bluuebunny/crossref_metadata_2025 huggingface dataset:
Hybrid Epoxy Nanocomposites: Improvement in Mechanical Properties and Toughening Mechanisms—A Review
Authors: Białkowska Anita , Bakar Mohamed , Kucharczyk Wojciech , Zarzyka Iwona
Published: March 2023

This article presents a review on the recent advances in the field of ternary diglycidyl ether of bisphenol A epoxy nanocomposites containing nanoparticles and other modifiers. Particular attention is paid to their mechanical and thermal properties. The properties of epoxy resins were improved by incorporating various single toughening agents, in solid or liquid states. This latter process often resulted in the improvement in some properties at the expense of others. The use of two appropriate modifiers for the preparation of hybrid composites, possibly will show a synergistic effect on the performance properties of the composites. Due to the huge amount of modifiers that were used, the present paper will focus mainly on largely employed nanoclays with modifiers in a liquid and solid state. The former modifier contributes to an increase in the flexibility of the matrix, while the latter modifier is intended to improve other properties of the polymer depending on its structure. Various studies which were carried out on hybrid epoxy nanocomposites confirmed the occurrence of a synergistic effect within the tested performance properties of the epoxy matrix. Nevertheless, there are still ongoing research works using other nanoparticles and other modifiers aiming at enhancing the mechanical and thermal properties of epoxy resins. Despite numerous studies carried out so far to assess the fracture toughness of epoxy hybrid nanocomposites, some problems still remain unresolved. Many research groups are dealing with many aspects of the subject, namely the choice of modifiers and preparation methods, while taking into account the protection of the environment and the use of components from natural resources.
A Novel Framework for Qualification of a Composite-Based Main Landing Gear Strut of a Lightweight Aircraft
Authors: Ahmad Muhammad Ayaz , Ali Shah Syed Irtiza , Khan Sabih Ahmad , Khan Haris Ali , Shams Taimur Ali
Published: March 2023

The determination of suitable testing and qualification procedures for fiber-reinforced polymer matrix composite structures is an active area of research due to the increased demand, especially in the field of aerospace. This research illustrates the development of a generic qualification framework for a composite-based main landing gear strut of a lightweight aircraft. For this purpose, a landing gear strut composed of T700 carbon fiber/epoxy material was designed and analyzed for a given lightweight aircraft having mass of 1600 kg. Computational analysis was performed on ABAQUS CAE® to evaluate the maximum stresses and critical failure modes encountered during one-point landing condition as defined in the UAV Systems Airworthiness Requirements (USAR) and Air Worthiness Standards FAA FAR Part 23. A three-step qualification framework including material, process and product-based qualification was then proposed against these maximum stresses and failure modes. The proposed framework revolves around the destructive testing of specimens initially as per ASTM standards D 7264 and D 2344, followed by defining the autoclave process parameters and customized testing of thick specimens to evaluate material strength against the maximum stresses in specific failure modes of the main landing gear strut. Once the desired strength of the specimens was achieved based on material and process qualifications, qualification criteria for the main landing gear strut were proposed which would not only serve as an alternative to drop test the landing gear struts as defined in air worthiness standards during mass production, but would also give confidence to manufacturers to undertake the manufacturing of main landing gear struts using qualified material and process parameters.
Sargassum@magnetite Composite EDTA-Functionalized for the Potential Removal of Mercury
Authors: Sandoval-Cárdenas Diana Issell , Pool Hector , Favela-Camacho Sarai E. , Santos-Cruz José , Campos-Guillén Juan , Ramos-López Miguel Angel , Rodríguez-deLeón Eloy , Urbina-Arroyo Jessica Viridiana , Amaro-Reyes Aldo
Published: March 2023

