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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".