bluuebunny/crossref_metadata_2025 huggingface dataset:
Improvement of Persistent Scatterer Interferometry to Detect Large Non-Linear Displacements with the 2π Ambiguity by a Non-Parametric Approach
Authors: Ogushi Fumitaka , Matsuoka Masashi , Defilippi Marco , Pasquali Paolo
Published: October 2019

Persistent scatterer interferometry (PSI) is commonly applied to monitor surface displacements with millimetric precision. However, this technique still has trouble estimating non-linear displacements because the algorithm is designed for the slow and linear displacements. Additionally, there is a variety of non-linear displacement types, and finding an appropriate displacement model for PSI is still assumed to be a fairly large task. In this paper, the conventional PSI technique is extended using a non-parametric non-linear approach (NN-PSI), and the performance of the extended method is investigated by simulations and actual observation data processing with TerraSAR-X. In the simulation, non-linear displacements are modeled by the magnitudes and periods of the displacement, and the evaluation of NN-PSI is conducted. According to the simulation results, the maximum magnitude of the displacement that can be estimated by NN-PSI is two and a half times the magnitude of the SAR sensor’s wavelength (2.5 λ that is roughly equivalent to 8 cm for X-band, 14 cm for C-band, and 60 cm for L-band), and the period of the displacement is about three months. However, this displacement cannot be reconstructed by the conventional PSI due to the limitation, known as the 2 π displacement ambiguity. The result of the observation data processing shows that a large displacement with the 2 π ambiguity can be estimated by NN-PSI as the simulation results show, but the conventional PSI cannot reconstruct it. In addition, a different approach, Small BAseline Subset (SBAS), is applied to the same data to ensure the accuracy of results, and the correlation between NN-PSI and SBAS is 0.95, while that between the conventional PSI and SBAS is −0.66. It is concluded that NN-PSI enables the reconstruction of non-linear displacements by the non-parametric approach and the expansion of applications to measure surface displacements that could not be measured due to the limitations of the traditional PSI methods.
Estimating Rainfall Interception of Vegetation Canopy from MODIS Imageries in Southern China
Authors: Wu Jianping , Liu Liyang , Sun Caihong , Su Yongxian , Wang Changjian , Yang Ji , Liao Jiayuan , He Xiaolei , Li Qian , Zhang Chaoqun , Zhang Hongou
Published: October 2019

The interception of rainfall by vegetation canopies plays an important role in the hydrologic process of ecosystems. Most estimates of canopy rainfall interception in present studies are mainly through field observations at the plot region. However, it is difficult, yet important, to map the regional rainfall interception by vegetation canopy at a larger scale, especially in the southern rainy areas of China. To obtain a better understanding of the spatiotemporal variation of vegetation canopy rainfall interception with regard to the basin scale in this region, we extended a rainfall interception model by combining the observed rainfall data and moderate resolution imaging spectroradiometer leaf area index (MODIS_LAI) data to quantitatively estimate the vegetation canopy rainfall interception rate (CRIR) at small/medium basin scales in Guangdong Province, which is undergoing large changes in vegetation cover due to rapid urban expansion in the area. The results showed that the CRIR in Guangdong declined continuously during 2004–2012, but increased slightly in 2016, and the spatial variability of CRIR showed a diminishing yearly trend. The CRIR also exhibited a distinctive spatial pattern, with a higher rate to the east and west of the mountainous areas and a lower rate in the central mountainous and coastal areas. This pattern was more closely related to the spatial variation of the LAI than that of rainfall due to frequent extreme rainfall events saturating vegetation leaves. Further analysis demonstrated that forest coverage, instead of background climate, has a certain impact on the canopy rainfall interception, especially the proportion of broad-leaved forests in the basin, but more in-depth study is warranted in the future. In conclusion, the results of this study provide insights into the spatiotemporal variation of canopy rainfall interception at the basin scale of the Guangdong Province, and suggest that forest cover should be increased by adjusting the species composition to increase the proportion of native broad-leaved species based on the local condition within the basin. In addition, these results would be helpful in accurately assessing the impacts of forest ecosystems on regional water cycling, and provide scientific and practical implications for water resources management.
Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition
Authors: He Luxiao , Wang Mi , Chang Xueli , Zhang Zhiqi , Feng Xiaoxiao
Published: October 2019

