Adaptive Framing and Virtual Channel Scheduling Algorithm Based on Advanced Orbiting System for Consumer Sustainability in Industry 5.0
Bibliographic record
Abstract
Based on the development needs of Industry 5.0, Advanced On-orbit Systems (AOS) can be integrated with terrestrial 5G networks, with large-scale IoT links as well as with flexible deployment and resource optimization, enabling efficient transmission of multiple types of industrial data, human-machine collaboration, and improving the flexibility, innovation and efficiency of production processes. This paper first proposes an AOS adaptive framing algorithm based on an optimization threshold. The algorithm adaptively adjusts the frame waiting time according to the packet arrival conditions and optimizes the frame waiting time threshold using a differential evolution algorithm. Furthermore, an AOS virtual channel scheduling algorithm based on a Deep QNetwork (DQN) is proposed. The algorithm considers the service priority, scheduling delay and frame residual to find the optimal virtual channel scheduling order. Through simulation, it can be seen that the adaptive framing algorithm based on optimized threshold values can effectively reduce the average framing time and average packet delay while ensuring the efficiency of frame reuse. Moreover, the virtual channel scheduling algorithm based on DQN can better meet the needs of the network, effectively reducing the average scheduling delay and frame residual. The combination of AOS framing and virtual channel scheduling can improve transmission efficiency and optimize system performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".