bluuebunny/crossref_metadata_2025 huggingface dataset:
A-BFPN: An Attention-Guided Balanced Feature Pyramid Network for SAR Ship Detection
Authors: Li Xiuqin , Li Dong , Liu Hongqing , Wan Jun , Chen Zhanye , Liu Qinghua
Published: August 2022

Thanks to the excellent feature representation capabilities of neural networks, target detection methods based on deep learning are now widely applied in synthetic aperture radar (SAR) ship detection. However, the multi-scale variation, small targets with complex background such as islands, sea clutter, and inland facilities in SAR images increase the difficulty for SAR ship detection. To increase the detection performance, in this paper, a novel deep learning network for SAR ship detection, termed as attention-guided balanced feature pyramid network (A-BFPN), is proposed to better exploit semantic and multilevel complementary features, which consists of the following two main steps. First, in order to reduce interferences from complex backgrounds, the enhanced refinement module (ERM) is developed to enable BFPN to learn the dependency features from the channel and space dimensions, respectively, which enhances the representation of ship objects. Second, the channel attention-guided fusion network (CAFN) model is designed to obtain optimized multi-scale features and reduce serious aliasing effects in hybrid feature maps. Finally, we illustrate the effectiveness of the proposed method, adopting the existing SAR Ship Detection Dataset (SSDD) and Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0). Experimental results show that the proposed method is superior to the existing algorithms, especially for multi-scale small ship targets under complex background.
A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China
Authors: Wang Yuedong , Feng Guangcai , Li Zhiwei , Luo Shuran , Wang Haiyan , Xiong Zhiqiang , Zhu Jianjun , Hu Jun
Published: August 2022

In recent years, increasing available synthetic aperture radar (SAR) satellite data and gradually developing interferometric SAR (InSAR) technology have provided the possibility for wide-scale ground-deformation monitoring using InSAR. Traditionally, the InSAR data are processed by the existing time-series InSAR (TS–InSAR) technology, which has inefficient calculation and redundant results. In this study, we propose a wide-area InSAR variable-scale deformation detection strategy (hereafter referred to as the WAVS–InSAR strategy). The strategy combines stacking technology for fast ground-deformation rate calculation and advanced TS–InSAR technology for obtaining fine deformation time series. It adopts an adaptive recognition algorithm to identify the spatial distribution and area of deformation regions (regions of interest, ROI) in the wide study area and uses a novel wide-area deformation product organization structure to generate variable-scale deformation products. The Turpan–Hami basin in western China is selected as the wide study area (277,000 km2) to verify the proposed WAVS–InSAR strategy. The results are as follows: (1) There are 32 deformation regions with an area of ≥1 km2 and a deformation magnitude of greater than ±2 cm/year in the Turpan–Hami basin. The deformation area accounts for 2.4‰ of the total monitoring area. (2) A large area of ground subsidence has occurred in the farmland areas of the ROI, which is caused by groundwater overexploitation. The popularization and application of facility agriculture in the ROI have increased the demand for irrigation water. Due to the influence of the tectonic fault, the water supply of the ROI is mainly dependent on groundwater. Huge water demand has led to a continuous net deficit in aquifers, leading to land subsidence. The WAVS–InSAR strategy will be helpful for InSAR deformation monitoring at a national/regional scale and promoting the engineering application of InSAR technology.
Assessment and Hydrological Validation of Merged Near-Real-Time Satellite Precipitation Estimates Based on the Gauge-Free Triple Collocation Approach
Authors: Cao Daling , Li Hongtao , Hou Enguang , Song Sulin , Lai Chengguang
Published: August 2022

Obtaining accurate near-real-time precipitation data and merging multiple precipitation estimates require sufficient in-situ rain gauge networks. The triple collocation (TC) approach is a novel error assessment method that does not require rain gauge data and provides reasonable precipitation estimates by merging data; this study assesses the TC approach for producing reliable near-real-time satellite-based precipitation estimate (SPE) products and the utility of the merged SPEs for hydrological modeling of ungauged areas. Three widely used near-real-time SPEs, including the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) early/late run (E/L) series, and the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Dynamic Infrared Rain Rate (PDIR) products, are used in the Beijiang basin in south China. The results show that the TC-based merged SPEs generally outperform all original SPEs, with higher consistency with the in-situ observations, and show superiority over the simple equal-weighted merged SPEs used for comparison; these findings indicate the superiority of the TC approach for utilizing the error characteristics of input SPEs for multi-SPE merging for ungauged areas. The validation of the hydrological modeling utility based on the Génie Rural à 4 paramètres Journalier (GR4J) model shows that the streamflow modeled by the TC-based merged SPEs has the best performance among all SPEs, especially for modeling low streamflow because the integration with the PDIR outperforms the IMERG products in low streamflow modeling. The TC merging approach performs satisfactorily for producing reliable near-real-time SPEs without gauge data, showing great potential for near-real-time applications, such as modeling rainstorms and monitoring floods and flash droughts in ungauged areas.
Evolution and Structure of a Dry Microburst Line Observed by Multiple Remote Sensors in a Plateau Airport
Authors: Huang Xuan , Zheng Jiafeng , Che Yuzhang , Wang Gaili , Ren Tao , Hua Zhiqiang , Tian Weidong , Su Zhikun , Su Lianxia
Published: August 2022

