High data rate underwater optical wireless communication systems with ICSM codes within green spectrum
Bibliographic record
Abstract
Abstract Underwater Optical Wireless Communication (UOWC) is emerging as a promising technology for high-speed data transmission in marine environments. This paper introduces a new UOWC system that integrates Optical Code Division Multiple Access (OCDMA) using Identity Shift Column Matrix (ICSM) codes, specifically designed to enhance communication capacity. For generating the relevant optical signals for the ICSM code sequences, Laser Diodes (LDs) sources are utilized, operating in the green spectrum ranging from 532 to 538.4 nm, known for their low attenuation in UOWC environments, to achieve extended transmission ranges. The performance of the proposed system is rigorously evaluated across five distinct water types—Pure Sea (PS), Clear Ocean (CL), Coastal Sea (CS), Harbor I (HI), and Harbor II (HII)—which exhibit varying absorption and scattering coefficients. The key performance metrics are investigated including eye diagrams, Bit Error Rate (BER), Underwater (UW) transmission distances, and Quality Factor (Q-factor). The obtained results indicate that the system can achieve an overall capacity of 30 Gbps with a maximum UW transmission distance of 50 m in PS water, characterized by the lowest extinction coefficient, while the shortest effective range of 5.2 m is observed in HII water, with the highest extinction coefficient. These ranges are achieved below the threshold value of BER (3.8 $$\times 10^{ - 3}$$ × 10 - 3 ).
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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 | 0.000 |
| 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".