Stripes are common in remote sensing imaging systems equipped with multichannel time delay integration charge-coupled devices (TDI CCDs) and have different scale characteristics depending on their causes. Large-scale stripes appearing between channels are difficult to process by most current methods. The framework of column-by-column nonuniformity correction (CCNUC) is introduced to eliminate large-scale stripes. However, the worst problem of CCNUC is the unavoidable cumulative error, which will cause an overall color cast. To eliminate large-scale stripes and suppress the cumulative error, we proposed a destriping method via unidirectional multiscale decomposition (DUMD). The striped image was decomposed by constructing a unidirectional pyramid and making difference maps layer by layer. The highest layer of the pyramid was processed by CCNUC to eliminate large-scale stripes, and multiple cumulative error suppression measures were performed to reduce overall color cast. The difference maps of the pyramid were processed by a designed filter to eliminate small-scale stripes. Experiments showed that DUMD had good destriping performance and was robust with respect to different terrains.
Detecting Patterns of Vegetation Gradual Changes (2001–2017) in Shiyang River Basin, Based on a Novel Framework
Authors: Wang Ju , Xie Yaowen , Wang Xiaoyun , Dong Jingru , Bie Qiang
Published: October 2019

A lot of timeseries satellite products have been well documented in exploring changes in ecosystems. However, algorithms allowing for measuring the directions, magnitudes, and timing of vegetation change, evaluating the major driving factors, and eventually predicting the future trends are still insufficient. A novel framework focusing on addressing this problem was proposed in this study according to the temporal trajectory of Normalized Difference Vegetation Index (NDVI) timeseries of Moderate Resolution Imaging Spectroradiometer (MODIS). It divided the inter-annual changes in vegetation into four patterns: linear, exponential, logarithmic, and logistic. All the three non-linear patterns were differentiated automatically by fitting a logistic function with prolonged NDVI timeseries. Finally, features of vegetation changes including where, when and how, were evaluated by the parameters in the logistic function. Our results showed that 87.39% of vegetation covered areas (maximum mean growing season NDVI in the 17 years not less than 0.2) in the Shiyng River basin experienced significant changes during 2001–2017. The linear pattern, exponential pattern, logarithmic pattern, and logistic pattern accounted for 36.53%, 20.16%, 15.42%, and 15.27%, respectively. Increasing trends were dominant in all the patterns. The spatial distribution in both the patterns and the transition years at which vegetation gains/losses began or ended is of high consistency. The main years of transition for the exponential increasing pattern, the logarithmic increasing pattern, and the logarithmic increasing pattern were 2008–2011, 2003–2004, and 2009–2010, respectively. The period of 2006–2008 was the foremost period that NDVIs started to decline in Liangzhou Oasis and Minqin Oasis where almost all the decreasing patterns were concentrated. Potential disturbances of vegetation gradual changes in the basin are refer to as urbanization, expansion or reduction of agricultural oases, as well as measures in ecological projects, such as greenhouses building, afforestation, grazing prohibition, etc.
Cropland Classification Using Sentinel-1 Time Series: Methodological Performance and Prediction Uncertainty Assessment
Authors: Tomppo Erkki , Antropov Oleg , Praks Jaan
Published: October 2019

Methods based on Sentinel-1 data were developed to monitor crops and fields to facilitate the distribution of subsidies. The objectives were to (1) develop a methodology to predict individual crop species or or management regimes; (2) investigate the earliest time point in the growing season when the species predictions are satisfactory; and (3) to present a method to assess the uncertainty of the predictions at an individual field level. Seventeen Sentinel-1 synthetic aperture radar (SAR) scenes (VV and VH polarizations) acquired in interferometric wide swath mode from 14 May through to 30 August 2017 in the same geometry, and selected based on the weather conditions, were used in the study. The improved k nearest neighbour estimation, ik-NN, with a genetic algorithm feature optimization was tailored for classification with optional Sentinel-1 data sets, species groupings, and thresholds for the minimum parcel area. The number of species groups varied from 7 to as large as 41. Multinomial logistic regression was tested as an optional method. The Overall Accuracies (OA) varied depending on the number of species included in the classification, and whether all or not field parcels were included. OA with nine species groups was 72% when all parcels were included, 81% when the parcels area threshold (for incorporating parcels into classification) was 0.5 ha, and around 90% when the threshold was 4 ha. The OA gradually increased when adding extra Sentinel-1 scenes up until the early August, and the initial scenes were acquired in early June or mid-May. After that, only minor improvements in the crop recognition accuracy were noted. The ik-NN method gave greater overall accuracies than the logistic regression analysis with all data combinations tested. The width of the 95% confidence intervals with ik-NN for the estimate of the probability of the species with the largest probability on an individual parcel varied depending on the species, the area threshold of the parcel and the number of the Sentinel-1 scenes used. The results ranged between 0.06–0.08 units (6–8% points) for the most common species when the Sentinel-1 scenes were between 1 June and 12 August. The results were well-received by the authorities and encourage further research to continue the study towards an operational method in which the space-borne SAR data are a part of the information chain.
Change Detection of High Spatial Resolution Images Based on Region-Line Primitive Association Analysis and Evidence Fusion
Authors: Huang Jiru , Liu Yang , Wang Min , Zheng Yalan , Wang Jie , Ming Dongping
Published: October 2019

