Optical Back Propagation With Optimal Step Size for Fiber Optic Transmission Systems
Bibliographic record
Abstract
An optical back propagation scheme consisting of an optical phase conjugator, fiber Bragg gratings (FBGs), and highly nonlinear fibers (HNLFs) is investigated. Transmission fiber dispersion is compensated by the FBGs and the nonlinearity is compensated by HNLFs. Several sections of FBGs and HNLFs are concatenated in a way analogous to the split-step Fourier scheme used for solving the nonlinear Schrödinger equation. The optimum accumulated dispersion of each section of the FBG and the optimum nonlinear phase shift of the each section of the HNLF are calculated by minimizing the mismatch between the area under the exponentially increasing nonlinearity profile and its stepwise approximation. The method of Lagrange multipliers is used for optimization. The proposed optimization technique leads to significant performance improvement and/or reach enhancement as compared to uniformly spaced sections, for the given number of sections.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".