bluuebunny/crossref_metadata_2025 huggingface dataset:
Copernicus Imaging Microwave Radiometer (CIMR) Benefits for the Copernicus Level 4 Sea-Surface Salinity Processing Chain
Authors: Ciani Daniele , Santoleri Rosalia , Liberti Gian , Prigent Catherine , Donlon Craig , Buongiorno Nardelli Bruno
Published: August 2019

We present a study on the potential of the Copernicus Imaging Microwave Radiometer (CIMR) mission for the global monitoring of Sea-Surface Salinity (SSS) using Level-4 (gap-free) analysis processing. Space-based SSS are currently provided by the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. However, there are no planned missions to guarantee continuity in the remote SSS measurements for the near future. The CIMR mission is in a preparatory phase with an expected launch in 2026. CIMR is focused on the provision of global coverage, high resolution sea-surface temperature (SST), SSS and sea-ice concentration observations. In this paper, we evaluate the mission impact within the Copernicus Marine Environment Monitoring Service (CMEMS) SSS processing chain. The CMEMS SSS operational products are based on a combination of in situ and satellite (SMOS) SSS and high-resolution SST information through a multivariate optimal interpolation. We demonstrate the potential of CIMR within the CMEMS SSS operational production after the SMOS era. For this purpose, we implemented an Observing System Simulation Experiment (OSSE) based on the CMEMS MERCATOR global operational model. The MERCATOR SSSs were used to generate synthetic in situ and CIMR SSS and, at the same time, they provided a reference gap-free SSS field. Using the optimal interpolation algorithm, we demonstrated that the combined use of in situ and CIMR observations improves the global SSS retrieval compared to a processing where only in situ observations are ingested. The improvements are observed in the 60% and 70% of the global ocean surface for the reconstruction of the SSS and of the SSS spatial gradients, respectively. Moreover, the study highlights the CIMR-based salinity patterns are more accurate both in the open ocean and in coastal areas. We conclude that CIMR can guarantee continuity for accurate monitoring of the ocean surface salinity from space.
BDS-3 Time Group Delay and Its Effect on Standard Point Positioning
Authors: Dai Peipei , Ge Yulong , Qin Weijin , Yang Xuhai
Published: August 2019

The development of the BeiDou navigation system (BDS) is divided into three phases: The demonstration system (BDS-1), the regional system (BDS-2) and the global BeiDou navigation system (BDS-3). At present, the construction of the global BeiDou navigation system (BDS-3) constellation network is progressing very smoothly. The signal design and functionality of BDS-3 are different from those of BDS-1 and BDS-2. The BDS-3 satellite not only broadcasts B1I (1561.098 MHz) and B3I (1268.52 MHz) signals but also broadcasts new signals B1C (1575.42 MHz) and B2a (1176.45 MHz). In this work, six tracking stations of the international GNSS monitoring and assessment system (iGMAS) were selected, and 41 consecutive days of observation data, were collected. To fully exploit the code observations of BDS-2 and BDS-3, the time group delay (TGD) correction model of BDS-2 and BDS-3 are described in detail. To further verify the efficacy of the broadcast TGD parameters in the broadcast ephemeris, the standard point positioning (SPP) of all the signals from BDS-2 and BDS-3 with and without TGD correction was studied. The experiments showed that the B1I SPP accuracy of BDS-2 was increased by approximately 50% in both the horizontal and vertical components, and B1I/B3I were improved by approximately 70% in the horizontal component and 47.4% in the vertical component with TGD correction. The root mean square (RMS) value of B1I and B1C from BDS-3 with TGD correction was enhanced by approximately 60%–70% in the horizontal component and by approximately 50% in the vertical component. The B2a-based SPP was increased by 60.2% and 64.4% in the east and north components, respectively, and the up component was increased by approximately 19.8%. For the B1I/B3I and B1C/B2a dual-frequency positioning accuracy with TGD correction, the improvement in the horizontal component ranges from 62.1% to 75.0%, and the vertical component was improved by approximately 45%. Furthermore, the positioning accuracy of the BDS-2 + BDS-3 combination constellation was obviously higher than that of BDS-2 or BDS-3.
Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou
Authors: Lou Ge , Chen Qiuxiao , He Kang , Zhou Yue , Shi Zhou
Published: August 2019

