bluuebunny/crossref_metadata_2025 huggingface dataset:
Cloud Detection Using an Ensemble of Pixel-Based Machine Learning Models Incorporating Unsupervised Classification
Authors: Yu Xiaohe , Lary David J.
Published: August 2021

Remote sensing imagery, such as that provided by the United States Geological Survey (USGS) Landsat satellites, has been widely used to study environmental protection, hazard analysis, and urban planning for decades. Clouds are a constant challenge for such imagery and, if not handled correctly, can cause a variety of issues for a wide range of remote sensing analyses. Typically, cloud mask algorithms use the entire image; in this study we present an ensemble of different pixel-based approaches to cloud pixel modeling. Based on four training subsets with a selection of different input features, 12 machine learning models were created. We evaluated these models using the cropped LC8-Biome cloud validation dataset. As a comparison, Fmask was also applied to the cropped scene Biome dataset. One goal of this research is to explore a machine learning modeling approach that uses as small a training data sample as possible but still provides an accurate model. Overall, the model trained on the sample subset (1.3% of the total training samples) that includes unsupervised Self-Organizing Map classification results as an input feature has the best performance. The approach achieves 98.57% overall accuracy, 1.18% cloud omission error, and 0.93% cloud commission error on the 88 cropped test images. By comparison to Fmask 4.0, this model improves the accuracy by 10.12% and reduces the cloud omission error by 6.39%. Furthermore, using an additional eight independent validation images that were not sampled in model training, the model trained on the second largest subset with an additional five features has the highest overall accuracy at 86.35%, with 12.48% cloud omission error and 7.96% cloud commission error. This model’s overall correctness increased by 3.26%, and the cloud omission error decreased by 1.28% compared to Fmask 4.0. The machine learning cloud classification models discussed in this paper could achieve very good performance utilizing only a small portion of the total training pixels available. We showed that a pixel-based cloud classification model, and that as each scene obviously has unique spectral characteristics, and having a small portion of example pixels from each of the sub-regions in a scene can improve the model accuracy significantly.
Detecting Moving Target on Ground Based on Its Shadow by Using VideoSAR
Authors: He Zhihua , Li Zihan , Chen Xing , Yu Anxi , Yi Tianzhu , Dong Zhen
Published: August 2021

Video synthetic aperture radar (VideoSAR) can detect and identify a moving target based on its shadow. A slowly moving target has a shadow with distinct features, but it cannot be detected by state-of-the-art difference-based algorithms because of minor variations between adjacent frames. Furthermore, the detection boxes generated by difference-based algorithms often contain such defects as misalignments and fracture. In light of these problems, this study proposed a robust moving target detection (MTD) algorithm for objects on the ground by fusing the background frame detection results and the difference between frames over multiple intervals. We also discuss defects that occur in conventional MTD algorithms. The difference in background frame was introduced to overcome the shortcomings of difference-based algorithms and acquire the shadow regions of objects. This was fused with the multi-interval frame difference to simultaneously extract the moving target at different velocities while identifying false alarms. The results of experiments on empirically acquired VideoSAR data verified the performance of the proposed algorithm in terms of detecting a moving target on the ground based on its shadow.
Parametric Decomposition of Pulsed Lidar Signals with Noise Corruption Using FRFT Spectrum Analysis
Authors: Xu Fan , Chen Jun , Liu Ya , Wu Qihui , Zhang Xiaofei , Shu Zhengyang
Published: August 2021

The parametric decomposition of full-waveform Lidar data is challenging when faced with heavy noise scenarios. In this paper, we report a fractional Fourier transform (FRFT)-based approach for accurate parametric decomposition of pulsed Lidar signals with noise corruption. In comparison with other joint time-frequency analysis (JTFA) techniques, FRFT is found to present a one-dimensional Lidar signal by a particular two-dimensional spectrum, which can exhibit the mathematical distribution of the multiple components in Lidar signals even with a heavy noise interference. A FRFT spectrum-processing solution with histogram clustering and moving LSM fitting is designed to extract the amplitude, time offset, and pulse width contained in the mathematical distribution. Extensive experimental results demonstrate that the proposed FRFT spectrum analysis method can remarkably outperform the conventional Levenberg–Marquardt-based method. In particular, it can accurately decompose the amplitudes, time offsets, and pulse widths of the pulsed Lidar signal with a −10-dB signal-to-noise-ratio by mean deviation ratios of 4.885%, 0.531%, and 7.802%, respectively.
The Water Availability on the Chinese Loess Plateau since the Implementation of the Grain for Green Project as Indicated by the Evaporative Stress Index
Authors: Qiu Linjing , Chen Yuting , Wu Yiping , Xue Qingyue , Shi Zhaoyang , Lei Xiaohui , Liao Weihong , Zhao Fubo , Wang Wenke
Published: August 2021

