## 4 Case Study
### Working Memory, Inference Engine, and Visual Notifications  
In this section, we discuss how we applied the working memory, inference engine, and notification modules of our architecture to this case study. After analysing the models generated by different pattern recognition and prediction algorithms, it was discovered that for this case study, models generated by classification decision trees and regression decision trees offered the best results. As a result, these algorithms were used to generate models for the working memory section of the proposed methodology. The generated models were used for analysing aggregated data bundles (ADBs) that were held-out during training. These ADBs served as the testing dataset for the generated models. The ADBs were passed to the generated models in chunks. The generated models were used for identifying network disruptions and transients in the satellite network caused by abnormal behaviour or environmental factors such as passing clouds. 1. Identifying disruptions: A disruption means a terminal suffers a hardware fault. In this section, how the knowledge of the network and classification decision trees were used to identify faults in a network is discussed. As discussed in the proposed framework section, the functional map of the network was used to derive a simple rule in this work. The rule states that \"if a terminal is predicted to belong to another network different from its assigned network, then it is assumed that either the model that did the prediction is wrong or there is something wrong with the terminal\". An important advantage of using decision tree is the transparency of the decision process. By using data containing all sub route groups and terminal data, a trained decision tree can be built to enable users to easily observe which sub route group a particular data entry belongs to, and highlights the important characteristics. The decision tree, illustrated in Figure 6, highlights that SNR_X1, MIS_X4, MIS_X1, MODCOD_X4, MODCOD_X5, TEMP, MIS_X5, MIS_X6, TIME_X1, MIS_X7, RX_X16, RX_X12, and TX_X3 are characteristics that distinguish between specific sub route groups. For example, in Figure 6, when a data entry has SNR_X1 smaller or equal to 696.5, MODCOD_X5 smaller or equal to 16, TIME_X1 smaller or equal than 5385, MIS_X4 smaller or equal to 29674.5, and RX_X17 smaller or equal to 6.5, then this entry will belong to sub route group 7342133. This tree representation enables the highlighting of terminal characteristics to enable users to distinguish between specific sub route groups. It was discovered that for the training dataset used, SNR_X1, MODCOD_X1, and MODCOD_X5 are the main characteristics that can be used in sub route group distinction. This is because they are loca \begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithm** & **RMSE** \\ \hline Regression Tree & 47.876 \\ Linear Regression & 49.372 \\ SVM Regression & 154.466 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of regression algorithms for predicting terminal disruption based on the terminal attribute RX_15. Using the decision tree as a predictor, ADBs containing terminal data were classified into specific sub route groups based on characteristics. There were in total 89531 correctly classified entries and 10,469 misclassified entries, which give 0.8953 accuracy for the method. Table 5 presents the confusion matrix representing how the terminals were classified into sub route groups. By using the earlier rule that states \"if a terminal is predicted to belong to another network different from its assigned network, then it is assumed that either the model that did the prediction is wrong or there is something wrong with the terminal\", all entries that were misclassified were deemed as a good source of information relating to outliers or problem scenarios. If a terminal was classified to a wrong sub route group it means that some of its characteristics were not typical for that group, thereby indicating a problem.