From f41bacb9936a32b45d1a06568ff18e666f2537e5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Christian=20Paul=20Alexandre=20Reisner-S=C3=A9n=C3=A9lar?=
 <uqnwo@student.kit.edu>
Date: Wed, 11 Dec 2024 20:12:31 +0000
Subject: [PATCH] Upload New File

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "885c7767-e912-4e31-b5d6-3a3443ffa58e",
+   "metadata": {},
+   "source": [
+    "# Fakultät für Physik\n",
+    "\n",
+    "## Physikalisches Praktikum P1 für Studierende der Physik\n",
+    "\n",
+    "Versuch P1-61, 62, 63 (Stand: **Oktober 2024**)\n",
+    "\n",
+    "[Raum F1-16](https://labs.physik.kit.edu/img/Klassische-Praktika/Lageplan_P1P2.png)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Ferromagnetische Hysterese"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cda71a9-2111-4282-a28a-821dc2202093",
+   "metadata": {},
+   "source": [
+    "Name:Reisner Vorname: Christian E-Mail: uqnwo@student.kit.edu\n",
+    "\n",
+    "\\begin{equation*}\n",
+    "\\begin{split}\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "\\end{split}\n",
+    "\\end{equation*}\n",
+    "\n",
+    "Name:Ben Tov Vorname: Jonthan E-Mail: ufoxj@student.kit.edu\n",
+    "\n",
+    "\\begin{equation*}\n",
+    "\\begin{split}\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "\\end{split}\n",
+    "\\end{equation*}\n",
+    "\n",
+    "Gruppennummer: Do06\n",
+    "\n",
+    "\\begin{equation*}\n",
+    "\\begin{split}\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "\\end{split}\n",
+    "\\end{equation*}\n",
+    "\n",
+    "\n",
+    "Betreuer: Finn Rosumek\n",
+    "\n",
+    "\\begin{equation*}\n",
+    "\\begin{split}\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "\\end{split}\n",
+    "\\end{equation*}\n",
+    "\n",
+    "Versuch durchgeführt am: 05.12.2024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9861759e-6c07-4ec5-a750-f307ec3d8028",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "**Beanstandungen zu Protokoll Version _____:**\n",
+    "\n",
+    "\\begin{equation*}\n",
+    "\\begin{split}\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "&\\\\\n",
+    "\\end{split}\n",
+    "%\\text{\\vspace{10cm}}\n",
+    "\\end{equation*}\n",
+    "\n",
+    "<br>\n",
+    "Testiert am: __________________ Testat: __________________"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e27d6b-3390-4401-8300-1dc26021fb2d",
+   "metadata": {},
+   "source": [
+    "# Durchführung"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be70efea-bff5-42d5-b5aa-0b9598c33846",
+   "metadata": {},
+   "source": [
+    "**Detaillierte Hinweise zur Durchführung der Versuche finden Sie in der Datei [Ferromagnetische_Hysterese_Hinweise.ipynb](https://gitlab.kit.edu/kit/etp-lehre/p1-praktikum/students/-/blob/main/Ferromagnetische_Hysterese/Ferromagnetische_Hysterese_Hinweise.ipynb)**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fe74fe8-1b63-48de-832b-ad03e49b0132",
+   "metadata": {},
+   "source": [
+    "## Aufgabe 1: Induktivität und Verlustleistung einer Spule"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f05f94c-cb93-4ac6-b323-7a73a8bffbb0",
+   "metadata": {},
+   "source": [
+    "### Aufgabe 1.1: Luftgefüllte Spulte\n",
+    "\n",
+    " * Bestimmen Sie die **Spuleninduktivität** $L$, den **Verlustwiderstand** $R_{L}$ und die **elektrische Verlustleistung** $P_{L}$ (aufgrund von $R_{L}$) einer luftgefüllten Spule.\n",
+    " * Bestätigen oder Widerlegen Sie die Hypothese, dass weder $L$ noch $R_{L}$ von der effektiven Stromstärke $I_{\\mathrm{eff}}$ im Wechselstromkreis abhängen.\n",
+    " * Berechnen Sie aus den angegebenen Spulendaten im [Datenblatt](https://gitlab.kit.edu/kit/etp-lehre/p1-praktikum/students/-/blob/main/Ferromagnetische_Hysterese/Datenblatt.md) zum Versuch die erwarteten Werte für $L$ und $R_{L}$ und vergleichen Sie Ihre Erwartung mit der Messung.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "72523276-c7ae-467f-97b4-b03495806d9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import PhyPraKit as PPK\n",
+    "import kafe2\n",
+    "from uncertainties import ufloat\n",
+    "from uncertainties.umath import sin,cos\n",
+    "def n(a):\n",
+    "    return np.array([x.n for x in a])\n",
+    "def s(a):\n",
+    "    return np.array([x.s for x in a])\n",
+    "def utl(u):\n",
+    "    return [f\"${x.nominal_value:.2f} \\\\pm {x.std_dev:.2f}$\" for x in u]"
+   ]
+  },
+  {
+   "attachments": {
+    "7cd64ca7-6bdc-45d3-a85b-d703b5a97f03.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "b699a264-3663-45b1-9ce4-ab2424a308d5",
+   "metadata": {},
+   "source": [
+    "**V E R S U C H S B E S C H R E I B U N G**\n",
+    "\n",
+    "![1_1FH.png](attachment:7cd64ca7-6bdc-45d3-a85b-d703b5a97f03.png)   \n",
+    "Quelle: Hinweise für den Versuch Ferromagnetische Hysterese-Impedanz der Spule \n",
+    "\n",
+    "\n",
+    "Es wurde eine Spule mit dem der Windungszahl $N=1000$ der Länge $l=6.8\\,\\mathrm{cm}$, einer Drahtdicke $d=0.7\\,\\mathrm{mm}$ und einer mittleren Wicklungsradius $r=3.4 \\,\\mathrm{cm}$  und einer Querschnitsfläche von $A\\approx 15.21 \\,\\mathrm{cm^2}$ mit dem Wiederstand $R_1=10 \\pm 0.5 \\,\\mathrm{\\Omega}$ in Reihe geschaltet. Diese Schaltung wurde mit einer sinusförmigen Spannung der Frequenz $f= 50 \\pm 0.1 \\,\\mathrm{Hz}$ und einer Ampitude $U_0=12 \\,\\mathrm{V}$. Mit einem Ampermeter wurde der Efektiv Strom durch die Schaltung gemessen. Desweiteren wurden die Spannungen an $R_1$ und $L$ gemessen. Aufgezeichnet wurden für verschiedene Werte für $I_{eff}$ die jeweiligen Pickspannungen $U_{L,0}$ und $U_{L,0}$, sowie die Zeitdifferenz der Nulldurchläufe von $U_R$ und $U_L$. \n",
+    "Für die Impedanz einer reelen Spule gilt:   \n",
+    "$Z=R_L+ i\\omega L=|Z|\\cdot e^{i\\Delta \\varphi}$   \n",
+    "und somit nach der Aufspalltung in einen Real und Imaginärteil sowie mit $|Z|=\\dfrac{U_{L,0}}{U_{R,0}}R$ und $\\Delta\\varphi = \\omega\\,\\Delta t$:   \n",
+    "$$\n",
+    "\\begin{equation*}\n",
+    "\\begin{split}\n",
+    "&L = \\frac{U_{L,0}}{U_{R,0}}\\,\\frac{R}{\\omega}\\,\\sin(\\omega\\Delta t);\\\\\n",
+    "&\\\\\n",
+    "&R_{L} = \\frac{U_{L,0}}{U_{R,0}}\\,R\\,\\cos(\\omega\\Delta t).\n",
+    "\\end{split}\n",
+    "\\end{equation*}\n",
+    "$$ \n",
+    "\n",
+    "sowie aus weiteren Überlegungen:   \n",
+    "$$\n",
+    "\\begin{equation*}\n",
+    "\\begin{split}\n",
+    "&L \\approx N^{2}\\,\\frac{\\mu_{0}\\,\\,A}{\\ell+0.91r}.\n",
+    "&\\\\\n",
+    "&R_{L} =\\rho\\,\\frac{l_{Draht}}{A_{Draht}}.\n",
+    "\\end{split}\n",
+    "\\end{equation*}\n",
+    "$$ \n",
+    "\n",
+    "Wobei $l_{Draht}$ die Länge des Drahtes ist und $A_{Draht}$ der Querschnitt des Drahtes ist. \n",
+    "\n",
+    "Die Verlustleistung an $L$ lässt sich über $P_{ver}=R_L\\cdot I_{eff}^2$\n",
+    "\n",
+    "Desweiteren kann über eine Anpassung von $\\mu_r$ und $R_L$ als konstante Werte gegen $I_{eff}$ kann gezeigt werden, dass die beiden Werte von $I_{eff}$ unbhängig sind. \n",
+    "\n",
+    "Die Fehler wurden aus den Unsicherheiten beim Ablesen und den Begerentzheit der Anzeigen, von den wir ausgehen, dass sie Näherungsweise den gesamten Fehler verantworten, wie follgt abgeschäzt:   \n",
+    "$\\Delta\\omega=0.1\\,\\mathrm{Hz}$   \n",
+    "die Restlichen Unsicherheiten können der darstellung der Messwerte entnommen werden.   \n",
+    "Die Berechnung der weiteren Unsicherheiten erfollgte mitels der Pythonbibliothek *uncertainties* mithilfe liniarer Fehlerpfortpflanzung berechnet. \n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b92d6f12-dd01-47e0-af99-1a77716ee6da",
+   "metadata": {},
+   "source": [
+    "**L Ö S U N G**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8e80a26f-775b-44f1-a90e-21ad5f95ce46",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Es wurde gemessen:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$I_{eff}\\mathrm{[mA]} $</th>\n",
+       "      <th>$U_{R,0} \\,\\mathrm{mV} $</th>\n",
+       "      <th>$U_{L,0} \\,\\mathrm{mV} $</th>\n",
+       "      <th>$\\Delta t \\,\\mathrm{ms} $</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>$34.35 \\pm 0.10$</td>\n",
+       "      <td>$0.51 \\pm 0.01$</td>\n",
+       "      <td>$0.72 \\pm 0.01$</td>\n",
+       "      <td>$2.72 \\pm 0.30$</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>$89.80 \\pm 0.10$</td>\n",
+       "      <td>$1.32 \\pm 0.01$</td>\n",
+       "      <td>$1.87 \\pm 0.01$</td>\n",
+       "      <td>$2.15 \\pm 0.30$</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>$126.00 \\pm 0.10$</td>\n",
+       "      <td>$1.85 \\pm 0.01$</td>\n",
+       "      <td>$2.66 \\pm 0.01$</td>\n",
+       "      <td>$2.71 \\pm 0.30$</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>$223.40 \\pm 0.10$</td>\n",
+       "      <td>$3.31 \\pm 0.01$</td>\n",
+       "      <td>$4.67 \\pm 0.01$</td>\n",
+       "      <td>$2.70 \\pm 0.30$</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>$301.50 \\pm 0.10$</td>\n",
+       "      <td>$4.44 \\pm 0.01$</td>\n",
+       "      <td>$6.27 \\pm 0.01$</td>\n",
+       "      <td>$2.72 \\pm 0.30$</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  $I_{eff}\\mathrm{[mA]} $ $U_{R,0} \\,\\mathrm{mV} $ $U_{L,0} \\,\\mathrm{mV} $  \\\n",
+       "0        $34.35 \\pm 0.10$          $0.51 \\pm 0.01$          $0.72 \\pm 0.01$   \n",
+       "1        $89.80 \\pm 0.10$          $1.32 \\pm 0.01$          $1.87 \\pm 0.01$   \n",
+       "2       $126.00 \\pm 0.10$          $1.85 \\pm 0.01$          $2.66 \\pm 0.01$   \n",
+       "3       $223.40 \\pm 0.10$          $3.31 \\pm 0.01$          $4.67 \\pm 0.01$   \n",
+       "4       $301.50 \\pm 0.10$          $4.44 \\pm 0.01$          $6.27 \\pm 0.01$   \n",
+       "\n",
+       "  $\\Delta t \\,\\mathrm{ms} $  \n",
+       "0           $2.72 \\pm 0.30$  \n",
+       "1           $2.15 \\pm 0.30$  \n",
+       "2           $2.71 \\pm 0.30$  \n",
+       "3           $2.70 \\pm 0.30$  \n",
+       "4           $2.72 \\pm 0.30$  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "UUr=0.01\n",
+    "UUl=0.005\n",
+    "UIf=0.1\n",
+    "Udelt=0.3\n",
+    "UR=np.array([ufloat(0.509,UUr),ufloat(1.316,UUr),ufloat(1.846,UUr),ufloat(3.313,UUr),ufloat(4.438,UUr)]) #mV\n",
+    "UL=np.array([ufloat(0.724,UUl),ufloat(1.866,UUl),ufloat(2.656,UUl),ufloat(4.665,UUl),ufloat(6.268,UUl)]) #mv\n",
+    "delt=np.array([ufloat(22.85-20.13,Udelt),ufloat(16.41-14.26,Udelt),ufloat(26.35-23.64,Udelt),ufloat(16.73-14.03,Udelt),ufloat(16.02-13.30,Udelt)]) #ms\n",
+    "delts=delt/1000\n",
+    "If=np.array([ufloat(34.35,UIf),ufloat(89.8,UIf),ufloat(126.0,UIf),ufloat(223.4,UIf),ufloat(301.5,UIf)]) #mA\n",
+    "omega=2*np.pi*ufloat(50,0.1)\n",
+    "R=ufloat(10,0.5)\n",
+    "print(\"Es wurde gemessen:\")\n",
+    "pd.DataFrame({\"$I_{eff}\\mathrm{[mA]} $\":utl(If),\n",
+    "              \"$U_{R,0} \\,\\mathrm{mV} $\":utl(UR),\n",
+    "              \"$U_{L,0} \\,\\mathrm{mV} $\":utl(UL),\n",
+    "              \"$\\Delta t \\,\\mathrm{ms} $\":utl(delt)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7b0c0e29-f5ba-4c38-87ca-4749fca945f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$I_{eff}\\mathrm{[mA]} $</th>\n",
+       "      <th>$P_{verl} \\,\\mathrm{W} $</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>$34.35 \\pm 0.10$</td>\n",
+       "      <td>$0.01 \\pm 0.00$</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>$89.80 \\pm 0.10$</td>\n",
+       "      <td>$0.08 \\pm 0.00$</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>$126.00 \\pm 0.10$</td>\n",
+       "      <td>$0.15 \\pm 0.01$</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>$223.40 \\pm 0.10$</td>\n",
+       "      <td>$0.49 \\pm 0.02$</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>$301.50 \\pm 0.10$</td>\n",
+       "      <td>$0.89 \\pm 0.04$</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  $I_{eff}\\mathrm{[mA]} $ $P_{verl} \\,\\mathrm{W} $\n",
+       "0        $34.35 \\pm 0.10$          $0.01 \\pm 0.00$\n",
+       "1        $89.80 \\pm 0.10$          $0.08 \\pm 0.00$\n",
+       "2       $126.00 \\pm 0.10$          $0.15 \\pm 0.01$\n",
+       "3       $223.40 \\pm 0.10$          $0.49 \\pm 0.02$\n",
+       "4       $301.50 \\pm 0.10$          $0.89 \\pm 0.04$"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "L=np.array([(UL[i]/UR[i])*(R/omega)*sin(omega*delts[i]) for i in range(len(UR))])\n",
+    "RL=np.array([(UL[i]/UR[i])*R*cos(omega*delts[i]) for i in range(len(UR))])\n",
+    "PL=RL*If**2\n",
+    "L1=L*1000\n",
+    "def l(r,L2=1):\n",
+    "    return L2\n",
+    "data = kafe2.XYContainer(x_data=n(If),y_data=n(L1))\n",
+    "data.add_error(axis='x', err_val=s(If))\n",
+    "data.add_error(axis='y', err_val=s(L1))\n",
+    "data.label = '$Induktivität$ '\n",
+    "fit1=kafe2.XYFit(xy_data=data,model_function=l)\n",
+    "fit1.do_fit()\n",
+    "fit1.assign_model_function_latex_name(r\"L\")\n",
+    "def R2(r,R_L=1):\n",
+    "    return R_L\n",
+    "data = kafe2.XYContainer(x_data=n(If),y_data=n(RL))\n",
+    "data.add_error(axis='x', err_val=s(If))\n",
+    "data.add_error(axis='y', err_val=s(RL))\n",
+    "data.label = '$Widerstand$ '\n",
+    "fit2=kafe2.XYFit(xy_data=data,model_function=R2)\n",
+    "fit2.do_fit()\n",
+    "RL=ufloat(fit2.parameter_values[0],fit2.parameter_errors[0])\n",
+    "kafe2.plot({fit1,fit2}, x_label=r\"$I_f\\,\\mathrm{[mA]}$\", y_label=r'$ L\\,\\mathrm{[mH]} \\mathrm{und\\ } R \\,\\mathrm{[\\Omega]} $')\n",
+    "Pver=RL*(If/1000)**2\n",
+    "pd.DataFrame({\"$I_{eff}\\mathrm{[mA]} $\":utl(If),\n",
+    "              \"$P_{verl} \\,\\mathrm{W} $\":utl(Pver)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8bd0860c-c28b-4442-b42e-1d0f048deddb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rechnerisch ergibt sich L=0.04612598620440388 H und R=9.880816326530613 Ohm\n"
+     ]
+    }
+   ],
+   "source": [
+    "N=1000\n",
+    "r=0.034\n",
+    "l=0.068\n",
+    "d=0.0007\n",
+    "A=np.pi*r**2\n",
+    "mu0=4*np.pi*10**(-7)\n",
+    "rhocu=1.78*10**(-8)\n",
+    "le=N*2*np.pi*r\n",
+    "Ad=np.pi*(d/2)**2\n",
+    "Lrech=N**2*mu0*A/(l+0.91*r)\n",
+    "Rl=rhocu*(le/Ad)\n",
+    "print(f\"Rechnerisch ergibt sich L={Lrech} H und R={Rl} Ohm\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04620cbe-dd13-479c-b3ef-5a7ef7f31455",
+   "metadata": {},
+   "source": [
+    "Aus den Daten von $U_{R,0},U_{R,0}$ und $\\Delta t$ follgt also:   \n",
+    "$R_L=9.78\\pm 0.75 \\,\\mathrm{\\Omega}$   \n",
+    "$L=33.1\\pm 2.3 \\,\\mathrm{mH}$  \n",
+    "\n",
+    "Für die Verlustleistung an der Spule lässt sich berechnen:   \n",
+    "$P_{verl}=$\n",
+    "Beide Fits mit einem konstanten Wert beschreiben die Daten im Rahmen der Unsicherheiten sehr gut, wie sich aus $\\chi^2/ndf < 1$ und $chi^2 \\ \\mathrm{proability}\\approx 0.7$ follgern lässt. Sie sind also nicht von $I_{eff}$ abhängig.\n",
+    "\n",
+    "\n",
+    "Eine Berechnung von $R_L$ und $L$ aus den Dimensionierungen der Spule ergibt:   \n",
+    "$R_L\\approx9.88\\,\\mathrm{\\Omega}$   \n",
+    "$L\\approx 46.1\\,\\mathrm{mH}$ \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d2e8948-810e-4240-9f99-8cb2c8bb2db2",
+   "metadata": {},
+   "source": [
+    "**D I S K U S S I O N**\n",
+    "\n",
+    "Beide Werte von $R_L$ sind im Rahmen der Messunsicherheit verträglich, die beiden Werte für $L$ nicht. Ein Vergleich mit den Herstellerangben ($L=44\\,\\mathrm{mH}$) legt nahe, dass der Wert für L aus der Berechnung von mithilfe von $U_{R,0},U_{R,0}$ und $\\Delta t$ warscheinlich zu klein ist. Es könnte eine Unsicherheit zu klein Abgeschätz worden sein, oder Beschädigung in der Spule fühert zu einer faktischen Änderung der Daten der Spule von denn Herstellerangaben.    \n",
+    "\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20887c3e-9c2e-4cf2-918e-c6091ec8f900",
+   "metadata": {},
+   "source": [
+    "### Aufgabe 1.2: Spule mit Eisenkern\n",
+    "\n",
+    " * Wiederholen Sie die Messungen von **Aufgabe 1.1**, diesmal jedoch mit einem Eisenkern in der Spule.\n",
+    " * Berechnen Sie aus den Daten der Spule und den gemessenen Werten für $L$ die (mittlere) **relative Permeabilität** $\\langle\\mu_{r}\\rangle$ als Funktion von $I_{\\mathrm{eff}}$.\n",
+    " * Berechnen Sie aus den gemessenen Werten für $R_{L}$ und $I_{\\mathrm{eff}}$ die **Verlustleistung $P_{L}$ der Spule**, als Funktion von $I_{\\mathrm{eff}}$ und vergleichen Sie mit dem Ergebnis aus **Aufgabe 1.1**.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "attachments": {
+    "61b1d54c-4a46-4ef5-b20b-d113f03ded4e.png": {
