Chromatic dispersion measurements of optical fiber based on time-of-flight using a tunable multi-wavelength semiconductor fiber laser
Bibliographic record
Abstract
We have developed a tunable multi-wavelength semiconductor fiber laser (SFL) for chromatic dispersion measurements of optical fiber based on the time-of flight method. The SFL incorporates a programmable high-birefringence fiber loop mirror to select the separation of the lasing wavelengths between 3.2nm and 1.6nm. The SFL emits 5 wavelengths with an average power of 11.96dBm per wavelength and 11 wavelengths with an average power of 18.35dBm per wavelength, for separations of 3.2nm and 1.6nm respectively, all within the C-band. The linewidth of each oscillating wavelength resides in the 0.16nm - 0.28nm range, the signal-to-noise ratio varies between 33.5dB and 39.2dB, and the uniformity of the output power is within 3.2dB. Stability measurements for each lasing peak show a wavelength deviation of +/-0.09nm/hour and a power variation of +/-0.90dB/hour. Results from time-of-flight measurements are compared with standard phase-shift techniques and the differences analyzed. The percent error between the two methods is better than -0.73 to 1.13% for measurements on various standard optical fiber lengths. The time-of-flight method is easier and faster to use for the characterization of sufficiently dispersive media such as deployed fiber spools. Our tunable laser provides a simple low cost solution for such measurement applications. The tunable nature of our SFL source also provides the following advantages for chromatic dispersion measurements: (1) greater precision can be obtained since two independent measurements (one at each wavelength separation) can be performed using a single optical source and (2) there is increased flexibility since the wavelength spacing can be tailored for a specific situation; shorter lengths benefiting from larger wavelength separations.
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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 | 0.001 |
| 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".