The vegetation coverage on the Loess Plateau (LP) of China has clearly increased since the implementation of the Grain for Green Project in 1999, but there is a debate about whether the improved greenness was achieved at the expense of the balance between the supply and demand of water resources. Therefore, developing reliable indicators to evaluate the water availability is a prerequisite for maintaining ecological sustainability and ensuring the persistence of vegetation restoration. This study was designed to evaluate water availability on the LP during 2000–2015, using the evaporative stress index (ESI) derived from a remote sensing dataset. The relative dependences of the ESI on climatic and biological factors (including temperature, precipitation and land cover change) were also analyzed. The results showed that the leaf area index (LAI) in most regions of the LP showed a significant increasing trend (p < 0.05), and larger gradients of increase were mainly detected in the central and eastern parts of the LP. The evapotranspiration also exhibited an increasing trend in the central and eastern parts of the LP, with a gradient greater than 10 mm/year. However, almost the whole LP exhibited a decreased ESI from 2000 to 2015, and the largest decrease occurred on the central and eastern LP, indicating a wetting trend. The soil moisture storage in the 0–289-cm soil profiles showed an increasing trend in the central and eastern LP, and the area with an upward trend enlarged with the soil depth. Further analysis revealed that the decreased ESI on the central and eastern LP mainly depended on the increase in the LAI compared with climatic influences. This work not only demonstrated that the ESI was a useful indicator for understanding the water availability in natural and managed ecosystems under climate change but also indicated that vegetation restoration might have a positive effect on water conservation on the central LP.
Mapping Invasive Phragmites australis Using Unoccupied Aircraft System Imagery, Canopy Height Models, and Synthetic Aperture Radar
Authors: Anderson Connor J. , Heins Daniel , Pelletier Keith C. , Bohnen Julia L. , Knight Joseph F.
Published: August 2021

Invasive plant species are an increasing worldwide threat both ecologically and financially. Knowing the location of these invasive plant infestations is the first step in their control. Surveying for invasive Phragmites australis is particularly challenging due to limited accessibility in wetland environments. Unoccupied aircraft systems (UAS) are a popular choice for invasive species management due to their ability to survey challenging environments and their high spatial and temporal resolution. This study tested the utility of three-band (i.e., red, green, and blue; RGB) UAS imagery for mapping Phragmites in the St. Louis River Estuary in Minnesota, U.S.A. and Saginaw Bay in Michigan, U.S.A. Iterative object-based image analysis techniques were used to identify two classes, Phragmites and Not Phragmites. Additionally, the effectiveness of canopy height models (CHMs) created from two data types, UAS imagery and commercial satellite stereo retrievals, and the RADARSAT-2 horizontal-horizontal (HH) polarization were tested for Phragmites identification. The highest overall classification accuracy of 90% was achieved when pairing the UAS imagery with a UAS-derived CHM. Producer’s accuracy for the Phragmites class ranged from 3 to 76%, and the user’s accuracies were above 90%. The Not Phragmites class had user’s and producer’s accuracies above 88%. Inclusion of the RADARSAT-2 HH polarization caused a slight reduction in classification accuracy. Commercial satellite stereo retrievals increased commission errors due to decreased spatial resolution and vertical accuracy. The lowest classification accuracy was seen when using only the RGB UAS imagery. UAS are promising for Phragmites identification, but the imagery should be used in conjunction with a CHM.
Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
Authors: Li Qin , Liu Xiuguo , Zhong Yulong , Wang Mengmeng , Zhu Shuang
Published: August 2021

Terrestrial water storage changes (TWSCs) retrieved from the Gravity Recovery and Climate Experiment (GRACE) satellite mission have been extensively evaluated in previous studies over large basin scales. However, monitoring the TWSC at small basin scales is still poorly understood. This study presented a new method for calculating TWSCs at the small basin scales based on the water balance equation, using hydrometeorological and multi-source data. First, the basin was divided into several sub-basins through the slope runoff simulation algorithm. Secondly, we simulated the evapotranspiration (ET) and outbound runoff of each sub-basin using the PML_V2 and SWAT. Lastly, through the water balance equation, the TWSC of each sub-basin was obtained. Based on the estimated results, we analyzed the temporal and spatial variations in precipitation, ET, outbound runoff, and TWSC in the Ganjiang River Basin (GRB) from 2002 to 2018. The results showed that by comparing with GRACE products, in situ groundwater levels data, and soil moisture storage, the TWSC calculated by this study is in good agreement with these three data. During the study period, the spatial and temporal variations in precipitation and runoff in the GRB were similar, with a minimum in 2011 and maximum in 2016. The annual ET changed gently, while the TWSC fluctuated greatly. The findings of this study could provide some new information for improving the estimate of the TWSC at small basin scales.
Dual-Satellite Alternate Switching Ranging/INS Integrated Navigation Algorithm for Broadband LEO Constellation Independent of Altimeter and Continuous Observation
Authors: Ye Lvyang , Yang Yikang , Jing Xiaolun , Li Hengnian , Yang Haifeng , Xia Yunxia
Published: August 2021

