Effects of Rain Rate With Various Integration Times on ITU-R P530-18 Attenuation Prediction
Bibliographic record
Abstract
All wireless communication systems are progressively transitioning to higher frequencies, which are significantly degraded by rainfall in outdoor environments. A dependable RF system can be designed using a globally accepted approach for accurately predicting rain attenuation, based on local rain intensity measurements. The necessary rain intensity for attenuation prediction is often measured at a single location with a 1 -minute integration period or converted from a longer integration time to a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1}$</tex>-minute duration. Recent measurements of rain intensity with a 10 -second integration time indicate that intensity is not uniform across a 1 -minute duration, hence affecting the statistics of rain intensity distribution and attenuation predictions when measured with an integration time shorter than 1 minute. This paper presents the influence of integration times on rain rate statistics and eventually it's impact on rain attenuation prediction method. The distance factor proposed by ITU-R P530-18 is investigated by utilizing rain rate data collected with integration durations of 2 minutes, 1 minute, 30 seconds, 20 seconds, and 10 seconds. The effects are found not significant with any path length. Measured rain attenuation for a 300 m path at 26 GHz were compared to those predicted with rain rates measured in 5 integration times. For propagation paths longer than 1 km, the ITU-R model's 1minute integration time may be sufficient to “average out” the spatial inhomogeneity of the rain rate; however, for shorter propagation paths, the rain intensity with lower integration times reflect more accurate predictions.
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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) | 0.001 | 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".