ArticleScientific data2026
Cognitive Radio for Satellite TT & C System: A General Dataset Using Software-defined Radio.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Cognitive Radio for Satellite TT & C System: A General Dataset Using Software-defined Radio.Scientific data · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The rapid advancement of satellite communication systems necessitates the integration of cognitive radio technologies to ensure system adaptability. However, existing public datasets are predominantly centered on terrestrial signal features, severely constraining their applicability in space environments. Here, we introduce RML24, the first open-source benchmark dataset specifically engineered for deep learning-based satellite Telemetry, Tracking, and Command (TT&C) signal processing. We generated over 1.3 million signal samples using Software Defined Radio (SDR) and Radio Frequency (RF) transceiver platforms, encompassing 22 modulation schemes prevalent in TT&C systems. RML24 incorporates simulated satellite channel models, authentic RF link effects, and signal propagation phenomena to align the data distribution with real-world signal characteristics. All datasets and associated model codes are publicly available. Furthermore, to facilitate broader cognitive radio research, RML24 includes comprehensive and precise annotations for tasks such as deep learning-based parameter estimation and demodulation. By providing a high-quality resource tailored for satellite communications, RML24 aims to accelerate the development of intelligent, adaptive, and deep learning-driven satellite systems.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.