bluuebunny/crossref_metadata_2025 huggingface dataset:
Ant-Mutated Immune Particle Filter Design for Terrain Referenced Navigation with Interferometric Radar Altimeter
Authors: Kang Suktae , Yu Myeong-Jong
Published: June 2021

This study aims to design a robust particle filter using artificial intelligence algorithms to enhance estimation performance using a low-grade interferometric radar altimeter (IRA). Based on the synthetic aperture radar (SAR) interferometry technology, the IRA can extract three-dimensional ground coordinates with at least two antennas. However, some IRA uncertainties caused by geometric factors and IRA-inherent measurement errors have proven to be difficult to eliminate by signal processing. These uncertainties contaminate IRA outputs, crucially impacting the navigation performance of low-grade IRA sensors in particular. To deal with such uncertainties, an ant-mutated immune particle filter (AMIPF) is proposed. The proposed filter combines the ant colony optimization (ACO) algorithm with the immune auxiliary particle filter (IAPF) to bring individual mutation intensity. The immune system indicates the stochastic parameters of the ACO, which conducts the mutation process in one step for the purpose of computational efficiency. The ant mutation then moves particles into the most desirable position using parameters from the immune system to obtain optimal particle diversity. To verify the performance of the proposed filter, a terrain referenced navigation (TRN) simulation was conducted on an unmanned aerial vehicle (UAV). The Monte Carlo simulation results show that the proposed filter is not only more computationally efficient than the IAPF but also outperforms both the IAPF and the auxiliary particle filter (APF) in navigation performance and robustness.
Health Assessment of Eucalyptus Trees Using Siamese Network from Google Street and Ground Truth Images
Authors: Khan Asim , Asim Warda , Ulhaq Anwaar , Ghazi Bilal , Robinson Randall W.
Published: June 2021

Urban greenery is an essential characteristic of the urban ecosystem, which offers various advantages, such as improved air quality, human health facilities, storm-water run-off control, carbon reduction, and an increase in property values. Therefore, identification and continuous monitoring of the vegetation (trees) is of vital importance for our urban lifestyle. This paper proposes a deep learning-based network, Siamese convolutional neural network (SCNN), combined with a modified brute-force-based line-of-bearing (LOB) algorithm that evaluates the health of Eucalyptus trees as healthy or unhealthy and identifies their geolocation in real time from Google Street View (GSV) and ground truth images. Our dataset represents Eucalyptus trees’ various details from multiple viewpoints, scales and different shapes to texture. The experiments were carried out in the Wyndham city council area in the state of Victoria, Australia. Our approach obtained an average accuracy of 93.2% in identifying healthy and unhealthy trees after training on around 4500 images and testing on 500 images. This study helps in identifying the Eucalyptus tree with health issues or dead trees in an automated way that can facilitate urban green management and assist the local council to make decisions about plantation and improvements in looking after trees. Overall, this study shows that even in a complex background, most healthy and unhealthy Eucalyptus trees can be detected by our deep learning algorithm in real time.
Inter-Annual Variability in the Antarctic Ice Sheets Using Geodetic Observations and a Climate Model
Authors: Kaitheri Athul , Mémin Anthony , Rémy Frédérique
Published: June 2021

Quantifying the mass balance of the Antarctic Ice Sheet (AIS), and the resulting sea level rise, requires an understanding of inter-annual variability and associated causal mechanisms. Very few studies have been exploring the influence of climate anomalies on the AIS and only a vague estimate of its impact is available. Changes to the ice sheet are quantified using observations from space-borne altimetry and gravimetry missions. We use data from Envisat (2002 to 2010) and Gravity Recovery And Climate Experiment (GRACE) (2002 to 2016) missions to estimate monthly elevation changes and mass changes, respectively. Similar estimates of the changes are made using weather variables (surface mass balance (SMB) and temperature) from a regional climate model (RACMO2.3p2) as inputs to a firn compaction (FC) model. Elevation changes estimated from different techniques are in good agreement with each other across the AIS especially in West Antarctica, Antarctic Peninsula, and along the coasts of East Antarctica. Inter-annual height change patterns are then extracted using for the first time an empirical mode decomposition followed by a principal component analysis to investigate for influences of climate anomalies on the AIS. Investigating the inter-annual signals in these regions revealed a sub-4-year periodic signal in the height change patterns. El Niño Southern Oscillation (ENSO) is a climate anomaly that alters, among other parameters, moisture transport, sea surface temperature, precipitation, in and around the AIS at similar frequency by alternating between warm and cold conditions. This periodic behavior in the height change patterns is altered in the Antarctic Pacific (AP) sector, possibly by the influence of multiple climate drivers, like the Amundsen Sea Low (ASL) and the Southern Annular Mode (SAM). Height change anomaly also appears to traverse eastwards from Coats Land to Pine Island Glacier (PIG) regions passing through Dronning Maud Land (DML) and Wilkes Land (WL) in 6 to 8 years. This is indicative of climate anomaly traversal due to the Antarctic Circumpolar Wave (ACW). Altogether, inter-annual variability in the SMB of the AIS is found to be modulated by multiple competing climate anomalies.
Estimating the Earth’s Outgoing Longwave Radiation Measured from a Moon-Based Platform
Authors: Ye Hanlin , Guo Huadong , Liu Guang , Ping Jinsong , Zhang Lu , Zhang Yiwen
Published: June 2021