The worldwide development of multi-center structures in large cities is a prevailing development trend. In recent years, China’s large cities developed from a predominantly mono-centric to a multi-center urban space structure. However, the definition and identification city centers is complex. Both nighttime light data and point of interest (POI) data are important data sources for urban spatial structure research, but there are few integrated applications for these two kinds of data. In this study, visible infrared imaging radiometer suite (NPP-VIIRS) nighttime imagery and POI data were combined to identify the city centers in Hangzhou, China. First, the optimal parameters of multi-resolution segmentation were determined by experiments. The POI density was then calculated with the segmentation results as the statistical unit. High–high clustering units were then defined as the main centers by calculating the Anselin Local Moran’s I, and a geographically weighted regression model was used to identify the subcenters according to the square root of the POI density and the distances between the units and the city center. Finally, a comparison experiment was conducted between the proposed method and the relative cut-off_threshold method, and the experiment results were compared with the evaluation report of the master plan. The results showed that the optimal segmentation parameters combination was 0.1 shape and 0.5 compactness factors. Two main city centers and ten subcenters were detected. Comparison with the evaluation report of the master plan indicated that the combination of nighttime light data and POI data could identify the urban centers accurately. Combined with the characteristics of the two kinds of data, the spatial structure of the city could be characterized properly. This study provided a new perspective for the study of the spatial structure of polycentric cities.
Ocean Optical Profiling in South China Sea Using Airborne LiDAR
Authors: Chen Peng , Pan Delu
Published: August 2019

Increasingly, LiDAR has more and more applications. However, so far, there are no relevant publications on using airborne LiDAR for ocean optical profiling in the South China Sea (SCS). The applicability of airborne LiDAR for optical profiling in the SCS will be presented. A total of four airborne LiDAR flight experiments were conducted over autumn 2017 and spring 2018 in the SCS. A hybrid retrieval method will be presented here, which incorporates a Klett method to obtain LiDAR attenuation coefficient and a perturbation retrieval method for a volume scattering function at 180°. The correlation coefficient between the LiDAR-derived results and the traditional measurements was 0.7. The mean absolute relative error (MAE) and the normalized root mean square deviation (NRMSD) between the two are both between 10% and 12%. Subsequently, the vertical structure of the LiDAR-retrieved attenuation and backscattering along airborne LiDAR flight tracks was mapped. In addition to this, ocean subsurface phytoplankton layers were detected between 10 to 20 m depths along the flight track in Sanya Bay. Primary results demonstrated that our airborne LiDAR has an independent ability to survey and characterize ocean optical structure.
Remote Sensing of Explosives-Induced Stress in Plants: Hyperspectral Imaging Analysis for Remote Detection of Unexploded Threats
Authors: Manley Paul V. , Sagan Vasit , Fritschi Felix B. , Burken Joel G.
Published: August 2019

Explosives contaminate millions of hectares from various sources (partial detonations, improper storage, and release from production and transport) that can be life-threatening, e.g., landmines and unexploded ordnance. Exposure to and uptake of explosives can also negatively impact plant health, and these factors can be can be remotely sensed. Stress induction was remotely sensed via a whole-plant hyperspectral imaging system as two genotypes of Zea mays, a drought-susceptible hybrid and a drought-tolerant hybrid, and a forage Sorghum bicolor were grown in a greenhouse with one control group, one group maintained at 60% soil field capacity, and a third exposed to 250 mg kg−1 Royal Demolition Explosive (RDX). Green-Red Vegetation Index (GRVI), Photochemical Reflectance Index (PRI), Modified Red Edge Simple Ratio (MRESR), and Vogelmann Red Edge Index 1 (VREI1) were reduced due to presence of explosives. Principal component analyses of reflectance indices separated plants exposed to RDX from control and drought plants. Reflectance of Z. mays hybrids was increased from RDX in green and red wavelengths, while reduced in near-infrared wavelengths. Drought Z. mays reflectance was lower in green, red, and NIR regions. S. bicolor grown with RDX reflected more in green, red, and NIR wavelengths. The spectra and their derivatives will be beneficial for developing explosive-specific indices to accurately identify plants in contaminated soil. This study is the first to demonstrate potential to delineate subsurface explosives over large areas using remote sensing of vegetation with aerial-based hyperspectral systems.
Capture Point-Based Controller Using Real-Time Zero Moment Point Manipulation for Stable Bipedal Walking in Human Environment
Authors: Hong Young-Dae
Published: August 2019

For collaboration of humans and bipedal robots in human environments, this paper proposes a stability control method for dynamically modifiable bipedal walking using a capture point (CP) tracking controller. A reasonable reference CP trajectory for the CP tracking control is generated using the real-time zero moment point (ZMP) manipulation without information on future footstep commands. This trajectory can be modified at any time during the single support phase according to a given footstep command. Accordingly, this makes it possible for the robot to walk stably with dynamically modifiable walking patterns, including sudden changes in navigational commands during the single support phase. A reference CP trajectory during the double support phase is also generated for continuity. The CP of the robot is controlled to track the reference trajectory using a ZMP-based CP tracking controller. The ZMP while walking is measured by the force-sensing resistor sensors mounted on the sole of each foot. A handling method for infeasible footstep commands is utilized so that the manipulated ZMP satisfies the allowable ZMP region for stability. The validity of the proposed method is verified through simulations and experiments.
Autonomous Vision-Based Aerial Grasping for Rotorcraft Unmanned Aerial Vehicles
Authors: Lin Lishan , Yang Yuji , Cheng Hui , Chen Xuechen
Published: August 2019