In challenging environments such as forests, valleys and higher latitude areas, there are usually fewer than four visible satellites. For cases with only two visible satellites, we propose a dual-satellite alternate switching ranging integrated navigation algorithm based on the broadband low earth orbit (LEO) constellation, which integrates communication and navigation (ICN) technology. It is different from the traditional dual-satellite integrated navigation algorithm: the difference is that it can complete precise real-time navigation and positioning without an altimeter and continuous observation. First, we give the principle of our algorithm. Second, with the help of an unscented Kalman filter (UKF), we give the observation equation and state equation of our algorithm, and establish the mathematical model of multipath/non-line of sight (NLOS) and noise interference. Finally, based on the SpaceX constellation, for various scenarios, we analyze the performance of our algorithm through simulation. The results show that: our algorithm can effectively suppress the divergence of the inertial navigation system (INS), in the face of different multipath/NLOS interference and various noise environments it still keeps good robustness, and also has great advantages in various indicators compared with the traditional dual-satellite positioning algorithms and some existing 3-satellite advanced positioning algorithms. These results show that our algorithm can meet the real-time location service requirements in harsh and challenging environments, and provides a new navigation and positioning method when there are only two visible satellites.
Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Networks for Hyperspectral Classification
Authors: Chen Zhitao , Tong Lei , Qian Bin , Yu Jing , Xiao Chuangbai
Published: August 2021

Hyperspectral classification is an important technique for remote sensing image analysis. For the current classification methods, limited training data affect the classification results. Recently, Conditional Variational Autoencoder Generative Adversarial Network (CVAEGAN) has been used to generate virtual samples to augment the training data, which could improve the classification performance. To further improve the classification performance, based on the CVAEGAN, we propose a Self-Attention-Based Conditional Variational Autoencoder Generative Adversarial Network (SACVAEGAN). Compared with CVAEGAN, we first use random latent vectors to obtain more enhanced virtual samples, which can improve the generalization performance. Then, we introduce the self-attention mechanism into our model to force the training process to pay more attention to global information, which can achieve better classification accuracy. Moreover, we explore model stability by incorporating the WGAN-GP loss function into our model to reduce the mode collapse probability. Experiments on three data sets and a comparison of the state-of-art methods show that SACVAEGAN has great advantages in accuracy compared with state-of-the-art HSI classification methods.
Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations
Authors: Duan Mingshan , Xia Jiangjiang , Yan Zhongwei , Han Lei , Zhang Lejian , Xia Hanmeng , Yu Shuang
Published: August 2021

Radar reflectivity (RR) greater than 35 dBZ usually indicates the presence of severe convective weather, which affects a variety of human activities, including aviation. However, RR data are scarce, especially in regions with poor radar coverage or substantial terrain obstructions. Fortunately, the radiance data of space-based satellites with universal coverage can be converted into a proxy field of RR. In this study, a convolutional neural network-based data-driven model is developed to convert the radiance data (infrared bands 07, 09, 13, 16, and 16–13) of Himawari-8 into the radar combined reflectivity factor (CREF). A weighted loss function is designed to solve the data imbalance problem due to the sparse convective pixels in the sample. The developed model demonstrates an overall reconstruction capability and performs well in terms of classification scores with 35 dBZ as the threshold. A five-channel input is more efficient in reconstructing the CREF than the commonly used one-channel input. In a case study of a convective event over North China in the summer using the test dataset, U-Net reproduces the location, shape and strength of the convective storm well. The present RR reconstruction technology based on deep learning and Himawari-8 radiance data is shown to be an efficient tool for producing high-resolution RR products, which are especially needed for regions without or with poor radar coverage.
Remote Sensing Image Classification with a Graph-Based Pre-Trained Neighborhood Spatial Relationship
Authors: Guan Xudong , Huang Chong , Yang Juan , Li Ainong
Published: August 2021

Previous knowledge of the possible spatial relationships between land cover types is one factor that makes remote sensing image classification “smarter”. In recent years, knowledge graphs, which are based on a graph data structure, have been studied in the community of remote sensing for their ability to build extensible relationships between geographic entities. This paper implements a classification scheme considering the neighborhood relationship of land cover by extracting information from a graph. First, a graph representing the spatial relationships of land cover types was built based on an existing land cover map. Empirical probability distributions of the spatial relationships were then extracted using this graph. Second, an image was classified based on an object-based fuzzy classifier. Finally, the membership of objects and the attributes of their neighborhood objects were joined to decide the final classes. Two experiments were implemented. Overall accuracy of the two experiments increased by 5.2% and 0.6%, showing that this method has the ability to correct misclassified patches using the spatial relationship between geo-entities. However, two issues must be considered when applying spatial relationships to image classification. The first is the “siphonic effect” produced by neighborhood patches. Second, the use of global spatial relationships derived from a pre-trained graph loses local spatial relationship in-formation to some degree.
Development and Laboratory Evaluation of an Online Controlling Algorithm for Precision Tillage
Authors: Sabouri Yashar , Abbaspour-Gilandeh Yousef , Solhjou Aliakbar , Shaker Mohammad , Szymanek Mariusz , Sprawka Maciej
Published: August 2021

