Cloaking and Antennas: From Theoretical Paradigms to Next‐Generation Intelligent Systems
Bibliographic record
Abstract
Abstract Electromagnetic cloaking techniques have evolved significantly over the past two decades, transitioning from mere concepts to functional implementations. Though their applications abound, those involving antennas are among the most widely investigated, spurred by the potential to render radiating/receiving components invisible or tailor their scattering properties—a pursuit of interest since the earliest metamaterial‐based cloaks. This review maps the development of leading cloaking methodologies, from the pioneering theories of transformation optics, scattering cancellation, and transmission‐line networks to the rise of dynamic, self‐adaptive systems fueled by recent advances in artificial intelligence. Each approach is examined in terms of its underlying mechanisms, prevailing limitations, and antenna integration capacity, particularly for addressing mutual coupling and blockage effects in dense array configurations. Finally, a comparative analysis reveals that intelligent cloaking systems combining deep learning with transformation optics and reconfigurable metasurfaces offer particularly promising solutions for modern stealth, sensing, and communication systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".