In the face of an increasingly congested near-Earth space environment, the need for more efficient and accurate tracking and orbit prediction capabilities for active satellites has become more pressing than ever. As space activities continue to expand, the demand for sophisticated technologies to monitor and manage satellite behavior has reached a critical stage. This challenge seeks to address this urgent need by developing cutting-edge AI algorithms that can autonomously characterize satellite patterns of life using astrometric time-series data.