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Tgt3mzZs9pMWzuqiV1suWLfMXtS1btgT3pI/mMmoRzOjRo4MjQO6hv4e1a9f6NJD169f7HuvAnuhEfM6cOab9WNTWSFupli9f3s4991x79913bePGjcEj9w6BEUBMv//+u79hHXvsscGRzBs3bpytXr3ar+sMWGfDGVG2bFkrXbq0TZ06NdMvgkCy0Bu+pllogVnr1q3t0EMPtc6dO9vrr7/uJ1dANHoN1Vat2ujgmWee8ZZjjRo1ssMPP9ymTZvmoVEtyTRitLcIjABi+vvvv/3FKF6LXf744w97++237frrr/d5kYsWLbINGzYE96ZfgwYN/F+t4AZyOoXF8ePHe1P87t27+9abenMfNmyYdenSxR588EFbt25d8GhgV1988YX/nmhh4MMPP2wfffSRNxH//PPPbejQoT5CpLnoTzzxhI/q7A0CI4CYpk+f7oGxePHiwZG9p7mKGi7RloI1atTwBSwafstohVHCWxJmdDgbSEarVq2yHj162OTJk4Mju1LVSNtiAmlplEW9af/880+fF37VVVf5KIymEGnqjrpK9OvXz5o3b+6/Qxo12hvs9JIgqproTRHI6bQln+ZSKexldg6jznr1+e677z4feuvUqZPPs3nppZfs4IMPDh6VPrfffruvsj755JMTup81kBU0NeOHH37wuYvR6CTrkEMOCW4lN3U/UGjJazu96LUyq1+P/v33X1+YeOKJJ3o1MdoOWGPGjLELL7zQT9pvvPHG4GgGKDAi/mbMmKEgzoVLrrmsXLky+O3eOytWrAi1atUqtGTJEr+9cOHCUIcOHUINGzYMTZ8+3Y9lxG233Rbx6+TChUtyXCpWrBj8teYN7dq1i/g8ZNWld+/eoa1btwZfze4WLVoUSjnpD1177bWhbdu2BUfTjyHpLPDqq6/6/BQuXHLiRWejOmONVfnYE30ezZ/RBH5VHUS7xey3334+5B1eAJMR4YqnhmHSfs1cuOS0y/vvv+9TNGKpU6dOxI9NxkvJkiWDrzpvmTFjRsTnI5EXzVU88sgjbfHixX47mnCLHT1Gcx0zisAIICbNgZHM7N2s1jxa7KKA+Msvv/jtmTNn+lDK3rTV0QueVgRKsWLF/F8gJ2vYsKEHxmjTPvLnz880J0QU7pGr7VKjLWjRa6a6U1SsWNFXUu8NAiOAmI4++mgrUKBA1Mn4e6LKpM6AtZpZ82v69u3rl+eee27H5Gutko51ZpyWKpLq5agXyXgsxgGyW7ly5TwQRupGoBOt8847z1fBAmmVKlXKq8/qwah53Wk7R+i19csvv/T7DjzwwL1ukUZgBBBT3bp1vbqh9h4Z3btZj1c1UaugBw4caG+88caOy+DBg+2CCy7wx+msOCOBUUFTIVNbFQK5gYYI1VKnV69ewRHzBWHqpXfbbbf5Kleq6YhEVWktIDz77LOtZ8+e3m9Rr69aRPXJJ5/46vuuXbt6kLzuuut88dTeIDACiKlChQrelkEhTfs3Z4ReoL799ls77rjjfCu/1FS1LFy4sF9XYMxIGP3ggw98NSKBEbmJQmOHDh2CW2bNmjWzZ5991u6++26vQALRqMm7+i9edNFF9vLLL1u3bt3s6quv9tXQDz30kO8YpN8lrZLW/PG9QWAEEJNeXNRkW72+Ro4cGRxNnxdffNGbDyswxqI2VOkNjGrToTPnAw44wCd6A7lJ6jmMlSpV8t/xokWLBkeA6KpXr25PPvmkff/9915pbNq0qYfGsWPHegPvdu3a+Yn63iIwAtgjbVOmwKg5iCtWrAiORqcFLTfddJP3W9S8Gu0Wk5q2GtTOBNrfVDTcNmjQoHTtmfvOO+/4C+K1116712fKAJAbqQuF5p1fdtllvqvLLbfcYo0bN7aaNWtm+vWSwAhgj7SF32OPPWazZ8/2+Yda3RyNAqW2/tOZrEKjJmPPnTs3uPc/Gk7WsLJWhmp+1uWXX+7tdbSKLxat1FbQVMsONckFAGQNdnpJEFVYwh351YdRb4hATqYeX02aNPGJ+P379/ewF60FSCKowqm5OY888oifNd9www0eZIHcJvx3pakgAwYM8Os5jVbuao5yXtvpRVvz1a5dOziSu1BhBJAuGuq49957vcqnJtxZ+Uag89off/zRnn/+eV+A07ZtW8IiAGQhAiOAdFFrndNPP937wWk4+fXXX/eqX1ZQONWqP/1/11xzjdWqVSu4BwCQFQiMANJNrXFuvfVW3+RezbfVpiHRtMparSI0f/L222+3M844w8MrACDrMIcxQZjDiNxKLxn6/b744ottypQpvj/0008/nZAVy9OmTbNWrVr5ftFqTKv/R7teALlZbpnDqB2ZihQpEhzJ/bRXs/rV5tY5jATGBCEwIrfT77iGh7/55htr3ry5bzt18MEHZzrQ6SVp3bp13jvszjvvtGXLltmjjz7q/1d6QqletNXweNasWf6xWq2tNy81IFdbIO27CiSz3BAYtfWndhjJS7Tt3ltvveX/5kYExgQhMCIv0A4tV1xxhY0fP95XT6uNjsLaQQcdtFfBUftDq2r53nvvebhT42K13bnkkkuCR6SfWvSoce3JJ5/sq7rVADkrV3UDeys3BEbkPsxhBLDXihcvbm+++aa3ulG46969u+9VqtYS2hVmyZIltmXLluDRu9P56tq1az0k6sRKq7C1ddXo0aN9cY0W1mj+4t4I94rUXtjaNouwCAB7jwpjglBhRF6iHVpmzJjhG92///77NmHCBCtfvrz3alST7WrVqlnlypW9FY629Pvjjz9s8+bNNn/+fF8BPW/ePJs8ebIVKlTIq4ktWrTw7QQ1lLy3FF6feeYZu//++71vI5BTUGFEMiIwJgiBEXmR5g9qgYqCoPZ7HjVqlAdBvcxo8rtWNys0aih727ZtXl2sWLGih0PNg6xXr563zFHIzGxFUDvM6HOoZ6S2xgJyCgIjkhGBMUEIjMjL9LKiIWH1TdRFWwFu2LDBVxDqX1UcVYEsVqyYNwTXfEdVF+PVLkfbE+rzN2rUyPeeLleuXHAPkPwIjEhGzGEEEHd6w1MI1EIYDUGr2tegQQPr2LGjr3Zu2bKl31b7idKlS1vRokXj2ltx4sSJHlr1/+9NWFT1U0Pmqpjquj6X5mJq6B0A8iICI4BcR1VFhVA1+c4IBcOFCxfaF198YR9++KG9++673iZDK66HDRvmq7dVvWRgBkBeQ2AEkKuoMvjxxx/7XEkNSWeE+jaqBY/aBGle5ZlnnumLZ9Q0XAtwNNVEi3sAIK8hMALIVebOnWurVq3yIfGM7LigquH06dN9xbZ6S6pNkOZYahHOMccc49VKBUe16cnsghwAyGkIjAByFe08o3mHJ5xwglcZI9m+ffuOPo2pafV2wYIFvQVQmPbPrlGjhs+x1HxMzYsEgLyGwAggV/nggw98ccpZZ50VHNlJQVLDzerL2KVLF1+BqhXccsMNN/gxzVPUFocKibqor+SLL77o17X9IQDkRQRGALmGhqI1JK3hZTX/TuuJJ56wc88913r16mVPP/20Dz8/9NBDXllUeHzppZe8H+TPP//szcV1adu2rV155ZV+XdsUAkBeRGAEkOMpKP7yyy8e+tQ4XBQcddHws6hJeO/evX3rwWnTptns2bN9PqK2JVy3bp0/BgAQGYERQI6mauKsWbM8LGrRihanaP/pp556yoeXw3tZa3Wzhqq184yCoy6fffaZHXHEET5vEQAQHYERQI6mFcvHHnusvfzyyzZkyJAdl8GDB9vNN9/sO8hI1apVfdHKBRdcsMvj1Eg82uIYIDtpyoSmR2i3JCC7ERgB5AnaKlBtcd58802vMmqoWsPYas6tHV20+OWjjz6yRYsW+ZC17p86dar99ttv/ngdAxJJv3OaXxv29ddf29VXX22dO3feMdUCyC4ERgB5RteuXe3xxx+3Tz75xG6//XYPg+ecc44VL17ch6yXL19uBx54oFd1tBDm7bffturVq3s/Rg1v//XXX8FnAuKvZ8+euwTG1atX2/fff++7DZ199tk+Vzer6ETq8MMP98uCBQuCo8jL9gmxx1VCaEeIQw45xK+/+uqrdvnll/t1ANkv9cte6ibcaY+nfXmkYTcSRRVE7SykRVjR6GTnjjvuCG4llt631MBeNEe4Zs2afh15FxVGAHmOgl/4klra46lvh48BifDrr7/usYKoSiOQXQiMAABks6VLl/pc2ljUBWDixIm2YsWKHe2igKxCYAQAIJtpyFdzZWPZsGGDT2/q3r27DR8+fMcuRUBWIDACAJDNtLhEe5jHmvqgqqLmx7/wwgu+aEvbWWooG8gKBEYAALKZVur37dvXQ2MkajSvfdLbt2/vtxcuXGhDhw71rS4nTJjgx4BEIjACAJDNVFk8+uij7aeffrJbb73VDjroICtcuLAdf/zxHgyvu+46a9WqlTebV7/Q+vXr27Zt27ziePrpp9srr7wSfCYgMQiMAAAkAYXGcuXKWZ8+fXwfdM1ZVEDUdpfasUj3a1ci3R4zZow3oleo1F7oDz30kH333Xe7tYIC4oXACABADqOdi7R/eseOHf32/PnzvUfjnDlz/DYQbwRGAAByoCJFivi8x9atW/tt7QozaNAgW79+vd8G4onACABADqUh6n79+lndunVt48aNNnLkSN/7HIg3AiMAADmY9ju/+eab/fq0adO8ufeemoADGUVgBAAgB9NimKZNm9qxxx7rK6e/+uormnoj7giMAADkcAcccIC1a9fOr7///vv2119/+XUgXgiMAADkcGqvU7t2bStQoIDfVmNvWuwgngiMAADkcBqWLlu2rFWqVMlvz5s3j8CIuCIwAgCQCxQtWtTKlCnj11n0gngjMAIAkAsUK1bMSpcuHdwC4ovACABALrBmzRpbvnx5cAuILwIjAABJSnMT00stdf7991+/npGPA9KDwAgAQJIqXry4FSxYMLgV3fbt230f6d9//91v16lTh9CIuCIwAgCQw2n/6HBYLFmypFWtWpXAiLgiMAIAkMOpUbcadkvjxo2tQoUKfh2IFwIjAAA5mIaj33vvPZs6darfbtasGaulEXcERgAAcjCtjO7Vq5df137SjRo1Ste8RyAjCIwAAORQWhndokULW7lypRUqVMjat29v9evXD+4F4ofACABADqNt/5YuXWrnnXeeTZ482fLly2fNmze3iy66yPLnzx88CogfAiMAADmIei3+8ssvdv3119uHH37oxw477DC7+eab7aCDDvLbQLwRGAEAyAFUVfzjjz/slVde8bA4cuRI27x5s9WoUcPuv/9+a9q0afBIIP4IjAAAJKmtW7faggULbPTo0da9e3e7+uqr7Z577rEJEyb4fe3atbMXXnjBzj77bPouIqEIjAAAJJFPPvnExowZ461yBg8ebF26dLGuXbvaU089ZZ9++qmvii5btqxXFXVMcxcJi0g0AiMAAElEVUOtdr7gggu8qvjNN9/YwoULbePGjb5V4A033OAVRt3HnEVkFQIjAABJZN99993lkrp6uGbNGhs3bpwvelm7dq037QayAoERAIAkojmK4cs111xjZ5xxhjVs2NCqV6/u9//222/WoUMHv+/LL7+0LVu2+HEgkfYJadkV4m7mzJl2yCGH+PVXX33VLr/8cr8OAEB6aVW0hqHXr19vU6ZM8f2if/jhB/v999+9vY5C5C233GJXXXVVXHd30fvWFVdc4ddnzZplNWvW9OvIu6gwAgCQxAoUKGDVqlWzNm3aWL9+/fxy6aWXWokSJWzu3Ll2991325NPPmmbNm0KPgKIPwIjAAA5xP7772+NGze2Rx991G6//XavPq5bt8769+9v//d//+e9GoFEIDDGmc7wVMpXx/2wQYMG2dtvv+0NVgEAyAwtgjnggAM8MN51111+bPHixfbiiy/6YhggEQiMcaKzOv3Bnnrqqd5Ydfz48f4HfeCBB9qPP/7o+3uqV9bq1auDjwAAYO8VKlTI/ve//+2Yazhx4kR7+eWXfSU1EG8ExjhRjywFxenTp/u/v/76qy1atMjnl2hj+E6dOnlwPP/8873pKgAA8fDss8/aEUcc4dc/+ugj++677xiaRtwRGONAf5jqyP/FF19Y586dffJx1apVvX9W/vz5vbGquvFfdtllXnkcMWJE8JEAAGTOfvvt50PTRYsW9VXVep/ZsGFDcC8QHwTGONC8Rf2RFilSxK688koPiWlpovJDDz3knfrVN4uzPwBAPGhO40knnWTHHXec3/7ss89s5cqVfh2IFwJjHOgPU/MXTzzxRCtZsmRwdHfa+/PQQw/14Wt16AcAIB70/nL66af7dfVp1JQoIJ4IjHGkbvuxKoc6C1y1apX/m3qrJwAAMkNNu9VcW6NZGslSEYORLMQTgTEOdGan1dCjRo2yJUuWBEd3p2Fr/RFXrFjRihUrFhwFAGQl7b+8p0tOE261U6NGDb+tBZgERsQTgTEOdGZXu3ZtK1OmjPXo0cP+/vvv4J6dFixY4Ati1Jm/bdu2wVEAQFZSGPz6669tyJAh1qtXL3viiSfspZdesqFDh/pFfXTVAFvDujmtPY3mz2sBDJDek4WMnBwRGOPkrLPO8l5Yo0ePthtuuME+/fRTP8PTZcyYMd5qR60OOnbsaO3btw8+CgCQlVSJ04iQqnHhLfW0J7O23tNFixc//PBDa9asmb3wwgu+h3NOoVXSpUqVCm4hr1I7v3nz5gW3YtNc16lTpwa3YtsnJYVSs46TFStWWJ8+faxnz57+gqQFLnp6FRo1d1G7v+gFqnDhwsFHAACyw9KlS61ChQq+svjzzz/3uX9huk89c+fPn++BUqNC2TXvXFOZNHql0ak9+f777+2aa66xn376yUe7unXr5u3d9oYqreGG4LNmzfL5kUgfnWTMmTMnuLWTGq1rNFJ0kqKwlrqKrfuVHVL/LmbUV1995QWrc88914488sjgaHQKltqb/JJLLrEGDRoERyOjwhhH+qPWH+hbb71lt956q/di1A//tttu8+0B1ZGfsAgA2U8jPgpTjRo12u0Nunz58j5qpMD4zTff+Jt7dlGIyJcvX3ArNnXfCG8MwXtN9lFg/Pbbb+2RRx6xq666yi/333+/7/kdtnXrVnv33Xd9RFL3X3/99d5yLzO/awp/+n9OOeWUHY3c90R9otu1a2ePP/64zZw5MzgaGYExztRWR8m+a9euO+bH6LqGoTXUAQDIfhp2VhALt6JJSyNDlSpVsqOOOsoKFCgQHM16miOfnsC4bds2b9n2119/+W2NcNGNI3uULl3aWrVq5WFMwfHoo4/2IFe3bt3gEf/9XDt06GAnnHCCB/2nn37aT1L2dkGs5iLed9991rJlS2vSpEnEn70CpabHqYKemk6ajj/+eK8qK8hGQ2BMEP0yqOKoFdS6zh8uACQHhSvNLdfr8jHHHBMc3UmVRS2EOfnkk+2cc87J9tfv9Pz/2tlFIVdv+CpcVKlShfedbBJefKTFrgqJF110kdWvX9+zQJiq2zohOeSQQ2zAgAE+fKy8sLdTCNSsXVsSq0AV7QRj2LBhPm0hbR9onRC1aNHCfv/9dw+40eS6wKhdV7Qtkl4Q9kSJXE+Q0jZTOQEgb9D8ss2bN/sbuN4s9QaqeeZqe6b3D4VEvclrd67Ub/LJTAsdNPVJNOdSw+rIPv/884+vxj/ssMN8MVUkyh0KeuFWSHtLWebtt9/2anm0qQj6v9555x1fGKUFX2mpGqqTp7Fjx0atMmZrYNR4vlbn/Pjjj7tcZsyY4X/MYZpwm/Yxkdod6Axr+PDhvvl6ekr4OvvS/z9w4MDdSrQAgNxJ8xL1/qOV0AqIX3zxhb388st26aWXekh87bXX/HLwwQcHH7GT3nvS836UlVQg0dx5DUdrPqa+r1i7jiHx9LPQRZXecuXKBUd3tXr1av9XQ9iZoVZ+qoo3b958t+yjDUWUjdQjWouhtK5C2SdtKFTQrF69uv3555+2bNmy4OiusjUw6ptQYNPYvcbVu3Tp4pNEf/75Z/8mw/SYO++80x+j+zX3JPxEh+mbV+NsTWTW5NFIlMJTTyjVk6Y5A3qiHnzwQe+ODwDIvRSutCpagVELDrRKWpUVLRJQQFRlSB0votGCBg1n6/1KK5EnTpzo72XZSZUstQASzYk79thjfVgU2UPVPGWR4sWLe+VOC5ciUXFMP6/M9s5UZtLoqoa4U09DUC7SELMKaVpToTCpIW/dVq5KPRKr46qE6vdfQ+mRZGtgVOpu3bq1l0enTZvmT6zG3zVpM/XET6XmCy+80L95LSi5/PLLfbeU1DR3Q5VFfb5opXgFU00KTU1/VFqhpI9/8803g6MAgNxIq4hVRVEl7sQTT/S+hXo/0Ryu22+/3VauXOkt0KI1NFYrHs1L05uqFgqoKhlpiC+raCGDvl59X5o3rwWWkSqjyDoKjMojCmCauxiN5hPqZ5XZRVXKL/odjtSDU/Mila1UXVRx7OKLL/bbCrNp57jq91hFNf19RJqml62BUV+sAptWdikd33TTTf5Hm7bflFYXa/Kx/lAVGFXiTX32pGqklqMrgOpMMdKkUaXmV155xYNpWgqn+oNTD8XwCjMAQO6j1iEacmvatGnUys8vv/yyyyhXanpD1RzIWrVq+WrU9PRHTBTNW1SvxMmTJ3v1SEPRKq6w20v20mjlpEmTPICF+y6mpQKYfn6aOpB2GFnh//nnn/cCV/ii3YeUkyLR/6ewmHa+rXJSnTp17KSTTvITHK2c1zxH5SmFxrRZScFVxzSfN9I6kGwNjKKzOc0f1Nmcztai0VlU48aNI05A1serJKuzvmhd7hUUNcwQbZeVM888c0fwBADkTppzqIbJes1PS/O8wkEx2mIXvWlrGFrzvfSmmx1U/dGw+tlnn73jPUvvn1ptqyojspdOOPR7oipetBFPnbRoPmGk+Y36Ger3U/NoVcjSCYBOTiL9Tmq9h6ZExFq3ofyjopmm4EUqqIWpOKevWWE26SqMorM9rUyL9mSEaZKymlFGEu49pV5HqUus+obDT6b+qDTvUfNVlMbTpmc92WeccYaNGDEi4hMVi8KufhiaW6n5kzrLO/XUU33o/KmnnvJfHoXVSGcHOranZpkAgMzTe4HmjWlVdKRdLTRsp/v0pqr3Er1P6H0j9Wu35j4qrKl6lJXhTO8z+vpV3VTFST389N4iCh6aw8jK6OSgaqAqh6pipx32DdNiXs051MhpWvoYTZnQ7582Abnnnnt8yDnS59Jj9tTsW/lJJ0IawY1FQ9ZaRR1NtgdG9Q1S4FNYi0ZPyCeffGKHH354cGQn3afApWFl7aySmiYn6w9by821nFyP1dmlgl2koWeFVh3P6Ippna1qNxe1YtAfreYlaMLrG2+8YbfccovPk1Gy1xZT48aN8z/yKVOm+A+xb9++URvHAgAyT2FLw39qTKw2Jnoz1rBf2sUt2v5OQ8y66P1D7y1Dhw71ymOYVqOqEqNmx5EKFNHmPmaUqjyaS6bWb9ruTbuCqPmzhhO1+FNfu0bU1CD6t99+26UpNLKXfjbKJNG22tMJiDJAOBhGEq5S6ucbiwptsTYF0e+jfuc13KwWP+FjkX5PdTKk4ehosjUw6smYPXu2f4Fa1RUtiSuQqUwaabhZf1QKnXrC0nZI1/h95cqV/eP0x6x0rSaZelGI1HJAx/W16IUlI1Ra1teoYKgKo84G7rrrLl/Aox+2vgbNZ9A+0m3atPFJpx07dvThhHvvvTdmiRgAkDmqrowePdp7zGkoWa/VKkLo5D610047zS644AJ/73juued8JakWJWjefJjCm4YR0+7Tq/ez9957L1Nbu4W9/vrr/v9rEY72htZUKhUkevfu7UUNhYSGDRv6drMqTGTnPErsTjlDo5aRgpyyiH6vNB1PC2Kivf9//PHHHur2tMWf/h/9vqr6rKJYWqqY6yQnPKVPn1MnGPoa0tLvsD6P5vZG+rqyNakoaGkipgKVSurRqEeWVqJFosCozdn1pKUdw9c3rT9qBUk9Eeedd54viqlXr95u4VIUIpWw1dMoI1RSVnsGzR/RfozaT1qhUXMPdOyZZ57xJe16IQq3bdD3ru+7U6dOXmUEACSG3lA1kqOtWtUtI9zKLVxxCdPjtPhS9+v1We8fmmKU+s1T05ZUwEg7oqVRpQkTJvjnyCy9n+h9RDtzaP5Z2pZvmtOm0Kv3RcJi8tHPReFNPT7TUrVaHVkUFnXyEomq2xoN1TS7WEPEYVoJrVCocJiWqtT6fBpBFa0HGTlyZMQ2giqYqaeo+kKmzVOSrYFRYVF/DPrFj9VkVEPKSseRKK3rjE5PWDT6Q9a8jz3trak/dJ2JZrQJq+YWaEhdLz6pl8frj1od3FXZvO666/zsUGeDGlrQD2zw4MH22GOPeVd+AEBi6LVdq5rTXtKGPtHrueYHarWxHpP6PUNvtnrPUuEhvMJaRQYFO8010whSpDfajFJoVXVRhYdrr73W58KlXvmsAPDWW2/5PPm0VVJkPy2GUq7RVDhNa1BG0Wiq+knrZ6YTEeWCaO109Hum8BYpGyhwqgCWmtZm6P+INJysubaam6jfGc3HVRDVepC0FXLR9ED9XqeuqKeWrYFRyVf9g/THF6niJ3pi9Jho34DoG4wWGFVeVQlfVcVo3dYzS2efezqr1B+7zlhVXtZQgi6ak6lJyrFCLAAge6mQ0LNnT2+yrHmKP/zwgwc5DRPrjffKK6/06o7al8RD9+7dd1w0zUmrZVVgUcVKU5o0rKj3tU8//dQrp4TG5KKTBo0uqsezNgXRiYemp6kYpXUOGm0Mn3BEojZJCoaaJ5uafu4PP/ywZ6fUVKzSSKem06VdtKtqodZP6HOqYKU+15E6zmi0Vr9TKnJF28ow2wKj/gD1hKhsqyAVbYW0OpLrjzHWPD/9EBQqI9H/obSuJ17DCKL/M+2TGqbwFuv/AgDkLTrhV2VIu2aoUqQ5hpo/qDdiTZnSnDAtUoj2PpZRChPhiz6n3rsUCFS1GjRokAdHLX5RMNEUJwVZLeiJNIcN2UMFKvWN1kiiFrhozqDmpSqMpa4Wp6VsojUPCnBaRR2mn626vWg0Nu00BP2eqN+ifjeUrVJTptGJjKZSaKRTQ9yRquDKSprXq68vWnvCbEtGqsipuhZthZAoReub1BZM0ejzaF6gnuRIIVDj+vpBKYFrAqoSuiY/a8JpWnqi9cPI7EbgAIDcRUN7GsKOdon1XhZPKmgoOGr1tgKAwoPChBZQqiuI3uOQPPTzUuDf0yikaF6hqsUaxg63ANR6B1UHNe9RP2MFukhT+DRvUotvNWUio5Sd9H8qpGpaRTTZFhj1JKqUr2ajOjvT+HqYvmiVVvv16+dl3HBlMBIlZQVGPbEafk5LZ1/63JqXoh+YSviaQxjpc6ovkpJ6tHQNAEAy0Hua5sBrgYwoLDzwwANRR9uQ3BTatOB2yJAhPhVP1UW13tNqafVzfvnll3es8I8091HD3urMot8BtYzKCOUkBVI1C9cK72jypXzyB4LrWU6hTU271RdR4+yah6h5GiqrKtjpCdDK5lg9hhQ8NSStEq5K9GmTt544LXrR9jx6nHaEueyyy/xsMS1VM/V4VTRjlYwBAMgKejNX9VLFjLQUHBQgFBY1JK6RM71fal1AZofH9fnUJkhuvPFGf49G4mjoWD9PZZXWrVt7MU3T9RQcNaVOx1T9U4Ux2rQ5jaSqwqjV+hqGTk9VUxVpNYLXCYiyT6zfm2wNjPpmtJpIwVDJWu1nNJlYfxzNmzf3lcd7qvaFF4zoCVLgTJuO9fF60jU+r/8nvIdiJDpbUwuF8PJzAACyU6zAKHof1fCl5lJqCpYWLigw6n0uMwsqCYxZT4UqzX2MdtHi4GhhUfS7oFY8WrmvIKgQuCcaWdXvmNaKxBrNlX1CkSb+ZTF9Yxo2Vnd0JWx90TpLivXEpKbVaep/qCdLy9bT/mFpiFvD1fqc0Voe6ElTUBw1apQ/4QAAZDf1GdYWhLH6LeptXG12tPpW75uPPvqot+ZRO5W9pUU0V1xxhV/X+6M2tkDOoBY7yj3p+flr1b9+f6KdkKSWbXMYU9MvuJKzzohUDdxTik5Lj9cWPFq9piHttBQkFUKjhUU9WfoDU6+rzIZFhV+l9Y8++si3C1RvLl1XGGYyMgAg3lRJ1NC0hiH1PqNqY6SFncgbVHhL78mChqDTExYlKQJjPKgJpsb6VSGMtPglGoVFbfU0ceJEb6ydGWqa+dBDD/nZ4CWXXOLNubXARtfVEmHgwIEZ+toAAEgPTcnSogUZM2aMrw8A4inXBEbN8VD5XCuutdF2eunxzz77rPdHUqjbW6og9u/f31czqVLZo0cP7yGpTu+aG6kzP7VA0P+TekU4AACZpSqRWsKFq0WRmjgDmZFrAqNowYuGgbW9TXqGf9W7SkPYWjWtrXIyQyH1xRdf9EUzWv6uDu8a3j7qqKN8z1I1eFUVVEPfkfaXBABgb2lYWr2Nw82e9T5IYEQ85arAKNpuT3uApmd1mB6jhS7aWina/Mb00ARTdXLXPo0KrJG21VFw1HC1qovvv/9+cBQAgPjQLh7hziJazADEU64LjKI/mPQERi2sibVyOr00uVhnc6ooquN/NAqN+oNWnyTmMgIA4kkLQDWHHkiEXBkYs5q6squ1j+ZAxmr4rdXaeozO/PQxAADEizp0qKcxkAgExjhQQ00NQ2sbn1itDLQwRts2qQIaaS9IAABS00hYekbMRNOjwkPR7FaGeCMwxoFWaGsoWnMYtc1hpLkj2pbw+eef9yHwtm3bZqjPJAAgb9Iwc3rCnxZ6zpkzx2bOnOm3Dz300HQHTSA9SC1xom0MtZ3hww8/7K11tEWTgqMu6pLft29fX/SiRTZ6LAAA8aJpUb/++qtf16hXlSpVCIyIKwJjnOhsTht4a+HLgw8+aNdcc4117tzZL7rep08fb6vTs2dP/0MGACBe/vzzTxsxYoRfP/74472RNxBPBMY40ZmcVkFr2Fm9FnV7yJAhflG/R1UXBwwYYHXr1g0+AgCAzFO/xS+//NLmzZvnt1WcYLU04o3AGEcKibVq1bIbbrjBevXqFRw13xpQx7RXNgAA8bRkyRLvASyNGze2k08+mUUviDsCYwKofY429A7TbV0AAIin5cuX23nnneet2ooXL25nnXUWI1lICAIjAAA5jFZFa0HlpZde6i3dChQoYC1btrQOHTpkejMKIBICIwAAOYi6b2jO4o033miff/75jjn0N910k1WuXDl4FBBfBEYAAHIAVRWnTp1qTzzxhN188802duxY27Jli9WpU8d69Ojhq6OBRCEwAgCQpLR7ixpyjxo1yrp06WIXXXSRB8YpU6Z4B46OHTva4MGD7dRTT6XvIhKKwAgAQBJ599137aWXXrLTTjvN6tWr5xs+aK6iguHkyZN957AKFSpY//797cknn/T+v4RFJBqBEQCAJHL22Wfb1Vdf7fMTtQpaF209u3XrVt9WViFy4MCBXnEsVapU8FFAYhEYAQBIIscdd5y1b9/eL02bNvUKYo0aNbxtjuYxqsp44YUXWvfu3X07QDXuBhKNwAgAQBK588477Z133vHLs88+a0OHDrVXXnnF7r77bmvTpo1VrVrVNmzY4MPRmsP48ccfExqRcARGAACSlHoqli9f3k455RTr1q2bD0U//vjjPr9RG0So2njdddfZ8OHDCY1IKAIjAAA5gBa2HHDAAXbOOef4ghcthClcuLDNnTvX7r33Xhs2bFjwSCD+CIwAAOQg2mr20EMP9Uqjhq9Fu75oZfWMGTP8NhBvBEYAAHIgrZC+5557rEWLFr4Y5rPPPrPXXnvNezcC8UZgBAAgh9IcR81fVMVRdH38+PHMZ0TcERgBAMjBihYtanfddZcVKFDAZs+ebZMmTbJNmzYF9wLxQWAEACAH02KYk08+2Ro0aOC3x4wZ482+gXgiMAIAkMNpq8B27dr59e+++87+/vtvvw7EC4ERAIAcrlChQlarVi3bf//9van3nDlzmMeIuCIwAgCQw2lYulKlSr6FoCxYsIDAiLgiMAIAkAuULFnSh6Zl48aN/i8QLwRGAAByAQ1HlyhRwq///PPPVBgRVwRGAAByAbXSWbdunV+vXr26D1MD8UJgBAAgF1ixYoX9+eeffl17TgPxRGAEACCH0/DzX3/95aujpUqVKlQYEVcERgAAcjgtcpk+fbpt3rzZb2urQAIj4onACABADrd48WJ7//33/fqpp566Y7U0EC8ERgAAcjANR3/++ef2/fffW758+ax58+ZWunTp4F4gPgiMAAAkqf32289DYCxz5861J5980q/Xrl3b95VWix0gngiMAAAkqcKFC1v+/PmDW7vTNoDdu3e3qVOn+mPbt29vRx55ZHAvED8ERiAXW7Vqlc9tinRZvny5bd++PXhk3qHhu6VLl9qoUaOsf//+dvfdd1u/fv1s3Lhx3pYEyCnWr19vN998s40YMcJvN2jQwDp27GjFixf320A8ERiBXEqrJh9//HG7/PLLrX79+taoUSO74YYb7IEHHrD777/fDj/8cDvssMPsscceS5qgpAA7YcIEe+6552zlypXB0d0p7L711ls+V6tbt242adKkdIXfbdu22RtvvOEVmN69e3vrkS5dunhl5pprrrFTTjnFhg0bZv/++2/wEUByWrhwof/ODh482LZs2WIHHXSQ9e3b14ekgYRIOdtGAsyYMUN7Mvnl1VdfDY4CWS8lTPnv4RVXXBFasmRJcDQU2rBhQ6hnz56h/Pnzh1KCZSglYAb3ZL2ZM2eGhg4dGrrgggtCBQsWDDVt2jQ0ffr04N6dUkKh/22dfPLJoZRg58fmzJkTOv/880PPPPNMKOWN049Fou+3bdu2oZRwGOrRo0coJTwG9/wnJSSG7rnnnlCRIkVCjzzySGjTpk3BPUDWGjlyZGjr1q3++7xs2bIdf5v6nV23bl1o9OjRoXr16u14jylWrFho+PDh/ph4STmxCpUoUcIv+hsDqDACuZiqbosWLbICBQp4NbFcuXLBPf/NjTrnnHPs9NNPtz59+ngPt+yi3SlKlixpt99+u1133XXB0d2lBF578MEHrUyZMt46RLQF2oUXXmgDBgywDz74wKuIaaW81vn9n3zyiZ144ol2yy232L777vryp3linTt3tqOPPtqef/55Gzt2bMTPBSSStvabNm2avfnmmz5V4sMPP7Qff/zRvv32W3vnnXfsoosusjPPPNMmT55sKSdXVrduXRszZoz/LcfTxRdf7FNadKlRo0ZwFHkZgRHIxbZu3ertNqpVq2Z16tTZrZFvsWLFrGzZsv4mtWbNGg9W2UHhT8FVb35FihQJju5KX5uGnhX6NLE/3DZE35OGmNWoePz48Tv20k3tn3/+8Tdb7bV75ZVX+httJOpd16pVKw+wX375pc8RA7KKpmHo5E3zajUX8Y477rDLLrvMfyd1otOhQ4cdvRarVq3q003effddO+GEE/wYkEgERiAX01y80aNH+5vLIYccEhz9jwLYzJkz7bvvvrOzzjor6XeG0A4Ws2fP9mrpgQceuEurEVUcNR/x66+/jjgfUwEwHP70fUajIFm+fHm//vvvv/sKVCAr6PdbrXEeffTR4MhOOpnTaIGq4jrx00IXPbZnz55Ws2bN4FFAYhEYgVxMQ7jz5s3zEFS5cuXg6H+07+yLL77o11XNUKUxmak6qOCn70WV0dQ0nKx+db/++qu/uaalkBkOw1oMFI1CdHgYukSJEjHbmQDxpJMhVcFjLbg69thj7bXXXvNpGTrJ0zQOIKsQGIFcbOLEiVaoUCEPg1pZrMClVciDBg3ykKg3oOHDh3uj37Rz+tJS8FQrmoxcNA8wXiuwVWHRatBYFAYjzTtUhTUcMv/++2//NxK9WWv4Wo466qiow+NAvGmXlj39rcyfP98aNmzobXPYJxpZjcAI5GIjR470IKjh1bvuussvbdq0sSuuuMLOPvtsu/76630xTLQ5falpTp9a2WTkov5wmsCflSIFRgU/DeWpYqi2OtGoOvnbb7/5G7KeF4VtICvob3BPJ20atgayTQgJQVsdZDe1hdHv35FHHhn6+++/g6Mhb9Hx8MMPhypUqBB67733gqPJQV/bvffeG7GtzooVK0Jdu3b1diLfffddcPQ/69evD912223+/f7www/B0V2pPUnNmjVDZcuW9dYkkUyePDlUuXJlb0GU+jkDEk2/7yknKTveNyJdOnfuHDwayHpUGIFcKiU4+bBVxYoVdyzkEFXNOnXq5EPUalKdV6oWGpZXw3I9J1ptnZaGvDUHUtXI8847zw444IDgHiDx1LqmSZMmUefNarHWjTfeGNwCsh6BEcilPv74Y3/zadas2W5DXZrLqKFbzdeLtQgkNQ0ta4g5Ixet0I41ZzAjwiuY9TWnXb2s26tXr/b7Iw2vq12JgvH5559vLVq0sLfffnu3FkJqQaQ+jupnpzdufd17mjMJxIsWZt13333e9iktzVvUzkTsEY3sRGAEciFVyxR+9Cak8JOW5jSKwtX+++/v1/dE/Q3/+OOPDF0WLFgQtwqmvk7NK1R7nPDClLC1a9fasmXLfPGOWuyk9c033/jXowCt3nZaIZ5260F9Tq1UVfVVVdjXX3/d95wGsor6gD788MPBLfPf51dffdW3ylSfUiA7ERiBXEjVscWLF3tlUXtGp6WV0qIApXY0CktqGKwqWzSqbmgnlIxctCuFeiaml6p+CoSq7KUNmhpK1rCcQqN2vgg36NbHzJo1y3fC0F7QkdoDqUWOds7QghbttduyZUsPs6mpBZGqj1pR/dVXX3nATFuFBBIt9QmPGtmrm4F2H4o2VA1kFQIjkIuoV6GCTq9evXxLLwXAcePGeQUutTvvvNP/VShT8HriiSesUqVKMVdpqhpZqlSpDF3S28tQX7cCnIaw1Y5Hw9/6d8aMGbtU+RR+L7nkEm8FpC0PRd+bht9PO+00n3uoqmpaBx98sH300UfWoEED712nhscKman9/PPP/rxphbSG8TWXUdeB7KLm9HtaOQ1klX1SzqA5hU4A7aAR3llDQwrawglIJP0pa+hVcwdT05tOo0aNrF27djvefPTYvn372v/93/95v0FV5rTdXtGiRf3+rKZgqL+TSMPXComqVoZpuL1///7e5FitgbS7i/bFDofeSFS1fOGFF2zhwoV+W1UcbbOmamOY+kxq796wtm3bWuPGjansIMuFeyyq7ZX2QAeSAYExQQiMSHYKXloookCkoJjTGgErXKqKqkpgdgVdIBEIjEhG1LqBPErVRg0baweUnBYWRUPkWiRAWASAxCMwAgAAICYCIwAAAGIiMAIAACAmAiMAAABiIjACAAAgJgIjAAAAYiIwAgAAICYCIwAAAGIiMAIAACAmtgZMELYGRLJbu3at7ymd12hvbW0nCCQrtgZEMiIwJgiBEclM+0iXKVPGtwesVatWcDT3+/HHH+3oo4+2b7/9NjgCJB8CI5IRgTFBCIxIZgqMqrSde+659vbbbwdHc79y5cr5m/HSpUuDI0DyITAiGTGHEQAAADERGAEAABATgREAAAAxERgBAAAQE4ERAAAgl9Ba5o0bN9qqVats/fr1vsgxHgiMQAJs3brVNmzYELc/VOQ++t3YtGmTv6ADkWzevNm2bNkS3AL2bM6cOTZs2DBfXX///fdb7969bfDgwTZu3LhM/y7RVidBaKuTN+mMTn+YkydP9sbYRYsWtcMPP9zatGljhQoVCh6V/Wirk31tdXQy8csvv9jnn39uy5Yt89sHHnigtWjRwo444ojgUcir/vzzT6tSpYpf1+9Dq1atrEmTJtasWTMrUKCAHwci+emnn+zBBx+00aNH20EHHWTVq1e3FStW2LRp0+yAAw7wNk3XXXfd3r8XKTAi/mbMmKEg7peUwBgcRW62bt26UK9evULVqlULpYSxHT//qlWrhh555JHQxo0bg0dmv23btvnXlhIYgyN5Q9myZUMpoTG4lfX0vH/66aehY445JpTyor3jd0TXUwJjaMKECcEjkRfNnz8/1Lp16x2/F+FLrVq1Qq+99lrwKGB3kydPDp1xxhmhfffdN9SjR4/QuHHjQlOnTg19++23obfffjvUsGHDUMmSJUOPPvpo6N9//w0+KmMIjAlCYMx79IJeqlSpXV7ow5cyZcqEevfuHdq+fXvw6OxFYMweK1euDFWpUmW33w9d8ufPH2rVqpW/diDv0Qln+/bt/Q0/0u+Hfm85oUAkej2/9tprQwUKFAgNHDgwtH79+uCe/+h9Z/bs2aEGDRqE6tWrF5oyZUpwT8YwJJ0gqYekU94IfAs25G4pf7R+iUZDwLokC81nyYtD0suXL7f99tsvOJK19HKbcnYf3Nqdhsv1ehHe6QN5i343Yr0l816CaDS1JSUQ2ldffRV1yPm9996zq666ynr06GFXXHFFcDQDFBgRf6krjFy4JOslL1YYIz0PXLhw4ZLTL3fffXfM4eZ58+b5466++urgSMZQYUyQRYsWWcoPJbiFvGDs2LExV6GVL1/e6tata/vvv39wJPvoz/7DDz/MkxXGNWvW2GmnnRYcyVqLFy+2H3/8Mbi1O1WQNDKhCevIW/755x9fLBdr1XyFChXsmGOOCW4B/9ECy19//dVuv/12u/POO6OOZM2fP99q1KhhXbt29VXUGR7JUGAEkHnHHXfcLmd7aS/nnXdeaOnSpcGjsxdzGLPH+PHjd/u9SH3R/MZhw4YFj0ZeMmvWrFDKCWXE34vw5a677goeDeykymGzZs18DvTmzZuDo7sbPnx4qESJEqE+ffoERzKGyRBAnKS8mO9oh5FWtWrV7IILLrDSpUsHR5AXqU1KtBZbBQsWtKZNm9opp5wSHEFeotcIVX70exBJ7dq1rXPnzsEtYKeKFSt6a66JEyfaqFGjgqO7+uOPP6xnz57+2EaNGgVHM4bACMTJGWecYY899thuobF48eL25JNPej+1ZFr0gqynvpwPPPCAderUKTiyk/owqoeahh2R96jHon4vnn322eDITlrM8Morr/hwIpCWFvHpRFTvNTfffLO9+OKLtnLlSr9PC6mmTJliF198sf3+++922WWXWb169fy+jGIOIxBHWqmmRqlqkDp8+HCfH3jqqadayZIlkyos0rg7+xp36yVX23bdc889fiIhCxcutFKlSlmRIkX8NvIuvYZoAwA1W1YTb725q3pUrFgxVs8jKnXomDRpko9SaCW9KtV6vdNxdYZYt26dPfTQQ9atW7e97hJBhRGIIy1aUIWoRIkSflu7M5QpU4bKInbQm74WPqUOh5UrVyYswuk1pGzZsta4cWO78MIL7bDDDvPKEWERseg9RkPN2hqwe/fuPoVh9erVvkVty5YtffcXnaRmpqUYgREAACAXUDVawXDChAlepZ47d67vLa3QmFkERgAAgFxElWqNbhUuXDg4knkERgAAAMREYAQAAEBMBEYAAADERFsdIAG6dOliL7/8svfCUruUZBNuq9OuXTsbNGhQcDT3Ux87tZzIzrY6Yffee6898sgjfl0/D1bBAkhmBEYgAXJCYKxUqZItWbIkOJJ3HHnkkb5nb3YjMALISQiMQAIke2DUn/2QIUPss88+C47kHeeff77vypPdCIwAchICI5AAyR4Ykf0IjAByEha9AAAAICYCIwAAAGIiMAIAACAmAiMAAABiIjACAAAgJgIjAAAAYiIwAgAAICYCIwAAAGIiMAIAACAmAiMAAABiIjACAAAgJgIjAAAAYiIwZoFNmzbZmjVrbPXq1bZ582bbtm2brV271m+vX7/eQqFQ8MjI/v33X39s+OP39HgAAIB4IjAm2N9//23dunWz5s2bW8uWLW3RokU2a9Ysu/zyy+20006zW2+91datWxc8OrKFCxfaJZdc4o/X51q+fHlwDwAAQOIRGBNo+/btNmrUKHvjjTfs+++/tyOPPNKqVavmVcUpU6b4senTp3vFMZZy5cpZ/fr1/fHPP/+8jRs3jiojAADIMgTGBFIlcfjw4fbPP/9Y+fLl7f7777d8+fIF