This paper presents the Visual Optical Recognition Telemetry EXtraction (VORTEX) system for extracting and analyzing drone telemetry data from First Person View (FPV) Uncrewed Aerial System (UAS) footage. VORTEX employs MMOCR, a PyTorch-based Optical Character Recognition (OCR) toolbox, to extract telemetry variables from drone Heads Up Display (HUD) recordings, utilizing advanced image preprocessing techniques, including CLAHE enhancement and adaptive thresholding. The study optimizes spatial accuracy and computational efficiency through systematic investigation of temporal sampling rates (1s, 5s, 10s, 15s, 20s) and coordinate processing methods. Results demonstrate that the 5second sampling rate, utilizing 4.07% of available frames, provides the optimal balance with a point retention rate of 64% and mean speed accuracy within 4.2% of the 1-second baseline while reducing computational overhead by 80.5%. Comparative analysis of coordinate processing methods reveals that while UTM Zone 33N projection and Haversine calculations provide consistently similar results (within 0.1% difference), raw WGS84 coordinates underestimate distances by 15-30% and speeds by 20-35%. Altitude measurements showed unexpected resilience to sampling rate variations, with only 2.1% variation across all intervals. This research is the first of its kind, providing quantitative benchmarks for establishing a robust framework for drone telemetry extraction and analysis using open-source tools and spatial libraries.

Machine learning (ML) is transforming industries by automating tasks and generating insights from large datasets. Cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) provide essential resources that enhance the development and deployment of ML models. This paper provides a comprehensive comparative analysis of these cloud platforms, highlighting their ML services, scalability, cost structures, and integration capabilities. Through case studies and performance benchmarks, we explore how these platforms facilitate different stages of the ML pipeline, addressing the strengths and limitations of each. The study serves as a guide for practitioners and researchers looking to leverage cloud platforms for ML applications [6], [7].

Virtualized RAN (vRAN) matches O-RAN Alliance specifications while transitioning towards virtualized functions on general-purpose computing platforms. However, the energy consumption of these systems remains a major concern. Although this issue has been addressed in the literature, previous works oversimplify routing decisions, overlook the benefits of flexible split choices, or neglect the energy consumption of the transport network. Additionally, most studies employing optimal solutions exhibit very limited scalability due to their high computational time. In this work, we present a comprehensive and efficient Mixed Integer Linear Programming model to minimize the energy consumption of O-RAN systems, addressing the limitations of current approaches. We also design and implement a synthetic data generator to evaluate our model across various network usage profiles, topologies, and reasonable-size networks. We achieved valuable insights and promising results in our evaluation. For example, our results show that when devices require high throughput, the transport network incurs significant energy costs and reduces the centralization rate. We also observed that hierarchical RAN topologies can achieve greater energy efficiency than ring topologies, with our approach enabling up to 15% more centralization while saving around 28% of energy and consuming at least one order of magnitude less time than other strategies.

This article describes numerous classification techniques for detecting numbers in handwritten digits written by different people or with manual input, convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), random forest classifier (RFC). The accuracy of different algorithms such as CNN, SVM, KNN and RFC in the handwriting recognition were compared. In the present work, to build and train neural networks or classifiers, I used the Modified National Institute of Standards and Technology Database (MNIST) dataset that contains 70000 digits with 250 different forms of writing, and the size of each image has 28×28. In this work, I proposed a model to implement a classification algorithm to recognize the handwritten digits. I have compared the results of some of the most used Machine Learning Algorithm like Random Forest Classifier (RFC), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM), and with Deep Learning Algorithms like Convolutional Neural Network (CNN) using Keras with TensorFlow. Using these algorithms, I achieved an accuracy of around 98.76% using CNN, 97.38% using SVM, 96.51% using RFC, and 96.38% using KNN. I found CNN algorithm gives the highest accuracy around 98.76% to detect the handwritten digit. This work paves the way to detect different handwritten digits by different people in different fields.

