Bibliographic record
Abstract
The application spectrum of visible light communication (VLC) in navigation is steadily broadening, primarily fueled by its remarkable precision achievable in indoor environments. VLC has emerged as a superior alternative to traditional navigation systems like the commercial global positioning system (GPS), consistently delivering precise results. Despite its success in-doors, addressing the challenges posed by ambient light remains paramount, especially in outdoor scenarios where natural lighting conditions exhibit significant variability. This article explores the intricate relationship between ambient light and outdoor VLC technology for navigation, particularly when coupled with solid-state lighting technologies. These technologies are pivotal in ensuring precise localization in dynamic outdoor environments. Through the application of advanced simulation and modeling techniques, the endeavor is to quantify the impact of ambient light on outdoor VLC systems. This article uses appropriate methodologies to calculate the angular error for the Angle of Arrival and measure received energy across different seasons to comprehensively assess ambient light's influence on system performance. Furthermore, the tables and graphs in the paper facilitate a detailed analysis of sunlight effects, offering valuable insights to optimize the positioning of light transmitters and receivers for enhanced performance and reliability in real-world applications. The findings presented hold significant implications for advancing outdoor VLC technology, paving the way for more robust and effective navigation systems operating seamlessly in diverse environmental conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".