This article introduces non-orthogonal frequency and time index modulation multiple access (NFT-IMMA) for sixth-generation (6G) communications. The proposed NFT-IMMA increases the number of communication resources by utilizing power levels, frequency, and time slots as transmission entities, which are selected through a predefined index activation pattern derived from partial user input data. NFT-IMMA increases spectral efficiency (SE) by employing index information and classical constellation symbols as methods of data transmission. Notably, it does not require prior scheduling at the base station, which simplifies the coordination process. Additionally, the use of three-dimensional indices for resource selection helps reduce user collisions. Performance evaluation at the physical layer, focusing on SE and bit error rate, indicates that NFT-IMMA outperforms conventional index modulation (IM) multiple access and IM-based orthogonal frequency division multiple access. At the MAC layer, NFT-IMMA demonstrates superior performance in terms of throughput and packet loss rate when compared to ALOHA and other competing techniques.

This paper investigates the computation of the radar cross section (RCS) of equilateral triangular targets using Monte Carlo numerical integration. The analysis employs the Physical Theory of Diffraction (PTD) to model the scattering behavior and numerically evaluates the RCS for a canonical planar geometry. A Python-based implementation is provided for an equilateral triangle with side lengths of 2 meters, illuminated by a 10 GHz radar wave. The simulation yields an RCS value of approximately 0.0292 m², showcasing the effectiveness of the Monte Carlo approach for RCS estimation in simple geometries. This work contributes to the understanding of electromagnetic scattering and stealth design methodologies.

This paper explores various methods for analyzing, manipulating, and evaluating survey data. Using a Jupyter notebook, we examine trends such as the increase in the number of published survey articles over the years. Additionally, we categorize each article into different taxonomy topics. Through data manipulation, we create a TF-IDF matrix, enabling us to predict an article’s taxonomy category based on key words from its title, summary, and associated categories

This study explores the influence of both laser spot size and sizing threshold in Surface Scanning Inspection Systems (SSIS) on cluster identification. Reducing the laser spot size enables the detection and differentiation of smaller point defects while simultaneously limiting the specified search distance, collectively influencing cluster identification. A smaller sizing threshold introduces more point defects, enhancing the likelihood of cluster identification. However, this also leads to a shift between clusters and scratches, as well as cluster merging, ultimately resulting in a decrease in the overall quantity of clusters.

The escalating frequency and sophistication of cyber threats means the need of the development of more effective detection mechanisms. The Dynamic Entropy Analysis (DEA) framework introduces a novel approach to ransomware detection through real-time monitoring of entropy variations within system files and processes, enabling the identification of malicious activities with high accuracy. Comprehensive evaluation of the DEA framework demonstrated a detection accuracy of 94.5%, surpassing traditional signature-based and behavior-based methods. The framework's adaptability to emerging ransomware variants was evidenced by a consistent detection rate exceeding 90% over a six-month period. Scalability assessments revealed effective operation across networks ranging from 50 to 500 devices, with minimal impact on system performance, as indicated by consistent detection accuracy and acceptable resource utilization metrics. Detection latency analysis indicated prompt identification of ransomware activities, with detection occurring within 0.5 to 2.5 seconds across various samples. The modular design of the DEA framework facilitates seamless integration with existing security infrastructures, enhancing its feasibility for real-world deployment. These findings underscore the potential of the DEA framework to serve as a proactive and efficient solution in mitigating the impact of ransomware attacks across diverse computing environments.

In this work, methods are presented to obtain flux linkages in the same elegant way as along magnetic vector potential. This is done by solving an additional field problem, retrieving the magnetic vector potential from H-based formulations. This additional problem can be solved using carefully chosen gauge conditions to obtain the magnetic vector potential efficiently. The methods are successfully tested on an industry-relevant benchmark problem to show their applicability. Their performance is compared with the standard approach in an academic example.

Scene classification in remote sensing (RS) imagery has been extensively investigated using both learning-from-scratch approaches and fine-tuning of ImageNet pre-trained models. Meanwhile, CLIP (Contrastive Language-Image Pretraining) has emerged as a powerful foundation model for vision-language tasks, demonstrating remarkable zero-shot capabilities across various domains. Its image encoder is a key component in many vision instruction-tuning models, enabling effective alignment of text and visual modalities for diverse tasks. However, its potential for supervised remote sensing (RS) scene classification remains unexplored. This work investigates the efficient adaptation of large CLIP models (containing over 300M parameters) through Low-Rank Adaptation (LoRA), specifically targeting the attention layers. By applying LoRA to CLIP's attention mechanisms, we can effectively adapt the vision- model for specialized scene classification tasks with minimal computational overhead, requiring fewer training epochs than traditional fine-tuning methods. Our extensive experiments demonstrate the promising capabilities of LoRA-CLIP. By training only on a small set of additional parameters LoRA-CLIP outperforms models pre-trained on ImageNet, demonstrating the clear advantages of using image-text pretrained backbones for scene classification.

