Miracle-dz/UniFAR-FTUs huggingface dataset:
The C*–algebras of Compact Transformation Groups

Abstract We investigate the representation theory of the crossed-product ${{C}^{*}}$ -algebra associated with a compact group $G$ acting on a locally compact space $X$ when the stability subgroups vary discontinuously. Our main result applies when $G$ has a principal stability subgroup or $X$ is locally of finite $G$ -orbit type. Then the upper multiplicity of the representation of the crossed product induced from an irreducible representation $V$ of a stability subgroup is obtained by restricting $V$ to a certain closed subgroup of the stability subgroup and taking the maximum of the multiplicities of the irreducible summands occurring in the restriction of $V$ . As a corollary we obtain that when the trivial subgroup is a principal stability subgroup; the crossed product is a Fell algebra if and only if every stability subgroup is abelian. A second corollary is that the ${{C}^{*}}$ -algebra of the motion group ${{\mathbb{R}}^{n}}\,\rtimes \,\text{SO}\left( n \right)$ is a Fell algebra. This uses the classical branching theorem for the special orthogonal group $\text{SO}\left( n \right)$ with respect to $\text{SO}\left( n-1 \right)$ . Since proper transformation groups are locally induced from the actions of compact groups, we describe how some of our results can be extended to transformation groups that are locally proper.
UAV Position & Cell Partition Optimization Considering User Distribution and Data Rate in UAV Network

In this paper, we investigate the effective use of UAV as flight base stations for users using wireless services. UAV's limited battery usage time leads to hover time limitation, and we propose Adaptive UAV-AP Positioning & Cell-Partition framework that can maximize average throughput within a fixed hover time. In the proposed model, UAV is deployed based on arbitrary spatial distribution and user's required data rate, and Cell-Partitioning is carried out. In this problem, the optimal cell-partition is derived by solving the optimization problem based on the Optimal Transport Theory. This framework can expect more adaptive cell-partitioning than cell-partition considering only the existing user distribution probability.
Printed Circuit Board Defect Detection Using Generative Deep Learning Model

Recently, Printed Circuit Board(PCB) design of complex structures is essential to design small electronic devices. Defect detection is one of the most important PCB processes because PCB defects have a fatal effect on product quality. For previous defect detection, methods such as Automated Optical Inspection(AOI) or In-Circuit Test(ICT) were used. But these methods have their disadvantage. They need high-cost inspection equipment, and new setting values are required each time as the surrounding environment changes. The proposed system is robust to environmental changes using generative deep learning models. In addition, it is more convenient to use than existing deep learning models using semi-supervised learning.
Outage Performance Analysis of Multiple Reflected Channels for OAM transmission

Previous OAM researches have focused on capacity analysis which has derived and improved by the orthogonality between OAM modes. However, the practical approach has a lack of attentions and is required to provide the feasible channel model. The channel model must include divergence issue and distorted channels. Therefore, this paper proposes a geometrical UCA transceiver and a generalized system model for orbital angular momentum (OAM) with alerting the divergence factor (DF) and distorted channels from multiple reflectors. This paper analyzes the outage performance (OP) of OAM transmission in the reflected channel and investigate numerical results based on the simulations and equations.
A Survey on Lock-free Binary Search Tree

Binary Search Tree is the data structure that has fast search speed and easy addition, removal of elements. Due to these advantages, binary search tree (BST) is one of the frequently used data structures of these days. However, most binary search tree schemes use locking technique to support multiple access of multiple threads for synchronization. Unfortunately, this locking techniques significantly reduce the performance of BST. Locking techniques block or spin the threads that has not obtained the lock until the thread that has obtained the lock completes its job. Thus, to improve the performance of BST, efficient concurrent programming technique should be applied, such as lock-free technique. In this paper, we conduct a comparison of existing lock-free/non-blocking BST methods. We analyze operations and evaluate the strengths and weaknesses of each schemes.
Varieties of apolar subschemes of toric surfaces

Powersum varieties, also called varieties of sums of powers, have provided examples of interesting relations between varieties since their first appearance in the 19th century. One of the most useful tools to study them is apolarity, a notion originally related to the action of differential operators on the polynomial ring. In this work we make explicit how one can see apolarity in terms of the Cox ring of a variety. In this way powersum varieties are a special case of varieties of apolar schemes; we explicitely describe examples of such varieties in the case of two toric surfaces, when the Cox ring is particularly well-behaved.
Backhaul Aggregator Design for Real-Time 3D Printer System

