Sensitivity analysis of electrically small objects in lossy inhomogeneous structures
Bibliographic record
Abstract
Accurate response Jacobians can significantly improve the convergence and the resolution of the reconstruction algorithms used in inverse microwave imaging. As the electrical size of an object decreases, however, the accuracy of the computed Jacobians with respect to its shape and constitutive parameters deteriorates. This is especially valid if finite-difference (FD) approximations are used. In this paper, we investigate the accuracy of the Jacobians provided by our self-adjoint sensitivity analysis (SASA) of lossy dielectric structures. We focus on time-domain solutions. The accuracy of the response gradients is investigated for electrically small objects. Recommendations are given for a proper choice of shape parameters when the object’s size becomes smaller than certain limits. We show that our approach yields accurate Jacobians even for objects as small as one grid cell while the FD approximations are prone to numerical noise.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".