Sargassum spp. affects the Caribbean shores; thus, its remotion or valorization is a priority. This work aimed to synthesize a low-cost magnetically retrievable Hg+2 adsorbent functionalized with ethylenediaminetetraacetic acid (EDTA) based on Sargassum. The Sargassum was solubilized to synthesize by co-precipitation a magnetic composite. A central composite design was assessed to maximize the adsorption of Hg+2. The solids yield magnetically attracted mass, and the saturation magnetizations of the functionalized composite were 60.1 ± 17.2%, 75.9 ± 6.6%, and 1.4 emu g−1. The functionalized magnetic composite yielded 29.8 ± 0.75 mg Hg+2 g−1 of chemisorption after 12 h, pH 5, and 25 °C achieving 75% Hg+2 adsorption after four reuse cycles. Crosslinking and functionalization with Fe3O4 and EDTA created differences in surface roughness as well as the thermal events of the composites. The Fe3O4@Sargassum@EDTA composite was a magnetically recovered biosorbent of Hg2+.
Carbon Nanotube-Based Intumescent Flame Retardants Achieve High-Efficiency Flame Retardancy and Simultaneously Avoid Mechanical Property Loss
Authors: Qu Qi , Xu Jin , Wang Huanhuan , Yu Yinrui , Dong Qianpeng , Zhang Xianhua , He Yan
Published: March 2023

Intumescent flame retardants (IFR) are an excellent solution to the problem of easy combustion of polymers. Still, the negative effect of the addition of flame retardants is the decline of the mechanical properties of polymers. In this context, carbon nanotubes (CNTs) are modified with tannic acid (TA) and then wrapped on the surface of ammonium polyphosphate (APP) to construct a special intumescent flame retardant structure (CTAPP). The respective advantages of the three components in the structure are explained in detail, especially the role of CNTs with high thermal conductivity in the flame retardant system. Compared with pure natural rubber (NR), the peak heat release rate (PHRR), total heat release (THR), and total smoke production (TSP) of the composites proposed with special structural flame retardants are decreased by 68.4%, 64.3%, and 49.3%, respectively, while the limiting oxygen index (LOI) increased to 28.6%. The TA-modified CNTs wrapped on the surface of APP can effectively reduce the mechanical damage caused by the flame retardant to the polymer. To sum up, the flame retardant structure of TA-modified CNTs wrapped on APP can effectively enhance the flame retardant properties of the NR matrix and reduce the negative impact on mechanics caused by adding APP flame retardant.
Enhancing the Properties of Water-Soluble Copolymer Nanocomposites by Controlling the Layer Silicate Load and Exfoliated Nanolayers Adsorbed on Polymer Chains
Authors: Wang Dongyin , Chen Changfeng , Hu Xiaojuan , Ju Fei , Ke Yangchuan
Published: March 2023

Novel polymer nanocomposites of methacryloyloxy ethyl dimethyl hexadecyl ammonium bromide-modified montmorillonite (O-MMt) with acrylamide/sodium p-styrene sulfonate/methacryloyloxy ethyl dimethyl hexadecyl ammonium bromide (ASD/O-MMt) were synthesized via in situ polymerization. The molecular structures of the synthesized materials were confirmed using Fourier-transform infrared and 1H-nuclear magnetic resonance spectroscopy. X-ray diffractometry and transmission electron microscopy revealed well-exfoliated and dispersed nanolayers in the polymer matrix, and scanning electron microscopy images revealed that the well-exfoliated nanolayers were strongly adsorbed on the polymer chains. The O-MMt intermediate load was optimized to 1.0%, and the exfoliated nanolayers with strongly adsorbed chains were controlled. The properties of the ASD/O-MMt copolymer nanocomposite, such as its resistance to high temperature, salt, and shear, were significantly enhanced compared with those obtained under other silicate loads. ASD/1.0 wt% O-MMt enhanced oil recovery by 10.5% because the presence of well-exfoliated and dispersed nanolayers improved the comprehensive properties of the nanocomposite. The large surface area, high aspect ratio, abundant active hydroxyl groups, and charge of the exfoliated O-MMt nanolayer also provided high reactivity and facilitated strong adsorption onto the polymer chains, thereby endowing the resulting nanocomposites with outstanding properties. Thus, the as-prepared polymer nanocomposites demonstrate significant potential for oil-recovery applications.
Ring Opening Polymerization Used for the Production of VOC Free High-Performance Ecofriendly Novel PBZ/PDA/CeO2 Nanocomposites
Authors: Hatami Mehdi , Abadi Zohreh Seifi Najaf , Yousefi Alireza , Qandalee Mohammad , Djafarzadeh Nader , Ghasemi Yaser , López-Coca Ignacio M. , Durán-Valle Carlos J.
Published: March 2023