5G and the industrial Internet of Things (IIoT) are cutting-edge technologies. Although they serve the communications and industrial sectors separately, the relevance of technology will still make them closely related. For example, in 3D printing, the intelligent manufacturing technology industry relies on high-speed developed Internet technology. However, advances in man-ufacturing activities, such as human-machine collaboration, and remote real-time monitoring, put forward higher requirements for communication latency and usually require lower latency at around 1 ms. Although the current wireless communication technology has made significant progress, and the end-to-end latency is at about 20–100 ms, it still cannot meet the urgent demand of communication latency of real-time monitoring systems. Therefore, the network operators required drastic changes in network architecture, which impact the backhaul directly for the 5G communication systems. Based on the technology and application characteristics of IIoT and 5G communication systems, this paper proposed direct fiber via layer two switches as an aggregator to sharply reduce latency and improve the quality of the 5G network technologh of IIoT.
The Internal Correlation: Its Applications in Statistics and Psychometrics

The internal correlation, a measure of dependency in a set of variables, is discussed and generalized. This coefficient is an upper bound to the product moment correlations, multiple correlations, and canonical correlations that can be defined in a set of variables. Applications of the internal correlation coefficient and its generalizations are given for a number of data-analytic situations. Where appropriate, we discuss tests of significance. We illustrate the internal correlation and expand the concept to a series of additional indices: local internal, up-internal, and down-internal correlations. Uses of these indices are illustrated in several areas: multicollinearity, ridge regression, factor analysis, principal components analysis, and test reliability.
Semi-supervised Intrusion Detection Joint Extreme Value Theory and Conditional Auto-encoder for Smart Grid

Intrusion detection is an important tool for smart grids to ensure safe and stable operation. Due to the few attack samples in the smart grid, the semi-supervised intrusion detection method has become a research hotspot. However, existing semi-supervised models always ignore the accuracy of the classifier itself, resulting in low accuracy and poor generalization ability. To solve this problem, a semi-supervised intrusion detection model joint extreme value theory and conditional auto-encoder is proposed for smart grids in this paper, simply called SSEC. This model tries to improve the accuracy of the classifier in three aspects: input, feature extraction, and output. Firstly, extreme value theory is used to improve the classifier and learn the prior tail distribution from a small number of posterior samples, thereby reducing the impact of incorrect pseudo labels in semi-supervised mode and optimizing the output accuracy of the classifier. Secondly, the conditional auto-encoder works with negative sample learning to optimize the accuracy of feature extraction for the classifier. Finally, a dynamic update strategy is designed to remove training samples with incorrect pseudo labels to ensure the accuracy of the classifier's input. Extensive experiments on the KDDCUP99 dataset and the UNSW_NB15 dataset illustrate the superiority of the SSEC model.
Performance Optimization and Analysis of energy efficient multipath routing protocols for Wireless Sensor Networks

The AODV or AdHoc On-Demand Distance Vector is a notable and broadly utilized protocol for MANET's. The Mobile AdHoc Network or MANET, without any infrastructure, is an assortment of remote nodes conveying and communicating over a wireless network. All wireless devices working in AdHoc mode inside range are allowed to have communication with each other in absence of base station. The routers are capable to roam and communicate arbitrarily and organize themselves as per the requirements when the nodes structure themselves into a random topology. Radio signals possess range limitations due to which multihop communication in MANETs is inevitable. The performance of traffic situations utilized in a mobile AdHoc network is responsible for the transmission and gathering of data between source and destination in a MANET. This paper provides a comprehensive comparative analysis of the routing protocols with respect to variation in node configuration
Ricinus communis-based biopolymer and epidermal growth factor regulations on bone defect repair: A rat tibia model

We report the effect of the addition of an epidermal growth factor to a Ricinus communis-based biopolymer in the healing of a rat tibia model. Bone repair and osteointegration after a period of three weeks were evaluated employing photoacoustic spectroscopy and x-ray diffraction. A parallel study was performed at 1, 2, 3, 4, 5, 6, 7, and 8 weeks with energy dispersive x-ray spectroscopy. We conclude that the use of an epidermal growth factor (group EGF) in vivo accelerates the process of bony repair in comparison with other groups, and that the employment of the Ricinus communis-based biopolymer as a bone substitute decreases bone production.
Current Trends on Deep Learning-aided Channel Coding

Artificial intelligence (AI)-aided approaches are emerging in solving physical layer communication problems. In this paper, we focus on applications of deep learning (DL) in channel coding for wireless communications and provide a survey on related topics. We describe various decoding problems of short linear codes and LDPC codes assisted by deep neural networks. The survey will be concluded with a list of future research directions.
Selección de Antenas UHF para una Misión Near-Space en un HAB y las frecuencias reguladas

