Electromagnetic Environment Situational Awareness
Bibliographic record
Abstract
Military radio, EW and RF sensor systems operate in a congested and contested electromagnetic environment. The NATO Science and Technology Organization established the Research Task Group 069 in order to take charge of the IST-146 project on Electromagnetic Environment Situational Awareness. The project was aimed at evaluating the operational benefits for NATO in line with the Electromagnetic Spectrum Strategy and at evaluating the Radio Environmental Map (REM) technology. The paper describes the military scenario considered for the study. Its operational analysis establishes the importance of Electromagnetic Spectrum Command and Control integrated with other C2 processes. The description of the data sources, models, and representation is done. Key user benefits are highlighted. Then proposals for possible evolution of electromagnetic operations and spectrum management within NATO are made. The paper further describes the proposed reference architecture based on the Internet of Things (IoT). It establishes how the relationships between the REM elements have been validated through the project scenario. Tests and simulations, carried out for the construction of measurement-based REMs and transmitter localization, are presented. The paper finally describes the proposed demonstration, which enables understanding through visualization of an interference situation and de-confliction by dynamically re-assigning frequencies.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".