Massive Point-by-Point Temporal Pulse Shaping for Ultra-High-Speed and Long-Time-Duration Arbitrary Microwave Waveform Generation
Bibliographic record
Abstract
Massive point-by-point temporal pulse shaping to generate ultra-high-speed and long-time-duration arbitrary microwave waveforms based on a temporal Vernier caliper is reported. The temporal Vernier caliper is implemented using a mode-locked laser (MLL) and a fiber loop. The period of a mode-locked pulse train is considered as the main temporal scale division, while the round-trip time of the fiber loop serves as the temporal Vernier scale division. A slight detuning and interpolation between the two temporal divisions result in a small temporal interval, enabling the generation of arbitrary microwave waveforms with a high sampling rate. By temporal duplication of the input pulse, massive temporal pulse shaping with an increased number of points can be realized, enabling the generation of arbitrary microwave waveforms with an extended time duration and increased memory depth. The approach is evaluated experimentally. Analog waveforms at a sampling rate of up to 1 tera-sample-per-second (TSa/s) and communication signals (OOK and PAM4) with a large memory depth of 10.4 kilopoints (kpts) are experimentally generated.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".