Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-Satellite Geolocation of Terrestrial GNSS Jammers from Low Earth Orbit

This paper explores two-step and direct geolocation of terrestrial Global Navigation Satellite System (GNSS) jammers from Low Earth Orbit (LEO). Within the past decade, there has been a sharp increase in GNSS outages due to deliberate GNSS jamming. Receivers in LEO are uniquely situated to detect, classify, and geolocate terrestrial GNSS jammers. The conventional two-step geolocation method first estimates the differential delay and differential Doppler, then uses a time history of these to estimate the transmitter location. By contrast, direct geolocation is a single-step search over a geographical grid that enables estimation of the transmitter location directly from the observed signals. Signals from narrowband, matched-code, and chirp jammers recently captured in the GNSS frequency bands by two time-synchronized LEO receivers over the Eastern Mediterranean are analyzed and the emitters geolocated. It is demonstrated that the direct approach is effective even for low signal-to-noise ratio interference signals based on short captures with multiple emitters. Moreover, the direct approach enables geolocation of multiple emitters with cyclostationary signals (e.g., chirp jammers), whereas the two-step method struggles in such cases to associate emitters with their corresponding structures in differential delay and Doppler space.
Enter the void

Life inside a 5D black hole is known to be rather more sustainable than it is in the 4D version. It is because there is no physical plughole in the 5D case and the tidal forces are negligible, allowing exploration without fear of dismemberment. The 5D black hole theory is supported by mathematical analyses.
Proximity-Based Positioning Scheme with Multi-layer Privacy

This article investigates the use of proximity-based positioning as a simple, yet effective, positioning system in crowded areas. Particularly, proximity-based positioning is a range-free solution where a receiver computes its position by knowing where nearby receivers are. The main requirement, therefore, is that collaborative receivers need to share their position, which poses a privacy concern and potential limitation in the widespread use of such approach. This article proposes a scheme whereby encrypted positions are shared among the collaborative agents in order to implement a proximity-based solution that does not reveal the position of neighboring nodes. With the use of the homomorphic encryption, we establish a framework that will perform the required operations to obtain position estimates, while the information of any participating user or device will remain private. The concept is investigated and then experimental results provided to support the proposed methodology, showing equivalent performance to the case where no privacy-security guarantees are provided.
Deformation calculation of membrane for pressure measurement

The mathematical model which is used to calculate the capacitance in the pressure-measurement links is determined by deformation of membrane for capactive pressure sensor. The establishment and derivation of characteristic equation for membrane deformation measurement are introduced in this paper. The characteristics of membrane under differentloads are also analyzed and calculated. The error mode for deformation calculation of membrane is corrected.
Multi-Constellation Blind Beacon Estimation, Doppler Tracking, and Opportunistic Positioning with OneWeb, Starlink, Iridium NEXT, and Orbcomm LEO Satellites

A novel blind spectral approach is proposed for blind beacon estimation, Doppler tracking, and opportunistic positioning with unknown low Earth orbit (LEO) satellite signals. The framework is agnostic to the modulation and multiple access scheme adopted by LEO satellites. First, an analytical derivation of the received signal frequency spectrum is presented, which accounts for the highly dynamic channel between the LEO satellite and a terrestrial receiver. Second, a frequency domain-based blind Doppler discriminator is proposed. Third, a Kalman filter (KF)-based Doppler tracking algorithm is developed. Fourth, a blind beacon estimation framework for LEO satellites is proposed and its convergence properties are studied. Simulation results are presented showing successful beacon estimation and Doppler tracking of Starlink LEO satellites transmitting 5G orthogonal division multiple access (OFDM) signals. Experimental results are presented demonstrating the efficacy of the proposed framework on multi-constellation LEO satellites, namely OneWeb, Starlink, Orbcomm, and Iridium NEXT. Despite adopting different modulation and multiple access transmission schemes, the proposed framework is capable of successfully estimating the beacon and tracking the Doppler, in a blind fashion, of 8 LEO satellites (2 OneWeb, 4 Starlink, 1 Iridium NEXT, and 1 Orbcomm) over a period of about 560 seconds with Hz-level accuracy. The produced Doppler measurements were fused through a nonlinear least-squares estimator to localize a stationary receiver to an unprecedented level of accuracy. Starting with an initial estimate about 3,600 km away, a final three-dimensional (3-D) position error of 5.8 m and 2-D position error of 5.1 m was achieved. Aside from achieving this unprecedented accuracy, these results represent the first successful opportunistic tracking of unknown OneWeb LEO signals and their exploitation for positioning.
Comprehensive Study on State of Art Energy Efficient Voltage Level Shifters for Multi-VDD Systems