This study analyzed the fabrication and characterization of polybenzoxazine/polydopamine/ceria as tertiary nanocomposites. To this end, a new benzoxazine monomer (MBZ) was fabricated based on the well-known Mannich reaction of naphthalene-1-amine, 2-tert-butylbenzene-1,4-diol and formaldehyde under ultrasonic-assisted process. Polydopamine (PDA) was used as dispersing polymer nanoparticles and surface modifier for CeO2 by in-situ polymerization of dopamine with the assistance of ultrasonic waves. Then, nanocomposites (NC)s were manufactured by in-situ route under thermal conditions. The FT-IR and 1H-NMR spectra confirmed the preparation of the designed MBZ monomer. The FE-SEM and TEM results showed the morphological aspects of prepared NCs and illustrated the distribution of CeO2 NPs in the polymer matrix. The XRD patterns of NCs showed the presence of crystalline phases of nanoscale CeO2 in an amorphous matrix. The TGA results reveal that the prepared NCs are classified as thermally stable materials.
Optimal Planning of Hybrid Electricity–Hydrogen Energy Storage System Considering Demand Response
Authors: Lu Zijing , Li Zishou , Guo Xiangguo , Yang Bo
Published: March 2023

In recent years, the stability of the distribution network has declined due to the large proportion of the uses of distributed generation (DG) with the continuous development of renewable energy power generation technology. Meanwhile, the traditional distribution network operation mode cannot keep the balance of the source and load. The operation mode of the active distribution network (ADN) can effectively reduce the decline in operation stability caused by the high proportion of DG. Therefore, this work proposes a bi-layer model for the planning of the electricity–hydrogen hybrid energy storage system (ESS) considering demand response (DR) for ADN. The upper layer takes the minimum load fluctuation, maximum user purchase cost satisfaction, and user comfort as the goals. Based on the electricity price elasticity matrix model, the optimal electricity price formulation strategy is obtained for the lower ESS planning. In the lower layer, the optimal ESS planning scheme is obtained with the minimum life cycle cost (LCC) of ESS, the voltage fluctuation of ADN, and the load fluctuation as the objectives. Finally, the MOPSO algorithm is used to test the model, and the correctness of the proposed method is verified by the extended IEEE-33 node test system. The simulation results show that the fluctuation in the voltage and load is reduced by 62.13% and 37.06%, respectively.
From Courtesan to Wŏn Buddhist Teacher: The Life of Yi Ch’ŏngch’un
Authors: Song Grace J.
Published: March 2023

This study examines the life of Yi Ch’ŏngch’un 李靑春 (1886–1955), one of the first female disciples of Wŏn Buddhism, a 20th-century Korean religious movement. Her story shows modernity’s impact on Korean women’s psyche and social roles, and the progressive initiative of male teachers who advanced gender equality in new Korean religious movements. A range of data, including periodicals, letters, and newspaper articles, enable us to understand the changing face of women during the process of modernization in Korea. This paper gives voice to the significant role of Yi Ch’ŏngch’un, an exceptional financial sponsor in the formative years of Wŏn Buddhism and an advocate for women’s rights. By highlighting her contributions, it becomes clear that the progressive males who pushed for gender equality relied on the support of female-ordained devotees to actualize the doctrine’s ideal.
A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties
Authors: Al-Mahturi Ayad , Santoso Fendy , Garratt Matthew A. , Anavatti Sreenatha G.
Published: March 2023

This paper presents the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). Our system has two major learning stages, namely, structure learning and parameters learning. The structure phase does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one initial fuzzy rule. The rules are then continuously updated and pruned in an online fashion to achieve the desired set point. For the phase of learning parameters, all adjustable parameters of the fuzzy system are tuned by using a sliding surface method, which is based on the gradient descent algorithm. This method is used to minimize the difference between the expected and actual signals. Our proposed control method is model-free and does not require prior knowledge of the plant’s dynamics or constraints. Instead, data-driven control utilizes artificial intelligence-based techniques, such as fuzzy logic systems, to learn the dynamics of the system and adapt to changes in the system, and account for complex interactions between different components. A robustness term is incorporated into the control effort to deal with external disturbances in the system. The proposed technique is applied to regulate the dynamics of a mobile robot in the presence of multiple external disturbances, demonstrating the robustness of the proposed control systems. A rigorous comparative study with respect to three different controllers is performed, where the outcomes illustrate the superiority of the proposed learning method as evidenced by lower RMSE values and fewer fuzzy parameters. Lastly, stability analysis of the proposed control method is conducted using the Lyapunov stability theory.
Monitoring of Coastal Boulder Movements by Storms and Calculating Volumetric Parameters Using the Volume Differential Method Based on Point Cloud Difference
Authors: Yao Yao , Burningham Helene , Knight Jasper , Griffiths David
Published: March 2023