Soil compaction management relies on costly annual deep tillage. Variable-depth tillage or site-specific tillage modifies the physical properties of the soil at the required zones for the growth of crops. In this study, a depth control system was designed for the subsoiler of the tillage at various depths. For this purpose, an algorithm was written to investigate the subsoiler location and soil compaction. A program was also developed to implement this algorithm using Kinco Builder Software to control the subsoiler depth, which was evaluated on the experimental platform. In this study, four compression sensors were used at a distance of 10 cm up to a depth of 40 cm on the blade mounted at the front of the tractor. The data of these sensors were used as the input and compared with the pressure baseline limit (2.07 MPa), and with the priority to select the greater depth, the depth of subsoiler was determined. At all three modes of sensor activation (single, collective, and combined), this system was able to operate the hydraulic system of the tractor and place the subsoiler at the desired depth through the use of the position sensors.
ViTT: Vision Transformer Tracker
Authors: Zhu Xiaoning , Jia Yannan , Jian Sun , Gu Lize , Pu Zhang
Published: August 2021

This paper presents a new model for multi-object tracking (MOT) with a transformer. MOT is a spatiotemporal correlation task among interest objects and one of the crucial technologies of multi-unmanned aerial vehicles (Multi-UAV). The transformer is a self-attentional codec architecture that has been successfully used in natural language processing and is emerging in computer vision. This study proposes the Vision Transformer Tracker (ViTT), which uses a transformer encoder as the backbone and takes images directly as input. Compared with convolution networks, it can model global context at every encoder layer from the beginning, which addresses the challenges of occlusion and complex scenarios. The model simultaneously outputs object locations and corresponding appearance embeddings in a shared network through multi-task learning. Our work demonstrates the superiority and effectiveness of transformer-based networks in complex computer vision tasks and paves the way for applying the pure transformer in MOT. We evaluated the proposed model on the MOT16 dataset, achieving 65.7% MOTA, and obtained a competitive result compared with other typical multi-object trackers.
The Adaptive Dynamic Programming Toolbox
Authors: Xing Xiaowei , Chang Dong Eui
Published: August 2021

The paper develops the adaptive dynamic programming toolbox (ADPT), which is a MATLAB-based software package and computationally solves optimal control problems for continuous-time control-affine systems. The ADPT produces approximate optimal feedback controls by employing the adaptive dynamic programming technique and solving the Hamilton–Jacobi–Bellman equation approximately. A novel implementation method is derived to optimize the memory consumption by the ADPT throughout its execution. The ADPT supports two working modes: model-based mode and model-free mode. In the former mode, the ADPT computes optimal feedback controls provided the system dynamics. In the latter mode, optimal feedback controls are generated from the measurements of system trajectories, without the requirement of knowledge of the system model. Multiple setting options are provided in the ADPT, such that various customized circumstances can be accommodated. Compared to other popular software toolboxes for optimal control, the ADPT features computational precision and time efficiency, which is illustrated with its applications to a highly non-linear satellite attitude control problem.
Parallel Potentiometric and Capacitive Response in a Water-Gate Thin Film Transistor Biosensor at High Ionic Strength
Authors: AlQahtani Hadi , Alswieleh Abdullah , Al-Khurayyif Ibrahim , AlGarni Saad , Grell Martin
Published: August 2021

We show that an SnO2-based water-gate thin film transistor (WGTFT) biosensor responds to a waterborne analyte, the spike protein of the SARS-CoV-2 virus, by a parallel potentiometric and capacitive mechanism. We draw our conclusion from an analysis of transistor output characteristics, which avoids the known ambiguities of the common analysis based on transfer characteristics. Our findings contrast with reports on organic WGTFT biosensors claiming a purely capacitive response due to screening effects in high ionic strength electrolytes, but are consistent with prior work that clearly shows a potentiometric response even in strong electrolytes. We provide a detailed critique of prior WGTFT analysis and screening reasoning. Empirically, both potentiometric and capacitive responses can be modelled quantitatively by a Langmuir‒Freundlich (LF) law, which is mathematically equivalent to the Hill equation that is frequently used for biosensor response characteristics. However, potentiometric and capacitive model parameters disagree. Instead, the potentiometric response follows the Nikolsky-Eisenman law, treating the analyte ‘RBD spike protein’ as an ion carrying two elementary charges. These insights are uniquely possible thanks to the parallel presence of two response mechanisms, as well as their reliable delineation, as presented here.
Caenorhabditis elegans Multi-Tracker Based on a Modified Skeleton Algorithm
Authors: Layana Castro Pablo E. , Puchalt Joan Carles , García Garví Antonio , Sánchez-Salmerón Antonio-José
Published: August 2021

