xschem/xschem_library/examples/mos_power_ampli.sch

807 lines
62 KiB
Plaintext
Raw Normal View History

2021-12-28 00:44:59 +01:00
v {xschem version=3.0.0 file_version=1.2 }
2020-08-08 15:47:34 +02:00
G {}
K {}
2020-08-08 15:47:34 +02:00
V {}
S {}
2020-08-08 15:47:34 +02:00
E {}
L 15 270 -460 340 -390 {}
L 15 270 -330 340 -390 {}
B 2 1520 -750 2440 -70 {flags=graph
y1=-26.8382
y2=73.1618
2021-12-28 00:44:59 +01:00
divy=4
subdivy=4
x1=0.0157884
x2=0.0164453
divx=8
subdivx=1
node="tcleval($\{path\}ga $\{path\}gb $\{path\}outi vnn)"
2021-12-28 00:44:59 +01:00
color="7 8 10 11 12 13 14 15 16 17"
dataset=0
unitx=m
}
B 2 960 -890 1094 -803 {flags=image image=x/2.png image_data="
iVBORw0KGgoAAAANSUhEUgAAAkgAAAGXCAIAAADH/DifAAAABmJLR0QA/wD/AP+gvaeTAAAgAElEQVR4nOzdZ1xT5/sw8DshQCIrIFsQFcRRJ6IigoCACoIKVgWrddRV
Fy4UJ8HirFaq1lnrKIILrRUHigKKwk9BQNAKOJiyh+yZPC/up+efHpIYIIt4fV/4Se5zcnLlCLm4N4XD4SAAAABAXlClHQAAAAAgSpDYAAAAyBVIbAAAAOQKJDYAAABy
BRIbAAAAuQKJDQAAgFyBxAYAAECuQGIDAAAgV+Q2sVVWVsbExFRWVko7EFmHb5S0o+gCsrKy4EYJIzk5OTk5WdpRdAHJyclZWVnSjqIL6MCvHk1MoUhdeHj43Llzhw4d
ymQypR2LTKusrExJSbGzs5N2ILIuKysrOzsbbtQX4aw2bNgwaQci65KTk5lMZq9evaQdiKzDv3pVVVVqampCvkRua2wAAAC+ThR5XSsyOjrawcEhKirK3t5e2rHINHyj
5PXHQIRYLFZAQADcqC/Cv3HR0dFSjkPm2dvb29vbs1gsaQci6zrwqwc1NgAAAHIFEhsAAAC5AokNAACAXIHEBgAAQK5AYvvaMZlMGMIOAJAnkNi+dsOGDYMBbAAAeQKJ
DQAAgFyBxAYAAECuQGIDAAAgVyCxyRZFRUUKF2Vl5W+++Wb79u319fX4BGdnZwqFMmbMGO5X+fr6UigURUVF/PTkyZP45fn5+TzfpaysbPPmzcOGDVNXV9fR0Rk1atQv
v/xCvAVCSFNTk8KLjo4O6VIPHz7keSa2efNmIa92//79SZMmmZiYMBiMvn37zpgxIzExER/avn27gLeIiIjo1B0HAMgdSGxkaWlpnp6eBgYGWlpa48ePf/ToEXGIzWbv
2rXLwsJCS0vL1dX1/v374g6mqanpzZs3gYGB8+bNE9U1P3/+PHr06L1796akpFRXV5eWlr548WL9+vVOTk4tLS2iepd2+e233yZOnBgREZGTk9PQ0PDu3btr166NHDny
9u3bUokHANClQWL7j/T0dGtr6xs3bhQVFdXU1ERFRTk7OxN1grlz527bti0pKamuru7u3btubm5//fWXOMJYvnx5Xl5ebm7u8+fP8Vj8q1evZmZmiuTihw8ffv/+PYPB
OH36dE5OzsePH3G96tmzZxcuXOA+c86cOXn/9erVK9LVbGxsiKM//PADQqhbt25ECb6y4Ks1NTVt2LABIeTm5vbkyZOysrJHjx6Zm5tzOJwff/wRIeTr60u8ZODAgQgh
R0dHogTWAgUAkEBi+4/ffvuturraysoqOzu7uLh43rx5bDZ7//79CKGUlJSQkBAFBYWIiIiamppVq1Y1Nzf7+vqKIwx1dfUePXoYGRmNHDly9+7duPDNmzciufjLly8R
Qg4ODosWLTI2Nu7Vq9fu3bttbGzodHpSUhL3maqqqj3+y8DAgHQ1ZWVl4ijeVIJKpRIl6urqX7zaP//809DQgBDasWOHjY2NlpaWg4PD4cOH6XR6SUlJUVERvhuYkpIS
QojBYBAlysrKIrktAAC5AYntP0pKShBCs2bNMjY2ZjKZuAry6dMnhNDFixcRQpaWlhMmTKDRaD4+Pgihd+/epaSkIIScnZ1dXFyI6wwfPnzu3LnE0/T09FmzZhkZGamo
qAwYMIDFYtXU1AgZEv4qRwiJ6hu8W7duCKH4+PhHjx4RC2Y/efKkvr7+yJEjInmLDsSDEDp+/HhZWRl+PHHixPr6+vr6ej09PcmHBADo0iCx/YebmxtC6I8//khMTPzw
4cPBgwcRQq6urgihjIwMhJCpqSk+s0+fPhQKBSGUl5cn+JrFxcX29vZXrlwpKipiMBhv374NCAjw8vISJp6ioqK9e/cihCgUCm6F67zZs2cjhMrLyx0dHXV0dNzc3LZv
3/748ePW1lbSmSdOnCCN1MA3pGP4Xc3U1HTUqFEIobNnz+rq6g4fPnzx4sW///57QUFBJz4lAODrBYntP7777rtNmzalpqZaWlqamprevHnT0dExMDAQIVRVVYUQUlVV
xWdSKBQVFRWEUGlpqeBr3r9/v7CwkEaj5eXllZaWXr9+XVVVNSYmRsAX9969e/FXv76+flhYGELI29u7Z8+eIvmMkydPDgsLGzp0KEKorKzs9u3bgYGBdnZ2gwYNev36
tUjeol2oVOrt27d/+OEHdXV1NpudnJz8+++/L1682MjIaOvWrZKPBwDQ1UFi+4+wsLCgoCCE0PDhw8eNG0elUh89evTrr78ihGg0GulkXGNjs9mCr9mnTx+EUEtLi62t
7dq1axUVFT99+lRdXd22v4onVVXVxYsXnzp1qgMfhx9PT8/k5OT8/PywsLBNmzYNHjwYIfT27VvS2Mu2wz2WLl3a4TcVcDVtbe3ff/+9rKwsPj7+6NGjs2bNYjAYbDZ7
9+7d4eHhnfmkAICvECS2/9i4cWNjY+O6detevnwZExPz119/cTicHTt2VFVV4UlX1dXV+EwOh1NbW4sQ+mJ+sra29vf3NzIyyszMDAoKcnd319HR2bRpU3NzM7+X4FGR
eXl5nz59qqqqOnXqFK4dIoQUFBQQQqRmQzxMHx/6orKysrKysvr6ekNDQ09Pz71797569QqPP0xMTCQ+IOI13IOosHYAv6vV19fjkGg02ujRo1esWHHp0qW0tDTc9xYV
FdXhdwQAfJ0gsf2f0tLSDx8+IISsrKxwiaWlJUKoubk5KSnJyMgIIZSeno4PvX//HtfVDA0NcYmAnctZLFZubm5KSsrevXtHjx7d2Ni4f//+Bw8e8DufGAdoYGCA64WE
3r17I4Q+fPjQ1NREFL59+xb9WzX8onHjxmlray9evJi7EM/4VlBQaFsxFbfw8HBtbW1tbe13794RhX369NHX10cI0el0CccDAOjqILH9H21tbVz9OnToUEJCQnp6+pYt
WxBCVCr1m2++mT59OkIoOTk5JCSksLBw+/btCKH+/fvjdjyEENFBVVJSkpeXhytVbDZ7xYoVxsbGXl5effv23bRp0/Xr1/H4RjwCs71sbW0RQmVlZXPnzo2Li0tOTv7p
p5/u3buHEBo3bhzp5OLi4qL/Qgg5OTkhhEJDQw8ePFhQUFBTUxMbG4snFQwbNozBYBAvr6urK2pDQEVTMH5XGzduHB75OWfOnPj4+Jqamvz8fH9/f/xHBmmNFQAA+DKO
nMJNWFFRUe161ZUrV9reIhaLhY96e3tzlysoKFy+fBkfwtli//79f/3115QpU6ysrAYOHNjQ0PDbb7+Fh4dTqVSEEI1GMzIywo+NjY3LysraBoArTH5+fgKC9PDwaBtk
nz59ysvL8QknTpzg+X9Np9M5HM7nz5/79evX9iiDwXj58iW+ApPJ5PcDEx8fzy+wNWvWIIRUVVVJ5V+8Gr+AZ82aRbrUsGHDEEJubm4C7o+Y+Pv7I/n9fREhOzs7Ozs7
aUfRBdjZ2fn7+0s7ii6gA796UGP7jxkzZjx+/HjixIl6enpMJtPa2vry5cv4tiKEgoOD/f39hw8frqGhYW9vHx4ePnPmTHxIS0tr9uzZu3btOnTo0M8//3zkyJHm5uYh
Q4YMGzZs8uTJd+7cmTRpkq6ubnFxcY8ePebMmRMVFaWlpdWxIMPCwv788087Ozs9PT0NDY2RI0cGBASkpqZqamoK83J1dfUXL17s2rVrxIgR3bt3V1VVHTRo0I8//vjP
P/8MHz68YyF10tKlS2NjY6dNm2Zqakqn042MjOzt7S9evBgSEiKVeAAAXRqFw79nqEuLjo52cHCIioqSzJJL/fv3T05Ohg4hOcZisQICAuT190WE8G8c7F77Rfb29vb2
9iwWS9qByLoO/OpBjU0ECgoK0tPTnz59Ku1AAAAAQGITBRUVlX79+sHiTwAAIAskPbZbLqmrq+MB9wAAAKQOamwAAADkCiQ2AAAAcgUSGwAAALkCiQ0AAIBcgcQGAABA
rkBi+9olJyc7ODhIOwoAABAZSGxfu8rKSlgkAgAgTyCxAQAAkCuQ2AAAAMgVSGwAAADkCiQ2AAAAcgUSGwAAALkCiQ0AAIBcgcQGAABArkBiAwAAIFcgsQEAAJArkNgA
AADIFUhsAAAA5AokNgAAAHIFEhsAAAC5AokNAACAXIHEBgAAQK5AYgMAACBXZCixBQYGUv5LTU2NOBoWFmZvb89kMseOHXv48GEpxgkAAECW0aQdwP/5559/+B06derU
smXLOByOsrLys2fPnj17lpeXt3//fkmGBwAAoEuQoRrbP//8Q6fTc3Nz8/6Vnp6OEGpqatqxYweHw2GxWLW1tZcvX0YIHThwIDc3V9ohAwAAkDmyktg4HE56erqpqamR
kVGPfxkaGiKEIiMji4qKqFTqhg0bFBQUZs6caWRkxOFwrl+/Lu2oAQAAyBxZaYrMycmpq6tTUFCYNGnS06dPjYyMvLy8tm7dSqPRMjIyEEL6+voqKir4ZFNTU1ylk2LA
a9euraioMDQ0xAnYw8ODdEJxcbGurq5UYgMAgK+ZrCQ23MH26tWrN2/eqKiovH37lsViZWVlnT17tqqqCiGkqqpKnIwHlZSWln7xsufPn4+JiRFHwH/88QcODNu6daui
oiLphIaGhjFjxgwZMkRBQUEcMYhEVlYWQiggIEDagcg6/IMEN+qLsrOzEdwoIWRnZ8fExMCN+qIOfIfLSmLjcDjTpk3T0ND49ddfNTQ09u7du3nz5nPnzm3evJlGIwdJ
oVAQQmw2+4uXTUpK+vjxozgC5s5qNBotNjaW+2h1dXVOTg5C6ObNm3fu3DE0NOzVq5c4wui8yspKhFBUVJS0A5F1+C8AuFFfVFFRgeBGCaGiooLD4XA4HGkHIuvwr177
cGRSc3OzkpISQigkJOTUqVMIIQMDA+Kovb09QmjTpk0CroB/r6KiosQRHv6blDB48GDSCQsXLuQ+wcnJSRxhiAS+UdKOogvw9/eHGyUMOzs7Ozs7aUfRBdjZ2fn7+0s7
ii6gA796slJje/ToUUlJiampqaWlJUKopaUFV8hUVFSYTCZCqKioqLKyEj/OzMxECOGhJVJBGpBpbGzM/bSqqurixYvcJatXr5ZEWAAAAGRnVGRoaKiXl5enp2diYmJp
aen69etbWlqUlZVHjRo1fvx4JpPJZrMDAgKKi4sPHjyYn59Po9E8PT2lFa3gxHbmzJnGxkbuo25ubhKKDAAAvnqyktjWrVunoqKSm5traWmpo6Nz7NgxhNCuXbv09fWV
lZX37NlDoVCCgoL09PQ2bNiAEFq2bJmRkZG0ohWQ2Dgczm+//cZ9dOXKlbhTEAAAgATISlPkgAED4uPjt2/f/r///a++vn7w4MG+vr7u7u746LJly5hM5smTJ1++fGlm
ZjZ79uz169dLMVrSTIOePXsSj2NiYt6/f899dMmSJRIKCwAAZFh9fX0rl5aWltY22hbiwSNv3rwZOHCgkG8kK4kNITRo0KAbN27wO+rl5eXl5SXJeATAIx4J3HXHM2fO
cB+aN28e7hcEAACM+MpubGysrKz84jd7u9KAbBZyOj34c8eOHdeuXRPyZBlKbF0Iv6bI2traq1evch9avHix5MICoAtqaWnp5JemmL6LxVTIPU8pNjZ27969Urz5XUhr
a6vwJ0Ni6whSYjt//rylpSWTyUxOTuYeNmJmZjZ27FiJRwfkVl1dXUtLS3Nzc3NzM37Q9inxAH+NEmmD52Piy5co7/xX+bt37zgcTr9+/QScWVdXJ+17CboYSGxiV1xc
zP00MDCQ52kmJiY3b95kMplDhw6FBskuoaamppkP/NfMw4cPeR5tamoiHhPJRkD6adfThoYGad+YdsMr4QEgKpDYxIu0lImKikptbS3PMx8+fPjw4UPi6cGDB9etWyfe
4CQuNze3sLCQuAMt7dTJd+eZV9o+FZBympuba2pqhH9HJyenTsYMQJdGp9MV/otGoym0wV2oqKhIoVCEOZNn4ZMnT2JiYry9vYUPEhJbu5FGjjg7O0+dOnXVqlV4wA9R
rqioyOFwiO9uOzu7Pn36SDRQkcrNzc3JycH/fvjwITMz8927d6RbAcDXhsFgCP8FTSpMSEjQ1tYeMGCA8C/HGULIN+pYFmlb2K1bN+neZBaLFRMTM2fOHOFfAomt3Ujf
5q6urvPnz582bZq/v//hw4epVCruHG5ubsYnrFmzxsfHR2bXiuSptrY2OTk56V+vX79uamqSdlBARlGpVO4vxLq6OgqF0r17d3F8yXagUBzvQqWKYAawvb29vb09i8Xq
/KUACSS2dsvPz+d+ihcRxss3e3h4rFq1Ki0tjTiqoaERFBQUFBRkbm6+Y8eOKVOm4K0JZM2HDx9evXqVmpqamJiYlpZGmooHxE1VVZVGoykqKuJ/iQcde4q/jjHux6Sn
pMcd+H6n0+ltPwteyjU6OlrC9xAAAiS2diPV2PBY/0uXLjU1Nc2bNy81NTUoKGj79u2450ZNTS06Ojo6OjooKAhXpadPn+7l5eXm5sbzS0Ey6uvrk5OTU1JSXr16hfcl
MDU1lVYwktGtWzdFLkQOID0W4Pnz50+fPg0ICPjimcrKygoKCl9MP1Jv4QFAXkFiazfSsiM4sYWHh4eEhJw7d+7cuXNr1qyZP3++j4/PhQsX8vLyvv/++8ePH69Zs6a0
tPTq1auhoaEzZ85UU1ObNm2at7e3k5NT2315RO7Dhw9paWnJyckvX74UbYWMyWQaGhrq6Ojgp1QqlcYL/kJvW0i0YvGsEAhGXARTUlLizi74qQj/emCxWE+fPt2xY4eo
LggAEBNIbO1G2rMGLzvy4MEDhFB0dHSvXr1YLNb27dvPnz+/YMGClStX4joci8XS1tb+8ccff/zxx9zc3EuXLl26dMnFxUVHR+fbb7/19va2sbER7ZKSz58/j4mJefLk