Change detection (CD) remains an important issue in remote sensing applications, especially for high spatial resolution (HSR) images, but it has yet to be fully resolved. This work proposes a novel object-based change detection (OBCD) method for HSR images that is based on region–line primitive association analysis and evidence fusion. In the proposed method, bitemporal images are separately segmented, and the segmentation results are overlapped to obtain the temporal region primitives (TRPs). The temporal line primitives (TLPs) are obtained by straight line detection on bitemporal images. In the initial CD stage, Dempster–Shafer evidence theory fuses the multiple items of evidence of the TRPs’ spectrum, edge, and gradient changes, and obtains the initial changed areas. In the refining CD stage, the association between the TRPs and their contacting TLPs in the unchanged areas is established on the basis of the region–line primitive association framework, and the TRPs’ main line directions (MLDs) are calculated. Some changed TRPs omitted in the initial CD stage are recovered by their MLD changes, thereby refining the initial CD results. Different from common OBCD methods, the proposed method considers the change evidence of TRPs’ internal and boundary information simultaneously via information complementation between TRPs and TLPs. The proposed method can significantly reduce missed alarms while maintaining a low level of false alarms in OBCD, thereby improving total accuracy. In our experiments, our method is superior to common CD methods, including change vector analysis (CVA), PCA-k-means, and iterative reweighted multivariate alteration detection (IRMAD), in terms of overall accuracy, missed alarms, and Kappa coefficient.
Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins
Authors: Melo Davi de C. D. , Getirana Augusto
Published: October 2019

The Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage. Attempts to decompose GRACE-based TWS signals into its different water storage layers, i.e., surface water storage (SWS), soil moisture, groundwater and snow, have shown that SWS is a principal component, particularly in the tropics, where major rivers flow over arid regions at high latitudes. Here, we demonstrate that water levels, measured with radar altimeters at a limited number of locations, can be used to reconstruct gridded GRACE-based TWS signals in the Amazon basin, at spatial resolutions ranging from 0.5 to 3°, with mean absolute errors (MAE) as low as 2.5 cm and correlations as high as 0.98. We show that, at 3° spatial resolution, spatially-distributed TWS time series can be precisely reconstructed with as few as 41 water-level time series located within the basin. The proposed approach is competitive when compared to existing TWS estimates derived from physically based and computationally expensive methods. Also, a validation experiment indicates that TWS estimates can be extrapolated to periods beyond that of the model regression with low errors. The approach is robust, based on regression models and interpolation techniques, and offers a new possibility to reproduce spatially and temporally distributed TWS that could be used to fill inter-mission gaps and to extend GRACE-based TWS time series beyond its timespan.
A Comprehensive Study of Three Different Portable XRF Scanners to Assess the Soil Geochemistry of An Extensive Sample Dataset
Authors: Declercq Ynse , Delbecque Nele , De Grave Johan , De Smedt Philippe , Finke Peter , Mouazen Abdul M. , Nawar Said , Vandenberghe Dimitri , Van Meirvenne Marc , Verdoodt Ann
Published: October 2019

The assessment of soil elemental concentrations nowadays mainly occurs through conventional laboratory analyses. However, proximal soil sensing (PSS) techniques such as X-ray fluorescence (XRF) spectrometry are proving to reduce analysis time and costs, and thus offer a worthy alternative to laboratory analyses. Moreover, XRF scanners are non-destructive and can be directly employed in the field. Although the use of XRF for soil elemental analysis is becoming widely accepted, most previous studies were limited to one scanner, a few samples, a few elements, or a non-diverse sample database. Here, an extensive and diverse soil database was used to compare the performance of three different XRF scanners with results obtained through conventional laboratory analyses. Scanners were used in benchtop mode with built-in soil calibrations to measure the concentrations of 15 elements. Although in many samples Cu, S, P, and Mg concentrations were up to 6, 12, 13, and 5 times overestimated by XRF, and empirical recalibration is recommended, all scanners produced acceptable results, even for lighter elements. Unexpectedly, XRF performance did not seem to depend on soil characteristics such as CaCO3 content. While performances will be worse when expanding to the field, our results show that XRF can easily be applied by non-experts to measure soil elemental concentrations reliably in widely different environments.
Correction: Pereira, O.; et al. Assessing Pasture Degradation in the Brazilian Cerrado Based on the Analysis of MODIS NDVI Time-Series. Remote Sensing 2018, 10(11) 1761
Authors: Pereira Osvaldo , Ferreira Laerte , Pinto Flávia , Baumgarten Leandro
Published: October 2019

After the publication of the research paper [...]
Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series
Authors: Recuero Laura , Litago Javier , Pinzón Jorge E. , Huesca Margarita , Moyano Maria C. , Palacios-Orueta Alicia
Published: October 2019

