Machine-learning ready data on the thermal power consumption of the Mars Express Spacecraft

Area of Science:

• Space Science
• Planetary Science
• Aerospace Engineering

Background:

• The Mars Express Spacecraft (MEX) provides critical telemetry data for understanding spacecraft operations.
• Analyzing this data is essential for spacecraft health monitoring and decision-making.

Purpose of the Study:

• To present six comprehensive datasets of Mars Express Spacecraft telemetry data.
• To highlight the utility of these datasets for artificial intelligence and machine learning research.
• To facilitate advanced analysis of spacecraft status and thermal power consumption.

Main Methods:

• Data collection of telemetry, context, and thermal power consumption from MEX.
• Sampling data at six different time resolutions (1 min to 60 min) over three Martian years.
• Utilizing advanced artificial intelligence and machine learning techniques for data analysis.

Main Results:

• The datasets capture detailed spacecraft status over an extended period.
• The data's complexity presents challenges and opportunities for state-of-the-art AI methods.
• Potential applications include multi-target regression, anomaly detection, and clustering.

Conclusions:

• The presented MEX telemetry datasets are a valuable resource for space mission analysis.
• These datasets can serve as a benchmark for developing and testing novel AI algorithms.
• Further analysis can yield insights into spacecraft health, operation, and scientific discovery.