GalaxAI：用于航天器遥测数据可解释分析的机器学习工具箱
GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data
摘要：
我们介绍GalaxAI -一个多功能的机器学习工具箱，用于航天器遥测数据的高效和可解释的端到端分析。GalaxAI采用各种机器学习算法进行多变量时间序列分析、分类、回归和结构化输出预测，能够处理高吞吐量的异构数据。这些方法允许构建鲁棒和准确的预测模型，这些模型又被应用于航天器监测和操作规划的不同任务。更重要的是，除了精确构建模型外，GalaxAI还实现了可视化层，为任务专家和操作员提供了数据分析过程的完整，详细和可解释的视图。我们展示了GalaxAI在两个不同航天器的两个用例上的实用性和多功能性：i）火星快车热功耗的分析和规划，ii）预测INTEGRAL穿过范艾伦带的交叉点。
英文摘要：
We present GalaxAI - a versatile machine learning toolbox for efficient and interpretable end-to-end analysis of spacecraft telemetry data. GalaxAI employs various machine learning algorithms for multivariate time series analyses, classification, regression and structured output prediction, capable of handling high-throughput heterogeneous data. These methods allow for the construction of robust and accurate predictive models, that are in turn applied to different tasks of spacecraft monitoring and operations planning. More importantly, besides the accurate building of models, GalaxAI implements a visualisation layer, providing mission specialists and operators with a full, detailed and interpretable view of the data analysis process. We show the utility and versatility of GalaxAI on two use-cases concerning two different spacecraft: i) analysis and planning of Mars Express thermal power consumption and ii) predicting of INTEGRAL's crossings through Van Allen belts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
