Packet Routing and Energy Cooperation for RTU Satellite-Terrestrial Multi-Hop Network in Remote Cyber-Physical Power System
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it