bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced Adsorptive Bioremediation of Heavy Metals (Cd2+, Cr6+, Pb2+) by Methane-Oxidizing Epipelon
Authors: Faheem Muhammad , Shabbir Sadaf , Zhao Jun , Kerr Philip G. , Sultana Nasrin , Jia Zhongjun
Published: April 2020

Cadmium (Cd), chromium (Cr) and lead (Pb) are heavy metals that have been classified as priority pollutants in aqueous environment while methane-oxidizing bacteria as a biofilter arguably consume up to 90% of the produced methane in the same aqueous environment before it escapes into the atmosphere. However, the underlying kinetics and active methane oxidizers are poorly understood for the hotspot of epipelon that provides a unique micro-ecosystem containing diversified guild of microorganisms including methane oxidizers for potential bioremediation of heavy metals. In the present study, the Pb2+, Cd2+and Cr6+ bioremediation potential of epipelon biofilm was assessed under both high (120,000 ppm) and near-atmospheric (6 ppm) methane concentrations. Epipelon biofilm demonstrated a high methane oxidation activity following microcosm incubation amended with a high concentration of methane, accompanied by the complete removal of 50 mg L−1 Pb2+ and 50 mg L−1 Cd2+ (14 days) and partial (20%) removal of 50 mg L−1 Cr6+ after 20 days. High methane dose stimulated a faster (144 h earlier) heavy metal removal rate compared to near-atmospheric methane concentrations. DNA-based stable isotope probing (DNA-SIP) following 13CH4 microcosm incubation revealed the growth and activity of different phylotypes of methanotrophs during the methane oxidation and heavy metal removal process. High throughput sequencing of 13C-labelled particulate methane monooxygenase gene pmoA and 16S rRNA genes revealed that the prevalent active methane oxidizers were type I affiliated methanotrophs, i.e., Methylobacter. Type II methanotrophs including Methylosinus and Methylocystis were also labeled only under high methane concentrations. These results suggest that epipelon biofilm can serve as an important micro-environment to alleviate both methane emission and the heavy metal contamination in aqueous ecosystems with constant high methane fluxes.
A qPCR-Based Survey of Haplosporidium nelsoni and Perkinsus spp. in the Eastern Oyster, Crassostrea virginica in Maine, USA
Authors: Marquis Nicholas D. , Bishop Theodore J. , Record Nicholas R. , Countway Peter D. , Fernández Robledo José A.
Published: April 2020

Eastern oyster (Crassostrea virginica) aquaculture is increasingly playing a significant role in the state of Maine’s (USA) coastal economy. Here, we conducted a qPCR-based survey for Haplosporidium nelsoni, Perkinsus marinus, and Perkinsus chesapeaki in C. virginica (n = 1440) from six Maine sites during the summer–fall of 2016 and 2017. In the absence of reported die-offs, our results indicated the continued presence of the three protozoan parasites in the six sites. The highest H. nelsoni qPCR-prevalence corresponded to Jack’s Point and Prentiss Island ( x ¯ = 40 and 48% respectively), both located in the Damariscotta River Estuary. Jack’s Point, Prentiss Island, New Meadows River, and Weskeag River recorded the highest qPCR-prevalence for P. marinus (32–39%). While the P. marinus qPCR-prevalence differed slightly for the years 2016 and 2017, P. chesapeaki qPCR-prevalence in 2016 was markedly lower than 2017 (<20% at all sites versus >60% at all sites for each of the years, respectively). Mean qPCR-prevalence values for P. chesapeaki over the two-year study were ≥40% for samples from Jack’s Point (49%), Prentiss Island (44%), and New Meadows River (40%). This study highlights that large and sustained surveys for parasitic diseases are fundamental for decision making toward the management of the shellfish aquaculture industry, especially for having a baseline in the case that die-offs occur.
Radar-Derived Internal Structure and Basal Roughness Characterization along a Traverse from Zhongshan Station to Dome A, East Antarctica
Authors: Luo Kun , Liu Sixin , Guo Jingxue , Wang Tiantian , Li Lin , Cui Xiangbin , Sun Bo , Tang Xueyuan
Published: April 2020

