With the rapid development of satellite communication systems, the development of cognitive radios for satellite systems is significant. However, traditional data sets are no longer able to meet the unique challenges of signal identification and demodulation in satellite channels. This paper proposes a general dataset for satellite cognitive radio (RML24), the first dataset designed specifically for deep learning applications in satellite signal identification and demodulation. RML24 integrates telemetry and communication signals in Telemetry, Tracking, and Command (TT & C) systems and simulates the effects of signal impairments in real satellite channels. The dataset utilizes a software-defined radio (SDR) platform and radio frequency (RF) transceivers to perform rigorous over-the-air measurements and validate the collected data. RML24 provides researchers with fundamental data and modeling benchmarks for widely used modulation classification models in the hope of facilitating the algorithmic validation and development of intelligent and adaptive satellite communication systems and advancing the development of data-driven satellite communication technologies. The experimental results demonstrate that RML24 is more consistent with the data distribution of real-world signals, and the trained model is more capable of generalization.