96ZfsWLFrHXr1h4at2zZYg8//LAPcwMAAGQFAmOCbN261X744QcbP3683+7UqZOVKVPGr0eiauTKlStt8eLFtmrVqt2qjocddpg1a9bMChQoYL/99puNGDEiuAcAACCxCIwJokUuH3/8sW3YsMFKlSplZ555poe9tPbdd18PiBq2fuCBB3yO4oMPPmgjR470jw1TlfHEE0+0SpUq+e0BAwZ4yAQAAEg0AmOCaBX0hAkT/PoRRxxhlStXtn322cdvp6ZKYq9eveymm26y5557zgYPHmxPPfWUL4YZMmRI8Kj/HH744ValShW//uOPP9qff/7p1wEAABKJwJggWh2t1c1St25dK1GihF9PS8PLX3zxhfXr18/ee+8969q1qxUtWtQ/VnMVV6xYETzSrGrVqh48VZXUkLcWwQAAACQagTFBpk6d6v0TpVatWj6kHIkWxKjCeNFFF3nbnVtuucVOOeUUv0+h8cMPP/TrUqhQIatRo4YVLFjQb//yyy/+LwAAQCIRGBNk6dKlwTWzkiVLRpy/KAqAmpuYP39+H7I++OCDrU6dOl5FlPCwdljp0qV3fK5ly5b5vwAAAIlEYEwQ7coiqgrqEmn+omhOosJimIKiAma4irhkyRL/N0zD1eHWPFosAwAAkGgExgQJD0cr3MXqvRipAXfqcJn2flUXw9XH8P8BAACQSATGBClevLj/q72fdYnmr7/+8gUsYbquyqEadEvFihX93zDtEhPu0Rj+PwAAABKJwJggmmsoCoCpQ15aWtiibQLDFCC1Q0z48Q0bNvR/w9TfMRww1d8RAAAg0QiMCVKzZs3gmnm/xNRNuDXMHB5qViXxtttus6+//tqD4sCBA73NjigQtm3b1q+HLViwYMe2gLVr1/Z/AQAAEonAmCBqpRPuvTht2jSvDIYpLIYriI0bN/Yha7XSUQB89NFHfUha8xTVhzFcqRStilZFMlxhPPbYY/1fAACARCIwJkiRIkXsqKOO8utqzq19osP2228/q1atmjf0PuaYY2z06NHWsWNHD4xqs9OgQQPr37+/7z+dmiqQaggu2pdaO8gAAAAkGoExQbQg5bTTTvMV0rNnz/at/MJVxXr16vmws4Jknz59rHr16vbqq6/aN998Y2PHjrXPP//crrvuOtt///398aKq4nfffWfz5s3z2wqYCp4AAACJRmBMkMKFC/sws+Yybt++3V577TVbt25dcO/u1EqnXLlyXmGMtPpZcxfHjRvn/R3Lli1rV111VXAPAABAYhEYE0QBUEPObdq08UrgxIkT7d133w3uzRj1W9SiGF0kPHwNAACQFQiMCaRFLx06dLD69ev7auh7773X2+Zk1KJFi+yFF17wfaf1ua644oodzbsBAAASjdSRQKoyalHL0KFD7dtvv7Xhw4dH3VM6FrXX6devn02aNMnee+89O/TQQ4N7AAAAEo/AmGAKjVrUogbcumieYkapUqkWOscdd5xVrVo15laDAAAA8UZgBAAAQEwERgAAAMREYAQAAEBMBEYAAADERGAEAABATARGAAAAxERgBAAAQEwERgAAAMREYAQAAEBMBEYAAADERGAEAABATARGAAAAxERgBAAAQEwERgAAAMREYAQAAEBMBEYAAADERGAEAABATARGAEnp999/t4ULF1ooFAqORKb7Z86caRMnTtzjYwEAe4fACGAXCl2rVq3ywJb6MnXqVFu2bFnwKLOtW7fajBkzdnnMggULbMuWLcEj9o4+72effWZPP/20rV692vbZZ5/gnsh0f/78+W3o0KE2YsQI+/fff4N7AADxQmAEsIvt27fbzz//bHfeeaeddtppdvjhh1uDBg3s1ltvtS+//DJ4lNm6devsgQcesGOOOcYaNmxoV155pQ0aNMj++eef4BEZp7A6e/Zse+utt6xVq1Z22GGHBffspECp/1tfZ1iNGjXsnHPOsXHjxnlwpdIIAPFFYATiRCFFQea3336zxYsX+zENky5atMhDTk6RL18+O/XUU+3ZZ5+1o446yo9169bNXnrpJTv33HP9tpQsWdIGDBhgnTp1sqZNm9rLL79st99+u1WoUCF4RMZt3rzZRo4caWXLlrUWLVrYvvvu/hL19ddfe1BNXe2Uxo0b20EHHWQDBw7MdJUzkfS1TZs2zauxYd9//72tWLEiuAUAyYfACMTJ/PnzrWvXrnb11Vd7UJQ77rjDunTpYn379rVNmzb5sZxCoVehTBVGVRcPPPDA4J6dFOzq1atnl112mdWpU8cKFSoU3LN39H9+8skndtZZZ1nBggWDoztpuHnMmDH20Ucf7fZ8amhaVcYJEybYTz/9FBxNLgrECrtXXXWVffzxx8FRs2uvvdZuuOEGmzRpUnAEAJILgRGIAw3DXnTRRfb+++9b+fLl7cknn7RRo0ZZx44dfUHGE088Yf379/fAkFNozuKsWbPs9NNPjxkEVVls1KhRcCtzFBYPOOAAD6GRrFy50v78808fIi9atGhwdCdVGFVp/OCDD4IjyWPbtm12/fXX2zPPPOPB9+6777axY8faO++8Y82aNbO3337bunfv7tVGAEg6IQCZsmHDhlCtWrVChQsXDnXr1i20cePGUEo4CG3fvj20devW0MKFC0NHHXVUqFixYqFXXnnFjye7lGAbeuihhzQRMDRs2DD/PiJZsWJF6OCDD47b93T88ceHUgJocGt3KWEq1LBhw1Dfvn1DmzZtCo7uavTo0aH9998/6tecXR555JFQvnz5Qk2aNAktW7bMvz49b7qkBMjQq6++Gtpnn31CZ599dmju3LnBRwFAcqDCCGSShkDnzZtn7dq1sx49eng1TnPvNESq+YAayn3hhResXLlyXrVbu3Zt8JHJS5W8OXPm+LBwlSpV/PuIREOoqprtaSVzeqhyOH36dDvyyCODIzulBG0ftr333nv9Me+++67ddNNN1q9fv+ARO6n6mPLallTDu6ouam5lkSJFfDW3hvL1nOp500WrvDt06ODD1d99952vPgeAZEJgBDJJc+oUCLTgI9IiDalataqdcsopvthh+fLlwdHk9ffff9svv/ziX7OGiKPR0K9WUkeycePG3S56nqL59ttvPThpWDmtCy+80J/fQw891KpXr+7B8cEHH/T5omlp0Y1C2FdffRUcyX46odAwtJ7PaIuCChcu7PNFNW9U/Se3p1oFDgDZjcAIZJIWWKiiFWlRSJjm21WqVMkXdaxfvz44mpz0vYSrfWqXo8poJJqPOXnyZKtVq1ZwZCctSNHcvMcee8zncw4bNsxvDx8+3P9VpTUSBb0SJUoEt3ZSmNJFQeqII46wunXrevCKNbdSoTdZ6GvRSvnwqvNI9L1r5XmpUqW8Ck0/SQDJhMAIZJL6EOrNXkO40WzYsMGWLl3qQ5H7779/cDQ5qe1L+HupVq1a1K9XVbOjjz7a9ttvv+DITno+FJC1wEOVQ12vXLmyh0ENyf7vf//zj09N/RP1cQUKFAiO7EqVWbXU0ddUpkyZ4GhkhxxySMyfR1ZThVnf1zfffBMc2Z2C+po1a7xZuU4wVG0FgGRBYAQy6cwzz/T5aOpTGG0YURU7hQW1ntlT2Mluqg4qvGno9+CDD446zP755597c+1INPdR1TSFSc3tVE9F9XY844wzvKejVgcrTKamsBiNwtRff/3lQ9r6miKF1NQUcjPb4ieeFJh1svDDDz94Y/JIFBQ//fRTr5zquY82bxQAsgOBEcgkBSO9yYeHYNOGRlXGbr75Zm/UrDl4xYsXD+5JTuEKo+YSRhtm17C6dlXRnLtoFAoV2o477rjgyH9UJdTwctrjqgpGo6Co3Wf0NWk4ek+mTJniIS1ZqFqoRTsaatZ8zEjVTy2Y0klHkyZNrH79+sFRAEgOBEYgkzR8qAUiCo3qt3j++ed7Tz1to6fb6lGoeY5a1asG19EqdslC1TtttafgG6liqrl46jGpxS6qmkWjoWcFRoVOBUztZKLn4fHHH/eKo5pzp6YhWwXDSHMP9XWoOqfhaIVuVRz1/Gq4Oy3dp88TbWg7u2iBjk4oVL1VpVUB8YsvvrDXXnvNF8P06dPHmjdvbg8//HDM5xUAssM+KS+ubLoKxIGaXGu3DlUSNV9RQ7taYawhaG2dd99993llLdnpJUG7kOh76dmzp++eouFiHVeFTBVCVRc7d+7sQ+yRaEGMwuLxxx/vzai1C87gwYN9IYe2HExbXRQthDnppJP886etImqFtdrl6KLP88cff/guOkOGDNlt6FkLixR4dV/aUJrdFLbVCkiLgNTsXVtJKqCXLl3aatasab179/ZQDADJhsAIxJECgFrnqBqmipoCU3hFb7JXFlPTghT1BNQ8uvPOO88DmKqDmn+nMKjqonZUiTbvUKunNVSvYfqTTz7Zw7Oek7vuust69eplbdq0CR65k6qIqh4qWF9yySXB0f9omFwVOs1j1IKZuXPn+lxGVebSUuWxffv2/nOI1RIou+j71AmFArJWfWtltIb21e9yT3MzASC7EBgB7EaVMIXD8ePHe3VPQVgXzQts2bKlzyWMtUhFrXRuu+02r/Zpq0TRog6FTK2s1jBsJOqvqOrhG2+8scvn18uUhnJV2VQI1Mp0VeIihfB77rnHG19rWgAAID4IjACiUjVMrV5UEdPQutripKfdi4aWNRdR+2iHV/uqr6NWTLdu3drn6ymUSurPp7mgnTp1shEjRvhK4dT0UhXeizvaCmitRtcwtJp6R1vBDQDIOBa9AIhKFTwNmdauXdsD457CouYoqjqocKhh69StYTQsrcUoGl5W5VFDzxpiTk3DzMcee6wPZaeliqOCYrSwKNp1R5VHDYMDAOKHwAggLlQxHDRokH300Uc+R1HVQLWSUUgUtYpRdfH111/3OYqqQqqhdWrFihXzCqP2gdYQdEZoXqP6GGo1uvZsBgDED0PSALKUKo0SrVKoCqRCpYaXFf60gnhPVq5cac8995zPl1QYzQmr0QEgJyEwAkg6WmDzySef+L7RkfaqTkvVSK2KVn/HZG+MDgA5EYERQFIKD2WnZ4s8PVYLdJKtWTcA5BYERgAAAMTEohcAAADERGAEAABATARGAAAAxERgBAAAQEwERgAAAMREYAQAAEBMBEYAAADERGAEAABATARGAAAAxERgBAAAQEwERgAAAMREYAQAAEBMBEYAAADERGAEAABATARGAAAAxERgBAAAQEwERgAAAMREYAQAAEBMBEYAAADERGAEAABATARGAAAAxERgBAAAQAxm/w/aW/qIygwa6AAAAABJRU5ErkJggg=="
+    }
+   },
+   "cell_type": "markdown",
+   "id": "0722c5d3-c073-4d86-a6c0-e6dc05073693",
+   "metadata": {},
+   "source": [
+    "**V E R S U C H S B E S C H R E I B U N G**\n",
+    "![1_2FH.png](attachment:61b1d54c-4a46-4ef5-b20b-d113f03ded4e.png)   \n",
+    "Quelle: Hinweise für den Versuch Ferromagnetische Hysterese-Impedanz der Spule \n",
+    "\n",
+    "\n",
+    "Der Versuch wurde wie in Aufgabe 1.1 Wiederholt und in soweit Verändert als das ein geschlossenen Eisenkern, welcher durch Schichten von islierdem Matrial Wirbelströme weitestgehend unterdrückt, in die Spule eingefügt wurde. In diesem ist die mittler Feldlinienlänge $l=48 \\,\\mathrm{cm}$. Zur Messbereichserweiterung wurde Das zur Messung von $U_L$ durch das zuschalten eines Wiederstnad $R_S=9 \\,\\mathrm{M\\Omega}$ verzehnfacht. \n",
+    "Aus:   \n",
+    "\n",
+    "\\begin{equation*}\n",
+    "\\begin{split}\n",
+    "&L = N^{2}\\,\\frac{\\mu_{0}\\cdot \\mu_r\\cdot A}{\\ell}\n",
+    "\\end{split}\n",
+    "\\end{equation*}\n",
+    "follgt:   \n",
+    "$\\mu_r = \\dfrac {\\ell\\cdot L}{N^{2}\\cdot\\mu_{0}\\cdot A}$.\n",
+    "\n",
+    "wobei $\\mu_r$ in dem Fall ein Zeitlicher Mittelwert darstellt.\n",
+    "\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa723eed-4a46-41a3-a823-7e61a1d252c7",
+   "metadata": {},
+   "source": [
+    "**L Ö S U N G**\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "93e1f6ca-f090-4c7b-bef2-d5276ef6412b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$I_{eff}\\mathrm{[mA]} $</th>\n",
+       "      <th>$U_{R,0} \\,\\mathrm{[mV]} $</th>\n",
+       "      <th>$U_{L,0} \\,\\mathrm{[mV]} $</th>\n",
+       "      <th>$\\Delta t \\,\\mathrm{[ms]} $</th>\n",
+       "      <th>$\\mu_r $</th>\n",
+       "      <th>$P_{verl} \\,\\mathrm{[W]} $</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>$10.02 \\pm 0.10$</td>\n",
+       "      <td>$0.15 \\pm 0.01$</td>\n",
+       "      <td>$22.01 \\pm 0.10$</td>\n",
+       "      <td>$1.44 \\pm 0.50$</td>\n",
+       "      <td>$526.03 \\pm 175.41$</td>\n",
+       "      <td>$0.13 \\pm 0.01$</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>$15.04 \\pm 0.10$</td>\n",
+       "      <td>$0.23 \\pm 0.01$</td>\n",
+       "      <td>$43.30 \\pm 0.10$</td>\n",
+       "      <td>$3.52 \\pm 0.50$</td>\n",
+       "      <td>$1416.89 \\pm 145.91$</td>\n",
+       "      <td>$0.19 \\pm 0.06$</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>$20.09 \\pm 0.10$</td>\n",
+       "      <td>$0.30 \\pm 0.01$</td>\n",
+       "      <td>$73.70 \\pm 0.10$</td>\n",
+       "      <td>$2.95 \\pm 0.50$</td>\n",
+       "      <td>$1670.88 \\pm 221.32$</td>\n",
+       "      <td>$0.60 \\pm 0.13$</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>$22.68 \\pm 0.10$</td>\n",
+       "      <td>$0.33 \\pm 0.01$</td>\n",
+       "      <td>$91.63 \\pm 0.10$</td>\n",
+       "      <td>$2.99 \\pm 0.50$</td>\n",
+       "      <td>$1875.76 \\pm 241.72$</td>\n",
+       "      <td>$0.84 \\pm 0.19$</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  $I_{eff}\\mathrm{[mA]} $ $U_{R,0} \\,\\mathrm{[mV]} $  \\\n",
+       "0        $10.02 \\pm 0.10$            $0.15 \\pm 0.01$   \n",
+       "1        $15.04 \\pm 0.10$            $0.23 \\pm 0.01$   \n",
+       "2        $20.09 \\pm 0.10$            $0.30 \\pm 0.01$   \n",
+       "3        $22.68 \\pm 0.10$            $0.33 \\pm 0.01$   \n",
+       "\n",
+       "  $U_{L,0} \\,\\mathrm{[mV]} $ $\\Delta t \\,\\mathrm{[ms]} $  \\\n",
+       "0           $22.01 \\pm 0.10$             $1.44 \\pm 0.50$   \n",
+       "1           $43.30 \\pm 0.10$             $3.52 \\pm 0.50$   \n",
+       "2           $73.70 \\pm 0.10$             $2.95 \\pm 0.50$   \n",
+       "3           $91.63 \\pm 0.10$             $2.99 \\pm 0.50$   \n",
+       "\n",
+       "               $\\mu_r $ $P_{verl} \\,\\mathrm{[W]} $  \n",
+       "0   $526.03 \\pm 175.41$            $0.13 \\pm 0.01$  \n",
+       "1  $1416.89 \\pm 145.91$            $0.19 \\pm 0.06$  \n",
+       "2  $1670.88 \\pm 221.32$            $0.60 \\pm 0.13$  \n",
+       "3  $1875.76 \\pm 241.72$            $0.84 \\pm 0.19$  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "UUr=0.01\n",
+    "UUl=0.01\n",
+    "UIf=0.1\n",
+    "Udelt=0.5\n",
+    "UR=np.array([ufloat(0.154,UUr),ufloat(0.230,UUr),ufloat(0.297,UUr),ufloat(0.332,UUr)]) #mV\n",
+    "UL=np.array([ufloat(2.201,UUl),ufloat(4.330,UUl),ufloat(7.370,UUl),ufloat(9.163,UUl)])*10 #mv\n",
+    "delt=np.array([ufloat(36.39-34.95,Udelt),ufloat(19.68-16.16,Udelt),ufloat(28.92-25.97,Udelt),ufloat(20.95-17.96,Udelt)]) #ms\n",
+    "delts=delt/1000\n",
+    "If=np.array([ufloat(10.02,UIf),ufloat(15.04,UIf),ufloat(20.09,UIf),ufloat(22.68,UIf)]) #mA\n",
+    "omega=2*np.pi*ufloat(50,0.1)\n",
+    "R=ufloat(10,0.5)\n",
+    "L=np.array([(UL[i]/UR[i])*(R/omega)*sin(omega*delts[i]) for i in range(len(UR))])\n",
+    "RL=np.array([(UL[i]/UR[i])*R*cos(omega*delts[i]) for i in range(len(UR))])\n",
+    "PL=RL*(If/1000)**2\n",
+    "N=1000\n",
+    "r=0.034\n",
+    "l=0.48\n",
+    "d=0.0007\n",
+    "A=0.038**2\n",
+    "mu0=4*np.pi*10**(-7)\n",
+    "rhocu=1.78*10**(-8)\n",
+    "le=N*2*np.pi*r\n",
+    "Ad=np.pi*(d/2)**2\n",
+    "mur=(L*l)/(N**2*mu0*A)\n",
+    "pd.DataFrame({\"$I_{eff}\\mathrm{[mA]} $\":utl(If),\n",
+    "              \"$U_{R,0} \\,\\mathrm{[mV]} $\":utl(UR),\n",
+    "              \"$U_{L,0} \\,\\mathrm{[mV]} $\":utl(UL),\n",
+    "              \"$\\Delta t \\,\\mathrm{[ms]} $\":utl(delt),\n",
+    "              \"$\\mu_r $\":utl(mur),\n",
+    "              \"$P_{verl} \\,\\mathrm{[W]} $\":utl(PL)\n",
+    "             })\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b7500752-447c-4130-9711-a14b88d21c0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.errorbar(n(If),n(PL),s(PL),s(If),marker='None', linestyle='None')  \n",
+    "plt.ylabel(r'$P_L\\,\\mathrm{ [W]}$')\n",
+    "plt.xlabel('$I_{eff} \\,\\mathrm{ [mA]}$')\n",
+    "plt.title(\"Verlustleistung\")\n",
+    "plt.show()\n",
+    "plt.errorbar(n(If),n(mur),s(mur),s(If),marker='None', linestyle='None')  \n",
+    "plt.ylabel(r'$\\mu_r$')\n",
+    "plt.xlabel('$I_{eff} \\,\\mathrm{ [mA]}$')\n",
+    "plt.title(\"Relative Permeabilität\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41f840e3-c7de-4945-8370-b54745a50f95",
+   "metadata": {},
+   "source": [
+    "**D I S K U S S I O N**\n",
+    "\n",
+    "Die Wert von $\\mu_r$ sind mit den Angaben auf Wikipedia für die magnetische Permeabilität von Eisen (300 bis 10000) (https://de.wikipedia.org/wiki/Magnetische_Permeabilit%C3%A4t) verträglich. Dies ist aber aufgrun des sehr weiten Intervalls nur begernzt Ausagekräftig. \n",
+    "\n",
+    "Es lässt sich feststellen, dass die Verlustleistung tendenziell viel größer ist wenn der Eisenkern in die Spule eingeführt ist. ES gilt also $P_{verlustLuft}<P_{verlustLuft}$ wenn nicht $I_{effLuft}\\gg I_{effEisen}$. Es liegt nahe dass die (Um-)Magnetesierung Eisens im Fall mit einem Eisenkern einen erheblichen Teil der Verlustleistung ausmacht. \n",
+    "\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3aadb392-61cb-4657-b068-9d9c1216460e",
+   "metadata": {},
+   "source": [
+    "## Aufgabe 2: Hysterese"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "e42096c8-8cb9-4937-8b94-8e0c78cdc05c",
+   "metadata": {},
+   "source": [
+    "### Aufgabe 2.1: Hysteresekurve\n",
+    "\n",
+    " * Stellen Sie die **Hysteresekurve** $B(H)$ für den Eisenkern aus **Aufgabe 1.2** für zwei geeignete effektive Stromestärken von $I_{\\mathrm{eff}}$ (im Primärkreis der Schaltung) auf dem Oszilloskop dar.\n",
+    " * Bestimmen Sie aus den aufgezeichneten Kurven $\\langle\\mu_{r}\\rangle$ und vergleichen Sie mit Ihren Ergebnissen aus **Aufgabe 1.2**.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "attachments": {
+    "9d8bfa84-6f03-4d9c-81b6-460e11615910.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "e96d00f4-6793-429d-b1f6-7287723557c8",
+   "metadata": {},
+   "source": [
+    "**V E R S U C H S B E S C H R E I B U N G**\n",
+    "![Hysteresemessung.png](attachment:9d8bfa84-6f03-4d9c-81b6-460e11615910.png)\n",
+    "Quelle: Hinweise für den Versuch Ferromagnetische Hysterese-Magnetisierung und Polarisation\n",
+    "\n",
+    "Es wurde die Spule aus der Aufgabe 1.1 und 1.2 wie berits in den Aufgaben 1.2 geschaltet. Es wird zusätzlich eine Spule mit den gleich Spezifikationen aber einer Windungszahl von $N_2=50$ um den Eisenkern gestülpt und die Anordung so zu einem Transformator ergänzt. Diese zweite Spule wird mit einem Wiederstand $R_2=10 \\pm 0.5 \\,\\mathrm{k\\Omega}$ und einem Kondsator mit $C= 10 \\pm 0.5 \\,\\mathrm{\\mu F}$ in Reihe geschaltet. Gemessen wurde die Spannung $U_H$ am Wiederstand $R_1$ und die Spannung $U_H$ an der Spule $C$. \n",
+    "\n",
+    "Es gilt:   \n",
+    "$H=N_1\\dfrac{I_1}{l}=N_1\\dfrac{U_H}{l\\cdot R_1}$\n",
+    "\n",
+    "sowie im hier vorliegenden Fall $R_2\\cdot 2\\cdot \\pi \\cdot f \\cdot C \\gg 1$:   \n",
+    "$$\n",
+    "\\begin{equation*}\n",
+    "U_{B} = \\frac{1}{C\\,R_{2}}\\int U_{i}\\,\\mathrm{d}t = \\frac{N_{2}\\,A}{C\\,R_{2}}\\int \\dot{B}\\,\\mathrm{d}t = \\frac{N_{2}\\,A}{C\\,R_{2}}\\,B.\n",
+    "\\end{equation*}\n",
+    "$$    \n",
+    "also\n",
+    "$$\n",
+    "\\begin{equation*}\n",
+    "B = \\frac{C\\,R_{2}}{N_{2}\\,A}\\,U_{B}\n",
+    "\\end{equation*}\n",
+    "$$\n",
+    "\n",
+    "Die so erhalten Werte für $B$ können gegen $H$ aufgetragen werden um die Hysterkurve zu erhalten, dabei wurden die Messwerte auf 300 Daten ($H,B,t$) runtergesampelt und mittels des Phyenbibliothek *PhryPraKit* geglättet. Des weitern wurden mithilfe von Spliens zwischen den Dten interpoliert um eine Stetige Hysteriekurve zu erhalten. \n",
+    "\n",
+    "Des weiteren lässt sich $\\mu_r$ als zeitlicher Mittelwert wie follgt berechnen:   \n",
+    "$\\mu_{r} = \\frac{B_{S}}{\\mu_{0}\\,H_{S}}$   \n",
+    "Da für $H\\approx 0$ zu sehr hohen Werten für $mu_r$ auftreten wurde der Median anstelle des arithmetischen Mittels betrachtet.\n",
+    "\n",
+    "Die Fehler wurden aus den Unsicherheiten beim Ablesen und den Begerenzheit der Anzeigen bzw. aus den Herstellerangben wie follgt abgeschätzt:   \n",
+    "$\\Delta U_H=\\Delta U_B=0.05\\,\\mathrm{\\mu V}$   \n",
+    "$\\Delta l=0.01\\,\\mathrm{m}$      \n",
+    "$\\Delta R_2=500\\,\\mathrm{\\Omega}$    \n",
+    "$\\Delta R_1=0.5\\,\\mathrm{\\Omega}$     \n",
+    "$\\Delta C=0.5\\,\\mathrm{\\mu F}$        \n",
+    "Die Berechnung der weiteren Unsicherheiten erfollgte mitels der Pythonbibliothek *uncertainties* mithilfe liniarer Fehlerpfortpflanzung berechnet. \n",
+    "\n",
+    "Die Messungen wurden bei $I_{eff}=10.02 \\pm 0.05 \\,\\mathrm{mA}$ und $I_{eff}=24.24 \\pm 0.05 \\,\\mathrm{mA}$ durchgeführt.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db113a38-b1df-4ab9-b89c-a24bfd10b7c8",
+   "metadata": {},
+   "source": [
+    "**L Ö S U N G**\n",
+    "\n",
+    "*Fügen Sie numerische Berechnungen zur Lösung dieser Aufgabe hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument. Um Code-Fragmente und Skripte in [Python](https://www.python.org/), sowie ggf. bildliche Darstellungen direkt ins [Jupyter notebook](https://jupyter.org/) einzubinden verwandeln Sie diese Zelle in eine Code-Zelle. Fügen Sie ggf. weitere Code-Zellen zu.* \n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9a5e3128-2be6-4b8e-9c54-449e1ddff14b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resampling by factor 13\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wir erhalten mur=(9.0+/-0.8)e+02 als Median. \n"
+     ]
+    }
+   ],
+   "source": [
+    "#Ur ist Channel B und UC ist Channel A\n",
+    "#R2 = 10 kOhm\n",
+    "#C = 10 uF\n",
+    "N1=1000\n",
+    "N2=50\n",
+    "l=ufloat(0.48,0.01)\n",
+    "R2=ufloat(10000,500)\n",
+    "R1=ufloat(10,0.5)\n",
+    "UHfak=N1/(R1*l)\n",
+    "C=ufloat(10*10**(-6),0.5*10**(-6))\n",
+    "UHfak=N1/(R1*l)\n",
+    "UBfak=C*R2/(N2*(0.039)**2)\n",
+    "mu0=4*np.pi*10**(-7)\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy import interpolate\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Read cvs file as pandas dataframe\n",
+    "#df = pd.read_csv(\"Christian_ist_ein_Schatz2_2_10_02mA2.csv\")\n",
+    "# Translate dataframe columns into native python lists\n",
+    "#t  = df[\"Frequenz\"].to_list()[1:-1:10]\n",
+    "#UB = df[\"Kanal A\"].to_list()[1:-1:10] \n",
+    "#UH = df[\"Kanal B\"].to_list()[1:-1:10] \n",
+    "#UB=np.array(UB)\n",
+    "#UH=np.array(UH)\n",
+    "data = np.genfromtxt('Christian_ist_ein_Schatz2_2_10_02mA2.csv', delimiter=\",\", skip_header=3 )\n",
+    "t, UB, UH= data[:,0], data[:,1], data[:,2]\n",
+    "t=t[1:-1:]\n",
+    "UB=UB[1:-1:]/1000\n",
+    "UH=UH[1:-1:]/1000\n",
+    "from PhyPraKit.phyTools import resample, meanFilter\n",
+    "\n",
+    "# If length is too large, resample by an appropriate factor, we are fine with \n",
+    "# 350 data points\n",
+    "il=len(UH)\n",
+    "size=300\n",
+    "if il > size:\n",
+    "    g = int(il/size)\n",
