Space‐time analysis of energy localization: A Poynting flow perspective with applications to pattern reconfigurable dipoles
Bibliographic record
Abstract
In this paper, we explore the dynamics of electromagnetic energy, especially in the near-field region of radiating antennas, from a fundamental perspective (ie, no limitations on antenna shape and nature of excitation signal) and identify some key future research directions. First, we provide a comprehensive critique of the frequency-domain reactive energy and circuit-theoretic Q-factor based approach, which is predominantly adopted in literature. In this way, we emphasize on the importance of adopting a general time-domain approach to characterize the near-field electromagnetic energy of arbitrary antennas. Next, we revisit the inherent ambiguities associated with the Poynting power-flux term in the context of electromagnetic energy, and point out the nonuniqueness of the reactive energy, conventionally obtained by subtracting the far-field radiation density from the total electromagnetic energy density around antennas. Furthermore, we discuss the concept of Poynting localized energy and its potential integration with FDTD techniques, and investigate its space-time behavior for a Yagi-Uda principle based pattern reconfigurable dipole system.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".