The internal layers of ice sheets from ice-penetrating radar (IPR) investigation preserve critical information about the ice-flow field and englacial conditions. This paper presents a new detailed analysis of spatial distribution characteristics of internal layers and subglacial topography of the East Antarctic ice sheet (EAIS) from Zhongshan Station to Dome A. The radar data of 1244 km along a traverse between Zhongshan Station and Dome A of EAIS were collected during the 29th Chinese National Antarctic Research Expedition (CHINARE 29, 2012/2013). In this study, the Internal Layering Continuity Index (ILCI) and basal roughness were taken as indicators to provide an opportunity to evaluate the past internal environment and dynamics of the ice sheet. Except for the upstream of Lambert Glacier, the fold patterns of internal layers are basically similar to that of the bed topography. The relatively flat basal topography and the decrease of ILCI with increasing depth provide evidence for identifying previous rapid ice flow areas that are unavailable to satellites, especially in the upstream of Lambert Glacier. Continuous internal layers of Dome A, recording the spatial change of past ice accumulation and ice-flow history over 160 ka, almost extend to the bed, with high ILCI and high basal roughness of the corresponding bed topography. There are three kinds of basal roughness patterns along the traverse, that is, “low ξt low η”, “low ξt high η”, and “high ξt high η”, where ξt represents the amplitude of the undulations, and quantifies the vertical variation of the bedrock, and η measures the frequency variation of fluctuations and the horizontal irregularity of the profile. The characteristics of internal layers and basal topography of the traverse between Zhongshan Station and Dome A provide new information for understanding the ancient ice-flow activity and the historical evolution of EAIS.
Estimating Water pH Using Cloud-Based Landsat Images for a New Classification of the Nhecolândia Lakes (Brazilian Pantanal)
Authors: Pereira Osvaldo J. R. , Merino Eder R. , Montes Célia R. , Barbiero Laurent , Rezende-Filho Ary T. , Lucas Yves , Melfi Adolpho J.
Published: April 2020

The Nhecolândia region, located in the southern portion of the Pantanal wetland area, is a unique lacustrine system where tens of thousands of saline-alkaline and freshwater lakes and ponds coexist in close proximity. These lakes are suspected to be a strong source of greenhouse gases (GHGs) to the atmosphere, the water pH being one of the key factors in controlling the biogeochemical functioning and, consequently, production and emission of GHGs in these lakes. Here, we present a new field-validated classification of the Nhecolândia lakes using water pH values estimated based on a cloud-based Landsat (5 TM, 7 ETM+, and 8 OLI) 2002–2017 time-series in the Google Earth Engine platform. Calibrated top-of-atmosphere (TOA) reflectance collections with the Fmask method were used to ensure the usage of only cloud-free pixels, resulting in a dataset of 2081 scenes. The pH values were predicted by applying linear multiple regression and symbolic regression based on genetic programming (GP). The regression model presented an R2 value of 0.81 and pH values ranging from 4.69 to 11.64. A lake mask was used to extract the predicted pH band that was then classified into three lake classes according to their pH values: Freshwater (pH < 8), oligosaline (pH 8–8.9), and saline (≥9). Nearly 12,150 lakes were mapped with those with saline waters accounting for 7.25%. Finally, a trend surface map was created using the ALOS PRISM Digital Surface Model (DSM) to analyze the correlation between landscape features (topography, connection with the regional drainage system, size, and shape of lakes) and types of lakes. The analysis was in consonance with previous studies that pointed out that saline lakes tend to occur in lower positions compared to freshwater lakes. The results open a relevant perspective for the transfer of locally acquired experimental data to the regional balances of the Nhecolândia lakes.
Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data
Authors: Jiang Xiandie , Li Guiying , Lu Dengsheng , Chen Erxue , Wei Xinliang
Published: April 2020

Species-rich subtropical forests have high carbon sequestration capacity and play important roles in regional and global carbon regulation and climate changes. A timely investigation of the spatial distribution characteristics of subtropical forest aboveground biomass (AGB) is essential to assess forest carbon stocks. Lidar (light detection and ranging) is regarded as the most reliable data source for accurate estimation of forest AGB. However, previous studies that have used lidar data have often beenbased on a single model developed from the relationships between lidar-derived variables and AGB, ignoring the variability of this relationship in different forest types. Although stratification of forest types has been proven to be effective for improving AGB estimation, how to stratify forest types and how many strata to use are still unclear. This research aims to improve forest AGB estimation through exploring suitable stratification approaches based on lidar and field survey data. Different stratification schemes including non-stratification and stratifications based on forest types and forest stand structures were examined. The AGB estimation models were developed using linear regression (LR) and random forest (RF) approaches. The results indicate the following: (1) Proper stratifications improved AGB estimation and reduced the effect of under- and overestimation problems; (2) the finer forest type strata generated higher accuracy of AGB estimation but required many more sample plots, which were often unavailable; (3) AGB estimation based on stratification of forest stand structures was similar to that based on five forest types, implying that proper stratification reduces the number of sample plots needed; (4) the optimal AGB estimation model and stratification scheme varied, depending on forest types; and (5) the RF algorithm provided better AGB estimation for non-stratification than the LR algorithm, but the LR approach provided better estimation with stratification. Results from this research provide new insights on how to properly conduct forest stratification for AGB estimation modeling, which is especially valuable in tropical and subtropical regions with complex forest types.
Understanding Fields by Remote Sensing: Soil Zoning and Property Mapping
Authors: Yuzugullu Onur , Lorenz Frank , Fröhlich Peter , Liebisch Frank
Published: April 2020