+    "    # This is an example of smoothing by averaging over n neighbors\n",
+    "    #print(\"Smoothing with window size \", n)\n",
+    "    #t  = meanFilter(vUH, width=n)\n",
+    "    #UH = meanFilter(vUH, width=n)\n",
+    "    #UB = meanFilter(vUB, width=n)\n",
+    "    # This is an example of down sampling by averaging over n neighbors\n",
+    "    print(\"Resampling by factor\", g)\n",
+    "    t  = resample(t , n=g)\n",
+    "    UH = resample(UH, n=g)\n",
+    "    UB = resample(UB, n=g)\n",
+    "\n",
+    "CALIB_UH2H = UHfak.n  # U_H -> H <-- adjust !\n",
+    "CALIB_UB2B = UBfak.n   # U_B -> B <-- adjust !\n",
+    "H = UH * CALIB_UH2H\n",
+    "B = UB * CALIB_UB2B\n",
+    "# Interpolate the points of (t,H) by spline functions; s=0 means that no extra \n",
+    "# smoothing will be applied, each point of H will be used for the spline\n",
+    "spl_Ht = interpolate.UnivariateSpline(t, H, s=0)\n",
+    "spl_Bt = interpolate.UnivariateSpline(t, B, s=0)\n",
+    "\n",
+    "# Plot hysteresis curve as Channel A vs. Channel B\n",
+    "tplt = np.linspace(t[0], t[-1], 200)\n",
+    "unitH = \"(A/m)\"; unitB = \"(T)\"\n",
+    "fig = plt.figure(1, figsize=(6.0, 6.0))\n",
+    "ax1 = fig.add_subplot()\n",
+    "ax1.scatter(H, B, color=\"blue\", marker=\"o\", s=5.0, label=\"Data points\")\n",
+    "ax1.plot(spl_Ht(tplt), spl_Bt(tplt), color=\"red\", label=\"Spline function\")\n",
+    "ax1.set_xlabel(\"H  \" + unitH)\n",
+    "ax1.set_ylabel(\"B \" + unitB)\n",
+    "ax1.legend(numpoints=1, loc=\"best\")\n",
+    "ax1.grid(linestyle=\"dashed\")\n",
+    "plt.title(r\"Hysteresekurve bei $I_{eff}=10.02 \\pm 0.05 \\,\\mathrm{mA}$\")\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "stdU=0.00000005\n",
+    "Uh=np.array([ufloat(x,stdU) for x in UH])\n",
+    "Ub=np.array([ufloat(x,stdU) for x in UB])\n",
+    "b=UBfak*Ub\n",
+    "h=UHfak*Uh\n",
+    "murt=((b/(mu0*h))**2)**0.5\n",
+    "#murt2=[x for x in murt if x<2000]\n",
+    "#murt2=np.sort(murt)[49:-100]\n",
+    "#plt.plot(range(len(murt)),n(murt))\n",
+    "#plt.show()\n",
+    "#mur=np.mean(murt)\n",
+    "mur=np.median(murt)\n",
+    "#plt.plot(range(len(murt2)),n(murt2))\n",
+    "#plt.plot(range(len(murt2)),np.ones(len(murt2))*mur.n)\n",
+    "#plt.show()\n",
+    "print(f\"Wir erhalten mur={mur} als Median. \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "219e67b9-1416-4540-8ba1-7362ed829606",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resampling by factor 13\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wir erhalten mur=(1.71+/-0.15)e+03 als Median. \n"
+     ]
+    }
+   ],
+   "source": [
+    "#Ur ist Channel B und UC ist Channel A\n",
+    "#R2 = 10 kOhm\n",
+    "#C = 10 uF\n",
+    "N1=1000\n",
+    "N2=50\n",
+    "l=ufloat(0.48,0.01)\n",
+    "R2=ufloat(10000,500)\n",
+    "R1=ufloat(10,0.5)\n",
+    "C=ufloat(10*10**(-6),0.5*10**(-6))\n",
+    "mu0=4*np.pi*10**(-7)\n",
+    "UHfak=N1/(R1*l)\n",
+    "UBfak=C*R2/(N2*(0.039)**2)\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy import interpolate\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Read cvs file as pandas dataframe\n",
+    "#df = pd.read_csv(\"Christian_ist_ein_Schatz2_2_10_02mA2.csv\")\n",
+    "# Translate dataframe columns into native python lists\n",
+    "#t  = df[\"Frequenz\"].to_list()[1:-1:10]\n",
+    "#UB = df[\"Kanal A\"].to_list()[1:-1:10] \n",
+    "#UH = df[\"Kanal B\"].to_list()[1:-1:10] \n",
+    "#UB=np.array(UB)\n",
+    "#UH=np.array(UH)\n",
+    "data = np.genfromtxt('Christian_ist_ein_Schatz2_1_24_24_02mA2.csv', delimiter=\",\", skip_header=3 )\n",
+    "t, UB, UH= data[:,0], data[:,1], data[:,2]\n",
+    "t=t[1:-1:]\n",
+    "UB=UB[1:-1:]/1000\n",
+    "UH=UH[1:-1:]/1000\n",
+    "from PhyPraKit.phyTools import resample, meanFilter\n",
+    "\n",
+    "# If length is too large, resample by an appropriate factor, we are fine with \n",
+    "# 150 data points\n",
+    "il=len(UH)\n",
+    "size=300\n",
+    "if il > size:\n",
+    "    g = int(il/size)\n",
+    "    # This is an example of smoothing by averaging over n neighbours\n",
+    "    #print(\"Smoothing with window size \", n)\n",
+    "    #t  = meanFilter(vUH, width=n)\n",
+    "    #UH = meanFilter(vUH, width=n)\n",
+    "    #UB = meanFilter(vUB, width=n)\n",
+    "    # This is an example of down sampling by averaging over n neighbours\n",
+    "    print(\"Resampling by factor\", g)\n",
+    "    t  = resample(t , n=g)\n",
+    "    UH = resample(UH, n=g)\n",
+    "    UB = resample(UB, n=g)\n",
+    "\n",
+    "CALIB_UH2H = UHfak.n  # U_H -> H <-- adjust !\n",
+    "CALIB_UB2B = UBfak.n   # U_B -> B <-- adjust !\n",
+    "H = UH * CALIB_UH2H\n",
+    "B = UB * CALIB_UB2B\n",
+    "# Interpolate the points of (t,H) by spline functions; s=0 means that no extra \n",
+    "# smoothing will be applied, each point of H will be used for the spline\n",
+    "spl_Ht = interpolate.UnivariateSpline(t, H, s=0)\n",
+    "spl_Bt = interpolate.UnivariateSpline(t, B, s=0)\n",
+    "\n",
+    "# Plot hysteresis curve as Channel A vs. Channeel B\n",
+    "tplt = np.linspace(t[0], t[-1], 200)\n",
+    "unitH = \"(A/m)\"; unitB = \"(T)\"\n",
+    "fig = plt.figure(1, figsize=(6.0, 6.0))\n",
+    "ax1 = fig.add_subplot()\n",
+    "ax1.scatter(H, B, color=\"blue\", marker=\"o\", s=5.0, label=\"Data points\")\n",
+    "ax1.plot(spl_Ht(tplt), spl_Bt(tplt), color=\"red\", label=\"Spline function\")\n",
+    "ax1.set_xlabel(\"H  \" + unitH)\n",
+    "ax1.set_ylabel(\"B \" + unitB)\n",
+    "ax1.legend(numpoints=1, loc=\"best\")\n",
+    "ax1.grid(linestyle=\"dashed\")\n",
+    "plt.title(r\"Hysteresekurve bei $I_{eff}=24.24 \\pm 0.05 \\,\\mathrm{mA}$\")\n",
+    "plt.show()\n",
+    "\n",
+    "stdU=0.00000005\n",
+    "Uh=np.array([ufloat(x,stdU) for x in UH])\n",
+    "Ub=np.array([ufloat(x,stdU) for x in UB])\n",
+    "b=UBfak*Ub\n",
+    "h=UHfak*Uh\n",
+    "murt=((b/(mu0*h))**2)**0.5\n",
+    "#murt2=[x for x in murt if x<2000]\n",
+    "#murt2=np.sort(murt)[49:-100]\n",
+    "#plt.plot(range(len(murt)),n(murt))\n",
+    "#plt.show()\n",
+    "#mur=np.mean(murt2)\n",
+    "mur=np.median(murt)\n",
+    "#plt.plot(range(len(murt2)),n(murt2))\n",
+    "#plt.plot(range(len(murt2)),np.ones(len(murt2))*mur.n)\n",
+    "#plt.show()\n",
+    "print(f\"Wir erhalten mur={mur} als Median. \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3814593-e9d4-428c-8ca9-2c980d56bfd3",
+   "metadata": {},
+   "source": [
+    "Es lässt sich aus den Daten errechen: \n",
+    "Für $I_{eff}=10.02 \\pm 0.05 \\,\\mathrm{mA}$:   \n",
+    "$\\mu_r=900 \\pm 80$   \n",
+    "Für $I_{eff}=24.24 \\pm 0.05 \\,\\mathrm{mA}$:   \n",
+    "$\\mu_r=1720 \\pm 150$   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eaa372be-36be-4914-9477-b3025bdee5c5",
+   "metadata": {},
+   "source": [
+    "**D I S K U S S I O N**\n",
+    "\n",
+    "Der Vergleich mit den Werten aus für $\\mu_r$ aus der Aufgabe 1.2 legt nahe, dass die Werte miteinader weitestgehen Verträglich sind.   \n",
+    "Zwar ist der Pull für die Werte mit $I_{eff}=10.02 \\pm 0.05 \\,\\mathrm{mA}$, mit 1.94 zwar größer als 1 da aber der Wert in der 1.2 aus nur einer Einzigen Messung beruht und somit stastistische Ausgen nur eine begerenzt Ausgekraft haben und der Wert aus der 2.1 aufgrund seiner Bestimmung über den Median auch Unsicher ist, kann von einer groben Verträglichkeit beider Messungen Augegangen werden. \n",
+    "\n",
+    "Für den Wert bei $I_{eff}=24.24 \\pm 0.05 \\,\\mathrm{mA}$ gibt es keine direckten Vergleichswerte in der Aufgabe 1.2 zwar ist $\\mu_r$ hier kleiner als $\\mu_r$ für $I_{eff}=22.68 \\pm 0.10 \\,\\mathrm{mA}$, was dem Ablesbaren Trend von $\\mu_r$ für steigenden $I_{eff}$ wiederspricht. Dies bewegt sich aber im Rahmen der Unsicherheiten.  \n",
+    "\n",
+    "\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66f60b58-6aac-4a8b-bb6a-adad17f3c95a",
+   "metadata": {},
+   "source": [
+    "### Aufgabe 2.2: Hystereseverlust \n",
+    "\n",
+    "**Diese Aufgabe ist nur für Studierende mit Hauptfach Physik verpflichtend. Studierende mit Nebenfach Physik und Lehramtstudierende können diese Aufgabe überspringen.**\n",
+    "\n",
+    " * Bestimmen Sie den **Hystereseverlust** $P_{\\mathrm{hyst}}$ und den dazu äquivalenten **Verlustwiderstand** $R_{\\mathrm{hyst}}$ aus den Magnetisierungskurven von **Aufgabe 2.1** für die verwendeten Werte von $I_{\\mathrm{eff}}$.\n",
+    " * Vergleichen Sie Ihr Ergebnis für $P_{\\mathrm{hyst}}$ mit Ihren Ergebnissen für $P_{L}$ aus **Aufgabe 1**.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1874d536-02ab-4d26-a701-b6b3b3003a75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Area enclosed by slope: 1.0320554354050826\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The derivatives of H(t) will be used to determine the upper and lower branches \n",
+    "# of the hysteresis slope\n",
+    "spl_Ht_deriv = spl_Ht.derivative()\n",
+    "Hp = []; Bp = []; Hm = []; Bm = []\n",
+    "for i, ti in enumerate(t):\n",
+    "    if spl_Ht_deriv(ti) > 0.0:\n",
+    "        # The branch when H was increased (lower branch)\n",
+    "        Hp.append(H[i])\n",
+    "        Bp.append(B[i])\n",
+    "    else:\n",
+    "        # The branch when H was decreased (upper branch)\n",
+    "        Hm.append(H[i])\n",
+    "        Bm.append(B[i])\n",
+    "# Convert to numpy arrays and sort at the same time increasing in H\n",
+    "ip = np.argsort(Hp)\n",
+    "Hp = np.array(Hp)[ip]\n",
+    "Bp = np.array(Bp)[ip]\n",
+    "im = np.argsort(Hm)\n",
+    "Hm = np.array(Hm)[im]\n",
+    "Bm = np.array(Bm)[im]\n",
+    "# Provide spline approximations for the upper and lower bracnches of the curve; \n",
+    "# adjusting the parameter s is a cruical ingredient here to get useful and \n",
+    "# stable interpolations\n",
+    "prec = (max(Hp) - min(Hp))*10e-7\n",
+    "spl_BHp = interpolate.UnivariateSpline(Hp, Bp, s=prec)\n",
+    "spl_BHm = interpolate.UnivariateSpline(Hm, Bm, s=prec)\n",
+    "# Detemine boundaries of the contour for integration; choose the smallest \n",
+    "# interval out the upper and lower branch of the curve\n",
+    "Hmin = max(np.min(Hp), np.min(Hm))\n",
+    "Hmax = min(np.max(Hp), np.max(Hm))\n",
+    "# Calculate the integral of the contour\n",
+    "integral = spl_BHm.integral(Hmin, Hmax) - spl_BHp.integral(Hmin, Hmax)\n",
+    "# Print the result to screen\n",
+    "print(\"Area enclosed by slope:\", integral)\n",
+    "\n",
+    "# Plot hysteresis curve as Channel A vs. Channeel B and highlight enclosed \n",
+    "# area\n",
+    "fig = plt.figure(1, figsize=(6.0, 6.0))\n",
+    "ax2 = fig.add_subplot()\n",
+    "ax2.scatter(Hp, Bp, color=\"red\", marker=\"o\", s=15.0, label=\"Increasing H\")\n",
+    "ax2.scatter(Hm, Bm, color=\"blue\", marker=\"o\", s=15.0, label=\"Decreasing H\")\n",
+    "Hplt = np.linspace(Hmin, Hmax, 200)\n",
+    "ax2.plot(Hplt, spl_BHp(Hplt), color=\"darkred\", label=\"Lower Spline\")\n",
+    "ax2.plot(Hplt, spl_BHm(Hplt), color=\"darkblue\", label=\"Upper Spline\")\n",
+    "# Form a single contour to plot a filled area\n",
+    "ax2.fill(\n",
+    "    np.concatenate((Hplt, np.flipud(Hplt))),\n",
+    "    np.concatenate((spl_BHp(Hplt), np.flipud(spl_BHm(Hplt)))),\n",
+    "    color=\"blue\",\n",
+    "    alpha=0.25, \n",
+    "    label=\"Enclosed area\"\n",
+    ")\n",
+    "ax2.legend(numpoints=1, loc=\"best\")\n",
+    "ax2.set_xlabel(\"H uncalib. \" + unitH)\n",
+    "ax2.set_ylabel(\"B uncalib. \" + unitB)\n",
+    "ax2.grid(linestyle=\"dashed\")\n",
+    "ax2.text(-275.0, -0.025, r\"$\\oint B\\,\\mathrm{d}H\\,=\\,%.4g\\,\\mathrm{\\frac{J}{m^{3}}}$\" % integral)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "2dd701bc-0c2d-4b9e-bf47-9dd8f5403579",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Area enclosed by slope: 20.08840840627854\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The derivatives of H(t) will be used to determine the upper and lower branches \n",
+    "# of the hysteresis slope\n",
+    "spl_Ht_deriv = spl_Ht.derivative()\n",
+    "Hp = []; Bp = []; Hm = []; Bm = []\n",
+    "for i, ti in enumerate(t):\n",
+    "    if spl_Ht_deriv(ti) > 0.0:\n",
+    "        # The branch when H was increased (lower branch)\n",
+    "        Hp.append(H[i])\n",
+    "        Bp.append(B[i])\n",
+    "    else:\n",
+    "        # The branch when H was decreased (upper branch)\n",
+    "        Hm.append(H[i])\n",
+    "        Bm.append(B[i])\n",
+    "# Convert to numpy arrays and sort at the same time increasing in H\n",
+    "ip = np.argsort(Hp)\n",
+    "Hp = np.array(Hp)[ip]\n",
+    "Bp = np.array(Bp)[ip]\n",
+    "im = np.argsort(Hm)\n",
+    "Hm = np.array(Hm)[im]\n",
+    "Bm = np.array(Bm)[im]\n",
+    "# Provide spline approximations for the upper and lower bracnches of the curve; \n",
+    "# adjusting the parameter s is a cruical ingredient here to get useful and \n",
+    "# stable interpolations\n",
+    "prec = (max(Hp) - min(Hp))*10e-7\n",
+    "spl_BHp = interpolate.UnivariateSpline(Hp, Bp, s=prec)\n",
+    "spl_BHm = interpolate.UnivariateSpline(Hm, Bm, s=prec)\n",
+    "# Detemine boundaries of the contour for integration; choose the smallest \n",
+    "# interval out the upper and lower branch of the curve\n",
+    "Hmin = max(np.min(Hp), np.min(Hm))\n",
+    "Hmax = min(np.max(Hp), np.max(Hm))\n",
+    "# Calculate the integral of the contour\n",
+    "integral = spl_BHm.integral(Hmin, Hmax) - spl_BHp.integral(Hmin, Hmax)\n",
+    "# Print the result to screen\n",
+    "print(\"Area enclosed by slope:\", integral)\n",
+    "\n",
+    "# Plot hysteresis curve as Channel A vs. Channeel B and highlight enclosed \n",
+    "# area\n",
+    "fig = plt.figure(1, figsize=(6.0, 6.0))\n",
+    "ax2 = fig.add_subplot()\n",
+    "ax2.scatter(Hp, Bp, color=\"red\", marker=\"o\", s=15.0, label=\"Increasing H\")\n",
+    "ax2.scatter(Hm, Bm, color=\"blue\", marker=\"o\", s=15.0, label=\"Decreasing H\")\n",
+    "Hplt = np.linspace(Hmin, Hmax, 200)\n",
+    "ax2.plot(Hplt, spl_BHp(Hplt), color=\"darkred\", label=\"Lower Spline\")\n",
+    "ax2.plot(Hplt, spl_BHm(Hplt), color=\"darkblue\", label=\"Upper Spline\")\n",
+    "# Form a single contour to plot a filled area\n",
+    "ax2.fill(\n",
+    "    np.concatenate((Hplt, np.flipud(Hplt))),\n",
+    "    np.concatenate((spl_BHp(Hplt), np.flipud(spl_BHm(Hplt)))),\n",
+    "    color=\"blue\",\n",
+    "    alpha=0.25, \n",
+    "    label=\"Enclosed area\"\n",
+    ")\n",
+    "ax2.legend(numpoints=1, loc=\"best\")\n",
+    "ax2.set_xlabel(\"H uncalib. \" + unitH)\n",
+    "ax2.set_ylabel(\"B uncalib. \" + unitB)\n",
+    "ax2.grid(linestyle=\"dashed\")\n",
+    "ax2.text(-275.0, -0.025, r\"$\\oint B\\,\\mathrm{d}H\\,=\\,%.4g\\,\\mathrm{\\frac{J}{m^{3}}}$\" % integral)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e125ea09-a94b-43e1-a873-b7b4634df092",
+   "metadata": {},
+   "source": [
+    "**V E R S U C H S B E S C H R E I B U N G**\n",
+    "\n",
+    "Aus dem Folder \"tools\" wurde der Code \" Differentiation und Integration von Spline-Funktionen\" zur Berechnung der Fläche der Hysteresis-Kurve verwendet. Die Ergebnisse und Plots sind oben betrachtbar.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "691ea74e-c262-436d-9f9f-a512e2f7f5b1",
+   "metadata": {},
+   "source": [
+    "**L Ö S U N G**\n",
+    "\n",
+    "Die Arbeit pro Volumeneinheit, die während eines Magnetisierungszyklus aufgewendet werden muss, um den Spulenkern mit dem Volumen $V$ zu magnetisieren ist: $\\omega=\\dfrac{W}{V}=\\displaystyle\\oint B\\,\\, \\mathrm{d}H$ mit $W$ die Arbeit. Das Integral wurde mit dem obrigen Code berechnen und ergibt: \n",
+    "\n",
+    "Für $I_{eff}=10.02 \\pm 0.05 \\,\\mathrm{mA}$: $\\,\\,\\,\\,\\,\\,\\,\\displaystyle\\oint B\\,\\, \\mathrm{d}H=1.032\\,\\mathrm{\\dfrac{J}{m^2}}$\n",
+    "\n",
+    "Für $I_{eff}=24.24 \\pm 0.05 \\,\\mathrm{mA}$: $\\,\\,\\,\\,\\,\\,\\,\\displaystyle\\oint B\\,\\, \\mathrm{d}H=20.09\\,\\mathrm{\\dfrac{J}{m^2}}$\n",
+    "\n",
+    "Mit der Gleichung $P_{hyst}=\\omega\\,\\nu\\,V$ zur Bestimmung des Hystereseverlustes wobei $\\nu$ die Frequenz der sinusförmigen Wechselspannung ist. Das Volumen $V$ des Spulenkers ist: $V=A\\,l=3.9\\cdot3.9\\cdot48\\approx0.00073\\,\\mathrm{m^3}$.\n",
+    "\n",
+    "Somit gilt bei einer Spannung von $\\nu= 50 \\pm 0.1 \\,\\mathrm{Hz}$:\n",
+    "\n",
+    "Für $I_{eff}=10.02 \\pm 0.05 \\,\\mathrm{mA}$: $\\,\\,\\,\\,\\,\\,\\,P_{hyst}=\\omega\\,\\nu\\,V=1.032\\cdot50\\cdot0.00073\\approx0.038 \\,\\mathrm{W}$\n",
+    "\n",
+    "Für $I_{eff}=24.24 \\pm 0.05 \\,\\mathrm{mA}$: $\\,\\,\\,\\,\\,\\,\\,P_{hyst}=\\omega\\,\\nu\\,V=20.09\\cdot50\\cdot0.00073\\approx0.73 \\,\\mathrm{W}$\n",
+    "\n",
+    "Die Berechnung des Verlustwiderstandes $R_{hyst}$ folgt aus der Gleichung: $R_{hyst}=\\dfrac{U_{eff}^2}{P_{hyst}}=\\dfrac{(I_{eff}\\cdot Z)^2}{P_{hyst}}$. Somit gilt:\n",
+    "\n",
+    "Für $I_{eff}=10.02 \\pm 0.05 \\,\\mathrm{mA}$: $\\,\\,\\,\\,\\,\\,\\,R_{hyst}=\\dfrac{(I_{eff}\\cdot Z)^2}{P_{hyst}}\n",
+    "\n",
+    "Für $I_{eff}=24.24 \\pm 0.05 \\,\\mathrm{mA}$: $\\,\\,\\,\\,\\,\\,\\,R_{hyst}=\\dfrac{(I_{eff}\\cdot Z)^2}{P_{hyst}}\n",
+    "\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a27d59cd-4be9-49dc-b15b-d74960881da5",
+   "metadata": {},
+   "source": [
+    "**D I S K U S S I O N**\n",
+    "\n",
+    "Aus den Ergebnissen läst sich ablesen, dass hohe Ströme $I_{eff}$ und damit große Magnetisierungszyklen zu unerwünschten Energieverlusten führen können. Ein größerer Teil der Energie verschwindet durch Verluste (in Form von Wärme) im Gegensatz zu niedrigeren Strömen.\n",
+    "Ein Kernmaterialien mit einer schmalen Hystereseschleife führt auch zu weniger Verlusten an Energie. Im Vergleich zu $P_L$ aus Aufgabe 1 ist erkennbar, dass\n",
+    "\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "5aaf2521-dbac-4802-a743-f267363680fc",
+   "metadata": {},
+   "source": [
+    "## Aufgabe 3: Magnetische Härte\n",
+    "\n",
+    " * Stellen Sie die Hysteresekurve für einen **Ferrit-Schalenkern** am Oszilloskop dar und vergleichen Sie diese mit der Hysteresekurve des Eisenkerns. \n",
+    " * Ermitteln Sie hierzu zusätzlich jeweils die folgenden Größen:\n",
+    "   * **Remanenz** $B_{R}$,\n",
+    "   * **Koerzitivfeldstärke** $H_{C}$ und\n",
