Polarization evolution and periodic power oscillation in recirculating loops
Bibliographic record
Abstract
For the last decade, recirculating loops have been a useful tool in the research and development of long haul transmission links. A loop experiment can emulate the transmission of an optical signal over thousands of kilometers by using a relatively short link of a few hundred kilometers and recirculating the signal several times. Although recirculating loops accurately replicate most physical effects encountered in point-to-point links (loss, noise, chromatic dispersion, nonlinear effects, etc), the statistics of polarization effects (polarization mode dispersion (PMD) and polarization-dependent loss (PDL)) may not be properly emulated. In an optical link, PDL can induce statistical fluctuations of the optical signal-to-noise ratio (OSNR) and consequently of the bit-error-rate (BER). Due to environmental changes, the effects of PDL vary stochastically in time. The periodic nature of fiber loop may artificially produce an unrealistic PDL distribution and the statistical distribution of PDL effect may be significantly different from that in a installed link. We report the analysis and observation of a power oscillation effect caused by PDL due to the periodic nature of the polarization evolution in a recirculating loop. The oscillation is expected to affect the OSNR and consequently the BER as a function of recirculation.
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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 | 0.000 | 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".