Permittivity measurement of disk and annular dielectric samples using coaxial transmission line fixtures. part II: experimentation and accuracy analyses
Bibliographic record
Abstract
An improved permittivity measurement technique for dielectric disks involving S-parameter measurement of a two-port coaxial transmission line fixture is presented. The previous form of the method suffers from variation of the retrieved permittivity with frequency, which leads to inaccuracies that may be severe at some frequencies. An extension of the method that reduces these errors is devised. In addition, an independently developed new technique for measuring the permittivity of annular samples via quadratic curve fitting is presented. This technique also involves S-parameter measurement of a coaxial fixture and requires measurement of only three known materials (one of them may be free space, in which case the requirement is reduced to only two solid dielectrics). The permittivity of any unknown dielectric may subsequently be determined with high accuracy over a wide frequency range. The method is based on the premise that the variation of the reflection characteristics of the two-port coaxial transmission line fixture with the permittivity of the sample displays a strongly quadratic behaviour. This paper constitutes the second of two parts of this work. Part I, also appearing in this issue, presents the theoretical formulation for the moment method mode-matching treatment of the coaxial fixture for treating annular samples. In the present paper, the measurement techniques are described, and numerical simulations of the experimental procedure based on the theory of Part I are presented. In addition, comparative results of accuracy for these two approaches are given. Sensitivity analyses are also presented, along with preliminary experimental results.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".