The measurements of boulder volume and axial length play significant roles in exploring the evolution of coastal boulder deposition, which provides a theoretical framework to examine the hydrodynamics of extreme wave events. At present, the application of structure-from-motion (SfM) to unmanned aerial system (UAS) imagery is one of the most used boulder surveying techniques. However, the monitoring of boulder movement and the accurate measurement of boulder morphometrics are rarely investigated in combination. In this study, UAS surveys were used to monitor moving boulders and measure boulder volumes using the volume differential method based on the differences of dense point clouds. This was undertaken at a site on the rocky shoreline of northwest Ireland in three repeated UAS surveys conducted in 2017, 2018, and 2019. The results from UAS monitoring and mapping of the distribution of 832 moving boulders in the study area over the 3-year period showed that boulders located in different zones of the coast vary significantly in their mobility. The main findings reveal that the theoretical error of the volume, obtained using the volume differential method, was estimated as 1–3.9%, which is much smaller than that of the conventional method of estimating volume using a tape measure.
Transformation of Agricultural Landscapes and Its Consequences for Natural Forests in Southern Myanmar within the Last 40 Years
Authors: Tun Phyu Thaw , Nguyen Thanh Thi , Buerkert Andreas
Published: March 2023

Kyunsu township comprises coastal regions and a multitude of small islands covered by vast tropical evergreen and mangrove forests, and a large water body in the Adman Sea of Myanmar. Due to population growth, residents have increasingly expanded their agricultural land areas into natural tropical evergreen and mangrove forests, leading to deforestation. Understanding the processes and consequences of landscape transformation for surrounding ecosystems is crucial for local policy making and for fostering sustainable crop production in this area. Landsat datasets from 1978, 1989, 2000, 2011, and 2020 were used in a time-series post-classification approach to investigate land use land cover (LULC) changes in the Kyunsu township of Southern Myanmar across the last 40 years. Our study also attempted to assess the effects of the transformation of LULC on carbon stocks. Between 1978 and 2020, major LULC changes occurred with the expansion of Paddy Fields (+90%), Plantations (+11%), Open Forests (+81%), Settlement Areas (+115%), Aquaculture Areas (+1594%), and Others (+188%) while the area covered with Closed Forests shrunk by 44% and with Mangrove Forests by 9%. Water Bodies expanded by 0.13%. Our analyses show that between 1978 and 2020 2453 ha of Paddy Fields expanded into Plantations, 1857 ha to Open Forests, and 1146 ha to Mangrove Forests. Additionally, 12,135 ha of Open Forests, 8474 ha of Closed Forests, and 2317 ha of Mangrove Forests became Plantations. Across the 40 year study period, a total of 40,523 ha of Closed Forests were transformed to Open Forests. Our findings show that transformation of agricultural landscapes in the study area significantly affected deforestation and forest degradation of tropical evergreen rain forests and mangrove forests which are vital sources of ecosystem services. These transformations led to estimated losses of carbon stocks between 1978 and 2020 ranged from 89,260–5,106,820 Mg (average of 1,723,250 Mg) in our study area. Our findings call for sustainable resource intensification to increase production efficiency in existing cultivated areas rather than crop land expansion into natural forests. In addition, our data highlight the need for rigorous policies to conserve and protect tropical natural evergreen and mangrove forest, as key local resources providing multiple ecosystem services.
Mapping Crop Leaf Area Index and Canopy Chlorophyll Content Using UAV Multispectral Imagery: Impacts of Illuminations and Distribution of Input Variables
Authors: Li Wenjuan , Weiss Marie , Garric Bernard , Champolivier Luc , Jiang Jingyi , Wu Wenbin , Baret Frédéric
Published: March 2023

