## 1 Introduction  
There has been an increase in the number of satellites launched into space in recent years. For example, recently, SpaceX launched 60 internet-beaming satellites [1]. This will bring great advantages to global positioning for navigation, remote sensing, and telecommunication between remote points on Earth, among other uses, as well as increasing global connectivity and further enabling applications that rely on Internet of Things technology. Maritime transportation is one of the main application domains of satellite communication. Over 90% of worldwide trade is served by the maritime market [2]. Vessels at sea regularly utilise satellite communications to stay in touch with operations on land, and about 47.15% of the vessels are out of coverage of the coastal AIS transceiver and would rely on the connectivity to a satellite system [3]. Satellite services offer the advantages of increased productivity and improvement in the quality of work for over one million seafarers, over 20,000 vessels are currently online through a broadband satellite communications connection, and over 50,000 vessels are expected to be connected by 2025 [4]. At over EUR 2.5 billion in retail revenue in 2018, growing at an average 7.8% over the next ten years, the seas have the potential to generate significant connectivity revenues for the satellite industry [5]. Currently, network monitoring is still heavily dependent on humans using a reactive approach (that is, when a customer calls to report a fault). Engineers use their expertise to decide what attributes and values provide or predict a snapshot of a satellite infrastructure's health. For example, if the attribute is outside the specified range it might indicate poor terminal condition, e.g., faulty or about to develop a fault. However, with the proliferation of IoT devices, the number of terminals is set to increase, and this approach quickly becomes unmanageable by human operator alone. A single threshold value indicating terminal health does not fit all scenarios as each terminal in a network has unique operating conditions. Moreover, generating a threshold for each terminal separately is not feasible for more than a million terminals in the satellite network [4]. In this work, a knowledge-based cognitive architecture is proposed that can autonomously monitor a satellite infrastructure with a large number of terminals towards achieving two objectives:  
1. Predictive maintenance is to keep track of a terminal's degrading signal quality to timely verify equipment in order to anticipate eventual problems that may lead to higher costs with corrective maintenance. Hence, it could reduce the maintenance cost, device failures, downtime for repairs, and human workload. 2. Reactive maintenance is to automatically inform network operators when a terminal suddenly drops out of operation, e.g., due to a sudden hardware failure, so that engineers can attempt onsite repairs quickly before a customer calls. Hence, it could greatly reduce the unexpected consequences brought to the customers, thus maintaining a high quality of services and improving satellite network operator and customer relations. In the context of this work, the factors that could lead to abnormality of satellite networks are: 1. Environmental factors--obstacles around satellite dishes, cloud coverage, and ageing subcomponent hardware issues. High cloud coverage has usually temporary effects, although there are a limited set of actions that engineers can use to mitigate the effects. Moreover, obstacles around the dishes such as trees might cause intermittent drops of signal quality caused by branch waving. Such cases, although temporary, might have a high impact on connectivity and be difficult to detect. 2. Device health factors--ageing subcomponent hardware issues could also cause a long-term degradation of a terminal's performance over time. Identifying and detecting all the events discussed above in a satellite network with more than a million nodes is beyond the capability of a human.