## 1 Introduction  
With the rapid development of satellite technology, the satellite transmitter identification method based on radar and optical imaging has matured. However, deep-learning methods that rely on a single image modality still have inherent limitations. Specific Emitter Identification (SEI), as a physical layer-based identity authentication mechanism, analyzes the unique radio frequency characteristics of the received radio signals. Radio Frequency Fingerprints (RFFs) for identifying different emission sources [1] have received extensive attention in recent years. RFFs originate from the inherent differences in the design or manufacturing process of hardware circuits and have the characteristics of being tamperresistant and difficult to forge [2], laying the physical foundation for SEI and providing an important complementary perspective for satellite transmitter identification. Deep Learning (DL), with its powerful data analysis capabilities, has been extensively applied in wireless communication technologies [3, 4, 5], including SEI [6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. For instance, [PERSON]. [16] proposed an SEI method utilizing Complex-Valued Neural Networks (CVNNs) to process In-phase and Quadrature (I/Q) signals directly, significantly enhancing recognition accuracy. [PERSON]. [17] introduced Knowledge Graph (KG) technology to SEI, designing an Adaptive Feature Combination (AFC) strategy via attention mechanisms to assign optimal weights to features for building an efficient classifier. [PERSON]. [18] explored the application of Graph Neural Networks (GNNs) in SEI, effectively improving system robustness in a complex wireless communication environment. [PERSON]. [19] proposed a Transformer-based SEI method to better capture long-term dependencies within signals. These DL-based SEI approaches rely on massive historical RF signal samples and deep neural networks to extract robust and effective RFFs, demonstrating superior performance compared to traditional methods based on handcrafted features. However, most existing DL-based SEI methods depend on supervised learning, requiring large amounts of precisely labeled data for effective training. In practical applications, especially for satellite signal data, obtaining reliable annotations during data acquisition is often challenging, resulting in scarce, accurately labeled samples. Consequently, the proportion of effectively labeled data is typically low, making the application of conventional supervised learning methods difficult under such constraints. To address the issue of limited labeled training samples, Few-Shot Learning (FSL) has recently been introduced to SEI. Ref. [20] applied meta-learning to SEI, directly utilizing I/Q data for training to reduce manual preprocessing steps. Validated on data collected from ZigBee devices and drones, this method achieved optimal performance with only 15 samples per class. Ref. [21] improved a meta-learning algorithm to better handle high-dimensional input data by calculating the distance and scatter between features, using this information to predict emitters. Compared to traditional meta-learning, the enhanced algorithm incorporates richer signal information to extract higher-quality, low-dimensional features. Ref. [22] proposed a novel FSL-SEI method based on Deep Metric Ensemble Learning (DMEL). Leveraging Complex-Valued Convolutional Neural Networks (CVCNNs) combined with contrastive loss and SoftMax loss, it extracts discriminative features characterized by compact intra-class distances and separable inter-class distances, ultimately enabling ADS-B signal identification via an ensemble classifier. The above-mentioned FSL-SEI methods assume that only a small number of labeled samples exist. However, a more realistic scenario is that a large number of unlabeled samples and limited labeled data are available. Semi-supervised learning provides an effective approach for this scenario. It utilizes unlabeled samples to offer auxiliary information and regularization constraints during model training.