Leaf area index (LAI) and canopy chlorophyll content (CCC) are important indicators that describe the growth status and nitrogen deficiencies of crops. Several studies have been performed to estimate LAI and CCC using multispectral cameras onboard an unmanned airborne vehicle (UAV) system. However, the impacts of illuminations during UAV flight and problems of how to invert still need more investigation. UAV flights with a multispectral camera were performed under clear (diffuse ratio 0) and cloudy illumination conditions (diffuse ratio 1) over rapeseed, wheat and sunflower (only clear) fields. One-dimension radiative transfer model PROSAIL was run twice to generate a clear-sky model and a cloudy-sky model, respectively. The LAI and CCC of flights under a clear sky were inverted from the clear-sky model, and the flights under cloudy conditions were inverted from both clear-sky and cloudy-sky models to compare the results. Moreover, three Look-Up-Tables (LUT) were built with same input variables but different distributions of LAI. Results showed that LAI from uniform dense LUT had better correspondence with ground measurements for all crops (R2 = 0.51~0.69). The illumination condition had little impact on small to medium LAI (LAI < 5) and CCC. However, the inversion of imageries during cloudy sky conditions from the clear-sky model led to an overestimation of high LAI values.
Polarimetric Imaging via Deep Learning: A Review
Authors: Li Xiaobo , Yan Lei , Qi Pengfei , Zhang Liping , Goudail François , Liu Tiegen , Zhai Jingsheng , Hu Haofeng
Published: March 2023

Polarization can provide information largely uncorrelated with the spectrum and intensity. Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields, e.g., ocean observation, remote sensing (RS), biomedical diagnosis, and autonomous vehicles. Recently, with the increasing amount of data and the rapid development of physical models, deep learning (DL) and its related technique have become an irreplaceable solution for solving various tasks and breaking the limitations of traditional methods. PI and DL have been combined successfully to provide brand-new solutions to many practical applications. This review briefly introduces PI and DL’s most relevant concepts and models. It then shows how DL has been applied for PI tasks, including image restoration, object detection, image fusion, scene classification, and resolution improvement. The review covers the state-of-the-art works combining PI with DL algorithms and recommends some potential future research directions. We hope that the present work will be helpful for researchers in the fields of both optical imaging and RS, and that it will stimulate more ideas in this exciting research field.
A ViSAR Shadow-Detection Algorithm Based on LRSD Combined Trajectory Region Extraction
Authors: Yin Zhongzheng , Zheng Mingjie , Ren Yuwei
Published: March 2023

Shadow detection is a new method for video synthetic aperture radar moving target indication (ViSAR-GMTI). The shadow formed by the target occlusion will reflect its real position, preventing the defocusing or offset of the moving target from making it difficult to identify the target during imaging. To achieve high-precision shadow detection, this paper proposes a video SAR moving target shadow-detection algorithm based on low-rank sparse decomposition combined with trajectory area extraction. Based on the low-rank sparse decomposition (LRSD) model, the algorithm creates a new decomposition framework combined with total variation (TV) regularization and coherence suppression items to improve the decomposition effect, and a global constraint is constructed to suppress interference using feature operators. In addition, it cooperates with the double threshold trajectory segmentation and error trajectory elimination method to further improve the detection performance. Finally, an experiment was carried out based on the video SAR data released by Sandia National Laboratory (SNL); the results prove the effectiveness of the proposed method, and the detection performance of the method is proved by comparative experiments.
Automated Detection of Glacier Surges from Sentinel-1 Surface Velocity Time Series—An Example from Svalbard
Authors: Koch Moritz , Seehaus Thorsten , Friedl Peter , Braun Matthias
Published: March 2023

Even though surge-type glaciers make up only a small percentage of all glaciers, related research contributes considerably to the general understanding of glacier flow mechanisms. Recent studies based on remote sensing techniques aimed to disentangle underlying processes related to glacier surges. They have proven the possibilities yielded by combining high performance computing and earth observation. In addition, modelling approaches to surges have seen increasing popularity, yet large spatial and temporal data about timing of surge incites are missing. We aimed to develop an algorithm that not only detects surge type glaciers but also determines the timing of a surge onset, while being computationally inexpensive, transferable, and expandable in time and space. The algorithm is based on time series analyses of glacier surface velocity derived from Sentinel-1 data. After seasonal and trend decomposition, outlier detection is performed by the General Studentized Extreme Deviate Test, an iterative algorithm well suited for outlier detection in univariate time series. To determine surges, cluster analysis is performed to identify outlier clusters, which are linked to glacier surges. We demonstrate the viability on the Svalbard archipelago for the period 2015 to 2021 where we have identified 18 glacier surges and the timing of their active phase.
On the Ion Line Calibration by Plasma Line in ISR Measurements
Authors: Yue Xinan , Liu Fanyu , Wang Junyi , Wang Yonghui , Cai Yihui , Ding Feng , Ning Baiqi , Li Mingyuan , Zhang Ning , Wang Zhongqiu , Xu Su
Published: March 2023

