Channel addition/removal response in Raman fiber amplifiers: modeling and experimentation
Bibliographic record
Abstract
In this article, we investigate, both theoretically and experimentally, transient effects in Raman fiber amplifiers (RFA) caused by channel addition/removal. A comprehensive large-signal numerical model of RFA, which incorporates time variation effects and the downstream and upstream propagation of multiple signals, pumps, and amplified spontaneous emission components, has been used for the theoretical analysis. The effect of pumping scheme, pump power, the length and type of Raman fiber, and number of added and/or dropped channels on the dynamics of surviving channel power fluctuations has been studied. In our experimental setup, signals from two laser diodes (LDs) were transmitted through counterdirectionally pumped RFA consisting of 15.6 km of dispersion compensating fiber (DCF). To simulate channel addition/removal, one signal was square-wave-modulated at 500 Hz. At the output of the RFA, the signal of the continuous-wave LD was selected with an optical bandpass filter, and power fluctuations of the surviving channel were recorded with a high-speed digital oscilloscope. Power fluctuations as high as 0.45 dB with typical saturated amplifier overshoots were observed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".