Fraud conducted through chat applications has grown into a serious global threat affecting millions of people each year. Most detection systems still classify each message in isolation rather than reasoning over the conversation as a whole, which limits their ability to recognise the gradual manipulation that characterises social-engineering fraud. This paper presents StageGuard, a hierarchical deep-learning architecture that models the full trajectory of a fraudulent conversation. The framework builds on the Manipulation Stage Progression Model (MSPM), a five-stage taxonomy grounded in Cialdini's principles of psychological influence [4], in which offenders build trust gradually across turns before exploiting it. On this basis we define the Stage Transition Anomaly Score (STAS), a single-valued, human-readable feature that quantifies how abruptly a conversation moves between stages and serves as an interpretable indicator of manipulative intent. BERT-base-uncased encodes each turn, and the resulting turn embeddings are processed by a bidirectional LSTM [6], [19] with Bahdanau attention [7] that captures temporal dependencies across the conversation. The attention context vector is fused with the STAS and urgency features before a multi-layer classifier produces the fraud decision, and training uses a multi-task objective that jointly optimises fraud detection and per-turn stage classification. Under a leakage-free protocol in which messages are partitioned into disjoint train, validation and test pools before conversations are constructed, and on a realistic task in which both classes contain spam so that content alone is insufficient, StageGuard attains an F1-macro of 0.9919 and an ROC-AUC of 0.9998. The hierarchical design substantially outperforms flat baselines that treat the conversation as a single text (F1 up to 0.952); ablation and McNemar tests indicate that STAS does not change accuracy significantly, and its value is instead the per-turn interpretability it provides for human operators. We further report bootstrap confidence intervals, five-fold cross-validation and an external-domain evaluation that exposes a sharp generalisation gap, which we discuss candidly as a limitation.
This paper presents a technical and OSINT-based analysis of the German 8.8 cm FlaK (Flugzeugabwehrkanone) family of weapons, integrating engineering examination of all major weapon subsystems with a methodological demonstration of open-source intelligence techniques for historical weapons research. The study covers the complete weapon architecture - including the barrel and liner system, breech mechanism, recoil and recuperator assemblies, cruciform mount, and the layered optical fire-control hierarchy - as well as the ammunition family for the FlaK system. Analytical sections address terminal ballistics of both high-explosive (HE) and armor-piercing (AP) ammunition. A five-stage OSINT research workflow (scoping, discovery, verification, preservation, and synthesis) structures the entire investigation. All technical claims are traced to primary sources, principally TM E9-369A (1943), TM 9-1985-3 (1953), OP 1666 (1946), and BRL Report No. 517 (1944), supplemented by official museum records, Nuremberg trial transcripts, corporate histories, and Bundesarchiv archival holdings. The study concludes that the FlaK’s operational supremacy was a systems-engineering achievement, and that disciplined OSINT methodology, when combined with quantitative ballistic analysis, substantially strengthens technical-historical weapons research.
Quantum communication and machine learning convergence is one of the promising directions in the development of communication systems that could be secure, efficient, and scalable. The modern communication networks employ quantum communication technologies, such as quantum key distribution (QKD), entangement, and teleportation, that can ensure high security and allow the transmission of reliable data. Combined with machine learning, these technologies can be used to process data better, provide better security, and perform better in many applications, including the Internet of Things (IoT), intelligent communication networks, health care, and artificial intelligence. This paper will provide a review of the key principles of quantum communication and the application of machine learning in communication systems, and how the two concepts can be applied to advantage. Besides that, the paper discusses critical technical problems, such as scalability, system integration, and the unavailability of standardization, that constrain the present application of hybrid quantum-classical systems. In addition, the future research directions are mentioned, specifically quantum-enhanced federated learning and the construction of a quantum internet. In general, this paper offers an analytical and systematic review of the field, highlighting the existing challenges, as well as the opportunities that may be explored in the future to establish next-generation intelligent communication systems.
