bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis and Correction of the Crosstalk Effect in a Three-Axis SERF Atomic Magnetometer
Authors: Yan Yeguang , Lu Jixi , Zhou Binquan , Wang Kun , Liu Ziao , Li Xiaoyu , Wang Weiyi , Liu Gang
Published: September 2022

Three-axis atomic magnetometers have an excellent advantage for determining the complete vector information of a magnetic field to be measured. However, the crosstalk effect, which leads to an error output on one axis owing to a magnetic field on the other axes, can reduce the measurement accuracy. In this study, we propose an effective suppression method for the crosstalk effect in a three-axis atomic magnetometer. First, we investigated and analyzed the main factors that introduce the effect. Based on this, the modulation parameters were optimized to improve the scale factors, which obtained a coupling coefficient of less than 6% for the atomic magnetometer. Subsequently, the associated transfer matrix was corrected to further suppress the crosstalk effect. After correction, all the coupling coefficients were decreased to less than 3%, with the majority being lower than 1%.
Tribological and Rheological Properties of Poly(vinyl alcohol)-Gellan Gum Composite Hydrogels
Authors: Feng Yang , Dai Shao-Cong , Lim Khoon , Ramaswamy Yogambha , Jabbarzadeh Ahmad
Published: September 2022

Polymeric poly(vinyl alcohol) (PVA)-based composite hydrogels are promising materials with various biomedical applications. However, their mechanical and tribological properties should be tailored for such applications. In this study, we report the fabrication of PVA-gellan gum (GG) composite hydrogels and determine the effect of GG content on their rheological and tribological properties. The rheology tests revealed an enhanced storage (elastic) modulus with increased gellan gum (GG) concentration. The results showed up to 89% enhancement of the elastic modulus of PVA by adding 0.5 wt% gellan gum. This elastic modulus (12.1 ± 0.8 kPa) was very close to that of chondrocyte and its surrounding pericellular matrix (12 ± 1 kPa), rendering them ideal for cartilage regeneration applications. Furthermore, the friction coefficient was reduced by up to 80% by adding GG to PVA, demonstrating the increased elastic modulus improved chance of survival under mechanical shear stresses. Examining PVA/GG at different concentrations of 0.1, 0.3, and 0.5 wt% of GG, we demonstrate that at a load of 5 N, the friction coefficient decreases by increasing the GG concentration. However, at higher loads of 10 and 15 N, a 0.3 wt% concentration was sufficient to significantly reduce the friction coefficient. For PVA and PVA/GG composites, we observed a reduction in friction coefficient by increasing the load from 5 to 15 N. We also found the friction to be independent of the sliding velocity. Possible mechanisms of achieving a reduced friction coefficient are discussed.
Efficient All-Polymer Solar Cells Enabled by Interface Engineering
Authors: Zhang Guoping , Wang Lihong , Zhao Chaoyue , Wang Yajie , Hu Ruiyu , Che Jiaxu , He Siying , Chen Wei , Cao Leifeng , Luo Zhenghui , Qiu Mingxia , Li Shunpu , Zhang Guangye
Published: September 2022

All-polymer solar cells (all-PSCs) are organic solar cells in which both the electron donor and the acceptor are polymers and are considered more promising in large-scale production. Thanks to the polymerizing small molecule acceptor strategy, the power conversion efficiency of all-PSCs has ushered in a leap in recent years. However, due to the electrical properties of polymerized small-molecule acceptors (PSMAs), the FF of the devices is generally not high. The typical electron transport material widely used in these devices is PNDIT-F3N, and it is a common strategy to improve the device fill factor (FF) through interface engineering. This work improves the efficiency of all-polymer solar cells through interfacial layer engineering. Using PDINN as the electron transport layer, we boost the FF of the devices from 69.21% to 72.05% and the power conversion efficiency (PCE) from 15.47% to 16.41%. This is the highest efficiency for a PY-IT-based binary all-polymer solar cell. This improvement is demonstrated in different all-polymer material systems.
Analysis of the Polymer Two-Layer Protective Coating Impact on Panda-Type Optical Fiber under Bending
Authors: Lesnikova Yulia I. , Trufanov Aleksandr N. , Kamenskikh Anna A.
Published: September 2022

The article discusses the effects of thermal-force on the Panda-type optical fiber. The studies used a wide temperature range. The research used two thermal cycles with exposures to temperatures of 23, 60 and −60 °C. The field of residual stresses in the fiber formed during the drawing process was determined and applied. Panda was considered taking into account a two-layer viscoelastic polymer coating under conditions of tension winding on an aluminum coil in the framework of a contact problem. The paper investigated three variants of coil radius to analyze the effect of bending on fiber behavior. The effect of the coating thickness ratio on the system deformation and optical characteristics was analyzed. Qualitative and quantitative patterns of the effect of temperature, bending, thickness of individual polymer coating layers and relaxation transitions of their materials on the Panda optical fiber deformation and optical characteristics were established. Assessment of approaches to the calculation of optical characteristics (values of the refractive indices and fiber birefringence) are given in the framework of the study. The patterns of deformation and optical behavior of the Panda-type fiber with a protective coating, taking into account the nonlinear behavior of the system materials, were original results.
Preparation of Waste-LDPE/SBS Composite High-Viscosity Modifier and Its Effect on the Rheological Properties and Microstructure of Asphalt
Authors: Yu Haosheng , Jin Yong , Liang Xingmin , Dong Fuqiang
Published: September 2022