Moon-based Earth observations have attracted significant attention across many large-scale phenomena. As the only natural satellite of the Earth, and having a stable lunar surface as well as a particular orbit, Moon-based Earth observations allow the Earth to be viewed as a single point. Furthermore, in contrast with artificial satellites, the varied inclination of Moon-based observations can improve angular samplings of specific locations on Earth. However, the potential for estimating the global outgoing longwave radiation (OLR) from the Earth with such a platform has not yet been fully explored. To evaluate the possibility of calculating OLR using specific Earth observation geometry, we constructed a model to estimate Moon-based OLR measurements and investigated the potential of a Moon-based platform to acquire the necessary data to estimate global mean OLR. The primary method of our study is the discretization of the observational scope into various elements and the consequent integration of the OLR of all elements. Our results indicate that a Moon-based platform is suitable for global sampling related to the calculation of global mean OLR. By separating the geometric and anisotropic factors from the measurement calculations, we ensured that measured values include the effects of the Moon-based Earth observation geometry and the anisotropy of the scenes in the observational scope. Although our results indicate that higher measured values can be achieved if the platform is located near the center of the lunar disk, a maximum difference between locations of approximately 9 × 10−4 W m−2 indicates that the effect of location is too small to remarkably improve observation performance of the platform. In conclusion, our analysis demonstrates that a Moon-based platform has the potential to provide continuous, adequate, and long-term data for estimating global mean OLR.
Mapping Outburst Floods Using a Collaborative Learning Method Based on Temporally Dense Optical and SAR Data: A Case Study with the Baige Landslide Dam on the Jinsha River, Tibet
Authors: Yang Zhongkang , Wei Jinbing , Deng Jianhui , Gao Yunjian , Zhao Siyuan , He Zhiliang
Published: June 2021

Outburst floods resulting from giant landslide dams can cause devastating damage to hundreds or thousands of kilometres of a river. Accurate and timely delineation of flood inundated areas is essential for disaster assessment and mitigation. There have been significant advances in flood mapping using remote sensing images in recent years, but little attention has been devoted to outburst flood mapping. The short-duration nature of these events and observation constraints from cloud cover have significantly challenged outburst flood mapping. This study used the outburst flood of the Baige landslide dam on the Jinsha River on 3 November 2018 as an example to propose a new flood mapping method that combines optical images from Sentinel-2, synthetic aperture radar (SAR) images from Sentinel-1 and a Digital Elevation Model (DEM). First, in the cloud-free region, a comparison of four spectral indexes calculated from time series of Sentinel-2 images indicated that the normalized difference vegetation index (NDVI) with the threshold of 0.15 provided the best separation flooded area. Subsequently, in the cloud-covered region, an analysis of dual-polarization RGB false color composites images and backscattering coefficient differences of Sentinel-1 SAR data were found an apparent response to ground roughness’s changes caused by the flood. We carried out the flood range prediction model based on the random forest algorithm. Training samples consisted of 13 feature vectors obtained from the Hue-Saturation-Value color space, backscattering coefficient differences/ratio, DEM data, and a label set from the flood range prepared from Sentinel-2 images. Finally, a field investigation and confusion matrix tested the prediction accuracy of the end-of-flood map. The overall accuracy and Kappa coefficient were 92.3%, 0.89 respectively. The full extent of the outburst floods was successfully obtained within five days of its occurrence. The multi-source data merging framework and the massive sample preparation method with SAR images proposed in this paper, provide a practical demonstration for similar machine learning applications using remote sensing.
Spatial Autocorrelation of Martian Surface Temperature and Its Spatio-Temporal Relationships with Near-Surface Environmental Factors across China’s Tianwen-1 Landing Zone
Authors: Luo Yaowen , Yan Jianguo , Li Fei , Li Bo
Published: June 2021

