Comparison of electron density measurements from CSES and Swarm satellites with GNSS ionospheric tomography data

Electron density measurements obtained from China Seismo‐Electromagnetic Satellite (CSES) and Swarm-B can play an increasingly important role in the study of ionosphere above F2 peak height. This study presented a comprehensive comparison of electron density products obtained from Langmuir probe mounted on CSES and Swarm-B with ionospheric tomography for a whole year period of 2019. CSES was fully compared with Swarm-B on a global scale, including both absolute and relative differences, and a new index called NFI was developed to better quantify the similarity between two latitudinal profiles of electron density. CSES and Swarm-B were then compared with tomography respectively in four regions, roughly located in America, Europe, Australia and China. Results indicated that CSES data are consistent with Swarm-B, as NFI values exceed 0.6 for most of the analyses. Tomography and Swarm-B were found to have a good agreement as their biases are less than 0.2 × 10⁵ el/cm³ in general. For the comparison between CSES and tomography, the bias increased to around 0.6 × 10⁵ el/cm³ but the standard deviation changed slightly, validating the underestimation of electron density by CSES. The spatiotemporal comparisons of CSES and Swarm-B with tomography showed that: 1) the differences in electron density were relatively low in middle latitudes and increased rapidly in the regions of equatorial ionization anomaly; 2) Swarm-B has a better consistent with tomography than CSES, but both are capable of detecting ionosphere anomalies such as midlatitude arcs; and 3) CSES and Swarm-B both can capture the seasonal changes of electron density, while their values are basically smaller than those from tomography in Spring and Summer months.     

空间核动力装置控制系统设计分析及启示

空间核动力装置执行深空任务具有复杂性和特殊性，对其控制系统的研发提出较多独特的挑战。研发具有高可靠性和高自主性的控制系统，一直是空间核反应堆领域的研究重点及难点之一。对美俄(苏联)从20世纪80年代至今典型空间堆控制系统的研发方案展开调研，分别从控制系统的总体要求、架构设计、启动控制策略、额定工况下功率控制方案等方面，对其设计现状进行分析和归纳，总结美俄不同型号空间堆控制系统的设计及其特殊性。在美俄空间堆控制系统设计的经验基础上，为不同型号空间堆装置控制系统的设计提供借鉴。     

A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning, flight scheduling, airport operation, and passenger service. Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity, which brings critical challenges to accurate flight delay prediction. In recent years, Graph Convolutional Networks (GCNs) have become popular in flight delay prediction due to the advantage in extracting complicated relationships. However, most of the existing GCN-based methods have failed to effectively capture the spatial–temporal information in flight delay prediction. In this paper, a Geographical and Operational Graph Convolutional Network (GOGCN) is proposed for multi-airport flight delay prediction. The GOGCN is a GCN-based spatial–temporal model that improves node feature representation ability with geographical and operational spatial–temporal interactions in a graph. Specifically, an operational aggregator is designed to extract global operational information based on the graph structure, while a geographical aggregator is developed to capture the similar nature among spatially close airports. Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.