This study introduces a novel control framework for Human-Drone Interaction (HDI) in industrial warehouses, targeting pick-and-delivery operations. The goals are to enhance operator safety as well as well-being and, at the same time, to improve efficiency and reduce production costs. To these aims, the Speed and Separation Monitoring (SSM) operation method is employed for the first time in HDI, drawing an analogy to the safety requirements outlined in collaborative robots' ISO standards. The so-called protective separation distance is used to ensure the safety of operators engaged in collaborative tasks with drones. Additionally, we employ the Rapid Upper Limb Assessment (RULA) method to evaluate the ergonomic posture of operators during interactions with drones. To validate the proposed approach in a realistic industrial setting, a quadrotor is deployed for pick-and-delivery tasks along a predefined trajectory from the picking bay to the palletizing area, where the interaction between the drone and a moving operator takes place. The drone navigates towards the interaction space while avoiding collisions with shelves and other drones in motion. The control strategy for the drone cruise navigation integrates simultaneously the time-variant Artificial Potential Field (APF) technique for trajectory planning and the iterative Linear Quadratic Regulator (LQR) controller for trajectory tracking. Differently, in the descent phase, the receding horizon LQR algorithm is employed to follow a trajectory planned in accordance with the SSM, which starts from the approach point at the border of the interaction space and ends in the volume with the operator's minimum RULA. The presented control strategy facilitates drone management by adapting the drone's position to changes in the operator's position while satisfying HDI safety requirements. The results of the proposed HDI framework simulations for the case study demonstrate the effectiveness of the method in ensuring a safe and ergonomic HDI within industrial warehouses.

This article investigates the essential cultural, gender, and contextual elements that shape the educational experiences and volunteerism attitudes of Arab and non-Arab students.

Integrating renewable energy in distribution or transmission power systems poses a challenge to traditional protection methods. Photovoltaic and wind energy generation is connected to the grid through power electronic converters. The converters do not respond to faults in the same way as traditional generation units based on synchronous generators. It is known that distance protection has issues detecting different faults when converter-based generation is presented in the grid. Therefore, this paper analyses the problem using a new geometric interpretation of unbalanced faults in power systems with converter-based resources. Simulation results of a grid with 100% wind farm generation have been used. The paper shows characteristics based on geometric algebra and differential geometry for an unbalanced fault.

Dielectric spectroscopy is a label-free, non-contact, real-time, multi-layer sensing technology, and has been used for identification and quantification of many biological materials. A combination of such sensing features is in demand for monitoring of organ-on-chip systems; however available sensing technologies have yet to address this need. In this work, we explore the possibility of leveraging the inherent features of dielectric spectroscopy for the application in organ-on-chip systems, by investigating three key technological developments using open-ended coaxial probes. Firstly, biocompatible non-contact sensing capabilities are proved by showing similar sensing performance of Parylene C-coated probes and uncoated probes. Secondly, a setup and methodology are developed for highly accurate and non-destructive height positioning of the probe to allow for precise extraction of intermediate sample layers. Finally, non-contact multi-layer sensing performance of the presented technology is successfully demonstrated by means of a biological phantom in a three-layered system. With further integration, dielectric spectroscopy can potentially become a cornerstone sensing technique for organ-on-chip by enabling real-time non-contact tracking of various tissue contents and properties.

Assessing urban environments using intelligent tools helps decision-makers improve citizen quality of life. Our research introduces a Geospatial Augmented Reality (AR) System for Aesthetic and Environmental Assessment of the University. This framework integrates user presence from geospatial analysis, greenery evaluated through image processing, user sentiments derived from biosensor data using deep learning, and contextual information. These factors are synthesized via Multi-Criteria Decision Making (MCDM) to generate scores. Subsequently, the system utilizes voice recognition to identify user-selected areas, overlaying scores, and relevant contextual data through AR technology. This approach empowers decision-makers to enhance urban spaces effectively.

Synthetic Aperture Radar (SAR) systems are increasingly at risk of Radio Frequency Interference (RFI), driving the need for effective mitigation techniques to maintain data quality in challenging operational settings. This paper introduces a lightweight and effective framework designed to mitigate the impact of RFI directly on raw Radio Frequency (RF) SAR data. Given the scarcity of publicly labeled raw SAR data with interference events, a synthetic RFI dataset is created using a realistic RF-level SAR emulation tailored for spaceborne scenarios. A twin U-Net model is developed to mitigate RFI in affected segments, inferring clean SAR data from contaminated RFI inputs. The model's mitigation performance is quantitatively validated on unseen synthetic data. This approach has the potential to enhance the quality of SAR imagery impacted by RFI with the advantage of operating directly in the raw signal domain.