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.
Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
Authors: Millan Romain , Mouginot Jérémie , Rabatel Antoine , Jeong Seongsu , Cusicanqui Diego , Derkacheva Anna , Chekki Mondher
Published: October 2019

We explore and compare the capabilities and limitations of different optical sensors (Sentinel-2/ESA, Landsat 7/8/USGS, Ven μ s/CNES-ISA, Pléiades/AirbusD&S and Planet Labs images) for mapping the surface speeds of mountain glaciers on a regional scale. We present here our automated workflow designed to download data from institutional or commercial servers, prepare images, launch the feature tracking algorithm, calibrate glacier surface speeds, and our post-processing treatment to obtain filtered and time-averaged velocity maps. We applied our methodology to three regions: (1) the European alps; (2) the Peruvian Cordillera Blanca; and (3) the Southern Alps of New Zealand for years 2017 and 2018 and quantified ice velocity for every possible repeat cycle from few days up to 400 days. For these regions, we demonstrate the ability of our processing chain to derive precise time-averaged ice flow maps. The statistical analysis of the results provided by each individual repeat cycles shows that velocity mapping from Sentinel-2 is about twice more precise than that from Landsat 7/8. If Sentinel-2 captures more details than Landsat, some of the smallest glaciers (<250 m wide) remain challenging. Given the estimated precision for Sentinel-2, we also conclude that velocity fluctuations of the order of 10 m/yr can only be captured with repeat cycles longer than 60 days. Comparing Sentinel-2 with Pléiades, Planet and Ven μ s imagery, we finally highlight the advantages of high-resolution sensors to map glacier surface speed with finer details in space and time.
Towards Multiscale and Multisource Remote Sensing Mineral Exploration Using RPAS: A Case Study in the Lofdal Carbonatite-Hosted REE Deposit, Namibia
Authors: Booysen René , Zimmermann Robert , Lorenz Sandra , Gloaguen Richard , Nex Paul A. M. , Andreani Louis , Möckel Robert
Published: October 2019

Traditional exploration techniques usually rely on extensive field work supported by geophysical ground surveying. However, this approach can be limited by several factors such as field accessibility, financial cost, area size, climate, and public disapproval. We recommend the use of multiscale hyperspectral remote sensing to mitigate the disadvantages of traditional exploration techniques. The proposed workflow analyzes a possible target at different levels of spatial detail. This method is particularly beneficial in inaccessible and remote areas with little infrastructure, because it allows for a systematic, dense and generally noninvasive surveying. After a satellite regional reconnaissance, a target is characterized in more detail by plane-based hyperspectral mapping. Subsequently, Remotely Piloted Aircraft System (RPAS)-mounted hyperspectral sensors are deployed on selected regions of interest to provide a higher level of spatial detail. All hyperspectral data are corrected for radiometric and geometric distortions. End-member modeling and classification techniques are used for rapid and accurate lithological mapping. Validation is performed via field spectroscopy and portable XRF as well as laboratory geochemical and spectral analyses. The resulting spectral data products quickly provide relevant information on outcropping lithologies for the field teams. We show that the multiscale approach allows defining the promising areas that are further refined using RPAS-based hyperspectral imaging. We further argue that the addition of RPAS-based hyperspectral data can improve the detail of field mapping in mineral exploration, by bridging the resolution gap between airplane- and ground-based data. RPAS-based measurements can supplement and direct geological observation rapidly in the field and therefore allow better integration with in situ ground investigations. We demonstrate the efficiency of the proposed approach at the Lofdal Carbonatite Complex in Namibia, which has been previously subjected to rare earth elements exploration. The deposit is located in a remote environment and characterized by difficult terrain which limits ground surveys.
Blind Fractionally Spaced Channel Equalization for Shallow Water PPM Digital Communications Links
Authors: Scarano Gaetano , Petroni Andrea , Biagi Mauro , Cusani Roberto
Published: October 2019

Underwater acoustic digital communications suffer from inter-symbol interference deriving from signal distortions caused by the channel propagation. Facing such kind of impairment becomes particularly challenging when dealing with shallow water scenarios characterized by short channel coherence time and large delay spread caused by time-varying multipath effects. Channel equalization operated on the received signal represents a crucial issue in order to mitigate the effect of inter-symbol interference and improve the link reliability. In this direction, this contribution presents a preliminary performance analysis of acoustic digital links adopting pulse position modulation in severe multipath scenarios. First, we show how the spectral redundancy offered by pulse position modulated signals can be fruitfully exploited when using fractional sampling at the receiver side, which is an interesting approach rarely addressed by the current literature. In this context, a novel blind equalization scheme is devised. Specifically, the equalizer is blindly designed according to a suitably modified Bussgang scheme in which the zero-memory nonlinearity is replaced by a M-memory nonlinearity, M being the pulse position modulation order. Numerical results not only confirm the feasibility of the technique described here, but also assess the quality of its performance. An extension to a very interesting complex case is also provided.
Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network
Authors: Park Pangun , Marco Piergiuseppe Di , Shin Hyejeon , Bang Junseong
Published: October 2019

