Surface-Current Measurements as Data for Electromagnetic Imaging Within Metallic Enclosures
Bibliographic record
Abstract
A 3-D microwave imaging method within metallic enclosures is investigated and improved. This method uses the components of the surface-current vector at receiver points on the enclosure wall as data. At the metallic wall, the normal component of the magnetic field as well as the tangential components of the electric field is negligible, whereas the vectorial surface current, which is directly related to the tangential components of the magnetic field, is dominant. After presenting the results of a numerical investigation based on synthetic data, the method is validated using an experimental system comprised of 24 co-resident shielded, coaxial half-loop antennas, distributed in four layers, within a cylindrical metallic enclosure. These antennas are used in receiver-transmitter pairs to introduce an electromagnetic field into the chamber and collect the magnetic field at the receiver points. The measured data are used as input to a multiplicatively regularized finite-element contrast source inversion algorithm. Due to their relatively small size and minimal protrusion into the chamber, these antennas minimally perturb the field distribution inside the chamber and thereby allow the use of a simple numerical inversion model, which does not need to account for the passive antennas. These attributes are especially useful for large computationally intensive industrial applications. The experimental system described herein is a laboratory-scale prototype for a stored-grain imaging application where metallic silos are utilized.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".