On D2D communications for public safety applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Providing seamless wireless coverage is essential for maintaining reliable communications in cellular networks. However, in extraordinary circumstances such as catastrophes, cellular infrastructure could be severely damaged; therefore, coverage is lost. For this reason, device-to-device (D2D) communication emerged as a promising technology to maintain wireless connectivity especially for search and rescue missions. This paper presents a review on recent advances in D2D communications regarding public safety applications such as search and rescue missions, coverage extension, and road safety. In search and rescue missions, discovery of devices in impacted areas can be achieved by other devices that have access to the cellular network. Moreover, the close proximity among mobile devices enables high data rate provisioning thus allowing rescue teams to send real-time high quality video and image reports to control centers. Cellular coverage can also be extended using relays such as drones that could play a significant role in coverage extension in extraordinary conditions. Furthermore, the safety of roads can be highly improved with vehicle-type communications, whereby information among vehicles are exchanged to avoid collisions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it