Accurate phase measurement of passive non-reciprocal quasi-optical components
Bibliographic record
Abstract
A method for accurate measurement of the phase of passive non-reciprocal quasi-optical components is presented. The measurements are performed using a two-port free-space measurement system that is composed of two horn-fed axially aligned lenses calibrated using a through-reflect-match (TRM) technique. The proposed method for accurate phase measurement consists in measuring the component twice, i.e. once facing the first port and then facing the second port. A simple relationship is derived between the phase at the desired location, recorded phase values and the phase due to the thickness of the sample. The method was verified by measuring two different structures at Ka band, i.e. a polarisation-sensitive frequency-selective surface (FSS) and a multilayer isotropic dielectric sample. Another method requiring two identical components is used to measure the phase of the reflection coefficient based on a Fabry–Perot interferometer (FPI) architecture. Neither time-domain gating nor data smoothing were applied to the results. The S-parameter magnitude and phase results are in good agreement with simulations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".