## 3 Case Study--Space Systems
Through radiofrequency links, the ground segment sends telecommands to the satellite--typically asynchronous data packets received by the satellite's central  
Figure 4: Conceptual Architecture of Space System. computer--to initiate specific behaviors or override internal onboard decisions. Depending on the mission profile, the satellite-ground link may be continuous or only available during specific orbital windows. In return, the satellite transmits data to the ground segment, which generally falls into two categories: payload data and telemetry (or housekeeping) data. Payload data consists of mission-related outputs such as images or measurements, while telemetry data provides insights into the satellite's operational health and status. Space systems are characterized by the generation of large volumes of spatio-temporal data, often collected at high frequency and transmitted to a distributed network of ground stations for archiving and processing. This data is then made available to end-users through the User Segment. In addition to storage and dissemination, ground systems increasingly apply data analytics and machine learning techniques to derive actionable insights from both telemetry and payload data. These insights support decision-making in areas such as satellite health monitoring, anomaly detection, trend analysis, and predictive maintenance, helping to ensure mission success and continuity. Payload data is further processed to produce mission-specific outputs, which may feed into downstream applications or be shared with external stakeholders. To illustrate the diversity and shared structure within a typical product family of space systems, Table 2 presents five satellite-based systems developed under a common architectural framework. Each of these systems has a distinct mission focus--ranging from earth observation to disaster response--but all rely on machine learning capabilities to analyze data, guide operations, or deliver downstream services. Despite their common needs for image classification, anomaly detection, or predictive modeling, we observed that the machine learning assets--such as datasets, pretrained models, and processing pipelines--were developed and maintained independently for each system. This absence of structured reuse in the development and evolution of machine learning components reflects a broader gap in current Product Line Engineering (PLE) practices. While traditional core assets like requirements, design models, and source code are often \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Product Name** & **ML Requirements** & **ML Core Assets** \\ \hline Earth Observation & Image classification, land use segmentation, anomaly detection. & Pretrained classification models, segmentation pipelines, labeled imagery datasets (multispectral, hyperspectral, elevation). \\ \hline Communications & Predictive traffic routing and signal optimization using historical data. & Traffic prediction models, signal processing pipelines, time-series datasets (signal strength, latency logs). \\ \hline Scientific Research & Spectral pattern recognition and anomaly classification in scientific datasets. & Anomaly detection models, preprocessing modules, scientific sensor datasets (e.g., particle counts, radiation). \\ \hline Disaster Monitoring Constellation & Change detection, rapid classification, and priority mapping for emergency zones. & Change detection models, emergency classifiers, temporal pipelines with time-series and thermal imagery. \\ \hline Reconnaissance Satellite & Object detection, movement tracking, and automatic target recognition. & Object detection models, secure deployment configs, encrypted radar, and optical imagery datasets. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Example set of products in a Space System PLE with the ML requirements and ML Core Assets. reused across system variants, assets related to machine learning are rarely integrated into the core asset base.