Plenary lecture I: microwave applications other than communication fields
Bibliographic record
Abstract
Microwave Energy is intensively used in Communication and Radar Technology. The introduction of the domestic microwave oven in everyday life is known nowadays by a large number of individuals around the world. But today the industry and scientific worlds use microwave energy at different levels of power and at very specified frequencies. These frequencies are called IMS frequencies (Industrial, Medical and Scientific). In some applications microwave power is very suitable for industrial drying and heating of materials. Medical applications of microwave energy present some advantages over conventional hyperthermia treatments. Different energy applications in medical areas are being developed these recent years. Another important question concerns the safety measures and power levels in electronic technology, especially for cellular telephones. Low-level microwave power on health effects are still not so clarified and may concern public health these coming years. Microwave sensors are also an important part of today's technologies. For the development of such techniques, the knowledge of permittivity (dielectric constant and dielectric losses) of material becomes a key factor for such applications. Different methods exist for the measurement of these parameters. Microwave sensing and microwave heating depend heavily on the evolution of these parameters with time, temperature and physical or chemical change of materials under study. More accurate measurements are made with the use of resonant cavities tuned to the frequency of interest. By introduction of a sample into the cavity, resonant frequency and quality factor change. These changes can be related to the complex permittivity or permeability of the material under investigation. Active cavity measurement system permits real-time measurements with microprocessor-based acquisition systems and algorithms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.197 | 0.178 |
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".