Variations in the Martian surface temperature indicate patterns of surface energy exchange. The Martian surface temperature at a location is similar to those in adjacent locations; but, an understanding of temperature clusters in multiple locations will deepen our knowledge of planetary surface processes overall. The spatial coherence of the Martian surface temperature (ST) at different locations, the spatio-temporal variations in temperature clusters, and the relationships between ST and near-surface environmental factors, however, are not well understood. To fill this gap, we studied an area to the south of Utopia Planitia, the landing zone for the Tianwen-1 Mars Exploration mission. The spatial aggregation of three Martian ST indicators (STIs), including sol average temperature (SAT), sol temperature range (STR), and sol-to-sol temperature change (STC), were quantitatively evaluated using clustering analysis at the global and local scale. In addition, we also detected the spatio-temporal variations in relations between the STIs and seven potential driving factors, including thermal inertia, albedo, dust, elevation, slope, and zonal and meridional winds, across the study area during 81 to 111 sols in Martian years 29–32, based on a geographically and temporally weighted regression model (GTWR). We found that the SAT, STR, and STC were not randomly distributed over space but exhibited signs of significant spatial aggregation. Thermal inertia and dust made the greatest contribution to the fluctuation in STIs over time. The local surface temperature was likely affected by the slope, wind, and local circulation, especially in the area with a large slope and low thermal inertia. In addition, the sheltering effects of the mountains at the edge of the basin likely contributed to the spatial difference in SAT and STR. These results are a reminder that the spatio-temporal variation in the local driving factors associated with Martian surface temperature cannot be neglected. Our research contributes to the understanding of the surface environment that might compromise the survival and operations of the Tianwen-1 lander on the Martian surface.
Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN
Authors: Ji Fengcheng , Ming Dongping , Zeng Beichen , Yu Jiawei , Qing Yuanzhao , Du Tongyao , Zhang Xinyi
Published: June 2021

Aircraft is a means of transportation and weaponry, which is crucial for civil and military fields to detect from remote sensing images. However, detecting aircraft effectively is still a problem due to the diversity of the pose, size, and position of the aircraft and the variety of objects in the image. At present, the target detection methods based on convolutional neural networks (CNNs) lack the sufficient extraction of remote sensing image information and the post-processing of detection results, which results in a high missed detection rate and false alarm rate when facing complex and dense targets. Aiming at the above questions, we proposed a target detection model based on Faster R-CNN, which combines multi-angle features driven and majority voting strategy. Specifically, we designed a multi-angle transformation module to transform the input image to realize the multi-angle feature extraction of the targets in the image. In addition, we added a majority voting mechanism at the end of the model to deal with the results of the multi-angle feature extraction. The average precision (AP) of this method reaches 94.82% and 95.25% on the public and private datasets, respectively, which are 6.81% and 8.98% higher than that of the Faster R-CNN. The experimental results show that the method can detect aircraft effectively, obtaining better performance than mature target detection networks.
Modify the Accuracy of MODIS PWV in China: A Performance Comparison Using Random Forest, Generalized Regression Neural Network and Back-Propagation Neural Network
Authors: Xiong Zhaohui , Sun Xiaogong , Sang Jizhang , Wei Xiaomin
Published: June 2021

Water vapor plays an important role in climate change and water cycling, but there are few water vapor products with both high spatial resolution and high accuracy that effectively monitor the change of water vapor. The high precision Global Navigation Satellite System (GNSS) Precipitable Water Vapor (PWV) is often used to calibrate the high spatial resolution Moderate-resolution Imaging Spectroradiometer (MODIS) PWV to produce new PWV product with high accuracy and high spatial resolution. In addition, the machine learning method has a good performance in modifying the accuracy of MODIS PWV. However, the accuracy improvement of different machine learning methods and different modeling timescale is different. In this article, we use three machine learning methods, namely, the Random Forest (RF), Generalized Regression Neural Network (GRNN), and Back-propagation Neural Network (BPNN) to calibrate MODIS PWV in 2019, at annual and monthly timescales. We also use the Multiple Linear Regression (MLR) method for comparison. The root mean squares (RMSs) at the annual timescale with the three machine learning methods are 4.1 mm (BPNN), 3.3 mm (RF), and 3.9 mm (GRNN), and the average RMSs become 2.9 mm (BPNN), 2.8 mm (RF), and 2.5 mm (GRNN) at the monthly timescale. Those results are all better than the MLR method (5.0 mm at the annual timescale and 4.6 mm at the monthly timescale). When there is an obvious variation pattern in the training sample, the RF method can capture the pattern to achieve the best results since the RF achieves the best performance at the annual timescale. Dividing such samples into several sub-samples each having higher internal consistency could further improve the performance of machine learning methods, especially for the GRNN, since GRNN achieves the best performance at the monthly timescale, and the performance of those three machine learning methods at the monthly timescale is better than that of annual timescale. The spatial and temporal variation patterns of the RMS values are significantly weakened after the modeling by machine learning methods for both three methods.
SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality
Authors: Stebel Kerstin , Stachlewska Iwona S. , Nemuc Anca , Horálek Jan , Schneider Philipp , Ajtai Nicolae , Diamandi Andrei , Benešová Nina , Boldeanu Mihai , Botezan Camelia , Marková Jana , Dumitrache Rodica , Iriza-Burcă Amalia , Juras Roman , Nicolae Doina , Nicolae Victor , Novotný Petr , Ștefănie Horațiu , Vaněk Lumír , Vlček Ondrej , Zawadzka-Manko Olga , Zehner Claus
Published: June 2021

