Low Earth Orbit (LEO) satellite networks serve as a cornerstone infrastructure for providing ubiquitous connectivity in areas where terrestrial infrastructure is unavailable. With the emergence of Direct-to-Cell (DTC) satellites, these networks can provide direct access to mobile phones and IoT devices without relying on terrestrial base stations, leading to a surge in massive connectivity demands for the serving satellite. To address this issue, group communication is an effective paradigm that enables simultaneous content delivery to multiple users and thus optimizes bandwidth reuse. Although extensive research has been conducted to improve group communication performance, securing this communication without compromising its inherent spectrum efficiency remains a critical challenge. To address this, we introduce StarCast, a secure group encryption scheme for LEO satellite networks. Our solution leverages ciphertext-policy attribute-based encryption (CP-ABE) to implement fine-grained access control by embedding access policies directly within the ciphertext. Unlike standard secure communication approaches that require dedicated per-user channels and significantly deplete limited satellite spectrum resources, StarCast maintains efficient spectrum reuse within user groups while ensuring that only authorized users can access transmitted data. Additionally, it significantly reduces the costly key management overhead associated with conventional encryption schemes.

This paper presents a novel image guide crossover design leveraging the uniaxial anisotropic characteristics of subwavelength dielectric gratings for millimeter-wave (mmWave) applications. The structure moreover, exploits the engineered polarization differences of the dominant mode in orthogonal channels for the designed image guides, which employ anisotropic features to achieve a compact, high-isolation, and wideband crossover. Results with optimized parameters demonstrate competitive performances for the compact structure, including an ultra-wide 30 dB isolation bandwidth from 22 GHz to over 50 GHz, insertion loss values around 0.5 dB, and group delay variations within 0.04 ns. To the best of the authors' knowledge, this is the first design achieving such performances for a mmWave crossover. Additionally, the proposed structure and design methodology is versatile, facilitating adaptation to other frequency ranges and different waveguide technologies. A fabricated prototype also shows agreement with analytical studies using the finite difference frequency domain (FDFD) method and full-wave simulations completed by a commercial solver. All in all, the combination of high isolation, low insertion loss, compact size, minimal group delay variations, and wide bandwidth operation positions the design as a promising solution for mmWave crossover circuit systems and other high density transmission line scenarios.

Quantum Neuromorphic Intelligence Analysis (QNIA) represents the convergence of quantum computing and neuromorphic computing to revolutionize predictive analytics in complex geopolitical events, terrorism threat assessment, and warfare strategy formulation. QNIA leverages the exponential computational capabilities of quantum computing employing algorithms such as Grover's and Shor's to perform rapid, high-dimensional data analysis while concurrently harnessing the adaptive, energy-efficient processing of neuromorphic architectures that mimic biological neural networks. This synergistic integration facilitates the development of advanced quantum-assisted deep learning models capable of detecting subtle anomalies and forecasting conflicts with unprecedented accuracy. This paper investigates the potential of QNIA to transform intelligence gathering and military decision-making processes in real time. It details how quantum-enhanced machine learning algorithms, when combined with neuromorphic processors, can process vast streams of global intelligence data, enabling the prediction of diplomatic tensions, economic warfare indicators, and cyber-attack patterns. By incorporating probabilistic models, QNIA quantitatively assesses QNIA A Preprint threat levels, optimizes resource allocation, and supports the formulation of proactive military strategies. Moreover, the study explores the ethical and strategic implications of deploying such integrated systems in high-stakes environments, emphasizing both their potential to enhance security measures and the risks associated with autonomous decision-making. By synthesizing theoretical frameworks from quantum mechanics, neuromorphic engineering, and artificial intelligence and incorporating insights from quantum machine learning (Biamonte et al., 2017) and recent evaluations of brain-inspired computing (Mehonic & Kenyon, 2020) this research provides a comprehensive overview of QNIA's capabilities and its transformative impact on modern defense and intelligence strategies. The convergence of these emerging technologies signifies a pivotal shift toward more agile, data-driven, and anticipatory security solutions, setting the stage for a new era in predictive geopolitical analysis and military strategy.

