Improved Metallic Enclosure Electromagnetic Imaging Using Ferrite Loaded Antennas
Bibliographic record
Abstract
Three-dimensional electromagnetic imaging can be used to monitor grain within metallic grain bins. Data acquisition requires multiple antennas surrounding the imaging space, which are used to transmit and receive the electromagnetic energy inside the bin. Due to their presence inside a metallic enclosure and due to very large mechanical forces these antennas are required to be low profile. In addition, since they are part of the imaging domain, they should be simple to model in the imaging software (i.e., using a point source). Existing half-loop magnetic field antennas meet these design criteria, but can be improved, particularly with better radiation efficiency. Herein, we present an enhanced antenna design: a ferrite-loaded shielded half-loop antenna designed to measure only the tangential component of the magnetic field against the metal enclosure wall, while rejecting the normal component of the electric field. Experimental results in two bins show that the enhanced design improves the signal level over existing probes by 6–18 dB inside a small-scale enclosure and around 20 dB inside a larger 28 m3 (800 bushel) bin. Full 3D imaging results of a high-moisture target within a low-moisture grain background inside the test enclosure show that the enhanced antennas improve the quality of the reconstructed results in the smaller bin, particularly where the antenna performance improvements are prominent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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".