Fault detection and diagnosis is one of the most critical components of preventing accidents and ensuring the system safety of industrial processes. In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time series data. The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory (LSTM) network to classify different types of faults. The autoencoder is trained with offline normal data, which is then used as the anomaly detection. The predicted faulty data, captured by autoencoder, are put into the LSTM network to identify the types of faults. It basically combines the strong low-dimensional nonlinear representations of the autoencoder for the rare event detection and the strong time series learning ability of LSTM for the fault diagnosis. The proposed approach is compared with a deep convolutional neural network approach for fault detection and identification on the Tennessee Eastman process. Experimental results show that the combined approach accurately detects deviations from normal behaviour and identifies the types of faults within the useful time.
Fluviograph Design Based on an Ultra-Small Pressure Sensor
Authors: Li Shanshan , Duan Qingming , Chu Xunyu , Yang Chao
Published: October 2019

Groundwater dynamic monitoring of assessment points and evaluation areas has a significant predictive effect for controlling the occurrence of disasters. Obtaining water level and water temperature change data can provide important theoretical significance and reference values. However, in some remote areas of China, the measurement data concerning water level change are mostly obtained by manual measurement. This measurement method not only wastes manpower, but also cannot ensure the accuracy and real-time nature of the data. Therefore, this paper carried out research and design on a fluviograph, based on the relationship between hydraulic pressure and water depth. In the paper, the fluviograph used ultra-small pressure sensors to complete the data acquisition of the water level, a STM32L011 single-chip microcomputer (STMicroelectronics, Geneva, Switzerland) to process the data, and LabVIEW software to display the final data. Additionally, the water level data record and water temperature information record can be fed back to the user and the manager. After laboratory testing, the water level variation error range of this fluviograph was 1–2 cm, and the water temperature error range was less than 1 °C, which indicates the accuracy of the metrical data. The results show that the fluviograph realizes the function of automatically recording the water level and water temperature of the monitoring point, and it improves the social production efficiency greatly.
A Single-Connector Stent Antenna for Intravascular Monitoring Applications
Authors: Liu Chien-Hao , Chen Shuo-Chih , Hsiao Hao-Ming
Published: October 2019

Recently, smart stents have been developed by integrating various sensors with intravascular stents for detecting vascular restenosis or monitoring intravascular biomedical conditions such as blood pressure or blood flow velocity. The information on biomedical signals is then transmitted to external monitoring systems via wireless communications. Due to the limited volumes of blood vessels and limited influence of blood flow, antennas with good radiation performance are required for intravascular applications. In this paper, we propose a stent antenna composed of multiple rings containing crowns and struts, where each ring is connected with one connector. Unlike a conventional stent, wherein each ring is connected with several connectors, the single connector prevents the random distribution of electrical current and thus achieves good radiation performance. The implantable stent antenna is designed for the frequency range of 2 to 3 GHz for minimum penetration loss in the human body and tissues. Mechanical FEM simulations were conducted to ensure that the mechanical deformation was within specific limits during balloon expansions. A prototype was fabricated with laser cutting techniques and its radiation performance experimentally characterized. It was demonstrated that the fabricated stent antenna had an omnidirectional radiation pattern for arbitrary receiving angles, a gain of 1.38 dBi, and a radiation efficiency of 74.5% at a resonant frequency of 2.07 GHz. The main contribution of this work was the manipulation of the current distributions of the stent for good EM radiation performances which needed to be further examined while inserted inside human bodies. These research results should contribute to the further development of implantable wireless communications and intravascular monitoring of biomedical signals such as blood pressure and blood flow velocity.
Screen-Printed Voltammetric Biosensors for the Determination of Copper in Wine
Authors: Norocel Liliana , Gutt Gheorghe
Published: October 2019