Digital prototypes of a ground-effect aircraft use an upper-surface trailing-section ducted fan to reduce both drag and rolling losses of trucks and railcars with exceptionally good lift-drag ratio (L/D) efficiencies for multimodal rail-highway clearances of 1-5 cm. Extending the multimodality to include flight over water would provide unprecedented seamless connectivity and passenger-oriented end-to-end transit with nonstop routing extending from inland to off-shore. The electric vehicles have a design constraint of a core ground-effect flight transit (GEFT) vehicle having a width of 2.5 meters to allow operation on railways and highways. The problem is the rapid decrease of L/D efficiency as clearances approach and exceed ~0.2 meters. This paper evaluates the impact of fences “skiing” on the water and wing extensions to increase L/D efficiency over water. The data identifies a water-corridor market entry position of extending rail-highway multimodality to rivers, river crossings, and calmer coast waters. The high L/D efficiency teaches toward electric vehicles. Solar panels would be particularly effective at locations with high solar irradiance.

Understandability plays a significant role in the effective deployment of autonomous robots, as it ensures the robot clearly communicates its decisions, actions, and plans to the human it interacts with. To advance robot understandability, this work proposes a model to structure levels of explanation (LoE) and methods to minimize existing discrepancies between the human's and robot's state of mind. The applicability of the LoE model is demonstrated through two different scenarios, i.e. search and rescue operations and physical training robots. Two Markov process models are utilized to estimate existing discrepancies, a hidden Markov model and a partially observable Markov decision process model. Following these models, robot utterances are generated to minimize these discrepancies using the LoE model. The results show that both models successfully estimate existing discrepancies and can generate appropriate communicative actions for the robot, thereby enhancing its understandability.

Image data augmentation is a fundamental data pre-processing technique within Artificial Intelligence (AI). It comprises various operations in the image space, such as translation, rotation, reflection, and dilation. The watershed algorithm facilitates image transformation, wherein pixel values are determined. These values (n) are subsequently categorized into three distinct groups: (n≤0), (0<n <2), and (n ≥2). Following this transformation, PCA-clustering post-natural disaster building damage levels into three clusters: lightly, moderately, and severely damaged. These clusters correspond to three different value ranges: group 1 (n<0), group 2 (0≤n <2), and group 3 (n≥2) Our experiment revealed two key findings. First, image augmentation can mitigate the weaknesses of the watershed algorithm. Second, variations in image sizes do not affect the accuracy of building damage assessment after natural disasters if we utilize the watershed algorithm.

The OWASP Top 10 list highlights SQL Injection as one of the most significant and widespread security risks in web applications. An outline of its design and implementation process is presented in this paper. "SQL Injection Vulnerability Scanner", A specialized tool that is designed to identify and address vulnerabilities within web applications. Through payload injection techniques, the scanner can detect exploitable input points, analyze server responses, and generate detailed vulnerabilities reports. The focus is on the scanner's modular design, which comprises elements for input handling, payload injection, and response analysis. The scanner's performance was tested using both real-life applications and known vulnerabilities such as DVWA and bWAPP. This proved its effectiveness. Its results showed that it can correctly identify weaknesses, classify them in terms of severity and provide actionable advice for fixing them. In this study, it is highlighted that the use of automated tools can significantly improve web application security and that effective coding practices are closely monitored. By detecting and detaching from vulnerabilities, this scanner contributes to the proactive approach of protecting web applications against SQL Injection attacks.

This research delves into the advancements in embedded systems, with a focus on three pivotal areas: IoT Implementation, Real-time computing, and Energy consumption. Embedded systems are now the foundation of most current technology, networked systems, smart gadgets, or even straightforward automation systems. In this paper, the author discusses ways IoT integration has increased the range of applications in embedded systems and enhanced their interaction. It also explores new developments in real-time processing; here, the focus is on the parts played by fast computation and quick response to them, such as medical, automobile, and industrial control. Also, the research raises the issue of the growing relevance of energy efficiency. It focuses on energy-efficient notions, innovative approaches, and hardware solutions aimed at reducing power consumption and sufficient performance. Security issues, system issues in general, and system scalability are also presented, providing a perspective of the complexity of the field. The research results should be helpful for young researchers, developers, and other industry members to understand the current trends that should be continued and the gaps that need to be filled in the future.