Automatic tracking of Caenorhabditis elegans (C. egans) in standard Petri dishes is challenging due to high-resolution image requirements when fully monitoring a Petri dish, but mainly due to potential losses of individual worm identity caused by aggregation of worms, overlaps and body contact. To date, trackers only automate tests for individual worm behaviors, canceling data when body contact occurs. However, essays automating contact behaviors still require solutions to this problem. In this work, we propose a solution to this difficulty using computer vision techniques. On the one hand, a skeletonization method is applied to extract skeletons in overlap and contact situations. On the other hand, new optimization methods are proposed to solve the identity problem during these situations. Experiments were performed with 70 tracks and 3779 poses (skeletons) of C. elegans. Several cost functions with different criteria have been evaluated, and the best results gave an accuracy of 99.42% in overlapping with other worms and noise on the plate using the modified skeleton algorithm and 98.73% precision using the classical skeleton algorithm.
Phase Noise of SAW Delay Line Magnetic Field Sensors
Authors: Durdaut Phillip , Müller Cai , Kittmann Anne , Schell Viktor , Bahr Andreas , Quandt Eckhard , Knöchel Reinhard , Höft Michael , McCord Jeffrey
Published: August 2021

Surface acoustic wave (SAW) sensors for the detection of magnetic fields are currently being studied scientifically in many ways, especially since both their sensitivity as well as their detectivity could be significantly improved by the utilization of shear horizontal surface acoustic waves, i.e., Love waves, instead of Rayleigh waves. By now, low-frequency limits of detection (LOD) below 100 pT/Hz can be achieved. However, the LOD can only be further improved by gaining a deep understanding of the existing sensor-intrinsic noise sources and their impact on the sensor’s overall performance. This paper reports on a comprehensive study of the inherent noise of SAW delay line magnetic field sensors. In addition to the noise, however, the sensitivity is of importance, since both quantities are equally important for the LOD. Following the necessary explanations of the electrical and magnetic sensor properties, a further focus is on the losses within the sensor, since these are closely linked to the noise. The considered parameters are in particular the ambient magnetic bias field and the input power of the sensor. Depending on the sensor’s operating point, various noise mechanisms contribute to f0 white phase noise, f−1 flicker phase noise, and f−2 random walk of phase. Flicker phase noise due to magnetic hysteresis losses, i.e. random fluctuations of the magnetization, is usually dominant under typical operating conditions. Noise characteristics are related to the overall magnetic and magnetic domain behavior. Both calculations and measurements show that the LOD cannot be further improved by increasing the sensitivity. Instead, the losses occurring in the magnetic material need to be decreased.
Readout Integrated Circuit for Small-Sized and Low-Power Gas Sensor Based on HEMT Device
Authors: Lee Seungjun , Jin Joohwan , Baek Jihyun , Lee Juyong , Chae Hyungil
Published: August 2021

This paper presents a small-sized, low-power gas sensor system combining a high-electron-mobility transistor (HEMT) device and readout integrated circuit (ROIC). Using a semiconductor-based HEMT as a gas-sensing device, it is possible to secure high sensitivity, reduced complexity, low power, and small size of the ROIC sensor system. Unlike existing gas sensors comprising only HEMT elements, the proposed sensor system has both an ROIC and a digital controller and can control sensor operation through a simple calibration process with digital signal processing while maintaining constant performance despite variations. The ROIC mainly consists of a transimpedance amplifier (TIA), a negative-voltage generator, and an analog-to-digital converter (ADC) and is designed to match a minimum target detection unit of 1 ppm for hydrogen. The prototype ROIC for the HEMT presented herein was implemented in a 0.18 µm complementary metal–oxide–semiconductor (CMOS) process. The total measured power consumption and detection unit of the proposed ROIC for hydrogen gas were 3.1 mW and 2.6 ppm, respectively.
Ambient Light Rejection Integrated Circuit for Autonomous Adaptation on a Sub-Retinal Prosthetic System
Authors: Kang Hosung , Choi Hojong , Kim Jungsuk
Published: August 2021

This paper introduces an ambient light rejection (ALR) circuit for the autonomous adaptation of a subretinal implant system. The sub-retinal implants, located beneath a bipolar cell layer, are known to have a significant advantage in spatial resolution by integrating more than a thousand pixels, compared to epi-retinal implants. However, challenges remain regarding current dispersion in high-density retinal implants, and ambient light induces pixel saturation. Thus, the technical issues of ambient light associated with a conventional image processing technique, which lead to high power consumption and area occupation, are still unresolved. Thus, it is necessary to develop a novel image-processing unit to handle ambient light, considering constraints related to power and area. In this paper, we present an ALR circuit as an image-processing unit for sub-retinal implants. We first introduced an ALR algorithm to reduce the ambient light in conventional retinal implants; next, we implemented the ALR algorithm as an application-specific integrated chip (ASIC). The ALR circuit was fabricated using a standard 0.35-μm CMOS process along with an image-sensor-based stimulator, a sensor pixel, and digital blocks. As experimental results, the ALR circuit occupies an area of 190 µm2, consumes a power of 3.2 mW and shows a maximum response time of 1.6 s at a light intensity of 20,000 lux. The proposed ALR circuit also has a pixel loss rate of 0.3%. The experimental results show that the ALR circuit leads to a sensor pixel (SP) being autonomously adjusted, depending on the light intensity.
The Effect of Chemical Corrosion on Mechanics and Failure Behaviour of Limestone Containing a Single Kinked Fissure
Authors: Wu Yulin , Dong Qianqian , He Jian
Published: August 2021