Certain heavy metals present in wine, including copper, can form insoluble salts and can induce additional casse, so their determination is important for its quality and stability. In this context, a new biosensor for quantification of copper ions with BSA protein (bovine serum albumin) and using SPE electrodes (screen-printed electrodes) is proposed. The objective of this research was to develop a miniaturized, portable, and low-cost alternative to classical methods. A potentiostat, which displays the response in the form of a cyclic voltammogram, was used in order to carry out this method. Values measured for the performance characteristics of the new biosensor revealed a good sensitivity (21.01 μA mM−1cm−2), reproducibility (93.8%), and limit of detection (0.173 ppm), suggesting that it has a high degree of application in the analysis proposed by our research. The results obtained for wine samples were compared with the reference method, atomic absorption spectrometer (AAS), and it was indicated that the developed biosensor is efficient and can be used successfully in the analysis of copper in wine. For the 20 samples of red wine analyzed with AAS, the concentration range of copper was between 0.011 and 0.695 mg/L and with the developed biosensor it was between 0.037 and 0.658 mg/L. Similar results were obtained for the 20 samples of white wine, 0.121–0.765 mg/L (AAS) and 0.192–0.789 mg/L (developed biosensor), respectively.
Greedy Mechanism Based Particle Swarm Optimization for Path Planning Problem of an Unmanned Surface Vehicle
Authors: Xin Junfeng , Zhong Jiabao , Li Shixin , Sheng Jinlu , Cui Ying
Published: October 2019

Recently, issues of climate change, environment abnormality, individual requirements, and national defense have caused extensive attention to the commercial, scientific, and military development of unmanned surface vehicles (USVs). In order to design high-quality routes for a multi-sensor integrated USV, this work improves the conventional particle swarm optimization algorithm by introducing the greedy mechanism and the 2-opt operation, based on a combination strategy. First, a greedy black box is established for particle initialization, overcoming the randomness of the conventional method and excluding a great number of infeasible solutions. Then the greedy selection strategy and 2-opt operation are adopted together for local searches, to maintain population diversity and eliminate path crossovers. In addition, Monte-Carlo simulations of eight instances are conducted to compare the improved algorithm with other existing algorithms. The computation results indicate that the improved algorithm has the superior performance, with the shortest route and satisfactory robustness, although a fraction of computing efficiency becomes sacrificed. Moreover, the effectiveness and reliability of the improved method is also verified by its multi-sensor-based application to a USV model in real marine environments.
Cryptanalysis and Improvement of a Privacy-Preserving Three-Factor Authentication Protocol for Wireless Sensor Networks
Authors: Renuka Km , Kumar Sachin , Kumari Saru , Chen Chien-Ming
Published: October 2019

Wireless sensor networks (WSNs) are of prominent use in unmanned surveillance applications. This peculiar trait of WSNs is actually the underlying technology of various applications of the Internet of Things (IoT) such as smart homes, smart cities, smart shopping complexes, smart traffic, smart health, and much more. Over time, WSNs have evolved as a strong base for laying the foundations of IoT infrastructure. In order to address the scenario in which a user wants to access the real-time data directly from the sensor node in wireless sensor networks (WSNs), Das recently proposed an anonymity-preserving three-factor authentication protocol. Das’s protocol is suitable for resource-constrained sensor nodes because it only uses lightweight cryptographic primitives such as hash functions and symmetric encryption schemes as building blocks. Das’s protocol is claimed to be secure against different known attacks by providing formal security proof and security verification using the Automated Validation of Internet Security Protocols and Applications tool. However, we find that Das’s protocol has the following security loopholes: (1) By using a captured sensor node, an adversary can impersonate a legal user to the gateway node, impersonate other sensor nodes to deceive the user, and the adversary can also decrypt all the cipher-texts of the user; (2) the gateway node has a heavy computational cost due to user anonymity and thus the protocol is vulnerable to denial of service (DoS) attacks. We overcome the shortcomings of Das’s protocol and propose an improved protocol. We also prove the security of the proposed protocol in the random oracle model. Compared with the other related protocols, the improved protocol enjoys better functionality without much enhancement in the computation and communication costs. Consequently, it is more suitable for applications in WSNs
Cylindrical Cavity Sensor for Distinction of Various Driveability Index Gasoline with Temperature Robustness
Authors: Lee Chong Hyun , Jeong Yoon-Sang , Ashraf Hina
Published: October 2019

In this paper, a cylindrical cavity sensor based on microwave resonant theory is proposed to distinguish between various driveability index gasolines under temperature variations. The working principle of the proposed sensor is based on the fact that the change in permittivity of gasoline samples inside cavity sensor will also cause a change in resonant frequency. The proposed sensor has good sensitivity in terms of resonant frequency separation, which enables it to capture the minute permittivity changes and distinguish different gasolines. By using a normal gasoline permittivity of 2.15 and changing sensor dimension parameters, the sensor was designed by high-frequency structure simulator (HFSS). The designed sensor has a resonant frequency of 7.119 GHz for the TM012 mode with a 19.2 mm radius, a 35 mm height, and one-port coupling probe of 8 mm height. The proposed cylindrical cavity sensor shows advantages of excellent resonant characteristics of small cavity size and small sample amount. To optimize and verify the parameters of the sensor, many experiments have been carried out using HFSS and a vector network analyzer (VNA). Consequently, the proposed sensor is proven to be robust to temperature changes in terms of resonant frequency separation. The minimum frequency separation to distinguish gasoline samples is found to be larger than 29 MHz with reflection coefficients under −11 dB for temperature changes from −35 °C to 0 °C. The consistency of experimental and theoretical results also are presented, which guarantees accuracy of the sensor for the distinction of gasoline.
Reconstruction of Barely Visible Impact Damage in Composite Structures Based on Non-Destructive Evaluation Results
Authors: Wronkowicz-Katunin Angelika , Katunin Andrzej , Dragan Krzysztof
Published: October 2019

