Satellite Scheduling Problems: A survey of applications in Earth and outer space observation
Bibliographic record
Abstract
With the growing interest in leveraging space technologies to provide both knowledge and services, the need for efficient space mission management also increases. Among all the related problems, the scheduling of tasks performed by observation satellites is not only crucial for the astrophysical community, but it also poses challenging optimization problems, which have been studied for nearly 30 years. The aim of this survey is to provide a comprehensive overview of Satellite Scheduling Problems (SSPs), with a particular focus on applications. First, we propose a novel literature classification of SSPs based on the main variants that have been defined over the years. We address both imaging and communication tasks in the context of Earth-centered missions and, for the first time, of outer space missions. Then, for each class of problems we provide a review of the main contributions available in the literature, offering insights about solution methodologies. Finally, we outline some promising future research directions. • The optimal scheduling of satellite tasks is surveyed. • Imaging, communication and integrated scheduling problems are considered. • A comprehensive literature classification of the problem variants is provided. • Both Earth and Outer Space applications are discussed. • Solution methodologies for each class of problems are presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".