本书探讨了航天技术中机器学习和数据挖掘的主要概念、算法和技术。
This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology.
卫星是太空的“鹰眼”，它能让我们同时观察地球的大片区域，比地面上的设备工具更快地收集更多的数据。
Satellites are the ‘eagle eyes’ that allow us to view massive areas of the Earth simultaneously, and can gather more data, more quickly, than tools on the ground.
因此，开发智能化的人造卫星健康监测系统是当前航天工程中的一个重要课题，该系统可以根据遥测数据确定卫星的当前状态并预测其故障。
Consequently, the development of intelligent health monitoring systems for artificial satellites – which can determine satellites’ current status and predict their failure based on telemetry data – is one of the most important current issues in aerospace engineering.
本书分为三个部分，第一部分讨论人造卫星健康监测中的核心问题，包括基于张量的卫星遥测数据异常检测和卫星监测中的机器学习，以及卫星模拟器的设计、实现和验证。
This book is divided into three parts, the first of which discusses central problems in the health monitoring of artificial satellites, including tensor-based anomaly detection for satellite telemetry data and machine learning in satellite monitoring, as well as the design, implementation, and validation of satellite simulators.
第二部分讨论遥测数据分析和挖掘的问题，而最后一部分则关注遥测数据中的安全问题。
The second part addresses telemetry data analytics and mining problems, while the last part focuses on security issues in telemetry data.
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