Technique to Decompose Near-Field Reflection Data Generated From an Object Consisting of Thin Dielectric Layers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it