This study concerns the influence of chemical corrosion and geometric parameters on the macroscopic damage characteristics of brittle limestone containing a kinked fissure under uniaxial compression. The specimens are prepared in chemical solutions with different NaCl concentrations and pH values. The acoustic emission (AE) technique is adopted to detect the inner distortion of the failure behaviour. The physical process of the crack coalescence of kinked fissures is synchronously captured by a high-speed camera. Seven failure patterns are identified based on the final failure mode and the failure process. Furthermore, the stress intensity factor of kinked cracks under chemical corrosion is obtained by a theoretical analysis. Chemical erosion with an acidic solution has a relatively strong effect on the compressive strength of the tested specimen, while the initial crack angle is not affected by short-term chemical corrosion.
5G Standalone and 4G Multi-Carrier Network-in-a-Box Using a Software Defined Radio Framework
Authors: Kiela Karolis , Jurgo Marijan , Macaitis Vytautas , Navickas Romualdas
Published: August 2021

In this work, an open Radio Access Network (RAN), compatible, scalable and highly flexible Software Defined Radio (SDR)-based Remote Radio Head (RRH) framework is proposed and designed. Such framework can be used to implement flexible wideband radio solutions, which can be deployed in any region, have common radio management features, and support various channel bandwidths. Moreover, it enables easier access for researchers to nonsimulated cellular networks, reduce system development time, provide test and measurement capabilities, and support existing and emerging wireless communication technologies. The performance of the proposed SDR framework is validated by creating a Network-in-a-Box (NIB) that can operate in multiband multicarrier 4G or 5G standalone (SA) configurations, with an output power of up to 33 dBm. Measurement results show, that the 4G and 5G NIB can achieve, respectively, up to 883 Mbps and 765 Mbps downlink data transfer speeds for a 100 MHz aggregated bandwidth. However, if six carriers are used in the 4G NIB, 1062 Mbps downlink data transfer speed can be achieved. When single user equipment (UE) is used, maximum uplink data transfer speed is 65.8 Mbps and 92.6 Mbps in case of 4G and 5G, respectively. The average packet latency in case of 5G is up to 45.1% lower than 4G. CPU load by the eNodeB and gNodeB is proportional to occupied bandwidth, but under the same aggregated DL bandwidth conditions, gNodeB load on the CPU is lower. Moreover, if only 1 UE is active, under same aggregated bandwidth conditions, the EPC CPU load is up to four times lower than the 5GC.
A Deep Learning-Based Fault Detection Model for Optimization of Shipping Operations and Enhancement of Maritime Safety
Authors: Theodoropoulos Panayiotis , Spandonidis Christos C. , Giannopoulos Fotis , Fassois Spilios
Published: August 2021

The ability to exploit data for obtaining useful and actionable information and for providing insights is an essential element for continuous process improvements. Recognizing the value of data as an asset, marine engineering puts data considerations at the core of system design. Used wisely, data can help the shipping sector to achieve operating cost savings and efficiency increase, higher safety, wellness of crew rates, and enhanced environmental protection and security of assets. The main goal of this study is to develop a methodology able to harmonize data collected from various sensors onboard and to implement a scalable and responsible artificial intelligence framework, to recognize patterns that indicate early signs of defective behavior in the operational state of the vessel. Specifically, the methodology examined in the present study is based on a 1D Convolutional Neural Network (CNN) being fed time series directly from the available dataset. For this endeavor, the dataset undergoes a preprocessing procedure. Aspiring to determine the effect of the parameters composing the networks and the values that ensure the best performance, a parametric inquiry is presented, determining the impact of the input period and the degree of degradation that our models identify adequately. The results provide an insightful picture of the applicability of 1D-CNN models in performing condition monitoring in ships, which is not thoroughly examined in the maritime sector for condition monitoring. The data modeling along with the development of the neural networks was undertaken with the Python programming language.
Integrating Personal Air Sensor and GPS to Determine Microenvironment-Specific Exposures to Volatile Organic Compounds
Authors: Breen Michael S. , Isakov Vlad , Prince Steven , McGuinness Kennedy , Egeghy Peter P. , Stephens Brent , Arunachalam Saravanan , Stout Dan , Walker Richard , Alston Lillian , Rooney Andrew A. , Taylor Kyla W. , Buckley Timothy J.
Published: August 2021

