Unmanned Aircraft Systems (UAS) research increasingly requires high-performance, multi-agent simulation environments that integrate realistic dynamics with immersive visualization. This paper introduces a distributed co-simulation framework that seamlessly combines the dynamic modeling capabilities of Hopsan with the advanced rendering and interaction tools of Unreal Engine 5.3. Communication between the two platforms is achieved through a lightweight, User Datagram Protocol (UDP) based plugin, which supports bi-directional real-time data exchange and is complemented by a USB Raw Input plugin to integrate human-in-the-loop joystick control. The proposed framework was validated across progressively complex scenarios. First, a single F‑16 aircraft data model was imported from Hopsan, encompassing waypoint-guided dynamics, atmospheric effects, actuation, and geoid-based altitude calibration. Its state reinforced by Hopsan was visualized in Unreal in real-time. Second, the platform was extended to support two independent F‑16 agents, each communicating via dedicated UDP ports, thereby demonstrating modular, scalable, multi-agent operation. Third, we introduced a Human Machine Interface (HMI) scenario, where one aircraft was piloted manually via joystick input, while the second autonomously followed the same waypoint sequence. This validated the framework’s capacity to handle human interaction in a multi-agent context. Results evidence synchronized simulation at real-time performance, accurate environmental interactions (terrain, wind, collision), and reliable human-in-the-loop control. The framework’s architecture promotes modularity, scalability, and deployment flexibility across multiple machines. Future enhancements will explore tighter coupling with Unreal’s environmental physics, adoption of fluid dynamics, and scaling to larger agent ensembles. By integrating open-source dynamical modeling with high-fidelity graphical simulation, this platform offers a robust foundation for UAS mission planning, operator training, and AI-driven control validation.

This concept paper focuses on integrating the Hydraulic Infinite Linear Actuator - Multi Rod (HILA-MR) technology into Variable Camber Continuous Trailing Edge Flap (VCCTEF) systems for Blended Wing Body (BWB) aircraft. HILA-MR is a novel actuator design enabling precise control of multiple flap segments in a VCCTEF via shared pistons. The HILA-MR/VCCTEF system demonstrates significantly lower power requirements compared to traditional simple flap systems and promotes a more optimal use of force and speed potential of hydraulic actuation throughout the flight envelope, reducing power requirements and contributing to overall efficiency and lower fuel burn. The system offers potential for drag reduction, improved efficiency, and reduced weight and volume for actuation and secondary power components in BWB aircraft. The aim of this paper is to give a comprehensive presentation of the HILA-MR system in a VCCTEF application, evaluate different aspects, design issues and benchmark the technology against present solutions.   

In the realm of model verification, ensuring traceability and repeatability is paramount for achieving Modeling & Simulation (M&S) credibility and development efficiently. This paper explores challenges and solutions for expressing complex model Verification & Validation (V&V) workflows that ensures readability while still enables automation, parallelization and advanced customization of verification activities. This paper contributes a practical solution that is evaluated through a detailed implementation within an in-house developed multipurpose automation and Verification & Validation (V&V) framework.

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This chapter proposes a reflection on the cognitive process used by designers when building a solution during design activities. The assumption is that diversity in cognition is critical to the design process. As a result, multisensory, or multimodal experiences should be incorporated into design teams more so than interdisciplinary ones. These claims are founded on a body of research that describes how designers solve problems and how our minds interpret the outside environment via sensory perception. It is important to consider the cognitive requirements of multisensory experiences in the conceptual phase of design. It is a challenge to move forward the knowledge in the design field by including people with diverse cognitive perspectives not to test a solution, but during the design process that creates an entire new system. The only way to achieve that is to embrace diversity in design schools together with the way design education is delivered.

System of Systems Engineering (SoSE) studies how independent constituent sys-tems interact to generate emergent capabilities. However, it is challenging linking the studyoperations and SoSE to software implementation and agent-based simulations. This paperconsiders communication as the primary architectural concern and examines how softwarearchitectures can improve SoS modelling and simulation. In particular, by relating soft-ware architectures to Observe–Orient–Decide–Act (OODA) loops, which are a promisingstructure for aligning communication, decision logic, and agent interactions with modularand traceable agent architectures. The goal of this work is to explore how communicationwithin SoS architectures can be better structured, represented, and analysed through soft-ware engineering and agent-based modelling frameworks in the context of hierarchical andcooperative operational environments, where information exchange directly influences sys-tem performance and emergent outcomes.

Agent-Based Modelling and Simulation (ABMS) is widely used in System-of-Systems (SoS) studies to represent constituent systems operating in dynamic environments.However, the absence of standardised agent architectures hinders scalability, behaviour trace-ability, and comparative evaluation of emergence, coordination, and operational perfor-mance. This work introduces a modular agent design based on Observe–Orient–Decide–Act(OODA) loops as a framework for SoS simulations, enabling transparent information flow andArtificial Intelligence (AI) integration at the decision layer. A wildfire suppression scenariois used for the study, modelling firefighting crews, aircraft, helicopters, bulldozers, and anincident commander as OODA-driven agents. Results show that OODA-based agents exhibitcoherent and traceable team behaviours, adaptive task switching, and communication-drivencoordination, supporting the study of emergence and interoperability in SoS operations. TheDecision stage is designed for future improvements in the form of learning-based AI and LargeLanguage Models to enhance autonomy and operational fidelity.

This article proposes a method for improved model verification within Large-Scale Simulators (LSS). The approach relies on machine-interoperable traceability of model verification information, such as model Operational Domains (ODs). This enables automated evaluation of model relevance and facilitates the combination of models for a broader evaluation of credible simulation results. The paper introduces a proof-of-concept testbed for verification of black-box models against model requirements. Furthermore, the results also include a proposal for a machine-readable format to capture model requirement Verification & Validation (V&V) results, along with the resulting model and updated model OD information.