Generation of a Long Linearly Chirped Microwave Waveform Based on a Fourier Domain Mode-Locked Optoelectronic Oscillator Incorporating a Frequency Shifting Loop
Bibliographic record
Abstract
Broadband chirped microwave waveform with a large time–bandwidth product (TBWP) is highly needed in a radar system to achieve pulse compression with a large pulse compression ratio. A chirped microwave waveform with a wide bandwidth can be generated optically thanks to the ultrawide bandwidth offered by modern photonics, but the pulsewidth is usually very limited. In this work, we propose and experimentally demonstrate a novel approach based on a Fourier domain mode-locked optoelectronic oscillator (FDML-OEO) to generate a broadband chirped microwave waveform with a long pulsewidth. The key is to increase the effective loop length of the FDML-OEO, which is done by incorporating a recirculation frequency shifting loop (FSL) in the OEO loop. Since the time duration of a microwave waveform is proportional to the OEO loop length, the pulsewidth is increased if a light is recirculating in the FSL for multiple times. The proposed approach is evaluated experimentally. For an FDML-OEO with a length of 1.6 km, a linearly chirped microwave waveform (LCMW) with an increased pulsewidth to$202.67 \mu \text{s}$is generated by controlling the light recirculating in an FSL with a length of 3.2 km for 12 times.
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".