Bibliographic record
Abstract
Active-scattering-cancellation techniques provide a means of achieving electromagnetic cloaking without dealing with passivity-based design and performance limitations. However, a major drawback is that the cloak must be adjusted to accommodate a specific set of illumination conditions. Until recently, configuration has been performed with these conditions known a priori. Although adequate for initial validation, this presents a major obstacle to the development of a practical device that must contend with an unknown and dynamic electromagnetic environment. As a solution, this paper presents the development of an estimation algorithm that uses field measurements, sampled within the vicinity of the object to be cloaked, to deduce incident-wave properties. This information can then be used to adaptively reconfigure the cloak without any prior knowledge of its surroundings. In addition, the use of iterative solvers is avoided in estimator development, sidestepping convergence issues that afflict previous designs while reducing run time. After presenting the development process and operation principles, performance evaluation is conducted by using the algorithm to estimate the incident angle, signal magnitude, and phase offset of an incident wave impinging on either a circular or polygonal quasi-two-dimensional conductive target. Input data sets are initially provided via full-field simulation models and later supplanted by real-world field measurements.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".