Personal exposure to volatile organic compounds (VOCs) from indoor sources including consumer products is an understudied public health concern. To develop and evaluate methods for monitoring personal VOC exposures, we performed a pilot study and examined time-resolved sensor-based measurements of geocoded total VOC (TVOC) exposures across individuals and microenvironments (MEs). We integrated continuous (1 min) data from a personal TVOC sensor and a global positioning system (GPS) logger, with a GPS-based ME classification model, to determine TVOC exposures in four MEs, including indoors at home (Home-In), indoors at other buildings (Other-In), inside vehicles (In-Vehicle), and outdoors (Out), across 45 participant-days for five participants. To help identify places with large emission sources, we identified high-exposure events (HEEs; TVOC > 500 ppb) using geocoded TVOC time-course data overlaid on Google Earth maps. Across the 45 participant-days, the MEs ranked from highest to lowest median TVOC were: Home-In (165 ppb), Other-In (86 ppb), In-Vehicle (52 ppb), and Out (46 ppb). For the two participants living in single-family houses with attached garages, the median exposures for Home-In were substantially higher (209, 416 ppb) than the three participant homes without attached garages: one living in a single-family house (129 ppb), and two living in apartments (38, 60 ppb). The daily average Home-In exposures exceeded the estimated Leadership in Energy and Environmental Design (LEED) building guideline of 108 ppb for 60% of the participant-days. We identified 94 HEEs across all participant-days, and 67% of the corresponding peak levels exceeded 1000 ppb. The MEs ranked from the highest to the lowest number of HEEs were: Home-In (60), Other-In (13), In-Vehicle (12), and Out (9). For Other-In and Out, most HEEs occurred indoors at fast food restaurants and retail stores, and outdoors in parking lots, respectively. For Home-In HEEs, the median TVOC emission and removal rates were 5.4 g h−1 and 1.1 h−1, respectively. Our study demonstrates the ability to determine individual sensor-based time-resolved TVOC exposures in different MEs, in support of identifying potential sources and exposure factors that can inform exposure mitigation strategies.
Transcending Conventional Biometry Frontiers: Diffusive Dynamics PPG Biometry
Authors: de Pedro-Carracedo Javier , Fuentes-Jimenez David , Ugena Ana María , Gonzalez-Marcos Ana Pilar
Published: August 2021

This paper presents the first photoplethysmographic (PPG) signal dynamic-based biometric authentication system with a Siamese convolutional neural network (CNN). Our method extracts the PPG signal’s biometric characteristics from its diffusive dynamics, characterized by geometric patterns in the (p,q)-planes specific to the 0–1 test. PPG signal diffusive dynamics are strongly dependent on the vascular bed’s biostructure, unique to each individual. The dynamic characteristics of the PPG signal are more stable over time than its morphological features, particularly in the presence of psychosomatic conditions. Besides its robustness, our biometric method is anti-spoofing, given the complex nature of the blood network. Our proposal trains using a national research study database with 40 real-world PPG signals measured with commercial equipment. Biometric system results for input data, raw and preprocessed, are studied and compared with eight primary biometric methods related to PPG, achieving the best equal error rate (ERR) and processing times with a single attempt, among all of them.
Integrated Pose Estimation Using 2D Lidar and INS Based on Hybrid Scan Matching
Authors: Park Gwangsoo , Lee Byungjin , Sung Sangkyung
Published: August 2021

Point cloud data is essential measurement information that has facilitated an extended functionality horizon for urban mobility. While 3D lidar and image-depth sensors are superior in implementing mapping and localization, sense and avoidance, and cognitive exploration in an unknown area, applying 2D lidar is inevitable for systems with limited resources of weight and computational power, for instance, in an aerial mobility system. In this paper, we propose a new pose estimation scheme that reflects the characteristics of extracted feature point information from 2D lidar on the NDT framework for exploiting an improved point cloud registration. In the case of the 2D lidar point cloud, vertices and corners can be viewed as representative feature points. Based on this feature point information, a point-to-point relationship is functionalized and reflected on a voxelized map matching process to deploy more efficient and promising matching performance. In order to present the navigation performance of the mobile object to which the proposed algorithm is applied, the matching result is combined with the inertial navigation through an integration filter. Then, the proposed algorithm was verified through a simulation study using a high-fidelity flight simulator and an indoor experiment. For performance validation, both results were compared and analyzed with the previous techniques. In conclusion, it was demonstrated that improved accuracy and computational efficiency could be achieved through the proposed algorithms.
A Comparison of Motives by Gender and Age Categories for Training at Norwegian Fitness Centres
Authors: Larsen Stian , Mozdoorzoy Tarron , Kristiansen Eirik , Nygaard Falch Hallvard , Aune Tore Kristian , van den Tillaar Roland
Published: August 2021

Examining participatory motives clarifies what engages and keeps individuals participating in exercise. The popularity of training at fitness centres has greatly increased over the last two decades, but individual determinants for motivation remain uncertain. This study compared motives between gender and age categories in training and performing physical activity at Norwegian fitness centres. To compare motives, a survey utilising a standardised questionnaire (MPAM-R) was conducted at six different Norwegian fitness centres. It was hypothesised that the intrinsic motive socialisation and extrinsic motive fitness would be more important among the older age categories for both genders, while the extrinsic motive appearance and intrinsic motive enjoyment would be more important among younger age groups. A total response of 183 men and 150 women, aged 14–80 years, was divided into seven categories based on their age and included in the statistical analysis. The main findings after conducting a two-way analysis of variance (ANOVA) with repeated measures, were that the most important motive for training at fitness centres was increasing fitness, followed by enjoyment, competence, vitality and appearance. The social motive was rated the lowest. Women rated fitness and enjoyment higher compared to men, and men rated the motive for appearance higher than women, but this decreased with age in both genders. With increasing age, the importance of enjoyment and competence decreased in men, while women seemed to place increased importance on vitality with age. The importance of the social motive decreased first as age increased, but then increased again in the age group 41–50 years and older. It was concluded that the motives for participating in exercise at fitness centres was dependent on individual characteristics and that motives about training at fitness centres differed by gender and changed with age.
Bridging Education and Engineering Students through a Wind Energy-Focused Community Engagement Project
Authors: Benitz Maija A. , Yang Li-Ling
Published: August 2021