Quantum computing has made significant strides, with hardware platforms now advanced enough to tackle the challenges of scaling up to larger and more robust systems. Central to this progression is the interface between quantum processors, comprising isolated qubits, and the classical systems required for their control and readout. As these systems transition from few-qubit devices to fault-tolerant architectures, the complexity of this quantum-classical interaction poses significant engineering challenges. Field-Programmable Gate Arrays (FPGAs) have emerged as an adaptable and efficient solution, offering realtime control, low-latency operations, and seamless integration. This review delves into current FPGA-based platforms, including commercial systems, as well as open-source solutions developed by academic research groups. The architecture, features, and contributions of these platforms to addressing scalability and integration issues are thoroughly examined. Additionally, the review highlights innovations in cryogeniccompatible controllers and explores the potential of Cryo-FPGAs to reduce noise and improve fidelity in quantum systems. By providing a comprehensive analysis of current technologies and identifying opportunities for advancement, this review offers valuable insights for researchers and engineers working to develop scalable quantum computing solutions.

Photoplethysmography (PPG) has been widely used in consumer and medical devices for assessing heart rate blood oxygen and blood pressure levels, together with electrocardiography (ECG). One of the major challenges is recording PPG with fast changing ambient light and motion artefacts that generating a changing baseline of the signal. Light to digital converters (LDCs) proposed in recent years show excellent resolution and power efficiency, however it may get saturated during fast ambient and motion events. Existing chopping ambient light removal and hysteresis DC removal techniques cannot track fast-changing components. Therefore, this paper proposes a current domain level crossing-based technique to assist a dual slope LDC, including a dynamic biased low-power continuous time comparator. The PPG readout frontend is implemented in a 55nm standard CMOS process and consumes 12.4μW-56.6μW depending on the ambient light intensity. It achieves a dynamic range of 102dB and an SNDR of 75dB at AC signal path.

The work reported in this article addresses the issue of reliability assessment from a systemic perspective. The general idea of the paper is based on the observation that failures occur at the level of elementary components but are induced by stresses emanating from the form of use of the system and the environment in which they are placed. Within this scope, a so-called U-Cycle approach is developed. It allows, in a matrix framework, to match the levels of decomposition of a system from a functional viewpoint and the resulting failures from a dysfunctional perspective. The modelling of this U-Cycle thus involves taking into account the repercussions of the systems' mission profiles in order to convert them into levels of stress on the basic components in a functional top-down analysis. Then, in a dysfunctional bottom-up study, it leads to characterise the impact of the failure of these components on the operation of the sub-systems to which they belong. The paper describes the principles of methodological instrumentation of this U-cycle based on the superposition of four descriptive views representing the structural, temporal, physical and logical forms of organisation. A case study based on the qualitative modelling of the reliability of an aircraft electrical flight control function is proposed to illustrate the developments.

This paper presents a novel framework for enhancing transfer learning capabilities in artificial intelligence agents, focusing on the crucial challenge of knowledge generalization across diverse tasks. We introduce the Adaptive Knowledge Transfer Network (AKTN), a hierarchical architecture that enables AI agents to decompose learned behaviors into fundamental cognitive primitives and recombine them for novel task execution. Our research demonstrates significant improvements in cross-domain knowledge application, reducing the learning curve for new tasks by up to 73% compared to traditional approaches.

This paper conducts a novel study of the P-Q coupling characteristics of grid-forming converters (GFM) under current-limiting and internal voltage-limiting in large disturbance scenarios. Initially, a simplified large-signal model is developed using internal voltage magnitude E and phase angle θ as key state variables. This model elucidates the nonlinear coupling between P , Q, E and θ, and examines how current and voltage thresholds affect the existence and accessibility of stable equilibrium points. By analyzing the trajectory evolution of operating points in the (θ, E) plane, the study uncovers the mechanisms and factors causing transient loss of synchronism in GFMs during phase angle jumps. It further details how internal voltage and currentlimiting influence the dynamic critical phase angle for loss of synchronization under the coupling effects of the P-f and Q-E control loops. Additionally, the paper addresses the dual effects of small-signal based P-Q decoupling control under large disturbances: enhancing resilience to minor phase jumps while potentially extending transient recovery. Hardware-in-the-loop tests confirm the theoretical analysis.