The civilian airplane is a common transportation mode for the local people in the Qinghai-Tibet Plateau (QTP). Due to the profound dynamic and thermal effects, the QTP can trigger strong windstorms during the warm season, during which downbursts can cause severe low-level wind shear and threaten aviation safety. However, the study of downbursts over QTP has not been given much attention. This study analyzes and interprets a typical traveling dry microburst line that happened at the Xining Caojiapu International Airport (ZLXN) on 14 May 2020, intending to show a better understanding of the dry downbursts over QTP and explore the synergetic usage of different remote sensing technologies for downburst detection and warning in plateau airports. Specifically, the characteristics of synoptic conditions, the convective system formation process, and the structure and evolution of downbursts and relevant low-level winds are comprehensively investigated. The results show that, under the control of an upstream shallow trough, features of the local atmosphere state, including a dry-adiabatic stratification, a shallow temperature inversion, increases in solar radiation heating, and strong vertical shears of horizontal winds, can be favorable atmospheric prerequisites for the formation and development of dry storms and downbursts. Low-reflectivity storm cells of the Mesoscale Convective System (MCS) organize to form narrow bow echoes, and downbursts show features of radial wind convergences and rapid descending reflectivity cores with hanging virga as observed by a Doppler weather radar. Moreover, details of gales, gust fronts, convergences, turbulences, wind collisions, and outflow interactions triggered by the downburst line are also detected and interpreted by a scanning Doppler wind lidar from different perspectives. In addition, the findings in this work have been compared with the results observed in Denver, U.S., and some simulation studies. Finally, a few conceptual models of low-level wind evolutions influenced by the dry downburst line are given.
A 7.4-Bit ENOB 600 MS/s FPGA-Based Online Calibrated Slope ADC without External Components
Authors: Zhang Mengdi , Zhao Ye , Chen Yong , Crovetti Paolo , Wang Yanji , Ning Xinshun , Qiao Shushan
Published: August 2022

A slope analog-to-digital converter (ADC) amenable to be fully implemented on a digital field programmable gate array (FPGA) without requiring any external active or passive components is proposed in this paper. The amplitude information, encoded in the transition times of a standard LVDS differential input—driven by the analog input and by the reference slope generated by an FPGA output buffer—is retrieved by an FPGA time-to-digital converter. Along with the ADC, a new online calibration algorithm is developed to mitigate the influence of process, voltage, and temperature variations on its performance. Measurements on an ADC prototype reveal an analog input range from 0.3 V to 1.5 V, a least significant bit (LSB) of 2.6 mV, and an effective number of bits (ENOB) of 7.4-bit at 600 MS/s. The differential nonlinearity (DNL) is in the range between −0.78 and 0.70 LSB, and the integral nonlinearity (INL) is in the range from −0.72 to 0.78 LSB.
A Comparative Study of Parameter Identification Methods for Asymmetric Nonlinear Systems with Quadratic and Cubic Stiffness
Authors: Wang Shibo , Tang Bin
Published: August 2022

Understanding the nonlinear dynamic characteristics of engineering structures is challenging, especially for the systems that exhibit asymmetric nonlinear behavior. This paper compared four parameter identification methods for asymmetric nonlinear systems incorporating quadratic and cubic stiffness nonlinearities. Hilbert transform, zero-crossing, direct quadrature, and wavelet transform were used to obtain the backbone, envelope, and restoring force curves from the free vibration time history. A nonlinear curve-fitting method was then applied to estimate the stiffness parameters of the asymmetric systems, and a linear least square fitting approach was utilized to estimate the damping parameters of the asymmetric systems. We used the Helmholtz–Duffing oscillator as a numerical example and a nonlinear vibration absorber with geometric imperfections to verify the feasibility and accuracy of these methods. The advantages and disadvantages of these methods and the deviations in estimated results are discussed.
Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach
Authors: Ghiță Alexandra Ștefania , Florea Adina Magda
Published: August 2022

