From e817b0cf791f3b92c1d4e92a27e75f23659f7a04 Mon Sep 17 00:00:00 2001 From: k8ik Date: Fri, 20 Jun 2025 22:12:01 +0200 Subject: [PATCH 1/2] added (not very useful) tunneling-current calculations --- Calculations/Tunneling-Current-Distance.ipynb | 111 ++++++++++++++++++ 1 file changed, 111 insertions(+) create mode 100644 Calculations/Tunneling-Current-Distance.ipynb diff --git a/Calculations/Tunneling-Current-Distance.ipynb b/Calculations/Tunneling-Current-Distance.ipynb new file mode 100644 index 0000000..a8ec059 --- /dev/null +++ b/Calculations/Tunneling-Current-Distance.ipynb @@ -0,0 +1,111 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "id": "3487469a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " [Text(0.0, 0, '0.00'),\n", + " Text(0.05, 0, '0.05'),\n", + " Text(0.1, 0, '0.10'),\n", + " Text(0.15000000000000002, 0, '0.15'),\n", + " Text(0.2, 0, '0.20')])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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GhZ+O8a7KRETUPulcqMycOROPP/44UlNTsWnTJmzatAkpKSl44oknMHPmTENkbJcEQcCz/QMAAN8eTkONSi1xIiIiotanc6Fy+fJlzJ07t96dks3MzDBnzhxcvnxZr+Hau1FdveFqZ4ms4krsOJctdRwiIqJWp3OhEhUVpRmbcrvExER07dpVL6GojpWFGZ7uUzcB3JcHUyGKnACOiIjaF60G0549e1bz/axZszB79mxcvnwZffr0AQAcOXIEH3/8MZYuXWqYlO3Y032U+CQ2GWcyinAqvRA9lBywTERE7YcgavFnukwmgyAI9/yLXhCEVp1LpaSkBAqFAsXFxXBwcGi1921tr244g/UnMjEiwhOfPNVD6jhEREQtosv5W6sWldTUVL0Eo+aZ1j8A609kYue5bGQUlMPX2UbqSERERK1CqzEqSqUSSqUS3t7eWLRoEdRqtWbZnQ/SvzBPB/QPdoVarBurQkRE1F7oNJjWwsICGzduNFQWuosXBgYCANYdz0DhjWqJ0xAREbUOna/6GTNmDLZs2WKAKHQ3/YNd0dnLARU1Knx/5IrUcYiIiFqFzlPoh4SEYPHixTh06BB69OgBW1vbeutnzZqlt3D0P4Ig4IWBgZi99jRi/krD9PsDYWVhdu8XEhERmTCtrvq5XUBAQNM7EwSkpKS0OJS22stVP7fUqNQY9H4srhZV4O2xXfBUb44JIiIi06P3q35uxyuApGNhJsOz/QOweHsC1uxPwRM9/WAmE6SORUREZDA6j1EhaU3o6QuFtQXS8svxRwKn1SciorZN5xYVAMjMzMS2bduQnp6O6ur6V6CsWLFCL8GocbZyczzTR4n/7r2Mz/alYFi4JwSBrSpERNQ26Vyo7NmzB48++igCAwNx4cIFdOnSBWlpaRBFEVFRUYbISHeY3M8fnx9IwemMIhxPK0SvAE6rT0REbZPOXT/z58/HvHnzEB8fDysrK2zcuBEZGRkYOHAgxo8fb4iMdAc3ezn+FuUDAPhsX7LEaYiIiAxH50IlMTERkyZNAgCYm5ujoqICdnZ2WLx4Md599129B6TGPX9/IGQC8OeFXCRcK5E6DhERkUHoXKjY2tpqxqV4eXkhOfl/f9Ffv35df8norgJcbTEy0hsA8EnsZYnTEBERGYbOhUqfPn1w8OBBAMCIESMwd+5cvP3225g2bRr69Omj94DUtJcGBQEAfo3PQkpemcRpiIiI9E/nQmXFihXo3bs3AGDRokUYMmQI1q1bB39/f3z55Zd6D0hN6+TlgKGd3CGKHKtCRERtk84z0xqT9jYzbWNOpRdi3Cd/wVwmYN+rD6CDo7XUkYiIiO5Kl/M3J3wzcVF+TugX5IJatYg1+1vv9gVEREStQatCxdnZWaeBsn5+frhyhXf4bS0zHwgGAPx0LB15pVUSpyEiItIfrSZ8Kyoqwo4dO6BQKLTaaX5+PlQqVYuCkfb6Bbmgm68jTmcU4atDqXhteJjUkYiIiPRC65lpJ0+ebMgc1AKCIGDmA8GY/u0JfHf4Cl68PwgKGwupYxEREbWYVl0/arVa50dgYKChs9NthoS5I8zTHmVVtfj6L97hmoiI2gYOpm0jZDIBfx8cAgD48mAqiitqJE5ERETUcixU2pCHu3iio4cdSitr8fUhtqoQEZHpY6HShshkAmYNqWtV+YqtKkRE1AawUGljRnTxQoi7HUoqaxFzKE3qOERERC3CQqWNkckEzB56a6xKCkoq2apCRESmS+dCZeDAgfj2229RUVFhiDykB2xVISKitkLnQqV79+6YN28ePD09MX36dBw5csQQuagFbh+r8sUBtqoQEZHp0rlQWblyJa5du4avv/4aubm5uP/++9G5c2csW7YMOTk5hshIzTAiwgvBbFUhIiIT16wxKubm5hg3bhy2bt2KzMxMPPnkk1iwYAF8fX0xZswY/Pnnn/rOSToyu6NVhVcAERGRKWrRYNpjx45h4cKFWL58Odzd3TF//ny4urrikUcewbx58/SVkZppZIQXOnrUtarwzspERGSKdC5UcnNzsXz5cnTp0gUDBgxAXl4efvrpJ6SlpWHRokX44osvsGvXLnz22WeGyEs6MJMJmPNgKADgq0OpuF7GOysTEZFp0fqmhLf4+PggKCgI06ZNw5QpU+Dm5tZgm8jISPTs2VMvAallhoV7INJHgbOZxfg0NhkLHuksdSQiIiKtCaIoirq84MCBAxgwYICh8uikpKQECoUCxcXFcHBwkDqO0dp3KQ+TvzoGS3MZ9r0yCF4Ka6kjERFRO6bL+Vvnrp+FCxeiqKio0TcdPHiwTvv69NNPERkZCQcHBzg4OKBv377YsWOHrpHoHu4PcUUvf2dU16qx6s/LUschIiLSms6Fyr59+1BdXd1geWVlJQ4cOKDTvnx8fLB06VKcPHkSJ06cwODBgzF69GicP39e11h0F4IgYN6wurEq649nID2/XOJERERE2tF6jMrZs2cBAKIoIiEhAdnZ2Zp1KpUKO3fuRIcOHXR681GjRtV7/vbbb+PTTz/FkSNHEB4e3mD7qqoqVFX9b0BoSUmJTu/XnvUKcMb9Hd2w/1IeVu6+hBUTukkdiYiI6J60LlS6desGQRAgCEKjXTzW1tZYtWpVs4OoVCr8/PPPuHHjBvr27dvoNkuWLMGiRYua/R7t3byHOmL/pTxsPn0VMwYFIcTDXupIREREd6X1YNorV65AFEUEBgbi2LFj9a72sbS0hLu7O8zMzHQOEB8fj759+6KyshJ2dnb48ccfMWLEiEa3baxFxdfXl4NpdfDidyex83w2HuzsgTWToqWOQ0RE7ZAug2m1blFRKpUAALVa3bJ0dwgNDcXp06dRXFyMDRs2YPLkydi3bx86d254Ga1cLodcLtfr+7c384aF4o/EHPyRkIMTaQWI9neWOhIREVGTtGpR2bZtGx5++GFYWFhg27Ztd9320UcfbVGgoUOHIigoCKtXr77ntrw8uXnmb4rHT8fS0UPphA0v9oUgCFJHIiKidkTvLSpjxoxBdnY23N3dMWbMmCa3EwQBKpVKp7B3UqvV9bp3SP/+MTQEm+MycfJKIXYl5GBYuKfUkYiIiBql1eXJarUa7u7umu+beuhapMyfPx/79+9HWloa4uPjMX/+fMTGxuKpp57S/ZOQ1jwcrPBc/0AAwHs7L6BWpd/uPCIiIn1p0U0JWyo3NxeTJk1CaGgohgwZguPHj+P333/Hgw8+KGWsduH5gYFwsrFAct4N/HwyU+o4REREjdJqjMpHH32k9Q5nzZrVokC64BiVlvnqYCoWb0+Au70csa8Mgo2lzrd+IiIi0pku52+tCpWAgACt3lgQBKSkpGiXUg9YqLRMVa0KQ5bvQ2ZhBeY91BEvDw6ROhIREbUDeh9Mm5qaqpdgZFzk5mZ4ZVgoZq89jc/2peCJXn5wtePl30REZDwkHaNC0hsV6Y2IDgqUVdVixR+XpI5DRERUT7MGJWRmZmLbtm1IT09vcIPCFStW6CUYtQ6ZTMCCRzrj8dWHsfZYOib39UeoJ6fWJyIi46BzobJnzx48+uijCAwMxIULF9ClSxekpaVBFEVERUUZIiMZWK8AZzzcxRM7zmXjrV8T8O20XpwEjoiIjILOXT/z58/HvHnzEB8fDysrK2zcuBEZGRkYOHAgxo8fb4iM1Ar+9XAYLMwEHEi6jthLeVLHISIiAtCMQiUxMRGTJk0CAJibm6OiogJ2dnZYvHgx3n33Xb0HpNahdLHFlH7+AIC3f01EDSeBIyIiI6BzoWJra6sZl+Ll5YXk5GTNuuvXr+svGbW6lweHwMnGApdzy7D2WLrUcYiIiHQvVPr06YODBw8CAEaMGIG5c+fi7bffxrRp09CnTx+9B6TWo7C2wD8f7AgA+GB3EooraiRORERE7Z3OhcqKFSvQu3dvAMCiRYswZMgQrFu3Dv7+/vjyyy/1HpBa18Refghys0XBjWqs2pMkdRwiImrntJqZ1lhxZlrDiL2YiylfH4e5TMDOfwxAsDsvVyYiIv3R5fzd7AnfqqurkZmZifT09HoPMn2DQt0xtJM7atUiFv2SABOuZYmIyMTpXKhcunQJAwYMgLW1NZRKJQICAhAQEAB/f3+t7wlExu//RnaGpZkMB5KuY1dCjtRxiIiondJ5wrepU6fC3Nwc27dvh5eXFycGa6P8XW0x/f4AfLw3GW9uT8DAjm6wsjCTOhYREbUzOhcqp0+fxsmTJxEWFmaIPGREZj4QjI0nryKzsAKf70/BrCG8uzIREbUunbt+OnfuzPlS2gkbS3P8e2QnAMAnsZdxtahC4kRERNTe6FyovPvuu3j11VcRGxuL/Px8lJSU1HtQ2zIq0gu9ApxRWaPGO78mSh2HiIjaGZ0vT5bJ6mqbO8emiKIIQRCgUqn0l+4eeHly60jMKsHIjw5ALQLfPdsLA0LcpI5EREQmTJfzt85jVPbu3dvsYGSaOnk5YFJff8T8lYbXt57HjtkDOLCWiIhahc6FysCBAw2Rg4zcnIc64tf4LKRev4HV+1IweygH1hIRkeHpXKgAQFFREb788kskJtaNWQgPD8e0adOgUCj0Go6Mh4OVBRY80hmzforDx7GXMaa7N5QutlLHIiKiNk7nwbQnTpxAUFAQPvjgAxQUFKCgoAArVqxAUFAQTp06ZYiMZCRGRXqhf7ArqmvVeH3rec5YS0REBqfzYNoBAwYgODgYa9asgbl5XYNMbW0tnnvuOaSkpGD//v0GCdoYDqZtfSl5ZRi+8gCqVWp88lQURkR4SR2JiIhMjEHv9XPixAm89tprmiIFAMzNzfHqq6/ixIkTuqclkxLoZocXBwYCABb/koCyqlqJExERUVumc6Hi4ODQ6M0HMzIyYG/Pu+y2By89EAw/Zxtkl1Ri+a6LUschIqI2TOdCZcKECXj22Wexbt06ZGRkICMjA2vXrsVzzz2HiRMnGiIjGRkrCzO8OaYLAOCbv9JwOqNI2kBERNRm6XzVz7JlyyAIAiZNmoTa2rpmfwsLC8yYMQNLly7Ve0AyTgM7umFMN29sOX0N/9p4Fr/8vT8szHSue4mIiO5K58G0t5SXlyM5ORkAEBQUBBsbG70G0wYH00orv6wKQ1fsQ2F5DV4ZFoqZDwRLHYmIiEyAQQfT3mJjY4OIiAhERERIUqSQ9Fzs5Pi/kZ0BAB/uSULq9RsSJyIiorZGq66fcePGISYmBg4ODhg3btxdt920aZNegpFpGBfVAVtOX8WBpOuYv+ksfprep8F9oIiIiJpLqxYVhUKhOfk4ODhAoVA0+aD2RRAEvD0mAlYWMhxJKcD6ExlSRyIiojak2WNUjAHHqBiPNftT8PZviXCwMsfuOQPh7mAldSQiIjJSBh2jMnjwYBQVFTX6poMHD9Z1d9RGTL3PHxEdFCiprMW/N5/j9PpERKQXOhcqsbGxqK6ubrC8srISBw4c0EsoMj3mZjK8Pz4SFmYCdifmYOvpa1JHIiKiNkDreVTOnj2r+T4hIQHZ2dma5yqVCjt37kSHDh30m45MSpinA2YNDsHyPy5h4bbz6BfsAnd7dgEREVHzaV2odOvWDYIgQBCERrt4rK2tsWrVKr2GI9Pz4qAg/J6QjXNXS/Cfzefw+TM9eBUQERE1m9aFSmpqKkRRRGBgII4dOwY3NzfNOktLS7i7u8PMzMwgIcl0WJjJ8P5jXfHofw/ij4QcbDtzDaO7saWNiIiaR+tCRalUoqamBpMnT4aLiwuUSqUhc5EJ6+TlgL8PDsGKm11AfYPYBURERM2j02BaCwsLbN682VBZqA2ZMSgI4d4OKCqvwb838SogIiJqHp2v+hk9ejS2bNligCjUlliYybBsfFfNVUA/n8iUOhIREZkgne+eHBISgsWLF+PQoUPo0aMHbG1t662fNWuW3sKRaevk5YC5D4Vi6Y4LWPRLXReQrzPvC0VERNrTeWbagICApncmCEhJSWlxKG1xZlrjp1KLmPj5ERxLK0BPfyesfb4vzGS8CoiIqD3T5fytc4tKampqs4NR+2MmE7D88a4YvnI/jqcVYs2BFLw4MEjqWEREZCJ0HqNyO1EUOUiS7snX2QYLR4UDAJbvuoiEayUSJyIiIlPRrELl22+/RUREBKytrWFtbY3IyEh89913+s5Gbcj4aB882NkDNSoRc9afRlWtSupIRERkAnQuVFasWIEZM2ZgxIgRWL9+PdavX4/hw4fjxRdfxAcffGCIjNQGCIKAJeMi4GpniQvZpXh3x0WpIxERkQlo1mDaRYsWYdKkSfWWf/PNN3jjjTdadQwLB9Oanj8v5GBazAkAwNdTe+KBUHeJExERUWvT5fytc4tKVlYW+vXr12B5v379kJWVpevuqJ0ZHOaBKf38AQDz1p9BbmmltIGIiMio6VyoBAcHY/369Q2Wr1u3DiEhIXoJRW3bvx4OQ5inPfJvVGPu+jNQqzkgm4iIGqfz5cmLFi