UNIC-Lab/RadioDiff
This is the code for the paper "RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction", IEEE TCCN.
317
Stars
22
Forks
36.6
Radar Score
Stable
Momentum
Scoring Breakdown
Impact41.1
Velocity22.2
Health49.6
Growth Metrics
7-Day Stars
+0
30-Day Stars
+0
Acceleration
x
Language
Python
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🛡️ Enterprise Readiness & Commercial Safety
Organic Growth
95%
High Confidence
Growth pattern looks organic.
License
NOASSERTION
Review license terms carefully before commercial adoption.
Health Grade
B
Based on maintenance, issue response, and release cadence.
Radar Score
36.6
Composite of Impact, Velocity, and Health.
Disclaimer: This is an automated heuristic assessment based on public GitHub signals. Always perform your own security audit, dependency review, and legal compliance check before production deployment.
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