bluuebunny/crossref_metadata_2025 huggingface dataset:
When Film Mediatizes Taoism, What Does It Bring to Taoism?
Authors: Sun Zhentao
Published: March 2023

Although Taoism is no longer what it was in its prime, Taoist culture still has great spiritual value in the present day and is well worth promoting worldwide. Taoist cinema plays a powerful role in the mass communication of Taoism. Current academic research on Taoist cinema has focused on analyzing how it can make good use of Taoism but has neglected to explore what cinema brings to Taoism in the cinematization process. This paper focuses on this thesis by considering the themes of the importance of the cinematization of Taoism for Taoism, the cultural transformation in the cinematization of Taoism, and the appropriate way to include Taoism in Taoist films. This paper indicates that Taoist cinema has communicated Taoist ideology, religious rituals and aesthetic styles to a global audience through the audiovisual language, and it has built a bridge between Taoism and the general public. In the cinematization of Taoism, in order to reflect the communication environment, communication media and audience influence, Taoist films have highlighted visual spectacles, entertainment and popularization, thus making them better accepted by audiences and achieving the expected communication effect. This paper argues that Taoist films express the sacred and exert a religious influence on the viewer through content rather than transcendental style.
Analysis of Religiosity in Slovakia since 1989 and Paths of Its Development
Authors: Kondrla Peter , Leskova Andrea , Durkova Eva
Published: March 2023

The study presents the results of research into the current state of selected religiosity phenomena in the Slovak Republic and models possible pathways for the future development of the selected phenomena. The starting point is an analysis of three decades of development of religiosity in the region from 1989 to the present day. The input data were obtained as part of a research project focused on development prospects. The data refer to the second and third decades of the reporting period. The data are subjected to statistical and correlation analyses. Analyses deal with the phenomenon of faith and its content, rational and liturgical interpretations of religiosity, and questions of the meaning of existence and the moral dimension of faith. Thanks to the application of selected religionist theories, this formulates possible paths which the religiosity of the selected region will take, describing the potential as well as the risks of development for individual directions of development.
AVHRR NDVI Compositing Method Comparison and Generation of Multi-Decadal Time Series—A TIMELINE Thematic Processor
Authors: Asam Sarah , Eisfelder Christina , Hirner Andreas , Reiners Philipp , Holzwarth Stefanie , Bachmann Martin
Published: March 2023

Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument has provided daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change-related environmental processes. For monitoring vegetation conditions, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as no standard procedure has been established. Thirteen different compositing methods have been implemented; daily, decadal, and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best-performing compositing algorithm considering all the investigated aspects. However, the combination of the NDVI value and viewing and illumination angles as the criteria for the best-pixel selection proved to be a promising approach, too. The generated NDVI time series, currently ranging from 1981–2018, shows a consistent behavior and close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites.
Evaluating the Combined Use of the NDVI and High-Density Lidar Data to Assess the Natural Regeneration of P. pinaster after a High-Severity Fire in NW Spain
Authors: Míguez Clara , Fernández Cristina
Published: March 2023

Pinus pinaster Ait. is an important timber species in NW Spain and is affected by forest fires every year. The persistence of this species after fire mainly depends on natural regeneration, which is very variable. In this study, we evaluated the combined use of the NDVI and LiDAR data for assessing P. pinaster regeneration success after fire in terms of density, cover and height. For this purpose, we selected a P. pinaster stand affected by a high-severity wildfire in October 2017. Field surveys and remotely piloted aircraft flights (with a high-density LiDAR sensor and multispectral camera) were conducted four years after the fire (October 2021). The study area is characterized as being particularly complex terrain, with a combination of pine trees and a high density of scrub and low vegetation. Field measurements were made in 16 study plots distributed over the burned area. Two different types of software and data processing methods were used to calculate the LiDAR-derived metrics. For pine variables, the LiDAR-based estimates of structural characteristics calculated with both data processing methods proved inadequate and were very poorly correlated with the field-measured data, while for shrubland the estimates proved to be more comparable to the field measurements. The inability of the laser pulses to reach the ground due to the complexity of the area/vegetation could lead to loss of information, calling into question the accuracy of LiDAR data in this type of scenario. LiDAR technology continues to expand in different areas and applications, and in forestry, future studies should focus on application in more complex terrain.
Tracing Archaeological Places via the Context of Paleo Geomorphic Footprints Using SAR/InSAR Data Fusion: A Case on Southern Mesopotamia
Authors: Kim Jungrack
Published: March 2023

