Hybrid De-embedding Technique for Microwave Absorber Characterization
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Bibliographic record
Abstract
In this paper, a data processing technique to obtain the true characteristic of microwave absorber material characterization is proposed. This technique addressed to overcome the limitations of port extension is known as hybrid de-embedding technique, which in principle is carried out by combining the structure model data of test fixture that is used with simulated data or experimental measurements. In this technique, the test fixture is simulated numerically to get S (scattering) parameter data. Then the S parameter is converted into the T parameter (transfer) to be used for de-embedding process by removing the characteristic of test fixture used so that the true characteristics of a microwave absorber material can be revealed. To verify the accuracy of technique proposed, the characteristics of a microwave absorber is simulated numerically and measured experimentally. The simulation and measurements results are then processed using the proposed technique to be compared with its ideal model. In general, the result of de-embedding process shows that the proposed technique has high accuracy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it