Spacecraft operating at great distances experience limited data bandwidth and high latency for communication with Earth. Data analysis algorithms that operate onboard, the spacecraft can perform detection and discovery of events of interest without human intervention. This capability serves to increase the quality and quantity of science data collected by the mission through data summarization, downlink prioritization, and adaptive instrument mode switching. However, before such technology can be adopted for use by a mission, it is necessary to characterize the required memory and computational resources. For operation in high-radiation environments, such as in orbit around the gas giants, a characterization of radiation tolerance is also important. In this article, we propose a framework to assess the resource and radiation profiles for machine learning algorithms in a simulated spacecraft computational environment. We apply this framework to several use cases designed for the Europa Clipper spacecraft, which plans to study Jupiter’s moon Europa. This approach can also benefit other remote deployments, such as the robotic exploration of hazardous environments on Earth.

中文翻译：

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航天器上数据分析算法的资源消耗和辐射耐受性评估


远距离运行的航天器与地球的通信会遇到有限的数据带宽和高延迟。航天器上运行的数据分析算法可以在无需人工干预的情况下检测和发现感兴趣的事件。该功能通过数据汇总、下行链路优先级和自适应仪器模式切换来提高任务收集的科学数据的质量和数量。然而，在任务采用此类技术之前，有必要确定所需内存和计算资源的特性。对于高辐射环境中的操作，例如在气态巨行星周围的轨道上，辐射耐受性的表征也很重要。在本文中，我们提出了一个框架来评估模拟航天器计算环境中机器学习算法的资源和辐射剖面。我们将此框架应用于为 Europa Clipper 航天器设计的几个用例，该航天器计划研究木星的卫星木卫二。这种方法还可以使其他远程部署受益，例如机器人探索地球上的危险环境。