To reduce the cost of high-viscosity modifier (HVM) and alleviate white pollution problems, we prepared the environment-friendly HVM (E-HVM) by using waste-low density polyethylene/styrene-butadiene-styrene (waste-LDPE/SBS) composite. The physical characteristics of the E-HVM modifier were first investigated. Additionally, the effects of E-HVM modifier dosage (8 wt% to 20 wt%) on the rheological properties and microstructure of asphalt were, respectively, researched by dynamic shear rheometer (DSR), bending beam rheometer (BBR), and fluorescence microscopy (FM). The results show that the E-HVM modifier has lower molecular weight, and its distribution is wider than that of the Tafpack-Super (TPS) modifier; thus, the E-HVM modifier had better compatibility with asphalt, which has also been proven by FM images. Due to these reasons, the E-HVM modifier improves the high-temperature performances of asphalt more effectively than the TPS modifier, which is shown by the higher dynamic viscosity (60 °C) and G* and the lower δ and Jnr(τ) Furthermore, compared to TPS modified asphalt, E-HVM modified asphalt also has a higher fatigue life at different strain levels (2.5% and 5.0%), but worse low-temperature performance. Following a comprehensive consideration of performances, the reasonable dosage range of E-HVM modifier is 12 wt% to 16 wt%.
Polymer Conductive Membrane-Based Circular Capacitive Pressure Sensors from Non-Touch Mode of Operation to Touch Mode of Operation: An Analytical Solution-Based Method for Design and Numerical Calibration
Authors: Zhang Qi , Li Fei-Yan , Li Xue , He Xiao-Ting , Sun Jun-Yi
Published: September 2022

Polymer-based conductive membranes play an important role in the development of elastic deflection-based pressure sensors. In this paper, an analytical solution-based method is presented for the design and numerical calibration of polymer conductive membrane-based circular capacitive pressure sensors from non-touch mode of operation to touch mode of operation. The contact problem of a circular membrane in frictionless contact with a rigid flat plate under pressure is analytically solved, and its analytical solution is used for the design of touch mode circular capacitive pressure sensors for the first time. The analytical relationship with input pressure as independent variable and output capacitance as dependent variable is precisely derived and is used for the numerical calibrations of the analytical relationships with input capacitance as the independent variable and output pressure as the dependent variable in order to meet the capacitive pressure sensor mechanism of detecting pressure by measuring capacitance. For the first time, an example showing the design and numerical calibration of a given (given design parameters) polymer conductive membrane-based circular capacitive pressure sensor from non-touch mode of operation to touch mode of operation is provided. Then, the influence of changing several important design parameters on input capacitance–output pressure relationships is comprehensively investigated in order to clarify the desired input–output relationships when changing design parameters.
Element Transfer Behavior for CaF2-Na2O-SiO2 Agglomerated Flux Subject in Submerged Arc Welding Process
Authors: Zhang Dan , Zhang Jin , Yang Shuchen , Shao Guoyou , Liu Zhongqiu
Published: September 2022

From a thermodynamic perspective, the present study has been performed to investigate the effect of SiO2 level in agglomerated fluxes on the element transfer behavior of essential elements, by applying CaF2-Na2O-SiO2 agglomerated fluxes with varying SiO2 contents. Element transfer behavior is quantified by the Δ value. The impact of SiO2 and heat input upon element transfer behavior is interpreted. Additionally, a possible thermodynamic approach to predict high basicity flux O potential and weld metal composition is proposed and evaluated. It is revealed that the consideration of the gas-slag-metal equilibrium is able to place constraints on the transfer behaviors (of O, Si, and Mn) and formation of gases. In submerged arc welding metallurgy, the empirically determined basicity index models proposed by Tuliani have been applied for more than 50 years to predict flux O potential and weld metal oxygen content. However, it is well known by welding practitioners that the flux basicity index model can only predict the changing trend of flux O potential when the flux basicity index is lower than 2.0. The present study has proposed a new thermodynamic method to identify the flux O potential for fluxes with a basicity index higher than 2.0. Additionally, the experimental evidence for the Mitra kinetic model has been provided.
Study of Plasma Interaction with Liquid Lithium Multichannel Capillary Porous Systems in SCU-PSI
Authors: Liu Jianxing , Jing Wenna , Guo Hengxin , Gao Yingwei , Wang Sishu , Chen Bo , Chen Jianjun , Wang Hongbin , Wei Jianjun , Ye Zongbiao , Gou Fujun
Published: September 2022