Big Data analytics has become an essential tool for industries to make informed decisions, enhance operational efficiency, and gain deep insights from diversified vast amounts of data. This paper explores the transformative power of Big Data analysis, focusing on four core types: Exploratory, Descriptive, Sentiment, and Predictive analysis. These methods are critical in sectors such as healthcare, retail, finance, and transportation, enabling businesses to identify trends, improve customer satisfaction, and forecast future outcomes. The paper discusses the methodology, required resources, and enabling technologies for effective Big Data utilization, offering a comprehensive framework for implementation. A discussion on challenges and strategies for overcoming them is included, alongside real-world applications that demonstrate Big Data's tangible benefits. Finally, a forward-looking conclusion emphasizes the importance of continuous adaptation and innovation to leverage Big Data's full potential.

Handwritten document recognition (HDR) is one of the most challenging tasks in the field of computer vision, due to the various writing styles and complex layouts inherent in handwritten texts. Traditionally, this problem has been approached as two separate tasks, handwritten text recognition and layout analysis, and struggled to integrate the two processes effectively. This paper introduces HAND (Hierarchical Attention Network for Multi-Scale Document), a novel end-to-end and segmentation-free architecture for simultaneous text recognition and layout analysis tasks. Our model's key components include an advanced convolutional encoder integrating Gated Depth-wise Separable and Octave Convolutions for robust feature extraction, a Multi-Scale Adaptive Processing (MSAP) framework that dynamically adjusts to document complexity and a hierarchical attention decoder with memoryaugmented and sparse attention mechanisms. These components enable our model to scale effectively from single-line to triple-column pages while maintaining computational efficiency. Additionally, HAND adopts curriculum learning across five complexity levels. To improve the recognition accuracy of complex ancient manuscripts, we fine-tune and integrate a Domain-Adaptive Pre-trained mT5 model for postprocessing refinement. Extensive evaluations on the READ 2016 dataset demonstrate the superior performance of HAND, achieving up to 59. 8% reduction in CER for line-level recognition and 31. 2% for page-level recognition compared to stateof-the-art methods. The model also maintains a compact size of 5.60M parameters while establishing new benchmarks in both text recognition and layout analysis. Source code and pre-trained models are available at https://github.com/MHHamdan/HAND.

This paper presents an innovative wind energy harnessing mechanism, grounded in the fundamental principles of electromagnetic rotary generation. The proposed design introduces a significant advancement through the modification of the generator's shaft, which is engineered to be hollow and incorporates a precision-designed fin tunnel to optimize wind energy conversion efficiency. This approach addresses key limitations of conventional wind turbines, including elevated costs, substantial spatial requirements, and suboptimal performance under low wind speed conditions. Empirical case studies conducted in urban, offshore, and microgrid contexts demonstrate the practicality and effectiveness of this design. Results from simulations and theoretical analyses indicate enhanced energy conversion efficiency, lower manufacturing costs, and a compact structural profile adaptable to a variety of environmental applications. This study aims to contribute to the corpus of renewable energy research by offering a scalable, efficient, and cost-effective solution for wind energy generation, supported by rigorous theoretical underpinnings and empirical evidence. The proposed methodology capitalizes on advancements in composite material science, aerodynamic optimization, and magnetic flux dynamics. Comparative analyses with conventional wind energy systems underscore the superior performance and versatility of this novel design. By synthesizing insights from peer-reviewed literature and performing comprehensive evaluations, this research establishes a robust framework for advancing renewable energy technologies and provides a foundation for subsequent investigative and developmental endeavors.

Soft magnetic composites cast with recycled ferrites and construction binders like cement, epoxy, or asphalt are highly robust and possess the lowest core up to 100 kHz in a wide temperature range. This paper shows how by using precision-enhanced magnetic testing and sophisticated hysteresis modeling, the performance of these materials could be significantly improved with respect to the stress effect on losses. A composite material grade with minimal losses was developed by benefiting from the behavior of ferrite compositions yielding zero crystalline anisotropy at temperatures below 0°C under compressive stress.