The satellite based monitoring initiative for regional air quality (SAMIRA) initiative was set up to demonstrate the exploitation of existing satellite data for monitoring regional and urban scale air quality. The project was carried out between May 2016 and December 2019 and focused on aerosol optical depth (AOD), particulate matter (PM), nitrogen dioxide (NO2), and sulfur dioxide (SO2). SAMIRA was built around several research tasks: 1. The spinning enhanced visible and infrared imager (SEVIRI) AOD optimal estimation algorithm was improved and geographically extended from Poland to Romania, the Czech Republic and Southern Norway. A near real-time retrieval was implemented and is currently operational. Correlation coefficients of 0.61 and 0.62 were found between SEVIRI AOD and ground-based sun-photometer for Romania and Poland, respectively. 2. A retrieval for ground-level concentrations of PM2.5 was implemented using the SEVIRI AOD in combination with WRF-Chem output. For representative sites a correlation of 0.56 and 0.49 between satellite-based PM2.5 and in situ PM2.5 was found for Poland and the Czech Republic, respectively. 3. An operational algorithm for data fusion was extended to make use of various satellite-based air quality products (NO2, SO2, AOD, PM2.5 and PM10). For the Czech Republic inclusion of satellite data improved mapping of NO2 in rural areas and on an annual basis in urban background areas. It slightly improved mapping of rural and urban background SO2. The use of satellites based AOD or PM2.5 improved mapping results for PM2.5 and PM10. 4. A geostatistical downscaling algorithm for satellite-based air quality products was developed to bridge the gap towards urban-scale applications. Initial testing using synthetic data was followed by applying the algorithm to OMI NO2 data with a direct comparison against high-resolution TROPOMI NO2 as a reference, thus allowing for a quantitative assessment of the algorithm performance and demonstrating significant accuracy improvements after downscaling. We can conclude that SAMIRA demonstrated the added value of using satellite data for regional- and urban-scale air quality monitoring.
Drivers of Organic Carbon Stocks in Different LULC History and along Soil Depth for a 30 Years Image Time Series
Authors: Tayebi Mahboobeh , Fim Rosas Jorge Tadeu , Mendes Wanderson de Sousa , Poppiel Raul Roberto , Ostovari Yaser , Ruiz Luis Fernando Chimelo , dos Santos Natasha Valadares , Cerri Carlos Eduardo Pellegrino , Silva Sérgio Henrique Godinho , Curi Nilton , Silvero Nélida Elizabet Quiñonez , Demattê José A. M.
Published: June 2021

Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km2. We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data.
Delay Analysis in IoT Sensor Networks
Authors: Althoubi Asaad , Alshahrani Reem , Peyravi Hassan
Published: June 2021

Internet of Things (IoT) devices, particularly those used for sensor networks, are often latency-sensitive devices. The topology of the sensor network largely depends on the overall system application. Various configurations include linear, star, hierarchical and mesh in 2D or 3D deployments. Other applications include underwater communication with high attenuation of radio waves, disaster relief networks, rural networking, environmental monitoring networks, and vehicular networks. These networks all share the same characteristics, including link latency, latency variation (jitter), and tail latency. Achieving a predictable performance is critical for many interactive and latency-sensitive applications. In this paper, a two-stage tandem queuing model is developed to estimate the average end-to-end latency and predict the latency variation in closed forms. This model also provides a feedback mechanism to investigate other major performance metrics, such as utilization, and the optimal number of computing units needed in a single cluster. The model is applied for two classes of networks, namely, Edge Sensor Networks (ESNs) and Data Center Networks (DCNs). While the proposed model is theoretically derived from a queuing-based model, the simulation results of various network topologies and under different traffic conditions prove the accuracy of our model.
A Test on the Potential of a Low Cost Unmanned Aerial Vehicle RTK/PPK Solution for Precision Positioning
Authors: Famiglietti Nicola Angelo , Cecere Gianpaolo , Grasso Carmine , Memmolo Antonino , Vicari Annamaria
Published: June 2021

This paper investigated the achievable accuracy from a low-cost RTK (Real Time Kinematic)/PPK (Post Processing Kinematic) GNSS (Global Navigation Satellite Systems) system installed on board a UAV (Unmanned Aerial Vehicle), employing three different types of GNSS Bases (Alloy, RS2 and RING) working in PPK mode. To evaluate the quality of the results, a set of seven GCPs (Ground Control Points) measured by means of the NRTK (Network Real Time Kinematic) technique was used. The outcomes show a RMSE (Root Mean Square Error) of 0.0189 m for an ALLOY Base, 0.0194 m for an RS2 Base and 0.0511 m for RING Base, respectively, on the vertical value of DEMs (Digital Elevation Models) obtained by a photogrammetric process. This indicates that, when changing the Base for the PPK, the solutions are different, but they can still be considered adequate for precision positioning with UAVs, especially when GCPs could be used with some difficulty. Therefore, the integration of a RTK/PPK GNSS module on a UAV allows the reconstruction of a highly detailed and precise DEM without using GCPs and provides the possibility to carry out surveys in inaccessible areas.
Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network
Authors: Mei Fan , Chen Hong , Lei Yingke
Published: June 2021