SWxsLN4iqzMYDIaxsXGvXr2MjIz69Oljampqbm4+YMAABoMhkmgBAEBUILG1G6nGZmJikpGRUVJSQpT8/vvveAMhe3v7tLS0c+fOTZs2jfslxsbGvr6+vr6+GRkZISEh
ly5dOn78uLGx8axZs7y9vS0sLDocW1RUFM5kT5486czkJzMzs2+++aZv377m5ub9+vUzMzOT4iZBAADQLpDY2qe+vr68vJx42qNHD4TQs2fPuM8ZP34899P58+fzu5q5
uTmLxWKxWC9fvgwNDb18+fKBAwf69evn5eU1e/Zsc3PzL8ZTV1f35MmT6OjomJiYuLi49n4cgq2trYWFxcCBA4cMGTJ06FCohwEgU1paWnR1dSsqKlatWnX48GGi/ObN
m/jv5pcvXw4fPlx6AfKWmJiIt9gMCwvj3mhs7dq1QUFBWlpaRUVF4uiL+cIV6+rqsrKyiouL6XS6np5e7969RR5B10JaTAu3Q+LxFwRSYhOGhYWFhYXF/v37Y2NjQ0ND
jx07FhAQYGFh4e3tPWvWLNJ+bwihJ0+ehIeHR0VFvXjxov0fApmbmw8cOHDEiBFDhgypq6vz9vZ+/PhxB64DAJAMGo3m7u5+4cKFiIgI7nLcCdKrVy8pZrXt27fzXH3J
zMwsMzOzZ8+eOTk5ERER3IkNhz1lyhQxjTDgfdH6+vrg4OCLFy/GxcVxT2DS1dWdMGHCjz/+aG1tLY5oZB/PIZFPnjzhLuzwzaFQKLa2tra2tocPH46MjAwNDf3pp582
btxoY2Pj7e3t7u6ekJBw+/btsLCw9vaZqaqqWllZ2djYWFtbjx07lns8HgzLBqBL8PDwuHDhQkZGRlZWFjEv9v79+wih6dOnSzMyPvCgAQ8Pj19//ZU7H+fn579+/RqJ
M2xyYmttbcXVhbKyMoSQoqJi7969mUxmU1NTaWlpUVFRcHBwcHCwtbV1UFDQyJEjxRSWzGq7rn95eTn3snhaWlp9+/bt5LvQaLRJkyZNmjSpoaHh9u3bR44cWbly5fLl
y9t1EQMDg3Hjxo0dO9ba2nrEiBGdDAkAIF0TJ07s1q1bXV1dRETE0qVLEUJZWVmZmZlI2onN19d32bJlxNPPnz87Ozt/+vRp06ZNCCFPT89ff/01Ozs7PT29X79+6N/q
mpqamrOzs5hCIie2YcOGpaWlaWlprVixYubMmSNHjuTubvn06VNUVNSFCxciIyNHjx598ODBtWvXiiky2dS2KfJ///sfdwluUBYVOp0+ffr06dOnf/r0KSAg4Pr166Wl
pfxOplKpgwcPtrGxGTt27NixY7m3PwUAdHUMBmPSpEnXr18nEhvOEIaGhlZWVlIMTF1dXV1dnXjq5+f36dOnmTNn/vDDDwghGxsbHR2dkpKSiIgI7sTm5uamrKwsppDI
C8NUVVUdPXo0Pz//6NGj48aNIw0iMDQ0/O677yIiIjIzMxctWvTu3TsxhSWzSImtZ8+ez58/5y4R1U/YmTNnFixYEBoaitcXNjQ0PHnyZElJydu3b728vDQ0NIgzzc3N
N2zYcPfu3crKyuTk5KNHj3p7e0NWA0D+4G6qhw8f4kVocTukh4eHaGcKdcbt27eDg4N1dHROnTqFS6hU6tSpUxFCuDWSw+HgxMbd5SZy5MT27t27FStWCJ7W+uHDhz59
+pw6dSooKEh8kcmmtomNNHxj1KhRnXyLBw8eDBkyZNGiRefOnZs9e7aenp63t/fff/+NOzv79esXGhpaWVn55s0bPz+/YcOGZWRknD9//u+//05OTu78NrUAAJnl5uam
qKhYVVUVHx/PZrPx5iGkdshz5865uLjo6+vr6OhYW1ufOXOGWLcWIZSdnb1kyRJzc3MGg2FiYuLu7k4a+0aYNWsWz4V19u/fzy+85uZmHx8fhNDWrVu5//j28PBACEVH
Rzc2NiYnJ5eUlDAYDBcXl87dDEHIiU1RUZHfqenp6bt377awsCCWXxJwsrz6YlNka2trVlZWxy6emZnp5uY2YcKE1NRUorC2tvbSpUtTp07V19c/cuRIQUEBLh8wYMCe
PXuSkpLS09PXrFnz5MmTcePGmZiY+Pr6vnz5smMBAABkmYaGBh50HRERkZCQUFFRoa2tPW7cOHy0qanJzc1twYIF9+7dKykpqaioiIuLW7Ro0fTp0/HGIyUlJTY2NqdP
n3737l337t1zc3PDw8Pt7OwiIyPbvhebzea51ZSAv56vXr36/v17IyMj7i43hJCTk5O6unpdXV1sbCyurk2aNElFRUWEd4bky2tUp6amslisQYMG9e/ff+vWrUlJSeKL
RvaREltDQwOp02vq1Km9e/emUCj9+/e3tbVdu3ZtQEDAzZs3Y2Ji8HLJPDU0NPj5+Zmbm9++fbvtUX19/cOHD3/8+HHVqlUGBgako+bm5lu2bElNTU1NTZ07d+7169dH
jBjRv39/FosFmz0CIGdw7SciIgK3Q06bNk1BQQEf2rdv3+3btzU1NS9fvlxdXV1XVxcWFqalpXXr1q1z584hhK5cuZKXlzd8+PCSkpK8vLyioqI5c+aw2ex9+/a1faOr
V69yeMFDQni6cOECQmjOnDmkzjMlJSVXV1fusMU+2oVn6BwOJyEhYfPmzaQBfioqKm5ubkeOHOH3KtkRFRWFEIqKihLhNUmZqWfPnleuXBF8e4mfOUL//v1XrlzJfdlX
r17xm4uNNw1ob5zx8fE+Pj44BVpYWPz88885OTn8TsY3qiO34yuDV5ORdhRdgJ2dnZ2dnbSj6ALs7Oz8/f3b+6qCggIqlUqlUgcNGoQQunPnDi6vq6vD6eTcuXPc54eH
h69Zs+b8+fMcDsfPzw8h5Ozs3Nraio/m5uYePXoUH+2kwsJC/HX3/PnztkfxV6W5ubmysrKSklJlZaXwV+7Arx757GfPnq1fv560eZiWlhZ+kJWV1a6rS5E4Ehv3fjQI
IRsbG19fX+6SX3/9Fa9FQkLq2l2+fDlxzVOnTvHr0ZwzZ05paWmHo21tbY2MjFy0aJGmpiaeIXfs2LGSkhLSaUImtoSEBBxVWFgYd/maNWvwT0hzc3OHQ5WK5uZmTU1N
hNCqVau4y//66y/8SV++fMldDolNSJDYhNSxxMbhcMaOHUv84dvY2IgL8W8ohUKpr6/n90LcDIgQ6tWr14IFC44cOfL06VN+v7kzZ87kuaD5vn37eJ5/6dIlhBCTyWSz
2W2PVldXE190Li4u7fq8HfjVIzdFWltbHzx4EPcSGRoarlix4uHDh0VFRfioBNahl2VtJ7ElJiZylyxfvjwtLQ3/N3DX1TgcDkLI3t4eJ9rffvsNl3///fdLlixpu6hj
3759o6Ki/vzzz+7du3c4WiqV6ujoePr06cLCwr/++qtHjx6+vr4GBgYuLi4XLlyorq5u19VGjBiBR1ryXPhAfCsItNfChQsNDAy4l+5ECIWFhdnb2zOZzLFjxxLLEeGl
HBCfTyTdpRwAEIAYT+ju7q6kpIQff/z4ESGkqalJ5I8ZM2ZQ/uXg4IAQcnJyCgsLs7Kyys3NPXv27KpVq8aOHauvrx8SEtL2Xdrbx4Yzq5mZGc8hmqqqqsSsNUnMuiMl
OqJ86tSpb9++JZXn5eW1K21KkThqbMQAVmzjxo1MJpO7hKhfJyUl2draojZ1NYSQg4PD2bNnORzOzJkzef6PbN68WYQxc6upqQkJCcG/DHiG3LVr14gxuF+ExzuZmJgQ
JcR60Ldu3RJTzMKrra3dvXs3vuGFhYVE+cmTJ3Eh0e7v6+uLD924cQOXfPz4kTgfN7+vX7+edH2osQkJamxC6nCN7cOHD/jn9saNG0Qh8Ud2VVUVLlmyZImRkZGuri5C
yN7envsK1dXVT58+DQoKwvNulZSUOv/dPmXKFITQrFmz+J1w9uxZhJCCgkLbdiPBRNAUSZr/NGjQoB07diQnJ0Ni43A427dv5745+Iue0KdPH9L5Z8+eVVVVxUdVVVVv
3Lhx6NAhbW1thBDPEUGDBw9+9eqVCAPmp7y8/PTp0+PHj6dSqTiSu3fvfrEtMSYmBsdJ/MWDf1LV1NQaGhrEHzVfxcXFgwcP5r6lRGJrbGzU09NDCLFYrJaWlrGAKAIA
ACAASURBVMuXLyOEKBQK7nSsq6vDq4udOHECn4//7EUIPXv2jPQukNiEBIlNSB1ObDzV19fjaceHDx/mLsdNFDixnT9/ftu2bZGRkcTRxsZG3CDPXShrRNAUmZWVFRcX
t3btWry8b1pa2s6dO4cNG4aPvnr1is1mt/1G/kqQmiKLi4u5n7Zdc2T+/Pm5ubk4/9XU1MybN2/atGklJSUJCQlz587lru0pKSnt3bv31atXgwcPFlv4/0dTU3PRokUP
Hz7Mz8/ftGmTurq6i4uLoaHh8uXLnzx5wuHT1IBXEEBcbXcSWEFAGBwOR1lZecCAAW3H4ERGRhYVFVGp1A0bNigoKMycOdPIyIjD4Vy/fh39u5QDavOJpL6UAwDtRafT
t27dihDy8/M7c+ZMXV1dQ0PD5cuXWSwWcc7bt28DAwNXrlyJ21o4HE58fHxVVRVCqH///lIKXDz4ZTw2m/306VMfHx/SaAhtbe05c+Z0Kv9KhDhqbI6Ojty3gvR0//79
/F6YlJSEV0YeMmQIUchmsx8/fuzt7Y2rGiNGjDhw4EB+fr4IAxbehw8fdu3ahcdZGRsbb9iwITExse1pixYtQgi5urpyOBw2m43zHL9hwZJHTMchamyHDh1CCBkaGhLn
2NnZIYQ2bNiAnwYHByOE1NXVcYX122+/RQitWLGi7cWhxiYkqLEJSbQ1Ng6H09jYOHnyZPwrgLeYRwgNHDiwV69euMZWWFiI122n0WhmZmbE39bLli0TYRgiJ4IaG4FC
oeCVjnNzc2NjY1evXo23miwtLcXfBV8hwTU2ARuEDhs27OnTp2fPnp0+ffqMGTP+/vvv1tZWPFIxJCSksrLy7t2733zzzc6dO42MjOzs7E6cOMG965sE9O7dG0+Ge/36
NTEZDq9Hx+2LKwjU19f7+/vb2tpqaGj07NnT1dUVL45AePbsmbu7u7Gxcbdu3fr167dkyRJ+89n19fV5LnxAmhEvGP5rlGgQRgipqakhhIjZh8Is5QBAl6CkpBQeHn7i
xInx48erqamZmpouXrw4KipqyZIlNjY2CCE9PT08F6hv376fPn1SUlKytrY+e/bs0aNHpR27qAmfA1tbWx8/frxy5Up9ff12ZlwpEEeNjbRyJveaMQih8vJywS/n3npb
SUlp6tSp165dIyaUcDichoaGsLCwb7/9lk6n02g0YviiCD9CW//73/927tzZtjwuLi4pKYlU2NjYiFc7jYyMxJM6PTw8iKPv37/Hi5yi/65KQ4wPfvz4Mf4rUklJCXd9
IYT09fU/ffrUNgDcGdlWfHw8v8/Stsa2a9cuhJC5uTlxDh4J+f333xMlEydORAht27YNp0xtbW089IsEamxCghqbkEReY5NXoqyxtUWlUm1tbY8cOZKfny/8q+RGeXl5
fX09d8nnz5+Jx71798Z9sPykpqZyt2I3NTXdv3/f0NCQSv2//wJlZWVPT8+rV6+WlJT88ccfCKGFCxfq6Oh8++23YWFhjY2NIvswXOrq6nbs2NG23MrKiuhbJQheQWDJ
kiXp6emDBg2Kj4+vr6+vrKzctWsXhULx8/PD62UfPny4paVlyZIlnz9/LiwszMzMHDRoUGFhIf6wJPyGTo0ePVr4T4cbS7nnNuDH3Au4CFjKAQDQFZET24oVK7Kzs7/w
GioVIXT37t1jx46JKy7ZI/i2CGiHRAgVFha6urrW1NQQJVQq9dq1a2PGjOF5vqqq6ty5c+/cuVNUVBQUFFRaWjpz5kxdXd158+bdu3cPL+wtLXgOzc2bN2NjY5WUlNzc
3HB5VFTUw4cPFRUVb9y4MXr0aAUFBQ0NjS1bthw6dMjHxwc32+I6q7GxMZ5qY2Zm9vvvvx89elR8IzXwGKiioiJi1Ri8fxVuV8emTp1KpVITExPxmEmxLjoOAJAAcmI7
ffp03759v/vuu8jISJ4DICsqKk6cODF8+HBXV1fSShzyjbshsS3B27C5u7uTXv7LL7/gqo9gWlpaS5cujY6Ozs3N9ff3f/PmjYuLi4GBgeDhi2Ll4uJCp9MzMjIaGxsd
HR2J9li88rKlpaWZmRn3+T4+PocOHcJjZ5ycnBBC27dvt7S09PHxOXv2rLq6+ooVK0jDcDCR9LGNHz8eL4UQEBBQXFx88ODB/Px8Go3Gnb309fXHjBnDZrPT0tI0NDR4
BgMA6ELIq0WkpKSsWrUqJCQkJCRES0vLysoKD55pbm4uLS1NSkpKSUlpbm6m0+k7duzAK499JUgjR0gE1NgWLFhArEeFLVu2jDQH7osMDQ3XrVu3bt26jx8/Xrx4MTQ0
9Pjx40ZGRrNmzfL29pbkBtl4BYFbt26h/7ZD4hlg3E18Ojo6xBgNFovl7++/Y8cOFRWVc+fOJSYmEvNJBw4cGBwc3HaZj9bW1s7XTZWVlffs2bN8+fKgoCBil6Vly5bh
mhzB09Pz6dOn6L9LOQAAuiqe3RgPHjyYPHkyd/cPQUtLa+PGjbm5ue3tAJQwkQ8eEbCmNeJac4Skbe+Rs7Mzz7EJ7ZWSkuLn54dX9ezbt++OHTvS09M7cJ0OLILMcwWB
gwcPIoRGjBhBlAwePNjIyAhX6VgsFvcVioqKIiIiWCwWnkwycODADkTeVtvBI1hoaKi9vb26urqFhcWBAwfavpDnUg4kMHhESDB4REgweERIHfjV472+n5OTk5OTU0VF
RVRUVHp6emlpKZ1O19XVHT169MiRI7/OrnUBTZHGxsakEZJYamrqihUruEvMzc2vXbsmkhs4ZMiQIUOG7NmzJz4+PjQ09NSpU3gqvbe3t7e3N56tIibz58+fP38+qRDX
GpOSkpKTk/Gok1evXiGEpk+fjmdDI4QCAgJaWloWLFjQp0+fCRMmTJgwwdXVddSoUf/8809zc3Pnt/dzdHTk8Gqe9fLy8vLyEvDC3r1783whAKArErRwraamJnSkEwQk
Nn4dbN9++y33QEo1NbVbt27h4fIiZGVlZWVldejQoaioqNDQ0L179/r5+Y0ZM8bb29vLy4vfoHmRs7Ozc3Z2fvDggaen5x9//GFjY1NdXX3kyBFisXyEUERERFxcXGZm
5pkzZ1RUVJqamqKjoxFCpqamX+GmtQAAMWnHcP+vnIA+Np4dbAcPHiTt83nt2jV++651Hl7L//fffy8sLLx586aJiYmfn5++vv6ECRPOnj3LPTNBfE6ePGlubv7x40cH
BwcVFRUtLS1/f3/u6dt79uxRVFS8fPmynp5e//791dXVN27ciBD66aefJBAeAOArAYlNKBwOJycnh9/RtgMfqqqqdu7cyV3y3XffTZgwQSzB/ZeSkpK7u3tISEhJSUlw
cDCDwVi2bJmenp6Hh8eVK1dIU/FEq3fv3i9fvty0adPIkSOVlZVHjhy5c+fOGzduzJ49G8/hs7Ozi4uLmzFjhq6ublZWlp6enpub26NHjwS3EwIAQLvIxB5asq+oqEjA
CL22gxJ3796NF3PC1NXV9+/fL67g+GAwGLhv6fPnz2FhYaGhobNnz2YwGFOmTPH29p44caI4Wv9UVFT27t1LKrx48SLxeMSIEV/cdhwAADqDXGNrbGwU0woXXZqAdkhd
