## 3 The Proposed Architecture
### The Modules in the Knowledge-Based Cognitive Architecture  
We shall now discuss the modules in the architecture presented in Figure 3, with the numbers corresponding to the module number in the architecture:  
**The monitored network** (1): In this work, satellite terminals were mostly static and situated in various geographical locations worldwide. Each terminal transmitted data in intervals of 0.3 s. The data received at base stations consisted of the aggregated data bundle (ADBt) from all sub route groups. Each aggregated data bundle comprised terminal data (\(G_{i}N_{k}T_{j}D_{t}\)), which were produced every 0.3 s, where t is the timestamp (see Figure 2). Figure 3: Proposed framework of the architecture. **Knowledge base** (2): The knowledge base was made up of rules derived from a team of 10 satellite engineers with a combined experience of more than 80 years. These rules were derived based on: (i) the knowledge of the satellite infrastructure (Figure 2), (ii) experience on the behaviour of terminals in networks, (iii) experience of signal propagation in satellite networks, and (iv) the behaviour of terminals at various geographical locations. Deriving rules from knowledge of satellite infrastructure and behaviour of terminals in networks (i and ii): One of the important rules applied in the knowledge base was closely linked to the satellite infrastructure design (Figure 2). From experience, satellite engineers discovered that terminals in the same network exhibited similar behaviour. As a result, when a terminal in a network was not behaving the same as the other terminals in the network or exhibited behaviour similar to another network, this was a cause for concern. However, capturing the behaviour of networks was a task that was not humanly possible using simple if-else rules. This is because a network \(G_{i}N_{k}\) is made up of various terminals \(G_{i}N_{k}T_{j}\) with different hardware behaviours (e.g., age of equipment, different component manufacturers, etc.) and unique geographical location conditions (e.g., weather conditions, trees growing close to terminals could obstruct line of sight to satellite, etc.) that is not common across all terminals. As a result, we had to use a variety of machine learning algorithms to model network behaviours and use the models to support the derivation of the following rule: 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. Deriving rules from experience of signal propagation in networks and behaviour of terminals at geographical locations (iii and iv): From experience, the satellite engineers were able to identify three different states that a terminal in the network could be in--a normal working state, a transient state with cloud covering or tree waving in front of satellite dish (i.e., geographical conditions), and an abnormal (broken down) state. In order to detect the state of a terminal, the engineers had previously identified a signal propagation variable and used it as a threshold value in an if-else rule structure to assess the state of a terminal. Furthermore, by using the value of this variable, it was possible to assess and estimate certain short transient conditions (passing cloud covering or rain), slow but continuous spatiotemporal conditions (tree growth) happening in the vicinity of the terminals, or a total hardware failure caused by a broken terminal. However, this approach caused a multiple of problems. For example, due to the differences in hardware of the terminals, one threshold value could not be rolled out to over 1000 terminals in the network. This called for an automated process of extracting this threshold value for each terminal in the network. Moreover, it was discovered that through the use of machine learning algorithms, other signal propagation variables were discovered.