-04-p0.3': 40,\n",
        "         'apply-patch-interleave-reference-25-07-11-03-p0.3': 40,\n",
        "         'apply-patch-interleave-modified-25-07-11-03-p0.3': 40,\n",
-       "         'short-within-trace-interleave-modified-25-07-11-04-p0.3': 40,\n",
-       "         'patch-interleave-25-07-11-02-p0.3': 40,\n",
        "         'simple-attack-25-07-07-01-p0.3': 33,\n",
        "         'simple-attack-25-07-07-01-p0.4': 33,\n",
        "         'Baseline-1-p0.3': 30,\n",
@@ -162,22 +162,22 @@
        "         'touch-interleave-touch-serial-5-25-07-02-01-p0.3': 23,\n",
        "         'attack-soldering-iron-25-07-08-01-p0.3': 22,\n",
        "         'patch-attack-25-07-09-02-p0.3': 21,\n",
-       "         'patch-attack-25-07-09-01-p0.3': 20,\n",
        "         'interleaved-25-07-02-01-p0.3': 20,\n",
        "         'interleaved-25-07-02-02-p0.3': 20,\n",
-       "         'touch-ground-interleave-touch-serial-5-25-07-02-01-p0.3': 20,\n",
        "         'hot-mesh-hot-serial-5-25-07-02-01-p0.3': 20,\n",
+       "         'touch-ground-interleave-touch-serial-5-25-07-02-01-p0.3': 20,\n",
+       "         'patch-attack-25-07-09-01-p0.3': 20,\n",
        "         'simple-attack-25-07-08-01-p0.3': 20,\n",
        "         'mods-25-07-01-01-p0.3': 16,\n",
        "         'mods-25-07-01-01-p0.4': 16,\n",
        "         'baseline-4-p0.4': 15,\n",
        "         'mods-25-07-01-01-p0.6': 15,\n",
        "         'mods-25-07-01-01-p1.0': 15,\n",
-       "         'attack-soldering-wire-25-07-08-01-p0.3': 12,\n",
        "         'mod-25-07-02-01-p0.3': 12,\n",
+       "         'attack-soldering-wire-25-07-08-01-p0.3': 12,\n",
        "         'baseline-repeat-25-07-02-01-p0.3': 11,\n",
-       "         'baseline-repeat-25-07-02-02-p0.3': 10,\n",
        "         'baseline-4-p0.3': 10,\n",
+       "         'baseline-repeat-25-07-02-02-p0.3': 10,\n",
        "         'environment-25-07-09-01-forest-x1c-p0.3': 10,\n",
        "         'environment-25-07-08-01-homeoffice-desktop-p0.3': 10,\n",
        "         'Baseline-2-alt-side-a-right-p0.4': 10,\n",
@@ -187,6 +187,12 @@
        "         'hot-mesh-hot-serial-5-25-07-02-01-p0.4': 10,\n",
        "         'environment-25-07-09-01-forest-x1c-p0.4': 10,\n",
        "         'environment-25-07-08-01-homeoffice-desktop-p0.4': 10,\n",
+       "         'baseline-6-p0.6': 10,\n",
+       "         'environment-25-07-09-01-forest-x1c-p0.6': 10,\n",
+       "         'environment-25-07-08-01-homeoffice-desktop-p0.6': 10,\n",
+       "         'baseline-6-p1.0': 10,\n",
+       "         'environment-25-07-09-01-forest-x1c-p1.0 ': 10,\n",
+       "         'environment-25-07-08-01-homeoffice-desktop-p1.0': 10,\n",
        "         'environment-25-07-08-01-office-p14s-p0.3': 10,\n",
        "         'environment-25-07-08-01-office-p14s-p0.4': 10,\n",
        "         'environment-25-07-08-01-office-p14s-p0.6': 10,\n",
@@ -199,19 +205,13 @@
        "         'environment-25-07-08-01-electronicslab-p14s-p0.4': 10,\n",
        "         'environment-25-07-08-01-electronicslab-p14s-p0.6': 10,\n",
        "         'environment-25-07-08-01-electronicslab-p14s-p1.0': 10,\n",
-       "         'environment-25-07-09-01-forest-x1c-p0.6': 10,\n",
-       "         'environment-25-07-08-01-homeoffice-desktop-p0.6': 10,\n",
-       "         'baseline-6-p0.6': 10,\n",
-       "         'environment-25-07-09-01-forest-x1c-p1.0 ': 10,\n",
-       "         'environment-25-07-08-01-homeoffice-desktop-p1.0': 10,\n",
-       "         'baseline-6-p1.0': 10,\n",
        "         'cal-open': 2,\n",
        "         'baseline-5-p0.6': 2,\n",
-       "         'baseline-4-p0.6': 1,\n",
-       "         'baseline-5-open': 1})"
+       "         'baseline-5-open': 1,\n",
+       "         'baseline-4-p0.6': 1})"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "50d21521-28d4-44da-b72f-223212424c9d",
    "metadata": {},
    "outputs": [
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "eda2dc4b-1641-4fce-a4ec-cba08d8df9f9",
    "metadata": {
     "scrolled": true
@@ -268,6 +268,72 @@
      "data": {
       "text/plain": [
        "[{'meta': 'mods-25-07-01-01-p0.3',\n",
+       "  'specimen': 'SP-PITCH-V2-0028',\n",
+       "  'mcu': '203632423631500f003f0034',\n",
+       "  'chip': 'pi3hdx12211',\n",
+       "  'timestamp': '2025-07-01T13:27:37.629Z',\n",
+       "  'mods': ['short_within_trace'],\n",
+       "  'mesh_id': 'M-SP-PITCH-V2-0028-0.3',\n",
+       "  'mesh_layout': 'SP-PITCH-V2',\n",
+       "  'pitch': '0.3',\n",
+       "  'serial': 28,\n",
+       "  'baseline': False},\n",
+       " {'meta': 'mods-25-07-01-01-p0.3',\n",
+       "  'specimen': 'SP-PITCH-V2-0030',\n",
+       "  'mcu': '203632423631500f003f0034',\n",
+       "  'chip': 'pi3hdx12211',\n",
+       "  'timestamp': '2025-07-01T13:26:21.618Z',\n",
+       "  'mods': ['short_within_trace'],\n",
+       "  'mesh_id': 'M-SP-PITCH-V2-0030-0.3',\n",
+       "  'mesh_layout': 'SP-PITCH-V2',\n",
+       "  'pitch': '0.3',\n",
+       "  'serial': 30,\n",
+       "  'baseline': False},\n",
+       " {'meta': 'mods-25-07-01-01-p0.3',\n",
+       "  'specimen': 'SP-PITCH-V2-0027',\n",
+       "  'mcu': '203632423631500f003f0034',\n",
+       "  'chip': 'pi3hdx12211',\n",
+       "  'timestamp': '2025-07-01T13:27:57.358Z',\n",
+       "  'mods': ['short_within_trace'],\n",
+       "  'mesh_id': 'M-SP-PITCH-V2-0027-0.3',\n",
+       "  'mesh_layout': 'SP-PITCH-V2',\n",
+       "  'pitch': '0.3',\n",
+       "  'serial': 27,\n",
+       "  'baseline': False},\n",
+       " {'meta': 'mods-25-07-01-01-p0.3',\n",
+       "  'specimen': 'SP-PITCH-V2-0026',\n",
+       "  'mcu': '203632423631500f003f0034',\n",
+       "  'chip': 'pi3hdx12211',\n",
+       "  'timestamp': '2025-07-01T13:28:17.183Z',\n",
+       "  'mods': ['short_within_trace'],\n",
+       "  'mesh_id': 'M-SP-PITCH-V2-0026-0.3',\n",
+       "  'mesh_layout': 'SP-PITCH-V2',\n",
+       "  'pitch': '0.3',\n",
+       "  'serial': 26,\n",
+       "  'baseline': False},\n",
+       " {'meta': 'mods-25-07-01-01-p0.3',\n",
+       "  'specimen': 'SP-PITCH-V2-0029',\n",
+       "  'mcu': '203632423631500f003f0034',\n",
+       "  'chip': 'pi3hdx12211',\n",
+       "  'timestamp': '2025-07-01T13:27:14.007Z',\n",
+       "  'mods': ['short_within_trace'],\n",
+       "  'mesh_id': 'M-SP-PITCH-V2-0029-0.3',\n",
+       "  'mesh_layout': 'SP-PITCH-V2',\n",
+       "  'pitch': '0.3',\n",
+       "  'serial': 29,\n",
+       "  'baseline': False},\n",
+       " {'meta': 'mods-25-07-01-01-p0.3',\n",
+       "  'specimen': 'SP-PITCH-V2-0029',\n",
+       "  'mcu': '203632423631500f003f0034',\n",
+       "  'chip': 'pi3hdx12211',\n",
+       "  'timestamp': '2025-07-01T14:20:23.363Z',\n",
+       "  'mods': ['short_within_trace'],\n",
+       "  'mesh_id': 'M-SP-PITCH-V2-0029-0.3',\n",
+       "  'mesh_layout': 'SP-PITCH-V2',\n",
+       "  'pitch': '0.3',\n",
+       "  'serial': 29,\n",
+       "  'baseline': False},\n",
+       " {'meta': 'mods-25-07-01-01-p0.3',\n",
        "  'specimen': 'SP-PITCH-V2-0035',\n",
        "  'mcu': '203632423631500f003f0034',\n",
        "  'chip': 'pi3hdx12211',\n",
@@ -323,72 +389,6 @@
        "  'serial': 31,\n",
        "  'baseline': False},\n",
        " {'meta': 'mods-25-07-01-01-p0.3',\n",
-       "  'specimen': 'SP-PITCH-V2-0030',\n",
-       "  'mcu': '203632423631500f003f0034',\n",
-       "  'chip': 'pi3hdx12211',\n",
-       "  'timestamp': '2025-07-01T13:26:21.618Z',\n",
-       "  'mods': ['short_within_trace'],\n",
-       "  'mesh_id': 'M-SP-PITCH-V2-0030-0.3',\n",
-       "  'mesh_layout': 'SP-PITCH-V2',\n",
-       "  'pitch': '0.3',\n",
-       "  'serial': 30,\n",
-       "  'baseline': False},\n",
-       " {'meta': 'mods-25-07-01-01-p0.3',\n",
-       "  'specimen': 'SP-PITCH-V2-0029',\n",
-       "  'mcu': '203632423631500f003f0034',\n",
-       "  'chip': 'pi3hdx12211',\n",
-       "  'timestamp': '2025-07-01T13:27:14.007Z',\n",
-       "  'mods': ['short_within_trace'],\n",
-       "  'mesh_id': 'M-SP-PITCH-V2-0029-0.3',\n",
-       "  'mesh_layout': 'SP-PITCH-V2',\n",
-       "  'pitch': '0.3',\n",
-       "  'serial': 29,\n",
-       "  'baseline': False},\n",
-       " {'meta': 'mods-25-07-01-01-p0.3',\n",
-       "  'specimen': 'SP-PITCH-V2-0029',\n",
-       "  'mcu': '203632423631500f003f0034',\n",
-       "  'chip': 'pi3hdx12211',\n",
-       "  'timestamp': '2025-07-01T14:20:23.363Z',\n",
-       "  'mods': ['short_within_trace'],\n",
-       "  'mesh_id': 'M-SP-PITCH-V2-0029-0.3',\n",
-       "  'mesh_layout': 'SP-PITCH-V2',\n",
-       "  'pitch': '0.3',\n",
-       "  'serial': 29,\n",
-       "  'baseline': False},\n",
-       " {'meta': 'mods-25-07-01-01-p0.3',\n",
-       "  'specimen': 'SP-PITCH-V2-0028',\n",
-       "  'mcu': '203632423631500f003f0034',\n",
-       "  'chip': 'pi3hdx12211',\n",
-       "  'timestamp': '2025-07-01T13:27:37.629Z',\n",
-       "  'mods': ['short_within_trace'],\n",
-       "  'mesh_id': 'M-SP-PITCH-V2-0028-0.3',\n",
-       "  'mesh_layout': 'SP-PITCH-V2',\n",
-       "  'pitch': '0.3',\n",
-       "  'serial': 28,\n",
-       "  'baseline': False},\n",
-       " {'meta': 'mods-25-07-01-01-p0.3',\n",
-       "  'specimen': 'SP-PITCH-V2-0027',\n",
-       "  'mcu': '203632423631500f003f0034',\n",
-       "  'chip': 'pi3hdx12211',\n",
-       "  'timestamp': '2025-07-01T13:27:57.358Z',\n",
-       "  'mods': ['short_within_trace'],\n",
-       "  'mesh_id': 'M-SP-PITCH-V2-0027-0.3',\n",
-       "  'mesh_layout': 'SP-PITCH-V2',\n",
-       "  'pitch': '0.3',\n",
-       "  'serial': 27,\n",
-       "  'baseline': False},\n",
-       " {'meta': 'mods-25-07-01-01-p0.3',\n",
-       "  'specimen': 'SP-PITCH-V2-0026',\n",
-       "  'mcu': '203632423631500f003f0034',\n",
-       "  'chip': 'pi3hdx12211',\n",
-       "  'timestamp': '2025-07-01T13:28:17.183Z',\n",
-       "  'mods': ['short_within_trace'],\n",
-       "  'mesh_id': 'M-SP-PITCH-V2-0026-0.3',\n",
-       "  'mesh_layout': 'SP-PITCH-V2',\n",
-       "  'pitch': '0.3',\n",
-       "  'serial': 26,\n",
-       "  'baseline': False},\n",
-       " {'meta': 'mods-25-07-01-01-p0.3',\n",
        "  'specimen': 'SP-PITCH-V2-0025',\n",
        "  'mcu': '203632423631500f003f0034',\n",
        "  'chip': 'pi3hdx12211',\n",
@@ -445,7 +445,7 @@
        "  'baseline': False}]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -456,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "54565f84-158c-4a91-9420-3b047c46b369",
    "metadata": {
     "scrolled": true
@@ -465,29 +465,29 @@
     {
      "data": {
       "text/plain": [
-       "{0.3: ['2025-07-02T15:01:37.534Z',\n",
-       "  '2025-07-02T15:01:44.213Z',\n",
-       "  '2025-07-02T15:01:50.886Z',\n",
-       "  '2025-07-01T13:00:21.356Z',\n",
-       "  '2025-07-07T11:12:08.214Z'],\n",
-       " 0.4: ['2025-07-08T19:01:27.348Z',\n",
+       "{0.3: ['2025-07-09T14:51:30.023Z',\n",
+       "  '2025-07-09T14:51:36.870Z',\n",
+       "  '2025-07-09T14:51:43.951Z',\n",
+       "  '2025-07-09T14:51:51.030Z',\n",
+       "  '2025-07-09T14:51:57.358Z'],\n",
+       " 0.4: ['2025-07-08T19:01:13.514Z',\n",
+       "  '2025-07-08T19:01:20.427Z',\n",
+       "  '2025-07-08T19:01:27.348Z',\n",
        "  '2025-07-08T19:01:34.785Z',\n",
-       "  '2025-07-08T19:01:40.874Z',\n",
-       "  '2025-07-01T13:03:55.562Z',\n",
-       "  '2025-07-07T11:22:15.688Z'],\n",
-       " 0.6: ['2025-07-08T19:02:42.237Z',\n",
+       "  '2025-07-08T19:01:40.874Z'],\n",
+       " 0.6: ['2025-07-08T19:02:28.753Z',\n",
+       "  '2025-07-08T19:02:35.523Z',\n",
+       "  '2025-07-08T19:02:42.237Z',\n",
        "  '2025-07-08T19:02:49.215Z',\n",
-       "  '2025-07-08T19:02:55.878Z',\n",
-       "  '2025-07-01T13:08:49.451Z',\n",
-       "  '2025-07-01T13:19:55.971Z'],\n",
-       " 1.0: ['2025-07-08T19:03:50.835Z',\n",
+       "  '2025-07-08T19:02:55.878Z'],\n",
+       " 1.0: ['2025-07-08T19:03:43.821Z',\n",
+       "  '2025-07-08T19:03:50.835Z',\n",
        "  '2025-07-08T19:03:57.466Z',\n",
        "  '2025-07-08T19:04:04.237Z',\n",
-       "  '2025-07-08T19:04:10.983Z',\n",
-       "  '2025-07-01T13:20:32.218Z']}"
+       "  '2025-07-08T19:04:10.983Z']}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -527,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "b8d7c4e0-7860-4058-8fd1-e7948ac06674",
    "metadata": {},
    "outputs": [
@@ -537,7 +537,7 @@
        "5"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -549,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "72b6defa-c833-4f5e-9166-42d0654625bc",
    "metadata": {},
    "outputs": [
@@ -557,16 +557,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Baseline threshold set at 0.999937\n",
+      "Baseline threshold set at 0.999941\n",
       "Within class: 1±8.75e-06 max: 1\n",
-      "Cross class: 1±7.56e-05 max: 1\n",
-      "Baseline false negative probability: 0.000000000108\n",
-      "Baseline false positive probability: 0.231346773343\n"
+      "Cross class: 1±7.32e-05 max: 1\n",
+      "Baseline false negative probability: 0.000000002181\n",
+      "Baseline false positive probability: 0.241450931829\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -576,7 +576,7 @@
     },
     {
      "data": {
-      "image/png": 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CV0r+8jerU/CeH0+BVWbOtToFr1Tf9pDVKXjMWVOl/177E5WWljbLtNGlNP4O/M8j5326BujF3m1N/1n8HR0gAADMVm94PHV12bHgMTZCBAAAAYcOEAAAJjN8uAjaZ4upAwwdIAAAEHDoAAEAYDI6QNajAAIAwGyGDxdBm/sw93WDKTAAABBw6AABAGAyo75eRn29z8aC5+gAAQCAgEMHCAAAk7EI2noUQAAAmI2doC3HFBgAAAg4dIAAADAZU2DW86gDlJWVpT59+igqKkpRUVFKTU3Ve++911y5AQAANAuPOkAdO3bUCy+8oG7dukmS1qxZo9GjR+vgwYPq1atXsyQIAMD1hg6Q9TwqgEaNGuX2efHixcrKytKuXbsogAAAuFIsgrac12uA6urqtGHDBlVWVio1NfWScQ6HQw6Hw/W5rKzM20sCAAD4hMcF0OHDh5Wamqrq6mpFRERo06ZN6tmz5yXjMzIy9Oyzz15VkgAAXE/YCdp6Hj8Gn5ycrEOHDmnXrl2aMWOGJk6cqPz8/EvGp6enq7S01HUUFhZeVcIAAPi7xjVAvjrgOY87QKGhoa5F0AMGDNDevXu1bNkyrVix4qLxdrtddrv96rIEAADwoaveB8gwDLc1PgAA4FsY8t3iZRpAXvGoAJo3b55GjBihhIQElZeXKycnR9u2bVNubm5z5QcAAOBzHhVAZ86c0YQJE1RcXKzo6Gj16dNHubm5uvPOO5srPwAArjvsA2Q9jwqgVatWNVceAAAEDMMwZBg+KoB8NE6g4WWoAAAg4PAyVAAAzMZO0JajAwQAAAIOHSAAAEzGTtDWowACAMBkPAVmPabAAABAwKEDBACA2VgEbTk6QAAAIODQAQIAwGSsAbIeBRAAACajALIeU2AAACDg0AECAMBkhuHDDhDvAvMKBRAAAGYzjIbDV2PBY0yBAQCAgEMHCAAAk/EqDOvRAQIAAAHHsg5Q+SdnVNPqK6su77Wonu2tTsErtRUOq1PwWmRMhNUpeK3gj/usTsEr59a9bnUKXrM5q6xOwWub13exOgWvvP6LFVan4LEqZ63+28oEfPgYPDtBe4cpMAAAzMarMCzHFBgAAAg4dIAAADAZO0Fbjw4QAAAIOHSAAAAwmWEYPtvBmZ2gvUMBBACA2erlw0XQvhkm0DAFBgAAAg4dIAAATMYiaOvRAQIAAAGHDhAAACbjXWDWowACAMBshtFw+GoseIwpMAAAEHDoAAEAYDLD8OEiaDpAXqEAAgDAbLwM1XJMgQEAgIBDBwgAAJOxD5D16AABAICAQwcIAACT8TJU63nUAcrKylKfPn0UFRWlqKgopaam6r333muu3AAAuD7V1/v2gMc8KoA6duyoF154Qfv27dO+ffv0wx/+UKNHj9bRo0ebKz8AAACf82gKbNSoUW6fFy9erKysLO3atUu9evXyaWIAAFyvWARtPa/XANXV1WnDhg2qrKxUamqqL3MCAABoVh4XQIcPH1Zqaqqqq6sVERGhTZs2qWfPnpeMdzgccjgcrs9lZWXeZQoAwHWCDpD1PH4MPjk5WYcOHdKuXbs0Y8YMTZw4Ufn5+ZeMz8jIUHR0tOtISEi4qoQBAPB7jS9D9dUBj3lcAIWGhqpbt24aMGCAMjIy1LdvXy1btuyS8enp6SotLXUdhYWFV5UwAAC4OsuXL1dSUpLCwsKUkpKiHTt2XDb+1VdfVY8ePRQeHq7k5GStXbvW7etOp1OLFi1S165dFRYWpr59+yo3N9ctpry8XDNnzlRiYqLCw8M1ePBg7d271y3mzJkzmjRpkuLj49WyZUvdddddOn78uFvMkCFDZLPZ3I5x48Z5fA+ueh8gwzDcpri+yW63y263X+1lAAC4blg5Bfb2229r5syZWr58uW699VatWLFCI0aMUH5+vjp16tQkPisrS+np6Vq5cqUGDhyoPXv2aNq0aWrdurXr4aj58+dr3bp1Wrlypbp3766tW7fq/vvv186dO9WvXz9J0tSpU3XkyBFlZ2crPj5e69at07Bhw5Sfn68OHTrIMAzdd999CgkJ0ebNmxUVFaXMzExXTKtWrVw5TZs2TYsWLXJ9Dg8P9/i+edQBmjdvnnbs2KEvvvhChw8f1tNPP61t27Zp/PjxHl8YAACYLzMzU1OmTNHUqVPVo0cPvfTSS0pISFBWVtZF47Ozs/XII49o7Nix6tKli8aNG6cpU6boxRdfdIuZN2+eRo4cqS5dumjGjBlKS0vT0qVLJUlVVVXauHGjlixZottvv13dunXTwoULlZSU5Lru8ePHtWvXLmVlZWngwIFKTk7W8uXLVVFRofXr17vl1LJlS7Vv3951REdHe3wfPCqAzpw5owkTJig5OVl33HGHdu/erdzcXN15550eXxgAgEDV2AHy1XGlampqtH//fg0fPtzt/PDhw7Vz586Lfo/D4VBYWJjbufDwcO3Zs0dOp/OyMR999JEkqba2VnV1dZeNaZxN+npMcHCwQkNDXTGN3nrrLcXExKhXr16aPXu2ysvLr+jn/zqPpsBWrVrl8QUAAMA3GPUNh6/GUtOnrC+2BOXcuXOqq6tTbGys2/nY2FiVlJRcdPi0tDS9/vrruu+++9S/f3/t379fq1evltPp1Llz5xQXF6e0tDRlZmbq9ttvV9euXfWXv/xFmzdvVl1dnSQpMjJSqampeu6559SjRw/FxsZq/fr12r17t2688UZJUvfu3ZWYmKj09HStWLFCrVq1UmZmpkpKSlRcXOzKZ/z48UpKSlL79u115MgRpaen6+OPP1ZeXp5Ht42XoQIAcB1ISEhwe+o6IyPjkrE2m83ts2EYTc41WrBggUaMGKFbbrlFISEhGj16tCZNmiSpoUMjScuWLdONN96o7t27KzQ0VI899pgmT57s+rrUME1mGIY6dOggu92ul19+WQ8++KArJiQkRBs3btSxY8fUpk0btWzZUtu2bdOIESPcxpk2bZqGDRum3r17a9y4cfrjH/+o999/XwcOHPDoflEAAQBgMsPw4TTYv2bACgsL3Z66Tk9Pb3LdmJgYBQcHN+n2nD17tklXqFF4eLhWr16tr776Sl988YUKCgrUuXNnRUZGKiYmRpLUrl07vfPOO6qsrNTJkyf1ySefKCIiQklJSa5xunbtqu3bt6uiokKFhYWuKbSvx6SkpOjQoUP68ssvVVxcrNzcXJ0/f94t5pv69++vkJCQJk+LfRsKIAAArgONLypvPC72BHZoaKhSUlKaTBfl5eVp8ODBlx0/JCREHTt2VHBwsHJycnTPPfcoKMi9jAgLC1OHDh1UW1urjRs3avTo0U3GadWqleLi4nThwgVt3br1ojHR0dFq166djh8/rn379l00ptHRo0fldDoVFxd32fy/6aofgwcAAJ6x8jH4WbNmacKECRowYIBSU1P12muvqaCgQNOnT5fUsH9fUVGRa6+fY8eOac+ePRo0aJAuXLigzMxMHTlyRGvWrHGNuXv3bhUVFenmm29WUVGRFi5cqPr6es2dO9cVs3XrVhmGoeTkZH322WeaM2eOkpOTNXnyZFfMhg0b1K5dO3Xq1EmHDx/WE088ofvuu8+1aPvvf/+73nrrLY0cOVIxMTHKz8/XU089pX79+unWW2/16D5QAAEAYDZf7uDs4Thjx47V+fPntWjRIhUXF6t3797asmWLEhMTJUnFxcUqKChwxdfV1Wnp0qX69NNPFRISoqFDh2rnzp3q3LmzK6a6ulrz58/XiRMnFBERoZEjRyo7O1s33HCDK6ZxWu7UqVNq06aNxowZo8WLFyskJMQVU1xcrFmzZunMmTOKi4vTQw89pAULFri+Hhoaqr/85S9atmyZKioqlJCQoLvvvlvPPPOM2zqhK2EzDHP30C4rK1N0dLQeffdT2VtFmnlpn4jq2d7qFLxSW3HpzSqvdcEtQ61OwWsFf9xndQpeObfudatT8JrNWWV1Cl7b/Earbw+6Br3+i6NWp+CxKmetfpa7S6WlpYqKijLtuo2/Ax9cnKfQMN/8+66prtTvn77T9J/F39EBAgDAZLwM1XoUQAAAmK2+vuHw1VjwGE+BAQCAgEMHCAAAkxmGIV8twTV5Ke91gw4QAAAIOHSAAAAwGYugrUcBBACA2SzcBwgNmAIDAAABhw4QAAAmYwrMenSAAABAwLGsA2Rv21L2CP/b9t1fXynRIqLpW4H9hfNL/321QWTSd6xOwSsXenv2UsFrSVCLkG8Pukbt/q/fW52CV7Yd97//vjjrPHtvlM+xEaLlmAIDAMBsPtwHiEXQ3mEKDAAABBw6QAAAmM0wJF8tXqYD5BU6QAAAIODQAQIAwGS8C8x6FEAAAJjMqK+X4aOnt3w1TqBhCgwAAAQcOkAAAJiNd4FZjgIIAACTGfW+e4WFwQyYV5gCAwAAAYcOEAAAZmMKzHJ0gAAAQMChAwQAgMl4DN56FEAAAJiNKTDLMQUGAAACDh0gAABMZtQbPnwMng6QN+gAAQCAgEMHCAAAszXshOi7seAxCiAAAEzGFJj1PJoCy8rKUp8+fRQVFaWoqCilpqbqvffea67cAAAAmoVHHaCOHTvqhRdeULdu3SRJa9as0ejRo3Xw4EH16tWrWRIEAOC6wxSY5TwqgEaNGuX2efHixcrKytKuXbsogAAAgN/weg1QXV2dNmzYoMrKSqWmpl4yzuFwyOFwuD6XlZV5e0kAAK4LhuHDNUBshOgVjwugw4cPKzU1VdXV1YqIiNCmTZvUs2fPS8ZnZGTo2WefvaokAQC4rtQbDYevxoLHPN4HKDk5WYcOHdKuXbs0Y8YMTZw4Ufn5+ZeMT09PV2lpqesoLCy8qoQBAACulscdoNDQUNci6AEDBmjv3r1atmyZVqxYcdF4u90uu91+dVkCAHAdMYx6GT5avOyrcQLNVe8DZBiG2xofAADwLZgCs5xHBdC8efM0YsQIJSQkqLy8XDk5Odq2bZtyc3ObKz8AAACf86gAOnPmjCZMmKDi4mJFR0erT58+ys3N1Z133tlc+QEAcN1hCsx6HhVAq1ataq48AAAATMO7wAAAMBtrgCxHAQQAgMkMw/DhFBgFkDc83gcIAADA39EBAgDAbEyBWY4OEAAACDh0gAAAMJtR33D4aix4jAIIAACTGfX1Mup9tAjaR+MEGqbAAABAwKEDBACA2Qyj4fDVWPAYHSAAABBw6AABAGAyw/DhGiAWQXuFAggAALPxFJjlmAIDAAABhw4QAABmYydoy1EAAQBgMsOo9+HLUJkC8wZTYAAAIOBY1gFyXKiSavyvARUZE2F1Cl5xfllldQpeC7kh3OoUvFb++VmrU/CK8/jHVqfgtaDqMqtT8FrfzPZWp+CVW/d9YnUKHqty1uoPxy1MoL6+4fDVWPAYHSAAABBw/K8FAwCAvzMMHz4GzyJob1AAAQBgMqPekOGjp7d8NU6gYQoMAAAEHDpAAACYjZ2gLUcHCAAABBw6QAAAmI3H4C1HBwgAALM1ToH56vDQ8uXLlZSUpLCwMKWkpGjHjh2XjX/11VfVo0cPhYeHKzk5WWvXrnX7utPp1KJFi9S1a1eFhYWpb9++ys3NdYspLy/XzJkzlZiYqPDwcA0ePFh79+51izlz5owmTZqk+Ph4tWzZUnfddZeOH3ffsMnhcOjxxx9XTEyMWrVqpXvvvVenTp3y+B5QAAEAEEDefvttzZw5U08//bQOHjyo73//+xoxYoQKCgouGp+VlaX09HQtXLhQR48e1bPPPqtHH31Uf/rTn1wx8+fP14oVK/Tb3/5W+fn5mj59uu6//34dPHjQFTN16lTl5eUpOztbhw8f1vDhwzVs2DAVFRVJkgzD0H333acTJ05o8+bNOnjwoBITEzVs2DBVVla6xpk5c6Y2bdqknJwcffTRR6qoqNA999yjuro6j+6DzTDM3UCgrKxM0dHRevS/j8neKtLMS/tE5He/Y3UKXqktq7Y6Ba/5807QJ/+w99uDrkFnN+RYnYLX/Hkn6D9kWp2Bd97I8M+doJ96f7dKS0sVFRVl2nUbfwfelbZQISFhPhnT6axW7taFV/yzDBo0SP3791dWVpbrXI8ePXTfffcpIyOjSfzgwYN166236le/+pXr3MyZM7Vv3z599NFHkqT4+Hg9/fTTevTRR10x9913nyIiIrRu3TpVVVUpMjJSmzdv1t133+2Kufnmm3XPPffo+eef17Fjx5ScnKwjR46oV69ekqS6ujp95zvf0YsvvqipU6eqtLRU7dq1U3Z2tsaOHStJOn36tBISErRlyxalpaVd8X2jAwQAQICoqanR/v37NXz4cLfzw4cP186dOy/6PQ6HQ2Fh7sVaeHi49uzZI6fTedmYxgKptrZWdXV1l41xOByS5BYTHBys0NBQV8z+/fvldDrd8o+Pj1fv3r0vmf+lUAABAGCyxrfB++qQGrpLXz8aC4qvO3funOrq6hQbG+t2PjY2ViUlJRfNNS0tTa+//rr2798vwzC0b98+rV69Wk6nU+fOnXPFZGZm6vjx46qvr1deXp42b96s4uJiSVJkZKRSU1P13HPP6fTp06qrq9O6deu0e/duV0z37t2VmJio9PR0XbhwQTU1NXrhhRdUUlLiiikpKVFoaKhat259xflfCgUQAABmMwzfHpISEhIUHR3tOi42ndXIZrN9Ix2jyblGCxYs0IgRI3TLLbcoJCREo0eP1qRJkyQ1dGgkadmyZbrxxhvVvXt3hYaG6rHHHtPkyZNdX5ek7OxsGYahDh06yG636+WXX9aDDz7oigkJCdHGjRt17NgxtWnTRi1bttS2bds0YsQIt3Eufjsvnf+lUAABAHAdKCwsVGlpqetIT09vEhMTE6Pg4OAm3ZKzZ8826Qo1Cg8P1+rVq/XVV1/piy++UEFBgTp37qzIyEjFxMRIktq1a6d33nlHlZWVOnnypD755BNFREQoKSnJNU7Xrl21fft2VVRUqLCw0DWF9vWYlJQUHTp0SF9++aWKi4uVm5ur8+fPu2Lat2+vmpoaXbhw4YrzvxQKIAAAzGbU/3svoKs9/jUFFhUV5XbY7fYmlw0NDVVKSory8vLczufl5Wnw4MGXTTkkJEQdO3ZUcHCwcnJydM899ygoyL2MCAsLU4cOHVRbW6uNGzdq9OjRTcZp1aqV4uLidOHCBW3duvWiMdHR0WrXrp2OHz+uffv2uWJSUlIUEhLiln9xcbGOHDnyrfl/ExshAgBgNgtfhTFr1ixNmDBBAwYMUGpqql577TUVFBRo+vTpkqT09HQVFRW59vo5duyY9uzZo0GDBunChQvKzMzUkSNHtGbNGteYu3fvVlFRkW6++WYVFRVp4cKFqq+v19y5c10xW7dulWEYSk5O1meffaY5c+YoOTlZkydPdsVs2LBB7dq1U6dOnXT48GE98cQTuu+++1yLnqOjozVlyhQ99dRTatu2rdq0aaPZs2frpptu0rBhwzy6DxRAAAAEkLFjx+r8+fNatGiRiouL1bt3b23ZskWJiYmSGjoqX98TqK6uTkuXLtWnn36qkJAQDR06VDt37lTnzp1dMdXV1Zo/f75OnDihiIgIjRw5UtnZ2brhhhtcMY3TcqdOnVKbNm00ZswYLV68WCEhIa6Y4uJizZo1S2fOnFFcXJweeughLViwwC3/3/zmN2rRooUeeOABVVVV6Y477tCbb775reuEvol9gDzEPkDmYx8g87EPkDXYB8g8Vu8DlDb05wpp0XSKyhvOWoe2fvCC6T+Lv/NoDVBWVpb69Onjml9MTU3Ve++911y5AQAANAuPpsA6duyoF154Qd26dZMkrVmzRqNHj9bBgwdduzYCAIBvYeEaIDTwqAAaNWqU2+fFixcrKytLu3btogACAOBKUQBZzutF0HV1ddqwYYMqKyuVmprqy5wAAACalccF0OHDh5Wamqrq6mpFRERo06ZN6tmz5yXjHQ6H23bcZWX+u0ARAACfaNzDx1djwWMeb4SYnJysQ4cOadeuXZoxY4YmTpyo/Pz8S8ZnZGS4bc2dkJBwVQkDAABcLY8LoNDQUHXr1k0DBgxQRkaG+vbtq2XLll0yPj093W1r7sLCwqtKGAAAv9e4BshXBzx21RshGoZx0TfONrLb7RfdjhsAgIDFImjLeVQAzZs3TyNGjFBCQoLKy8uVk5Ojbdu2KTc3t7nyAwAA8DmPCqAzZ85owoQJKi4uVnR0tPr06aPc3FzdeeedzZUfAADXHxZBW86jAmjVqlXNlQcAAIBpeBkqAABmYw2Q5SiAAAAwGwWQ5Tx+DB4AAMDf0QECAMBshuHDDpDhm3ECDAUQAABmYwrMckyBAQCAgEMHCAAAk9nqDdnqfTN15atxAg0dIAAAEHDoAAEAYDpfvsSUNUDeoAACAMBsLIK2HFNgAAAg4NABAgDAbHSALEcHCAAABBw6QAAAmMxm1Mvmo86Nr8YJNBRAAACYjSkwyzEFBgAAAg4dIAAAzEYHyHKWFUAt46Nlj4yy6vJeK/jjPqtT8Epk0nesTsFr5Z+ftToFryU+MNDqFLzSoqXd6hS8Fh4bbXUKXvuPBb+zOgWvfOdHmVan4LGa6grp/TusTgMWogMEAIDJbDJkk4/eBeajcQINBRAAAGYzDB9OgVEAeYNF0AAAIODQAQIAwGTsA2Q9OkAAACDg0AECAMBshuG7tTusAfIKBRAAACZjCsx6TIEBAICAQwcIAADT1f/r8NVY8BQFEAAAJrMZhmw+Wrvjq3ECDVNgAAAg4NABAgDAbLwM1XJ0gAAAQMChAwQAgMl4Gar1KIAAADAZ+wBZjykwAAAQcOgAAQBgOuNfh6/GgqfoAAEAgIDjUQGUlZWlPn36KCoqSlFRUUpNTdV7773XXLkBAHBdalwE7asDnvNoCqxjx4564YUX1K1bN0nSmjVrNHr0aB08eFC9evVqlgQBALjesAjaeh4VQKNGjXL7vHjxYmVlZWnXrl0UQAAAwG94vQi6rq5OGzZsUGVlpVJTU32ZEwAA1zX2AbKexwXQ4cOHlZqaqurqakVERGjTpk3q2bPnJeMdDoccDofrc1lZmXeZAgAA+IjHT4ElJyfr0KFD2rVrl2bMmKGJEycqPz//kvEZGRmKjo52HQkJCVeVMAAA/s4mXy6Ehjc8LoBCQ0PVrVs3DRgwQBkZGerbt6+WLVt2yfj09HSVlpa6jsLCwqtKGAAAf8dTYNa76o0QDcNwm+L6JrvdLrvdfrWXAQAA8BmPCqB58+ZpxIgRSkhIUHl5uXJycrRt2zbl5uY2V34AAFx3bIYhm+GjRdA+GifQeFQAnTlzRhMmTFBxcbGio6PVp08f5ebm6s4772yu/AAAuO7YVC+bfLQPkI/GCTQeFUCrVq1qrjwAAABMw8tQAQAwnS8XLzMF5g1ehgoAAAIOHSAAAExm+9fhq7HgOQogAABMZrMZstl89BSYj8YJNEyBAQCAgEMHCAAAk/EyVOvRAQIAAAGHDhAAACajA2Q9OkAAAJjM6pehLl++XElJSQoLC1NKSop27Nhx2fhXX31VPXr0UHh4uJKTk7V27Vq3rzudTi1atEhdu3ZVWFiY+vbt2+Q1WeXl5Zo5c6YSExMVHh6uwYMHa+/evW4xFRUVeuyxx9SxY0eFh4erR48eysrKcosZMmSIbDab2zFu3DiP7wEdIAAAAsjbb7+tmTNnavny5br11lu1YsUKjRgxQvn5+erUqVOT+KysLKWnp2vlypUaOHCg9uzZo2nTpql169YaNWqUJGn+/Plat26dVq5cqe7du2vr1q26//77tXPnTvXr10+SNHXqVB05ckTZ2dmKj4/XunXrNGzYMOXn56tDhw6SpCeffFIffPCB1q1bp86dO+vPf/6zfvrTnyo+Pl6jR4925TRt2jQtWrTI9Tk8PNzj+0AHCAAAk9l8fHgiMzNTU6ZM0dSpU9WjRw+99NJLSkhIaNJpaZSdna1HHnlEY8eOVZcuXTRu3DhNmTJFL774olvMvHnzNHLkSHXp0kUzZsxQWlqali5dKkmqqqrSxo0btWTJEt1+++3q1q2bFi5cqKSkJLfr/vWvf9XEiRM1ZMgQde7cWT/5yU/Ut29f7du3zy2nli1bqn379q4jOjraw7tAAQQAwHWhrKzM7XA4HE1iampqtH//fg0fPtzt/PDhw7Vz586LjutwOBQWFuZ2Ljw8XHv27JHT6bxszEcffSRJqq2tVV1d3WVjJOm2227Tu+++q6KiIhmGoQ8++EDHjh1TWlqa2/e99dZbiomJUa9evTR79myVl5df7tZcFAUQAAAma9wI0VeHJCUkJCg6Otp1ZGRkNLnuuXPnVFdXp9jYWLfzsbGxKikpuWiuaWlpev3117V//34ZhqF9+/Zp9erVcjqdOnfunCsmMzNTx48fV319vfLy8rR582YVFxdLkiIjI5WamqrnnntOp0+fVl1dndatW6fdu3e7YiTp5ZdfVs+ePdWxY0eFhobqrrvu0vLly3Xbbbe5YsaPH6/169dr27ZtWrBggTZu3Kgf/ehHHv87YA0QAAAmC5KhIB89vdU4TmFhoaKiolzn7Xb7Jb/HZnOfODMMo8m5RgsWLFBJSYluueUWGYah2NhYTZo0SUuWLFFwcLAkadmyZZo2bZq6d+8um82mrl27avLkyXrjjTdc42RnZ+vhhx9Whw4dFBwcrP79++vBBx/UgQMHXDEvv/yydu3apXfffVeJiYn68MMP9dOf/lRxcXEaNmyYpIb1P4169+6tG2+8UQMGDNCBAwfUv3//K71tdIAAALgeREVFuR0XK4BiYmIUHBzcpNtz9uzZJl2hRuHh4Vq9erW++uorffHFFyooKFDnzp0VGRmpmJgYSVK7du30zjvvqLKyUidPntQnn3yiiIgIJSUlucbp2rWrtm/froqKChUWFrqm0BpjqqqqNG/ePGVmZmrUqFHq06ePHnvsMY0dO1a//vWvL/lz9+/fXyEhITp+/LhH94sCCAAAk1m1CDo0NFQpKSnKy8tzO5+Xl6fBgwdf9ntDQkLUsWNHBQcHKycnR/fcc4+CgtzLiLCwMHXo0EG1tbXauHGj25NbjVq1aqW4uDhduHBBW7dudcU4nU45nc4mYwYHB6u+vv6SeR09elROp1NxcXGXzf+bLJsCO7P9U4WGR1h1ea+dW/e61Sl45ULvW61OwWvO4x9bnYLXWrS8dAv6Wtbhnj5Wp+C16pIyq1PwWnifgVan4BV//HNeb3Naen0rX4Y6a9YsTZgwQQMGDFBqaqpee+01FRQUaPr06ZKk9PR0FRUVufb6OXbsmPbs2aNBgwbpwoULyszM1JEjR7RmzRrXmLt371ZRUZFuvvlmFRUVaeHChaqvr9fcuXNdMVu3bpVhGEpOTtZnn32mOXPmKDk5WZMnT5bU0MH6wQ9+oDlz5ig8PFyJiYnavn271q5dq8zMTEnS3//+d7311lsaOXKkYmJilJ+fr6eeekr9+vXTrbd69nuONUAAAASQsWPH6vz581q0aJGKi4vVu3dvbdmyRYmJiZKk4uJiFRQUuOLr6uq0dOlSffrppwoJCdHQoUO1c+dOde7c2RVTXV2t+fPn68SJE4qIiNDIkSOVnZ2tG264wRVTWlqq9PR0nTp1Sm3atNGYMWO0ePFihYSEuGJycnKUnp6u8ePH65///KcSExO1ePFiV3EWGhqqv/zlL1q2bJkqKiqUkJCgu+++W88884xrPdKVshmGYeoe2mVlZYqOjtZDv/nQLztAZ1ZdfJ+Ea10wHSBLxE+caHUKXqEDZI3PVr9vdQpead2nm9UpeKymqkJrfnabSktL3RYON7fG34FTut+i0GDf9CBq6mq16pNdpv8s/o41QAAAIOAwBQYAgMmsXAOEBhRAAACYzGZrOHw1FjzHFBgAAAg4dIAAADBZc+wEDc/QAQIAAAGHDhAAAGbz4Rogj7aChgsFEAAAJguyNRy+GgueYwoMAAAEHDpAAACYzNOXmH7bWPAcHSAAABBw6AABAGCyIJuhIB/t4OyrcQINBRAAACZjJ2jrMQUGAAACDh0gAABMxmPw1qMAAgDAZEyBWY8pMAAAEHA8LoA+/PBDjRo1SvHx8bLZbHrnnXeaIS0AAK5fjVNgvjrgOY8LoMrKSvXt21evvPJKc+QDAADQ7DxeAzRixAiNGDGiOXIBACAgsAbIes2+CNrhcMjhcLg+l5WVNfclAQC4pvEUmPWafRF0RkaGoqOjXUdCQkJzXxIAAOCymr0ASk9PV2lpqesoLCxs7ksCAHBNC5IPF0Fb/cP4qWafArPb7bLb7c19GQAAgCvGRogAAJiMRdDW87gAqqio0Geffeb6/Pnnn+vQoUNq06aNOnXq5NPkAAC4Htl8uAiaAsg7HhdA+/bt09ChQ12fZ82aJUmaOHGi3nzzTZ8lBgAA0Fw8LoCGDBkiwzCaIxcAAAICU2DWY/E4AAAIOCyCBgDAZGyEaD0KIAAATEYBZD2mwAAAQMChAwQAgMlYBG09CiAAAEwWZLMpyEeVi6/GCTRMgQEAgIBDBwgAAJMxBWY9OkAAACDg0AECAMBkQUENh6/GgucogAAAMBn7AFmPuhEAAAQcOkAAAJjMZrPJ5qPVy74aJ9DQAQIAAAGHDhAAACZjDZD1KIAAADBbkGTz1RwMczlesa4A8mX5ayKbs8rqFLwS1CLE6hS8FlRdZnUKXguPjbY6Ba9Ul/jvPQ9rH2V1Cl6zx7S2OgWvRHePszoFjzkqy61OARajAwQAgMmYArMejTMAABBw6AABAGAyHoO3HgUQAAAmC5IPp8B8M0zA4b4BAICAQwcIAACT2Xz4GLzPHqcPMBRAAACYLMhmU5CP1u74apxAQ90IAAACDh0gAABMxj5A1qMDBAAAAg4dIAAATMYiaOtRAAEAYDI2QrQedSMAAAg4dIAAADAZi6CtRwcIAAAEHDpAAACYjEXQ1qMAAgDAZOwEbT3qRgAAEHA8KoAyMjI0cOBARUZG6jvf+Y7uu+8+ffrpp82VGwAA1yebZPPRIRpAXvGoANq+fbseffRR7dq1S3l5eaqtrdXw4cNVWVnZXPkBAAD4nEcFUG5uriZNmqRevXqpb9++euONN1RQUKD9+/c3V34AAFx3goJ8e3hq+fLlSkpKUlhYmFJSUrRjx47Lxr/66qvq0aOHwsPDlZycrLVr17p93el0atGiReratavCwsLUt29f5ebmusWUl5dr5syZSkxMVHh4uAYPHqy9e/e6xVRUVOixxx5Tx44dFR4erh49eigrK8stxuFw6PHHH1dMTIxatWqle++9V6dOnfL4HlzVGqDS0lJJUps2ba5mGAAAAkrjTtC+Ojzx9ttva+bMmXr66ad18OBBff/739eIESNUUFBw0fisrCylp6dr4cKFOnr0qJ599lk9+uij+tOf/uSKmT9/vlasWKHf/va3ys/P1/Tp03X//ffr4MGDrpipU6cqLy9P2dnZOnz4sIYPH65hw4apqKjIFfPkk08qNzdX69at09/+9jc9+eSTevzxx7V582ZXzMyZM7Vp0ybl5OToo48+UkVFhe655x7V1dV5dB+8LoAMw9CsWbN02223qXfv3peMczgcKisrczsAAIA1MjMzNWXKFE2dOlU9evTQSy+9pISEhCadlkbZ2dl65JFHNHbsWHXp0kXjxo3TlClT9OKLL7rFzJs3TyNHjlSXLl00Y8YMpaWlaenSpZKkqqoqbdy4UUuWLNHtt9+ubt26aeHChUpKSnK77l//+ldNnDhRQ4YMUefOnfWTn/xEffv21b59+yQ1NF5WrVqlpUuXatiwYerXr5/WrVunw4cP6/333/foPnhdAD322GP6v//7P61fv/6ycRkZGYqOjnYdCQkJ3l4SAIDrgq8WQLsWQktNmg0Oh6PJdWtqarR//34NHz7c7fzw4cO1c+fOi+bqcDgUFhbmdi48PFx79uyR0+m8bMxHH30kSaqtrVVdXd1lYyTptttu07vvvquioiIZhqEPPvhAx44dU1pamiRp//79cjqdbvnHx8erd+/el8z/UrwqgB5//HG9++67+uCDD9SxY8fLxqanp6u0tNR1FBYWenNJAABwGQkJCW4Nh4yMjCYx586dU11dnWJjY93Ox8bGqqSk5KLjpqWl6fXXX9f+/ftlGIb27dun1atXy+l06ty5c66YzMxMHT9+XPX19crLy9PmzZtVXFwsSYqMjFRqaqqee+45nT59WnV1dVq3bp12797tipGkl19+WT179lTHjh0VGhqqu+66S8uXL9dtt90mSSopKVFoaKhat259xflfikcbIRqGoccff1ybNm3Stm3blJSU9K3fY7fbZbfbPUoKAIDrWVCQTUE+eolX4ziFhYWKiopynb/c795vrhsyDOOSa4kWLFigkpIS3XLLLTIMQ7GxsZo0aZKWLFmi4OBgSdKyZcs0bdo0de/eXTabTV27dtXkyZP1xhtvuMbJzs7Www8/rA4dOig4OFj9+/fXgw8+qAMHDrhiXn75Ze3atUvvvvuuEhMT9eGHH+qnP/2p4uLiNGzYsEv+PJfL/1I86gA9+uijWrdunX7/+98rMjJSJSUlKikpUVVVlUcXBQAgkNnkwymwf40ZFRXldlysAIqJiVFwcHCTbsnZs2ebdIUahYeHa/Xq1frqq6/0xRdfqKCgQJ07d1ZkZKRiYmIkSe3atdM777yjyspKnTx5Up988okiIiLcGiVdu3bV9u3bVVFRocLCQtcUWmNMVVWV5s2bp8zMTI0aNUp9+vTRY489prFjx+rXv/61JKl9+/aqqanRhQsXrjj/S/GoAMrKylJpaamGDBmiuLg41/H22297dFEAAGC+0NBQpaSkKC8vz+18Xl6eBg8efNnvDQkJUceOHRUcHKycnBzdc889CvrGM/hhYWHq0KGDamtrtXHjRo0ePbrJOK1atVJcXJwuXLigrVu3umKcTqecTmeTMYODg1VfXy9JSklJUUhIiFv+xcXFOnLkyLfm/00eT4EBAICr0xxTYFdq1qxZmjBhggYMGKDU1FS99tprKigo0PTp0yU1rN0tKipy7fVz7Ngx7dmzR4MGDdKFCxeUmZmpI0eOaM2aNa4xd+/eraKiIt18880qKirSwoULVV9fr7lz57pitm7dKsMwlJycrM8++0xz5sxRcnKyJk+eLKmhg/WDH/xAc+bMUXh4uBITE7V9+3atXbtWmZmZkqTo6GhNmTJFTz31lNq2bas2bdpo9uzZuummmy47RXYxvAwVAACTff3pLV+M5YmxY8fq/PnzWrRokYqLi9W7d29t2bJFiYmJkho6Kl/fE6iurk5Lly7Vp59+qpCQEA0dOlQ7d+5U586dXTHV1dWaP3++Tpw4oYiICI0cOVLZ2dm64YYbXDGlpaVKT0/XqVOn1KZNG40ZM0aLFy9WSEiIKyYnJ0fp6ekaP368/vnPfyoxMVGLFy92FWeS9Jvf/EYtWrTQAw88oKqqKt1xxx168803XeuRrvi+GSa3dcrKyhQdHa2Hlu1QaHiEmZf2ibPLl1qdgldaDBxhdQpecx78/61OwWtJv5hvdQpeaZnQ+tuDrlFh7aO+Pegadfy1D6xOwStxP7z0XnDXKkdluX57VzeVlpa6LRxubo2/A9f9+Ha1DPVND+Krmlr9f+s/NP1n8Xd0gAAAMJmVHSA0uKpXYQAAAPgjOkAAAJjMykXQaEABBACAyZgCsx5TYAAAIODQAQIAwGQ2m83jVzdcbix4jg4QAAAIOHSAAAAwmS2o4fDVWPAcBRAAACYLstkU5KOpK1+NE2ioGwEAQMChAwQAgNl8OAVGK8M73DYAABBw6AABAGAyNkK0HgUQAAAmswXZZPPRKyx8NU6gYQoMAAAEHMs6QF+98gs5g/2vAbV5fRerU/DK7v/6vdUpeK1vZnurU/Dafyz4ndUpeCW8z0CrU/CaPaa11Sl47cafDLU6Ba98VXDB6hQ8FlQTbO31bQ2Hr8aC5/yvAgEAwM/Z5MNXYYgKyBtMgQEAgIBDBwgAAJPxKgzrcdsAAEDAoQMEAIDJbDYfrgFiIyCvUAABAGCyoKCGw1djwXPcNgAAEHDoAAEAYLKGV2H4agrMJ8MEHDpAAAAg4NABAgDAZDwGbz0KIAAATMZTYNajbgQAAAGHDhAAACbjMXjrcdsAAEDAoQMEAIDJbEE22YJ8tAbIR+MEGgogAABM1rAPkO/GgueYAgMAAAGHDhAAACZjCsx6V9UBysjIkM1m08yZM32UDgAAAcD272mwqz1E/eMVrwugvXv36rXXXlOfPn18mQ8AAECz86oAqqio0Pjx47Vy5Uq1bt3a1zkBAHBdC7LZFBTko4NV0F7xqgB69NFHdffdd2vYsGHfGutwOFRWVuZ2AAAAWMnjRdA5OTk6cOCA9u7de0XxGRkZevbZZz1ODACA6xWPwVvPow5QYWGhnnjiCa1bt05hYWFX9D3p6ekqLS11HYWFhV4lCgDA9aLxKTBfHfCcRx2g/fv36+zZs0pJSXGdq6ur04cffqhXXnlFDodDwcHBbt9jt9tlt9t9ky0AAIAPeFQA3XHHHTp8+LDbucmTJ6t79+76z//8zybFDwAAaIp9gKznUQEUGRmp3r17u51r1aqV2rZt2+Q8AADAtYqdoAEAMBmLoK131QXQtm3bfJAGAACBo3EPH1+NBc/xMlQAABBwmAIDAMBktqCGw1djwXPcNgAAEHDoAAEAYDKbzSabj1Yv+2qcQEMBBACAyZgCsx63DQAABBw6QAAAmIydoK1HAQQAgMmCbA2Hr8aC55gCAwAAAYcOEAAAJrPZfDgFxlNgXqEDBAAAAg4dIAAATMYiaOtRAAEAYDLeBm89psAAAEDAoQMEAIDZfDgFxnPw3qEDBAAAAo5lHaDq1B+rLjTcqst77fVfrLA6Ba9sO263OgWv3brvE6tT8Np3fpRpdQpeadHSf/+8RHePszoFr31VcMHqFLzSslNrq1PwWFB5sLXXD2o4fDUWPMcUGAAAJuMpMOtRNwIAgIBDBwgAAJPxGLz16AABABBgli9frqSkJIWFhSklJUU7duy4bPyrr76qHj16KDw8XMnJyVq7dq3b151OpxYtWqSuXbsqLCxMffv2VW5urltMeXm5Zs6cqcTERIWHh2vw4MHau3evW4zNZrvo8atf/coVM2TIkCZfHzdunMf3gA4QAAAms3IN0Ntvv62ZM2dq+fLluvXWW7VixQqNGDFC+fn56tSpU5P4rKwspaena+XKlRo4cKD27NmjadOmqXXr1ho1apQkaf78+Vq3bp1Wrlyp7t27a+vWrbr//vu1c+dO9evXT5I0depUHTlyRNnZ2YqPj9e6des0bNgw5efnq0OHDpKk4uJit2u/9957mjJlisaMGeN2ftq0aVq0aJHrc3i45w9V0QECAMBkjQWQrw5PZGZmasqUKZo6dap69Oihl156SQkJCcrKyrpofHZ2th555BGNHTtWXbp00bhx4zRlyhS9+OKLbjHz5s3TyJEj1aVLF82YMUNpaWlaunSpJKmqqkobN27UkiVLdPvtt6tbt25auHChkpKS3K7bvn17t2Pz5s0aOnSounTp4pZTy5Yt3eKio6M9ugcSBRAAANeFsrIyt8PhcDSJqamp0f79+zV8+HC388OHD9fOnTsvOq7D4VBYWJjbufDwcO3Zs0dOp/OyMR999JEkqba2VnV1dZeN+aYzZ87of/7nfzRlypQmX3vrrbcUExOjXr16afbs2SovL7/oGJdDAQQAgMka9wHy1SFJCQkJio6Odh0ZGRlNrnvu3DnV1dUpNjbW7XxsbKxKSkoummtaWppef/117d+/X4ZhaN++fVq9erWcTqfOnTvnisnMzNTx48dVX1+vvLw8bd682TWlFRkZqdTUVD333HM6ffq06urqtG7dOu3evbvJtFejNWvWKDIyUj/60Y/czo8fP17r16/Xtm3btGDBAm3cuLFJzJVgDRAAACazqWHxrq/GkqTCwkJFRUW5ztvtl97Q9JvXNgzjkvksWLBAJSUluuWWW2QYhmJjYzVp0iQtWbJEwcENG0ouW7ZM06ZNU/fu3WWz2dS1a1dNnjxZb7zxhmuc7OxsPfzww+rQoYOCg4PVv39/Pfjggzpw4MBFr7t69WqNHz++Sddo2rRprn/u3bu3brzxRg0YMEAHDhxQ//79L/kzfxMdIAAArgNRUVFux8UKoJiYGAUHBzfp9pw9e7ZJV6hReHi4Vq9era+++kpffPGFCgoK1LlzZ0VGRiomJkaS1K5dO73zzjuqrKzUyZMn9cknnygiIkJJSUmucbp27art27eroqJChYWFrim0r8c02rFjhz799FNNnTr1W3/u/v37KyQkRMePH//W2K+jAAIAwGS2IN8eVyo0NFQpKSnKy8tzO5+Xl6fBgwdf9ntDQkLUsWNHBQcHKycnR/fcc4+CvvEejrCwMHXo0EG1tbXauHGjRo8e3WScVq1aKS4uThcuXNDWrVsvGrNq1SqlpKSob9++3/ozHT16VE6nU3Fxnr0GhykwAAACyKxZszRhwgQNGDBAqampeu2111RQUKDp06dLktLT01VUVOTa6+fYsWPas2ePBg0apAsXLigzM1NHjhzRmjVrXGPu3r1bRUVFuvnmm1VUVKSFCxeqvr5ec+fOdcVs3bpVhmEoOTlZn332mebMmaPk5GRNnjzZLb+ysjJt2LDB9QTZ1/3973/XW2+9pZEjRyomJkb5+fl66qmn1K9fP916660e3QcKIAAATGblPkBjx47V+fPntWjRIhUXF6t3797asmWLEhMTJTXsxVNQUOCKr6ur09KlS/Xpp58qJCREQ4cO1c6dO9W5c2dXTHV1tebPn68TJ04oIiJCI0eOVHZ2tm644QZXTGlpqdLT03Xq1Cm1adNGY8aM0eLFixUSEuKWX05OjgzD0I9//OMmuYeGhuovf/mLli1bpoqKCiUkJOjuu+/WM88841qPdKVshmEYHn3HVSorK1N0dLTueeg1hfjh2+BHnuFt8Ga7Nana6hS8tvcu3gZvNn9+G3xQqH/+f1J/fBt8dXmZXvjuDSotLXVbONzcGn8HHn/zAUW2DPn2b7gC5V85deOkP5j+s/g71gABAICA45//dwMAAD/m6eLlbxsLnuO2AQCAgEMHCAAAkzV0gHy1CNonwwQcj27bwoULm7yCvn379s2VGwAA1yUrX4aKBh53gHr16qX333/f9dnTx84AAACs5nEB1KJFC7o+AABcBZut4fDVWPCcxzOHx48fV3x8vJKSkjRu3DidOHGiOfICAABoNh51gAYNGqS1a9fqu9/9rs6cOaPnn39egwcP1tGjR9W2bduLfo/D4ZDD4XB9Lisru7qMAQDwc7Ygm4Is2gkaDTzqAI0YMUJjxozRTTfdpGHDhul//ud/JMntfSDflJGRoejoaNeRkJBwdRkDAODnrHoZKv7tqm5bq1atdNNNN132FfTp6ekqLS11HYWFhVdzSQAAgKt2VfsAORwO/e1vf9P3v//9S8bY7XbZ7f77XiEAAHzNZvPhy1BZBe0Vjwqg2bNna9SoUerUqZPOnj2r559/XmVlZZo4cWJz5QcAwHXHyrfBo4FHBdCpU6f04x//WOfOnVO7du10yy23aNeuXUpMTGyu/AAAAHzOowIoJyenufIAACBgBAU1HL4aC57jtgEAgIDDy1ABADAZa4CsRwEEAIDJKICsxxQYAAAIOHSAAAAwmS93cGYnaO9w2wAAQMChAwQAgMmCfPgyVF+NE2gogAAAMBmLoK3HFBgAAAg4dIAAADAZi6Ctx20DAAABhw4QAAAms9l8uAbIxhogb1AAAQBgMp4Csx5TYAAAIODQAQIAwGQsgrYetw0AAAQc0ztAhmFIkpw1VWZf2ieqnLVWp+AVZ12w1Sl4zV/vuSTVVFdYnYJX6m1Oq1PwmqOy3OoUvBZU459/T4PK/S9vR0WZpH//TjJbeZXTZ4ugy6v89++rlWyGyf/2T506pYSEBDMvCQDARRUWFqpjx46mXa+6ulpJSUkqKSnx6bjt27fX559/rrCwMJ+Oez0zvQCqr6/X6dOnFRkZ6fNH98rKypSQkKDCwkJFRUX5dOzm5K95S+RuBX/NW/Lf3P01b4ncL8UwDJWXlys+Pl5BQeauBqmurlZNTY1PxwwNDaX48ZDpU2BBQUHNXm1HRUX53V90yX/zlsjdCv6at+S/uftr3hK5X0x0dLTPx7wSYWFhFCvXABZBAwCAgEMBBAAAAs51VQDZ7XY988wzstvtVqfiEX/NWyJ3K/hr3pL/5u6veUvkDlyK6YugAQAArHZddYAAAACuBAUQAAAIOBRAAAAg4FAAAQCAgEMBBAAAAg4FEAAACDgUQAAAIOBQAAEAgIDz/wCARhix+qWjOgAAAABJRU5ErkJggg==",
+      "image/png": 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rpe//1eoUvOfHU2BVmfOsTsErYY8+b3UKHquprtJb6XeqrKysWaaNLqfxd+B/Hjnv0zVAL/VuY/rP4u/oAAEAYLZ6w+OpqyuOBY+xESIAAAg4dIAAADCZ4cNF0D5bTB1g6AABAICAQwcIAACT0QGyHgUQAABmM3y4CNrch7lvGEyBAQCAgEMHCAAAkxn19TLq6302FjxHBwgAAAQcOkAAAJiMRdDWowACAMBs7ARtOabAAABAwKEDBACAyZgCs55HHaCsrCz16dNH0dHRio6OVmpqqt57773myg0AAKBZeNQB6tixo1588UV169ZNkrR27VqNGTNGBw8eVK9evZolQQAAbjR0gKznUQE0evRot89LlixRVlaWdu/eTQEEAMDVYhG05bxeA1RXV6eNGzeqqqpKqampl41zOBxyOByuz+Xl5d5eEgAAwCc8LoAOHz6s1NRUVVdXKzIyUps3b1bPnj0vG5+RkaHnnnvumpIEAOBGwk7Q1vP4Mfjk5GQdOnRIu3fv1syZMzVp0iQVFBRcNj49PV1lZWWuo6io6JoSBgDA3zWuAfLVAc953AEKDQ11LYIeMGCA9u3bp+XLl2vlypWXjLfb7bLb7deWJQAAgA9d8z5AhmG4rfEBAADfwpDvFi/TAPKKRwXQ/PnzNXLkSCUkJKiiokI5OTnavn27cnNzmys/AAAAn/OoADpz5owmTpyokpISxcTEqE+fPsrNzdVdd93VXPkBAHDDYR8g63lUAK1evbq58gAAIGAYhiHD8FEB5KNxAg0vQwUAAAGHl6ECAGA2doK2HB0gAAAQcOgAAQBgMnaCth4FEAAAJuMpMOsxBQYAAAIOHSAAAMzGImjL0QECAAABhw4QAAAmYw2Q9SiAAAAwGQWQ9ZgCAwAAAYcOEAAAJjMMH3aAeBeYVyiAAAAwm2E0HL4aCx5jCgwAAAQcOkAAAJiMV2FYjw4QAAAIOJZ1gCo+OaOaiK+surzXonu2szoFr9RWOqxOwWtRsZFWp+C1wj/stzoFr5xb/4bVKXjN5rxodQpe27Khi9UpeGXCjxZYnYLHnHW11ibgw8fg2QnaO0yBAQBgNl6FYTmmwAAAQMChAwQAgMnYCdp6dIAAAEDAoQMEAIDJDMPw2Q7O7ATtHQogAADMVi8fLoL2zTCBhikwAAAQcOgAAQBgMhZBW48OEAAACDh0gAAAMBnvArMeBRAAAGYzjIbDV2PBY0yBAQCAgEMHCAAAkxmGDxdB0wHyCgUQAABm42WolmMKDAAABBw6QAAAmIx9gKxHBwgAAAQcOkAAAJiMl6Faz6MOUFZWlvr06aPo6GhFR0crNTVV7733XnPlBgDAjam+3rcHPOZRAdSxY0e9+OKL2r9/v/bv369/+7d/05gxY3T06NHmyg8AAMDnPJoCGz16tNvnJUuWKCsrS7t371avXr18mhgAADcqFkFbz+s1QHV1ddq4caOqqqqUmprqy5wAAACalccF0OHDh5Wamqrq6mpFRkZq8+bN6tmz52XjHQ6HHA6H63N5ebl3mQIAcIOgA2Q9jx+DT05O1qFDh7R7927NnDlTkyZNUkFBwWXjMzIyFBMT4zoSEhKuKWEAAPxe48tQfXXAYx4XQKGhoerWrZsGDBigjIwM9e3bV8uXL79sfHp6usrKylxHUVHRNSUMAACuzYoVK5SUlKSwsDClpKRo586dV4x/7bXX1KNHD4WHhys5OVnr1q1z+7rT6dTixYvVtWtXhYWFqW/fvsrNzXWLqaio0KxZs5SYmKjw8HANHjxY+/btc4s5c+aMJk+erPj4eLVs2VJ33323jh8/7hYzdOhQ2Ww2t2P8+PEe34Nr3gfIMAy3Ka5vstvtstvt13oZAABuGFZOgb399tuaNWuWVqxYodtvv10rV67UyJEjVVBQoE6dOjWJz8rKUnp6ulatWqWBAwdq7969mj59ulq1auV6OGrBggVav369Vq1ape7du2vbtm164IEHtGvXLvXr10+SNG3aNB05ckTZ2dmKj4/X+vXrNXz4cBUUFKhDhw4yDEP333+/QkJCtGXLFkVHRyszM9MVExER4cpp+vTpWrx4setzeHi4x/fNow7Q/PnztXPnTn3xxRc6fPiwnnnmGW3fvl0TJkzw+MIAAMB8mZmZmjp1qqZNm6YePXro5ZdfVkJCgrKysi4Zn52drUcffVTjxo1Tly5dNH78eE2dOlUvvfSSW8z8+fM1atQodenSRTNnzlRaWpqWLVsmSbp48aI2bdqkpUuXasiQIerWrZsWLVqkpKQk13WPHz+u3bt3KysrSwMHDlRycrJWrFihyspKbdiwwS2nli1bql27dq4jJibG4/vgUQF05swZTZw4UcnJybrzzju1Z88e5ebm6q677vL4wgAABKrGDpCvjqtVU1Oj/Px8jRgxwu38iBEjtGvXrkt+j8PhUFhYmNu58PBw7d27V06n84oxH330kSSptrZWdXV1V4xpnE36ekxwcLBCQ0NdMY3eeustxcbGqlevXpozZ44qKiqu6uf/Oo+mwFavXu3xBQAAwDcY9Q2Hr8ZS06esL7UE5dy5c6qrq1NcXJzb+bi4OJWWll5y+LS0NL3xxhu6//771b9/f+Xn52vNmjVyOp06d+6c2rdvr7S0NGVmZmrIkCHq2rWr3n//fW3ZskV1dXWSpKioKKWmpur5559Xjx49FBcXpw0bNmjPnj26+eabJUndu3dXYmKi0tPTtXLlSkVERCgzM1OlpaUqKSlx5TNhwgQlJSWpXbt2OnLkiNLT0/Xxxx8rLy/Po9vGy1ABALgBJCQkuD11nZGRcdlYm83m9tkwjCbnGi1cuFAjR47UbbfdppCQEI0ZM0aTJ0+W1NChkaTly5fr5ptvVvfu3RUaGqrHH39cU6ZMcX1dapgmMwxDHTp0kN1u1yuvvKKHHnrIFRMSEqJNmzbp2LFjat26tVq2bKnt27dr5MiRbuNMnz5dw4cPV+/evTV+/Hj94Q9/0J///GcdOHDAo/tFAQQAgMkMw4fTYP+cASsqKnJ76jo9Pb3JdWNjYxUcHNyk23P27NkmXaFG4eHhWrNmjb766it98cUXKiwsVOfOnRUVFaXY2FhJUtu2bfXOO++oqqpKJ0+e1CeffKLIyEglJSW5xunatat27NihyspKFRUVuabQvh6TkpKiQ4cO6csvv1RJSYlyc3N1/vx5t5hv6t+/v0JCQpo8LfZtKIAAALgBNL6ovPG41BPYoaGhSklJaTJdlJeXp8GDB19x/JCQEHXs2FHBwcHKycnRvffeq6Ag9zIiLCxMHTp0UG1trTZt2qQxY8Y0GSciIkLt27fXhQsXtG3btkvGxMTEqG3btjp+/Lj2799/yZhGR48eldPpVPv27a+Y/zdd82PwAADAM1Y+Bj979mxNnDhRAwYMUGpqql5//XUVFhZqxowZkhr27ysuLnbt9XPs2DHt3btXgwYN0oULF5SZmakjR45o7dq1rjH37Nmj4uJi3XrrrSouLtaiRYtUX1+vefPmuWK2bdsmwzCUnJyszz77THPnzlVycrKmTJniitm4caPatm2rTp066fDhw3ryySd1//33uxZt/+1vf9Nbb72lUaNGKTY2VgUFBXr66afVr18/3X777R7dBwogAADM5ssdnD0cZ9y4cTp//rwWL16skpIS9e7dW1u3blViYqIkqaSkRIWFha74uro6LVu2TJ9++qlCQkI0bNgw7dq1S507d3bFVFdXa8GCBTpx4oQiIyM1atQoZWdn66abbnLFNE7LnTp1Sq1bt9bYsWO1ZMkShYSEuGJKSko0e/ZsnTlzRu3bt9fDDz+shQsXur4eGhqq999/X8uXL1dlZaUSEhJ0zz336Nlnn3VbJ3Q1bIZh7h7a5eXliomJ0WPvfip7RJSZl/aJ6J7trE7BK7WVl9+s8noX3DLU6hS8VviH/Van4JVz69+wOgWv2ZwXrU7Ba1t+G/HtQdehCT/6m9UpeMxZV6uNx7errKxM0dHRpl238XfgQ0vyFBrmm//eNdVV+t0zd5n+s/g7OkAAAJiMl6FajwIIAACz1dc3HL4aCx7jKTAAABBw6AABAGAywzDkqyW4Ji/lvWHQAQIAAAGHDhAAACZjEbT1KIAAADCbhfsAoQFTYAAAIODQAQIAwGRMgVmPDhAAAAg4lnWA7G1ayh7pf9u+++srJVpENn0rsL9wfum/rzaISvqO1Sl45UJvz14qeD0JahHy7UHXqT3/9TurU/BKeechVqfgMWdttXR8u3UJsBGi5ZgCAwDAbD7cB4hF0N5hCgwAAAQcOkAAAJjNMCRfLV6mA+QVOkAAACDg0AECAMBkvAvMehRAAACYzKivl+Gjp7d8NU6gYQoMAAAEHDpAAACYjXeBWY4CCAAAkxn1vnuFhcEMmFeYAgMAAAGHDhAAAGZjCsxydIAAAEDAoQMEAIDJeAzeehRAAACYjSkwyzEFBgAAAg4dIAAATGbUGz58DJ4OkDfoAAEAgIBDBwgAALM17ITou7HgMQogAABMxhSY9TyaAsvKylKfPn0UHR2t6Ohopaam6r333muu3AAAAJqFRx2gjh076sUXX1S3bt0kSWvXrtWYMWN08OBB9erVq1kSBADghsMUmOU8KoBGjx7t9nnJkiXKysrS7t27KYAAAIDf8HoNUF1dnTZu3KiqqiqlpqZeNs7hcMjhcLg+l5eXe3tJAABuCIbhwzVAbIToFY8LoMOHDys1NVXV1dWKjIzU5s2b1bNnz8vGZ2Rk6LnnnrumJAEAuKHUGw2Hr8aCxzzeByg5OVmHDh3S7t27NXPmTE2aNEkFBQWXjU9PT1dZWZnrKCoquqaEAQAArpXHHaDQ0FDXIugBAwZo3759Wr58uVauXHnJeLvdLrvdfm1ZAgBwAzGMehk+Wrzsq3ECzTXvA2QYhtsaHwAA8C2YArOcRwXQ/PnzNXLkSCUkJKiiokI5OTnavn27cnNzmys/AAAAn/OoADpz5owmTpyokpISxcTEqE+fPsrNzdVdd93VXPkBAHDDYQrMeh4VQKtXr26uPAAAAEzDu8AAADAba4AsRwEEAIDJDMPw4RQYBZA3PN4HCAAAwN/RAQIAwGxMgVmODhAAAAg4dIAAADCbUd9w+GoseIwCCAAAkxn19TLqfbQI2kfjBBqmwAAAQMChAwQAgNkMo+Hw1VjwGB0gAAAQcOgAAQBgMsPw4RogFkF7hQIIAACz8RSY5ZgCAwAAAYcOEAAAZmMnaMtRAAEAYDLDqPfhy1CZAvMGU2AAACDgWNYBcly4KNX4XwMqKjbS6hS84vzyotUpeC3kpnCrU/BaxednrU7BK87jH1udgteCqsutTsFrfTPbWZ2CVyI3fmR1Ch5z1tVam0B9fcPhq7HgMTpAAAAg4PhfCwYAAH9nGD58DJ5F0N6gAAIAwGRGvSHDR09v+WqcQMMUGAAACDh0gAAAMBs7QVuODhAAAAg4dIAAADAbj8Fbjg4QAABma5wC89XhoRUrVigpKUlhYWFKSUnRzp07rxj/2muvqUePHgoPD1dycrLWrVvn9nWn06nFixera9euCgsLU9++fZWbm+sWU1FRoVmzZikxMVHh4eEaPHiw9u3b5xZz5swZTZ48WfHx8WrZsqXuvvtuHT9+3C3G4XDoiSeeUGxsrCIiInTffffp1KlTHt8DCiAAAALI22+/rVmzZumZZ57RwYMH9f3vf18jR45UYWHhJeOzsrKUnp6uRYsW6ejRo3ruuef02GOP6Y9//KMrZsGCBVq5cqV+/etfq6CgQDNmzNADDzyggwcPumKmTZumvLw8ZWdn6/DhwxoxYoSGDx+u4uJiSZJhGLr//vt14sQJbdmyRQcPHlRiYqKGDx+uqqoq1zizZs3S5s2blZOTo48++kiVlZW69957VVdX59F9sBmGuRsIlJeXKyYmRo/99zHZI6LMvLRPRH33O1an4JXa8mqrU/CaP+8EffL3+7496Dp0dmOO1Sl4zZ93gv59ptUZeGfKj4usTsFjzrpabTr+/1RWVqbo6GjTrtv4O/DutEUKCQnzyZhOZ7Vyty266p9l0KBB6t+/v7KyslznevToofvvv18ZGRlN4gcPHqzbb79dv/jFL1znZs2apf379+ujjxp2AY+Pj9czzzyjxx57zBVz//33KzIyUuvXr9fFixcVFRWlLVu26J577nHF3Hrrrbr33nv1wgsv6NixY0pOTtaRI0fUq1cvSVJdXZ2+853v6KWXXtK0adNUVlamtm3bKjs7W+PGjZMknT59WgkJCdq6davS0tKu+r7RAQIAIEDU1NQoPz9fI0aMcDs