XV19fX3ukk+fPhEjy7HNmzdzzwiWMA0NjYULFz548ODTp0+7d+/++PGju7u7vr7+kiVLoqKivubtGgAAcomc2Oh0Op1O/zoXzRJAwMiRUaNGkUoCAgK4/zjQ1NRcuXKl
uCJrD11d3VWrVj179iwrK2vjxo3Pnz8fP3483u+0XbOeAQBAlkEfm1CE72ArKCggzV3bunUr9+ryssDExGTTpk3Jycnp6emLFy/W1NS0srIyNTXdunXrP//8I+3oAACg
UyCxCUX4IZGBgYHcvXE9evRYvXq1GCPrHHNz8z179pSXl7948cLDw+PChQsDBw4cPHgwbrHkPjMxMZFCoVAoFGJSGrZ27VoKhdK9e3fprmDJz8GDBykUCpVKJdZAwYYP
H06hUKZOnSqtwAAA4sN78MiJEye493dua/369eKJR0YJOYmttLSUtDD0rl27ZHyGVlVVlbq6uqWlpaWl5c8///zkyZPQ0NBDhw5t3bp19OjRXl5eq1atUlBQGDFiRM+e
PXNyciIiIrhnN+Itp6dMmYL3o5GW0tLSnTt33rlzp7Cw0NzcfNmyZYsWLaJSqR4eHnhP0QcPHnh7e+OTi4uLU1JSEKx3DIC8Iq1E0rFXidbbt28jIyMjIyPLyso6fBHR
LqmF1/BtS1tbm/s07l3YEULffPONSN5drKKiovC6GyR37tz5/vvvVVRUsrKycAle39LExIQ4h8j3t27dkky0PNXV1Q0dOhRHQqz0uHnzZnwUH5o3bx5xPh6lSaPRvriF
HjdYUktIsKSWkGBJLSGJdz82yaiqqrK3t8dreiUlJRHlYWFh9vb2TCZz7Nixhw8flnBU/JoiSTuWkaprpDwns0itjpiLi8v58+dLS0vxlmbo3/pNdnZ2eno6LsHVNTU1
NWdnZ0kFy8Pdu3dTUlLU1dUTEhKqq6vxdsCHDh1qampC/4aN91rD8OPx48cL3kIPANBF8U5sr1+/rhRIfAGxWKzCwkJS4alTp2bMmBETE9PQ0PDs2TMfHx+8YoVkcP67
+TU37sR25swZvOsY1qNHDzlo6aLT6d26dcOPbWxscJKLiIjAJTixubm5KSsrSytChFBJSQlCyMHBYcSIEUpKSkuWLGEwGA0NDRUVFejffUQLCgrw2pXo37C5tyYAAMgT
3olN40vEFM3r16+PHDlC+sZpamrasWMHh8NhsVi1tbV4N8gDBw4I3kdGhPLy8jh8Gmm5R478/PPP3IfWr1/Pc3uErotKpeLRFjixcTgcnCGknr8dHR3pdPr9+/fx1qyB
gYH19fUWFhZ6enoIocGDB+Mt4nDYr1+//vTpE/FZAADyR7a+eVeuXKmpqblnzx7uwsjIyKKiIiqVumHDBgUFhZkzZxoZGXE4HNLwPPERZkjk/fv3iQY6hJCamtqiRYvE
HpnE4dpPdHR0Y2NjcnJySUkJg8HgXg2yvr7e39/f1tZWQ0OjZ8+erq6uDx8+5L7Cs2fP3N3djY2Nu3Xr1q9fvyVLlmRlZfF8L+E3GjUzM7t+/Xp9ff3kyZP19fV37txp
YGDA/eOBw8aJDbdD2tjY4LQHAJA/5MRmY2NjY2Mjlb0WQ0NDo6Ojf/75Zy0tLe5yvJSwvr6+iooKLjE1NUVf2tJahPglNgaD0a9fP/z4l19+4T70ww8/qKmpiT0yiXNy
clJXV6+rq4uNjcXVtUmTJhH/Lx8+fBg+fPjOnTtjY2Pr6+tzc3Pv3r3r5ORELCf25MkTOzu78PDw4uJidXX1jIyM06dPjxkzpqCgoO174Y1G22p7ZkZGBv4zwtjYePLk
yQwGo6CgYPXq1c3NzfgEXKeMjY2tq6uDdkgA5J/oh7B0SHV1taGhoY2NDZvNJqYcRUZGcjicgIAAhJC5uTlxspubG0Jo/vz5Ai6IR0WCziNGRWJ4wWJfX19HR0eEUHBw
MHEIlwwaNCg+Pr6lpaWysnLXrl149ltmZiaHw/n2228RQkuWLKmvr+dwOJmZmYMGDUIIBQYGduaHZ+HChQghS0tLNpvN4XAKCgpwv+Ddu3fxCWw2Gy9pdv36dXwoJydH
yIuTdj8HAEhFu74TyHOPBgwYIMx7iHx9ioCAgOLi4oiICAqFQjrUdoIUPkeYRQ7nzZuH95jujHv37vFccWrUqFG4Fe7+/ftxcXFE+YABA/A6VV1CVlbW+fPn8YBankhd
qp6enpcuXbp582Z2draSkhL+IwMhFBUV9fDhQ0VFxRs3buA+LQ0NjS1btuAJA8XFxWZmZriSbWxsTKfTEUJmZma///57QkICXvu/w/D/zqhRo/APhr6+vpGRUUZGxv/+
979JkyYhhCgUyrRp044dO8Ziserq6kaNGiX8RqwGBgb45kRHR8fExAi4UQA7d+4cQqjtVrSA5Ny5c7169bK3t5d2ILIO/+q17zWkRNexV3WemZkZlUplMplMJpPYilNF
RSUwMPDUqVMIIQMDA+Jk/KOwadMmARcU4Tw23EPT1pkzZ/AJpN6a2NjYzr+pxOAbJfz51dXVOC0hhFxcXIjyAwcOIITGjBkj4LXbtm3DLxwxYsTq1av/+OOPN2/e8DtZ
T0+Pxkt8fDzpTFyJ7N69+507d/Ly8vBqIwihsLAw4hzcAont27dP+M9LgHlsQoJ5bEKCeWxC6sCvHrkyhBuL2srIyCBGS5MWsxcVNptNmkhQW1vb0NBgZGSEECoqKqqs
rMTroWRmZiKEJLZePr8+NrxKZFxcXFFREVHYt2/fsWPHSiYwqVBVVXV2dr516xb6b08VngxnYGBAlOjo6BCtyiwWy9/ff8eOHSoqKufOnUtMTExMTMSHBg4cGBwc3HZP
O9zHJkxIP/300927d8vKylxdXYlCR0fHadOmEU/t7e01NTXxBACpD+MEAIgVObFdvXqVVFJSUhIQEPDXX38hhFRVVX19fcWxnhbOVVhZWZm2tjZCKDIy0tHRsbGxkclk
VlZWBgQEbN68+c8//8zPz6fRaBL7euKZ2KhUKu4fIt0xfn8ZyBNPT89bt24pKChwj5jv06cPQig7O5soMTAwoNPp1dXVxP7dioqKfn5+fn5+xcXFycnJcXFxp0+ffvPm
zZw5c16/fk16Fzw7TRhmZmbJycn+/v5PnjwpKioyMzObNWvWunXruKdb0Gi0KVOmnD9/fujQobilFAAgtwTU5mprawMDA/HoPhqN9uOPPxYWFnauTikU0uARDodz/Phx
Ut/bypUrBV9EVE2Rzc3Nbbv9EELDhg3DJ5DaIRMSEjr5jhLW3qZIfqKjoxFCVCo1KSmJuxz//cFisTgcDovF2rZt2/v374mjz58/RwhRKJSmpqbOxyBW0BQpJGiKFBI0
RQpJZEtqtba2/vHHH3379t22bVt1dfW0adPS0tKOHTsmrak/y5YtCwkJsbe3V1dXt7CwOHDgwJEjRyTz1nj4XNty3HT27Nkz7nbInj17tt1N+ythZ2fn7OzMZrM9PT2j
o6NbWloqKip27tyJ6/pYREREYGDgli1bamtrEUJNTU04HZqamsr4UtEAgC6Ex4rsd+/e3bhxY1paGkLIysrqwIEDEu406t69e9tc4uXlhccISJjgDravsB1SgJMnT06a
NCkjI8PBwUFJSQkv1Th58uTbt2/jE/bs2ePs7Hz58uXw8HAjI6OsrCy8I+tPP/0kzbgBAPKFnNjGjx9PzABzdHT89ttv09LScJLjtnTpUklEJwP4bSaO1xy5du0ad+FX
nth69+798uXLn3766dGjR2/fvh06dKi7u7ufn9/8+fPxgH47O7u4uLh9+/YlJCRkZWXp6ekNGTJk3bp1Dg4O0o4dACA/yImNe17zw4cPSeshEb6exMZv72wLC4uEhATu
1U/09fWtrKwkFZeMUlFR2bt3L6kQbxODjRgx4sqVK5INCgDwdZGttSJlEM+myD59+jAYjJs3b3IXTpkyhecwEwAAAJJErrFJbAHGroJnYsO71ZASG6wWDwAAsoCc2Hr0
6CGVOGQWv8SWlZWVmppKlDAYDCcnJwnGBQAAgDdyU+SMGTNmzJhRXl6On+IVIohV0ouKihgMBoPBkGiMUsUvsZGqa5MnT5bKlggAAABIyInt2rVr165dq6+vx08tLS0t
LS25N4ZuaGhoaGiQXIBSVV9fX1ZW1ra8bWKDdkgAAJARMHhEEJ49jkwmU0NDg7QtzpQpUyQVFAAAAEEgsQnCsx3SwsKCNGDdwcGB2JEAAACAdEFiE4RfB9v58+e5S6Ad
EgAAZAckNkF4JjYNDY3Y2Fjuku+++05SEQEAAPgCHmtFIoRu3rzZvXv3tk9J+6XJPZ6JLTk5mfvp5MmT8SY7AAAAZAHvxLZixQoBT78ePBMbqbq2YMECSYUDAADgy6Ap
UhCeiY17A0wtLS3uXaQBAABIHbnGRmpn+8rxWwGZAL1rAAAga8iJbejQoVKJQwZVV1dXVVUJPmfhwoWSCQYAAICQePSxvXr1KiQk5MOHD9ra2jNnzrS3t0cI5efnZ2Zm
1tTU1NTUnD9//u7du5KOVOK+WF0zNzfHqyEDAACQHeTEdv369ZkzZ7a2tuKnx48fP3nyZF5e3le4x/EXNzpYvny5ZCIBAAAgPHJiCwgIwFnNyMiIw+Hk5+f7+voSS0di
dDpdcgFKj+Aam7KyMoyHBAAAGUQeFfn27VuE0LFjx3Jzc/Py8k6ePFlVVdXc3LxgwYKnT58mJiampqZyDwuUY/n5+QKOzpo1C5bRAgAAGUSusTU1NSGEXFxc8FNXV1f8
YN++fTo6OpKMTOqys7MFHF20aJHEIgEAACA83vPYFBUVSQ++tqyGBPaxmZiY2NraSjIYAAAAQoIJ2nzxnJ2NQe8aAADILN5Lal2/fl1LSwtxLQ558eJF7hO+honJAgaP
zJ07V5KRAAAAEB7vxLZ69WpSyZw5c7ifyn1iq6ioII0FJVhYWPTp00fC8QAAABASNEXyJqAdcsKECZKMBAAAQLuQa2x4uD8QkNjGjh0ryUgAAAC0Czmx9evXTypxyBoB
HWyWlpaSjAQAAEC78GiKrK+vv3fv3rFjx65cufL582eivLW1taamprCwcN++fRKMUDr41dhUVFT09fUlHAwAAADhkWtsqampbm5uRH2FyWSGh4eXlpZu2LDhw4cPbDYb
l2/atEmiYUocv8QGqx4DAICMIye2NWvWcLfCVVZWzps3r6Kiory8XLKBSRm/xDZgwAAJRwIAAKBdyIntxYsXCKGFCxdu2bKFw+Hs3bv3zJkzCCELC4vp06fT6XQlJaWv
4cudX2Lr37+/hCMBAADQLuTEVl1djRDavHmzqakpQmjr1q04sV26dKlv376Sj08qOBwOv8EjkNgAAEDG8Z7HxmAw8INu3brhB19PVkMIFRcXt7S08DwEiQ0AAGQcTNDm
gV87pIKCAq7IAgAAkFm8l9QqKCjAD4qLi/ED0uZkPXr0EGtY0sUvsUFWAwAA2cc7sY0cOZJUYmRkxP2Uw+GIKyIZwC+xmZmZSTgSAAAA7QVNkTxAYgMAgK6LXGM7ceKE
VOKQKfy2GP2qRtAAAEAXRU5sS5culUocMoXfWH/oYwMAANlHTmxPnz7leZ6CgkKfPn10dXXFH5L08WuKhBobAADIPnJis7GxEXB2v379zp49O2bMGHGGJH3QxwYAAF1X
+waPpKenOzk58euCkg+kiQ0EExMTCUcCAACgA8g1tvDwcJ7ntbS0vH37dv/+/eXl5QEBAadPnxZ/bNLBr7rWq1cvyQYCAACgI8iJbfLkyfxOnTp1qr6+/vz58+Pj48UR
SlZW1pYtWx4+fFhbW2tubr58+fIffviBQqHgo2FhYUeOHElOTv7mm29mzZq1evVqccSAILEBAEAXx3uCNj8WFhYIoQ8fPog8jvLycisrq6KiIgqFoqiomJSUtHjx4tLS
Uj8/P4TQqVOnli1bxuFwlJWVnz179uzZs7y8vP3794s8DMQ/sfXu3VscbwcAAEC02tfHhnvXxLGF9JkzZ4qKikxMTHJzc0tLS6dMmYIQ+u233xBCTU1NO3bs4HA4LBar
trb28uXLCKEDBw7wy0CdBDU2AADo0siJrZGPurq6ly9fbtmyBSE0aNAgkcdRUVHRq1ev7777rkePHmpqajix1dfXczicyMjIoqIiKpW6YcMGBQWFmTNnGhkZcTic69ev
izwMBIkNAAC6OHJTJJ1OF/wCCoWC05to7d69e/fu3fhxQUHBn3/+iRBasGABhULJyMhACOnr66uoqOATTE1N8/LyxDQ4ExIbAAB0ae3rY1NSUtq3b9/o0aPFFA1CaPny
5cePH0cIjRw5Eqe6qqoqhJCqqipxjpqaGkKotLT0i1dbu3Ytk8lsVwDJyck8y+fPn9+u63QVlZWVCCEHBwdpByLrsrKyENwoIaSkpCC4UUJISUnJzs6OiYkRfNrx48f7
9+/f0tKiq6tbUVGxatWqw4cPE0dv3rw5bdo0hNDLly+HDx8u3ojbLzEx0dLSEiEUFhbm6elJlK9duzYoKEhLS6uoqIhGoxUXF8+aNYvfRfCvXruQE1tgYCDP8xQUFHr3
7j169GhxV1wcHR2bmpouXrz44sULV1fXBw8e0GjkIPFQSTab/cWrcTicdm1E0Nra2tTU1LacTqfL64YG+HPJ66cTIbhRQoIbJSTOv4Q5mUajubu7X7hwISIigrv8wYMH
CKFevXpJPau9fv16165djx8/ptFotra2Bw8e1NXVHTFiRM+ePXNyciIiIrgTGw57ypQpxNe7gPvQkZ8ljkx6/PgxDi81NfXUqVMIIQMDA+Kovb09QmjTpk0CrhAVFYUQ
ioqKatf7vnz5kuddcnBw6NgHkX34Rkk7ii7A398fbpQw7Ozs7OzspB1FF2BnZ+fv7y/8+Tdu3MBfRx8/fiQK8Tp/69evF3l47ZKcnKytrY0QUlJSwhUPY2Pj8vJyDofj
4+ODEDIxMSFOJnqRbt26JczFO/Crx3tUZGNj4717916/fk2UPHr0KDY2trGxsd2ZUzhbt251cHDYuXMnfjpmzBicydPS0vBWcEVFRbjRDCGUmZmJEDI0NBR5GLg/ry3o
YAMASNfEiRO7deuGECIqbVlZWfjLcPr06dKMDCEfH5/S0tJ58+aVl5fn5uYOGTKEw+HcvXsXIYQratnZ2enp6fhkXF1TU1NzdnYWUzw8EltSUpKZmZmLi0tsbCxRGBoa
amtrO2jQIH51ms6Ljo4+cODA3bt3c3JyfH19W1paEEJDhw4dP348k8lks9kBAQHFxcUHDx7Mz8+n0WjcFVtR4ZfYYBIbAEC6GAzGpEmTEFdiwxnC0NDQyspKioGlp6fH
xMRQKJRDhw6pqKj06NEjJSUlNzd39uzZCCEbGxsdHZ22Ybu5uSkrZdCTyAAAIABJREFUK4spJHJiKysr8/DwaDvgENcu37175+DgUFxcLPI41qxZo6urW11d7erqamJi
EhQUhBCaP3/+gAEDlJWV9+zZQ6FQgoKC9PT0NmzYgBBatmwZaVNvkcB//rQFNTYAgNThv+YfPnyI/+6/f/8+QsjDw4NYoUkqXrx4gRBiMpkJCQmurq4jR45ctWoVMbiP
SqVOnToV/ZvYOBwOTmziqJkQyIlt27Zt2dnZurq6f//996JFi4jyo0eP/vXXXzo6OlVVVfwGmHSGjo7O8+fPv/vuu549e6qqqg4fPvzw4cPEipTLli0LCQmxt7dXV1e3
