Flexible Task Scheduling in Data Relay Satellite Networks
Bibliographic record
Abstract
The task-schedulingalgorithm is a key module to satisfy various complex user requirements, and improve the usage flexibility and efficiency of data relay satellites networks (DRSN). In this context, we first propose a novel application mode for DRSN, in which users are allowed to submit multiple optional service time windows and specify a preferred antenna as well as an expected execution duration for each task. Meanwhile, the start time of a service time window can be adjusted within a specified range. A mathematical programming model that maximizes the completion ratio of tasks and the expectation satisfaction of users is established. Moreover, a conflict resolution-assisted iterative task-scheduling algorithm (CRITS) is designed, composing of five closely dependent operators: resource matching, service durations generation, conflict evaluation, conflict resolution, and solution update. To verify the effectiveness of the proposed CRITS, extensive experiments are carried out. The experimental results demonstrate the competitive performance of CRITS in addressing the DRSN scheduling problem. In comparison with two heuristic algorithms (heuristic algorithm based on time-freedom degree and a heuristic algorithm based on task priority) and a meta-heuristic algorithm (adaptive variable neighborhood descent combined with a tabu list), the proposed CRITS increases the overall completion ratio of tasks by 6.65, 10.26, and 10.96%, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".