/YsQI7dq165Lf43A4FBbmXqyFh4dr7969cjqdV4xpLJBqa2tVV1d3xRiHwyFJbjHBwcEKDQ11xeTn58vpdLrlHx8fr969e182/8uhAAIAwGSNb4P31SE1dJe+fjQWFF937tw51dXVKS4uzu18XFycSktLL5lrWlqa3njjDeXn58swDO3fv19r1qyR0+nUuXPnXDGZmZk6fvy46uvrlZeXpy1btqikpESSFBUVpdTUVD3//PM6ffq06urqtH79eu3Zs8cV0717dyUmJio9PV0XLlxQTU2NXnzxRZWWlrpiSktLFRoaqlatWl11/pdDAQQAgNkMw7eHpISEBMXExLiOS01nNbLZbN9Ix2hyrtHChQs1cuRI3XbbbQoJCdGYMWM0efJkSQ0dGklavny5br75ZnXv3l2hoaF6/PHHNWXKFNfXJSk7O1uGYahDhw6y2+165ZVX9NBDD7liQkJCtGnTJh07dkytW7dWy5YttX37do0cOdJtnEvfzsvnfzkUQAAA3ACKiopUVlbmOtLT05vExMbGKjg4uEm35OzZs026Qo3Cw8O1Zs0affXVV/riiy9UWFiozp07KyoqSrGxsZKktm3b6p133lFVVZVOnjypTz75RJGRkUpKSnKN07VrV+3YsUOVlZUqKipyTaF9PSYlJUWHDh3Sl19+qZKSEuXm5ur8+fOumHbt2qmmpkYXLly46vwvhwIIAACzGfX/2gvoWo9/ToFFR0e7HXa7vcllQ0NDlZKSory8PLfzeXl5Gjx48BVTDgkJUceOHRUcHKycnBzde++9CgpyLyPCwsLUoUMH1dbWatOmTRozZkyTcSIiItS+fXtduHBB27Ztu2RMTEyM2rZtq+PHj2v//v2umJSUFIWEhLjlX1JSoiNHjnxr/t/ERogAAJjNwldhzJ49WxMnTtSAAQOUmpqq119/XYWFhZoxY4YkKT09XcXFxa69fo4dO6a9e/dq0KBBunDhgjIzM3XkyBGtXbvWNeaePXtUXFysW2+9VcXFxVq0aJHq6+s1b948V8y2bdtkGIaSk5P12Wefae7cuUpOTtaUKVNcMRs3blTbtm3VqVMnHT58WE8++aTuv/9+16LnmJgYTZ06VU8//bTatGmj1q1ba86cObrllls0fPhwj+4DBRAAAAFk3LhxOn/+vBYvXqySkhL17t1bW7duVWJioqSGjsrX9wSqq6vTsmXL9OmnnyokJETDhg3Trl271LlzZ1dMdXW1FixYoBMnTigyMlKjRo1Sdna2brrpJldM47TcqVOn1Lp1a40dO1ZLlixRSEiIK6akpESzZ8/WmTNn1L59ez388MNauHChW/6/+tWv1KJFCz344IO6ePGi7rzzTr355pvfuk7om9gHyEPsA2Q+9gEyH/sAWYN9gMxj9T5AacN+qpAWTaeovOGsdWjbBy+a/rP4O4/WAGVlZalPnz6u+cXU1FS99957zZUbAABAs/BoCqxjx4568cUX1a1bN0nS2rVrNWbMGB08eNC1ayMAAPgWFq4BQgOPCqDRo0e7fV6yZImysrK0e/duCiAAAK4WBZDlvF4EXVdXp40bN6qqqkqpqam+zAkAAKBZeVwAHT58WKmpqaqurlZkZKQ2b96snj17Xjbe4XC4bcddXu6/CxQBAPCJxj18fDUWPObxRojJyck6dOiQdu/erZkzZ2rSpEkqKCi4bHxGRobb1twJCQnXlDAAAMC18rgACg0NVbdu3TRgwABlZGSob9++Wr58+WXj09PT3bbmLiryv8clAQDwqcY1QL464LFr3gjRMIxLvnG2kd1uv+R23AAABCwWQVvOowJo/vz5GjlypBISElRRUaGcnBxt375dubm5zZUfAACAz3lUAJ05c0YTJ05USUmJYmJi1KdPH+Xm5uquu+5qrvwAALjxsAjach4VQKtXr26uPAAAAEzDy1ABADAba4AsRwEEAIDZKIAs5/Fj8AAAAP6ODhAAAGYzDB92gAzfjBNgKIAAADAbU2CWYwoMAAAEHDpAAACYzFZvyFbvm6krX40TaOgAAQCAgEMHCAAA0/nyJaasAfIGBRAAAGZjEbTlmAIDAAABhw4QAABmowNkOTpAAAAg4NABAgDAZDajXjYfdW58NU6goQACAMBsTIFZjikwAAAQcOgAAQBgNjpAlrOsAGoZHyN7VLRVl/da4R/2W52CV6KSvmN1Cl6r+Pys1Sl4LfHBgVan4JUWLe1Wp+C18LgYq1Pw2r8v/I3VKXil69IFVqfgMcdXFdL4PlanAQvRAQIAwGQ2GbLJR+8C89E4gYYCCAAAsxmGD6fAKIC8wSJoAAAQcOgAAQBgMvYBsh4dIAAAEHDoAAEAYDbD8N3aHdYAeYUCCAAAkzEFZj2mwAAAQMChAwQAgOnq/3n4aix4igIIAACT2QxDNh+t3fHVOIGGKTAAABBw6AABAGA2XoZqOTpAAAAg4NABAgDAZLwM1XoUQAAAmIx9gKzHFBgAAAg4dIAAADCd8c/DV2PBU3SAAABAwPGoAMrKylKfPn0UHR2t6Ohopaam6r333muu3AAAuCE1LoL21QHPeTQF1rFjR7344ovq1q2bJGnt2rUaM2aMDh48qF69ejVLggAA3GhYBG09jwqg0aNHu31esmSJsrKytHv3bgogAADgN7xeBF1XV6eNGzeqqqpKqampvswJAIAbGvsAWc/jAujw4cNKTU1VdXW1IiMjtXnzZvXs2fOy8Q6HQw6Hw/W5vLzcu0wBAAB8xOOnwJKTk3Xo0CHt3r1bM2fO1KRJk1RQUHDZ+IyMDMXExLiOhISEa0oYAAB/Z5MvF0LDGx4XQKGhoerWrZsGDBigjIwM9e3bV8uXL79sfHp6usrKylxHUVHRNSUMAIC/4ykw613zRoiGYbhNcX2T3W6X3W6/1ssAAAD4jEcF0Pz58zVy5EglJCSooqJCOTk52r59u3Jzc5srPwAAbjg2w5DN8NEiaB+NE2g8KoDOnDmjiRMnqqSkRDExMerTp49yc3N11113NVd+AADccGyql00+2gfIR+MEGo8KoNWrVzdXHgAAAKbhZagAAJjOl4uXmQLzBi9DBQAAAYcOEAAAJrP98/DVWPAcBRAAACaz2QzZbD56CsxH4wQapsAAAEDAoQMEAIDJeBmq9egAAQCAgEMHCAAAk9EBsh4dIAAATGb1y1BXrFihpKQkhYWFKSUlRTt37rxi/GuvvaYePXooPDxcycnJWrdundvXnU6nFi9erK5duyosLEx9+/Zt8pqsiooKzZo1S4mJiQoPD9fgwYO1b98+t5jKyko9/vjj6tixo8LDw9WjRw9lZWW5xQwdOlQ2m83tGD9+vMf3gA4QAAAB5O2339asWbO0YsUK3X777Vq5cqVGjhypgoICderUqUl8VlaW0tPTtWrVKg0cOFB79+7V9OnT1apVK40ePVqStGDBAq1fv16rVq1S9+7dtW3bNj3wwAPatWuX+vXrJ0maNm2ajhw5ouzsbMXHx2v9+vUaPny4CgoK1KFDB0nSU089pQ8++EDr169X586d9ac//Uk/+clPFB8frzFjxrhymj59uhYvXuz6HB4e7vF9oAMEAIDJbD4+PJGZmampU6dq2rRp6tGjh15++WUlJCQ06bQ0ys7O1qOPPqpx48apS5cuGj9+vKZOnaqXXnrJLWb+/PkaNWqUunTpopkzZyotLU3Lli2TJF28eFGbNm3S0qVLNWTIEHXr1k2LFi1SUlKS23X/8pe/aNKkSRo6dKg6d+6sH//4x+rbt6/279/vllPLli3Vrl071xETE+PhXaAAAgDghlBeXu52OByOJjE1NTXKz8/XiBEj3M6PGDFCu3btuuS4DodDYWFhbufCw8O1d+9eOZ3OK8Z89NFHkqTa2lrV1dVdMUaS7rjjDr377rsqLi6WYRj64IMPdOzYMaWlpbl931tvvaXY2Fj16tVLc+bMUUVFxZVuzSVRAAEAYLLGjRB9dUhSQkKCYmJiXEdGRkaT6547d051dXWKi4tzOx8XF6fS0tJL5pqWlqY33nhD+fn5MgxD+/fv15o1a+R0OnXu3DlXTGZmpo4fP676+nrl5eVpy5YtKikpkSRFRUUpNTVVzz//vE6fPq26ujqtX79ee/bsccVI0iuvvKKePXuqY8eOCg0N1d13360VK1bojjvucMVMmDBBGzZs0Pbt27Vw4UJt2rRJP/zhDz3+b8AaIAAATBYkQ0E+enqrcZyioiJFR0e7ztvt9st+j83mPnFmGEaTc40WLlyo0tJS3XbbbTIMQ3FxcZo8ebKWLl2q4OBgSdLy5cs1ffp0de/eXTabTV27dtWUKVP029/+1jVOdna2HnnkEXXo0EHBwcHq37+/HnroIR04cMAV88orr2j37t169913lZiYqA8//FA/+clP1L59ew0fPlxSw/qfRr1799bNN9+sAQMG6MCBA+rfv//V3jY6QAAA3Aiio6PdjksVQLGxsQoODm7S7Tl79myTrlCj8PBwrVmzRl999ZW++OILFRYWqnPnzoqKilJsbKwkqW3btnrnnXdUVVWlkydP6pNPPlFkZKSSkpJc43Tt2lU7duxQZWWlioqKXFNojTEXL17U/PnzlZmZqdGjR6tPnz56/PHHNW7cOP3yl7+87M/dv39/hYSE6Pjx4x7dLwogAABMZtUi6NDQUKWkpCgvL8/tfF5engYPHnzF7w0JCVHHjh0VHBysnJwc3XvvvQoKci8jwsLC1KFDB9XW1mrTpk1uT241ioiIUPv27XXhwgVt27bNFeN0OuV0OpuMGRwcrPr6+svmdfToUTmdTrVv3/6K+X+TZVNgZ3Z8qtDwSKsu77Vz69+wOgWvXOh9u9UpeM15/GOrU/Bai5aXb0Ffzzrc28fqFLxWXVpudQpeC+8z0OoUvBIUGmx1Ch4Lclqbs5UvQ509e7YmTpyoAQMGKDU1Va+//roKCws1Y8YMSVJ6erqKi4tde/0cO3ZMe/fu1aBBg3ThwgVlZmbqyJEjWrt2rWvMPXv2qLi4WLfeequKi4u1aNEi1dfXa968ea6Ybdu2yTAMJScn67PPPtPcuXOVnJysKVOmSGroYP3gBz/Q3LlzFR4ersTERO3YsUPr1q1TZmamJOlvf/ub3nrrLY0aNUqxsbEqKCjQ008/rX79+un22z37PccaIAAAAsi4ceN0/vx5LV68WCUlJerdu7e2bt2qxMRESVJJSYkKCwtd8XV1dVq2bJk+/fRThYSEaNiwYdq1a5c6d+7siqmurtaCBQt04sQJRUZGatSoUcrOztZNN93kiikrK1N6erpOnTql1q1ba+zYsVqyZIlCQkJcMTk5OUpPT9eECRP0j3/8Q4mJiVqyZImrOAsNDdX777+v5cuXq7KyUgkJCbrnnnv07LPPutYjXS2bYRim7qFdXl6umJgYPfyrD/2yA3Rm9aX3SbjeBdMBskT8pElWp+AVOkDW+GzNn61OwSsd7/me1Sl4zFFVoayxvVRWVua2cLi5Nf4OnNr9NoUG+6YHUVNXq9Wf7Db9Z/F3rAECAAABhykwAABMZuUaIDSgAAIAwGQ2W8Phq7HgOabAAABAwKEDBACAyZpjJ2h4hg4QAAAIOHSAAAAwmw/XAHm0FTRcKIAAADBZkK3h8NVY8BxTYAAAIODQAQIAwGSevsT028aC5+gAAQCAgEMHCAAAkwXZDAX5aAdnX40TaCiAAAAwGTtBW48pMAAAEHDoAAEAYDIeg7ceBRAAACZjCsx6TIEBAICA43EB9OGHH2r06NGKj4+XzWbTO++80wxpAQBw42qcAvPVAc95XABVVVWpb9++evXVV5sjHwAAgGbn8RqgkSNHauTIkc2RCwAAAYE1QNZr9kXQDodDDofD9bm8vLy5LwkAwHWNp8Cs1+yLoDMyMhQTE+M6EhISmvuSAAAAV9TsBVB6errKyspcR1FRUXNfEgCA61qQfLgI2uofxk81+xSY3W6X3W5v7ssAAABcNTZCBADAZCyCtp7HBVBlZaU+++wz1+fPP/9chw4dUuvWrdWpUyefJgcAwI3I5sNF0BRA3vG4ANq/f7+GDRvm+jx79mxJ0qRJk/Tmm2/6LDEAAIDm4nEBNHToUBmG0Ry5AAAQEJgCsx6LxwEAQMBhETQAACZjI0TrUQABAGAyCiDrMQUGAAACDh0gAABMxiJo61EAAQBgsiCbTUE+qlx8NU6gYQoMAAAEHDpAAACYjCkw69EBAgAAAYcOEAAAJgsKajh8NRY8RwEEAIDJ2AfIetSNAAAg4NABAgDAZDabTTYfrV721TiBhg4QAAAIOHSAAAAwGWuArEcBBACA2YIkm6/mYJjL8Yp1BZAvy18T2ZwXrU7BK0EtQqxOwWtB1eVWp+C18LgYq1PwSnWp/97zsHbRVqfgNXtsK6tT8Ep4B//LO6gy2OoUYDE6QAAAmIwpMOvROAMAAAGHDhAAACbjMXjrUQABAGCyIPlwCsw3wwQc7hsAAAg4dIAAADCZzYePwfvscfoAQwEEAIDJgmw2Bflo7Y6vxgk01I0AACDg0AECAMBk7ANkPTpAAAAg4NABAgDAZCyCth4FEAAAJmMjROtRNwIAgIBDBwgAAJOxCNp6dIAAAEDAoQMEAIDJWARtPQogAABMxk7Q1qNuBAAAAcejAigjI0MDBw5UVFSUvvOd7+j+++/Xp59+2ly5AQBwY7JJNh8dogHkFY8KoB07duixxx7T7t27lZeXp9raWo0YMUJVVVXNlR8AAIDPeVQA5ebmavLkyerVq5f69u2r3/72tyosLFR+fn5z5QcAwA0nKMi3h6dWrFihpKQkhYWFKSUlRTt37rxi/GuvvaYePXooPDxcycnJWrdundvXnU6nFi9erK5duyosLEx9+/ZVbm6uW0xFRYVmzZqlxMREhYeHa/Dgwdq3b59bTGVlpR5//HF17NhR4eHh6tGjh7KystxiHA6HnnjiCcXGxioiIkL33XefTp065fE9uKY1QGVlZZKk1q1bX8swAAAElMadoH11eOLtt9/WrFmz9Mwzz+jgwYP6/ve/r5EjR6qwsPCS8VlZWUpPT9eiRYt09OhRPffcc3rsscf0xz/+0RWzYMECrVy5Ur/+9a9VUFCgGTNm6IEHHtDBgwddMdOmTVNeXp6ys7N1+PBhjRgxQsOHD1dxcbEr5qmnnlJubq7Wr1+vv/71r3rqqaf0xBNPaMuWLa6YWbNmafPmzcrJydFHH32kyspK3Xvvvaqrq/PoPnhdABmGodmzZ+uOO+5Q7969LxvncDhUXl7udgAAAGtkZmZq6tSpmjZtmnr06KGXX35ZCQkJTTotjbKzs/Xoo49q3Lhx6tKli8aPH6+pU6fqpZdecouZP3++Ro0apS5dumjmzJlKS0vTsmXLJEkXL17Upk2btHTpUg0ZMkTdunXTokWLlJSU5Hbdv/zlL5o0aZKGDh2qzp0768c//rH69u2r/fv3S2povKxevVrLli3T8OHD1a9fP61fv16HDx/Wn//8Z4/ug9cF0OOPP67/+7//04YNG64Yl5GRoZiYGNeRkJDg7SUBALgh+GoBtGshtNSk2eBwOJpct6amRvn5+RoxYoTb+REjRmjXrl2XzNXhcCgsLMztXHh4uPbu3Sun03nFmI8++kiSVFtbq7q6uivGSNIdd9yhd999V8XFxTIMQx988IGOHTumtLQ0SVJ+fr6cTqdb/vHx8erdu/dl878crwqgJ554Qu+++64++OADdezY8Yqx6enpKisrcx1FRUXeXBIAAFxBQkKCW8MhIyOjScy5c+dUV1enuLg4t/NxcXEqLS295LhpaWl64403lJ+fL8MwtH//fq1Zs0ZOp1Pnzp1zxWRmZur48eOqr69XXl6etmzZopKSEklSVFSUUlNT9fzzz+v06dOqq6vT+vXrtWfPHleMJL3yyivq2bOnOnbsqNDQUN19991asWKF7rjjDklSaWmpQkND1apVq6vO/3I82gjRMAw98cQT2rx5s7Zv366kpKRv/R673S673e5RUgAA3MiCgmwK8tFLvBrHKSoqUnR0tOv8lX73fnPdkGEYl11LtHDhQpWWluq2226TYRiKi4vT5MmTtXTpUgUHB0uSli9frunTp6t79+6y2Wzq2rWrpkyZot/+9reucbKzs/XII4+oQ4cOCg4OVv/+/fXQQw/pwIEDrphXXnlFu3fv1rvvvqvExER9+OGH+slPfqL27dtr+PDhl/15rpT/5XjUAXrssce0fv16/e53v1NUVJRKS0tVWlqqixcvenRRAAACmU0+nAL755jR0dFux6UKoNjYWAUHBzfplpw9e7ZJV6hReHi41qxZo6+++kpffPGFCgsL1blzZ0VFRSk2NlaS1LZtW73zzjuqqqrSyZMn9cknnygyMtKtUdK1a1ft2LFDlZWVKioqck2hNcZcvHhR8+fPV2ZmpkaPHq0+ffro8ccf17hx4/TLX/5SktSuXTvV1NTowoULV53/5XhUAGVlZamsrExDhw5V+/btXcfbb7/t0UUBAID5QkNDlZKSory8PLfzeXl5Gjx48BW/NyQkRB07dlRwcLBycnJ07733Kugbz+CHhYWpQ4cOqq2t1aZNmzRmzJgm40RERKh9+/a6cOGCtm3b5opxOp1yOp1NxgwODlZ9fb0kKSUlRSEhIW75l5SU6MiRI9+a/zd5PAUGAACuTXNMgV2t2bNna+LEiRowYIBSU1P1+uuvq7CwUDNmzJDUsHa3uLjYtdfPsWPHtHfvXg0aNEgXLlxQZmamjhw5orVr17rG3LNnj4qLi3XrrbequLhYixYtUn19vebNm+eK2bZtmwzDUHJysj777DPNnTtXycnJmjJliqSGDtYPfvADzZ07V+Hh4UpMTNSOHTu0bt06ZWZmSpJiYmI0depUPf3002rTpo1at26tOXPm6JZbbrniFNml8DJUAABM9vWnt3wxlifGjRun8+fPa/HixSopKVHv3r21detWJSYmSmroqHx9T6C6ujotW7ZMn376qUJCQjRs2DDt2rVLnTt3dsVUV1drwYIFOnHihCIjIzVq1ChlZ2frpptucsWUlZUpPT1dp06dUuvWrTV27FgtWbJEISEhrpicnBylp6drwoQJ+sc//qHExEQtWbLEVZxJ0q9+9Su1aNFCDz74oC5evKg777xTb775pms90lXfN8Pktk55ebliYmL08PKdCg2PNPPSPnF2xTKrU/BKi4EjrU7Ba86D/8/qFLyW9LMFVqfglZYJrb496DoV1i7624OuU8df/8DqFLzS6YffszoFjzkqy5WZ2kFlZWVuC4ebW+PvwPU/GqKWob7pQXxVU6v/b8OHpv8s/o4OEAAAJrOyA4QG1/QqDAAAAH9EBwgAAJNZuQgaDSiAAAAwGVNg1mMKDAAABBw6QAAAmMxms3n86oYrjQXP0QECAAABhw4QAAAmswU1HL4aC56jAAIAwGRBNpuCfDR15atxAg11IwAACDh0gAAAMJsPp8BoZXiH2wYAAAIOHSAAAEzGRojWowACAMBktiCbbD56hYWvxgk0TIEBAICAY1kH6KtXfyZnsP81oLZs6GJ1Cl7Z81+/szoFr/XNbGd1Cl7794W/sToFr4T3GWh1Cl6zx7ayOgWv3fzjYVan4JV/5BdanYLHHF9VWHr9IFvD4aux4Dn/q0AAAPBzNvnwVRiiAvIGU2AAACDg0AECAMBkvArDetw2AAAQcOgAAQBgMpvNh2uA2AjIKxRAAACYLCio4fDVWPActw0AAAQcOkAAAJis4VUYvpoC88kwAYcOEAAACDh0gAAAMBmPwVuPAggAAJPxFJj1qBsBAEDAoQMEAIDJeAzeetw2AAAQcOgAAQBgMluQTbYgH60B8tE4gYYCCAAAkzXsA+S7seA5psAAAEDAoQMEAIDJmAKz3jV1gDIyMmSz2TRr1iwfpQMAQACw/Wsa7FoPUf94xesCaN++fXr99dfVp08fX+YDAADQ7LwqgCorKzVhwgStWrVKrVq18nVOAADc0IJsNgUF+ehgFbRXvCqAHnvsMd1zzz0aPnz4t8Y6HA6Vl5e7HQAAAFbyeBF0Tk6ODhw4oH379l1VfEZGhp577jmPEwMA4EbFY/DW86gDVFRUpCeffFLr169XWFjYVX1Penq6ysrKXEdRUZFXiQIAcKNofArMVwc851EHKD8/X2fPnlVKSorrXF1dnT788EO9+uqrcjgcCg4Odvseu90uu93um2wBAAB8wKMC6M4779Thw4fdzk2ZMkXdu3fXf/7nfzYpfgAAQFPsA2Q9jwqgqKgo9e7d2+1cRESE2rRp0+Q8AADA9YqdoAEAMBmLoK13zQXQ9u3bfZAGAACBo3EPH1+NBc/xMlQAABBwmAIDAMBktqCGw1djwXPcNgAAEHDoAAEAYDKbzSabj1Yv+2qcQEMBBACAyZgCsx63DQAABBw6QAAAmIydoK1HAQQAgMmCbA2Hr8aC55gCAwAAAYcOEAAAJrPZfDgFxlNgXqEDBAAAAg4dIAAATMYiaOtRAAEAYDLeBm89psAAAEDAoQMEAIDZfDgFxnPw3qEDBAAAAo5lHSD7IwsUGhZh1eW9NuFHC6xOwSvlnYdYnYLXIjd+ZHUKXuu61D//vASFBludgtfCO7SyOgWv/SO/0OoUvNI6pZPVKXisuqLc0usHBTUcvhoLnmMKDAAAk/EUmPWoGwEAQMChAwQAgMl4DN56dIAAAAgwK1asUFJSksLCwpSSkqKdO3deMf61115Tjx49FB4eruTkZK1bt87t606nU4sXL1bXrl0VFhamvn37Kjc31y2moqJCs2bNUmJiosLDwzV48GDt27fPLcZms13y+MUvfuGKGTp0aJOvjx8/3uN7QAcIAACTWbkG6O2339asWbO0YsUK3X777Vq5cqVGjhypgoICderUdEF7VlaW0tPTtWrVKg0cOFB79+7V9OnT1apVK40ePVqStGDBAq1fv16rVq1S9+7dtW3bNj3wwAPatWuX+vXrJ0maNm2ajhw5ouzsbMXHx2v9+vUaPny4CgoK1KFDB0lSSUmJ27Xfe+89TZ06VWPHjnU7P336dC1evNj1OTw83KN7INEBAgDAdI0FkK8OT2RmZmrq1KmaNm2aevTooZdfflkJCQnKysq6ZHx2drYeffRRjRs3Tl26dNH48eM1depUvfTSS24x8+fP16hRo9SlSxfNnDlTaWlpWrZsmSTp4sWL2rRpk5YuXaohQ4aoW7duWrRokZKSktyu265dO7djy5YtGjZsmLp06eKWU8uWLd3iYmJiPLoHEgUQAAA3hPLycrfD4XA0iampqVF+fr5GjBjhdn7EiBHatWvXJcd1OBwKCwtzOxceHq69e/fK6XReMeajjxq2MamtrVVdXd0VY77pzJkz+p//+R9NnTq1ydfeeustxcbGqlevXpozZ44qKiouOcaVUAABAGCyxn2AfHVIUkJCgmJiYlxHRkZGk+ueO3dOdXV1iouLczsfFxen0tLSS+aalpamN954Q/n5+TIMQ/v379eaNWvkdDp17tw5V0xmZqaOHz+u+vp65eXlacuWLa4praioKKWmpur555/X6dOnVVdXp/Xr12vPnj1Npr0arV27VlFRUfrhD3/odn7ChAnasGGDtm/froULF2rTpk1NYq4Ga4AAADCZTQ2Ld301liQVFRUpOjradd5ut1/+e75xbcMwLpvPwoULVVpaqttuu02GYSguLk6TJ0/W0qVLFRzcsGnq8uXLNX36dHXv3l02m01du3bVlClT9Nvf/tY1TnZ2th555BF16NBBwcHB6t+/vx566CEdOHDgktdds2aNJkyY0KRrNH36dNe/9+7dWzfffLMGDBigAwcOqH///pf9mb+JDhAAADeA6Ohot+NSBVBsbKyCg4ObdHvOnj3bpCvUKDw8XGvWrNFXX32lL774QoWFhercubOioqIUGxsrSWrbtq3eeecdVVVV6eTJk/rkk08UGRmppKQk1zhdu3bVjh07VFlZqaKiItcU2tdjGu3cuVOffvqppk2b9q0/d//+/RUSEqLjx49/a+zXUQABAGAyW5Bvj6sVGhqqlJQU5eXluZ3Py8vT4MGDr/i9ISEh6tixo4KDg5WTk6N7771XQd94D0dYWJg6dOig2tpabdq0SWPGjGkyTkREhNq3b68LFy5o27Ztl4xZvXq1UlJS1Ldv32/9mY4ePSqn06n27dt/a+zXMQUGAEAAmT17tiZOnKgBAwYoNTVVr7/+ugoLCzVjxgxJUnp6uoqLi117/Rw7dkx79+7VoEGDdOHCBWVmZurIkSNau3ata8w9e/aouLhYt956q4qLi7Vo0SLV19dr3rx5rpht27bJMAwlJyfrs88+09y5c5WcnKwpU6a45VdeXq6NGze6niD7ur/97W966623NGrUKMXGxqqgoEBPP/20+vXrp9tvv92j+0ABBACAyazcB2jcuHE6f/68Fi9erJKSEvXu3Vtbt25VYmKipIa9eAoL//Vi3rq6Oi1btkyffvqpQkJCNGzYMO3atUudO3d2xVRXV2vBggU6ceKEIiMjNWrUKGVnZ+umm25yxZSVlSk9PV2nTp1S69atNXbsWC1ZskQhISFu+eXk5MgwDP3oRz9qkntoaKjef/99LV++XJWVlUpISNA999yjZ5991rUe6WrZDMMwPPqOa1ReXq6YmBhNyHjfL98G71jpn2/39uu3wX/hv2+D/87SlVan4BXeBm+NqpPnrU7BK/76NvgXv3uTysrK3BYON7fG34HH33xQUS1Dvv0brkLFV07dPPn3pv8s/o41QAAAIOAwBQYAgMk8Xbz8bWPBc9w2AAAQcOgAAQBgsoYOkK8WQftkmIDj0W1btGhRk1fQt2vXrrlyAwDghmTly1DRwOMOUK9evfTnP//Z9dnTx84AAACs5nEB1KJFC7o+AABcA5ut4fDVWPCcxzOHx48fV3x8vJKSkjR+/HidOHGiOfICAABoNh51gAYNGqR169bpu9/9rs6cOaMXXnhBgwcP1tGjR9WmTZtLfo/D4ZDD4XB9Li8vv7aMAQDwc7Ygm4Is2gkaDTzqAI0cOVJjx47VLbfcouHDh+t//ud/JMntfSDflJGRoZiYGNeRkJBwbRkDAODnrHoZKv7lmm5bRESEbrnlliu+gj49PV1lZWWuo6io6FouCQAAcM2uaR8gh8Ohv/71r/r+979/2Ri73S673X4tlwEA4IZis/nwZaisgvaKRwXQnDlzNHr0aHXq1Elnz57VCy+8oPLyck2aNKm58gMA4IZj5dvg0cCjAujUqVP60Y9+pHPnzqlt27a67bbbtHv3biUmJjZXfgAAAD7nUQGUk5PTXHkAABAwgoIaDl+NBc9x2wAAQMDhZagAAJiMNUDWowACAMBkFEDWYwoMAAAEHDpAAACYzJc7OLMTtHe4bQAAIODQAQIAwGRBPnwZqq/GCTQUQAAAmIxF0NZjCgwAAAQcOkAAAJiMRdDW47YBAICAQwcIAACT2Ww+XANkYw2QNyiAAAAwGU+BWY8pMAAAEHDoAAEAYDIWQVuP2wYAAAKO6R0gwzAkSc7qKrMv7RPOulqrU/CKs7ba6hS85q/3XJIcX1VYnYJXgpzBVqfgtaBK/83dX/+8VFeUW52CxxyVDTk3/k4yW8VFp88WQVdcdPpknEBjM0z+r3/q1CklJCSYeUkAAC6pqKhIHTt2NO161dXVSkpKUmlpqU/HbdeunT7//HOFhYX5dNwbmekFUH19vU6fPq2oqCifP7pXXl6uhIQEFRUVKTo62qdjNyd/zVsidyv4a96S/+bur3lL5H45hmGooqJC8fHxCgoydzVIdXW1ampqfDpmaGgoxY+HTJ8CCwoKavZqOzo62u/+R5f8N2+J3K3gr3lL/pu7v+YtkfulxMTE+HzMqxEWFkaxch1gETQAAAg4FEAAACDg3FAFkN1u17PPPiu73W51Kh7x17wlcreCv+Yt+W/u/pq3RO7A5Zi+CBoAAMBqN1QHCAAA4GpQAAEAgIBDAQQAAAIOBRAAAAg4FEAAACDgUAABAICAQwEEAAACDgUQAAAIOP8/vPAXT8KB2ZAAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -586,7 +586,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -697,7 +697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "6f435bd9-ebbc-458d-bad6-044ca7d7a111",
    "metadata": {},
    "outputs": [
@@ -726,7 +726,7 @@
        " <Axes: >)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1031,7 +1031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 11,
    "id": "1d9b2596-b700-47ef-8533-6b61aef433c7",
    "metadata": {},
    "outputs": [
@@ -1060,7 +1060,7 @@
        " <Axes: >)"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1093,7 +1093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 12,
    "id": "a0f3458f-60cc-4808-bb05-bec6c7226432",
    "metadata": {},
    "outputs": [
@@ -1122,7 +1122,7 @@
        " <Axes: >)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1169,7 +1169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 13,
    "id": "cfb46a92-d059-4f86-98a2-04684dc48e37",
    "metadata": {},
    "outputs": [
@@ -1198,7 +1198,7 @@
        " <Axes: >)"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1239,7 +1239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 14,
    "id": "0e325869-ca39-43ea-af6a-5250b2291143",
    "metadata": {},
    "outputs": [
@@ -1264,7 +1264,7 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1297,7 +1297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 15,
    "id": "6d7f5196-3161-4e38-aab2-c43e46b6d71f",
    "metadata": {},
    "outputs": [
@@ -1306,11 +1306,11 @@
      "output_type": "stream",
      "text": [
       "setting threshold for quantile 0.001\n",
-      "Baseline threshold set at 0.978377\n",
+      "Baseline threshold set at 0.980253\n",
       "\n",
       "Distribution parameters:\n",
-      "Within class: 0.983±0.00138 min: 0.98 max: 0.985\n",
-      "Cross class: 0.564±0.0468 min: 0.495 max: 0.659\n",
+      "Within class: 0.984±0.0012 min: 0.982 max: 0.987\n",
+      "Cross class: 0.57±0.0427 min: 0.506 max: 0.657\n",
       "\n",
       "Type 1 error (false alarm rate): 0.001000000000\n",
       "Type 2 error (missed alarm rate): 0.000000000000\n"
@@ -1322,13 +1322,13 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1343,7 +1343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 16,
    "id": "43fa4b94-a509-4415-b5b2-a0ba4bfb2695",
    "metadata": {},
    "outputs": [
@@ -1352,11 +1352,11 @@
      "output_type": "stream",
      "text": [
       "setting threshold for quantile 0.001\n",
-      "Baseline threshold set at 0.978377\n",
+      "Baseline threshold set at 0.980253\n",
       "\n",
       "Distribution parameters:\n",
-      "Within class: 0.983±0.00138 min: 0.98 max: 0.985\n",
-      "Cross class: 0.434±0.0706 min: 0.351 max: 0.616\n",
+      "Within class: 0.984±0.0012 min: 0.982 max: 0.987\n",
+      "Cross class: 0.444±0.0679 min: 0.364 max: 0.616\n",
       "\n",
       "Type 1 error (false alarm rate): 0.001000000000\n",
       "Type 2 error (missed alarm rate): 0.000000000000\n"
@@ -1368,13 +1368,13 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1389,7 +1389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 17,
    "id": "3020e256-9eba-4872-b35e-af0a5b0e0642",
    "metadata": {},
    "outputs": [
@@ -1398,11 +1398,11 @@
      "output_type": "stream",
      "text": [
       "setting threshold for quantile 0.001\n",
-      "Baseline threshold set at 0.978377\n",
+      "Baseline threshold set at 0.980253\n",
       "\n",
       "Distribution parameters:\n",
-      "Within class: 0.983±0.00138 min: 0.98 max: 0.985\n",
-      "Cross class: 0.32±0.0419 min: 0.248 max: 0.373\n",
+      "Within class: 0.984±0.0012 min: 0.982 max: 0.987\n",
+      "Cross class: 0.327±0.0386 min: 0.257 max: 0.371\n",
       "\n",
       "Type 1 error (false alarm rate): 0.001000000000\n",
       "Type 2 error (missed alarm rate): 0.000000000000\n"
@@ -1414,13 +1414,13 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1435,7 +1435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 18,
    "id": "a239eab3-26a1-4aff-9cdf-9b3265ac0993",
    "metadata": {},
    "outputs": [
@@ -1444,11 +1444,11 @@
      "output_type": "stream",
      "text": [
       "setting threshold for quantile 0.001\n",
-      "Baseline threshold set at 0.978377\n",
+      "Baseline threshold set at 0.980253\n",
       "\n",
       "Distribution parameters:\n",
-      "Within class: 0.983±0.00138 min: 0.98 max: 0.985\n",
-      "Cross class: 0.191±0.0348 min: 0.158 max: 0.264\n",
+      "Within class: 0.984±0.0012 min: 0.982 max: 0.987\n",
+      "Cross class: 0.199±0.0329 min: 0.162 max: 0.274\n",
       "\n",
       "Type 1 error (false alarm rate): 0.001000000000\n",
       "Type 2 error (missed alarm rate): 0.000000000000\n"
@@ -1460,13 +1460,13 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1481,7 +1481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 19,
    "id": "2f2cae61-d0b7-4a15-8823-8e8f2cbcad9e",
    "metadata": {},
    "outputs": [
@@ -1490,14 +1490,14 @@
      "output_type": "stream",
      "text": [
       "setting threshold for quantile 0.001\n",
-      "Baseline threshold set at 0.978377\n",
+      "Baseline threshold set at 0.980253\n",
       "\n",
       "Distribution parameters:\n",
-      "Within class: 0.983±0.00138 min: 0.98 max: 0.985\n",
-      "Cross class: 0.697±0.0449 min: 0.634 max: 0.766\n",
+      "Within class: 0.984±0.0012 min: 0.982 max: 0.987\n",
+      "Cross class: 0.698±0.0441 min: 0.635 max: 0.766\n",
       "\n",
       "Type 1 error (false alarm rate): 0.001000000000\n",
-      "Type 2 error (missed alarm rate): 0.000000000204\n"
+      "Type 2 error (missed alarm rate): 0.000000000082\n"
      ]
     },
     {
@@ -1506,13 +1506,13 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1527,7 +1527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 20,
    "id": "5d284aae-5b02-4670-b571-7ab6772c836b",
    "metadata": {},
    "outputs": [
@@ -1536,11 +1536,11 @@
      "output_type": "stream",
      "text": [
       "setting threshold for quantile 0.001\n",
-      "Baseline threshold set at 0.978377\n",
+      "Baseline threshold set at 0.980253\n",
       "\n",
       "Distribution parameters:\n",
-      "Within class: 0.983±0.00138 min: 0.98 max: 0.985\n",
-      "Cross class: 0.554±0.0429 min: 0.497 max: 0.653\n",
+      "Within class: 0.984±0.0012 min: 0.982 max: 0.987\n",
+      "Cross class: 0.555±0.044 min: 0.498 max: 0.659\n",
       "\n",
       "Type 1 error (false alarm rate): 0.001000000000\n",
       "Type 2 error (missed alarm rate): 0.000000000000\n"
@@ -1552,13 +1552,13 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1573,7 +1573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 21,
    "id": "cde79668-ed47-4e87-9d21-43bc7253820e",
    "metadata": {},
    "outputs": [
@@ -1582,14 +1582,14 @@
      "output_type": "stream",
      "text": [
       "setting threshold for quantile 0.001\n",
-      "Baseline threshold set at 0.978377\n",
+      "Baseline threshold set at 0.980253\n",
       "\n",
       "Distribution parameters:\n",
-      "Within class: 0.983±0.00138 min: 0.98 max: 0.985\n",
-      "Cross class: 0.38±0.0898 min: 0.28 max: 0.549\n",
+      "Within class: 0.984±0.0012 min: 0.982 max: 0.987\n",
+      "Cross class: 0.383±0.0893 min: 0.285 max: 0.552\n",
       "\n",
       "Type 1 error (false alarm rate): 0.001000000000\n",
-      "Type 2 error (missed alarm rate): 0.000000000014\n"
+      "Type 2 error (missed alarm rate): 0.000000000011\n"
      ]
     },
     {
@@ -1598,13 +1598,13 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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VxikcCRGRVB7d/A+hR42dPBiEiFTF6RgRSaXDIgjdtZ6MDoMQkaKYEyIiqTSL6RiDEBG5yjIxrch8jEGISFFMTBORVMwJEZFUuqZBN4k0ZnX5xHYQisViGXvTRqNRVzpERPaMlZGQ7dRVKBRCcXFxuty5by0R3V1ej3VRge0gFAwG0dfXly7hcNjNfhGRBU3TLIsKbE/HDMOw3CCbiO4e3h0jIrl0QDOby3CdEBG5iSMhIpLKo2nwmEQaz1jLCRFRftEspmOmU7U8wiBEpKh7brEiEeUX5oSISCpOx255sb4UhePHOdEXAEDBeHeWecYHko63mUw4v4mvMcH57x/5+xeOtwm4syn9g//7teNt/rh2l+NtAsCiq/90rK0b8Tj+J8tzPLpFYlqRoRBHQkSK4kiIiKRiYpqI5LJ4ih5qxCAGISJVeTwaPB6TnFBSjSjEIESkqLGynxCDEJGidF2DbnIHzKwunzAIESlKg8VI6K71ZHQYhIgUxZEQEUml64PFrF4FDEJEiuJIiIik4t0xIpLKajP7MbfRPRHlFz47RkRS6RYrpnWTunzCIESkKOaEiEgqTdegmdwBM6vLJwxCRIri9q5EJJUGi7tjijy4wSBEpCjdo5kmn5mYJiJX8bGNW/xTDRRNcG6je1+hO3FROL/PvSs8Bc7/5Pxtgjtf/lKf8311Y1P6Rz/4L8fbBIDeF9ocayuWyP6/0eDdMbPFiqPp0d3DkRCRorhYkYik4mMbRCSVx6vB4zXZYzqhRhBSZMBGRHdKrZg2K7nYvXs3ysvL4fP5UFlZiaNHj9o67/jx4/B6vXjkkUeyuh6DEJGiUiumzUq2Dhw4gHXr1mHTpk04deoUampqUFdXh+7ubtPz+vr6sHz5cjzzzDNZX5NBiEhVVqOgHEZC27dvR0NDA1atWoV58+Zhx44dCAQCaGlpMT1v9erVWLp0KRYsWJD1NRmEiBSla1p6d8Vhy635WDQazSixWGzY9gYGBnDy5EnU1tZmHK+trcWJEydG7Mc//vEP/Pbbb9i8eXNu3yOns4hIutSKabMCAIFAAMXFxekSCoWGbe/y5ctIJBIoKSnJOF5SUoKenp5hz/n111/xxhtvoK2tDV5vbve5eHeMSFF2n6IPh8Pw+/3p44ZhmLd7R0ZbCDHs7f5EIoGlS5fi7bffxty5c7PpegbbQSgWi2UM46LRaM4XJaLRs7ufkN/vzwhCI5kyZQo8Hs+QUU9vb++Q0REA9Pf3o7OzE6dOncJrr70GAEgmkxBCwOv14vDhw1i0aJHldW1Px0KhUMaQLhAI2D2ViFxgmg+yeBPHcAoKClBZWYmOjo6M4x0dHaiurh7yeb/fj59++gldXV3p0tjYiAcffBBdXV14/PHHbV3X9kgoGAyiubk5/e9oNMpARCSRG0/RNzc3Y9myZaiqqsKCBQuwZ88edHd3o7GxEcBgHLhw4QL2798PXddRUVGRcf7UqVPh8/mGHDdjOwgZhmE5lySiu8eNZ8fq6+tx5coVbN26FZFIBBUVFWhvb0dZWRkAIBKJWK4ZyhYT00SKcmt716amJjQ1NQ1b19raanruli1bsGXLlqyuxyBEpChu70pEUmmaxUiIT9ETkZu4vSsRyWX1kKoi8zEGISJFcY9pIpKKLz8kIqn4Guhb/CUG/BMLnOgLAGCcz+NYW7cTSeFCm443Ca8Lb9voNGocbxMAPA+XOd7moqv/dLxNJ9+KcbuCr0be3iJbyf4oMHdSVufoXh26d+SfF7O6fMKREJGimBMiIqn4GmgikorvHSMiqXh3jIiksnzvmEldPmEQIlLU4HTMbCR0FzszCgxCRIridIyIpOJiRSKSSrPYR5ojISJyle7RoJskn7mVBxG5ijkhIpKKj20QkVQcCRGRVAxCRCQVV0wTkVQcCRGRVHyKnoik4nvHiEiqez4nJMTgns39fww41hkAGBfnHtNOuxmPOd4mACRvXne8zRvxuONtxhIu/IfCrX2hHRK7NthW6vfKjv7rN01HQv3Xb466X3eDJrL51rc5f/48AoGA0/0huqeFw2HMnDnT9DM3btxAeXk5enp6LNubNm0azp07B5/P51QXHZdzEEomk7h48SKKiopM557RaBSBQADhcBh+vz/njt4NqvRVlX4C6vRVdj+FEOjv70dpaSl0G0udb9y4gYEB61lIQUFBXgcgYBTTMV3XLSP27fx+f17/EN5Olb6q0k9Anb7K7GdxcbHtz/p8vrwPLnYpchOPiMYqBiEiksr1IGQYBjZv3gzDMNy+1Kip0ldV+gmo01dV+jkW5ZyYJiJyAqdjRCQVgxARScUgRERSMQgRkVQMQkQkFYMQEUnFIEREUjEIEZFU/w8FtOSNrwjUGwAAAABJRU5ErkJggg==",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1619,7 +1619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 22,
    "id": "cea0663e-1b3d-48e5-8329-37b020b6f0f0",
    "metadata": {},
    "outputs": [
@@ -1628,11 +1628,11 @@
      "output_type": "stream",
      "text": [
       "setting threshold for quantile 0.001\n",
-      "Baseline threshold set at 0.978377\n",
+      "Baseline threshold set at 0.980253\n",
       "\n",
       "Distribution parameters:\n",
-      "Within class: 0.983±0.00138 min: 0.98 max: 0.985\n",
-      "Cross class: 0.271±0.0861 min: 0.2 max: 0.454\n",
+      "Within class: 0.984±0.0012 min: 0.982 max: 0.987\n",
+      "Cross class: 0.283±0.0852 min: 0.21 max: 0.462\n",
       "\n",
       "Type 1 error (false alarm rate): 0.001000000000\n",
       "Type 2 error (missed alarm rate): 0.000000000000\n"
@@ -1644,13 +1644,13 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1665,7 +1665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 23,
    "id": "9a715c1a-2caa-4ecb-a182-c94c979dda9e",
    "metadata": {},
    "outputs": [
@@ -1674,14 +1674,14 @@
      "output_type": "stream",
      "text": [
       "setting threshold for quantile 0.001\n",
-      "Baseline threshold set at 0.998775\n",
+      "Baseline threshold set at 0.998737\n",
       "\n",
       "Distribution parameters:\n",
-      "Within class: 0.999±0.000201 min: 0.999 max: 1\n",
-      "Cross class: 0.937±0.0919 min: 0.736 max: 0.999\n",
+      "Within class: 0.999±0.00023 min: 0.999 max: 1\n",
+      "Cross class: 0.937±0.0918 min: 0.736 max: 0.999\n",
       "\n",
       "Type 1 error (false alarm rate): 0.001000000000\n",
-      "Type 2 error (missed alarm rate): 0.251223299160\n"
+      "Type 2 error (missed alarm rate): 0.249765370704\n"
      ]
     },
     {
@@ -1690,13 +1690,13 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1711,7 +1711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 24,
    "id": "1b0a2d88-8022-4ff7-828a-8651a91514d6",
    "metadata": {},
    "outputs": [
@@ -1719,16 +1719,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Baseline threshold set at 0.933097\n",
-      "Within class: 0.999±0.000772 max: 1\n",
-      "Cross class: 0.867±0.21 max: 1\n",
+      "Baseline threshold set at 0.931467\n",
+      "Within class: 0.999±0.000847 max: 1\n",
+      "Cross class: 0.864±0.215 max: 1\n",
       "Baseline false negative probability: 0.000000000000\n",
-      "Baseline false positive probability: 0.376374353664\n"
+      "Baseline false positive probability: 0.376108543689\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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IUzGEmMSYDZzRay6nx3wLS/IkZ/vgg6WOwjXXbB2B/c03/F/MV/yU9QeW5A1nmLmUSm0SzWYUb/2zjmtv6q5PpdgTDkg3EEjFy6wsiI0KA9BIDKUvfdbNo+peQiGpBgtiiu6IUJg5U8ROVJQEz7ZncrLbZfLLzJQaGvfcs+dumCuukP0uWCAbyHXt3/+Wi3ZdnZwHt98ulTGvu04q8159tbQWmDpVxlNdLc/54IM9z1raV1mzRmIeqqrErffww7sXKgUFcj7YbBKf1BEOO0wsZZWVkdotu+PCC2HAADFAPPNMz/rOQiEJQP7lF/Fi3HnnNkJl8WJRxrfeKo2z6uslsCUmBlJTty5Xe9xxsiI47DAJcqmrk+CX3/9eKqZt4zvzeERsWi6hqVN7XpZQRYXEqpSVyffmsQcZ5CngpqzX0Tbmi5kuLU0uUN99J08aO1Z+7BdeCElJZDSu4wjnXHA6SNMqcWghQtj5umCwKDXFPscBK1ZArCsZOdJYxEDnP/cVdO+AupnZs8WSEB/fdotIa+rqpBQ6wP/9X8dWs7GxsuKLjRW/+iOP7NlEk5UViXt49VW55j/8sLh+QP735JNy3d9WWGmapMQ+8YSU9TZNETQvv9yzJr+9wapV4qZobBRPwwMPiPdhd1ixKmPGyHnVEZxOceOBiOG2YLOJWLbbpaDhd9917L07G9OU+kLz58vnmjatlWusuloU+tSpMqGWl8uPKhgkZHNRXOlkVZ6LZcs11ixooGLOKowvvxZxUlIikc1XXy0Tt2lKM6U//lHyfludsFFRItQzMkQQ3Hnnjl2g3cXzz4vFyTBAwySOeqb3fZ7YukI5Ri6XbHa7XGTuuUc+xBlnSGGf55+HkSM5KfFnsDtoMqPwGM0Y6MwLjCL8xNPd/REVHeCA9nmMHg2p6Xa01RK38t+CQ/lrdw+qmwiHIzVVzj57500Ed8Ubb0gMyIABEjTbUTIzZVE5daqUVX/5Zbjsso7v77zzxJqelyfxFWVlMpldd11kEtwVLpdMfDk5IlY++EAKz1133YHhNczLk8nN7xeBf9ttbTs/TFMEMOx53MgJJ8icPH++zFdtEcJ9+0qn79dfl5X6yJG7r+/S1bz5psRlaZpk/IwciRyo776TVubV1VBaimmYrGcA324eyTf141gfzqXJjCKIE0O3oekadptJtN3PAFchR+Uv4/SVX5ATVSlf0u9/Lwd/zRpR4z/+KCdsy4FLSBBX6003SQ2ke++V73hP2l50Bj//LLFG1dUQDpvE2n2ckjqfie5fIL9MLlQul1wkeveWD9HSGKq5WbIJly93U155F70Dn6M57RiNkKGXUmfEU0c8P3xWw2TD2L0/UdGjOGAr2IKsJi68ED7/JEgIO9E0Ur+yGH3oflCfup3Mni0Wh7g4qUDaXrFSVCSLOKs8f2dUEbXGBCIyTj654/t64w1ZgDU3yyr/9ttlAdpevv1WYnIMQ+Jhbrml6y7wgYBYMhoaZPP5xIVgbeGwiC6XS8aQmCjX7ZiYzhtDVZVkqFRXS6G3u++W92sL69fDn/4kY3v99Y4J4NbcfLOUk//NbyQmpS2EQjL+/HypgnvzzXs2hj3hq6/ESgdiADnxRORLfeIJMUGVlRGoqGNm4GheqDqLZcYw6s1YwtgxthjBTeSCrRE25TFNk3nXroXp4yrlwszZ/C7nS5LH9JG4lTlzxKQYGyvqpFUUb36+iKbmZumA/be/ta34XFfg80mNpBkzWuLIDIM+rhLmD7uUBG9pJOc6I0Ny4P/xjy3i69tvJS6toaHVDpubKFtYRE2jgxiznhXmQWCaXO95gYe+PHjnZZEVnUJnz98HwLpw50RHi+v3p9lhKuvteIli4bQPGf9ON17RugHTFGsBwOmnd2xSee01mcDHj++8cudHHilWkP/8R1bGqakda6AIMtnW18sEf/zxHRMqIJaYuDjJMlqwQFxWU6d2LFPINMV6v3GjuN9KS+XzlpZKqEFH64RER8uic+hQ+ZzDh3fMAmQFgVZXS4zKtGltFyoQcQFNmLDnQgVkcl+5Uqxk553Xtngoux1uuEG2778Xwdsd5fiXLhX3D4jQOvFE5Mu+915YsYLGDWX8u/4Mnmy4lCIjkwAO0HTsDqkvEhOjodt0DMMkHDAwgiEMv4+gP0zAsOMPO/GaDlaHcrhj/W+4f8P5HPLLan6fM5Pjjx1DbH2RNNaZNk26AbYUw8nNlfN32jSJ433mmc7p1dUR3nhDvF+NjWJVibM1c0P2+yQYNfKjMAw5AQcMkB9eUhKGISLFquPTu3cktnjt2mjerHKxeWUsDcEo7ATw42ZO8BB45z9KrOxjHNBiBVriVrIdVK4wMdB45bNUOjgf7rOsWCHN4JzOjrlvNm2SiQAkVqUzOe88uU599ZUEJU6d2v5mdR9/LBNcZmakFH8wKGJh0yYJDaivl2uhlQnat+/OYyzGjRMLw913S8LG7beLCT02dtfjKC+XSqzr1olrJb8lVnBX6LrsNyZGJny7XdKF7XZZAYdC4p7x+0VU1NWJxXDNGtk+/FBef/TRO0wQ2SXPPy/Wkbg4mczaU6TM8myAiM7OYNIkGVN5ucSg7qgb847o109EymefSdD044/vXQ9Aaamcu4YhiToXX4ycCH//O761m3l5zWE85L2GEqMXhmZDt2nEesKkxzWQYq+luRnC9SYOgjh0A5sdcDnREj2YHg+hYIBgXR21tSZ1oWhqgzF4TSezag5mXu1ghuVt4oqBszhvpJPo0g3iy9ywYUsRpeHDJd17+nQJb0lI6Fgm4J6Qny9tFfJaEn3smsGgqEKuinpV1IsVaZ+eLj7ivn0xTfFwff21/OvCCyW13fpux46FyWOSOWF8NeVBDyYaBhrrQ7n4Z8+lHbpb0QM4IMVKUZH4RZOT5YKXlm5DWyFxK597j5QZrE+f7h7mXsOqDnrMMTIxtRcr1mXixD2rjbIjNE3cS3V1Us79nntk4jz44La9/qefIjVfbrpJyjEsXiyxDOEwGP4gNDaIHdzvB8OUWSUcIiOqnnFZxRzVv5CBvb1oGelysczKYtigQfz9706mTRPxcfPNIqRap4FWV0v2yrJlslVUbD8+p1NOtcxMqeyani63qanyXURFtW+V6/fLZG6VqJ8/X+JrPvpI+s4cc4xMRLuL3fjf/2Ti0jQ5bu0ROSACuLJSBE5HrVjb4nRK24dPPxXx2VaxAtLIb/ZssWJ9+WWLZWMv4PNJfZOGBsmcuvZa0GbPwnz4H3z2Swa3lj7POqM/BjZsuknvmHpOTplLKKSz2ZuCHoQYDZzOIEM9G+nnLCTVUUuUzU9Qd1FtJlJgpLEi5iBqknpBoJrmygKKvMmU+JPw42RB/SDylqbz8eZV3DhqFofbfpKDUVIiAUhJSRx2mLimnnpKfiMJCZHA9K4mFJISAhs3yvEKh03ibc3cn/GECI+KCjHzxcaKVajFIvLiiyJUNE0amO4oLiprcAx/PHkRd78zBK0lYLcZD7M39eX49evFSqPYJzggY1a+/16yGVJSZKX1f/8HH7wTwB924MTP+qseIfuZWzvt/TqKacoqubJy+62qSqwBNptMatZE17u3XBTbGkhYVibl9E1TXC07Kpy2KwoLRUyYprjed1TBtDMIBmXlZ2VR3HHH7mue5OXJijEQkOeaJnzzZZiN6wI4Q82M1JaRGCgnk2LizVp0M0wzUZSTSomWianbxYRht9Mvppxf5fzIocnrRDw4HDB4MAVZhzJ11jFU+aOJjdW47joRB3PmiNWl9a/LZpOyEEOGyGq/Xz853l0ZI2AYIpg+/lhSZUEExGWXSdbrjoRQebm4ArxeSa648ML2v+8zz8iC4NhjI/2fOoONG2XCt9nglVfaFzD7ySfye4+Lk9vOKGe/K0xTLCo//ijxRI8+Csnff8T6B9/nbwvP42v/4XjxoGsmWe4qrsr5lABufqwZCk4nmsvJob3yOSZ2HqONhdLLzDRldvf55OLQ2AimiRkTy3r3MP7nO4yvfEfgCztprAuyoSmDulA0QdOBhsGAqGL+L3sW12R/hMthyEVw6lQ5GZGFx2uvyXnx17/CEUd07TECqTD9wgtyftbXm9gJcUryXN7LuFZ+TJWVYi4ZO1ZUqtPJzJnSOwkkJmlXvcvKl5Rw8sRaVjdn04wHHYObPE/z96leCTpTdAkqZqUTmDBBzOqVlbICHD8eZn8LJeUmQRy8/qbG357ZgzfYuFFWLitWQEMDtYEoiqIG0pCSS6j/YILp2YSJzFDBYCSQsr5+a1HS0TojKSmyaBg4UG4HDNix1eSLL+T6N2pU+4UKyIrdNCX1t6uECog2uOWWiGC54w6xYu/sItXQIIG+FRUQCposmVWLVrCJ1PIKfIF03DTzG+0FLtDfQdM1sOlyQdQ0sNlo1mNYGh7GD6FD+CEwkbz6FP6+4nT6xZTz64xZTEjegLZ8OdnLl3Of931uyLuOhd4BnPV1NDm5NlJTNTRNxMmoURInMWRI58RutAddF7fVuHGSfvzPf4rV5cknpa3BdddtPWlbotPrlZiXCy5o/3uGQhG3YGdXj+3bV47p2rWSVXPOOW1/7UkniYAqKJCsnCuu6Nyxbct774lQsdvh1ltMYj98jUfub+SZvDsoMXoRxkac3sjvsmYwKKGCj6omEYpPQUvTOdrzExcmfE4vV23L3jRwxYo6czrlINfUbIm61mpqGFg1n4HeOVwY9xozXKfxcfgoop0hipvi2OxNxW+6WNPUm4c3nMmymiymD36VDLNCzIJ/+Qsccgjnny+7/ewzKRsQE9M+C1Z72bhRxEpeXotL1DSJtzfyUPx9cvI2NspnTUyUATmdrF4tCysQl9rumqymHZzBoUM2snlRMl48GNiYGxoLPz3UdR9M0ekckJYVkCzBGTPEn37YYXDDDSYLF0ifoFEs4pfl7vbXBq+vl4C5r76istHNJ81HMzcwlmIyQG+1fLbMIfFxEhjh2vUMFhcn4iM5WdwDyclyPz5eVs41NWIhsYI1Cwt3XAckLS0iXvr1k1TcG26I9AuZOLF9H7euTlbogYCsINt7uDqC1fTtp5/k/mmnSUn/1hk5pinX348/Mmmu9TPMXI6nrpQTjJmczfvMNQ/hn+YVJNvq+Gvqy5S7+1CjJxPQ3dg0g+hwPZmOCnrbS0n2FdFoRvNh9WQ+9h+PzxUPdgcD3ZsZm15MQTiT+cVZ+EJ28pt7URWMJcoV5pCDGrjrbp3Bx+V0T7TiTjAMEZj//rfMAVlZsrC2hOqXX4qQcTrlNjOz/e+xcKGUvYiPl9o2nW05ssaYmSmTVnsO76JFInRtNnlte91bbWX5cgmtME245mqTgQvf4tp/9GVhTT/qicVOiKHuPG4d+hHfNh1CXswICIYYE57Pb9M/p29UuajIQw+V9LUhQ+TH3/rDmqaYWNeuFR/pvHlyv6wMKipo9iTzlv5rPq47kuaAnVW+vtSHoggYDuxaiOGxG3ly8NOMS94oB+T//g/OOQfD1HjoIRGcTqfEY3Wk7cbuCIVEI/3wg1RGrqyUuJw/Z7zBfWlPSLBPba0M4qKL4OmnaWgQS11FhcQw3Xxz277/l29axTOP+vglNJwwdtIppbj/ZLR1a3vU73N/orPn7wNWrKxbJ+ZDp1OC9i67DGZ8EiKMjSiayDv/Vnq9/UTbd/jzz3DllQSKKnjNdy6fGCcTwg6GiWYapLnqSIgJ4tDC2PUwNqcdTQcNsMVFE9svlZhBmcQOySIl00lKSkSgtDc11uuVlcr69fI516/fcWv46moJz0lPFyvEuHHtK+T21luSkjpwoGQR7q3fvFWB9t135X52tjQutNxCU6fCS/8MYTQ2MTy0hPONtziH90jSavC6E/nacyq31d5EeTiJvvZCUu01yDcBRtgkZIBmgk03iLb5GKKvY6R7LVnxDcxsmsyHDUdTEUwkjE6io4ne0TX0z2jmiJxNNBdU8nnJaEKmjRi7l98f/DNHnZ2MdsgEmXAcjr1zkHbDunXynVdWRgrxZWZK6mhDA1x+uSSMdIRHHpGYl1NOke+ls/H5JO7G5+tYmvy0aeJymDJFrgGdTW2tTKjV1TDlKJOM1f/j7tf7U+WNohkPCdRyWPwqTsxdzafa6YTsLmLL87gq8yOOSF4J2dmYZ59D1bDJbC5xUFUl+2poEGODzSbXhF69RGRmZrYEd4fDEqj06aeS2lNeDiUlFNhzeS78O5bU51IYTGNzIAOf4cA0NVJdtdzX7yV+kz1bzs1jjoGrryakObjvPglGd7tlDTa4kys6vP66uPJWrhSNFQ5K6vXSrJOJchuy+mpulg+5cCFmbBzTp8tCJSND4lzamoW36Acv006az9cN4/HjxoWP+YknMGL2092THnYAoMRKJ2Ga4pffvFluf/oJnngkSE2DDR2De133cUvln9tWtOKzz+Cqqyhpjuc+35/Z5BoEoSAjbSs5RZvBqOB8onSfXGkSEsTEYRXJCIflSmNFUjoc4lM5+mixv3ZS1bGmJkkA2LBBJqr8fAlOq6+P1FcCWUGdcIJYWXYlkgIBEXh1deLb7o5mcdv29hk9GooKTT593w/NzRxpzOIh7Sb6aJup86Tznv0CvvBNptkWR6nZi82BXjj1ECNiN+IlivqQB3/YQdDQ8QVt+EJ2bGYYTIMwNkKmDSdBYu3NBG1ugnYPyY56Umw1nNlrLhf1/YGkKQezMeogHns9lQ15GhgGg2OKuKT3N4xILZUDPGqUbH36dOuqrrZWApatXlBDhkjwcU6OZMx0NN35ootESDz4oLiSuoKnn5ZqtkceKavz9mAtVDRNAkpzcjpvXIYhwm/xYsjKNEnYvJTXv00nEJBMlGytkKHJ5fQboLPYPh6KiznEsZCr+3xGbL9UFo3/Az/WDeOXxVq7yuBnZsopNWmSnGJ6WYkEKs2YASUlmEXFfG47lRebLqDaH8XqwADqw1GETBtRNj8X9/qKRwc+iz3GLQV1br2VgCeeu+6StOvoaAkU7qzGh4sXi2hcuVJEWFWliQs//864iXOyfpYLc02NvPE998Af/8g338jv3W6X+kvtGYvPBxcOX8Z3GzKoIgkdg4dj7uJP0+LkAqbodJRY6UQ++ECy+IYMkWDDG/9ksHIlmMAwVrD0wS/R//rn3e/kuuvI82YwLTSVOlsycYEKTtZmstnRj4XGaAqC6YSDBp5wE+mUMkDfwKjodRzav5KclGa0xpZKRi3BnHg8cj8+XiIUTzpJllGdyIK5Qa680qSmGvr2DlFR56S+yYbDoeF2i5465xyZeHaUwmsVuEpJkeC47qrk2tAgAYGffAKrVoQpKTLQjDCjWcSD+i301ouZ5z6Sd0JniQvH6SAhNoyzVwJfrutHwHSQm6sRl6ATCMhFzes18TaZNNYbBPwGoYBBKGASMjQ0TKJoohdlJGu1hKNi8SZlkkAdScFSTkxdxBnpP5M0aSjvR/+Gd2ZE4Sutg/o6xkat5pyMHxkeu0k0SmyszOYHHSTbgAF73fJiZav8+KPEtAwZIhNCRxs2/vCDuARTUyVbo6u0mCU47Hb5Dbe3j9X06fKZDztM3DWdxZtvSr0Qmw3MgkJ+XhYFwSBJVJGulROd5CT5oHSqfNHohZu4POsLjspcy4wB1/JZ6Thq6yM51boui4jUVLF4Wm7fcFi+t5IS6VS+TfsfUlMlVfvUU8FdUyKFimbPhuJiistsPGL+iZWN2awP5FAaTsUfdmDXw4yOXcebB91HdnKzXG+mTcOXlsO0aXJuREeLwDjooD07RuXl8t2tXWtZd000I8ykmCV8m/kbNL9PhEowKAps1izKq+1bgr4vuUR6l7WXv/16M5+81cgqczAmGqc7ZvLRKS9EikwpOhUlVjqR6mrJhDMMcWNcfz18NTNMGJ0omvgq81Imrnpp5/m8M2bAVVexqSmFm4P30EQ0qf4idIfGz8YEioJpBE07hilXbM000c0QOgY2wjgIkmBrYJCngHHJGzkpawljnCuwxXjkiqRpkW3sWLkCjR3bsSIR1dWEFy/j+8/q+HR2LF+u7UupP4EERxODoosBCGguKm29KA8mENBcaJpGYqK87Z/+FLG+tLZKXXYZnHVWR45+51FSAjdeUceXX+s0BxwkUcUoFuPVY8gnF68ejcNuEBuv4UqKoc7nwufTtlSFtdlkIthSCbSllonTKQav7GwRZYEGH5sXVlJX6qXO66DJ8BBDI9E00eBIIhCVQLzDS0K4khNSF3FW+lyyxmXw36hL+WJZBuEGL9TXk6tv4jTnFxwRuxi3LRj5IE6nRI9a4mXIkK5PWUGCuKdMEatbRoYsyDtqbbBEwDnnSCxRV/LXv8Lq1VJb46KL2vfaggJJ1TVNcVvtriFjW7CsBYEAVObVU7AxhC3o5xDm4rdF4UtIxzWoD/bqCuIbC/lLv/fZnDWR171n0xx2EWw5Ffr0EfEVHy+nREKCeEIGDZIg9m0FYFOTxMjMmycWYquKa0KCZHMddxzYN6yR1cXatYQ2FfF6/Wm86z+VCm8M68L9aAy50TWTNGct9/d/gQv6zkOP9sDNN9M0ZCx33y1WEKdT4tvGjevYMWpullT4FSvEBV1TA431BjF6E3PTzmBwep3E3FRVyUF44w3MI4/ittvEwzV0qIjhjlwC33jZz7NXLmFuYDQh7GRpxRQOPFpOIhW30ukosbIDwuEwwWBwp//fFU8/LT/0E06QSe/Ff4VobNLRMThZm8nDl6/esZ35p59g6lSqmtw8FLqBulAMnnA9pWSwPtwPn+HAppnY9RAuPYjeUiQ7bGoEwjZCho0wNjRM7ISxaWEcephkez2jEvM5PWM+YzNK0Txu+eFaP6akJPG5jBu3pZOcw+HA1jqK0TQlAm31ali2DHPpMuYsS+S1wqMo9SdimBpL6nPx6H5+m/oJ48I/ozc2YIQMvKaLMiOVbziG+dohNDniwWbD4bZz7PE6N90kE/wdd4jr4JVX9sp8ulMKCuC235Wz4Acfjc06o8xf+APP87Z+Id9zBD5bNLhduOI91DXaCAQksM9ulwtvc7McrujoSOE1t1uMW1FRsul6JNZx5Ej5/D98Us2iGSXUFnupC0VTTywOgoRtTvyOWNA1os0mstxVHJf6C4eN9rEy50TmbMgiENDANHCFmjg0fRNHRc1jVO0s7A01W384TZOZ66CDZKYaOFAUYydXNJszRyaA9etlMkxPFxdOe415TU0SSxIMihupJRu2y7BKEMTFSf+o9sZ2PfaYZBSNGiWehj2htlYEfEUFbFjpo6HMizPYxOW8yArbSMoSh+BPyyatYQP9bJs4p/dc3o79HXlmX6qrNUIhKc6amLjrrzcxUaxBJ5yw4+MbCEgxvrfflusZiND54x9h5EEhCfR6+20oK2NhXiKPBK6h1BfPquAAasOxhE2dGLuPU5Lmcsvg9xmaUAJXXIH/+NO4/wGNBQsiTSJ3l4WzLT6fBOv+8oucaw4HrF9nYjOCXJ34Gv/o/5wE1dbUyA/01FPh3//m4080XnhBjs+TT25dy6g9LF8Of5mygFmVB+HHgxM/jan9ccz/8YCqq7W3UGKlFaZpUlpaSm1tbYffw++XmAddl8mquhqCQTkkNsKk6VU4U+O3zjn1eqG6WjJxSCBs2sA0CWEnhB0TCdfUNRObbqJp4lrCNCVLxwQDDbPF4mJ9AWZLkKeGKSJGM4i2+4hxBtHteqTsqoXN1mJvNkkoLSV97Vq08nKZwX0+TBOW1OfycsGx5HnTMTzR2KLduAMNLN6ciC0c4CBtNQGcW0aAaeIwAvRlI1kUslnryyxtCqVaOtjtODx2YhPtJKQ4uORSrUsCKNvKxnyT2/9vM2uXeKlo9DDUXMkd2t18aj+TxZ7DMFPTSBndm6UbYigqEpESHS1m8qws8e1XV0fiNO6/X66RXq/ow/x8qQK7du3WXWmjoiQ2YOxYKCsK8u3jy9m4qIomv4NaM54GYvHqHsK6E1OT78euh0lyNjE0o5ak4ZkUh1LxerUtp1VcnMnhw+uYELeaEd55ONcuj8w2rXG5RLBYkZWWj8DaLBNRGwkGJQi2vFysIfPni8UsJ0fiAiyPZFuwXIM5ORIL0tWL1XBY+vWVl7fqtdMOysrks4dCe9bPyjSlmvHcubBkUYhwbSPOYCP3mFNZYT+Y7xNPpcqZyaDwKg6OWU92rwDvmedQXBtFfb18lSkpcryys0WXpqfLsQ+HZe7euFHORa838r6jRknq7o4CX0Mhiel5661ITNdJJ4kl2VOxWb6o5cupyqvj4YpLWBIexnpvJiVGOkHDhkMPMywqn7Mz5vKbPnPIOG0coSuu5LFnnFuaU55xhljP2uICbmwUd+OSJeLCS0qCpUtNjECY3o5SVuachAt/pH5DdjZ88gkF7oHccIOIsD/+cc8apPr9cMGYtcxZmUxNS9zK+3GXcsaTx3V+6W2FEiutKSkpoba2lrS0NKKiotA6cHU0DMmUCYflglFVBY0NZovgMImjjt7OCrS0NBEs9fVQW0s4bFJmphEwJcYgYDoI4sBEQ9dMXC7QWpZI4bC8lxxpqw2ZgWYYImAMeTRs6hhoWHJFQ5SNXTOItvlJjPLjidbQgwH55ZkyzuZwmPKaGhJ++omMloYsq7x9eaHqbOY1DqWRWHxeg7iGIlK9BawO9qeRaHpTRKZegqZpROvNoOk06bGYNpsMyO8H0yCZKjyal585lAKzN/VmLDYdJg6u5No/OTjh4tS9Xj9kw+ogUy/aQHGej4K6OAaZazmTD5nnPJzyuP6E+/Sn3p3K+vUapikCY9QoqRty6KGRhVRRkdTbqKuT7JfcXJnAo6JEvMbFycRRVyeT0Y8/yjlikZYmK8x+OSGWP/sdsz5rosFrI2jYqDUT8GoearRE/KYL07S8ehoeRxBPnAMzOoaQ5kCz6TidGna7nGYDB8LBA5sZHpNPr5o1OEo24yjMxxFowqGHsbdY4hxaaKu/NV2TQce1pMVbm/WYVYCnZXb8+GOJOUpKkhosTU3i8quuliDrv/2t7aLj1lvFVN/RmIKOYI0/NVXG397YKauEwfDhIlg6IrA+/1xW/AvmGxgNjXgC9TzB9Zh2Bw/E3UtBOIvhjlUcnriCoqjBfOcdS3WtjdRU0Z2DBkkZ/iOOkK9lZ4RCMtl/842ch9Z1Zdw4EQ07Mg40N0v6+IwZcj81VcoVjBxuSKDXv/9NuLSCN9eM4e3AWZT641kfyiVo2AlrNvq5ixgQVcIJGcu4YHIJSXffwH8+S+Kdd2R/ffqIRWnIkJ2Pe906sdQVFkqWYmqqGH3ra8O48PNB0uUc23eDqE6r7PHVVxO6dRo33SSvHzNG0uH3VAD/6TeVfPB6E5vN3oDGr13v8drv5sgXqOhUlFhpIRwOs3btWtLS0khucYd0FCs10OORhWtRkUkoJP9zEqC3XkySvSVvMBwmbGoUk4XPcIAJftOJr6XThN1mEh2j05YO5KYp1iEzbIJpoBshwkGToGEjjN7Sy0LfYmnRNQOXHiLWHSIq3oHTCTYziGYa1DTWU1xSwTdvJfLN0gxW57vwNQbRvM30ChWTGS7ATgidMGvMwcRpDTwYfSeDootJ0aoIGjZCho6dEHVGLOvN/vyij+Vn7wgaAi4pQ49JMVmsN8Rx7tYDxDj8DOlVwyXnNHHKdf2J7rOLq20nsWZRI3dcvIGaEj+FdTH0MTeRTQEVzkwCGX2oTh3M5hInwaBc3A49VOIbDjlErCVLlog1Zd06uZgXFckWFbXzehJut7iARo8WEbN0qbghWq90Bw+GI0dU45kzk7lfNjC/MhcjZGCa0GBGU00SjWY0XtODgS6p7YBDC+FxhNAdOgHNSdB0RAJoNA2nA2KiDWKiIcYdJMruRw8FwR+AYAACQVFYoSA2jBbxEsKph0h11pPhqibDXcOQmEKGxWzCZQtBRgbNYw7nd1+eR33Qs5VlYvVqiUsIhdre4biyUuKXTFMCa9PS9uw7biuBgFRgrq7u2Mq7slJeHwpJem5b2zhYFBVJYb2ffjJprvYRE6zhEeNPTHbO5Zzoz1kf6kuubTOHJqxmVnASpcEUdJs0EDzxRBHPgwa17z1B5vW33hLhYhhiYD3rLInf2VGzyaVLxTVXXi6n1llniVXGUVEs/1i6lMVro3i4/P8oNnqx1DeQoOkgbNpIctQzPDofl0tjSvZ6znn4MApcA3jySVm7gVgZjz1WfiOxsXI6rlkj1rZZs0QEFxWJCycvD8pKDbRQkP+LfpfnBz4SaWwVDsuX8O67vP5VGm+9Jdrl6ae3eL33iOeeNXn2+lWsCA4ijM4APZ91R/5O2jYrOhUlVlrw+Xzk5+fTt29fPO2xVe+AUEjMrKYpP6aCAmhqksOiYxCFl3R7JbFaAz7NQ4WZSjCsgWkSMJ14EbOCwy5CxTqiUVGymLUa0Jmm/BYDLYaRQCCyRb4FE0JhQoEwoRCETZu4jIgsKXQM7JqBrpuyktZ0QqaP0tLN/O2GDIryQAsFSTXLGc98xmiLGcJqhjo38KHrAj7kDDKj6xmSWEq+YzAVpBKyuyBsgLcZV2M1Wf48Btg3MpSV+Lzwk38MC4MjWRwehmHaGOlcSY2ZQEU4GT8uEhxNDIwt5ZKjCznjxv44J4zqEj/A8i+KuOuqUpprvFTWOkmiCi8eotwmodz+FNGbyipRib16iWtn3DgpJPbVV2yXDqppYlWYN0++gxNPlMmjuVlM13V1W7xqWzF0qIQOOZ2yyl20KPIdahoMyWpgSOMCbCuWUlEJK+p6U+mLBdMgiJM6I4aacBw1JBDCgYFlVROBbNMgrOmEWwSNZW9DAx0Ttx4gyuYjxuEn1hUgyh1Gt9mg9SE3YYs5x24Dmx230cSRnvmckfYTc6qG8VbJZDL7OHn6g0zsmRGF8cUXEVfO1Km773b93nsSvzR8uATZ7k2sMvrJyVIzqT2doUFe+8kn8p0+8EDbT9tQSIJFP/oISguDxIZquNO4gz84XuLXCTP41nsonlAjfaLKWGsMxHC6iYrS+NWvRCB1RkxPSYnE61hFEtPSxCU2Zsz2z/X5pE/WF1/I/X79JBwvu7cpB+DVV6ku8vKPVSezKHwwy739qDXicWhBYmxeBkcV4HaBZtMYd1wSh1w0gOXLJebJMCLv43CwJVg4HBZ3WyAgXsuCAijYbBD0hhjszOPHPhcRbTbKD66mRp7097+zfMi5W4rqdWZphFmz4LazVjK/th9BXMTRwJqc40jP/2nvdrc8AFBipQVLrOTm5uLuBB9Eaam4S2Nj5YdXVmoSbml8ZSNMlO5Ds9taenOEsRHCa3paCRWIjhGXg90uE2VbCxYZBlvSZv1+uQ0EANPACITw+yFk2rYSLBpmy/Qmj5r4qKjcyD+uDJG+eQnH8C39