Space-Time Modulation in Lossy Dispersive Media and the Implications for Amplification and Shielding
Bibliographic record
Abstract
We investigate the dispersion, attenuation, amplification, and the area of solutions of electromagnetic (EM) waves in a lossy progressively disturbed medium. Approximate, rigorous, and numerical methods are fully developed for a lossy environment, and the differences, merits, and drawbacks are discussed. A new representation of the Floquet theorem accounts for the loss exposed to both the pumped wave and the signal wave. The second-order small perturbation approximation is employed to yield closed-form solutions for transverse EM waves’ dispersion relation. It is proven to be valid in small-perturbed lossy space–time-modulated (STM) media with nonsuperluminal modulation. Analyzing the dispersion relation, an elaborated sufficiency condition is proposed. Moreover, the nonunique solutions and abnormal effects created by the loss factor are analyzed thoroughly. The special harmonic amplification and shielding properties experienced in a lossy STM media are brought to attention throughout the process. The developed approximate and rigorous analytical results are finally compared to finite-difference time-domain (FDTD) simulations in a realistic test, where a signal is going through upconversion/downconversion in an STM medium. Finally, a set of useful conclusions, implications, and applications has been raised to give more insight into how amplification and shielding might be affected in a lossy STM environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".