Precision agriculture aims to optimize field management to increase agronomic yield, reduce environmental impact, and potentially foster soil carbon sequestration. In 2015, the Copernicus mission, with Sentinel-1 and -2, opened a new era by providing freely available high spatial and temporal resolution satellite data. Since then, many studies have been conducted to understand, monitor and improve agricultural systems. This paper presents results from the SolumScire project, focusing on the prediction of the spatial distribution of soil zones and topsoil properties, such as pH, soil organic matter (SOM) and clay content in agricultural fields through random forest algorithms. For this purpose, samples from 120 fields were investigated. The zoning and soil property prediction has an accuracy greater than 90%. This is supported by a high agreement of the derived zones with farmer’s observations. The trained models revealed a prediction accuracy of 94%, 89% and 96% for pH, SOM and clay content, respectively. The obtained models for soil properties can support precision field management, the improvement of soil sampling and fertilization strategies, and eventually the management of soil properties such as SOM.
Multi-Network Asynchronous TDOA Algorithm Test in a Simulated Maritime Scenario
Authors: Gioia Ciro , Sermi Francesco , Tarchi Dario
Published: April 2020

In the last few years, the number of applications relying on position of vessels at sea has grown significantly. Usually, these applications exploit information provided by the Automatic Identification System (AIS). Unfortunately, the cooperative nature of AIS makes it vulnerable to different types of attack. Therefore, especially for critical applications, the veracity of the position information reported in the AIS message needs to be verified. Several techniques can be adopted to this end. This paper presents a mathematical extension of the traditional Time Difference Of Arrival (TDOA) localisation technique allowing merging TDOA measurement from synchronous and non-synchronous receivers. This technique was tested in a simulated scenario, where the position of a moving target was estimated using different configurations of the receivers network. The robustness of the proposed algorithm with respect to the traditional one is demonstrated. The proposed approach, which is derived form satellite applications, is not limited to the AIS signals or to the maritime domain, and it can be adopted to estimate the position of any radiofrequency transmitter, by employing a suitable number of non-synchronous receivers.
Data Block and Tuple Identification Using Master Index
Authors: Kvet Michal , Matiasko Karol
Published: April 2020

Relational databases are still very often used as a data storage, even for the sensor oriented data. Each data tuple is logically stored in the table referenced by relationships between individual tables. From the physical point of view, data are stored in the data files delimited by the tablespaces. Files are block-oriented. When retrieving data, particular blocks must be identified and transferred into the memory for the evaluation and processing. This paper deals with storage principles and proposes own methods for effective data block location and identification if no suitable index for the query is present in the system. Thanks to that, the performance of the whole system is optimized, and the processing time and costs are significantly lowered. The proposed solution is based on the master index, which points just to the blocks with relevant data. Thus, no sequential block scanning is necessary for consuming many system resources. The paper analyzes the impact of block size, which can have a significant impact on sensor oriented data, as well.
Spurious Absorption Frequency Appearance Due to Frequency Conversion Processes in Pulsed THz TDS Problems
Authors: Trofimov Vyacheslav A. , Wang Nan-Nan , Qiu Jing-Hui , Varentsova Svetlana A.
Published: April 2020

The appearance of the spurious absorption frequencies caused by the frequency conversion process at the broadband THz pulse propagation in a medium is theoretically and experimentally discussed. The spurious absorption frequencies appear due to both the frequency doubling and generation of waves with sum or difference frequency. Such generation might occur because of the nonlinear response of a medium or its non-instantaneous response. This phenomenon is confirmed by the results of a few physical experiments provided with the THz CW signals and broadband THz pulses that are transmitted through the ordinary or dangerous substances. A high correlation between the time-dependent spectral intensities for the basic frequency and generated frequencies is demonstrated while using the computer simulation results. This feature of the frequency conversion might be used for the detection and identification of a substance.
A Novel Fuzzy PID Congestion Control Model Based on Cuckoo Search in WSNs
Authors: Lin Lin , Shi You , Chen Jinfu , Ali Sher
Published: April 2020

