Cellular Communications in Ocean Waves for Maritime Internet of Things
Bibliographic record
Abstract
The rapid advancement of Internet of Things (IoT) and fifth generation and beyond technologies is transforming the marine industry and research. Our understanding of the vast sea that covers 71% of the Earth's surface is being enhanced by the various ocean sensor networks equipped with effective communication technologies. In this article, we begin with a review of the research and development status-quo of Maritime IoT (MIoT) enabled by multiple wireless communication technologies. Then, we study the impact of sea waves on radio propagation and the communications link quality. Due to the severe attenuation of sea water to radio-frequency electromagnetic wave propagation, large ocean waves can easily block the communication link between a buoy sensor and a cell tower near shore. This article for the first time uses the ocean wave modeling of coastal and oceanic waters to examine the condition of line-of-sight communications. Real wave measurement data parameters are applied in the numerical evaluation of the developed model. Finally, the critical antenna design taking into account the wave impact is numerically studied with implementation solutions proposed, and the system hardware and protocol aspects are discussed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".