As the number of satellites in orbit continues to increase, automated health monitoring of satellites has become increasingly critical. Given the complexity and diversity of satellite telemetry, it is essential to employ methods specifically tailored to these characteristics. A method named Quantile Gaussian Process Regression (Q-GPR) has been developed by the authors based on operational insights of actual satellite missions, but the method had not yet been evaluated using publicly available datasets. In this study, Q-GPR is evaluated by participating in the Spacecraft Anomaly Challenge, a widely open competition focused on algorithmic satellite anomaly detection held in early 2025. The competition utilized the ESA-ADB dataset, which comprises real satellite telemetry provided by the European Space Agency, enabling a realistic and practical evaluation environment. To accommodate the competition and dataset-specific requirements, Q-GPR was extended through adaptive learning and ensemble learning techniques. The proposed method outperformed all baseline methods included with the dataset. Notably, it demonstrated superior performance across diverse telemetry channels, highlighting its effectiveness and practicality for real-world satellite operations.