Multiwavelength Characterization of Optical Wireless Communication in Complex Water-Filled Pipe Environment
Bibliographic record
Abstract
This paper presents an in-depth investigation of optical wireless communication through water-filled PVC pipelines using high-brightness light-emitting diodes (HB-LEDs) operating at visible wavelengths: 475 nm (blue), 528 nm (green), 583 nm (yellow), and 625 nm (red). Simulations were conducted in Ansys Zemax OpticStudio using ray-tracing techniques and Bidirectional Scattering Distribution Function (BSDF) models to evaluate the effects of surface roughness, interface reflection, and wavelength-dependent absorption. A custom experimental setup was developed using a 375 mm long, 50 mm diameter PVC pipe and a Thorlabs S121C photodiode sensor to validate the simulation. Optical power was measured under five water fill conditions (0%, 25%, 50%, 75%, and 100%). Results show that the greatest transmission loss occurs at the 50% water level, where multiphase scattering dominates, with experimental power decreasing to −11.82 dBm at 583 nm (yellow). Full immersion improves transmission, with recovered power levels up to −2.3 dBm at 475 nm (blue). Absorption coefficients were calculated using the Beer–Lambert Law, with peak values exceeding 0.09 cm⁻¹ at 50% fill. Simulation results aligned with experimental measurements within 1–2 dB, validating the model’s reliability. These findings support the development of adaptive gain control strategies and wavelength-optimized optical links for autonomous robotic inspection in submerged or semi-submerged pipeline environments.
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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".