面向一体化测试与运管的航天器指令技术研究

现有的航天器测试与运管程序通过word文档采用自然语言的方式进行描述，需要人工录入、集成、整合和校验，既浪费人力、时间，又容易因人为原因出错，不利于测试与运管程序在多用户间的快速交互，且存在安全隐患。针对该问题，借鉴NASA及ESA的PLEXIL、PLUTO、Timeliner-TLX、SCL等航天器指令设计经验，研究了面向航天器地面测试与在轨运管的航天器指令技术，该指令架构具备层次化、模块化、抽象化等高级程序特点，且具备对航天器进行工作过程监测与异常状态处置能力，便于航天器测试与运管程序的人机交互、数字化设计、交换共享与解译执行，可作为国内航天器测试与运管程序一体化设计的技术参考。     

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.