Since the deployment of EO resources into orbit, archaeological surveys have made extensive use of space imaging. In particular, synthetic aperture radar (SAR) and interferometric synthetic aperture radar (InSAR) data have often proved useful in many geomorphic investigations. In this study, we tested the potential of SAR/InSAR datasets for archaeological applications, which were conducted for southern Mesopotamia. While identifying the embedded human settlements, we attempted to reconstruct the paleoclimatic conditions and the paleotopography imprinted onto contemporary geography as key features of the environmental context of ancient human activity. First, Sentinel-1 and PALSAR-2 SAR/InSAR time-series data were compiled and used to identify the paleoshoreline and other ancient hydrologic backgrounds in southern Mesopotamia. We defined regions of interest (ROI) based on this and further used a rotational decomposition of the time-series signature to detect shallow subsurface features in predefined ROI. SAR/InSAR data processing identified ancient channels and shorelines that match the historical/archaeological records of key Sumerian cities. Our findings in Eridu and Larsa-Uruk-Umma, as well as their hydrological contexts, are archaeologically significant and suggest the need for more ground surveys. In terms of global coverage and resolving power, this study shows that the potential of SAR/InSAR for probing the background environment of ancient civilizations is comparable to high-resolution commercial optical imaging. Longer wavelength and higher resolution SAR/InSAR time-series datasets are highly anticipated for such applications.
Mutual Information Boosted Precipitation Nowcasting from Radar Images
Authors: Cao Yuan , Zhang Danchen , Zheng Xin , Shan Hongming , Zhang Junping
Published: March 2023

Precipitation nowcasting has long been a challenging problem in meteorology. While recent studies have introduced deep neural networks into this area and achieved promising results, these models still struggle with the rapid evolution of rainfall and extremely imbalanced data distribution, resulting in poor forecasting performance for convective scenarios. In this article, we evaluate the amount of information in different precipitation nowcasting tasks of varying lengths using mutual information. We propose two strategies: the mutual information-based reweighting strategy (MIR) and a mutual information-based training strategy (time superimposing strategy (TSS)). MIR reinforces neural network models to improve the forecasting accuracy for convective scenarios while maintaining prediction performance for rainless scenarios and overall nowcasting image quality. The TSS strategy enhances the model’s forecasting performance by adopting a curriculum learning-like method. Although the proposed strategies are simple, the experimental results show that they are effective and can be applied to various state-of-the-art models.
Enhancing Contrast of Dark Satellite Images Based on Fuzzy Semi-Supervised Clustering and an Enhancement Operator
Authors: Trung Nguyen Tu , Le Xuan-Hien , Tuan Tran Manh
Published: March 2023

Contrast enhancement of images is a crucial topic in image processing that improves the quality of images. The methods of image enhancement are classified into three types, including the histogram method, the fuzzy logic method, and the optimal method. Studies on image enhancement are often based on the rules: if it is bright, then it is brighter; if it is dark, then it is darker, using a global approach. Thus, it is hard to enhance objects in all dark and light areas, as in satellite images. This study presents a novel algorithm for improving satellite images, called remote sensing image enhancement based on cluster enhancement (RSIECE). First, the input image is clustered by the algorithm of fuzzy semi-supervised clustering. Then, the upper bound and lower bound are estimated according to the cluster. Next, a sub-algorithm is implemented for clustering enhancement using an enhancement operator. For each pixel, the gray levels for each channel (R, G, B) are transformed with this sub-algorithm to generate new corresponding gray levels because after clustering, pixels belong to clusters with the corresponding membership values. Therefore, the output gray level value will be aggregated from the enhanced gray levels by the sub-algorithm with the weight of the corresponding cluster membership value. The test results demonstrate that the suggested algorithm is superior to several recently developed approaches.
A Comparative Study of the Temperature Change in a Warm Eddy Using Multisource Data
Authors: Yang Xiaohong , Yang Yanming , Weng Jinbao
Published: March 2023

An ocean acoustic tomography (OAT) experiment conducted in the northern South China Sea in 2021 measured a month-long record of acoustic travel times along paths of over one hundred kilometers in range. A mesoscale eddy passed through the experimental region during the deployment of four acoustic moorings, providing unique OAT data for examining the deep temperature change in the eddy and for comparison with the Hybrid Coordinate Ocean Model (HYCOM) data. The existence of the eddy is first confirmed by the merged sea level anomaly (MSLA) image and HYCOM data and it can exceed the depth of the sound channel axis. The temperature changes measured by temperature and depth (TD)/conductivity–temperature–depth (CTD) loggers and by the OAT sound speed are in accordance with those reflected on the MSLA image during the movement of the eddy. However, the eddy movement prompted by temperature changes in the HYCOM data is different from that measured by TD/CTD. The modeled eddy intensity is at least two times less than the measured eddy intensity. At the sound channel axis depth, a factor of approximately 4.17 ms−1 °C−1 can be used to scale between sound speed and temperature. The transmission/reception path-averaged temperature of the eddy derived from the OAT-computed sound speed at the depth of the sound channel axis is five times greater than those in the HYCOM data. OAT is feasible as a tool to study mesoscale eddy properties in the deep ocean, while HYCOM data are not accurate enough for this mesoscale eddy at the sound channel axis depth. It is suggested that the model be refined by the OAT path-averaged temperature as constraints when the HYCOM data capture the mesoscale eddies.
Band Ratios Combination for Estimating Chlorophyll-a from Sentinel-2 and Sentinel-3 in Coastal Waters
Authors: Tran Manh Duy , Vantrepotte Vincent , Loisel Hubert , Oliveira Eduardo N. , Tran Kien Trung , Jorge Daniel , Mériaux Xavier , Paranhos Rodolfo
Published: March 2023