3ChAkTsH//ftx3330AgEOHDmHPnj2NFjBEd7KyMMOqid0x6r8HcSDpOr48mIrp9wdKHYuIiIyQzi0qf/vb33D06FG4urpiy5Yt2LJlC1xdXXHs2DGMHTvWEBmpDQrxsMeCRzoDAN77/QLiM4slTkRERMZI58G0xoSDaU2bKIqY8f0p7DyfDX8XG/zy9/6wt7KQOhYRERmYQWemBQCVSoXNmzcjMTERANC5c2eMHj0a5ubN2h21U4IgYOnfIhB/tRhp+eWYvykeqyZ2hyBwin0iIqqjc9fP+fPn0bFjR0yePBmbN2/G5s2bMXnyZISEhODcuXOGyEhtmKONJT6a2B3mMgHbz2bh+6PpUkciIiIjonOh8txzzyE8PByZmZk4deoUTp06hYyMDERGRuL55583REZq43oonfDa8DAAwJu/JODcVY5XISKiOjqPUbG2tsaJEycQHh5eb/m5c+fQs2dPVFRU6DXg3XCMStshiiKmf3sSuxNzoLw5XsWB41WIiNokg0741rFjR+Tk5DRYnpubi+DgYF13RwSgbrzKsvGR6OBojSv55fjXxrO84SUREeleqCxZsgSzZs3Chg0bkJmZiczMTGzYsAH/+Mc/8O6776KkpETzINKFo40l/vtkd1iYCfgtPhtfH0qTOhIREUlM564fmex/tc2tqzNu7eL254IgQKUy7B1y2fXTNn11MBWLtyfAXCbgx+l90CvAWepIRESkRwa9PHnv3r3NDnanJUuWYNOmTbhw4QKsra3Rr18/vPvuuwgNDdXbe5DpmXqfP+IyivDLmWuY+eMp/Pr3/nB3sJI6FhERSUDSCd+GDx+OJ554Aj179kRtbS3+/e9/49y5c0hISICtre09X88WlbarvLoWYz/+CxdzShGtdMKP0/vA0lznnkoiIjJCupy/m1WoFBUV4csvv9RM+BYeHo5p06ZBoVA0L/FNeXl5cHd3x759+3D//fc3WF9VVYWqqirN85KSEvj6+rJQaaNSr9/Ao6sOorSqFlP6+eONR8Pv/SIiIjJ6Br3q58SJEwgKCsIHH3yAgoICFBQUYMWKFQgKCsKpU6eaHRoAiovr5s9wdm58TMKSJUugUCg0D19f3xa9Hxm3AFdbrJjQDQAQ81catsRdlTYQERG1Op1bVAYMGIDg4GCsWbNGM2V+bW0tnnvuOaSkpGD//v3NCqJWq/Hoo4+iqKgIBw8ebHQbtqi0T8t+v4j/7r0MKwsZNrzYD106tKzljoiIpGXQrh9ra2vExcUhLCys3vKEhARER0ejvLxc98QAZsyYgR07duDgwYPw8fHR6jUco9I+qNQinv3mOGIv5sFLYYVtL/eHm71c6lhERNRMBu36cXBwQHp6w/uxZGRkwN7eXtfdAQBefvllbN++HXv37tW6SKH2w0wm4MMnuiPQzRZZxZWY8f1JVNUa9tJ3IiIyDjoXKhMmTMCzzz6LdevWISMjAxkZGVi7di2ee+45TJw4Uad9iaKIl19+GZs3b8aff/6JgIAAXeNQO6GwtsCaSdGwtzLHiSuFWLj1PGeuJSJqB3SeR2XZsmUQBAGTJk1CbW0tAMDCwgIzZszA0qVLddrXzJkz8eOPP2Lr1q2wt7dHdnY2AEChUMDa2lrXaNTGBbnZYdXE7pgWcxxrj2egk5cDJvfzlzoWEREZkE5jVFQqFQ4dOoSIiAjI5XIkJycDAIKCgmBjY6P7m9+cyfZOX3/9NaZMmXLP13OMSvv0+f5kvPPbBZjJBHwztRf6h7hKHYmIiHRg0MG0VlZWSExMNIpuGhYq7ZMoipi7/gw2xV2FvZU5Nr/UD8HuzRsfRURErc+gg2m7dOmClJSUZocjailBEPDOuAhEK51QWlmLaTEnkF9Wde8XEhGRydG5UHnrrbcwb948bN++HVlZWfXulsw7JlNrsbIww+pnesDP2QbpBeV44buTqKzhlUBERG2NXu6eDLTeHZNvx64fupxbhrGfHEJpZS1Gd/PGygndmhz7RERExsFk7p5M1FLB7nb47OkemPzVMWw9fQ1KF1vMebCj1LGIiEhPdCpURFGEt7c3qqurERoaqplCn0hK9wW74q0xXfCvTfH4aE8SfJys8Xg07wNFRNQWaD1GJTU1FZGRkQgLC0NkZCSCgoJw4sQJQ2Yj0toTvfwwY1AQAGD+pnjEXsyVOBEREemD1oXKK6+8gtraWnz//ffYsGEDfHx88MILLxgyG5FOXh0WirHdO0ClFvHSD6cQn1ksdSQiImohrQfTenp6YsOGDejfvz8AICsrCz4+PigpKYGtra1BQzaFg2npTtW1akyLOY6Dl6/D1U6OzS/1g6+z7pMREhGR4RhkHpXc3FyEhIRonnt5ecHa2hq5uWxiJ+NhaS7Dp09HoZOXA66XVWHyV8dQcKNa6lhERNRMWhcqgiCgrKys3pwpMpkMpaWlnEeFjIq9lQVipvZEB0drpFy/gakxx3GjqlbqWERE1Axad/3IZLIG81Pcmjvl9u85jwoZi8u5pRj/2WEUltegf7ArvpwSDbm5mdSxiIjaPYPMo8L5U8jUBLvb4+upvfDkmiM4ePk6/rnuNFZNjIKZjBPCERGZCp1npjUmbFEhbRxMuo5pMcdRrVJjYi9fvDM2grPXEhFJyKA3JbzdyJEjkZWV1ZJdEBlc/xBXfPhEN8gE4KdjGXjv94tSRyIiIi21qFDZv38/Kioq9JWFyGAejvDCO2MjAACfxibj472XJU5ERETaaFGhQmRKnujlh3+PCAMAvP/7RXxxIEXiREREdC8tKlSUSiUsLCz0lYXI4J6/Pwj/HFp308K3fk3Ed0euSJyIiIjupkV3FTx37py+chC1mllDglFZq8KnsclYsOUc5OYy3sSQiMhI6VyonD17ttHlgiDAysoKfn5+kMvlLQ5GZCiCIODVYaGoqFYh5q80vLbxLOTmMozu1kHqaEREdAedC5Vu3bo1mOTtdhYWFpgwYQJWr14NKysr/aQk0jNBELBwVGdU1arx07F0/HPdaQBgsUJEZGR0HqOyefNmhISE4PPPP8eZM2dw+vRpfP755wgNDcWPP/6IL7/8En/++Sf+7//+zxB5ifRGEAS8PaYLJkT7Qi0C/1x3Glvirkodi4iIbqNzi8rbb7+NDz/8EMOGDdMsi4iIgI+PDxYsWIBjx47B1tYWc+fOxbJly/QalkjfZDIBS8ZFQBCAtcczMGf9aYgQMba7j9TRiIgIzShU4uPjoVQqGyxXKpWIj48HUNc9xIngyFTIZMLN2WrrJoSbu/4MALBYISIyAjp3/YSFhWHp0qWorq7WLKupqcHSpUsRFlY3R8XVq1fh4eGhv5REBiaTCXh7TAQm9vKDWgTmrD+D9cczpI5FRNTu6dyi8vHHH+PRRx+Fj48PIiMjAdS1sqhUKmzfvh0AkJKSgpdeekm/SYkMrK5Y6QKZAPxwNB2vbjyLihoVJvfzlzoaEVG71aybEpaWluKHH37ApUuXAAChoaF48sknYW9vr/eAd8ObEpIhiKKIN7cn4qtDqQCA14aHYcagIIlTERG1Hbqcv3n3ZKJGiKKIFX9cwqo/6+4JNGtwMP75YEfedZmISA90OX83a2bapKQk7N27F7m5uVCr1fXWvf76683ZJZFREQQBcx8KhbWlGd7beREf/XkZZVUq/N/ITpDJWKwQEbUWnQuVNWvWYMaMGXB1dYWnp2e9vzAFQWChQm3KS4OCYWtpjoXbzuOrQ6koqqjGu3+LhIUZ7+dJRNQadO76USqVeOmll/Daa68ZKpPW2PVDrWXjyUy8uvEsVGoRg8Pc8fGTUbC2NJM6FhGRSdLl/K3zn4WFhYUYP358s8MRmaK/9fDB58/0gNxchj8v5OKZL4+iuLxG6lhERG2ezoXK+PHjsWvXLkNkITJqQzp54PvnesPByhwnrhTi8dWHkV1cKXUsIqI2TecxKsHBwViwYAGOHDmCiIgIWFhY1Fs/a9YsvYUjMjY9/Z2x7oW+mPzVMVzMKcW4Tw4hZlovdPRo3UvziYjaC53HqAQEBDS9M0FASkpKi0Npi2NUSCoZBeWY/PUxpOTdgL2VOT5/Jhp9g1ykjkVEZBI4jwpRKyi8UY3p357AiSuFsDST4f3xkRjdrYPUsYiIjJ5BB9MSUR0nW0t8/1xvPNzFE9UqNWavPY1PY5NhwrU/EZHR0apFZc6cOXjzzTdha2uLOXPm3HXbFStW6C3cvbBFhYyBWi3i7d8S8eXBuin3H4/2wVtjImBpzr8DiIgao/eZaePi4lBTU6P5vimcXpzaI5lMwIJHOsPXyRqLtydg/YlMXMkvx2dP94CTraXU8YiITBrHqBDp0d6Lufj7j3Eoq6qFv4sNvpzSE0FudlLHIiIyKhyjQiSRB0LdsXFGP3RwtEZafjnGfnwIB5OuSx2LiMhkadWiMm7cOK13uGnTphYF0gVbVMhYXS+rwvPfnsCp9CLIBODfIzrh2f4B7B4lIoIBxqgoFAq9BCNqL1zt5Phxeh/8Z/M5bDyVibd+TURCVgneGRsBKwveI4iISFsco0JkQKIo4utDaXj7t0So1CK6+iiw+ploeCqspI5GRCQZg49Rqa2txe7du7F69WqUlpYCAK5du4aysrLm7I6ozRIEAdP6B+Dbab3gaGOBM5nFeGTVQRxNyZc6GhGRSdC5ReXKlSsYPnw40tPTUVVVhUuXLiEwMBCzZ89GVVUVPvvsM0NlbYAtKmRK0vPL8fx3J3AhuxRmMgH/Gh6G5wZw3AoRtT8GbVGZPXs2oqOjUVhYCGtra83ysWPHYs+ePbqnJWon/FxssOmlfhjTzRuqm5PEzfzxFMqqaqWORkRktHS+e/KBAwfw119/wdKy/kRW/v7+uHr1qt6CEbVFNpbm+GBCN0QpnfDm9gT8Fp+NC9ml+OzpHrwDMxFRI3RuUVGr1VCpVA2WZ2Zmwt6e/9ES3YsgCJjU1x/rXugLTwcrpOTdwKP/PYj1JzJ4nyAiojvoXKg89NBDWLlypea5IAgoKyvDwoULMWLECH1mI2rTovycsH1WfwwIcUVljRqvbjiLuevP4Aa7goiINHQeTJuZmYlhw4ZBFEUkJSUhOjoaSUlJcHV1xf79++Hu7m6orA1wMC21BWq1iE/3JWP5rotQi0CQmy0+fioKYZ78N01EbZMu5+9mzaNSW1uLtWvX4uzZsygrK0NUVBSeeuqpeoNrWwMLFWpLjqUWYNZPccguqYTcXIb/jOyEZ/ooeVUQEbU5Bi9UjAULFWprCm5UY+7609h7MQ8AMCTMHe89FgkXO7nEyYiI9MfghUpSUhL27t2L3NxcqNXqeutef/11XXfXbCxUqC0SRRExf6VhyW8XUK1Sw81ejuXju+L+jm5SRyMi0guDFipr1qzBjBkz4OrqCk9Pz3rN0oIg4NSpU81L3QwsVKgtS8wqwayf4pCUWzfj87T7AvDq8FDeK4iITJ5BCxWlUomXXnoJr732WotC6gMLFWrrKmtUeOe3RHx7+AoAINjdDise74pIH0dpgxERtYBBZ6YtLCzE+PHjmx2OiLRnZWGGxaO74OspPeFmL8fl3DKM/eQvrNx9CTUq9b13QERk4nQuVMaPH49du3YZIgsRNeGBMHfs+sf9GBnhBZVaxMrdSfjbp3/hYnap1NGIiAxK5yn0g4ODsWDBAhw5cgQRERGwsLCot37WrFl6C0dE/+Nka4n/PtkdD53xwIIt53A2sxiPrDqAWYND8OKgIFiYNetm6ERERk3nMSoBAQFN70wQkJKSovW+9u/fj/fffx8nT55EVlYWNm/ejDFjxmj9eo5RofYqu7gS/9kcjz0XcgEA4d4OeP+xrujszd8DIjJ+upy/dW5RSU1NbXawO924cQNdu3bFtGnTMG7cOL3tl6it81RY4YvJ0dhy+ire2JaA89dK8Oh/D+KlQUF46YFgXhlERG2G0Uz4JggCW1SImiG3tBILtpzD7+dzAACBbrZ4Z2wE+gS6SJyMiKhxBm1RUalUiImJwZ49exqd8O3PP//UdZdaq6qqQlVVleZ5SUmJwd6LyFS421vhs6d7YMe5bCzcdh4peTfwxOdH8ERPX8x/uBMUNhb33gkRkZHSuVCZPXs2YmJiMHLkSHTp0qVV70OyZMkSLFq0qNXej8hUCIKAERFeuC/YFe/tvIAfjqZj7fEM7E7MwX9GdsKYbh14zyAiMkk6d/24urri22+/xYgRI/QbRIuun8ZaVHx9fdn1Q3SH42kFmL8pHpdvzmrbO8AZb43pghAPe4mTEREZeMI3S0tLBAcHNztcS8jlcjg4ONR7EFFDPf2d8dusAXhlWCisLGQ4mlqAhz88gCU7EnGjqlbqeEREWtO5UJk7dy4+/PBDGMkYXCJqgqW5DDMfCMYf/xyIBzt7oFYtYvW+FAxZvg9bT1/l7zARmQSdx6gcPHgQe/fuxY4dOxAeHt5gwrdNmzZpva+ysjJcvnxZ8zw1NRWnT5+Gs7Mz/Pz8dI1GRI3wdbbBmknR2JOYgzd+OY+MggrMXnsa3x+5goWjwtGlg0LqiERETdJ5jMrUqVPvuv7rr7/Wel+xsbF44IEHGiyfPHkyYmJi7vl6Xp5MpJvKGhW+OJCCj/cmo6JGBUEAnujph7kPdYSrnVzqeETUThj07snGhIUKUfNkFVdgyW8XsO3MNQCAndwcLz0QhGn3BXCyOCIyOBYqRKSV42kFWPxLAuKvFgMAOjha49XhoRgV6Q2ZjJczE5FhGLRQCQgIuOt8DLrc66elWKgQtZxaLWLrmat4b+dFZBVXAgC6+ijw2sNh6BfkKnE6ImqLDDoz7T/+8Y96z2tqahAXF4edO3filVde0XV3RCQxmUzA2O4+GB7uhS8PpuDT2GScySzGk2uO4v6ObnhteCjCvTngloikobeun48//hgnTpzQaTBtS7FFhUj/8kqr8N8/k/DD0XTUquv+exjdzRtzHuwIpYutxOmIqC2QZIxKSkoKunXr1qr332GhQmQ4V/JvYPmuS5oBt+YyAeOjffDy4BB0cLSWOB0RmTKDzkzblA0bNsDZ2VlfuyMiiSldbPHRxO7Y/vf+GNjRDbVqET8dy8AD78fijW3nkVtSKXVEImoHtG5RWbx4MebOnYv+/fvXG0wriiKys7ORl5eHTz75BM8//7zBwt6JLSpEredEWgGW7bqIIykFAAC5uQxP9VbixYGBcHewkjgdEZkSg3T9mJmZISsrC5988km9QkUmk8HNzQ2DBg1CWFhYy5LriIUKUesSRRF/Jedj+a6LOJVeBKBuqv4ne/nhxYFB8FSwYCGiezNIoSKTyZCdnQ13d3e9hNQHFipE0hBFEQeSruPDPUk4eaUQAGBpJsP4aB+8cH8Q/FxsJE5IRMbMYIVKTk4O3Nzc9BJSH1ioEElLFEUcTs7Hyj1JOJZa1yVkJhMwKtILMwYFI9TTXuKERGSMDFaoKBSKu072BgAFBQXaJ20hFipExuNoSj4+iU3Gvkt5mmVDO7njhYFBiFY63fP/DiJqPww24duiRYugUHDiJyJqqHegC3oHuuDc1WJ8GpuM385lYXdiLnYn5qK7nyOeHxCIh8I9Ycap+YlIBxyjQkQGkZxXhi8OpGDjqauorlUDAJQuNni2fwD+FuUDW7nOE2MTURth0Kt+WKgQkS7ySqvw7eE0fHfkCorKawAA9lbmmNjLD5P6KuHjxIG3RO0Nr/ohIqNTXl2Ln09kIuavNKRevwEAkAnAsHBPTO7nj94BzhzHQtROSDKFvhRYqBCZHrVaROylXHx1MA0HL1/XLA/1sMczfZUY270Du4WI2jgWKkRkEi5ml+Kbw2nYfOoqKmpUAAB7uTnGRXXAk72VvLyZqI1ioUJEJqW4ogYbT2bi+yNXkHKzWwgAeiid8GQvP4yM9IKVhZmECYlIn1ioEJFJUqtFHEq+jh+PpuOPhBzUquv+e1JYW2BMN2883tMX4d6cIoHI1LFQISKTl1tSiZ9PZuLHo+m4WlShWd6lgwMmRPvi0W4doLC2kDAhETUXCxUiajNUahEHL1/H+hMZ+ON8DqpVdXOyWJrL8GBnDzwW5YMBIa4wN5NJnJSItMVChYjapIIb1dgSdxXrT2TgQnapZrmrnRxjunljTPcOCPd24GXOREaOhQoRtWmiKOL8tRJsPJWJraevoeBGtWZdiLsdxnTvgEe7esPXmZPJERkjFipE1G7UqNSIvZiHTacysedCrma6fgCIVjrhkUgvjIj0gru9lYQpieh2LFSIqF0qqazBzvhsbD1zFX8l5+PW/24yAegd4IJHunpheLgnXOzk0gYlaudYqBBRu5dTUolfz2bhl7PXEJdepFkuE4A+gS54uIsnhoV7wt2BLS1ErY2FChHRbTIKyvFrfBa2n72Gc1dLNMsFoa57aFi4Jx7q7Ak/F45pIWoNLFSIiJqQUVCOHeey8Ft8Nk5nFNVbF+Zpj2HhnniwswevHiIyIBYqRERauFZUgV3ns/H7+RwcSyuASv2//w69FFYYHOaOoZ090DfQhVP4E+kRCxUiIh0V3qjGngu5+P18Ng4mXdfcJBEArC3McF+wCwaFuuOBMHd0cLSWMCmR6WOhQkTUApU1KhxOzsfuxBzsScxFdkllvfUdPewwKNQd94e4Idrfia0tRDpioUJEpCeiKCIhqwSxF/Ow90IuTqUX4rYeIlhZyNAn0AX3h7hhQIgrgt3tOLaF6B5YqBARGUhReTX2J13H/kt52H8pD7mlVfXWezjIcV+wK/oHu+K+YFd48PJnogZYqBARtQJRFHEhuxT7L+XhQNJ1HEsrqDczLgAEutmiX5AL+ga6ok+gMyebIwILFSIiSVTWqHDySiEOXr6Og0nXce5aMe78H7ajhx16B7igd6Azege4wM2ehQu1PyxUiIiMQHF5DY6m5uNwSj4OJ+fXu+PzLYGutujp74xofyf0CnCGn7MNx7hQm8dChYjICOWXVeF4WgGOpBTgaGoBLmSXNGhxcbOXI1rphCg/J0QpndClgwPk5ryqiNoWFipERCaguLwGJ9MLcCy1ECfSCnA2sxjVqvpjXCzNZOjSwQHd/ZzQ3c8R3Xwd0cHRmq0uZNJYqBARmaDKGhXOZhbjVHohTl4pxKkrhci/Ud1gOzd7Obr6OKKrjwKRvnVfHW0sJUhM1DwsVIiI2gBRFHElvxxxGYWISy9CXHoRErNKUKtu+N+2n7MNInwUiOhQ9+jirYDCxkKC1ET3xkKFiKiNqqxR4dzVYpzJLMaZjCKczSxCWn55o9v6Odsg3Nvh5kOBcG8HuNnL2W1EkmOhQkTUjhSX1+Ds1SLEXy3GuavFiL9ajIyCika3dbG1RCcvB3Tysr/51QFBbnawNJe1cmpqz1ioEBG1c0Xl1Ui4VoLz10pw/loxzl8rQXJeGRrpNYK5TECQmx1CPe3rHh726OhhDx8na8hkbH0h/WOhQkREDVRUq3AppxSJWSVIzCpBQlYJLmSXorSyttHtrS3MEOJhhxB3ewS72yHE3Q4hHnbwcbKBGQsYagEWKkREpBVRFHGtuBIXs+uKlovZpbiUU4bkvLIGtwO4RW4uQ4CrLYLc7RDkZocgN1sEudkhwNUWtnLzVv4EZIpYqBARUYvUqtS4UlCOS9mluJxbhqSbj5S8MlQ1UcAAdTdlDHC1RYCrHQJcbeDvYosAV1v4udhw4jrS0OX8zdKXiIgaMDeT3Wwtsau3XKUWkVlYjuS8MiTn3kDK9bqvyXllyL9RjZySKuSUVOFISkG91wkC4K2whtLFBkoX27qvzjbwc7GBr7MNHKx4KTU1ji0qRESkF8XlNUi5XobU6zeQkncDafk3H9fLUVbV+DiYWxxtLODnXFe0+DhZw9fpf993cLSGlQVbY9oSdv0QEZHREEUR18uqcSX/Bq7kl9d9LShHWn45MgvKG519906udnJ0cLKGj5M1fByt4a15WKGDozUU1hacH8aEsFAhIiKTUVZVi4yCcqQXlCOjoByZhRXILCxHRkEFMgrLUV6tuuc+rC3M4OVoBW+FNTwVVvBWWMFTYQ0vhRU8HKzgpbCCow2LGWPBMSpERGQy7OTmmsnn7iSKIorKa3C1qK54ySyswNWiClwrqsC1okpkFVfgelk1KmpUSMmr63JqiqW5DB4Ocng61BUvdQ853O2t4G4vh7uDFdwd5LCXm7OgMSIsVIiIyGgJggAnW0s42VqiSwdFo9tU1qiQVVxXtGTdLF6uFVcip7gSWcWVyCmpRP6NalTXqutaaZqYtfcWKwsZ3OzlcLOrK2Jc7S3hZmcFN3s5XO0s4XpznYudJWwseRo1NP6EiYjIpFlZmN28JNq2yW0qa1TILalCbmklsksqb16dVInckkrklt78vrQKpZW1qKzRrqABABtLM7jYWcLFtq6Icba1hIudHC62dd/f+bC2MGNrjY5YqBARUZtnZWEGP5e6y6HvpqJahbzSKuSVVSKvtAq5pVW4XlqFvLIq5JVWI6+s7vn1sipU1apRXq1CuZZFDVA3WZ6zrSWcbCzhZGsBRxtLONtYwsmm7nsnWws4WltCYWMBJxtLOFpbwMHaol3PBMxChYiI6CZrS+0KGlEUcaNahfyyuqIlv6wa+TeqUXCjWvO8sLz6f19vdj1V1apvdlNV6pTL3socCmsLKKwt4GhT99XB6uZX6/99dbAyv/nVAg7W5nCwsoDcXGbSrTgsVIiIiHQkCALs5Oawk5tD6dJ0l9MtoiiivFqFght1hcutr4U3alBUXo3C8hoUllejqLwGRRU3v5bXaOafKa2sRWllLTILtWu5uZ2lmQz2VuY3HxYNv5ebw87KHHZyC9jdfG5787PZW5lDYWMh6YR8LFSIiIgMTBAE2N4sAHyd795ac7salRrFFTUorqgrXEoq6gqZkopazfLiirrlJZU1KKmoRUll3bKyqlqIIlCtUiP/RrVW89U05uEunvj06R7Neq0+sFAhIiIyUhZmMrjayeFqJ9f5tWq1iBvVtSiprEVpZc3NVpm6r7eWlVXWoqyqFmU3l5VV1eBGlQplVXUtOGVVNZLfaJKFChERURskkwk3u3csAFg3ez9Szwsrk/Tdb/r444/h7+8PKysr9O7dG8eOHZM6EhEREQGSD8SVvFBZt24d5syZg4ULF+LUqVPo2rUrhg0bhtzcXKmjERERkcQkL1RWrFiB6dOnY+rUqejcuTM+++wz2NjY4KuvvpI6GhEREUlM0kKluroaJ0+exNChQzXLZDIZhg4disOHDzfYvqqqCiUlJfUeRERE1HZJWqhcv34dKpUKHh4e9ZZ7eHggOzu7wfZLliyBQqHQPHx9fVsrKhEREUlA8q4fXcyfPx/FxcWaR0ZGhtSRiIiIyIAkvTzZ1dUVZmZmyMnJqbc8JycHnp6eDbaXy+WQy3W/lpyIiIhMk6QtKpaWlujRowf27NmjWaZWq7Fnzx707dtXwmRERERkDCSf8G3OnDmYPHkyoqOj0atXL6xcuRI3btzA1KlTpY5GREREEpO8UJkwYQLy8vLw+uuvIzs7G926dcPOnTsbDLAlIiKi9kcQpZ4btwVKSkqgUChQXFwMBwcHqeMQERGRFnQ5f5vUVT9ERETUvrBQISIiIqPFQoWIiIiMluSDaVvi1vAaTqVPRERkOm6dt7UZJmvShUppaSkAcCp9IiIiE1RaWgqFQnHXbUz6qh+1Wo1r167B3t4egiC0eH8lJSXw9fVFRkYGryIyYTyOpo/H0PTxGLYNhjqOoiiitLQU3t7ekMnuPgrFpFtUZDIZfHx89L5fBwcH/mK1ATyOpo/H0PTxGLYNhjiO92pJuYWDaYmIiMhosVAhIiIio8VC5TZyuRwLFy7kHZpNHI+j6eMxNH08hm2DMRxHkx5MS0RERG0bW1SIiIjIaLFQISIiIqPFQoWIiIiMFgsVIiIiMlptvlD5+OOP4e/vDysrK/Tu3RvHjh276/Y///wzwsLCYGVlhYiICPz222/11ouiiNdffx1eXl6wtrbG0KFDkZSUZMiP0O7p+xhOmTIFgiDUewwfPtyQH4Gg23E8f/48/va3v8Hf3x+CIGDlypUt3ie1nL6P4RtvvNHgdzEsLMyAn4B0OYZr1qzBgAED4OTkBCcnJwwdOrTB9q1yThTbsLVr14qWlpbiV199JZ4/f16cPn266OjoKObk5DS6/aFDh0QzMzPxvffeExMSEsT/+7//Ey0sLMT4+HjNNkuXLhUVCoW4ZcsW8cyZM+Kjjz4qBgQEiBUVFa31sdoVQxzDyZMni8OHDxezsrI0j4KCgtb6SO2Srsfx2LFj4rx588SffvpJ9PT0FD/44IMW75NaxhDHcOHChWJ4eHi938W8vDwDf5L2S9dj+OSTT4off/yxGBcXJyYmJopTpkwRFQqFmJmZqdmmNc6JbbpQ6dWrlzhz5kzNc5VKJXp7e4tLlixpdPvHH39cHDlyZL1lvXv3Fl944QVRFEVRrVaLnp6e4vvvv69ZX1RUJMrlcvGnn34ywCcgfR9DUawrVEaPHm2QvNQ4XY/j7ZRKZaMnuZbsk3RniGO4cOFCsWvXrnpMSXfT0t+Z2tpa0d7eXvzmm29EUWy9c2Kb7fqprq7GyZMnMXToUM0ymUyGoUOH4vDhw42+5vDhw/W2B4Bhw4Zptk9NTUV2dna9bRQKBXr37t3kPqn5DHEMb4mNjYW7uztCQ0MxY8YM5Ofn6/8DEIDmHUcp9klNM+TPOykpCd7e3ggMDMRTTz2F9PT0lsalRujjGJaXl6OmpgbOzs4AWu+c2GYLlevXr0OlUsHDw6Pecg8PD2RnZzf6muzs7Ltuf+urLvuk5jPEMQSA4cOH49tvv8WePXvw7rvvYt++fXj44YehUqn0/yGoWcdRin1S0wz18+7duzdiYmKwc+dOfPrpp0hNTcWAAQNQWlra0sh0B30cw9deew3e3t6awqS1zokmffdkouZ44oknNN9HREQgMjISQUFBiI2NxZAhQyRMRtS+PPzww5rvIyMj0bt3byiVSqxfvx7PPvushMnoTkuXLsXatWsRGxsLKyurVn3vNtui4urqCjMzM+Tk5NRbnpOTA09Pz0Zf4+npedftb33VZZ/UfIY4ho0JDAyEq6srLl++3PLQ1EBzjqMU+6SmtdbP29HRER07duTvogG05BguW7YMS5cuxa5duxAZGalZ3lrnxDZbqFhaWqJHjx7Ys2ePZplarcaePXvQt2/fRl/Tt2/fetsDwB9//KHZPiAgAJ6envW2KSkpwdGjR5vcJzWfIY5hYzIzM5Gfnw8vLy/9BKd6mnMcpdgnNa21ft5lZWVITk7m76IBNPcYvvfee3jzzTexc+dOREdH11vXaudEvQ3LNUJr164V5XK5GBMTIyYkJIjPP/+86OjoKGZnZ4uiKIrPPPOM+K9//Uuz/aFDh0Rzc3Nx2bJlYmJiorhw4cJGL092dHQUt27dKp49e1YcPXo0L082IH0fw9LSUnHevHni4cOHxdTUVHH37t1iVFSUGBISIlZWVkryGdsDXY9jVVWVGBcXJ8bFxYleXl7ivHnzxLi4ODEpKUnrfZJ+GeIYzp07V4yNjRVTU1PFQ4cOiUOHDhVdXV3F3NzcVv987YGux3Dp0qWipaWluGHDhnqXkJeWltbbxtDnxDZdqIiiKK5atUr08/MTLS0txV69eolHjhzRrBs4cKA4efLketuvX79e7Nixo2hpaSmGh4eLv/76a731arVaXLBggejh4SHK5XJxyJAh4sWLF1vjo7Rb+jyG5eXl4kMPPSS6ubmJFhYWolKpFKdPn86TWyvQ5TimpqaKABo8Bg4cqPU+Sf/0fQwnTJggenl5iZaWlmKHDh3ECRMmiJcvX27FT9T+6HIMlUplo8dw4cKFmm1a45woiKIo6q99hoiIiEh/2uwYFSIiIjJ9LFSIiIjIaLFQISIiIqPFQoWIiIiMFgsVIiIiMlosVIiIiMhosVAhIiIio8VChYiIiIwWCxWiViQIArZs2SJ1jFaRlpYGQRBw+vRpqaMYhL+/P1auXNns19/6+QiCgG7duuktV1NiY2M17zdmzBiDvx+RvrBQIWqhKVOmaE4AFhYW8PDwwIMPPoivvvoKarW63rZZWVn1bm1/N6ZU1EyZMqXByc/X1xdZWVno0qWLNKFMxO7duxvcSNMQ+vXrh6ysLDz++OMGfy8ifWKhQqQHw4cPR1ZWFtLS0rBjxw488MADmD17Nh555BHU1tZqtvP09IRcLpcwaesxMzODp6cnzM3NpY5i1FxcXODi4mLw97G0tISnpyesra0N/l5E+sRChUgP5HI5PD090aFDB0RFReHf//43tm7dih07diAmJkaz3e2tJNXV1Xj55Zfh5eUFKysrKJVKLFmyBEBdtwIAjB07FoIgaJ4nJydj9OjR8PDwgJ2dHXr27Indu3fXy+Lv74933nkH06ZNg729Pfz8/PD555/X2yYzMxMTJ06Es7MzbG1tER0djaNHj2rWb926FVFRUbCyskJgYCAWLVpUr+C63RtvvIFvvvkGW7du1bQsxcbGNuj6udX18OuvvyIyMhJWVlbo06cPzp071+TPVRRFvPHGG/Dz84NcLoe3tzdmzZqlWf/dd98hOjoa9vb28PT0xJNPPonc3FzN+lvv+fvvv6N79+6wtrbG4MGDkZubix07dqBTp05wcHDAk08+ifLycs3rBg0ahJdffhkvv/wyFAoFXF1dsWDBAtzt1mhFRUV47rnn4ObmBgcHBwwePBhnzpxpcvum3GqdWrZsGby8vODi4oKZM2eipqZGs42/vz/eeustTJo0CXZ2dlAqldi2bRvy8vIwevRo2NnZITIyEidOnND5/YmMDQsVIgMZPHgwunbtik2bNjW6/qOPPsK2bduwfv16XLx4ET/88IOmIDl+/DgA4Ouvv0ZWVpbmeVlZGUaMGIE9e/YgLi4Ow4cPx6hRo5Cenl5v38uXL0d0dDTi4uLw0ksvYcaMGbh48aJmHwMHDsTVq1exbds2nDlzBq+++qqmm+rAgQOYNGkSZs+ejYSEBKxevRoxMTF4++23G/0c8+bNw+OPP65pVcrKykK/fv2a/Lm88sorWL58OY4fPw43NzeMGjWq3kn4dhs3bsQHH3yA1atXIykpCVu2bEFERIRmfU1NDd58802cOXMGW7ZsQVpaGqZMmdJgP2+88Qb++9//4q+//kJGRgYef/xxrFy5Ej/++CN+/fVX7Nq1C6tWrar3mm+++Qbm5uY4duwYPvzwQ6xYsQJffPFFk59r/PjxmgLo5MmTiIqKwpAhQ1BQUNDka5qyd+9eJCcnY+/evfjmm28QExNTr+AFgA8++AD33Xcf4uLiMHLkSDzzzDOYNGkSnn76aZw6dQpBQUGYNGnSXYsrIpOg13sxE7VDkydPFkePHt3ougkTJoidOnXSPAcgbt68WRRFUfz73/8uDh48WFSr1Y2+9vZt7yY8PFxctWqV5rlSqRSffvppzXO1Wi26u7uLn376qSiKorh69WrR3t5ezM/Pb3R/Q4YMEd955516y7777jvRy8uryQyN/QxSU1NFAGJcXJwoiqK4d+9eEYC4du1azTb5+fmitbW1uG7dukb3u3z5crFjx45idXV1k+99u+PHj4sAxNLS0nrvuXv3bs02S5YsEQGIycnJmmUvvPCCOGzYMM3zgQMHip06dap3bF577bV6x1KpVIoffPCBKIqieODAAdHBwUGsrKyslycoKEhcvXp1o1nv/PncMnnyZFGpVIq1tbWaZePHjxcnTJhQ771vP8ZZWVkiAHHBggWaZYcPHxYBiFlZWQ3239S/VyJjxBYVIgMSRRGCIDS6bsqUKTh9+jRCQ0Mxa9Ys7Nq16577Kysrw7x589CpUyc4OjrCzs4OiYmJDVpUIiMjNd8LggBPT09Nl8jp06fRvXt3ODs7N/oeZ86cweLFi2FnZ6d5TJ8+HVlZWfW6R5qrb9++mu+dnZ0RGhqKxMTERrcdP348KioqEBgYiOnTp2Pz5s31uqBOnjyJUaNGwc/PD/b29hg4cCAA3PXn4eHhARsbGwQGBtZbdnuXEQD06dOn3rHr27cvkpKSoFKpGuQ8c+YMysrK4OLiUu/nlpqaiuTkZG1+LPWEh4fDzMxM89zLy6tBvjs/E4B6rU23lt35OiJTw1FuRAaUmJiIgICARtdFRUUhNTUVO3bswO7du/H4449j6NCh2LBhQ5P7mzdvHv744w8sW7YMwcHBsLa2xmOPPYbq6up621lYWNR7LgiCpmvnXoMpy8rKsGjRIowbN67BOisrq7u+Vt98fX1x8eJF7N69G3/88QdeeuklvP/++9i3bx+qq6sxbNgwDBs2DD/88APc3NyQnp6OYcOG3fXncevqrNvd/vNpjrKyMnh5eSE2NrbBOkdHR533p02+Oz9TU8ta8rmIjAELFSID+fPPPxEfH49//vOfTW7j4OCACRMmYMKECXjssccwfPhwFBQUwNnZGRYWFg3+ej906BCmTJmCsWPHAqg7QaalpemUKzIyEl988YXmfe4UFRWFixcvIjg4WOt9WlpaNtrS0JgjR47Az88PAFBYWIhLly6hU6dOTW5vbW2NUaNGYdSoUZg5cybCwsIQHx8PURSRn5+PpUuXwtfXFwD0Onj09sHFt3KHhITUa+m4JSoqCtnZ2TA3N9eMMyIi/WChQqQHVVVVyM7OhkqlQk5ODnbu3IklS5bgkUcewaRJkxp9zYoVK+Dl5YXu3btDJpPh559/hqenp+YvcH9/f+zZswf33Xcf5HI5nJycEBISgk2bNmHUqFEQBAELFizQ+S/miRMn4p133sGYMWOwZMkSeHl5IS4uDt7e3ujbty9ef/11PPLII/Dz88Njjz0GmUyGM2fO4Ny5c3jrrbca3ae/vz9+//13XLx4ES4uLlAoFE2+/+LFi+Hi4gIPDw/85z//gaura5MTkMXExEClUqF3796wsbHB999/D2trayiVSqjValhaWmLVqlV48cUXce7cObz55ps6/SzuJj09HXPmzMELL7yAU6dOYdWqVVi+fHmj2w4dOhR9+/bFmDFj8N5776Fjx464du0afv31V4wdOxbR0dF6y0XU3nCMCpEe7Ny5E15eXvD398fw4cOxd+9efPTRR9i6dWujf4EDgL29Pd577z1ER0ejZ8+eSEtLw2+//QaZrO7Xcvny5fjjjz/g6+uL7t27A6grbpycnNCvXz+MGjUKw4YNQ1RUlE5ZLS0tsWvXLri7u2PEiBGIiIjA0qVLNTmHDRuG7du3Y9euXejZsyf69OmDDz74AEqlssl9Tp8+HaGhoYiOjoabmxsOHTrU5LZLly7F7Nmz0aNHD2RnZ+OXX36BpaVlo9s6OjpizZo1uO+++xAZGYndu3fjl19+gYuLC9zc3BATE4Off/4ZnTt3xtKlS7Fs2TKdfhZ3M2nSJFRUVKBXr16YOXMmZs+ejeeff77RbQVBwG+//Yb7778fU6dORceOHfHEE0/gypUrmrEiRNQ8gijy2jUiMrzY2Fg88MADKCwsbNa4jdY0aNAgdOvWrUVT5N9LWloaAgICEBcX1ypT6N8yZcoUFBUVmcysx0RsUSEiklC/fv3uOu+Mvhw4cAB2dnb44YcfDP5eRPrEMSpERBLw8fFBUlISALTKbRWio6M1swTb2dkZ/P2I9IVdP0RERGS02PVDRERERouFChERERktFipERERktFioEBERkdFioUJERERGi4UKERERGS0WKkRERGS0WKgQERGR0fp/EQCweAdUr8EAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from pint import UnitRegistry\n", + "\n", + "# Set up unit system\n", + "unit = UnitRegistry()\n", + "unit.formatter.default_format = \"~\"\n", + "unit.setup_matplotlib()\n", + "\n", + "# Physical constants\n", + "e=-1.602176634e-19 * unit.C # electron charge\n", + "m=9.109e-31 * unit.kg # electron mass\n", + "hbar=6.62607015e-34/2/np.pi * unit.joule * unit.second # Planck constant\n", + "phi=4 * unit.eV # Work function (see table)\n", + "phi_joule=phi.to(\"joule\")\n", + "U=5 *unit.V\n", + "\n", + "# Table working functions different metals\n", + "\n", + "# Metal F(eV)\n", + "# (Work Function)\n", + "# Ag (silver) 4.26\n", + "# Al (aluminum) 4.28\n", + "# Au (gold) 5.1\n", + "# Cs (cesium) 2.14\n", + "# Cu (copper) 4.65\n", + "# Li (lithium) 2.9\n", + "# Pb (lead) 4.25\n", + "# Sn (tin) 4.42\n", + "# Chromium 4.6\n", + "# Molybdenum 