Effect of Turbulence Layer Height and Satellite Altitude on Tropospheric Scintillation on Ka-Band Earth–LEO Satellite Links
Bibliographic record
Abstract
Tropospheric scintillation on Earth-space paths greatly increases at low elevation angles and/or higher carrier frequencies, may impair low margin systems, and can interfere with the power control algorithms used to mitigate rain fading. The amplitude and spectral characteristics of tropospheric scintillation have been well studied for Earth-geostationary-Earth-orbit (Earth-GEO) links, which have fixed elevation angles and path lengths. However, little has previously been reported concerning tropospheric scintillation on Earth-low-Earth-orbit (Earth-LEO) links, which are distinguished by the rapid change of the elevation angle as the satellite passes from horizon to horizon. In such cases, both the length of the slant path to the turbulence layer and the velocity at which the slant path passes across the turbulence layer rapidly change as the satellite passes across the sky. This affects both the intensity of the scintillation process, which generally reaches its maximum value at low elevation angles and/or during periods of rain, and the corner frequency of the scintillation process, which generally reaches its maximum value at high elevation angles. In this paper, we use a geometric model of propagation through the turbulence layer during a LEO satellite pass in conjunction with Tatarskii's theory of propagation through turbulent media to show that the corner frequency of the scintillation process increases as 1) the orbital altitude decreases and 2) the height of the turbulence layer increases. We also discuss the implications of our results for the simulation of tropospheric scintillation on Earth-LEO links.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".