Near-field detection at microwave frequencies based on self-adjoint response sensitivity analysis
Bibliographic record
Abstract
A new detection method is proposed for the localization of electrically small scatterers in a known background medium. The method requires the knowledge of the electric field distribution inside the known background medium where no scatterers are present. It is based on a self-adjoint response sensitivity computation which can be performed in real time. Using the E- field distribution in the background medium, it provides three-dimensional maps of the Fréchet derivative within the imaged volume. The peaks and dips in these maps identify the locations where the permittivity and conductivity of the measured medium differ from those in the background medium. The background medium can be heterogeneous. In a homogeneous-medium example, the performance of the detection algorithm is studied in terms of the number of transmission/reception points, the dielectric contrast of the scatterer compared to the background medium, and the size of the scatterer. Its resolution is also addressed. The detection of a small scatterer in a heterogeneous background is demonstrated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".