Miracle-dz/UniFAR-FTUs huggingface dataset:
The Importance of Using Simulators in Dosimetry for Radiation Protection

The main objective of radiological protection dosimetry is to evaluate the absorbed dose in sensitive organs and tissues of the human body by internal or external sources of radiation (ICRP 103, 2007). Simulators are physical or computational models used to simulate the transport of ionizing radiation, and its interactions in the tissues of the human body and to evaluate energy deposition in regions of interest. The Monte Carlo method is applied to reproduce a statistical process similar to the interaction of nuclear particles with human tissues. The objective of this work is, through literary research, to compare computational Simulators with physical Simulators, and to show the importance and evolution of the use of computational Simulators in the dosimetry of radiological protection. Physical Simulators, especially anthropomorphic ones, are very expensive and limited, besides being difficult to position (AP/PA/PERFIL). Computer simulators do not have radiation emissions, which protects researchers and ensures that there is no variation of the simulated radiation beam. To reduce uncertainty in dose calculations caused by anatomical variations, the scientific community has developed several computational Simulators with modified ICRP standard reference values about body mass, height, positioning, size and position of organs and structures, etc.
Optimization Method of SLAM Mapping Based on Plane Fitting and Intensity Information

The traditional SLAM (Simultaneous Localization and Mapping) algorithm function is that under the condition of uncertain position the robot creates a map in a completely unknown environment then uses the map to carry out autonomous positioning and navigation tasks. Because of the large error of the original LiDAR data, this paper proposes a method to improve the optimization method of LOAM odometry by using the least square method. In addition, the current SLAM algorithm based on LiDAR mainly uses geometric features to optimize while ignoring the intensity information of LiDAR, so this paper proposes to use LiDAR intensity information in feature extraction, odometry and back-end optimization. experiments show that the method proposed in this paper further improves the accuracy of mapping.
Energy Efficiency Improvement Rate for Low Power UAV Identification Environment

In this paper, we present an protocol for identifying UAVs with low power and try to check how much energy efficiency is improved while maintaining the UAV identification success probability almost the same as before when the algorithm is applied. The simulation results were derived by dividing the UAVs into scattered and densified distributed flying cases, and the UAV identification success probability and energy efficiency improvement rate results were presented according to each distribution.
Atmospheric Effects on Downlink Channel of Satellite to Ground Free Space Optical Communications

The free space optical (FSO) communication system promises to be one of the most key technologies for dealing with high bandwidth requirements which have prompted the use of optical links between space and ground stations. In this paper, we investigate the atmospheric effect on downlink satellite-to-ground FSO communication. To consider various atmospheric conditions, we present FSO channel factors such as fog attenuation coefficient, refractive index parameter, coherence length, turbulence models and angle of arrival fluctuations. From the simulation result, we examined the influence of optical channel conditions such as atmospheric attenuation and intensity fluctuation on channel performance of a satellite to ground FSO downlink. Based on this, we proved that the use of longer wavelengths is preferable to minimize the effect of turbulence whilst using larger receiver apertures reduce angle of arrival variations. Moreover, the location of the optical receiver has a significant impact on atmospheric turbulence. The obtained mathematical results can be utilized to discover the optimal channel parameters for the design of a satellite to ground the FSO link.
Dual-Directional Polarization Insensitive Metamaterial Absorber based on Modified Circular Ring Structure

This paper presents the designs and analysis of dual-directional polarization insensitive metamaterial absorbers based on circular ring structure. The circular ring structure is then extended and modified to achieve new performance. The unit cell of the metamaterial consists of a circular ring copper with and without copper lines printed on dual layer of 0.8 mm thick FR4 substrate with dielectric constant of 4.6 and loss tangent of 0.019. The simulated result shows that the circular ring shape metamaterial absorber achieves very high absorbance up to 98.43%. The measurement result confirms the calculated performance in simulation. The circular ring structure can also be modified to achieve new performance such as frequency reduction and dual band response. The new modified metamaterial absorbers also confirm the polarization-independent behavior. Thin metamaterial absorbers are very important for various application such as stealth technology, radar cross section and mutual coupling reduction.
Deep models optimization on embedded devices to improve the orientation estimation task at sea

The environmental monitoring task has greatly benefited from the improvements achieved in the robotics field. The enhancement of navigation and control algorithms, together with the use of performing, small and low-cost sensors, allows in fact to reduce the implementation costs while improving the system reliability. This is strongly supported by the developments of embedded hardware, smart computing devices able to collect and process data in real-time and in low-resource settings. Following the results obtained by DOES, this work aims at putting another step towards its deployment in live scenarios: we propose a study on the performances of DOES tested on embedded systems, using lighter backbone architectures and model optimization techniques.
Mining Hub Nodes Based on Fast Brandes Betweenness Centrality in a Weighted Complex Transportation Network

