1：Experimental Design and Method Selection:

The methodology involves designing a software platform that integrates streaming data analysis algorithms with high-performance computing solutions. It uses modifications of the Expectation-Maximization (EM) algorithm for orientation determination, X-ray Cross-Correlation Analysis (XCCA) for preliminary structure information, and phase retrieval algorithms like Error Reduction (ER) and Hybrid Input-Output (HIO). The design is based on High Performance Data Analysis (HPDA) principles to handle big data from XFEL experiments.

2：Sample Selection and Data Sources:

The data sources are diffraction patterns from single-particle imaging experiments at the European XFEL facility, specifically from the AGIPD detector. Data selection involves filtering empty images and classifying diffraction patterns into categories (single hit, multiple hit, non-informative) using machine learning algorithms.

3：List of Experimental Equipment and Materials:

The primary equipment includes the European XFEL facility and the AGIPD detector. Computing resources involve high-performance clusters, such as those at the Kurchatov Institute, with CPUs (e.g., Intel Xeon E5-2650 v3) and GPUs (e.g., NVIDIA Tesla K80). Software tools include existing packages for data preprocessing, classification, orientation determination (e.g., EMC algorithm), and phase retrieval (e.g., libspimage).

4：0). Software tools include existing packages for data preprocessing, classification, orientation determination (e.g., EMC algorithm), and phase retrieval (e.g., libspimage). Experimental Procedures and Operational Workflow:

4. Experimental Procedures and Operational Workflow: The workflow includes preprocessing of detector data, filtering empty images, classification of diffraction patterns, XCCA analysis, batch processing for orientation determination using EM modifications, iterative phase retrieval, and validation of 3D structure reconstruction. Data is processed asynchronously in batches to handle the data stream in quasi-online mode.

5：Data Analysis Methods:

Data analysis employs statistical methods and machine learning for classification, likelihood maximization in EM algorithms, and iterative constraints in phase retrieval. Performance metrics include computation time and resource requirements, analyzed using software like SVM for classification and custom implementations for orientation and phase steps.