The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board
equipment within its pre-defined operating temperatures range. To plan and
optimize the scientific operations of MEX, its operators need to estimate in
advance, as accurately as possible, the power consumption of the thermal
subsystem. The remaining power can then be allocated for scientific purposes.
We present a machine learning pipeline for efficiently constructing accurate
predictive models for predicting the power of the thermal subsystem on board
MEX. In particular, we employ state-of-the-art feature engineering approaches
for transforming raw telemetry data, in turn used for constructing accurate
models with different state-of-the-art machine learning methods. We show that
the proposed pipeline considerably improve our previous (competition-winning)
work in terms of time efficiency and predictive performance. Moreover, while
achieving superior predictive performance, the constructed models also provide
important insight into the spacecraft's behavior, allowing for further analyses
and optimal planning of MEX's operation.

文章提出了一种机器学习方案用于构建精确的预测模型以预测 Mars Express 空间船上热子系统的功耗， 通过对原始遥测数据进行转换，使用不同的机器学习方法构建精确的模型，旨在优化 Mars Express 的科学任务的分配，并提供更好的深入分析和规划。