Wireless Sensor Networks (WSNs) consist of multiple sensor nodes, each of which has the ability to collect, receive and send data. However, irregular data sources can lead to severe network congestion. To solve this problem, the Proportional Integral Derivative (PID) controller is introduced into the congestion control mechanism to control the queue length of messages in nodes. By running the PID algorithm on cluster head nodes, the effective collection of sensor data is realized. In addition, a fuzzy control algorithm is proposed to solve the problems of slow parameter optimization, limited adaptive ability and poor optimization precision of traditional PID controller. However, the parameter selection of the fuzzy control algorithm relies too much on expert experience and has certain limitations. Therefore, this manuscript proposes the Cuckoo Fuzzy-PID Controller (CFPID), whose core idea is to apply the cuckoo search algorithm to optimize the fuzzy PID controller’s quantization factor and PID parameter increment. Simulation results show that in comparison with the existing methods, the instantaneous queue length and real-time packet loss rate of CFPID are better.
Impact Damage Evaluation in Composite Structures Based on Fusion of Results of Ultrasonic Testing and X-ray Computed Tomography
Authors: Katunin Andrzej , Wronkowicz-Katunin Angelika , Dragan Krzysztof
Published: April 2020

Barely visible impact damage (BVID) is one of the most dangerous types of structural damage in composites, since in most practical cases the application of advanced non-destructive testing (NDT) methods is required to detect and identify it. Due to its character of propagation, there are minor signs of structural damage on a surface, while the internal damage can be broad and complex both in the point of view of fracture mechanisms and resulting geometry of damage. The most common NDT method applied e.g., in aircraft inspections is ultrasonic testing (UT), which enables effective damage detection and localization in various environments. However, the results of such inspections are usually misestimated with respect to the true damage extent, and the quantitative analysis is biased by an error. In order to determine the estimation error a comparative analysis was performed on NDT results obtained for artificially damaged carbon fiber-reinforced composite structures using two UT methods and X-ray computed tomography (CT). The latter method was considered here as the reference one, since it gives the best spatial resolution and estimation accuracy of internal damage among the available NDT methods. Fusing the NDT results for a set of pre-damaged composite structures with various energy values of impact and various types of impactor tips applied for introducing damage, the evaluation of estimation accuracy of UT was possible. The performed analysis allowed for evaluation of relations between UT and X-ray CT NDT results and for proposal of a correcting factor for UT results for BVID in the analyzed composite structures.
Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs
Authors: Xiao Zhongyang , Yang Diange , Wen Tuopu , Jiang Kun , Yan Ruidong
Published: April 2020

Real-time vehicle localization (i.e., position and orientation estimation in the world coordinate system) with high accuracy is the fundamental function of an intelligent vehicle (IV) system. In the process of commercialization of IVs, many car manufacturers attempt to avoid high-cost sensor systems (e.g., RTK GNSS and LiDAR) in favor of low-cost optical sensors such as cameras. The same cost-saving strategy also gives rise to an increasing number of vehicles equipped with High Definition (HD) maps. Rooted upon these existing technologies, this article presents the concept of Monocular Localization with Vector HD Map (MLVHM), a novel camera-based map-matching method that efficiently aligns semantic-level geometric features in-camera acquired frames against the vector HD map in order to achieve high-precision vehicle absolute localization with minimal cost. The semantic features are delicately chosen for the ease of map vector alignment as well as for the resiliency against occlusion and fluctuation in illumination. The effective data association method in MLVHM serves as the basis for the camera position estimation by minimizing feature re-projection errors, and the frame-to-frame motion fusion is further introduced for reliable localization results. Experiments have shown that MLVHM can achieve high-precision vehicle localization with an RMSE of 24 cm with no cumulative error. In addition, we use low-cost on-board sensors and light-weight HD maps to achieve or even exceed the accuracy of existing map-matching algorithms.
Margin-Based Deep Learning Networks for Human Activity Recognition
Authors: Lv Tianqi , Wang Xiaojuan , Jin Lei , Xiao Yabo , Song Mei
Published: April 2020

Human activity recognition (HAR) is a popular and challenging research topic, driven by a variety of applications. More recently, with significant progress in the development of deep learning networks for classification tasks, many researchers have made use of such models to recognise human activities in a sensor-based manner, which have achieved good performance. However, sensor-based HAR still faces challenges; in particular, recognising similar activities that only have a different sequentiality and similarly classifying activities with large inter-personal variability. This means that some human activities have large intra-class scatter and small inter-class separation. To deal with this problem, we introduce a margin mechanism to enhance the discriminative power of deep learning networks. We modified four kinds of common neural networks with our margin mechanism to test the effectiveness of our proposed method. The experimental results demonstrate that the margin-based models outperform the unmodified models on the OPPORTUNITY, UniMiB-SHAR, and PAMAP2 datasets. We also extend our research to the problem of open-set human activity recognition and evaluate the proposed method’s performance in recognising new human activities.
Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers
Authors: Toma Rafia Nishat , Prosvirin Alexander E. , Kim Jong-Myon
Published: April 2020

Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults.
Hybridized-GNSS Approaches to Train Positioning: Challenges and Open Issues on Uncertainty
Authors: Spinsante Susanna , Stallo Cosimo
Published: April 2020

In recent years, the development of advanced systems and applications has propelled the adoption of autonomous railway traffic and train positioning, with several ongoing initiatives and experimental testbeds aimed at proving the suitability and reliability of the Global Navigation Satellite System signals and services, in this specific application domain. To satisfy the strict safety and accuracy requirements aimed at assuring the position solution’s integrity, availability, accuracy and reliability, recent proposals suggest the hybridization of the Global Navigation Satellite System with other technologies. The integration with localization techniques that are expected to be available with the upcoming fifth generation mobile communication networks is among the most promising approaches. In this work, different approaches to the design of hybrid positioning solutions for the railway sector are examined, under the perspective of the uncertainty evaluation of the attained results and performance. In fact, the way the uncertainty associated to the positioning measurements performed by different studies is reported is often not consistent with the Guide to the Expression of Uncertainty in Measurement, and this makes it very difficult to fairly compare the different approaches in order to identify the best emerging solution. Under this perspective, the review provided by this work highlights a number of open issues that should drive future research activities in this field.
Pattern Recognition and Anomaly Detection by Self-Organizing Maps in a Multi Month E-nose Survey at an Industrial Site
Authors: Licen Sabina , Di Gilio Alessia , Palmisani Jolanda , Petraccone Stefania , de Gennaro Gianluigi , Barbieri Pierluigi
Published: April 2020

Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses.
Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV’s Autonomous Motion Planning in Complex Unknown Environments
Authors: Hu Zijian , Wan Kaifang , Gao Xiaoguang , Zhai Yiwei , Wang Qianglong
Published: April 2020

Autonomous motion planning (AMP) of unmanned aerial vehicles (UAVs) is aimed at enabling a UAV to safely fly to the target without human intervention. Recently, several emerging deep reinforcement learning (DRL) methods have been employed to address the AMP problem in some simplified environments, and these methods have yielded good results. This paper proposes a multiple experience pools (MEPs) framework leveraging human expert experiences for DRL to speed up the learning process. Based on the deep deterministic policy gradient (DDPG) algorithm, a MEP–DDPG algorithm was designed using model predictive control and simulated annealing to generate expert experiences. On applying this algorithm to a complex unknown simulation environment constructed based on the parameters of the real UAV, the training experiment results showed that the novel DRL algorithm resulted in a performance improvement exceeding 20% as compared with the state-of-the-art DDPG. The results of the experimental testing indicate that UAVs trained using MEP–DDPG can stably complete a variety of tasks in complex, unknown environments.
Limited Angle Electrical Resistance Tomography in Wastewater Monitoring
Authors: Wu Chenning , Hutton Martin , Soleimani Manuchehr
Published: April 2020

Electrical resistance tomography (ERT) has been investigated in monitoring conductive flows due to its high speed, non-intrusive and no radiation hazard advantages. Recently, we have developed an ERT system for the novel application of smart wastewater metering. The dedicated low cost and high-speed design of the reported ERT device allows for imaging pipes with different flow constituents and monitoring the sewer networks. This work extends the capability of such a system to work with partially filled lateral pipes where the incomplete data issue arises due to the electrodes losing contact with the conductive medium. Although the ERT for such a limited region has been developed for many years, there is no study on imaging content within these limited regions. For wastewater monitoring, this means imaging the wastewater and solid inclusions at the same time. This paper has presented a modified ERT system that has the capacity to image inclusions within the conductive region using limited data. We have adjusted the ERT hardware to register the information of the non-contact electrodes and hence the valid measurements. A limited region image reconstruction method based on Jacobian reformulation is applied to gain robustness when it comes to inclusion recovery in limited data ERT. Both simulation and experimental results have demonstrated an enhanced performance brought by the limited region method in comparison to the global reconstruction.
On the Orbital Angular Momentum Incident Fields in Linearized Microwave Imaging
Authors: Pavone Santi Concetto , Sorbello Gino , Di Donato Loreto
Published: April 2020

Orbital angular momentum (OAM) is gaining great attention in the physics and electromagnetic community owing to an intriguing debate concerning its suitability for widening channel capacity in next-generation wireless communications. While such a debate is still a matter of controversy, we exploit OAM generation for microwave imaging within the classical first order linearized models, i.e., Born and Rytov approximation. Physical insights into different fields carrying ℓ-order OAM are conveniently exploited to propose possible alternative imaging approaches and paradigms in microwave imaging.
Multi-Objective Optimization of Loop Closure Detection Parameters for Indoor 2D Simultaneous Localization and Mapping
Authors: Han Dongxiao , Li Yuwen , Song Tao , Liu Zhenyang
Published: April 2020

