## 1 Introduction  
The occurrence of impacts from random objects, such as ice particles and debris, presents a considerable obstacle in maintaining the structural integrity of composite components for aircraft systems. Conventionally, C-Scan inspections have been utilized for the appraisal of impact-induced damage [1; 2; 3]. However, this approach is prone to human inaccuracies, is time-consuming, and does not provide real-time results, as assessments can only be conducted between flight operations. The emergence of new sensor technologies and advanced data processing methods enables the deployment of structural health monitoring (SHM) systems [4; 5; 6; 7; 8; 9]. Some SHM systems have the capacity to autonomously identify and evaluate impact damage while continuously monitoring aircraft components during flight operations [10; 11; 12]. As a result, reliance on labor-intensive and error-susceptible visual examinations may be diminished. The automated detection and localization of impact damage stemming from debris and hail through the incorporation of SHM systems offer a more timely, accurate, and efficient evaluation approach. In recent years, various methods have been applied for SHM in aircraft structures. PZT sensors and vibration analysis are widely used due to their sensitivity to a broad frequency range and their ability to detect different types of damage [13]. Ultrasonic testing and guided wave techniques are also utilized due to their ability to inspect large areas of a structure [14]. However, it is noteworthy that each of these methods has its inherent limitations. For instance, the deployment of PZT sensors and the implementation of vibration analysis may necessitate intricate signal processing and interpretation, which may impose substantial complexity. Ultrasonic testing often demands direct contact with the structure, a requirement that may not be practicable for aircraft in service, thereby restricting its usability in these contexts. Acoustic emission (AE) monitoring is one SHM method worth investigating as it is a passive technique, detecting the energy released by the structure itself when it undergoes a change (e.g., impact or cracking) [15; 16]. This allows for continuous, real-time monitoring without the need for external excitation. AE sensors can be mounted on the surface of the structure, making it a noninvasive and nondestructive method. Furthermore, AE has shown high sensitivity to various types of damage and can provide information about the damage's location and severity [17; 18; 19; 20]. Several studies have explored the use of AE in monitoring damage to composites [21; 22; 23; 24; 25; 26]. [PERSON] et al. [23] utilized AE to identify the type of damage in carbon fiber-reinforced polymer (CFRP) samples by decomposing the AE signal using a Wavelet Packet Transform (WPT). Results showed that the signals could be categorized based on the type of damage mechanism. Similarly, [PERSON] et al. [24] applied AE to unidirectional (UD) CFRP tendons and used Hilbert marginal energy spectrum (HMES) and instantaneous energy spectrum (IES) to identify damage patterns and evolution during tensioning. [PERSON] et al. [25] studied the application of AE to assess the low-velocity impact (LVI) on fiber composites by applying AE to graphite-epoxy composite plates. Results showed that impact loads could be identified from the AE signals, and delamination could be assessed by examining the waveform of the recorded signal. [PERSON] et al. [26] placed four AE sensors on CFRP specimens and conducted impact experiments. The AE waveforms and their corresponding frequency domain spectrum were analyzed to distinguish between different AE signatures, enabling the determination of whether the structure had been damaged due to impact events. While the use of AE in monitoring the impact on fiber composite materials has shown promise, traditional methods of analyzing AE signals are often challenging, especially for complex datasets, and require manual analysis based on expertise.