bluuebunny/crossref_metadata_2025 huggingface dataset:
Wide-Area and Real-Time Object Search System of UAV
Authors: Li Xianjiang , He Boyong , Ding Kaiwen , Guo Weijie , Huang Bo , Wu Liaoni
Published: March 2022

The method of collecting aerial images or videos by unmanned aerial vehicles (UAVs) for object search has the advantages of high flexibility and low cost, and has been widely used in various fields, such as pipeline inspection, disaster rescue, and forest fire prevention. However, in the case of object search in a wide area, the scanning efficiency and real-time performance of UAV are often difficult to satisfy at the same time, which may lead to missing the best time to perform the task. In this paper, we design a wide-area and real-time object search system of UAV based on deep learning for this problem. The system first solves the problem of area scanning efficiency by controlling the high-resolution camera in order to collect aerial images with a large field of view. For real-time requirements, we adopted three strategies to accelerate the system, as follows: design a parallel system, simplify the object detection algorithm, and use TensorRT on the edge device to optimize the object detection model. We selected the NVIDIA Jetson AGX Xavier edge device as the central processor and verified the feasibility and practicability of the system through the actual application of suspicious vehicle search in the grazing area of the prairie. Experiments have proved that the parallel design of the system can effectively meet the real-time requirements. For the most time-consuming image object detection link, with a slight loss of precision, most algorithms can reach the 400% inference speed of the benchmark in total, after algorithm simplification, and corresponding model’s deployment by TensorRT.
Capsule–Encoder–Decoder: A Method for Generalizable Building Extraction from Remote Sensing Images
Authors: Tang Zhenchao , Chen Calvin Yu-Chian , Jiang Chengzhen , Zhang Dongying , Luo Weiran , Hong Zhiming , Sun Huaiwei
Published: March 2022

Due to the inconsistent spatiotemporal spectral scales, a remote sensing dataset over a large-scale area and over long-term time series will have large variations and large statistical distribution features, which will lead to a performance drop of the deep learning model that is only trained on the source domain. For building an extraction task, deep learning methods perform weak generalization from the source domain to the other domain. To solve the problem, we propose a Capsule–Encoder–Decoder model. We use a vector named capsule to store the characteristics of the building and its parts. In our work, the encoder extracts capsules from remote sensing images. Capsules contain the information of the buildings’ parts. Additionally, the decoder calculates the relationship between the target building and its parts. The decoder corrects the buildings’ distribution and up-samples them to extract target buildings. Using remote sensing images in the lower Yellow River as the source dataset, building extraction experiments were trained on both our method and the mainstream methods. Compared with the mainstream methods on the source dataset, our method achieves convergence faster, and our method shows higher accuracy. Significantly, without fine tuning, our method can reduce the error rates of building extraction results on an almost unfamiliar dataset. The building parts’ distribution in capsules has high-level semantic information, and capsules can describe the characteristics of buildings more comprehensively, which are more explanatory. The results prove that our method can not only effectively extract buildings but also perform great generalization from the source remote sensing dataset to another.
Development of a GPU-Accelerated NDT Localization Algorithm for GNSS-Denied Urban Areas
Authors: Jang Keon Woo , Jeong Woo Jae , Kang Yeonsik
Published: March 2022

There are numerous global navigation satellite system-denied regions in urban areas, where the localization of autonomous driving remains a challenge. To address this problem, a high-resolution light detection and ranging (LiDAR) sensor was recently developed. Various methods have been proposed to improve the accuracy of localization using precise distance measurements derived from LiDAR sensors. This study proposes an algorithm to accelerate the computational speed of LiDAR localization while maintaining the original accuracy of lightweight map-matching algorithms. To this end, first, a point cloud map was transformed into a normal distribution (ND) map. During this process, vector-based normal distribution transform, suitable for graphics processing unit (GPU) parallel processing, was used. In this study, we introduce an algorithm that enabled GPU parallel processing of an existing ND map-matching process. The performance of the proposed algorithm was verified using an open dataset and simulations. To verify the practical performance of the proposed algorithm, the real-time serial and parallel processing performances of the localization were compared using high-performance and embedded computers, respectively. The distance root-mean-square error and computational time of the proposed algorithm were compared. The algorithm increased the computational speed of the embedded computer almost 100-fold while maintaining high localization precision.
DTS-Depth: Real-Time Single-Image Depth Estimation Using Depth-to-Space Image Construction
Authors: Ibrahem Hatem , Salem Ahmed , Kang Hyun-Soo
Published: March 2022