In this paper, an embedded multichannel capillary porous system (EM-CPS) was designed and fabricated with 304 stainless steel using the laser ablation method. The EM-CPS revealed its excellent ability to wick liquid lithium to its surface effectively. The interaction between Li-prefilled EM-CPS and plasma was studied, and the results showed that the surface temperature decreased by ~140 °C compared with the results of the experiment of EM-CPS without lithium filling. Additionally, EM-CPS displayed a better heat transfer performance and stronger radiation loss of the vapor cloud than the traditional woven tungsten-based meshes. In addition, the drift of the lithium vapor cloud center was found during plasma irradiation and led to a decrease in the intensity of the Li 670.78 nm emission line detected by the spectrometer at the observation point. When the thermal load deposited on the sample surface is reinforced by increasing the magnetic field, the rise in surface temperature is restrained due to the enhanced heat dissipation capability of lithium. SEM images of irradiated samples showed that the 304 stainless steel-based EM-CPS has corrosion problems due to the interaction between liquid lithium and argon plasma, but it still showed good plasma-facing characteristics. These findings provide a reference for further studies of embedded multichannel CPSs with plasma-facing components (PFCs) in linear plasma devices and tokamaks in the future.
Changes over the Last 35 Years in Alaska’s Glaciated Landscape: A Novel Deep Learning Approach to Mapping Glaciers at Fine Temporal Granularity
Authors: Roberts-Pierel Ben M. , Kirchner Peter B. , Kilbride John B. , Kennedy Robert E.
Published: September 2022

Glaciers are important sentinels of a changing climate, crucial components of the global cryosphere and integral to their local landscapes. However, many of the commonly used methods for mapping glacier change are labor-intensive and limit the temporal and spatial scope of existing research. This study addresses some of the limitations of prior approaches by developing a novel deep-learning-based method called GlacierCoverNet. GlacierCoverNet is a deep neural network that relies on an extensive, purpose-built training dataset. Using this model, we created a record of over three decades long at a fine temporal cadence (every two years) for the state of Alaska. We conducted a robust error analysis of this dataset and then used the dataset to characterize changes in debris-free glaciers and supraglacial debris over the last ~35 years. We found that our deep learning model could produce maps comparable to existing approaches in the capture of areal extent, but without manual editing required. The model captured the area covered with glaciers that was ~97% of the Randolph Glacier Inventory 6.0 with ~6% and ~9% omission and commission rates in the southern portion of Alaska, respectively. The overall model area capture was lower and omission and commission rates were significantly higher in the northern Brooks Range. Overall, the glacier-covered area retreated by 8425 km2 (−13%) between 1985 and 2020, and supraglacial debris expanded by 2799 km2 (64%) during the same period across the state of Alaska.
Capability of a Ground-Based Passive Surveillance System to Detect and Track Spaceborne SAR in LEO Orbits
Authors: Novák David , Gregor Ladislav , Veselý Jiří
Published: September 2022

Monitoring the Earth’s surface from the low Earth orbit (LEO) by synthetic aperture radar (SAR) provides meaningful information about a number of human activities such as the ecosystem, agriculture, and of course security matters. During its operation in LEO orbits, spaceborne SAR, at altitudes between 200 km and 2000 km, inherently emit electromagnetic signals toward the Earth’s surface. It has been experimentally verified that it is possible to detect and intercept spaceborne SAR signals at a distance of hundreds of kilometers by a single electronic support measure (ESM) non-comms sensor, placed on the Earth’s surface. The work described in this document is a follow-up to the paper and describes the principle of a new non-cooperative passive tracking method of spaceborne SAR carriers, tracked by four ground-based passive ESM non-comms sensors. These sensors used the time difference of arrival (TDOA) method in order to determine the current spaceborne SAR position in LEO orbits. Simulations were performed to assess the impact of ESM sensor configurations on the SAR’s signal interception and positioning error. The simulation results showed that the proposed method is sufficient to detect a spaceborne SAR position in LEO.
RaftNet: A New Deep Neural Network for Coastal Raft Aquaculture Extraction from Landsat 8 OLI Data
Authors: Su Hua , Wei Susu , Qiu Junlong , Wu Wenting
Published: September 2022

The rapid development of marine ranching in recent years provides a new way of tackling the global food crisis. However, the uncontrolled expansion of coastal aquaculture has raised a series of environmental problems. The fast and accurate detection of raft will facilitate scientific planning and the precise management of coastal aquaculture. A new deep learning-based approach called RaftNet is proposed in this study to extract the coastal raft aquaculture in Sansha Bay using Landsat 8 OLI images accurately. To overcome the issues of turbid water environments and varying raft scales in aquaculture areas, we constructed the RaftNet by modifying the UNet network with dual-channel and residual hybrid dilated convolution blocks to improve the extraction accuracy. Meanwhile, we adopted the well-known semantic segmentation networks (FCN, SegNet, UNet, UNet++, and ResUNet) as the contrastive approaches for the extraction. The results suggested that the proposed RaftNet model achieves the best accuracy with a precision of 84.5%, recall of 88.1%, F1-score of 86.30%, overall accuracy (OA) of 95.7%, and intersection over union (IoU) of 75.9%. We then utilized our RaftNet to accurately extract a raft aquaculture area in Sansha Bay from 2014 to 2018 and quantitatively analyzed the change in the raft area over this period. The results demonstrated that our RaftNet is robust and suitable for the precise extraction of raft aquaculture with varying scales in turbid coastal waters, and the Kappa coefficient and OA can reach as high as 88% and 97%, respectively. Moreover, the proposed RaftNet will unleash a remarkable potential for long time-series and large-scale raft aquaculture mapping.
Enhancing Aboveground Biomass Estimation for Three Pinus Forests in Yunnan, SW China, Using Landsat 8
Authors: Tang Jing , Liu Ying , Li Lu , Liu Yanfeng , Wu Yong , Xu Hui , Ou Guanglong
Published: September 2022

