Performance investigation of UOWC system based on OAM beams for various Jerlov water types
Bibliographic record
Abstract
This paper introduces a novel high-speed underwater optical wireless communication (UOWC) system employing three distinct orbital angular momentum (OAM) beams. A single green laser source operating at 532 nm generates these beams: LG 0 , 0 , LG 0 , 20 , and LG 0 , 50 , each transmitting data at 10 Gbps. The paper provides a comprehensive analysis of absorption and scattering coefficients for five Jerlov water types: I (JI), IA (JIA), IB (JIB), II (JII), and III (JIII). Simulation results demonstrate the system ability to transmit multiple data streams simultaneously using distinct OAM modes, achieving an overall capacity of 30 Gbps. The longest underwater (UW) transmission distance of 22 m is achieved in JI water as it exhibits the lowest attenuation. This range decreases by 9.09 %, 31.82 %, 59.09 %, and 80.91 % in JIA, JIB, JII, and JIII, respectively, due to increased attenuation in these water types. These results are obtained with a log (BER) below −5 and a Q-factor above 4, indicating successful data reception. The findings highlight the potential of OAM multiplexing for enhancing data capacity in challenging underwater environments. • A new UOWC system is proposed based on using OAM beams. • The performance is investigated under 5 different Jerlov water conditions. • Scattering and absorption coefficients of Jerlov waters are calculated at 532 nm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".