sLA4cODAkSNHRB4DgsQGAJBhbm5uioqKVVVV8fHxbDb74cOHqE075Llz51xcXPT19XV0dKytrc+cOdPc3Ewczc7OXrJkibm5OYPBMDExcXd3526Z4zZr1ixFXtou+YRz
WHV19aRJk+7evZuQkHD06NGRI0fW1tbiEzw8PBBC0dHRjY2NycnJJSUlDAbDxcVFdDeGjDzg8N69ewihffv2ubu7c5crKSlNnTq1srJy/vz5T548EUcoJiYmwcHB/I56
eXl5eXmJ43258Uts0BQJAJA6DQ2N8ePHR0REREREKCkpVVRUaGtrjxs3Dh9tamry9PS8ffs2QohKpVIolLi4uLi4uJs3b964cUNBQaGkpMTGxiYvL49CoRgaGubm5ubk
5Ny5cyciIsLJyYn0Xmw2G9cLSThtxijiAeotLS3btm3z9fV9+fLlpEmTsrKyjh8/jhvYnJyc1NXVq6qqYmNjExMTEUKTJk0i5iWLA7nGVlhYiBAaMmQIz7PxDLZ3796J
LyApqqmpKSsra1uuoKAgjmZPAABoL1z7iYiIwO2Q06ZNU1BQwIf27dt3+/ZtTU3Ny5cvV1dX19XVhYWFaWlp3bp169y5cwihK1eu5OXlDR8+vKSkJC8vr6ioaM6cOWw2
e9++fW3f6OrVqzwHHG7atIl0Ju5C09LSCggIUFdXt7e3x7UxnMMQQkpKSq6urtxhi3u0C7nG1rt373/++efNmzd4vWMSXKHp0aOHWGOSFu5RoNygHRIAICOmTp26fPny
xMTE+vp6xNVTVV9fv2vXLoTQoUOHZs6ciQs9PT2VlZUjIyMVFRXRv4v9amtra2pqIoR0dHT27NljZWWFl7zoMJwRKBQK0dWnpaWFEFJXVyfO8fT0vHTp0s2bN7Ozs5WU
lNzc3Drzjl9ErrHhWX7bt2/HVTduVVVV27ZtQ/+u8S9/oIMNACDj9PX1x4wZw2az09LSNDQ0HB0dcfmbN28aGxspFAppCY/JkycfOnTo+++/Rwjhkx88eGBqarpw4cKj
R4/m5OQsXboUHyURvo9t3LhxBgYGZWVlLBaroqLi7t27oaGhCCFra2viHBcXFzqdnpGR0djY6OjoqKGhIdK7QkZObGvXrlVUVMzKyjI3N1+9ejWe6B4SEuLn52dqavrq
1SsajbZq1SqxxiQt0MEGAJB9RC3N3d1dSUkJP/748SNCSFNTk1jvd8aMGZR/4RXOnJycwsLCrKyscnNzz549u2rVqrFjx+rr64eEhLR9F9zH1lbbPjYajRYUFESj0Xbu
3KmlpeXq6lpfXz9q1Kg5c+YQ56iqqhKz1iQw647cFGlpafnLL7/4+PhUV1e3HXlIoVD27t07ZswYcYclFVBjAwDIPg8Pj/Xr16P/Zog+ffoghMrLy6urq3HTopaWlpGR
UVNTE/cELU9PT09Pz5qamlevXr148SI4ODghIWHBggV2dnakPqarV68KH9LMmTM1NDQOHz4cFxfXvXt3Nze3wMBAovOPeOtbt24pKCjg0f9ixWOC9sqVK5OSklxdXbln
zykpKTk7O7948QLfULkEiQ0AIPt69+6Nx3Hg5Y+xgQMHMhgMhBAeJ4IQOnnyZG5uLu4/wi5cuLB9+/aHDx+qqqpaW1v7+Pg8ffpUU1Ozqanp7du3nYxq4sSJt2/fLi8v
z8zMxDO1SSfMnz+fw+G0tLTgxbfEivfq/kOGDLl9+zabzc7Ly8vNzTU0NOzZsycp/coffv+1kNgAADKOTqdv3bp127Ztfn5+3bp18/b2plKpN2/eZLFYxDlv377ds2fP
tWvXHjx4gHe1jI+Px9un9O/fX2qhi4GgbWuoVGrPnj179uwpsWikqKSkpKamhuch6GMDAMg+X1/fuLi427dvL1q0aOnSpRQKpaWlZeDAgcToRB8fn+Dg4Ldv3/bu3btX
r16lpaV4Ad5ly5bJ2Vh33osgf4X4rRKpoKAgjtWWAQBAtJSUlMLDw0+cODF+/Hg1NTVTU9PFixdHRUUtWbLExsYGIaSnpxcfH+/j49O3b99Pnz4pKSlZW1ufPXv26NGj
0o5dxNq30agc49fBhrtkAQCgS1i6dOnSpUu5SzZv3kw8NjQ0xCvxyjeosf1/MHIEAADkAyS2/w8msQEAgHwgN0VevnxZmJeRJrfLAX4LYEJiAwCAzmhqamppaWlubsb/
tra2Eo/xpO/Gxsbi4uKmpqampqbm5uYmLs3NzXgTg7179/r5+Qn5juTEJuQK+vKX2N68ecOz3MTERMKRAACAqDQ0NPDMFsI/7eTLW1tbRfJB2rXHNQweQQihgoKCxsZG
noegxgYA6BicVPh9+1dUVLx79+7vv/8WX2oRVVKRBU1NTcKfTE5saWlpbU/KycnZvn073oNAS0tr69atnYlPBvHrYEMweASALgg3dokkN3T45cIklVevXl28eFECN0QO
dCqxffPNN9xPP3/+vHfv3qCgoIaGBmVl5dWrV2/ZsoXJZIogTFnCL7EpKCjo6+tLOBgAujQOh8Nmsz9//izF1IK3vgTyhHsf8C/i2xTZ1NR0/Pjxn376qaysjEKhzJkz
Z9euXfK6Csn79+95lpuZmUk4EgA6qa6uTiS5oQMv5167R/7+/AUCqKqqKioqKnFp11PBJ1y8ePH8+fMHDhwQPh7eie3KlSubN2/+8OEDQsjJyWn//v14nzZ5lZ6ezrMc
OtiA8FpaWjqfGzr58rZbigA5RqFQOpxL2pVaBDyl0cQ+UOPp06cIoaFDhwr/EnJMjx8/9vX1ff78OUJoyJAh+/fvnzhxoghDlE38xvpDB1tXgZOKSHIDv6f4h2TYsGH8
ToCk8lWhUqmdzCXBwcGmpqaurq4dTi0SSCpdFPm+2NnZEY8LCgrmz5/P82UFBQXii0ny+CU2WE9LsNraWmImCmliCvcDsdZaamtrJfmRU1JSJPl2QAA1NbVOppYOv7xb
t26dj//Jkyc2Njbr1q3r/KUAiaCEX1JSIrE4pOjTp091dXU8D8lgU2RdXV0jL83NzdxjiwmNjY3cyaDtv/hvFGdnZ5yB+KUo4im/ewW+KjiptP32ZzAYKSkpFArFxsZG
tLmEeCqSpALkGzmxLVq0SCpxSBG/6hrqUFNkdXV1VVVVVVXV58+fa2tr6+rq6v+roaGhqqqq5l+4q19wLmlubhZ3OomMjBTr9YGo0Ol0ft/+DAZDQUFBQG4Q8gQBqQVv
ZSmYvb09Qujvv/8W+70AgA9yYjt9+rRU4pAifiNHEEKCR4HW1NTEx8cnJCRkZmZmZWVlZWXh4TZAjqmrq3esnkEklY69XMikAgBAbRNbWVmZMC/r3r27GIKRDn41tm7d
uunq6rYtj42NvXr16qNHj3hOZgdiwmAw+H370+l0Go3Wmc4VOp3OnXXank+n01ksVkBAwOfPn6V9JwAAX0BObNra2sK8TJ4GgPGrZpE62F68eBEaGhocHPyVdD3ypKam
pqysrKSkRPxLetq2kMFg0Gg0AckGpyUBqYhOp0v7cwMAuhIYLfrlsf7Xrl3z9/fnt0qy5KmrqyvzQqqRcJdw952Q/s3IyNixY0dERATphdz5SVVVVdofGgAAhEVObO2a
3S0fBGwxmpiYuGbNmtjYWCEv1bdv3549e2poaDCZTA0NDXV19bbnUKlUFRUVVVVV/K+6ujqej4Ib04h/2z4VUxdLdHQ0QmjChAniuDgAAEgeObGtX7/+i6+RpyESnz59
4jcR6tatW7/99puA16qoqFhZWdna2lpbW/ft2xdmcwMAgCxoR1Nkenp6WFjYtWvXkpKS5KaPTcC6/jk5OTzLLSwsJk+e7OzsbGtrK7a4AAAAdNCXE1tqairOZ69fv5ZA
QBImILGRaGtrL1++fO7cubAyMgAAyDK+iS0xMRHnM+6vfhUVFQcHB3laPVLA7GwCk8kMDAxcsWKFBOIBAADQSeTEFhcXFxYWFhYWlpWVRRRqaWmVl5cjhF6/fm1iYiLJ
+MQtIyND8AkGBgbPnj2D/jMAAOgqyInN2tqaeGxoaOjh4eHp6Tlu3DhFRUWEkPwtJv3Fpsg///wTshoAAHQhfBPV1KlT9+3b169fP0lGI3mCmyJnzJjh6OgosWAAAAB0
HpX0nFgd8ebNm/379x88eLC/v7+8btWRm5vb0NDA76iiouLPP/8syXgAAAB0HjmxZWVlxcXFrV271sjICCGUlpa2c+fOYcOG4aOvXr1is9mSjlFsBFfXfvjhBznrUAQA
gK8BObFRKBQrK6tffvklJyfn6dOnPj4+PXr0II66urrq6enNnTtXskGKi4DERqPR/Pz8JBkMAAAAkSAnNgKFQrG2tg4KCsrNzY2NjV29erWhoSFCqLS0NDg4WIIRipGA
xDZnzhyorgEAQFfEN7ERKBTK2LFjf/3119zc3MePH69cuVJfX18CkUnA+/fv+R3auHGjJCMBAAAgKl9ObP93KpVqa2t75MiR/Px88QUkSfxqbOrq6gMGDJBwMAAAAESC
R2Krr6+/d+/esWPHrly5wr2tYmtra01NTWFhodyMFXz79i3Pcpi4BgAAXRd5Hltqaqqbmxux/i+TyQwPDy8tLd2wYcOHDx+IIZGbNm2SaJhiUFBQ0NjYyPMQaYtRAAAA
XQg5sa1Zs4Z7VfvKysp58+ZVVFTgJbXkiYA1R6DGBgAAXRe5KfLFixcIoYULF7579y4zM/OHH354//59eXm5hYXFrl27Dh48eOTIkcjISHGEUlpaumTJElNTUzU1NQsL
i8OHD7e0tBBHw8LC7O3tmUzm2LFjDx8+3Pm3EzAkEhIbAAB0XeQaW3V1NUJo8+bNpqamCKGtW7eeOXMGIXTp0qW+ffuKL47m5mZ7e3u8M46iomJSUlLS/2vvvsOiuNqG
gd+z9CYdVwQLzQJRUZMHERUBEwuoEKOixhcSEeKrIjFqLEkwUQlqIo8lUaOvaIw1iCjGB4WADUkQBUVCUXFlAemwKp2d749zOd8821jqwnL//uDaPTM7c++wO/eeMnMe
PHj06NEvv/wCAIcPHw4KCqJpWkNDIzk5OTk5mc/n79y5syN7lDFdKiY2hBDqvSSPitTS0iIPtLW1yYMuzWoAcPbsWZLVkpOTq6urSR/ekSNHnjx50tjY+PXXX9M0HRoa
+ubNm7NnzwLA7t27CwoKOrJHbIpECCGl1Ibh/l3q0aNHADBmzJgJEyZoa2uvX7+ew+EAwIMHD+Lj40tKSjgczhdffKGiojJ//nwLCwuapi9cuNCRPcqosdnZ2XVkywgh
hBRI8t39i4uLyYPS0lLyQOTaNfZ9tjqFt7f3qFGjmFswUxRFRmBqa2uTKdO4XK6Ojg5Zam1tzefz+Xx+R/YobSY2AwMDpp6KEEKo15Gc2N59912REnJPZAZN050bh5OT
k5OTE3ksFAo///xzAOjfv7+bm1taWhoA6OrqMivr6ekBQHl5eaubzcjIoChKvPzVq1cCgUDiS8zMzG7cuNH2d9Bbkakb+tRbbh8ejwd4oORALn7FA9WqmpoaHo+HB6pV
5KvXNvR/a9+rOlFlZeXcuXMBQF9fPyUlhabp7du3A4CdnR2zjpeXFwAsXbpUxnYSExPbfCwQQgj1SG3KI6I1toMHDyokaCI5OXnhwoUFBQUODg4XLlwgI1ZMTU3h7XBN
gjweMGBAqxvcs2cPM+cOW0JCwrZt2yS+5KOPPlqxYkX74u+N0tPTQ0JC8HdAqyIjI48fP44HqlVr1qwBgIiICEUH0tOtWbNmzJgxfn5+ig6kpyNfvTa9RDSxBQYGdl48
bXPq1Ck/P7+mpqaAgIC9e/dqamqSctIKWlJSUl1dbWBgAG8HNJLZBmQbM2aMq6urePnt27elvcTFxUXiS5RbH3zLbZWUlAR4oORAvqR4oFplYGAwZMgQPFCtIl+9NpHc
xwYAqampqampz549KyoqMjc3t7KyGj9+/HvvvdehAKXLy8v75JNPmpqa5s2b9+2339bU1JCWen19fTc3NwMDg+rq6q1bt27cuPHXX38tLCxUVVX18fFp9+7w6myEEFJW
EhLb33//vWnTpoSEBPFF7u7uO3bs6Ir0duzYMXLnxt9///33339nys+cObNgwYKwsLAVK1ZEREQw7RtBQUEi41naRMaENXijSIQQ6tVEE9vz58+nTZvGjBjU19c3MjKq
qqqqrq4GgISEhGnTpj18+LDTJ+HMysqSsTQoKMjAwODQoUP379+3sbFZtGjR2rVrO7I7GYmtqy9FRwgh1KVEE9vGjRsFAoG6uvqOHTv+53/+x8TEhJRXVFScOHGCLN24
ceOpU6c6N46LFy/KXmHhwoULFy7slH01NDQwF+qJMDIyYvr2EEII9Uaiie3hw4cAsGvXrtWrV7PLjY2NQ0JC1NXVV65cSe4S0nvhzbQQQkiJid5Si9xo6v3335e49gcf
fAAyR170CtjBhhBCSkw0sdXX18PbW3uII+Vknd4L7+uPEEJKTPJw/5qaGvYtrNjlXRxPd5BRY8PEhhBCvZ3kxGZvb9/NcXQnTGwIIaTEesq0Nd0J+9gQQkiJidbYvv/+
e4XE0Z1kjIq0srLqzkgQQgh1OtHERqauVmIvXryQtsjExISZOhwhhFAv1eeaIrEdEiGElFufS2w41h8hhJQbJrb/DxMbQggpgT6X2HCsP0IIKbc+l9hk1Niwjw0hhJSA
1IlGJcrNzX3w4AEAGBgYvPfee4aGhl0TVRfCpkiEEFJubUtsV69eXbNmDQDY2NiUlpZev3696+bU7goNDQ0VFRXSlmKNDSGElEA7myLz8vLCw8M3b97cudF0tZycHGmL
zMzMcCY2hBBSAm2rsS1evNjV1ZU8DggImDRpUudH1JWwgw0hhJSevInt2bNnJ06cyM3N5fP5FhYWdnZ2S5cu7XX3Ss7Pz5e2CDvYEEJIOciV2DZs2BAREdHY2MguDAsL
CwkJ6V33lsTEhhBCSq/1xHb06NGdO3cCwDvvvOPi4mJiYlJRUXH79u2HDx+Gh4cPGzbM39+/6+PsHJjYEEJI6bU+eOTIkSMAsG3btocPH/7000/ffvvtgQMHMjIySF2N
LO0tnj9/Lm0R9rEhhHqs5uZmIyMjiqJWr17NLo+JiaEoiqIociFWT5OWlkbCu3DhArs8JCSEoihjY+Pm5uau2G/riS0vL09dXX39+vUi5WvXrtXU1JQxBUwPhLcdQQj1
Rqqqql5eXgAQFxfHLr9+/ToADBkyxNHRUTGRAQDA7du3KTFpaWnjxo0bNGgQSAl79uzZqqptG8Aop9YTW0VFhampqZqamki5qqpq//79y8rKuiKsrlBZWVlXVydtqY2N
TXcGgxBCbeLt7Q0Aubm57Jana9euAcCHH36oqKiIf/75R9oiEjY7sRUWFj5+/Bi6Mmy5rmOjKKpN5T2TjHbIAQMGqKiodGMsCCHUNh988IG2tjawksTz589Jm1kPSWyX
Ll3is4waNQoAfHx8AIDH4zGXEZPqmp6e3rRp07ooHrkSW1FRkakkMibt7IF4PJ60RdgOiRDq4bS0tKZPnw6sxEYyhLm5uZOTkyIjA8jOzgaACRMmDGQh7XwuLi6mpqYg
