Penetration Depth Quantification of Open-ended Coaxial Probes for Dielectric Spectroscopy of Layered Media
Bibliographic record
Abstract
Dielectric spectroscopy using open-ended coaxial probes is a powerful tool for biological tissue classification. It measures the complex permittivity of a medium as a function of frequency by applying an electromagnetic field and observing the energy reflected back. In heterogeneous tissue, a critical parameter that defines the accuracy of permittivity measurement is the penetration depth (PD) of the electromagnetic field for a given probe geometry, however, it is still unclear how the tissue characteristics affect the PD and the accuracy of the measurements. This paper evaluates the effect of various tissue and probe parameters on the PD in the context of dielectric spectroscopy through an open-ended coaxial probe. The PD is evaluated under different simulation conditions considering a probe inserted into a 2-layered tissue with different dielectric characteristics in 54,000 different simulations. A model for extracting the permittivity from the simulated reflection coefficient is also described. The results show for the first time that the PD increases as the difference in permittivity and conductivity between the layers increases, suggesting that measurement accuracy is sensitive to changes of contrast in the layer’s characteristics. The results also show that the PD decreases with the excitation frequency but increases with the diameter of the coaxial probe. These findings can greatly aid in quantifying and understanding the sensitivity of biological tissue classification using dielectric spectroscopy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".