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 study presents an end-to-end deep learning pipeline for bleeding detection and classification in Wireless Capsule Endoscopy (WCE) imagery. ResNet-18 [1] is employed for classifying bleeding and non-bleeding frames, while UNet [2] performs precise segmentation of bleeding regions. For detection, YOLOv8 [3], implemented via the Ultralytics library, efficiently identifies bleeding areas. The dataset was adapted to the YOLO format, with a custom YAML file crafted for seamless model integration. This multi-model approach addresses critical challenges in WCE analysis, offering scalable and accurate AI-driven solutions for gastrointestinal bleeding diagnostics.

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.

We present a novel AI-based ideation assistant and evaluate it in a user study with a group of innovators. The key contribution of our work is twofold: we propose a method of idea exploration in a constrained domain by means of LLM-supported semantic navigation of problem and solution spaces, and employ novel automated data input filtering to improve generations. We found that semantic exploration is preferred to the traditional prompt-output interactions, measured both in explicit survey rankings, and in terms of innovation assistant engagement, where 2.1x more generations were performed using semantic exploration. We also show that filtering input data with metrics such as relevancy, coherence and human alignment leads to improved generations in the same metrics as well as enhanced quality of experience among innovators.

Observations made using antenna arrays are often limited by the noise coming from the antenna system itself and from the environment. In demanding applications such as radioastronomy, the thermal noise emitted by the multilayered substrate below the antenna array may become significant and needs to be properly characterized. Both the intensity of the noise at each port and the correlation of the noise at pairs of ports need to be quantified. In this paper, we provide a spectral formulation to calculate the noise correlation matrix of antenna arrays. The method is based on the propagative and evanescent plane wave spectrum emitted by each pair of active antenna ports into the lossy layered medium, and can thus be implemented as a post-processing step combined with any full-wave numerical solver. This formulation allows us to account for layered media with different physical temperatures at each layer. Numerical examples are provided using Square Kilometer Array (SKA) stations lying on a finite ground plane, itself lying on a layered semi-infinite soil. The behaviours of noise power and noise correlation coefficients are studied for different positions of the antennas on the ground plane. This is done considering a multilayered soil with different moisture levels. A very good agreement with the commercial software FEKO is observed regarding the evaluation of the noise correlation coefficients. Moreover, the efficiency of the algorithm in terms of computational time is shown by comparison with FEKO. The direction-dependent system noise temperature picked up by a full SKA station in presence of the soil is also given.

This paper addresses the optimization of the radiation pattern of surface-wave (SW) based metasurface (MTS) antennas. Those antennas are considered as a promising alternative to parabolic reflectors, and phased arrays due to their extremely low profile and their ability to provide high gain, shaped beams and multibeams. However, pattern synthesis with MTS antennas is very challenging because of the single active control point and the need to control surface and leaky-waves through the MTS. An accurate optimization of the radiation pattern, along with the sidelobe level requires a full-wave modelling of the feeding structure, including its coupling with the MTS. MTS synthesis methods existing in the literature usually approximate the feeder model, and neglect its coupling with the MTS. Such approximation may lead to more than 1 dB error in the predicted antenna directivity. This paper presents a technique for optimization of the far-field pattern, built on a Method of Moments (MoM) analysis tool in which the MTS coupling with the feeder, a coax probe, is fully considered. The MTS is modeled as an arbitrarily shaped, spatially modulated electric sheet impedance in a layered medium. At each optimization iteration, the complexity of the underlying analysis is O(N log N) owing to the use of a FFT based acceleration. The effectiveness of the method is demonstrated through the optimization of MTSs radiating a pencil beam and a conical beam with orbital angular momentum (OAM).

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.