La presente investigación consiste en explicar las condiciones y parámetros que conlleva una misión cercana al espacio utilizando un globo de gran altitud, mostrando la importancia de tener una comunicación constante a través de radiofrecuencias, también tomando en cuenta los transmisores y antenas de radio, las consideraciones técnicas de radiofrecuencia y las regulaciones de telecomunicaciones.
The Influence of Electrical Equipment Structural Elements on their Wireless Communications

The paper presents a review of the main obstacle that occurs in electrical equipment wireless communication networks. Wireless communications represent one of the biggest advantages in today's technology evolution. Remote access to the monitoring of electrical equipment is a necessity nowadays. All wireless connectivity devices are susceptible to wireless interference, communication devices within electrical equipment are no exception. There are many obstacles in the construction of power management equipment and buildings that can interfere with wireless communication signals. However, often the building elements of electrical equipment are more difficult to replace. The paper proposes a presentation of the main types of obstacles known in wireless communications. Also, it was proposed solutions to diminish the influences on the wireless communication network, in order to avoid operation malfunction of the electrical equipment.
Detecting Minimum Distance of LDPC Codes with Improved Approximately Nearest Codeword Method

Approximately nearest codeword (ANC) is a classic approach for detecting minimum distance of low-density parity-check (LDPC) codes. With the principle of local search, ANC method is capable of detecting minimum distance of LDPC codes within acceptable time. However, the message passing (MP) decoding algorithm and scheduling scheme, as well as the local search strategy, still have much space to improve. To fill the gap, this paper proposes an improved-ANC method which employs novel noise introduction method, decoding algorithm, scheduling scheme and searching strategy, to accelerate the computation of the minimum distance of LDPC codes significantly. Finally, we run simulations on LDPC codes in Wi-Fi5 and 5G-NR standards. The simulation results are presented and demonstrate the effectiveness of our proposal.
Convolutional Residual Blocks With Edge Guidance for Image Denoising

We propose an image denoising Convolutional Neural Network (CNN), comprises of alternative vertical and horizontal Sobel edge guided residual blocks. The input images are corrupted by additive white Gaussian noise. Initially, the first convolutional layer maps three channel (RGB) input noisy image into depth feature maps. Next, each residual block extracts vertical/horizontal edge using the Sobel edge mask. The edge guidance helps in preserving edges and textures in the given input image. Further, the input depth feature map along with corresponding edges are fed into simple $1\times 1,1\times 3,3\times$ 1 and $3\times 3$ convolutional layers with residual connections. Usage of simple convolutional blocks reduces the overall network parameter count, while maintaining the better performance. The simple and efficient design of the proposed residual convolution block can easily be plugged into any other image reconstruction networks. Experimental results show that the proposed model is more effective in preserving textures and edges while removlng noise.
New lower bounds for t‐coverings

Fisher proved in 1940 that any 2‐ (v,k,λ) design with v>k has at least v blocks. In 1975, Ray‐Chaudhuri and Wilson generalized this result by showing that every t‐ (v,k,λ) design with v⩾k+⌊t/2⌋ has at least v(⌊t/2⌋) blocks. By combining methods used by Bose and Wilson in proofs of these results, we obtain new lower bounds on the size of t‐ (v,k,λ) coverings. Our results generalize lower bounds on the size of 2‐ (v,k,λ) coverings recently obtained by the first author.
Relabeling Method for Improving Vehicle Part Detection

The lightweight deep learning detection method does not properly distinguish partial objects that are named differently depending on their location but has the same shape and appearance like a tire. To solve the problem, this paper introduces a method to relabel the detection result of vehicle parts. We define vehicles parts with super-class and sub-class according to the shape and location, and then detect super-classes. For relabeling super-class to sub-class, we classify vehicle viewing direction, and generate a set of sub-class combinations from super-classes. The spatial distributions among the detected partial objects are analyzed using the likelihood. Then, the labels of the detected parts are determined. We tested our method with self-collected and open dataset, and achieved a mAP value of 87.7%, which is about 11 % better than tiny YOLO v4.
Investigating Correlation between the mm-wave Array Antenna and Digital Wireless Communication in the 5G Mobile System

In this paper, an intuitive approach to assessing advantages of beamforming in 5G wireless communication is proposed as a novel try and practical demonstration of importance of alignment between the transmitter's and receiver's beams working in millimeter-wave frequency bands. The effects of the misalignment and alignment between beams need to be checked for, which was conducted with a horn antenna and the 4 × 4 Butler matrix RF-to-RF wireless connectivity between the horn and the microstrip line beamformer, concerning the changing angle of the beam from the Butler matrix, was tested, showing over 12 dB enhancement in received power. This direct electromagnetic link test was accompanied by examining 64-QAM constellations for beam-angle change.
Automatic Detection of Subjective, Annotated and Physiological Stress Responses from Video Data