Autonomous vision-based aerial grasping is an essential and challenging task for aerial manipulation missions. In this paper, we propose a vision-based aerial grasping system for a Rotorcraft Unmanned Aerial Vehicle (UAV) to grasp a target object. The UAV system is equipped with a monocular camera, a 3-DOF robotic arm with a gripper and a Jetson TK1 computer. Efficient and reliable visual detectors and control laws are crucial for autonomous aerial grasping using limited onboard sensing and computational capabilities. To detect and track the target object in real time, an efficient proposal algorithm is presented to reliably estimate the region of interest (ROI), then a correlation filter-based classifier is developed to track the detected object. Moreover, a support vector regression (SVR)-based grasping position detector is proposed to improve the grasp success rate with high computational efficiency. Using the estimated grasping position and the UAV?Äôs states, novel control laws of the UAV and the robotic arm are proposed to perform aerial grasping. Extensive simulations and outdoor flight experiments have been implemented. The experimental results illustrate that the proposed vision-based aerial grasping system can autonomously and reliably grasp the target object while working entirely onboard.
Comprehensive Analysis of the Energy Harvesting Performance of a Fe-Ga Based Cantilever Harvester in Free Excitation and Base Excitation Mode
Authors: Liu Huifang , Cong Chen , Zhao Qiang , Ma Kai
Published: August 2019

Vibration energy harvesting attempts to generate electricity through recycling the discarded vibration energy that is usually lost or dissipated, and represents an alternative to traditional batteries and may even lead to reliable self-powered autonomous electronic devices. Energy harvesting based on magnetostrictive materials, which takes advantage of the coupling characteristics of the Villari effect and the Faraday electromagnetic induction effect, is a recent research field of great interest. Aiming to develop a new type of magnetostrictive energy harvester using Fe-Ga alloy, which is suitable for harvesting the vibration energy from base excitations and free excitations, a Fe-Ga based cantilever harvester was proposed. The energy harvesting performance of the harvester prototype, including its resonance characteristics, open-circuit output voltage-frequency response and amplitude characteristic under base excitation, influence of external resistance, energy harvesting performance under free excitation, the function of pre-magnetization and so on was studied systematically and carefully by experiments. In terms of the volume power density, the harvester prototype without pre-magnetized magnet when in series with the optimal resistor load displays a value of 2.653 mW/cm3. The average conversion efficiency without a pre-magnetic field is about 17.7% when it is in series with a 200 Ω resistance. The energy harvesting and converting capability can therefore be improved greatly once the Fe-Ga beam is highly pre-magnetized. The prototype successfully lit up multi-LEDs and digital display tubes, which validates the sustainable power generation capacity of the fabricated prototype.
Simulation, Design, and Test of a Dual-Differential D-Dot Overvoltage Sensor Based on the Field-Circuit Coupling Method
Authors: Zhao Pengcheng , Wang Jingang , Wang Qian , Xiao Qianbo , Zhang Ruiqiang , Ou Shucheng , Tao Yaqin
Published: August 2019

Accurate measurement of overvoltage in power grids is of great significance to study the characteristics of overvoltage and design of insulation coordination. Based on the research of D-dot voltage sensor, we designed a Dual-Differential D-dot overvoltage sensor. In order to quantify the structural parameters of the sensor, improve the performance and measurement accuracy of the sensor. The Field-Circuit Coupling method was proposed to be used in the parameter design of D-dot overvoltage sensor. The joint simulation of space electromagnetic field model and equivalent circuit model of the Dual-Differential D-dot overvoltage sensor was established with the finite element simulation software Ansoft Maxwell and circuit simulation software Simplorer. Finally, the actual sensor was manufactured. A test platform was built to verify the steady-state and transient performance of the sensor. The results show that the Dual-Differential D-dot sensor has excellent steady-state and transient performance, the error of phase and amplitude are small, and the sensor can achieve the non-contact measurement of power transmission line. Simultaneously, the rationality of the Field-Circuit Coupling method was further verified.
Magnetic Communication Using High-Sensitivity Magnetic Field Detectors
Authors: Hott Maurice , Hoeher Peter A. , Reinecke Sebastian F.
Published: August 2019

In this article, an innovative approach for magnetic data communication is presented. For this purpose, the receiver coil of a conventional magneto-inductive communication system is replaced by a high-sensitivity wideband magnetic field sensor. The results show decisive advantages offered by sensitive magnetic field sensors, including a higher communication range for small receiver units. This approach supports numerous mobile applications where receiver size is limited, possibly in conjunction with multiple detectors. Numerical results are supported by a prototype implementation employing an anisotropic magneto-resistive sensor.
A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System
Authors: Zhu Longtu , Jia Honglei , Chen Yibing , Wang Qi , Li Mingwei , Huang Dongyan , Bai Yunlong
Published: August 2019

Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures was used to collect soil gases. From the response curve of each sensor, four features were extracted (maximum value, mean differential coefficient value, response area value, and the transient value at the 20th second). Then, soil organic matter regression prediction models were built based on back-propagation neural network (BPNN), support vector regression (SVR), and partial least squares regression (PLSR). The prediction performance of each model was evaluated using the coefficient of determination (R2), root-mean-square error (RMSE), and the ratio of performance to deviation (RPD). It was found that the R2 values between prediction (from BPNN, SVR, and PLSR) and observation were 0.880, 0.895, and 0.808. RMSEs were 14.916, 14.094, and 18.890, and RPDs were 2.837, 3.003, and 2.240, respectively. SVR had higher prediction ability than BPNN and PLSR and can be used to accurately predict organic matter contents. Thus, our findings offer brand new methods for predicting SOM.
An Event Recognition Method for Φ-OTDR Sensing System Based on Deep Learning
Authors: Shi Yi , Wang Yuanye , Zhao Lei , Fan Zhun
Published: August 2019

Phase-sensitive optical time domain reflectometer (Φ-OTDR) based distributed optical fiber sensing system has been widely used in many fields such as long range pipeline pre-warning, perimeter security and structure health monitoring. However, the lack of event recognition ability is always being the bottleneck of Φ-OTDR in field application. An event recognition method based on deep learning is proposed in this paper. This method directly uses the temporal-spatial data matrix from Φ-OTDR as the input of a convolutional neural network (CNN). Only a simple bandpass filtering and a gray scale transformation are needed as the pre-processing, which achieves real-time. Besides, an optimized network structure with small size, high training speed and high classification accuracy is built. Experiment results based on 5644 events samples show that this network can achieve 96.67% classification accuracy in recognition of 5 kinds of events and the retraining time is only 7 min for a new sensing setup.
Machine Learning Classification for Assessing the Degree of Stenosis and Blood Flow Volume at Arteriovenous Fistulas of Hemodialysis Patients Using a New Photoplethysmography Sensor Device
Authors: Chiang Pei-Yu , Chao Paul C. -P. , Tu Tse-Yi , Kao Yung-Hua , Yang Chih-Yu , Tarng Der-Cherng , Wey Chin-Long
Published: August 2019

The classifier of support vector machine (SVM) learning for assessing the quality of arteriovenous fistulae (AVFs) in hemodialysis (HD) patients using a new photoplethysmography (PPG) sensor device is presented in this work. In clinical practice, there are two important indices for assessing the quality of AVF: the blood flow volume (BFV) and the degree of stenosis (DOS). In hospitals, the BFV and DOS of AVFs are nowadays assessed using an ultrasound Doppler machine, which is bulky, expensive, hard to use, and time consuming. In this study, a newly-developed PPG sensor device was utilized to provide patients and doctors with an inexpensive and small-sized solution for ubiquitous AVF assessment. The readout in this sensor was custom-designed to increase the signal-to-noise ratio (SNR) and reduce the environment interference via maximizing successfully the full dynamic range of measured PPG entering an analog–digital converter (ADC) and effective filtering techniques. With quality PPG measurements obtained, machine learning classifiers including SVM were adopted to assess AVF quality, where the input features are determined based on optical Beer–Lambert’s law and hemodynamic model, to ensure all the necessary features are considered. Finally, the clinical experiment results showed that the proposed PPG sensor device successfully achieved an accuracy of 87.84% based on SVM analysis in assessing DOS at AVF, while an accuracy of 88.61% was achieved for assessing BFV at AVF.
Artificial Neural Networks for Forecasting Passenger Flows on Metro Lines
Authors: Gallo Mariano , De Luca Giuseppina , D’Acierno Luca , Botte Marilisa
Published: August 2019

Forecasting user flows on transportation networks is a fundamental task for Intelligent Transport Systems (ITSs). Indeed, most control and management strategies on transportation systems are based on the knowledge of user flows. For implementing ITS strategies, the forecast of user flows on some network links obtained as a function of user flows on other links (for instance, where data are available in real time with sensors) may provide a significant contribution. In this paper, we propose the use of Artificial Neural Networks (ANNs) for forecasting metro onboard passenger flows as a function of passenger counts at station turnstiles. We assume that metro station turnstiles record the number of passengers entering by means of an automatic counting system and that these data are available every few minutes (temporal aggregation); the objective is to estimate onboard passengers on each track section of the line (i.e., between two successive stations) as a function of turnstile data collected in the previous periods. The choice of the period length may depend on service schedules. Artificial Neural Networks are trained by using simulation data obtained with a dynamic loading procedure of the rail line. The proposed approach is tested on a real-scale case: Line 1 of the Naples metro system (Italy). Numerical results show that the proposed approach is able to forecast the flows on metro sections with satisfactory precision.
A Node Density Control Learning Method for the Internet of Things
Authors: Lou Shumei , Srivastava Gautam , Liu Shuai
Published: August 2019