In both analogue and digital systems, the Level Shifter (LS) circuit has emerged as a crucial circuit element. Research papers towards the advancement of LS circuits have multiplied during the last few decades. their applications as well as their performances. As a result, this review article offers an in-depth analysis of the Level shifter design, covering circuit design features as well as several design approaches like testing approach, size methods, layout design process, and circuit evaluation methodology. This study evaluates the most modern LS circuits and to provide performance measurements for them in the last section.
Modern Techniques for Rumor Detection from the Perspective of Natural Language Processing

Rumor detection on online social network (OSN) aims to help people retrieve reliable information and prevent public panic when emergencies occur suddenly. However, it is a waste of human efforts to detect rumors from the rapid growth of large-scale datasets. Due to the development of artificial intelligence, many architectures and frameworks are proposed to provide solutions for this issue. The first proposed traditional feature related methods are time-consuming and heavily depend on well-designed features, which calls for novel methods to detect rumors more efficiently. Thus deep neural networks related methods are successively born, and recent research on propagation related methods has captured much attention of both academia and industry. However, there lacks a systematic and global survey in the field of modern rumor detection. In this paper, we introduce rumors and OSN, and then present a comprehensive study of rumor detection methods on OSN, classifying them according to their search approaches and providing a comparison of the selected works. Finally, this survey deliver unique views on key challenges and several future research directions of rumor detection on OSN, such as multi-task learning, multi-modal detection and developing standard datasets and benchmarks. This work is supported by the Department of System Science, College of Liberal Arts and Sciences in National University of Defense Technology.
Factor Graph Dimensionality Reduction using Lateral Motion Constraints for Aided Dead Reckoning Navigation

For navigation problems involving dead reckoning of odometry measurements aided with additional sensors, we introduce a method that treats the lateral components of the odometry measurements as constraints, thereby reducing the dimensionality of the state representation. The constrained lateral motion approach is best suited for factor graph representations of ground vehicle and fixed-wing aerial vehicle navigation, whereby the tangential component of motion is typically much greater than the lateral component. We conduct experiments in both 2D and 3D cooperative navigation scenarios aided by inter-vehicle range measurements, and show that we achieve faster convergence with more efficient optimization with our new parameterization.
Vision and UWB-Based Collaborative Positioning Between Ground and UAS Platforms

The rapid development of autonomous ground vehicle technologies (AV) and the recent proliferation of unmanned aerial system (UAS) applications motivate the search for appropriate solutions ensuring safe and effective navigation of such autonomous platforms in any operational environment and condition, including situations where air and ground platforms share the same space. Because of the easy availability and efficiency of GNSS (Global Navigation satellite System) for civilian use, GNSS positioning has been widely used in a large variety of consumer applications, in particular in mobile devices and, in fact, it has become an enabling technology for many applications, allowing positioning and navigation of ground and aerial platforms almost everywhere. Since reliable GNSS signal reception cannot be guaranteed in urban and vegetated areas as well as when signals are subject to unintentional and intentional interferences, the provision of alternate and complementary positioning solutions has been of high interest for a long while. One of the applicable techniques is the use of collaborative navigation (CN), where platforms navigating in close vicinity can share navigation information and a joint navigation solution can potentially provide better individual navigation solutions for all platforms. In this work we investigate the feasibility and performance of CN in areas where ground and airborne vehicles operate in the same space. A field test, including multiple ground vehicles and drones was conducted to acquire experimental data to assess accuracy, robustness and accuracy of CN in a simulated intersection area. Initial results of this research work are reported.
Cloud Computing and IoT Integration: Issues, Challenges and Opportunities

The demand for connection in our daily lives has led to the adoption of more devices than ever before that are connected to the Internet and one another. As a result, the storage, exchange, and processing of data in modern times are significantly influenced by integrating technologies. With the help of the Internet of Things (IoT), many devices can connect to the web and generate a ton of data. In contrast, cloud computing serves as a paradigm for providing universal, useful, on-demand network access to a shared pool of computing resources with the least amount of administrative burden. IoT benefits greatly from cloud computing since it offers essential services. IoT development is heavily reliant on the availability of cloud services like computational power and storage. The cloud-based IoT paradigm opens up new possibilities while efficiently utilizing infrastructure, apps, and data. This study draws attention to the issues and practical challenges associated with integrating these two technologies. The study also makes an effort to describe the benefits of integration and discusses future applications for cloud IoT.
Research of Reactive Power Coordination and Optimization Control Strategy for Hydro-PV-PS Complementary Power Plant Group