4.37\n", + "# Stainless Steel 4.4\n", + "# Gold 4.8\n", + "# Tungsten 4.5\n", + "# Copper 4.5\n", + "# Nickel 4.6\n", + "\n", + "# Distance range\n", + "Distance_tip_sample=np.linspace(10e-13,2e-10,100)* unit.m\n", + "Tunneling_current=U*np.exp(-2*np.sqrt(2*m*phi_joule)/hbar*Distance_tip_sample) /unit.V #please note: This is not the tunneling current as this formular gives just the proportionality. Calculating the current constant is difficult as there are for us unknown parameters\n", + "Distance_tip_sample_nm=Distance_tip_sample.to(\"nm\")\n", + "\n", + "plt.plot(Distance_tip_sample_nm, Tunneling_current)\n", + "plt.xlabel(f\"Distance tip sample [{Distance_tip_sample_nm.units:~P}]\")\n", + "plt.ylabel(f\"Tunneling-Proportionality [arb. Unit]\")\n", + "plt.xticks(ticks=np.linspace(0, 0.2, 5), labels=[f\"{x:.2f}\" for x in np.linspace(0, 0.2, 5)])\n", + "#plt.yscale(\"log\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} -- 2.47.2 From ae9c00dea4156704371f1a59f14a6713d5d87a2a Mon Sep 17 00:00:00 2001 From: k8ik Date: Sat, 21 Jun 2025 17:16:06 +0200 Subject: [PATCH 2/2] started setting up calculations for eddy current brake simulations --- .../Eddy-current-brake-simulation.ipynb | 1110 +++++++++++++++++ 1 file changed, 1110 insertions(+) create mode 100644 Calculations/Eddy-current-brake-simulation.ipynb diff --git 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\n", + "# Dimensions assumed are 0.5, 1.5 and 3 cm (x,y,z) \n", + "\n", + "\n", + "# Question: Is there a more elegant way to do this? Should I make a for-loop for creating our magnet array?\n", + "\n", + "magnet1 = magpy.magnet.Cuboid(polarization=(1.5, 0, 0), dimension=(0.005, 0.015, 0.03)) \n", + "magnet1.position = (0, 0, 0)\n", + "magnet1.orientation = R.from_euler(\"y\", 90, degrees=True)\n", + "\n", + "magnet2 = magpy.magnet.Cuboid(polarization=(1.5, 0, 0), dimension=(0.005, 0.015, 0.03)) \n", + "magnet2.position = (0.04, 0, 0)\n", + "magnet2.orientation = R.from_euler(\"y\", 90, degrees=True)\n", + "\n", + "magnet3 = magpy.magnet.Cuboid(polarization=(1.5, 0, 0), dimension=(0.005, 0.015, 0.03)) \n", + "magnet3.position = (0, 0.04, 0)\n", + "magnet3.orientation = R.from_euler(\"y\", 90, degrees=True)\n", + "\n", + "magnet4 = magpy.magnet.Cuboid(polarization=(1.5, 0, 0), dimension=(0.005, 0.015, 0.03)) \n", + "magnet4.position = (0.04, 0.04, 0)\n", + "magnet4.orientation = R.from_euler(\"y\", 90, degrees=True)\n", + "\n", + "magnet5 = magpy.magnet.Cuboid(polarization=(1.5, 0, 0), dimension=(0.005, 0.015, 0.03)) \n", + "magnet5.position = (0, 0.08, 0)\n", + "magnet5.orientation = R.from_euler(\"y\", 90, degrees=True)\n", + "\n", + "magnet6 = magpy.magnet.Cuboid(polarization=(1.5, 0, 0), dimension=(0.005, 0.015, 0.03)) \n", + "magnet6.position = (0.04, 0.08, 0)\n", + "magnet6.orientation = R.from_euler(\"y\", 90, degrees=True)\n", + "\n", + "magnet7 = magpy.magnet.Cuboid(polarization=(1.5, 0, 0), dimension=(0.005, 0.015, 0.03)) \n", + "magnet7.position = (0, 0.12, 0)\n", + "magnet7.orientation = R.from_euler(\"y\", 90, degrees=True)\n", + "\n", + "magnet8 = magpy.magnet.Cuboid(polarization=(1.5, 0, 0), dimension=(0.005, 0.015, 0.03)) \n", + "magnet8.position = (0.04, 0.12, 0)\n", + "magnet8.orientation = R.from_euler(\"y\", 90, degrees=True)\n", + "\n", + "\n", + "# Grouping the magnets to collection\n", + "coll = magpy.Collection(magnet1, magnet2, magnet3, magnet4, magnet5, magnet6, magnet7, magnet8)\n", + "\n", + "# Plotting the magnet array\n", + "magpy.show(coll, backend=\"plotly\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70a4312d", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "31021d1f168d4c86a26e512b68ff75d1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Widget(value='