When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on Internet of Things architectures. Firstly, the characteristics of wireless sensors networks and the structure of mobile nodes are analyzed. Combined with the flexibility of wireless sensor networks and the degree of freedom of real-time processing and configuration of field programmable gate array (FPGA) data, a one-step transition probability matrix is introduced. In addition, the probability of arrival of signals between any pair of mobile nodes is also studied and calculated. Finally, the probability of signal connection between mobile nodes is close to 1, approximating the minimum node density at T. We simulate using a fully connected network identifying a worst-case test environment. Detailed experimental results show that our novel proposed method has shorter completion time and lower power consumption than previous attempts. We achieve high node density control as well at close to 90%.
Smartphone-Based Platform for Affect Monitoring through Flexibly Managed Experience Sampling Methods
Authors: Bailon Carlos , Damas Miguel , Pomares Hector , Sanabria Daniel , Perakakis Pandelis , Goicoechea Carmen , Banos Oresti
Published: August 2019

The identification of daily life events that trigger significant changes on our affective state has become a fundamental task in emotional research. To achieve it, the affective states must be assessed in real-time, along with situational information that could contextualize the affective data acquired. However, the objective monitoring of the affective states and the context is still in an early stage. Mobile technologies can help to achieve this task providing immediate and objective data of the users’ context and facilitating the assessment of their affective states. Previous works have developed mobile apps for monitoring affective states and context, but they use a fixed methodology which does not allow for making changes based on the progress of the study. This work presents a multimodal platform which leverages the potential of the smartphone sensors and the Experience Sampling Methods (ESM) to provide a continuous monitoring of the affective states and the context in an ubiquitous way. The platform integrates several elements aimed to expedite the real-time management of the ESM questionnaires. In order to show the potential of the platform, and evaluate its usability and its suitability for real-time assessment of affective states, a pilot study has been conducted. The results demonstrate an excellent usability level and a good acceptance from the users and the specialists that conducted the study, and lead to some suggestions for improving the data quality of mobile context-aware ESM-based systems.
A Unique Interactive Nanostructure Knitting based Passive Sampler Adsorbent for Monitoring of Hg2+ in Water
Authors: Chouhan Raghuraj S. , Žitko Gregor , Fajon Vesna , Živković Igor , Pavlin Majda , Berisha Sabina , Jerman Ivan , Vesel Alenka , Horvat Milena
Published: August 2019

This work reports the development of ultralight interwoven ultrathin graphitic carbon nitride (g-CN) nanosheets for use as a potential adsorbent in a passive sampler (PAS) designed to bind Hg2+ ions. The g-CN nanosheets were prepared from bulk g-CN synthesised via a modified high-temperature short-time (HTST) polycondensation process. The crystal structure, surface functional groups, and morphology of the g-CN nanosheets were characterised using a battery of instruments. The results confirmed that the as-synthesized product is composed of few-layered nanosheets. The adsorption efficiency of g-CN for binding Hg2+ (100 ng mL−1) in sea, river, rain, and Milli-Q quality water was 89%, 93%, 97%, and 100%, respectively, at natural pH. Interference studies found that the cations tested (Co2+, Ca2+, Zn2+, Fe2+, Mn2+, Ni2+, Bi3+, Na+, and K+) had no significant effect on the adsorption efficiency of Hg2+. Different parameters were optimised to improve the performance of g-CN such as pH, contact time, and amount of adsorbent. Optimum conditions were pH 7, 120 min incubation time and 10 mg of nanosheets. The yield of nanosheets was 72.5%, which is higher compared to other polycondensation processes using different monomers. The g-CN sheets could also be regenerated up to eight times with only a 20% loss in binding efficiency. Overall, nano-knitted g-CN is a promising low-cost green adsorbent for use in passive samplers or as a transducing material in sensor applications.
Congestion Control in CoAP Observe Group Communication
Authors: Suwannapong Chanwit , Khunboa Chatchai
Published: August 2019

The Constrained Application Protocol (CoAP) is a simple and lightweight machine-to-machine (M2M) protocol for constrained devices for use in lossy networks which offers a small memory capacity and limited processing. Designed and developed by the Internet Engineering Task Force (IETF), it functions as an application layer protocol and benefits from reliable delivery and simple congestion control. It is implemented for request/response message exchanges over the User Datagram Protocol (UDP) to support the Internet of Things (IoT). CoAP also provides a basic congestion control mechanism. In dealing with its own congestion, it relies on a fixed interval retransmission timeout (RTO) and binary exponential backoff (BEB). However, the default CoAP congestion control is considered to be unable to effectively perform group communication and observe resources, and it cannot handle rapid, frequent requests. This results in buffer overflow and packet loss. To overcome these problems, we proposed a new congestion control mechanism for CoAP Observe Group Communication, namely Congestion Control Random Early Detection (CoCo-RED), consisting of (1) determining and calculating an RTO timer, (2) a Revised Random Early Detection (RevRED) algorithm which has recently been developed and primarily based on the buffer management of TCP congestion control, and (3) a Fibonacci Pre-Increment Backoff (FPB) algorithm which waits for backoff time prior to retransmission. All the aforementioned algorithms were therefore implemented instead of the default CoAP mechanism. In this study, evaluations were carried out regarding the efficiency of the developed CoCo-RED using a Cooja simulator. The congestion control mechanism can quickly handle the changing behaviors of network communication, and thus it prevents the buffer overflow that leads to congestions. The results of our experiments indicate that CoCo-RED can control congestion more effectively than the default CoAP in every condition.
UHF Partial Discharge Location in Power Transformers via Solution of the Maxwell Equations in a Computational Environment
Authors: Nobrega Luiz , Costa Edson , Serres Alexandre , Xavier George , Aquino Marcus
Published: August 2019

