Enhancing Satellite Data Integrity Through Online Learning for Memory Dump Scheduling
Bibliographic record
Abstract
Managing data downlinks through memory dump scheduling in spacecraft operations is paramount to maintaining data integrity and keeping high mission performance. Traditional static scheduling methods lack the flexibility to adapt to random events and place a significant amount of manual workload on operators. Although machine learning techniques have shown promise, they generally require large datasets and high computational resources, both of which can be limited in practice. This paper proposes to optimize time offsets for memory dump using online learning techniques, specifically, leveraging follow-the-leader strategies. These lightweight sequential algorithms are based on the intuitive idea of choosing offsets with the currently best historical performance and are known to be optimal under realistic assumptions. By integrating real-time telemetry feedback and online learning, the proposed method dynamically adjusts memory dump timings to account for variations in spacecraft operations and ground station availability, reducing the probability of data loss due to memory saturation or ground station outage. The proposed algorithm is tested using data coming from live telemetry within the mission planning system of Sentinel-6A, demonstrating its effectiveness in optimizing memory dump by showing a remarkable improvement in data key performance index. The algorithm achieved an 86% reduction in data loss relative to the loss experienced in the real-world scenario.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".