This article presents a comprehensive simulation framework for performance analysis of hybrid monitoring and control systems in power grids. The model integrates the dynamics of the power grid and the communication network to deal with how these two interact and the effects that arise due to the interactions between them. The framework is demonstrated on a 15-node MATLAB-based simulation of generation stations, substations, industrial and residential loads, and renewable energy sources, with a real-world topology. The simulation incorporates profiles of daily loads, renewable generation profiles, and various forms of system disturbances, and a distributed control system that considers the delay of communication and the loss of packets. Verify the framework with simulations of voltage drops, load surges, generation losses, and cyber-attacks, and examine their impact on system performance based on the Voltage Quality Index, Frequency Quality Index, and Communication Reliability Index. Findings show that communication performance contributes greatly to control performance, particularly when there is a cyber event characterized by high voltage-quality degradation. The suggested framework will offer a guide that a smart grid planner will use to determine resilience and develop the respective strategies, optimize communication infrastructure, and continue to introduce renewable energy into future power systems.
This paper presents an analysis of the James Webb Space Telescope (JWST) Optical Telescope Element (OTE) based on its published optical prescription and system architecture. Using modeling, key optical performance parameters were evaluated, including spot diagrams, footprint diagrams, wavefront error (WFE), point spread function (PSF), Strehl ratio, encircled energy (EE), and modulation transfer function (MTF). The segmented 6.6 m primary mirror was modeled using a User-Defined Aperture (UDA) approach to accurately reproduce the hexagonal pupil geometry and diffraction effects. Results confirm diffraction-limited performance across the evaluated field, with RMS wavefront error well below the Maréchal criterion, high Strehl ratios (≥0.87 off-axis and ≥0.97 on-axis at 1 µm), and encircled energy closely matching the diffraction limit. MTF curves demonstrate strong contrast transfer up to the cutoff spatial frequency with minimal field-dependent degradation. Additional analyses were performed to assess the impact of individual segment piston and tilt misalignments. Controlled perturbations show that segment-level phase discontinuities significantly degrade WFE, emphasizing the importance of precise segment phasing and wavefront sensing and control (WFS&C).
The traditional black box, or Flight Data Recorder (FDR) and Cockpit Voice Recorder (CVR), has been instrumental in post-crash investigations. Its limitations, such as data loss, difficulty in retrieval from remote or underwater crash sites, and the inability to provide real-time insights, prompted the need for technological advancements. This paper aims to explore the integration of cutting-edge technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Satellite Communication, GPS, and advanced telemetry systems to revolutionize black box functionality and enhance aviation safety. Next-generation black boxes can leverage real-time encrypted data transmission through satellite connectivity, ensuring immediate access to crucial flight parameters, even before an accident occurs. The incorporation of GPS enables precise aircraft tracking, while telemetry techniques allow monitoring of critical flight parameters, engine health, and environmental conditions. Additionally, the use of underwater acoustic beacons and buoyant ejection modules can expedite black box recovery in case of oceanic crashes, reducing search and rescue operation time. AI-driven predictive analytics further strengthen aircraft monitoring by detecting anomalies and potential system failures, enabling preemptive measures to prevent disasters. The integration of IoT allows seamless connectivity between onboard sensors and ground control stations, ensuring that aviation authorities receive real-time alerts regarding abnormal flight behavior or malfunctions. Moreover, cloud-based data storage ensures redundancy, eliminating the risks of data loss due to hardware damage. By implementing IoT-enabled black boxes, the aviation industry can significantly reduce the risk of flight disappearance, improve accident investigations, and enhance proactive safety measures. The ability to access real-time flight data enhances situational awareness, minimizes investigation delays, and facilitates faster decision-making during in-flight emergencies. This technological evolution in flight data recording and transmission marks a significant step toward a safer, more efficient, and more transparent aviation ecosystem.