Tibetan's low-resource status has hindered its digital preservation, particularly in Handwritten Character Recognition (HCR). The field lacks foundational models aligned with modern technological advances as well as effective data augmentation methods. For online HCR, the primary challenge is appropriately leveraging temporal information. To address these challenges, we propose TSTN, a spatiotemporal network model tailored for Tibetan online HCR. TSTN excels at capturing both temporal and spatial features from online samples. Using the MRG-OHTC dataset with 583 Tibetan characters, experimental results demonstrate TSTN's superiority, achieving a highest accuracy of 89.26% and a stable average accuracy of 85.13%. To further mitigate data scarcity, we implement geometric data augmentation to enhance accuracy and validate TSTN's robustness in trajectory sequence feature extraction. Furthermore, we generate new samples by segmenting and recombining Tibetan character components, demonstrating the reliability and effectiveness of this approach in boosting recognition performance. Our structureaware data augmentation SplitMix methods further improve performance by a maximum of 1.75%. Our code will be made publicly available soon.

Additive codes may have better parameters than linear codes. However, still very few cases are known and the explicit construction of such codes is a challenging problem. Here we show that a Griesmer type bound for the length of additive codes can always be attained with equality if the minimum distance is sufficiently large. This solves the problem for the optimal parameters of additive codes when the minimum distance is large and yields many infinite series of additive codes that outperform linear codes.

Mathematical modelling of visual perception in two dimensions is described. Although perception is ubiquitously mentioned in literature and it is used in various contexts, its explicit measurable presentation has not yet been given beyond verbal descriptions. In this seminal work the visual perception is explicitly defined for clarity. Based on this definition, it is treated as a mathematical entity in the form of probability in a particular circular region. Theoretically, human vision is expressed in advanced probabilistic terms and the enigmatic relation between vision and perception is mathematically formulated. Here, the perception concerns a special range of interest, coined as vision circle, where percepts describe the vision act of human as the retinal ophthalmic sensing. Perceptual Intelligence Quotient (PIQ) is introduced, that it is subject to measurement. The results of the theoretical considerations are shown to be closely matching the common visual perception experiences as well as the reported visual perception studies, including the variation of peripheral acuity. Two eye-tracking experiments demonstrate that saccadic movement behavior of human is reasonably matching the prediction based on the novel perception theory. The work demystifies the visual perception and understanding its role in sciences. As to the cybernetics, perception plays an important role in the control of complex, integrated processing systems, which are interfaced by indispensable man-machine control systems. In these systems, the role of perception is critical, referring to the hazard situations. The implications of the study are investigated and explained in two such cybernetics applications, too.

This paper presents the analysis and effects of frequency diversity on finite uniform circular array for generation of range dependent Orbital Angular Momentum (OAM) modes. The analysis reveals that the array factor expression in terms of Bessel's function for a circular array with finite no. of elements possess a phase error in the azimuthal domain at any transverse plane due to which the range dependency of OAM modes is affected. In this report a detailed analysis of the phase distribution on concentric circles for an exact array factor expression without considering any assumption or approximation has been carried out. Effect of changing parameters such as total number of elements and frequency increment value is analyzed and compared with the approximated array factor expression. Fourier analysis in the azimuthal domain reflects the presence of desired modes at a specific range for finite array cases. Generated phase and amplitude profile validate the occurrence of range dependent OAM modes at specified distance. Analysis of phase error for different frequency increment for finite no. of elements is also done. A prior knowledge of the available modes at a specified distance away from the antenna is crucial for improving the spectral efficiency of a communication system or radar.

In this paper, a new method for temperature prediction combining Random Forest (RF) and Long Short-Term Memory Network (LSTM) is proposed to address the challenges in the field of weather prediction. The method combines the advantages of RF and LSTM, which considers the temporal correlation of meteorological data and captures the intrinsic correlation among meteorological elements. While LSTM mines the temporal patterns in the historical weather data, RF provides more informative input data, which improves the timeliness and accuracy of the prediction. Compared with the LSTM model, the RF-LSTM model has improved the RMSE, MAE, and MAPE values by 0.27, 0.22, and 0.07, respectively, and the RF-LSTM model performs significantly in the temperature prediction in Nanjing with high prediction accuracy and stability. The method provides a new idea for meteorological prediction, which not only improves the overall level of prediction, but also provides valuable guidance for people's production and life.

