A liquid crystal switched passive Van Atta array for automobile radar target enhancement in heavy rainfall
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
Severe weather poses a major challenge to safe driving. Sudden precipitation or heavy fog can reduce visibility and extend braking distances, increasing the risk of an accident. To combat this, modern cars are adopting a wide variety of driver aids such as braking-assist, lane detection and blind spot alerts (P. Green et al, US D.O.T., Int. Vehicle-Based Safety Sys., 2008). These require vehicle sensors, the most popular being radar and VANETs (Vehicular Ad-hoc NETworks), which allow inter-car data sharing. Both use radio frequencies, which are more resistant to weather and obstacles than older optical systems. However, radar range is still degraded by heavy rainfall (M. I. Skolnik, Introduction to Radar Systems, 442–449, 2001). Also, global regulatory bodies have moved from 24 GHz radars to 76–82 GHz radars, which are even more susceptible. Regulatory power limitations imposed upon radar have also limited the success of increasing range with more powerful transmitters.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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