Chlorophyll-a concentration (Chl-a) is a crucial parameter for monitoring the water quality in coastal waters. The principal aim of this study is to evaluate the performance of existing Chl-a band ratio inversion models for estimating Chl-a from Sentinel2-MSI and Sentinel3-OLCI observation. This was performed using an extensive in situ Rrs-Chl-a dataset covering contrasted coastal waters (N = 1244, Chl-a (0.03–555.99) µg/L), which has been clustered into five optical water types (OWTs). Our results show that the blue/green inversion models are suitable to derive Chl-a over clear to medium turbid waters (OWTs 1, 2, and 3) while red/NIR models are adapted to retrieve Chl-a in turbid/high-Chl-a environments. As they exhibited the optimal performance considering these two groups of OWTs, MuBR (multiple band ratio) and NDCI (Normalized Difference Chlorophyll-a Index)-based models were merged using the probability values of the defined OWTs as the blending coefficients. Such a combination provides a reliable Chl-a prediction over the vast majority of the global coastal turbid waters (94%), as evidenced by a good performance on the validation dataset (e.g., MAPD = 21.64%). However, our study further illustrated that none of the evaluated algorithms yield satisfying Chl-a estimates in ultra-turbid waters, which are mainly associated with turbid river plumes (OWT 5). This finding highlights the limitation of multispectral ocean color observation in such optically extreme environments and also implies the interest to better explore hyperspectral Rrs information to predict Chl-a.
Investigating the Backscatter of Marine Plastic Litter Using a C- and X-Band Ground Radar, during a Measurement Campaign in Deltares
Authors: Simpson Morgan David , Marino Armando , de Maagt Peter , Gandini Erio , de Fockert Anton , Hunter Peter , Spyrakos Evangelos , Telfer Trevor , Tyler Andrew
Published: March 2023

In recent years, marine plastic pollution has seen increased coverage in the public interest and research due to a greater understanding of the scale and impact of plastic pollution within the marine environment. Considering the hazard that plastic waste poses on the environment, marine life, and on humans, remote-sensing techniques could provide timely information on their detection and dynamics. The remote sensing of marine plastic is a relatively new field and research into the capabilities of radar for detecting and monitoring marine plastic pollution is generally limited, with several interactions and mechanisms being largely unknown. Here, we exploit the use of a C- and X-band radar to understand the capabilities of monitoring marine plastics. Our results show that backscattering differences in the C- and X-band between the reference water (called here as “clean”) and the test water filled with plastic can be detected in some conditions (based on statistical analysis). Overall, the results indicate that the X-band frequency performs significantly better than the C-band frequency, with X-band detecting significant differences in backscattering in 48/68 test cases compared with C-band detecting differences in 20/67 test cases. We also find that the difference in backscattering is dependent on the size and shape of the plastic object, as well as the wave conditions which the plastic is moving on. This study provides new insights on the radar capabilities for detecting marine plastic litter and new information which can be used in the planning of future missions and studies on the remote sensing of marine plastic pollution.
Vehicle Detection in High-Resolution Aerial Images with Parallel RPN and Density-Assigner
Authors: Kong Xianghui , Zhang Yan , Tu Shangtan , Xu Chang , Yang Wen
Published: March 2023

Vehicle detection in aerial images plays a significant role in many remote sensing applications such as city planning, road construction, and traffic control. However, detecting vehicles in aerial images remains challenging due to the existence of tiny objects, the scale variance within the same type of vehicle objects, and dense arrangement in some scenarios, such as parking lots. At present, many state-of-the-art object detectors cannot generate satisfactory results on vehicle detection in aerial images. The receptive field of the current detector is not fine enough to handle the slight scale variance. Moreover, the densely arranged vehicles will introduce ambiguous positive samples in label assignment and false predictions that cannot be deleted by NMS. To this end, we propose a two-stage framework for vehicle detection that better leverages the prior attribution knowledge of vehicles in aerial images. First of all, we design a Parallel RPN that exploits convolutional layers of different receptive fields to alleviate the scale variation problem. To tackle the densely arranged vehicles, we introduce a density-based sample assigner in the vehicle-intensive areas to reduce low-quality and occluded positive samples in the training process. In addition, a scale-based NMS is proposed to filter out redundant proposals hierarchically from different levels of the feature pyramid. Moreover, we construct two challenging vehicle detection datasets based on the AI-TOD and xView datasets which contain many tiny objects. Extensive experiments on these two datasets demonstrate the effectiveness of our proposed method.
LASSO (L1) Regularization for Development of Sparse Remote-Sensing Models with Applications in Optically Complex Waters Using GEE Tools
Authors: Cardall Anna Catherine , Hales Riley Chad , Tanner Kaylee Brooke , Williams Gustavious Paul , Markert Kel N.
Published: March 2023

