Bibliographic record
Abstract
Explains and resolves the electromagnetic compatibility challenges faced by engineers in transportation and communications This book is a mathematically-rich extension of courses required to maintain the Federal Communications Commission (FCC), the Canadian Standards Association (CSA), and the European Union certifications. The text provides an in-depth study of the electromagnetic compatibility (EMC) issues related to specific topics in transportation and communications, including Light Rail Transit, shadow effects, and radio dead spots, through the analysis of real-world case studies in the United States and Europe. The author provides Cartesian, cylindrical, and spherical solutions that can be applied to Maxwell's and Wave Equations. The book covers topics such as SCADA Systems, shielding, and complexities of radio frequencies and their effect on communication houses. The author also provides information for alternative industries to apply the solutions from the case studies and background content to their own professions. Presents a series of over twenty real-world case studies related to EMC in transportation and communications Covers power line radiation, shadow effects on subway cars, train control systems, and edge distortions Includes the OATS testing method and Department of Transportation (DOT) test Provides access to a companion website housing power point slides and additional appendices Electromagnetic Compatibility: Analysis and Case Studies in Transportation is a reference for practicing engineers involved in transportation and communications, as well as post-graduate engineering students studying transportation and communications in engineering.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.124 | 0.096 |
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".