Forward error correction coding is the most common way of channel coding and the key point of error correction coding. Therefore, the recognition of which coding type is an important issue in non-cooperative communication. At present, the recognition of FEC codes is mainly concentrated in the field of semi-blind identification with known types of codes. However, the receiver cannot know the types of channel coding previously in non-cooperative systems such as cognitive radio and remote sensing of communication. Therefore, it is important to recognize the error-correcting encoding type with no prior information. In the paper, we come up with a neoteric method to identify the types of FEC codes based on Recurrent Neural Network (RNN) under the condition of non-cooperative communication. The algorithm classifies the input data into Bose-Chaudhuri-Hocquenghem (BCH) codes, Low-density Parity-check (LDPC) codes, Turbo codes and convolutional codes. So as to train the RNN model with better performance, the weight initialization method is optimized and the network performance is improved. The experimental result indicates that the average recognition rate of this model is 99% when the signal-to-noise ratio (SNR) ranges from 0 dB to 10 dB, which is in line with the requirements of engineering practice under the condition of non-cooperative communication. Moreover, the comparison of different parameters and models show the effectiveness and practicability of the algorithm proposed.
Measuring User Experience, Usability and Interactivity of a Personalized Mobile Augmented Reality Training System
Authors: Papakostas Christos , Troussas Christos , Krouska Akrivi , Sgouropoulou Cleo
Published: June 2021

Innovative technology has been an important part of firefighting, as it advances firefighters’ safety and effectiveness. Prior research has examined the implementation of training systems using augmented reality (AR) in other domains, such as welding, aviation, army, and mathematics, offering significant pedagogical affordances. Nevertheless, firefighting training systems using AR are still an under-researched area. The increasing penetration of AR for training is the driving force behind this study, and the scope is to analyze the main aspects affecting the acceptance of AR by firefighters. The current research uses a technology acceptance model, extended by the external constructs of perceived interactivity and personalization, to consider both the system and individual level. The proposed model was evaluated by a sample of 200 users, and the results show that both the external variables of perceived interactivity and perceived personalization are prerequisite factors in extending the TAM model. The findings reveal that the usability is the strongest predictor of firefighters’ behavioral intentions to use the AR system, followed by the ease of use with smaller, yet meaningful, direct and indirect effects on firefighters’ intentions. The identified acceptance factors help AR developers enhance the firefighters’ experience in training operations.
Voice Multilateration System
Authors: Burczyk Robert , Cwalina Krzysztof , Gajewska Malgorzata , Magiera Jaroslaw , Rajchowski Piotr , Sadowski Jaroslaw , Stefanski Jacek
Published: June 2021

This paper presents an innovative method of locating airplanes, which uses only voice communication between an air traffic controller and the pilot of an aircraft. The proposed method is described in detail along with its practical implementation in the form of a technology demonstrator (proof of concept), included in the voice communication system (VCS). A complete analysis of the performance of the developed method is presented, including the results of simulation and measurement tests in real conditions. The obtained results are very optimistic and indicate that the proposed solution may constitute an alternative method of locating aircraft in emergency conditions, i.e., a backup solution in the case of failure of other positioning systems.
Determination of Nutrients in Liquid Manures and Biogas Digestates by Portable Energy-Dispersive X-ray Fluorescence Spectrometry
Authors: Horf Michael , Gebbers Robin , Vogel Sebastian , Ostermann Markus , Piepel Max-Frederik , Olfs Hans-Werner
Published: June 2021

Knowing the exact nutrient composition of organic fertilizers is a prerequisite for their appropriate application to improve yield and to avoid environmental pollution by over-fertilization. Traditional standard chemical analysis is cost and time-consuming and thus it is unsuitable for a rapid analysis before manure application. As a possible alternative, a handheld X-ray fluorescence (XRF) spectrometer was tested to enable a fast, simultaneous, and on-site analysis of several elements. A set of 62 liquid pig and cattle manures as well as biogas digestates were collected, intensively homogenized and analysed for the macro plant nutrients phosphorus, potassium, magnesium, calcium, and sulphur as well as the micro nutrients manganese, iron, copper, and zinc using the standard lab procedure. The effect of four different sample preparation steps (original, dried, filtered, and dried filter residues) on XRF measurement accuracy was examined. Therefore, XRF results were correlated with values of the reference analysis. The best R2s for each element ranged from 0.64 to 0.92. Comparing the four preparation steps, XRF results for dried samples showed good correlations (0.64 and 0.86) for all elements. XRF measurements using dried filter residues showed also good correlations with R2s between 0.65 and 0.91 except for P, Mg, and Ca. In contrast, correlation analysis for liquid samples (original and filtered) resulted in lower R2s from 0.02 to 0.68, except for K (0.83 and 0.87, respectively). Based on these results, it can be concluded that handheld XRF is a promising measuring system for element analysis in manures and digestates.
Command Recognition Using Binarized Convolutional Neural Network with Voice and Radar Sensors for Human-Vehicle Interaction
Authors: Oh Seunghyun , Bae Chanhee , Cho Jaechan , Lee Seongjoo , Jung Yunho
Published: June 2021

