Robust Broadband Maritime Communications: Theoretical and Experimental Validation
Bibliographic record
Abstract
Abstract Atmospheric ducts, which are caused by the rapid decrease in the refractive index of the lower atmosphere, have been recognized as the dominant propagation mechanism for long range maritime links. Recent advances in radio technology are making reliable high‐capacity overwater links a reality. A ray‐tracing simulation model was developed to obtain reliable prediction of maritime link performance beyond 30 km for realistic conditions. A series of static and mobile long range maritime multiple‐input multiple‐output links were tested with representative antenna heights at 1.39 (Band 3 or L‐Band) and 4.5 GHz (Band 4 or C‐Band) during periods with various levels of ducting activities, for example, sunset and sunrise times. Measured link performance and observed ducting occurrences are compared to predictions to validate the model ability to accurately predict and explain link performance for various ducting conditions. The test results agree with the model predictions and illustrate the various Received Signal Level degradation, enhancement, and fluctuations that can be expected in long range maritime links below 6 GHz. We also report on short‐term (few to several minutes) fluctuations of the received signal level at the two measurement frequency bands during high ducting activity periods. Measurements and theoretical analysis of the effect of ducting on the quality of maritime communication links indicate that both reflection‐induced and ducting‐induced disruptions are different and more frequent in higher‐frequency bands. This work opens interesting perspectives on providing more robust and more predictable communications in the presence of ducting.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".