yKfRnkCpuw+rYiewODiMH2uGEsBJfBw4o+zY7NqWYFO3OxJU6sQvJqfKSvB66esoorYOfm4aiaHpDLWtI2hz4cZHkZlBRSgJn+EixV7LyPiN/OGQJUy+8iC0445t/+yxExY9N4/77jHwNwQINngx0Ckgm94xNTT3PYiSxniam2WVeeKJ4s748EOJw2gtIEeMEJfQ8OEiTpubJf31008lfuDWW0XAJCfLhdc0ZTX4yy8iSlr3/HG55EI6aZKYub/9Vp67BdPA3lRPrpZPRv1a3A0VmIEggYBJIKBR5Ysi35fJxlBvSklrKQeub/mudQxcBHAQAl0jYDoxYUvANkhslUfzkW6voK+zmIM8+YyOW0/f2GpqHGmUhHtR6EthSWM/Kt29ISWVsAGlG/2kh4u4deA7HJ6+XswoZ5yxZba2apnExopw2VXBwGuvFcF/zTUS/Lk3CQQk9qSiQqxA7e0YXF0t4iEQkADQHU30O+LddyUOY9WKMLFGHX8IP8u9jrt4MnEadzb8iUBAI8nRgN+dAC4XI0eKt6Gzi6uB1KR87rlIGvOxx4p7c0fB73PnSshKQ4P87i+/XCxSWmkJPP44xpJlvL18GG9UnUBeOIe8QG9cBIjVGxkTvZqQIwrdboO0NKIPymHIUJ36ejn/q6vlt2Fdy0xTFmwej1h18vIMmuvC9LJV8kHKFYxJ3AihEKHicsrdOfimnETzrffywD/s1NZKjbobbui841RSAv93eB4/5KXjxY2DEB/EX8opP9/RNV/MAYwSKy10tljx+UT1gwiNggKptwEyGdgJ4dSC2Anhxk+NmRBx/dghOlou8C6XLA72tO5IOCwuBtlMmhvDBHwmYUNcQxaWmNJopqoyn39fWULFZpOww02DM5laEqgLxxKwufHjwhuwYbNpZGRIaqOVAWO3b/FyEQzK54iKAr/fpKmsEUdNOUs3JRAydCbZ5pJmFFNo9Eaz2wg6onAZXorDqZSGUgkZOr1slRyVsJjrx/1I7kUTJbJ/RwVb2oLPx8+3f8L9r/Um1ODF3VxNOank0Y+chHqaMgZSWS9un5gYmThDIbYEAoJYlk88UYTKypURN1BxsZioQyExlYdCsuK0Ygfi4yWOZeDAyKZpUqH166+3rgw8cKBc9IcOldTMhQvl1jKVA3Jwm5owmprB58cRaibkDdHgtREKmoSCJt6Qk4ZwFI1mFCHT3uIE1HDhx40XnTAB3ISwEcSxpc+U3iJgRcQaeDQfudomJtgWcIZjBoc5F7EqehwfmmfwX+9plGm9SE0xee/IJxlcMkvGN3asLGWjo7eUQ9+wQcTd3Xfv2OpgNRe026WkR1vqKHY2P/0kMSd2uwir9va5+te/xEIyeLC0c9iddaWkRNwoP/5o4g42cqzxBW/zK/4dcxXX+x/EF7QR5/Bii/aQnOHkvPPg9tu7th6RzyeVnT/9VERCUpKIxx1ZxaqrJRvKam45frxk+MTHmbKDV14hr8DBs2uO5vvgeJZ7BxLChkfzM9K5isPjl1Hk6EulO3ur+kCGIae4FfuvaXI+bNgAeRsMGuvCpNmquM3zKJPT1/J93QiW1vRmk5GNGROHefAo1pdEU1Mj47/2WtHPHelbtiNMEy46tpQZ33qoIxYdgz95nufhx52iWBWdhhIrLXS2WIHIxBUXJz+60lJTyp1g4iCES/OjadBseAi1TBB2O0RFSdM6j0dW6l3RRdcSL3U1YepqDAJBHcPUWq2vfVRWbuSGP2ayaZMHdA2bXUO369gcOjabRiAQyYZNTo7Un3O5ImIFZHJtfVaEQiLeSooNbOEAw6LyifZWYnp9YBj4TDdR7jBBVwxOw0dhII3SQDKaESZTL+G3KZ9yybAFRJ91vESxtvXKY5owbx4zp/3Is4sPxaipJy1YyDJGsJkc0lNCeBMyaWjSCYflc/32tyJSmprksx51FJx+usSqfPutiIdtz3hNk5TQwkJJqXS3FPG04pa2JSEhIlzsdrG0LFkSCXiMjpYCxEcdJdfx8nJZ+c6bJ/EgeXliGree3/Jh0TCJdoXx2APEu3zEmvUEGgNU1Tup9bsJhXWCplRYTtaqiNWaMEwNv+nEa7ppIgqf6caHe0uwt2WBsxEiiRqO5Wsu5nXu025jvTaI3AwvKeNzuemIuRzywyNiXsjOlmjGtDQKC2USCwRkpX7GGdsfj1deETdQZxdYaw+mKVaRhQtFcFiZXW2ltlYsDIGApOXvqo6I1Xvq+edNws1+BodW8Ag38pL9D3ysn0Fz0IFDC5OTY5I1IIqLL5Zkk71VymPlSglDKZbySRx9tMzD24pI05QA5VdekXM9Pl6sGOPGIWrsiScwlyzl61VZ/LP8DOb6x1ARTkQDEvVaDnMu5NDMzbhS4igfexJlwSSamuT616uXXGM2bJDfXX1tmOaGEGlaJaP1pSRHeSkLJkq8lWFIeYFhA6mJ7s3q1fLbOOgg+S1pmgSxX3FFS1uBPeTuaUGev7eSElPcnofaF/H9ZS+hP//snu9csQUlVlroCrFiWVc0TawjmzdL7IppRszuEmaqoQF2B7jdGrouVoiMDJnw/X6ZLFsaom6p72azyQJk3rw5PPPMQyxZspDS0hI++OADzmxHIxbTlP3X1Zo0Nhgt7+GnrHwjN93Uh6JiyYxyu2ViTUiQMaxbJxfwUaN27Z6NiZH5KiFBXCSrV8tka81jmCY1ZQEc3npim0rQwwHChk4UXuJcPpzRTkI2J+uae1Pt8+AyfAzS13Fzyksc2y8P7YTjJU926NAdX8HDYZg/n8A7H/Hil72ZUTASGhroa2zga46hmhRiUt2Y0bE0e8WNNX68GAV+/ll20b+/TKxLlkjdiUAgsvusrIgbKDtbvjenU57zhz+IKf3ii6UYXmlppEXBunVSyKq1f771d+L3y/O93oiVymaT4+nxyK1VC8QKikxLkwuwzRapStp6rBZ+P1RUmJSXifXFDBnEuv2kRnkxwiaBIAQCGoGQRsCw4w/aCBg2AmEbwbCNsKlvyXCTOKxmTmQmw2yrme88HG3COG78fRNHfXGLLLtTUyX4pFcvPv8cnnlGzp1HHtm6u3Y4LJN8VVXHmmF2JuXlIqwaG+W7b29X5ZdfhvffF4H5yCM7Fxdffy3BxGXFIWLC9RzNN9ToySx3jKIuGI1bD9CvH2QOiuXcc/euULHw+6Wys9UVPTFRYlkOOWT7527cKGnqmzbJ/SlT5PMlxLdYWV59lUBFHV+uyeEf9b9nkXcIfpyARiwNpDpqSYvxknlwCrGDMmhq0ti4UfYbDJoEvUGCzSGS9Drshp/B7k3YNAO30cwk8wfGxa1lyK/HUHrp35h6h04oJEX++vSRYz1vnowrKUmMfnvaVPH11+Gey/JYF8jBRCdX38TMCXcw8Kd/79mOFVuhxEoLXSFWQCaLxsZIcKz84CRrR0O6MksLHw2HIzIZ9eq1pfzKdsGY2zJ79ucsWvQDBx00hmuvPYfnnvuAU089E6czEjvidLbtAmeZXRsbfWzenE9DQy42m5uoKBmb3S63TzwhouOUU8Snb7dHGhnW1soEXVQkEfyta4qkpcln++47ec2RR0rp7eZmscCUl4SpKW7G7mvEZfrAhAxKSHA2kRwfotTVhzV1GXi9JlFmM0fYfuSWhGcZmt0os9748aIYPB5544ICWLKE5RtjeHbDcWyuS4BAgBwzn084HZ8ehZ4YjzvGgdcrq8FTT5XPaBWinDRJxrdoUeRzZGWJ//vII3edqTJrllQzdjolBiA1dev/BwJiGVm3Tuqv5OWJQLEEhmnKcamoiCQ3gIwrKkr2N2iQXHAPOkhEsVUuxQrCrq0Vy0tZmfxdXy+b3y/n2C+/iIXIMGSfkyfLZ2pJHtqyBQJyHMrKZKzikjIIhyOBvOOZxyTtR1bqI9EPGswtDyZx6Id/k5MhLQ2mT8dMTePee2XS6NMn0p8F5LF77hHB9eqr3d+n0RoPiDXh9NPb/tq6OhE4Pp+4bHY0sdfUyHm0dIm0dTiIlWRpJaxxDMOvuQmZNjxJHoaP83DmmTLpd2dx1NWr5fuy3JVHHSW1aba1UAQC8v198glbUv0twW4rFysLS5cSKq3k3eLDeKTmt6z3Z+PHiYmOkwAOLYTutLcE62mEgwZa0A9hA80IEac1MMiRz4jYTZzk+JpDvf+TkgfHHkvJHc/xl2lSd+bwwyVw2Tpua9fCo4+K5dPhkP8demjHj8ncuXDNKXksqe5NGBvJWg3Ppt/NuUWPq0q2nYgSKy10lVgJBmUCN02pr2G5QKzVtOXicTplcoiLE8VfURGZ5K2JKSoq8jwrEygYlH1aWUD9+mk8/fQHHHfcmVuNw2bbuorq7iaBXR2PzZvFPdLUJBefigqZwFqn3Vo4HCK+bDYRbjabTHI+n7z2rrtkcs3LkxiPZctk8tyYZ1C+qZlwk4+QqeM0AwzQ8kh21NE7oZFfPBPZWJNA2Bcgzqxnsv0HznF+ysFR68iIrkdz2KkOx7OiOZev6g9hsX8oGAaOYBMhbMzjEEIOD8TGEhOr4/OJALnkEhlfcbEc46ysiPnbEi5nnCGugbZch0xTXBnLl8sF8bbb2vaaqioRLT5fJEDa6ZTHrNgY67zaEZomZnNLHKamym3r+63dGnl5EltRWCiP//GPUlZ9V3i9ktoqqaQGRlgCeOOoYwLz0TVw9cvk/teyGfzKLVsJljpXGldfLRP6RRfJfkDiWObPl1TYyy7b/bHaG1j9eUDOj3POafsc9OqrEjibmyuuFE2LBFh//70EyC7+xcQ0DNIo53ztHapdmRS6+7OquQ+mJ5qho1z86ldipesJc18gIMfj/fflsyQkSEDyxInbj2/NGhHp69fL/awsES2TJppoMz4Tn1FjI6GSCj5pOJJ/lp1BqTeeejOGAC5shHARwNRteDUPAcOJhkkvvYLT3V/yq/7zGdb4s6hvmw2OOILqvz/HTQ8kU1Ym8WIPPMB2dZv8fjnff/5ZrqfTpokltSOUl8O5E4v4eUMKAexE4ePmmKeZtuHSvZdzfwCgxEoLO5ycLVv8HlJVJSsoq5FYOCyTQuvAMV2P5P1XVUHY7kLTNeLjxeTaVn+5pmm8/fYHnHjimVtSmn2+7V0NVp+a6Gj5IW/rxml9PBwON3l5MuGuWCGm4M2bZVzb9kCJjY2kGjY2bj2ZGoZ87sJCeb9x4+RzTZiwtRcnEBDB8vXX8PEHYTav8xNs8uMLO/DgZaC2nlx7AcnxIWY5T6C8OQozECLGqCdZryVeq8Oj+SUDRtfwBzQa/Q7izRo2k0OFlobfGY8zxomjpXDakCGyCv74Yzn+dXVbH5tjj5UxZma277sHERXXXy/f+003SbGuzsDnk0mgsFDES3GxCMLWlpmdoWkiWNLTIyLG6ZRsnTVr5P9HHy1CwopB2nZbvFiyV3RdhNjzz4YpKpLv2YOXTIrI1orI7B/Fo58NIuUft8gg09Ph/vuZsyKZhx6SfT3xhBzryy+Xc+a55zovCHJPMU0J9LUKl40bJ9aEtpRpb2gQ0dXcLJlFpimp6SUlIvZ/+gn8PoMEapnJiaxyj+Fd98WsCeRSa0siKdPD5ZdLYHJPECqtWbtWrCxWIsGgQdI7aMyYrcdqGJLe/J//RPoM9esnAvWQPqXoTz4uFxefD1/Qxqz6MXxTOJg1tWnUBGMoJZ164tAxSNJqOMXxJVemvEu/pDq5sAYCchIefTTldz/HtIfjttRgeeCBnTelDIfFwjJ7tizkHnwQ+vZt/3EwTTjn2Fq++NZBM27sGJzj+ow333OinXJy+3eo2CFKrLSwQ7Hi88F55+3x+5otuwqH5Tfldkv5inBYaqPpLfW6/IGImb/m+Xfo1cfd7v4kmqZtF7Niaa7mZtl8vq1FhKbJhOR0Riw9gYC4gT79NJfVq91bNFswKHEbmiYuoCOPFCtDRoZMfq2zikOhiMvAKpw2a5asyJOTI6ud1FR572HDZNXV2occCMBnn8FTTxgsXxQg2BzAF3IQRRP9yOcQfT66y8aP+hFU21JpDrswwqAZYWxhP27DS6xZi4sApaTTaE8g5IklOkZ82ampIh5+9SupqbFpk7iz+vSRMU2YIFYkq+liR3n9dSm61Za03T3Fcv2Ul8tWVhaxfln3dyVmiovFCGKtmgcM2F7MmmbEdTRhgkxSmgaPPmKwYG4Yf1DHgZ846hmkbWDsCD8PzBiJ685bRE317o05/X7ueSKe+fNFLI4YIYJg5EgRQT0J0xQh98IL8huwBNqECRLPZJ37Xq+I9IYGmcQ3bhQBPG+enO/Wue10SuzSiuUGrnAzL3IFua5i7rHfSY2jF2sCudhiPZx7rsajj3a/O2xnBIPw3/9Ko2HrGjF4sGSxTZq0tUWjuVkWOh98ELHC9uoFp51qcmz4C6Lffgm8XvxBne/tR/HfkiNYV5lAuL4Zj7+W421fc0HcTLISm+VCaflFnU64+mrWnflX7p1uo7pasu/uv3/3/ahCIbGqLFsmC5HHH9/eCtMWbr0pxHMPNVBDHDomhzvm8foNC+j94HXt35lihyix0kJXihUQUeLzyq2mgcsJug2p2RZqiWNBRIzTCfYP3pGmg+1kR2JlW6xMoKYmuYDsKEslHPZRVpbPK6/kUlnpJjpaYiLKy2WSGjNGgujas9r73//EzN/YEl5ipVKXl0faE8TFyWR18cViaWnN99/D/dNN5s8N4WsI4g/qRNFMNoUM15aTqlXQaMSwiRyCLYXRmommmkR89ljCMfEENSd2u0wsffuKWX/cOMnYWL1ajoXVjfbKK2UsnUEoBH/+s5j/Dz5YjkN31YyyxExpaWSrrJRrf329fD9FRSIwrf5HltXLOlfKykTYORxyjCyRm5wMq1aZrFrix+u3YyeIh2YG6Rs46+h6bnptJNrfbpY37NePyj/dxx9viqGxMZI5192Btbti0yZx7cyf3/bXWGXtbTZxIZ5/vkzs559n4q0PMNH8gfdt53ON/TmqY/uwMjAArzuBgQM1vviiext7tpXaWsngmjEjIoTdbhEsY8aISLMEen291CuaOTNiabHbITvVi756JYXrfPgNUWfuaJ0TD6nljKPqSAkUy4FfvlwuXA4HDBhAcPrDfLS8P6+9JteRnBxxL++q1UBrGhokpbmqSlzTV13V/s//r3/BXVeVUBRKRQOG21bz8ORPOO7bW9q/M8UOUWKlha50A1kEArs20bvdEXO8NANqv923LWKlNaYpQsnvl9uW9kKEQj5KSvIxzVzS091kZMgkdtll8tzdpWNuSyAgF4HycgnIPessMYF//rlcewIBWdE3NsoqNSVF9n/ZZdubZhcskKDHn382aaoLEQyA3QwSRRMJWh1ReDF0GyG7B58zjgZbAkHDhqbJsU1PF//0tdfKZ73mGklFjomRSfnXvxaXT2evZgsK4MYbRQOfe64IpZ7MqlVynBsa5JjdeaesPmtrJX6ipkYyxzMyJN5o6VI5h7xeWLdWMry8AR0bYdx46a9v5KbfVnLBXQdJk6DaWhg0iBkT7+XOBzwUFoq77ZVXen7xz/x8+OEHESFSoTryP7dbBEZmppy7ffuKa+2LL2QifeQRCUz9ZX6QaKOR75jEu86LmB91FJu1bMpsvXG6dWbOFGvjvkRNjVR2/vrr7ftmZmRI7LtlVbWCu+fNk+uCNXPYtRBZzkpOiZnF7zJnkOmp2f6NUlJoOv1CZtuP4cNPbFvea9Ik+V23V+AtXiyVlUGseu1dpPzvf/DH0wtZ25gOQLZezI3Z73LdxhvbtyPFTlFipYWuCrDdFsOQH3RDA1t6zXg8koVi1QDYE9orVnbGjo6HVeiqf3/x9bZnrO++KyvS5GSJR2h9iDdtkhXZt9/K6r6kRFY5yckySZ53ngiIbes6LFgg1p0FC+T5rcVW6ywWKbInQnDIELjwQllBffqppC7W18vxP/NMiS3pyliJ774T3zhIDYpjjum69+oMiopEmJaViQtr6lTpH/PFF+Iqe/zxiFWloUG+w/fekziaFctN6quDmGFpkOmhmVxbAc/dW8Uhv8qVyOOGBkI5/Rg7+x+UVtg55hhxmfW0+IxdYbZYR/3+SEHEbWlqknicpiaxUD71eBjDH+ByXuRk+1c867qeInsfqmNy8IcdXHFFJAtpX8Q0xVL5ww/iYsnP33kwuFU6we8Xy4jV9JNwGBoaSLNVkh1dQ1KUD0dCFI1xWRSHe5GXr22JxUtKkpTuo4/u+LljNaHc9rxuCxs3wjmTSllanEwYjUStnvNjZvBs1QU914e3j6HESgt7S6xYSNPBzllBNjY2sr4l3H706NE88sgjTJkyhaSkJHJycjq0z22PR1mZuEVCIXFhjB7d9n1VV8tK3OcTy8KUKTt+XnOzxLR89pnEuVjltmNixC1zww0iMrY9ZgUFspL7+msJOPV65djabJEKwIccIqvZcePEijNtmkyKhiF+7enTZd97Y5K0ip7puqS07q5PTndTWyvf+bp18h16vRK0OH36jmtUBAISe/LCC7BiWZj6GgObGSKETgxN9Ldv4r03g+SM7wW3386X63J5aPMFrDYHM2K0nZtuku+q3YRCEgiyerWY6erq5CRITpZI8LFj296zogv473+lDcOPP5j4GoOkU8pXHM+tjocojh5AfXw2ld4YBg2STJtt09z3ZZqaIllsNTUiTKxMwfR0CbhNS5Pf45o1sgCZP19EwK7o0weOP162Pb1sNzTIdaqhQazAJ7cjNjYUgimja5i33NOSEeTnaOcPvDG3H7GjB+zZwBSAEitb2FOxYq3mu4NZs2YxZQcK4JJLLuGVV17p0D63PR4PPSQNxkaNav+Kb/p0yYAYOFBqjuzuOFnBmzNmiJsoP18mSJtNgj2vvVbcSDtawVpBvVZPn8RECRLVNLlAfvaZpCxa/XbGj5cJZG9mGJqmWKb+9z/5DH/9a8+N0bDw+eR7fPVV+fv006Vh367Iz5faJEsWhaitBYcZJIiNOOoZ5srj0+/icafF8fvjN1JVa2NIXDGrEw4lblAvnn1Wo03XIyt17KefJA+1sXHnz3W5RCn/+tc7TxHpQrxeEcurVxrYCfA0V7PBPoR50UdT5O5HICqR+HiZKH//+70+vB5JU5Poz9JSEc3BoFhJU1LEStrWuJS2MmOGWFji4qTbd3umgt+c1cgHH0IzHuyEON45h3vvhVF/3U0NAEWb6Gyx0oWdKnou4bCsGOLiOt6uZk846qij6EqNuHixCBVNk8yY9vDTTyJUdF1iQ9oi6DRNVuzDh8tk9/nnIihWrpRV1zXXyMR5wQUyyY8cGTnudvvWbhzTFAvNrFmyn8WLxWXkdks8zLRpe19kahpcd51ceL//XrIWrrgCTjut57o/3G5ZAcfGRmoFvfjirouU5eZKIOU559hZODdAXYMDuxmkjnhW+vtx7pQ1XPpAGlX9x5NSspy70v/DzattbKzsywu3RPPnR3uzXTqcaYoaXbpUqvQtXLh11USrmJDfL8LF65UfqN8v6vXzzyVX9bLLpEPiXjzghYVQtDmEiUYUzSRTw/vOCeSbfUjNiaemViwM55+/14bU44mOlt93ZwW6744TTpBztqRETpWzzmr7a4eO8fDpR41gSlXyZtPN+gWljOqqwSr2iANSrDQ0RIp3Weme+wuBgHTLBans2p429DU1kdeec07HWtgnJspC+MILIzEqP/wgsRSPPw5vvy1unNzcrYP3fD4J2tuwIZLpYpmU+/eX2IuTTmr/eDoLy6Li8UhA4gsviJn8yit7ZvbH/PkRf/5vfiOxNx9+KMf2T3/aXlNYxMRInNPZZzuZN8dLXbMDuxmilngWNA1h6Q1hxhwZ5uKHDiYq9Duue3Imf154IbPe9XLUmuelUJdVTreuTgr8VFRs/SYpKVQOOJSla93kLW+msjkKE4ix+egfVcLo+DwyYloCxawCQU8/LZHd112388F3IqYJt/zNoLnRwI7JANZznz6VZlsKSX3jqG/Q6ddPsoX2p+vHvobNJjFyTzwhrriTT257k/fcATY89iB1QQCNeiOWDeuKdvcyRTdxQIqV+HhZbdbUyHXUMOR62FNXye3hvffEBJuSIpNUWzEMERZ1dZINYVUo7SiaJi6bt98WC8vf/y6BezU1Mr7CQgmyi4mJxLSEwxI8W1kpk0V2triibr55+7To7kDXxaXVp49YKWbNkvnzD3+QGJuecv4UFYn7DsT98/vfS32Rxx8Xy1BNjVTm3VlTOLdbuvde+CsXv/zgpd7vRDcNakjAEQqzfE4Vox8JwcgTGXj44Zzxp+V8+IWLp9YcxzPOZ/HYVm69Q5sNBg8mPPxgfjbG8/n7XhY/HYBwS7Slxx05GWx28AYZbt/Ir1xvcnCfVbJsrq+XA15VJYFDXawQZ82Cb2YE0IFxzKeGZIq1bGLcThJjnMQ4RKS0ZyWv6BqmTJGaSOXlspA49dS2vS4nB2I8YbSWMhR1ZizrS3rgykMBHKAxK6Yp1z9Ni7jMExNlld9TJpz24vP5WLUqnyeekDor7Q2qtZq4uVxS5XJPi6pti2mKheWNNyT+pHWjR49HhEAwKBNlTIwE8516qlhoeqLlYtUqiWOxUjCHDxdr1Nix3XsO1dWJuCsqEoH3979HYoWWLpU0z+ZmcRHddNP2FY1bs3IlXHtVkKU/e6n1RxFuaecZTx29XZW89FgDE64cg88H11xtUrbRy6mji/jDpOXyhcfGQq9eVCYN4suvdb54uZjq9VWYwTB+w05StB8zNp6y5mjqm500Bh34TRcBnLhiHMTGmExKWs306HtI8rWknPXpI1XM7r23y4JvfT446jAfvyzWiKGB57mSmx2PUGRm0ivTzqBBcp5efLG4NhXdz2efRaooP/ts236DBQVw6mFVrCiKx8AkVathQtRy3iyZQkzsPjoR9CBUgG0LeyJWamrEjW7137GqM8bGRizY+xp1dT6WLMnn5ZdzOfpod7usKlaQGoirY/LkrhkjyBy2ZImsXOfPlwVza9LS5P1PPHH31Sy7G59Psmjefz9SfC07W2JZJk7c+/FQjY1iMcnLE8vao49u76LYuFECrsvLRcRccIHUkNlZe4jPP4eb/+RlzVobIdOGhrElhiNG93L2mHzu/GcWRVo2U6fKJPHAA+K6W7QIvvnUy7wvqgkVlVPrc1LvdxEI22k0o6kNxRAwHBjIxCDdzE1sGDj0EHanjivaQZzbz01pr/C7hHfRKsplSTxypFQS6wKX0FOPh7nlz35CYY0LeAuby84X9lNp0BOIjdPo3Vs+37/+JdcPRffj9UodJK9XTosxY3b/mlAIjji4noUr3YTRiaeeSa6F3Pf5GEZOSe76Qe/nKLHSwp6IFa9XLuiGIZYElytS+M3lklXnXnCLdxqBAGza5KO4OJ+5c3P5y1/cba458Pnn8Mwz8vfeXimapgjHyspIxmpn+/8DgUh/o4ICcftVV29dO0e6aos1xzof4uNlLNaWmBjZkpLkMWuCr6yUEu0zZ0aEr6ZJgbAJE8TC0a9f155TZWWSrrx5s4zt/vt3Xn+msVEa8v34o9zPypI4o4kTtxYtdXVicXvxRSgtkBbO/rADn+EgjI6NEDE0E+MMMia7An90Aptq4nHZDfom1OKt8VJdAxXeOHxhB6Zh4tdc+EwXQdPeIk4MHISwE8JAw0QnhI6Jjg0D0PBEabg8Gkd5fuYfOU+QVbdSfJUTJkjtl7Y24moDRUVw1KgaNldGkUYZf9Ye5XnPDTTEZdG3v52NG+VcefBB6cOk6Dm88IL8DsePl0D8tnD+KY18OsOGDydRNDPF+RO/nZrF2bfvY9X9eiAqG6gTMAwx4/r9keQDtzuShFBQIBNnfPyemfRNU94rHI7UabG23aHr8t66Htl2NBavV1wR4bBcs6+8sm3FkUxTutO++abcP/30vZ/VoGky8XdW3x3TFDGyenVky8uL9G/aHT5fpJx4URvi7KyO25aAOfZYCRBet07Ew48/Snau3S7nV79+UkMmPV0sSDEx4snweORYGEakSJ5hyMpvZxuI+HG5JOPqk0/k3E1LEw/JrgrlxcRIQdrvvpN05qIiSQ+PjZXWAomJYnn55RcRe337wogRTmrX1lCytoIyfyJVwTgM00YdcTQEwny9IRpNMwngwgTW4ETHwEYYuxkiqDkImA7CppycUiXXR5a9nP6eYoZEbWZ41AZcho+Pqyfxv8bx1JlxhNHxNYcI+jS+DU/g12vu4J7ezzO5cI58mEcf7bSugaYJd15bSUmlBwcBzuID3nZfQrUzg0ED7Jx/vlSzDYXYIlr2Vbfx/sgpp4hYWbBArgNtqXszaIQbx+defKaTEHaCpp0NC+u6frCKdnNAipXoaLEkl5XJKtOqxGg1BjQMOdnr6+XCHR29a9dQOCwXdatrciAgq3VLpHQW2woYvz/SXcB67LXXJB7Eet/W4sj6u6kJ5s6NxO2MGSMX4JdfjlgXYmNli4mRzfo7KqrnXKADASkq11qc1Oyg0ndsrMTgZGeLULAERmysfOdOp3wmq42Bzyc1ImprxbpQWyv7tSwyNTWRYOD6+h0XwoqOlufV18vxDgalTLndLueYdWsJDrc7cttWsdnQIN9hXV3kPTVN4lJyciS8Y9Agsexsu7DRNHG3jR8vF/hPPpH9fP/91s8bOBAuvVRqZNx9dy+WaAZJ+UU0hipZ5+1NU9CJaWr4cWGaJhomYewtlpcwGnZM3GimiYmGjTAuLUC6s5qcmBrcyVEQP4TVMWNZ7XKj2zUmjvLyu14befyxCubk9abJ9KAZYZpr/ayM7sstm6/k+l6JnFfyGdqcOfLhfv/7PT4xZ3/exKefQhAbA8mj1pVOvn0g8alOrr5aKijn5sr5MXeuVP/t6RWNDyQyM6W55rJl8t20xUo8ZLgduy0MIQ0DjUYzmvz1u2mBrugWDkixAjIpZ2XJBFRVFbGyOBwygRiGPFZaKiLA45HJRNdlogiHI5Nb68aC1qq4tUCwRIa1tRYdsONrrLWf1vuySlVbwsi6b7fLvgIBWVVUVu74MxuGrJiLiiIdpfv2lUlqxoy2HTe7PeIa2dZVYm1JSWKZ6kxhYxgy7nXrIgIlP3/7po42m1gwhgyJbKmpnSuwTFNESGsBY902Nka2hobI31YTSktgWgK5qSlizYFIR21LJMbFyWa5kIJB+b5KS+V1VhxrZqYIMMMQAVNSIlYdi969pUDguHFyQbf25/FE4lZWrJBjWlsr39+AARLLah27adPgfmcG8z934MrbyMFx37HG14cNviwavHaMsGRVBE0IYwM0XHqAgGHHphnE25sYGlvE+UOXMuGs3sQePR49Ppa6OrFIzZsngcvfL45hrn04598Nh6718vTDXiqb3ITQCTT5WRfM4omy8ymLj+cqx/vYP/1UDtKFF3b4O21qMLj18nLqg2nE0MSh2jy+sJ+FFhXFWWfJeVVXJ7Eqxx4ri4LnnpPzqyvbPSjax7HHilj55huxFO/ud5+TA1Eug5qQiYlOvRFDUUUDwaCqut/TOCBjVrbFqvHR1CQTvqbJiWqzbd23pjWWlaK1oLCOZOvnb/t368da32+92rb+tu5b4sjnk4nPsthomggGjwf8fh8FBfmUleUSDm99PMrLJcV28WKZMEEusKedJhOcZQmybr3e7SfdhoadN3TcGS6XBHomJ0e2be9HRW1tKbLer6pKCveVlEgvory8rWuJWSQmbi1MBgzoefFGVn/N1sd0289pfdZtA453hdstZe7POUcsRlZ35oICiV3Jz5fJv6Bg69e5XOLuGT9etuR2xBKGQpLW/MFrTZCfR2ygigHRJfhNF2vD/aj0xVDvdVDQmIgv7MBjCzA0poBBMSVcenwxR1w5DG3czlOmNm4UC9/8+XLMcnMl0PrFZwNU10gQroMANg1SXfX0dRYxKX45rigbtkPGoR80dMv5ZLNFFh+WBcvpjFgPrcc9Hnj+hpW8PScTHy7GsIBKTx9qo7M4eLSN11+X8i6NjdJ+4sgjJXt62TKx1v3jHyrQtqfg80nJBquC847aS7QmLw9OPKSGDZVxaITpqxcyNKaQx3+Z3KE6U4oIKsC2hc7uDWStlisqtraW2Gxbi4ptLSfWa7cVKjsSONs+t/U+thU0O3LjWPu0spisFbdM9CJWGhtzqapyU1MjQaVr1sgEZpGWJjVUjjmm/VlPgUDELbKtm6T1Vl29tbWgs3C7ZWU7YIC4N7rCatLdNDVFhIt1W17OlpVeXFyk/szo0W0TZg0NYjWx+rdUV2/9//79JVZ1wgT5uy3Hc+FCeOF5g6KllVBaFoksbsEXdrCiMQfdbuPMyVU8+moyjtSErZ5jmmIhysuTbdMm+f2Vl4to2bRJhLnHI4Jq1UoDv0+aLNoJogPRNh/xtkYGRRfLSrhPn3ankVWtq2LxGg9+XLjw4tAMAo4Y3FE6l18ugnLZMrFCPv64dCOuq5PeV9XVcOihcMst+2YW4f7IE09IvZXjjhORuSt8Phg/pJ5VmzyYQLZWzAj3em78cDJTjlemlT1hnxQrzzzzDA899BAlJSUMGzaMxx57jCOOOGK3r+uORobhcGQC9vnk/rbiBLZ367RexVnWEdjeAmMY8NRT0/nii/fJy1uNy+Vh9OiJ/PnPD5CbO3i7QNzWYsXa745iGsJhH2Vl+bzyitRZaY3bLRPbMceIG6A93Uk7SiAgF/mqKnFLVVbKhb2yMvJYTc32x9XpFLdGQoJMCpmZ4sIYMECsQWpC2DNMUywu8+fLtnbt1t9BUlJEuBx88K7FUCgkQcRzZpusWeqntqQZLRwmOT7IsGEaUb0TmfGNG02TwnnDh4so2bAhIlAsS9+O0DQZXygkFrSxY+Hddwzqa8Li/qIOOyZZrgoy3dVcnD2HWE8IY/JRGJOOIGxoWyyGrePJWm+VizbywRcxVIdjAQ0XfgL2KNBtjBwp5+Hy5fK7HThQxuF0yjnpdktbC7dbXA5XXrl/Ced9laVLI0UP//3v3SeLHT2hgR/mOwlhI5VKxruWcdY9Y7jsryp9eU/Y57KB3n77bW644QaeeeYZJk2axPPPP89JJ53EypUrO9xhuCux2SIBmF6vbD6fXNisrBK7fWuTsmVObutEumzZbG688WrGjh1PIBBi6tTb+MMfjmfBgpVERUVvJYRau6O2zQgJBuXWNOVvu10m9SFDZCWamioX2IED977/1ekUsZGRsfPnhMMyfkvEWcdS0XVomsT09OsnsSq1tWJxmTdPsn+qqyUFe+ZMOZ8GDoSDDpIg3ZwcMVpY57ndLkG6kydrgJtg0I3dLu6SwkIRRalLpbXP734n77lt5pfdLhaLfv0iLp+0NNk8HhG1t94qFiZdh7fe1vntpVBREqLJiCWDYur9bpIxmVk6itsGv8ugNQ9Byo9w9dU7L9MbDtPw3OtcPjcTMzwCN34yKaYiui+eKBsnnywvf+opGUNyshyHoiK5FliNNTVNiufddZcEKp99tpSAGTZMYo4Ue59hw8Q9XlcnNZ3Gjt3183P6O/h5fpggdny4wTTZtKwOUGKlJ9HllpVDDjmEMWPG8KxVdQwYOnQoZ555JtOnT9/la7vDstIdVFRUkJaWxuzZs5ncwYps+9PxUHQPgYC4O+bPF/GybUsfEHGRkSEaIDraipcSUd/UFMmwszBNcelUVMhrp0wRgdO/v2y9e+9+5VteLlV5KytF1Jx+OvzmNwZVZWL27Es+Jjq97aUkePz8ccBXHJO5Ci0+LuLztKrdGgYsXozvX69xw38n8knVofjw4MFLozsVV5yLgw6CL76QeJ/bbxeR9OSTItYMQz6jFRO0dq0Ec65eLbvPzIxYAPv1E+EyZoxMoCpgc+/x7LOSNHDssXD99bt+7r33mjx0RyMNRjRumpnsmEev0Rm8+nMP6PGxD7NPWVYCgQALFy7kb3/721aPH3/88fxoVaTqTKxIxu7A5eqwDbiuJfc0qbMKjigUHcDplFXo2LHitiktFavBypWShWVZFbYN2N0RKSkyuffvL5aTb76Rirb19fJ4e1J+rdoxN98sFo0vvoCXXtK55BKN2sowm4xcBrCWslAy/qYGHl95LIvKe/O7gbNI/Oc/JWI3J0c+YHEx1cU+bv/5VD5vHk8IGyYajZ5UdLeLjAyZ6Gw2KTIGUr/DMgLresRieMgh8tjUqeJueP55iQ+qrhbL7IYNsn3wQcQVO368HF/1U+9ajjhCxMpPP4mFbFeCeMgQDbvNBEOy2JpND9UVYRoadm6YU+x9ulSsVFZWEg6H6bVNwFuvXr0oLS3d7vl+vx9/K7FR357UCNmBtODsDt55R65I7cQ0TW688UYOP/xwhu8udF2h2EtoWmRStoSFaYqVo6Qkkord3Cw63Spul5oqr9m28+2kSVIp+csvpYHypk1w+eVtLz6blSVtAm65RSwe8fHwwgsaV1xho65aY70xiFzy8BkONvozCZbpLKzqy1lx33BSwk/Er1+PGQrzQ9kAnqi5mLnmeJqJIowdLSaKkOlicK7sf8gQqamyaZNMVrvLiNY0KfWeni6f0TAkzve442QfCxZIfNZPP8lmVTc+4gg5Lnu7LcOBwEEHSbxRba3EHI0atfPn5uaC22VC0MTEJjFczV42bpQ0f0XPYK/UWdG2sTiYprndYwDTp0/nrrvu2htD6jFcc801LF26lO+3rcalUPQwNE1iSjrSs8lmg2uuEYvLG2+IGFi5Ukz0bU0Rzc0Vt8zUqVKULSkJHn9c44YbbNRWa2w2+pKiVZFuFrEh1BdHKEh+IIMnK39FklZDnRFLkZlBBamEsePR/OixsfhCdoYPFwvKOeeIm+fVV+U9L7647avrE06Qz/fQQyJS3nwTLrtMPndeXiQ2aN06mUCXLxdrzKhR4ho79NCe2bRzX0TXJUj8yy+l3tCuxEpmJrg9Glqj1AkKYAe/j4ICJVZ6El0asxIIBIiKiuKdd97hrFa91K+//noWL17M7Nmzt3r+jiwr2dnZbY9Z2cfcQNdeey0ffvghc+bMITc3d4/eXsWsKPYV5s2TKvmNjTKpnHuuBPu2Nbj6hx+kWaJpSk0Nv19cRNVVBi4tQKJRjT3sw2EGaDBj8OLBQCeAkzA2XASIjwoSjk8kENTJyhKxcP/9Yh26/XbJKBk2TGp1tNe7W14uvYPWrJH7gwfDb38r+wOJ3/n+e8kkWr8+8jq7XVxEkyfLRKt+xnvG/PnSLyslBV56aeffo2HAyH6NrN7kBkyGaGvJ9ZRx6kNH8oc/7oXUyf2Uzo5Z6dJEUKfTydixY/nqq6+2evyrr75i4sSJ2z3f5XIRFxe31dYuNE1+4d2xteOKZpom11xzDe+//z7ffvvtHgsVhWJfYsIEcZdMmiQTxX//C1ddBf/7X6Qq866YNEmyiwD+8x9xo0yfDimpOobDTZ27F2nZLlwxDmLcIeIczcTZm8lwVTMktYpLf+1j3AnJoOnExkoA7J13iivrnXdEqLhcYvXpSBhaWpqIlcsvl0vDmjXSi+nmm0WgxMbCWWeJYHv+ebHe5ORIZtzPP4tl5uKLZR8//dT+YowKwUq9r6yUYOidoeuQmgY2DEygwYwGI0zB6sadv0ix1+lyN9CNN97Ib37zG8aNG8dhhx3GP//5TzZv3syVV17Z1W/dY7n66qt54403+Oijj4iNjd0SvxMfH49HlcJUHAAkJsoE/uOPMmGXl0uTwPfeE2vJhAm7FgqnnSbBrG++KYGwl18Ojz0mFWa9XhubglmMn2KQHOVFCwTIyIQRh0QzaoKTu+6S99V1qUb78MPiUlq0SMrogwQY7yrtfnfoOpx5plhJ3nwTvv46Eqzscklw7oQJEltxwQWybdokzSXnzJG4oO++ky0qSuojjRkjQboqOLdtOJ1yvH7+Wax5u3I3ZuXYsc0PE8JJM1FgmBSs6yYrvWKH7LWicA8++CAlJSUMHz6cRx99tE0puvtr6vKO4nUAXn75ZS699NIO7XNfPh6KAxu/XxopvvuuBO6C1He5+GKZbHYmWkxTsnDefVfun3GGuFyuuEICKz0eqQ8zbJhU/q2oECFQWir7vPBCyTKKjpbU46lTpabSCSdInElnUl0Nn38uNWdKSrb+X0pKJI07K0sCdZuaJI38xx+37/WVnS0VnAcNktdZqeSqIN32fPWVVLQdOFDE8M647VaTJx9ootGIIpoGJjvnoQ8dzFs/5Kg4og6yT1aw7Sj7q1jpCtTxUOzrNDaKZeWTTyKhZ0OHiqVlZ4GOpimvsQJiBw8W0XLvveJ+CQZlRR0VJZaLmhoRLtdeGynF/ssvEq/S3Cx1Ue64o+uKE5qmxKl8950E2G7YsHPXl6aJBcpmk7FVV4sIa93ryGoHEh0tmVitu6NbPZJaF5m03suqqm39bZqRrK7oaHlfKxssIWHfFUJVVdI1XNPEarazOfOll+AvVzZQG4zGhZ9TnV/hyxnEQ58MYciQvTrk/YZ9qs6KQqFQtJWYGEkBPuMMsZbMmCFpyrfeKm6Q3/1OMjdao2kSoJuRIYXb1qyRWI+RI2UiLy6WarqaJlVo+/WDv/5VitPV1IjQ+fhjmayHD5fO0l1ZRVnTIlWlQSw569aJkLLGWlYmVqBgcOs+Ti6XfIbGRhEtjY0S5+JwiEgpKxOh0dnCIjZWrFMjRkgcSJ8+nbv/riQ5Wer8bNwojVx3ZtAfNAjsDg0zCAY6NlM6uhYUoMRKD0GJFYVC0aNISBBXztlnS/DtF19I2u/ixRIHcsEF22fKTJokAuDllyVbaPVqmbTDYamua9mPBw0SV8wnn0jQpdWw9KST5D33drsHt1tEwLaWI6uxanm5CJcdbVaDUsOI9D0yDLEuDRgggb6tG6+aZqRVQutGq9Zjfr+4n5qaxCJRUiLv09AgqeJz58rz+vWTyrBHHbVvFE0bPVrEyqJFOxcrubngcGpozWAAzYYb/D42b96bI1XsCuUG2k9Qx0Oxv1JUJEG0CxfK/fR0CaQdupNq6IWFkXomIIKltFRK5Fv9vSyGDBHxM25c142/qwgEZBJeuVK6ai9ZsnXz62HDpG7MuHEdt7YEg1IjZtky2ZYujQg8u11E3nnniduop7J4scQjJSXBK6/s+FgEgzAkp4mNpW50wkzS5xIbbTL2T5O586591AfWzaiYlRbU5Lw16ngo9mdMUwTI88/Lal/TZJK88MK2V8FtbBS3Ul2dWDT69xfhs6/GY2xLICAWqO++EyuIJSr69oX/+z8p9b+nNDSIZeqrryLNHJ1Oyc4677yeWdQuEIBf/1osR08+KcdjR4wb2sSS1U5MTIazkmxPFennH8ELr6juqh1hn6qzolAoFJ2Bpkm675NPSryJaYqL6Kabts+W2RkxMTJhH3ssHH64xLnsL0IFRDRMnCj1XP71L6nl4vGI9eXuu2UrLt6z94iNhVNPlTTxe+8Vl1MgILE/f/yjpAn3NJxOiUcCsT7tjPQsvaXWik4t8WAYlBUECQb3zjgVu0aJFYVCsc8QHS0uoJtvFvGxbp3ct6rFKoTkZCn1//LL4gqy26Wi69VXw1tvbe8Oay+aJsG2Dz0kQcmZmRIMfO+9Ul24pTdrj+Hgg+V2V2Ilq68TuxbGRKOBWDw0YzZ72UEbO0U3oMSKQqHY5zj8cKkA26ePZPX87W/S2VmxNdHRkrr71FNSyj8UgtdfF7G3p1YWENEyfrxYvM49V4J1v/9eUsOXLdvz/XcWI0fK7fLlOxdquQNs2HXJ5/bhJlWrgqamTjlOij1HiRWFQrFPkp4u1WcPO0wm4cceE9dQz43C6z6ysqR+zJ//LAJmzRqpM/PVV51zvJxOSTt/5BEpWldTA7fdBm+/3TO+j9xcscR5vVv3Y2pN//6SBm6iEcKO25S24kqs9AyUWFEoFPssbjfccgucf77c/89/4MUXe8YE2dPQNEk3fuopsTT4/VLd9YknOq//UP/+IliOOUa+g9dek75LDQ2ds/+OouuR9PClS3f8nEGDQLdrSBiTRoMRDV4vRUV7aZCKXaLEikKh2KfRNKlye8UVcv+jj8RFtKdxGfsrKSkSW/J//yfH7uuv4S9/2b4NQEdxu+GGG2RzOqW+yZ/+JIG+3YnlCtqZWMnNBbtdQ8PEAKrDceD3K7HSQ1BipRt49tlnGTly5JbO0ocddhiff/55dw9LodinOeMMCbbVdeng/PDDkfRdxdZYqd/33itdq/PzRVAsWtR573HMMfId9Ool1XX/8hfpzdRdWJaV1at3fF7Exkq7AQ0T0Kg2kyDgp7hYmel6AkqsdAO9e/fm/vvvZ8GCBSxYsICjjz6aM844gxUrVnT30BSKfZopU6Q8v90ugZ4PPqgEy64YORIef1xSkJuaxGXz/vud50bLzRUr1+jR4nZ66CHpw9MdVq+cHInX8flEnG2LpkFSkomOgYlGlZkI4TDV5WF8vr0/XsXWKLHSDZx22mmcfPLJDBo0iEGDBnHfffcRExPDXKuetUKh6DCHHCLBnQ4H/PQT/P3vqFoZuyA5GaZPh+OOE5Hy8stiEbGaSe4psbEigs47T+5/8IGkO9fXd87+24qmRaoer1q14+ekZ9qwaSJWakkknroW68reG6dix+xfYsU0RTZ3x9bBpUg4HOatt96iqamJww47rJMPiEJxYDJunJRYdzqlvsh993VeEOn+iMMh6cZXXSUNIOfMkYJ75eWds39dlxiZv/1NYlqWLhW304YNnbP/tnLQQXK7cuWO/5+V68TWUmulnjgyKAGfilvpCexfjQz9/oh839u888723dV2wbJlyzjssMPw+XzExMTwwQcfcJD1S1IoFHvM6NGSrnv33dJX6J574PbbpXuxYns0DU4+WWrXTJ8u5fRvuEEEhhWcuqdMmiSpzffeKwG9N90k3bRPOGHvVBNubVkxze3fM6evjsNmggF+XCSa1S3py0ldPzjFLtm/LCv7EIMHD2bx4sXMnTuXq666iksuuYSVO5P7CoWiQ4wcKS4It1sa2t11Fyr+YDcMGyY1awYMkJTjqVPh4487L44lJ0fiWMaNE2vX00+LoKyu7pz974pBgySeqbpagn63pX9/+b+JRhgdTAO8zcqy0gPYvxoZmmbnOVrbi8u1R0uDY489lv79+/P888936PWqkaFCsXNWrRIri9crroA775S+OYqdEwhITZb//U/uH3OM9P9xdlJfP9OEDz+U2jjBoBRtu+IKOProrrWy/OUvUhTvxhslILs1ixfDCUc0UdHowUGQa+zPsTZzCoPPG8nDD3fdmPZHVCPDXaFpsoTqjm0Pf12maeLvLqGlUOznDB0qbqDoaIlXmDZNsl8UO8fplLiS3/1OYk6++UZcQm1tHLk7NE2aLT72mFg0Ghvl7xtu2HktlM5gV3Er/fqBza5vqbVSYaSA36csKz2A/Uus7CPceuutfPfdd2zcuJFly5Zx2223MWvWLC666KLuHppCsd8yeLAIlpgYqbUxdapMkIqdo2lw+unipomNlcaRN9yw8wDVjpCTI9lHv/2t1DnJy5NsrqlTJTjaMDrvvWDXYkVqrZhbaq2UGSngD9DY2P1VeA909q8A232EsrIyfvOb31BSUkJ8fDwjR45k5syZHHfccd09NIViv2bgQMkMuv12mXj/8heZFLOyuntk3YflPW9oEPHm90tcj98f2Xw+OPFE6b20Zo0IixNOgDFjJHvIZpNYD2trfd/6e0fPsdnE3eTzyXdz5ZXw2Wfw44/wxRcwY4ZYwwYOlF5Qbrc81+uF5mZ5raaJ5ce6dbkgLk6ER2IiZGTI1rs3pKVFgmw3b5bPHBsbORZSawU2FxsYaJSSwRhzPZWmQVGRzpAh3fMdKZRY6RZefPHF7h6CQnHA0q+f1F656y4oKpLmfn/9q3Ql3p8IBKCiQtw2lZXyd1WV1DeprxdhUl8vE3Zb69AYhryuuhqef17EQJ8+nRfHYhETI2nTFRVS1G/NGnnc6ZSKu9HRYoWJihKB0lbi4iTI1uuVwnQrV0pdntakptuwrTAIm3aqSCKTEioDQYqKXEqsdCNKrCgUigOOvn0lI+Xvf5fg27vugosuksoH7Zn8egKGIVaC/Hy53bwZCgqgtLR9GTw2m4gEj0esEy6XWDJcLhEJNps876ijYPlyKc1vGJKCPGqUiEDTFHERCokY2PY2GNz6fjgs+7bey9pcLhmHwwGFhfKZCgtl/5ZFxjDEuhIXJ4XtkpLkNi5OXu9wiGArL5cxFhWJOFuwQI5NRQVcfz2cc46kVI8bJ2PJyHFg13z4TWgkhkyzkKV+P8XFKue9O1FiRaFQHJAkJIhL6Nln4auvpEPwwoVSHC07u7tHt3Pq6mDFCrE2rF0L69fvPB3b7YbUVGleaG3x8eL6iIsTcWK5TNqbJ7BpEzz5pIyjpkZEwYUXwuGHd002TyAAy5aJNSQvTz53ba2IlooKSUUOBkUImaY8rmni+snMFEESEyMCad48aeBYWSkF8ObMEXF06KHgcOrYbQYYGgGcJIYqwa+q2HY3SqwoFIoDFqty67Bh4tZYtUrun3KKrLiTekAtsIYGsWQsWyZZMps2bf8ct1vqouTkyJadLe6ZuLiuSwPu00d6L33+uaQfFxTI/bfekqaSkyaJu6azcDrFVTdggHRw3rhRhNKaNWJ1qaqS+BorBiYQEMFildbXNBEkUVHyveu67HPkSHl9dbWkaRcUgM9wYaIRQsdnOKBZ1VrpbvavOisHMOp4KBR7Rnk5/POf8PPPct/hkH45Z50lwZ17C59PLCdLlsiWn7+9O6dvXwkUHTRItt69u9d91dQEn3widVOslHCnU+JBDj1UMnBSUtq3z/p6cd0UFopA27RJBEpt7c5f43SK9cTplCBen0+sLtXVkaDc5mYRNfX1csz69JHjacWj/PIL/PR9mEBYR8PgRP1rmrIGkTohl3fe2TuVdvcHOrvOihIr+wnqeCgUncPixfDmm1untg4eDJMnw8SJ7Z90d4cVQGqJkzVrtu9KnJ0NI0bAwQfD8OFiMemJNDWJpeXbb8VC0Zq0NBEGKSkSWxIVJY8bhoiH2lrZKitFoOwsVVjTRDxaIqNvX8n2SU0VobIjMWGast/8fHEhLVggmU21tRLfouviHktLE/fgt1+F8AZ0wKQPm/G5k3ClxXPHHWI1Sk7upAO2H6PESgtqct4adTwUis7DNMX18s47Il5aXyXT0sRtNGSIWDSyssRdtLsVt2nKar64WLYNGyTmJC9v+2yctDQRJgcfLCKlJ7ij2oNpyuebPVuO44YNHSvXn5oqxzg7W0RJnz7i5uqMS9yLL8JLL4nwCwTE+lJVJcKlpChMIKQBBgPJI+iOozkunaFD5fljx4rVzQrKVWxPZ4sVFbOiUCgU26BpIhJGjJDg0R9+kCDM1avFXVReHilDD5GUWito1XLJmKZYG6x0Ya93x+8XHy+xE5ZA2Ztup65A0yS2ZMAAue/zicWopEQEQVWVHAvrODkckgadkCDCLCtLgmK7ct1licCcHGnFMGMGzJwpQrKkWAM0QKecNAbphXjTepGertHUJJaZBQskBmbCBAkqHjWqa8d7oKPEikKhUOyCxEQ49VTZvF6ZdFesEGtBYaFkoVg1TSoqdr+/lBSZiPv2jcScpKfv37EQbndEiPUUBg6U24ICyYa69FJJXf/oI1i9CqoqDQx0moiiPuihpsykd2+N666LiNWqKrEezZ4tMTIHHSTdvkeMgNxcZXXpTJRYUSgUijbi8cgKetSoyGOhkIiUhoZIsbXWLo/o6EiqcEqKxEgoup/ERHEzVVRIGvSIEfJd/frX8P77Gp+8HyRgOgAImHb8PpP8fHjmGXEBXn+9iLAff5StvFyytay+Rna7CJaBA0WQ9u0rFiNlfekYSqwoFArFHmC3R0q6K/YtBg0SsbJ2rYgVi759NTz2EIGgTJG9KKfRlkl9vY36ekmHnjZNgp0vugguu0zcR7/8IjFOq1dLPZx162SbMSOy75SUSBxO795iZevVS4STXc3IO0Udmm5m+vTp3HrrrVx//fU89thj3T0chUKhOGAYOFDikdat2/rxnByw20ALgo0w59g+5An7MMKaA49HrGmGIcHDt9wi8UYXXxxxF5qmiCCrcN+6deIyrKuLtD9YvHjr99Q0ETLp6SJetr1NSNi/XYW7Q4mVbmT+/Pn885//ZOTIkd09FIVCoTjgGDRIbteu3frx3FzQbaIMTHSSjXJGpJZS6umH0yliJBAQl57XK66fm26Sxo6//rWkuqelyXbEEZH9NjSIaLE2qy3CtnFPy5ZtP1anU6wvrVsLWJt1Pz5+/42TUWKlm2hsbOSiiy7ihRde4N577+3u4SgUCsUBx4ABYq2oqJCsr8REeXzgQNC2iBVYZw4kmSr0zH7cdht8/z3MmiX1YTRN4pFqaqRf0qJFMH68uIf699/6/WJjpZif1fnZwjSl5ktZWUS8WLdlZTK+QECK5O2ukq7bLeOxstN2dmttO6tN09PYv8SK1eu8O3C52vWNX3311Zxyyikce+yxSqwoFApFN+DxSOzI5s3iqpkwQR7PzgabXce6oueF+9LHKKYGmWL+/Gc47TT4178kfqW+Xi7/0dEiWubPl234cDjpJDjsMEnP3hmaJkIpMZEddna2grittO9tt+pq2UIhSRP3+STgty3outSNmTatPUdu77N/iRW/X3LPuoN33mlzmPdbb73FokWLmD9/fhcPSqFQKBS7YtAgEStr10bESnQ0REWb6NUGJhqFZhaHBhexEqkVY73ugQckqPaNNyQ+palJ1szBoLiHli2TuJb4eKmAfMQRIkbaa8loSxC3aUorAaumT13djm9b/7+5WWJv9oVO4/uXWNkHKCgo4Prrr+fLL79UlWYVCoWimxk0SDowbxu3kpioU1BgEEannFQygpvBNCgpiczsmiZxKqNHS8Dshx+KGwgirp1QSMTBxx9L/6TUVOmXNHasZCB1VoyJZdmJjm57Zpo1NsPonDF0JfuXWHG5xMLRXe/dBhYuXEh5eTljx47d8lg4HGbOnDk89dRT+P1+bDZbV41SoVAoFK1oHWRrmhGrR2qGHfsyPyHTRh3xZJhFEAxSUrL9tV7TRLCMHi0pzDNmSH8kKxi3rk5iTUIhcc9s3izixeUSS4u1DR68d/s+2e37TiuH/UusaFqPr7hzzDHHsGybUO/f/va3DBkyhJtvvlkJFYVCodiL9Okj8SRNTeLiycyUx9Mzdew2E0IazUSREiiGQIDS0l0vTDMz4YorpCLukiXw3Xcwd67s3zBEuJSVSWaQ3S4F6f73P2ns6HJJ4bg+fSRuxqrFkpUVafx4oLJ/iZV9gNjYWIYPH77VY9HR0SQnJ2/3uEKhUCi6Frsd+vWTmJP16yNiJSsLbDYTMwRhbHh9QCBAdbUEsO5uXWy3i6tn7FgRKevWSXzLkiXyPj6fxLU0NkYaXPp80srB44lsLpdsVgVka0tIkEwey/UTHS2iy24Hm23rWxALj2nKWAwj8rdpyvtYn7unosSKQqFQKA5o+vePiJXJk+Wxvn3B1pK+bKBRFEgjhiYaSaa0VP7fVnRdXDyDB8OvfiUioaAgUuF2wwZxDTU0SNCr1ytbfb0ImGBQRIclXByOrTe7Xd7DZovctg7i1bSIWGktUqzbUaPg4Yc762h2DUqs9ABmzZrV3UNQKBSKAxarqeH69ZHH+vaNFIYz0Fkd6k+GrZx15FBS0j6xsi26Lq6ePn3g2GPlMdOUNORNm0S4FBTIbWmppEP7/VJrxdqCQREyoRCEw7JZ4mPbzcISMNve7gvRB0qsKBQKheKAZsAAuV2/PhJkO2CAFIazDBRrjYEMNotYxzhKSzt/DFa5/ZQUcR21prk5UiiuvFyyjGpqIrdNTRG3UiDQtvez22VzOHpWN+ydocSKQqFQKA5osrMlc8frldiRrCxJMXY6NTRMQGOTmc2RgTWAPGdvEhUlLQByc3f/XMMQ4RIOy/3W1hVLnDgc+0bV2tbsA6VgFAqFQqHoOmw2CbKFiCsoKgqiYzR0DAyg1Ewn058P0CWWlc5C12XssbGyWSX24+MlCNfp3PeECiixolAoFArFlriV1h2Yk1J0bJoBaNSQSEbzBjCNvW5ZUSixolAoFArFlqaDGzZEHktNt2HXTUw0GomRWivBIBUVEtiq2HsosaJQKBSKA57WGUFW+fmMTA2bLgEfQex4G8O48GOaEvCq2HsosaJQKBSKA57evaWGic8npfHBKgwHoGGgs86fTUZMI9Cz41b2R7pUrNx3331MnDiRqKgoEhISuvKtFAqFQqHoMLq+fZBtnz5b11pZ788mw10D7P2MoAOdLhUrgUCA8847j6uuuqor30ahUCgUij1m2+Jw/ftHxIqJxtrwANINMbsoy8repUvrrNx1110AvPLKK135NgqFQqFQ7DFWcTgrIygjA+wOWmqtQJ7Zl+MDmwBpeqjYe6iYlW7gzjvvRNO0rbb09PTuHpZCoVAc0FhiJS9PgmxTUsDllsJwJhpFZibpzXmAEit7mx5Vwdbv9+P3+7fcr6+v78bRdC3Dhg3j66+/3nLfti80Z1AoFIr9mKws6abs80FhoVS29cRIrZWQqVNFMpmNayFB3ECGIbEuiq6n3Yd5R1aBbbcFCxZ0aDDTp08nPj5+y5adnd2+HZimnGXdsbXuFtUG7HY76enpW7bU1NT2fVaFQqFQdCq6vrUrSNMgOdW2pTBcPXEkVq3HbjMIhaTxoGLv0G7LyjXXXMOvfvWrXT6nbwfbUd5yyy3ceOONW+7X19e3T7D4/XDeeR167z3mnXdEkreRdevWkZmZicvl4pBDDuHvf/87/axQdIVCoVB0C/37w/LlEmR7zDGQmqZj001MA/w4qW5w0CveT1G1h5IS6SGk6HraLVZSUlJISUnpirHgcrlwuVxdsu+exCGHHMK///1vBg0aRFlZGffeey8TJ05kxYoVJCcnd/fwFAqF4oBl24ygjAyw24AQGNjY0JhGekzjFrEycmS3DfWAoktjVjZv3kx1dTWbN28mHA6zePFiAAYMGEBMTEznv6HLJRaO7qAdIuukk07a8veIESM47LDD6N+/P6+++upWliWFQqFQ7F1aB9mGwxK30rrWyjpfNplRtSwkVQXZ7kW6VKxMmzaNV199dcv90aNHA/C///2Po446qvPfUNPa5YrpKURHRzNixAjWte6gpVAoFIq9TmYmeDzg9UJBgVUYDkAjjMaGYA7j7JXAQCVW9iJdGsf8yiuvYJrmdluXCJV9GL/fz6pVq8jIyOjuoSgUCsUBjaZFrCvr14tlxWbXWmqtaKw3+pERLgRU+vLeRCVddQN/+ctfmD17Nvn5+fz888+ce+651NfXc8kll3T30BQKheKAp3VGUGoquFwaekutlUKzNxmNYgUvKWl3Iqiigyix0g0UFhZy4YUXMnjwYM4++2ycTidz586lT58+3T00hUKhOOBpbVlJSQGnWwdNVEkZqfSqWY2mSdWKurpuHOgBRI8qCneg8NZbb3X3EBQKhUKxE6yMoPx8iIkBd7SOTQsSNG3UkYBeVEDKAKioEOuK6tPb9SjLikKhUCgUrUhPh+hoCAalkm1Sig2bJm4gPy6qiv1k9DIAFbeyt1BiRaFQKBSKVmiaFIcDcQWlpbXUWgGCOCiqjyUjrglQYmVvocSKQqFQKBTb0Lo4XEaGlOLXaKm10pxFRpQEqyixsndQYkWhUCgUim1oHWSbkwNWr9kwGnmB3mQ4KgFpaKjoepRYUSgUCoViGyyxkp8v3ZitKrYmGnnhHDL0MgCKi7trhAcWSqwoFAqFQrENvXpJJlAoJLVUnC7QWwrDbTJySPdvAqChAZqaunesBwJKrCgUCoVCsQ2tK9nW10utFU0zMYES0vGU5m9JWVZxK12PEisKhUKhUOwAK8i2qgpcHn2LZaWaJIx1G7A6pKi4la5HiRWFQqFQKHaAZVkpKQGnx4auGZho+HBTvbF+i1hRcStdjxIrCoVCoVDsAEusFBZCfLy2JSMogJPSUshICwPKsrI3UGKlG5g+fTrjx48nNjaWtLQ0zjzzTNasWbPd81atWsXpp59OfHw8sbGxHHrooWzevLkbRqxQKBQHHqmpEBcnQbYxMWBrmTHD6Gxo7EVGdD2gYlb2BkqsdAOzZ8/m6quvZu7cuXz11VeEQiGOP/54mlqFlG/YsIHDDz+cIUOGMGvWLJYsWcLUqVNxu93dOHKFQqE4cGgdZOt0gt5iWTHQ2eDPIt1RBSixsjfYrxoZmib4/d3z3i6XnNhtYebMmVvdf/nll0lLS2PhwoVMnjwZgNtuu42TTz6ZBx98cMvz+vXr12njVSgUCsXuGTAAFi2Sv61aKwY6m4KZZNrKgAFUVUEgIIJG0TXsV2LF74fzzuue937nHeio0aOupcd4UlISAIZh8Nlnn3HTTTdxwgkn8Msvv5Cbm8stt9zCmWee2UkjVigUCsXusDKCfD5wOKTWiglsMrKJaSojOlrqrJSWSqVbRdeg3EDdjGma3HjjjRx++OEMHz4cgPLychobG7n//vs58cQT+fLLLznrrLM4++yzmT17djePWKFQKA4cLDdQXR04XRoa0n25yMxEKynekhGkXEFdy35lWXG5xMLRXe/dEa655hqWLl3K999/v+Uxw5DW42eccQZ/+tOfABg1ahQ//vgjzz33HEceeeQej1ehUCgUuyc5GeLjpTCcbtPRtTCGqVNJCsba9aRnSf8glb7ctexXYkXTOu6K6Q6uvfZaPv74Y+bMmUPv3r23PJ6SkoLdbueggw7a6vlDhw7dStQoFAqFomvRNHEFlZaCYbOhayFME7x4qF5TQeZ4eZ5KX+5alBuoGzBNk2uuuYb333+fb7/9ltzc3K3+73Q6GT9+/HbpzGvXrqVPnz57c6gKhUJxwDNggATPhsM6mg4aGn5cVGz2kp4uz1GWla5lv7Ks7CtcffXVvPHGG3z00UfExsZS2iLJ4+Pj8Xg8APz1r3/lggsuYPLkyUyZMoWZM2fyySefMGvWrG4cuUKhUBx4DBgANptknFq1VkLYKaj0kJkaAuzKstLFKMtKN/Dss89SV1fHUUcdRUZGxpbt7bff3vKcs846i+eee44HH3yQESNG8K9//Yv33nuPww8/vBtHrlAoFAceVkYQgN5SosJAZ31zBhnuGgDKy6V4nKJrUJaVbsA0zTY977LLLuOyyy7r4tEoFAqFYlckJUFioiRSNOqyxg+jkx/sTVK4AqczlUAAKirYkh2k6FyUZUWhUCgUit0wYEBLAocm6csGGptDWWhVlSpuZS+gxIpCoVAoFLth4EAJstV0fUutlQKzNxQWbrGmqLiVrkOJFYVCoVAodoOVEWSgYdMMQKOcVELrN6rCcHsBJVYUCoVCodgN/fuLWDERywpIrZXy1dVKrOwFlFhRKBQKhWI3JCVBWhpoOi1xK+DDRcm6RiVW9gJKrCgUCoVC0QaGDgVd1zA0HRMI4qSwWN8qZqWNyZ6KdqLEikKhUCgUbWDYMCkOBzoaUmtlXV0aqYkhbDYIBqGqqpsHuZ+ixIpCoVAoFG1g0CCptWJqGmASRicv0BtbXTVpafIclb7cNSixolAoFApFG7BqrZimlLE10CkIpUNFBZmZ8hwlVroGJVYUCoVCoWgD8fGQkgItRWwx0Sg2MzE3bSY7Wx4rKOi+8e3PKLHSDUyfPp3x48cTGxtLWloaZ5555nYdljVN2+H20EMPddOoFQqFQpGbC7pNJk8TqCWeuuUF9O4t/1dipWtQYqUbmD17NldffTVz587lq6++IhQKcfzxx9PU1LTlOSUlJVttL730Epqmcc4553TjyBUKheLAZsgQ0HQNEyt92U3JqlplWeli9qtGhqYJfn/3vLfLBZrWtufOnDlzq/svv/wyaWlpLFy4kMmTJwOQbjWbaOGjjz5iypQp9OvXr1PGq1AoFIr2c/DBYLNpBDQNTBMfbkrX1jOuRaxUVoLP19JHSNFp7Fdixe+H887rnvd+552On5x1dXUAJCUl7fD/ZWVlfPbZZ7z66qsdHZ5CoVAoOoGhQ8Hu0ABZnQZxsLlAY0qsxLTU1UFhoQTjKjoP5QbqZkzT5MYbb+Twww9n+PDhO3zOq6++SmxsLGefffZeHp1CoVAoWpOSAjExgCZF9w101tfIQlO5grqO/cqy4nKJhaO73rsjXHPNNSxdupTvv/9+p8956aWXuOiii3Aru6JCoVB0K4mJEBcHJUifIAOdjb4MCIXo3dvO8uViWVF0LvuVWNG0fctPeO211/Lxxx8zZ84celuh5Nvw3XffsWbNGt5+++29PDqFQqFQbIvNBjk5sGaVpC6H0CgMp0NhIdnZfQFlWekKlBuoGzBNk2uuuYb333+fb7/9ltzc3J0+98UXX2Ts2LEcfPDBe3GECoVCodgZAwdatVYkK6jSTMG/dI1yA3UhSqx0A1dffTWvvfYab7zxBrGxsZSWllJaWorX693qefX19bzzzjtcccUV3TRShUKhUGxLWhq4nCYaJiY6zXgoXVy6pdZKSQmEQt07xv0NJVa6gWeffZa6ujqOOuooMjIytmzbunreeustTNPkwgsv7KaRKhQKhWJbUlLA4xG7Cmh48VC6spqUFAlFCIdFsCg6DyVWugHTNHe4XXrppVs97/e//z3Nzc3Ex8d3z0AVCoVCsR3JyRAdq6NpErfiw0VpXjOaxhbrigqy7VyUWFEoFAqFoh2kpkJsnN5SaUUjiJ1NJU5ApS93FUqsKBQKhULRDlJTISoKdN0EIISDDbXJgLKsdBVKrCgUCoVC0Q5SU8HptDKCTMLY2ehLB59PWVa6CCVWFAqFQqFoB0lJYllx2MWyEkanOJxGML9wi1gpLJR+dYrOocvEysaNG7n88svJzc3F4/HQv39/7rjjDgKBQFe9pUKhUCgUXY6uQ3o6uJzSIchEo96MpWTuJtLTpXCczydNDRWdQ5eJldWrV2MYBs8//zwrVqzg0Ucf5bnnnuPWW2/tqrdUKBQKhWKv0KsXuDwaNsJoQB1xFC6uxG6HjAx5jnIFdR5dVm7/xBNP5MQTT9xyv1+/fqxZs4Znn32Whx9+uKveVqFQKBSKLic1FdweHYfmJ2TaaCKaotUNgGQEFRaKWBkzppsHup+wV2NW6urqSEpK2ptvqVAoFApFp5OaCq5oG049CIAfN5vzpWxtnz7ynE2bumt0+x97rZHhhg0bePLJJ/nHP/6x0+f4/X78fv+W+/X19XtjaAqFQqFQtIvUVHC7NRw2Ey0MBhqLyrIA6NtXnvP/7d13eBTV+sDx7+xudjcdAglJSAihl1AUkPJDOgHEK4gNRYSrcn9IEbAgyM8rVhAVG4pdUdQoIghXRVBJECGaAJEqRBN6CgHS6+7O748jy40Uk5BkN+H9PM88CTOzO+8c53nyeuac9xw86LLw6p1K96zMnz8fTdMuuiUmJpb7zPHjxxk+fDg33XTTRde5WbBgAf7+/s4t/Myw6npmwYIF9OjRA19fX4KCghg9ejT79+8vd05+fj7Tpk0jLCwMT09P2rdvz9KlS10UsRBCiP8WGAgWC2A0YsAOaOwtbI5eUupMVg4dAofDhUHWI5XuWZk2bRpjx4696DnNz/yXQiUqAwcOpHfv3rz55psX/dzcuXO57777nP/Ozc2tlwlLXFwcU6dOpUePHthsNubNm0d0dDR79+7F29sbgFmzZrFx40aWL19O8+bNWb9+PVOmTCE0NJRRo0a5+A6EEOLyFhSk1gFyGEwYsWPHSKa9Ebm/ZxLSPgyzGUpLIT0dQkNdHW3dV+lkpXHjxjRu3LhC5x47doyBAwfSrVs33nvvPQyGi3fkWCwWLBZLZUOqc9atW1fu3++99x5BQUFs27aNfv36AbB161YmTJjAgAEDALVO0BtvvEFiYqIkK0II4WLOnhWDEQNq3EqB7k3q9sN07RhGRAQkJ6tXQZKsXLoaG2B7/PhxBgwYQHh4OM899xwnTpwgPT2d9PT0mrokuq7mtrtiu5TiPzk5OQDlBh/37duXNWvWcOzYMXRdZ+PGjRw4cIBhw4ZdajMJIYS4RFYr+PuDxQoGHGjo2DCyJVbVEjszyFbGrVSPGhtgu379en7//Xd+//13ws4slvAnvYbK+pWUwE031chX/60VK9TDW1m6rnPffffRt29foqKinPtffvllJk2aRFhYGCaTCYPBwNtvv03fvn2rMWohhBBVFRgIVi8DRQY7JQ4HNoz8nGhgCjLItrrVWM/KxIkT0XX9vJs4a9q0aezcuZNPPvmk3P6XX36Z+Ph41qxZw7Zt23j++eeZMmUK3333nYsiFUII8d8CA8HqbcRDs2HAjgMjuw/5oeuSrFS3Wpu6XBssFtXD4aprV9b06dNZs2YNmzZtKtf7VFRUxMMPP8yqVasYOXIkAJ07dyYpKYnnnnuOIUOGVFfYQgghqigwECxWAyaDjsHuwI5GZqE3R46cTVbS09VQgar0vIuz6lWyoml144HQdZ3p06ezatUqYmNjiYyMLHe8rKyMsrKycwYkG41GHDIPTggh3MKZGUGa0YChTEfDQZHdzNYfCrlloheNGsHJk5CaCu3buzrauk1WXXaBqVOnsnz5cj7++GN8fX2dA4+LiooA8PPzo3///jz44IPExsaSmprK+++/zwcffMD111/v4uiFEELA2RlButGIAR0DDuy6gdh1xQC0aKHO++MPFwZZT0iy4gJLly4lJyeHAQMGEBIS4tw+/fRT5zkxMTH06NGDcePG0aFDBxYuXMhTTz3F5MmTXRi5EEKIM84kKw6DB4Aat6Ib2L1H49QpaNlSnSfJyqWrV6+B6oqKDDIODg7mvffeq4VohBBCVEVgIJhMoJmMGLABGgbsFOXZ2Lr1bLKSkuLSMOsF6VkRQgghqqBhQ5WsWL0NeGg2NEDDQXGBnZ9+OpusHDoEZWUuDbXOk2RFCCGEqAJNg8aNweJlwqypYnAODBQV6uzeDR4e4OsLdjscPuziYOs4SVaEEEKIKgoMBIuXAQ/NjgboGNBsZeh2B1u3yiDb6iLJihBCCFFFgYFgtWoYjAaM2HGgYdZLKMsrJjZWBtlWF0lWhBBCiCoKCvqzKKjJiAYY0LHoxRTllLB3LzRqpM47cMCVUdZ9kqwIIYQQVaR6VkA3eqCjoQM2h5EmFrVAbUaGOi81FUpLXRdnXSfJihBCCFFFQUFqRhAeJgw40NEo0i0EkglAYqJandluVwmLqBpJVoQQQogqCg5Ws4IMZg/MWhngoED3wph7CosFjh9XU5xBXgVdCklWhBBCiCoKDASDAcw+Hli0EgBKsHA0y4ure9sAyM5W50qyUnWSrAghhBBVZDT+uaChjwkPzY4RHR2N7GIr3cPSATh2DGw2SVYuhSQrLrBgwQJ69OiBr68vQUFBjB49mv3795c7JyMjg4kTJxIaGoqXlxfDhw8nOTnZRRELIYS4kODgM9OXtT9nBDkosnlgykqjeXM1WygrS70SystzdbR1kyQrLhAXF8fUqVOJj49nw4YN2Gw2oqOjKSgoANTaQaNHjyYlJYUvv/ySHTt2EBERwZAhQ5znCCGEcA/BweDpCRjVcnsaOkV2M4d25zNihBqAm58Pug6//ebaWOsqWcjQBdatW1fu3++99x5BQUFs27aNfv36kZycTHx8PLt376Zjx44AvPbaawQFBfHJJ59w9913uyJsIYQQ56F6VkA3mZzTl4scZlIPlDFjEHz4oXpdlJ0Ne/dCjx6ujrjuqVfJiq5DSYlrrm2xqBHhVZGTo+bjBwQEAFDy501YrVbnOUajEbPZzObNmyVZEUIINxIcrJIRD4sBY54dG0YKdU9SDhqwWuGaa9S05fR02LfP1dHWTfUqWSkpgZtucs21V6xQmXVl6brOfffdR9++fYmKigKgXbt2REREMHfuXN544w28vb1ZvHgx6enppKWlVXPkQgghLkVwsPpp9DRj0Qqx6VaKdCtHs70pOn6akSMb8tFHKmFJTFQrMHt4uDbmukbGrLjYtGnT2LlzJ5988olzn4eHBytXruTAgQMEBATg5eVFbGwsI0aMwGg0ujBaIYQQf3UmWdGsZjy1YgyocSvFZSYObk0jIACuvVYlKIcOwe+/uzTcOqle9axYLKqHw1XXrqzp06ezZs0aNm3aRFhYWLlj3bp1IykpiZycHEpLSwkMDKRnz5507969miIWQghRHby9wdcXPH1MlGoODDgwYaPQ5kHKttO0vwFuuQVeew1OnoRvv4X27V0ddd1Sr3pWNE29inHFVpnxKrquM23aNL744gt++OEHIiMjL3iuv78/gYGBJCcnk5iYyKhRo6qhpYQQQlSn4GBVa+VM57eGTpHNg5S9xYAqHnf11erY55+rMZai4upVslJXTJ06leXLl/Pxxx/j6+tLeno66enpFBUVOc9ZsWIFsbGxzunLQ4cOZfTo0URHR7swciGEEOfTpAl4empgMuLAgB2NIruFlP9aD2jSJFXtNiUFYmNdFmqdJMmKCyxdupScnBwGDBhASEiIc/v000+d56SlpTF+/HjatWvHvffey/jx48uNaxFCCOE+QkLUmBQPswEDDsrwoFD35FCGFVuhWm75yishMlItarhkietmr9ZF9WrMSl2hV6D/79577+Xee++thWiEEEJcqjMLGnr5gEe2jWI8KMNEUamRw1uO0mJICwwGGDYM3nkHDh6Ejz6CO+90deR1g/SsCCGEEJfozIwgg6cVT60YIzpmyigq8+DAlizneVdeCRERquz+6tWyXlBFSbIihBBCXKIzyYrdaMFqKMGAAyN2Cm1mkpPOLpPSuTM0aKAmZtjt8MILUFzsmpjrEklWhBBCiEvUuLGqYmv2MuJhsKOjga7WCEr+4+x00WbNwN8fmjZV5x89Cq+/7sLA6whJVoQQQohLZDBAUBB4+nlgNOKcEVRot3Aow0pJrhpNq2nQtata3LBnT/Xv779XtVfEhUmyIoQQQlSD4GDw8DLjabJhxE4RXtgwYbfppGw66jyvWzf1MyMDbr9d/b50Keza5YKg6whJVoQQQohqEBwMmkGjgU8ZJuw4MGDSyigpM3Jg60nneVdeqXpUUlNh0CDo10+NX3n6aTVLSJxLkhUhhBCiGoSEqJ8WXwtWrRij6lehwGbmwM6zo2j9/aF1a/X7jh0wYwa0awf5+fDII3DsmAuCd3OSrAghhBDVoEkT9VOzmvEylKABBhwU2CzsPVB+Edozr4ISE8FshvnzVcG47Gx46CHpYfkrSVaEEEKIanCmZ6XE6I2nsRQdA7oDCu0WsrI9yErJdZ57Zk3a7duhtFQthvjEE9CiBeTkwJw5kJDggptwU5KsCCGEENXgTLKimy2YTXYcaBTpVrXQrcPOvg1nB9m2bg2NGqkaK7/+qvb5+6txKx06QEGBSl5iYmTRQ5BkxSWWLl1K586d8fPzw8/Pj969e/PNN984j+u6zvz58wkNDcXT05MBAwawZ88eF0YshBDi71itf9Zb8bLQyFKgisLhidVQRnGZkX3x2c5zNQ169VK/b9169ju8veGpp+Caa1SS8tFH8OST6vXQ5UySFRcICwtj4cKFJCYmkpiYyKBBgxg1apQzIVm0aBGLFy9myZIlJCQkEBwczNChQ8nLy3Nx5EIIIS6maVPAaqGxZz4m7Bix46HZKCgzs2+3o9y5vXurnz//rGYDnWEywT33qIG3Hh7wyy8weTKsX3/59rJIsuIC//jHP7jmmmto06YNbdq04amnnsLHx4f4+Hh0XefFF19k3rx5jBkzhqioKJYtW0ZhYSEff/yxq0MXQghxEU2bAiYTFqsBT0MRmqajYafQZiHlmIXigrNZSceO4OMDubmwd++53zVkCDz3HLRsqV4LvfIKzJ17ea4nVK+SFV1X7/9csVU127Xb7cTExFBQUEDv3r1JTU0lPT2d6Oho5zkWi4X+/fuzZcuWamopIYQQNaFpUwANvDzxNpSArlHmMOFwgMOu89t3Z8etnKliC7B58/m/r0ULeP55uPtusFhgzx64/354/HFISanx23EbJlcHUJ1KSuCmm1xz7RUr1PvKitq1axe9e/emuLgYHx8fVq1aRYcOHZwJSZMzc+D+1KRJEw4dOlSdIQshhKhmKlmBErMvXqZS7DYjBTYr3h6l6HYHv36fRddREc7z+/dX5fZ//BEmTVIJzF8ZjTBqFPTpAx9/rM5PSFBb165w3XVqdpGmnfvZ+qJeJSt1Sdu2bUlKSiI7O5uVK1cyYcIE4uLinMe1vzx1uq6fs08IIYR7OZOsZNOARubDpBTrFOlWAk35FJaZ2LWttNz5Z1Zhzs6GpKSzU5rPJzBQjWO58Ub45BPYtEl9JilJzUQaMkRVwz2zAnR9Uq+SFYtF9XC46tqVYTabadWqFQDdu3cnISGBl156iYceegiA9PR0Qs7MgwMyMzPP6W0RQgjhXoKCVO+IzcOTFn4nSMxtDehYTTYKyiwkH4SiAgee3moUhtEIV18Na9dCXNzFk5UzmjaFBx6A8ePhq6/UwNu0NPjwQ7W1a6d6bK6+Wk2Hrg/q1ZgVTVOvYlyxXWqnh67rlJSUEBkZSXBwMBs2bHAeKy0tJS4ujj59+lxiCwkhhKhJBsOf9VYsVgI8i/HUSjBoOmV2Dc1hw2FzsGfD8XKf6d9f/dy6FQoLK36tJk3gzjvh/fdh5kz1SkjT4Lff4I03VDLz0EPw+edw5EjdnklUr3pW6oqHH36YESNGEB4eTl5eHjExMcTGxrJu3To0TWPmzJk8/fTTtG7dmtatW/P000/j5eXFbbfd5urQhRBC/I2mTeHIHxY8jSV4aUVk634U2sx4asWAzs7vT9B9dJjz/DZt1GeOHVNjV4YNq9z1rFYYPFhtp06p74iLg+RkNcto715Ytky9HurRA666Ss1E8vCo3vuuSZKsuEBGRgbjx48nLS0Nf39/OnfuzLp16xg6dCgAs2fPpqioiClTpnD69Gl69uzJ+vXr8fX1dXHkQggh/k54OMSbzTgMJryMxWTZAsi3WbFabdjKbCQllpU7X9NUgvLuu+qVTmWTlf8WEKAG444aBSdOqEG4v/wCO3dCerp63bR2rUpwunRRr526dVPjYdyZpuvu2zGUm5uLv78/OTk5+Pn5lTtWXFxMamoqkZGRWCszDaeekvYQQgj3sHEjLF4MHY+tZ2eylW0F7fDSimjrn0FBqQcN/BwsS+pCQNDZ/oKcHJgwQRWHe+UVaN68emMqLlYDcX/5RS2eePr02WPNmsGrr1bv9S7297sq6tWYFSGEEMLVmjVTP484mtLZ/xAaoAO6wYAnRWC3s33tsXKf8fc/W3Nl3brqj8lqVeX9771XvRJ66SU1pqV9e/VayN1JsiKEEEJUo7Aw9WonV/Mjwu80nloRmg4FpR4YNVXBdtuGU+d8bsQI9fP77ys30LayNE0Vm7v5Zli0CO64o+auVV0kWRFCCCGqkcWipjBjteJrKcXbUIRdM1JQZqFY88Ruc7Ajqfx6QKDGkISHq1c2339fe/HWhRJeNZqsXHfddTRr1gyr1UpISAjjx4/n+PHjf/9BIYQQog5r1gywWvHQbHgbiinVzdh18DbbcNh0CrLL2L+joNxnNA2uvVb9vnZt3Z5qXN1qNFkZOHAgn332Gfv372flypX88ccf3HjjjTV5SSGEEMLlVLLiSbbNl2bmdHQ0Su0mNKOBBqY80CF+5bn/8z5oEHh7qyJv8fG1H7e7qtFkZdasWfTq1YuIiAj69OnDnDlziI+Pp6ys7O8/LIQQQtRR4eGAycRhe1O6N/gdg+bArhspLjNw5q1LfGzROb0nViuMHKl+X7FCelfOqLUxK6dOneKjjz6iT58+eFygEk1JSQm5ubnlNiGEEKKuOTMj6LAeTgf/Y3hrRTjQyC+zkoc3mr2MtMM2DqXYz/nsddeB2ayKuu3aVcuBu6kaT1YeeughvL29adSoEYcPH+bLL7+84LkLFizA39/fuYWHh9d0eEIIIUS1O/PnK1trSFO/PHwMhRTrFgyajmbyINiUBTYb8V+c+yrI3x/+rBFKTEwtBu3GKp2szJ8/H03TLrolJiY6z3/wwQfZsWMH69evx2g0cscdd3ChOnRz584lJyfHuR05cqTqdyaEEEK4iNX65+rHViuYjDT0yMemm7DZ1d8/q9kBwE/f5p338zfcoBZE3LVLVZ+93FW63P60adMYO3bsRc9p/l+l9xo3bkzjxo1p06YN7du3Jzw8nPj4eHr37n3O5ywWC5bKLl8shBBCuKHISEjf58mRokCu9Etmb3Ek+XYvGtmKyMOMSbNxMNnGkcM64c3Kzx8ODFR1V9auVSspL1pUN6YY15RK96w0btyYdu3aXXS7ULn3Mz0qJSUllxZ1PbJgwQLn4oX/bd++fVx33XX4+/vj6+tLr169OHz4sGuCFEIIUWnNmwOenqQWNqGH1x6shlJsDgMFDitZemPaWg5BcTGxn2ed9/M33qjGrvz2myqTfzmrsTErv/zyC0uWLCEpKYlDhw6xceNGbrvtNlq2bHneXpXLUUJCAm+++SadO3cut/+PP/6gb9++tGvXjtjYWH799VceeeQRWfNHCCHqkMhIwGLhYEkIUX5H8DEWUeDwxNtUCppGgG8pAHFrcs476+fMooSgFjm02WovdndTY8mKp6cnX3zxBYMHD6Zt27bceeedREVFERcXJ696gPz8fMaNG8dbb71Fw4YNyx2bN28e11xzDYsWLeKKK66gRYsWjBw5kqCgIBdFK4QQorKaNwc0jcN6OC0DTtPAlE+Zw4TDrsar5JZYsBpLyUgtZP9v5x/LeeONasDt8ePw1Ve1F7u7qbFkpVOnTvzwww+cPHnSuSLw0qVLadq0aU1dEl1XZYpdsVV2LvzUqVMZOXIkQ4YMKbff4XDw1Vdf0aZNG4YNG0ZQUBA9e/Zk9erV1ddQQgghalxwsBpfW2bxJovGXOH7O2hworQBDruDvXnhdPfdD8XFfPfZyfN+h5cX3H67+v3jj+Hk+U+r9yo9wNadlZTATTe55torVqiHsiJiYmLYvn07CQkJ5xzLzMwkPz+fhQsX8uSTT/LMM8+wbt06xowZw8aNG+nfv381Ry6EEKImaJrqXfktxZPUomB6NUrmy6y+FNrNmM1QZvcgPLAEsiHuP/nc/VDj8/4diY6G776D/fvhjTfg4Ydr+05cTxYyrGVHjhxhxowZLF++/LxjUBwO1T04atQoZs2aRdeuXZkzZw7XXnstr7/+em2HK4QQ4hL89yDbrr4p+JoKybN50cBDrQuUVeZHU+tJitOz2RTrOO93GAwwbRoYjbB1K2zZUnvxu4t61bNisageDldduyK2bdtGZmYm3bp1c+6z2+1s2rSJJUuWUFBQgMlkokOHDuU+1759ezZv3lydIQshhKhhLVsCVk9+zwxhnN9aGlvzOZnrR06RBdD55VgYo8MSWJbaiHXLs4gefv6xic2bq9orn32mele6dFFrCF0u6lWyomkVfxXjKoMHD2bXX+on//Of/6Rdu3Y89NBDWCwWevTowf79+8udc+DAASIiImozVCGEEJdIJSsW/igKxehhoJffPvbnhnCkJJBI3yxyyrwICTdhOmgneUce+/cH0bbt+b/rllvgp5/g2DF47z3V23K5kNdAtczX15eoqKhy25nlCKKiogBV9ffTTz/lrbfe4vfff2fJkiWsXbuWKVOmuDh6IYQQldG8OZg8DOSZGpJpCKZvg11YjDayy7yJ8M8GYGdWU/o32g2nTrPm89ILfpfZfDZB+fbby+t1kCQrbuj666/n9ddfZ9GiRXTq1Im3336blStX0rdvX1eHJoQQohI8PCAiAvDy4vfCUK4MScfPo4g8myemsmIAfvqjCf/odBAcDjavPUXW+WvEARAVBWPGqN9ffhnS02v8FtyCJCtuIDY2lhdffLHcvjvvvJPk5GSKiopISkpi1JnKQEIIIeqU1q0BTy9+LwihqW8uEd4n0XU4kBuMj6WMnBJPCpq0oLPfQRwZJ/hy9cVrYYwfD+3aQUGBKsNfVlY79+FKkqwIIYQQNahlS/7sWQlBKytlSGASmgYHCprSOeQEALG/NWFMeAIUFvHNijxyci78fSYTzJ4Nvr6QnKzGr9R3kqwIIYQQNah1a1SyUhCCXlRMv4a78TEUkW3zpqEhG4DNuxvScWAQrb2PU3Ikk7+rAxoYCLNmqd/XrlVjWOozSVaEEEKIGhQRASZPD/I1P9IdQXRqdJzG1nxK7UYOpPvRpEEJRTYPfs7rwK1NN8Gp0/xnZclFe1cAevSA225Tv7/2GmzbVvP34iqSrAghhBA1yGQ6+yrot/wwzAE+9AnYBwYDP2V35OoWRwH4/kcz3ftaae11jOLDmcTE/P13jx0LgwaBwwELF0JKSs3ei6tIsiKEEELUsHbtAC9PfssPA6uVa4ISMRvtZJb4E6xlggZJqX6c6NCfieHfw4lMvvmPjWPHLv69mgbTp0Pnzmqdusceq58zhCRZEUIIIWqYSlZUzwo2Gz0bJhNgyqPQbiUxpQFdW+Sh6xrrNlro3L6M7j6/YU/LrNDgWZNJrRfUrBmcOgVz59a/hEWSFSGEEKKGtWsHeHuTWtSE4sxcfL0d9GzwGxgNfJvZjeEdjwCw/gcTZf8Yw53h32HMOM7PW+0kJv7993t7wxNPQNOmkJWlkpfMzJq9p9okyYoQQghRwxo3hsZhVnSDiQPZQRASwpgmWzCaDBwpDqThyWQCfMvIKTKzdX8A4ZEmrgv4CTIzefNNKL1wYVungAB4+mmVsJw4oXpY0tJq/t5qgyQrQgghRC1o104D7z9fBfn50bfRPgI8iyh0WPh6XwuGX3UKgNUf5aPfeBO3Nt1EwMlk0o7aWb68Ytf474QlMxMefFDVYqnrJFkRQgghakH79oC3N/vywwDwMRXT228PeJhZk9GTEa2SMZvsJB/1Yk9hJJ7NApnWdDWkp7F6NezeXbHrBATAggVqBlJOjnolVNenNUuy4mILFixA0zRmzpzp3Kdp2nm3Z5991nWBCiGEuCQdOwLePuzJi8CeWwAeHtzaeD1Gi5GDRU04tvUwg6/KB+CL19Lhjjvo0SCZYfq36KWlvPACFBZW7FoNG6qEpWtXNUvo8cfh669Bv3glf7clyYoLJSQk8Oabb9K5c+dy+9PS0spt7777LpqmccMNN7goUiGEEJcqMhK8Az0psptJ+a0U2rWjb8A+gv2KKNEsfHKwF6P7ZqFpOgm7LKQaW0HbttwV8jVNsveTmQlvv13x63l6wqOPwsCBqg7L0qXwyisVG//ibiRZcZH8/HzGjRvHW2+9RcOGDcsdCw4OLrd9+eWXDBw4kBYtWrgoWiGEEJfKYICoblYwmdh1OgxCQjAbbESH7QWzhW8yuxOU/BN9o3JAh4+fTIH//V88TWXMsr6OVpDHhg2weXPFr2kyqbL8//ynqsmyYQPMmcNFV3Z2R/UqWdF11d3liq2yXWtTp05l5MiRDBky5KLnZWRk8NVXX3HXXXddQssIIYRwB506a+Djza68CDCbARjvvRIPbw+OlzZi868+3HqLA03TiU8wkpLTCIYOpaPvYW4wrAbdwQsvVK5SrabBmDHqVdCZxQ9nzqz4GBh3YHJ1ANWppARuusk1116xAqzWip0bExPD9u3bSUhI+Ntzly1bhq+vL2PGjLnECIUQQrhap06Ajw970iKwn9qC0dubDvkHaNusmN15Ft4+PIzl6Zvp16EbcXsa88H8FOZ/dAf8/DPj9RWkeHRme2kXnngCXnwR/P0rfu2uXeGFF9RsoZQUmDcPJk6E0aNVQuPO6lXPSl1w5MgRZsyYwfLly7FWILt59913GTduXIXOFUII4d4iI8EnyJsiu5nfE7OhUyc0De64YidYLGw+3YHszbu57S5PjJqDbYk6SdvsMGUKBk1ndulTNPXJIStLJR02W+Wu36QJLFoEAwaocSzvvlu176lt9apnxWJRPRyuunZFbNu2jczMTLp16+bcZ7fb2bRpE0uWLKGkpASj0QjAjz/+yP79+/n0009rImQhhBC1TNOgcx9vtuyAHakNaHtzBMTHc4t5NU8H9CK7yJt3jkRz/4lfGdmtOWsSQ3nn0cO8FNcHQ//+eMfF8YjHQu6zPsnevUaefx4eeAD+/LNRIRYL3HcfdOgAb74JDRqosS3urF71rGiaehXjiq2iXWiDBw9m165dJCUlObfu3bszbtw4kpKSnIkKwDvvvEO3bt3o0qVLDbWYEEKI2tatpwd4ebEtp5Wqkw9YU/YybGAJWK18fKw/+sZYxt4XirepmIPJpaxblgH/+78QEEDT07uZ02EtJpMabPvCC6qXpDI0DUaMgMWLYdKkGrjJalavkpW6wNfXl6ioqHKbt7c3jRo1Iioqynlebm4uK1as4O6773ZhtEIIIarblVcCPr7sL2hKXsoJ9W5I17mv/zZMFhNH7cF8lXYFvnviuX1oJujwwbMZZNt8YNo0AK7Y/g5zrtmJ0QhxcfDyy1WroRIZ6Rzn69YkWXFTMTEx6LrOrbfe6upQhBBCVKPGjaFZSw90XePXH3OhRw8Amh/ZTK9egNXKktRr4fvvuWZGa1r5ZVBwspi3/u+QOnf0aAB6rn+C2RMyMBjg++/h+eehrMx191WTJFlxA7Gxsbz44ovl9v3rX/+isLAQ/8oM9RZCCFEndBvkB8C2fV5/1uEHtm9n1r02MJpIKm5D0ulmGNavY8pkHU3T2bQmm03ri9UUns6dobiYPuv+zf1Tipw9LP/+N+Tnu+6+aookK0IIIUQtu/JqH7BaScxuhaOkTI1yLSigr+9O2rcH3eLFk7+Phc2baT0onFva74LSUl57+ChZp40wezYEBsLx4/Tb+gyP/p8dT09VO+WBByA93dV3WL0kWRFCCCFqWVSUKr2fXebN/u+Pot7/AFu28MADgNHITwVd2ZMbDsuWccuz3WntfZyCo6d44eET6H7+qlCK2QzbtnHF5ldY9IxO48Zw7JiqWluBUl51hiQrQgghRC0zmaB7TzVfeGtcKfzP/6gD8fGMHG6ndWtwWDx5LGU8/PEHpszj3D/xJBatjJ3rjrE6plgtqzx7NmcGrTT/4V2ef06nTRv1Kujxx2HZMrDbXXij1USSFSGEEMIFeo8KAiD+90boYeHg4wM5OWh793D//aAZDGwuuIKfT7eB99+n6cSh3NU5AUpKWfbUEXbv0qFnT7j3XvWFq1cT8MPnPPMM/OMfatfnn6sOmMxMF91kNZFkRQghhHCBbv288fCxkFYcwOGNf5x9FfTTT4wercbd2j0sPHL0XzgKi+H99xm+OJqrG+3FfuI0T087RkYGMHgwnFk/7oMPMH25kn/9Cx56SNUB27MHpk+H2FgX3Wg1kGRFCCGEcAGrFa7orOrcb15zEvr1Uwd+/BHNbuORR8Bk0thV2JIVGf0gIQHt6BFmPBZAK+808vYf5/EZJyksRE1nPlPq4v33ISaGvv+j8/LL0K4dFBaqqc3PPls3ZwtJsiKEEEK4yNXXNgAgLt6KHtUJGjaEvDzYvp2BA+Gqq8ChmXjm5N2cLPWFt97C0vtK/m/iUQI88jj84yGeeyxfjUu57TYYP1598UcfwQcfEBKss3AhjBunhrZs2qR6WXbtctktV4kkK0IIIYSL9LolArOHg7Rcb36PO3a2dyU2Fk2DJ59UQ1mOF/rz1Okp6MUlsGABjWaOZ96geMyOYhI+O8hLz5WpCrY333z2ldDnn8PSpRg1B2PHqgUMQ0IgK0uNY3nvvbpTRE6SFSGEEMJFrD4menUqACDuswy1HDLAzz9DQQEdO8JNNwForM3qzddFA+DwYVi6lDbPTuKhLuswFuWz8e0/ePUlm0pYRo+Ge+5RCwB98w088wyUltK2rSrLP2yYKs3/xReqJsuRIy659UqRZEUIIYRwof4j1GKGP/5kwN68JYSHQ2kp/PgjoFZIjoyE3AIjC7Pv4WBJiFrB8KuvuGrJHTzQ4Su0vBy+fT2Ft990qITlmmvUtGaTCbZsgUcfhYICrFa1vNC8eeDrCykpMH8+2Gyuu/+KkGTFxRYsWICmacycOdO5Lz8/n2nTphEWFoanpyft27dn6dKlrgtSCCFEjbny5lb4mQo5ddLB9rg81fUBsH49AEFBKmFp0ABS0r14yvQopQ6T6hrZu5e+L93MjFZfw+ls1rx8kNde1dUqzH37wmOP4SxtO3cunDoFqIlHS5ZAt24webLKadyZJCsulJCQwJtvvknnzp3L7Z81axbr1q1j+fLl7Nu3j1mzZjF9+nS+/PJLF0UqhBCippiCGzOwQwbosOHDNBg4UGUPycmq6wO4/nro31+92dmc2pQ3g/+tPvz225CTw+Dnr2F6i6/QTp5g3dJUnlvkUL0lnTvDwoUq00lNhQcfhOPHAQgIUB0uf66j6NYkWXGR/Px8xo0bx1tvvUXDhg3LHdu6dSsTJkxgwIABNG/enH/961906dKFxMREF0UrhBCiJkWP8QHgl60Osh1+Z2uufPstoHKXOXOgdWvVOfLR3q78p9k9avDJ88+Dw0H0M4OZ3WoVppMZ/LgshScec1BcDLRooeYsh4So6nCzZ6tECJX81AX1KlnRdSguds2m65WLderUqYwcOZIhQ4acc6xv376sWbOGY8eOoes6Gzdu5MCBAww70zUohBCiXmk26gra+hzDnpPPD18VwYgR6sD33zsLo7RoAVOnQlgYHDqk8dKBESRG3qQGnCxcCAYDfZ8bzSPtP8eSncH2z5J56AEbWVlAcLCaDtSyJeTkwMMPQ1KSy+63stz8LVXllJScGTVd+1asUAV+KiImJobt27eTcIFVpl5++WUmTZpEWFgYJpMJg8HA22+/Td++fasxYiGEEG4jNJRhUcfYH9+Urz86xei1nTA0bw4HD6relRtuAGDMGNi+HdasgQMHNJ72Gc/iLqU0//VLlYzMmsWVi2/nqYfe5Ym9Y0j52sb9Wa145EkLrVo1gAUL4OmnVaLy2GNqxcMz06XdWL3qWakLjhw5wowZM1i+fDnWC2Q3L7/8MvHx8axZs4Zt27bx/PPPM2XKFL777rtajlYIIURt6XdjED6mIjJ+zydxmwajRqkD//mPc7qOwaDK6F95pVpwec9ejflH7iK95yjVxf/CC3D0KG1fmsLiqz6lmeMgp37ax5zpBcTHowbbPvqoSlBsNvV6aM0a1910BWm6XtkXGLUnNzcXf39/cnJy8PPzK3esuLiY1NRUIiMjnX/0dV31rriCxVKxd3+rV6/m+uuvx2g0OvfZ7XY0TcNgMJCTk0PDhg1ZtWoVI0eOdJ5z9913c/ToUdatW3fe7z1fewghhKhD0tJ4b8SnfJHeh663d+KJp41w553qtc3s2XD11c5TDxxQY2V//VVNQe7eTWdBhw8J/nGFOuGGGyA6moJHFrLox95sz2+N1rIlE2c15PrrQUOHt96CtWvVQJhFi6p1StDF/n5XRb3qWdE09SrGFVtFBykNHjyYXbt2kZSU5Ny6d+/OuHHjSEpKwm63U1ZWhsFQ/j+N0WjE4XDUQKsJIYRwCyEhjOyTjYZOUlwOh9LMql4KwF9mg7Zpo3pY2rX7szr/Do25e8eTft0kdcLKlfDJJ3g/9xj/vmEP1zT6Bf1AMu89cZSXXnRQZtNg0iSYMkX1tLj53OV6lazUBb6+vkRFRZXbvL29adSoEVFRUfj5+dG/f38efPBBYmNjSU1N5f333+eDDz7g+uuvd3X4QgghalDQtVfRu+FvkJXFF1+gkhWTCfbvh99+K3dunz4wcya0bas6X7bv0Ji75ToO3/oQGI1qmeXFizE+/BCTp5n4V8Q6tLRjfL80mXkPlpKTq6mBvP7+rrjVSpFkxQ3FxMTQo0cPxo0bR4cOHVi4cCFPPfUUkydPdnVoQgghatLVV3Nj2M9QWEjcNwWcKGtwtgT/qlXnnD58uBojeyZh2bYNHvyyL0k3P626/ZOS4OGH0a4fzT+e7c9jUZ/jXZDBvi/2MuuuXFJTa/Pmqq5ejVm5nEl7CCFEPbFwIfPeacFOz6u4bnpzJkUfUjXyAV55BZo3P+cj69ersbXJyeDhAa1awbTrjzFs4xzIzla9Jw8/DF5eHJv3Go9vHsTx0kZYWzfj/kVNnGVdqkudHLNSUlJC165d0TSNpDo0r1sIIYSodcOHc2PIT5B1knVf2TnlG6FK5wMsX37ej0RHqzG4HTuqGUN79sALnzXl1Y6vURrRWnW7zJsHBw7Q9M1HeW78r3TxSaF4/yHef2A3tmL3XhyoVpKV2bNnExoaWhuXEkIIIeq2Ll3o2qaQ9p4HKc04xeefA7fdpmZy/Pyzs/rsXw0cCE89pSrsN2gAe/fCyvW+3G9fxLFOw9VU5VdegY8+wnf+/cx/3MD1IfH839VxmCzG836nu6jxZOWbb75h/fr1PPfcczV9KSGEEKLu0zS04cO4PSwWMjL45hudE9ZwlY3ABXtXAKKiYPFiuOoq9bYoORl+3mZi5oEp/NBllqq2vnYt/PvfmEYM5c4PBhD27zvdvu5+jSYrGRkZTJo0iQ8//BAvL6+/Pb+kpITc3NxymxBCCHHZiY6mU6PjdDLuxXY6nw8/BMaOVbN8tm9X3SYXEBKilgsaMQI6dFBrCe3eo/Hc9kEsaraEfFMD2LULZsxQA1w8PWvttqqqxpIVXdeZOHEikydPpnv37hX6zIIFC/D393du4eHhNRWeEEII4b78/NAGDeSfYd9BRjobN8IfhSEwdKg6/uGHF12UzttbLXw4daoax+LlpfKb9fsjmO7xOju9eqksZu5c1dPivnNtgCokK/Pnz0fTtItuiYmJvPLKK+Tm5jJ37twKf/fcuXPJyclxbkeOHKlseEIIIUT9cN11tPZJY4DhRygu5u23Qb/5FlVnf/du+Omni35c0+C661Rx2i5dIDISUlJgz0Fv/i9/Du9ap1BWBsTFgd1eO/dURZWeupyVlUVWVtZFz2nevDljx45l7dq1aP/1Hsxut2M0Ghk3bhzLli3722vJ1OWKk/YQQoh6aP58TmxJZnLGY5Q2a8V998HAtI/hk0+gcWNYurRCq+jm5cGLL8LWrXDkCJSWQmSkTlvLQR543J/wLgHVGnZ1T12usTorhw8fLjfm5Pjx4wwbNozPP/+cnj17EhYW9rffIclKxUl7CCFEPbRvH8yezecZfVnWYCb+QRZef7kUnwfvgcxMuOUWuP32Cn2VrsPXX8O770JGBhw9CuHhqnT/G29Ub8X9OlNnpVmzZuVKyrdp0waAli1bVihREUIIIS577dtD166MDtxCeNEBcnLgzffNcNdd6vgXX0B6eoW+StNg5EhVPO6KK1TV24wMCAqqwfiriZTbF0IIIdzZbbdhMjiYYV6KVlrCxo2w2d4bunaFsjK1enIlNGumZgvdeKPqVfHxUZOM3FmtJSvNmzdH13W6du1aW5esExYsWICmacycOdO5LyMjg4kTJxIaGoqXlxfDhw8n+QJFgIQQQtRz7dtDly609TrCzcGbAHj1NY1TN09WWcYvv8DmzZX6SrNZLbr8xBMwfbrbl1mRnhVXSkhI4M0336Rz587OfbquM3r0aFJSUvjyyy/ZsWMHERERDBkyhIKCAhdGK4QQwmVuvRWAsdmv06ppIfn58NLnTdFvvEkdf/11VVK/kq64AqphSEmNk2TFRfLz8xk3bhxvvfUWDRs2dO5PTk4mPj6epUuX0qNHD9q2bctrr71Gfn4+n3zyiQsjFkII4TIdO0LnzpgcpdwXuByzWdWGW+M1VpWqzclRo2TrqXqVrOg6FBe7ZqvsnKqpU6cycuRIhgwZUm5/SUkJQLkZPUajEbPZzOZKdvMJIYSoR/75T9A0wpPWctewowC8u8zIr9EPqtULf/wRtmxxcZA1oxonKrleSQncdJNrrr1iRYWmugMQExPD9u3bSUhIOOdYu3btiIiIYO7cubzxxht4e3uzePFi0tPTSUtLq+aohRBC1BmtWsGgQfD994zY/yIHBj3L9z9oPPNJM14YejtNvv0AXntN9cL4+7s62mpVr3pW6oIjR44wY8YMli9fft56KB4eHqxcuZIDBw4QEBCAl5cXsbGxjBgxAqO7D9cWQghRs+64A6xWtAP7mdLpR1q3VgXfnvptDMVNW6rXQYsXu335/MqqsaJw1aGyReF0XfWuuILFUrHR1KtXr+b6668vl3jY7XY0TcNgMFBSUuI8lpOTQ2lpKYGBgfTs2ZPu3bvz6quvnvd7pSicEEJcJj77TK0N1LgxWU8sZeYcKzk50Kvdaeb+MQlDWQlMnAg33OCyEOtMUThX0DT1KsYVW0WnfQ0ePJhdu3aRlJTk3Lp37864ceNISkoql8T4+/sTGBhIcnIyiYmJjBo1qoZaTgghRJ0xerSq5JaVReONK5g3Ty2eHP9bQ14PfVx1qnzwAfz2m6sjrTb1KlmpC3x9fctV9o2KisLb25tGjRoRFRUFwIoVK4iNjXVOXx46dCijR48mOjraxdELIYRwObMZ7r5b/b5yJe09D/LAA+p/mr851J7P/CeBw6FWMMzLc22s1USSFTeUlpbG+PHjadeuHffeey/jx4+XactCCCHO6t0b+vRRqyW//DJ9ejmYPBlAY/npkay3D4YTJ+DZZ1XiUsfVqzErlzNpDyGEuMycOgVTpkBBgVoraPRoPvxQDWnRigqYVbqIgf7bYcwYNe25FsmYFSGEEEJAQMDZJOTDDyE9ndtvh2uuAd3Tmxfs97LpZEe12OGmTa6N9RJJsiKEEELUVdHR0KkTlJbCSy+h6ep10LBhoAc04vniKfx0qj289BKkpLg62iqTZEUIIYSoqzRNrURotcLu3fD552gaTJ0KgweDIzScZ0/eyc8ZzeHJJ6u0fpA7kGRFCCGEqMtCQvhzdC189BHs34+mwb33wsBBGvbIViw8ejs/H2gICxeCzebaeKtAkhUhhBCirhs0CPr1UzN/nnsOCgsxGGDGDOg3yIStVTueThnLpjhdLXjovnNrzkuSFSGEEKKu0zQ1MygoCNLT4dVXQdcxGuG++2DQSE8cLVvzXMr1rP8wA/7zH1dHXCmSrAghhBD1gbc3PPCAWoF50yZYvRoAoxFmzoRrbmuAHt6MV1Kv5csndsK2bS4NtzIkWRFCCCHqi/btYdIk9ft778H27YDqeJk8GcbcEwyBjXn70FA+m74J/fARFwZbcZKsCCGEEPXJyJEwdKgal7JoEaSlASphmfhPjXFzmoGvDx/+8T98MH4Deq77l+SXZEUIIYSoTzQN7rkH2rZV1W0ff9y5RpCmwdjbTdz1WARYzHz+WxRv3Pwdepl7zxCSZMUF5s+fj6Zp5bbg4GDncV3XmT9/PqGhoXh6ejJgwAD27NnjwoiFEELUKR4e8PDD0KgRHD0KTzyhCsf9afQ4b6Y+EYJmMpDl3wo7RhcG+/ckWXGRjh07kpaW5tx27drlPLZo0SIWL17MkiVLSEhIIDg4mKFDh5JXT1bPFEIIUQsCAlSvirc37NunXgnZ7c7Dwyc04bFP2jL7w06YPDQXBvr3JFlxEZPJRHBwsHMLDAwEVK/Kiy++yLx58xgzZgxRUVEsW7aMwsJCPv74YxdHLYQQok5p1gweeUT1tPz8MyxdWq7GyhX9fDGbXRhfBdWrZEXXobjYNVtl6+skJycTGhpKZGQkY8eOJeXPNRtSU1NJT08nOjraea7FYqF///5s2bKlOptLCCHE5aBjR5g9Ww1Y+fZb+OQTV0dUaSZXB1CdSkrgpptcc+0VK9TSDBXRs2dPPvjgA9q0aUNGRgZPPvkkffr0Yc+ePaSnpwPQpEmTcp9p0qQJhw4dqu6whRBCXA569VKDbl97TSUrDRqo5ZnriHqVrNQVI0aMcP7eqVMnevfuTcuWLVm2bBm9evUCQNPKvz/Udf2cfUIIIUSFjRgBp05BTAy8/jr4+KgS/XVAvUpWLBbVw+Gqa1eVt7c3nTp1Ijk5mdGjRwOQnp5OSEiI85zMzMxzeluEEEKISrntNsjNha+/hsWL1eDbbt1cHdXfqldjVjRNvYpxxXYpnR4lJSXs27ePkJAQIiMjCQ4OZsOGDc7jpaWlxMXF0adPn2poJSGEEJetM6Vs+/VTM4OeflrNFHJz9SpZqSseeOAB4uLiSE1N5eeff+bGG28kNzeXCRMmoGkaM2fO5Omnn2bVqlXs3r2biRMn4uXlxW233ebq0IUQQtR1mgazZkH37qr2yrPPgs29i8LVq9dAdcXRo0e59dZbycrKIjAwkF69ehEfH09ERAQAs2fPpqioiClTpnD69Gl69uzJ+vXr8fX1dXHkQggh6gWTCebMUa+CbrlF/duNabpe2Um3tSc3Nxd/f39ycnLw8/Mrd6y4uJjU1FQiIyOxVnQaTj0m7SGEEMJdXOzvd1XIayAhhBBCuDVJVoQQQgjh1iRZEUIIIYRbk2RFCCGEEG5NkhUhhBBCuLU6n6w4HA5Xh+AWpB2EEELUV+49sfoizGYzBoOB48ePExgYiNlsvizXztF1ndLSUk6cOIHBYMBcF9b6FkIIISqhziYrBoOByMhI0tLSOH78uKvDcTkvLy+aNWuGwVDnO8uEEEKIcupssgKqd6VZs2bYbDbsdrurw3EZo9GIyWS6LHuWhBBC1H91OlkB0DQNDw8PPDw8XB2KEEIIIWqAvDMQQgghhFuTZEUIIYQQbk2SFSGEEEK4Nbces3JmQejc3FwXRyKEEEKIijrzd/vM3/FL5dbJSl5eHgDh4eEujkQIIYQQlZWXl4e/v/8lf4+mV1faUwMcDgfHjx/H19e32qfl5ubmEh4ezpEjR/Dz86vW767PpN2qRtqt6qTtqkbarWqk3armr+2m6zp5eXmEhoZWS/0vt+5ZMRgMhIWF1eg1/Pz85IGsAmm3qpF2qzppu6qRdqsaabeq+e92q44elTNkgK0QQggh3JokK0IIIYRwa5dtsmKxWHj00UexWCyuDqVOkXarGmm3qpO2qxppt6qRdquamm43tx5gK4QQQghx2fasCCGEEKJukGRFCCGEEG5NkhUhhBBCuDVJVoQQQgjh1i7LZOW1114jMjISq9VKt27d+PHHH10dkluZP38+mqaV24KDg53HdV1n/vz5hIaG4unpyYABA9izZ48LI3adTZs28Y9//IPQ0FA0TWP16tXljlekrUpKSpg+fTqNGzfG29ub6667jqNHj9biXdS+v2u3iRMnnvMM9urVq9w5l2O7LViwgB49euDr60tQUBCjR49m//795c6RZ+5cFWk3eebOtXTpUjp37uws9Na7d2+++eYb5/HafNYuu2Tl008/ZebMmcybN48dO3Zw9dVXM2LECA4fPuzq0NxKx44dSUtLc267du1yHlu0aBGLFy9myZIlJCQkEBwczNChQ51rOV1OCgoK6NKlC0uWLDnv8Yq01cyZM1m1ahUxMTFs3ryZ/Px8rr32Wux2e23dRq37u3YDGD58eLln8Ouvvy53/HJst7i4OKZOnUp8fDwbNmzAZrMRHR1NQUGB8xx55s5VkXYDeeb+KiwsjIULF5KYmEhiYiKDBg1i1KhRzoSkVp81/TJz1VVX6ZMnTy63r127dvqcOXNcFJH7efTRR/UuXbqc95jD4dCDg4P1hQsXOvcVFxfr/v7++uuvv15LEbonQF+1apXz3xVpq+zsbN3Dw0OPiYlxnnPs2DHdYDDo69atq7XYXemv7abruj5hwgR91KhRF/yMtJuSmZmpA3pcXJyu6/LMVdRf203X5ZmrqIYNG+pvv/12rT9rl1XPSmlpKdu2bSM6Orrc/ujoaLZs2eKiqNxTcnIyoaGhREZGMnbsWFJSUgBITU0lPT29XBtaLBb69+8vbfgXFWmrbdu2UVZWVu6c0NBQoqKiLvv2jI2NJSgoiDZt2jBp0iQyMzOdx6TdlJycHAACAgIAeeYq6q/tdoY8cxdmt9uJiYmhoKCA3r171/qzdlklK1lZWdjtdpo0aVJuf5MmTUhPT3dRVO6nZ8+efPDBB3z77be89dZbpKen06dPH06ePOlsJ2nDv1eRtkpPT8dsNtOwYcMLnnM5GjFiBB999BE//PADzz//PAkJCQwaNIiSkhJA2g3UeIH77ruPvn37EhUVBcgzVxHnazeQZ+5Cdu3ahY+PDxaLhcmTJ7Nq1So6dOhQ68+aW6+6XFM0TSv3b13Xz9l3ORsxYoTz906dOtG7d29atmzJsmXLnAPOpA0rriptdbm35y233OL8PSoqiu7duxMREcFXX33FmDFjLvi5y6ndpk2bxs6dO9m8efM5x+SZu7ALtZs8c+fXtm1bkpKSyM7OZuXKlUyYMIG4uDjn8dp61i6rnpXGjRtjNBrPyegyMzPPyQ7FWd7e3nTq1Ink5GTnrCBpw79XkbYKDg6mtLSU06dPX/AcASEhIURERJCcnAxIu02fPp01a9awceNGwsLCnPvlmbu4C7Xb+cgzp5jNZlq1akX37t1ZsGABXbp04aWXXqr1Z+2ySlbMZjPdunVjw4YN5fZv2LCBPn36uCgq91dSUsK+ffsICQkhMjKS4ODgcm1YWlpKXFyctOFfVKStunXrhoeHR7lz0tLS2L17t7Tnfzl58iRHjhwhJCQEuHzbTdd1pk2bxhdffMEPP/xAZGRkuePyzJ3f37Xb+cgzd366rlNSUlL7z1oVBwTXWTExMbqHh4f+zjvv6Hv37tVnzpype3t76wcPHnR1aG7j/vvv12NjY/WUlBQ9Pj5ev/baa3VfX19nGy1cuFD39/fXv/jiC33Xrl36rbfeqoeEhOi5ubkujrz25eXl6Tt27NB37NihA/rixYv1HTt26IcOHdJ1vWJtNXnyZD0sLEz/7rvv9O3bt+uDBg3Su3TpottsNlfdVo27WLvl5eXp999/v75lyxY9NTVV37hxo967d2+9adOml3273XPPPbq/v78eGxurp6WlObfCwkLnOfLMnevv2k2eufObO3euvmnTJj01NVXfuXOn/vDDD+sGg0Ffv369ruu1+6xddsmKruv6q6++qkdEROhms1m/8sory01fE7p+yy236CEhIbqHh4ceGhqqjxkzRt+zZ4/zuMPh0B999FE9ODhYt1gser9+/fRdu3a5MGLX2bhxow6cs02YMEHX9Yq1VVFRkT5t2jQ9ICBA9/T01K+99lr98OHDLrib2nOxdissLNSjo6P1wMBA3cPDQ2/WrJk+YcKEc9rkcmy387UZoL/33nvOc+SZO9fftZs8c+d35513Ov9WBgYG6oMHD3YmKrpeu8+apuu6Xrm+GCGEEEKI2nNZjVkRQgghRN0jyYoQQggh3JokK0IIIYRwa5KsCCGEEMKtSbIihBBCCLcmyYoQQggh3JokK0IIIYRwa5KsCCGEEMKtSbIihBBCCLcmyYoQQggh3JokK0IIIYRwa5KsCCGEEMKt/T+Bhn8g4Rm/oAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1738,7 +1738,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1753,7 +1753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 25,
    "id": "5560ec94-f8d9-4828-a75f-d006416733bd",
    "metadata": {},
    "outputs": [
@@ -1762,14 +1762,14 @@
      "output_type": "stream",
      "text": [
       "setting threshold for quantile 0.001\n",
-      "Baseline threshold set at 0.978377\n",
+      "Baseline threshold set at 0.980253\n",
       "\n",
       "Distribution parameters:\n",
-      "Within class: 0.983±0.00138 min: 0.98 max: 0.985\n",
-      "Cross class: 0.635±0.0628 min: 0.514 max: 0.704\n",
+      "Within class: 0.984±0.0012 min: 0.982 max: 0.987\n",
+      "Cross class: 0.636±0.0639 min: 0.517 max: 0.706\n",
       "\n",
       "Type 1 error (false alarm rate): 0.001000000000\n",
-      "Type 2 error (missed alarm rate): 0.000000021955\n"
+      "Type 2 error (missed alarm rate): 0.000000037002\n"
      ]
     },
     {
@@ -1778,13 +1778,13 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1799,7 +1799,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 26,
    "id": "9f626678-0b7e-4c37-86ca-d1c2746e6c20",
    "metadata": {},
    "outputs": [
@@ -1808,11 +1808,11 @@
      "output_type": "stream",
      "text": [
       "setting threshold for quantile 0.001\n",
-      "Baseline threshold set at 0.978377\n",
+      "Baseline threshold set at 0.980253\n",
       "\n",
       "Distribution parameters:\n",
-      "Within class: 0.983±0.00138 min: 0.98 max: 0.985\n",
-      "Cross class: 0.346±0.00811 min: 0.326 max: 0.354\n",
+      "Within class: 0.984±0.0012 min: 0.982 max: 0.987\n",
+      "Cross class: 0.345±0.00723 min: 0.326 max: 0.352\n",
       "\n",
       "Type 1 error (false alarm rate): 0.001000000000\n",
       "Type 2 error (missed alarm rate): 0.000000000000\n"
@@ -1824,13 +1824,13 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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tCgaDSXOGpqencfjwYTz55JMZ1wcwCBGJS6dvxzweDzweT8q8vr6+pGMlJSU4d+5cVnUBDEJEwpIgQ4LCt2MKefmEQYhIUHqMCRmBQYhIWPKlpJSf/xiEiARlkqMwKQxMm7IYmDbCFQehllOtsF2f2eQkJXqtcy/I7ie6LEr/+Jqnc14mALz/bPolHrJlr70t52WG3juT8zIBoOC61PNtshE5dxbAcxldwzEhIjKUBOVAo88eI7nHIEQkKPaEiMhQkixDUljMXikvnzAIEQlKQhQmhYcuCdfIwDQRGUOC8rgPx4SISFeSJEOSFB7HFPLyCYMQkaA4ME1EhmIQIiJDmSDDpBBolPLyCYMQkaA4JkREhmJPiIgMxVf0RGQoPo4RkaHMkgyzQqBRyssnDEJEgpKkuaSULwIGISJBLZaBaZ2WECMi3Un/6Q2lStmOTHd1dcHpdMJqtcLlcmFwUHnxunA4jN27d6O8vBwWiwW33HILent7NdfHnhCRoMzSXFLKz1R/fz9aWlrQ1dWF9evXY//+/airq8Px48exatWqlNc89NBDOHPmDA4cOICPf/zjmJiYwOzsrOY6GYSIBGWSZJgUBp+V8tLp6OhAY2MjmpqaAACdnZ3405/+hO7ubni93qTzX3zxRRw5cgQffPABbrzxRgDA6tWrM6qTj2NEglJ6FFs4aB0KhRJSOBxOWV4kEsHIyAjcbnfCcbfbjaGhoZTX/O53v0NVVRV+/OMf4+abb8att96KXbt24fz585rvgz0hIkGZpLmklA8ADocj4fiePXuwd+/epPMnJycRjUZht9sTjtvtdoyPj6es44MPPsCrr74Kq9WKF154AZOTk/B4PPjXv/6leVzoioNQ8dICFC/J/1gmKf3bylJsNvdvHwqKct851WNXDAC4ZWttzst8p/t/c17mzFsncl4mAJhtJTkr62JEe89hntZX9IFAADabLX7cYlHeJUS6rFBZlpOOzYvFYpAkCYcOHUJJydzvo6OjA1//+texb98+FBcXq95H/kcPIkrJbJpLafMv/T/SZrMlBKF0SktLYTabk3o9ExMTSb2jeWVlZbj55pvjAQgAKioqIMsyTp06hTVr1qjWyzEhIkHNP44ppUwUFRXB5XLB5/MlHPf5fKipqUl5zfr163H69GmcPXs2fuydd96ByWTCypUrtd1HZs0konxhgkoQyqLM1tZWPPPMM+jt7cWJEyewc+dO+P1+NDc3AwDa2tqwefPm+PmbNm3CsmXLsHXrVhw/fhyvvPIKHnnkEXzrW9/S9CgG8HGMSFh6fLZRX1+PqakptLe3IxgMorKyEgMDAygvLwcABINB+P3++PlLliyBz+fD9u3bUVVVhWXLluGhhx7CY489prlOBiEiQUkqj1zZfjvm8Xjg8XhS5vX19SUdu+2225Ie4TLBIEQkKNWBaUEGWxiEiASldZ5QvmMQIhIUl/IgIkOZJAkmhUijlJdPNAehcDic8M1JKBTSpUFEpM1i6QlpHrryer0oKSmJp8u/RyGiq6vArJ5EoDkItbW1YXp6Op4CgYCe7SIiFZIkqSYRaH4cs1gsqh++EdHVw7djRGQsEyApPctwnhAR6Yk9ISIylFmSYFaINObFNiZERPlFUnkcU3xUyyMMQkSCuuYmKxJRfuGYEBEZio9jl5yfmUVhLHd3a9LpFxeL6VNurpmz2bFOhb32tpyXCeizKP2t/3VPzsucfP2WnJcJAKYcrpURPjcDHMrsGrNJZWBakK4Qe0JEgmJPiIgMxYFpIjKWylf0ECMGMQgRicpslhTHEM0xMaKQIE+NRHQ5rXvRZ6qrqwtOpxNWqxUulwuDg+l38H355ZdTfr3/j3/8Q3N97AkRCcpkkmBSeAOmlJdOf38/Wlpa0NXVhfXr12P//v2oq6vD8ePHsWrVqrTXvf322wm7vH70ox/VXCd7QkSCkqDSE8qizI6ODjQ2NqKpqQkVFRXo7OyEw+FAd3e34nU33XQTli9fHk9ms/YV1RiEiAQ13xNSSsDcUswL08JlmheKRCIYGRmB2+1OOO52uzE0NKTYlk996lMoKyvDhg0b8Je//CWz+8jobCLKGyaTegIAh8ORsDSz1+tNWd7k5CSi0SjsdnvCcbvdjvHx8ZTXlJWVoaenB4cPH8Zvf/tbrF27Fhs2bMArr7yi+T44JkQkKK1jQoFAIGG8Rm2F1MuXhZVlOe1SsWvXrsXatWvjP1dXVyMQCOCJJ57A5z//edV7ANgTIhKW1rdjNpstIaULQqWlpTCbzUm9nomJiaTekZI777wT7777rubzGYSIBJXrhe6LiorgcrmS9pX3+XyoqanRXM7o6CjKyso0n8/HMSJB6fHtWGtrKxoaGlBVVYXq6mr09PTA7/ejubkZwNyuO2NjYzh48CAAoLOzE6tXr8a6desQiUTwy1/+EocPH8bhw4c118kgRCQok8qMaVMWKzLU19djamoK7e3tCAaDqKysxMDAAMrLywEAwWAQfr8/fn4kEsGuXbswNjaG4uJirFu3Dn/4wx9w7733aq6TQYhIUHrtwOrxeODxeFLm9fX1Jfz86KOP4tFHH82uoksYhIgEJZkkSApvx5Ty8gmDEJGguLwrERlKgvIbMEmQtTwYhIgEZTJLioPP2QxMG4FBiEhQCz/NSJcvgisOQmq/iExlOsFKK5NJ1qXcXNNjMDH03pmclwkAM2+dyHmZeixKX3qnM+dlAsD4/2lfM0fNxXMXMr5m7u2YwuOYGB0h9oSIRMWF7onIUGqfZuj1VJFrDEJEgjIXSDAXKKwxHWUQIiId6TVj+mpjECISFGdME5GxuO8YERnJJKmsrCjI8xiDEJGgOGOaiAx1zY0JhcPhhK1CQqGQLg0iIm0Wy9sxzXMqvV5vwrYhDodDz3YRkQqt+47lO81BqK2tDdPT0/EUCAT0bBcRqZgfE1JKItD8OGaxWFT3KyKiq4ffjhGRoa65gWkiyi+LZXlXQTpsRHQ5SZLivaGUKcvXY11dXXA6nbBarXC5XBgcHNR03WuvvYaCggJ88pOfzKg+BiEiQekxMN3f34+Wlhbs3r0bo6OjqK2tRV1dXcJeY6lMT09j8+bN2LBhQ+b3kfEVRJQflHpBas9qaXR0dKCxsRFNTU2oqKhAZ2cnHA4Huru7Fa/btm0bNm3ahOrq6sxvI+MriCgvzC+trJSAuYnFC9PCSccLRSIRjIyMwO12Jxx3u90YGhpK245nn30W77//Pvbs2ZPdfWR1FREZTnE8aMGbM4fDkTDR2Ov1pixvcnIS0WgUdrs94bjdbsf4+HjKa959911897vfxaFDh1BQkN17Lr4dIxKU1s82AoEAbDZb/LjafL/LB7RlWU45yB2NRrFp0yb88Ic/xK233qq94Ze54iCkts7tYqbHfUvmnBeJguv0mWRqtpXkvEyTOfed81zuirHQ8g235aysCzOZf4tpKjDBVJD+9zWfZ7PZEoJQOqWlpTCbzUm9nomJiaTeEQDMzMxgeHgYo6Oj+Pa3vw0AiMVikGUZBQUFeOmll3D33Xer1sueEJGgcr3vWFFREVwuF3w+H7761a/Gj/t8PjzwwANJ59tsNvztb39LONbV1YU///nPeP755+F0attqiUGISFB6bAPd2tqKhoYGVFVVobq6Gj09PfD7/WhubgYw9w3p2NgYDh48CJPJhMrKyoTrb7rpJlit1qTjShiEiASlx7dj9fX1mJqaQnt7O4LBICorKzEwMIDy8nIAQDAYVJ0zlCkGISJB6fXtmMfjgcfjSZnX19eneO3evXuxd+/ejOpjECISlOq+Ywp5+YRBiEhQc49jSj2hq9iYK8AgRCQoLuVBRIZaLGtMMwgRCUpSWUeaPSEi0pXJLMGkMPi86NaYJqL8wjEhIjJUrj/bMAqDEJGg2BMiIkMxCBGRoThjmogMxZ4QERmKO7ASkaHm9x1TyhcBgxCRoK75MSFZlgEAM+ciOWuMnubbm0t6/J/GVJj7MiPnzua8TAC4GDmf8zLD52ZyXubFcxdyXiaQ3brQ6YTPzpWVyX+nM+cvKvaEZs5fvOJ2XQ2SnOVf56lTp+BwOHLdHqJrWiAQwMqVKxXPuXDhApxOZ9pteBZavnw5Tp48CavVmqsm5lzWQSgWi+H06dNYunSpYo8gFArB4XAkbTuSj0RpqyjtBMRpq9HtlGUZMzMzWLFiBUwapjpfuHABkYj6U0hRUVFeByDgCh7HTCaTasReSOu2I/lAlLaK0k5AnLYa2c6SEu1bKFmt1rwPLloJ8hKPiBYrBiEiMpTuQchisWDPnj2qW8/mA1HaKko7AXHaKko7F6OsB6aJiHKBj2NEZCgGISIyFIMQERmKQYiIDMUgRESGYhAiIkMxCBGRoRiEiMhQ/w8+n0XXc9on0gAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1845,7 +1845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 27,
    "id": "a25858aa-b730-4898-bc83-8e3bc54cb36c",
    "metadata": {},
    "outputs": [
@@ -1854,11 +1854,11 @@
      "output_type": "stream",
      "text": [
       "setting threshold for quantile 0.001\n",
-      "Baseline threshold set at 0.978377\n",
+      "Baseline threshold set at 0.980253\n",
       "\n",
       "Distribution parameters:\n",
-      "Within class: 0.983±0.00138 min: 0.98 max: 0.985\n",
-      "Cross class: 0.264±0.0184 min: 0.233 max: 0.304\n",
+      "Within class: 0.984±0.0012 min: 0.982 max: 0.987\n",
+      "Cross class: 0.275±0.02 min: 0.235 max: 0.314\n",
       "\n",
       "Type 1 error (false alarm rate): 0.001000000000\n",
       "Type 2 error (missed alarm rate): 0.000000000000\n"
@@ -1870,13 +1870,13 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1901,7 +1901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 28,
    "id": "a9fce3aa-2108-48e5-afae-c3f1e75c7db3",
    "metadata": {},
    "outputs": [
@@ -1940,13 +1940,13 @@
        " <Axes: >)"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1956,7 +1956,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -1991,7 +1991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 29,
    "id": "3e0f0a08-205f-4621-a366-9f80b88ed5ce",
    "metadata": {},
    "outputs": [
@@ -2032,13 +2032,13 @@
        " <Axes: >)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -2058,7 +2058,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -2093,7 +2093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 30,
    "id": "72805716-cee3-4a1b-94c8-d810756b8c18",
    "metadata": {},
    "outputs": [
@@ -2130,13 +2130,13 @@
        "(<Figure size 300x300 with 2 Axes>, <Axes: >, None, None)"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -2146,7 +2146,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 300x300 with 2 Axes>"
       ]
@@ -2173,7 +2173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 31,
    "id": "165344df-8bb0-4ed9-8aa8-c99499a39b81",
    "metadata": {},
    "outputs": [
@@ -2214,7 +2214,7 @@
        " <Axes: >)"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2295,7 +2295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 32,
    "id": "10bf6e6d-598c-4e4d-942d-e7db92986c95",
    "metadata": {},
    "outputs": [
@@ -2325,7 +2325,7 @@
        " <Axes: >)"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2370,7 +2370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 33,
    "id": "000d8897-a36d-4000-a86c-291d5ac6c34c",
    "metadata": {},
    "outputs": [
@@ -2399,7 +2399,7 @@
        " <Axes: >)"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2444,7 +2444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 34,
    "id": "a91c74ef-31c5-415d-a868-c5d64e095848",
    "metadata": {},
    "outputs": [
@@ -2473,7 +2473,7 @@
        " <Axes: >)"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2509,9 +2509,7 @@
   {
    "cell_type": "markdown",
    "id": "ca721bcb-8b6f-4066-b685-1c3bcb14c29f",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## Is our circuit sensitive to grounding? Would we detect if the mesh ground is touched?"
    ]
@@ -2583,16 +2581,14 @@
   {
    "cell_type": "markdown",
    "id": "94017b41-28af-4697-93ea-0fc4a9aa032d",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## Compare the two above cases."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 39,
    "id": "49058b85-96bc-469a-b3d5-fe98f28b132f",
    "metadata": {},
    "outputs": [
@@ -2615,21 +2611,21 @@
     {
      "data": {
       "text/plain": [
-       "(<Figure size 350x350 with 2 Axes>,\n",
+       "(<Figure size 200x250 with 2 Axes>,\n",
        " <Axes: >,\n",
        " <Figure size 300x150 with 1 Axes>,\n",
        " <Axes: >)"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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KPNYqFdu2baOVK1fSpUuX6OjRozRkyBDav38/U3XDisO7hVLBzVskSSKdTkc3btygc+fOKfu1Wi1169aNOnbsSEeOHCGTyeRU2RyNUAoVyKt19M033yhtb0+cOEF+fn40bdo0Cg4OpqysLFq4cCF9/fXXNHr0aEpOTqYlS5Y4NXZoz5499Mwzz1Dbtm1p/vz5lJycTL6+vjRy5Eg6cOBAgYrhaDQaDV27do1SU1OJiGjdunU0a9YsIiJ68sknKSsriw4ePKh0GJIkiZo3b05Xr16l27dvO1U2h1O6A1XZY8uWLfD09ES3bt3QpEkTVKhQAV988QUA4MCBA+jduzeCg4PRpEkTdOrUCceOHXOaLGoPTkxMDHNs586daNu2LZo1a6a4XJ1lmpjNZmRlZSEoKAgvvfQSPv74Y0iSpCQppaamol69enj22Wexf/9+5X3/+c9/0K5duzK5FmEPoRTIf5ju3LmDadOmMSuxY8eOhZubm5K+aTAYcOvWLdy6dQv37t0rEfneffdd1K9fn+kRAVgW7SRJQu3atZmH0Vn89ddf8PLyglarVTxx1vnCmTNn0KBBA0RFReGJJ55Ajx494OPjU2prJA+Dy5pP//vf/+jGjRtEZBnqjxw5QrVq1aItW7YwptDcuXNp1KhRNGTIECWkuVKlSlSpUiXy8vIqEVkbNmxIkiTRzp07KTs7W9nfuHFjatu2LXXp0kXpO+csDAYD+fr6kslkIo1GQ0ePHqWLFy+SRqMhs9lM9evXp927d1NcXBxFRkZSREQE7d+/n5o2bepUuZxCaWtlSSPLMk6dOgVfX18mEV6WZfTt25cJTVCbIxMmTIAkSVi7dq3TZLPe78iRI/j222/x7bffKsd69eqFkJAQfPbZZ0qoRnx8PAYPHlwiKaRWrl+/jhMnTsDDwwN9+vRhKpCrcWZdJmfjckphTX+0ZnX9/vvvuHLlinKsT58+8PPzww8//GDz3ilTpjg9qX/t2rXw9vZG3bp14enpySwQ9unTB40aNUJQUBBatWoFT09PpxYqU2N9yK1/v/3798PDwwP9+vVTIm7fffddfPDBBwAeDddrYbiUUqSkpGDFihXKgtLNmzchSRL69++vpGPKsowXXngBlStXLlAxnIH1AcrMzETLli3x6aef4o8//lByrPv27aucu2vXLixYsABz5szBmTNnSkQ+Huvayf79++Ht7Y327dvjueeeg5eXV4lE4Dobl1EKs9mMtm3bIjw8HGvWrFEUY/PmzfDw8MCwYcOUEcOqGNWrV8euXbtKRL7t27djxIgRGDp0KBNPtXPnTvj4+KBfv34lIkdB3/AFmUJWxThy5AgGDx6MwYMHl9io5WxcQims/2ij0YjY2Fg0bdoUq1evVrxH27dvh1artVGMmJgY1KlTB1lZWU6XMSUlBRqNBjVr1lRaZlnl3rlzJ6pUqYIePXo43Va33vP8+fM4efKkjcdLjVUWg8FQok1cnI1LKAWQ/81mNBrx73//265iqE0pZ5dYsc5tDAYDPvvsM7i7u2PSpEk2523ZsgW1atVSlNaRzJ49G9u2bVO2161bB39/f9SpUwf+/v7YvHnzIz1xLi4uoxRqjEYjunbtWqBiWL0qzuz4aeXXX39FYGAg9u7dC8CiuEuXLoVOp8OUKVNsznfWukhsbCwqVKiA3bt348KFCwgNDcWCBQvw3XffYdSoUXBzc8OKFSvK1Whgj3KvFFZz4Nq1a0hLS1NKMVpHjIiICEYxNm3ahCpVqjitDhJPq1atEBoaip9//hmAZXSyKgbff85ZyLKMgQMHonLlyli5ciXGjRvHHH/jjTfg5uaGTz/91CUUo1wrhboNbqtWrfD444+jYcOGePvttwGwptSXX36phCM4KyyhsPzu6Oho1KxZk1EMay7zW2+95RRZCpLNuk7TunVrm1Fp0qRJqFChAhYvXlzuFaNcKwVg6cHs4eGBBQsW4LfffkNSUhIkScKOHTsA5E++Q0JCsG7dOgDO9bH/9NNPSn1U633MZjOio6MRHBzMKMaqVatw8uRJp8miDm+xMmrUKHh4eDBzDCujR4+Gv79/qVXuKynKvVLExcUpJVUuXryIunXrKglCau/JSy+9ZNMXwdHk5uYiPDwc9erVUxa8rA9mdnY2mjRpgqioqBJZH7Hed/v27Rg7dqyijCaTCQMGDEClSpWYFXUrN27ccLpspU25VoqcnBw0bdoUK1asQEZGBoKCgjB8+HDlgVi0aBG+++67EpXp6tWraNasGZo2bcpU1DabzRgwYAAkSUJ4eLhDmqQXxbp16+Dl5YW3337bZkTq3bs3KlWqhN27dztdjrJGuVIK9beu9fXEiRPx8ssvKznV1tEhKysLAwcORFJSEoxGo1NMJrU86of8xo0bCA8PR7NmzXDu3DlFpkmTJmH//v0lkkJ65MgRBAYGYvny5cx+dSyTdY5RUiv7ZYVyoxTWB3Dbtm0YN26c0mp2xYoVqFKlClq2bKkEABqNRkyZMgW1a9d2WmMQqzybN2/GCy+8gMaNG+PVV1/Fl19+CcASWNe4cWPUrVsXEyZMwCuvvAI/Pz9cvHjRKfLwbN68GREREcjOzobBYMCKFSvQoUMHhIWFoXfv3sp5w4cPZ1r6ugLlRikAizng4+OD+Ph4nD59WtmflJSE0NBQPP300+jXrx969uyJKlWqOL1u6aZNm+Dm5obJkydj/Pjx6NGjB4KDg5n2WX379kWHDh3Qrl07HD161KnyqNmxYwfCwsIQFxeHyMhIdO/eHXFxcVi2bBl8fX3x1VdflZgsZY1yoxRHjhyBv7+/TXcca5jCzp078cYbb+CZZ55BQkKC07/9MjMzERMTg4SEBGXfxYsXkZiYiNq1azMh6AaDwamhJNZR6/bt28yi5EcffYTnn38e48ePVzII//nnH7Ro0aLE51pliXKjFNu3b0ebNm2QmZmJzMxMLF++HNHR0XjssccQFxenxBM5E+vDd+vWLciyjLCwMMyYMYM55/z58+jatWuBK9bOlGnDhg1o3749goKC0L17d8yaNYs5biUhIQF169YtsaaLZZFHOvMOqmR9o9FIv/zyC7333nvUtm1bWr9+PTVq1Ij69+9PO3fupIMHDzpdHkmS6KuvvqKRI0fS+fPnKSoqii5dukRpaWnKObVr16bAwED66aeflCR/Z8u0fft26tOnD3Xv3p02b95MISEhNHXqVNqyZYtSXWPdunU0evRoWrBgAa1Zs4aCg4OdLluZpbS18kFQL3qpeffddxEbG4vx48crE22DwYCmTZsqi3XOlOf8+fMICwtDSkoKAEvka+XKlfHf//4Xf//9t3L+wIEDMXToUIevDPMr5rIsIzs7G/3790diYiIAi3kUFBSk9LSwsnDhQgwcONCpi4WPChLg5NooDgZ5ZWj27NlDGzduJJPJRA0aNKDRo0cTEdHt27epUqVKyvnTpk2j1atX0/fff081a9Z0mly7d++mY8eO0YkTJ2j+/PlK/vZbb71F8+fPp06dOlGNGjUoMzOTVq9eTT///DOFh4c77P5ms5k0Gg2lpaWRTqdj/gYdO3akMWPGUFRUFD355JP073//mxYvXkxERBs3bqQaNWpQixYt6N69e1ShQgWHyfTIUspK+UCsX78evr6+6N+/P3r16oXw8HAMGTJEOS7LMpYtW4ahQ4eiatWqDvUyFRZCPWLECKXTKB9h++mnnyIuLg4tWrRAnz59nJaMc/bsWYSGhiIuLk4ZmTIzM9GpUyfExcWhbt26GDp0qDKipKen4+WXX8aiRYse6fRRR/PIKcWvv/6K2rVrIzk5GYClC2i1atXg5uaGF154QTkvJSUFPXr0cGghX6tCXL58GZ9//jmWLFnCrCtMnToVkiRh4cKFBYZ5GwwGpVOpo5FlGdOnT4ckSejYsSP+85//KAUOtmzZAnd3d0RGRjLvmTp1KurWrev08JZHjTKrFNYH0GobW1m5ciWGDx8OwOLiDA0NxeDBg5GSkgIPDw+mpqkjo12tMhw/fhxNmzZFv379CkwGGjlyJDw8PLBq1SpmFbskvomPHDmCSpUqoWPHjoiJicFrr72mKIa1RlTv3r0xaNAgDBgwAL6+vmWix1xZo0wqhfUBPH36NMaMGYMePXooVSIAS6U+k8mELl264OWXXwYA/P3336hfvz4kSUKfPn0AOO5BtF7n+PHj8PPzw8SJE5l+D5s2bWIaK8bFxcHDwwOff/6509Yf+Em1NbNw+vTpmDBhAqZPn47IyEiMGzcOaWlpACwLdr169cJzzz2HiRMnOr0yyaNKmVMKq0IcPXoU1apVw3PPPYfevXtDr9fjvffeU867dOkSwsPDlWrf6enp6N+/P1atWsUE2jmK9PR0tGvXDq+++irzQL733nuQJAkdOnRgFGPUqFGQJEkJ63Ak1r9Renq6ErVqlSklJQUtW7ZEZmYm5s2bh8jISIwdO1YZMayjlyullxaXMqUU1n/UsWPH4OnpqSxwmUwmjBkzBmPHjlX+qTdu3EC9evUwZswY3L59G5MnT0ZUVJTTOvacPHkSdevWxe7duxU5Fy1aBL1ej4ULF6JTp07o1q2bkpMBWBo0Ouvb+MyZM6hbty4ef/xxbNq0iVmhf/rppxXT7q233kKLFi0wYcIEpt+dmFgXTplSCsAyAlStWhUvvvgis79Xr16IiIhAw4YN0aVLFyxZsgRz585FYGAggoODERAQ4FT7eNWqVdBqtczDlJqaqrTc+v3339GxY0e0aNHC6fVTZVnGlClTUKFCBdSoUQPNmzfHiy++iJEjRyIjIwMpKSkYPHiwMql/++230aBBA0yZMkWMEPdBmVOK8+fPIyoqCrGxsfjxxx8BWAL6vLy8MHPmTKSkpKBhw4aIiIjAnj17cOLECWzatMnpYQl79+6Fu7t7gdl51gftk08+QVRUVIkUPbh69Spee+01PPfcc3jllVfwzTff4IknnkBsbCyio6MhSRITB/bBBx84tXd2eaLMKQVgMQ26dOmC2NhYDB06FP7+/syK9IULFyBJUoF9mp1Famoq/P39ERsbW2j91AkTJuDFF18ssQaHV65cwejRo9G6dWssWrQIgMX9aq17u2bNmhKRo7xRJpUCsHieOnXqBE9PT3z44YcALN/OBoMBly9fRpMmTZR/eknZx2vXroWbmxsGDBjArH9kZGRg4sSJ8PPzK/F+blevXsWYMWMQGRmJuXPnKvvF2sODU2aVAgD+/PNPdO7cGV27dlVsd8DidgwNDS3xSE6TyYTk5GTodDo0bNgQr7zyCuLi4vDMM8+gevXqpebzv3btGsaMGYMWLVrgnXfeYeQVFJ8yrRRAvikVExODw4cPY9asWfDw8CjVRadffvkFPXv2REREBNq2bYvJkyfj7NmzpSYPkK8Ybdq0wZtvvlmqsjzqPBIBgWfPnqXx48fTgQMH6NatW7Rv3z6KjIwsVZlkWSatVluqMvBcv36d4uPj6fLly7R69WqqUqVKaYv0SPJIKAUR0enTp2nSpEn07rvv0uOPP17a4ijRuvzr0sbanSkgIKCUJXl0eWSUgsiSSGRt1SsQOItHSikEgpLgkU5HFQicgVAKgYBDKIVAwCGUQiDgEEohEHCUGaXIzc2lGTNmUG5ubmmLoiBkKj579uyh7t27U2BgIEmSRBs2bCjyPT/88ANFRkaSh4cH1alTh5KTk50vqD1KaymdJyMjA0RUIpX87hchU/HZunUrpk6dinXr1oGIiqxJe+7cOXh5eeG1117DyZMnsWTJEuj1eqasaEmjK12VFJQ3unbtSl27dr3v85OTk6lWrVo0b948IiJq1KgRHTx4kD788EN6/vnnnSSlfcqM+SRwTfbt20edO3dm9sXExNDBgwfJaDSWikwPPFKYzWa6evUqeXt7OyTu586dO8zvskB5lAkAZWZmUmBgIGk07HdiTk4OGQyGAt/D/4/d3d3J3d39gWRQc/36dZs4rYCAADKZTJSWlkY1atR46HsUl/tWitzcXGZyd+XKFXrsscccLlBZLOxbHmVKTU1lyojm5ORQtZBQunvzus25FStWpLt37zL7EhISaMaMGQ8lgxVe4ZAXeVRaQZb3rRRJSUmUmJhos3/c4UvkXtGHiIhuHU3NP2AuIqRKY+cD8+8t6loqwFXyllTh3fwx4r4pJa1qm7unzXs5mPuowskCfdxoXLsgmjpwKl04fZGIiEzuvsrxp/3OMtfZe706K6I5/5s715uthavNvsVsVzNdZrZv1OuuvPY7s5WIiIyyidae20/e3t7MuQaDge7evE5jfz6n/D+JiHLv3qF5retQamoq+fjk73fEKEFEVL16dbp+nVXEmzdvkk6nK7XQ9/tWivj4eBo/fryyfefOHQoODib3ij7k4W35Y7l7qf7QDlQKlJBSaFRKwd/zQZXCo4Ib+fj4kLvenfRafd65bspxTx37L7Ceo8gk5V/LrHNjjmm5c904v4le75F/TMseK+xbWO9RkfSeFfPvaTITEZGPjw+jFI6iVatW9PXXXzP7du7cSU888USpRUTft1IUZkPeOpqqKEPlyFrK/tvH2G+tYo0M9s4lIrMh/wHV6NgH22zifAeqa0sa7uMWS6bCFYiISNIVrFAanUVZZK0HyTpLJXKoHubMLHPhMhCRRKrjnPxmvScrk4l7s0pmkMT8Lgyz0URmg4nZLg53796lP//8U9k+f/48HT16lCpXrky1atWi+Ph4unLlCq1cuZKIiEaMGEELFiyg8ePH07Bhw2jfvn20dOlS+uKLL4p1X0ciXLICBpjMBJOZ2S4OBw8epKefflrZtloXAwcOpBUrVtC1a9fo0qVLyvHQ0FDaunUrjRs3jhYuXEiBgYE0f/78UnPHEgmlEHA8rFK0b9+eMR95VqxYYbPvqaeeosOHDxfrPs7k4ZXCDMXUUJtMlSLYSaGNOaXCZs5QxBxC/Y/i/2X2/on25htERJLKnLL5x5rZ64L3mBgKnnNAtrxPAkiC5bXGcE85XsGHM2cyCpdfMmZxF2fPtff46iTLUbNk/yE3G2XOfHJ+C7KyhhgpBAxm2Uxm2cxsuxpCKQQMMMkEk8xsuxpCKQQMZoNMZr3MbLsajlUKlU3OzyFs5hi/X8nf4OYQGjfO9Whg3YJq9yfvkgXnZlXPMSSdfb+3ek6hsbkOu61xK7zmk/o62rzzoNES8lyqULlWDUb2s5u1Hsy2pJrbQMce05jY8PFcM7cW4ZbvQjfkrWEYiljyMctmZW3Cuu1qiJFCwABZZhwSRS1alkeEUggYYJDJrJOZbVdDKIWAATIUN7J129V4eKXQSEWGZRBxcwgiqtQ4KP8YN/8AtyYg3cf1lfdy6xTMGgg3d+Gvqz7K++eL8sIwaxzq65gscwqNbCCNbJkDmNwKb+Bu1rLxTW6GDPXFWJl0bNiNVuIeYJXp4yblvVeyH7ZhNslkVn1Ws/A+CVwdmGSCUbhkBQIFsXjnaKVQmSdFhW7YCwm5dSSV2S5W6LgdE8k24pNzq6qjW7koWDMf5gE+tLxgGa0PFSQtQbLcT5LzcyT0Os7VK7OZb0w4CWdakTGb2TSBixBWvVfKM+okKiqERizeiZFCwADZzE20xUhRKHw6alnKWxY4DrPRRGbdg+dTlAfuu5pHUlIS+fr6Kj9lMW9Z8PCYZZDZZM7/ES7ZwiksHVUdOs64ZosI3VC7Xfk5hF8zVuHszTHszSFsjnPZc/y8QKNXpZTyIeh8PjcXOs7cVx0ukpd5pzEblfmC2u2aU0TcBRPmwaeUatg5kVsR7tb7QZhPDkhHFZQvzEYTmbWubT6JibaAQYwUQikEPFxAIImAwAfAjPtaR7AJ/1aHRXDvL2qO8c+h/MT3osrhMPMGs/15ArPOUlQ6qs17818ys42860DSEDS24eZu3DqF1sSuPcia/PmHZMxhjkkyW1ZSr+FkNOUft65h2KxlcJhNJsZkMpuE+SRwcURAoFAKAYfIpxBKIeCAyURQeZ8gzKeHQ53Py/v5Jd19rxPazAuYOQSxlQj5Y7Z1aFXBbZxMfMFAvmyNPZkk7uOw36iq9Q7lfVBK0kiq0jQyd10+dFxvzC9sDK5sJuWwn8c29il/+35L3IiRQowUAg7IXDE04ZIVuDpmk5HMGiOz7Wo4VCnUlTX47xe+6oY9igo7V5tMalOKiCh9/wVmW10FUGunIiCPxBUi5j+PTZiHu6pSCFPU2XKerK9Asrulare6moefD+emZavrk0mbH0WgvZfO3pOrEGg0c6EoKrdxDiyml7EIl6xl8U5mtl0NMVIIWGSZXbATcwqBqyMm2kIpBBwwGQmSjtl2NR5aKdTfLOqGKTZVNexU7itW+DcRY7Pzc4gqLWsz2+oqIjZuYn5OYS/0nU9d5ZB06rDzAprK6L0IbpYOQdq7N5XjV/7mKqGb2W2jd37VE6oSxBzz+usHZptvyKL1raS8ztFYKogYYf8hh2wiqLq/QBbrFIUiMu9cA8hmgsa1J9oi807AYjLY/rgY960U8fHxlJGRofykpqYW/SbBIwdkk82Pq/HQmXeSVpu/FmAnhNxe5b6i5hD2wrj5bkT2KhHyISE2oSd20lyLmveoZUJBIehyLpHJEvqtDiHX8uEifKiJfP/f1Hz5mgfyHJk5l6xZeJ8Ero7JSIwBIbxPApdHNhFJWnbbxXCoS1bdp9qm6SLfMEVlYtgkx9tktXEND1WmGB+6wZtp9kJC+Aw/xpwqqhklb14V1ggyTxy4VSR45IV5qJqtuHN/Fok3FVVRs8jiPX58gWg7Jl3eg44iomTJbCIya9ltF0OMFAIWk4EYZXNB75NQCgGLWWZHBzHRFrg6klkmSWX68ivsroADmrZo8ucAKjcl7yrlYV2a3LyAt6u5OYY6Y453jdpsq+YJRVUJUVdC5921/JzB3n2Y5pN5uyVTbn41DpVLtshaY6rwcMmNbQRp5tL/AG5Oofo7SgVk/RWEJBuYmYlUDJdweUGMFAIWs4nNtRUTbYGrI8FMEmRm29UQSiFg0MhGJvZHI4vFu2IjaTU2XX+Iirb1mVkDH6atL7zDEJH9qhs2TSnV4STcPIFvQKnuqMQfs2kmz8usmkOpv1ulAv42GkOW8tqgLaLYmNqU4ap5aLhvcT7MQ+POzkHuBwkySaqUVfWo4SqIkULAIJRCKIWAQyiFowss2ylQrOHMGnWfat78somo5V20KviqG3YLLvORrnbMKb45pW2vb66IGRNZWsA9JY1iCpn1nsphT3fOrMzitlWNWqQiVpdlzrWtLk9j30GejxYm0qpcu2YI75PAxdEQSKOaFWmK6KZaHhHpqAIGDWTSqEYKjQuaTyIdVcCgkcykVf1oioqqLYc8dCNIpk6QOhyDb3Ji4qt5yKpTi2qmUnjWnk0lQj5kxF42IBe6oXa72nPXFnS8sOokSndRmJWQDY2qKby7nnNd8/MnlUuWTw3lJ8E64o4zoTbgfheMhsyc+SSUolBEI0jXwDKnALPtaoiJtoBBJ5mVsv1ERZfuL48IpRAwaCWQVgKz7Wo4tuq4ar2BD8WwCZNQwa9D8BW9+aaL6sgH/lweew0nbStyFF7NozhzjALnMWajUpnD7Oal7L6bza2r8M0i1aHjXHN5WaPnTs1lt1VrJ9q8yoDmIioEajQgjUZlPtlZIyqviJFCwKDTgPQqpTALpRC4OsJ8KsY6hcA10Gpsf4rLxx9/TKGhoeTh4UGRkZG0d+9eu+cvXLiQGjVqRJ6enhQWFkYrV658QOkdgwNCx/MrBDIpmYWUfFHep7b15aJsfXZTbStLXI0YdfVvy3tV6wd8SikX+8SEf3Mlevh4rPsNO9do8z6LKvZJvU5xj59PmVmbX5OdkS9DpQCyBx/7pP7ssqTP+233EqTTWn7yr1k8vvzySxo7dix9/PHH1KZNG1q8eDF17dqVTp48SbVq1bI5f9GiRRQfH09LliyhqKgoOnDgAA0bNoz8/Pyoe/fuxby7YxAjhYBBJxHpNKqfIpSIZ86cOTRkyBAaOnQoNWrUiObNm0fBwcG0aNGiAs9ftWoVxcXFUa9evahOnTrUu3dvGjJkCM2aNcsBn+bBEEohYNBwppPV8Xfnzh3mRx0HZ8VgMNChQ4eoc+fOzP7OnTvTzz//XOD9cnNzycODTYby9PSkAwcOkNFYOll/D18hEFBcqvZCKuxm3vHwWXs2J6gr2HEuTRNn9hSj6DOzVUSBZXsuW7UpVbGAvzBUPuUKfOi4kSsZqHbRcqHjfEiI1o6xI+V9OqmIsA1+HmF9zce6JSQk0IwZM5h9aWlpJMsyBQSwZl5AQABdv369wPvFxMRQSkoKPffcc9S8eXM6dOgQLVu2jIxGI6WlpVGNGjXsyusMhPdJwKDXSqTX5iuqnBcxm5qaSj4+Psp+eyE/NutMQKHrSdOnT6fr16/Tk08+SQAoICCABg0aRO+//75NSdSSQphPAgat1vaHiMjHx4f5KUgpqlatSlqt1mZUuHnzps3oYcXT05OWLVtGWVlZdOHCBbp06RLVrl2bvL29qWrVqg7/fPfDQ48UgT5u5FHBklCvUfd949pCad34rDC1p4c9V8N7kFC46cKbZXy/bvVqOV+thaslxhYZsMmsKyK6VPXtqjaZquZ9lJoh+Q+FZLinvK7uwX7WOlnVmW05r08eERH5sg+Wu1dddpuyme17IVWU19q7lnNzjblEpwv7FERajURa1d9Ua6fXOI+bmxtFRkbSN998Qz169FD2f/PNN/Tss8/afa9er6eaNS1m5+rVq+mZZ54hDR8tXUI8tFKMaxfEDKuCgnl95iulLEEsEVkmzPN9kws9S6cj0queiuImo44fP54GDBhATzzxBLVq1Yo++eQTunTpEo0YMYKILCkIV65cUdYizpw5QwcOHKCWLVvSrVu3aM6cOXT8+HH69NNPi3lnx/HQmXdTB04ld71lKJW1+V4EfhLIx/RoVOUYIXHHOF89+K901TRd1ldgD+m92G05X2aov3XJUsqyUPh78sMMJ6PN+XnUDAmg12e+QutugdLy5sH/jByiHH9tIuu7P7CVNT2OncnJv5Y/+3eqWpmdlO84xspQUZP/3n/CnyciotycLLKHVuJGiiJiy3h69epF6enpNHPmTLp27RqFh4fT1q1bKSQkhIiIrl27Rpcu5bdHkGWZZs+eTadPnya9Xk9PP/00/fzzz1S7du1i3deR3LdSJCUlUWJios3+C6cvkl6btzCky38g+cpy0LC30qgfVl4puPql9oLkZHd2lOIffGtLLSJS+kMoMhpzqFCKUgq+xmohSmElTSa6nve1e+PUGWW/+S57Xsali8z2lT/yTSLPTPbvoL/L2vUXTrIy+GrzFeCGzz9ERGQ0sCYWj1YrkVZlCmrNxVyoIKJRo0bRqFGjCjy2YsUKZrtRo0Z05MiRYt/DmYhGkAIGnU6y+XE1HjrzzuTuS1Jetx1o84dzjWoySWQ7Upjc8s0evrK1WctWwuNRj0L8ddWN2y3H1U3fOXOJN+lUlfvUZWiI2NAMIjb8mz+uNvesk+p/Rg5RRoiAXfmxQJ6X45jr/N8v7N+4a8N8M82vEmsuffETK3/jKpnMtqqKEHU0LCcioiyDiTZT4ag9TkREWtfLMRLrFAIWDed94ut1uQJCKQQMvMmk43teuAAPrRRP+50lT53lMplZ+WNtBR/2j2kwFu7n13N2a46BPdeNOy6r1hD8fFgT4srfbKiDeumBb7rIN0xRFzvmK/fxVTf4jDl1tKs6dMO6DvHaxFrKpFptMr1dczFznfcn9WG23Tz9lNfDFocwx1Z+wEbqVq4ZymyvnvWH8rp1N8v6R2aWkeh/VChaDTfRLiqsthwiRgoBg433SSuUQuDi2JhPD+CSfdQRSiFg0GglJmRFI0aK4rP3enVl8Y4hgwvL1nJNDFVuV36xjkdr4tyhapftLfZcvpunuqqITfU9O/DVv4taoVdnzKnDv62xTAe2XlcW5tRuV34OsaLLF8y29vk2yusBIVeZY4lT2L/beX0DVkb3Jsrr8KuWdaW7OUVU8+DMJ6EUApdHjBRCKQQcOi03pxDeJ4GrI7xPDlAKjdlAmrzaQPZSHXmb3M2gqlIh2bffZQ0b9qE35kfRmbRsWITRO4i9sXq+woeP2EmwAFeNr6gAQXXVDXX4iDUf4tiZHCW4Tx26oV6HIGLnEERE8rqflNc/RP+LOdarKxvgmH6TDSZc/kf+HON//7NE3+bK9oPBNVrizCe7p5dLxEghYNDpNaTT538B6Myul5wplELAICbaDlCKXO+aZLb2d1ZFrEpGNpkFOq6ns1k1jHNmDW+68HkPUPWT1t5LZ69bhTOf1O/LYluSSW6cTKrr8k0XbRqmcDIyhcrU781LIa3pr1XyIdTRrnzoBu92VZtM6uhaIqKsxBhmOyCIjez1+jU/mSc0zHLPbKP9h1zDhXmIgECBy6PVa0irMp+0sjCfCkU0gnQNhPdJNIIUcFjnFOofV+OhG0Fqs2+RNi/Mg8lW412WXNYbdCpXKpfVJvEhFDIXmpCjamTC3cfrrx/sfAquwT3nZtXwLlr1O7mmi3zDFPbcfJeytQxN1cp6JadanTHHh3/zoRtqtys/hziQsIPZDp/Ukdnu2TRfjtZdLXOtzGwj0ZZCRSedXiKdXize3ReiEaRroNFqmNpZmgepxf+IIybaAgaJM5kkYT4JXB0b88kklKLYVDNdJjdYLqNRufJ56zzXzFXdULWNMoEdot0kdk1Ar2Gvpj7fyK24gps3SKrCafwxoPBz+QYofON2vumi+nx19W9rKcsdxzRKXSZ11Q0+hZQP/1aHbvDrEPwc4vj73zLbp5/On4PI8jUiIsric3A5NDrOfNIJ80ng4tjkaIvFO4Gro3PTkM5NFftkEiOFwMXR6Ig0qnwKjQs+IQ/9kW/U6056fV4MkZ3S6ZIb585VN1osoogvTIUXM5a4e2p9K7HvVd/HzM10uBB1jXt+LJSZu6fENRAB1yjS5tp5WMvhV9TkKLVd1ZX71GVoiNgUUiI2/Fsdy0TErkMQsXMIIqKw7/LXMc7lhaTnmIoKHRcBgS74PSCwh0YnkZYZKYRSCFwcnZ6bUxjFnKLY+J3ZSm55YdRql6dOYs0JA9hbqd2ukv22kDYuW/W1c8CGnedouH4VKviS/3yIiBrbhDOu6SJY88rap5qIzUC0dBCKpX/Cn1fK4VuLHRPlV+6zYq26YcWaMUeUH/6tvLcrGyZvdbtaOafK4rNm8MmZd4gaVKLCEC5ZMVIIOLSc+aQV5pPA1dHqNaRVmU9agxgp7htrg0WjKiNNbT6Zbcwn7gIPYT6pr23kjhlReLEvcDLZM58KeDcrg435pLquynzKNeZamrHnZCldhLIM+Z89M4u9Dl+sTF1ogM+ay8xmz+VXq9WeJjnTkv+Se9fyG4UUhruXa2RGh3u5pdPgvTSRUNhfpwguX74scioeYVJTU5VupEREOTk5FBoaWmAT+OrVq9P58+fJw8PD5lh55IGVwmw209WrV8nb27vQxuHFwZqfwTcxL03Ko0wAKDMzkwIDA21a8ubk5JDBYFvC1M3NzWUUgughzCeNRsN80zgKa/PyskR5k8nX17fA/R4eHi718BeG682iBIIiEEohEHCUGaVwd3enhISEMpXyKmRyTR54oi0QlFfKzEghEJQVhFIIBBxCKQQCDqEUAgGHUAqBgEMohUDAIZRCIOAQSiEQcPw/HSwszqWJKSsAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 350x350 with 2 Axes>"
+       "<Figure size 200x250 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -2655,7 +2651,7 @@
     "\n",
     "batchlabels = ([5, 15, 25], ['Baseline', 'Ground touched', 'Mesh touched'])\n",
     "smooth = None\n",
-    "plot_vector_covar(group_baseline+group_touch_ground, group_touch, vector_method='norm', distance_method='corr', smooth=smooth, plot_cdf=True, fig_out='touch_combined', do_lines=True, figsize=(3.5,3.5), batchlabels=batchlabels, label_placement='x')"
+    "plot_vector_covar(group_baseline+group_touch_ground, group_touch, vector_method='norm', distance_method='corr', smooth=smooth, plot_cdf=True, fig_out='touch_combined', do_lines=True, figsize=(2,2.5), batchlabels=batchlabels, label_placement='x')"
    ]
   },
   {
@@ -4982,7 +4978,9 @@
   {
    "cell_type": "markdown",
    "id": "1702f98f-74e2-4e13-b2f3-9276e9112606",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
    "source": [
     "## Repeat looped patch experiment with multiple runs per sample\n",
     "\n",
@@ -5313,7 +5311,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.3"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
diff --git a/jupyter_notebooks/multi_trace_viewer.ipynb b/jupyter_notebooks/multi_trace_viewer.ipynb
index f45a08a..5ed8c1c 100644
--- a/jupyter_notebooks/multi_trace_viewer.ipynb
+++ b/jupyter_notebooks/multi_trace_viewer.ipynb
@@ -588,7 +588,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.3"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
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index 2c0351f..698cb32 100644
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index 52cfcc8..2e983b0 100644
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index 8e91c96..bf8b1f8 100644
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index 0c01085..ae0d359 100644
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diff --git a/paper/fig_covar_touch_combined.pdf b/paper/fig_covar_touch_combined.pdf
index 56272f0..bbc8aaf 100644
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diff --git a/paper/fig_covar_touch_mesh.pdf b/paper/fig_covar_touch_mesh.pdf
index 452211a..dc897e2 100644
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diff --git a/paper/fig_results_distinguish_copies.txt b/paper/fig_results_distinguish_copies.txt
index e1f689b..5f3db8e 100644
--- a/paper/fig_results_distinguish_copies.txt
+++ b/paper/fig_results_distinguish_copies.txt
@@ -1,4 +1,4 @@
-Results calculated from plots fig_covar_distinguish_copies.pdf / fig_cdf_distinguish_copies.pdf on 2025-07-10T11:36:13.912164
+Results calculated from plots fig_covar_distinguish_copies.pdf / fig_cdf_distinguish_copies.pdf on 2025-07-15T13:50:47.042666
 
