Repetition-rate-tunable return-to-zero and carrier-suppressed return-to-zero optical pulse train generation using a polarization modulator
Bibliographic record
Abstract
An approach is proposed to generating return-to-zero (RZ) and carrier-suppressed return-to-zero (CS-RZ) pulse trains with a high and tunable repetition rate by using a polarization modulator (PolM). The PolM is a special phase modulator that can support both TE and TM modes with opposite phase modulation indices. A linearly polarized cw light with its polarization state oriented with an angle of 45 degrees to one principal axis of the PolM is modulated by a sinusoidal signal at the PolM. The output light from the PolM is then sent to a polarization beam splitter (PBS), with one axis of the PBS aligned at an angle of 45 degrees to one principle axis of the PolM. At the two outputs of the PBS, two optical signals with one consisting of the even-order optical sidebands and the other consisting of the odd-order optical sidebands are obtained, leading to the generation of an RZ and a CS-RZ pulse train having a repetition rate that is twice the frequency of the drive sinusoidal signal. The proposed approach is demonstrated by a proof-of-concept experiment in which an RZ and a CS-RZ pulse train with tunable repetition rates of 7.2 and 8 GHz are generated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".