The radar constant calibration in incoherent scatter radar ion line processing is essential for the data quality and was not paid enough attention in previous studies. In this investigation, based on several experiments made by the newly built Sanya incoherent scatter radar (SYISR), we made and evaluated the ion line calibration by plasma line both in case study and statistically. The calibration factor had local time and altitude variations, due to the corresponding variations of the transmitted power, the radar gain, and the noise temperature. We obtained a mean calibration factor of 1.35 by the simultaneous measured plasma line and ion line electron density and applied it to a one-month ion line observation calibration. Through a co-located ionosonde measured foF2 evaluation, the calibration decreased the mean deviation from −1.92 MHz (−18%) to −0.33 MHz (−3%), which resulted in much better agreement between the ion line foF2 after calibration and the ionosonde results. The existed deviations between after calibration and ionosonde results were due to the uncertainties either in the used calibration factor or the ionosonde measurements. An empirical Te/Ti usage in raw electron density estimation and ignoring the seasonal and short-term variations of the effecting factors might influence the calibration performance. Using the to-be-completed SYISR Tristatic System, the performance of plasma line calibration technique is expected to be improved in the future.
Impact of Climate Change Parameters on Groundwater Level: Implications for Two Subsidence Regions in Iran Using Geodetic Observations and Artificial Neural Networks (ANN)
Authors: Haji-Aghajany Saeid , Amerian Yazdan , Amiri-Simkooei Alireza
Published: March 2023

This study aims to investigate how changes in meteorological indicators affect groundwater resources, and hence to predict groundwater levels using these indicators, particularly in regions experiencing drought and subsidence. Precipitation, temperature, evapotranspiration and precipitable water vapor (PWV) are important meteorological parameters to predict groundwater levels. Two subsidence areas with different weather conditions were selected to conduct a comprehensive study on the effect of temperature and precipitation on groundwater level changes. The correct locations of the two subsidence areas were determined by analyzing Interferometric Synthetic Aperture Radar (InSAR) images of Sentinel-1A using the small baseline subset algorithm. The interferograms were processed to correct tropospheric effects using the advanced integration method. Specifying the exact locations of the two areas, the meteorological parameters were downscaled using the Statistical DownScaling Model (SDSM), synoptic observations, meteorological data, and the General Circulation Model (GCM). An Artificial Neural Network (ANN) was then employed to predict the groundwater level changes as a function of meteorological data, including Global Positioning System (GPS)-based PWV and the evapotranspiration index. The trained ANN, along with the downscaled meteorological indicators, was used to predict groundwater level changes over two time periods. In the first period, the prediction was performed over the current years to investigate the performance of the method using the available data, whereas in the second period, the prediction was performed for the coming years, up until 2030. The results confirmed the high performance of the prediction algorithm, and the importance of including PWV and evapotranspiration in groundwater level predictions. The Pearson correlation coefficient was used to check the relationship between groundwater level changes and meteorological variables. The statistical significance of these coefficients was tested at the significance level α=0.05. In more than 80% of the cases, the correlation coefficients were statistically significant, reaching more than 0.70 in some of the months. It is also observed that an increase in the depth of groundwater level has an obvious relationship with an increase in temperature and a decrease in rainfall.
A Fast Hyperspectral Tracking Method via Channel Selection
Authors: Zhang Yifan , Li Xu , Wei Baoguo , Li Lixin , Yue Shigang
Published: March 2023