Currently, the importance of autonomous operating devices is rising with the increasing number of applications that run on robotic platforms or self-driving cars. The context of social robotics assumes that robotic platforms operate autonomously in environments where people perform their daily activities. The ability to re-identify the same people through a sequence of images is a critical component for meaningful human-robot interactions. Considering the quick reactions required by a self-driving car for safety considerations, accurate real-time tracking and people trajectory prediction are mandatory. In this paper, we introduce a real-time people re-identification system based on a trajectory prediction method. We tackled the problem of trajectory prediction by introducing a system that combines semantic information from the environment with social influence from the other participants in the scene in order to predict the motion of each individual. We evaluated the system considering two possible case studies, social robotics and autonomous driving. In the context of social robotics, we integrated the proposed re-identification system as a module into the AMIRO framework that is designed for social robotic applications and assistive care scenarios. We performed multiple experiments in order to evaluate the performance of our proposed method, considering both the trajectory prediction component and the person re-identification system. We assessed the behaviour of our method on existing datasets and on real-time acquired data to obtain a quantitative evaluation of the system and a qualitative analysis. We report an improvement of over 5% for the MOTA metric when comparing our re-identification system with the existing module, on both evaluation scenarios, social robotics and autonomous driving.
Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review
Authors: Li Rihui , Yang Dalin , Fang Feng , Hong Keum-Shik , Reiss Allan L. , Zhang Yingchun
Published: August 2022

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS–EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS–EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS–EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS–EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS–EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS–EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS–EEG data analyses in future research.
ST-CRMF: Compensated Residual Matrix Factorization with Spatial-Temporal Regularization for Graph-Based Time Series Forecasting
Authors: Li Jinlong , Wu Pan , Li Ruonan , Pian Yuzhuang , Huang Zilin , Xu Lunhui , Li Xiaochen
Published: August 2022

Despite the extensive efforts, accurate traffic time series forecasting remains challenging. By taking into account the non-linear nature of traffic in-depth, we propose a novel ST-CRMF model consisting of the Compensated Residual Matrix Factorization with Spatial-Temporal regularization for graph-based traffic time series forecasting. Our model inherits the benefits of MF and regularizer optimization and further carries out the compensatory modeling of the spatial-temporal correlations through a well-designed bi-directional residual structure. Of particular concern is that MF modeling and later residual learning share and synchronize iterative updates as equal training parameters, which considerably alleviates the error propagation problem that associates with rolling forecasting. Besides, most of the existing prediction models have neglected the difficult-to-avoid issue of missing traffic data; the ST-CRMF model can repair the possible missing value while fulfilling the forecasting tasks. After testing the effects of key parameters on model performance, the numerous experimental results confirm that our ST-CRMF model can efficiently capture the comprehensive spatial-temporal dependencies and significantly outperform those state-of-the-art models in the short-to-long terms (5-/15-/30-/60-min) traffic forecasting tasks on the open Seattle-Loop and METR-LA traffic datasets.
Development of an Adaptive Computer-Aided Soft Sensor Diagnosis System for Assessment of Executive Functions
Authors: Mohai Katalin , Kálózi-Szabó Csilla , Jakab Zoltán , Fecht Szilárd Dávid , Domonkos Márk , Botzheim János
Published: August 2022

The main objective of the present study is to highlight the role of technological (soft sensor) methodologies in the assessment of the neurocognitive dysfunctions specific to neurodevelopmental disorders (for example, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and specific learning disorder). In many cases neurocognitive dysfunctions can be detected in neurodevelopmental disorders, some of them having a well-defined syndrome-specific clinical pattern. A number of evidence-based neuropsychological batteries are available for identifying these domain-specific functions. Atypical patterns of cognitive functions such as executive functions are present in almost all developmental disorders. In this paper, we present a novel adaptation of the Tower of London Test, a widely used neuropsychological test for assessing executive functions (in particular planning and problem-solving). Our version, the Tower of London Adaptive Test, is based on computer adaptive test theory (CAT). Adaptive testing using novel algorithms and parameterized task banks allows the immediate evaluation of the participant’s response which in turn determines the next task’s difficulty level. In this manner, the subsequent item is adjusted to the participant’s estimated capability. The adaptive procedure enhances the original test’s diagnostic power and sensitivity. By measuring the targeted cognitive capacity and its limitations more precisely, it leads to more accurate diagnoses. In some developmental disorders (e.g., ADHD, ASD) it could be very useful in improving the diagnosis, planning the right interventions, and choosing the most suitable assistive digital technological service.
Human Standing Posture Motion Evaluation by the Visual Simulation of Multi-Directional Sea-Waves
Authors: Doine Renon , Sakamaki Takanori
Published: August 2022

