Carbon-based radar absorbing materials: A critical review
Bibliographic record
Abstract
With the development of radar (Radio Detection and Ranging) systems, the study of materials with the capability to block and reduce the reflected electromagnetic radiation to avoid or confuse detection systems, or to protect sensitive devices and living beings exposed to electromagnetic radiation, has become a topic of great interest. This review describes some concepts of the electromagnetic spectrum, radar systems, frequency bands, and radar applications based on their operating frequency, the radar cross-section, and the mechanisms to reduce it, as well as the microwave absorption theory. Furthermore, different carbon-based materials such as carbon black, carbon fibers, nanotubes, graphene, graphene oxide, reduced graphene oxide, and its composites have been used as electromagnetic absorber materials due to their remarkable intrinsic characteristics as lightweight, flexibility, and suitable electric and magnetic properties, are described. This review also explains the principal mechanisms by which these materials can attenuate the radiation. The review is concluded with a summary of the perspectives and challenges for future investigation of carbon-based materials and his electromagnetic characterization for radar signals absorption, interference protection and human security.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".