This paper presents an algorithm for the localisation of partial discharge (PD) sources in power transformers based on the electromagnetic waves radiated by a PD pulse. The proposed algorithm is more accurate than existing methods, since it considers the effects of the reflection, refractions and diffractions undergone by the ultra-high frequency (UHF) signal within the equipment tank. The proposed method uses computational simulations of the electromagnetic waves generated by PD, and obtains the time delay of the signal between each point in the 3D space and the UHF sensors. The calculated signals can be compared with the signals measured in the field, so that the position of the PD source can be located based on the best correlation between the simulated propagation delay and the measured data. The equations used in the proposed method are defined as a 3D optimisation problem, so that the binary particle swarm optimisation algorithm can be used. To test and demonstrate the proposed algorithm, computational simulations were performed. The solutions were sufficient to identify not only the occurrence of defects, but also the winding and the region (top, centre or base) in which the defect occurred. In all cases, an accuracy of greater than 15 cm was obtained for the location, in a 180 MVA three-phase transformer.
Analysis of Byzantine Attacks for Target Tracking in Wireless Sensor Networks
Authors: Yang Yukun , Xiong Pengwen , Wang Qing , Zhang Qiang
Published: August 2019

Herein, the problem of target tracking in wireless sensor networks (WSNs) is investigated in the presence of Byzantine attacks. More specifically, we analyze the impact of Byzantine attacks on the performance of a tracking system. First, under the condition of jointly estimating the target state and the attack parameters, the posterior Cramer–Rao lower bound (PCRLB) is calculated. Then, from the perspective of attackers, we define the optimal Byzantine attack and theoretically find a way to achieve such an attack with minimal cost. When the attacked nodes are correctly identified by the fusion center (FC), we further define the suboptimal Byzantine attack and also find a way to realize such an attack. Finally, in order to alleviate the negative impact of attackers on the system performance, a modified sampling importance resampling (SIR) filter is proposed. Simulation results show that the tracking results of the modified SIR filter can be close to the true trajectory of the moving target. In addition, when the quantization level increases, both the security performance and the estimation performance of the tracking system are improved.
Monitoring of Chemical Risk Factors for Sudden Infant Death Syndrome (SIDS) by Hydroxyapatite-Graphene-MWCNT Composite-Based Sensors
Authors: Sudhan Narayanan , Lavanya Nehru , Leonardi Salvatore Gianluca , Neri Giovanni , Sekar Chinnathambi
Published: August 2019

Sensing properties of chemical sensors based on ternary hydroxyapatite-graphene-multiwalled carbon nanotube (HA-GN-MWCNT) nanocomposite in the detection of chemical substances representing risk factors for sudden infant death syndrome (SIDS), have been evaluated. Characterization data of the synthesized composite have shown that the graphene-MWCNT network serves as a matrix to uniformly disperse the hydroxyapatite nanoparticles and provide suitable electrical properties required for developing novel electrochemical and conductometric sensors. A HA-GN-MWCNT composite-modified glassy carbon electrode (HA-GN-MWCNT/GCE) has been fabricated and tested for the simultaneous monitoring of nicotine and caffeine by cyclic voltammetry (CV) and square wave voltammetry (SWV), whereas a HA-GN-MWCNT conductive gas sensor has been tested for the detection of CO2 in ambient air. Reported results suggest that the synergic combination of the chemical properties of HA and electrical/electrochemical characteristics of the mixed graphene-MWCNT network play a prominent role in enhancing the electrochemical and gas sensing behavior of the ternary HA-GN-MWCNT hybrid nanostructure. The high performances of the developed sensors make them suitable for monitoring unhealthy actions (e.g., smoking, drinking coffee) in breastfeeding women and environmental factors (bad air quality), which are associated with an enhanced risk for SIDS.
Navigating Transitions for Sustainable Infrastructures—The Case of a New High-Speed Railway Station in Jingmen, China
Authors: Liu Xinyu , Schraven Daan , de Bruijne Mark , de Jong Martin , Hertogh Marcel
Published: August 2019