+    "   * **Sättigungsinduktion** $B_{S}$.\n",
+    " * Diskutieren Sie Ihre Erwartung für $P_{\\mathrm{hyst}}$ für den Ferrit-Schalenkern im Vergleich zum Eisenkern. \n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "93c09936-b52e-4bc6-b66d-329f5ab3e5a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resampling by factor 13\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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nYrPZaNmyJb///juRkZHcf//9tGnTJtfnhPReq3vuuYfx48czfvz4XH32eklJSezbt8+pMjdp0sT+o+V69913H+Hh4Tz33HMkJSVx6623smDBAlauXMm8efMcekJuzHduP//YY49RtWpVmjVrRtmyZTl06BDvvPMOZ8+eZc6cOU6VIzd++uknTp8+zdSpU7n77rszvX/77bfz4YcfMnv2bGn45MS1T9qESJefBQyrV6+u6tatmyk9JSVFDR48WDVs2FCVKFFCBQQEqDp16qgJEyZkWnxv7NixqlKlSspoNCpArV27Viml1NGjR9XTTz+tKleurHx8fFRISIhq1aqVev311+2fvdniejc7R27ymdf8XLp0SbVu3VqVLl3aIcbR0dGqcePGKiAgQNWsWVN9+OGHOY7xya6MGTOfyGFwpTN5z07G9bdt26YeeOABFRgYqIKCglSfPn0yLZbn7HWcGePz9ttvZxoTM23aNNW/f/9sP9O2bVuHQdQ3vi4M7dq1y3Fs0/WuXbumRo8erapUqaLMZrOqWrWqGjt2rMOkgNyeM2M21oQJE3L92azO48xfTt/b5cuX1YsvvqgqVKigfH19VcOGDdWCBQuyvV5GvnP7+cmTJ6vGjRur4OBgZTKZVEhIiOrRo4fasmVLtnnLzxif7t27K19f3yzH2mXo3bu3MpvN6syZM9ke4+0MSl23QIoQHmbXrl00atSIGTNmMGTIEFdnR3iBjRs30qtXL1avXk3dunW5cOECW7dupXPnzkD6mKbdu3dzyy23ZPlaCOFasoCh8EiHDx9mzZo1PPvss1SsWJH+/fu7OkvCS7Rt25YRI0bQsWNHAgMDadGihX3g8smTJzEYDPZGzo2vhRCuJz0+wiP179+fr776irp16/Lxxx/TunVrV2dJCCGEB5CGjxBCCCG8hjzqEkIIIYTXkIaPEEIIIbyGNHyEEEII4TVkAUM3YrPZOH36NEFBQYWy1LkQQgihV0opLl++TKVKlbLcfiWDNHzcyOnTp6lSpYqrsyGEEEJ4rJMnT+a4hIQ0fNxIUFAQkP6llShRwsW5uTmLxUJMTAwdO3bEx8fH1dnxGhJ315HYu47E3jU8Ke5JSUlUqVLFfi/NjjR83EjG460SJUp4TMOnWLFilChRwu3/g9ATibvrSOxdR2LvGp4Y95sNFZF1fNxIUlISwcHBJCYmekTDJ+N5qoxJKloSd9eR2LuOxN41PCnuzt5DZVaXyJeAgABXZ8ErSdxdR2LvOhJ719Bb3KXhI/JM0zSio6PRNM3VWfEqEnfXkdi7jsTeNfQYdxnj42GsVisWi8XV2QDSn/2azWZSUlKwWq2uzo7XKKy4+/j4YDKZCux8QgjhjqTh40GuXLnCqVOncJdhWUopKlSoYN+BWhSNwop7xi7igYGBBXZOIYRwN9Lw8RBWq5VTp05RrFgxQkJC3KKhYbPZuHLlCoGBgTkuFiUKVmHEXSnF+fPnOXXqFLVr15aeHyGEbsmsLjeS04j0lJQUjh49SvXq1d1moJlSCqUUBoPBLRpi3qKw4p6cnMyxY8eoUaMG/v7+BXZePVFKoWkaZrNZ6nwRk9i7hifFXWZ16ZS7VTxpN7tGYcTd3eqWu0pOTnZ1FryWxN419BZ3afiIPMtY30EaP0VL4u46mqaxdu1aXc1w8RQSe9fQY9yl4SOEEEIIryENHyHyKCIigsaNG7s6G0IIIXJBGj6iUPXv398+CNfHx4fy5csTHh7O559/js1my9W55syZQ8mSJQsno3nwv//9j9WrV+fqM9WrV2fatGmFkyFRJMxmmQzrKhJ719Bb3KXhI/LMaDRSsmTJm06p7ty5M/Hx8Rw7doyffvqJ9u3bM2zYMO6//36Pfm4cGBhImTJlivy6zsZdFDwfHx+6du3qMZs16onE3jX0GHf5P6fIM6UUFovlpoNs/fz8qFChApUrV+aOO+5g3LhxfPfdd/z000/MmTPHfty7775LgwYNKF68OFWqVGHIkCFcuXIFgHXr1vHUU0+RmJho70GKiIgAYN68eTRr1oygoCAqVKjAY489xrlz53LMU/Xq1Zk0aRKPPfYYgYGBVKpUiQ8++MDhmBMnTtCtWzcCAwMpUaIEPXv25OzZs/b3b3zU1b9/f7p3787bb79NxYoVKVOmDM8//7x9pe27776b48ePM2LECIep6MePH+eBBx6gVKlSFC9enPr16xMdHZ3vuIuCZ7PZOHfuXK57K0X+SexdQ49xl4aPyDOlFFevXs3TDbhDhw40atSIpUuX2tOMRiPTp09nz549zJ07lzVr1jBq1CgAWrVqxbRp0yhRogTx8fHEx8fzv//9D4C0tDQmTZrEH3/8wfLlyzl69Cj9+/e/aR7eeustGjZsyPbt2xk7diwjRoxg1apV9rJ1796df/75h/Xr17Nq1SoOHz5Mr169cjzn2rVrOXz4MGvXrmXu3LnMmTPH3rhbunQpt9xyCxMnTrSXAeD5558nNTWVDRs2sHv3bqZOnZrj6sn5ibvIH6vVSlxcnGzR4gISe9fQY9z19eBO3JSmQVQUbNoEbdrAuHHgqse3oaGh7Nq1y/56+PDh9n+vUaMGkyZN4rnnnmPmzJn4+voSHByMwWCgQoUKDud5+umn7f9es2ZNpk+fTosWLeyrG2endevWjBkzBoDbbruNzZs389577xEeHs4vv/zCrl27OHr0KFWqVAHgq6++on79+mzdupXmzZtnec5SpUrx4YcfYjKZCA0NpWvXrqxevZqBAwdSunRpTCaTvWcqw4kTJ3j44Ydp0KCBvQxCCCEKh/T4eJmoKIiIgFWr0v8ZFeW6vGSsPpxh7dq1hIeHU7lyZYKCgnjyySe5cOECV69ezfE8O3bsoFu3blSrVo2goCDuvvtuIL1BkZOwsLBMr/fv3w/A/v37qVKlir3RA1CvXj1KlixpPyYr9evXd9juoWLFijd97Pbiiy/y+uuv07p1ayZMmODQGBRCCE+kaTBxInTsmP7PlBTH164c3ikNHy+zaRNkPCFRKv11XhkMBoxGY55X/N2/fz81atQA0se5dOnShdtvv50lS5awbds2ZsyYAZDjbvRXr16lY8eOBAYGMm/ePLZu3cqyZcuA9EdguZVRlhsbZRmyS89w4wBAg8Fw02fjzzzzDEeOHKFv377s3r2bZs2aZRpvdOM58xN3kXcGg4GgoCCJvQtI7F3DmbhnNHI6hdv4dOAW1rWbwM7ANjSY0IMyqxbw1oTLtCx/lC0TfuTyqjiX/+iWR11epk0b+OWX9EaPwZD+Oq8MBkOO+6HkZM2aNezevZsRI0YA8Pvvv6NpGu+88459ttK3337r8BlfX99Mz5kPHDhAQkICU6ZMsffO/P77707l4ddff830OjQ0FEjv3Tlx4gQnT560n3ffvn0kJiZSt27dXJY25zIAVKlShcGDBzN48GDGjh3Lp59+ygsvvJDlOfITd5E/ZrOZDh06uDobXkli7xo3jbtSzH92A1W+mMOXRFOe/3q4mwE9WJ7+Ium/j3RTy5k2rRvgmuEW0vDxMuPGpf/z+jE+eaWUIi0tDV9f3xx/DaSmpnLmzBmsVitnz55l5cqVTJ48mfvvv58nn3wSgFq1aqFpGh988AEPPPAAmzdvZtasWQ7nqV69OleuXGH16tU0atSIYsWKUbVqVXx9ffnggw8YPHgwe/bsYdKkSU7lf/Pmzbz55pt0796dVatWsWjRIlasWAHAvffeS8OGDXn88ceZNm0amqYxZMgQ2rVrR7NmzfIYsfQybNiwgd69e+Pn50fZsmUZPnw49913H7fddhsXL15kzZo1OTaunI27KHg2m83eGJblBIqWxN41soq7psFbEVcJWDqfPv98SL+zu+3HJxFEDB1ZSWeqc4xefENt/iIVX85QgWqcYB5P0PLir0yYUN/eW1SklHAbiYmJClCJiYmZ3ktOTlb79u1TycnJLshZ1qxWq7p48aKyWq3ZHtOvXz8FKECZzWYVEhKi7r33XvX5559n+ty7776rKlasqAICAlSnTp3Ul19+qQB18eJF+zGDBw9WZcqUUYCaMGGCUkqpr7/+WlWvXl35+fmpsLAw9f333ytA7dixI9t8VatWTUVGRqqePXuqYsWKqfLly6tp06Y5HHP8+HH14IMPquLFi6ugoCD16KOPqjNnztjfnzBhgmrUqJFDWbt16+ZwjmHDhql27drZX8fFxamGDRsqPz8/lfGf39ChQ1WtWrWUn5+fCgkJUX379lUJCQnZ5t2ZuOeFO9Yxd5OWlqaWL1+u0tLSXJ0VryOxd42MuE+alKY63mtVM5/foza3HKn+oaRS6Q8P1DVjMfUxz6oO/KJ8SM1IVqBUqZI2VZ54ZcKizKSp1bRXCtQfNFA+pCp/f6UsloLJa0730OsZlJI5se4iKSmJ4OBgEhMTMz3KSElJ4ejRo9SoUQN/f38X5dCRzWYjKSmJEiVKeNwvsOrVqzN8+HCHmWSeorDi7o51zN1YLBaio6Pp0qWLrhZ08wQS++xn5WaVDnk4dqOifYurvDwoCRIT+er9f7j8224eqPI9Z36+xO223QRxxZ6fv6jFDJ7nWLv+NOlQik2boFWr9CZPXFz6+UeNgjffhLlz4cgRCOEce6lPCAk8y8d8yrNMmJA+2Sa/crqHXk8edQkhhBBuRLMoPn9xJ6fiTtKo9lW633sVU8pVNvxwFfPqq3ThKoGrrrJ/8TUa1NX4c4+N+vtsNMCGaZWVI/NtGJSNFodstMSGcZWNk3Ns1Khq4+9jNjoct3Hvv8cmzEzBPzWRoZeSGEcS5l+s8O/A46cyMrQHavz7r8n481tAe95Ofp5o7gODkYgOMH589uUZPx6sVrBMjKIlv3KJkoSQQAXOAPDVVwXT8HGWNHxEnhkMBsxms4wzKWISd9cxGAyEhIRI7F3AK2KfmgrffMO5Me/zbPz29LQ/gMXp/9rh3z+73el/9Uj/s/sz/R+1r087mv5XjfQ/u7NkYjWYuGoqwQUtmL8MtQkIK8enWzqxVWvKIW7jlVFmWhghLRdjRU/9coDZvOKQ9he33vyDhUAaPiLPDAZDjgsEurNjx465Ogt55slx93Rms5lWrVq5OhteSY+xz3jstHf1GYYYZ3HX/lkYzp6lEuk9K7toyFWKU6xscVreU5wdfxZn047iXKE41yhOm07F6XS/D9ErjfywwogVIwojD3QzYjOaWLrsv7SHHjXyyKNGvl1iZOE3/6X37OtHqn8wb38azCWCuUwJRk0oBgYDERH/zgCOg7vvhlvM0DsPC99qGrQ5nT5LdyvNWEYPUvFjCQ8D8MQTBR7aHEnDR+SZUoqUlBT8/f31/SvMzUjcXcdqtXLo0CFq167tsFClKHx6jP0XQ7dR/eP3GcNCfPl3vbLKlVkd+jy9Vw8kgbIYDBDxArQcDw00+OG68Tn3jAPM0HEw/H5depd/e2B2XZfW/d9jH+oBB+r9l97732P/viXr2b6xsVZ69DhE//618fPLW9yjouDhY98A8CFD+crQD6XA3x9eeQleey1v8csrGdzsRmRws3CGDG52HRlg6zqeHvuM3p2day/St/QKup+ZhSF2s/39WMJY22AYr2x7CM3g4zZbC+U37poGD1bfRfTfjUjFl3Kc487wYGJiCj6vMrhZCCGEcAeHDrHmhR9o+/MPjGMjZtIXMbWafFhg7cn7DGOboTkRjwA+6TfmnAYLe5KoKHj+77EA/MADJBGcr4VzC4I0fIQQQogComkw+XUr/6yI41G/7wm78AOGAwfoeN0xe6jPjpqP0GfdII58UZFSmyAinwvKuiNNgxMzf2Q80aThwziiqFnT9eWUho/IM4PBIKsHu4DE3XWMRiNVq1aVR7suUJixz2l9nCmTLOxed4H2t59nYI8ETIn/QFIS1n8S2fhjIglHkqhZNpHGNRIxXk7izL5Enjv1F2W58N8FzGaOVG3H9CMP8D0PcMxQk4h+YK7i/j07uYm7psHbYy9wZc4iymnxVCl2gYizywF4jxEc4jYi+7nusV0GGePjRjxtjI/QF6ljwlu9+7/TJL7zGVU4QQjnuaNKAlX8z5N88jwBKZfydM5/KEU0XTje4AFe2dgZrXiw24zbKWiaBlFvKP6ZuZBx54ZRjvMO7/9NJUI5QLmaQRw8WHjlljE+otAppUhOTiYgIKBQex+OHTtGjRo12LFjB40bN2bdunW0b9+eixcvUrJkyUK7rlKKQYMGsXjxYi5evGi/vitcH4NGjRoVSdxFZlarlV27dtGwYUPdzCzyFIUS+5QUeO89npv2BgFc/S/9ZPo/Av59acPABcpwrXgI1RqXhuBg1m4rwcGzwSRRgkSCqVgnmKHjSrBwZTDvLyjPVpphM5jTx+0Ee+64HWfiPmWShQYTe9o3JN1HXdZxNxcowwXKEE0XrhqC6OcGvT0gDR+RD+rfzTJzmlZ97tw5XnvtNX766SfOnj1LqVKlaNSoEREREYSFheXpuq1atSI+Pp7g4OD8ZP+mVq5cyZw5c1i3bh01a9akbNmyhXq9DP379+fSpUssX77cnlalShXi4+MpW7asU3EXhcNms3HixAluv/12afgUsQKNvVKwfDm89BIcPUoA8Ct38iP3k0AIHXqVpeeQEGYuCmHChyH8QymUwUTEqP8aLxsn8t86NwaIeAx4Eh55DP4MhaAC2AjaHTgT9xLffkYPlpOKL6/zKlMZjQVfANq3h+pmeMKNYiENH1GoHn74YSwWC3PnzqVmzZqcPXuW1atX888//+T5nL6+vlSoUKEAc5m1w4cPU7FiRbdYNM1kMtnLbLPZXJwbITyTpsFnw3Zz58LhNPlnTXpipUpYJ79JzJE+bIk10qYNPPTvmjfPtoKEkKzXt8n49xvfM5s9s2cnz65c4elTkQC8xDvMYCilSkGpUtC3L7z6qnv08jgomD1RRUHI1e7sNptSV6645s9mU0rdfJfwixcvKkCtW7cux3IDaubMmapz587K399fVa9eXX377bf2948ePeqw2/ratWsddm3/4osvVHBwsFq5cqUKDQ1VxYsXV506dVKnT592uM7nn3+uQkNDlZ+fn6pTp46aMWNGtnm6fld5QFWrVk0plb6r+3vvvedwbKNGjew7xWeU59NPP1Xdu3dXAQEB6tZbb1Xfffedw2f27NmjunTpooKCglRgYKBq06aN+uuvv9SECRMcrguotWvXOsQgI+5r1qxRzZs3V76+vqpChQpq9OjRynLdNsft2rVTL7zwgnr55ZdVqVKlVPny5R3yeSPZnf3mZIdw18l37M+fV1uaD1EaRqVAJeOn1t/1qlKXLxdsRnXmpnGfNEkpUBdK1VT33ZOqIiMLbrf13HJ2d3Zp+LiRXDV8rlxRKr2Xtej/rlxRSills9nUtWvXlO3fhtCNLBaLCgwMVMOHD1cpKSnZlhtQZcqUUZ9++qk6ePCgevXVV5XJZFL79u1TSjnX8PHx8VH33nuv2rp1q9q2bZuqW7eueuyxx+zX+OSTT1TFihXVkiVL1JEjR9SSJUtU6dKl1Zw5c7LM06VLl9TEiRPVLbfcouLj49W5c+eUUs43fG655Rb19ddfq0OHDqkXX3xRBQYGqgsXLiillDp16pQqXbq0euihh9TWrVvVwYMH1eeff64OHDigLl++rHr27Kk6d+6s4uPjVXx8vEpNTXWIgc1mU4cOHVLFihVTQ4YMUfv371fLli1TZcuWdchHu3btVIkSJVRERIT6888/1dy5c5XBYFAxMTFZllkaPjenaZrav3+/0jTN1VnxOrmJvcWiVGSkUuH32tRbL59V2rvvK1WypP3/Yd/yiKrGURUeXgQZ93A5xd1y+pxK8QtSCtTiRxa4rMGTQRo+HsjTGj7OWLx4sSpVqpTy9/dXrVq1UmPHjlV//PGHwzGAGjx4sEPanXfeqZ577jmllHMNH0D99ddf9s/PmDFDlS9f3v66SpUq6uuvv3a4xqRJk1RYWFi2eX/vvffsPT0ZnG34vPrqq/bXV65cUQaDQf30009KKaXGjh2ratSoke0vqH79+qlu3bo5pN0Yg3Hjxqk6deo4NDpnzJihAgMD7T1w7dq1U23atHE4T/PmzdXo0aOzvK40fIS7sjdkwpVDj4LFotTrryarp1odUPOfiFbatA+UGjFC7Q/tpv6ggbpMcYf/d8VXaKTasU6BUgZD+rmE8ywWpSIjbOrhtmfV4KZb1JLAJ5UCtY0myojV5fF0tuHjbk/ehLOKFYMrV1x3bdIHN1+9epXixYtnO8j24YcfpmvXrmzcuJG4uDhWrlzJm2++yWeffUb//v3tx9040DksLIydO3fmIkvFqFWrlv11xYoVOXfuHADnz5/n5MmTDBgwgIEDB9qP0TSt0AZIN2zY0P7vxYsXJygoyJ6fnTt30rZt2zwvu6+UYvfu3YSFhTnEvXXr1ly5coVTp05RtWrVTPkAx7iI3NM0jS1bttCiRQvMbjdwwfNlrKcTu0FjuO9MOiUtwpB8jQun0uhzPpUXSySRvMqAZUoqZmMahuRUXrFp6R+OBeal/2vodee0YSA+oCaVp42ibL8BdJhqwlcnA4+LgqZp/PbbFtaubcGaT47xwclujGefwzGjmYoNI5s2uSiTuST/5XoqgwGKF3dpFpRSaJqGUirH2UX+/v6Eh4cTHh7O+PHjeeaZZ5gwYYJDwycruZmxdGMjwmAwoP5doipjMPCnn37KnXfe6XBcbmeHGI1G+3kzWCwWp/KTkY+AgIBMx+eGUirLAc4Z+bo+bjnlQ+SeUorz589nqgOiYERFweoJG/iQ52nAHnt6+X//SISSAMnp6Rn/9V4mkCPU5EpITVr3rUn0wVp8sKImh6nJCaoxbowf45/13CnlrqSUIiHhPD++sZPlKfdTgbPpjUkqcpxqrKQzvxCOwYDLt6JwlsctPzpz5kz7AmtNmzZl48aNOR6/fv16mjZtir+/PzVr1mTWrFmZjlmyZAn16tXDz8+PevXqsWzZsjxdd//+/Tz44IMEBwcTFBREy5YtOXHiRN4Lq1P16tXj6tWrDmm//vprptehoaEUhPLly1O5cmWOHDnCrbfe6vBXo0aNXJ0rJCSE+Ph4++ukpCSOHj2aq3M0bNiQjRs3ZtlggvRZa1arNcdz1KlTh7i4OIcbcGxsLEFBQVSuXDlX+RHCLZw+TZuPHmc97WjAHhIow8w60yA6mq/6/cK9fmtZ/9ZbtPTfyoyh++HwYd576RRluEAJkmhi+IPVQ5fBO+/QcfkQwiI7Uz38NsZF+knPTj6V3r+flSn3UoGz7KAxlTjNLfxNa2KZxHhq1kyf2u8pcfaoHp9vvvmG4cOHM3PmTFq3bs3HH3/Mfffdx759++xd+9c7evQoXbp0YeDAgcybN4/NmzczZMgQQkJCePjhhwGIi4ujV69eTJo0iR49erBs2TJ69uzJpk2b7L0Dzlz38OHDtGnThgEDBhAZGUlwcDD79+/36hVwL1y4wKOPPsrTTz9Nw4YNCQoK4vfff+fNN9+kW7duDscuWrSIZs2a0aZNG+bPn8+WLVuYPXt2geUlIiKCF198kRIlSnDfffeRmprK77//zsWLFxk5cqTT5+nQoQNz5szhgQceoFSpUrz22mu57jUaOnQoH3zwAb1792bs2LEEBwfz66+/0qJFC+rUqUP16tX5+eefOXjwIGXKlMnycdyAAQOYNWsWL7zwAkOHDuXgwYNMmDCBkSNHynYKwrNYLDB9OkRE0OHKFWwY+JhBvMbrvPhYGbgP+oTD8VstXKqdyP2vNGLQmPSdPF+YApdLyJTyQnXwIGEREZhJZSNteIAfKF2jJO2rg8kEbdt64CrUhTjOqMC1aNEi0yDY0NBQNWbMmCyPHzVqlAoNDXVIGzRokGrZsqX9dcYMmut16tRJ9e7dO1fX7dWrl3riiSdyV6Ab5Gpwsxuw2WwqJSUl21ldKSkpasyYMeqOO+5QwcHBqlixYqpOnTrq1VdfVdeuXbMfB6gZM2ao8PBw5efnp6pVq6YWLFhgf9/Z6ezXW7Zsmbqxes+fP181btxY+fr6qlKlSqm77rpLLV26NNvyZTW4OTExUfXs2VOVKFFCValSRc2ZMyfLwc3Lli1z+FxwcLD64osv7K//+OMP1bFjR1WsWDEVFBSk2rZtqw4fPqyUUurcuXMqPDxcBQYGZjmdPSPua9euvel09mHDhjnko1u3bqpfv35Zltcd65i7sVqt6tixY9ku4SByafVqperWtQ8+trW4U30y6PdMg5iVkti7giXNpv6qda9SoHaWvEt1bX/VpdPVb8bZwc0es1dXWloaxYoVY9GiRfTo0cOePmzYMHbu3Mn69eszfeauu+6iSZMmvP/++/a0jB6da9eu4ePjQ9WqVRkxYgQjRoywH/Pee+8xbdo0jh8/7tR1bTYbwcHBjBo1ik2bNrFjxw5q1KjB2LFj6d69e7ZlSk1NJTU11f46KSmJKlWqkJCQYN9nxGg0YjKZuHr1KsePH6d69er2FXszxrFc/xXeLP3G8R0Z40FurAbZpWeMccnNNW+WbjKZWLJkCT169NBNmfKb7ooyJScnc/ToUapUqYK/v7+97lmtVof8ZKRnjO/KYDKZMBqN2abf+GgvY3CwpmlOpfv4+GCz2RweAxoMBsxmc7bp2eVdyuS6MqWmWvlk/CmazB/FXfGLAVBly2KdPBnVty8YjR5XJj1+T1arle/6LuGRb/qQgh+3s5d+E6szZoz7lik5OVlfe3UlJCRgtVopX768Q3r58uU5c+ZMlp85c+ZMlsdrmkZCQgIVK1bM9piMczpz3XPnznHlyhWmTJnC66+/ztSpU1m5ciUPPfQQa9eupV27dlnmb/LkyURGRmZKj4mJodi/M6eqVq1KkyZNOHjwIEoprly5QlpaGn5+fgQEBHD16lWHLz8gIAA/Pz8uX77sUBGLFy+Oj48PSUlJDtcKCgrCYDBkSi9RogRKKS5fvuyQXrJkSTRNcxijYzQaKVGiBGlpaSQnJ9vTzWYzgYGBpKSkODTwfH19KVasGMnJyaSlpQHpFTYlJUVXZQI86ntKTU0lOTmZDRs2oGmave7t2rXLYaxanTp1CA0NZcuWLZw//99mhI0bN6ZatWps2LDBIT9hYWGUK1eOmJgYhxi0b9+egIAAoqOjHfLepUsXkpOTWbt2rUMeu3btSkJCAnFxcQ5x6dChAydPnnSYBRgSEkKrVq04dOgQBw8etKcXVJlatGhBxYoVdVWmgvie0tLM7N/flYH949mzcgXFzp6l2LlzGI5fwngcavsehKNHePGffzAohc1g5PfmQyg5/Qn2nzkDK1fmWCZfX1/Cw8O9uu4VRZmOHTvJvi1xdP1lKAB/PdSDv6OrkZJyiOho9y1T27ZtcUoue5Jc5u+//1aAio2NdUh//fXXVZ06dbL8TO3atVVUVJRD2qZNmxSg4uPjlVJK+fj4ZFrfZd68ecrPz8/p62Yc06dPH4djHnjgAYdHZjdKSUlRiYmJ9r+TJ08qQCUkJKi0tDSVlpZmXzTqypUrau/everq1avKarXaHy/ZbDZltVrtfzdLvz4tI/3GY3NKv/7cmqY5rNyc27xkpANqyZIlblGm3Oa9sNJzyntG3DVNK9AyXbt2Te3du1clJSU51D1N0+z18fp0i8XikJ5x3ezSr09LS0uz593Z9Iy4XJ+W8Wgvu/Ts8p7XMl29elUtX77cviCnHspUEN/TpElpqkTAVfWEzzy1iVbKajDedD2wTcbWqrnPVhUe7lyZMmKfEQtvq3tFUabkZIuKjFSqfq2r6gvTU0qBOmqsrr7/5htVrFiamjjRvcuku3V8ypYti8lkytS7c+7cuUy9MRkqVKiQ5fFms5kyZcrkeEzGOZ25btmyZTGbzdSrV8/hmLp167Iph4UN/Pz88PPzy5Tu4+OTaRqyyWTCYDBg/LcbOEPGY4obZZee3cDX7KaO53TurB7H5CYv1z96yc95CrJM7pKeU5ky4n79ZwsyLzfWP5PJlOUA7uzWsckuPbt1i3KTfmP9v1l6dnnPb5kyrqWnMuWUxxzTr1yh/Jefsif5A6pwKj1RAX5+nDRVZ++16hylBseoTokG1SnfsgavfFqdc7ZyGBREtHHDMunxe3KiTJOjFPsjFvIjY6jOcQBeMr/Hk34Gxo6FMWNMmM3uW6bre7Jz4jHTP3x9fWnatCmrVq1ySF+1alW2m0iGhYVlOj4mJoZmzZrZA5bdMRnndOa6vr6+NG/e3KFbE+DPP/+kWrVquSypEEK4L02DiRPhqdZ/srXF86hbbmHgodFU4RRnKUcEEbw/4hhcu8YXow/QxbCSIXzEW4bRWB/pRf+ZLXg+shzh4Z41BVr3Dh/mkfdasYA+VOc4p6hMH75mpbkrAKNHe9jMrZzk2B/kZhYuXKh8fHzU7Nmz1b59+9Tw4cNV8eLF1bFjx5RSSo0ZM0b17dvXfvyRI0dUsWLF1IgRI9S+ffvU7NmzlY+Pj1q8eLH9mM2bNyuTyaSmTJmi9u/fr6ZMmaLMZrP69ddfnb6uUkotXbpU+fj4qE8++UQdOnRIffDBB8pkMqmNGzc6XT5nZnVdPxvK1TK6GrOb1SUKR2HF/dq1azKr6yasVqs6e/as984sstnU3P5r1Pfc7/DYytagoVre/QvVpUNypi0lstpqIi+8PvaFJGMbir3BLZUCdZni6hUmqQCuqpo1lYqMtKrTpz0j7rrdq2vGjBmqWrVqytfXV91xxx1q/fr19vf69eun2rVr53D8unXrVJMmTZSvr6+qXr26+uijjzKdc9GiRapOnTrKx8dHhYaGqiVLluTquhlmz56tbr31VuXv768aNWqkli9fnquy5fSlpaWlqX379qlLly7l6pxCOOvSpUtq37599uf/QthpmlKLFinVrJm9sWPFoL7jAfW/pmuUkh8/HisyUqlH+dbe6Ond8miBNFRdQXfT2b1BUlJStlPxlFKcOHECi8VCpUqV3GKROpvNxpUrVwgMDHSL/HiLwoi7zWbj9OnT9iUecrNdiDexWCzExMTQsWPHPO+15lFSU+HLL+Gtt+DQIQAsPgF8anmK9xjOYUNtIiKKZrFAr4t9EelybxrTV9fjVg4zgQjiwicQE/Pf+54U95zuodfTyxM73TMYDFSsWJGjR49y/PhxV2cHSG+MJScnExAQIDfKIlRYcTcajdLoccKNa4fokXYhkXW9Z9FkwzTKpP07saNUKRg6FMNzL5DwaQg1NkHfIt7o0xtiX5Q0Dboc/4hbOUw8FXiXl3g5i/229BZ3afh4EF9fX2rXru2wTowrWSwWNmzYwF133eX2vwT0pLDi7uvrKz13XihjR/RNm+C+BqcYZvoQbfpH3JuavmbUSW5hX6eRdFo8EAIDZaNPD3f9913t4k6m/DURgAlE0rx9oFcMNpeGj4cxGo1us/9Xxsqc/v7+0vApQhJ3kRfX3/DatIFxw69h3rWdNZN+IzTmN55kC9VXpfcm+wP7qMtURrOAPtxt86VToGvzLwpGVBTETNjMWKLoSvrCgHupx+c8TQezjmZu5cALiigKi9lspn379tmuySAKh8TddTw59pNft3Ig8lseYj13rvoNY+RusFnpeN0xNgzsK9mKPfeN4vEF92PDiMGQ3lByNU+Ovds4e5YHp/VmPOsAsGLkW3oylsnYDOYsv2c9xl0/JREuERAQ4OoseCWJu+t4ZOxPneLB6U/wGtftaWgDKlTgQMk7+fLAnfxGC7bRjJEjghk3Dv4Mzbzruat5ZOzdQEZv3z0fDqb1xXWk4cMc+vMmo6ja/lZuM8PTOXzPeou7NHxEnmmaRnR0NF26dJFHLkVI4u46Hhn75cthwAAaXfyHywTyCc/yK2G0Hnknw9++hVutBvyjwLQJRv578zOb3W8cj0fG3k1ERcHOCcsYz3IsmHm+2RaOl2rMk9d939nRY9yl4SOEEHqUnAwvvQQffQSAuqMpc9ss4Of9tWnTBoaOAwzu2cgRBWv72kRmkL7h6JuM4nipxg5T1r2NNHyEEEJntJ17+KdTb8qd2wuA7aWXMUa9zlBf339vf8KbvHJ1HJU5zSFu5Q1eZYwbjNlyJWn4CCGEB3OYrdVa8UqZWagRIymnpXCG8vTjS1qX6Mh4X1fnVBQ1TYMvB8fSf2t6r9/Hd3zCmG4BbjNmy1Vk5WY34uyqk+5CKYWmaZjNZln0rghJ3F3HHWM/cSK8MSGNbiznBT6gLZsAiOY++jOH86RvCOrpjzbcMfbuLurVa/R4oyl1OcAXPMXJyM9z/VjTk+Lu7D1UVisT+ZKcnOzqLHglibvruFXsT52i5pzxHKMa39KLtmwizeDLz53f4wF+5Dzl3GY6ekFwq9i7MU1LbxCHvD2auhzgNBX5H2+xaVPezqe3uEvDR+SZpmmsXbtWd8uZuzuJu+u4OvaaBhMjbIxpuooDdXugqlXjiaOTqMgZ4qnAJF5jxojD3PPDcCZEGgkPh4gI95mOnh+ujr0niYqC2Ak/MzD1QwCe4gsuGsrkqQGsx7jLGB8hhPAQCwes4pEvh1GP/fY0W7u7WVJ+CJ9f6E7YXT684KbT0UXR+WPNBT7nKQA+YCi/l+5ExDB9NIALgjR8hBDC3Z06BSNH8sSiRQAkUoK59GNX2GA+W1ePR4FHXZtD4S6UYtL5QVQinv2EMoapjB4mDeHryaMukS96Wsbck0jcXadIY2+xwNtvQ2goLFqEzWDkfYZRlRMMN0ynaud6RZcXNyD1/ibOnIGuXam3bwlWo5kP75zH6Mhi+e7p0VvcZVaXG/G0WV1CiIKTaRPRNusxvfg8hr3pa/GcrBJGuUUzmfpzY4etJHR2TxK5lFFvLEu+56X9AyhpSUAz+2GYNQvTgP6uzl6RcvYeKv/JiDyz2WwkJCRQtmxZjEbpPCwqEnfXKczYR0WlD0Sur3ZTc9VUzMwH4DxlGc1U5p7sz4SfjV77yELqfdaiXrdRJvJFnmcGADtpxBPafHr+XZ+CqCp6jLs+SiFcwmq1EhcXh9VqdXVWvIrE3XUKLfYpKfgtmscG1YbdNOQJ5mPDwI+VB1GHg3zB09gw5nk6sh5IvXeUMWU99a33eZ4Z2DDwFv/jTn5jL/ULrK7oMe7S4yOEEEUs4/HEkZi/eFZ9TNjBLxh94QIAFswspzuJz47idOXmXIoAFLpaj0fkX1QUfDdhO3GMBuAFPmAmzwNSV25GGj5CCFHEPhh9isbvDmE8P9jTVJUqrKv9LB9rA6h3T0WHAanXj+kRAuD3dVf4mj74YmEZ3VlQcgjtm4DJBG3bSl3JiTR8RJ4ZDAaCgoLcfhlzvZG4u06BxH7BAp6ZPoQgLmHDwE/cx2+NBzPx9y60N5lof8Ph3jqm50ZS7x1FXhpGHf7kFJUZyGcMH2EolLqix7jLrC43IrO6hNCxixdhyBBYuBCA32hBP+bypyGUiAhp4IhcePddeOkllMHA/+5YS/CD7WSGHzKrSxQBm83GyZMnqVKlim5G+3sCibvr5DX21p9/4WrP/pRI+hub0YT1lfHEGMZRNc7MY/IIyylS7/81fTq89BIAhjfe4J2x7Qr1cnqMuzR8RJ5ZrVZ27txJpUqVdPMfhCeQuLuOs7HPGLy8ZX0yr10dw52/TacE8Ce16WubR1dzC+nhySVvrvcZ9anUgpm8cGAYAF/XeIW/0sYwTivcnh49xl0aPkIIUcCiouCLCcdYSg+asBOAGQxhFG9yjeIEe/G0dJF7UVHw94RPGP/vrK0pjGbs0UkYIg1gkMekuSUNHyGEKGBJ369jC48SQgLnCGFMhbnMOXsfSqalizz4dUMaU/kAgHcYyVgmAwaUwqvXdsorafiIPDMYDISEhOhqtL8nkLi7zk1jrxTMmMGbO4ZjxMo27qAHy3l6UBUijDItPT+8ud63vMuXe1ev5knm8jb/A9JjUBSNaD3GXWZ1uRGZ1SWEB0tNheefh9mzAdjV8HHGlf2UFu0CZMaNyJfr93ELC0tv8MTGyn5tN3L2HioNHzfiaQ0fq9XKoUOHqF27NiaTydXZ8RoSd9fJKvaaBtPHxtPp04epnxiHMhoxTJ2aPvNGR7+SXU3qvWt4UtydvYfqY4i2cAmbzcbBgwex2WyuzopXkbi7TlaxnzvkN3q93Yz6iXFcpCRfPx4N//ufNHoKmNR719Bj3KXhI4QQuZSxQeRbt8/hiU/vojKn2Us9mrOVuWc6uTp7Qgc0DSZN0OjYMb2uaZqrc6Qf0vARQohcevdNjeAJw3l571P4kcZyutGSXzliuFVmbIkC8fGIAzw5sRYVVn1JxARFVJSrc6QfMiRK5JnRaKRq1aq6WdTKU0jcXcdoNFKjRAmqTe9KY9YCEMl4vqoxgbBbjTJjqxB5Vb1XihYLhlONEzzKIr7iSZdNW9dj3KXhI/LMZDLRpEkTV2fD60jcXce0dy8NBwyAf45yheI8yZcsNzxERH9ZRK6weUO9z5i9pS37kYkXfiYVX0bwnkvXftJj3PXThBNFzmq1smPHDqxWq6uz4lUk7kVP02BRr8Wk3BEGR49iq1GTeUPiuBL+EBER0stTFLyh3kdFweQJKfTbORyAJVVHUjP8VpfWMT3GXXp8RJ7ZbDZOnDjB7bff7vbTHPVE4l7EbDZi75nAoxteB+Bco0Z82eVn/hdVnsEuzpo38YZ6v2kTDOc9anGEv6nEwlqvEBPj2jzpMe7S4yOEENlJSoIePbjr30bPdNMwfh0/nrV/lHZxxoQe3V/vCK+SXtdG8ybN7g50cY70SRo+QghxnYyp6v1bH+L8rS3h++/RzH70Yy5jfN9CmUyEhbk6l0JvtLMX6DOvC8W5xlb/Ntw6/jF5hFpI5FGXyDOj0UidOnV0NdrfE0jcC1dUFMRO+JkF9KYUl0gKqkSxn5dTa1Vz7oq1cuZMHV5+WWJf1HRd75OTiW/+IFUuHOQEVeie8g2DTAa32IpCj3GXLSvciKdtWSGEHk1tMI//7emHCRuxhDH9riUsXF/R1dkSOqRpMPl1K3d/1JO255ZykZK0YRP7qE94OC4f3+NpZMsKUeg0TSM2NhZNlhQtUhL3QvTJJ4za+yQmbMzlSTqwlnr3/Nfokdi7jh5jHxUFxsgJtD23lFR86c5y9lHfpdPXb6THuLtBR5rwVEopzp8/j3QaFi2Je8HKWDul7NfvM+TgcAzAlhZD+brE+4xra3QYZyGxdx09xv7QqmN8xlsAPMUXnKrZjvBauNVCmHqMuzR8hBBeLSoKUiZMZgjpd5rY1i/TauNUfpZNRkUh+9/lCfiRxmo6sJA+RPSThTCLgjR8hBDeSylqzRnP4/9OIZ5ABHEB44mRRo8obLt303DXVwAsazGFiK4Gt+nl0Ttp+Ig8M5lMNG7cWDeLWnkKiXsBsVhgzBgeP/ouAKOYytuGUUS0zf4jEnvX0VPsNQ2OPDKO25RiX71HmLa5uVvM4MqKnuKeQWZ1uRGZ1SVE4dMsiiVPLqfV92Oocu1PAH7q8gHvWYbax1a4601IeD5Ng5EtNjF9R1s0TNRnH49H3iaPuAqAzOoShU7TNNasWaOr0f6eQOKeOxkLEnbsCF88s5nTNdvQa+FDVLn2J+cpy9KH53HfiqHExKSPr8ip0SOxdx29xD4qCrrsSH+0OpsB/MltLtt53Rl6ifv15HeNyDOlFJcvX9bVaH9PIHHPnagoWDDhAJMZQ3e+A+AqxXiXkbzFy7RMKsFDTp5LYu86eon9iZX7GM/PWDEyhTGA+0xdz4pe4n49afgIIfRtyRJ20RsfNKwY+TboGV66HEE8Fd1qvRThHZ5LmwbAd3TjGDVo3959pq57C2n4CCH0a80axu19DDMaP9GZl3iXniPrMtiYvhO2O62XIrxAQgJ37E2fybWp2QgiH5AxZa4g4RZ5ZjKZCAsL09Vof08gcXfStm3QrRtmaxr