Aiming at addressing the issues related to the tuning of loop closure detection parameters for indoor 2D graph-based simultaneous localization and mapping (SLAM), this article proposes a multi-objective optimization method for these parameters. The proposed method unifies the Karto SLAM algorithm, an efficient evaluation approach for map quality with three quantitative metrics, and a multi-objective optimization algorithm. More particularly, the evaluation metrics, i.e., the proportion of occupied grids, the number of corners and the amount of enclosed areas, can reflect the errors such as overlaps, blurring and misalignment when mapping nested loops, even in the absence of ground truth. The proposed method has been implemented and validated by testing on four datasets and two real-world environments. For all these tests, the map quality can be improved using the proposed method. Only loop closure detection parameters have been considered in this article, but the proposed evaluation metrics and optimization method have potential applications in the automatic tuning of other SLAM parameters to improve the map quality.
Monitoring of Strain and Temperature in an Open Pit Using Brillouin Distributed Optical Fiber Sensors
Authors: Lanciano Chiara , Salvini Riccardo
Published: April 2020

Marble quarries are quite dangerous environments in which rock falls may occur. As many workers operate in these sites, it is necessary to deal with the matter of safety at work, checking and monitoring the stability conditions of the rock mass. In this paper, some results of an innovative analysis method are shown. It is based on the combination of Distributed Optical Fiber Sensors (DOFS), digital photogrammetry through Unmanned Aerial Vehicle (UAV), topographic, and geotechnical monitoring systems. Although DOFS are currently widely used for studying infrastructures, buildings and landslides, their use in rock marble quarries represents an element of peculiarity. The complex morphologies and the intense temperature range that characterize this environment make this application original. The selected test site is the Lorano open pit which is located in the Apuan Alps (Italy); here, a monitoring system consisting of extensometers, crackmeters, clinometers and a Robotic Total Station has been operating since 2012. From DOFS measurements, strain and temperature values were obtained and validated with displacement data from topographic and geotechnical instruments. These results may provide useful fundamental indications about the rock mass stability for the safety at work and the long-term planning of mining activities.
Monitoring Aircraft Position Using EGNOS Data for the SBAS APV Approach to the Landing Procedure
Authors: Krasuski Kamil , Wierzbicki Damian
Published: April 2020

The aim of this paper is to present the problem of the implementation of the EGNOS (European Geostationary Navigation Overlay Service) data for the processing of aircraft position determination. The main aim of the research is to develop a new computational strategy which might improve the performance of the EGNOS system in aviation, based on navigation solutions of an aircraft position, using several GNSS (Global Navigation Satellite System) onboard receivers. The results of an experimental test conducted by the Cessna 172 at EPDE (European Poland Deblin) (ICAO (International Civil Aviation Organization) code, N51°33.07’/E21°53.52’) aerodrome in Dęblin are presented and discussed in this paper. Two GNSS navigation receivers with the EGNOS positioning function for monitoring changes in the parameters of the aircraft position in real time during the landing phase were installed onboard a Cessna 172. Based on obtained research findings, it was discovered that the positioning accuracy was not higher than 2.1 m, and the integrity of positioning did not exceed 19 m. Moreover, the availability parameter was found to equal 1 (or 100%); also, no intervals in the continuity of the operation of the EGNOS system were recorded. In the paper, the results of the air test from Dęblin were compared with the parameters of positioning quality from the air test conducted in Chełm (ICAO code: EPCD, N51°04’57.8” E23°26’15”). In the air test in Chełm, the obtained parameters of EGNOS quality positioning were: better than 4.9 m for accuracy, less than 35.5 m for integrity, 100% for availability, and no breaks in continuity. Based on the results of the air tests in Dęblin and Chełm, it was concluded that the parameters of the EGNOS positioning quality in aviation for the SBAS (Satellite Based Augmentation System) APV (Approach to Vertical guidance) procedure were satisfied in accordance with the ICAO (International Civil Aviation Organization) requirements. The presented research method can be utilized in the SBAS APV landing procedure in Polish aviation. In this paper, the results of PDOP (Position Dilution of Precision) are presented and compared to the two air tests in Dęblin and Chełm. The maximum results of PDOP amounted to 1.4 in the air test in Dęblin, whereas they equaled 4.0 in the air test in Chełm. The paper also shows how the EGNOS system improved the aircraft position in relation to the only GPS solution. In this context, the EGNOS system improved the aircraft position from about 78% to 95% for each ellipsoidal coordinate axis.
GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction
Authors: Ding Jiakai , Huang Liangpei , Xiao Dongming , Li Xuejun
Published: April 2020