Remote-sensing data are used extensively to monitor water quality parameters such as clarity, temperature, and chlorophyll-a (chl-a) content. This is generally achieved by collecting in situ data coincident with satellite data collections and then creating empirical water quality models using approaches such as multi-linear regression or step-wise linear regression. These approaches, which require modelers to select model parameters, may not be well suited for optically complex waters, where interference from suspended solids, dissolved organic matter, or other constituents may act as “confusers”. For these waters, it may be useful to include non-standard terms, which might not be considered when using traditional methods. Recent machine-learning work has demonstrated an ability to explore large feature spaces and generate accurate empirical models that do not require parameter selection. However, these methods, because of the large number of included terms involved, result in models that are not explainable and cannot be analyzed. We explore the use of Least Absolute Shrinkage and Select Operator (LASSO), or L1, regularization to fit linear regression models and produce parsimonious models with limited terms to enable interpretation and explainability. We demonstrate this approach with a case study in which chl-a models are developed for Utah Lake, Utah, USA., an optically complex freshwater body, and compare the resulting model terms to model terms from the literature. We discuss trade-offs between interpretability and model performance while using L1 regularization as a tool. The resulting model terms are both similar to and distinct from those in the literature, thereby suggesting that this approach is useful for the development of models for optically complex water bodies where standard model terms may not be optimal. We investigate the effect of non-coincident data, that is, the length of time between satellite image collection and in situ sampling, on model performance. We find that, for Utah Lake (for which there are extensive data available), three days is the limit, but 12 h provides the best trade-off. This value is site-dependent, and researchers should use site-specific numbers. To document and explain our approach, we provide Colab notebooks for compiling near-coincident data pairs of remote-sensing and in situ data using Google Earth Engine (GEE) and a second notebook implementing L1 model creation using scikitlearn. The second notebook includes data-engineering routines with which to generate band ratios, logs, and other combinations. The notebooks can be easily modified to adapt them to other locations, sensors, or parameters.
An Adaptive Hybrid Sampling Method for Free-Form Surfaces Based on Geodesic Distance
Authors: Chen Chen , Jia Huakun , Lu Yang , Zhang Xiaodong , Chen Haohan , Yu Liandong
Published: March 2023

High precision geometric measurement of free-form surfaces has become the key to high-performance manufacturing in the manufacturing industry. By designing a reasonable sampling plan, the economic measurement of free-form surfaces can be realized. This paper proposes an adaptive hybrid sampling method for free-form surfaces based on geodesic distance. The free-form surfaces are divided into segments, and the sum of the geodesic distance of each surface segment is taken as the global fluctuation index of free-form surfaces. The number and location of the sampling points for each free-form surface segment are reasonably distributed. Compared with the common methods, this method can significantly reduce the reconstruction error under the same sampling points. This method overcomes the shortcomings of the current commonly used method of taking curvature as the local fluctuation index of free-form surfaces, and provides a new perspective for the adaptive sampling of free-form surfaces.
A Novel Dual-Permanent-Magnet Mechanical Antenna for Pipeline Robot Localization and Communication
Authors: Dong Yahao , Wu Jing , Zhang Xinran , Xie Tianyu
Published: March 2023

The demand for pipeline inspection has promoted the development of pipeline robots and associated localization and communication technologies. Among these technologies, ultra-low-frequency (30–300 Hz) electromagnetic waves have a significant advantage because of their strong penetration, which can penetrate metal pipe walls. Traditional low-frequency transmitting systems are limited by the size and power consumption of the antennas. In this work, a new type of mechanical antenna based on dual permanent magnets was designed to solve the above problems. An innovative amplitude modulation scheme that involves changing the magnetization angle of dual permanent magnets is proposed. The ultra-low-frequency electromagnetic wave emitted by the mechanical antenna inside the pipeline can be easily received by the antenna outside to localize and communicate with the robots inside. The experimental results showed that when two N38M-type Nd–Fe–B permanent magnets with a volume of 3.93 cm3 each were used, the magnetic flux density reached 2.35 nT at 10 m in the air and the amplitude modulation performance was satisfactory. Additionally, the electromagnetic wave was effectively received at 3 m from the 20# steel pipeline, which preliminarily verified the feasibility of using the dual-permanent-magnet mechanical antenna to achieve localization of and communication with pipeline robots.
Reliability Assessment of a Vision-Based Dynamic Displacement Measurement System Using an Unmanned Aerial Vehicle
Authors: Kim Hongjin , Kim Guyeon
Published: March 2023