 setting threshold for quantile 0.001
 Baseline threshold set at 0.976282
diff --git a/paper/fig_results_distinguish_copies_large_run.txt b/paper/fig_results_distinguish_copies_large_run.txt
index f9a9681..d60cf39 100644
--- a/paper/fig_results_distinguish_copies_large_run.txt
+++ b/paper/fig_results_distinguish_copies_large_run.txt
@@ -1,4 +1,4 @@
-Results calculated from plots fig_covar_distinguish_copies_large_run.pdf / fig_cdf_distinguish_copies_large_run.pdf on 2025-07-11T13:24:24.365583
+Results calculated from plots fig_covar_distinguish_copies_large_run.pdf / fig_cdf_distinguish_copies_large_run.pdf on 2025-07-15T13:50:52.376029
 
 setting threshold for quantile 0.001
 Baseline threshold set at 0.995906
diff --git a/paper/fig_results_distinguish_layouts.txt b/paper/fig_results_distinguish_layouts.txt
index dfa5b92..1d65b31 100644
--- a/paper/fig_results_distinguish_layouts.txt
+++ b/paper/fig_results_distinguish_layouts.txt
@@ -1,4 +1,4 @@
-Results calculated from plots fig_covar_distinguish_layouts.pdf / fig_cdf_distinguish_layouts.pdf on 2025-07-10T11:36:14.156461
+Results calculated from plots fig_covar_distinguish_layouts.pdf / fig_cdf_distinguish_layouts.pdf on 2025-07-15T13:50:52.609772
 
