Evaluation of transmission dispersion characteristics of nonuniform gratings for DWDM systems
Bibliographic record
Abstract
Summary form only given. In their role as optical add-drop multiplexers for channel-specific routing in optical networks, as well as in their use as clean-up filters to reduce network crosstalk, fiber Bragg gratings will provide an important enabling technology for dense wavelength-division multiplexed (DWDM) communication systems. While grating-based filters have strong reflectivities over narrow frequency ranges, especially when apodization is used to reduce out-of-band reflections, dispersion is still present in the wings of the grating spectrum where the transmission is essentially unity. Because in a DWDM network a given channel may pass numerous adjacent gratings during propagation, the degradation of the signal due to the dispersion of the fiber gratings could limit the bit rate or transmission distance achievable. With the advent of grating structures with engineered profiles, it becomes important to be able to estimate fiber grating dispersion in the wings of arbitrarily designed grating so that various system parameters can be varied to achieve optimum performance. In this paper, we analyze theoretically and experimentally the dispersion in the wings of such gratings and derive asymptotic expression, which should find use in system designs involving many gratings.
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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) | 0.001 | 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".