In recent years, many studies have been conducted on the vision-based displacement measurement system using an unmanned aerial vehicle, which has been used in actual structure measurements. In this study, the dynamic measurement reliability of a vision-based displacement measurement system using an unmanned aerial vehicle was examined by measuring various vibrations with a frequency of 0 to 3 Hz and a displacement of 0 to 100 mm. Furthermore, free vibration was applied to model structures with one and two stories, and the response was measured to examine the accuracy of identifying structural dynamic characteristics. The vibration measurement results demonstrated that the vision-based displacement measurement system using an unmanned aerial vehicle has an average root mean square percentage error of 0.662% compared with the laser distance sensor in all experiments. However, the errors were relatively large in the displacement measurement of 10 mm or less regardless of the frequency. In the structure measurements, all sensors demonstrated the same mode frequency based on the accelerometer, and the damping ratios were extremely similar, except for the laser distance sensor measurement value of the two-story structure. Mode shape estimation was obtained and compared using the modal assurance criterion value compared with the accelerometer, and the values for the vision-based displacement measurement system using an unmanned aerial vehicle were close to 1. According to these results, the vision-based displacement measurement system using an unmanned aerial vehicle demonstrated results similar to those of conventional displacement sensors and can thus replace conventional displacement sensors.
Practical Use of Starlink Downlink Tones for Positioning
Authors: Jardak Nabil , Adam Ronan
Published: March 2023

The large availability of Low Earth Orbit (LEO) satellite systems makes them useful beyond their original purposes, such as in positioning, where their signals can be passively used. In order to determine their potential for this purpose, newly deployed systems need to be investigated. This is the case with the Starlink system, which has a large constellation and is advantageous for positioning. It transmits signals in the 10.7–12.7 GHz band, the same as that of geostationary satellite television. Signals in this band are typically received using a low-noise block down-converter (LNB) and a parabolic antenna reflector. Regarding opportunistic use of these signals in small vehicle navigation, the dimensions of the parabolic reflector and its directional gain are not practical for tracking many satellites simultaneously. In this paper, we investigate the feasibility of tracking Starlink downlink tones for opportunistic positioning in a practical situation, when signals are received without a parabolic reflector. For this purpose, an inexpensive universal LNB is selected, and then signal tracking is performed to determine the signal and frequency measurement quality, as well as the number of satellites that can be tracked simultaneously. Next, the tone measurements are aggregated to handle tracking interruptions and to recover the traditional Doppler shift model. After that, the use of measurements in multi-epoch positioning is defined, and its performance discussed as a function of the relevant measurement rate and the required multi-epoch interval duration. The results showed promising positioning which can be improved by selecting a better-quality LNB.
Alternating-Current Microgrid Testbed Built with Low-Cost Modular Hardware
Authors: Haidekker Mark A. , Liu Maohua , Song WenZhan
Published: March 2023

With the growing popularity of microgrids for alternative energy management, there is demand for tools that allow us to study the effect of microgrids in distributed power systems. Popular methods involve software simulation and prototype validation with physical hardware. Simulations often do not capture the complex interactions, and combinations of software simulations with hardware testbeds promise to give a more accurate picture. These testbeds, however, usually aim at the validation of hardware for industrial-scale use, which makes them expensive and not readily accessible. To fill the gap between full-scale hardware and software simulation, we propose a modular lab-scale grid model at a 1:100 power scale over residential single-phase networks with 12 V AC and 60 Hz grid voltage. We present different modules—power sources, inverters, demanders, grid monitors, and grid-to-grid bridges—that can be assembled into distributed grids of almost arbitrary complexity. The model voltage poses no electrical hazards, and microgrids can readily be assembled with an open power line model. Unlike a prior DC-based grid testbed, the proposed AC model allows us to examine additional aspects, such as frequency, phase, active and apparent power, and reactive loads. Grid metrics, including the discretely sampled voltage and current waveforms, can be collected and sent to higher-tier grid management systems. We integrated the modules with Beagle Bone micro-PCs, which in turn connect any such microgrid with an emulation platform built on CORE (Common Open Research Emulator) and the Gridlab-D power simulator, thereby allowing hybrid software/hardware simulations. Our grid modules were shown to fully operate in this environment. Through the CORE system, multitiered control and even remote grid management is possible. However, we also found that the AC waveform poses design challenges that require us to balance accurate emulation (most notably with respect to harmonic distortion) with per-module costs.
Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning
Authors: Tian Fengkai , Vieira Caio Canella , Zhou Jing , Zhou Jianfeng , Chen Pengyin
Published: March 2023

