New Modified Urban Canyon Models for Satellite Signal Propagation Prediction
Bibliographic record
Abstract
The effects of high-rise buildings on satellite propagation in the vicinity of urban canyons are investigated. A comparison between a conventional canyon model and the two modified canyon models, which take into account the presence of high-rise buildings, is presented for both narrow-band and wideband signal cases. The narrow band is developed using ray tracing (RT) and includes the direct wave, the specular reflection from building walls and ground, and the diffracted waves. In addition, multiple shadow boundaries are defined and used to carry out the uniform theory of diffraction calculations. The incident shadow boundary is the dominant boundary and is used to determine the line-of-sight region for all cases, while wall and ground reflection shadow boundaries are used to obtain higher precision due to multiple reflections. The wideband model is developed by applying a channel transfer function to the data obtained from the RT method. The proposed models are used to predict the received signal in a realistic urban environment from satellites. The models are applicable to any satellite link application, such as global navigation satellite systems, low Earth-orbiting, and high-altitude platform systems, and the results are obtained for a satellite transmitting two linearly polarized signals at a frequency of 1.625 GHz. It is found that the presence of high-rise buildings next to a street canyon can significantly alter the visibility of satellites, which, in turn, lead to an increase in path loss. Consequently, ignoring high-rise buildings in the proximity of a street canyon can lead to a path loss difference of as much as 30 dB.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".