As most of the recent high-resolution depth-estimation algorithms are computationally so expensive that they cannot work in real time, the common solution is using a low-resolution input image to reduce the computational complexity. We propose a different approach, an efficient and real-time convolutional neural network-based depth-estimation algorithm using a single high-resolution image as the input. The proposed method efficiently constructs a high-resolution depth map using a small encoding architecture and eliminates the need for a decoder, which is typically used in the encoder–decoder architectures employed for depth estimation. The proposed algorithm adopts a modified MobileNetV2 architecture, which is a lightweight architecture, to estimate the depth information through the depth-to-space image construction, which is generally employed in image super-resolution. As a result, it realizes fast frame processing and can predict a high-accuracy depth in real time. We train and test our method on the challenging KITTI, Cityscapes, and NYUV2 depth datasets. The proposed method achieves low relative absolute error (0.028 for KITTI, 0.167 for CITYSCAPES, and 0.069 for NYUV2) while working at speed reaching 48 frames per second on a GPU and 20 frames per second on a CPU for high-resolution test images. We compare our method with the state-of-the-art methods on depth estimation, showing that our method outperforms those methods. However, the architecture is less complex and works in real time.
Traffic Flow Detection Using Camera Images and Machine Learning Methods in ITS for Noise Map and Action Plan Optimization
Authors: Fredianelli Luca , Carpita Stefano , Bernardini Marco , Del Pizzo Lara Ginevra , Brocchi Fabio , Bianco Francesco , Licitra Gaetano
Published: March 2022

Noise maps and action plans represent the main tools in the fight against citizens’ exposure to noise, especially that produced by road traffic. The present and the future in smart traffic control is represented by Intelligent Transportation Systems (ITS), which however have not yet been sufficiently studied as possible noise-mitigation tools. However, ITS dedicated to traffic control rely on models and input data that are like those required for road traffic noise mapping. The present work developed an instrumentation based on low-cost cameras and a vehicle recognition and counting methodology using modern machine learning techniques, compliant with the requirements of the CNOSSOS-EU noise assessment model. The instrumentation and methodology could be integrated with existing ITS for traffic control in order to design an integrated method, which could also provide updated data over time for noise maps and action plans. The test was carried out as a follow up of the L.I.S.T. Port project, where an ITS was installed for road traffic management in the Italian port city of Piombino. The acoustic efficacy of the installation is evaluated by looking at the difference in the acoustic impact on the population before and after the ITS installation by means of the distribution of noise exposure, the evaluation of Gden and Gnight, and the calculation of the number of highly annoyed and sleep-disturbed citizens. Finally, it is shown how the ITS system represents a valid solution to be integrated with targeted and more specific sound mitigation, such as the laying of low-emission asphalts.
Properties of Fiber Bragg Grating in CYTOP Fiber Response to Temperature, Humidity, and Strain Using Factorial Design
Authors: Nan Ying-Gang , Yazd Nazila Safari , Chapalo Ivan , Chah Karima , Hu Xuehao , Mégret Patrice
Published: March 2022

The characteristics of fiber Bragg grating (FBG) in cyclic transparent fluoropolymer (CYTOP) optical fiber have attracted more and more attention in recent years. However, different results of the FBG response to environmental parameters are reported. This work presents a three-variable two-level factorial experimental method to investigate the FBG response to temperature, humidity, and strain in CYTOP fiber. Two uniform FBGs are inscribed separately in CYTOP fiber with and without over-clad. With only eight measuring points, the interactions among three variable parameters are computed and the parameter sensitivities and cross-sensitivities are estimated. Similar temperature and strain sensitivities were found for both gratings, whereas significant cross-sensitivity between humidity and temperature was present only in FBG inscribed in CYTOP fiber with over-clad.
The Improved WNOFRFs Feature Extraction Method and Its Application to Quantitative Diagnosis for Cracked Rotor Systems
Authors: Liang Haiying , Zhao Chencheng , Chen Yungao , Liu Yang , Zhao Yulai
Published: March 2022

During its operation, a rotor system can be exposed to multiple faults, such as rub-impact, misalignment, cracks and unbalancing. When a crack fault occurs on the rotor shaft, the vibration response signals contain some nonlinear components that are considerably tougher to be extracted through some linear diagnosis methods. By combining the Nonlinear Output Frequency Response Functions weighted contribution rate (WNOFRFs) and Kullback–Leibler (KL) divergence, a novel fault diagnosis method of improved WNOFRFs is proposed. In this method, an index, improved optimal WNOFRFs (IOW), is defined to represent the nonlinearity of the faulty rotor system. This method has been tested through the finite element model of a cracked rotor system and then verified experimentally at the shaft crack detection test bench. The results from the simulation and experiment verified that the proposed method is applicable and effective for cracked rotor systems. The IOW indicator shows high sensitivity to crack faults and can comprehensively represent the nonlinear properties of the system. It can also quantitatively detect the crack fault, and the relationship between the values of IOW and the relative depth of the crack is approximately positively proportional. The proposed method can precisely and quantitatively diagnose crack faults in a rotor system.
Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data through Google Earth Engine
Authors: Youssefi Fahimeh , Zoej Mohammad Javad Valadan , Hanafi-Bojd Ahmad Ali , Dariane Alireza Borhani , Khaki Mehdi , Safdarinezhad Alireza , Ghaderpour Ebrahim
Published: March 2022