Weeds can cause significant yield losses and will continue to be a problem for agricultural production due to climate change. Dicamba is widely used to control weeds in monocot crops, especially genetically engineered dicamba-tolerant (DT) dicot crops, such as soybean and cotton, which has resulted in severe off-target dicamba exposure and substantial yield losses to non-tolerant crops. There is a strong demand for non-genetically engineered DT soybeans through conventional breeding selection. Public breeding programs have identified genetic resources that confer greater tolerance to off-target dicamba damage in soybeans. Efficient and high throughput phenotyping tools can facilitate the collection of a large number of accurate crop traits to improve the breeding efficiency. This study aimed to evaluate unmanned aerial vehicle (UAV) imagery and deep-learning-based data analytic methods to quantify off-target dicamba damage in genetically diverse soybean genotypes. In this research, a total of 463 soybean genotypes were planted in five different fields (different soil types) with prolonged exposure to off-target dicamba in 2020 and 2021. Crop damage due to off-target dicamba was assessed by breeders using a 1–5 scale with a 0.5 increment, which was further classified into three classes, i.e., susceptible (≥3.5), moderate (2.0 to 3.0), and tolerant (≤1.5). A UAV platform equipped with a red-green-blue (RGB) camera was used to collect images on the same days. Collected images were stitched to generate orthomosaic images for each field, and soybean plots were manually segmented from the orthomosaic images. Deep learning models, including dense convolutional neural network-121 (DenseNet121), residual neural network-50 (ResNet50), visual geometry group-16 (VGG16), and Depthwise Separable Convolutions (Xception), were developed to quantify crop damage levels. Results show that the DenseNet121 had the best performance in classifying damage with an accuracy of 82%. The 95% binomial proportion confidence interval showed a range of accuracy from 79% to 84% (p-value ≤ 0.01). In addition, no extreme misclassifications (i.e., misclassification between tolerant and susceptible soybeans) were observed. The results are promising since soybean breeding programs typically aim to identify those genotypes with ‘extreme’ phenotypes (e.g., the top 10% of highly tolerant genotypes). This study demonstrates that UAV imagery and deep learning have great potential to high-throughput quantify soybean damage due to off-target dicamba and improve the efficiency of crop breeding programs in selecting soybean genotypes with desired traits.
In-Mould OCT Sensors Combined with Piezo-Actuated Positioning Devices for Compensating for Displacement in Injection Overmoulding of Optoelectronic Parts
Authors: Hannesschläger Günther , Schwarze Martin , Leiss-Holzinger Elisabeth , Rankl Christian
Published: March 2023

When overmoulding optoelectronic devices with optical elements, precise alignment of the overmoulded part and the mould is of great importance. However, mould-integrated positioning sensors and actuators are not yet available as standard components. As a solution, we present a mould-integrated optical coherence tomography (OCT) device that is combined with a piezo-driven mechatronic actuator, which is capable of performing the necessary displacement correction. Because of the complex geometric structure optoelectronic devices may have, a 3D imaging method was preferable, so OCT was chosen. It is shown that the overall concept leads to sufficient alignment accuracy and, apart from compensating for the in-plane position error, provides valuable additional information about the sample both before and after the injection process. The increased alignment accuracy leads to better energy efficiency, improved overall performance and less scrap parts, and thus even a zero-waste production process might be feasible.
Bayesian-Based Hybrid Method for Rapid Optimization of NV Center Sensors
Authors: Tian Jiazhao , Said Ressa S. , Jelezko Fedor , Cai Jianming , Xiao Liantuan
Published: March 2023

NV centers are among the most promising platforms in the field of quantum sensing. Magnetometry based on NV centers, especially, has achieved concrete development in areas of biomedicine and medical diagnostics. Improving the sensitivity of NV center sensors under wide inhomogeneous broadening and fieldamplitude drift is a crucial issue of continuous concern that relies on the coherent control of NV centers with high average fidelity. Quantum optimal control (QOC) methods provide access to this target; nevertheless, the high time consumption of current methods due to the large number of needful sample points as well as the complexity of the parameter space has hindered their usability. In this paper, we propose the Bayesian estimation phase-modulated (B-PM) method to tackle this problem. In the case of the state transforming of an NV center ensemble, the B-PM method reduced the time consumption by more than 90% compared with the conventional standard Fourier basis (SFB) method while increasing the average fidelity from 0.894 to 0.905. In the AC magnetometry scenario, the optimized control pulse obtained with the B-PM method achieved an eight-fold extension of coherence time T2 compared with the rectangular π pulse. Similar application can be made in other sensing situations. As a general algorithm, the B-PM method can be further extended to the open- and closed-loop optimization of complex systems based on a variety of quantum platforms.
Between-Class Adversarial Training for Improving Adversarial Robustness of Image Classification
Authors: Wang Desheng , Jin Weidong , Wu Yunpu
Published: March 2023