Franp6eGhkYXxSNXYhMKheWSCIXCLgqrK2BiQwj1aqT2k5CQQMZckHZIb29vhTee/fPPPxoaGps2bTI2NrawsPDz8ysvLyeLOBzOnDlz4G1io2maJDbyXrpI6x13Z86c
6brddycZ9UtMbAihns/T01NNTU0gEKSkpDg7OyckJIBYO2RkZOTZs2cfPHjQ0tJia2v76aefLl26lBkkwePxtm/fnpSUVFBQYGZmNmrUqA0bNri4uIjva8GCBSJDGYnt
27eLjCWsq6vj8Xg0Tf/yyy/GxsaFhYXHjx9PSUnJyMggdTJvb+8jR44kJSU1NDRkZWWVlZVpaWnNmDGjsw6LuNYT24IFC7pu992poKBA2iIc648Q6vn09fXd3Nzi4uLi
4uLU1dWrqqpMTEwmT55MljY2Nvr4+Fy5cgUAOBwORVF37969e/duTExMdHS0iopKWVmZi4sLn8+nKMrc3LygoODFixd//PFHXFych4eHyL6EQqHEsfg0TYuUlJeXz5kz
h6KorVu3vvPOO7du3XJzc8vJyTl+/Pjy5csBwMPDo1+/fgKB4Pbt22lpaQAwffp0HR2dTj8+DNHEFhUVJc/LFN5X2Q5YY0MI9Xbe3t4ksZHK0Ny5c5mBb+Hh4VeuXDE0
NDx48KCnp6eqqmpsbGxAQMDly5cjIyM//fTTc+fO8fl8R0fH69evGxsbl5WVff755ydPngwPDxdPbOfPn5czJEtLy+joaObppEmTJk2alJiYSHIYAKirq8+cOfPMmTNx
cXH379+Hrs8goolt3rx58rxMPGn3fDJqbJjYEEK9wpw5c1asWJGWlkYuXmJ6qurq6rZv3w4Ae/bsmT9/Pin08fHR0NCIj48nTZF8Ph8ATExMyFSapqamYWFhTk5Oenp6
HQkpMzPz8ePHenp6M2fOJCXk/ovsOpmPj8+ZM2diYmJ4PJ66urqnp2dH9tiqPjSDdnFxsbRFtra23RkJQgi1D5fLnTBhglAozMzM1NfXd3d3J+VZWVkNDQ0URYl0Hs2a
NWvPnj1Lly4FALLy9evXra2tP/nkk/3797948SIwMJAsFbFgwQI1ScgdFtkyMjIWLlzo6el54cKFqqqq48ePJycnAwB7+pcZM2Zoamrm5uY2NDS4u7vr6+t36lERJVpj
u3fvXpfuT1FkVNcGDhzYnZEghFBH+Pj43LlzBwC8vLzU1dVJIbkRrqGhITOv5EcfffT777+Tx66uromJiR4eHlFRUbt27UpNTT127NixY8cAwNjYeO/evYsWLRLZi/x9
bN7e3g4ODpmZmewGRk9PT3JpNqGrqztt2rTLly9Dt/RkiSa2cePGdfUuFYLUwSXCdkiEUC/i7e29du1a+O8MYWVlBQCVlZWvXr0iTYtGRkYWFhaNjY2lpaXMaj4+Pj4+
Pq9fv3748GFqaurJkyfv3bvn7+8/ZcoUkZ/48vexaWtrJyYmbt269erVqy9fvrSzs/P19Q0JCRFZzcfH5/LlyyoqKmT0f5fqkvt09UDYwYYQUg5Dhw4VrzaNHDlSS0ur
rq4uMjJy1apVAHDo0CEA2LdvH3Pf5BMnTuTl5bm6urq7uzs7Ozs7O3/22WdcLreqqio7O7sjbVcmJib79u2TvY6fn5+fn1+7d9EmfaWPDcf6I4SUmKam5ubNmwHgyy+/
PHr0aG1tbX19/dmzZ0NDQ5l1srOzt23btnLlStKCRdN0SkqKQCAAgOHDhyso8C7RVxJbYWGhtEVYY0MIKYF169bNmjWrtrZ22bJl/fr109PTW7hwIZfLZU5xwcHBlpaW
2dnZQ4cOtbW1NTIymjJlSktLS1BQkJINNcDEhokNIaQM1NXVY2NjDx486ObmpqenZ21tHRAQkJiYuHz5cnJvkf79+6ekpAQHB9va2hYVFamrqzs7Ox87dmz//v2Kjr2T
9ZU+NkxsCKG+IDAwMDAwkF2yceNG5rG5uXlERES3B9XdsMaGiQ0hhJRKX0ls0ob7W1pa4kxsCCGkTPpEYisvL5d4pSFgdQ0hhJROn+hjKyoqkrYIx/ojhFCXqq2tbWlp
EQqFzF8ZD8TLnzx5AgAXL16cO3eunHvsE4kNbzuCEOpcQqGw1TOy7PKamhoej5eYmNiOc327k0RXb5n9tHPvlf/rr79iYvsvMmpsmNgQEteRs5tAIKBp+vbt28p3pmY/
6JTjnJ6eHhkZ2SmbUnpCoVD+lXtiYvvkk0+uXr368OFDU1NTpjAqKmrfvn3p6en29vYLFixgbhIjDxn39cfE1ge17+xGPkV//fVXbz9Ty7Pljh9k9p3dEeq4Nv2Y6FmJ
rba29t///ndkZCRN0+xv1+HDh4OCgmia1tDQSE5OTk5O5vP54rMnSIN9bHKqra1tbm5ubm5uampiPxB52qYVmpube8KZmkxe1XFOTk6dsh2EUJu0qWGzpyS2srIyd3f3
Z8+evXnzRmRRY2Pj119/TdN0aGjoli1boqKiFixYsHv37lWrVllaWsqz8S6tsQkEgqqqqurq6pqamurqaoFAUFNTIxAIGhoa2r1NdlZoaWlp9bFQKCQlDObMLo69qLa2
luyRoqgOHgeEkLLS0tJSeYvD4aiwyH7a1vUlvvzWrVs3btzw9fWVP+CekthIbWzEiBECgSA3N5e9KD4+vqSkhMPhfPHFFyoqKvPnz1+7di2fz79w4UJwcLA8G5dWYxs8
eLD8Eebn52dmZj569CgrKys/P7+8vLyysrK8vFz+LSCEegVtbe2On45bfRobG2thYTFhwoROzw1qamoURbX75dra2or+D/yX0NDQGzduiM8YJ0NPSWxmZmapqakAkJCQ
4OHhwV5E8hyXy2UmGre2tubz+TLGOoqQlthkV9fq6uqSkpJu37599+7dtLQ0cg9shJSPtrZ2p5xPyYPbt29TFOXh4dENuYFQVVVln8fb+nItLS2FHHZXV1dXV1f23fdR
Z+kpiU0GklF0dXWZEjKNnvy1JWn30yJT84m7c+fOjz/++Mcff9TX17ctVtQbkPN4W8+nfD7/xYsXbm5unXU6bvWptBVUVVXJg/a9vKvP466urgA7j8PVAAAgAElEQVRw
6tSpLt0LQjL0gsSmqioaJOkQkmfsVkhIiIxqdWJi4tSpU0UKnzx5In9dsKfhcDgAQL0l8bFIIelpMzAwgLcHVuJfGYtkrCD+VGIkXbeUoihyTDrr2AqFQqFQ2NTU1Cnb
VEoZGRkAIP7NQiIyMjJ4PN6NGzcUHUhP9/z587a+pBckNjLo/9WrV0wJeTxgwIBWX0vTtIxBHJqamuyRNkKhMCsrq6Kioq0RqqmpqaqqqqmpkV/TBIfDkZhg5C+RViii
gyfu6urqjIyM0aNHd2QjPVlnXSVKttO515wqJTxQcqLfUnQgPV07DlEvSGwWFhYAUFJSUl1dTSoWeXl5AGBubt7qayMiImpra2fNmiVx6eHDh5mrbWpqambNmiUjq9nb
2zs7O9va2vbv33/AgAFGRkbGxsYmJibsNtLeKCkpaerUqUlJSYoOpKcLDQ3dunUrHqhWkaZIPFCtwj42OZGvXpte0gsSm5ubm4GBQXV19datWzdu3Pjrr78WFhaqqqr6
+PjI8/KXL19KW8RcxFZZWTl16tSHDx+KrDB+/Hg/P7/Jkyfb2tpqamq2+y0ghBDqNr0gsWloaISFha1YsSIiIoKZIi8oKIjU5Fol4+pssoWamppp06aJZDVTU9Po6OiJ
Eyd2IHCEEEIK0AsSGwAEBQUZGBgcOnTo/v37NjY2ixYtWrt2rZyvlXZ1NjMkcubMmffv32cvGjhwYGJioq2tbUdiRgghpBA9LrG5u7tL7CpcuHDhwoUL27FBaYmNtEMG
BAQkJyezy+3s7JKSkuQZmYIQQqgHUv6JRqUltiFDhuzfv//IkSPsQlNT0/j4eMxqCCHUe/XdxNbS0rJq1SqRwujoaDnvP4kQQqhnUv7ExuPxJJaLT4O0Y8cOHC2CEEK9
nZIntpqaGjnX/Ne//vXll192aTAIIYS6gZIntsrKSnlW09PTO3/+PIVTtyCEUO+HiQ0A4ODBg9i1hhBCykHJE5s8MwAMHz68TTP9IIQQ6smUPLHJU2Pz9/fvhkgQQgh1
DyVPbK3eqp+iqCVLlnRPMAghhLqBkie2Vmts7u7u8swSgBBCqLdQ8sTWao1t8eLF3RMJQgih7tHXE5unp2f3RIIQQqh7KHlikzEZGwBYWlqamJh0WzAIIYS6gZIntpaW
FhlLx48f322RIIQQ6h5KnthkGzt2rKJDQAgh1MkwsSGEEFIqfTqxjR49WtEhIIQQ6mR9N7Fpa2sPHDhQ0VEghFDrmpubjYyMKIpavXo1uzwmJoaiKIqiHjx4oKjY2EpL
SxMSEhISEp4+fQoAaWlpJLwLFy6wVwsJCaEoytjYuLm5uSvC6LuJbdy4cYoOASGE5KKqqurl5QUAcXFx7PLr168DwJAhQxwdHRUT2X/z9/f38PDw8PAgE16OGzdu0KBB
ICXs2bNnq6qqdkUYfTexOTg4KDoEhBCSl7e3NwDk5uY+f/6cKbx27RoAfPjhh4qKiu3SpUt//PGHSCEJm53YCgsLHz9+DF0Zdt9NbPb29ooOASGE5PXBBx9oa2sDK0k8
f/48Ly8PekZiq6+vX7NmjYODw7Bhw9jlPj4+AMDj8XJyckgJqa7p6elNmzati4Lpu4lt5MiRig4BIYTkpaWlNX36dGAlNpIhzM3NnZycFBkZAAB8//33+fn5P/30k5qa
GrvcxcXF1NQUxML29PTU0NDoomD6bmLDGhtCqHchtZ+EhAQy5oK0Q3p7e1MUpdjA8vPzw8PD/fz8Jk2aJLKIw+HMmTMH3iY2mqZJYiPvpYsobWKjaVrGUiMjIzMzs24L
BiGEOs7T01NNTU0gEKSkpAiFwoSEBBBrh4yMjJwxYwaXyzU1NXV2dj569GhTUxOzlMfjLV++3M7OTktLa/DgwV5eXrdv35a4rwULFqhJsnPnTvGVg4ODtbS0JC6Ct91s
SUlJDQ0N6enpZWVlWlpaM2bMaPdxaFWXjEjpCWRPWIMjRxBCvY6+vr6bm1tcXFxcXJy6unpVVZWJicnkyZPJ0sbGRh8fnytXrgAAh8OhKOru3bt3796NiYmJjo5WUVEp
KytzcXHh8/kURZmbmxcUFLx48eKPP/6Ii4vz8PAQ2ZdQKJQ4Fl+8znDlypXLly8fPHiQNDmK8/Dw6Nevn0AguH37dlpaGgBMnz5dR0eng0dDBqWtsZWXl8tYOmLEiG6L
BCGEOgszyJC0Q86dO1dFRYUsCg8Pv3LliqGh4dmzZ1+9elVbWxsVFWVkZHT58mUy+P7cuXN8Pt/R0bGsrIzP55eUlCxZskQoFIaHh4vv6Pz587QkGzZsEFnz4sWLALB+
/XpDQ0NDQ8OsrCwA2LVrF1MnU1dXnzlzJjvsrh7torQ1tqqqKhlLMbEhhHqjOXPmrFixIi0tra6uDlg9VXV1ddu3bweAPXv2zJ8/nxT6+PhoaGjEx8eTAR18Ph8ATExM
DA0NAcDU1DQsLMzJyUlPT6/jgQkEAvbThoaG169fM099fHzOnDkTExPD4/HU1dW7er4wpa2xyW6KxMSGEOqNuFzuhAkThEJhZmamvr6+u7s7Kc/KympoaKAoasGCBez1
Z82atWfPnqVLlwIAWfn69evW1taffPLJ/v37X7x4ERgYSJaKkL+P7ZdffmFX6UhHz5YtW27dusWsM2PGDE1Nzdzc3IaGBnd3d319/U49KqL6aGLDsf4IoV6KqaV5eXmp
q6uTx/n5+QBgaGioqalJSj766CPqralTpwKAh4dHVFSUk5NTQUHBsWPHVq1aNXHiRC6Xe+rUKfG9kD42cbLH5Umjq6vLXLXWDVfdKW1ik9EUqaura2Fh0Z3BIIRQZyHd
bPDfGcLKygoAKisrX716RUqMjIwsLCxEhn/7+PjcvXu3urr6zp07ERER48ePr6io8Pf3LywsFNmL/H1sciL5WEVFhYz+71JKm9hkDB4RuTAeIYR6kaFDh5IcM3fuXKZw
5MiRWlpaAEDGiQDAoUOHCgoKtmzZwqxz4sSJr776KiEhQVdX19nZOTg4+M6dO4aGho2NjdnZ2Z0V3qNHj2ia/u6770TK/fz8aJpubm42MTHprH1Jo7SJTUaNDTvYEEJK
RlNTc/PmzQDw5ZdfHj16tLa2tr6+/uzZs6Ghocw62dnZ27ZtW7lyJRlFQtN0SkoKGfQxfPhwBQXeJZQ2scnoY8MaG0JI+axbt27WrFm1tbXLli3r16+fnp7ewoULuVzu
kCFDyArBwcGWlpbZ2dlDhw61tbU1MjKaMmVKS0tLUFCQks3hpbSJ7eXLl9IWYWJDCCkfdXX12NjYgwcPurm56enpWVtbBwQEJCYmLl++3MXFBQD69++fkpISHBxsa2tb
VFSkrq7u7Ox87Nix/fv3Kzr2Tqac17G9fv26paVF2lJMbAghZRUYGBgYGMgu2bhxI/PY3Nw8IiKi24PqbspZYysuLpaxFBMbQggpMeWssZWUlEhbNHTo0K6bKwEhhBBb
fX19S0uLUChsaWlhHkh8Km0dMrHq48eP5Z+SRTkTm4wam52dXXdGghBSMjRNd/BMTR5UV1fn5+dfv35dnpXbsf2OP+3gFjr3sH/99ddRUVFyrqyciQ1HjiDUDp1yaquu
rgaA+Ph4JTg1S9xg+269IVFGRsaJEyc6a2vKrUX6sAlxmNiQ8uuUMx2PxwOAhISE3n5qlrb9TjxfAwBz/ySEOkWbqoC9JrFFRUXt27cvPT3d3t5+wYIFq1evlrGyjD42
9nWIL168ePLkyZs3b+reqq2traurY8/Lp/RI+zX7Ks4Oam5u7gmnfnLv804nPm0VQqgbKGFiO3z4cFBQEE3TGhoaycnJycnJfD5f2mytILPGZmNjc/z48UuXLt28eVP2
nG19ytatWxUdAkJICWlra3M4HBUVFQ6Hwzxg/5X9VEVF5cWLFzwe75133pF/p1Tntj90hcbGxkGDBpWUlISGhm7ZsiUqKmrBggUURfF4PEtLS4kvGT9+PJmnVZyLi4u0
qdARQkiclpaWnKdgaeuILCIPkpOTzczM7O3t27o1VVVViqI6mC264oXkZpWdLjQ0dOvWrW1KVb2gxhYfH19SUsLhcL744gsVFZX58+evXbuWz+dfuHAhODhY4kuKioqk
bQ2zGuo7uue8JvKUTJHs5eXVDfvquo1TFNXV/x1XV1dXV9dO7AVAjF6Q2HJzcwGAy+Xq6OiQEmtraz6fT+7jKZHsC7RRb6SpqanYU15qampKSsq6deu66Ows8jO8Ixtn
ZuRSCFdXVwA4fPiwAmNAfVwvSGzk5tO6urpMCZnIXFoPWZt6zqytrS0tLXV1dXVY+sgV3OQkyOPxjhw5snPnTvYpUvyx/E87Jc0o9tQsUWhoaEpKioyeXYRQD9ELEpuq
qmiQpJVA2iAZGSNHGGPGjDlw4ICzs3PHw+vtkpKSjhw5sm7dOkUHghBCnaMXJDZTU1MAYKaFZR4PGDBA4voyOtiI999//+LFi13Uz4kQQkixesFNkC0sLACgpKSE3NEA
APLy8gDA3Nxc4vqya2zTpk2Li4vDrIYQQsqqFyQ2Nzc3AwMDoVC4devW0tLSH374obCwUFVV1cfHR+L6si9iO3fuXJdFihBCSPF6QWLT0NAICwujKCoiIqJ///5ffPEF
