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Using a Dynamic Channel Emulator to Support Over the Air Testing by Student Design Teams

2025· article· W7139940733 on OpenAlexaff
David G. Michelson

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChannel (broadcasting)Reliability (semiconductor)Field (mathematics)Process (computing)Air traffic control

Abstract

fetched live from OpenAlex

Student design teams provide students with opportunities to participate in intramural competitions or major projects and gain practical experience with leading edge technologies. Many of these projects use wireless technology to connect student operators with remote devices such as solarpowered vehicles, electric vehicles, drones, rockets and satellites. Initially, many students pursue such wireless design efforts with a hobbyist's mindset and implement designs based on their best understanding of the problem followed by fixes once issues are observed in the field. In the case of rockets, such an approach is both time-consuming and expensive. In the case of satellites, only a very limited number of software changes are possible once the satellite launched. Accordingly, we are encouraging student design teams to adopt a test culture in which requirements are defined in advance and implementation-independent tests are devised before design begins. This permits a significant number of potential flaws to be identified before the system is fielded and allows students to more fully develop their design intuition. This is not a moot problem. Failure of the communication subsystem has been responsible for approximately 20% of small satellite mission failures over the period between 2000 and 2019. Insufficient system-level integration and testing has been cited as one reason why corrective actions weren't taken to prevent such failures before launch.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.708
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.302
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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