The hub nodes in transportation network have great influences on the connectivity, reliability and safety of the entire network. However, most of the currentresearch only consider the structure of transportation network and neglect the traffic-related factors. Thus, present study proposes hub nodes mining algorihm that combines traffic flow feature and Betweenness centrality, the importance of the node is related to the Betweenness of nodes, the traffic flow, and the grade of the connected road. Moreover, it introduced fast algorithm Ulrik Brandes reduce the complexity of calculation. The results of the simulation indicate that the proposed algorithm can mining hub nodes effectively in the road network including bridge nodes. The findings of this paper can be applied to provide decision-making support for the management of transportation network and the optimization of traffic construction
A Novel Scene Matching Algorithm via Deep Learning for Vision-based UAV Absolute Localization

Almost all military systems and combat platforms rely on GNSS to achieve military missions. However, they are facing more and more intensive jamming and spoofing attacks. Due to the rapid development of computer vision technology and vision sensor, visual navigation and localization technology have gradually revealed their advantages to solve this kind of problem. In this paper, a new framework of visual localization under a GNSS-denied environment is proposed. Firstly, a novel image preprocessing method based on information entropy and granular segmentation is presented which can significantly filtrate the Google tiles in the georeferenced area to parsimony set to reduce UAV localization time. Secondly, we introduce an end-to-end image matching method based on convolutional neural network and image preprocessing to realize UAV localization which has strong robustness and localization accuracy. Finally, the proposed localization algorithm achieves experimental performance on the real images captured by DJI Phantom 3 drone. The good accuracy and fast execution time shown by experiments indicate that the proposed localization algorithm can serve as a powerful tool to keep the UAVs working continuously in the face of intensive jamming and spoofing attacks.
Weight-Counting Based Greedy Pilot Allocation in Cell-Free Massive MIMO

Pilot reuse caused by the limited number of orthogonal pilots brings serious pilot contamination to cell-free multiple-input multiple-output system. This paper focuses on the issue of pilot assignment to mitigate pilot contamination. In the proposed scheme, the existing Basic pilot allocation algorithm which proposes a greedy pilot allocation algorithm based on weighted counting is improved. Specifically, we first use large-scale fading coefficient to evaluate channel status of users, so as to determine the pilot allocation sequence for users. Then, a threshold for the number of pilot frequencies is introduced to avoid reusing one pilot too much. Simulation results show that the proposed algorithm outperforms the original algorithm and achieves a significant improvement than most of classical algorithms.
Synthesis of Artificial Neural Network Training Algorithm with Interval Target Tuning in the Implementation of the Method for Constructing a Decentralized Hierarchical Network of Modular Type

The paper describes the application of artificial neural network training algorithm with interval target tuning in the implementation of the method for constructing a decentralized hierarchical network of modular type. The main concept of combining these two methods is devoted to the idea of improving the solution of the problem of constructing a model of difficult formalized systems. The proposed hierarchical structure of the network will allow solving a complex initial problem asynchronously in time and in parallel in space, involving distributed computing resources and geographically dispersed teams of researchers.
Segmentation and Edge Detection for Ionogram Automatic Scaling

Ionograms, which are captured by ionosondes, include crucial data about the ionosphere. The first step in analyzing ionospheric weather is scaling ionograms. Due to the current rapid expansion of data collection, it is impossible for human experts to manually scale a sizable number of ionograms in a timely manner. In this study, a three-stage method based on semantic segmentation networks, an edge detection module, and a feature fusing module is suggested to obtain precise ionospheric parameters of the E, F1, and F2 layers. The segmentation networks are trained using 3448 ionograms from the Chinese Academy of Sciences Digital Ionosonde situated in Hainan, Huailai, and Wuhan. The test results over 863 images indicate that 99.1% of the critical frequency foF2 autoscaled values have an error of within 0.2 MHz, and 98.7% of the minimum virtual height h'F2 autoscaled values have an error of within 10 km, demonstrating that our technique performs of almost as well as that of human specialists. Thus, the investigation into ionospheric physics could benefit greatly from our work.
Experimental measurements of radar absorbing materials in semi anechoic chambers

This paper focuses on radar absorbing materials (RAM) characterization simple techniques that can be used in typical semi anechoic chambers for research and development projects. The aim of our work is to evaluate RAM materials performances using common measurement setups available in EMC laboratories. The obtained results made us conclude that further studies may be necessary for conducting more accurate measurements of these materials properties. Anyhow it's possible to use these kind of results in preliminary R&D stages to compare different technologies / project results.
Six-phase Fractional Slot Concentrated Winding Permanent Magnet Synchronous Motor with Reduced Torque Ripple