The vibration signal of an early rolling bearing is nonstationary and nonlinear, and the fault signal is weak and difficult to extract. To address this problem, this paper proposes a genetic mutation particle swarm optimization variational mode decomposition (GMPSO-VMD) algorithm and applies it to rolling bearing vibration signal fault feature extraction. Firstly, the minimum envelope entropy is used as the objective function of the GMPSO to find the optimal parameter combination of the VMD algorithm. Then, the optimized VMD algorithm is used to decompose the vibration signal of the rolling bearing and several intrinsic mode functions (IMFs) are obtained. The envelope spectrum analysis of GMPSO-VMD decomposed rolling bearing fault signal IMF1 was carried out. Moreover, the feature frequency of the four fault states of the rolling bearing are extracted accurately. Finally, the GMPSO-VMD algorithm is utilized to analyze the simulation signal and rolling bearing fault vibration signal. The effectiveness of the GMPSO-VMD algorithm is verified by comparing it with the fixed parameter VMD (FP-VMD) algorithm, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm and empirical mode decomposition (EMD) algorithm.
Measurement of the Acoustic Non-Linearity Parameter of Materials by Exciting Reversed-Phase Rayleigh Waves in Opposite Directions
Authors: Yan Bingsheng , Song Yuzhou , Nie Shijie , Yang Mingchao , Liu Ziran
Published: April 2020

The acoustic non-linearity parameter of Rayleigh waves can be used to detect various defects (such as dislocation and micro-cracks) on material surfaces of thick-plate structures; however, it is generally low and likely to be masked by noise. Moreover, conventional methods used with non-linear Rayleigh waves exhibit a low detection efficiency. To tackle these problems, a method of exciting reversed-phase Rayleigh waves in opposite directions is proposed to measure the acoustic non-linearity parameter of materials. For that, two angle beam wedge transducers were placed at the two ends of the upper surface of a specimen to excite two Rayleigh waves of opposite phases, while a normal transducer was installed in the middle of the upper surface to receive them. By taking specimens of 0Cr17Ni4Cu4Nb martensitic stainless steel subjected to fatigue damage as an example, a finite element simulation model was established to test the proposed method of measuring the acoustic non-linearity parameter. The simulation results show that the amplitude of fundamentals is significantly reduced due to offset, while that of second harmonics greatly increases due to superposition because of the opposite phases of the excited signals, and the acoustic non-linearity parameter thus increases. The experimental research on fatigue damage specimens was carried out using this method. The test result was consistent with the simulation result. Thus, the method of exciting reversed-phase Rayleigh waves in opposite directions can remarkably increase the acoustic non-linearity parameter. Additionally, synchronous excitation with double-angle beam wedge transducers can double the detection efficiency.
The Performance of the Magneto-Impedance Effect for the Detection of Superparamagnetic Particles
Authors: García-Arribas Alfredo
Published: April 2020

The performance of magneto-impedance sensors to detect the presence and concentration of magnetic nanoparticles is investigated, using finite element calculations to directly solve Maxwell’s equations. In the case of superparamagnetic particles that are not sufficiently magnetized by an external field, it is assumed that the sensitivity of the magneto-impedance sensor to the presence of magnetic nanoparticles comes from the influence of their magnetic permeability on the sensor impedance, and not from the stray magnetic field that the particles produce. The results obtained not only justify this hypothesis, but also provide an explanation for the discrepancies found in the literature about the response of magneto-impedance sensors to the presence of magnetic nanoparticles, where some authors report an increasing magneto-impedance signal when the concentration of magnetic nanoparticles is increased, while others report a decreasing tendency. Additionally, it is demonstrated that sensors with lower magneto-impedance response display larger sensitivities to the presence of magnetic nanoparticles, indicating that the use of plain, nonmagnetic conductors as sensing materials can be beneficial, at least in the case of superparamagnetic particles insufficiently magnetized in an external magnetic field.
South African National Climate Change Response Policy Sensitization: An Assessment of Smallholder Farmers in Amathole District Municipality, Eastern Cape Province
Authors: Popoola Oluwabunmi Oluwaseun , Yusuf Shehu Folaranmi Gbolahan , Monde Nomakhaya
Published: April 2020

The South African National Climate Change Response Policy (NCCRP) was instituted in October 2011. Amongst the policy’s priorities is building capacity and resilience in the country’s agricultural sector, with the public agricultural extension system being a principal component for climate change sensitization, education, and capacity building. This study, therefore, investigated the level of the policy awareness amongst smallholder farmers in the study area and the sensitization and response implementation. A multistage sampling procedure was used in the selection of the study population, with a cross-sectional household survey conducted using semi-structured questionnaires. Findings revealed that there was an extremely low awareness of the policy, and a dearth of sensitization campaigns and capacity-building training by extension officers in the study area. The practical and policy implications outlined could aid an increase in the resilience of farmers, with support from extension advisers and other relevant stakeholders.
Impacts of International Commodity Trade on Conventional Biofuels Production
Authors: Mizik Tamás
Published: April 2020