Crew fatigue from standing posture motion, caused by ship motion, can lead to marine accidents. Therefore, the mechanism of fatigue in crew members ought to be elucidated. The standing posture of humans is maintained by postural state detection through the visual, vestibular, and somatosensory systems. Humans can adjust their posture through corrective postural reactions (CPR) generated after anticipatory postural adjustments (APAs) by using information from these sensory systems. APAs refer to skills acquired by learning from past motions and perturbations and are prepared by the central nervous system based on visual information before the actual perturbation occurs. We hypothesized that APAs would decrease fatigue in crew members by stabilizing their standing posture motions. We aimed to clarify the human standing posture control influenced by APAs based on visual information. To this end, we presented wave images with different wave directions to the participants using a visual simulator and analyzed their standing posture motion. We found that the participants stabilized their standing posture based on the projected wave directions. This showed that the participants predicted ship motion from the wave images and controlled their center of pressure (COP) through APAs. Individual differences in standing postural motion may indicate the subjective variation of APAs based on individual experiences. This study was limited to males aged 20–23 years. To generalize this study, randomized controlled trials should be performed with participants of multiple age groups, including men and women.
Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning Techniques
Authors: Bahache Mohamed , Tahari Abdou El Karim , Herrera-Tapia Jorge , Lagraa Nasreddine , Calafate Carlos Tavares , Kerrache Chaker Abdelaziz
Published: August 2022

Remotely monitoring people’s healthcare is still among the most important research topics for researchers from both industry and academia. In addition, with the Wireless Body Networks (WBANs) emergence, it becomes possible to supervise patients through an implanted set of body sensors that can communicate through wireless interfaces. These body sensors are characterized by their tiny sizes, and limited resources (power, computing, and communication capabilities), which makes these devices prone to have faults and sensible to be damaged. Thus, it is necessary to establish an efficient system to detect any fault or anomalies when receiving sensed data. In this paper, we propose a novel, optimized, and hybrid solution between machine learning and statistical techniques, for detecting faults in WBANs that do not affect the devices’ resources and functionality. Experimental results illustrate that our approach can detect unwanted measurement faults with a high detection accuracy ratio that exceeds the 99.62%, and a low mean absolute error of 0.61%, clearly outperforming the existing state-of-art solutions.
A Multi-Sensor Data-Fusion Method Based on Cloud Model and Improved Evidence Theory
Authors: Xiang Xinjian , Li Kehan , Huang Bingqiang , Cao Ying
Published: August 2022

The essential factors of information-aware systems are heterogeneous multi-sensory devices. Because of the ambiguity and contradicting nature of multi-sensor data, a data-fusion method based on the cloud model and improved evidence theory is proposed. To complete the conversion from quantitative to qualitative data, the cloud model is employed to construct the basic probability assignment (BPA) function of the evidence corresponding to each data source. To address the issue that traditional evidence theory produces results that do not correspond to the facts when fusing conflicting evidence, the three measures of the Jousselme distance, cosine similarity, and the Jaccard coefficient are combined to measure the similarity of the evidence. The Hellinger distance of the interval is used to calculate the credibility of the evidence. The similarity and credibility are combined to improve the evidence, and the fusion is performed according to Dempster’s rule to finally obtain the results. The numerical example results show that the proposed improved evidence theory method has better convergence and focus, and the confidence in the correct proposition is up to 100%. Applying the proposed multi-sensor data-fusion method to early indoor fire detection, the method improves the accuracy by 0.9–6.4% and reduces the false alarm rate by 0.7–10.2% compared with traditional and other improved evidence theories, proving its validity and feasibility, which provides a certain reference value for multi-sensor information fusion.
Two-Stage Channel Estimation for Semi-Passive RIS-Assisted Millimeter Wave Systems
Authors: Peng Chengzuo , Deng Honggui , Xiao Haoqi , Qian Yuyan , Zhang Wenjuan , Zhang Yinhao
Published: August 2022