76j3M+5W+oXdbU75uNBJ719FF7GfNwpCSAk2b8u6WNuABqyboIu438LjBzTNnzqRGjRr4+/vTtGlTNm7cmOPx69evp2nTpvj7+1OzZk1mzZqV6ZglS5ZQr149/Pz8qFevHsuWLcvXdQcNGoTBYGDatGm5Lp8nMRqNlCtXTleb13kCifvNafsPcbXdfXDlCkdrdOC2LfNZucp008HLNyOxdx1Pj712NZXLU2cAsLT6CDSrB7R68Py4Z8WjSvLNN98wfPhwXnnlFXbs2EHbtm257777OHHiRJbHHz16lC5dutC2bVt27NjBuHHjePHFF1myZIn9mLi4OHr16kXfvn35448/6Nu3Lz179uS3337L03WXL1/Ob7/9RqVKlQo+AG7GYrGwYsUKLBaLq7PiVSTuN3H6NJdbdaT41fNs4w4aH11G1Dt+BXJqib3reGrsM2YVflDlTYKunOE0Femz5FGiolydM+d4atxzpDxIixYt1ODBgx3SQkND1ZgxY7I8ftSoUSo0NNQhbdCgQaply5b21z179lSdO3d2OKZTp06qd+/eub7uqVOnVOXKldWePXtUtWrV1Hvvved02ZRSKjExUQEqMTExV59zlbS0NLV8+XKVlpbm6qx4FYl71iwWpSaPTVSHAxsoBepPblUhnFWgVHh4wVxDYu86nhr7yEil2rNGaRiVAvUkcwq0ThY2T4q7s/dQjxnjk5aWxrZt2xgzZoxDeseOHYmNjc3yM3FxcXTs2NEhrVOnTsyePRuLxYKPjw9xcXGMGDEi0zEZj6mcva7NZqNv3768/PLL1K9f36kypaamkpqaan+dlJQEpLewM1rXRqMRk8mE1WrFZrPZj81I1zTNYZqhyWTCaDRmm35jq938b7//jWs0ZJfu4+ODzWbDarXaz6VpmkN6BoPBgNlszjbv7limG/PujmXKeM9iseimTM6k36xMU6PSuGNqH2radhNPBToSw0VzGYr5WrjrrvSFmvNbpowyZHzW2+qeK8t0fb48qUx/bjjDAvpgwsbnPMVC3ycoZs5cJ921TNdfx93rnrM8puGTkJCA1WqlfPnyDunly5fnzJkzWX7mzJkzWR6vaRoJCQlUrFgx22MyzunsdadOnYrZbObFF190ukyTJ08mMjIyU3pMTAzFihUDoGrVqjRp0oRdu3Y5PFqrU6cOoaGhbNmyhfPnz9vTGzduTLVq1diwYQOXL1+2p4eFhVGuXDliYmIcKkv79u0JCAggOjraIQ9dunQhOTmZtWvX2tPMZjNdu3YlISGBuLg4e3psbCz33HMPJ0+eZOfOnfb0kJAQWrVqxaFDhzh48KA93RPKFBQURIcOHdy6TKtWrdJdmSDv31PPnSOobYvG6uvLD/1ex7i6BqN6H6BFi/QyRUcXXJkuXLhApUqVvLbuuapMGTymTPv28d7ZCYRwltOlb2XoPx8ycuQuWrZML1N0tGd8Txncve61bZvDfjPX8ZgtK06fPk3lypWJjY0lLCzMnv7GG2/w1VdfceDAgUyfue2223jqqacYO3asPW3z5s20adOG+Ph4KlSogK+vL3PnzqVPnz72Y+bPn8+AAQNISUlx6rrbtm2ja9eubN++3T62p3r16gwfPpzhw4dnW6asenyqVKlCQkKCfbltd2tRX/8rQSnFlStXCAoKkh6fIixTRtwDAwPx8fHRRZmcSc+xTHPnYuzfH4B+Pl/yrakPY8eaeeWVgi1TRuyDg4MxmUxeV/dcWSalFNeuXSM4ONj+/x93LxNvv41pzBhSfQN5vsVvVA2vx+jRVoxGz/melFJcvXqVkiVLYrPZ3LruJScnO7Vlhcf0+JQtWxaTyZSpd+fcuXOZemMyVKhQIcvjzWYzZcqUyfGYjHM6c92NGzdy7tw5qlatan/farXy0ksvMW3aNI4dO5Zl/vz8/PDzyzzo0sfHx35Dy2AymbKcTphRAZxNv/G8eUk3Go0YjUaUUgQFBdmvlZF+o+zy7o5lcjbdlWW6Pu6Gf3cR9/QyOZueZd5/+w3joEEAbGw7jnj/voz9d32egi5TRuwz8uBtdc/Z9MIok1KK4sWL55h3dyqTOnEa2/hJmICfO09j1pJ6/84oNP3751zeXV0mpRSBgYGA+9e95OTkLI+7kcfM6vL19aVp06asWrXKIX3VqlW0atUqy8+EhYVlOj4mJoZmzZrZA5bdMRnndOa6ffv2ZdeuXezcudP+V6lSJV5++WV+/vnnvBfazWmaRnR0tK72cPEEEvfrnDoF3btDaip060bbdZOc2nMrryT2ruNpsf/z/hH4pl1lE63p8f1THjOL60aeFndneEyPD8DIkSPp27cvzZo1IywsjE8++YQTJ04wePBgAMaOHcvff//Nl19+CcDgwYP58MMPGTlyJAMHDiQuLo7Zs2ezYMEC+zmHDRvGXXfdxdSpU+nWrRvfffcdv/zyC5uu2zXuZtctU6aMvQcpg4+PDxUqVKBOnTqFHRYhvI6mwZsR13hkenduu3wGVf92DF99BTpaa0R4Jk2Db5/6icf2LUHDxBBmYsPo1huRehuPavj06tWLCxcuMHHiROLj47n99tuJjo6mWrVqAMTHxzsMsKpRowbR0dGMGDGCGTNmUKlSJaZPn87DDz9sP6ZVq1YsXLiQV199lddee41atWrxzTffcOeddzp9XSFE0dC09E1Hv5xj4/WjT3Mb2zhPWRaEf8+LQUGuzp4QTI1Mode8FwB4n2HspqHsCedmPKrhAzBkyBCGDBmS5Xtz5szJlNauXTu2b9+e4zkfeeQRHnnkkTxfNyvZjesRQuRdVBRERECUGkdvvsGCmUdYjN/eGjg/n1KIwmNetohbOczfVCKCCEqXhmHDZE84dyL9wiLPzGYzXbp0yXagmigc3hz3TZvgefUBY5gKwEA+ZaOhXZH9mvbm2Luap8T+4dT5AHzCs1w1BDFsWOGNOSsKnhL33JCGj8gXZ0fRi4LlrXEfWGox7zMMgFd4nY01+xMRUbS/pr019u7A7WN/9iy1jqRPhDne+vEir5uFxe3jnkvS8BF5pmkaa9eu1dVof0/gtXHfvJlHvnsCI4ofbnkOv4hxHDxYtL+mvTb2bsDdY69psLL/Qgw2G6duuZPP1t3q0T09Gdw97nkhDR8hhFvTNJj20kkudngIQ2oqtm7deeDYB4yfYPD4m4rQj6goKL0y/THXm6ce99jp695AGj5CCLf2ZsQ12r7bnVJp59hJI6bePg+yWCxNCFc68vMhWrAVDRML6SXT192YNHxEvuhpwJsn8Zq4K0XL2QNpynbOU5buLGftluIuzZLXxN4NuWvsNQ06n/wUgFWEk2Aop6vp6+4a97zymL26vEFSUpJT+4wI4TXefhtefhkLZu7lFzYa2hERkT6uRwh3MfmVKzwXdQslSeQBvudq+weIifH88T2extl7qPT4iDyz2WycO3fOYXM6Ufi8Ie6aBvP7rsT68mgAYu6bhl94O5fPkvGG2Lsrd4590JI5lCSRP6nNCrpiNuun0ePOcc8rafiIPLNarcTFxTns7isKnzfEfeb/jtBlXh9M2PiMZ9h255BC3YPLWd4Qe3fltrG3Wnns/DQgfaVmDEZdPeZy27jng07apEII3VCKVvOeoxSXiCWM5/mQdpsNrs6VEFn78UdK/3OY5IBSnAzrT0Q7fazdo2fS8BFCuJdFi2h2IYYU/OjHXCwGP139ghb6oWnw97B3qQZsb/osS38urptHXHomj7pEnhkMBoKCgjAY5Nd4UdJz3LV/krj8zHAAFlYfS7V7art8XM/19Bx7d+eOsf9i6DaqHd+ABTO9Nw3V5do97hj3/JJZXW5EZnUJb/dby2Hc+dt0DnErDdnN2Eh/mcEl3NbWsvfR/MJK5vE4fZlHeDjExLg6V95LZnWJQmez2Th+/LiuRvt7At3Gfft2mv32IQBDmEkK/m63CJxuY+8B3CX2mgYTJ8LLzdbS/MJKLJiJIAKDAV0+knWXuBckafiIPLNarezcuVNXo/09gd7irmkwMVKx+55hmLCxgN78Qrhb3kj0FntP4i6xj4qCiAmKR7elL7WwovIgaobf6laPZAuSu8S9IMkwLCGES0VFwW8RPzGeTSTjz7KwtwgPTG/06PFGIjzbpk3wEEtowVauUJyva70mj7c8jDR8hBAutXmjjSm8AsCHDOVS4C1yIxFu664wCz1XpbfI3+Ulbr+nvItzJHJLHnWJPDMYDISEhOhqtL8n0FvcB5ZaRBN2kkQQUxnjdo+3rqe32HsSd4n92MpfchuHuOQTgu/Yl3TfK+kucS9IMqvLjcisLuF1LBZU/foYDh1ibs1Ijvcbz7hx+lnuX+iMUtC0KezYAW++CS+/7OocievIrC5R6KxWKwcOHNDVoDdPoJe4axr88PAcDIcOcbV4CI//PsLlW1LcjF5i74ncIfZa7BbYsYM0ox9vXXgaTXNZVoqMO8S9oEnDR+SZzWbj4MGDuprm6An0EvepkSk0+SESgFevjiPqgyAX5+jm9BJ7T+QOsd8zdBYAC209Gf1mGV0uWHgjd4h7QZOGjxDCJcp+O5Nb+JsTVOEjBrvdmj1COLh4kbq7FgLwEc+hFFJnPZQ0fIQQRS8pib6n0n8uRzKBNIO/Ww9qFoIvv8TPlsIfNORXWrrlOlPCOW78NF24O6PRSNWqVTEapf1clHQR93ffpdi1CySUrcPfjfoRcZdnrNmji9h7KFfGXrMoLr4+ixBg7W2DubeqgbZtPaPO5pce67zM6nIjMqtLeAPtTALWajXwS7vCop7f0mP+o249oFmIuU+to9+c9lyhOJU5zUuRJWQPOTcks7pEobNarezYsUNXo/09gafHfetDk/FLu8I27qD3tw971ABRT4+9J3Nl7Kv9lD6oeT6Pk0QJrxrbo8c6Lw0fkWc2m40TJ07oarS/J/DouJ88SdPfZgDwCm9gw+hRNxGPjr2Hc1nsz56lbcJSAGYx2OvG9uixzkvDRwhRJDSLYl/nkfjaUlnPXfxMJ6+7iQjPommw+okvMFkt7CtxJ2XvbaLbzUi9iTR8hBBFYmPnN6i3bzEWzLzMW9SsaZCbiHBrk1+3UvOXjwF4K2kwbdvi9otsipuTho/IM6PRSJ06dXQ12t8TeGTclyyh/ZrXABjCTLbSglq1PO8m4pGx1wlXxD7l+xhqcIyLlOQbenrUY9mCosc6r5+SiCJnMpkIDQ3FZDK5OitexZPirmnwyXM7SOn5JADv8yKfMdBjH3F5Uuz1xhWxf/Ja+qDmufQjxVDMI+tsfumxzkvDR+SZpmnExsaiecOGNW7Ek+I+7ZXzdJ7VDX/bNVbSiR/avUN4OB77iMuTYq83RR77Eye47dCPAOxuNdhj62x+6bHOe1Ans3A3SinOnz+PLAVVtDwp7rd8+y5VOckB6tCbhbTwNRMT4+pc5Z0nxV5vijz2Y8disNmgfXtmrwktmmu6IT3WeenxEUIUjmvXePDsJwCMZipJhpJe+ahAeBZNg6/6r4avv0YZDGhRb7o6S6KAScNHCFGgNA0mToT3ms2nWPI//FOyBqn33u+1jwqEZ5k6MZUWc4cAMEM9T1RMMxfnSBQ0edQl8sxkMtG4cWNdDXrzBO4e96goiJig+IPpAPx+51BWrnTPvOaWu8dez4oi9poGxT58kzr8STwVeIXXudMLZ3JdT491Xnp8RJ4ZjUaqVaumq2mOnsDd475pE9zNWhqwhysU56PUp12dpQLj7rHXs6KI/cz/HeG5i28AMJJ3SSLY6x/P6rHO66ckoshpmsaaNWt0NdrfE7h73Nu0gWG8D8CX9KNJ+5KuzVABcvfY61lRxL7i8pn4k8oa2rOQ3tSsKY9n9VjnpeEj8kwpxeXLl3U12t8TuHvcx/U+woOGHwBQQ1/Q1Y3D3WOvZ4Uee6uVzv98DcB0XsRgMNCvn2ctsFkY9FjnvfwrFUIUJE2DHU+8TXOl+OvWTgx6L9TrbxzCQ6xZQ9DleK4FlCatVRci7pLeHr2SHh8hRIGZ9+wGmm5NX+120F+jiIpycYaEcNZXXwFQrH8von/x9bjtVITzpOEj8sxkMhEWFqar0f6ewG3jfuUKnRb2x4jiMwawhg6629vIbWPvBQoz9lriVdK+WQrA55a+6Gg4S77psc5Lw0fkmdFopFy5croa7e8J3DHumga/t3+ZislHOU5VRvKux+7HlRN3jL23KMzYfz9gOb5pV/mLWjzzWUvpqbyOHuu8fkoiipzFYmHFihVYLBZXZ8WruGPcFwz4hWa/pz/ieoovCKlZQpcLFrpj7L1FYcQ+Y7HNkt99CcA8nkBh0F1PZX7osc5Lw0fki56mOHoSt4q7UoQtexmAD3metXSgVi10O0bCrWLvZQo69lFRMHfCEe7WVgHwJU/qsqcyv/RW53X4vyUhRJFas4ZbL+/kKsWYQKTcOITH2LQJBvIJRhQr6URi6ZpEDNNfT6VwJA0fIUT+vPUWAHtbPE3T4DK0aSM3DuEZ2rVM5elVnwPwMYMZNiy9p1Lom0HpaVUiD5eUlERwcDCJiYmUKFHC1dm5qYyFrYKCgjAYDK7Ojtdwp7hr23dhbtoIK0ZmDP+LIW/V0OXjrQzuFHtvUxixt85fiOmJPiT4VeKj0ccZ+5pZ1/U3Lzypzjt7D5UxPiJfAgICXJ0Fr+Qucd/79DsALOYRhr9fwytmw7hL7L1RQcZe0+DUKx8BsDdsoDR6cqC3Oi8NH5FnmqYRHR2tu4Fv7s5t4n7qFPV3pS/x/zb/Qyl0PxvGbWLvhQo69p8M30e14xvQMPHEume8otGeF3qs89LwEULkSsYU4EVtpmFWGutox+80l0HNwnMoRYNFrwHwAw9wilt032gX//G4hs/MmTOpUaMG/v7+NG3alI0bN+Z4/Pr162natCn+/v7UrFmTWbNmZTpmyZIl1KtXDz8/P+rVq8eyZctydV2LxcLo0aNp0KABxYsXp1KlSjz55JOcPn06/wUWws1ERcFnE05y//EZAKxsOJrwcHS5bo/Qqa++ou25pVgwM5Hx0mj3Mh7V8Pnmm28YPnw4r7zyCjt27KBt27bcd999nDhxIsvjjx49SpcuXWjbti07duxg3LhxvPjiiyxZssR+TFxcHL169aJv37788ccf9O3bl549e/Lbb785fd1r166xfft2XnvtNbZv387SpUv5888/efDBBws3IEK4wKZNEMl4AkhhPXexvVxnYmL0u26P0JkTJ+CFFwDY1GECIeFNpNHubZQHadGihRo8eLBDWmhoqBozZkyWx48aNUqFhoY6pA0aNEi1bNnS/rpnz56qc+fODsd06tRJ9e7dO8/XVUqpLVu2KEAdP34850JdJzExUQEqMTHR6c+4ks1mU2lpacpms7k6K17F1XH/aMguZcWgFKg7+VVFRrokGy7h6th7s4KIvSXVqo7UaK8UqJO3tFSWZEsB5lCfPKnOO3sP9ZjfZ2lpaWzbto0xY8Y4pHfs2JHY2NgsPxMXF0fHjh0d0jp16sTs2bOxWCz4+PgQFxfHiBEjMh0zbdq0PF8XIDExEYPBQMmSJbM9JjU1ldTUVPvrpKQkIP3RWcby4EajEZPJhNVqxWaz2Y/NSNc0DXXdigQmkwmj0Zht+o3Ljpv//Yl+48C17NJ9fHyw2WxYrVaUUly5coWgoCCH9AwGgwGz2Zxt3t2xTDfm3R3LlBH3wMBAfHx8irxMA4+MxohiY4VHuP/5O3jpJQsWi3d8TxmxDw4OxmQy6aJMN6a7a5mUUly7do3g4OA8l2lt9+l0PLqWqxSjw6m5PPm2YvTo/8or31PmMimluHr1KiVLlnT7MjnLYxo+CQkJWK1Wypcv75Bevnx5zpw5k+Vnzpw5k+XxmqaRkJBAxYoVsz0m45x5uW5KSgpjxozhsccey3EtgcmTJxMZGZkpPSYmhmLFigFQtWpVmjRpwq5duxwe6dWpU4fQ0FC2bNnC+fPn7emNGzemWrVqbNiwgcuXL9vTw8LCKFeuHDExMQ6VpX379gQEBBAdHe2Qhy5dupCcnMzatWvtaWazma5du5KQkEBcXJw9PTAwkHvuuYeTJ0+yc+dOe3pISAitWrXi0KFDHDx40J7uCWUKCgqiQ4cOUqYbylR2925ar/wJq9FMsWmP0sA/mpgYzy4T5P57at68OZUqVdJVmTzhe8q47tatW3NdJv+jR7nnl7EAvFFqEqdSatCgQTTXF0u+p6zLlHGew4cPu3WZ2rZtizM8ZgHD06dPU7lyZWJjYwkLC7Onv/HGG3z11VccOHAg02duu+02nnrqKcaOHWtP27x5M23atCE+Pp4KFSrg6+vL3Llz6dOnj/2Y+fPnM2DAAFJSUnJ9XYvFwqOPPsqJEydYt25djg2frHp8qlSpQkJCgv1z7taivv5XgsViYdWqVXTs2JGAgADd//JxlzJlxD08PNy+vkZRlGnqZBv3RbamqW0bM3iei5OmMXq0d31PGbHv3Lkzfn5+uijTjenuWqaM2Hfp0gWDwZCrMiUnW/grrD8N/viaGGMn7lPRKAxMnKgxerTrynR9urt+T9fH3Wg0unWZkpOTnVrA0GN6fMqWLYvJZMrUy3Lu3LlMvTEZKlSokOXxZrOZMmXK5HhMxjlzc12LxULPnj05evQoa9asuenqy35+fvj5+WVK9/HxsT/CyGAymTCZTJmOzagAzqbfeN68pBuNRozG/8bFZ1zrxvQM2eXdnct0s3R3KJOPj499JdXCLJNSJiZPNnHkrcU0tW3jMoFEMp7GG8y8+mrBlulm6e7yPWVcS09lyimPuU13tzK9NdXEc3+kd02+YRtL9ZpG+vWDMWN8shyQ7wll0uP3lN8yJScnZ3ncjTxmVpevry9NmzZl1apVDumrVq2iVatWWX4mLCws0/ExMTE0a9bMHrDsjsk4p7PXzWj0HDp0iF9++cXesNK77CqwKFxFGfeoKIiYoBh6ZQoA7zKSBEM5r53+K3XedfIa+7M/bSeEBJIIIo4watWSWYi5obs6n78x1EVr4cKFysfHR82ePVvt27dPDR8+XBUvXlwdO3ZMKaXUmDFjVN++fe3HHzlyRBUrVkyNGDFC7du3T82ePVv5+PioxYsX24/ZvHmzMplMasqUKWr//v1qypQpymw2q19//dXp61osFvXggw+qW265Re3cuVPFx8fb/1JTU50un6fN6hLeITxcqbtYpxSoa/ir2iXPqchIpSwyIUZ4iNUdXlcK1DK6KYNBedVMRG/i7D3Uoxo+Sik1Y8YMVa1aNeXr66vuuOMOtX79evt7/fr1U+3atXM4ft26dapJkybK19dXVa9eXX300UeZzrlo0SJVp04d5ePjo0JDQ9WSJUtydd2jR48qIMu/tWvXOl02T2v4WK1WdfbsWWW1Wl2dFa9S1HGPjFTqOx5QCtRHDPbqm4bUedfJT+xtrdsoBWpa6EfSaM8lT6rzzt5DPWZwszfwtN3ZLRYL0dHRdOnSJdtnsKLgFXXctT0HMDeoiw0DM184wOB3b/PaRwRS510nr7HXLiRiLFcGo83K9OFHGPJWDa+tv3nhSXVedmcXQhQI84z3ATB278bQ6d7b6BGeaenzv2C0WTnIbQx/v4ZsRiqk4SOEyJ52JYWUOQsA+DL4BXS0QbPwEpXWzgfSNyNVCtmMVEjDR+SdwWAgKCjIPqVaFI2ijPuyAT/gn5LICarw1Ny7vf7XstR518lT7M+fJyzhBwDm0F82I80DPdZ56bQWeWY2m+nQoYOrs+F1ijLut6z5CoB5PIENo9f/WpY67zq5jb2mweon59PJpnEgqBkV77ydnm1lM9Lc0mOdlx4fkWc2m43jx487rNgpCl+Rxf38eVr88xMAX9FXfi0jdd6Vchv7qDcUFVZ+AcAHl5+ibVtZuycv9FjnpeEj8sxqtbJz506HJc9F4SuyuC9ciMmm8XelZlQJr0tEhPxaljrvOrmN/ZmfdtCIXaTgx9f08freyrzSY52Xtq8QIhNNg7NTvqQysKvRk0R/L7+UhWfprz4HYDndSTSU8vreSvEf6fERQmTyyYj9VD79OxbM9Pupt9cPahYeJiWF5oe+BuCPJk9Jb6VwIL/hRJ4ZDAZCQkJ0NdrfExRF3Ct+NwuAn7iP84TIY4J/SZ13HWdjr2mw+b4ptLt4kcTgKkyKuxdz5r2ghZP0WOdl5WY34mkrNwt90bT0DUnPRG/ng9+aY8JGJ1ayytCJiIj0gaFCuLuZL+xnwIeN8SONXnxD/cieUne9hKzcLAqd1WrlwIEDuhr05gkKK+5RUTBpgsYzvz2DCRtryvVChXeSxwTXkTrvOk7F3maj7bxB+JHGCrrwLY9Kb2U+6bHOS8NH5JnNZuPgwYO6muboCQor7ps2wXDe4w528A+lmBX6PjExMgX4elLnXedmsdc0+KHH5zS4tJGrFGMIMzEYDDKoOZ/0WOel4SOEAOD+ekeIZAIA/+Mdbr+nvItzJITz3nv1Am2+fxmA15iEuWY16a0UWZLfcUIIAIbuH4KRZHaU7kD1F/vLDUN4FPP3SynFJfZSj+m8SIdaMi5NZE0aPiLPjEYjVatWxWiUjsOiVNBx1zT4dPhenov5GavRTIPNH9MkVD8zOAqS1HnXuVns79eWATCfx7EZzPKIq4Dosc7LrC43IrO6hCtMnAgBE0bxMm+xjO7sjlwmv5SFZ0lKQoWEYEhLY0DYPqp1rsu4cTI2zdvIrC5R6KxWKzt27NDVaH9PUNBxj92g8QTpm5HOob/MgsmB1HnXyS72mgZLBkRjSEsjoWwdPt5QVwbkFyA91nlp+Ig8s9lsnDhxQlej/T1BQce9X4WfqcgZzhHCT3SRRwQ5kDrvOtnFPioKtMXpj7k+S+ghq4wXMD3WeWkTC+HFNA0a7pgDwA/BTzB2mI8MahYe5bf1KYwgGoCl9KCk9FiKm5AeHyG82HuvXuDWfd8DMD2xPyaTPCIQnuWJiqsJ4gqnqMw2mkmPpbgp+V+cyDOj0UidOnV0NdrfExRk3P2/W4gfaWynCbtoSHn5tZwjqfOuk13se/mmP+baVqU7E54xSo9lAdNjnZeGj8gzk8lEaGioq7PhdQoy7j2upA9qnks/DAbk1/JNSJ13naxir6VaSf32e4oDSR16yEyuQqDHOq+fJpwocpqmERsbi6Zprs6KVymwuP/5J7ec+g2b0UT8Xb1llVsnSJ13naxiP++5zRS/ep5/KMWAuXfJwOZCoMc6L21jkWdKKc6fP48sBVW0CiLumgabn51PO+BwrY58vbq8/FJ2gtR518kq9iVWpz/m+oEHsOAjSzEUAj3WeenxEcILRb2huGX9PAAiDj0hv5SF51GKDknpDZ9l9JBHtcJp0vARwgv9syKOWhzhMoEso7v8UhYeRdPgkyE7KXnpOCnGALT2HeVRrXCadG6LPDOZTDRu3BiTyeTqrHiVgoj746T39izlIVIMxeSXspOkzrvO9bF//XUwzErv7Ym2dabF3cVkm5VCosc6Lw0fkWdGo5Fq1aq5OhteJ99xT0uj2eFvANh/xxNEdJNfys6SOu8618d+00bFdL4F0h9znZUey0Kjxzovj7pEnmmaxpo1a3Q12t8T5CfumgbfPvkjhn/+4XJQRV6P7SD7GuWC1HnXuT72j1XZSCgHuUJxvqO79FgWIj3WeWn4iDxTSnH58mVdjfb3BPmJe1QUlP/mfQBmXO5H1FT9dF8XBanzrnN97J9M/RSADZUf43+RQdJjWYj0WOfld54QXuTsit9pxwYsmPmAodSXRwTC0/zzD8YliwDosvxZujRzcX6Ex5EeHyG8hKZBj+PvAbCQ3pymsjwiEB5nzdPzITWV+AqN0Ro1dXV2hAeSho/IM5PJRFhYmK5G+3uCvMb9g9GnaHc2fUDoe4ygfXsZ1JxbUuddx2QyceRwS26Jng3ApDPPEjXZ4OJc6Z8e67w0fESeGY1GypUrp6vN6zxBXuNecemH+KCxlrvZwR2YzTKoObekzruO0Wjk+MK/qM8+rlKM+Twm608VAT3Wef2URBQ5i8XCihUrsFgsrs6KV8lT3K9coduZj4H03h5Z5TZvpM67jsViYUSx14D0R7WXDcFSh4uAHuu8/N4T+aKnKY6eJNdxnzOHgJRLXCh9K6l33E9EW3nMlVdS54uepsHHY44yZNMGAHY2f5aI+6UOFxW91Xlp+Aihd1YrTJsGQJlJI/h5iHT0Cs8SFQXV34/CaLWy2ngvIfffKSs1izyT/wMKoXPW736Ew4dJMpdi8ul+6OzHm/ACR3/+k8etXwEw0SdCxvaIfJGGj8gzs9lM+/btMcsI2SKV27ifGvkuADO0QbwSVVx2Ys8HqfNFT9Pg8cORmLDxi39nNmutZGxPEdJjnc9TSU6ePMmxY8e4du0aISEh1K9fHz8/v4LOm/AAAQEBrs6CV3I67r//TrXj6QsWfshQlEJ+LeeT1Pmi9enwvQw6uwCA11Qkd90lY3uKmt7qvNM9PsePH2fs2LFUr16d6tWr065dO+677z6aNWtGcHAw4eHhLFq0CJvNVpj5FW5E0zSio6N1N/DN3Tkbd02DP56ZDvy3YKHM5sofqfNFr9ryaRhRfGfsxphv4gkI0GQZhiKkxzrvVMNn2LBhNGjQgEOHDjFx4kT27t1LYmIiaWlpnDlzhujoaNq0acNrr71Gw4YN2bp1a2HnWwhxE1MmWaj+x3cAfMRz1KwJERHya1l4kEuXCD83H4APzMMACAtzZYaEHjjVbvb19eXw4cOEhIRkeq9cuXJ06NCBDh06MGHCBKKjozl+/DjNmzcv8MwKIZx3acVmgkniHCH8SkvurYXMhBGeZe5cfCzJnC3fAL87WgM/8dJLrs6U8HRONXzeeustTpw4gVIKgyHnJcK7dOlSIBkTQuTPw34/AvAT94HBKI+4hEfR0mxcmjSTssDvzYew+FsDMTGy2rjIP4Nycq95k8lEfHw85cqVK+w8ea2kpCSCg4NJTEykRIkSrs7OTSml0DQNs9l80waxKDjOxl3VrYvhwAFeb/ANtkd6Mm6c3DTyS+p80fmq3y/0/TKcJIK4hb/5X2QgY8dK7IuaJ9V5Z++hTg9udrJ9JLxMcnKyq7PglXKKu6bBB8MPYzhwAJvRxJg1HRk/Xho9BUXqfNGosXImAHPpx2WC2LxZYu8qeou7rOMj8kzTNNauXaur0f6e4GZxj4qCv95fAcB6W1uiZpYswtzpm9T5IhIfT6vz/w3MNxigbVuJvSvosc7n6jfgZ599RmBgYI7HvPjii/nKkBAifzZtgpdIb/isoCu7ZN0e4UE0DX4ZsIjOysbeEmFUblGP3m3hpZcgJsbVuRN6kKuGz6xZszCZTNm+bzAYpOEjhIs9UnM74awCYAX300cGNQsPEhUF9/z0DQCfJvWibdv02Yg62hxcuFiuGj6///67DG4WDvS0jLknyTbuNhsDdz6PAcXa8r3pMyRU1u0pYFLnC9fBX04ynlhsGPiWR7n9uh5Lib1r6C3uTpfG3Udzi6Ln4+ND165dXZ0Nr5NT3K1ffInpt1+5Zgpk5xNvy0yuAiZ1vvD1C/gWgI205YyhEoP/7bGU2LuGHuPucbO6Zs6cSY0aNfD396dp06Zs3Lgxx+PXr19P06ZN8ff3p2bNmsyaNSvTMUuWLKFevXr4+flRr149li1bluvrKqWIiIigUqVKBAQEcPfdd7N37978FdbN2Ww2zp07J9uUFLFs437pEinDRgEwwTqel96tLBuSFjCp84Uv/GJ6w2dXaE+HlcYl9q6hy7grJ0VERKirV686e3ihWLhwofLx8VGffvqp2rdvnxo2bJgqXry4On78eJbHHzlyRBUrVkwNGzZM7du3T3366afKx8dHLV682H5MbGysMplMKioqSu3fv19FRUUps9msfv3111xdd8qUKSooKEgtWbJE7d69W/Xq1UtVrFhRJSUlOV2+xMREBajExMQ8RKfopaWlqeXLl6u0tDRXZ8WrZBv3F15QCtQ+QpUPqQqUCg93TR71Sup84bFYlHp/+BGlQFkNRmU5dcbhfYm9a3hS3J29hzrV8MmuYZGdU6dO5ep4Z7Vo0UINHjzYIS00NFSNGTMmy+NHjRqlQkNDHdIGDRqkWrZsaX/ds2dP1blzZ4djOnXqpHr37u30dW02m6pQoYKaMmWK/f2UlBQVHBysZs2a5XT5pOEjnHFj3C0Wpd5++YyyGMxKgbqHVQqUMhiUiox0cWZ1Rup84YmMVGo0U5QC9QsdMtVdib1reFLcnb2HOvX0v3nz5nTr1o1nnnmGFi1aZHlMYmIi3377Le+//z6DBg3ihRdeKLBeKYC0tDS2bdvGmDFjHNI7duxIbGxslp+Ji4ujY8eODmmdOnVi9uzZWCwWfHx8iIuLY8SIEZmOmTZtmtPXPXr0KGfOnHG4lp+fH+3atSM2NpZBgwZlmb/U1FRSU1Ptr5OSkgCwWCxY/p3CYDQaMZlMWK1Wh67GjHRN0xweQ5pMJoxGY7bplhumRmQMWrtxjYbs0n18fLDZbFitVvu5NE1zSM9gMBgwm83Z5t0dy3Rj3t2xTBnvWSwWzGYzUVGQNO1rzEpjq7E55vB2hNugTRsbL79stc+GcecyOZPuDt9TRhkyPquHMt2Y7qoy7dqcyAe8D8AS06P8tQmHMl2fL08pkx6+p+uv4+5lcpZTDZ/9+/cTFRVF586d8fHxoVmzZlSqVAl/f38uXrzIvn372Lt3L82aNeOtt97ivvvuy1NmcpKQkIDVaqV8+fIO6eXLl+fMmTNZfubMmTNZHq9pGgkJCVSsWDHbYzLO6cx1M/6Z1THHjx/PtkyTJ08mMjIyU3pMTAzFihUDoGrVqjRp0oRdu3Zx4sQJ+zF16tQhNDSULVu2cP78eXt648aNqVatGhs2bODy5cv29LCwMMqVK0dMTIxDZWnfvj0BAQFER0c75KFLly4kJyezdu1ae5rZbKZr164kJCQQFxdnT4+NjeWee+7h5MmT7Ny5054eEhJCq1atOHToEAcPHrSne0KZgoKC6NChg1uXadWqVXTp0oV9+5L5qPpHcAj8BjRmQHgMjz7alXPnEoiJ8awyecr39M8//1CxYkVdlclV31OLFq2YPfsQUUGvUpF4rlSogG+3xrSpRKYy+fr6YjAY3L5MevueTCYTBoPB7cvUtm1bnOH0Xl0AKSkpREdHs3HjRo4dO0ZycjJly5alSZMmdOrUidtvv93ZU+Xa6dOnqVy5MrGxsYSFhdnT33jjDb766isOHDiQ6TO33XYbTz31FGPHjrWnbd68mTZt2hAfH0+FChXw9fVl7ty59OnTx37M/PnzGTBgACkpKU5dNzY2ltatW3P69GkqVqxoP2bgwIGcPHmSlStXZlmmrHp8qlSpQkJCgn2fEXdrUXvzLx93LdPMYQd5/oO6aJioFXCCgeNCePVVzy5TVume/j1JmbIuU1SUmYWTDrBDa4QfaTx/yxIqPNuNsWNNGAyeWSY9fk+eUKbk5GSn9urK1URXf39/HnroIR566KHcfKxAlC1bFpPJlKl359y5c5l6WjJUqFAhy+PNZjNlypTJ8ZiMczpz3QoVKgDpPT/XN3xyyhukPw7z8/PLlO7j44OPj49DmslkynLxyOzWV8gu/cbz5iXdaDRiNBqx2WycPHmSKlWqOKTfKLu8u2OZnE13ZZmuj7vBYOC5El8D8HvZzgx4oRIZT2U9qUzOpru6TDfWeT2UyZk85jY9N2XatAmmai/jRxor6cSh0B7MeC1j+ZT/ynR97N29TKCf7+n6uLt7mZzdU8xj9ury9fWladOmrFq1yiF91apVtGrVKsvPhIWFZTo+JiaGZs2a2QOW3TEZ53TmujVq1KBChQoOx6SlpbF+/fps86YHVquVnTt3OvwyEIXv+rhrFkXizHkA/H33E7JuTyGTOl/w+pf/iQf4EQtmRjCNNm2zXjNOYu8auox7vodRF6GMaeWzZ89W+/btU8OHD1fFixdXx44dU0opNWbMGNW3b1/78RnT2UeMGKH27dunZs+enWk6++bNm5XJZFJTpkxR+/fvV1OmTMl2Ont211UqfTp7cHCwWrp0qdq9e7fq06ePTGcXheL6uM8esFkpUEkEqmJclVlchUzqfMGz3t1eKVCLqo5UkZHpsxSzIrF3DU+Ke4HO6nIXvXr14sKFC0ycOJH4+Hhuv/12oqOjqVatGgDx8fEOA6xq1KhBdHQ0I0aMYMaMGVSqVInp06fz8MMP249p1aoVCxcu5NVXX+W1116jVq1afPPNN9x5551OXxdg1KhRJCcnM2TIEC5evMidd95JTEwMQUFBRRAZ4a1KxywAYCkPcY1ibJINSYUH0a6kwKZYjMDZBwdKj6UoErka3CwKV1JSklMDs9yFpmls2bKFFi1a6G4vF3dmj3vz5lwudxulLh3jQb7jR8ODRESkb+goCofU+YI15+kN9P+iHWcoTyXiiYg0ZFt/Jfau4Ulxd/Ye6t6lEG7NbDbregyTu8qIu7ZrH6UuHSPV4MeRavfw2pPIhqSFTOp8wTJsWA/ABu5CYcixx1Ji7xp6jHuuBzdfuHDB/u8nT55k/PjxvPzyyzfdM0voj9Vq5cCBA/oa9OYBMuK+7uUfAFij2rPveHFMJnlMUNikzhestiq94bOedhgM0KZN9sdK7F1Dj3F3uuGze/duqlevTrly5QgNDWXnzp00b96c9957j08++YT27duzfPnyQsyqcDc2m42DBw/qa/M6D5AR9/K/rwBgBV1RChnfUwSkzhegtDRqxKevfm8Ja+ewIWlWJPauoce4O93wGTVqFA0aNGD9+vXcfffd3H///XTp0oXExEQuXrzIoEGDmDJlSmHmVQjxL58rV6h/Kf2msYKuN/21LIS70X79HUNyMok+ZbilYz0Z2CyKjNPVbOvWraxZs4aGDRvSuHFjPvnkE4YMGWJf5OiFF16gZcuWhZZRIcR/yu3YgdFm5VxIPWo3rsFTbWR8j/As6yeu5x5gteUuIiYawSgD80XRcLrh888//9hXKA4MDKR48eKULl3a/n6pUqUc9t4Q+mc0GqlatWqWK3+KwmOzGSm9cQ8Af93Wleho+aVcVKTOF5xbti4F4BfudepRrcTeNfQY91yVxGAw5PhaeBeTyUSTJk2yXKpcFJ73Jl6jwpYNAAzf/ChRUS7OkBeROl9ADh6kTtLvaJhYzCNOPaqV2LuGHuOeq9+J/fv3t+8tlZKSwuDBgylevDiAw2abwjtYrVZ27dpFw4YNdfUfhbvz+WExxUjmT25jK80oKYOai4zU+fzTNIgdNJ+7gG2lO9LojnK0bXvzR7USe9fQY9yd7vHp168f5cqVIzg4mODgYJ544gkqVapkf12uXDmefPLJwsyrcDM2m40TJ07oarS/J+hxbT4AC8yPYTAYZFBzEZI6n39Rbygqr0+vwx/88zht26aP7bnZ41qJvWvoMe5O9/h88cUXhZkPIYQz/v6bGkfWAHC8TR8i2sugZuFZElb8Ri2OcJViLKcbraTHUhQxGRIphIfQNFj79ALCleJC3brMiK5BQICrcyVE7jxhSO/tWU53rhkCpcdSFDn9DNMWRc5oNFKnTh1djfZ3Z1FRUC7mKwCWFe/F229L3Iua1Pl8Skuj+dFvANjX+PGbLlp4PYm9a+gx7rJJqRvxtE1KRdEaFLaLj39tRCq+VOAMzcNLERPj6lwJ4RxNg+/6LODhxY9xOagiAWePYw7wcXW2hI44ew/VTxNOFDlN04iNjUXTNFdnRdc0DSZOhKb75wHwk7ELwybs5667JO5FTep83kVFQfnFMwB45/Igot7KXaNHYu8aeoy7jPEReaaU4vz580inYeGKioKJE6wc42sAVld8jHubnKdjR4l7UZM6n3enf9pJGzZjwczHPEuDXA5qlti7hh7jLj0+Qri5TZugHeu4hb/5h1KcuP0+QFZrFp7l2bT03p6lPMRZQ0UZ1CxcRv7XKYSba9MGqqxKf8y1iJ40b+Pn4hwJkUsXL9Jkf/psrt+aDSXiAVmGQbiONHxEnplMJho3bqyb1Tzd1bjh17C+sRjSwHdAX0aNMhEfL3F3BanzuadpsLrPHDolJ3O2fAPejG2DOQ9jmiX2rqHHuMujLpFnRqORatWq6Wqaozsyr/gOv7QrUKMGT33aCl9fiburSJ3Pvag3FDV/ngnAhLPPEzU5b3s8SuxdQ49x109JRJHTNI01a9boarS/u9HSbMSPfBOA9VX7olkNEncXktjn3vnordTmL65QnHk8ftNd2LMjsXcNPcZdGj4iz5RSXL58WVej/d3ND33mU/HMTi4RzMPrXyQqSuLuShL73NE0aHVmKQAr6JqvlZol9q6hx7hLw0cId5WcTOvoVwCYzFguUCbPv5aFcIWoNxTNTiwBYAkPc/fdMqhZuJ40fIRwQ5oGv3T7gHIpJzlBFabzIgYDMgVYeJRTK/dQm79IwY+fuA+zWZZhEK4nVVDkmclkIiwsTFej/d3Fe69eYOCqKABe5XUq1QygX7/0X8tGo8TdVaTO585j/umPuX6mE1cNQflquEvsXUOPcZeGj8gzo9FIuXLlXJ0NXSqz5BNKkshOGjGfx7mnFowfn/GuxN1VpM7nglK0O78YgD/rP0REz/w95pLYu4Ye4y6PukSeWSwWVqxYgcVicXVWdKdT8nIAZjIEZTA5/FKWuLuOxN45mgYrHvwYw949aGY/Rqx5gPHj8/eYS2LvGnqMuzR8RL7oaYqj2zh9msp/bwEg6a4HiIjI/EtZ4u46Evub+3jEAdr/OBKAUdpkomaVLpDzSuxdQ29xl0ddQrgRTYOVg37gfuDvyi2Yt7qiDAYVHkPTYMrENO7/6HGKkUwM4UxjGPfKbEThRqTHRwg3EhUFhh+/B2Dm392IinJxhoTIhago8Jk0nsbW7SRQhv7MAYNRZiMKt2JQelqVyMMlJSURHBxMYmIiJUqUcHV2bipjYaugoCAMhrwtQy8cPdjhCt+uLYs/qdRnD5XD6xMT43iMxN11JPY5G9D6AJ/G1sOIogdL2VC6B8OGpT+qzW/PpcTeNTwp7s7eQ6XHR+RLQECAq7OgK0+U+xl/UvmLWuynXra/lCXuriOxz94zfIYRxfc8wHeG9EZPfgc1X09i7xp6i7s0fESeaZpGdHS07ga+udIj5u8A2Fm1GxGRhiyn/0rcXUdinwOLhZaHvgTg98YDsxyUnx8Se9fQY9xl2KQQbkI7cRrboqX4Alfu6VYgjweEKDIrVmA4fx4qVGDi1vvk7iLclvT4COEm9nR7Bd+0q8TRkqe/aCsDm4VnmT07/Z/9+kmLXbg1afgI4Q62baPxzjkADGcaCoNsSCo8hnbiNLYV0QDMuPYUOnoqInRIGj4iz8xmM126dMEsv+7yTNNgYqRi973DAfiKJ9jCnTluSCpxdx2JfdbWDfgKo7Kxida88GGdQumtlNi7hh7jLg0fkS/JycmuzoJHi4qCPRGLaXBpE9cI4IewyYSHc9OBoRJ315HY30Ap6sR+DsDnPI1SFFpvpcTeNfQWd2n4iDzTNI21a9fqarR/UYvbYGEqowB4k1FcCryFmJicpwBL3F1HYu9I0+CLZzZT5dqfXKE4i3g0x97K/F1LYu8Keoy7NHyEcKH+ISuowTHOEcLbvCwr3AqPEhUFQZ9PA+AbelGuZlCBT2MXoqBJw0cIF3r0cvpMmHXV+jMqsrjcMIRHOf7zAR5iKQDvMpJatQp2wUIhCoM0fES+6GnAW1HTTpyG6PSZMOcfeDpX6/ZI3F1HYv+fF65NxYhiOd3Yb6hf6D2WEnvX0FvcZa8uN+Jpe3WJ/Fl972TuWT2OTbTmLsMmIiLSfy0L4RFOnEDVqoVB03ixxa+U7XqnLLopXEr26hKFzmazce7cOWw2m6uz4nlsNkJj0x9zfcYzuZoJI3F3HYl9Ok2D33q+jUHTOFqjA+9uvrPQH3FJ7F1Dj3GXho/IM6vVSlxcHFar1dVZ8SiaBnMHbKBy8mGSCMr1TBiJu+tI7NNNG3eOhr99CsDAo+OKZJVxib1r6DHu0ikpRBGLioLb5swCYAF9qFCzOP36yUwY4TmqLHqHAFLYQnNW0wGjrDIuPIj0+AhRxPauPsPDLAHgI56TmTDCsyQk0OP0DAAmMh6DwSDLMAiPIg0fkWcGg4GgoCAMBoOrs+JRBps/wweNWMLYZWic65uGxN11vD32mgabHnoX37Sr/Bl4B6n3dC2ydXu8Pfauose4y6wuNyKzuryApqFq1sRw8iRTb/+K1EefkJkwwmO8NeYCg6dWJ4grdGc5d0R2k5mIwm3IrC5R6Gw2G8ePH9fVaP/CpGmw8MkVGE6e5GqxsrwU90ieHnFJ3F3H22Nf6dtpBHGFnTTiOx4stD25suLtsXcVPcZdGj4iz6xWKzt37tTVaP/CFBUFJRd8BMCMa08T9a5/ns4jcXcdb429psGbYy/y4LHpgGvG9nhr7F1Nj3GXDnYhisiRmL8Yz8/YMDCLQdwqM2GEh4iKAjXlfYJIYhcN2FWjOxH9ZSai8Ewe0+Nz8eJF+vbtS3BwMMHBwfTt25dLly7l+BmlFBEREVSqVImAgADuvvtu9u7d63BMamoqL7zwAmXLlqV48eI8+OCDnDp1KlfX/uOPP+jTpw9VqlQhICCAunXr8v777xdU0YVODFLpU9hX0pljhpoyE0Z4jB1rLzGMaUB6b0/NW40yE1F4LI9p+Dz22GPs3LmTlStXsnLlSnbu3Enfvn1z/Mybb77Ju+++y4cffsjWrVupUKEC4eHhXL582X7M8OHDWbZsGQsXLmTTpk1cuXKF+++/36Fb72bX3rZtGyEhIcybN4+9e/fyyiuvMHbsWD788MOCD4QbMRgMhISE6Gq0f2HRLifTaMcXACwtP4TXXsv7r2WJu+t4a+xfNHxASRLZQ32W8ZBLGu3eGntX02XclQfYt2+fAtSvv/5qT4uLi1OAOnDgQJafsdlsqkKFCmrKlCn2tJSUFBUcHKxmzZqllFLq0qVLysfHRy1cuNB+zN9//62MRqNauXJlnq+tlFJDhgxR7du3z1U5ExMTFaASExNz9Tnh/pb1mKMUqKNUUyY0FRnp6hwJ4aTERGUrVUopUK83WKgiI5WyWFydKSEyc/Ye6hEdlXFxcQQHB3PnnXfa01q2bElwcDCxsbHUqVMn02eOHj3KmTNn6Nixoz3Nz8+Pdu3aERsby6BBg9i2bRsWi8XhmEqVKnH77bcTGxtLp06d8nRtgMTEREqXLp1juVJTU0lNTbW/TkpKAsBisWCxWAAwGo2YTCasVqvDqPqMdE3TUNetSGAymTAajdmmZ5w3Q8auu5qmOZXu4+ODzWbDarVitVo5fPgwt956K35+fvb0DAaDAbPZnG3e3bFMN+a9oMoUumYmAJ+bn8HXx0ZsrAHIW5ky4l6rVi38/PxcViY9fk83K1NG7G+77TZ8fHx0UaYb068vk6ZBXI/pdLh4kfMhoYyM7YbZz4JSkHH7KKoyWa1Wjhw5Qp06dVBKeV3dc1WZro874NZlcpZHNHzOnDlDuXLlMqWXK1eOM2fOZPsZgPLlyzukly9fnuPHj9uP8fX1pVSpUpmOyfh8Xq4dFxfHt99+y4oVK3Is1+TJk4mMjMyUHhMTQ7FixQCoWrUqTZo0YdeuXZw4ccJ+TJ06dQgNDWXLli2cP3/ent64cWOqVavGhg0bHB7phYWFUa5cOWJiYhwqS/v27QkICCA6OtohD126dCE5OZm1a9fa08xmM127diUhIYG4uDh7+pkzZ7jnnns4efIkO3futKeHhITQqlUrDh06xMGDB+3pnlCmoKAgOnToUDBlunqV0MQt2Mxmwj6rwoKS0Zw82RjIX5n++usv15VJj99TLspUqlQpKlWqpKsyZfU9mZOTabXuXQCOP3Eff6/+2aVlAqhduzZbt2712rrnijJlxP3w4cNuXaa2bdviDJcuYBgREZHljf96W7duJSYmhrlz5zoEHNK/iAEDBjBmzJhMn4uNjaV169acPn2aihUr2tMHDhzIyZMnWblyJV9//TVPPfWUQ68LQHh4OLVq1WLWrFlERUXl6tp79+6lffv2vPjii7z66qs5li2rHp8qVaqQkJBgX3zJ3VrU1/9KsFgsrFq1io4dOxIQEKD7Xz55LtOgQTB7Nnsa9GJ0la8IC4NRo0z4+uatTBlxDw8PJyAgwDVl0uP35ESZMmLfuXNn/Pz8dFGmG9OvL9PnjT5g0P6XOMhtNPX/g3YdTCxf7poyZcS+S5cuGAwGr6t7rirT9XE3Go1uXabk5GSnFjB0aY/P0KFD6d27d47HVK9enV27dnH27NlM750/fz5Tj06GChUqAOm9Edc3fM6dO2f/TIUKFUhLS+PixYsOvT7nzp2jVatW9mOcvfa+ffvo0KEDAwcOvGmjB9IfvWU8qriej48PPj4+DmkmkwmTyZTp2IwK4Gz6jefNS7rRaMRo/G9cfMa1bkzPkF3e3blMN0t3pkyaBu+PPs3zX8zHDwid/jwr7na8dn7K5OPjYx9wWFRlcibvnvY9Xc/ZMmVcS09lypRHpXjofPoO7NMYzrVUf+68E24sgkeVKR/pUib3L1NycnKWx93IpbO6ypYtS2hoaI5//v7+hIWFkZiYyJYtW+yf/e2330hMTLQ3UG5Uo0YNKlSowKpVq+xpaWlprF+/3v6Zpk2b4uPj43BMfHw8e/bssR/j7LUzenr69evHG2+8UTABcnNGo5GqVatm+R+ISF/7JOCd1/GzpbCJ1kStL5ipMBJ31/GW2GsazH16PSEJB7hmCuTvu58osj25suMtsXc3uox7/sdRF43OnTurhg0bqri4OBUXF6caNGig7r//fodj6tSpo5YuXWp/PWXKFBUcHKyWLl2qdu/erfr06aMqVqyokpKS7McMHjxY3XLLLeqXX35R27dvVx06dFCNGjVSmqY5fe09e/aokJAQ9fjjj6v4+Hj737lz53JVRpnVpS99Wx9WaZiVAtWW9So83NU5EsI5kZFKLaSnUqBmMUhmIQqP4Ow91GMaPhcuXFCPP/64CgoKUkFBQerxxx9XFy9edDgGUF988YX9tc1mUxMmTFAVKlRQfn5+6q677lK7d+92+ExycrIaOnSoKl26tAoICFD333+/OnHiRK6uPWHCBAVk+qtWrVquyuhpDR9N09T27dsdGoniPzsbPakUqJV0VAaDKrCbh8Tddbwl9j3virc32huy0y0a7d4Se3fjSXF39h4qu7O7EU/bnd1isRAdHU2XLl2yfQbrtfbvR91+OwabjaEttlCua/MC24Vd4u463hL7Nfe8QYc1rxJLGG0MsURE4PJd2L0l9u7Gk+Lu7D3UI6azC+FJNA3+fGQ89Ww2DoR2Z9rm5rK0v/AcVivt//oEgE31BxPRU/bkEvqio9FKQriHz1/YQb19i7Fh4NEDk4iKcnWOhMiFefMwnDgBpUszauujsieX0B1p+Ig8MxqN1KlTR1+j/QtA1e/T92j7hl7s4XY2FfAu7BJ319F77LX9h0h9digAaxqPRPMJcHGO/qP32LsrPcZdPyURRc5kMhEaGprl+g3eSNMg6tVrtI5fBMBHPIfBQIFv6Chxdx1dxz41lfP39MIv7QrraEfHNWPcqrdS17F3Y3qMuzR8RJ5pmkZsbGye90vRm6go2PPGdwSpyxylOqdrtCmUtU8k7q6j19hrGvx692gqxu/gPGV5nPlYMRV4b2V+6DX27k6PcZeGj8gzpRTnz59HJgam27QJ+vIlAF/Rl5q3GgtlfITE3XX0GvtF/VfQ8tf3AejPHE5TuVB6K/NDr7F3d3qMuzR8hCggnRvF05EYAObR161uGkJkKzGR8MXPAvAuI4imK6VL4/KVmoUoLDJWX4gCMqzc15iwsTe4FU+MrC03DeEZRo+mbOpp/qQ2r/AGBgMMG+b6dXuEKCzS8BF5ZjKZaNy4sa4GveWZzYbpq7kA1J/6JPUHFd6lJO6uo7vYr18PH38MQNxTn9L2VABt2rhnT4/uYu8h9Bh3WbnZjXjays0inabBhs5RdFj9ChafAAx//405pJSrsyVEjrTLySTWaESZC4fY1vRZGv36sazXIzyas/dQGeMj8kzTNNasWaOr0f55sWDAL7Rb/RoAgy0fEvVR4TZ6JO6uo4fYaxpMnAifVp1ImQuH+JtK3LPtTbeaup4VPcTeE+kx7tK+F3mmlOLy5cu6Gu2fG5oGH446wRNf9cGEjU95hs95mvBCngLs7XF3JT3EPioK5k/4k728BcAQZpJIsFtNXc+KHmLvifQYd+nxESKPpk5MpdV7j1BWJbCNO3iBD9xuCrAQN9q0CUbwLmasRHMf39NN6q3wKtLjI0Qelfz2E1qwlX8oxSMspnhpf8YNc8+BoUJk6Nj4HP1XzQFgCmOoWRP69ZN6K7yHNHxEnplMJsLCwnQ12j837rN8D8DrvMpxQw0iimgKsLfH3ZX0EPuRfjMwksqBEi24d2Rbxr3iGZuQ6iH2nkiPcZdZXW5EZnV5kCtXUGXKYEhL4+lWB6jeqQ7jxnnGDUR4sWvXoGpVuHABvv0WHn3U1TkSosDIrC5R6CwWCytWrMBisbg6K0Vv7VoMaWlQvTqfb7qtULamyI5Xx93FPD72c+akN3pq1IAePVydm1zx+Nh7KD3GXRo+Il/0NMXRWZoGWyetBGDr/9u787io6v2P468ZBhANcUElXNPMJZfMMsFcUHHP8vbLXDIzU6vrdiuX1BQtSS2r2+Jyb2aZ6zVtRwPXVHCH1ExTc8EFBUU0ZJkz8/39QUwiqIDAmTnzeT4ePq59OTPn+33P8c6Hc77fcyp1RbOZdOiD++XuLFw1ey3DxqU33gNgTYNX0FxwpoOrZu/qjJa7FD5CFFD4dEXFXWsAeHNXV6e//4kQAKsHfkOFS8e4RHmeihgkx61wW65X8guhsxORv1Ob42TgxQZCSHfy+58IgVI0WTMTgDm8TCplnP6+PUIUFznjIwrNYrEQEhKCxc1m9PYum3WZawutuWa6q8Tvf+KuuTsDl81+/XrqX9nFNXz4kJEued8el83exRkxd+OMROjCx8dH7y6UuE72rMtcx+p2JewZfe5/4o65OwtXy17TIH5oOPcAa6oNoWn9yrRu7Zr37XG17I3CaLnLGR9RaJqmERERYbiJb7einb2AfcMmAGyduuqyhN0dc3cWrpj9opdiuOf4RqxY+Nfp12jdmhJdhVhUXDF7IzBi7lL4CFEAO596B4uWwS4eYvgnDWSCqHB6Db55G4BFPEs81WVuj3B7UvgIkV8JCTy4/RMAJjMNhUm+RIRz++UXgpK+x46JmYxzybk9QhQ1FzvZKYQ+NA12/2MWLe1pxNCStXSRLxHh1LR0jfPdh1AV2FS5NzWb3MczLjq3R4iiJI+scCKu9sgKpRSapmGxWDCZSv4mfiXpvdfO8tLsOviQTid+4ljtTo4HO5b0XAl3yt3ZuFL26zu+TYf1E7iMH434laFTq5bIs+SKiytlbySulLs8skKUiLS0NL27UCLu+d9MfEhnK62IIpQ6dfSdIOouuTsjl8j+wAFabwgDYCQfcoaqhrgs6xLZG5DRcpfCRxSapmls3LjRULP983TpEt0TPgUgjDBMJpOul7jcJncn5BLZW63w3HN4qUy+pwdfMsAQl2VdInsDMmLuMsdHiNv59FO8rNdICGiKuVEHwmSehHBSmgZbuswkZM8erpUqz+8vzyd0f1ahLsesEFmk8BHiVqxW+OgjAALeHk3kc859jVu4twUjf2HQhmkADE3/iPv8AomM1LlTQjgZudQl7oiRbmOeF9tXX8Pp0yR7VWb6sT44y9leo+fuzJw2+8xMOix+Di+sfM0TLKGfIeb1XM9pszc4o+Uuq7qciKut6jIyTYPwcOgRHsyDGTGEMYVppjDCwnDplTHCmDQNtoWG0XbTVC5Sgfv5lQumADlehVuRVV2i2Nntdi5cuIDdbte7K0UuPByipmzlwYwYMvBiHi+iFE7xG7SRc3d2zpr9ZyNiCd40HYB/8gllamcVPUaa1+Os2RudEXOXwkcUms1mIyYmBpvNpndXitzWrRDGFAA+5znOE+A0K2OMnLuzc8rsMzPpuHggnmh8xZOs4Gndb7dQHJwyezdgxNwN9M9CiKLTr9rPdGADmXgSzgRq18Zxw0IhnMqbb1L7z/0k4s/LzNH9dgtCODspfITIw7PHwwCIrPo8g4fW1OUOzULciqbB2l7z6fbDdMzAxv+bwwMplWXpuhC3If9XLgrNZDLh6+vr9LcxL7DNmzFv2gienvSInkCPGnp3KCfD5u4CnCn7Dd1n0yPyNQA+ZCSXGz9FpIEnMjtT9u7EiLnLqi4nIqu69KdpcLpuCLVObGLXwy/RLHqOnOkRzkUpmDYNwsIAeJvxTCCc0FCT3LNHuDVZ1SWKnd1u5+TJk4aa7b9kyCZqndhEBl48uet1wsP17lFuRszdVeidvWZVxDw6xlH0TGQ6E3jbLeb16J29uzJi7lL4iEKz2WzExcUZZra/pkHg/z4AYAGDiae6Uyxfv5HRcncleme/M2QcQdGzARjJv4kJmUBoKIZbup4XvbN3V0bMXU7iC/GXf7+ewKhrPwDwESMA51i+LgQA69YRvO0dAAbzKZ8xmFALcnlLiAKSMz5C/MX328VYsBFDSw7RgNq1jf9btHARly/DoEEAzOUlPmOw09xXSghXI2d8RKGZTCYqVapkjNn+StHr8mcAfMbzmExZ9+1xxonNhsrdxeiRvabBr+1G0fT0aS6Wr8OlF98hdDdut2xdjnt9GDF3WdXlRGRVl462b4egIKyePjz1aAIPtisr9+4RTmFF3695evk/sGGmDVvoPDVYnr8lRB5kVZcodjabjUOHDhlj0tunnwLg2fcpvtlQ1qlv92+o3F1MiWd/4QKdVw0DYBZjiSbYKSfclwQ57vVhxNyl8BGFZrfbOXz4sMsvc9R2xWJf+DkAn1teQNP07c/tGCV3V1RS2WsaTAuzs/3+5ylnTeQXmhBGmFvP65HjXh9GzN1Jf6cVovhpGsx400qvGc9zv93GCnrz/MLWnKqJXEoQugoPB23qdFryI+l489HDi2hbztvt5vUIURyk8BFuKzwc0qbN5n7iuER5RvIhSuG2lxKE88j4Zg1vMgWAF5nH2XJNZdm6EEVELnWJQjObzdSoUQOz2TUPo+M//c4UwgAYzQdcoIpLXEpw9dxdWYlkf+wYk37rhxnFXF5kkek5pz8mS4Ic9/owYu6yqsuJyKquEmS3c7J2O2qe3MJaOtOVNdSubWLgQGQ1l9DPtWsQHAy//MLpai0ZVm8Tj7TxlmNSiHww3Kqu5ORkBgwYgJ+fH35+fgwYMIDLly/f8jVKKcLCwggMDMTHx4d27drx66+/5tgmIyODESNG4O/vT5kyZejZsyenT58u9L4vXrxItWrVMJlMt+2fq7PZbMTGxrrmbP/586l5cguZXmVY/Oh8pk41cfgwTr2aK5tL5+7iijN7zarYFzQUfvmFP8tUJmDrV/y4ztsljsmSIMe9PoyYu8sUPv369SMuLo61a9eydu1a4uLiGDBgwC1fM2vWLN577z0+/vhjdu3aRUBAAKGhoVy9etWxzejRo/n6669Zvnw5W7du5c8//6RHjx45PuSC7Hvw4ME0adKkaAbt5Ox2O6dOnXKp2f6aBh+8Gk/qiHEAeMx8m8VbarrUl4sr5m4UxZn9+p7/psm+JWh40CP1f4R/UbXI9+HK5LjXhyFzVy7g4MGDClDbt293tMXExChAHTp0KM/X2O12FRAQoGbMmOFoS09PV35+fmrevHlKKaUuX76sPD091fLlyx3bnDlzRpnNZrV27doC73vOnDmqbdu2av369QpQycnJBRpnSkqKAlRKSkqBXqeXzMxM9c0336jMzEy9u5JvU8Ps6nu6KwVqK8Fq2hRN7y4VmCvmbhRFmb3VqtTUqUqFdrSr9R2mKwVKgRrNewqUCg0tgg4biBz3+nCl3PP7HeoSv+PGxMTg5+fHI4884mhr2bIlfn5+REdHU69evVyvOX78OAkJCXTq1MnR5u3tTdu2bYmOjmbYsGHs2bMHq9WaY5vAwEAaNWpEdHQ0nTt3zve+Dx48yLRp09ixYwd//PFHvsaVkZFBRkaG47+vXLkCgNVqxWq1AlkTyzw8PLDZbDkq7ux2TdNQ103T8vDwwGw237Q9+32zWf46zaHdcPOam7V7enpit9ux2WyO99I0LUd7NpPJhMViuWnfS3pMYCF5zlJ68CMZeDHcey6Bu+yAx0377oxjyv6Z1WrN1+fkCmPKT7szjCl7DNmvvZMxzZwJb04x8TH/pD3/AeA9y6vM8xiOKRNat7ZjtcrnlFfWRhnT9e3OOqbr9+PsY8ovlyh8EhISqFy5cq72ypUrk5CQcNPXAFSpUiVHe5UqVTh58qRjGy8vL8qXL59rm+zX52ffGRkZ9O3bl3feeYcaNWrku/B5++23mTp1aq72yMhISpcuDUCNGjVo1qwZ+/bt49SpU45t6tWrR/369dm5cyeJiYmO9gceeICaNWvy888/57ikFxQUROXKlYmMjMxxsISEhODj40NERESOPnTr1o20tDQ2btzoaLNYLHTv3p2kpCRiYmIc7dHR0XTo0IH4+Hji4uIc7ZUqVSI4OJgjR45w+PBhR7teYzrwS2fGXRwDwB/9/o+w3sex2eKB3GPy9fWlffv2Tj2mqKioAn1OrjAmKNixp9eYLl26xN13331HY2pYU2MVX9CT71EmE/tfeIE63Vsz59IW4uPb079/PBERJTcmV/icKlasiNlsZseOHYYZkyt8TqVLl8ZsNjv9mFq3bk1+6LqqKywsLM8v/uvt2rWLyMhIvvjiixyBA9StW5fBgwczfvz4XK+Ljo6mVatWnD17lrvvvtvRPmTIEOLj41m7di1Lly5l0KBBOc66AISGhlKnTh3mzZtHeHj4bff9yiuvcPbsWZYvXw7Apk2bCAkJITk5mXLlyt10bHmd8alevTpJSUmOGenOVlG78m8+44O2MXtPCMmU455S8QTe482ePeDj47pjMuLn5BZjunCBCy17UfX0LtIoxSCvRTR64wnGjXPhMf3FUJ+TjMmlxpSWlpavVV26nvEZPnw4ffr0ueU2tWrVYt++fZw/fz7XzxITE3Od0ckWEBAAZJ2xub7wuXDhguM1AQEBZGZmkpycnOOsz4ULFwgODnZsc7t9b9iwgf379/PVV18BOD5Qf39/Jk6ceNPiztvbG29v71ztnp6eeHp65mjz8PDAw8Mj17bZB0B+229838K0m81mx8G7c+dOWrRokaP9Rjfre0mP6WmPVQB8wxNcybiLV/qAj0/OMd3IGcd0fe7ZT0y+1eeU33Zn+Zxu1a73mK7P3mw2F25MR49Cly5UPX2Maz4VmNDoexr2CGb8+JwT7OVzytmuaRo7duygRYsWhhnT9Zx1TNcf8xaLxanHlJaWlud2ufafr62Kib+/P/7+/rfdLigoiJSUlBxfsjt27CAlJcVRoNzonnvuISAggKioKJo1awZAZmYmmzdvZubMmQA0b94cT09PoqKi6N27NwDnzp3jwIEDzJo1K9/7XrVqVY7Ad+3axfPPP8+WLVuoU6dOYaJxCUopEhMT0fGkYf7Z7Twcn1X4HH3gKcJ6ue6t/10qd4O54+yPHIG2beHcObjnHkqvWcMHecxRFLnJca8PI+buEnN8GjRoQJcuXRgyZAjz588HYOjQofTo0SPHxOb69evz9ttv06tXL0wmE6NHjyY8PJy6detSt25dwsPDKV26NP369QPAz8+PwYMH8+qrr1KxYkUqVKjAa6+9RuPGjenYsWO+931jcZOUlOR47a0udYkStG0bpnPnoFw5pu/oCF56d0i4G+3wMa61CKHslXOcr9yIilvWYama9xlrIUTxcZn7+CxZsoTGjRvTqVMnOnXqRJMmTfjyyy9zbHP48GFSUlIc/z127FhGjx7Nyy+/zEMPPcSZM2eIjIzE19fXsc3777/PE088Qe/evWnVqhWlS5fm+++/z3HaLj/7Fs5L02DHmJUAxNV6HM0sVY8oYcePk/pICGWvnOFXGtLkwnrCF0jRI4Qe5JEVTsTVHllht9uJj4+nevXqTv0cl2lhdgZPrU5VzvIY3/Pw1B4u/fR1V8ndiAqV/cmTWZe3Tp7kEPVoxybOE0BoKPLg0QKQ414frpR7fr9DXeJSl3BOZrOZmjVr6t2Nm9K0rCewn313KVU5SwpliSSUDBd/+rqz525kBc1eOx7P1eYhlE8+SbxPXTqkbeA8AS7xMFxnI8e9PoyYu3OXb8KpaZrGhg0bCn0TqeIWHg6fTznOrNSXAZjNq1hN3i7/hePsuRtZgbI/coSrzdtSPvk4R6lDy7SN1AsJJDQUwsJcd3K9XuS414cRc5czPqLQlFJcvXrVaWf7R/+ssZj+lOUqW2nFvPITCBvt+l84zp67keU7+927oVs3yicncozatGcDZ6nK/Ra5vFVYctzrw4i5yxkfYViva28STAwplGUAixk+2uJSDyMVrkfTYPGzkaS1bAeJiZy9+0FaEU08NeTylhBOQr4ChDFt3UqbLW8B8EmjeQx6qpbLn+kRzu+7Pkt4etVzeKIRRUd2Prual0v7snVrVtEjx6AQ+pNVXU7EFVd1JSUl4e/v71Sz/bWky/x5b1PKpZzil6bPcv/uLwx1lsdZc3cHt8x+9mx47TUAltKX5/icdqFecmmriMhxrw9Xyj2/36HOPQrh1MxmM5UrV3aafwyaBtOmKtbWepFyKac4Rm1a//Ix4eF696xoOVvu7uTG7DUNwt9I4/vqLzmKnvf5F8+wGM3kJZe2ipAc9/owYu7GGYkocVarlR9//DHXg+X0Eh4Of4QtokfqCjQ86MdSrpJ1mcFInC13d3Jj9gtG/sITbzXnsdPzAIjsOIurU9+jY6hZVm4VMTnu9WHE3A10AUDowZmWOB6LPMYn/BOAyUxjJ48YdkKpM+XubjRNA7sd3n+fQfPG40Um5whgIF+AqRORLnxzTGcnx70+jJa7FD7CGJRiyrlh3EUqm2nDTMZRuzYMHCi/dYuio2ngfekS+6o9RvOkKLyAb+nJC3zKRVMlwgxYZAthNFL4CGNYvJjaf6zHainFwkcWMKWTBxMmyNJ1UbR+GPwNPX8cjfeVK1zDh/Xd3uOXFsNots0kq7aEcBGyqsuJuNqqruwbW/n6+mIymXTrh5aQREadBpS5lsT6DuG0Xfu6oQseZ8ndrZw/DyNGwMq/HnZLU/qyjOqhDWTVVgmR414frpS7rOoSJcLHx0fvLnCg62uUuZbEfhrRdf1rhlvFlRdnyN0tKAVffgkNG8LKldjNHrxjGcsjbOewqYEh5485Mznu9WG03KXwEYWmaRoRERG6THzTNJg2DcY+tIEH4r7Ajomh/AcrnoZbxXUjPXN3J9ofpzhSrzs8+yxcuoRq+gCZW6K576tgOnbzkFVbJUyOe30YMXcDXxAQRhYeDh9NSWI7QwCYx4tsJ8iwq7hE8dO0rONq6xbFqFLz6Rg5hrqZf5KBF9OYgs/jYxj3MBBxjm++AU9PvXsshCgMKXyES9qxOZ2veYI6/MEJajK/1tuE1pXHAojCCw+HmVOu8V9eoDvLANhGMINZwGHqExoD4zDOvUyEcFdS+AiXomkQ/padl3cM5FG2cRk/uhPB04P8mCz3TxF34GjUcbbRiwf4BSsWZlWYyZRLo7DhIWcShTAQWdXlRFxxVZemaVgslhKb7T9tGpSaMo6xzCITTwbd/RP1Xgxxq6XreuRueOvWca3n05ROu8QFKvEUX9Fuchs8PMjxgFEPD8leL3Lc68OVcs/vd6ibfFWI4pKWloavr2+J7c9v2TxGMQuAwSwgsVEIS9zwTE9J525Emgbh0xVVlrzHkKNjKa3snAl8iLF1VtOhY/U8i2mlJHs9Sfb6MFrusqpLFJqmaWzcuLHkZvtHRDDicPYjKaayxDTALS8/lHjuBjUr7Br3hvVn2JHXMCs7cQ88R9VjW1jyc3UmT877DKJkrx/JXh9GzF3O+AjXEBsLvXs7vqC2+79BWGuZyCwK4fRp+O9/GfbOf6nIOaxY+Bfv87v/P4ks5dyn8oUQd04KH+HUNA0+HhfPgI+7UzEzlb3lOxDx2HwiJpvcZk6PuHOaVbFsyAZqrZlDq6RvMdttVATOEEhflrHV1Iaw1nr3UghREuSrQ9wRSzFXH+HTFe3f60tFznGA+wlJXsXVt7ywW3DrVVzFnbthpKXBf/7D5bfmMiDpsKP5ZM02VAt/mYWHelFquxdhBbgNgmSvH8leH0bLXVZ1ORFXW9VVEsY8tJF39rQnjVLU5xCnqAlAaCjyjCRxU5oG70+6SI953WmQsgOAK/iyiGeZx4sEhjaS40cIg5FndYliZ7fbuXDhAna7vdj28eLlGQAsYLCj6HH3e6qURO6u7uNx8XSf2ZoGKTu4SAU+uG8O1TjDCD7moKlRoY8fyV4/kr0+jJi7FD6i0Gw2GzExMdhstuLZwZ491DkWid3swfZWrxESAh064PbPSCr23F3doUP0m9OKhvxGPNVozRZ+qP4Sr031JTT0zo4fyV4/kr0+jJi7sS7cCWOZORMAc7++LP6ylr59Ea5h1y7o2pXK6Rc5RD06EclpUw36tHHvOWFCiL9J4SOcknbwdzy++goTMLfsOIZo7nNnZlFI69bBE09Aaiqq+UP80CGC+rGVeEGe3yaEuI58lYhCM5lM+Pr6FsltzB1Pxt4K7R++So9vJtFIKb7jMf45txGJVeQ39mxFmburuf44uf6BtF/3X8kTK/vjqazY23fA/M3XvObry2tFvH93zl5vkr0+jJi7rOpyIu68qmvaVMX3YXsYwn/ox1LuIhWAVmwlmlayiksAWc9qCwsDlJ2H2c3MdmuoeySCqmd2ArCS/+P3NxYzcZq3rv0UQpQ8WdUlip3dbufkyZN3NNtf0+CjUUfp+ebD7OJhhvJf7iKVY571eI7PiaaV26/iulFR5O6SLl7EsnIZX6gBJBDADh6h3aYwR9HzEcPpw3I2by++osdts3cCkr0+jJi7XOoShWaz2YiLiyMwMBCzuXA1dHg4NP9wFA+wh3S8+Yr/478MJeT11tT2MBF6wyUNUTS5u5TkZHj1VfjiCyZc93++KZTlTMNQjtbtxkvfduEsgcVeJLtd9k5EsteHEXOXwkfo6syafUwmAhtmHiCOxAr1GTWKPJ+MLdyPbfW3XBv4Ir5/JgBgb9SYGL+urE7vRvnuwYx/w5P7gGF5zPsRQoi8yFeL0NXw1KwbFK7kKX431SdslExiFkBiIowYgceKFfgCh6jHYD6j81PBTJ4MrW7YXI4ZIUR+SeEjCs1kMlGpUqVCz/bXDh/j/gMrAFhcdTxvDJbf1vPjTnN3akrBihUwYgQkJWEzeTBLjWEqU8igFGW26ts9Q2fv5CR7fRgxd2NcsBO6sFgsBAcHF+gBdpqWtTKnUydYGzITs7Kzhi5EnH0ADw+5vJUfhcndFWhHjnOoYS/o2xeSklCNm/DZ0B1MNL1NBqWcYpK7UbN3BZK9PoyYu3FGIkqczWbjyJEj1K1bFw8Pj3y9JjwcwqYoJvEmPfgvAG/zOkplzdEQt1eY3J1N9v14orfYGFhlLU8nz8VjTQT1lSITT6YzCc8nxjN+shfnAp1n/o4RsndVkr0+jJi7FD6i0Ox2O4cPH6ZOnTr5/gexbYudfzOKEXwMwBTC2EIbp/ht3lUUJndn88HERK7N+oy5zOMeTjjaIwnlVWZzgMaEbodJFueav2OE7F2VZK8PI+YuhY8oOZmZvHv+ORqzDDsmRvEhv4YMJ9TiHL/Ni2KmFERHw9y5jFy6Ei8yAbhEeTbUGETCEy8y8qO6KIUUwkKIYiOFjygZ6enQqxeN96/FZrYws+EiKj3Vl0hZtu4efvkFRo6En38GwAvYycPM4WX+x9OMH+zDhAlwqaLzXNYSQhiTfOWIQjObzdSoUeO2N7XSNIht8yoP71pLpmdpPL5exYTuXUqol8aT39ydgXb+IrGPvcGDu+bjgR1VqhSmfv3QhrzE2siHOLsVxj/6932bnOmyVl5cKXujkez1YcTc5VldTsSoz+pa0fdrnl7+DwC6soagqV2c/gtO3CFNg/nzSRvzBj5pyQAsow/nX5nF6NnVde6cEMKI5FldotjZbDZiY2Ox2Ww33+jUKbp+NRiAWYxhLV1k9dYdylfuetq0CR58EIYPxyctmTia0obN9GMZEftdu+hx+uwNTLLXhxFzl8JHFJrdbufUqVO5Hl6Xfa+ezh009jXpT1ktmZ08zCTekkmrReBmuZeU7M+3S6iNtyeloiUkQXw82u44fm3UG0JCYP9+VIUK/NhjLg+xxzAr9/TO3p1J9vowYu4yx0cUudlvXObCjCW8w3yasJ8r+NKXZVSv7cXAgTJp1ZVk32/nSNQJJp9/mXuv7EW7nMb4jDS8sMI6YHrWthbgfsCGmXm8ROqQabzyVgUmy3O0hBBORAofUWQ0DX7uPJ0RG6ZTmjQAruHDQL7gD+oQWsf5J6+KnMLD4eCUFcxnKH5cAaBUXht6epKqfNiqtWQM77CfJoTuhbEuMGFZCOFe5FKXKDSz2Uy9evUcs/2/67OU9hsmUZo0DnA/I/iQQM7yDb0McZnDWdyYe7FJTaXF/MEspw9+XGEbwYx8OIaPRxymBqfwJ5G7+JM3p2iQmcnsN1LoavqJ/TQx7OddYtmLXCR7fRgxd1nV5URcelXXwYOkNWmBjy2V6UxgEm9RoYKJpk3BbIY2bf5esixcQGxs1jOzDh/Gjom3mMSbTOaNqRYmTMg6E3T95SuL5e/LYje2CyFEScjvd6gUPk7E1QofTdPYuXMnLRo2xBIcDL/9xjo60JmfUCYPwsLkMkdxcOTeokXRPzhQKfjwQxg7FjIzUVWrsih0MUvOtJNihmLOXtySZK8PV8o9v9+hzj0K4dSUUiReuMBvL31M499+44pvIHuHLqXDPg+ZyFqMlFIkJiZSlL+zaFrW87Me/WwQLZN+zGp8/HFMCxYwsGJFBhbZnlxbcWQv8key14cRc5fCRxSKpsG70zN48sBi7tu3Cg0Pul9dQWjZykRG6t07kUN6OqSlgd2OlmHjw/dt7N5h45GHbPzzRRsWs52vpvxGv6XDCOQc6Xizodtsun39ctZDs4QQwkBcZrZScnIyAwYMwM/PDz8/PwYMGMDly5dv+RqlFGFhYQQGBuLj40O7du349ddfc2yTkZHBiBEj8Pf3p0yZMvTs2ZPTp08Xat+ff/45TZo0oVSpUgQEBDB8+PA7HbbTWj44iienPch9q1YBMI6ZbOVRuTmhE8i+z07/NvHEPvg86q67oEIF8PfHUrUKr7wbyNIt1Rn1fi0s9epA3br0WdqTQM5xkAa0YCcfWP8pRY8QwpiUi+jSpYtq1KiRio6OVtHR0apRo0aqR48et3zNjBkzlK+vr1q1apXav3+/evrpp9Xdd9+trly54tjmxRdfVFWrVlVRUVFq7969KiQkRDVt2lRpmlagfc+ePVsFBgaqJUuWqKNHj6oDBw6o7777rkBjTElJUYBKSUkp0OtKktWq1LZWY5TKmg2ikrwqqf7mxQrsymRSaupUvXtofDabTZ04cULZbLY8fz5z/CU1k7HqGqUcn9P1f2yYVCYWlYa3SjOXVsrXV6X6VFCf8LLyIVU+x1u4Xfai+Ej2+nCl3PP7HeoShc/BgwcVoLZv3+5oi4mJUYA6dOhQnq+x2+0qICBAzZgxw9GWnp6u/Pz81Lx585RSSl2+fFl5enqq5cuXO7Y5c+aMMpvNau3atfne96VLl5SPj49at27dHY3TFQqfzwdtcnyBfsBIVZbLCpSqXTvry9Jq1buHbiwtTal331UplvKOz2gTbdSIh2OUyshQStPU1LCsAhVUjgLHas36e2iofI5CCNeU3+9Ql5jjExMTg5+fH4888oijrWXLlvj5+REdHU29evVyveb48eMkJCTQqVMnR5u3tzdt27YlOjqaYcOGsWfPHqxWa45tAgMDadSoEdHR0XTu3Dlf+46KisJut3PmzBkaNGjA1atXCQ4OZvbs2VSvfvNnE2VkZJCRkeH47ytXsm4QZ7VasVqtQNY9FDw8PLDZbDluGZ7drmlajklnHh4emM3mm7Znv2+27Fn6mqbdvj0zk3b/ewmA/3oMZWrZGcyYHk1ycivGji2F2WzHZrORvQuTyYTFYrlp351iTICnpyd2uz3Hs2iy+36zdj3HpGka0dHRBAcHU6pUKbDZsC1ahMfUqZhOnaIscID7mWAKZ32pLrze04TVBCalmDDRhMlsZ/t2G0FB8OqroGlZY5o48e++KwU2m3xON44pO/vWrVvj5eVliDHd2O6sY8rOvm3btgCGGNP17c76OV2fu8lkcuox5ZdLFD4JCQlUrlw5V3vlypVJSEi46WsAqlSpkqO9SpUqnDx50rGNl5cX5cuXz7VN9uvzs+8//vgDu91OeHg4//73v/Hz82PSpEmEhoayb98+vLy88uzj22+/zdSpU3O1R0ZGUrp0aQBq1KhBs2bN2LdvH6dOnXJsU69ePerXr8/OnTtJTEx0tD/wwAPUrFmTn3/+matXrzrag4KCqFy5MpGRkTkOlpCQEHx8fIiIiMjRh27dupGWlsbGjRsdbXVWrqZR6m9ctPhT9fM2LLwraxbzffdtw2LpwMmT8cTFxTm2r1SpEsHBwRw5coTDhw872p