The integrated agile Earth observation satellite scheduling problem
Bibliographic record
Abstract
Satellites are widely used for different purposes, such as monitoring deforestation, rising sea levels, and ecological changes. As a scarce resource, scheduling the activities performed by these spacecrafts is of paramount importance. This paper addresses the integrated agile Earth observation satellite scheduling problem (IAEOSSP) that employs a constellation of agile satellites to observe the Earth. The objective of the IAEOSSP is to maximize the profits associated with observed targets and the amount of data downloaded to ground stations. Critical aspects of the problem are considered, such as the onboard storage capacity, time windows, setup times, and energy dynamics. We propose a mixed-integer programming (MIP) formulation for the problem, as well as an improved MIP model that is computationally more tractable at the expense of possibly reducing the feasible search space. Moreover, we implement a MIP-based heuristic (MBH) to solve the proposed model with an efficient heuristic strategy for decreasing the size of the graph, as well as a heuristic from the literature initially designed for the non-agile version of the problem. Computational experiments evaluate the performance of the MBH not only on the IAEOSSP but also on a particular case involving conventional satellites denoted as constellation mission scheduling problem (CMSP). The results show that the MBH compares favorably against the improved MIP model for the IAEOSSP and against two existing algorithms for the CMSP.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".