In many studies regarding the field of malaria, environmental factors have been acquired in single-time, multi-time or a short-time series using remote sensing and meteorological data. Selecting the best periods of the year to monitor the habitats of Anopheles larvae can be effective in better and faster control of malaria outbreaks. In this article, high-risk times for three regions in Iran, including Qaleh-Ganj, Sarbaz and Bashagard counties with a history of malaria prevalence were estimated. For this purpose, a series of environmental factors affecting the growth and survival of Anopheles were used over a seven-year period through the Google Earth Engine. The results of this study indicated two high-risk times for Qaleh-Ganj and Bashagard counties and three high-risk times for Sarbaz county over the course of a year observing an increase in the abundance of Anopheles mosquitoes. Further evaluation of the results against the entomological data available in previous studies showed that the high-risk times predicted in this study were consistent with an increase in the abundance of Anopheles mosquitoes in the study areas. The proposed method is extremely useful for temporal prediction of the increase in abundance of Anopheles mosquitoes in addition to the use of optimal data aimed at monitoring the exact location of Anopheles habitats.
Announcement Signals and Automatic Braking Using Virtual Balises in Railway Transport Systems
Authors: Santiso Enrique , Jiménez José A. , Mazo Manuel , Losada Cristina , Rodríguez Francisco J.
Published: March 2022

In rail transport, various automatic protection systems are available to ensure the safe operation of trains and to facilitate automation and optimization tasks. For this purpose, a set of physical balises is used, which are placed at fixed points along the railway track. Based on the information provided by these balises, different information is displayed to the driver, and control actions are generated. The use of physical balises located at fixed points does not allow for automatic protection actions on sections of track where they are not installed. This is a major drawback as in many cases, temporary automatic protection actions are necessary on sections of the railway line without balises due to various circumstances (work on the track, accidents, etc.). To solve this problem, this paper presents a solution called announcement signals and automatic braking using virtual balises (ASAB-VB). This proposal allows the incorporation of virtual balises at points on the track where it is necessary to temporarily perform automatic protection actions. For this purpose, the ASAB-VB system allows obtaining the train position in real-time and storing a digital map of the track that will be made by each train. This digital map includes geographic information about the balises (both physical and virtual ones) located on the track. At the same time, the train position is obtained by merging the information provided by a GNSS, an odometer, and an inertial system (gyro and accelerometers).
A Distributed Real-Time Transcoding CDN System Design and Performance Simulation Study
Authors: Ren Yuan , Huang Cheng , Lv Yongbo , Lv Wanjun , Zhang Han
Published: March 2022

Content Delivery Network (CDN) technology is one of the core technologies for performance optimization of mobile WEB applications, but it often encounters bottlenecks in video storage. In this paper, based on studying the basic technical model of distributed real-time transcoding CDN, we propose an overall technical architecture of a distributed real-time transcoding CDN system, present the business process of distributed real-time transcoding CDN resource negotiation in the model, design a resource storage policy model and algorithm, including the hot storage ratio resource storage value evaluation method and storage resource allocation algorithm. Through simulation experiments, we verify the technical efficiency of the distributed real-time transcoding CDN system by simulation experiments.
Evaluation of Acoustic Noise Level and Impulsiveness Inside Vehicles in Different Traffic Conditions
Authors: Flor Daniel , Pena Danilo , Oliveira Hyago Lucas , Pena Luan , de Sousa Vicente A. , Martins Allan
Published: March 2022

Recently, the issue of sound quality inside vehicles has attracted interest from both researchers and industry alike due to health concerns and also to increase the appeal of vehicles to consumers. This work extends the analysis of interior acoustic noise inside a vehicle under several conditions by comparing measured power levels and two different models for acoustic noise, namely the Gaussian and the alpha-stable distributions. Noise samples were collected in a scenario with real traffic patterns using a measurement setup composed of a Raspberry Pi Board and a microphone strategically positioned. The analysis of the acquired data shows that the observed noise levels are higher when traffic conditions are good. Additionally, the interior noise presented considerable impulsiveness, which tends to be more severe when traffic is slower. Finally, our results suggest that noise sources related to the vehicle itself and its movement are the most relevant ones in the composition of the interior acoustic noise.
An Atlas of Piezoelectric Energy Harvesters in Oceanic Applications
Authors: Kargar Seyyed Masoud , Hao Guangbo
Published: March 2022

