A broad-band digital filtering approach for time-domain Simulation of pulse propagation in optical fiber
Bibliographic record
Abstract
A broad-band digital filtering approach for the simulation of pulse propagation in the optical fiber has been developed. Unlike the most popular frequency-domain split-step method, the pulse propagation is realized by letting the signal samples pass through a preextracted digital filter where the convolution is simply made by a series of operations that consist of shift and multiplication only. It also differs from the existing time-domain split-step method in a sense that the digital filter is extracted to match the frequency-domain fiber linear transfer function in the full bandwidth range rather than in a reduced portion. This approach is verified through comparisons made with the conventional frequency-domain split-step method and is applied to the simulation of multiple-channel narrow-pulse propagation over the long-haul fiber. The main advantage brought by this approach lies in that the simulator is fully realized in a "data-flow" fashion; that is, the signal (long sample stream) is treated sample by sample, rather than block (a collection of neighboring samples) by block. Matching the fiber frequency-domain response over the full bandwidth does not require any further reduction on the propagation step since the error can be controlled through the filter length. The authors' preliminary effort on the filter length reduction on a given error reveals that a savings on both memory and computation time is also achievable in comparison with the frequency-domain split-step method.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".