 setting threshold for quantile 0.001
 Baseline threshold set at 0.078150
diff --git a/paper/fig_results_probe_0.3.txt b/paper/fig_results_probe_0.3.txt
index 1693493..9ef4bcb 100644
--- a/paper/fig_results_probe_0.3.txt
+++ b/paper/fig_results_probe_0.3.txt
@@ -1,4 +1,4 @@
-Results calculated from plots fig_covar_probe_0.3.pdf / fig_cdf_probe_0.3.pdf on 2025-07-10T11:36:25.765389
+Results calculated from plots fig_covar_probe_0.3.pdf / fig_cdf_probe_0.3.pdf on 2025-07-15T13:50:57.178835
 
 setting threshold for quantile 0.001
 Baseline threshold set at 0.972987
diff --git a/paper/fig_results_probe_0.4.txt b/paper/fig_results_probe_0.4.txt
index d9dbf6b..0b48a76 100644
--- a/paper/fig_results_probe_0.4.txt
+++ b/paper/fig_results_probe_0.4.txt
@@ -1,4 +1,4 @@
-Results calculated from plots fig_covar_probe_0.4.pdf / fig_cdf_probe_0.4.pdf on 2025-07-10T11:36:25.993768
+Results calculated from plots fig_covar_probe_0.4.pdf / fig_cdf_probe_0.4.pdf on 2025-07-15T13:50:57.408773
 