This paper introduces Web 3.0 NEXT , a network design that pushes Web3 decentralization even further by reducing the dependency on centralized or traditional internet service providers (ISPs) and data centres. By integrating peer-to-peer mesh networking, decentralized storages, blockchain-based authentication and verification system, and a suite of emerging off-grid connectivity technologies like Wi-Fi mesh, LoRaWan, and satellite networks, the suggested system seeks to establish a strong, self-sustaining network infrastructure. This paper explores the benefits and technical challenges of such a system. That area can be used in a disaster-prone area, national security and economic innovations, which may force centralized authority from suppressing it. We will also discuss ethical, legal, financial, energy, user adoption, and security considerations, alongside relevant case studies and provide a holistic view of the potential challenges faced in deploying WEB 3.0 NEXT.

This study presents a systematic approach to optimizing drone airframe geometry with a specific focus on enhancing flight dynamics for racing applications. The proposed method integrates an elitist multi-objective evolutionary algorithm with mathematical procedures of varying natures, encapsulated within an algorithmic system of a simulation platform. The framework combines implicit numerical methods with a custom implementation to seamlessly interact across multiple simulation environments. This approach ensures the mathematical model closely reflects real-world conditions, providing reliable optimization outcomes. The preliminary findings suggest that optimized airframe geometries lead to notable advancements in thrust efficiency and overall performance. Symmetrical and non-symmetrical designs exhibit distinct benefits, highlighting the potential for significant improvements in trajectory precision and flight efficiency. These results emphasize the practical impact of shape design and dynamic optimization in drone airframes, offering valuable insights for developing high-performance racing drones.

Adversarial attacks on Natural Language Processing (NLP) models expose vulnerabilities by introducing subtle perturbations to input text, often leading to misclassification while maintaining human readability. Existing methods typically focus on word-level or local text segment alterations, overlooking the broader context, which results in detectable or semantically inconsistent perturbations. We propose a novel adversarial text attack scheme named Dynamic Contextual Perturbation (DCP). DCP dynamically generates context-aware perturbations across sentences, paragraphs, and documents, ensuring semantic fidelity and fluency. Leveraging the capabilities of pre-trained language models, DCP iteratively refines perturbations through an adversarial objective function that balances the dual objectives of inducing model misclassification and preserving the naturalness of the text. This comprehensive approach allows DCP to produce more sophisticated and effective adversarial examples that better mimic natural language patterns. Our experimental results, conducted on various NLP models and datasets, demonstrate the efficacy of DCP in challenging the robustness of state-of-theart NLP systems. By integrating dynamic contextual analysis, DCP significantly enhances the subtlety and impact of adversarial attacks. This study highlights the critical role of context in adversarial attacks and lays the groundwork for creating more robust NLP systems capable of withstanding sophisticated adversarial strategies.

As minimum area SRAM bit-cells are obtained when using cell ratio and pull-up ratio of 1, we analyze the possibility of decreasing the cell ratio from the conventional values comprised between 1.5-2.5 to 1. The impact of this option on area, power, performance and stability is analyzed showing that the most affected parameter is read stability, although this impact can be overcome using some of the read assist circuits proposed in the literature. The main benefits are layout regularity enhancement, with its consequent higher tolerance to variability, cell area reduction by 25% (with respect to a cell having a cell ratio of 2), leakage current improvement by a 35%, as well as energy dissipation reduction and a soft error rate per bit improvement of around 30%.

Gig economy platforms depend significantly on effective data processing to manage the requirements of changing environments. Nonetheless, conventional data pipelines often face obstructions and delays, which restrict overall operational efficiency. In this research, we present a novel approach aimed at enhancing data pipelines tailored for gig economy platforms. Our system integrates sophisticated design concepts with responsive algorithms to improve data flow and minimize latency. By analyzing current challenges, we intentionally distribute resources based on real-time data needs. We evaluate the effects of our optimizations through precise performance metrics that gauge data throughput and processing speed. Experimental findings show that our method greatly improves the efficiency of data operations, establishing a basis for expanding gig economy platforms while upholding service quality. This study provides important perspectives on the complex dynamics of data management within fast-evolving business environments, promoting additional investigation into effective and scalable strategies in the gig economy.