Artificial Intelligence is an emerging, transforming, and revolutionizing technology of the 21st century. It has wide-ranging applications from education and healthcare to defense and security. It is rapidly transforming the modern defense system, making it faster, smarter, and more efficient. The ethics regarding the use of AI are in turmoil, but global powers would not wait for them to become established principles, as they find it imperative to maximize their power and capability in every sector, especially defense, where, from automated drones to real-time threat situations, AI is redefining warfare, national security, and defense strategies. National Defense strategy 2022 of the United States is one such strategy that entails within it the concept of integrated deterrence to use all means at disposal with the cooperation of allies and create a defense infrastructure with advanced and effective Artificial Intelligence. Followed by Data analytics and adoptive strategy 2023 that outlines the ways in which Artificial intelligence will be incorporated to answer the operational needs and prepare United States of America for the Future Advanced Warfare.
This study aims to automate threat assessment and target assignment processes in air defense systems using a dynamic, learning artificial intelligence-based model. Unlike threat assessment studies in the literature that use different criteria and methods, this study integrates missing data completion, multi-criteria analysis, and artificial neural networks to dynamically update the threat score. Furthermore, unlike studies in the literature, the number of criteria used has been increased to enable the model to provide a broader perspective. Most studies are static and use a small number of criteria; this study presents a dynamic, multi-criteria model that can handle incomplete data. The developed Geometric Threat Score proposes an average perspective for threat assessment, which varies depending on individuals and geographical conditions. The model generates threat scores using criterion data obtained from radars and sensors and can respond adaptively to changing conditions. The results achieved demonstrated high performance with mean square errors (MSE) of 0.0005–0.0072 and a correlation coefficient (R) above 95%. This approach accelerates decision support processes in air defense systems, reducing human influence and increasing system effectiveness.
A split-ring integrated hybrid fractal antenna has been designed with improved bandwidth from 3 GHz to 9.56 GHz and is presented for WiFi/WiMAX/WLAN/5G midband applications. The proposed antenna is planned to deliver an extended bandwidth and is consists of split-ring resonators integrated with a fractal-shaped patch. The Fractal Shape in the antenna provides a compact size due to its self-similarity, space-filling property, and additional split-ring resonators to improve the bandwidth. The antenna, compact in size with dimensions of 31×37mm2 and a thickness of 1.8mm, is constructed upon an FR4 epoxy substrate showing a dielectric constant of 4.4. The proposed antenna performance is presented with the help of reflection coefficient and VSWR plots, gain, field distribution, and radiation pattern. The comparison of simulated and measured analysis and results showed good agreement, indicating that the design antenna is suitable for WiFi, WiMAX, WLAN, and 5G applications.
The aerospace manufacturing industry is facing increasing pressure to align with the United Nations' Sustainable Development Goals, such as responsible production, climate action, and industry innovation. Industry 4.0 notwithstanding, most of these technologies are concerned with efficiency and automation while paying little heed to human-centric, sustainable goals that can be achieved in line with the SDGs. This would present a possibility to bridge this gap with Industry 5.0, which is on the way to collaborating with human-machine interaction and sustainability practices. This paper discusses the possibility of how this new paradigm, focused on collaborative work between advanced automation and human-centric, sustainable production, can help aerospace manufacturers achieve those key Sustainable Development Goals. Industry 5.0 will provide an opportunity for a symbiotic partnership between humans and machines with avenues that open opportunities to reduce environmental impacts, optimize the use of resources, and enhance innovation in aerospace manufacturing processes. The research illustrates the implementation strategy of Industry 5.0, smart factory, robotics, digital twin, and artificial intelligence towards the achievement of SDG 8, SDG 9, SDG 12, and SDG 13.
This study presents the detection and characterization of the exoplanet WASP-10b using differential photometry performed with amateur astronomical equipment. An overview of exoplanet properties, detection techniques, and the transit method is provided, followed by a description of the observational setup, recording procedures, calibration, and data processing workflow. More than one hundred CCD images were acquired using an 8-inch Schmidt–Cassegrain telescope and a monochrome Atik 383L+ camera, with additional calibration frames to correct instrumental noise and vignetting. Differential photometry was carried out using AstroImageJ, enabling precise extraction of the stellar flux of WASP-10 relative to nearby reference stars. The transit light curve was modeled using the Mandel & Agol analytic formalism, from which key planetary parameters were estimated. Results yield a planetary radius of approximately 1.12 RJ and an orbital inclination of 89°, both in good agreement with published values. The study demonstrates that high-precision exoplanet transit measurements are achievable with advanced amateur-level equipment, offering valuable scientific and educational contributions to exoplanetary research.
