本書探討了航天技術中機器學習和數據挖掘的主要概念、算法和技術。
This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology.
衛星是太空的“鷹眼”，它能讓我們同時觀察地球的大片區域，比地面上的設備工具更快地收集更多的數據。
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，開發智能化的人造衛星健康監測系統是當前航天工程中的一個重要課題，該系統可以根據遙測數據確定衛星的當前狀態並預測其故障。
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.
本書分爲三個部分，第一部分討論人造衛星健康監測中的核心問題，包括基於張量的衛星遙測數據異常檢測和衛星監測中的機器學習，以及衛星模擬器的設計、實現和驗證。
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.
第二部分討論遙測數據分析和挖掘的問題，而最後一部分則關注遙測數據中的安全問題。
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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