The occurrence of barely visible impact damage (BVID) in aircraft composite components and structures being in operation is a serious problem, which threatens structural safety of an aircraft, and should be timely detected and, if necessary, repaired according to the obligatory regulations of currently applied maintenance methodologies. Due to difficulties with a proper detection of such a type of damage even with non-destructive testing (NDT) methods as well as manual evaluation of the testing results, supporting algorithms for post-processing of these results seem to be of a high interest for aircraft maintenance community. In the following study, the authors proposed new approaches for BVID reconstruction based on results of ultrasonic and X-ray computed tomographic testing using authored advanced image processing algorithms. The studies were performed on real composite structures taking into consideration failure mechanisms occurring during impact damaging. The developed algorithms allow extracting relevant diagnostic information both from ultrasonic B-and C-Scans as well as from tomographic 3D arrays used for the validation of ultrasonic reconstructed damage locations, which allows for a significant improvement of the detectability of BVID in tested structures. The developed approach can be especially useful for NDT operators evaluating the results of structural NDT inspections.
A Computer Mouse Using Blowing Sensors Intended for People with Disabilities
Authors: Chen Hsin-Chuan , Huang Chiou-Jye , Tsai Wei-Ru , Hsieh Che-Lin
Published: October 2019

The computer is an important medium that allows people to connect to the internet. However, people with disabilities are unable to use a computer mouse and thus cannot enjoy internet benefits. Nowadays, there are various types of assistive technologies for controlling a computer mouse, but they all have some operational inconveniences. In this paper, we propose an innovative blowing-controlled mouse assistive tool to replace the conventional hand-controlled mouse. Its main contribution is that it uses microphones to induce small signals through the principle of airflow vibration, and it then converts the received signal into the corresponding pulse width. The co-design of software programming enables various mouse functions to be implemented by the identification of the blowing pulse width of multiple microphones. The proposed tool is evaluated experimentally, and the experimental results show that the average identification rate of the proposed mouse is over 85%. Additionally, compared with the other mouse assistive tools, the proposed mouse has the benefits of low cost and humanized operation. Therefore, the proposed blowing control method can not only improve the life quality of people with disabilities but also overcome the disadvantages of existing assistive tools.
Flow Measurement of Oil-Water Two-Phase Flow at Low Flow Rate Using the Plug-in Conductance Sensor Array
Authors: Jin Ningde , Zhou Yiyu , Liang Xinghe , Wang Dayang , Zhai Lusheng , Wei Jidong
Published: October 2019

In order to improve the flow measurement accuracy of oil-water two-phase flow at low flow rate, this paper presents a plug-in conductance sensor array (PICSA) for the measurement of water holdup and cross-correlation velocity. Due to the existence of the insert body in PICSA, the effect of slippage and the non-uniform distribution of dispersed phase on the measurement of oil-water two-phase flow at low flow rate can be reduced. The finite element method is used to analyze the electric field distribution characteristics of the plug-in conductance sensor, and the sensor geometry is optimized. The dynamic experiment of oil-water two-phase flow is carried out where water cut Kw and mixture velocity Um are set in the range of 10–98% and 0.0184–0.2580 m/s respectively. Experimental results show that the PICSA has good resolution in water holdup measurement for dispersed oil-in-water slug flow (D OS/W), transition flow (TF), dispersed oil-in-water bubble flow (D O/W) and very fine dispersed oil-in-water bubble flow (VFD O/W). In addition, the cross-correlation velocity of the oil-water two-phase flow is obtained by using the plug-in upstream and downstream conductance sensor arrays. The relationship between the cross-correlation velocity and mixture velocity is found to be sensitive to the change of flow pattern, but it has a good linear relationship under the same flow pattern. Based on the flow pattern identification, a good prediction result of the mixture velocity is obtained using kinematic wave theory. Finally, a high precision prediction of the individual phase volume fraction of oil-water two-phase flow at low flow rate is achieved by using the drift flux model.
Feminist New Materialisms: Activating Ethico-Politics through Genealogies in Social Sciences
Authors: Revelles-Benavente Beatriz , Ernst Waltraud , Rogowska-Stangret Monika
Published: October 2019