AAQFBZGanDhpiU1HR+fSpUsGBgZdGCtCCCGFo3uJ06dPu7q69uvXb+zYsbt375axpq+vr6IPKkIIoc7UpnzRC+480lZTp05NSkoSKfT19V2+fLkiwkEIIdRR5PpIOfWC
UZFtVVpaKlIyadKkU6dOKSQYhBBC3UwJa2zGxsaVlZXskpycHJxfFCGE+oheMHikTerr69lZjcvlJiYmYlZDCKG+Q9maIjU1NauqqoqKioqKigoLC4VCYZtaZhFCCPV2
StgUiRBCqC9TtqZIhBBCfZyyNUUi1J1KS0sfPXoEAEOGDLG2tlZ0OKh3yMnJIbNuOTo6GhkZKTocJaTMNbbMzMzZs2eTOWrXrFlTV1en6Ih6kCNHjgwYMCAmJoZdePHi
xQkTJujr61taWs6bNy87O1tR4fUE5eXly5cvt7a21tPTGzt27N69e5ubm0XW8ff39/Dw8PDwiIyMVESMPcLz588XLVrUv39/XV3dsWPHHjlyhN3BIRAIVq1a5eDgYGJi
Mn369NTUVAWG2hMIBAJXV1fysXnw4AEpbGpqioiIGDVqlI6OzrBhwzZs2ECm6+qDaJretWvXxIkT9fX1J06ceOLECWaRUCjcvn372LFjjYyMZs6cSaa0lboVpXTv3r1+
/foBgLq6OnmnkydPrq2tVXRcitfS0nLx4kVya7EzZ84w5SdPniQHSk1NjTzQ1tbOyclRYKgK1NjYaG9vL3JAli1bxl6H/bNgy5YtigpVsSoqKvr37w8AFEUx37WwsDCy
tKqq6t1332Uv1dTUvHXrlmJjVqyQkBDmYxMfH08KAwICSAn7fNXU1KTYUBWCORTMpIy7du0iixYtWkRKyJSZampq0dHREjeitInN29sbAKZPn/7mzZt//vnH0NAQAE6e
PKnouBRs9uzZJiYmzPeKndhIS9rs2bMbGhoyMjLIrTgXL16swGgV6NdffyWHKDk5+c2bNxs2bCBP8/LyyAp1dXVDhw51cHAYNmxYX05sZObVwYMH8/l8gUAwe/ZsALCw
sCBLv/nmGwBwcHAoLi6ura2dN2+ehYXFypUrFRuzAmVmZqqqqn744YfsxMbMjXzs2LHGxsbk5GRyTr9586ai4+1uiYmJAKCjo/PXX3+1tLSEhYVZWFiMGzeOpun09HQA
UFFRiYuLa2pqWrVqFQDY2NhI3I5yJjaBQKCiogIAV69eJSVLliwBgLlz5yo2MIWbO3fu+PHjx40bJ5LY3rx5Q6ZvvXjxIilZs2YNAIwcOVJxwSrS+vXrAWDMmDHkaUVF
BYfDAYBz586REnLKvnnzpoODQ19ObBs3bhwyZMimTZvI0yNHjgCAsbGxUCgUCoVk0kTmQ4VcXV1NTU1zc3PZiY20+RsaGgqFQrKam5ubyO/OPmLhwoUAsGbNGvFFZDLk
f/3rX+TpkydPyDFMT08XX1k5B488ffq0paUFAJj+fPKAdNj2ZdHR0QDQ0tIiPi85qaMwl/2RCcq1tbW7Nb4ew9vbe9SoUYMGDSJPKYpiH5D8/Pzw8HA/P79JkyYpMsoe
YMeOHTt27CCPi4uLyafI39+foig+n19cXAwAQqHQ19c3Ozt7+PDhGzduHDVqlCIjVpzTp08nJSVFRkaKDBgZNmyYra1tXl7erl27/Pz87ty5c/PmTV1d3cmTJysqVEUh
XbCmpqYhISG3bt3icrn+/v6kgkt+DTCndCsrK4qiaJrm8/mjR48W3VCXpl9FuXHjBnl3RUVFpGT37t0AMGTIEMUG1kMwgyCk/SRMT0/X09MDgB9//LGbY+uBWlpa/Pz8
AKB///6km9bLy8vQ0LC0tJSm6T5eY2N89tln5EP17rvvNjY20jTNjIxg+o0AQEND4/79+4oOVgFevXplbm7u4uIiFAqZtkemj43P57OznYqKCtPa1Kfo6+uLfGAAYP/+
/TRNT506FQCWL1/OrKyrqwsAkZGR4ttRzlGR4tUR0s5GfnQj2aKjo6dMmfLq1StfX9/g4GBFh6NgVVVVH374YWRkpL6+fkxMjJaW1pUrVy5fvhwWFmZqaqro6HoQd3f3
Tz/9VFNTMzU1debMmcD6urm5uRUXF+fl5Y0YMaKhoeGrr75SaKSKQeZJ/vnnn8m5iK22tnbRokWVlZX9+vXz8vIyMzNraWlZtWpVYWGhQkJVIPKZMTc3T09Pr6iomD9/
PgBs3rxZYiOTrLN69+XibsQ0YTOD+kJDQ4HVPtvHSaux1dfXr1ixAgBUVFS2bdvGtPj3WXfu3LG0tAQABweH3NxcUrhs2TIA6Nevn4GBgYGBAel709DQmD59umKj7Qlu
3rxJPlqPHj168eIFecz0gkRERAAAl8tVbJAKYWNjw+FwyGeGDNgGAB0dnW3btpER7RoaGqQNoLm5mQwl3bBhg6Kj7m5WVlYAEBERQZ6SASMAkJ2dTYZE+vr6kkVCoZB8
9SRWbZWzxsblcsngkZycHFKSl5cHAObm5ooMq2d7/fr1lClTfvrppwEDBiQlJW3evFn8p2WfcurUKVdX14KCgoCAgNTUVFtbW/ZSgUBQXV1dXV1NfjA2NDS8fv1aQZEq
0ubNm6dOnfrtt9+SpxMmTCC/rDMzM83MzMiVEswHibS2Maf1vkYoFJLPDHON2ps3b+rr6//66y8AsLa2Jm0AKioqjo6OAEDK+5SBAweC2AcGAPr160fGaTOn9KdPnzLV
Owkb6r5c3L1mzJgBAJMnTy4oKIiPj9fS0gKA06dPKzquHkFijc3f3x8ANDQ0kpKSXr5VVlamwDgVKDc3l1wrM2/evOLiYuaA1NXViazZx/vYNm3aBAB6enp//PEHj8cj
g2kBICsri6bpjz76CABmzpxZWFiYmZk5fPhwAPDz81N01Aom0sd28OBB8nT//v1FRUUXLlwwNjYGgFWrVik60u524MABALCyskpPTy8uLibDRsjYCJLmORzOb7/9Vlxc
TMZPDh8+XGLDktImtr/++ov0QzLGjh1L+rSReGITCATkPC5i+PDhig1VUTZu3CjxF6X4cJs+nthKS0vNzMxEjhKTurKyssjl2wwDA4OCggLFxqxwIomtvr6euQKHweVy
i4uLFR1pd3v9+rWzszP7OFAUFRMTQ5b6+vqyF6moqJw9e1bidpSzKRIA3nvvvT///HPu3LmmpqY2NjafffbZzZs3mVtIIBFPnz5taGhQdBQ9SFZWlqJD6B1MTU3//vvv
xYsXDxo0SFdX19HRce/evb/88gtZOmLEiNu3b/v6+lpaWnK53A8//PDBgwekTQkxSDPJ5s2b7e3ttbW1bWxsli9ffv/+fS6Xq+jQupuOjs7169eDg4MdHBz09fWnTJny
559/kqv+AeDkyZPffPONo6Ojvr6+q6trbGwsGV0iDqetQQghpFSUtsaGEEKob8LEhhBCSKlgYkMIIaRUMLEhhBBSKpjYEEIIKRVMbAghhJQKJjaEEEJKBRMbQgghpYKJ
DSGEkFLBxNYXNTc3GxkZURS1evVqdnlMTAxFURRFMVNEdpEZM2ZQLIaGhu+9994XX3xRW1vLrHPo0CFKjL6+voeHx6NHj0Q2+PTpU2YFkXuD/fDDD2RRRUUFANja2lIU
ZWxsXF1dzaxz4MABsk5JSYnIlidNmiQeBoPsi6bpQ4cOzZw5c+DAgWZmZu7u7mFhYU1NTTKOwD///LN06dJhw4Zpa2tbWlq6u7tfuHBB5DZArW72zp07JIy4uDim8M2b
N6Rw+/btpGTAgAEiYauoqFhbW3/33XdkonnGmTNn5s6dO3jwYF1d3TFjxoSHh7NX8Pb2lnYcHj9+LPIGvby8KIoSmSy7qqpKRUWFoqg9e/Yw0W7atGny5MmGhoZDhw6d
PXv2uXPn2C9hPgYik5PJ838BgI8//piUfP/99+L/heLi4tWrVzs7O+vp6Q0dOtTT0/P69evsFdr0lomGhgYbGxsHB4empqZWv2WLFi3S1tbm8XgSN4Xar5vubYl6mKVL
lwKAnZ0du/B///d/oVvmGZ8+fbrET6OdnV1NTQ1Zh7nlubjBgweTmawZYWFhzNLLly+zF5HJ0wGgvLycpmkbGxvydP369cw6+/fvJ4UvX74UCdXFxUXG16e+vr6ysnLa
tGnii8aNG/f8+XOJbz8tLU3iLafXrVvHrCPPZm/fvk0K//Of/zAvZGbP2bZtGymRccvBAwcOkHUaGxvJLOEi3N3dmRke5s6dK207mZmZIu/x//7v/8iiwsJCpvD8+fOk
MD8/n6bp9PR05t/Btnjx4oaGBpGPAZ/Pb9P/habp+vp6ZoqccePGiUQYGxtLbqIvYsWKFcw6bXrLxHfffQcAv/32Gy3Ht+zRo0cURfn4+EjcFGo3TGx9VHR0NPsUQ5Ap
x9auXdvVeyeJbeTIkXw+n8/np6amBgUFkXi+++47sg5zRktNTSWr5ebmkrl1QGx2QTJ/FSEyK4q0xKapqcnj8cg6MhJbaWkp2TvzC33Lli38t2iaZiJfsmTJ1atX//zz
T+YXurSpR93c3Mj57tKlS2VlZRkZGZ6engBAUVReXh5ZR57Ntimx+fr6kphfvHgRHx9P5r5gpt5ljoCbm1tMTMzff//97bffkokcP/roI7IOOctbW1vzxTQ1NYm8x/Ly
cjIn4rFjx5jCgIAAJsc0NTWRiRHU1NS++eabW7duXbx4kfnFEx4eLvIxEElsrf5faJqOiYlhpyL2R72goEBHRwcAzMzMfv7557///vvSpUvk/8Kkpba+ZZqmq6qqdHV1
LS0tm5ubafm+ZWSCrdTUVIkfFdQ+mNj6qNraWm1tbQA4ePAgKcnPzydfwuTk5K7eOzl/jRkzhl04a9YsADAyMiI/tyWe0Z48eUIKmaoGTdNkFlkAmDNnDgAYGhqy5yeS
ltgA4OOPPybryEhsDKbpcvfu3Uxheno6OfuLTJ21a9cusrJI9ZEwMDAAgJ07dzIlAoFAV1dXU1Pz3Llz8m+2TYlt2bJl7E0tWbIEAExNTclLyNQzTk5O7EP3+eefAwCH
w3nz5g399iwv/0xGU6dOBYCFCxcyJYMHDwaAHTt20DS9d+9eEmdUVBSzQktLC7mVu66uLpm0RVpiY0j8vxCLFy8GgKlTp5J6G3sFMh2zjo4Ou1bd0tJCKoLMT4e2vmXy
D9q8eTN5Ks+3jNRiFy1aJOcukDywj62P0tLSItmF6Z4hvQvm5uZOTk4KCYk0hVVWVj59+lTaOvTbXigTExOmkPTKDBw4kPSjVFVVJSYmyt6Xs7OzpqbmyZMnO9ibGBMT
IxQKNTU1v/76a3b5qlWrSDq5ePGi+KvIye78+fO5ubmkRE9P79WrV3V1dWRmzvZttk3IkSSHMSMjo7S0FADWrVvHntopJCTk4MGDP/30U/umNPL29gaA69evk5mO8/Ly
SGeSj48PAJDazLhx48hTgsPhkMm4X79+HR8f3/63B1BfX3/p0iUA8PX1Jb+ZoqKiyCKhUEj2vmzZMpJrmb2fP3/+3r17Ejvk5PHbb78BwLx588hTeb5lXl5empqaUVFR
9fX17dspEoeJre8iJ5SEhAQy7+i1a9fgbW+5QuJh6lLPnj1jl5eWlpaUlJSUlOTn54eHhwOAvr7+Bx98wKxAEpu3t/fw4cPt7e2BdQqTZtCgQWvWrKFpet26dR2JmbSD
2dnZsRMtAGhoaJCpIyXO60aqC6mpqcOGDbOysvL19d29ezd7MEL7NitbbW0tOYzFxcU3btyIjY0FgAULFgAAUw8eP348+yUWFhaBgYGBgYGGhoZMYXZ2tsgwCi8vL4l7
JJ+lioqKtLQ0ePsBs7e3HzZsGPMeJ06cKPKq0aNH6+rqtu89sv3nP/959eoVh8OZM2cO+ainpKSQESg8Hq+urg4AxH/DcbnccePGjR49ml0o51uuqKjIyMjQ1dV95513
mMJWv2Xkf9rQ0HDnzp2OvF/Ehomt7/L09FRTUxMIBCkpKUKhMCEhAQDIXOwAUFJSwv4m6+rqjh079ssvv2T/eOfxeMuXL7ezs9PS0ho8eLCXlxfTONYORkZG5EFRURG7
fOzYsVwul8vlWllZHTlyxMrKKjY2lpkePTc3NyMjA96eQZjagMh4P3FffvmlsbFxQkICe0hhWz1//hwABgwYIL5o4MCBIJakiR07dmzfvp3Mt5mfn3/mzJl169Y5ODh4
eXkJBIJ2b1a2U6dOkcNobm7u6ur66tWroKCgzZs3A0BBQQFZR2S26w6ysLAgmZIcYXJOJx+wuro6UkeU+B7Nzc2hXe+RjfzccXFxMTMzmzFjhqamJk3TFy5cgLeHFwDY
w2q0tLSYTzs7kcsvNzeXpmkrKyvSuUjI/pYRdnZ2ADZLR/QAAAjUSURBVJCTk9OOnSKJMLH1Xfr6+qS3PC4u7t69e1VVVSYmJpMnTxZZrX///hYWFhwO58GDB+Hh4a6u
rqQVq6yszMXF5Zdffnny5ImxsXFBQUFsbOyUKVPa3YJUVVVFHpDzmjQ1NTWVlZXMU9JFYWxsTCInia2srOzWrVuyd6evr//VV18BwPr161vNgtKQhqyXL1+KLyKFgwYN
El+kpqa2adOmgoKCrKys48ePBwUFkfN7bGwsaYhr32bbRCgU8vl88saZA06uiJBNfCRFZGSktJXJvyMuLq65uZm0D5NzupaWlqmpKXTZe6yvr798+TK8bQ7V0dEhVXxS
lbe0tCSriVxCII2cb7m8vBwASAcqQ55vGXkJyfSoU2Bi69PI1z4uLo78mp47dy77xyaRkJBQUFBQU1MTGxurra2dkpJy+vRpADh37hyfz3d0dCwrK+Pz+SUlJUuWLBEK
haS1sB2YBjErKyt2OTNqICcnx9HRsaKiYu3atcxS8sO8oqJCVVWVoihmeOTvv//e6h4/++wzKyurhw8fnjhxon0xk5bPnJwcdq4FgKamJtL+JnIhFwA0NzdXVFRUVFQ0
NzePGDFi6dKlP//887Nnz0jk5Owv52aZfxY7MZMmL/ZSghk8UltbSwbLxMbGkqNExukBgMgFgpmZmQEBAQEBAewEoKamNvC/SRw0T5APWEpKCmkYtLa2Zg4IeY93794V
eUlWVhaptoofOvldvXqVDKIJCQkhlTAyQvLWrVslJSVWVlaampoAcOPGDeYlT5484fP5zJAWNjnfMtmm+PWLrX7LGhsbmZejToGJrU+bM2cOh8NJS0s7e/YsvP19LRFF
UbNmzfrkk08A4PDhwwDA5/MBwMTEhLTbmJqahoWF7d+//+OPP25fMCS7GBoaiiQ2hp2dHemWf/bsGTmV5+TkPHz4UOLK0dHR9H9f7yxOXV2dXABHskU7eHl5cTic+vr6
bdu2scv3799PkgEZqMlGfrObmJiwf/VramqSxEbObnJudujQoaQ8OzubWYd5LO0wamlpBQYGamlpAQAZUDp69GjSDrxnzx72QTt69OiRI0dOnjxJalftMGzYsJEjRzY3
N5M2T3YTHHkLZJw9U0jTNFlTR0fH3d29fTuFtz93xAmFwosXL3I4HDL2MjIykvn8DBw4sH///qStsn3IyFJSb2Nr9VtGXtK57cB9XbePw0Q9C9N7T+7ZwZQzbUTsC1HJ
N3PAgAE0TTP3aBgyZIi/v/++ffvu3LnDXNxTVlYWERERERGRk5MjvlMyVMze3v7ly5cvX75MT09ftWoV2Zr4dWzscd4//PADKXz16hVN06Thrl+/fjwej2kmYk6Ud+7c