Deep neural networks (DNNs) have been known to be vulnerable to adversarial attacks. Adversarial training (AT) is, so far, the only method that can guarantee the robustness of DNNs to adversarial attacks. However, the robustness generalization accuracy gain of AT is still far lower than the standard generalization accuracy of an undefended model, and there is known to be a trade-off between the standard generalization accuracy and the robustness generalization accuracy of an adversarially trained model. In order to improve the robustness generalization and the standard generalization performance trade-off of AT, we propose a novel defense algorithm called Between-Class Adversarial Training (BCAT) that combines Between-Class learning (BC-learning) with standard AT. Specifically, BCAT mixes two adversarial examples from different classes and uses the mixed between-class adversarial examples to train a model instead of original adversarial examples during AT. We further propose BCAT+ which adopts a more powerful mixing method. BCAT and BCAT+ impose effective regularization on the feature distribution of adversarial examples to enlarge between-class distance, thus improving the robustness generalization and the standard generalization performance of AT. The proposed algorithms do not introduce any hyperparameters into standard AT; therefore, the process of hyperparameters searching can be avoided. We evaluate the proposed algorithms under both white-box attacks and black-box attacks using a spectrum of perturbation values on CIFAR-10, CIFAR-100, and SVHN datasets. The research findings indicate that our algorithms achieve better global robustness generalization performance than the state-of-the-art adversarial defense methods.
Transfer Learning for Image-Based Malware Detection for IoT
Authors: Panda Pratyush , C U Om Kumar , Marappan Suguna , Ma Suresh , S Manimurugan , Veesani Nandi Deeksha
Published: March 2023

The tremendous growth in online activity and the Internet of Things (IoT) led to an increase in cyberattacks. Malware infiltrated at least one device in almost every household. Various malware detection methods that use shallow or deep IoT techniques were discovered in recent years. Deep learning models with a visualization method are the most commonly and popularly used strategy in most works. This method has the benefit of automatically extracting features, requiring less technical expertise, and using fewer resources during data processing. Training deep learning models that generalize effectively without overfitting is not feasible or appropriate with large datasets and complex architectures. In this paper, a novel ensemble model, Stacked Ensemble—autoencoder, GRU, and MLP or SE-AGM, composed of three light-weight neural network models—autoencoder, GRU, and MLP—that is trained on the 25 essential and encoded extracted features of the benchmark MalImg dataset for classification was proposed. The GRU model was tested for its suitability in malware detection due to its lesser usage in this domain. The proposed model used a concise set of malware features for training and classifying the malware classes, which reduced the time and resource consumption in comparison to other existing models. The novelty lies in the stacked ensemble method where the output of one intermediate model works as input for the next model, thereby refining the features as compared to the general notion of an ensemble approach. Inspiration was drawn from earlier image-based malware detection works and transfer learning ideas. To extract features from the MalImg dataset, a CNN-based transfer learning model that was trained from scratch on domain data was used. Data augmentation was an important step in the image processing stage to investigate its effect on classifying grayscale malware images in the MalImg dataset. SE-AGM outperformed existing approaches on the benchmark MalImg dataset with an average accuracy of 99.43%, demonstrating that our method was on par with or even surpassed them.
Advances in the Monitoring, Diagnosis and Optimisation of Water Systems
Authors: Cugueró-Escofet Miquel Àngel , Puig Vicenç
Published: March 2023

In the context of global climate change, with the increasing frequency and severity of extreme events—such as draughts and floods—which will likely make water demand more uncertain and jeopardise its availability, those in charge of water system management face new operational challenges because of increasing resource scarcity, intensive energy requirements, growing populations (especially in urban areas), costly and ageing infrastructures, increasingly stringent regulations, and rising attention towards the environmental impact of water use [...]
An ECG Stitching Scheme for Driver Arrhythmia Classification Based on Deep Learning
Authors: Kim Do Hoon , Lee Gwangjin , Kim Seong Han
Published: March 2023

This study proposes an electrocardiogram (ECG) signal stitching scheme to detect arrhythmias in drivers during driving. When the ECG is measured through the steering wheel during driving, the data are always exposed to noise caused by vehicle vibrations, bumpy road conditions, and the driver’s steering wheel gripping force. The proposed scheme extracts stable ECG signals and transforms them into full 10 s ECG signals to classify arrhythmias using convolutional neural networks (CNN). Before the ECG stitching algorithm is applied, data preprocessing is performed. To extract the cycle from the collected ECG data, the R peaks are found and the TP interval segmentation is applied. An abnormal P peak is very difficult to find. Therefore, this study also introduces a P peak estimation method. Finally, 4 × 2.5 s ECG segments are collected. To classify arrhythmias with stitched ECG data, each time series’ ECG signal is transformed via the continuous wavelet transform (CWT) and short-time Fourier transform (STFT), and transfer learning is performed for classification using CNNs. Finally, the parameters of the networks that provide the best performance are investigated. According to the classification accuracy, GoogleNet with the CWT image set shows the best results. The classification accuracy is 82.39% for the stitched ECG data, while it is 88.99% for the original ECG data.
A Critical Review on Factors Affecting the User Adoption of Wearable and Soft Robotics
Authors: Ang Benjamin Wee Keong , Yeow Chen-Hua , Lim Jeong Hoon
Published: March 2023