Nowadays, a large number of sensors are employed in the oceans to collect data for further analysis, which leads to a large number of demands for battery elimination in electronics due to the size reduction, environmental issues, and its laborious, pricy, and time-consuming recharge or replacement. Numerous methods for direct energy harvesting have been developed to power these low-power consumption sensors. Among all the developed harvesters, piezoelectric energy harvesters offer the most promise for eliminating batteries from future devices. These devices do not require maintenance, and they have compact and simple structures that can be attached to low-power devices to directly generate high-density power. In the present study, an atlas of 85 designs of piezoelectric energy harvesters in oceanic applications that have recently been reported in the state-of-the-art is provided. The atlas categorizes these designs based on their configurations, including cantilever beam, diaphragm, stacked, and cymbal configurations, and provides insightful information on their material, coupling modes, location, and power range. A set of unified schematics are drawn to show their working principles in this atlas. Moreover, all the concepts in the atlas are critically discussed in the body of this review. Different aspects of oceanic piezoelectric energy harvesters are also discussed in detail to address the challenges in the field and identify the research gaps.
Recognition of Upper Limb Action Intention Based on IMU
Authors: Cui Jian-Wei , Li Zhi-Gang , Du Han , Yan Bing-Yan , Lu Pu-Dong
Published: March 2022

Using motion information of the upper limb to control the prosthetic hand has become a hotspot of current research. The operation of the prosthetic hand must also be coordinated with the user’s intention. Therefore, identifying action intention of the upper limb based on motion information of the upper limb is key to controlling the prosthetic hand. Since a wearable inertial sensor bears the advantages of small size, low cost, and little external environment interference, we employ an inertial sensor to collect angle and angular velocity data during movement of the upper limb. Aiming at the action classification for putting on socks, putting on shoes and tying shoelaces, this paper proposes a recognition model based on the Dynamic Time Warping (DTW) algorithm of the motion unit. Based on whether the upper limb is moving, the complete motion data are divided into several motion units. Considering the delay associated with controlling the prosthetic hand, this paper only performs feature extraction on the first motion unit and the second motion unit, and recognizes action on different classifiers. The experimental results reveal that the DTW algorithm based on motion unit bears a higher recognition rate and lower running time. The recognition rate reaches as high as 99.46%, and the average running time measures 8.027 ms. In order to enable the prosthetic hand to understand the grasping intention of the upper limb, this paper proposes a Generalized Regression Neural Network (GRNN) model based on 10-fold cross-validation. The motion state of the upper limb is subdivided, and the static state is used as the sign of controlling the prosthetic hand. This paper applies a 10-fold cross-validation method to train the neural network model to find the optimal smoothing parameter. In addition, the recognition performance of different neural networks is compared. The experimental results show that the GRNN model based on 10-fold cross-validation exhibits a high accuracy rate, capable of reaching 98.28%. Finally, the two algorithms proposed in this paper are implemented in an experiment of using the prosthetic hand to reproduce an action, and the feasibility and practicability of the algorithm are verified by experiment.
Heterogeneous Skin Phantoms for Experimental Validation of Microwave-Based Diagnostic Tools
Authors: Boparai Jasmine , Popović Milica
Published: March 2022

Considerable exploration has been done in recent years to exploit the reported inherent dielectric contrast between healthy and malignant tissues for a range of medical applications. In particular, microwave technologies have been investigated towards new diagnostic medical tools. To assess the performance and detection capabilities of such systems, tissue-mimicking phantoms are designed for controlled laboratory experiments. We here report phantoms developed to dielectrically represent malign skin lesions such as liposarcoma and nonsyndromic multiple basal cell carcinoma. Further, in order to provide a range of anatomically realistic scenarios, and provide meaningful comparison between different phantoms, cancer-mimicking lesions are inserted into two different types of skin phantoms with varying tumor–skin geometries. These configurations were measured with a microwave dielectric probe (0.5–26.5 GHz), yielding insight into factors that could affect the performance of diagnostic and detection tools.
Light Field Reconstruction Using Residual Networks on Raw Images
Authors: Salem Ahmed , Ibrahem Hatem , Kang Hyun-Soo
Published: March 2022

Although Light-Field (LF) technology attracts attention due to its large number of applications, especially with the introduction of consumer LF cameras and its frequent use, reconstructing densely sampled LF images represents a great challenge to the use and development of LF technology. Our paper proposes a learning-based method to reconstruct densely sampled LF images from a sparse set of input images. We trained our model with raw LF images rather than using multiple images of the same scene. Raw LF can represent the two-dimensional array of images captured in a single image. Therefore, it enables the network to understand and model the relationship between different images of the same scene well and thus restore more texture details and provide better quality. Using raw images has transformed the task from image reconstruction into image-to-image translation. The feature of small-baseline LF was used to define the images to be reconstructed using the nearest input view to initialize input images. Our network was trained end-to-end to minimize the sum of absolute errors between the reconstructed and ground-truth images. Experimental results on three challenging real-world datasets demonstrate the high performance of our proposed method and its outperformance over the state-of-the-art methods.
Adapting a Dehazing System to Haze Conditions by Piece-Wisely Linearizing a Depth Estimator
Authors: Ngo Dat , Lee Seungmin , Kang Ui-Jean , Ngo Tri Minh , Lee Gi-Dong , Kang Bongsoon
Published: March 2022