oaUM92fGoAuFwvfee4/5OrR1uD9N08uWLSPl/v7+8fHxt27dIrUEAHj//fclborURczMzKKjoysqKiorK0+fPk1GTDCXN8m5WdI9Y2xsfOLEiUePHl29epVsXENDg7l2
SuJwfz09PfbumKM0f/78mzdvPnr06NtvvyUViyVLlpB1yNh3Gxubl2Jev34t7biRREX89ddfTHlTU9OIESMAQF1dfdu2bSkpKVeuXJk5cyZZMywsjKzGfAzu378vslNp
/5fa2lpyjdpXX33Fbj90dXUFgGnTptE0zePxSGrX09Pbs2dPampqdHQ000JoYGDQjrfc2Nioq6vL4XAEAoHIImnfMoLUmLOysqQdQ9RWmNj6OiZVMOcvQmJiI13fFEWR
60+joqKcnJzY7SrGxsbk4tb09HRScubMGfGdtunOI+zEtm/fPlJILnIiV/iK3LihqamJDC0JCQmhW0tsNE2z26PakdgqKiqYC3vZxowZw74sl+3+/fvkrCrj7cu52eTk
ZHLxABu5ZxWzjsTERhrTAgMDydP6+vr58+eL727EiBFMSDJuw/H9999LO25MbdjS0lIoFIosYiqdbPPnzyfXMtLSb0BDBoNI/L8wrdAPHz5k7458ElRVVSsqKmiajomJ
EekPA4Bp06Y5OjqKJDb53zIZLRkbGytSLu1bRtM0afmwsLCQdgBRO2BTZF9HOgBAbKSWRGRwAZfLJcnMx8fn7t271dXVd+7ciYiIGD9+fEVFhb+/v5x98gx9ff3x48ev
Xbv2wYMHzD2QJCK/8QHgxIkT//zzT2ZmJgCQezcwVFVVyc2oWh30T0yePFnagHV5GBkZxcfH79+/f9q0aVwu19jY2NXVddu2bX///feQIUMkvsTR0TErKyswMNDe3l5H
R8fExGT8+PE7duy4d+8e8/bl3OyECRNycnKWLVtmb2+vra1NbreYnJy8Zs0a2WGTI3nx4kXSoaWhoXH27NnIyMhZs2YNGDDA0NDQyclp586drf5HWjV27FgyFsbHx0fk
SpKxY8dmZGSsW7fO2dlZX19/0KBBs2bNOn369NmzZyXeckxOzHWN7GH3AEB+TjU3N5P+ttmzZ6enpwcFBTk6Ourp6U2cODE0NPTy5cuffvqpjDZ52T777DMA+PXXX0XK
ZXzLSAs8eSHqLBTdWj8E6ptKSkrIL/3MzEzSzw8AwcHBe/funTJlSlJS0okTJ/Ly8lxdXZm+kMbGRi6XW1VVFR8fTwptbGwOHDjAvuYMISVG0/SoUaOePHny/PlzefrM
Wlpahg0bVlpamp+fL2MMDmorrLEheV27du3o0aMAQLp/srOzt23btnLlStKWQtN0SkoK+fk/fPjwlpaWQ4cOlZWVsXuwEFJuFEX9+9//rq+v//HHH+VZ/+zZs0+fPv3m
m28wq3UuVUUHgHq6999/X11dvbq6mnRmODk5kVvwBQcHnzx5Mjs7e+jQoUOGDCkvLycrBAUFDRw48PHjx+fPn7927Vr7rnVFqJdyc3PbsWNHaWkpTdOt3sSnqKho5cqV
IvPaoI7DpkgkGdMUSejo6NjY2EyfPj00NJS54KaoqGjnzp3Xrl3j8Xi6uro2NjYBAQEff/yx+MVwCCHUbTCxIYQQUirYx4YQQkipYGJDCCGkVDCxIYQQUir/D82Dmcxe
UU7IAAAAAElFTkSuQmCC"}
2020-08-08 15:47:34 +02:00
T {ANALOG AUDIO AMPLIFIER
2020-12-16 18:30:33 +01:00
N-Channel only power stage} 430 -270 0 0 0.5 0.5 {layer=8}
2020-08-08 15:47:34 +02:00
N 180 -500 180 -470 {lab=E9}
N 260 -470 340 -470 {lab=E9}
N 340 -500 340 -470 {lab=E9}
N 1110 -700 1110 -670 {lab=SA}
N 840 -1020 840 -980 {lab=E4}
N 1110 -590 1110 -570 {lab=OUTI}
N 1110 -510 1110 -470 {lab=#net1}
2020-08-08 15:47:34 +02:00
N 180 -1020 180 -980 {lab=E1}
N 390 -790 520 -790 {lab=C2}
N 230 -950 230 -900 {lab=#net2}
N 180 -900 230 -900 {lab=#net2}
2020-08-08 15:47:34 +02:00
N 390 -790 390 -740 {lab=C2}
N 340 -740 390 -740 {lab=C2}
N 180 -920 180 -900 {lab=#net2}
N 220 -950 230 -950 {lab=#net2}
2020-08-08 15:47:34 +02:00
N 340 -760 340 -740 {lab=C2}
N 380 -790 390 -790 {lab=C2}
N 180 -1120 180 -1080 {lab=VBOOST}
N 840 -1120 840 -1080 {lab=VBOOST}
N 180 -1120 690 -1120 {lab=VBOOST}
2020-08-08 15:47:34 +02:00
N 690 -1120 840 -1120 {lab=VBOOST}
N 1110 -1180 1400 -1180 {lab=VPP}
N 840 -1120 1240 -1120 {lab=VBOOST}
N 860 -440 1070 -440 {lab=GB}
2020-08-08 15:47:34 +02:00
N 560 -460 560 -440 {lab=C8}
N 1240 -1120 1400 -1120 {lab=VBOOST}
N 1240 -900 1240 -870 {lab=#net3}
2020-08-08 15:47:34 +02:00
N 560 -580 560 -520 {lab=E8}
N 560 -760 560 -640 {lab=C6}
N 840 -780 840 -760 {lab=GA}
N 690 -810 690 -790 {lab=B1}
N 690 -810 800 -810 {lab=B1}
N 690 -1120 690 -880 {lab=VBOOST}
N 690 -820 690 -810 {lab=B1}
N 260 -470 260 -460 {lab=E9}
N 150 -70 260 -70 {lab=VSS}
2020-08-08 15:47:34 +02:00
N 50 -210 220 -210 {lab=B3}
N 260 -180 260 -130 {lab=E3}
N 150 -150 150 -70 {lab=VSS}
N 50 -150 50 -70 {lab=VSS}
N 50 -70 150 -70 {lab=VSS}
N 690 -600 1110 -600 {lab=OUTI}
2020-08-08 15:47:34 +02:00
N 180 -470 260 -470 {lab=E9}
N 1110 -610 1110 -600 {lab=OUTI}
N 860 -380 1110 -380 {lab=SB}
N 860 -700 1110 -700 {lab=SA}
N 1240 -1120 1240 -960 {lab=VBOOST}
2020-08-08 15:47:34 +02:00
N 1110 -410 1110 -380 {lab=SB}
N 1110 -730 1110 -700 {lab=SA}
N 860 -760 1070 -760 {lab=GA}
2020-08-08 15:47:34 +02:00
N 340 -740 340 -690 {lab=C2}
N 340 -630 340 -560 {lab=C9}
N 220 -630 340 -630 {lab=C9}
N 180 -600 180 -560 {lab=C5}
N 1110 -320 1110 -280 {lab=VNN}
N 1330 -590 1390 -590 {lab=OUT}
N 1110 -590 1240 -590 {lab=OUTI}
N 1110 -600 1110 -590 {lab=OUTI}
N 340 -860 340 -820 {lab=#net4}
N 560 -860 560 -820 {lab=#net5}
2020-08-08 15:47:34 +02:00
N 560 -1020 560 -920 {lab=E6}
N 340 -1020 340 -920 {lab=E2}
N 260 -280 260 -240 {lab=C3}
N 730 -440 770 -440 {lab=#net6}
N 560 -440 670 -440 {lab=C8}
N 690 -650 690 -600 {lab=OUTI}
N 690 -730 690 -710 {lab=#net7}
2020-08-08 15:47:34 +02:00
N 180 -840 180 -660 {lab=C7}
N 840 -860 840 -840 {lab=E11}
N 1240 -810 1240 -590 {lab=OUTI}
N 860 -760 860 -750 {lab=GA}
N 860 -710 860 -700 {lab=SA}
N 860 -440 860 -430 {lab=GB}
N 860 -390 860 -380 {lab=SB}
N 1240 -590 1270 -590 {lab=OUTI}
N 830 -440 860 -440 {lab=GB}
N 840 -760 860 -760 {lab=GA}
N 340 -1180 340 -1080 { lab=VPP}
N 560 -1180 560 -1080 { lab=VPP}
N 60 -1180 340 -1180 {lab=VPP}
N 340 -1180 560 -1180 {lab=VPP}
N 1110 -1180 1110 -790 { lab=VPP}
N 560 -1180 1110 -1180 {lab=VPP}
N 230 -950 800 -950 { lab=#net2}
2020-08-08 15:47:34 +02:00
C {ipin.sym} 530 -160 0 0 {name=p0 lab=PLUS}
C {ipin.sym} 530 -120 0 0 {name=p2 lab=VPP}
C {ipin.sym} 530 -100 0 0 {name=p3 lab=VNN}
C {nmos3.sym} 1090 -440 0 0 {name=xm2 model=irf540 m=1
program=evince
url="https://www.vishay.com/docs/91021/91021.pdf"
net_name=true}
C {res.sym} 960 -410 0 1 {name=R7 m=1 value=190 net_name=true}
2020-08-08 15:47:34 +02:00
C {nmos3.sym} 1090 -760 0 0 {name=xm1 model=irf540 m=1
program=evince
url="https://www.vishay.com/docs/91021/91021.pdf" net_name=true}
2022-01-14 14:56:13 +01:00
C {res.sym} 960 -730 0 1 {name=R0 m=1 value=190 net_name=true}
2020-08-08 15:47:34 +02:00
C {lab_pin.sym} 1390 -590 0 1 {name=p14 lab=OUT}
C {lab_pin.sym} 1110 -280 0 0 {name=p18 lab=VNN}
C {lab_wire.sym} 920 -440 0 0 {name=l8 lab=GB}
C {res.sym} 340 -1050 0 1 {name=R2 m=1 value=50 net_name=true}
C {res.sym} 180 -1050 0 1 {name=R3 m=1 value=50 net_name=true}
2020-08-08 15:47:34 +02:00
C {lab_pin.sym} 340 -1080 0 0 {name=p10 lab=VPP}
C {npn.sym} 160 -530 0 0 {name=Q5 model=q2n2222 area=1 net_name=true}
C {pnp.sym} 820 -950 0 0 {name=Q4 model=q2n2907p area=1 net_name=true}
C {res.sym} 840 -1050 0 1 {name=R9 m=1 value=50 net_name=true}
C {pnp.sym} 540 -790 0 0 {name=Q6 model=q2n2907p area=1 net_name=true}
C {res.sym} 560 -1050 0 1 {name=R4 m=1 value=50 net_name=true}
2020-08-08 15:47:34 +02:00
C {lab_pin.sym} 180 -580 0 0 {name=p15 lab=C5}
C {lab_pin.sym} 340 -580 0 1 {name=p16 lab=C9}
C {lab_pin.sym} 180 -1000 0 0 {name=p17 lab=E1}
C {lab_pin.sym} 560 -1080 0 0 {name=p25 lab=VPP}
C {lab_pin.sym} 340 -970 0 1 {name=p23 lab=E2}
C {lab_pin.sym} 560 -970 0 1 {name=p28 lab=E6}
C {lab_pin.sym} 840 -1000 0 0 {name=p29 lab=E4}
2020-08-08 15:47:34 +02:00
C {lab_pin.sym} 180 -1120 0 0 {name=p34 lab=VBOOST}
C {ammeter.sym} 1110 -540 0 0 {name=vd net_name=true current=0.2069}
C {ammeter.sym} 1110 -640 0 0 {name=vu net_name=true current=0.2005}
2020-08-08 15:47:34 +02:00
C {lab_pin.sym} 60 -1180 0 0 {name=p27 lab=VPP}
C {pnp.sym} 200 -950 0 1 {name=Q1 model=q2n2907p area=1 net_name=true}
C {pnp.sym} 360 -790 0 1 {name=Q2 model=q2n2907p area=1 net_name=true}
2020-08-08 15:47:34 +02:00
C {lab_pin.sym} 140 -530 0 0 {name=p6 lab=PLUS}
C {lab_pin.sym} 380 -530 0 1 {name=p24 lab=MINUS}
C {npn.sym} 360 -530 0 1 {name=Q9 model=q2n2222 area=1 net_name=true}
2020-08-08 15:47:34 +02:00
C {lab_pin.sym} 560 -670 0 0 {name=p9 lab=C6}
C {code.sym} 1040 -180 0 0 {name=STIMULI
only_toplevel=true
value=".option PARHIER=LOCAL RUNLVL=6 post MODMONTE=1 warn maxwarn=6000
.option sampling_method = SRS ingold=1
.option method=gear
.option savecurrents
2020-08-08 15:47:34 +02:00
.temp 30
vvss vss 0 dc 0
vvpp vpp 0 dc 50
vvnn vnn 0 dc -50
rfb1 vpp minus 100k
rfb2 out minus 100k
rfb3 minus vdc '100k/43'
vvdc vdc 0 dc 21.3
eref vref 0 plus vdc 45
vplus plus vdc dc 0
evboost vboost 0 vol='v(out)>=40 ? v(out)+10 : 50'