1pTBaLhe7du5OUlERMTIyj3dfXl/bt2xMf77xjivzpJ1K/ttDq29epmXIAgLSKFfm9f3++K/8eleIvsrTnGgAiIv4e07PPxtOkSdaYIiOda0yu8jklJSURGBhoqDG5wucEWQXPrl27DDMmV/icsnM/evSoU4+pdevW5Ieu9/EJCwvL84v/ert27SIyMpIvvvgiR+AAdevWZfDgwYwfPz7X66Kjo2nVqhVnz57l7rvvdrQPGTKE+Ph41q5dy9KlSxk0aFCOsy4AoaGh1KlTh3nz5hEeHn7bfYeHhzNx4kR++uknx9mjxMREAgICiIiIoHPnznmOLa8zPtWrVycpKclxDwJnqajnj/uDwe8/gA/pPGv6gt31+/LMM1buvz+KTp064ePjY/jffJxiTFevYtu0ifiFCym7/ShVErIKnsv4sSd0LG2+Gg4+Pq41Jhf6nKxWK1FRUXTp0gVvb29DjOnGdmcdU3b23bp1w2QyGWJM17c76+d0fe5ms9mpx5SWlub89/EZPnw4ffr0ueU2tWrVYt++fZw/fz7XzxITE3Od0ckWEBAAZJ2xub7wuXDhguM1AQEBZGZmkpycnOOsz4ULFwgODnZsc7t9Z79/w4YNHT+vVKkS/v7+uX5buZ63tzfe3t652j09PfH09MzR5uHhgYeHR65tb3ZDqZu13/i+N2vXNHjrLfhykYWW6ZsYe3EcPqSzgRC+VAMIrWZizJisMwnZ+zKbzXne1vxmfS/pMd2q/WZ9L44xZWebdYdjS447H//d7pnVbrZDXBy2n6I4+WkU1U5spZQ9g7p/vVcGXnzMcMKZQHMq0uG6f+slOab8tLva53Sr9ux9GWlMt+pjQdtlTDImKPkxpaWl5bldrv3na6ti4u/vj7+//223CwoKIiUlxXH3SIAdO3aQkpLiKFBudM899xAQEEBUVBTNmjUDIDMzk82bNzNz5kwAmjdvjqenJ1FRUfTu3RuAc+fOceDAAWbNmpXvfbdq1QqAw4cPU61aNQAuXbpEUlISNWvWLFQ2esl+7MDyhWkEnVjKaj6kKfsASKU0LzMHk8nEo49mHexBQUF5HvAij0c4vJSMZesm9szcRO0dF6kNEAUHV0GTRorf9kOd/VDnr/ajy1Kpn7gVLl7EA7K2B05TlZSHW3O4RndeXNWZRCoZ9tlYzkaOef1I9vowZO53OIm6xHTp0kU1adJExcTEqJiYGNW4ceNcS8rr1aunVq9e7fjvGTNmKD8/P7V69Wq1f/9+1bdv3zyXs1erVk2tW7dO7d27V7Vv3z7P5ey32/fjjz+u7r//frVt2za1f/9+1aNHD9WwYUOVmZmZ7zE6w6qu8Il/qulMUIlUdKwM+pPS6hNeUu2r/y6rfgogfOKfqgtr1EzGqF00V/bs5VQF/ePrq6L9H1PD+VDV4zcFdhUaKiuxhBDieoZazq6UUhcvXlT9+/dXvr6+ytfXV/Xv318lJyfn2AZQCxcudPy33W5XU6ZMUQEBAcrb21u1adNG7d+/P8dr0tLS1PDhw1WFChWUj4+P6tGjhzp16lSB952SkqKef/55Va5cOVWhQgXVq1evXO9zO85Q+Gyo0sfxhXucmupV3lHluKQg971dMjMz1Q8//FCg4s7QbDalNm9WavJkpR59VGWaPHMXMQ0aqB0t/qle5V31L2arV5itfuo8W6n33lNrO7+n/sV7ahTvq9G8ryK6fqjU1q1KZWaqqVOVYxl66dKZatUqyV0PcszrR7LXhyvlnt/vUHlIqRPR+yGltvWb8OgYgg0zz7CYlTxF2fIWypeHZ56BN97I+YBKq9VKREQE3bp1u+k1WKPTNAifrrB//S0vnXuDKhcO5Pj5CWqyng5spD0PvhrCK+8G3vQp5rd6uvn1P2vTxkrjxu6du17kmNePZK8PV8pdHlIqCkbTSOo/kirAfIaxnL6EhGQtd3byB/LqRylWDPqJrovf4GF2A5DuXZZST/aA9u3R2rRn0bJ7HIXMyL8e2mqx5P3U+pu13/gzqzVrUrkQQoiCk680kWXuXKqc308SFZnEW0DWl60UPTexaRNMmkT/bdsA+JMyfMBo9ga9yuolWSsELdy8kBFCCKEPl3lIqSgeWpqV7574jNRXJgEwkekkUyFfq4QsFgshISE3XZroirIf8NmpU9b/Zt8mIrt9ZIvt/FEnFEJCYNs2rJZSzOZV7uE4k01v8UBI+VvvoAgYMXdXIdnrR7LXhxFzN85IRL5kzxXZvjmD0NMLeeqPGfTUsu5kHUNLjrV7gVDPv+eY3I6Pj08x9/jO5Jo3M96O5c/LaAlJLHoviRN7LtK8VhI9Wl3Gw2RnQyRciYRGmEiJgg3R0KmzifU/QbOfNvAYPwBg8/DEY9gQTOMmkvp5IM225j+zouDsuRuZZK8fyV4fRstdJjc7keKe3Kxp0DnUToNNcxjPDKpxBoAEqvAOY5jHi7QKLUNkZP7ez6knvcXHw9at7Hp/C+m79uNPEv4kUdF0CbOy3/71N6HhwRcMZNOjb/DlllpF198CcOrcDU6y149krw9Xyl0mN4tcwsOh4aZP+IiRQNaN8GYyjk95gXR8XPcmeHY72v7fWDtpKz67t9A8fSvlLmedxXr4xm3/KvNTLWVJ0Py5SEWS8KdUQHnah3qw7xfFL/vA9NeGTZpk3Vxw/37YtL8iHzGco6b7CAstueEJIYQoOlL4uJGtWxQfMBeAmYxlMtPIJOuRGbVrw8CBJXepprCyL13t3pzKMJ9FdDWtwRy9DculS/S4bju72QPzg83YbnmUD7e34Cx3cxF/nnvNn1enV2D2DC/CwrJurmMyQdhL0H4yNNTgm+sujfWZAFiggQZfh0OtrfBMCV7SEkIIUbSk8HETmgZ1L+2gIb9xDR+mM5HAWt7UqQNt2uS8Z4wz+/D1c1je/ZjPmUsFkh3t6ebSbLO3ZCuPsoXWlGn7CN9u8OUhDeqHQ9JWeOpRGPVXIZNduGy9YW5OYZaaCyGEcB0yx8eJFOccn2nTIGDKMIbyHxYxgM9DFt3xPXqUUmiahsViwWQyFV1n83LgALz3HpmfL8FLZQJwlDpsrDuUIYvb8daPzZj8puffZ3DCjFuolGjuIgfJXj+SvT5cKXeZ4yNy+GVdIv9iKQALGYSHuWjO8KSlpeHr63vnb5QHzapY9sJ66v84m4cvrgXAC9hGMO/yGt/Tk8nPeEALGP8g2D1yn8ExquLMXdyaZK8fyV4fRstdCh8DS0+Hrl3hbMxJxmS8hS9/socH2UQ7Qgq/sMkxz2bHDo2hg9fRoUM33n/fk63bTLRq9VfRYTJlLSPfZrrpYxnatsxg3KALcP48Kz8+z9nY8zxw93lCGp7HnHieSxv2M+DCrwDYMHO4YS/um/cq6zcGkboVJufjEpURaZrGxo0bXWKVhdFI9vqR7PVhxNyl8DGwLl2g7ub/spGhjrYJhAMmPDzy9x6aBh9MTOT0xt9pV+0oPRsc5dB3R+ly4BjDOUqFiKx5Nm9kv2AdMDXrr446JAqYkvVXCzCJrNOl5igFb2a1983e9iCwPuuvlYFUSvMZz/MBo6lTtQ6RrWFy63xHIIQQQuQghY+B7dyhWPxXFWLHxL8ZRSSdAGh9u+Lh7FlYuZJzs1fwWnxMVtsu4GtodIf9MvP3tDLNZOGyV2VOZVThPFl/fGpV4ekRVVgVczdDv+rMpb/uJD3QFZfaCyGEcCpS+BiYTylFcnp5qnGGI9RlLLMAE2XLwuefwxdfwIABMGkSoBTzXzmMilpH9/RV1Dq5GROK6mQVTSepyVHuJaNqHbzvv5c5kfdy2rsG4/99iOUr2rNxoyegMAFjx2bdB2fWrKzb5phRjBkDr70Gs99VvPNOVruGJ6MmlwOzOefS8kHAK/C4Br82dp95OwVhpNvHuxrJXj+SvT6Mlrus6nIiRb2qa/Jk+OzN08TSjEokcYh6HKMOD7IXDQtRhBJLMzrctYNHMzfgn3kux+u3Ecxy+vAV/0cCdztWS02YkPMxEGPHwqxZuQuUHI+KyGOOz+22FUIIIfIrv9+hUvg4kaIufDQNKlWCNpe/ZTHP4Muft9w+HW+iCWYtXVjB05yiJpB1c8M6dXIXJXa7naSkJPz9/TGb5Xm3JUVy149krx/JXh+ulHt+v0OdexTijlgsMGoUfMfjBHKWQXzGy3xCS2LoSBSzeYUIuvImkwhhA+W4TAc28A5jHUWPyZR1R+fIyKwzSNefibHZbMTExGCz2XQaoXuS3PUj2etHsteHEXOXCwoGN2lS1tyZL7/05dvLgyhXDqpXh+OnYXpyR5L/vvkxISFZhU1QUFbBEx0tc2uEEEIYixQ+BmexwNSpWX9ulNd8G5lbI4QQwsjka86N3elN/0wmE76+vk5/G3Ojkdz1I9nrR7LXhxFzl8nNTqQ4n9UlhBBCGJlMbhbFzm63c/LkSez2O3j+hSgwyV0/kr1+JHt9GDF3KXxEodlsNuLi4gw1298VSO76kez1I9nrw4i5S+EjhBBCCLchhY8QQggh3IYUPqLQTCYTlSpVMtRsf1cguetHstePZK8PI+Yuq7qciKzqEkIIIQpHVnWJYmez2Th06JChJr25AsldP5K9fiR7fRgxdyl8RKHZ7XYOHz5sqGWOrkBy149krx/JXh9GzF0KHyGEEEK4DSl8hBBCCOE2pPARhWY2m6lRowZmsxxGJUly149krx/JXh9GzF1WdTkRWdUlhBBCFI6s6hLFzmazERsba6jZ/q5ActePZK8fyV4fRsxdCh9RaHa7nVOnThlqtr8rkNz1I9nrR7LXhxFzl8JHCCGEEG7DoncHxN+yp1tduXJF557kj9Vq5dq1a1y5cgVPT0+9u+M2JHf9SPb6kez14Uq5Z3933m7qshQ+TuTq1asAVK9eXeeeCCGEEK7p6tWr+Pn53fTnsqrLidjtds6ePYuvr69LPBDuypUrVK9enfj4eFmFVoIkd/1I9vqR7PXhSrkrpbh69SqBgYG3XH4vZ3yciNlsplq1anp3o8DKli3r9P8gjEhy149krx/JXh+ukvutzvRkk8nNQgghhHAbUvgIIYQQwm1I4SMKzdvbmylTpuDt7a13V9yK5K4fyV4/kr0+jJi7TG4WQgghhNuQMz5CCCGEcBtS+AghhBDCbUjhI4QQQgi3IYWPEEIIIdyGFD4i35KTkxkwYAB+fn74+fkxYMAALl++fNPtrVYr48aNo3HjxpQpU4bAwECeffZZzp49W3KdNoiCZg+wevVqOnfujL+/PyaTibi4uBLpq6ubM2cO99xzD6VKlaJ58+Zs2bLllttv3ryZ5s2bU6pUKWrXrs28efNKqKfGUpDcz507R79+/ahXrx5ms5nRo0eXXEcNqCDZr169mtDQUCpVqkTZsmUJCgrip59+KsHe3jkpfES+9evXj7i4ONauXcvatWuJi4tjwIABN93+2rVr7N27lzfeeIO9e/eyevVqfv/9d3r27FmCvTaGgmYPkJqaSqtWrZgxY0YJ9dL1rVixgtGjRzNx4kRiY2Np3bo1Xbt25dSpU3luf/z4cbp160br1q2JjY1lwoQJjBw5klWrVpVwz11bQXPPyMigUqVKTJw4kaZNm5Zwb42loNn//PPPhIaGEhERwZ49ewgJCeGxxx4jNja2hHt+B5QQ+XDw4EEFqO3btzvaYmJiFKAOHTqU7/fZuXOnAtTJkyeLo5uGdKfZHz9+XAEqNja2GHtpDC1atFAvvvhijrb69eur8ePH57n92LFjVf369XO0DRs2TLVs2bLY+mhEBc39em3btlWjRo0qpp4Z351kn61hw4Zq6tSpRd21YiNnfES+xMTE4OfnxyOPPOJoa9myJX5+fkRHR+f7fVJSUjCZTJQrV64YemlMRZW9uLXMzEz27NlDp06dcrR36tTppjnHxMTk2r5z587s3r0bq9VabH01ksLkLopGUWRvt9u5evUqFSpUKI4uFgspfES+JCQkULly5VztlStXJiEhIV/vkZ6ezvjx4+nXr59LPOzOWRRF9uL2kpKSsNlsVKlSJUd7lSpVbppzQkJCnttrmkZSUlKx9dVICpO7KBpFkf3s2bNJTU2ld+/exdHFYiGFj5sLCwvDZDLd8s/u3bsBMJlMuV6vlMqz/UZWq5U+ffpgt9uZM2dOkY/DFZVU9qJgbsz0djnntX1e7eLWCpq7KDqFzX7ZsmWEhYWxYsWKPH85c1YWvTsg9DV8+HD69Olzy21q1arFvn37OH/+fK6fJSYm5vpt4UZWq5XevXtz/PhxNmzYIGd7/lIS2Yv88/f3x8PDI9dvuhcuXLhpzgEBAXlub7FYqFixYrH11UgKk7soGneS/YoVKxg8eDArV66kY8eOxdnNIieFj5vz9/fH39//ttsFBQWRkpLCzp07adGiBQA7duwgJSWF4ODgm74uu+g5cuQIGzdulC+D6xR39qJgvLy8aN68OVFRUfTq1cvRHhUVxeOPP57na4KCgvj+++9ztEVGRvLQQw/h6elZrP01isLkLopGYbNftmwZzz//PMuWLaN79+4l0dWipefMauFaunTpopo0aaJiYmJUTEyMaty4serRo0eOberVq6dWr16tlFLKarWqnj17qmrVqqm4uDh17tw5x5+MjAw9huCyCpq9UkpdvHhRxcbGqh9//FEBavny5So2NladO3eupLvvMpYvX648PT3VggUL1MGDB9Xo0aNVmTJl1IkTJ5RSSo0fP14NGDDAsf0ff/yhSpcurf71r3+pgwcPqgULFihPT0/11Vdf6TUEl1TQ3JVSKjY2VsXGxqrmzZurfv36qdjYWPXrr7/q0X2XVtDsly5dqiwWi/rkk09y/H/65cuX9RpCgUnhI/Lt4sWLqn///srX11f5+vqq/v37q+Tk5BzbAGrhwoVKqb+XUef1Z+PGjSXef1dW0OyVUmrhwoV5Zj9lypQS7bur+eSTT1TNmjWVl5eXevDBB9XmzZsdPxs4cKBq27Ztju03bdqkmjVrpry8vFStWrXU3LlzS7jHxlDQ3PM6tmvWrFmynTaIgmTftm3bPLMfOHBgyXe8kExK/TUTTwghhBDC4GRVlxBCCCHchhQ+QgghhHAbUvgIIYQQwm1I4SOEEEIItyGFjxBCCCHchhQ+QgghhHAbUvgIIYQQwm1I4SOEEEIItyGFjxBC/CUzM5N7772Xbdu26bL/jz/+mJ49e+qybyHchRQ+QgiX9dxzz/HEE0/kat+0aRMmk4nLly8X6P3+85//ULNmTVq1apXrZ0OHDsXDw4Ply5ff9PVhYWH06dOnQPu83pAhQ9i1axdbt24t9HsIIW5NCh8hhPjLRx99xAsvvJCr/dq1a6xYsYIxY8awYMGCm77+u+++u6Mnint7e9OvXz8++uijQr+HEOLWpPARQghg7969HD16lO7du+f62cqVK2nYsCGvv/4627Zt48SJE7m2iY+P58CBA3Tt2hUAk8nE/Pnz6dGjB6VLl6ZBgwbExMRw9OhR2rVrR5kyZQgKCuLYsWM53qdnz5588803pKWlFcs4hXB3UvgIIQTw888/c99991G2bNlcP1uwYAHPPPMMfn5+dOvWjYULF+ba5rvvvqNNmzaUK1fO0fbmm2/y7LPPEhcXR/369enXrx/Dhg3j9ddfZ/fu3QAMHz48x/s89NBDWK1Wdu7cWbQDFEIAUvgIIVzcDz/8wF133ZXjT/ZZl4I4ceIEgYGBudqPHDnC9u3befrppwF45plnWLhwIXa7Pcd23377ba7LXIMGDaJ3797cd999jBs3jhMnTtC/f386d+5MgwYNGDVqFJs2bcrxmjJlylCuXLk8zyoJIe6cFD5CCJcWEhJCXFxcjj+ffvppgd8nLS2NUqVK5WpfsGABnTt3xt/fH4Bu3bqRmprKunXrHNtcuXKFzZs351qR1aRJE8ffq1SpAkDjxo1ztKWnp3PlypUcr/Px8eHatWsFHoMQ4vYsendACCHuRJkyZbj33ntztJ0+fbrA7+Pv78/+/ftztNlsNhYtWkRCQgIWiyVH+4IFC+jUqRMAa9asoUGDBtSsWTPH6z09PR1/N5lMN2278ezRpUuXqFSpUoHHIIS4PSl8hBACaNasGXPnzkUp5ShIIiIiuHr1KrGxsXh4eDi2PXToEP379+fixYtUrFiRb7/9tsjuv3Ps2DHS09Np1qxZkbyfECInudQlhBBkXTJLTU3l119/dbQtWLCA7t2707RpUxo1auT48+STT1KpUiUWL16MpmmsWbPmjpaxX2/Lli3Url2bOnXqFMn7CSFyksJHCCGAihUr8o9//IMlS5YAcP78eX788UeefPLJXNuaTCb+8Y9/sGDBAjZv3sxdd91F8+bNi6Qfy5YtY8iQIUXyXkKI3ExKKaV3J4QQwhns37+fjh07cvToUXx9ffP1mpEjR6JpGnPmzLnj/R84cIAOHTrw+++/4+fnd8fvJ4TITeb4CCHEXxo3bsysWbM4ceJEjtVXt9KoUSOCgoKKZP9nz55l0aJFUvQIUYzkjI8QQggh3IbM8RFCCCGE25DCRwghhBBuQwofIYQQQrgNKXyEEEII4Tak8BFCCCGE25DCRwghhBBuQwofIYQQQrgNKXyEEEII4Tak8BFCCCGE2/h/Zscy1EQ03rgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wir erhalten mur=(2.38+/-0.21)e+03 als Median. \n"
+     ]
+    }
+   ],
+   "source": [
+    "#Ur ist Channel B und UC ist Channel A\n",
+    "#R2 = 10 kOhm\n",
+    "#C = 10 uF\n",
+    "N1=250\n",
+    "N2=50\n",
+    "l=ufloat(0.48,0.01)\n",
+    "R2=ufloat(10000,500)\n",
+    "R1=ufloat(10,0.5)\n",
+    "UHfak=N1/(R1*l)\n",
+    "C=ufloat(10*10**(-6),0.5*10**(-6))\n",
+    "UHfak=N1/(R1*l)\n",
+    "UBfak=C*R2/(N2*(0.039)**2)\n",
+    "mu0=4*np.pi*10**(-7)\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy import interpolate\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Read cvs file as pandas dataframe\n",
+    "#df = pd.read_csv(\"Christian_ist_ein_Schatz2_2_10_02mA2.csv\")\n",
+    "# Translate dataframe columns into native python lists\n",
+    "#t  = df[\"Frequenz\"].to_list()[1:-1:10]\n",
+    "#UB = df[\"Kanal A\"].to_list()[1:-1:10] \n",
+    "#UH = df[\"Kanal B\"].to_list()[1:-1:10] \n",
+    "#UB=np.array(UB)\n",
+    "#UH=np.array(UH)\n",
+    "data = np.genfromtxt('Jonathan_ist_Gott_3_Eisen.csv', delimiter=\",\", skip_header=3 )\n",
+    "t, UB, UH= data[:,0], data[:,1], data[:,2]\n",
+    "t=t[1:-1:]\n",
+    "UB=UB[1:-1:]/1000\n",
+    "UH=UH[1:-1:]/1000\n",
+    "from PhyPraKit.phyTools import resample, meanFilter\n",
+    "\n",
+    "# If length is too large, resample by an appropriate factor, we are fine with \n",
+    "# 350 data points\n",
+    "il=len(UH)\n",
+    "size=300\n",
+    "if il > size:\n",
+    "    g = int(il/size)\n",
+    "    # This is an example of smoothing by averaging over n neighbors\n",
+    "    #print(\"Smoothing with window size \", n)\n",
+    "    #t  = meanFilter(vUH, width=n)\n",
+    "    #UH = meanFilter(vUH, width=n)\n",
+    "    #UB = meanFilter(vUB, width=n)\n",
+    "    # This is an example of down sampling by averaging over n neighbors\n",
+    "    print(\"Resampling by factor\", g)\n",
+    "    t  = resample(t , n=g)\n",
+    "    UH = resample(UH, n=g)\n",
+    "    UB = resample(UB, n=g)\n",
+    "\n",
+    "CALIB_UH2H = UHfak.n  # U_H -> H <-- adjust !\n",
+    "CALIB_UB2B = UBfak.n   # U_B -> B <-- adjust !\n",
+    "H = UH * CALIB_UH2H\n",
+    "B = UB * CALIB_UB2B\n",
+    "# Interpolate the points of (t,H) by spline functions; s=0 means that no extra \n",
+    "# smoothing will be applied, each point of H will be used for the spline\n",
+    "spl_Ht = interpolate.UnivariateSpline(t, H, s=0)\n",
+    "spl_Bt = interpolate.UnivariateSpline(t, B, s=0)\n",
+    "\n",
+    "# Plot hysteresis curve as Channel A vs. Channel B\n",
+    "tplt = np.linspace(t[0], t[-1], 200)\n",
+    "unitH = \"(A/m)\"; unitB = \"(T)\"\n",
+    "fig = plt.figure(1, figsize=(6.0, 6.0))\n",
+    "ax1 = fig.add_subplot()\n",
+    "ax1.scatter(H, B, color=\"blue\", marker=\"o\", s=5.0, label=\"Data points\")\n",
+    "ax1.plot(spl_Ht(tplt), spl_Bt(tplt), color=\"red\", label=\"Spline function\")\n",
+    "ax1.set_xlabel(\"H  \" + unitH)\n",
+    "ax1.set_ylabel(\"B \" + unitB)\n",
+    "ax1.legend(numpoints=1, loc=\"best\")\n",
+    "ax1.grid(linestyle=\"dashed\")\n",
+    "plt.title(r\"Hysteresekurve bei $I_{eff}=262.2 \\pm 0.05 \\,\\mathrm{mA}$\")\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "stdU=0.00000005\n",
+    "Uh=np.array([ufloat(x,stdU) for x in UH])\n",
+    "Ub=np.array([ufloat(x,stdU) for x in UB])\n",
+    "b=UBfak*Ub\n",
+    "h=UHfak*Uh\n",
+    "murt=((b/(mu0*h))**2)**0.5\n",
+    "#murt2=[x for x in murt if x<2000]\n",
+    "#murt2=np.sort(murt)[49:-100]\n",
+    "#plt.plot(range(len(murt)),n(murt))\n",
+    "#plt.show()\n",
+    "#mur=np.mean(murt)\n",
+    "mur=np.median(murt)\n",
+    "#plt.plot(range(len(murt2)),n(murt2))\n",
+    "#plt.plot(range(len(murt2)),np.ones(len(murt2))*mur.n)\n",
+    "#plt.show()\n",
+    "print(f\"Wir erhalten mur={mur} als Median. \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "b17d3a37-5dec-4013-9d9f-bfd171e86f5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resampling by factor 13\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wir erhalten mur=(3.89+/-0.35)e+03 als Median. \n"
+     ]
+    }
+   ],
+   "source": [
+    "#Ur ist Channel B und UC ist Channel A\n",
+    "#R2 = 10 kOhm\n",
+    "#C = 10 uF\n",
+    "N1=250\n",
+    "N2=50\n",
+    "l=ufloat(0.48,0.01)\n",
+    "R2=ufloat(10000,500)\n",
+    "R1=ufloat(10,0.5)\n",
+    "UHfak=N1/(R1*l)\n",
+    "C=ufloat(10*10**(-6),0.5*10**(-6))\n",
+    "UHfak=N1/(R1*l)\n",
+    "UBfak=C*R2/(N2*(0.039)**2)\n",
+    "mu0=4*np.pi*10**(-7)\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy import interpolate\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Read cvs file as pandas dataframe\n",
+    "#df = pd.read_csv(\"Christian_ist_ein_Schatz2_2_10_02mA2.csv\")\n",
+    "# Translate dataframe columns into native python lists\n",
+    "#t  = df[\"Frequenz\"].to_list()[1:-1:10]\n",
+    "#UB = df[\"Kanal A\"].to_list()[1:-1:10] \n",
+    "#UH = df[\"Kanal B\"].to_list()[1:-1:10] \n",
+    "#UB=np.array(UB)\n",
+    "#UH=np.array(UH)\n",
+    "data = np.genfromtxt('Jonathan_ist_ein_Augenschmaus_252.csv', delimiter=\",\", skip_header=3 )\n",
+    "t, UB, UH= data[:,0], data[:,1], data[:,2]\n",
+    "t=t[1:-1:]\n",
+    "UB=UB[1:-1:]/1000\n",
+    "UH=UH[1:-1:]/1000\n",
+    "from PhyPraKit.phyTools import resample, meanFilter\n",
+    "\n",
+    "# If length is too large, resample by an appropriate factor, we are fine with \n",
+    "# 350 data points\n",
+    "il=len(UH)\n",
+    "size=300\n",
+    "if il > size:\n",
+    "    g = int(il/size)\n",
+    "    # This is an example of smoothing by averaging over n neighbors\n",
+    "    #print(\"Smoothing with window size \", n)\n",
+    "    #t  = meanFilter(vUH, width=n)\n",
+    "    #UH = meanFilter(vUH, width=n)\n",
+    "    #UB = meanFilter(vUB, width=n)\n",
+    "    # This is an example of down sampling by averaging over n neighbors\n",
+    "    print(\"Resampling by factor\", g)\n",
+    "    t  = resample(t , n=g)\n",
+    "    UH = resample(UH, n=g)\n",
+    "    UB = resample(UB, n=g)\n",
+    "\n",
+    "CALIB_UH2H = UHfak.n  # U_H -> H <-- adjust !\n",
+    "CALIB_UB2B = UBfak.n   # U_B -> B <-- adjust !\n",
+    "H = UH * CALIB_UH2H\n",
+    "B = UB * CALIB_UB2B\n",
+    "# Interpolate the points of (t,H) by spline functions; s=0 means that no extra \n",
+    "# smoothing will be applied, each point of H will be used for the spline\n",
+    "spl_Ht = interpolate.UnivariateSpline(t, H, s=0)\n",
+    "spl_Bt = interpolate.UnivariateSpline(t, B, s=0)\n",
+    "\n",
+    "# Plot hysteresis curve as Channel A vs. Channel B\n",
+    "tplt = np.linspace(t[0], t[-1], 200)\n",
+    "unitH = \"(A/m)\"; unitB = \"(T)\"\n",
+    "fig = plt.figure(1, figsize=(6.0, 6.0))\n",
+    "ax1 = fig.add_subplot()\n",
+    "ax1.scatter(H, B, color=\"blue\", marker=\"o\", s=5.0, label=\"Data points\")\n",
+    "ax1.plot(spl_Ht(tplt), spl_Bt(tplt), color=\"red\", label=\"Spline function\")\n",
+    "ax1.set_xlabel(\"H  \" + unitH)\n",
+    "ax1.set_ylabel(\"B \" + unitB)\n",
+    "ax1.legend(numpoints=1, loc=\"best\")\n",
+    "ax1.grid(linestyle=\"dashed\")\n",
+    "plt.title(r\"Hysteresekurve bei $I_{eff}=25.3 \\pm 0.05 \\,\\mathrm{mA}$\")\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "stdU=0.00000005\n",
+    "Uh=np.array([ufloat(x,stdU) for x in UH])\n",
+    "Ub=np.array([ufloat(x,stdU) for x in UB])\n",
+    "b=UBfak*Ub\n",
+    "h=UHfak*Uh\n",
+    "murt=((b/(mu0*h))**2)**0.5\n",
+    "#murt2=[x for x in murt if x<2000]\n",
+    "#murt2=np.sort(murt)[49:-100]\n",
+    "#plt.plot(range(len(murt)),n(murt))\n",
+    "#plt.show()\n",
+    "#mur=np.mean(murt)\n",
+    "mur=np.median(murt)\n",
+    "#plt.plot(range(len(murt2)),n(murt2))\n",
+    "#plt.plot(range(len(murt2)),np.ones(len(murt2))*mur.n)\n",
+    "#plt.show()\n",
+    "print(f\"Wir erhalten mur={mur} als Median. \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf498ca8-ac2f-4d18-829e-3edd1b05b6e9",
+   "metadata": {},
+   "source": [
+    "**V E R S U C H S B E S C H R E I B U N G**\n",
+    "\n",
+    "*Fügen Sie Ihre Versuchsbeschreibung hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f5600ea-c2ea-4d77-a77e-e3b471c99146",
+   "metadata": {},
+   "source": [
+    "**L Ö S U N G**\n",
+    "\n",
+    "*Fügen Sie numerische Berechnungen zur Lösung dieser Aufgabe hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument. Um Code-Fragmente und Skripte in [Python](https://www.python.org/), sowie ggf. bildliche Darstellungen direkt ins [Jupyter notebook](https://jupyter.org/) einzubinden verwandeln Sie diese Zelle in eine Code-Zelle. Fügen Sie ggf. weitere Code-Zellen zu.* \n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3fb8b19-3453-4ee0-8be3-79e63c9d428f",
+   "metadata": {},
+   "source": [
+    "**D I S K U S S I O N**\n",
+    "\n",
+    "*Fügen Sie eine abschließende Diskussion und Bewertung Ihrer Lösung hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b669bee3-e4f2-4a1d-be44-6ef3be4f6170",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "# Beurteilung"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4ab1a4b-9cdf-43aa-a33c-ae863bd60902",
+   "metadata": {},
+   "source": [
+    " * Nach Abschluss des Versuchs haben Sie die Möglichkeit diesen Versuch individuell zu beurteilen.\n",
+    " * **Folgen Sie zur Beurteilung dieses Versuchs diesem [Link](https://www.empirio.de/s/mlNVWZpooS)**.\n",
+    " * Beachten Sie, dass jede:r Studierende nur einmal pro Versuch eine Beurteilung abgeben kann.\n",
+    " * Wir empfehlen die Beurteilung nach der Besprechung Ihrer Versuchsauswertung mit Ihrem:r Tutor:in auszufüllen.  "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
-- 
GitLab