Packet Routing and Energy Cooperation for RTU Satellite-Terrestrial Multi-Hop Network in Remote Cyber-Physical Power System
Bibliographic record
Abstract
The cutting-edge applications of cyber-physical power systems (CPPS) must transmit large volumes of data packets collected by massive remote terminal units (RTUs) to the control center. To develop high-reliability and self-sustainable communication networks for the RTUs deployed in hard-to-reach areas, we propose an RTU satellite-terrestrial multi-hop network with energy cooperation for remote CPPS. Specifically, data packets generated by RTUs are either transmitted to faraway base station (BS) in a multi-hop manner or uploaded to satellite network, and each RTU harvests ambient renewable power with the capacity to transfer harvested energy to the relay RTU. We then develop a multi-agent learning-based packet routing and energy cooperation approach (MAQMIX-PREC) to maximize the network throughput by jointly optimizing relay selection, sub-slot partition, and channel allocation. This approach effectively decouples the decision-making and coordinates the training among RTUs in the RTU multi-hop network. Experimental evaluations illustrate that the proposed approach achieves congestion-awareness and energy cooperation, and outperforms benchmark methods in terms of training convergence, network throughput, and traffic intensity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".