A comparative study on a fractional slot concentrated winding permanent magnet synchronous motor with 10 rotor poles and 12 stator coils connected in different arrangements and supplied from a six-phase inverter is presented in this paper. The aim of this investigation is to reduce the torque ripple of the machine by using the appropriate winding connection and power supply unit.
Improved Preprocessing for Machine Learning Intrusion Detection in IEEE 802.11

WiFi networks have become the most used technology when connecting various personal devices to the Internet at home, in the office, or even in public places. Such popularity and widespread use make WiFi networks an attractive target for attackers. Despite the encryption of network frames and other security features implemented as part of the IEEE 802.11 family of standards, different kinds of intrusion activities that disrupt regular WiFi communication are still on the rise. Detection of such sophisticated attacks is the first step in protecting WiFi networks. Therefore, Intrusion Detection Systems (IDS) are of paramount importance. This paper evaluates different machine learning algorithms applicable for implementing an IDS. We train and evaluate attack detection models with respect to the well-known and publicly available Aegean WiFi Intrusion Dataset (AWID), using the version having 4 labels and a reduced number of items (AWID-CLS-R). Our focus is especially on the preprocessing phase, where we apply specific feature selection procedures. Based on the dataset features, we also infer new feature variables that add information about relations between individual dataset items (frames). Compared to other similar works, our evaluation results show that feature reduction combined with specific machine learning algorithms can improve attack detection rate in terms of accuracy, precision, and recall.
Indoor Location Based Tracking using Euclidean Distance Estimation (LTS-ED)

To provide location-based services like indoor navigation systems or indoor crowd monitoring systems in an indoor environment, indoor location tracking systems are required. The implementation of an indoor location tracking system employing Wi-Fi signal strength will be the main topic of this study. However, there are several methods for tracking an indoor location, including fingerprinting, triangulation, and trilateration. The proposed system was created using a fingerprinting technique. The server, which is based on Java, and the client, which is based on Android, make up the system's two primary parts. Regarding Wi-Fi RSSI scanning, the client's application oversees removing weak received signal strength (RSSI), more specifically RSSI lower than −85dBm, while the server provides the location ID with the highest likelihood of matching based on offline training data kept in a database. The software algorithm powered by MySQL will be the only foundation for the indoor location monitoring system. The findings demonstrate that smaller-area location tracking provides more accuracy than larger-area tracking.
The Radiographic view classification and localization of Lumbar spine using Deep Learning Models

The radiographic view of the Lumbar spine as anteroposterior (AP) and lateral (LA) taken from the front and side view of the patient should classify in the first step of computer-aided diagnosis; it helps to select an algorithm to localize Lumbar spine using Deep Learning Models in the following steps. However, the inclusion of radiology images into the analysis program may be inaccurate. and thus, it cannot be used as a baseline for subsequent spinal localization analysis. Object detection based on a deep learning approach, therefore, needed to determine the percentage of model confidence before it led to a more accurate analysis of the lumbar spine localization. The accuracy of the radiographic view classification of this experiment was 99.4%, Lumbar spine localization on AP view was 95.6%, and, on LA view, localization was 93.5%.
Attention Mechanism-based CNN for Surface Damage Detection of Wind Turbine Blades

Wind turbine blades undertake the task of energy conversion. Wind turbine blade damages detection can provide guidance to maintain wind turbine. In an effort to address the low accuracy of current blade damage detection, this paper proposed an attention mechanism-based CNN the classification of common damage to wind turbine blades. This framework uses morphological transformations to enlarge the dataset and adjusts contrast and chromaticity to enhance damage features. The preprocessed blade images are sent to CNN for feature extraction, and then the feature maps are re-calibrated by applying the proposed mechanism to build up the score of the damage feature in the classifier. The performance of the proposed framework for detecting blades damage was confirmed by manually collected images from commercial wind farms, with a multi-classification accuracy of 92.85%. And this paper analyzes the role of the attention mechanism in damage detection by visualizing the attention mechanism using grad-CAM.
Object Detection with Dataset Augmentation for Fire Images Based on GAN

Objection detection is the task to find and classify objects in images. Many object detection models based on a deep learning algorithm have been proposed. Deep learning algorithms require the models to be trained with affluent images with accurate annotations. However, in the case of fire detection, neither enough datasets to train the detection model nor correct and sufficient annotations exist. In this paper, we propose a GAN-based model to generate fire images with bounding boxes to enhance the performance of the fire detection model. Through the experiments, we demonstrated that the model can inject flame images into the clean images within specified areas and the generated images are enough to augment the fire detection dataset so that the model's object detection performance can be improved.
Access Point Selection Schemes for Cell-free Massive MIMO UDN Systems