Recently, as technology has advanced, the use of in-vehicle infotainment systems has increased, providing many functions. However, if the driver’s attention is diverted to control these systems, it can cause a fatal accident, and thus human–vehicle interaction is becoming more important. Therefore, in this paper, we propose a human–vehicle interaction system to reduce driver distraction during driving. We used voice and continuous-wave radar sensors that require low complexity for application to vehicle environments as resource-constrained platforms. The proposed system applies sensor fusion techniques to improve the limit of single-sensor monitoring. In addition, we used a binarized convolutional neural network algorithm, which significantly reduces the computational workload of the convolutional neural network in command classification. As a result of performance evaluation in noisy and cluttered environments, the proposed system showed a recognition accuracy of 96.4%, an improvement of 7.6% compared to a single voice sensor-based system, and 9.0% compared to a single radar sensor-based system.
Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization
Authors: Pardo-Zamora Oscar N. , Romero-Troncoso Rene de J. , Millan-Almaraz Jesus R. , Morinigo-Sotelo Daniel , Osornio-Rios Roque A. , Antonino-Daviu Jose A.
Published: June 2021

The study of power quality (PQ) has gained relevance over the years due to the increase in non-linear loads connected to the grid. Therefore, it is important to study the propagation of power quality disturbances (PQDs) to determine the propagation points in the grid, and their source of generation. Some papers in the state of the art perform the analysis of punctual measurements of a limited number of PQDs, some of them using high-cost commercial equipment. The proposed method is based upon a developed proprietary system, composed of a data logger FPGA with GPS, that allows the performance of synchronized measurements merged with the full parameterized PQD model, allowing the detection and tracking of disturbances propagating through the grid using wavelet transform (WT), fast Fourier transform (FFT), Hilbert–Huang transform (HHT), genetic algorithms (GAs), and particle swarm optimization (PSO). Measurements have been performed in an industrial installation, detecting the propagation of three PQDs: impulsive transients propagated at two locations in the grid, voltage fluctuation, and harmonic content propagated to all the locations. The results obtained show that the low-cost system and the developed methodology allow the detection of several PQDs, and track their propagation within a grid with 100% accuracy.
Using Smart Virtual-Sensor Nodes to Improve the Robustness of Indoor Localization Systems
Authors: Pedrollo Guilherme , Konzen Andréa Aparecida , de Morais Wagner Ourique , Pignaton de Freitas Edison
Published: June 2021

Young, older, frail, and disabled individuals can require some form of monitoring or assistance, mainly when critical situations occur, such as falling and wandering. Healthcare facilities are increasingly interested in e-health systems that can detect and respond to emergencies on time. Indoor localization is an essential function in such e-health systems, and it typically relies on wireless sensor networks (WSN) composed of fixed and mobile nodes. Nodes in the network can become permanently or momentarily unavailable due to, for example, power failures, being out of range, and wrong placement. Consequently, unavailable sensors not providing data can compromise the system’s overall function. One approach to overcome the problem is to employ virtual sensors as replacements for unavailable sensors and generate synthetic but still realistic data. This paper investigated the viability of modelling and artificially reproducing the path of a monitored target tracked by a WSN with unavailable sensors. Particularly, the case with just a single sensor was explored. Based on the coordinates of the last measured positions by the unavailable node, a neural network was trained with 4 min of not very linear data to reproduce the behavior of a sensor that become unavailable for about 2 min. Such an approach provided reasonably successful results, especially for areas close to the room’s entrances and exits, which are critical for the security monitoring of patients in healthcare facilities.
High-Gain Millimeter-Wave Patch Array Antenna for Unmanned Aerial Vehicle Application
Authors: Anim Kyei , Lee Jung-Nam , Jung Young-Bae
Published: June 2021