We propose a method to harden sensors against laser radiation by defocusing the employed optics by purpose, and to reconstruct the sought focused images of the scene via image reconstruction. The introduced defocus widens the laser spot incident on the sensor and greatly reduces its damage potential. We employ a coded aperture and optimize its pattern jointly with the free parameters of the image reconstruction pipeline. For the image reconstruction, we combine a state-of-theart alternating direction method of multipliers (ADMM)-based physically informed deconvolution stage with a U-Net-like neural network to remove remaining reconstruction artifacts. To evaluate the performance of our proposed approach, we conducted reconstruction experiments on simulated data, including ablation experiments and on real data and performed sensor destruction tests with and without sensor protection. Destructive experiments with increasing laser power suggest that our approach has the potential to increase the tolerable radiation threshold by about three orders of magnitudes.

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.

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.

This paper presents optimal reference voltage saturation (ORVS) strategies for nonlinear current control of synchronous machine drives. The ORVS are formulated as analytically solvable optimization problems which maximize the feasible current change in the presence of voltage constraints due to the limited dc-link voltage of voltage-source converters (VSCs). Two ORVS strategies are proposed: one for circular voltage constraint and one for hexagonal voltage constraint, enabling full utilization of the voltage hexagon. The effectiveness of the proposed ORVS are validated through laboratory experiments conducted with a highly nonlinear, anisotropic reluctance synchronous machine (RSM) and an interior permanent magnet synchronous machine (IPMSM). The performance of ORVS under both circular and hexagonal voltage constraints are compared with classical reference voltage saturation (CRVS). The experimental results demonstrate that the proposed ORVS outperform CRVS regarding faster current dynamics and current tracking accuracy with significantly reduced cross-coupling effects.

Load forecasting plays a crucial role in energy management, directly impacting grid stability, operational efficiency, cost reduction, and environmental sustainability. Traditional Vanilla Recurrent Neural Networks (RNNs) face issues such as vanishing and exploding gradients, whereas sophisticated RNNs such as Long Short-Term Memory Networks (LSTMs) have shown considerable success in this domain. However, these models often struggle to accurately capture complex and sudden variations in energy consumption, and their applicability is typically limited to specific consumer types, such as offices or schools. To address these challenges, this paper proposes the Kolmogorov-Arnold Recurrent Network (KARN), a novel load forecasting approach that combines the flexibility of Kolmogorov-Arnold Networks with RNN's temporal modeling capabilities. KARN utilizes learnable temporal spline functions and edge-based activations to better model non-linear relationships in load data, making it adaptable across a diverse range of consumer types. The proposed KARN model was rigorously evaluated on a variety of real-world datasets, including student residences, detached homes, a home with electric vehicle charging, a townhouse, and industrial buildings. Across all these consumer categories, KARN consistently outperformed traditional Vanilla RNNs, while it surpassed LSTM and Gated Recurrent Units (GRUs) in six buildings. The results demonstrate KARN's superior accuracy and applicability, making it a promising tool for enhancing load forecasting in diverse energy management scenarios.

Explainable Artificial Intelligence (XAI) emerged to reveal the internal mechanism of machine learning models and how the features affect the prediction outcome. Collinearity is one of the big issues that XAI methods face when identifying the most informative features in the model. Current XAI approaches assume the features in the models are independent and calculate the effect of each feature toward model prediction independently from the rest of the features. However, such assumption is not realistic in real life applications. We propose an Additive Effects of Collinearity (AEC) as a novel XAI method that aim to considers the collinearity issue when it models the effect of each feature in the model on the outcome. AEC is based on the idea of dividing multivariate models into several univariate models in order to examine their impact on each other and consequently on the outcome. The proposed method is implemented using simulated and real data to validate its efficiency comparing with the a state of arts XAI method. The results indicate that AEC is more robust and stable against the impact of collinearity when it explains AI models compared with the state of arts XAI method.