Bibliographic record
Abstract
The main goal of this research is to study the rain effects on microwave and millimeter wave radio links.Thus, the rain-induced attenuation, depolarization and scattering are studied.To carry out this study, a realistic rain model is proposed, which consider a realistic rain medium composed by a cluster of raindrops with the shape-size relation proposed by Chuang and Beard, a raindrop size distribution given by de Wolf, index of refraction of water for a given temperature and frequency suggested by Ray and a distribution of the orientation angle of the symmetry axis.The realistic rain model is evaluated with two different applications of systems operating at microwave and millimeter wave frequencies.One of the applications involves wireless telecommunication systems, which are strongly affected by the presence of precipitation.To design an efficient radio communication system, the realistic rain model is applied for the analysis and quantification of rain-induced effects on links operating at millimeterwave frequencies in urban environments.Another application involves weather radars (X-band radars in particular).Considering their relatively low cost and high resolution, X-band radars would be among the best options to monitor meteorological events.However, they are susceptible to attenuation by fog, snow or rain.To solve this problem, a realistic and improved rain model is implemented to compute backscattering cross sections and estimate rain attenuation at each range gate.The proposed method is evaluated using radar data provided by the CASA OTG X-band (λ = 3cm) radar located in Mayaguez, Puerto Rico, and X-band radar METEOR 50DX -Selex located in Belém, Brazil.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".