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首页Second Simulation of the Satellite Signal in the Solar Spectrum使用手册翻译

Second Simulation of the Satellite Signal in the Solar Spectrum使...

6S,大气校正

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以下手册介绍了6S（在太阳光谱中卫星信号的二次模拟），一个能够上述问题模拟的代码。6S代码是由Laboratoire OPTIQUE Atmospherique 10年前开发的5S升级版。

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6 s 用户指南第 2 版,1997 年 7 月

太阳光谱中卫星信号的二次模拟

（1）马里兰大学地理系通信地址: 格林贝尔特戈达德航天中心 923

位于美国马里兰州 20771 号

（2）法国国家科学研究院大气光学实验室 713 位于法国维尔里尔

科技大学 59655 号

6 s 用户指南第 2 版,1997 年 7 月

（3）欧洲中期天气预报中心位于英国伯克郡雷丁 Shinfield 公园 RG

2 9 号

6 s 用户指南第 2 版,1997 年 7 月

摘要 5 说明

描述卫星观测的大气效应 11

1.吸收的影响 11

2 .散射的影响 12

2.1 朗伯体的目标例子 12

2.2 环境函数 15

2.3 大气的内部反射 17

2.4 飞机和高目标模拟 19 2.5 目标的

方向效应 21

2.6 大气校正模型组合 23

3-吸收和散射效应 24

结论 26

图 27

鸣谢 31

附录:

I 计算机代码 33

II 输入和输出的例子 51

III 子程序 55

6 s 用户指南第 2 版,1997 年 7 月

摘要

在可见光和近红外波段，从卫星或者陆地及海面的空中平台的遥感，

很大程度上受气太阳-目标-传感器传输路径的影响。

以下手册介绍了 6S（在太阳光谱中卫星信号的二次模拟），一个能

够上述问题模拟的代码。6S 代码是由 Laboratoire OPTIQUE

Atmospherique 10 年前开发的 5S 升级版。6S 模拟

飞机观测，考虑到较高的目标及非朗伯面边界条件，新的气体

（CH4 ,N2O,CO）已经被集成到计算气态传输的计算里。关于瑞利

6 s 用户指南第 2 版,1997 年 7 月

和气溶胶散射效应的计算精度已经通过使用本领域的近似散射法的

连续顺序和使用状态得以改善。用于光谱整合的一步，已经使精度

已经提高到 2.5 纳米。最后，所有的社会认可的结果（如：瑞利光

学厚度计算）已经集成在代码中。

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(a) Consider the case of a European Vanilla Call option which is path independent. Examine the convergence of the Monte Carlo Method using the programme given in ‘MC Call.m’. How does the error vary with the number of paths nP aths? The current time is t = 0 and the Expiry date of the option is t = T = 0.5. Suppose that the current value of the underlying asset is S(t = 0) = 100 and the Exercise price is E = 100, with a risk free interest rate of r = 0.04 and a volatility of σ = 0.5. (b) Now repeat part (a) above but assume that the volatility is σ = 0.05. Does the change in the volatility σ influence the convergence of the Monte Carlo Method? (c) Now repeat part (a) but instead of taking one big step from t = 0 to t = T divide the interval into nSteps discrete time steps by using the programme given in ‘MC Call Small Steps.m’. Confirm that for path independent options, the value of nP aths determines the rate of convergence and that the value of nSteps can be set to 1. (d) Now let us consider path dependent options. The programme given in ‘MC Call Small Steps.m’ is the obvious starting point here. We assume that the current time is t = 0 and the expiry date of the option is t = T = 0.5. The current value of the underlying asset is S(t = 0) = 100 and the risk free interest rate is r = 0.05 and the volatility is σ = 0.3. (i) Use the Monte Carlo Method to estimate the value of an Arithematic Average Asian Strike Call option with Payoff given by max(S(T) − S, ¯ 0). (ii) Use the Monte Carlo Method to estimate the value of an Up and Out Call option with Exercise Price E = 100 and a barrier X = 150. (iii) Comment on the the rate of convergence for part (i) and (ii) above with respect to the parameters nP aths and nP aths使用matlab编程

(a) In Monte Carlo simulation, the error decreases as the number of paths (nP aths) increases. This is because the more paths are simulated, the more accurate the estimate of the option price becomes....

python 大气校正_基于6s模型的遥感影像大气校正方法

该方法基于6S（Second Simulation of Satellite Signal in the Solar Spectrum）模型，通过对大气散射和吸收等物理量的模拟，计算出不同波段下的大气透过率和反射率等参数，再根据这些参数对遥感影像进行校正。...

You could also create code to simulate the Monty Hall problem. Create a function named random_door, which uses numpy.random.choice to sample 1 door randomly from a list of integer door indices (1-3 in this case). Use this function to randomly select the door the car is behind and the contestant’s initial choice of doors. Create a function monty_choice, which chooses the door Monty opens, conditional on the contestant’s choice of doors and the door with the car. For the case where the contestant has selected the door with the car, select the door to open by simulating the flip of a fair coin using the np.random.binomial function with n = 1. Create a function win_car, which determines if the contestant wins the car, conditional on the strategy selected, {switch, noswitch}, the door the contestant selected, the door with the car, and the door Monty opened. Create a function named simulation that allows you to run the simulation n = 1000 times, with a switch or noswitch strategy. Execute your simulation for each possible strategy. For the two strategies, plot side by side bar charts showing the numbers of successes and failures for each strategy. Describe the strategy a contestant should adopt for this game. How much will the chosen strategy change the probability of winning a car? Is this result consistent with the conditional probability of this problem?

The code then runs the simulation for both the switch and no switch strategies, and plots the results in side-by-side bar charts. According to the simulation results, the contestant should switch ...