In a reconfigurable intelligent surface (RIS) assisted millimeter Wave (mmWave) communication system, the channel coefficient increases exponentially with the number of RIS elements which results in expensive pilot overhead. Most previous works have proposed some channel estimation algorithms for the estimation accuracy of cascaded channels, which have improved the estimation accuracy, but the pilot overhead is discouraging in the estimation process. To improve the channel estimation accuracy with reduced pilot overhead, we propose a two-stage channel estimation protocol by exploiting semi-passive elements and the coherent time difference of the channel, where the quasi-static channel between the base stations (BS) and RIS is estimated at the RIS, and the user (UE)-RIS time-varying channel is estimated at the BS. In the first stage, we formulate the BS-RIS channel estimation as a mathematical optimization problem by an iterative weighting method and then propose a gradient descent (GD)-based algorithm to solve it. In the second stage, we first transform the received the UE-RIS signal model into an equivalent parallel factor (PARAFAC) tensor model and estimate the UE-RIS channel by the least-squares (LS) algorithm. The simulation results show that the proposed method has better estimation accuracy than the LS, compression sensing (CS) and minimum mean square error (MMSE) methods with less pilot overhead, and the spectral efficiency is improved by at least 10.5% compared to the other three methods.
A Novel Decentralized Blockchain Architecture for the Preservation of Privacy and Data Security against Cyberattacks in Healthcare
Authors: Kumar Ajitesh , Singh Akhilesh Kumar , Ahmad Ijaz , Kumar Singh Pradeep , Anushree  , Verma Pawan Kumar , Alissa Khalid A. , Bajaj Mohit , Ur Rehman Ateeq , Tag-Eldin Elsayed
Published: August 2022

Nowadays, in a world full of uncertainties and the threat of digital and cyber-attacks, blockchain technology is one of the major critical developments playing a vital role in the creative professional world. Along with energy, finance, governance, etc., the healthcare sector is one of the most prominent areas where blockchain technology is being used. We all are aware that data constitute our wealth and our currency; vulnerability and security become even more significant and a vital point of concern for healthcare. Recent cyberattacks have raised the questions of planning, requirement, and implementation to develop more cyber-secure models. This paper is based on a blockchain that classifies network participants into clusters and preserves a single copy of the blockchain for every cluster. The paper introduces a novel blockchain mechanism for secure healthcare sector data management, which reduces the communicational and computational overhead costs compared to the existing bitcoin network and the lightweight blockchain architecture. The paper also discusses how the proposed design can be utilized to address the recognized threats. The experimental results show that, as the number of nodes rises, the suggested architecture speeds up ledger updates by 63% and reduces network traffic by 10 times.
Collision Detection of a HEXA Parallel Robot Based on Dynamic Model and a Multi-Dual Depth Camera System
Authors: Hoang Xuan-Bach , Pham Phu-Cuong , Kuo Yong-Lin
Published: August 2022

This paper introduces a Hexa parallel robot and obstacle collision detection method based on dynamic modeling and a computer vision system. The processes to deal with the collision issues refer to collision detection, collision isolation, and collision identification applied to the Hexa robot, respectively, in this paper. Initially, the configuration, kinematic and dynamic characteristics during movement trajectories of the Hexa parallel robot are analyzed to perform the knowledge extraction for the method. Next, a virtual force sensor is presented to estimate the collision detection signal created as a combination of the solution to the inverse dynamics and a low-pass filter. Then, a vision system consisting of dual-depth cameras is designed for obstacle isolation and determining the contact point location at the end-effector, an arm, and a rod of the Hexa robot. Finally, a recursive Newton-Euler algorithm is applied to compute contact forces caused by collision cases with the real-Hexa robot. Based on the experimental results, the force identification is compared to sensor forces for the performance evaluation of the proposed collision detection method.
The Impact of Base Cell Size Setup on the Finite Difference Time Domain Computational Simulation of Human Cornea Exposed to Millimeter Wave Radiation at Frequencies above 30 GHz
Authors: Foroughimehr Negin , Vilagosh Zoltan , Yavari Ali , Wood Andrew
Published: August 2022

Mobile communication has achieved enormous technology innovations over many generations of progression. New cellular technology, including 5G cellular systems, is being deployed and making use of higher frequencies, including the Millimetre Wave (MMW) range (30–300 GHz) of the electromagnetic spectrum. Numerical computational techniques such as the Finite Difference Time Domain (FDTD) method have been used extensively as an effective approach for assessing electromagnetic fields’ biological impacts. This study demonstrates the variation of the accuracy of the FDTD computational simulation system when different meshing sizes are used, by using the interaction of the critically sensitive human cornea with EM in the 30 to 100 GHz range. Different approaches of base cell size specifications were compared. The accuracy of the computation is determined by applying planar sensors showing the detail of electric field distribution as well as the absolute values of electric field collected by point sensors. It was found that manually defining the base cell sizes reduces the model size as well as the computation time. However, the accuracy of the computation decreases in an unpredictable way. The results indicated that using a cloud computing capacity plays a crucial role in minimizing the computation time.
An Accurate and Robust Method for Absolute Pose Estimation with UAV Using RANSAC
Authors: Guo Kai , Ye Hu , Gao Xin , Chen Honglin
Published: August 2022