The estimation of forest aboveground biomass (AGB) using Landsat 8 operational land imagery (OLI) images has been extensively studied, but forest aboveground biomass (AGB) is often difficult to estimate accurately, in part due to the multi-level structure of forests, the heterogeneity of stands, and the diversity of tree species. In this study, a habitat dataset describing the distribution environment of forests, Landsat 8 OLI image data of spectral reflectance information, as well as a combination of the two datasets were employed to estimate the AGB of the three common pine forests (Pinus yunnanensis forests, Pinus densata forests, and Pinus kesiya forests) in Yunnan Province using a parametric model, stepwise linear regression model (SLR), and a non-parametric model, such as random forest (RF) and support vector machine (SVM). Based on the results, the following conclusions can be drawn. (1) As compared with the parametric model (SLR), the non-parametric models (RF and SVM) have a better fitting performance for estimating the AGB of the three pine forests, especially in the AGB segment of 40 to 200 Mg/ha. The non-parametric model is more sensitive to the number of data samples. In the case of the Pinus densata forest with a sample size greater than 100, RF fitting provides better fitting performance than SVM fitting, and the SVM fitting model is better suited to the AGB estimation of the Pinus yunnanensis forest with a sample size of less than 100. (2) Landsat 8 OLI images exhibit superior accuracy in estimating the AGB of the three pine forests using a single dataset. Variables, such as texture and vegetation index variables, which can reflect the comprehensive reflection information of ground objects, play a significant role in estimating AGBs, especially the texture variables. (3) By incorporating the combined dataset with characteristics of tree species distribution and ground object reflectance spectrum, the accuracy and stability of AGB estimation of the three pine forests can be improved. Moreover, the employment of a combined dataset is also effective in reducing the number of estimation errors in cases with AGB less than 100 Mg/ha or exceeding 150 Mg/ha.
Wildfire Risk Assessment in Liangshan Prefecture, China Based on An Integration Machine Learning Algorithm
Authors: Xie Lingxiao , Zhang Rui , Zhan Junyu , Li Song , Shama Age , Zhan Runqing , Wang Ting , Lv Jichao , Bao Xin , Wu Renzhe
Published: September 2022

Previous wildfire risk assessments have problems such as subjectivity of weight allocation and the linearization of statistical models, resulting in generally low robustness and low generalization ability of fire risk assessment models. Therefore, in this paper, we explored the potential of integration machine learning algorithms to build wildfire risk assessment models. Based on analyzing fire data’s spatial and temporal distribution, we selected 10 triggering factors of topography, meteorology, vegetation, and human activities, using frequency ratio (FR) to provide uniform data representation of triggering factors. Next, we used the Bayesian optimization (BO) algorithm to perform hyperparametric optimization solutions for various machine learning models: support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Finally, we constructed an integration machine learning algorithm to acquire a fire risk grading map and the importance evaluation corresponding to each triggering factor. For validation purposes, we selected Liangshan Prefecture in Sichuan Province as the specific study area and obtained MCD64A1 burned area product to extract the extent of burned areas in Liangshan Prefecture from 2011 to 2020. The accuracy, kappa coefficient, and area under curve (AUC) were then applied to assess the predictive power and consistency of the fire risk classification maps. The experimental analysis showed that among the three models, FR-BO-XGBoost had the best performance in wildfire risk assessment in the Liangshan region (AUC = 0.887), followed by FR-BO-RF (AUC = 0.876) and FR-BO-SVM (AUC = 0.820). The feature importance result indicated that the study area’s most significant effects on wildfires were precipitation, NDVI, land cover, and maximum temperature. The proposed method avoided the subjective weighting and model linearization problems. Compared with the previous methods, it automatically acquired the importance of the triggering factors to the wildfire, which had certain advantages in wildfire risk assessment, and was worthy of further promotion.
Evaluation of the Spatiotemporal Evolution of China’s Ecological Spatial Network Function–Structure and Its Pattern Optimization
Authors: Liu Hongjun , Niu Teng , Yu Qiang , Yang Linzhe , Ma Jun , Qiu Shi
Published: September 2022

(1) Background: Eco−spatial networks play an important role in enhancing ecosystem services and landscape connectivity. It is necessary to study landscape structure optimization to achieve synergistic gains in network connectivity and ecosystem functionality. (2) Method: Based on remote sensing data, RS and GIS were used to evaluate the spatiotemporal changes in ecosystem services in China. Combined with complex network theory, the spatiotemporal evolution of China’s ecological spatial network and its topological structure from 2005 to 2020 is discussed. Network function–structure co−optimization was carried out using the edge augmentation strategy. (3) Result: The “three River resource” has high water conservation and high soil and water conservation in southeastern hilly areas. There is strong windbreak and sand fixation in southeastern Inner Mongolia. In the past 15 years, there have been about 8200 sources and about 14,000 corridors. The network has the characteristics of small−world and heterogeneity. After optimization, 18 sources and 3180 corridors are added, and the network connectivity and robustness are stronger. Finally, five regions are divided according to the network heterogeneity and corresponding protection and management countermeasures are proposed to provide scientific guidance for the country’s territorial space planning.
Automatic Matching of Multimodal Remote Sensing Images via Learned Unstructured Road Feature
Authors: Yu Kun , Xu Chengcheng , Ma Jie , Fang Bin , Ding Junfeng , Xu Xinghua , Bao Xianqiang , Qiu Shaohua
Published: September 2022