In order to improve the coordination ability of energy supply and demand, promote clean energy production and consumption nearby, reduce waste light and water, promote the consumption of renewable energy, and improve the comprehensive efficiency of the energy system, multi-energy complementation is an important direction for the development of clean energy in the future. This paper mainly introduces a conventional hydropower unit, full power variable speed pumping storage unit, new energy can complementary power station group of reactive power coordination optimization control strategy.
A Study of Deep Learning based Techniques for the Detection of Maize Leaf Disease: A Short Review

The process of detecting and recognizing disease in plants are crucial for the early control of pests and diseases that significantly affect plant growth. Although maize is the most productive food crop worldwide, it can suffer from physiological lesions caused by viral and fungal infections primarily visible in the leaves. The automatic identification and classification of plant diseases are essential for sustainable agriculture, but it remains a significant challenge. Existing techniques proposed for this purpose are limited in scope and rely solely on models for deep learning. Using cropped photos, convolutional neural networks have demonstrated to be the most adequate method for identifying and forecasting illnesses. An overview of deep learning methods for identifying plant diseases is given in this article, which also includes data collection sources, deep learning architectures, and image processing methods. The study evaluates existing deep learning models and highlights their results, with a focus on future research to improve system performance and accuracy for detecting crop diseases using better deep learning capabilities. The main objective of this review is to strengthen the quality of maize plant leaf disease detection and inspire further research in this field.
Maximizing the Influence of Social Networks Based on Graph Attention Networks

It is a crucial algorithmic challenge in influence analysis to choose k important members to maximize the influence spread. This is known as the influence maximization (IM) problem in social networks. There are two main problems with the previous research for the IM problem: (1) the complex graph data can not be effectively used in the graph topology, (2) the problem of overlapping influences is prone to occur, which will lead to the solutions of the seed set being suboptimal. This paper suggests an influence maximization approach based on graph attention embedding (GAEIM) to address the aforementioned issues. GAEIM learns the shallow graph structure by Node2Vec and uses the graph attention mechanism to learn the deeper graph structure to extract information related to IM problems. Then, to reduce influence overlapping, we apply the cost-effective lazy forward algorithm (CELF) to choose the most influential seed nodes after selecting prospective seed nodes using the heuristic algorithm. In addition, we have demonstrated the monotone and submodular nature of the GAEIM. Thus, the proposed approach guarantees the approximate optimal solution to the IM problem. Across various datasets, the GAEIM algorithm exhibits superior influence propagation and effectiveness compared to existing IM algorithms, according to experimental results.
Intelligent Control Design for Quadrotor Perching Application using Neural-Network Augmented Direct Inversion Control Approach

Abstract-The quadrotor’s altitude(height), attitude(roll, pitch, yaw), and position (x-y directions) controller design are challenging research areas because of their non-linear coupled dynamics and under-actuated system architecture. This study proposes a quadrotor control system based on neural networks of the Elman recurrent learning mechanism. To solve the desired trajectory tracking problem for a quadrotor, a direct inverse control strategy utilizing Elman recurrent neural networks (ERNN) is demonstrated and tested through MATLAB simulation. The simulation findings show that the ERNN-based control systen operates with a minimum mean square error when using the reference flight testing dataset. Theerror-based comparativ analysis shows that ERNN-based altitude, attitude, and position controllers outperform the backpropagation neural network, according to our experiments.
A Review on Brain Tumor Prediction using Deep Learning

Detection and segmentation of brain tumors is important in the healthcare domain. Since brain tumors can possibly lead to cancer, it is a crucial task to detect it early through Magnetic Resonance Imaging (MRI) or Computed Tomography (CT), which are the techniques that use radio waves and magnetic fields to present a detailed view of the body organs. The images obtained from the MRI makes it hard to locate the exact position of the tumor and hence it is a challenging task to detect the tumor accurately. Thus, computer-aided methods (segmentation, detection and classification processes) with better accuracy are required for early tumor diagnosis. The segmentation of brain tumor which is usually carried out manually by the radiologists through their expertise and skill is a highly prolonged task and there can be chances of some faulty predictions, hence, the semantic segmentation is proven to be an effective method to overcome this problem. Semantic segmentation method is applied to brain tumors which are automatically segmented with the aid of deep learning techniques (CNN, RNN, GAN, LSTMs, etc.). The usage of deep learning techniques with greater accuracy and robustness are proven to be effective for the precise diagnosis of brain tumor. The primary objective of this paper is to examine the previously published techniques using deep learning for the human brain tumor prediction.
An Efficient Way to Predict Stock Price using LSTM-RNN Algorithm