With the rapid development of hyperspectral imaging technology, object tracking in hyperspectral video has become a research hotspot. Real-time object tracking for hyperspectral video is a great challenge. We propose a fast hyperspectral object tracking method via a channel selection strategy to improve the tracking speed significantly. First, we design a strategy of channel selection to select few candidate channels from many hyperspectral video channels, and then send the candidates to the subsequent background-aware correlation filter (BACF) tracking framework. In addition, we consider the importance of local and global spectral information in feature extraction, and further improve the BACF tracker to ensure high tracking accuracy. In the experiments carried out in this study, the proposed method was verified and the best performance was achieved on the publicly available hyperspectral dataset of the WHISPERS Hyperspectral Objecting Tracking Challenge. Our method was superior to state-of-the-art RGB-based and hyperspectral trackers, in terms of both the area under the curve (AUC) and DP@20pixels. The tracking speed of our method reached 21.9 FPS, which is much faster than that of the current most advanced hyperspectral trackers.
Robustness of Physiological Synchrony in Wearable Electrodermal Activity and Heart Rate as a Measure of Attentional Engagement to Movie Clips
Authors: Stuldreher Ivo V. , van Erp Jan B. F. , Brouwer Anne-Marie
Published: March 2023

Individuals that pay attention to narrative stimuli show synchronized heart rate (HR) and electrodermal activity (EDA) responses. The degree to which this physiological synchrony occurs is related to attentional engagement. Factors that can influence attention, such as instructions, salience of the narrative stimulus and characteristics of the individual, affect physiological synchrony. The demonstrability of synchrony depends on the amount of data used in the analysis. We investigated how demonstrability of physiological synchrony varies with varying group size and stimulus duration. Thirty participants watched six 10 min movie clips while their HR and EDA were monitored using wearable sensors (Movisens EdaMove 4 and Wahoo Tickr, respectively). We calculated inter-subject correlations as a measure of synchrony. Group size and stimulus duration were varied by using data from subsets of the participants and movie clips in the analysis. We found that for HR, higher synchrony correlated significantly with the number of answers correct for questions about the movie, confirming that physiological synchrony is associated with attention. For both HR and EDA, with increasing amounts of data used, the percentage of participants with significant synchrony increased. Importantly, we found that it did not matter how the amount of data was increased. Increasing the group size or increasing the stimulus duration led to the same results. Initial comparisons with results from other studies suggest that our results do not only apply to our specific set of stimuli and participants. All in all, the current work can act as a guideline for future research, indicating the amount of data minimally needed for robust analysis of synchrony based on inter-subject correlations.
A Comparison of Two Types of Acoustic Emission Sensors for the Characterization of Hydrogen-Induced Cracking
Authors: Liu Dandan , Wang Bin , Yang Han , Grigg Stephen
Published: March 2023

Acoustic emission (AE) technology is a non-destructive testing (NDT) technique that is able to monitor the process of hydrogen-induced cracking (HIC). AE uses piezoelectric sensors to convert the elastic waves generated from the growth of HIC into electric signals. Most piezoelectric sensors have resonance and thus are effective for a certain frequency range, and they will fundamentally affect the monitoring results. In this study, two commonly used AE sensors (Nano30 and VS150-RIC) were used for monitoring HIC processes using the electrochemical hydrogen-charging method under laboratory conditions. Obtained signals were analyzed and compared on three aspects, i.e., in signal acquisition, signal discrimination, and source location to demonstrate the influences of the two types of AE sensors. A basic reference for the selection of sensors for HIC monitoring is provided according to different test purposes and monitoring environments. Results show that signal characteristics from different mechanisms can be identified more clearly by Nano30, which is conducive to signal classification. VS150-RIC can identify HIC signals better and provide source locations more accurately. It can also acquire low-energy signals better, which is more suitable for monitoring over a long distance.
Delay-Aware and Link-Quality-Aware Geographical Routing Protocol for UANET via Dueling Deep Q-Network
Authors: Zhang Yanan , Qiu Hongbing
Published: March 2023

In an unmanned aerial vehicles ad hoc network (UANET), UAVs communicate with each other to accomplish intricate tasks collaboratively and cooperatively. However, the high mobility of UAVs, the variable link quality, and heavy traffic loads can lead to difficulties in finding an optimal communication path. We proposed a delay-aware and link-quality-aware geographical routing protocol for a UANET via the dueling deep Q-network (DLGR-2DQ) to address these problems. Firstly, the link quality was not only related to the physical layer metric, the signal-to-noise ratio, which was influenced by path loss and Doppler shifts, but also the expected transmission count of the data link layer. In addition, we also considered the total waiting time of packets in the candidate forwarding node in order to decrease the end-to-end delay. Then, we modeled the packet-forwarding process as a Markov decision process. We crafted an appropriate reward function that utilized the penalty value for each additional hop, total waiting time, and link quality to accelerate the learning of the dueling DQN algorithm. Finally, the simulation results illustrated that our proposed routing protocol outperformed others in terms of the packet delivery ratio and the average end-to-end delay.
Colorimetric and Fluorescent Sensing of Copper Ions in Water through o-Phenylenediamine-Derived Carbon Dots
Authors: Pizzoferrato Roberto , Bisauriya Ramanand , Antonaroli Simonetta , Cabibbo Marcello , Moro Artur J.
Published: March 2023