In recent years, the advent of soft robotics has changed the landscape of wearable technologies. Soft robots are highly compliant and malleable, thus ensuring safe human-machine interactions. To date, a wide variety of actuation mechanisms have been studied and adopted into a multitude of soft wearables for use in clinical practice, such as assistive devices and rehabilitation modalities. Much research effort has been put into improving their technical performance and establishing the ideal indications for which rigid exoskeletons would play a limited role. However, despite having achieved many feats over the past decade, soft wearable technologies have not been extensively investigated from the perspective of user adoption. Most scholarly reviews of soft wearables have focused on the perspective of service providers such as developers, manufacturers, or clinicians, but few have scrutinized the factors affecting adoption and user experience. Hence, this would pose a good opportunity to gain insight into the current practice of soft robotics from a user’s perspective. This review aims to provide a broad overview of the different types of soft wearables and identify the factors that hinder the adoption of soft robotics. In this paper, a systematic literature search using terms such as “soft”, “robot”, “wearable”, and “exoskeleton” was conducted according to PRISMA guidelines to include peer-reviewed publications between 2012 and 2022. The soft robotics were classified according to their actuation mechanisms into motor-driven tendon cables, pneumatics, hydraulics, shape memory alloys, and polyvinyl chloride muscles, and their pros and cons were discussed. The identified factors affecting user adoption include design, availability of materials, durability, modeling and control, artificial intelligence augmentation, standardized evaluation criteria, public perception related to perceived utility, ease of use, and aesthetics. The critical areas for improvement and future research directions to increase adoption of soft wearables have also been highlighted.
Pansharpening Model of Transferable Remote Sensing Images Based on Feature Fusion and Attention Modules
Authors: Liu Hui , Deng Liangfeng , Dou Yibo , Zhong Xiwu , Qian Yurong
Published: March 2023

The purpose of the panchromatic sharpening of remote sensing images is to generate high-resolution multispectral images through software technology without increasing economic expenditure. The specific method is to fuse the spatial information of a high-resolution panchromatic image and the spectral information of a low-resolution multispectral image. This work proposes a novel model for generating high-quality multispectral images. This model uses the feature domain of the convolution neural network to fuse multispectral and panchromatic images so that the fused images can generate new features so that the final fused features can restore clear images. Because of the unique feature extraction ability of convolution neural networks, we use the core idea of convolution neural networks to extract global features. To extract the complementary features of the input image at a deeper level, we first designed two subnetworks with the same structure but different weights, and then used single-channel attention to optimize the fused features to improve the final fusion performance. We select the public data set widely used in this field to verify the validity of the model. The experimental results on the GaoFen-2 and SPOT6 data sets show that this method has a better effect in fusing multi-spectral and panchromatic images. Compared with the classical and the latest methods in this field, our model fusion obtained panchromatic sharpened images from both quantitative and qualitative analysis has achieved better results. In addition, to verify the transferability and generalization of our proposed model, we directly apply it to multispectral image sharpening, such as hyperspectral image sharpening. Experiments and tests have been carried out on Pavia Center and Botswana public hyperspectral data sets, and the results show that the model has also achieved good performance in hyperspectral data sets.
Influence of the Backward Fall Technique on the Transverse Linear Acceleration of the Head during the Fall
Authors: Mroczkowski Andrzej , Taiar Redha
Published: March 2023

Background: The formation of large accelerations on the head and cervical spine during a backward fall is particularly dangerous due to the possibility of affecting the central nervous system (CNS). It may eventually lead to serious injuries and even death. This research aimed to determine the effect of the backward fall technique on the linear acceleration of the head in the transverse plane in students practicing various sports disciplines. Methods: The study involved 41 students divided into two study groups. Group A consisted of 19 martial arts practitioners who, during the study, performed falls using the side aligning of the body technique. Group B consisted of 22 handball players who, during the study, performed falls using the technique performed in a way similar to a gymnastic backward roll. A rotating training simulator (RTS) was used to force falls, and a Wiva® Science apparatus was used to assess acceleration. Results: The greatest differences in backward fall acceleration were found between the groups during the buttocks’ contact with the ground. Larger changes in head acceleration were noted in group B. Conclusions: The lower changes in head acceleration obtained in physical education students falling with a lateral body position compared to students training handball indicate their lower susceptibility to head, cervical spine, and pelvis injuries when falling backwards as caused by horizontal force.