Stock price prediction is a challenging and important task in finance. Machine learning algorithms have shown promise in predicting stock prices, with the ability to analyze large datasets and identify complex patterns. This study presents a comprehensive study of machine learning algorithms for stock price prediction. The proposed approach uses a variety of machine learning algorithms, including decision tree, random forest, support vector regression, and artificial neural networks. The models are trained on historical stock price data and evaluated using real-world data from various companies. This study calculates the RMSE, Error rate and the closing price of the stock for that day.
A Smart Spotify Assistance and Recommendation System

This project will be used to discover correlation between the Spotify user and the respective song searched by him. Based on the found relations, this system will recommend songs accordingly. This Project also consist of multiple features to assist user and provide a better experience overall. This project will be build using Spotify library for python along with SpotifyWEB API built for developers. It also uses few more libraries like Numpy, Pandas, and OS etc. To access the songs/albums we will be using ‘‘Universal Resource Identifier’’ which is a special six-digit unique code. The recommender system’s objective is to adjust to the particular user-related requirements of the music sector.He primary emphasis of this paper is the creation of a dynamic system just to assist people who love to listen music for entertainment through the recent technologies and frameworks.
Vision Guided Drone Flight for Entering Confined Spaces for Inspection

Unmanned aerial vehicle (UAV) flight in confined spaces is an emerging area of interest because solutions can create opportunities for developing drone-based inspections for societal benefits, military and other applications. This article describes new work in the area of UAV flight in confined spaces. To develop the UAVs' ability to operate in small space environments, the task of identifying an opening to the confined space and determine its center was solved, with the goal being to autonomously minimize the distance between the UAV's position and the gate's center. There were three main approaches for gate detection, each involving placing an object in the four corners of the gateway. The first approach to gate detection was grayscale and HSV filtering of LED lights. Using a combination of both HSV and grayscale filtering caused glare from the LED lights to skew center detection of each light. The second strategy was to train YOLOv5 using over 200 annotated images of LED lights, but it still produced an issue of identifying false positives which caused the center to skew. The third detection strategy utilized april tags instead of LED lights. The specific patterns solve the issue of false positives entirely, for each april tag has a unique recognizable pattern. The new issue was visibility as a result of lighting. However, the positives of size manipulation and accuracy overshadowed this. The maximum resolutions and distances at which the April tags could be detected were tested with the picamera V1. Based on algorithms for the RaspberryPi, the UAV was able to adjust it's flight with the center of the april tag gateway achieving the project goal.
Prediction of Alopecia Areata using CNN

Alopecia Areata is a disease which is chronic in nature, it leads to drastic hair damage. This is an autoimmune disease that can have several aftermaths like brittle nails and bald spots. Its origination can be affiliated with hormonal changes and hereditary factors. There is no clinical test for its detection and the majority of diagnosis is conducted by examining the patients with the naked eye. Thus the expertise of the doctor and hearsay is the only means of detection, the obvious repercussion being low credibility of results. The aim of this study is to present a novel CNN architecture that can facilitate and streamline the arena of detection by taking an image-based dataset. The results of the CNN are compared with four machine learning models that are Naive Bayes. Support Vector Machine, Logistic Regression, and Decision Tree. The dataset has been procured by web scraping and the Dermnet dataset. Image preprocessing techniques haw been used to augment and enhance the dataset. The best accuracy obtained is 98%.
Review of the Theory and Application of Infrared Thermography in Transmission Line Monitoring and Equipment Monitoring

Infrared thermography temperature measurement technology has become a hot spot in the field of temperature measurement at home and abroad due to its non-contact temperature measurement, nondestructive detection, and fast response characteristics. After years of development, infrared thermal imaging technology gradually plays an important role in various fields. This paper briefly introduces the infrared thermal imaging temperature measurement technology and modern infrared thermal imager performance principle, as well as the application of infrared thermal imager in the field of electric power in recent years, focusing on the analysis of the application scheme on transmission line monitoring, analyzing the current problems and the direction that can be improved.
Harmonic Circulating Analysis of Modular Multilevel Matrix Converter based on Two-Dimensional Phasor Method

The most promising topology for AC-to-AC conversion in high-voltage and high-power applications is the modular multilevel matrix converter (M3C). M3C is the core equipment for constructing Fractional Frequency Transmission System (FFTS). The harmonic characteristics will affect the system stability of FFTS. Based on the proposed 2-D phasor method, the interaction between the two sets of different frequency components from both sides is analyzed, and a general formula for arm current and capacitor voltage is established. Under the control strategy of M3C system, the validness of the proposed model has been confirmed through simulation tests conducted in the MATLAB/Simulink.