Guest Editorial Localisation, Communication and Networking With VLC
Bibliographic record
Abstract
We are at the dawn of an era in information and communication technology with unprecedented demand for connected and automated everything. Both Shannon theories and industrial advances have clearly evidenced that more densely packed networks and a much wider operating bandwidth are key drivers for meeting the escalating wireless network demands. Recently, there have been substantial research efforts on the exploitation of higher frequency bands, in particular the millimetre wave and optical wireless bands. After a decade of active research and development, and along with the maturity of device technology, Visible Light Communications (VLC) has emerged as a very promising technology to enable next generation digital innovations and support wide range of applications. This special issue on VLC focuses on three core thrusts of the discipline:Localisation, CommunicationandNetworking. The overall aim of the special issue is to inspire multi-disciplinary international communities to work together in order to achieve further research advances. Indeed, a total of 96 high quality papers were received from both academia and industry. After a careful peer-reviewing process, 17 papers were selected based on their combined novelty, rigour, and impact. Owing to the highly selective nature of JSAC, many other interesting papers were not selected for the special issue, but we hope that these papers might appear elsewhere.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.035 | 0.021 |
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".