 setting threshold for quantile 0.001
 Baseline threshold set at 0.963759
diff --git a/paper/fig_results_short_within_0.3.txt b/paper/fig_results_short_within_0.3.txt
index 9ee11ea..3247284 100644
--- a/paper/fig_results_short_within_0.3.txt
+++ b/paper/fig_results_short_within_0.3.txt
@@ -1,4 +1,4 @@
-Results calculated from plots fig_covar_short_within_0.3.pdf / fig_cdf_short_within_0.3.pdf on 2025-07-14T19:58:37.881770
+Results calculated from plots fig_covar_short_within_0.3.pdf / fig_cdf_short_within_0.3.pdf on 2025-07-15T13:50:56.179657
 
 setting threshold for quantile 0.001
 Baseline threshold set at 0.991740
diff --git a/paper/fig_results_short_within_0.3_min_max.txt b/paper/fig_results_short_within_0.3_min_max.txt
index 3d00598..1c14ab1 100644
--- a/paper/fig_results_short_within_0.3_min_max.txt
+++ b/paper/fig_results_short_within_0.3_min_max.txt
@@ -1,4 +1,4 @@
-Results calculated from plots fig_covar_short_within_0.3_min_max.pdf / fig_cdf_short_within_0.3_min_max.pdf on 2025-07-14T19:58:37.994713
+Results calculated from plots fig_covar_short_within_0.3_min_max.pdf / fig_cdf_short_within_0.3_min_max.pdf on 2025-07-15T13:50:56.330460
 
 setting threshold for quantile 0.001
 Baseline threshold set at 0.447921
diff --git a/paper/fig_results_touch_combined.txt b/paper/fig_results_touch_combined.txt
index b37b6bd..017451d 100644
--- a/paper/fig_results_touch_combined.txt
+++ b/paper/fig_results_touch_combined.txt
@@ -1,4 +1,4 @@
-Results calculated from plots fig_covar_touch_combined.pdf / fig_cdf_touch_combined.pdf on 2025-07-11T17:06:11.411547
+Results calculated from plots fig_covar_touch_combined.pdf / fig_cdf_touch_combined.pdf on 2025-07-15T13:52:25.576345
 
 setting threshold for quantile 0.001
 Baseline threshold set at 0.978979
diff --git a/paper/fig_results_touch_mesh.txt b/paper/fig_results_touch_mesh.txt
index f9c08d2..75932c5 100644
--- a/paper/fig_results_touch_mesh.txt
+++ b/paper/fig_results_touch_mesh.txt
@@ -1,4 +1,4 @@
-Results calculated from plots fig_covar_touch_mesh.pdf / fig_cdf_touch_mesh.pdf on 2025-07-10T11:36:26.224634
+Results calculated from plots fig_covar_touch_mesh.pdf / fig_cdf_touch_mesh.pdf on 2025-07-15T13:50:57.639982
 
 setting threshold for quantile 0.001
 Baseline threshold set at 0.981066
diff --git a/paper/paper.tex b/paper/paper.tex
index d523867..c3ce267 100644
--- a/paper/paper.tex
+++ b/paper/paper.tex
@@ -78,9 +78,8 @@
     approach is both low-cost and precise, and enables the use of inexpensive standard Printed Circuit Boards (PCBs) as
     security mesh material. We demonstrate a working prototype of our TDR circuit costing less than \price{10}{\euro} in
     components that achieves both time resolution and rise time better than \qty{200}{\pico\second}---a $25\times$
-    improvement over previous work. We demonstrate our prototype's capability to detect and localize faults in several
-    practical attack scenarios including probing using a high impedance oscilloscope probe and a patching attempt using
-    micro soldering.
+    improvement over previous work. We demonstrate a simple classifier that detects several classes of advanced attacks
+    such as probing using an oscilloscope probe or micro-soldering attacks with perfect accuracy.
 \end{abstract}
 
 \section{Introduction}
@@ -96,9 +95,9 @@ Security meshes continue to be the state of the art for tamper sensing in applic
 attacks such as attempts at drilling or sawing through the device's enclosure to place probes must be prevented. Common
 applications for such meshes include Hardware Security Modules (HSMs) used to store and process cryptographic keys
 applying security standards such as
-FIPS-140-2\cite{usnationalinstituteofstandardsandtechnologySecurityRequirementsCryptographic2002} or ISO/IEC
-24759\cite{ISOIEC24759}. Other applications include card payment terminals where PCI PTS HSM
-standards\cite{pcisecuritystandardscouncilPaymentCardIndustry2021} are applicable. Security meshes usually consist of
+FIPS-140-2~\cite{usnationalinstituteofstandardsandtechnologySecurityRequirementsCryptographic2002} or ISO/IEC
+24759~\cite{ISOIEC24759}. Other applications include card payment terminals where PCI PTS HSM
+standards~\cite{pcisecuritystandardscouncilPaymentCardIndustry2021} are applicable. Security meshes usually consist of
 two or more conductive traces that are laid out in a meandering pattern to cover a surface. A sensing circuit
 electrically monitors these traces to detect attempts at penetrating this surface.
 
@@ -112,23 +111,23 @@ lower-security applications such as card payment terminals, simpler approaches a
 implementation. Often, standard copper/polyimide Flexible Printed Circuits (FPCs) or even standard Printed Circuit
 Boards (PCBs) are used because of the wide availability of manufacturing services.
 