The vulnerability analysis of a critical infrastructure is certainly a complex and articulated study; therefore, it requires the use of reliable decision support tools. In this article, after a brief review of the state of the art on risk analysis studies for critical infrastructures, a vulnerability analysis will be performed on a railway infrastructure under the hypothesis of an intentional attack by a criminal group. Specifically, the aim of this paper is to show how the application of the event tree technique - derived from the more well-known fault tree analysis - can allow to define the different scenarios. For each one, a probabilistic assessment will be carried out through numerical simulations with the Monte Carlo method in order to calculate the security level of this infrastructure.
Ballistics, the science of projectile motion, encompasses the study of objects such as bullets, missiles, and rockets from launch to impact, divided into internal, external, terminal, and forensic ballistics. This paper explores recent advancements in ballistic materials, methods, and sustainability challenges. Traditional and self-healing materials, including microcapsule-based, bio-inspired, and metallic self-repairing systems, are examined for their applications in armor, projectiles, and thermal protection. Experimental techniques like light gas guns and high-speed photography, alongside numerical simulations such as finite element analysis (FEA) and smoothed particle hydrodynamics (SPH), are compared for their efficacy in ballistic research. High-velocity projectiles exceeding Mach 5, including hypersonic and kinetic energy penetrators, are analyzed for their aerodynamic and material challenges, with future directions pointing toward AI-guided systems and 3D-printed materials. The study also highlights green ammunition innovations, such as lead-free bullets and biodegradable cartridges, to address environmental concerns like toxic propellants and heavy metal contamination. Sustainability efforts focus on resource efficiency, including recycled composites and additive manufacturing, while military and civilian applications explore hypersonic swarms and non-lethal munitions. The paper concludes with future perspectives, emphasizing digital twins in forensics, space ballistics, and closed-loop ammunition recycling. By integrating experimental and computational approaches, this research aims to advance ballistic technologies while addressing ecological and ethical challenges in the field.
The integration of Micro, Small, and Medium Enterprises (MSMEs), particularly start-ups, into India's defense sector is emerging as a transformative force in modernizing the country's military capabilities. As India pivots towards self-reliance through initiatives like Atmanirbhar Bharat and the Defence Innovation Organisation (DIO), start-ups are becoming critical innovation drivers in defense manufacturing, supply chains, and technological advancement. This paper investigates the current and potential convergence of start-ups and MSMEs with Indian defense needs, analyzing policy frameworks, innovation hubs, funding ecosystems, and dual-use technologies. A mixed-methods approach comprising policy analysis, case studies (e.g., Tonbo Imaging, IdeaForge), and primary interviews with defense ecosystem stakeholders is employed. The findings suggest a positive correlation between MSME innovation intensity and their integration into strategic defense functions. However, structural bottlenecks such as procurement delays, intellectual property risks, and lack of sustained funding restrict their scaling. The study proposes a techno-policy roadmap to deepen civil-military-industrial integration through innovation clusters, regulatory sandboxing, and joint development programs. The outcomes offer a blueprint for leveraging entrepreneurial vigor to meet national security imperatives while fostering indigenous defense technology capabilities.
This study will evaluate the transition from conventional combat methods to a technology-driven battlefield. In particular, the Lebanon-Israel conflict serves a crucial case study to analyze how digital tools and AI reshape battlefield strategies, operational efficiency, and psychological warfare, triggering a broader evolution in modern military doctrine. This study will tackle a longitudinal comparison of digital strategy evolution in a single dyadic conflict zone. It adds to fields of security studies, cyberwarfare strategy, and AI-driven conflict analysis by analyzing how asymmetric technological adoption constructs long-term power dynamics. Through a theoretical lens of realism and a complementary military geopolitical framework, this research will analyze the impact of cyber warfare, AI-driven decision-making, intelligence, and precision missile systems on military strategies, political decision-making, and regional security.