Haze is the most frequently encountered weather condition on the road, and it accounts for a considerable number of car crashes occurring every year. Accordingly, image dehazing has garnered strong interest in recent decades. However, although various algorithms have been developed, a robust dehazing method that can operate reliably in different haze conditions is still in great demand. Therefore, this paper presents a method to adapt a dehazing system to various haze conditions. Under this approach, the proposed method discriminates haze conditions based on the haze density estimate. The discrimination result is then leveraged to form a piece-wise linear weight to modify the depth estimator. Consequently, the proposed method can effectively handle arbitrary input images regardless of their haze condition. This paper also presents a corresponding real-time hardware implementation to facilitate the integration into existing embedded systems. Finally, a comparative assessment against benchmark designs demonstrates the efficacy of the proposed dehazing method and its hardware counterpart.
Cross Channel Scripting and Code Injection Attacks on Web and Cloud-Based Applications: A Comprehensive Review
Authors: M Indushree , Kaur Manjit , Raj Manish , R Shashidhara , Lee Heung-No
Published: March 2022

Cross channel scripting (XCS) is a common web application vulnerability, which is a variant of a cross-site scripting (XSS) attack. An XCS attack vector can be injected through network protocol and smart devices that have web interfaces such as routers, photo frames, and cameras. In this attack scenario, the network devices allow the web administrator to carry out various functions related to accessing the web content from the server. After the injection of malicious code into web interfaces, XCS attack vectors can be exploited in the client browser. In addition, scripted content can be injected into the networked devices through various protocols, such as network file system, file transfer protocol (FTP), and simple mail transfer protocol. In this paper, various computational techniques deployed at the client and server sides for XCS detection and mitigation are analyzed. Various web application scanners have been discussed along with specific features. Various computational tools and approaches with their respective characteristics are also discussed. Finally, shortcomings and future directions related to the existing computational techniques for XCS are presented.
The Identification of ECG Signals Using WT-UKF and IPSO-SVM
Authors: Li Ning , Zhu Longhui , Ma Wentao , Wang Yelin , He Fuxing , Zheng Aixiang , Zhang Xiaoping
Published: March 2022

The biometric identification method is a current research hotspot in the pattern recognition field. Due to the advantages of electrocardiogram (ECG) signals, which are difficult to replicate and easy to obtain, ECG-based identity identification has become a new direction in biometric recognition research. In order to improve the accuracy of ECG signal identification, this paper proposes an ECG identification method based on a multi-scale wavelet transform combined with the unscented Kalman filter (WT-UKF) algorithm and the improved particle swarm optimization-support vector machine (IPSO-SVM). First, the WT-UKF algorithm can effectively eliminate the noise components and preserve the characteristics of ECG signals when denoising the ECG data. Then, the wavelet positioning method is used to detect the feature points of the denoised signals, and the obtained feature points are combined with multiple feature vectors to characterize the ECG signals, thus reducing the data dimension in identity identification. Finally, SVM is used for ECG signal identification, and the improved particle swarm optimization (IPSO) algorithm is used for parameter optimization in SVM. According to the analysis of simulation experiments, compared with the traditional WT denoising, the WT-UKF method proposed in this paper improves the accuracy of feature point detection and increases the final recognition rate by 1.5%. The highest recognition accuracy of a single individual in the entire ECG identification system achieves 100%, and the average recognition accuracy can reach 95.17%.
Motion Similarity Evaluation between Human and a Tri-Co Robot during Real-Time Imitation with a Trajectory Dynamic Time Warping Model
Authors: Gong Liang , Chen Binhao , Xu Wenbin , Liu Chengliang , Li Xudong , Zhao Zelin , Zhao Lujie
Published: March 2022

Precisely imitating human motions in real-time poses a challenge for the robots due to difference in their physical structures. This paper proposes a human–computer interaction method for remotely manipulating life-size humanoid robots with a new metrics for evaluating motion similarity. First, we establish a motion capture system to acquire the operator’s motion data and retarget it to the standard bone model. Secondly, we develop a fast mapping algorithm, by mapping the BVH (BioVision Hierarchy) data collected by the motion capture system to each joint motion angle of the robot to realize the imitated motion control of the humanoid robot. Thirdly, a DTW (Dynamic Time Warping)-based trajectory evaluation method is proposed to quantitatively evaluate the difference between robot trajectory and human motion, and meanwhile, visualization terminals render it more convenient to make comparisons between two different but simultaneous motion systems. We design a complex gesture simulation experiment to verify the feasibility and real-time performance of the control method. The proposed human-in-the-loop imitation control method addresses a prominent non-isostructural retargeting problem between human and robot, enhances robot interaction capability in a more natural way, and improves robot adaptability to uncertain and dynamic environments.
Security Requirements and Challenges of 6G Technologies and Applications
Authors: Abdel Hakeem Shimaa A. , Hussein Hanan H. , Kim HyungWon
Published: March 2022

