The interaction of polarized microwaves with planar arrays of femtosecond laser-produced plasma filaments in air
Bibliographic record
Abstract
The interaction of polarized microwaves with subwavelength arrays of parallel plasma filaments, such as those produced by the propagation of high-power femtosecond laser pulses in ambient air, was investigated by calculating the reflection and transmission coefficients as a function of the incidence angles using the finite-difference time-domain (FDTD) method. The time evolution of these coefficients was calculated and compared with experiments. It is found that the plasma filaments array becomes transparent when the polarization of the microwave radiation is perpendicular to the filaments axis, regardless the incidence angle of the microwave with respect to the filaments, except near grazing incidence. Increasing the filaments electron density or diameter, or decreasing the electron collision frequency or filaments spacing, decreases the transmission and increases the reflection. Transmission decreases when increasing the number of filament layers while reflection remains unchanged as the number of filament layers exceeds a given number (∼3 in our case). Transmission slightly increases when disorder is introduced in the filament arrays. The detailed calculation results are compared with those obtained from the simple birefringent slab model, which provides a convenient framework to calculate approximately the properties of filament arrays.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".