Cell-free massive MIMO(mMIMO) is considered as one of technologies for the next generation wireless communication where higher spectral efficiency (SE) and energy efficiency are required. In this paper, we focus on the Access Point(AP) selection in scalable cell-free mMIMO systems, where each user is served by a selected APs in the network. We propose an AP selection scheme using a linear assignment algorithm. Numerical results show that the proposed scheme offers better downlink SE for users with bad channel conditions compared to exiting AP selection schemes.
Elastic Network Cache Control Using Deep Reinforcement Learning

Thanks to the development of virtualization technology, content service providers can flexibly lease virtualized resources from infrastructure service providers when they deploy the cache nodes in edge networks. As a result, they have two orthogonal objectives: to maximize the caching utility on the one hand and minimize the cost of leasing the cache storage on the other hand. This paper presents a caching algorithm using deep reinforcement learning (DRL) that controls the caching policy with the content time-to-live (TTL) values and elastically adjusts the cache size according to a dynamically changing environment to maximize the utility-minus-cost objective. We show that, under non-stationary traffic scenarios, our DRL-based approach outperforms the conventional algorithms known to be optimal under stationary traffic scenarios.
Industrial Humanless Monitoring System over 5G Networks in the Shipyard Environment

As the development of hardware technology and the application of AI technology become popular, efforts to apply image-based services to real industrial sites are increasing. In particular, the development of image-based worker recognition technology is a field in which various studies are being conducted as a base technology that can be widely used in many industrial fields, such as industrial health, safety monitoring, work process management, and collaboration between humans and robots. However, opposition from labor unions due to workers' privacy protection issues and outdated on-site network facilities are acting as obstacles to the application of video-based application services in actual industrial sites. To solve this privacy problem, we introduced a technology that recognizes a worker at the end device where an image sensor is installed and replaces the recognized worker with an existing background image. In this paper, we introduce the previously proposed worker deletion technology [1] and analyze the problems that occurred in field experiments. In addition, the structure linked with the 5G network is additionally presented, and advanced performance improvement techniques are presented. The performance of the implemented algorithm is applied to various hardware platforms to find out its performance and applicability.
Survey: Strategies for Loss-Based Discrete-Time Hazard and Survival Function Estimation

Survival analysis (time-to-event analysis) addresses a fundamental problem of understanding the relationship between input and the survival outcomes and predicting the survival outcomes. Survival analysis has been widely used in many areas - including economics, finance, engineering, and medicine - and recently has gained substantial interest from the view of non-parametric machine learning and deep learning frameworks. In this paper, we review strategies for non-parametric, especially deep learning based, estimation of the dynamics underlying discrete-time event processes. More specifically, we focus on loss functions that can be used for implementation and then briefly mention specific instantiation of such approaches from related work.
Classification of Group Actions Extended to Symplectic Deformation Quantizations

Consider a group $\Gamma$ acting on a formal (Fedosov) deformation quantization $\mathbb{A}_\hbar(M)$ of a symplectic manifold $(M,\omega)$. This canonically induces an action of $\Gamma$ by symplectomorphisms on $M$. We examine the reverse problem of extending group actions by symplectomorphisms to the deformation quantization. To do this we first define a notion of extension that does not impose restrictions on the Fedosov connection realizing $\mathbb{A}_\hbar(M)$ in its gauge equivalence class by considering composition of pull-back with certain inner automorphisms of sections of the Weyl bundle. We reformulate the well known sufficient conditions for existence of such extensions and show by way of example that they are not necessary. We then turn to the corresponding classification problem of extensions of a given action by symplectomorphisms. To this end we associate a group $\bar{\mathcal{G}_\nabla}$ to each Fedosov connection $\nabla$ and show that the (non-Abelian) group cohomology $\mbox{H}^1(\Gamma,\bar{\mathcal{G}_\nabla})$ classifies extensions up to a natural notion of equivalence. Finally we develop several tools that allow for computation of $\mbox{H}^1(\Gamma,\bar{\mathcal{G}_\nabla})$ and apply these to compute $\mbox{H}^1(\Gamma,\bar{\mathcal{G}_\nabla})$ in a range of examples.
Dynamic Rating Technology Approach for Real-Time Monitoring of Overhead Transmission Lines

Current paper introduces the Romanian innovative solution for online monitoring system of high voltage overhead power lines, a smart grid technology.The online monitoring system uses sensors for online measurement of power line operating parameters (conductor current, conductor tilt angle/sag, conductor mechanical tension, conductor temperature, tower inclination, conductor oscillations, etc.) and ambient weather parameters (solar irradiation, environment temperature, wind speed and direction), for optimization of the high-voltage electrical network by using DLR (dynamic line rating) application. The paper describes the hardware of the OHLM system, emphasizing also on the developed software.