Evaluation of Near-Field Electromagnetic Shielding Effectiveness at Low Frequencies
Bibliographic record
Abstract
Magnetic induction tomography (MIT) is a novel technology for flow measurement offering significant promise in the measurement of multiphase flows containing low-conductivity fluids such as saline water. Such measurements rely on optimal effective shielding to avoid external field interference and extraneous capacitive coupling that can lead to false readings and overestimations of the eddy current-induced fields. The performance of various attenuation materials in the low megahertz frequency spectrum is presented and compared with outcomes from a numerical computational method. The results demonstrate that the shielding mechanism that prevails at low frequencies is that of reflection. Consequently, hard shields such as metals show superior wave attenuation performance for MIT systems operating below 13 MHz. For higher frequencies, the absorption effect on the incident wave path within soft electromagnetic shields presents enhanced shielding properties. This paper also explores the limitations of traditional testing geometry for shielding effectiveness and proposes an alternative approach to near-field, free-space measurement for MIT sensors. The proposed semi-enclosed approach shows enhanced shielding effectiveness measurements compared with the traditional transversal barrier method. The proposed method was used to assess the electromagnetic shielding effectiveness of ferromagnetic and various metallic materials.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".