After implementing 5G technology, academia and industry started researching 6th generation wireless network technology (6G). 6G is expected to be implemented around the year 2030. It will offer a significant experience for everyone by enabling hyper-connectivity between people and everything. In addition, it is expected to extend mobile communication possibilities where earlier generations could not have developed. Several potential technologies are predicted to serve as the foundation of 6G networks. These include upcoming and current technologies such as post-quantum cryptography, artificial intelligence (AI), machine learning (ML), enhanced edge computing, molecular communication, THz, visible light communication (VLC), and distributed ledger (DL) technologies such as blockchain. From a security and privacy perspective, these developments need a reconsideration of prior security traditional methods. New novel authentication, encryption, access control, communication, and malicious activity detection must satisfy the higher significant requirements of future networks. In addition, new security approaches are necessary to ensure trustworthiness and privacy. This paper provides insights into the critical problems and difficulties related to the security, privacy, and trust issues of 6G networks. Moreover, the standard technologies and security challenges per each technology are clarified. This paper introduces the 6G security architecture and improvements over the 5G architecture. We also introduce the security issues and challenges of the 6G physical layer. In addition, the AI/ML layers and the proposed security solution in each layer are studied. The paper summarizes the security evolution in legacy mobile networks and concludes with their security problems and the most essential 6G application services and their security requirements. Finally, this paper provides a complete discussion of 6G networks’ trustworthiness and solutions.
Implementation of Hemispherical Resonator Gyroscope with 3 × 3 Optical Interferometers for Analysis of Resonator Asymmetry
Authors: Kim Myeongseop , Cho Bobae , Lee Hansol , Yoon Taeil , Lee Byeongha
Published: March 2022

A hemispherical resonator gyroscope (HRG) has been implemented by using a consumer wineglass as the resonator and 3 × 3 optical interferometers as the detectors. The poorness of the off-the-shelf wineglass as the resonator can be overcome by the high performance of the optical interferometer. The effects of asymmetries in stiffness and absorption of the resonator are analyzed theoretically and confirmed experimentally. We prove that the trace of the amplitude ratio of two n = 2 fundamental resonant modes of the resonator follows a straight line in a complex plane. By utilizing the straightness of the ratio and the high performance of the optical interferometer, we extract four real constant parameters characterizing the HRG system. Experimentally, by using a resonator having an average resonance frequency of 444 Hz and Q value of 1477.2, it was possible to measure the Coriolis force at the level of industrial grade. The bias stability was measured as small as 2.093°/h.
Market Survey of Modern Oral Nicotine Products: Determination of Select HPHCs and Comparison to Traditional Smokeless Tobacco Products
Authors: Jablonski Joseph J. , Cheetham Andrew G. , Martin Alexandra M.
Published: March 2022

In an effort to combat the risks associated with traditional tobacco products, tobacco product innovation has been redirected towards reducing the consumer’s potential exposure to harmful or potentially harmful constituents (HPHCs). Among these innovations are modern oral nicotine products (MONPs). This product class aims to deliver nicotine while limiting the consumer’s potential toxicant exposure. This body of work sought to investigate the potential for select HPHC exposure (tobacco-specific nitrosamines, carbonyls, benzo[a]pyrene, nitrite, and metals) from MONPs and to compare it to that from traditional tobacco products. This work expands on previously published studies both in terms of diversity of products assessed and analytes tested. In total, twenty-one unique MONPs were assessed and compared to four traditional tobacco products. We found that there was a difference in the potential exposure based on the MONP filler—plant material vs. granulate/powder. Typically, the HPHC levels observed in plant-based MONPs were higher than those observed for granulate/powder products, most notably within the metals analysis, for which the levels were occasionally greater than those seen in traditional smokeless tobacco products. Generally, the overall HPHC levels observed in MONP were at or below those levels observed in traditional tobacco products.
Economic Framework of Smart and Integrated Urban Water Systems
Authors: Grigg Neil
Published: March 2022

Smart and integrated urban water systems have important roles in advancing smart cities, but their contributions go much further by supplying needed public services and connecting other sectors to meet sustainability goals. Achieving integration and gaining access to financing are obstacles to implementing smart water systems and both are implicit in the economic framework of smart cities. Problems in financing the start-up of smart water systems are reported often. The local and diverse nature of water systems is another barrier because an approach that works in one place may not work in another with different conditions. The paper identifies the challenges posed by the economic framework and provides examples from four cities with diverse characteristics. It outlines pathways to advance implementation of smart water systems by improving control strategies, advancing instrumentation and control technologies, and most of all, to help transform cities by raising customer awareness and trust through reliable and useful water information.
Bridging the Gender Gap in the Agricultural Sector: Evidence from European Union Countries
Authors: Fanelli Rosa Maria
Published: March 2022