The study gives an overview of raw materials and biofuel generation, markets, production, and regulation. The major aim of this study was to reveal the impacts of biofuel production on international commodity trade. According to the results of the country-level regressions, the export of corn and sugar cane have generally negatively impacted ethanol production. This effect was positive at the global level which indicates that some of the imported raw materials are used for ethanol production. Although the explanatory power of the models was relatively high (from 0.35 (EU) to 0.94 (USA)), none of models proved to be significant, even at the 10% level. These values were higher for the biodiesel models (from 0.53 (USA) to 0.97 (Brazil)) and the EU model results were significant at the 5% level. The export of raw materials had a positive impact on biodiesel production. This implies that some part of the biodiesel was produced from the imported raw materials. The export of processed products (different oils) had a negative impact on biodiesel production, as they are normally used for other purposes.
Pinpointing the Barriers to Recycling at Destination
Authors: Díaz-Meneses Gonzalo , Vilkaite-Vaitone Neringa
Published: April 2020

This paper aims to gain further understanding of the barriers that prevent tourists at tourist destinations from recycling. Methodologically, a survey was carried out with a questionnaire, reaching 371 units through a convenience sampling procedure on the island of Gran Canaria. The measuring instruments consisted of a Likert ‘beliefs’ scale, comprising statements related to a wide range of recycling barriers, as well as both sociodemographic and situational information. Firstly, we performed an exploratory factor analysis on the barriers scale, and several obstacles to recycling were identified, such as a lack of knowledge and familiarity, blaming of the system of collection, assumed incompatibility with a relaxing holiday, erroneous beliefs about environmental usefulness, disregard towards the place being visited and a certain sense of detachment, the non-existence of incentives, and an unwillingness to comply with perceived demands. Secondly, by means of ANOVA tests, we measured the causal relationship between these barriers to recycling and the tourists’ sociodemographic and situational profiles. The practical implications shed light on how to overcome the difficulties that occur for the tourist at the destination with regards to recycling, by considering the tourists’ beliefs and their sociodemographic and situational background.
Public Preference for Increasing Natural Gas Generation for Reducing CO2 Emissions in South Korea
Authors: Seo Seong-Jae , Kim Ju-Hee , Yoo Seung-Hoon
Published: April 2020

To meet international efforts to prevent climate change, the South Korean government is seeking to transform its main power source from coal to natural gas (NG), which emits less carbon dioxide (CO2) than coal. The government needs information about public preferences for increasing NG-fired generation. Therefore, the purpose of this paper is to provide the government with this information by gathering the data on public preferences for increasing NG-fired generation for reducing CO2 emissions, analyzing these data, and obtaining the implications from the analysis. To this end, a survey of 1000 people, after explaining the merits and demerits of NG-fired power, were asked to determine the proper ratio of NG-fired generation by choosing one of six examples: lower than 10%, 10%–20%, 20%–30%, 30%–40%, 40%–50%, and higher than 50%. An interval data model was employed to deal with the data. The average value of the ratio was estimated to be 26.7% with statistical significance, which is greater than the actual value of 22.2% from 2017. Finally, whether and how much some variables such as education level, age, gender, and income level of the respondent affected their preferences for NG-fired generation were investigated.
Biocorrosion of Concrete Sewers in Greece: Current Practices and Challenges
Authors: Fytianos Georgios , Baltikas Vasilis , Loukovitis Dimitrios , Banti Dimitra , Sfikas Athanasios , Papastergiadis Efthimios , Samaras Petros
Published: April 2020

This paper is intended to review the current practices and challenges regarding the corrosion of the Greek sewer systems with an emphasis on biocorrosion and to provide recommendations to avoid it. The authors followed a holistic approach, which included survey data obtained by local authorities serving more than 50% of the total country’s population and validated the survey answers with field measurements and analyses. The exact nature and extent of concrete biocorrosion problems in Greece are presented for the first time. Moreover, the overall condition of the sewer network, the maintenance frequency, and the corrosion prevention techniques used in Greece are also presented. Results from field measurements showed the existence of H2S in the gaseous phase (i.e., precursor of the H2SO4 formation in the sewer) and acidithiobacillus bacteria (i.e., biocorrosion causative agent) in the slime, which exists at the interlayer between the concrete wall and the sewage. Biocorrosion seems to mainly affect old concrete networks, and the replacement of the destroyed concrete pipes with new polyvinyl chloride (PVC) ones is currently common practice. However, in most cases, the replacement cost is high, and the authors provide some recommendations to increase the current service life of concrete pipes.