-Several academic approaches exist that target low-cost\cite{
+Several academic approaches exist that target low-cost~\cite{
     vasileActiveTamperDetection2017,
     vasileTemperatureSensitiveActive2017,
     dupontMiniaturizedUltraLowPowerTamper2022,
     vasileProtectingSecretsAdvanced2019,
-} or high-performance mesh monitoring\cite{
+} or high-performance mesh monitoring~\cite{
     immlerBTREPIDBatterylessTamperresistant2018,
     immlerSecurePhysicalEnclosures2018,
     garbTamperSensitiveDesignPUFBased,
-}. Some academic works even try to replace the security mesh with entirely different tamper sensing primitives\cite{
+}. Some academic works even try to replace the security mesh with entirely different tamper sensing primitives~\cite{
     staatAntiTamperRadioSystemLevel2022,
     vaiSecureArchitectureEmbedded2015,}.
 High-performance mesh monitoring approaches try to characterize the mesh's physical properties with high accuracy, but
 often come at the cost of specialized, expensive circuitry. Low-cost approaches utilize advanced analog techniques in
 their circuitry to extract precise measurements using few components. They trade off measurement precision for lower
 component cost. Besides simple monitoring, detecting tamper attempts by replacing the mesh with a macro-scale Physically
-Unclonable Function (PUF) has also been researched\cite{
+Unclonable Function (PUF) has also been researched~\cite{
     immlerBTREPIDBatterylessTamperresistant2018,
     staatAntiTamperRadioSystemLevel2022,
     vaiSecureArchitectureEmbedded2015,}, albeit this comes with complex monitoring circuits that utilize expensive,
@@ -162,7 +161,7 @@ specimen is shown in Figure\ \ref{fig_pic_board}.
 
 Compared to previous academic designs, our approach can be implemented at a lower cost using exclusively inexpensive,
 commercially available mass-market components. Our TDR frontend improves upon previous, delay-based approaches in
-monitoring fidelity\cite{vasileActiveTamperDetection2017,vasileTemperatureSensitiveActive2017}. Our design achieves
+monitoring fidelity~\cite{vasileActiveTamperDetection2017,vasileTemperatureSensitiveActive2017}. Our design achieves
 sufficient sensitivity to detect high-impedance oscilloscope probes despite such probes being specifically designed to
 conduct measurements without disturbing the circuit under test. Unlike previous, capacitance-based approaches, our
 design is compatible with inexpensive signal switch ICs, enabling the protection of arbitrarily large meshes at minimal
@@ -174,7 +173,7 @@ The contributions of our work are as follows:
     \item To our knowledge, our design is the first to apply a low-cost embedded differential Time Domain Reflectometry
         (TDR) frontend to security mesh monitoring. Our design achieves pulse rise times below \qty{200}{\pico\second},
         a $25\times$ improvement over the closest previous
-        work\cite{vasileActiveTamperDetection2017,vasileTemperatureSensitiveActive2017}.
+        work~\cite{vasileActiveTamperDetection2017,vasileTemperatureSensitiveActive2017}.
     \item Our approach provides higher fidelity compared to state-of-the-art security mesh conductivity monitoring or
         previous low-cost approaches. It enables the use of meshes manufactured using less advanced technologies such as
         standard FPC or PCB processes. Our TDR frontend produces 70 data points for each meter of mesh length, resulting
@@ -192,12 +191,12 @@ The contributions of our work are as follows:
 \section{Related Work}
 
 Tamper sensing meshes are used in numerous applications from Hardware Security Modules (HSMs) to card payment
-terminals\cite{andersonCryptographicProcessorsASurvey2006,tehranipoorHardwareSecurityPrimitives2023}. Despite their
+terminals~\cite{andersonCryptographicProcessorsASurvey2006,tehranipoorHardwareSecurityPrimitives2023}. Despite their
 widespread use, security mesh design and monitoring is covered by a sparse research corpus. Commercially,
 security-by-obscurity is often considered a good idea and little detail is published on physical security
-implementations\cite{andersonSecurityEngineeringGuide2020}.
+implementations~\cite{andersonSecurityEngineeringGuide2020}.
 
-Patent literature gives a partial view of commercial developments in this area. Even in recent patents such as\cite{
+Patent literature gives a partial view of commercial developments in this area. Even in recent patents such as~\cite{
     brodskyTamperRespondentAssemblyFlexible2019, % IBM. ok, mentions conductivity monitoring but mostly on mesh
     nortonTamperDetectingCases2019, % HP. ok, mentions continuity monitoring only but mostly on mesh
     razaghiTamperDetectionSystem2020, % Square. ok. mentions what is effectively conductivity monitoring
@@ -210,13 +209,13 @@ manufacturers Texas Instruments and Zilog, cited monitoring methods are basic an
 of resistance or capacitance.
 
 Academic research in the area is more advanced and spans both improvements to security meshes and their monitoring
-circuits\cite{
+circuits~\cite{
     immlerBTREPIDBatterylessTamperresistant2018,
     dupontMiniaturizedUltraLowPowerTamper2022,
     vasileProtectingSecretsAdvanced2019},
 as well as approaches that entirely replace the security mesh with other primitives based on e.g.\ radio frequency or
 optical measurements that aim to sense tampering
-with a device\cite{staatAntiTamperRadioSystemLevel2022,vaiSecureArchitectureEmbedded2015}. A drawback of techniques
+with a device~\cite{staatAntiTamperRadioSystemLevel2022,vaiSecureArchitectureEmbedded2015}. A drawback of techniques
 aiming to replace security meshes with other sensor types is that it is difficult to prove such sensors do not have
 blind spots.
 
@@ -232,16 +231,16 @@ security mesh as a Physically Unclonable Function (PUF), combining tamper sensin
 their design, the mesh consists of a cross-hatch pattern made from several dozen individually addressable capacitive
 electrodes. They manufacture their meshes in a specialized process that results in unpredictable, random variations in
 capacitance between electrodes. They propose an analog frontend that measures the precise mutual capacitance of each
-pair of electrodes\cite{obermaierMeasurementSystemCapacitive2018} using an approach similar to
+pair of electrodes~\cite{obermaierMeasurementSystemCapacitive2018} using an approach similar to
 \textcite{satoToucheEnhancingTouch2012}, and they use the resulting capacitance matrix as the basis of their PUF. In
 further work, they demonstrate a custom IC integrating the monitoring
-circuit\cite{garbFORTRESSFORtifiedTamperResistant2021}.
+circuit~\cite{garbFORTRESSFORtifiedTamperResistant2021}.
 
 Advantages of their system include high sensitivity to modifications, as well as that as a PUF, the system does not
 require a continuous power supply. Disadvantages include the limited mesh size a single circuit can support due to
 dynamic range constraints, the specialized manufacturing process needed for the mesh as well as the high cost of the
 monitoring circuit. Common physical security standards require systems to actively destroy all key material when
-tampering is detected\cite{
+tampering is detected~\cite{
     usnationalinstituteofstandardsandtechnologySecurityRequirementsCryptographic2002,
     ISOIEC24759,
     pcisecuritystandardscouncilPaymentCardIndustry2021}.
@@ -283,7 +282,7 @@ to any signal characteristics apart from total signal power.
 
 \paragraph{Time domain mesh monitoring.}
 Time-Domain Reflectometry has been proposed for tamper sensing in nuclear arms control
-applications\cite{parsonsTamperRadiationResistant1977}. However, compared to our design, the systems proposed in this
+applications~\cite{parsonsTamperRadiationResistant1977}. However, compared to our design, the systems proposed in this
 field are usually much larger, using standard benchtop measurement equipment to perform TDR. Additionally, they target
 lower time resolution since they are designed to monitor spans of cable up to several hundred meters in length.
 
@@ -323,7 +322,7 @@ downconverting mixers. This development was enabled by both the increasing avail
 hundreds of megasamples per second at a reasonable resolution, and by the increase in speed of CPUs,
 FPGAs, and other components of the digital processing chain. However, this is largely a development of this
 millennium--meanwhile, signals far into the gigahertz range have been studied since the advent of radar technology in
-the Second World War\cite{kahrs50YearsRF2003}. Enabled by the progress from vacuum tubes to semiconductor devices,
+the Second World War~\cite{kahrs50YearsRF2003}. Enabled by the progress from vacuum tubes to semiconductor devices,
 equivalent time sampling became the technology of choice for the latter half of the twentieth century until around the
 turn of the millennium the introduction of high-speed digital processing and fast ADCs enabled real-time conversion up
 into higher microwave frequencies, today reaching beyond the \qty{100}{\giga\hertz} boundary.
@@ -331,10 +330,10 @@ into higher microwave frequencies, today reaching beyond the \qty{100}{\giga\her
 \textcite{kahrs50YearsRF2003} trace back the style of four-diode balanced bridge sampling gate that we use to a vacuum
 tube implementation presented in \textcite{chanceWaveforms1949}. This style of sampling gate found application in a
 number of sampling oscilloscopes throughout the twentieth century in several oscilloscope sampling frontends such as
-HP's 187B\cite{HP187BDualTrace1962}. 
+HP's 187B~\cite{HP187BDualTrace1962}. 
 
 While initially equivalent time sampling was used to circumvent technological limitations, more recently it has also
-been used to achieve cost-optimized designs\cite{houtman1GHzSamplingOscilloscope2000}. Going along similar principles,
+been used to achieve cost-optimized designs~\cite{houtman1GHzSamplingOscilloscope2000}. Going along similar principles,
 \textcite{polasekReflektometrCasoveOblasti2020} presents a design for a minimal sampling TDR circuit that uses a CMOS
 clock generator IC along with a CML fanout buffer for pulse generation. The circuit improves upon the double sampling
 design first presented by \textcite{houtman1GHzSamplingOscilloscope2000} to reconstruct a downsampled copy of the input
@@ -376,7 +375,7 @@ length.
 In this paper, we apply TDR to monitor a security mesh for changes caused by an attack. Our prototype setup consists of
 a custom circuit board containing a low-cost embedded TDR frontend that can be connected to a security mesh specimen to
 measure its response, creating a fingerprint of the mesh. In a standard PCB manufacturing process, we construct a
-security mesh with a ground plane underneath that works similarly to previous work\cite{
+security mesh with a ground plane underneath that works similarly to previous work~\cite{
     immlerBTREPIDBatterylessTamperresistant2018,
     obermaierMeasurementSystemCapacitive2018,
     garbTamperSensitiveDesignPUFBased}.
@@ -447,7 +446,7 @@ to use a comparatively lossy but simple \qty{-6}{\deci\bel} resistive tee instea
 We implemented the sub-nanosecond sampler using a simple four-diode bridge sampling gate made from commodity
 \partno{BAT17-04W} RF Schottky diodes, which offer turn-on times better than \qty{100}{\pico\second} at
 \price{0.13}{\euro} per device at quantity 1000. In contrast to prior
-work\cite{polasekReflektometrCasoveOblasti2020,houtman1GHzSamplingOscilloscope2000}, we precisely control the timing of
+work~\cite{polasekReflektometrCasoveOblasti2020,houtman1GHzSamplingOscilloscope2000}, we precisely control the timing of
 our ADC and avoid the need for a second sampling stage.
 
 We base our circuit around an \partno{STM32G474RB} microcontroller, \price{5}{\euro}-class commodity ARM
@@ -458,10 +457,10 @@ adjustable, phase-locked stimulus and sampling pulses.
 
 While the HRTIM peripheral provides sub-nanosecond phase adjustment, the digital outputs of the \partno{STM32G4} series
 are limited to a minimum transition time of $t_r=t_f=\qty{1.7}{\nano\second}$\footnote{Datasheet specification, when
-driving a \qty{10}{\pico\farad} load\cite{stmicroelectronicsSTM32G474xBDatasheet2021}.}. We work around this issue with
+driving a \qty{10}{\pico\farad} load~\cite{stmicroelectronicsSTM32G474xBDatasheet2021}.}. We work around this issue with
 two circuit tricks. First, we send the output through a fast amplifier to square up the edges to a rise time better than
 \qty{500}{\pico\second}. We then reduce the \qty{10}{\nano\second} minimum pulse width supported by the \partno{HRTIM}
-peripheral by applying a clip line\cite{tektronixinc.TektronixS6Sampling1982} pulse forming network--i.e.\ we connect
+peripheral by applying a clip line~\cite{tektronixinc.TektronixS6Sampling1982} pulse forming network--i.e.\ we connect
 the amplifier's output to the load in parallel with a short, terminated transmission line stub. The length of this stub
 determines the pulse width.
 
@@ -506,7 +505,7 @@ such as the CML-output comparators made by Analog Devices due to cost.
 \paragraph{Standard logic ICs.}
 As a baseline, we evaluated the \partno{74LVC2G157} CMOS multiplexer configured to provide complementary outputs.
 According to manufacturer specifications, this part provides slightly faster rise and fall times than
-oumicrocontroller\cite{renesaselectronicscorporationApplicationNoteAN2242019}.
+oumicrocontroller~\cite{renesaselectronicscorporationApplicationNoteAN2242019}.
 
 \paragraph{Optical Networking Chipsets.}
 Optical transceivers use CML-output limiting amplifiers and laser drivers, some of which are still available as discrete
@@ -813,8 +812,8 @@ lines here and for \partno{TDP0604} since the other amplifiers' output did not c
     \end{center}
     \caption{Specifications of mesh test specimens used in the experiments in this paper. Approximate signal delays were
         calculated using wave velocity
-        $v=\frac{c}{\sqrt{\epsilon_r}}\approx\frac{c}{2}$\cite{wheelerTransmissionLinePropertiesParallel1965} assuming
-        $\epsilon_r\approx 4$\cite{mumbyDielectricPropertiesFR41989} for the test specimens' \partno{FR-4} substrate.}
+        $v=\frac{c}{\sqrt{\epsilon_r}}\approx\frac{c}{2}$~\cite{wheelerTransmissionLinePropertiesParallel1965} assuming
+        $\epsilon_r\approx 4$~\cite{mumbyDielectricPropertiesFR41989} for the test specimens' \partno{FR-4} substrate.}
     \label{tab_mesh_spec}
 \end{table}
 
@@ -830,7 +829,7 @@ We validated the results from Figure\ \ref{fig_mesh_length} by calculating speed
 substrate based on them. The resulting measurements are shown in Table\ \ref{tab_speed_of_light}. All amplifier
 configurations yield comparable measurements of approximately \qty{1.6}{\meter\per\second}, which corresponds with the
 expected signal propagation velocity in \partno{FR-4} PCB material of
-\qty{1.5d8}{\meter\per\second}\cite{wheelerTransmissionLinePropertiesParallel1965,mumbyDielectricPropertiesFR41989}.
+\qty{1.5d8}{\meter\per\second}~\cite{wheelerTransmissionLinePropertiesParallel1965,mumbyDielectricPropertiesFR41989}.
 
 The graphs in Figure~\ref{fig_mesh_length} show a dispersion effect that increasingly rounds off the trailing edge of
 the response with longer mesh lengths. This effect stems from higher-frequency components coupling into adjacent trace
@@ -904,33 +903,38 @@ switching.
 \subsection{Classification performance}
 \label{sec-class-perf}
 
-To evaluate the practical performance of our system in a baseline scenario, we captured approximately 1250 measurement
-series under a variety of environmental and attack conditions. In each series, we captured 7 differential traces with
-$2\times768$ points per trace. One differential trace served as a calibration reference with the multiplexers configured
-to disconnect the mesh. The other six traces cover each of open circuit, short circuit, and matched load termination
-measuring the mesh once from each of both ends.
+To evaluate the practical performance of our system, we captured approximately 1250 measurement series under a variety
+of environmental and attack conditions and evaluated its performance using a simple template-matching classifier. In
+each measurement series, we captured 7 differential traces with $2\times768$ points per trace. One differential trace
+served as a calibration reference with the multiplexers configured to disconnect the mesh. The other six traces cover
+each of open circuit, short circuit, and matched load termination measuring the mesh once from each of both ends for 12
+channels total ($\{\text{open}, \text{short}, \text{load}\} \times \{\text{forward}, \text{reverse}\} \times
+\{\text{positive}, \text{negative}\}$).
 
-We explored two variants of our baseline classifier, each consisting of three steps: First, traces are passed through a
-B-spline smoothing filter. This filter serves as a low-pass filter, evening out noise contributions. We only applied
-this filter where necessary. Second, we calculate a distance between each channel
-($\{\text{open},\text{short},\text{load}\}\times\{\text{forward},\text{reverse}\}\times\{\text{positive},\text{negative}\}$
-of the baseline trace and the corresponding channel of the experiment traces, resulting in a vector with 12 entries.
-Third, we apply a norm to this vector to reduce it to a single, scalar distance value.
+Our classifier is designed to compare two measurement series and produce a scalar score indicating their similarity. A
+simple threshold can then be applied on the similarity score to decide the class. Type 1 and type 2 error rates can be
+tuned by adjusting this threshold.
 
-The two variants of our classifier differ in the distance function and the vector norm. The first variant uses the
-pearson ccorrelation coefficient as its distance function and mean as its vector norm. The second variant uses the
-maximum distance at any one trace point as its distance function, and selects the maximum component in its vector norm.
-The first variant is sensitive to changes in the overall shape of a trace, while the second variant is sensitive to
-localized changes to one or a few points of a trace.
+Our classifier proceeds in four steps: B-spline smoothing, per-channel pearson correlation coefficient, channel score
+mean, and threshold. B-spline smoothing serves as a low-pass filter, evening out random noise. We only applied this
+filter where necessary---most attacks leave a strong signal that stands out from noise. We calculate the pearson
+correlation coefficient for each measurement channel separately, producing a vector with 12 entries. We average the
+components of this vector to a single, scalar similarity score.
 
-Figure~\ref{fig_layout_identity} shows the performance of the correlation classifier on intact meshes. For each
-performance measurement, we show the correlation matrix between a set of baseline measurements and a set of experiment
-measurements. High values indicate similarity, low values indicate differences. We show the baseline set top
-left, and the experiment set bottom right. Uniform color within the top left indicates high similarity between baseline
-measurements. Nonuniform color in the bottom right is expected, and indicates that mutliple experiment (attack)
+\subsubsection{Interpreting these performance plots}
+Figure~\ref{fig_layout_identity} shows the similarity score of multiple intact meshes. For each performance measurement,
+we show the similarity scores for each pair of measurements as a matrix, with each measurement appearing once in each
+row and column. High values indicate similarity, low values indicate differences. We show the baseline measurement set
+top left, and the experiment set bottom right. Uniform color within the top left indicates high similarity between
+baseline measurements. Nonuniform color in the bottom right is expected, and indicates that mutliple experiment (attack)
 measurements are unlike each other. Classification performance is indicated by the top right and bottom left quadrants,
 which indicate misclassification probability. Misclassification is likely when the top left and top right quadrants look
-alike. Misclassification is unlikely the more they differ.
+alike. Misclassification is less likely the more they differ. Under each figure, we give the False Negative Rate (FNR),
+i.e. the rate of missed alarms, when the threshold is adjusted for a False Positive Rate, i.e. a false alarm rate, of
+$0.1\%$. These values are calculated assuming a normal distribution of similarity scores. Additionally, we provide the
+Crossover Error Rate (CER) derived from the empirical cumulative distribution function of the results, i.e. the error
+rate where for some threshold FPR is equal to FNR. A CER near $50\%$ indicates the classifier cannot distinguish the
+classes, lower values indicate good performance.
 
 Figure~\ref{fig_layout_identity_layout} compares several copies of the same mesh (top left) to four variants that have
 the same pitch and area, but different layout of the traces (bottom right). Here and in all following graphs we list the
@@ -942,27 +946,26 @@ The variance between samples of the baseline group in Figure~\ref{fig_layout_ide
 possibility that while all mesh samples of the same layout were supposed to be identical copies, our measurement circuit
 might be sensitive enough to pick up on manufacturing variations from one copy to another in a PUF-like manner. To
 evaluate this scenario, in Figure~\ref{fig_layout_identity_identity} we show the result of repeated measurements of
-three copies of the same mesh. The measurements were taken interleavedi (i.e. $1, 2, 3, 1, 2, \hdots$) to exclude
-systematic errors from affecting the conclusion. As we can see, our system indeed exhibits a PUF-like response and can
-distinguish multiple copies of the same mesh with precision. We leave a detailed analysis of this effect to future work.
-For the scope of this paper, the presense of this effect indicates good performance of our design, and increases the
-detection efficiency of our approach. 
+three copies of the same mesh. The measurements were taken interleaved ($1, 2, 3, 1, 2, \hdots$) to exclude systematic
+errors. We found our system can indeed distinguish multiple copies of the same mesh at a 1.7\% FNR at 0.1\% FPR. We
+leave a detailed analysis of this effect to future work. For the scope of this paper, the presense of this effect
+indicates good performance of our design, and increases the detection efficiency of our approach. 
 
 \begin{figure}
     \centering
     \begin{subfigure}[t]{0.28\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_distinguish_layouts.pdf}
-        \caption{Different mesh layouts, False negative rate 18\% at 0.1\% false positive rate, CER=0\%}
+        \caption{Five copies of the same layout compared to four other layouts, FNR 18\% at 0.1\% FPR, CER=0\%}
         \label{fig_layout_identity_layout}
     \end{subfigure}
     \hspace*{5mm}
     \begin{subfigure}[t]{0.28\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_distinguish_copies_large_run.pdf}
-        \caption{Three identical copies, False negative rate 1.7\% at 0.1\% false positive rate, CER=0\%}
+        \caption{Three identical copies, FNR 1.7\% at 0.1\% FPR, CER=0\%}
         \label{fig_layout_identity_identity}
     \end{subfigure}
     \hfill
-    \caption{Measurements of intact meshes, correlation classifier.}
+    \caption{Similarity matrices of measurement series on intact meshes.}
     \label{fig_layout_identity}
 \end{figure}
 
@@ -971,104 +974,86 @@ detection efficiency of our approach.
 \begin{figure}
     \begin{subfigure}[t]{0.23\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_open_p0.3.pdf}
-        \caption{Open, p=\qty{0.3}{\milli\meter}. Missed alarm rate 0.0\% at 0.1\% false alarm rate, CER=0\%.}
+        \caption{One trace interrupted, p=\qty{0.3}{\milli\meter}. FNR 0.0\% at 0.1\% FPR, CER=0\%.}
     \end{subfigure}
     \hfill
     \begin{subfigure}[t]{0.23\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_short_across_traces_p0.3.pdf}
-        \caption{Short, p=\qty{0.3}{\milli\meter}. Missed alarm rate 0.0\% at 0.1\% false alarm rate, CER=0\%.}
+        \caption{Both traces shorted, p=\qty{0.3}{\milli\meter}. FNR 0.0\% at 0.1\% FPR, CER=0\%.}
     \end{subfigure}
     \hfill
     \begin{subfigure}[t]{0.23\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_open_p0.4.pdf}
-        \caption{Open, p=\qty{0.4}{\milli\meter}. Missed alarm rate 0.0\% at 0.1\% false alarm rate, CER=0\%.}
+        \caption{One trace interrupted, p=\qty{0.4}{\milli\meter}. FNR 0.0\% at 0.1\% FPR, CER=0\%.}
     \end{subfigure}
     \hfill
     \begin{subfigure}[t]{0.23\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_short_across_traces_p0.4.pdf}
-        \caption{Short, p=\qty{0.4}{\milli\meter}. Missed alarm rate 0.0\% at 0.1\% false alarm rate, CER=0\%.}
+        \caption{Both traces shorted, p=\qty{0.4}{\milli\meter}. FNR 0.0\% at 0.1\% FPR, CER=0\%.}
     \end{subfigure}
-    \caption{Covariance matrix of intact (top left) and modified meshes (bottom right). Shown are two pitches. Ten
-        specimens each with either one trace interrupted, or both traces shorted in a random location.}
+    \caption{Similarity matrix of 10 intact and 10 modified meshes with two pitch sizes.}
     \label{fig_covar_basic_attacks}
 \end{figure}
 
 Figure~\ref{fig_covar_basic_attacks} shows the performance of our classifier under the two basic attack scenarios of an
-interrupted trace, and a short between the mesh's differential traces. Such attacks lead to large changes in the
-location of the reflected pulse edge, which our classifier picks up with perfect accuracy across our test set.
+interrupted trace, and a short circuit between the mesh's differential traces. Such attacks lead to large changes in the
+location of the reflected pulse edge, leading to 0\% Crossover Error Rate.
 
-\subsubsection{Hairpin shortening}
+\subsubsection{Trace shortening}
 
 \begin{figure}
     \centering
-    \begin{subfigure}[t]{0.28\textwidth}
-        \includegraphics[width=\textwidth]{fig_covar_short_within_0.3.pdf}
-        \caption{Correlation classifier, False negative rate 18\% at 0.1\% false positive rate, CER=17\%}
-        \label{fig_short_within_corr}
-    \end{subfigure}
-    \hspace*{5mm}
-    \begin{subfigure}[t]{0.28\textwidth}
-        \includegraphics[width=\textwidth]{fig_covar_short_within_0.3_min_max.pdf}
-        \caption{Min/Max classifier, False negative rate 23\% at 0.1\% false positive rate, CER=23\%}
-        \label{fig_short_within_minmax}
-    \end{subfigure}
-    \hfill
+    \includegraphics[width=0.3\textwidth]{fig_covar_short_within_0.3.pdf}
     \caption{Classification results of several mesh specimens that have one trace shorted to an adjacent location on the
-    same trace.}
+    same trace. FNR 18\% at 0.1\% FPR, CER=17\%.}
     \label{fig_short_within}
 \end{figure}
 
-When one trace is not shorted to the other mesh trace, but instead shorted to another location within the same trace,
-the resulting distortion in response shape is harder to detect. The reason for this is that such modifications introduce
-a skew in the delay of the differential pair. Depending on the length of the shorted-out section, this skew may be as
-little as a few picoseconds, which is hard to detect given our system's measurement resolution.
-
-Figure~\ref{fig_short_within} shows the performance of our classifier under this scenario. As we can see in the
-structure of the correlation plots, for some samples which have longer sections of mesh trace shorted out, this attack
-is easy to distinguish, but for others, where only a short section of trace is shorted out, it is harder to distinguish.
+Figure~\ref{fig_short_within} shows classification results when one trace is short circuited to another location within
+the same trace. Here, the resulting distortion in response shape is harder to detect. Depending on the length of the
+shorted-out section, the timing skew such modifications introduce may be as little as a few picoseconds. For some
+samples which have longer sections of mesh trace shorted out, this attack is easy to distinguish, but for others, our
+classifier cannot distinguish it leading to an overall FNR of 18\% at 0.1\% FPR, with some specimens reliably detected,
+and others never detected.
 
 \subsubsection{Advanced attacks}
 
 \begin{figure}
     \begin{subfigure}[t]{0.23\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_probe_0.3.pdf}
-        \caption{Oscilloscope probe. Missed alarm rate 0.0\% at 0.1\% false alarm rate, CER=0\%.}
+        \caption{Oscilloscope probe. FNR 0.0\% at 0.1\% FPR, CER=0\%.}
         \label{fig_covar_adv_probe}
     \end{subfigure}
     \hfill
     \begin{subfigure}[t]{0.23\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_soldering_p0.3.pdf}
-        \caption{Soldering iron. Missed alarm rage 0.0\% at 0.1\% false alarm rate, CER=0\%.}
+        \caption{Soldering iron. FNR 0.0\% at 0.1\% FPR, CER=0\%.}
         \label{fig_covar_adv_soldering}
     \end{subfigure}
     \hfill
     \begin{subfigure}[t]{0.23\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_antenna_wire_30mm_p0.3.pdf}
-        \caption{30mm wire soldered. Missed alarm rage 9.6\% at 0.1\% false alarm rate, CER=1\%.}
+        \caption{30mm wire soldered. FNR 9.6\% at 0.1\% FPR, CER=1\%.}
         \label{fig_covar_adv_antenna}
     \end{subfigure}
     \hfill
     \begin{subfigure}[t]{0.23\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_probe_points_p0.3.pdf}
-        \caption{Baseline vs. specimens with soldermask removed for previous plots.}
+        \caption{Baseline vs. experiment specimens with no attack.}
         \label{fig_covar_adv_baseline}
     \end{subfigure}
-    \caption{}
+    \caption{Classifier performance under advanced attack scenarios.}
     \label{fig_covar_adv_attack}
     %too much: fig_covar_soldering_p0.3_minmax.pdf
     %too much: fig_covar_antenna_wire_30mm_p0.3_minmax.pdf
 \end{figure}
 
-Figure~\ref{fig_covar_adv_attack} shows our classifier's performance under a set of more advanced attacks: An
-oscilloscsope probe touching one mesh trace (Figure~\ref{fig_covar_adv_probe}, Rigol PVP3150 probe), a soldering iron
-touching one mesh trace (Figure~\ref{fig_covar_adv_soldering}), and a mesh where one trace has a
-$l=\qty{30}{\milli\meter},d=\qty{120}{\micro\meter}$ copper wire soldered to one trace
-(Figure~\ref{fig_covar_adv_probe}). The probing attack is interesting since oscilloscope probes are specifically
-designed to disturb the probed circuit as little as possible. The wire attack simulates an attacker attaching a wire in
-an attempt to patch a trace in preparation for an attack. Our classifier is able to clearly distinguish each attack.
-Figure~\ref{fig_covar_adv_baseline} compares baseline specimens against the three specimens that had soldermask removed
-for these attacks while no attack is being conducted. This result shows that this preparation has no effect on the
-measurement.
+Figure~\ref{fig_covar_adv_attack} shows our classifier's performance under conditions similar to actions an attacker
+would perform during an attack: An oscilloscsope probe\footnote{Part number Rigol PVP3150.} touching one mesh trace
+(Figure~\ref{fig_covar_adv_probe}), a soldering iron touching one mesh trace (Figure~\ref{fig_covar_adv_soldering}), and
+a mesh where one trace has a $l=\qty{30}{\milli\meter},d=\qty{120}{\micro\meter}$ piece of copper wire soldered to one
+trace (Figure~\ref{fig_covar_adv_probe}). Our classifier is able to clearly distinguish the probing and soldering iron
+cases at 0\% FNR, with a maximum of 9.6\% FNR at 0.1\% FNR in the soldered wire case.
 
 \subsubsection{Patching attacks}
 \label{sec_attack_probe}
@@ -1076,50 +1061,43 @@ measurement.
 \begin{figure}
     \begin{subfigure}[t]{0.27\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_patch_interleave_baseline.pdf}
-        \caption{Test boards before experiment}
+        \caption{Test boards before experiment.}
         \label{fig_covar_patch_attack_baseline}
     \end{subfigure}
     \hfill
     \begin{subfigure}[t]{0.27\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_patch_ref_exp_interleave_direct.pdf}
-        \caption{Experiment specimen compared to reference before and after}
+        \caption{Experiment specimen compared to reference before and after attack.}
         \label{fig_covar_patch_attack_direct}
     \end{subfigure}
     \hfill
     \begin{subfigure}[t]{0.4\textwidth}
         \includegraphics[width=\textwidth]{fig_patch_interleave_scatter.pdf}
-        \caption{Trajectory of experiment and control speciments}
+        \caption{Trajectory of relative difference to reference specimens.}
         \label{fig_covar_patch_attack_scatter}
     \end{subfigure}
     \hfill
-    \caption{Classifier performance under a patching attack that bridges a short gap within a mesh trace using wire.
-        B-spline smoothing was applied during classification.}
+    \caption{Classifier performance under a patching attack that bridges a short gap within a mesh trace using wire.}
     \label{fig_covar_patch_attack}
 \end{figure}
 
-While our proposed measurement setup significantly increases the level of effort required from an attacker, as long as
-standard PCBs are used as meshes, the attacker can apply PCB rework techniques like they are widely used in the industry
-for PCB repair. If we assume a standard PCB process with \qty{100}{\micro\meter} trace/space design rules, a drilling
-attack targeting a \qty{300}{\micro\meter} hole size as proposed by \textcite{immlerSecurePhysicalEnclosures2018} will
-break at least one trace. Patching the resulting break using a wire is possible, but with increasing wire length, the
-TDR response of the mesh is increasingly distorted. We experimentally performed an attack comparable to the one shown by
-\textcite{immlerSecurePhysicalEnclosures2018} on a \qty{300}{\micro\meter} pitch mesh specimen. In this attack, we
-removed a small part of one mesh trace and bridged it with a wire. Figure\ \ref{fig_drill_mod_shape} shows our
-modification and the resulting change in the time-domain response.
+PCB tamper sensing meshes are susceptible to industry-standard PCB rework techniques. If we assume a standard PCB
+process with \qty{100}{\micro\meter} trace/space design rules, a drilling attack targeting a \qty{300}{\micro\meter}
+hole size requires cutting and patching at least one trace~\cite{immlerSecurePhysicalEnclosures2018}. We performed such
+an attack on a set of \qty{300}{\micro\meter} pitch meshes. Figure\ \ref{fig_drill_mod_shape} shows our modification and
+the resulting change in the time-domain response.
 
-Figure~\ref{fig_covar_patch_attack} shows the classification result of this attack. Because the patch is small,
-this type of attack leaves only subtle traces in the measurement data. To extract this effect, we performed two
-experiments in a row. First, we interleaved measurements of two reference specimens, a control specimen, and the
-unmodified experiment specimen to establish a baseline. Then, we modified the experiment specimen and repeated the
-experiment. Temperature drift and other possible external factors affecting the measurement can be excluded by comparing
-both control and experiment measurements against the two references before and after the modification.
-Figure~\ref{fig_covar_patch_attack_baseline} shows the four samples before the attack, exhibiting the same subtle
-PUF-like effect that we described in Section~\ref{sec-class-perf}. Since we peform both before and after measurements on
-the same sample, we can separate this effect from the effect of the attack. Figure~\ref{fig_covar_patch_attack_direct}
-compares both control and experiment samples before and after the attack, and shows a clear change in the experiment
-sample during the attack. Figure~\ref{fig_covar_patch_attack_scatter} plots the similarity of both samples to each of
-the two reference samples. We can see that the control distribution stays in one place, while the experiment
-distribution shifts.
+Figure~\ref{fig_covar_patch_attack} shows the classification result of this attack. To extract the subtle effect of this
+attack, we measured two reference specimens, one control, and one experiment specimen twice in a row, once before the
+attack, and once after. Measurements were repeated 10 times interleaved. Factors such as temperature drift can be
+excluded by comparing both control and experiment measurements against the two references before and after the
+modification. Figure~\ref{fig_covar_patch_attack_baseline} shows the four samples before the attack, exhibiting the same
+subtle PUF-like effect that we described in Section~\ref{sec-class-perf}. Since we peform both before and after
+measurements on the same sample, we can separate this effect from the effect of the attack.
+Figure~\ref{fig_covar_patch_attack_direct} compares both control and experiment samples before and after the attack, and
+shows a clear change in the experiment sample during the attack. Figure~\ref{fig_covar_patch_attack_scatter} plots the
+similarity scores of both samples to each of the two reference samples. We can see that the control distribution stays
+in one place, while the experiment distribution shifts.
 
 \begin{figure}
     \centering
@@ -1141,74 +1119,73 @@ distribution shifts.
 
 Based on the above results, we peformed a larger-scale experiment using seven samples with patches applied compared
 against baseline measurements taken before and after measuring the experiment samples. Each sample was measured ten
-times in an interleaved order. Figure~\ref{fig_patch_large_scale} shows the results of this experiment. As we can see,
-the min/max classifier is better at distinguishing the subtle, localized effects of such patches. Using the min/max
-classifier, half of attack attempts are detected in a single measurement when fixing the false alarm rate at 0.1\%.
+times, interleaved. Figure~\ref{fig_patch_large_scale} shows the results of this experiment, resulting in a FNR of
+71.5\% at 0.1\% FPR. Since such patches only affect few data points along the reflection response, we included a variant
+of our classifier that uses the maximum difference across all channels instead of the averaged pearson correlation
+coefficient to better at distinguishing the subtle, localized effects of such patches. Using this classifier variant,
+FNR improves to 51.1\%, detecting half of all attack attempts in a single measurement when fixing the false alarm rate
+at 0.1\%.
 
 \begin{figure}
     \centering
     \begin{subfigure}{0.3\textwidth}
         \centering
         \includegraphics[width=\textwidth]{fig_covar_patch_repeat_p0.3.pdf}
-        \caption{Correlation classifier. Missed alarm rate 71.5\% at 0.1\% false alarm rate, CER=34\%.}
+        \caption{Micro-soldering patching attack. FNR 71.5\% at 0.1\% FPR, CER=34\%.}
         \label{fig_patch_large_scale_corr}
     \end{subfigure}
+    \hspace*{5mm}
     \begin{subfigure}{0.3\textwidth}
         \centering
         \includegraphics[width=\textwidth]{fig_covar_patch_repeat_p0.3_minmax.pdf}
-        \caption{Min/max classifier. Missed alarm rate 51.1\% at 0.1\% false alarm rate, CER=15\%.}
+        \caption{\emph{maximum} classifier variant. FNR 51.1\% at 0.1\% FPR, CER=15\%.}
         \label{fig_patch_large_scale_minmax}
     \end{subfigure}
     \caption{Classification performance in a larger-scale experiment using 10 measurements each of 7 samples with
-        traces patched through micro-soldering. B-spline smoothing was applied before classification.}
+        traces patched through micro-soldering.}
     \label{fig_patch_large_scale}
 \end{figure}
 
 \subsubsection{Environmental susceptibility}
 
-The measurement sensitivity of our design raises the question of how environmental factors such as handling, or
-electromagnetic interference affect the measurements. Figure~\ref{fig_env_effects} shows the result in several
-scenarios. As shown in Figure~\ref{fig_env_effects_time}, time alone does not contribute significantly to the
-measurement results. As indicated by Figure~\ref{fig_env_effects_touch}, touching parts of the device other than the
-mesh during normal handling also does not disturb measurements. However, when the mesh is directly touched, this can
-easily be detected. In a practical application, this is of little concern since any PCB tamper sensing mesh would lie on
-the inside of the device. Since the meshes we use have a continous ground plane, a simple solution to touch sensitivity
-is to put the ground plane on the outside of the device, shielding the mesh from touching.
+Figure~\ref{fig_env_effects} shows the results of a series of experiments evaluating the effect of environmental factors
+such as handling or electromagnetic interference on our measurements. Figure~\ref{fig_env_effects_time} shows our
+measurements exhibit little time drift (CER=60\%). Figure~\ref{fig_env_effects_touch} shows that touching the mesh is
+easily detected (FNR=0\%), but the system is insensitive to touching other parts of the circuit. Our tamper-sensing mesh
+uses a continous ground plane. In a practical application the mesh would be on the inside of the protected envelope,
+with the ground plane on the outside, shielding it from touch.
 
-A significant effect on the measurements can be seen when the mesh is heated, as shown by the results in
-Figure~\ref{fig_env_effects_heat}. Figure\ \ref{fig_tempco_time} shows the relative difference between the time-domain
-response of a mesh at room temperature and a mesh heated to \qty{70}{\degree C}. This temperature dependence has two
-main factors. First, the resistance of the mesh's copper traces has a positive temperature coefficient, meaning that its
-resistance increases with temperature. Across the \qty{50}{\degree C} temperature difference shown here, this
-corresponds to a change in resistance of approximately 20\%. Besides the resistance of copper, the dielectric constant
-and dissipation factor of the FR-4 dielectric of the mesh PCB also have a significant temperature
-coefficient\cite{sagarStudiesTemperatureDependent2024,hinagaThermalEffectsPCB2010}. An increase in copper resistance can
-be seen in the overall shift of the response curve due to resistive attenuation. An increase in the dielectric
-dissipation factor can be seen in the slope of the difference, since pulse energy is dissipated more the longer the
-pulse travels through the material. Finally, a change in dielectric constant moves the response's trailing edge in time,
-with the pulse propagating slightly slower at high temperature.
+As shown in Figure~\ref{fig_env_effects_heat}, heating the mesh distors its measurements (FNR=0.6\%, CER=0\%).
+Figure~\ref{fig_tempco_time} shows the difference caused by heating the mesh to \qty{70}{\degree C} in the time domain.
+This temperature dependence stems from the resistance of the mesh's copper traces increasing with temperature, and the
+dielectric properties of the FR-4 PCB substrate changing. Both dielectric constant and dissipation factor of FR-4 change
+with temperature~\cite{sagarStudiesTemperatureDependent2024, hinagaThermalEffectsPCB2010}. The increase in copper
+resistance causes a shift of the response curve. An increase in the dielectric dissipation factor affects the slope of
+the difference in Figure~\ref{fig_tempco_time} since pulse energy is dissipated more the longer the pulse travels
+through the material. A change in dielectric constant moves the response's trailing edge in time, with the pulse
+propagating slightly slower at high temperature.
 
 Since these effects are consistent with physical predictions and only reach problematic levels at large temperature
 differences, it would be possible to design a classifier that is insensitive to temperature effects. Furthermore, given
 the predictable, physical nature of these effects, they could also be compensated before classification in the digital
-domain based on a temperature measurement and a set of per-mesh calibration data.
+domain based on a temperature measurement.
 
 \begin{figure}
     \begin{subfigure}[t]{0.25\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_time_drift.pdf}
-        \caption{Time drift (2.5h). False negative rate 100\% at 0.1\% false positive rate, CER=60\%.}
+        \caption{Time drift (2.5h). FNR 100\% at 0.1\% FPR, CER=60\%.}
         \label{fig_env_effects_time}
     \end{subfigure}
     \hfill
-    \begin{subfigure}[t]{0.4\textwidth}
+    \begin{subfigure}[t]{0.25\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_touch_combined.pdf}
-        \caption{Touch sensitivity. False negative rate 0.0\% at 0.1\% false positive rate, CER=0\%.}
+        \caption{Touch sensitivity. FNR 0.0\% at 0.1\% FPR, CER=0\%.}
         \label{fig_env_effects_touch}
     \end{subfigure}
     \hfill
     \begin{subfigure}[t]{0.25\textwidth}
         \includegraphics[width=\textwidth]{fig_covar_hot_mesh.pdf}
-        \caption{Mesh heated (\qty{70}{\degree C}). False negative rate 0.6\% at 0.1\% false positive rate, CER=0\%.}
+        \caption{Mesh heated (\qty{70}{\degree C}). FNR 0.6\% at 0.1\% FPR, CER=0\%.}
         \label{fig_env_effects_heat}
     \end{subfigure}
     \caption{Classification results of the same mesh under various environmental factors.}
@@ -1228,25 +1205,22 @@ our measurements. Although our system's equivalent-time sampling setup inherentl
 synchronous to the sampling clock, the setup is unshielded so we verified its actual susceptibility in several
 scenarios. Figure~\ref{fig_env_covar} shows the result of these measurement series. For comparison, we included several
 measurements from Figure~\ref{fig_patch_large_scale}. From these figures, we can see that there are some environmental
-effects, but these effects are small even when compared against a subtle attack like a patching attack.
+effects, but these effects are small even when compared against a subtle attack like a patching attack with the
+classification performance remaining approximately constant at 69.0\% FNR at 0.1\% FPR and a slightly reduced CER of
+20\%.
 
 \begin{figure}
-    \begin{subfigure}{0.3\textwidth}
-        % NOTE: not actually "tridelta" data, I'm just too lazy to rename these and fix up the notebook.
-        \includegraphics[width=\textwidth]{fig_covar_patch_repeat_tridelta_all_the_data_p0.3.pdf}
-        \caption{Covariance Metric, Missed alarm rate 69.0\% at 0.1\% false alarm rate, CER=20\%.}
-    \end{subfigure}
+    \centering
+    % NOTE: not actually "tridelta" data, I'm just too lazy to rename these and fix up the notebook.
+    \includegraphics[width=0.4\textwidth]{fig_covar_patch_repeat_tridelta_all_the_data_p0.3.pdf}
     \hspace*{2mm}
-    \begin{subfigure}{0.3\textwidth}
-        % NOTE: not actually "tridelta" data, I'm just too lazy to rename these and fix up the notebook.
-        \includegraphics[width=\textwidth]{fig_covar_patch_repeat_tridalta_all_the_data_p0.3_minmax.pdf}
-        \caption{Min/Max Metric, Missed alarm rate 63.5\% at 0.1\% false alarm rate, CER=17\%.}
-    \end{subfigure}
-    \caption{Covariance matrices comparing all environmental runs. For scale, measurements from
-        Figure~\ref{fig_patch_large_scale} are included on the bottom/right. B-spline smoothing was applied.}
+    \caption{Classifier similarity scores of measurements in different environments, 10 measurements each. For scale,
+        measurements from Figure~\ref{fig_patch_large_scale} are included on the bottom/right. FNR 69.0\% at 0.1\% FPR,
+        CER=20\%.}
     \label{fig_env_covar}
 \end{figure}
 
+\color{highlightred}
 \subsection{Countermeasures}
 
 As shown above, PCB security meshes can be manipulated through micro-soldering. Keeping the modifications as physically
@@ -1257,15 +1231,15 @@ done using a minimal amount of solder as well as a bespoke, insulated soldering
 tool out of a material like sintered ceramic is conceivable, to our knowledge, no such tool exists on the market.
 
 Furthermore, the actual drilling would have to happen with a dielectric drill bit, placing special attention on
-evacuating conductive copper chips before they can create shorts to nearby traces. Again, it is conceivable that such a
-tool could be manufactured, but to our knowledge, such a tool is not currently available as a standard component on the
-market.
+evacuating conductive copper chips before they can create short circuits to nearby traces. Again, it is conceivable that
+such a tool could be manufactured, but to our knowledge, such a tool is not currently available as a standard component
+on the market.
 
 Finally, any probes penetrating the mesh would have to be placed such that their presence in the vicinity of the mesh
 traces does not disturb the TDR response. Modifications would have to be carried out with great care, likely using
 micromanipulators or similar specialized equipment.
 
-The PCI PTS HSM DTR standard\cite{pcisecuritystandardscouncilPaymentCardIndustry2021a} contains a useful framework for
+The PCI PTS HSM DTR standard~\cite{pcisecuritystandardscouncilPaymentCardIndustry2021a} contains a useful framework for
 thinking about attacker capabilities. Applying their taxonomy, our monitoring system raises the skill level required for
 a patching attack from a \emph{skilled} attacker to an \emph{expert} attacker, and the equipment requirement from
 \emph{standard} equipment to \emph{bespoke} equipment such as dielectric drill bits and ceramic soldering tips.
@@ -1274,7 +1248,6 @@ a patching attack from a \emph{skilled} attacker to an \emph{expert} attacker, a
 % seems to work better.
 
 % FIXME peer review only, for major revision @ TCHES
-\color{highlightred}
 \section{Future Work}
 
 %\paragraph{Design variants.} We found that the timing jitter of our sampling frontend is low enough to reach the
@@ -1306,11 +1279,10 @@ similar to a VNA and it would be interesting to measure parts of the secure subs
 our TDR frontend.
 
 \color{highlightgreen}
-\paragraph{Characterization of PUF-like effects.} In Section~\ref{sec-class-perf}, we have described a PUF-like effect
-we observed during measurements, where our baseline classifier was repeatedly able to distinguish supposedly identical
-copies of the same mesh. It would be interesting to precisely characterize this effect and its dependence on factors
-such as the chosen PCB manufacturer, and to quantify if it indeed rises to the level of a PUF in entropy and
-repeatability.
+\paragraph{Characterization of PUF-like effects.} In Section~\ref{sec-class-perf}, we have described a PUF-like effect,
+where our classifier was able to distinguish supposedly identical copies of the same mesh. It would be interesting to
+precisely characterize this effect and its dependence on factors such as the chosen PCB manufacturer, and to quantify if
+it indeed rises to the level of a PUF in entropy and repeatability.
 
 \color{black}
 \section{Conclusion}