A high-gain millimeter-wave patch array antenna is presented for unmanned aerial vehicles (UAVs). For the large-scale patch array antenna, microstrip lines and higher-mode surface wave radiations contribute enormously to the antenna loss, especially at the millimeter-wave band. Here, the element of a large patch array antenna is implemented with a substrate integrated waveguide (SIW) cavity-backed patch fed by the aperture-coupled feeding (ACF) structure. However, in this case, a large coupling aperture is used to create strongly bound waves, which maximizes the coupling level between the patch and the feedline. This approach helps to improve antenna gain, but at the same time leads to a significant level of back radiation due to the microstrip feedline and unwanted surface-wave radiation, especially for the large patch arrays. Using the SIW cavity-backed patch and stripline feedline of the ACF in the element design, therefore, provides a solution to this problem. Thus, a full-corporate feed 32 × 32 array antenna achieves realized gain of 30.71–32.8 dBi with radiation efficiency above 52% within the operational band of 25.43–26.91 GHz. The fabricated antenna also retains being lightweight, which is desirable for UAVs, because it has no metal plate at the backside to support the antenna.
Quantitative Analysis of Fluorescence Detection Using a Smartphone Camera for a PCR Chip
Authors: Kim Jong-Dae , Park Chan-Young , Kim Yu-Seop , Hwang Ji-Soo
Published: June 2021

Most existing commercial real-time polymerase chain reaction (RT-PCR) instruments are bulky because they contain expensive fluorescent detection sensors or complex optical structures. In this paper, we propose an RT-PCR system using a camera module for smartphones that is an ultra small, high-performance and low-cost sensor for fluorescence detection. The proposed system provides stable DNA amplification. A quantitative analysis of fluorescence intensity changes shows the camera’s performance compared with that of commercial instruments. Changes in the performance between the experiments and the sets were also observed based on the threshold cycle values in a commercial RT-PCR system. The overall difference in the measured threshold cycles between the commercial system and the proposed camera was only 0.76 cycles, verifying the performance of the proposed system. The set calibration even reduced the difference to 0.41 cycles, which was less than the experimental variation in the commercial system, and there was no difference in performance.
Worldwide Research Analysis on Natural Zeolites as Environmental Remediation Materials
Authors: Morante-Carballo Fernando , Montalván-Burbano Néstor , Carrión-Mero Paúl , Jácome-Francis Kelly
Published: June 2021

Society faces a significant problem in regards to the spread of harmful products in the environment, primarily caused by accelerated growth and resource consumption. Consequently, there is a need for materials to be processed in less harmful ways and to remedy the contaminated sources they generate. Microporous materials have been studied for a long time and are used in waste treatment alternatives. Natural zeolites, on which this study is based, are attractive to the scientific and technological communities, due to their numerous applications as decontaminants and adsorption properties. This study analyzes the intellectual structures of publications related to natural zeolites in environmental remediation, using bibliometric methods to determine their volumes and trends. The methodology comprises of an analysis based on 1582 articles, using VOSviewer software, with data from 1974 to 2020, via the Scopus database. Results reflect a notable increase in publications from the end of the 1990s; the greatest contribution in the area comes from Eurasian countries. The study considers that development in this line of research will continue to increase and serve as a great contribution to preserve the environment in coming years, with themes that focus on water treatment (e.g., drinking water, wastewater, greywater), removal of heavy metals, ammonium, ammonia, and construction.
Sustainable Public Administration
Authors: Heinrichs Harald , Laws Norman
Published: June 2021

The 2030 Agenda for Sustainable Development, with its 17 Sustainable Development Goals (SDGs), was agreed upon by 193 member states of the United Nations in September 2015 [...]
Performance of Conventional and Innovative Single U-Tube Pipe Configuration in Vertical Ground Heat Exchanger (VGHE)
Authors: Eswiasi Adel , Mukhopadhyaya Phalguni
Published: June 2021

A ground source heat pump system (GSHP) with a ground heat exchanger (GHE) is a renewable and green technology used for heating and cooling residential and commercial buildings. An innovative U-Tube pipe configuration is suggested to enhance the heat transfer rate in the vertical ground heat exchanger (VGHE). Laboratory experiments are conducted to compare the thermal efficiency of VGHEs with two different pipe configurations: (1) an innovative U-Tube pipe configuration (single U-Tube with two outer fins) and (2) a single U-Tube. The results show that the difference between the inlet and outlet temperatures for the innovative U-Tube pipe configuration was 0.7 °C after 60 h, while it was 0.4 °C for the single U-Tube after the same amount of time. The borehole thermal resistance for the innovative U-Tube pipe configuration was 0.680 m·K/W, which is 29.22% lower than that of the single U-Tube. The heat exchange rate in the innovative U-Tube pipe configuration is increased by 57.95% compared to the conventional single U-Tube. Measured ground temperatures indicate that compared to single U-Tube pipe configuration, the innovative U-Tube pipe configuration has superior heat transfer performance. Based on the experimental results presented in this paper, it was concluded that increasing the surface area significantly by introducing external fins to the U-Tube enhances the heat transfer rate, resulting in increased thermal efficiency of the VGHE.
Graduate Students’ Behavioral Intention towards Social Entrepreneurship: Role of Social Vision, Innovativeness, Social Proactiveness, and Risk Taking
Authors: Tu Bingyan , Bhowmik Roni , Hasan Md. Kamrul , Asheq Ahmed Al , Rahaman Md. Atikur , Chen Xia
Published: June 2021