Situation Awareness (SA) is critical for effective decision-making in complex, dynamic environments. This paper introduces a novel hybrid generative framework combining theoretical advancements in Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) with domainspecific preprocessing techniques for synthetic data generation in SA training. We evaluate the framework's performance using metrics such as Frechet Inception Distance (FID) and a proposed Situational Fidelity Score (SFS). Applications in autonomous driving and cybersecurity demonstrate improved system performance, including enhanced anomaly detection and decision-making accuracy. This research advances generative AI methodologies while addressing the data scarcity challenges in SA systems, enabling robust and adaptable training environments. Impact Statement-This research specifically advances the state of the art in SA training by introducing a novel framework that integrates advanced generative models, such as GANs and VAEs, tailored for the unique requirements of SA systems. Unlike traditional data augmentation methods, this approach systematically addresses the scarcity and sensitivity of training data by producing synthetic datasets that closely replicate realworld complexities. The proposed framework also emphasizes the generation of rare and anomalous events, which are critical for improving the robustness and adaptability of SA algorithms. Furthermore, it outlines rigorous validation techniques and domainspecific adaptations, ensuring the fidelity and applicability of synthetic data in operational environments. By bridging gaps in data availability and diversity, this work not only enhances training efficiency but also fosters innovation in the development of collaborative multi-agent systems and autonomous operations for complex, dynamic scenarios.

The present work explores the geomorphotectonics of the Kathiawar (Saurashtra)-Kutchch Outlier in Gujarat, India north of the SONATA lineament. Using spatial statistics, the author explores intricate links between spatially resolved data, reveal patterns and correlations that drive the region's geological history. The work addresses an underlying issue, i.e., that of finding the spatial position of entities under study, which is a central issue in spatial analysis. The methodology combines several spatial analytic methods in order to derive a detailed representation of the geomorphotectonics of the area. Results of this study would make an important contribution to the ongoing task of understanding the geological history of the Kathiawar (Saurashtra)-Kutchch Outlier, providing insight to the complex processes that have created this peculiar geological unit.

This paper introduces a novel approach for jointly performing image denoising and 3D point cloud reconstruction from single noisy medical images. By leveraging a joint learning framework, our model improves the accuracy and robustness of 3D reconstructions, particularly under noisy conditions. The model employs a two-branch architecture: a denoising branch utilizing convolutional layers with dropout regularization and a reconstruction branch that predicts 3D point clouds. Quantitative experiments on the LIDC-IDRI dataset demonstrate the model's superior performance compared to sequential baselines, achieving a 15% improvement in PSNR and a 20% reduction in Chamfer Distance. These results highlight the potential clinical applications of our framework in surgical planning and anatomical modeling.

Surface-level human advancements, defined as the integration of technology with the human body to enhance physical capabilities (and, thus, in reference to mechanical systems designed to be worn on the body), represent a promising frontier in science and engineering. This paper explores the historical, cultural, and ethical dimensions of human enhancement, focusing on a novel concept that unites technology with artistic expression. By designing an ergonomic brace system equipped with lightweight materials and pneumatic muscles, this approach improves mobility and strength while maintaining aesthetic appeal. Technical insights delve into the choice of materials, including the use of lightweight alloys and advanced polymers for durability and flexibility. The proposed system leverages state-of-the-art pneumatic technologies to augment reflexes and physical support, with applications spanning rehabilitation and daily activities. By harmonizing functionality with artistic design, these advancements aim to inspire a modern reform in human enhancement, fostering a future where technology serves both practical needs and creative aspirations.

We present the reconfiguration of a circular feed horn operating in bandwidth from 1.4 GHz to 1.8 GHz for radio astronomical applications, adapting it to the 21 cm band (1.42 GHz), and performing observations of the hydrogen line. The proposed model was implemented using cylindrical structures. The design, simulation, measured performance, and reconfiguration are detailed. This feed horn is used at the prime focus of a parabolic reflector antenna. A prototype was designed and characterized, and the simulated and measured results were in good agreement. The reflection coefficients (S 11) of the feed horn was below-15 dB, and the voltage standing wave ratio (VSWR) remained below 1.43. The gains obtained from simulation and measurement were 12 dBi and 12.3 dBi, respectively. To determine the suitability of the proposed design in a practical environment, spectral measurements were performed using an FPGA-based spectrometer. The results showed that the intensity of the signal reached 3.5 dB more than that of the original feed horn. The overall performance of the proposed feed horn suggests that it can be applied to the optics of the 4.9 m reflector antenna.