The idea to create a Special Issue journal around the topic of feminist new materialisms emerged out of the editors’ collaboration in the frames of European project New Materialism: Networking European Scholarship on ‘How Matter Comes to Matter’ (European Cooperation in Science and Technology), and more specifically it was born at the 9th Annual Conference on the New Materialisms, held at Utrecht University in June 2018 [...]
Quantifying the Sustainability of Products and Suppliers in Food Distribution Companies
Authors: Segura Marina , Maroto Concepción , Segura Baldomero
Published: October 2019

Supplier evaluation is a relevant task of supply chain management where multicriteria methods make great contributions to manufacturing industries. This is not the case in food distribution companies, which have a key role in providing safe and affordable food to society. The purpose of this research is to measure the sustainability of products and suppliers in food distribution companies through a multiple criteria approach. Firstly, the system proposed provides indicators to qualify products and assess the food quality, using the compensatory Multi-Attribute Utility Theory (MAUT) model. Secondly, these indicators are included in supplier evaluation, which takes economic, environmental, and social criteria into account. MAUT and Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE), a non-compensatory method, are used for supplier evaluation. This approach has been validated for fresh food in a supermarket chain, mainly using historical data. Partial indicators, such as food safety scores, together with global indicators of suppliers, inform the most appropriate decisions and the most appropriate relations between companies and providers. Poor performance in food safety can lead to the disqualification of some suppliers. MAUT is good for qualifying products and is easy to apply at the operational level in logistic platforms, while PROMETHEE is more suitable for supplier segmentation, as it helps to identify supplier strengths and weaknesses.
The ‘Lunar Side’ of the Story: Exploring the Sustainability of Curricular Internships in Higher Education
Authors: Lopes Betina , Silva Patrícia , Melo Ana I. , Brito Elisabeth , Paiva Dias Gonçalo , Costa Marco
Published: October 2019

Curricular internships are increasingly seen as an effective strategy capable of contributing to the objectives and targets of the sustainable development paradigm, both in its social, economic and institutional dimensions. This positive outlook is ingrained within the large bulk of existing research, which has been focused on the discussion of positive outputs, such as the enhancement of graduates′ competences (employability) and their work integration (employment). An important, albeit relatively unexplored, feature of the sustainable institutional politics of curricular internship management pertains to the assessment of its associated liabilities and tensions. This article seeks to address this challenge, by analyzing two interrelated dimensions. First, it seeks to understand the internship processes ranging from the planning to the implementation stages. Second, it elicits the associated negative aspects (the ‘lunar side’) of internships, allowing for an assessment of the features where there is room for improvement. Empirically, this article draws on in-depth qualitative research. It presents the results of four focus groups, voicing the perceptions of top management structures and students involving first cycle degrees in the areas of Marketing, Management, and Accounting from four public Portuguese Higher Education Institutions (HEIs)-three polytechnics and one university. Findings confirm the general positive outlook of internships. Perceived negative perspectives report to both internal problems (such as the lack of institutional support and curricular design) and external problems (e.g., the shortage of suitable internship placements), constituting barriers towards sustainable institutional policies and strategies. Adaptive strategies to overcome specific difficulties are highlighted (e.g., establishment of advisory committees with local businessmen). Finally, recommendations regarding curricular internship management at HEIs are delineated (e.g., investment in monitoring and evaluation strategies of internships).
Willingness to Pay for More Sustainable Tourism Destinations in World Heritage Cities: The Case of Caceres, Spain
Authors: Jurado-Rivas Carlos , Sánchez-Rivero Marcelino
Published: October 2019

Sustainable tourism management is becoming an increasingly important factor in the image of tourist destinations. Therefore, it is appropriate to consider the possibility of charging a premium price on certain tourism products or services in exchange for making them more sustainable. Generally, eco-friendly management of cultural heritage sites, and particularly World Heritage sites, along with protected natural areas, is essential for the image enhancement of tourist destinations. In line with these considerations, this paper aims to quantify the willingness to pay (WTP) an extra amount in order for certain tourism products and services to become more sustainable in a World Heritage city such as Caceres, Spain. By using the contingent valuation method, the average price increase that tourists visiting the city would be willing to pay for a double room in a hotel, a restaurant, entrance to a museum, or a taxi ride, all of which would become more sustainable services, has been estimated. In addition to the aggregate analysis, diverse average-comparison statistical techniques have been used in order to determine the possible influence of sociodemographic factors on the WTP for more sustainable products and services. Specifically, factors including the effect resulting from a tourist’s place of origin, gender, the year when the survey was conducted (through t-tests), and the effect of age and educational levels (through ANOVA tests) on the WTP for sustainability, have been analyzed. When considering all of the sociodemographic factors involved, notable statistical differences have been found. This implies that when a hypothetical increase in tourism prices for the purpose of financing public or private sustainability actions is considered, different segments of the tourism market should be taken into account, since it would not be appropriate to apply the same policy of price increases to all tourists equally.