Bibliographic record
Abstract
The evolving role of modern navies has required increasingly higher levels of capability in the Radio Frequency (RF) shipboard systems that provide radar, communications, Electronic Attack (EA) and Electronic Support (ES) functions. The result has been a proliferation of topside antennas and associated hardware on naval vessels. The notion of MultiFunction RF (MFRF) systems has drawn considerable interest as an approach to reversing this trend. In a MFRF system, RF functions are consolidated within a shared set of electronics and antenna apertures that utilize Active Electronically Scanned Array (AESA) technology. This paper highlights a number of issues to be considered in the design and implementation of a naval MFRF system. Specifically, the key requirements of the RF functions of interest are first reviewed, and MFRF system design trade-offs resulting from costs and/or performance limitations in existing hardware technology are then discussed. It is found that limitations in hardware technology constrain the implementation of practical MFRF systems. MFRF system prototype development programs that have been conducted in other countries are described. MFRF resource allocation management is identified as an important future research topic.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".