Automatic matching of multimodal remote sensing images remains a vital yet challenging task, particularly for remote sensing and computer vision applications. Most traditional methods mainly focus on key point detection and description of the original image, thus ignoring the deep semantic feature information such as semantic road features, with the result that the traditional method can not effectively resist nonlinear grayscale distortion, and has low matching efficiency and poor accuracy. Motivated by this, this paper proposes a novel automatic matching method named LURF via learned unstructured road features for the multimodal images. There are four main contributions in LURF. To begin with, the semantic road features were extracted from multimodal images based on segmentation model CRESIv2. Next, based on semantic road features, a stable and reliable intersection point detector has been proposed to detect unstructured key points. Moreover, a local entropy descriptor has been designed to describe key points with the local skeleton feature. Finally, a global optimization strategy is adopted to achieve the correct matching. The extensive experimental results demonstrate that the proposed LURF outperforms other state-of-the-art methods in terms of both accuracy and efficiency on different multimodal image data sets.
Providing Real-Time Wearable Feedback to Increase Hand Use after Stroke: A Randomized, Controlled Trial
Authors: Schwerz de Lucena Diogo , Rowe Justin B. , Okita Shusuke , Chan Vicky , Cramer Steven C. , Reinkensmeyer David J.
Published: September 2022

After stroke, many people substantially reduce use of their impaired hand in daily life, even if they retain even a moderate level of functional hand ability. Here, we tested whether providing real-time, wearable feedback on the number of achieved hand movements, along with a daily goal, can help people increase hand use intensity. Twenty participants with chronic stroke wore the Manumeter, a novel magnetic wristwatch/ring system that counts finger and wrist movements. We randomized them to wear the device for three weeks with (feedback group) or without (control group) real-time hand count feedback and a daily goal. Participants in the control group used the device as a wristwatch, but it still counted hand movements. We found that the feedback group wore the Manumeter significantly longer (11.2 ± 1.3 h/day) compared to the control group (10.1 ± 1.1 h/day). The feedback group also significantly increased their hand counts over time (p = 0.012, slope = 9.0 hand counts/hour per day, which amounted to ~2000 additional counts per day by study end), while the control group did not (p-value = 0.059; slope = 4.87 hand counts/hour per day). There were no significant differences between groups in any clinical measures of hand movement ability that we measured before and after the feedback period, although several of these measures improved over time. Finally, we confirmed that the previously reported threshold relationship between hand functional capacity and daily use was stable over three weeks, even in the presence of feedback, and established the minimal detectable change for hand count intensity, which is about 30% of average daily intensity. These results suggest that disuse of the hand after stroke is temporarily modifiable with wearable feedback, but do not support that a 3-week intervention of wearable hand count feedback provides enduring therapeutic gains.
Dynamic Navigation and Area Assignment of Multiple USVs Based on Multi-Agent Deep Reinforcement Learning
Authors: Wen Jiayi , Liu Shaoman , Lin Yejin
Published: September 2022

The unmanned surface vehicle (USV) has attracted more and more attention because of its basic ability to perform complex maritime tasks autonomously in constrained environments. However, the level of autonomy of one single USV is still limited, especially when deployed in a dynamic environment to perform multiple tasks simultaneously. Thus, a multi-USV cooperative approach can be adopted to obtain the desired success rate in the presence of multi-mission objectives. In this paper, we propose a cooperative navigating approach by enabling multiple USVs to automatically avoid dynamic obstacles and allocate target areas. To be specific, we propose a multi-agent deep reinforcement learning (MADRL) approach, i.e., a multi-agent deep deterministic policy gradient (MADDPG), to maximize the autonomy level by jointly optimizing the trajectory of USVs, as well as obstacle avoidance and coordination, which is a complex optimization problem usually solved separately. In contrast to other works, we combined dynamic navigation and area assignment to design a task management system based on the MADDPG learning framework. Finally, the experiments were carried out on the Gym platform to verify the effectiveness of the proposed method.
Virtual Obstacles for Sensors Incapacitation in Robot Navigation: A Systematic Review of 2D Path Planning
Authors: Ngwenya Thabang , Ayomoh Michael , Yadavalli Sarma
Published: September 2022

