Performance evaluation of a UOWC system based on the FRS/OCDMA code for different types of Jerlov waters
Bibliographic record
Abstract
In this work, the fixed right shift (FRS) code is utilized for the optical code division multiple access (OCDMA) technique in an underwater optical wireless communication (UOWC) system. Additionally, in this system, a 532 nm laser diode (LD) source is employed to generate optical signals. The investigation encompasses an analysis of five distinct Jerlov water types, each exhibiting diverse chlorophyll concentrations. The performance of the proposed system is evaluated when each channel that is assigned a unique FRS code sequence carries different data rates (2.5, 5, and 10 Gbps). Underwater (UW) ranges, bit error rate (BER), eye diagrams, and quality factor (Q-factor) are the performance metrics used to evaluate the system performance. The proposed UOWC-FRS/OCDMA system is simulated, and the obtained results show that the eye diagram openings close, the BER increases, and the Q-factor decreases as the data rate per each channel increases from 2.5 to 10 Gbps, and the attenuation of water becomes higher. Moreover, the lower attenuation values caused by the Jerlov type I (JI) waterbody allow each channel to carry 10 Gbps of data to propagate longer UW for a range of 35 m with a log(BER) ≤−6.33 and Q-factor greater than 4.9. On the other hand, at the same values of BER and Q-factor, the shortest ranges of 12 and 5.15 m are obtained for JII and JIII waters, respectively, where their attenuation coefficient values are 0.5297 (JII) and 1.8998 m −1 (JIII). Furthermore, as our model uses three channels, the overall achieved capacity is 3×10Gbps=30Gbps.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".