High-fidelity modeling and simulation for wideband receiving system development
Bibliographic record
Abstract
Research experience has shown that it is expensive to design, build and test a RF/microwave system that is comprised of various RF/microwave components. In order to mitigate the problem, High-Fidelity Modeling and Simulation (HFM&S) is a practical approach that includes high-fidelity behavioural model (HFBM) of the receiving system and scenarios embedded with different Concept of Operations (CONOPS). HFM&S is also an essential way to develop receiving system specifications that can be used for system validation and verification. This paper presents the HFBM of a wideband digital receiver using Matlab/Simulink® and the RF Toolbox™/SimRF™, and the use of the model to build a multi-channel receiving system that has a linear antenna array. The receiving system is installed on an UAV to intercept a ground-based emitter signal in a scenario that is built in Systems Tool Kit®. Through the design and build of the UAV receiver and its deployment in a scenario, this paper demonstrates the following: what is meant by HFBM of a system and how it can simulate real hardware; how signal integrity in the HFM&S can be and should be retained; why amplitude and phase are important for signal waveform level M&S; why the modern high performance computing technology should be used for signal waveform level M&S; an
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".