Machine-learning-based stress detection systems differ with respect to the ground truth used for training the algorithms. It is unclear how models trained on different facets of the stress reaction (e.g., biological, psychological, social) can be compared, interpreted and applied. In this study, we investigate the influence of the stress label on the performance of machine learning models trained on either vocal characteristics or facial expressions extracted from videos. We collected videos from 40 male participants while being exposed to the Trier Social Stress Test (TSST) and assessed self-reported, live observed, video-annotated and neuro-endocrinological stress levels. We train three standard machine learning models to separately predict different stress labels using either voice or facial cues. Analyzing the relationships of different stress facets we found that observers' annotations were significantly positively associated (live vs. video annotated, $\boldsymbol{\rho}_{\mathbf{s}}\ =\ . 53$). Similarly, the neuro-endocrinological stress indices correlated with each other (cortisol vs. sAA, $\boldsymbol{\rho}_{\mathbf{s}}$ =.39). Machine learning experiments resulted in predictions that were positively associated with panel-annotated stress levels showing significantly stronger correlations in voice-based models $(\boldsymbol{\rho}_{\mathbf{s}=}.54\ \mathbf{v}\mathbf{s}. \boldsymbol{\rho}_{\mathbf{S}}=.30)$. Predictions of self-reported stress were positively related to ground truth values for face-based $(\boldsymbol{\rho}_{\mathbf{s}}$ =.24) but not for voice-based models. There was no evidence for successful predictions of video-annotations or endocrinological stress levels in both settings. We provide evidence that machine learning models trained on different stress assessments perform differently and should be interpreted and applied accordingly. Implications and recommendations for future work on video-based stress detection are discussed.
Study of the Characteristics of Few-Mode Microstructured Optical Fibers with 6 Cores Made of Highly Doped GeO2 Silica and Induced Chirality

The necessity of using silica-based few-mode microstructured optical fibers with induced longitudinal twisting (chirality) is experimentally substantiated. The case with a 6-core made of highly doped GeO2 silica for building the cross-section of an optical fiber is considered. Prototypes of microstructured optical fibers are fabricated and studied to exhibit the quasi-annular optical filed distribution. The results of experimental studies of the mentioned optical elements are presented and dependence of the basic characteristics on the parameters of the technological cycle of their production is estimated.
An Implementation of Traffic Volume Forecasting System using Distributed Deep Learning

The increase of means of transport, on the one hand, makes traveling easy and convenient, and unintentionally makes traffic congestion more and more serious, especially in big cities. Traffic congestion has a direct and indirect impact on a country's economy and its dwellers' health. Traffic congestion effects on individual level as well. Time loss, especially during peak hours, mental stress, and the added pollution to the global warming are also some important factors caused due to traffic congestion. In recent years, traffic volume prediction has received lots of interest from researchers. However, there are still many challenges that needs to be resolved. Models for traffic volume prediction needs not only to be accurate but also be able to handle the huge volume of input traffic data. This study present an implement of a traffic volume forecasting system using distributed deep learning. A state of the art deep learning model, that is LSTM, is used to provide accurate forecasting result. Especially, the model is trained in a distributed manner using BigDL library. Thank to the distributed training, the model now is able to receive a huge volume of traffic data to provide the most accurate results.
Reactive Power Compensation at Industrial Consumers: Romanian Study Case

The paper presents the results obtained at the end of a study which aim was to answer the question: What is the best solution for reactive power compensation in the case of industrial consumers? Reactive power compensation is an action needed to improve the operation of ac power systems. In the case of industrial consumers, the main goal of reactive power compensation is to increase their electrical system’s power factor that will benefit them economically and technically. The study had as support a Romanian industrial consumer, more specifically its electrical circuits’ structure, electrical equipment, active and reactive consumption, and power quality indices values. The first step of the study was to create a Simulink/MATLAB model for the industrial consumer. The next step was to analyze different technologies and equipment dedicated to reactive power compensation and to simulate diverse situations. The analysis of the results regarding the values of power factor could determine the best solution for the case study. The outcomes of the study were as follows: the use of capacitor banks is the simpler one, but it is not the best in case of fluctuating loads. The other solutions that imply the use of power electronic devices mean continuity in the compensation.
Computability of the packing measure of totally disconnected self-similar sets

We present an algorithm to compute the exact value of the packing measure of self-similar sets satisfying the so called Strong Separation Condition (SSC) and prove its convergence to the value of the packing measure. We also test the algorithm with examples that show both the accuracy of the algorithm for the most regular cases and the possibility of using the additional information provided by it to obtain formulas for the packing measure of certain self-similar sets. For example, we are able to obtain a formula for the packing measure of any Sierpinski gasket with contraction factor in the interval $(0,\frac{1}{3}]$ .