The field of mobile robot (MR) navigation with obstacle avoidance has largely focused on real, physical obstacles as the sole external causative agent for navigation impediment. This paper has explored the possible option of virtual obstacles (VOs) dominance in robot navigation impediment in certain navigation environments as a MR move from one point in the workspace to a desired target point. The systematically explored literature presented reviews mostly between the years 2000 and 2021; however, some outlier reviews from earlier years were also covered. An exploratory review approach was deployed to itemise and discuss different navigation environments and how VOs can impact the efficacy of both algorithms and sensors on a robotic vehicle. The associated limitations and the specific problem types addressed in the different literature sources were highlighted including whether or not a VO was considered in the path planning simulation or experiment. The discussion and conclusive sections further recommended some solutions as a measure towards addressing sensor performance incapacitation in a robot vehicle navigation problem.
Cooperative Energy-Efficient Routing Protocol for Underwater Wireless Sensor Networks
Authors: Ahmad Irfan , Rahman Taj , Zeb Asim , Khan Inayat , Othman Mohamed Tahar Ben , Hamam Habib
Published: September 2022

Underwater wireless sensor networks (UWSNs) contain sensor nodes that sense the data and then transfer them to the sink node or base station. Sensor nodes are operationalized through limited-power batteries. Therefore, improvement in energy consumption becomes critical in UWSNs. Data forwarding through the nearest sensor node to the sink or base station reduces the network’s reliability and stability because it creates a hotspot and drains the energy early. In this paper, we propose the cooperative energy-efficient routing (CEER) protocol to increase the network lifetime and acquire a reliable network. We use the sink mobility scheme to reduce energy consumption by eliminating the hotspot issue. We have divided the area into multiple sections for better deployment and deployed the sink nodes in each area. Sensor nodes generate the data and send it to the sink nodes to reduce energy consumption. We have also used the cooperative technique to achieve reliability in the network. Based on simulation results, the proposed scheme performed better than existing routing protocols in terms of packet delivery ratio (PDR), energy consumption, transmission loss, and end-to-end delay.
Ultrasensitive and Regenerative Transistor Sensor Based on Dynamic Covalent Chemistry
Authors: Cao Ban-Peng , Dai Changhao , Wang Xuejun , Xiao Qiang , Wei Dacheng
Published: September 2022

Field-effect transistor (FET) sensors require not only high sensitivity but also excellent regeneration ability before widespread applications are possible. Although some regenerative FETs have been reported, their lowest limit of detection (LoD) barely achieves 10−15 mol L−1. Here, we develop a graphene FET with a regenerative sensing interface based on dynamic covalent chemistry (DCvC). The LoD down to 5.0 × 10−20 mol L−1 remains even after 10 regenerative cycles, around 4–5 orders of magnitude lower than existing transistor sensors. Owing to its ultra-sensitivity, regeneration ability, and advantages such as simplicity, low cost, label-free and real-time response, the FET sensor based on DCvC is valuable in applications such as medical diagnosis, environment monitoring, etc.
A Usage Aware Dynamic Spectrum Access Scheme for Interweave Cognitive Radio Network by Exploiting Deep Reinforcement Learning
Authors: Wang Xiaoyan , Teraki Yuto , Umehira Masahiro , Zhou Hao , Ji Yusheng
Published: September 2022

Future-generation wireless networks should accommodate surging growth in mobile data traffic and support an increasingly high density of wireless devices. Consequently, as the demand for spectrum continues to skyrocket, a severe shortage of spectrum resources for wireless networks will reach unprecedented levels of challenge in the near future. To deal with the emerging spectrum-shortage problem, dynamic spectrum access techniques have attracted a great deal of attention in both academia and industry. By exploiting the cognitive radio techniques, secondary users (SUs) are capable of accessing the underutilized spectrum holes of the primary users (PUs) to increase the whole system’s spectral efficiency with minimum interference violations. In this paper, we mathematically formulate the spectrum access problem for interweave cognitive radio networks, and propose a usage-aware deep reinforcement learning based scheme to solve it, which exploits the historical channel usage data to learn the time correlation and channel correlation of the PU channels. We evaluated the performance of the proposed approach by extensive simulations in both uncorrelated and correlated PU channel usage cases. The evaluation results validate the superiority of the proposed scheme in terms of channel access success probability and SU-PU interference probability, by comparing it with ideal results and existing methods.
Integrity Verification of Distributed Nodes in Critical Infrastructures
Authors: Sisinni Silvia , Margaria Davide , Pedone Ignazio , Lioy Antonio , Vesco Andrea
Published: September 2022

The accuracy and reliability of time synchronization and distribution are essential requirements for many critical infrastructures, including telecommunication networks, where 5G technologies place increasingly stringent conditions in terms of maintaining highly accurate time. A lack of synchronization between the clocks causes a malfunction of the 5G network, preventing it from providing a high quality of service; this makes the time distribution network a very viable target for attacks. Various solutions have been analyzed to mitigate attacks on the Global Navigation Satellite System (GNSS) radio-frequency spectrum and the Precision Time Protocol (PTP) used for time distribution over the network. This paper highlights the significance of monitoring the integrity of the software and configurations of the infrastructural nodes of a time distribution network. Moreover, this work proposes an attestation scheme, based on the Trusted Computing principles, capable of detecting both software violations on the nodes and hardware attacks aimed at tampering with the configuration of the GNSS receivers. The proposed solution has been implemented and validated on a testbed representing a typical synchronization distribution network. The results, simulating various types of adversaries, emphasize the effectiveness of the proposed approach in a wide range of typical attacks and the certain limitations that need to be addressed to enhance the security of the current GNSS receivers.
Human Arm Motion Prediction for Collision Avoidance in a Shared Workspace
Authors: Zheng Pu , Wieber Pierre-Brice , Baber Junaid , Aycard Olivier
Published: September 2022

