Compact optical coherent receiver for avionics applications
Bibliographic record
Abstract
An optical coherent receiver for the down conversion of radio frequency (RF) signals from 10-18 GHz to 2 GHz is presented. Light from a distributed feedback semiconductor laser is split between two lithium niobate Mach-Zehnder modulators driven either by a tunable local oscillator (LO) tone or a RF signal coming, for example, from a receiving antenna. The modulated light signals are combined with an optical coupler and filtered by two fiber Bragg gratings (FBG) that select one optical sideband from each signal. Detection of the filtered light by a balanced photo-detector produces an electrical signal at an intermediate frequency equal to the beat difference between the RF and LO frequencies. Most current RF photonic systems are made from individually packaged devices that are interconnected with fiber-optic cables. In order to reduce size and weight and make the coherent receiver suitable for use in smaller airborne and mobile platforms, optical and opto-electronic components are packaged within a common enclosure where light routing is performed by micro-optics. A printed circuit board (PCB) is included within the module. It comprises a micro-processor to control and monitor the laser, the FBGs and thermo-electric coolers to ensure a robust operation over time and fluctuating environmental conditions. The module including the PCB, laser, modulators, optics, optical filters and balanced detector has a size of 89 x 64 x 32 mm3.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".