Sustainable development and Circular Economy (CE) have drawn massive attention worldwide. Construction practices consume large amounts of materials, resources and energy. Sustainability and CE could play a big role in reduction efforts. However, the potential influence of both concepts on the planning and construction of large infrastructures remains unexplored. This paper investigates how professionals involved in a high-speed railway station project in the Chinese city of Jingmen envision the use of sustainability and CE for the planning and construction of the railway station and its surrounding areas. We reviewed policy documents and interviewed local professionals with the Triple Bottom Line (TBL) framework. The analysis reveals opportunities for improvement towards sustainability and the interdependence between the dimensions in the TBL framework for the railway station and its surroundings. The case shows that local professionals identify ample opportunities for improvement (presented as “sustainability ideas”), but none appear truly sustainable. These insights provide evidence that the hierarchical introduction of transition(s) creates a cognitive silo for local professionals when envisioning sustainability ideas. In the TBL framework, this study finds a useful and novel approach to break down the silos, because the TBL stresses the interdependence between the various sustainability dimensions.
The Use of Artificial Intelligence as a Tool Supporting Sustainable Development Local Policy
Authors: Mrówczyńska Maria , Sztubecka Małgorzata , Skiba Marta , Bazan-Krzywoszańska Anna , Bejga Przemysław
Published: August 2019

This paper addresses the problem of noise in spa protection areas. Its aim is to determine the delimitation of the areas that exceed a permissible noise level around the sanatorium on the example of a health resort in Inowrocław. The determination of the exceedance of permissible noise levels allows us to develop directly effective local policy tools to be included in planning documents. In order to reduce noise infiltration, it is important to define environmental priorities. Taking into account their impact on the health of users in the protection area, environmental priorities enable us to introduce additional elements to street architecture. In order to properly manage space, in accordance with the idea of sustainable development, zones of environmental sensitivity—and their socio-environmental vulnerability—have been designated for assessing damage (exceeding permissible noise in health facilities) and defining methods of building resilience (proper management). This has provided the basis for a natural balance optimized for the people living in these areas. To achieve the goal above, non-linear support vector machine (SVM) networks were used. This technique allows us to classify the linearly inseparable data and to determine the optimal separation margin. The boundaries of the areas which exceeded permissible noise levels (separation margin) were estimated on the basis of noise pollution maps, created by means of the SVM technique. Thus, the study results in establishing buffer zones where it is possible to use varied land utilization in terms of form and function, as described in the planning documents. Such an activity would limit the spread of noise.
Sustainable Water Management in Agriculture—The Impact of Drainage Water Management on Groundwater Table Dynamics and Subsurface Outflow
Authors: Sojka Mariusz , Kozłowski Michał , Stasik Rafał , Napierała Michał , Kęsicka Barbara , Wróżyński Rafał , Jaskuła Joanna , Liberacki Daniel , Bykowski Jerzy
Published: August 2019

The paper presents the results of the effects of control drainage (CD) on the groundwater table and subsurface outflow in Central Poland. The hydrologic model DRAINMOD was used to simulate soil water balance with drain spacing of 7 and 14 m, different initial groundwater Table 40, 60 and 80 cm b.s.l., and dates at the beginning of control drainage of 1 March, 15 March, 1 April, and 15 April. The CD restricts flow at the drain outlet to maintain a water table during the growing season. Simulations were made for the periods from March to September for the years 2014, 2017, and 2018, which were average, wet, and dry, respectively. The simulations showed a significant influence of the initial groundwater tables and date blocking the outflow from the drainage network on the obtained results. In the conditions of central Poland, the use of CD is rational only when it is started between 1 and 15 March. In this case, the groundwater table can be increased from 10 to 33 cm (7 m spacing) and from 10 to 41 cm (14 m spacing) in relation to the conventional system (free drainage—FD). In the case of blocking the outflow on 1 March, the reduction is about 80% on average in the period from March to September. With a delay in blocking the outflow, the impact of CDs decreases and ranges from 8% to 50%. Studies have shown that the proper use of the drainage network infrastructure complies with the idea of sustainable development, as it allows efficient water management, by reduction of the outflow and, thus, nitrates from agricultural areas. Furthermore, CD solutions can contribute to mitigating the effects of climate change on agriculture by reducing drought and flood risk.
Scientific Decision Framework for Evaluation of Renewable Energy Sources under Q-Rung Orthopair Fuzzy Set with Partially Known Weight Information
Authors: Krishankumar R. , Ravichandran K. S. , Kar Samarjit , Cavallaro Fausto , Zavadskas Edmundas Kazimieras , Mardani Abbas
Published: August 2019

As an attractive generalization of the intuitionistic fuzzy set (IFS), q-rung orthopair fuzzy set (q-ROFS) provides the decision makers (DMs) with a wide window for preference elicitation. Previous studies on q-ROFS indicate that there is an urge for a decision framework which can make use of the available information in a proper manner for making rational decisions. Motivated by the superiority of q-ROFS, in this paper, a new decision framework is proposed, which provides scientific methods for multi-attribute group decision-making (MAGDM). Initially, a programming model is developed for calculating weights of attributes with the help of partially known information. Later, another programming model is developed for determining the weights of DMs with the help of partially known information. Preferences from different DMs are aggregated rationally by using the weights of DMs and extending generalized Maclaurin symmetric mean (GMSM) operator to q-ROFS, which can properly capture the interrelationship among attributes. Further, complex proportional assessment (COPRAS) method is extended to q-ROFS for prioritization of objects by using attributes’ weight vector and aggregated preference matrix. The applicability of the proposed framework is demonstrated by using a renewable energy source prioritization problem from an Indian perspective. Finally, the superiorities and weaknesses of the framework are discussed in comparison with state-of-the-art methods.
The Evolution of Sustainable Business Model Innovation: Evidence from a Sharing Economy Platform in China
Authors: Hu Haibo , Huang Tao , Cheng Yang , Lu Haitao
Published: August 2019