In European Union countries most farms are operated by smallholders in extensive agriculture and approximately 30% of agricultural farms are operated by woman. Despite the growing importance of the role of women in the agricultural sector, a more complete picture of the gender gap and the differences between female and male-operated farms is still lacking. The principal aim of this paper is to fill this gap by highlighting the differences between female and male-operated farms. The data used comes from the World Bank Open Data repository and Eurostat. The methodology consists of descriptive statistical analysis, principal component analysis, and multivariate regression models. The analysis is used to investigate how a set of indicators affects the gender gap in the agricultural sector. The research questions test how the socio-economic determinants and traditional agricultural land use influence the historical gender division of labour and the evolution and persistence of farm differences. The results from almost all 27 EU countries suggest that women, the same as men, can be considered “productive resources” and play an important role in the agriculture sector in areas such as crop and livestock production. However, their activities are less remunerative.
Surface Plasmons Excited by X-rays in the Surface Layers of Solids
Authors: Stozharov Valery M.
Published: March 2022

The phenomenon of total external reflection of X-rays at a sliding angle of incidence of a beam of incident X-rays is investigated. For metals, a quantitative law of direct dependence of the refractive index decrement on the interplane distance is obtained. The excitation of surface plasmons by X-rays that have experienced complete external reflection is detected. For surface plasmons, a dimensional effect was observed, expressed in an increase in the energy of plasmons and the concentration of conduction electrons with an increase in the depth of the output of surface plasmons. By the method of dispersion of surface plasmons, internal mechanical micro-stresses and spontaneous polarization of the surface layers of glassy dielectrics and in thin layers of vanadium dioxide were determined. The absence of micro-stresses in the lithium fluoride ionic single crystal was found out, and the polarization observed in it is due to the large dipole moment of the molecules of this crystal. In thin films of vanadium dioxide, the dependence of micro-stresses on the stresses in the substrates was found.
Vascular Function in Norwegian Female Elite Runners: A Cross-Sectional, Controlled Study
Authors: Kyte Karoline Holsen , Stensrud Trine , Berg Tore Julsrud , Seljeflot Ingebjørg , Hisdal Jonny
Published: March 2022

In general, aerobic exercise has a positive impact on the vascular system, but the syndrome of relative energy-deficiency in sports (RED-S) makes this impact less clear for the athlete. The present cross-sectional controlled study aimed to investigate the vascular function in female elite long-distance runners, compared to inactive women. Sixteen female elite long-distance runners and seventeen healthy controls were recruited. Assessments of vascular function and morphology included endothelial function, evaluated by flow-mediated dilatation (FMD), vascular stiffness, evaluated with pulse wave velocity (PWV), carotid artery reactivity (CAR %), and carotid intima-media thickness (cIMT). Blood samples included hormone analyses, metabolic parameters, lipids, and biomarkers reflecting endothelial activation. RED-S risk was assessed through the low energy availability in female questionnaire (LEAF-Q), and body composition was measured by dual-energy X-ray absorptiometry (DXA). We found no significant differences in brachial FMD, PWV, CAR %, cIMT, or biomarkers reflecting endothelial activation between the two groups. Forty-four percent of the runners had a LEAF-Q score consistent with being at risk of RED-S. Runners showed significantly higher HDL-cholesterol and insulin sensitivity compared to controls. In conclusion, Norwegian female elite runners had an as good vascular function and morphology as inactive women of the same age.
Is There a Regulation in the Expansion of Urban Spatial Structure? Empirical Study from the Main Urban Area in Zhengzhou, China
Authors: Wu Yanyan , Yuan Jiadong
Published: March 2022

