Study on Continuous Wavelength Tuning of Narrowband Multiwavelength Spectrum in Optical Fiber Multiwavelength Filter Using Contiguous Quarter-Wave Polarization Transformation
Bibliographic record
Abstract
In this study, we scrutinize the continuous wavelength tuning capability of the narrowband multiwavelength spectrum in an optical fiber multiwavelength filter using contiguous quarter-wave polarization transformation. The multiwavelength filter is based on a polarization-diversity loop structure composed of a polarization beam splitter, two equal-length polarization-maintaining fiber segments, four quarter-wave plates (QWPs) used to tune the spectrum wavelength, and a half-wave plate utilized to maximize the spectrum visibility. By utilizing the transmittance function of the filter derived through the Jones matrix formulation, we theoretically found specific orientation angles of the four QWPs for the continuous wavelength tuning of the narrowband multiwavelength spectrum, which could induce an extra phase difference φ from 0° to 360° in the filter transmittance. From the spectral calculation at the eight selected orientation angle sets of the four QWPs, it was revealed that the wavelength tuning of the narrowband multiwavelength spectrum could be implemented at 0.1nm intervals by properly adjusting the orientation angles of the four QWPs and this approach could be easily extended for the continuous wavelength tuning of the spectrum. The theoretical prediction was then experimentally verified.
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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 | 0.001 |
| 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".