In this paper, we explore the emergence of Sheke Network (SKN), an architecture design company whose network platform permits architecture design institutes to seek suitable architects for their architectural design projects. The concepts of sustainable business model (SBM) innovation are used to analyze the SKN’s novel business model, which is built around modern internet technologies, and SKN’s distinct appeal, which centers on cost savings, resource sharing, and the potential for access to more architects’ experiences. Although SBM innovation theory has had a significant impact on management practices and aroused plenty of rich debate within academia, it is still not clearly understood how a company might successfully build a sharing economy (SE) platform using SBM innovation. Thus, this article analyzes the process of SBM innovation in terms of its three essential factors—sustainable value proposition (SVP), sustainable value creation and delivery (SVC&D), and sustainable value capture (SVC)—aiming to disambiguate the black box by examining SKN as a case study. The findings highlight the dynamic evolution of SBM innovation in the development of a sharing economy platform.
Biowaste Management in Italy: Challenges and Perspectives
Authors: Demichelis Francesca , Piovano Francesco , Fiore Silvia
Published: August 2019

The aim of this work is the development of a methodology for the technical and environmental assessment of biowaste valorization in 2G biorefineries. Italy was chosen as case study, considering years 2016–2017. Approach: the Italian context was evaluated through the following key parameters: Gross domestic power, climate, demography, and population density distribution described the Italian framework. The four most abundant biowaste categories were defined through their amounts and geo-localization: wastewater and sewage sludge (WSS, 4.06 Mt/y), organic fraction of municipal solid waste (OFMSW, 1.7 Mt/y), agricultural livestock waste (ALW, 5.7 Mt/y), and waste deriving from the food industry (FIW, 2.6 Mt/y). The geo-localization and quantitative evaluations of the available biowaste amounts were aimed at defining the dimension and localization of the biorefinery plant and at optimizing supply and transport chains, while the qualitative characteristic were aimed to evaluate the most promising process among thermo-valorization (TH) and anaerobic digestion (AD). Results: All considered biowastes were appropriate for biorefinery processes, since carbon content exceeds 40% and the carbon–nitrogen ratio was between 10 and 30. All biowaste categories were evaluated as feedstocks for two biorefinery processes: anaerobic digestion (AD) and thermo-valorization (TH) with energy recovery. Compared to TH, AD achieved in all cases the best performances in terms of produced energy and avoided CO2 emissions. The primary energy production of AD and TH for WSS, OFMSW, ALW, and FIW were respectively: 7.89 vs. 2.4 kWh/kg; 8.7 vs. 2.6 kWh/kg; 10.85 vs. 5.5 kWh/kg; and 12.5 vs. 7.8 kWh/kg. The main findings of this work were: the adoption of AD was technically more suitable than TH; AD increased the avoided CO2 emissions of 10%–89.9% depending on biowaste category.
A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips
Authors: Gao Yong , Liu Jiajun , Xu Yan , Mu Lan , Liu Yu
Published: August 2019

Taxi services provide an urban transport option to citizens. Massive taxi trajectories contain rich information for understanding human travel activities, which are essential to sustainable urban mobility and transportation. The origin and destination (O-D) pairs of urban taxi trips can reveal the spatiotemporal patterns of human mobility and then offer fundamental information to interpret and reform formal, functional, and perceptual regions of cities. Matrices are one of the most effective models to represent taxi trajectories and O-D trips. Among matrix representations, non-negative matrix factorization (NMF) gives meaningful interpretations of complex latent relationships. However, the independence assumption for observations is violated by spatial and temporal autocorrelation in taxi flows, which is not compensated in classical NMF models. In order to discover human intra-urban mobility patterns, a novel spatiotemporal constraint NMF (STC-NMF) model that explicitly solves spatial and temporal dependencies is proposed in this paper. It factorizes taxi flow matrices in both spatial and temporal aspects, thus revealing inherent spatiotemporal patterns. With three-month taxi trajectories harvested in Beijing, China, the STC-NMF model is employed to investigate taxi travel patterns and their spatial interaction modes. As the results, four departure patterns, three arrival patterns, and eight spatial interaction patterns during weekdays and weekends are discovered. Moreover, it is found that intensive movements within certain time windows are significantly related to region functionalities and the spatial interaction flows exhibit an obvious distance decay tendency. The outcome of the proposed model is more consistent with the inherent spatiotemporal characteristics of human intra-urban movements. The knowledge gained in this research would be useful to taxi services and transportation management for promoting sustainable urban development.