LBLRTM 内容 层编号方案 LBLRTM的输出文件 运行LBLRTM的顺序 测验 经常问的问题 介绍 LBLRTM（逐行辐射传递模型）是一种准确高效的逐行辐射传递模型，源自快速大气特征码（FASCODE）。 LBLRTM已经并且一直在针对从紫外线到亚毫米的大气辐射光谱进行广泛验证。 HITRAN数据库为LBLRTM中使用的线路参数提供了基础。 这些线参数以及其他来源的其他线参数由称为LNFL的线文件创建程序提取，以便在LBLRTM中使用。 可以从Zenodo存储库中使用AER线文件检索代码或目录下载从HITRAN构建的，适用于LNFL的线参数数据库。 LBLRTM在计算中使用线路参数和MT_CKD连续体。 模型和数据因此被链接。 对于最新版本，关系为： LBLRTM版本 MT_CKD发布 线文件 v12.11 v3.5 v3.8 如果发生任何构建或运行问题，请创建问题或

一本介绍交流电机建模的专业书籍，分析很全面透彻。Mukhtar Ahmad编著。

IEC 62433 Series EMC IC modelling - 包含全部6份最新英文标准文件.7z

Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey 动态网络用于广泛的领域，包括社交网络分析、推荐系统和流行病学。将复杂网络表示为随时间变化的结构，网络模型不仅可以利用结构模式，还可以利用时间模式。然而，由于动态网络文学来自不同领域并使用不一致的术语，因此导航具有挑战性。同时，图神经网络 (GNN) 近年来因其在一系列网络科学任务（例如链接预测和节点分类）上表现出色的能力而受到广泛关注。尽管图神经网络很流行并且动态网络模型的好处已经得到证实，但很少有人关注用于动态网络的图神经网络。为了解决这项研究跨越不同领域以及调查动态图神经网络这一事实所带来的挑战，这项工作分为两个主要部分。首先，为了解决动态网络术语的歧义，我们建立了具有一致、详细的术语和符号的动态网络基础。其次，我们使用所提出的术语对动态图神经网络模型进行了全面调查。

Ultra-Dense Networks for 5G and Beyond：Modelling, Analysis, and Applications Ultra-Dense Networks for 5G and Beyond：Modelling, Analysis, and App.pdf (8.28 MB, 下载次数: 137 ) We are observing an ever-increasing number of connected devices and the rapid growth of bandwidth-intensive wireless applications. Te number of wirelessly connected devices is anticipated to exceed 11.5 billion by 2019, i.e. nearly 1.5 mobile devices per capita. In addition, it is expected that we will witness a 10 000-fold growth in wireless data traffic by the year 2030. Such unprecedented increases in mobile data traffic and network loads are pushing contemporary wireless network infrastructures to a breaking point. Tese predictions have raised alarm to the wireless industry and mobile network operators who are faced with the challenges of provisioning high-rate, low-delay, and highly reliable connectivity anytime and anywhere without ignificantly increasing energy consumption at the infrastructure, such as base stations, fronthaul and backhaul networks, and core networks.

Computational modeling plays an increasingly important role in biological and medical research, as well as in the medical device industry. Like other industries, successful development of medical devices and implants requires not only extensive testing (bench tests, animal experiments and human trials), but also extensive computational simulations which allow engineers to cost-effectively investigate system behavior and iterate the device design.

This textbook provides a step-by-step approach to numerical methods in engineering modelling. The authors provide a consistent treatment of the topic, from the ground up, to reinforce for students that numerical methods are a set of mathematical modelling tools which allow engineers to represent real-world systems and compute features of these systems with a predictable error rate. Each method presented addresses a specific type of problem, namely root-finding, optimization, integral, derivative, initial value problem, or boundary value problem, and each one encompasses a set of algorithms to solve the problem given some information and to a known error bound. The authors demonstrate that after developing a proper model and understanding of the engineering situation they are working on, engineers can break down a model into a set of specific mathematical problems, and then implement the appropriate numerical methods to solve these problems

Modelling Transport 4th edition by Juan de Dios Ortuzar and Luis G.Willumsen

利用多时相卫星图像进行农作物分类 该仓库提供了使用多时相卫星图像进行农作物分类的代码。 作物分类对于理解作物的供应很重要。 卫星图像有助于实时监测作物生长和健康状况。 如今，每天都有高分辨率的卫星图像。 利用高频数据和多个波段，可以使用深度学习对农作物进行分类。 有许多经典的机器学习农作物分类方法可用它使用单时间图像，并使用其结果精度相对较低作物的光谱特性和结构特性，但我们会使用由玫瑰M. Rustowicz笔者建议的方法 安装 conda create --name geo_py37 python=3.7 conda install gdal rasterio conda install numpy pandas geopandas scikit-learn jupyterlab matplotlib seaborn xarray rasterstats tqdm pytest sq

经典的无线通信英语教材 里面有各种信道的程序，对信道模型的精确分析