Regional growth in offshore wind energy development, changes to the state’s K-12 science standards, and a desire to deepen undergraduate student learning coalesced to inspire an interdisciplinary community engagement project bridging university courses in engineering and education. The project consists of three main activities: a professional development event for local fourth grade teachers, five classroom lessons designed and taught by undergraduate engineering and education majors, and a final celebration event, all focused around the topics of wind energy and engineering design. This spring, the project was carried out for the third consecutive year, though each year’s implementation has been unique due to the timing of the onset of COVID-19. Analysis of responses from the Teaching Engineering Self-Efficacy Scale and an end-of-semester course survey demonstrate growth in student learning and transferrable skills from participating in the semester-long project. Additionally, exploration of students’ narrative work provides a richness to further understanding their growth and challenges they confronted. This interdisciplinary community engagement project will continue into future years, with improvements informed by the findings of this work, most notably with the hope of returning to a fully in-person delivery of lessons to fourth-graders.
Spatial Diversity of Organic Farming in Poland
Authors: Kobylińska Małgorzata
Published: August 2021

Economic development requires following the principles of sustainable development for the socio-economic progress of a country. The organic farming sector is important in ensuring sustainable development. The advancement of organic farming is an important issue which combines the environment, human health and socio-economic development. It is a management method that facilitates supplying high-quality food products and aims at eliminating the use of artificial fertilisers and pesticides. Organic farming has a beneficial impact on natural environmental protection, biodiversity conservation and food safety and quality improvement. The natural conditions in a region have a decisive impact on organic farming development. The purpose of this study is to assess the spatial diversity of organic farming and selected organic crop production in Poland by voivodship in 2013 and 2018. The statistical analysis of organic farming spatial diversity was conducted in a one- and two-dimensional approach. The analysis conducted made it possible to identify four clusters of voivodships based on the production volume of selected organic crops using the k-means algorithm. Graphs of observation depth contours in a sample were used to visualise and to analyse the two-dimensional data. STATISTICA software and selected packages of the R environment, available under the GPL licence, were used in the analysis. The analysis shows that the organic farm number and acreage in Poland is characterised by considerable variability between voivodships, with their noticeable concentration in several country regions. In the analysed years, organic farming was the most widespread in the Warmińsko-Mazurskie Voivodship and the Zachodniopomorskie Voivodship.
Paying for Sustainable Coffee in a Developing Country: Consumers’ Profile in Costa Rica
Authors: Valenciano-Salazar Jorge A. , André Francisco J. , Soliño Mario
Published: August 2021

This article analyzes the willingness to pay of Costa Rican consumers for three environmental certifications in the coffee market, namely, Carbon Neutral, Fairtrade, and ISO 14001. A face-to-face survey was applied to 1191 Costa Rican inhabitants. The results show that Costa Rican consumers are willing to pay price premiums around 30% for all the considered environmental certifications. In addition, a Cragg’s hurdle model shows that household income, the level of education, and environmental or community activism increase the likelihood of consumers paying price premiums for environmental certifications, while men are less likely to pay than women. It was also found that the size of the price premiums that respondents are willing to pay are positively related to income, gender (female), and education, and negatively related to age. Once the effect of socioeconomic variables has been controlled for, we conclude that consumers are more willing to pay for the CN certification than for the other two. The results can be useful for participants in the coffee value chain; coffee producers can use environmental certifications both to enhance their participation in green markets, particularly in developing countries, and to improve their environmental performance.
Evaluation of an Algorithm for Automatic Grading of Forum Messages in MOOC Discussion Forums
Authors: Pérez-Nicolás Raquel L. , Alario-Hoyos Carlos , Estévez-Ayres Iria , Moreno-Marcos Pedro Manuel , Muñoz-Merino Pedro J. , Delgado Kloos Carlos
Published: August 2021

Discussion forums are a valuable source of information in educational platforms such as Massive Open Online Courses (MOOCs), as users can exchange opinions or even help other students in an asynchronous way, contributing to the sustainability of MOOCs even with low interaction from the instructor. Therefore, the use of the forum messages to get insights about students’ performance in a course is interesting. This article presents an automatic grading approach that can be used to assess learners through their interactions in the forum. The approach is based on the combination of three dimensions: (1) the quality of the content of the interactions, (2) the impact of the interactions, and (3) the user’s activity in the forum. The evaluation of the approach compares the assessment by experts with the automatic assessment obtaining a high accuracy of 0.8068 and Normalized Root Mean Square Error (NRMSE) of 0.1799, which outperforms previous existing approaches. Future research work can try to improve the automatic grading by the training of the indicators of the approach depending on the MOOCs or the combination with text mining techniques.