Reliability and Maintainability Simulation involves a large number of random variables. The use of dynamic arrays will greatly improve the efficiency of simulation and the scale of problem solving. Please design Vector. This problem requires the implementation of a vector class template, which can realize the storage and access of data. (1) [] operator can only access the existing elements. (2) The add method can automatically expand the internal storage space when accessing. Note that the behavior of this vector is different from that of std:: vector. Function interface definition: template <class T> class Vector { ... }

Sure, here's the code for the Vector class template that meets ... Therefore, it should be used with caution and tested thoroughly for reliability and maintainability in the context of your simulation.

Write the code about Process Scheduling Algorithm Simulation in C++.The requirements are as follows:1、 Simulate the operation of the round-robin algorithm for process scheduling. 2、 Create at least 15 processes (use function fork()) and output their scheduling situation under the scheduling algorithm mentioned above and output it to the terminal to check the execution of the algorithm. 3、 The output should include the arrival time of the processes, the end time, and the average execution time

Here is an example code for Process Scheduling Algorithm Simulation using Round-Robin in C++: ``` #include #include #include #include using namespace std; struct Process { int pid; // Process ...

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To generate a chirp signal in OptiSystem using MATLAB, follow these steps: 1. Open OptiSystem and create a new project. 2. Add a MATLAB component to...8. Run the simulation to generate the chirp signal.

matlab AGC simulation

2. Peak Envelope AGC: This technique involves using a feedback loop to adjust the gain of a signal based on the peak envelope of the input signal. 3. RMS AGC: This technique involves using a feedback...

the code of Reconstruction toolkit to do monte carlo simulation

Here is an example code of using Reconstruction toolkit (RTK) to perform Monte Carlo simulation for X-ray CT imaging: ```python import rtk # Set up simulation parameters source_position = [0, 0, ...

Write the code about Process Scheduling Algorithm Simulation in C++.1、 Simulate the operation of the round-robin algorithm for process scheduling. 2、 Create at least 15 processes (using function fork())and output their scheduling situation under the scheduling algorithm mentioned above and output it to the terminal to check the execution of the algorithm. 3、 The output should include the arrival time of the processes, the end time, and the average execution time

Here is the code for process scheduling algorithm simulation using round-robin algorithm in C++: ``` #include #include #include #include using namespace std; struct process { int pid; int ...

Write the code in C++ aboutProcess Scheduling Algorithm Simulation:1、 Simulate the operation of the round-robin algorithm for process scheduling. 2、 Create at least 15 processes and output their scheduling situation under the scheduling algorithm mentioned above and output it to the terminal to check the execution of the algorithm. 3、 The output should include the arrival time of the processes, the end time, and the average execution time.

Here's a possible implementation of the round-robin algorithm simulation in C++: ```cpp #include #include #include using namespace std; struct Process { int id; int arrival_time; int burst_...

abaqus/explicit vumat for the simulation of damage and failure in unidirecti

使用abaqus/explicit VUMAT进行单向材料的损伤和破裂模拟时，需要定义材料的力学行为模型、破坏准则以及材料参数。通过对材料进行实验测试，获取材料的力学性能和破坏参数，然后在VUMAT中进行网络化处理，对应用场景...

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Here are the detail information provided in PPTs:The option is an exotic partial barrier option written on an FX rate. The current value of underlying FX rate S0 = 1.5 (i.e. 1.5 units of domestic buys 1 unit of foreign). It matures in one year, i.e. T = 1. The option knocks out, if the FX rate:1 is greater than an upper level U in the period between between 1 month’s time and 6 month’s time; or,2 is less than a lower level L in the period between 8th month and 11th month; or,3 lies outside the interval [1.3, 1.8] in the final month up to the end of year.If it has not been knocked out at the end of year, the owner has the option to buy 1 unit of foreign for X units of domestic, say X = 1.4, then, the payoff is max{0, ST − X }.We assume that, FX rate follows a geometric Brownian motion dSt = μSt dt + σSt dWt , (20) where under risk-neutrality μ = r − rf = 0.03 and σ = 0.12.To simulate path, we divide the time period [0, T ] into N small intervals of length ∆t = T /N, and discretize the SDE above by Euler approximation St +∆t − St = μSt ∆t + σSt √∆tZt , Zt ∼ N (0, 1). (21) The algorithm for pricing this barrier option by Monte Carlo simulation is as described as follows:1 Initialize S0;2 Take Si∆t as known, calculate S(i+1)∆t using equation the discretized SDE as above;3 If Si+1 hits any barrier, then set payoff to be 0 and stop iteration, otherwise, set payoff at time T to max{0, ST − X };4 Repeat the above steps for M times and get M payoffs;5 Calculate the average of M payoffs and discount at rate μ;6 Calculate the standard deviation of M payoffs.

Based on the information provided in the PPTs, here is the Python code to simulate and price the partial barrier option using Monte Carlo simulation: ```python import numpy as np from scipy.stats ...

Write the code about Process Scheduling Algorithm Simulation in C++ using the function fork（）.The requirements are as follows:1、 Simulate the operation of the round-robin algorithm for process scheduling. 2、 Create at least 15 processes and output their scheduling situation under the scheduling algorithm mentioned above and output it to the terminal to check the execution of the algorithm. 3、 The output should include the arrival time of the processes, the end time, and the average execution time

Note that the fork() function is not used in this code as it is not necessary for simulating the round-robin algorithm. However, it can be used to create child processes for each process and simulate ...

2024-2030全球及中国PCB接触式探头行业研究及十五五规划分析报告.docx

2024-2030全球及中国PCB接触式探头行业研究及十五五规划分析报告

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