Technique to Decompose Near-Field Reflection Data Generated From an Object Consisting of Thin Dielectric Layers
Bibliographic record
Abstract
Extracting properties of hidden structures using ultra-wideband (UWB) radar is evolving into a promising technology. For these applications, a short-duration electromagnetic wave is transmitted into an object or structure of interest and the backscattered fields that arise due to dielectric contrasts at interfaces are measured. The time-of-arrival (TOA) between reflections and the amplitude of the reflections may be used to infer the geometrical and dielectric properties of hidden structures or objects. For electrically thin layers, the limited bandwidth of the illuminating signal typically gives rise to overlapping reflections, necessitating the use of high-resolution techniques. We investigate an iterative nonlinear parameter estimation technique that may be used for near-field applications. The effectiveness of the algorithm to decompose the reflection data is evaluated using numerical data generated from 2D and 3D dielectric slabs and experimental data from multi-layered slabs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".