Experimental assessment of snow‐induced attenuation on an Earth‐space link operating at Ka‐band
Bibliographic record
Abstract
Abstract This investigation assesses the attenuation induced by snowfall on an experimental slant‐path link that monitors the 20.199 GHz beacon signal of the Anik F2 satellite. Beacon data collected at Communications Research Centre Canada (CRC) in Ottawa over 2 years, including the winters of 2010–2011 and 2011–2012, were analyzed as part of this study. The antenna of one of the two receivers used in the propagation campaign with Anik F2 was shielded, the first year under a tent and the second year under the roof of a building, in order to prevent degradations on the measured beacon signal due to snow or ice accumulation on the parabolic reflector surface. One of the main challenges of the study was the unambiguous identification of snow events. Information provided by several weather sensors, a profiling radiometer, and meteorological reports were used to help identify the type of precipitation. Events of wet and dry snow along with freezing rain are presented and discussed. Radiometric measurements of sky noise temperature were particularly useful to detect light snowfall events and to estimate event durations. Statistics of snow attenuation were derived for the winter months of the study. It is found that snow attenuation is modest at 20.2 GHz; however, modest attenuation may be important for small‐margin communication systems.
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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 | 0.000 | 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 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".