Antenna Physical Poles Extracted From Measured Backscattered Fields
Bibliographic record
Abstract
This paper presents a new approach to extract the physical poles of antennas. The singularity expansion method (SEM) allows modeling the antenna backscattering using poles which are theoretically independent of the wave incident angle, making them useful for antenna identification. Nevertheless, only the physical poles respect this property while the spurious poles change for each incident angle. Indeed, we call the poles linked to the antenna itself as “physical” and the poles linked to anything but the antenna as “spurious”(excitation, noise, etc.). The goal of this paper is to highlight the method to define the optimal time windowing applied on the antenna backscattering in order to obtain the physical poles of the antenna. The approach is based on the window decreasing technique (WDT) and the window increasing technique. The SEM is applied on the backscattered field measured in the boresight direction of three antennas: 1) a narrowband patch antenna; 2) a wideband helix antenna; and 3) a UWB antenna. Using these poles to reconstruct the field backscattered in several directions, we show that the poles extracted from one direction with this new approach are relevant to reconstruct the backscattered field for any other directions. Moreover, we show that these poles can be extracted directly from these other directions.
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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 | 0.001 | 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 | 0.001 |
| 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".