In this paper, we proposed an accurate and robust method for absolute pose estimation with UAV (unmanned aerial vehicle) using RANSAC (random sample consensus). Because the artificial 3D control points with high accuracy are time-consuming and the small point set may lead low measuring accuracy, we designed a customized UAV to efficiently obtain mass 3D points. A light source was mounted on the UAV and used as a 3D point. The position of the 3D point was given by RTK (real-time kinematic) mounted on the UAV, and the position of the corresponding 2D point was given by feature extraction. The 2D–3D point correspondences exhibited some outliers because of the failure of feature extraction, the error of RTK, and wrong matches. Hence, RANSAC was used to remove the outliers and obtain the coarse pose. Then, we proposed a method to refine the coarse pose, whose procedure was formulated as the optimization of a cost function about the reprojection error based on the error transferring model and gradient descent to refine it. Before that, normalization was given for all the valid 2D–3D point correspondences to improve the estimation accuracy. In addition, we manufactured a prototype of a UAV with RTK and light source to obtain mass 2D–3D point correspondences for real images. Lastly, we provided a thorough test using synthetic data and real images, compared with several state-of-the-art perspective-n-point solvers. Experimental results showed that, even with a high outlier ratio, our proposed method had better performance in terms of numerical stability, noise sensitivity, and computational speed.
Data Collection from Buried Sensor Nodes by Means of an Unmanned Aerial Vehicle
Authors: Cariou Christophe , Moiroux-Arvis Laure , Pinet François , Chanet Jean-Pierre
Published: August 2022

The development of Wireless Underground Sensor Networks (WUSNs) is a recent research axis based on sensor nodes buried a few dozen centimeters deep. The communication ranges are, however, highly reduced due to the high attenuation of electromagnetic waves in soil, leading to issues of data collection. This paper proposes to embed a data collector on an Unmanned Aerial Vehicle (UAV) coming close to each buried sensor node. The whole system was developed (sensor nodes, data collector, gateway) and experimentations were carried out in real conditions. In hovering mode, the measurements on the RSSI levels with respect to the position of the UAV highlight the interest in maintaining a high altitude when the UAV is far from the node. In dynamic mode, the experimental results demonstrate the feasibility of carrying out the data collection task while the UAV is moving. The speed of the UAV has, however, to be adapted to the required time to collect the data. In the case of numerous buried sensor nodes, evolutionary algorithms are implemented to plan the trajectory of the UAV optimally. To the best of our knowledge, this paper is the first one that reports experiment results combining WUSN and UAV technologies.
Gender and Public Perception of Disasters: A Multiple Hazards Exploratory Study of EU Citizens
Authors: Cuesta Arturo , Alvear Daniel , Carnevale Antonio , Amon Francine
Published: August 2022

Aim: To explore gender influence on individual risk perception of multiple hazards and personal attitudes towards disaster preparedness across EU citizens. Method: An online survey was distributed to 2485 participants from Spain, France, Poland, Sweden and Italy. The survey was divided into two parts. The first part examined perceived likelihood (L), perceived personal impact (I) and perceived self-efficacy (E) towards disasters due to extreme weather conditions (flood, landslide and storm), fire, earthquake, hazardous materials accidents, and terrorist attacks. The overall risk rating for each specific hazard was measured through the following equation R = (L × I)/E and the resulting scores were brought into the range between 0 and 1. The second part explored people’s reactions to the Pros and Cons of preparedness to compute the overall attitudes of respondents towards preparation (expressed as a ratio between −1 and 1). Results: Although we found gender variations on concerns expressed as the likelihood of the occurrence, personal consequences and self-efficacy, the overall risks were judged significantly higher by females in all hazards (p < 0.01). We also found that, in general, most respondents (both males and females) were in favour of preparedness. More importantly, despite the gender differences in risk perception, there were no significant differences in the attitudes towards preparedness. We found weak correlations between risks perceived and attitudes towards preparedness (rho < 0.20). The intersectional analysis showed that young and adult females perceived higher risks than their gender counterparts at the same age. There were also gender differences in preparedness, i.e., females in higher age ranges are more motivated for preparedness than men in lower age ranges. We also found that risk perception for all hazards in females was significantly higher than in males at the same education level. We found no significant differences between sub-groups in the pros and cons of getting ready for disasters. However, females at a higher level of education have more positive attitudes towards preparedness. Conclusions: This study suggests that gender along with other intersecting factors (e.g., age and education) still shape differences in risk perception and attitudes towards disasters across the EU population. Overall, the presented results policy actions focus on promoting specific DRR policies and practices (bottom-up participatory and learning processes) through interventions oriented to specific target groups from a gender perspective.
Conveyor-Less Urban-Car Assembly Factory with VaaC and Matrix System
Authors: Oh Seog-Chan , Wells James W. , Arinez Jorge
Published: August 2022

