Queuing enhancements for in-vehicle time-sensitive streams using power line communications
Bibliographic record
Abstract
Power line communication (PLC) technology enables existing direct current power lines in an automobile to be used for data transmissions. Due to its potential for cost reduction and energy saving, PLC is an attractive supplement to the Ethernet in next generation in-vehicle networks. This paper considers using HomePlug Green PHY (HomePlug GP) as an in-vehicle PLC network protocol due to its low cost, compact size, universal applicability, strong reliability and reduced overhead. To address the challenge of long latency in data transfer by HomePlug GP, we propose two queuing enhancements to improve the real-time performance of HomePlug GP. Specifically, we introduce the virtual collision mechanism to improve the fairness of queuing. We further apply the principle of credit-based shaping in IEEE 802.1 Audio Video Bridging over the HomePlug GP to meet the latency requirements of different network traffic. OMNeT++ simulation results demonstrate the effectiveness of the two proposed enhancements.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".