This review examines the design principles and image quality performance of the three primary types of amateur astronomical telescopes: refractors, reflectors, and catadioptrics. The study begins with a thorough overview of existing literature, referencing over 150 sources to provide a theoretical introduction for the analysis. Each telescope type is evaluated in terms of its optical construction, aberration control, portability, maintenance, and suitability for both visual observation and astrophotography. Key differences and trade-offs in performance, usability, and cost are discussed. Special attention is given to how each design handles chromatic and spherical aberrations, field curvature, and diffraction effects -factors critical to image sharpness and contrast. The paper concludes with practical recommendations tailored to various user needs, such as planetary observation, deep-sky imaging, or beginner-level stargazing. By synthesizing theoretical insights with practical considerations, this review aims to guide amateur astronomers, educators, and enthusiasts in selecting the most appropriate telescope type for their specific interests and observing goals.
Supply Chain Management (SCM) in the Indian defense sector represents a strategic function that supports operational readiness and national preparedness. This study undertakes a qualitative analysis based solely on secondary data sources to examine key dimensions of defense logistics operations. The research draws from government audit reports, policy documents, defense procurement reviews, and institutional publications to synthesize insights into current SCM practices. The analysis identifies several areas of interest, including inventory visibility, supply alignment, infrastructure utilization, and inter-agency coordination. Observations indicate that existing logistical frameworks, shaped by well-established protocols and multi-tiered structures, are undergoing transformation with increased emphasis on integration, automation, and data-driven decision-making. The study also reflects on the evolving role of digital technologies and policy reforms in enabling more responsive and cohesive supply chain mechanisms. Recommendations emerging from the review include the implementation of enterprise resource planning (ERP) systems, application of predictive analytics for supply planning, and the formation of integrated logistics command structures. Additionally, the importance of organizational adaptation and capacity-building is emphasized to ensure readiness for future operational demands. The findings suggest that continuous improvement in defense SCM can be facilitated through structural realignments, informed policy direction, and the strategic adoption of modern technological solutions.
This review explores the security challenges and solutions associated with Wireless Sensor Networks (WSNs). It provides a comprehensive analysis of common security threats, including various types of attacks and their potential impacts on WSNs. The paper discusses critical security requirements such as data integrity, authentication, and secure communication, and examines defensive measures to mitigate these threats. Additionally, the review addresses trust management within WSNs, highlighting the importance of establishing reliable and secure networks. Emerging trends such as the integration of artificial intelligence, blockchain technology, quantum cryptography, edge computing, and secure multi-party computation are also discussed, showcasing the latest advancements aimed at enhancing WSN security. The findings underscore the necessity for ongoing research and development to improve the security and efficiency of WSNs, ensuring their viability for diverse applications.
This research paper focuses on the development of an innovative system applying the UGV (Unmanned Ground Vehicle) modular platform in the fields of humanitarian demining, where the scientific research process demonstrates the multifunctional application of such systems, or similar ones, in civil protection sectors. The use of the UGV platform enables more efficient mission completion in scenarios where human lives are directly at risk, such as demining operations. The UGV platform, with its modular design and development approach, represents a modern method for the advancement of such systems, offering multi-operation and multifunctional applications.
The demand for solid propellant rocket motors does not seem to fade, regardless of constant improvements in rocket technology. To make modifications that will result in improved design, it is crucial to use already existing motor models. Setting internal ballistic requirements and conducting calculations by following the design sequence, results in defining pressure- and thrust-time curves that are needed for rocket motor performance evaluation and providing a base for further external ballistic analysis. Calculation results are obtained by using the program SPPMEF, which is divided into modules for different parts of internal ballistic analysis using equations and shortens the time needed for theoretical integrations. NGR-176 is a type of double-base solid rocket propellant selected for the conceptual design of 107 mm rocket motor. The selected propellant configuration is a star with adapted and optimized geometric variables and new structural materials have been assigned to the newly-sized nozzle and motor case through examining various parameters.