Industry 4.0 transforms classical industrial systems into more human-centric and digitized systems. Close human–robot collaboration is becoming more frequent, which means security and efficiency issues need to be carefully considered. In this paper, we propose to equip robots with exteroceptive sensors and online motion generation so that the robot is able to perceive and predict human trajectories and react to the motion of the human in order to reduce the occurrence of the collisions. The dataset for training is generated in a real environment in which a human and a robot are sharing their workspace. An Encoder–Decoder based network is proposed to predict the human hand trajectories. A Model Predictive Control (MPC) framework is also proposed, which is able to plan a collision-free trajectory in the shared workspace based on this human motion prediction. The proposed framework is validated in a real environment that ensures collision free collaboration between humans and robots in a shared workspace.
Calibration-Aimed Comparison of Image-Cytometry- and Flow-Cytometry-Based Approaches of Ploidy Analysis
Authors: Jónás Viktor Zoltán , Paulik Róbert , Kozlovszky Miklós , Molnár Béla
Published: September 2022

Ploidy analysis is the fundamental method of measuring DNA content. For decades, the principal way of conducting ploidy analysis was through flow cytometry. A flow cytometer is a specialized tool for analyzing cells in a solution. This is convenient in laboratory environments, but prohibits measurement reproducibility and the complete detachment of sample preparation from data acquisition and analysis, which seems to have become paramount with the constant decrease in the number of pathologists per capita all over the globe. As more open computer-aided systems emerge in medicine, the demand for overcoming these shortcomings, and opening access to even more (and more flexible) options, has also emerged. Image-based analysis systems can provide an alternative to these types of workloads, placing the abovementioned problems in a different light. Flow cytometry data can be used as a reference for calibrating an image-based system. This article aims to show an approach to constructing an image-based solution for ploidy analysis, take measurements for a basic comparison of the data produced by the two methods, and produce a workflow with the ultimate goal of calibrating the image-based system.
A Comprehensive Analysis of the Validity and Reliability of the Perception Neuron Studio for Upper-Body Motion Capture
Authors: Wu Yiwei , Tao Kuan , Chen Qi , Tian Yinsheng , Sun Lixin
Published: September 2022

The Perception Neuron Studio (PNS) is a cost-effective and widely used inertial motion capture system. However, a comprehensive analysis of its upper-body motion capture accuracy is still lacking, before it is being applied to biomechanical research. Therefore, this study first evaluated the validity and reliability of this system in upper-body capturing and then quantified the system’s accuracy for different task complexities and movement speeds. Seven participants performed simple (eight single-DOF upper-body movements) and complex tasks (lifting a 2.5 kg box over the shoulder) at fast and slow speeds with the PNS and OptiTrack (gold-standard optical system) collecting kinematics data simultaneously. Statistical metrics such as CMC, RMSE, Pearson’s r, R2, and Bland–Altman analysis were utilized to assess the similarity between the two systems. Test–retest reliability included intra- and intersession relations, which were assessed by the intraclass correlation coefficient (ICC) as well as CMC. All upper-body kinematics were highly consistent between the two systems, with CMC values 0.73–0.99, RMSE 1.9–12.5°, Pearson’s r 0.84–0.99, R2 0.75–0.99, and Bland–Altman analysis demonstrating a bias of 0.2–27.8° as well as all the points within 95% limits of agreement (LOA). The relative reliability of intra- and intersessions was good to excellent (i.e., ICC and CMC were 0.77–0.99 and 0.75–0.98, respectively). The paired t-test revealed that faster speeds resulted in greater bias, while more complex tasks led to lower consistencies. Our results showed that the PNS could provide accurate enough upper-body kinematics for further biomechanical performance analysis.
Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier
Authors: Waqas Khan Prince , Byun Yung-Cheol
Published: September 2022

Wind turbines are widely used worldwide to generate clean, renewable energy. The biggest issue with a wind turbine is reducing failures and downtime, which lowers costs associated with operations and maintenance. Wind turbines’ consistency and timely maintenance can enhance their performance and dependability. Still, the traditional routine configuration makes detecting faults of wind turbines difficult. Supervisory control and data acquisition (SCADA) produces reliable and affordable quality data for the health condition of wind turbine operations. For wind power to be sufficiently reliable, it is crucial to retrieve useful information from SCADA successfully. This article proposes a new AdaBoost, K-nearest neighbors, and logistic regression-based stacking ensemble (AKL-SE) classifier to classify the faults of the wind turbine condition monitoring system. A stacking ensemble classifier integrates different classification models to enhance the model’s accuracy. We have used three classifiers, AdaBoost, K-nearest neighbors, and logistic regression, as base models to make output. The output of these three classifiers is used as input in the logistic regression classifier’s meta-model. To improve the data validity, SCADA data are first preprocessed by cleaning and removing any abnormal data. Next, the Pearson correlation coefficient was used to choose the input variables. The Stacking Ensemble classifier was trained using these parameters. The analysis demonstrates that the suggested method successfully identifies faults in wind turbines when applied to local 3 MW wind turbines. The proposed approach shows the potential for effective wind energy use, which could encourage the use of clean energy.
A Real-Time Cup-Detection Method Based on YOLOv3 for Inventory Management
Authors: Wu Wen-Sheng , Lu Zhe-Ming
Published: September 2022

