The next generation of spacecraft is anticipated to enable various new applications involving onboard processing, machine learning and decentralised operational scenarios. Even though many of these have been previously proposed and evaluated, the operational constraints of real mission scenarios are often either not considered or only rudimentary. Here, we present an open-source Python module called PASEOS that is capable of modelling operational scenarios involving one or multiple spacecraft. It considers several physical phenomena including thermal, power, bandwidth and communications constraints as well as the impact of radiation on spacecraft. PASEOS can be run both as a high-performance-oriented numerical simulation and/or in a real-time mode directly on edge hardware. We demonstrate these capabilities in three scenarios, one in real-time simulation on a Unibap iX-10 100 satellite processor, another in a simulation modelling an entire constellation performing tasks over several hours and one training a machine learning model in a decentralised setting. While we demonstrate tasks in Earth orbit, PASEOS is conceptually designed to allow deep space scenarios too. Our results show that PASEOS can model the described scenarios efficiently and thus provide insight into operational considerations. We show this in terms of runtime and overhead as well as by investigating the modelled temperature, battery status and communication windows of a constellation. By running PASEOS on an actual satellite processor, we showcase how PASEOS can be directly included in hardware demonstrators for future missions. Overall, we provide the first solution to holistically model the physical constraints spacecraft encounter in Earth orbit and beyond. The PASEOS module is available open-source online together with an extensive documentation to enable researchers to quickly incorporate it in their studies.

翻译：下一代航天器预计将催生多种涉及星载处理、机器学习及分散式运行场景的新应用。尽管其中许多方案已有前期提出与评估，但实际任务场景的运行约束往往仅被粗略考虑或完全忽略。本文提出名为PASEOS的开源Python模块，能够对涉及单星或多星的运行场景进行建模。该模块综合考虑热力学、电力、带宽、通信约束及辐射影响等多类物理现象。PASEOS既可运行高效数值仿真，也可直接在边缘硬件上执行实时模式。我们通过三个场景验证其能力：在Unibap iX-10 100卫星处理器上实时仿真、模拟整星座持续数小时的任务执行、以及训练分散式机器学习模型。虽然演示场景设定于地球轨道，但PASEOS在设计层面支持深空场景。实验结果表明，PASEOS能高效建模所述场景，通过运行时开销分析及星座温度、电池状态、通信窗口的建模数据，揭示运行考量因素。通过在真实卫星处理器部署PASEOS，我们展示了其可直接集成于未来任务硬件原型验证。总体而言，我们首次提出全面建模航天器在近地轨道及深空环境中物理约束的解决方案。该模块以开源形式在线发布，附有详实文档，便于研究者快速将其纳入研究。