Near-Field Probes for Subsurface Detection Using Split-Ring Resonators
Bibliographic record
Abstract
Most of the previous microwave near-field probes and imaging techniques focused on surface imaging, providing ultrahigh lateral resolution. Few microwave near-field probes were developed for subsurface detection, offering both high lateral and depth resolution with varying degrees of effectiveness. In this work, a novel microwave near-field probe using a single split-ring resonator is introduced with the primary focus of subsurface detection. The design is simple, compact, inexpensive, and easy to fabricate using printed circuit board technology. Fourier spatial analysis of the field of the new probe reveals a substantial enhancement of the evanescent field, thus making a significant difference in subsurface detection. Experimental results illustrate that a small 3.24-mm aluminum block immersed in 1% sodium chloride (NaCl) solution and positioned 4 mm away from the surface was successfully detected using a probe operating at 1.218 GHz. For this particular experiment, where the size of the object was λ/74 , the detection ability of the new probe was tested using 2% and 3% NaCl solution as well. The phase changes due to the depth of the object demonstrate that the new probe is able to sense the presence of the same object in very lossy medium (3% NaCl whose loss tangent is approximately unity) with depth of 1-2 mm in spite of a standoff distance of 1-mm air and a container thickness of 6.35 mm.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".