The city is a fundamental regional unit of development. Urban spatial structure is a relationship performance among the physical environment, functional activities, and cultural values. Identifying the urban spatial structure and functional areas accurately is of great significance for optimizing urban planning and promoting urban development. Previous studies have focused on the distribution for the single-functional area in some big cities and urban agglomerations from a view of a static time node, with little focus on multifunctional areas’ distribution from the perspective of comprehensive evolutionary in underdeveloped regions, especially in provincial capitals in the Central Chinese region. Therefore, taking Zhengzhou, a representative National Central City in the undeveloped central part of China as an example, we investigate urban spatial sprawling in the main urban area. Our interest is twofold. Firstly, we investigate the urban center as to whether, and to what extent, scaling in the spatial structure. The second point of importance concerns the comparison of specific types of functional area in the spatial structure and morphology from a microlevel perspective. The identification framework has been constructed to identify and evaluate the urban spatial structure. The research shows the following: (i) There is a strong correlation between the urban center which is extracted and the density in spatial distribution. The density value of POIs decreases from the urban center to the suburbs. This result is in line with objective facts. (ii) The urban center area has expanded by 25.16 km2 in 2 years. In the center of the main urban area of Zhengzhou, it is generally accepted that the spatial structure pattern monocentric, but is polycentrically patterned in function. The compactness is increasing in the urban center area. (iii) By identifying the mixed functional area, it presents a circle-layer expanded layout with the comprehensive functional area as the core in 2016 and 2018. The comprehensive functional area has developed significantly and maturely, and in the multifunctional area, the development of the public–residential–business functional area and the public–residential–leisure functional area is relatively mature. In short, this study not only helps strategic planners to strengthen refined management, practical planning, construction, and management integration but also to assess whether policies or actions have been delivered as effectively as planned by identifying the urban spatial structure and revealing the evolution regulation.
A Data-Driven Approach to Stormwater Quality Analysis in Two Urban Catchments
Authors: Larm Thomas , Wahlsten Anna , Marsalek Jiri , Viklander Maria
Published: March 2022

The StormTac Web model, representing a low-complexity conceptual model (LCCM), was applied to two urban catchments featuring stormwater quality controls, a stormwater pond or a biofilter. The model calculates annual average runoff from annual precipitation and land-use specific volumetric runoff coefficients and baseflows (in storm sewers), which are multiplied by the corresponding mean stormwater quality constituent concentrations obtained from the recently upgraded StormTac Database, to yield constituent loads. The resulting runoff loads pass through the stormwater quality control facilities (a stormwater pond or a biofilter) where treatment takes place and its efficacy is described by “reduction efficiencies”. For the four selected stormwater quality constituents (TP, Cu, Zn, TSS) and two study catchments, a 201-ha residential Ladbrodammen and an 8.2-ha Sundsvall traffic corridor, the compositions of stormwater entering and leaving the control facilities were calculated by StormTac Web and compared against the measured data. In general, the calculated concentrations were smaller than the measured ones, and these differences were reduced, but not eliminated in all cases, by considering uncertainties in both calculated and measured data. Uncertainties in calculated values consisted of two components, a flow component (assumed as 20%) and a concentration component, which was assumed equal to the relative standard error (RSE) of the data in the StormTac Database. Explanations of differences in calculated and measured stormwater data were discussed with respect to temporal changes and trends in environmental practices and stormwater quality monitoring and enhancement by treatment.
Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights
Authors: Shiau Yuo-Hsien , Yang Su-Fen , Adha Rishan , Muzayyanah Syamsiyatul
Published: March 2022

The study aims to adopt an artificial neural network (ANN) for modeling industrial energy demand in Taiwan related to the subsector manufacturing output and climate change. This is the first study to use the ANN technique to measure the industrial energy demand–manufacturing output–climate change nexus. The ANN model adopted in this study is a multilayer perceptron (MLP) with a feedforward backpropagation neural network. This study compares the outcomes of three ANN activation functions with multiple linear regression (MLR). According to the estimation results, ANN with a hidden layer and hyperbolic tangent activation function outperforms other techniques and has statistical solid performance values. The estimation results indicate that industrial electricity demand in Taiwan is price inelastic or has a negative value of −0.17 to −0.23, with climate change positively influencing energy demand. The relationship between manufacturing output and energy consumption is relatively diverse at the disaggregated level.
The Impact of Green HRM on Employees’ Eco-Friendly Behavior: The Mediator Role of Organizational Identification
Authors: Ribeiro Neuza , Gomes Daniel Roque , Ortega Eduardo , Gomes Gabriela Pedro , Semedo Ana Suzete
Published: March 2022

This study sought to explore the impact of green human resource management on organizational identification and employees’ eco-friendly behavior, as well as the mediating role of organizational identification in the relationship between green human resource management and employees’ eco-friendly behavior. To achieve the study objectives, a cross-sectional quantitative study was developed, for which the data were obtained through a structured questionnaire containing the measures of the study variables. Data were collected from 235 employees from several Portuguese tourism organizations participating in the study. The Harman test and bootstrapping were applied previously to the assessment of the results. The mediation study’s hypothesis was evaluated using Baron and Kenny’s linear regression method, and subsequently complemented using the Sobel test. The findings showed that the implementation of green HRM practices in tourism organizations has a positive impact on employees’ eco-friendly behavior and on organizational identification, with the latter mediating the relationship between green human resource management and employees’ eco-friendly behavior. The study is breaking new ground because it incorporates the impact of green human resource management on organizational identification and employees’ eco-friendly behavior in a single research model, thus expanding knowledge on the subject, namely in the tourism sector in Portugal.