Supervised machine learning classifiers often encounter challenges related to performance, accuracy, and overfitting. This paper introduces the Artificial Liver Classifier (ALC), a novel supervised learning classifier inspired by the human liver's detoxification function. The ALC is characterized by its simplicity, speed, hyperparameters-free, ability to reduce overfitting, and effectiveness in addressing multi-classification problems through straightforward mathematical operations. To optimize the ALC's parameters, an improved FOX optimization algorithm (IFOX) is employed as the training method. The proposed ALC was evaluated on five benchmark machine learning datasets: Iris Flower, Breast Cancer Wisconsin, Wine, Voice Gender, and MNIST. The results demonstrated competitive performance, with the ALC achieving 100% accuracy on the Iris dataset, surpassing logistic regression, multilayer perceptron, and support vector machine. Similarly, on the Breast Cancer dataset, it achieved 99.12% accuracy, outperforming XGBoost and logistic regression. Across all datasets, the ALC consistently exhibited lower overfitting gaps and loss compared to conventional classifiers. These findings highlight the potential of leveraging biological process simulations to develop efficient machine learning models and open new avenues for innovation in the field.

Parametric Semantic Lattices (PSL) offer a new approach to enhancing semantic representation and contextual processing in advanced language modeling architectures. By embedding hierarchical structures that capture both local and global linguistic dependencies, PSL addresses key limitations observed in traditional transformer-based frameworks. The adaptive nature of the lattice configurations enables the dynamic interpretation of syntactic and semantic relationships across diverse linguistic inputs. Quantitative analysis reveals significant reductions in perplexity and marked improvements in semantic coherence when compared to baseline models. Through leveraging multi-dimensional encoding, the PSL framework enhances interpretability while maintaining computational efficiency across long text sequences. Experiments demonstrate its scalability across extensive datasets and various hardware configurations, validating its application to resource-intensive natural language processing tasks. Evaluation under conditions of noisy input highlights its robustness in maintaining contextual fidelity and accuracy. Practical applications span areas such as automated translation, sentiment analysis, and advanced query resolution, showcasing its versatility. The integration of PSL into transformers introduces minimal computational overhead while yielding substantial performance gains. Results further highlight its ability to retain long-range dependencies, a critical factor for tasks requiring deeper contextual understanding. By redefining structural modeling within transformers, PSL opens new avenues for the development of contextually aware artificial intelligence systems. The findings collectively underscore its transformative potential in advancing the state of language modeling and representation.

This work proposes a methodology to estimate the statistical distribution of the probability that a 6T bit-cell starts up to a given logic value in SRAM memories for PUF applications. First, the distribution is obtained experimentally in a 65-nm CMOS device. As this distribution cannot be reproduced by electrical simulation, we explore the use of an alternative parameter defined as the distance between the origin and the separatrix in the bit-cell state space to quantify the mismatch of the cell. The resulting distribution of this parameter obtained from Monte Carlo simulations is then related to the start-up probability distribution using a two-component logistic function. The reported results show that the proposed imbalance factor is a good predictor for PUF-related reliability estimation with the advantage that can be applied at the early design stages.

The Distance-based Neighborhood k-Search (DNkS) algorithm, introduced in this article, offers a novel approach to enhancing meter-transformer connectivity modeling in low-voltage grids. By employing a local k-neighborhood search strategy, DNkS effectively subdivides the grid into manageable sections, ensuring robust connectivity assessments. Utilizing metrics such as Adjusted Mutual Information and Accuracy, DNkS demonstrated superior performance in trials, achieving up to 100% accuracy in certain cases, significantly outperforming existing state-of-the-art methods such as deep convolutional time-series clustering and spectral embedding-based meter-transformer mapping. Although DNkS is effective, its performance critically depends on the accuracy of meter and transformer coordinates. In comparative analyses across various network configurations, DNkS consistently outperformed other methods, affirming its utility and effectiveness. The versatile nature of the algorithm would allow its integration into existing systems in various ways, for example, through an API or a web interface. Implementing DNkS promises substantial improvements in the reliability and accuracy of utility network models, directly contributing to enhanced grid management practices.

Large language models (LLMs) have achieved significant success in reasoning tasks, including mathematical reasoning and logical deduction. Among these reasoning tasks, graph problems stand out due to their complexity and unique structural characteristics, attracting considerable attention from researchers. Previous studies have explored LLMs' graph reasoning abilities through various techniques, such as different encoding methods for graph structures and the use of carefully designed prompts. However, a critical factor has been mostly overlooked: the prompt sequential order in which graph descriptions are presented to the models. In this study, we present the first comprehensive analysis of how the order of graph descriptions impacts LLM performance. Specifically, we comprehensively evaluate four graph description orders across six graph problems using six mainstream LLMs. The results reveal that: (1) ordered graph descriptions significantly improve LLMs' comprehension of graph structures; (2) the robustness of LLMs to graph description order varies across different tasks; and (3) the impact of graph order on performance is closely related to the inherent characteristics of tasks. This study provides a critical advancement in the application of LLMs for solving graph-related problems, paving the way for future research to optimize model performance through strategic graph description ordering.