The advent of autonomous electric vehicles (AEVs) will give drivers time and space instead of focusing on driving. Because of this, some drivers may want to personalize their car for their work, while others may want to customize their vehicle space to be more suitable for relaxation, which will accelerate the megatrend of mass individualization. However, the production of individualized cars faces several challenges. For example, since high-level automation during individualized car production is difficult, a stable skilled labor supply is essential, low-volume/high-variety production is required, and customer proximity or involvement is also important. These conditions can be satisfied by building a car assembly plant in an urban area. The problem is that urban areas are often spatially and environmentally constrained. However, it is be possible to overcome these urban limitations by implementing a conveyor-less micro factory. The objective of this study is to propose a new iterative matrix-system layout design method that can realize a conveyor-less urban car assembly factory with two technologies—VaaC (vehicle as a conveyor) and matrix assembly system. VaaC consists of three novel ideas: sensor skid, safety-sensor guidance system, and vehicle-powered devices, and this paper views each of them in detail. The proposed iterative matrix-system layout design method consists of four steps: (1) layout refinement, (2) simulation, (3) cost analysis, and (4) optimization check, and will examine how each step is performed through simple examples. The authors hope that this paper will arouse interest and provide elements to spur future research on the conveyor-less urban car assembly system.
Hermeneutic-Phenomenological Interpretation of Coronavirus Experiences, Their Meanings, and the Prospects of Young Finns in Education and the Labor Market in Lapland
Authors: Helve Helena Marketta
Published: August 2022

In this paper I reflect on the methodological concepts of youth research, utilizing a hermeneutic-phenomenological approach to the interpretation of interview data from young adults who have been on short-term work or distant education at Finnish ski resorts in Lapland during the coronacirus pandemic. The study received background from a previous study “From higher education to working life: Work values of young Finns in changing labor markets". I try to distance myself from this research by interpreting young people’s coronavirus experiences and future perspectives hermeneutic-phenomenologically. In the spring of 2021, I interviewed a total of ten (5 women and 5 men) young people aged 19 to 27 I met at the ski resorts. Interviews on young people’s coronavirus experiences and their implications for the transitions from education to employment and future orientations were semi-structured, partly discussions of topics related to education, work and transition to adulthood combined with young people’s COVID-19 experiences and their implications. In the interviews, young people combined their previous life experiences and perceptions of the world with the coronavirus experiences. The coronavirus experiences of young people were situational. The study analyzes the individual experiences of young people with the COVID-19 pandemic, describing them with own youth spoken language, and interpreting the essential contents of the meanings hermeneutic-phenomenologically. The COVID-19 interpretations of young people had positive and negative meanings to their transitions in education and the labor market. The basic themes that cut across the entire material were: (1) The small impact of the pandemic on the young person’s own life. (2) The uncertainty of life and uncertain future and (3) the experienced loneliness, which can provide for youth to confront their true selves. The implications of these results are discussed in the article, which also critically considers the applicability of the hermeneutic-phenomenological research, and discusses about ethical points of the study of young people in exceptional contexts.
“Not Storing the Samples It’s Certainly Not a Good Service for Patients”: Constructing the Biobank as a Health Place
Authors: Riso Brígida
Published: August 2022

Biobanks have been established from the beginning of the millennium as relevant infrastructures to support biomedical research. These repositories have also transformed the paradigm of collecting and storing samples and associated clinical data, moving these practices from the healthcare services and research laboratories to dedicated services. In Portugal, the establishment of biobanks is happening in the absence of a specific legal framework, turning it difficult to fully understand the scope of their action. This ethnographic research explored how establishing a biobank challenges the dynamics between healthcare and biomedical research. The ethnography intended to follow the path of biological samples from the hospital, where they were collected, to the biobank in a research institute, where they were stored. Findings suggest that although the nature of the biobank’s technical work seemed to inscribe it as a research-oriented setting, the biobank’s daily work was performed through symbolic action in the logic of care. Biobank staff constantly recalled the human nature of the samples, and they built complex illness narratives of each sample, promoting a connection with the absent donor. These practices were crucial to constructing the biobank as a health place, one that was designed to be life-saving in the near future.
Language Practices within the Mixed Spanish-/Italian-/French- and Estonian-Speaking Families in Tallinn
Authors: Seppik Regina , Zabrodskaja Anastassia
Published: August 2022