rload out 0 4
** models are generally not free: you must download
** SPICE models for active devices and put them into the below
** referenced file in simulation directory.
.include \\"models_poweramp.txt\\"
.dc vplus '-1.5' '1.5' 0.1
.save all
.op
2020-08-08 15:47:34 +02:00
*.probe dc v(plus,vdc)
"}
C {lab_wire.sym} 920 -760 0 0 {name=l1 lab=GA}
C {res.sym} 800 -440 1 1 {name=R11 m=1 value=1300 net_name=true}
C {pnp.sym} 540 -490 0 0 {name=Q8 model=q2n2907p area=1 net_name=true}
C {capa.sym} 1240 -930 0 0 {name=C12 m=1 value="40u" net_name=true}
C {diode.sym} 1240 -1150 0 0 {name=D0 model=d1n4148 area=1
url="http://pdf.datasheetcatalog.com/datasheet/bytes/1N5406.pdf" net_name=true}
C {res.sym} 1240 -840 0 1 {name=R18 m=1 value=200 net_name=true}
C {zener.sym} 1400 -1150 0 0 {name=D1 model=d1n758 area=1
url="http://www.futurlec.com/Datasheet/Diodes/1N746-1N759.pdf" net_name=true}
C {res.sym} 690 -760 0 1 {name=R14 m=1 value=4k net_name=true}
C {pnp.sym} 820 -810 0 0 {name=Q11 model=q2n2907p area=1 net_name=true}
C {res.sym} 690 -850 0 1 {name=R15 m=1 value=4k net_name=true}
C {res.sym} 260 -430 0 1 {name=R5 m=1 value=120 net_name=true}
C {res.sym} 260 -370 0 1 {name=R6 m=1 value=120 net_name=true}
C {zener.sym} 150 -180 2 0 {name=D2 model=d1n755 area=1
2020-08-08 15:47:34 +02:00
url="http://www.futurlec.com/Datasheet/Diodes/1N746-1N759.pdf"
net_name=true}
C {npn.sym} 240 -210 0 0 {name=Q3 model=q2n2222 area=1 net_name=true}
C {res.sym} 150 -240 0 1 {name=R1 m=1 value=10k net_name=true}
C {lab_pin.sym} 150 -270 0 0 {name=p7 lab=VPP}
C {res.sym} 260 -100 0 1 {name=R10 m=1 value=170 net_name=true}
C {lab_pin.sym} 50 -70 0 0 {name=p11 lab=VSS}
C {capa.sym} 50 -180 0 0 {name=C3 m=1 value=100n net_name=true}
C {res.sym} 560 -610 0 1 {name=R12 m=1 value=1300 net_name=true}
2020-08-08 15:47:34 +02:00
C {lab_pin.sym} 690 -800 0 0 {name=p12 lab=B1}
C {lab_pin.sym} 340 -470 0 1 {name=p13 lab=E9}
C {lab_pin.sym} 560 -440 0 0 {name=p19 lab=C8}
C {lab_pin.sym} 560 -560 0 1 {name=p20 lab=E8}
C {lab_pin.sym} 840 -850 0 0 {name=p21 lab=E11}
2020-08-08 15:47:34 +02:00
C {lab_pin.sym} 260 -160 0 1 {name=p22 lab=E3}
C {lab_pin.sym} 260 -270 0 0 {name=p26 lab=C3}
2020-08-08 15:47:34 +02:00
C {lab_pin.sym} 50 -210 0 0 {name=p30 lab=B3}
C {lab_pin.sym} 520 -490 0 0 {name=p33 lab=VSS}
C {res.sym} 340 -660 0 1 {name=R13 m=1 value=300 net_name=true}
C {npn.sym} 200 -630 0 1 {name=Q7 model=q2n2222 area=1 net_name=true}
2020-08-08 15:47:34 +02:00
C {lab_pin.sym} 180 -690 0 0 {name=p8 lab=C7}
C {lab_pin.sym} 340 -710 0 1 {name=p31 lab=C2}
2020-08-08 15:47:34 +02:00
C {title.sym} 160 -30 0 0 {name=l2 author="Stefan Schippers"}
C {lab_pin.sym} 860 -700 0 0 {name=p32 lab=SA}
C {ammeter.sym} 1110 -350 0 0 {name=v0 net_name=true current=0.2263}
C {lab_pin.sym} 860 -380 0 0 {name=p35 lab=SB}
C {ammeter.sym} 560 -890 0 0 {name=v1 net_name=true current=0.01956}
C {ammeter.sym} 340 -890 0 0 {name=v2 net_name=true current=0.01947}
C {ammeter.sym} 260 -310 0 0 {name=v3 net_name=true current=0.03918}
C {ammeter.sym} 700 -440 3 0 {name=v4 net_name=true current=0.01941}
C {ammeter.sym} 690 -680 0 0 {name=v5 net_name=true current=0.006184}
C {ammeter.sym} 180 -870 0 1 {name=v6 net_name=true current=0.01949}
C {ammeter.sym} 840 -890 0 0 {name=v7 net_name=true current=0.01944}
C {spice_probe_vdiff.sym} 860 -410 0 0 {name=p37 voltage=3.688}
C {spice_probe_vdiff.sym} 860 -730 0 0 {name=p38 voltage=3.68}
C {ammeter.sym} 1300 -590 3 0 {name=v8 net_name=true current=-2.1216e-04}
2020-08-08 15:47:34 +02:00
C {opin.sym} 600 -130 0 0 {name=p5 lab=OUT}
C {ipin.sym} 530 -180 0 0 {name=p1 lab=MINUS}
2020-08-08 15:47:34 +02:00
C {ipin.sym} 530 -140 0 0 {name=p4 lab=VSS}
C {launcher.sym} 510 -330 0 0 {name=h2
descr="Ctrl-Click
Clear all probes"
tclcommand="
xschem push_undo
xschem set no_undo 1
xschem set no_draw 1
set lastinst [xschem get instances]
for \{ set i 0 \} \{ $i < $lastinst \} \{incr i \} \{
set type [xschem getprop instance $i cell::type]
if \{ [regexp \{(^|/)probe$\} $type ] \} \{
xschem setprop $i voltage fast
\}
if \{ [regexp \{current_probe$\} $type ] \} \{
xschem setprop $i current fast
\}
if \{ [regexp \{differential_probe$\} $type ] \} \{
xschem setprop $i voltage fast
\}
\}
xschem set no_undo 0
xschem set no_draw 0
xschem redraw
"
}
C {ngspice_probe.sym} 750 -1120 0 0 {name=p54}
C {ngspice_probe.sym} 180 -760 0 0 {name=p53}
C {ngspice_probe.sym} 560 -710 0 0 {name=p55}
C {ngspice_get_value.sym} 1130 -780 0 0 {name=nmos1 node=i(@r.$\{path\}xm1.rd[i])
descr="Id="}
C {ngspice_get_expr.sym} 800 -1000 0 1 {name=r8
node="[format %.4g [expr [ngspice::get_voltage e4] - [ngspice::get_voltage c7]]]"
descr = veb
}
C {ngspice_get_expr.sym} 860 -980 0 0 {name=r9
node="[format %.4g [expr [ngspice::get_current \{q4[ic]\}] / [ngspice::get_current \{q4[ib]\}] ] ]"
descr = beta
}
C {ngspice_probe.sym} 560 -830 0 0 {name=p41}
C {ngspice_probe.sym} 560 -530 0 1 {name=p42}
C {ngspice_probe.sym} 590 -440 2 1 {name=p47}
C {ngspice_get_expr.sym} 860 -920 0 0 {name=r15
node="[format %.4g [expr ([ngspice::get_voltage e4] - [ngspice::get_voltage e11]) * [ngspice::get_current \{q4[ic]\}]]] W"
descr = power
}
C {ngspice_probe.sym} 260 -260 0 0 {name=p48}
C {ngspice_probe.sym} 90 -70 0 0 {name=p49}
C {ngspice_probe.sym} 100 -210 0 1 {name=p52}
C {ngspice_probe.sym} 1190 -590 0 1 {name=p39}
C {ngspice_probe.sym} 890 -700 2 1 {name=p43}
C {ngspice_probe.sym} 460 -790 2 1 {name=p44}
C {ngspice_probe.sym} 730 -810 2 1 {name=p46}
C {ngspice_probe.sym} 440 -950 0 0 {name=p50}
C {ngspice_probe.sym} 200 -470 0 0 {name=p45}
C {ngspice_probe.sym} 340 -600 0 0 {name=p51}
C {ngspice_get_expr.sym} 330 -900 0 1 {name=r17
node="[ngspice::get_current v2]"
descr = current
}
C {ngspice_get_expr.sym} 360 -1040 0 0 {name=r18
node="[ngspice::get_current \{r2[i]\}]"
descr = current
}
C {ngspice_get_expr.sym} 860 -1040 0 0 {name=r19
node="[ngspice::get_current \{r9[i]\}]"
descr = current
}
C {ngspice_get_expr.sym} 820 -890 0 1 {name=r2
node="[ngspice::get_current \{q4[ic]\}]"
descr = current
}
C {ngspice_get_expr.sym} 800 -970 0 1 {name=r1
node="[ngspice::get_current \{q4[ib]\}]"
descr = Ib
}
C {ngspice_get_expr.sym} 580 -460 0 0 {name=r11
node="[format %.4g [expr ([ngspice::get_voltage e8] - [ngspice::get_voltage c8]) * [ngspice::get_current \{q8[ic]\}]]] W"
descr = power
}
C {ngspice_get_expr.sym} 190 -860 0 0 {name=r12
node="[ngspice::get_current v6]"
descr = current
}
C {ngspice_get_expr.sym} 240 -210 0 1 {name=r6
node="[format %.4g [expr [ngspice::get_current \{q3[ic]\}] / [ngspice::get_current \{q3[ib]\}] ] ]"
descr = beta
}
C {ngspice_get_expr.sym} 860 -780 0 0 {name=r16
node="[format %.4g [expr ([ngspice::get_voltage e11] - [ngspice::get_voltage ga]) * [ngspice::get_current \{q11[ic]\}]]] W"
descr = power
}
C {ngspice_get_expr.sym} 980 -720 0 0 {name=r20
node="[ngspice::get_current \{r0[i]\}]"
descr = current
}
C {ngspice_get_expr.sym} 280 -200 2 1 {name=r3
node="[format %.4g [expr ([ngspice::get_voltage c3] - [ngspice::get_voltage e3]) * [ngspice::get_current \{q3[ic]\}]]] W"
descr = power
}
C {ngspice_get_expr.sym} 240 -190 2 0 {name=r21
node="[format %.4g [expr [ngspice::get_voltage b3] - [ngspice::get_voltage e3]]]"
descr = vbe
}
C {ngspice_get_expr.sym} 340 -390 0 0 {name=r7
node="[format %.4g [expr ([ngspice::get_voltage e9] - [ngspice::get_voltage c3]) * [ngspice::get_current \{r5[i]\}]]] W"
descr = power
}
C {ngspice_get_expr.sym} 570 -760 0 0 {name=r10
node="[format %.4g [expr ([ngspice::get_voltage e6] - [ngspice::get_voltage c6]) * [ngspice::get_current \{q6[ic]\}]]] W"
descr = power
}
C {ngspice_get_expr.sym} 210 -650 0 0 {name=r14
node="[format %.4g [expr ([ngspice::get_voltage c7] - [ngspice::get_voltage c5]) * [ngspice::get_current \{q7[ic]\}]]]"
descr = power
}
C {ngspice_probe.sym} 180 -560 0 0 {name=p36}
C {ngspice_get_expr.sym} 200 -500 0 0 {name=r13
node="[format %.4g [expr ([ngspice::get_voltage c5] - [ngspice::get_voltage e9]) * [ngspice::get_current \{q5[ic]\}]]]"
descr = power
}
C {ngspice_get_expr.sym} 320 -500 0 1 {name=r22
node="[format %.4g [expr ([ngspice::get_voltage c9] - [ngspice::get_voltage e9]) * [ngspice::get_current \{q9[ic]\}]]]"
descr = power
}
C {ngspice_get_expr.sym} 330 -760 0 1 {name=r23
node="[format %.4g [expr [ngspice::get_current \{q2[ic]\}] / [ngspice::get_current \{q2[ib]\}] ] ]"
descr = beta
}
C {ngspice_get_expr.sym} 1090 -640 0 1 {name=r24
node="[ngspice::get_current vu]"
descr = current
}
C {ngspice_get_expr.sym} 1090 -530 0 1 {name=r25
node="[ngspice::get_current vd]"
descr = current
}
C {ngspice_get_expr.sym} 1210 -1140 0 1 {name=r5
node="[ngspice::get_current \{d0[id]\}]"
descr = current
}
C {ngspice_get_expr.sym} 1370 -1150 0 1 {name=r26
node="[ngspice::get_current \{d1[id]\}]"
descr = current
}
C {ngspice_get_value.sym} 1130 -460 0 0 {name=r27 node=i(@r.$\{path\}xm2.rd[i])
descr="Id="}
C {ngspice_get_expr.sym} 160 -230 0 0 {name=r28
node="[format %.4g [expr ([ngspice::get_node v(vpp)] - [ngspice::get_voltage b3]) * [ngspice::get_current \{r1[i]\}]]] W"
descr = power
}
C {ngspice_get_expr.sym} 1130 -740 2 1 {name=r29
node="[format %.4g [expr ([ngspice::get_node v(vpp)] - [ngspice::get_voltage outi]) * [ngspice::get_current \{vu\}]]] W"
descr = power
}
C {lab_wire.sym} 910 -600 0 1 {name=l40 lab=OUTI}
C {ngspice_get_expr.sym} 980 -400 0 0 {name=r30
node="[ngspice::get_current \{r7[i]\}]"
descr = current
}
C {ngspice_get_expr.sym} 700 -440 2 0 {name=r31
node="[ngspice::get_current \{v4\}]"
descr = current
}
C {ngspice_get_expr.sym} 1130 -420 2 1 {name=r32
node="[format %.4g [expr ([ngspice::get_voltage outi] - [ngspice::get_node v(vnn)]) * [ngspice::get_current \{vd\}]]] W"
descr = power
}
C {ngspice_get_expr.sym} 150 -160 2 0 {name=r33
node="[format %.4g [expr -[ngspice::get_voltage b3] * [ngspice::get_current \{d2[id]\}]]] W"
descr = power
}
C {launcher.sym} 770 -70 0 0 {name=h3
descr="Load file into gaw"
comment="
This launcher gets raw filename from current schematic using 'xschem get schname'
and stripping off path and suffix. It then loads raw file into gaw.
This allow to use it in any schematic without changes.
"
tclcommand="
set rawfile [file tail [file rootname [xschem get schname]]].raw
gaw_cmd \\"tabledel $rawfile
load $netlist_dir/$rawfile
table_set $rawfile\\"
unset rawfile"
}
C {spice_probe.sym} 1010 -760 0 0 {name=p40 voltage=3.543}
C {spice_probe.sym} 1000 -440 0 0 {name=p56 voltage=-46.18}
C {spice_probe.sym} 420 -790 0 0 {name=p57 voltage=48.06}
C {spice_probe.sym} 280 -950 0 0 {name=p58 voltage=47.27}
C {spice_probe.sym} 180 -720 0 0 {name=p59 voltage=47.27}
C {spice_probe.sym} 1020 -1120 0 0 {name=p62 voltage=49.04}
C {launcher.sym} 770 -110 0 0 {name=h1
descr=Backannotate
tclcommand="ngspice::annotate"}
C {launcher.sym} 770 -150 0 0 {name=h4
descr="View Raw file"
tclcommand="textwindow $netlist_dir/[file tail [file rootname [ xschem get schname 0 ] ] ].raw"
}
2021-12-28 00:44:59 +01:00
C {spice_probe.sym} 790 -600 0 0 {name=p60 analysis=tran voltage=-0.1364}