## 3 Case Study--Space Systems
### Need for Systematic Reuse of ML for Space Systems  
Machine Learning is used extensively in space systems, supporting critical tasks such as telemetry analysis, trajectory optimization, and payload data processing. These systems rely on ML to enable advanced capabilities in data-driven decision-making and operational efficiency. Given the increasing complexity of space missions, ML has become a key enabler of automation and analytics in both ground and space segments. Despite the diverse nature of tasks performed using ML in space systems, many of these tasks share similar underlying requirements and processes. For instance, anomaly detection in telemetry, path optimization for satellite control, and data preprocessing for payload analysis often involve comparable datasets, models, and workflows. This similarity presents a significant opportunity for reuse, as developing generic, reusable ML components could dramatically reduce development effort, costs, and time for new missions. However, reuse in ML for space systems is currently ad hoc and fragmented. Most developments focus on isolated projects without systematic reuse of datasets, models, or pipelines. This lack of a comprehensive approach leads to duplicated effort, inconsistencies, and inefficiencies when deploying ML solutions across missions. Without a structured framework, scaling and adapting ML-enabled capabilities for new missions becomes resource-intensive and prone to errors.  
A more systematic and comprehensive approach to reuse in ML is needed to address these challenges. This case study illustrates how space systems highlight the need for structured reuse practices. Variability in ML assets, such as differing telemetry formats, model architectures, and operational requirements, complicates reuse further. For instance, telemetry data across satellites often varies in format and frequency, necessitating mission-specific preprocessing. Similarly, the choice of ML algorithms and hyperparameters is influenced by mission goals, such as optimizing real-time decision-making or achieving high accuracy in anomaly detection. Additionally, the dynamic and iterative nature of ML workflows introduces further complexities. Models must frequently be retrained or updated to adapt to changes in data distributions, mission objectives, and operational environments. Maintaining consistency and traceability across multiple missions or iterations becomes challenging, especially when coupled with the rigorous testing and validation required for critical space operations.  
Given these observations, a more systematic and integrated reuse approach is essential to address the challenges of managing variability, scalability, and adaptability in ML-enabled space systems. By leveraging PLE principles and the process, organizations can develop a structured framework for systematically managing core ML assets such as datasets, models, and pipelines. We will elaborate on this in the following sections.  
While the case study focuses on space systems, the ML PLE framework is designed to be general and domain-agnostic. Its principles, such as large-scale systematic reuse, asset modularization, configuration variability, and lifecycle traceability, apply equally to sectors like industrial automation, financial risk modeling, and smart agriculture. For example, in agriculture, datasets from sensor networks and weather stations can be reused across farms, while configurable pipelines can be adapted for crop yield prediction or disease detection.