In prior studies, several researchers have adopted entrepreneurial orientation (EO) in determining students’ intention toward entrepreneurship, although the application of EO is scant in determining intention toward social entrepreneurship in existing literature. Hence, in consideration of this research gap, the current study empirically examines the influence of the dimensions of social entrepreneurial orientation (SEO): social vision, social proactiveness, innovativeness, and risk-taking motive on graduate students’ entrepreneurial intention toward social entrepreneurship-based business start-up. An online-based survey method was used to collect data from a sample of 465 students purposively who were studying at different universities in Bangladesh. A PLS-based SEM was applied to analyze the data and examined the proposed relationships in the conceptual model. The findings reveal that Graduate students’ social proactiveness, innovativeness, and risk-taking motive significantly affect their social entrepreneurial intention. However, students’ social vision does not have direct influence but has indirect influence on social entrepreneurial intention through their social entrepreneurial attitudes. The research contributes to the body of knowledge in the existing social entrepreneurship literature as well as provides practical implications for the policymakers, practitioners, and stakeholders working toward flourishing of social-based entrepreneurship, venture, and start-up.
A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network
Authors: El-Sharawy Karim M. , Diab Hatem Y. , Abdelsalam Mahmoud O. , Marei Mostafa I.
Published: June 2021

This article presents a control strategy that enables both islanded and grid-tied operations of a three-phase inverter in distributed generation. This distributed generation (DG) is based on a dramatically evolved direct current (DC) source. A unified control strategy is introduced to operate the interface in either the isolated or grid-connected modes. The proposed control system is based on the instantaneous tracking of the active power flow in order to achieve current control in the grid-connected mode and retain the stability of the frequency using phase-locked loop (PLL) circuits at the point of common coupling (PCC), in addition to managing the reactive power supplied to the grid. On the other side, the proposed control system is also based on the instantaneous tracking of the voltage to achieve the voltage control in the standalone mode and retain the stability of the frequency by using another circuit including a special equation (wt = 2πft, f = 50 Hz). This utilization provides the ability to obtain voltage stability across the critical load. One benefit of the proposed control strategy is that the design of the controller remains unconverted for other operating conditions. The simulation results are added to evaluate the performance of the proposed control technology using a different method; the first method used basic proportional integration (PI) controllers, and the second method used adaptive proportional integration (PI) controllers, i.e., an Artificial Neural Network (ANN).
A COVID-19 Pandemic Sustainable Educational Innovation Management Proposal Framework
Authors: Scavarda Annibal , Dias Ana , Reis Augusto , Silveira Haydee , Santos Isabel
Published: June 2021

The COVID-19 pandemic has promoted a big change in the educational sector. Suddenly, teachers, professors, and students had to migrate from presential classes to the online system without prior notice or a training course. This paper aimed to verify how the need of a rapid change to the online system in response to the impossibility of keeping the presential system due to the mandatory social distancing imposed by the COVID-19 pandemic has affected relationships and performance of teachers, professors, and students, as well as review the technologies and procedures adopted by them to innovate and achieve sustainable education. To address the empirical side of this exploratory research, the authors of this paper sent an email questionnaire to kindergarten, elementary and high school teachers in the City of Rio de Janeiro (Brazil), as well as to professors of the top 197 Brazilian universities. To address theoretical side of this exploratory research, an investigation was carried out through scientific databases. The data were analyzed with SPSS (Statistical Package for the Social Sciences), version 22.0, and with Microsoft Excel 2007. As a result, this paper showed that social isolation and transition to the online system greatly affected the work conditions of teachers and professors, as well as the learning process of students. Anyway, sustainable actions were taken to overcome these challenges. Furthermore, this paper proposed a framework that might support the development of new studies, filling the literature gap on the subject.
Removal of Volatile Organic Compounds by Means of a Felt-Based Living Wall Using Different Plant Species
Authors: Suárez-Cáceres Gina Patricia , Pérez-Urrestarazu Luis
Published: June 2021

Poor indoor quality affects people’s health and well-being. Phytoremediation is one way in which this problem can be tackled, with living walls being a viable option for places with limited space. The aim of this study was to evaluate the efficiency of five plant species in a living wall to remove Volatile Organic Compounds (VOCs) and to identify whether the type of pollutant has any influence. An enclosed chamber was used to add the contaminants n-hexane and formaldehyde independently. Total VOCs were measured for three days in two scenarios: (1) empty chamber, and (2) chamber with living wall. Five living walls were prepared, each with three plants of the same species: Spathiphyllum wallisii, Philodendron hederaceum, Ficus pumila, Tradescantia pallida, and Chlorophytum comosum. There was no correlation between leaf area/fresh weight/dry weight and the contaminant reduction. In general, all five species were more efficient in reducing TVOCs when exposed to formaldehyde than to n-hexane. Chlorophytum comosum was the most efficient species in reducing the concentration of TVOCs for both contaminants, Spathiphyllum wallisii being the least efficient by far.