PMD test method in the fiber link with optical amplifier
Bibliographic record
Abstract
To learn the surrounding conditions in the fiber link and its effect on PMD, and to provide the first-hand design basis, we have carried out the data observation of PMD in a fiber link for a long time. We have tested the first-order and second-order PMD. The fiber tested is the G652 fiber produced by Corning Co. of USA, and the testing distance is 1000km; n segments of same fibers are linked into one, and n equals to 40, that is to say, the length of every segment is 25km; for the requirement of dispersion compensation in the high-speed and long distance fiber optical communication system, one fiber grating dispersion compensator is added in the place of every 200km, and there are five compensators; one EDFA is added in the place of every 100km, and there are eleven EDFA. The result suggests that, with the increase of length of fiber link, the distribution of PMD intends to be stable, that is, with the number n increasing, the relative error of PMD becomes less. The testing methods are the Jones matrix eigenanalysis technique and interference technique. HP8509B fiber polarization analyzer of Agilent in USA is used for measuring instrument of the Jones matrix eigenanalysis technique; FPMD-5600 Femtosecond PMD Analyzer of EXFO in Canada is used for measuring instrument of interference technique. The difference between these two testing methods is analyzed. With the Jones matrix eigenanalysis technique, fibers of 1000km are inspected through 48 hours, and the result suggests that, at nine o'clock in the morning, PMD reaches the maximum, at nine o'clock in the evening, it reaches the minimum, during other time, its change is very little. So it can be concluded that, PMD in the long distance fiber link is affected by temperature of the lab. Stress testing is carried in the ultra-short fiber (less than one meter). PMD has no obvious change in the range of stress which can be endured by the fiber.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".