Inventory is the basis of business activities; inventory management helps industries keep their inventories stocked with reasonable quantities, which ensures consumers demand while minimizing storage costs. The traditional manual inventory management has low efficiency and a high labor cost. In this paper, we used improved YOLOv3 to detect the cups stored on the warehouse shelves and counted their numbers to realize automated inventory management. The warehouse images are collected by the camera and transmitted to the industrial computer, which runs the YOLOv3 network. There are three feature maps in YOLOv3, the two smaller feature maps and the structure behind them are removed, and the k-means algorithm is used to optimize the default anchor size. Moreover, the detection range is limited to a specified area. Experiments show that, by eliminating those two feature maps, the network parameter is reduced from 235 MB to 212 MB, and detection FPS is improved from 48.15 to 54.88 while mAP is improved from 95.65% to 96.65% on our test dataset. The new anchors obtained by the k-means algorithm further improve the mAP to 96.82%. With those improvements, the average error rate of detection is reduced to 1.61%. Restricted detection areas eliminate irrelevant items to ensure the high accuracy of the detection result. The accurately counted number of cups and its change provide significant data for inventory management.
Land Use Planning to Reduce Flood Risk: Opportunities, Challenges and Uncertainties in Developing Countries
Authors: Der Sarkissian Rita , Al Sayah Mario J. , Abdallah Chadi , Zaninetti Jean-Marc , Nedjai Rachid
Published: September 2022

Land use planning for flood risk reduction has been significantly addressed in literature. However, a clear methodology for flood mitigation oriented land-use planning and its implementation, particularly in developing countries like Lebanon, is still missing. Knowledge on land use planning is still in its earliest stages in Lebanon. A lack of hazard-informed land use planning coupled to random land cover pattern evolution characterize the country. In response, this study focuses on the opportunities, challenges and uncertainties resulting from the integration of land use planning into efficient Disaster Risk Reduction (DRR). For this purpose, GIS-based analyses were first conducted on the current land use/land cover (LU/LC) of the Assi floodplain. Then, the areas land cover was retraced and its evolution after several flood occurrences was assessed. Subsequently, a flood hazard-informed LU/LC plan was proposed. The latter is mainly based on the spatial allocation of land-uses with respect to different flood hazard levels. This approach resulted in the production of a land use planning matrix for flood risk reduction. The matrix approach can serve as a tool for designing sustainable and resilient land cover patterns in other similar contexts while simultaneously providing robust contributions to decision-making and risk communication.
ML-Based Delay Attack Detection and Isolation for Fault-Tolerant Software-Defined Industrial Networks
Authors: Ramani Sagar , Jhaveri Rutvij H.
Published: September 2022

Traditional security mechanisms find difficulties in dealing with intelligent assaults in cyber-physical systems (CPSs) despite modern information and communication technologies. Furthermore, resource consumption in software-defined networks (SDNs) in industrial organizations is usually on a larger scale, and the present routing algorithms fail to address this issue. In this paper, we present a real-time delay attack detection and isolation scheme for fault-tolerant software-defined industrial networks. The primary goal of the delay attack is to lower the resilience of our previously proposed scheme, SDN-resilience manager (SDN-RM). The attacker compromises the OpenFlow switch and launches an attack by delaying the link layer discovery protocol (LLDP) packets. As a result, the performance of SDN-RM is degraded and the success rate decreases significantly. In this work, we developed a machine learning (ML)-based attack detection and isolation mechanism, which extends our previous work, SDN-RM. Predicting and labeling malicious switches in an SDN-enabled network is a challenge that can be successfully addressed by integrating ML with network resilience solutions. Therefore, we propose a delay-based attack detection and isolation scheme (DA-DIS), which avoids malicious switches from entering the routes by combining an ML mechanism along with a route-handoff mechanism. DA-DIS increases network resilience by increasing success rate and network throughput.
A Research on the Sharing Platform of Wild Bird Data in Yunnan Province Based on Blockchain and Interstellar File System
Authors: Yang Huaiyuan , Li Yucheng , Zhou Hua , Zhao Yili , Song Lei
Published: September 2022

Sharing scientific data is an effective means to rationally exploit scientific data and is vital to promote the development of the industrial chain and improve the level of science and technology. In recent years, the popularity of the open data platform has increased, but problems remain, including imperfect system architecture, unsound privacy and security, and non-standardized interaction data. To address these problems, the blockchain’s decentralization, smart contracts, distributed storage, and other features can be used as the core technology for open data systems. This paper addresses the problems of opening, allocation-right confirmation, sharing, and rational use of wild-bird data from Yunnan Province, China. A data storage model is proposed based on the blockchain and interstellar file system and is applied to wild-bird data to overcome the mutual distrust between ornithology institutions in the collaborative processing and data storage of bird data. The model provides secure storage and secure access control of bird data in the cloud, thereby ensuring the decentralized and secure storage of wild-bird data for multiple research institutions.