Priority-Aware Parallel Transmission Toward Dense Satellite Remote Sensing and Communication Integrated Networks
Bibliographic record
Abstract
Dense satellite networks provide new potentials for prompt massive observational data backhaul, which has been the focus of the study. However, the dynamic and dense networks, coupled with the multi-priority task requirements of satellites, present significant challenges in designing effective offloading and transmission strategies. To address these challenges, we construct a remote sensing and communication integrated network (RSCIN) model and propose a task-splitting and parallel transmission approach that adequately utilizes the resources of both communication satellite (CS) and observation satellite (OS) for efficient data offloading. Specifically, we first investigate the priority-aware latency caused by the preemptive-resume scheme of OSs and employ a lognormal distribution to model the internal traffic intensity of CSs and analyze its influence on OS data relays. Furthermore, we formulate a mixed integer nonlinear programming (MINLP) problem to minimize the end-to-end (E2E) delay by jointly considering path selection, task-splitting strategy, transmit power, and queuing delay. With the proposed joint task-splitting and multi-path selection (JTMPS) algorithm, we equivalently decompose the MINLP problem into the constructed path set (CPS) problem and an optimal CPS-based task scheduling problem, which the benders decomposition algorithm can further solve. Extensive analysis and numerical results verify that the proposed JTMPS algorithm can achieve superior performance than various baseline schemes in RSCINs.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 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".