The Effect of Sea State on the Polarization of Reflected Beidou B1 Signals
Bibliographic record
Abstract
Background: Oceanographic buoys generally employ satellites for positioning and data transmission. However, sea surface conditions can affect these signals. The Signal to Noise Ratio (SNR) of small observation buoys can be improved by exploiting polarization diversity. Methods: This paper discusses the effect of sea surface conditions on the polarization and reflection loss of Beidou B1 reflected signals for the purposes of exploiting polarization diversity. The Rayleigh roughness criterion is used to assess the roughness of the sea surface. The Fresnel reflection coefficients are derived to analyze the polarization and reflection loss of the Beidou B1 reflected signals with different sea surface states. Results: The results obtained show that for the Beidou B1 signals, the sea surface is considered rough for most sea surface states and incident angles. For smooth sea surfaces, the Beidou B1 reflected signals are mainly Left Hand Circularly Polarized (LHCP) waves, but Right Hand Circularly Polarized (RHCP) waves dominate when the incident angles are larger than the Brewster angle. The reflected loss is between -2 dB to -3.4 dB. In rough sea surfaces and the signals propagation is dominated by diffuse reflection. The reflection loss decreases with the incident angle and there is a fluctuation when the incident angle is around 49 degrees. The specular reflection signal has a significant amplitude when the angle of incidence is large. RHCP waves are the main component of the reflected signals, and the reflection loss is relatively small which can be employed for polarization diversity or marine remote sensing. Conclusion: polarization diversity is only useful with good sea conditions, and the corresponding gain decreases with the deterioration of the sea surface conditions.
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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".