Line‐of‐sight probability for UAV communications in 3D grid urban streets
Bibliographic record
Abstract
Abstract In this letter, the authors present a novel expression for the probability of line‐of‐sight (LoS) between a UAV and ground users situated on a gridded street within urban and dense urban environments. Firstly, this study conducts extensive ray‐tracing simulations, encompassing over 50,000 receivers positioned along streets in Brooklyn and Manhattan, respectively. Moreover, the impact of street width on the probability of LoS is investigated, considering three different average widths. By fitting the LoS probability data corresponding to the UAV's elevation angle in two urban environments obtained by simulations, a unified expression representing the relationship between the LoS probability and the UAV's elevation angle is obtained. This obtained LoS probability expression is compared with three existing expressions. The findings presented in this letter can provide some valuable insights for analytical studies on channel modelling between UAVs and ground‐based street users including vehicles.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".