Absorption and scattering effects of Maalox, chlorophyll, and sea salt on a micro-LED-based underwater wireless optical communication [Invited]
Bibliographic record
Abstract
In this work, a blue gallium nitride (GaN) micro-light-emitting-diode (micro-LED)-based underwater wireless optical communication (UWOC) system was built, and UWOCs with varied Maalox, chlorophyll, and sea salt concentrations were studied. Data transmission performance of the UWOC and the influence of light attenuation were investigated systematically. Maximum data transmission rates at the distance of 2.3 m were 933, 800, 910, and 790 Mbps for experimental conditions with no impurity, 200.48 mg/m3 Maalox, 12.07 mg/m3 chlorophyll, and 5 kg/m3 sea salt, respectively, much higher than previously reported systems with commercial LEDs. It was found that increasing chlorophyll, Maalox, and sea salt concentrations in water resulted in an increase of light attenuation, which led to the performance degradation of the UWOC. Further analysis suggests two light attenuation mechanisms, e.g., absorption by chlorophyll and scattering by Maalox, are responsible for the decrease of maximum data rates and the increase of bit error rates. Based on the absorption and scattering models, excellent fitting to the experimental attenuation coefficient can be achieved, and light attenuation by absorption and scattering at different wavelengths was also investigated. We believe this work is instructive apply UWOC for practical applications.
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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) | 0.000 | 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".