Bibliographic record
Abstract
This chapter discusses optical wireless communication (OWC) and its application in vehicular environments, particularly focusing on visible light communication (VLC). OWC encompasses technologies that transmit data via light waves, offering a viable alternative to traditional radio frequency (RF) communications, especially in scenarios where RF is congested or subject to interference. VLC utilizes the existing LED lighting in vehicles and infrastructure, enabling high-speed, shortrange data exchange among vehicles (V2V), between vehicles and infrastructure (V2I), and with networks (V2N) or pedestrians (V2P). It highlights benefits such as improved collision avoidance, traffic management, and enhanced positioning precision. This chapter also acknowledges challenges like interference from sunlight, quick channel changes, and the necessity for line-of-sight communication. Overall, it underscores the potential of VLC in enhancing intelligent transportation systems (ITS) and supporting the development of autonomous vehicles through reliable, real-time communication. The exploration of research directions and practical applications of VLC aims to address these challenges while maximizing the benefits of this emerging technology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.418 | 0.285 |
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".