This phenomenological study examined six mixed families living in Tallinn who are composed by French-/Italian-/Spanish-Estonian native speakers, who have at least one child who is being raised simultaneously with the combination of French-/Italian-/Spanish-Estonian and who all appeared to follow the one parent one language strategy as family language policy. The semi-structured interviews were conducted with parents. The theoretical aspect features family language policies and strategies, identity and its types, globalisation forces, bilingualism, and multiculturalism. The research aimed at highlighting the reasons behind parents’ ideological decision, more specifically, on how these bilingual families manage and adapt their language policies. The study shows how families control their chosen strategies. Research revealed in which languages children prefer to speak if they have been raised in multilingual environment. The results demonstrated that parents prefer to use a one parent–one language approach and they are led by their intuition and desire to speak in their own mother tongue with their children. It was found that bilingual reading to children during their first years contributes to their ability to speak in both parents’ mother tongues. Data showed that bilingual children living in Tallinn prefer to speak Estonian while having competency in both languages. This study revealed that parents were content about their children being bilingual.
Impacts on Student Learning and Skills and Implementation Challenges of Two Student-Centered Learning Methods Applied in Online Education
Authors: Soubra Lama , Al-Ghouti Mohammad A. , Abu-Dieyeh Mohammed , Crovella Sergio , Abou-Saleh Haissam
Published: August 2022

Online education became more prevalent during the COVID-19 pandemic in many countries around the world, including the Gulf Cooperation Council (GCC) countries. This study aims at assessing the impacts on learning and skills of two student-centered instructional strategies (problem-based learning (PBL) and just-in-time teaching (JiTT)) used online and their implementation challenges. The PBL and JiTT were implemented in modules taught in various courses delivered at different bachelor’s study levels and disciplines. The research used a mixed design research method. Quantitative data were collected from exam scores and two self-administered surveys. Qualitative data were collected using individual structured interviews. The lecture-based learning method was used for comparisons. A total of 134 students participated in the quizzes and exams, 85 students completed the self-perceived impacts on learning and skills survey, and 82 students completed the implementation challenges survey. Ten students participated in the structured interviews. Tests and survey scores showed that both online PBL and JiTT had significant impacts on students learning and skills and that these effects are consistent across various disciplines. A non-conducive online learning climate, internet connectivity problems, heavy workloads, and time management issues were reported as the implementation challenges. The PBL and JiTT can be considered as effective teaching/learning strategies in online education.
The Impact of Public Services on Urban Innovation—A Study Based on Differences and Mechanisms
Authors: Qiu Yi , Wang Hana
Published: August 2022

Urban public services can significantly promote the improvement and capacity of urban innovation, yet there are differences. Although some studies have analyzed the impact of public services on technological innovation, few studies have taken Chinese cities at the prefecture level and above as samples to build an urban public service evaluation index system and focus on analyzing the differences and impact mechanisms. Based on panel data of 277 cities at the prefecture level and above from 2005 to 2019, this paper empirically studies the differences and mechanisms of the impact of urban public services on urban innovation. The results show that urban public services can significantly promote the improvement of urban innovation ability. After endogenous treatment and robustness testing, the empirical results remain robust. There are differences in the impact of urban public services on urban innovation: the positive effect of basic public services on urban innovation is greater than that livelihood public services. The innovation effect of public services in small and large cities is significant, but not in medium-sized cities. The effect of urban public services in eastern and central regions on improving innovation capability is higher than that in the western and northeastern regions. In cities with different levels of administration, the technological innovation effect of public services in key cities is not significant, while in generalized cities, it is significant. The study also found that public services can positively affect urban innovation through the talent agglomeration effect, industrial structure effect, and income scale effect.
Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm
Authors: Kumar Arun , Rajagopal Karthikeyan , Gugapriya G. , Sharma Himanshu , Gour Nidhi , Masud Mehedi , AlZain Mohammed A. , Alajmani Samah H.
Published: August 2022

Filtered Non-Orthogonal Multiple Access (F-NOMA) is a multi-carrier wave form and is considered a suitable contender for 5G radio. Peak to average power ratio (PAPR) is regarded as a major hurdle in the NOMA wave form because it hampers the efficiency of the power amplifier of the NOMA transmitter. In this study, a novel selective mapping (SLM) algorithm is used to minimize the PAPR of the NOMA. The conventional SLM increases the intricacy of the structure, and the projected SLM algorithm is applied to the transmitter part of the F-NOMA. Furthermore, we evaluate the performance of SLM on F-NOMA for 16, 64, and 256-Quadrature Amplitude Modulation (QAM) transmission methods. The parameters such as Bit Error Rate (BER), PAPR, power spectral density (PSD), and complexity are estimated and compared with different transmission patterns. The simulation outcomes of the work reveal that the optimal PAPR can be achieved by selecting the sub-block (S) and phase rotation elements (Ps). PAPR in F-NOMA achieves 1 dB gain in different QAM transmissions and its saving performance is 70.07%; however, complexity increases with an increase in modulation order.