The rapid growth of small Unmanned Aerial Systems (sUAS) for civil and commercial missions has intensified concerns about their resilience to cyber-security threats. Operating within the emerging UAS Traffic Management (UTM) framework, these lightweight and highly networked platforms depend on secure communication, navigation, and surveillance (CNS) subsystems that are vulnerable to spoofing, jamming, hijacking, and data manipulation. While prior reviews of UAS security addressed these challenges at a conceptual level, a detailed, system-oriented analysis for resource-constrained sUAS remains lacking. This paper presents a comprehensive survey of cyber-security vulnerabilities and defenses tailored to the sUAS and UTM ecosystem. We organize existing research across the full cyber-physical stack, encompassing CNS, data links, sensing and perception, UTM cloud access, and software integrity layers, and classify attack vectors according to their technical targets and operational impacts. Correspondingly, we review defense mechanisms ranging from classical encryption and authentication to adaptive intrusion detection, lightweight cryptography, and secure firmware management. By mapping threats to mitigation strategies and evaluating their scalability and practical effectiveness, this work establishes a unified taxonomy and identifies open challenges for achieving safe, secure, and scalable sUAS operations within future UTM environments.
The integration of Artificial Intelligence (AI) into software testing has emerged as a promising approach to addressing the limitations of conventional methods, particularly in safety-critical domains such as automotive and aerospace. Traditional software testing is often manual, resource-intensive, and prone to incomplete coverage. In contrast, AI-based techniques have been applied to automate test case generation, optimise testing processes, and improve defect prediction. This study extends these approaches to the context of Embodied AI, where the reliability of systems that perceive and act within complex environments is a primary concern. Autonomous satellites are considered as representative embodied agents, for which the robustness of the perception–action loop is mission-critical. The paper presents two main contributions. First, it reviews the state of the art in the application of AI techniques to software testing. Second, it demonstrates the practical viability of these techniques through a proof of concept (PoC) in the satellite domain. In this PoC, generative AI is used to create synthetic datasets simulating adverse atmospheric conditions, including varying levels of cloud opacity. These datasets are used to evaluate and improve an on-board object detection model. Experimental results indicate that this approach reveals limitations of the baseline model and, following retraining, enhances robustness under challenging conditions while maintaining performance in clear scenarios. The findings demonstrate the potential of AI-based testing methodologies to support the validation and improvement of mission-critical embodied systems.
Polymeric optical materials such as Cyclo Olefin Polymer (COP) are adopted in aerospace lighting systems due to their excellent optical clarity, dimensional stability, moldability and weight saving advantages over glass. However, their relatively low toughness and the presence of residual molding stress make them prone to crack initiation during mechanical fastening. During its installation, crack formation was consistently observed around self-tapping screw interfaces, raising concerns over reliability, maintainability, and compliance with durability requirements. A structured Design of Experiments (DOE) was performed to identify root causes and evaluate potential mitigation methods. The investigation revealed that residual stresses in the COP material, combined with localized stress concentrations during screw tightening, were the primary drivers of crack initiation. Two complementary process improvements were identified and validated as part of mitigation plan: (i) annealing of the optics prior to assembly to relieve internal stress, and (ii) using step-torquing method to fasten the screws, to gradually distribute applied loads and reduce localized stress peaks. Post-assembly observation over three days confirmed a significant reduction in crack initiation. The combined annealing and step-torquing approach demonstrated a substantial reduction in crack generation probability, providing a practical and repeatable process for enhancing the robustness of polymeric optic assemblies. This work contributes a generalizable methodology for mitigating assembly-induced failures in advanced polymer materials and supports broader adoption of lightweight, high-performance optics in aerospace applications.
Polypropylene, a commodity plastic, is the semi-crystalline thermoplastics widely used in high volume for general purpose application. Polypropylene is the macro molecules of soft and weak backbone, which by reinforcement of fillers in different forms such as fiber, spheroids, nanotubes, flakes, etc., can influence its mechanical, thermal, electrical, creep resistance, and flame resistance properties for use in aerospace applications. Currently, polycarbonate and nylon plastics are used in aerospace applications, however, they are expensive compared with polypropylene. In this thesis, efforts are put to study the effect of reinforcement fillers in the properties of polypropylene composite, primarily the mechanical and flammability properties. The matrix element, polypropylene co polymer and reprocessed polypropylene blended in equal ratio, are coupled with the dispersing phases such as graphene, mica, fumed silica, and polydimethylsiloxane polymer. Effect of graphene as reinforcing filler at different weight % to polypropylene composite’s properties are studied and compared with that of the neat polypropylene. Effect of coupling agent, Aminopropyltriethoxysilane (APTES), on mineral fillers and Polydimethylsiloxane polymer (PDMS) used for crosslinking with the polypropylene matrix is also studied and compared using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM) techniques.
As we often refer to term “System of Systems” (SoS) which encompasses multiple independent systems, similarly there of multiple Large and complex systems, such as aircraft which is made up of multiple intricate subsystems. Within an organization and/or across organizations, various development teams work on these subsystems, and each subsystem typically has its own product line to support different OEM requirements. However, the final decision on which Subsystem Configurations will be used is made at a higher system level. This creates a challenge where multiple product lines must be managed together, ensuring compatibility, consistency, and integration across the entire system, the traditional Product Line Engineering (PLE) approach is effective for managing configurations within an individual subsystem. However, when several subsystem product lines must work together within a broader system context, traditional PLE approaches fall short. Misalignment in interfaces, redundant functionalities, or inconsistent configuration decisions across teams can introduce integration delays and increase system complexity. Therefore, a new approach is needed to manage the collective behavior and coordination of these interconnected product lines. This paper explores these challenges and proposes a Product Line of Product Lines (PoPL) approach to systematically manage and coordinate multiple interconnected product lines in large-scale aerospace system development.