In an era where personalized viewing xperiences are key, this new-generation OTT platform introduces a groundbreaking feature: the Personalized "Watch Party" Mode with AI Hosts. This feature allows users to engage in a shared viewing experience tailored to a group's diverse content preferences and moods. The AI Host gathers input from all participants, processing their mood and genre preferences to recommend a movie that suits the group. Additionally, the AI Host enriches the experience by setting the atmosphere and plot before the movie, providing interactive commentaries, and guiding viewers throughout the watch party. Fully voice-enabled, the AI Host offers an immersive, realtime experience akin to a human host, redefining group movie nights with intelligent and personalized interaction.

Ground effect flight is able to seamlessly operate across highway, railway, water, and air corridors; but barriers emerge to on transfer to low-pressure tunnels and maglev. The barriers may be overcome with modes of operation that allow open entry to low pressure tunnels and higher speeds and efficiency with ground effect over rails than possible with maglev. Digital prototypes simulated in 2-Dimensional and 3-Dimensional computational fluid dynamics (CFD) have identified that open-entry low pressure tunnels can operate with seamless entry and exit with existing tunnel infrastructure and at speeds faster than airliners when operating with a favorable tunnel tailwind. Bernoulli Loops using tunnel air flow to transfer air between entry and exit locations are quantified with CFD simulations and create both resilience and degrees of multimodal optimization not possible with isolated tunnels. High-impact aspects of the multimodality include the ground-effect flight transit (GEFT) technology of this work including: a) seamlessness across modes from ranging from inner-city subways to open water, b) unprecedented increases in efficiency due to low drag and substantial elimination of rolling losses, c) higher speeds and higher efficiency than alternatives, d) a deployment strategy where inexpensive vehicles exhibit exceptional performance using existing infrastructure, and e) the rapid deployment of electric railcars using current infrastructure.

With the growing need for accessible information across diverse linguistic backgrounds, text summarization in multilingual contexts has become increasingly essential. Text summarization is a crucial task in natural language processing, particularly in multilingual settings involving Indian languages. This paper presents our approach for the FIRE 2024 task, where we leverage large transformer-­based language models for summarizing Indian languages, addressing the linguistic diversity and frequent instances of code­mixing and script-­mixing unique to this context. Our methodology incorporates both extractive and abstractive summarization techniques, optimized for Indian languages through advanced fine­tuning of models like mT5, IndicBART, and BART. While prompt engineering has predominantly been applied to English tasks, we adapt it alongside fine­tuning to enhance summarization performance and computational efficiency. Our models achieved top results in five languages—Hindi, Gujarati, English, Tamil, and Bengali—and ranked second in Telugu. These results demonstrate substantial improvements in summarization accuracy, underscoring our approach’s efficacy in handling the complexities of Indian languages and advancing text processing in multilingual, mixed­-language environments.

This paper presents novel observations on the impact of ground-bounce noise (GBN) on logic-HIGH levels of the output response of Complementary Metal Oxide Semiconductor (CMOS) inverter circuits. While the impact of power supply noise on logic-HIGH is undeniable and comprehensively studied in the literature, the impact of ground bounce is perceived, and the analysis is majorly focused on logic-LOW. This work demonstrates as well as analyses the significant impact of GBN on logic-HIGH and the observations are supported by a theoretical investigation using both the analytical and small-signal approaches. Several examples are considered to validate the novel observations considering both the simulation and measurement-based case studies. The simulation-based case studies are performed using several CMOS technologies, and measurement-based case studies are performed using standard HEX inverter Integrated Circuits (ICs). There is a close correlation observed between the practical observations and the corresponding theoretical foundation.

We propose in this paper a novel hierarchical multi-grid orthogonal matching pursuit based angle estimation scheme. The proposed scheme accurately estimates the angles of departure (AoDs) and the angles of arrival (AoAs) of a point-to-point ultra massive multiple input multiple output (UM-MIMO) system, ensuring ultra-resolution estimation accuracy. Specifically, we define the on-grid error range in the conventional compressive sensing (CS) estimators and build high-resolution grids centered at the estimated discrete on-grid angles. The proposed scheme proceeds hierarchically through multiple levels to refine the angle estimations. Numerical simulations demonstrate the efficacy and superiority of the proposed scheme over recent state-of-the-art literature, where sub-millidegree angle estimation accuracy can be attained.