Fluorescent nitrogen and sulfur co-doped carbon dots (NSCDs) were synthesized using a simple one-step hydrothermal method starting from o-phenylenediamine (OPD) and ammonium sulfide. The prepared NSCDs presented a selective dual optical response to Cu(II) in water through the arising of an absorption band at 660 nm and simultaneous fluorescence enhancement at 564 nm. The first effect was attributed to formation of cuprammonium complexes through coordination with amino functional groups of NSCDs. Alternatively, fluorescence enhancement can be explained by the oxidation of residual OPD bound to NSCDs. Both absorbance and fluorescence showed a linear increase with an increase of Cu(II) concentration in the range 1–100 µM, with the lowest detection limit of 100 nM and 1 µM, respectively. NSCDs were successfully incorporated in a hydrogel agarose matrix for easier handling and application to sensing. The formation of cuprammonium complexes was strongly hampered in an agarose matrix while oxidation of OPD was still effective. As a result, color variations could be perceived both under white light and UV light for concentrations as low as 10 µM. Since these color changes were similarly perceived in tap and lake water samples, the present method could be a promising candidate for simple, cost-effective visual monitoring of copper onsite.
Evaluation Protocol for Analogue Intelligent Medical Radars: Towards a Systematic Approach Based on Theory and a State of the Art
Authors: Battiston Geoffray , Régnier Rémi , Galibert Olivier
Published: March 2023

We propose the basis for a systematised approach to the performance evaluation of analogue intelligent medical radars. In the first part, we review the literature on the evaluation of medical radars and compare the provided experimental elements with models from radar theory in order to identify the key physical parameters that will be useful to develop a comprehensive protocol. In the second part, we present our experimental equipment, protocol and metrics to carry out such an evaluation.
Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities
Authors: Pietrzak Paweł , Szczęsny Szymon , Huderek Damian , Przyborowski Łukasz
Published: March 2023

Spiking neural networks (SNNs) are subjects of a topic that is gaining more and more interest nowadays. They more closely resemble actual neural networks in the brain than their second-generation counterparts, artificial neural networks (ANNs). SNNs have the potential to be more energy efficient than ANNs on event-driven neuromorphic hardware. This can yield drastic maintenance cost reduction for neural network models, as the energy consumption would be much lower in comparison to regular deep learning models hosted in the cloud today. However, such hardware is still not yet widely available. On standard computer architectures consisting mainly of central processing units (CPUs) and graphics processing units (GPUs) ANNs, due to simpler models of neurons and simpler models of connections between neurons, have the upper hand in terms of execution speed. In general, they also win in terms of learning algorithms, as SNNs do not reach the same levels of performance as their second-generation counterparts in typical machine learning benchmark tasks, such as classification. In this paper, we review existing learning algorithms for spiking neural networks, divide them into categories by type, and assess their computational complexity.
Coseismic Deformation Field and Fault Slip Distribution Inversion of the 2020 Jiashi Ms 6.4 Earthquake: Considering the Atmospheric Effect with Sentinel-1 Data Interferometry
Authors: Zhang Xuedong , Li Jiaojie , Liu Xianglei , Li Ziqi , Adil Nilufar
Published: March 2023

Due to some limitations associated with the atmospheric residual phase in Sentinel-1 data interferometry during the Jiashi earthquake, the detailed spatial distribution of the line-of-sight (LOS) surface deformation field is still not fully understood. This study, therefore, proposes an inversion method of coseismic deformation field and fault slip distribution, taking atmospheric effect into account to address this issue. First, an improved inverse distance weighted (IDW) interpolation tropospheric decomposition model is utilised to accurately estimate the turbulence component in tropospheric delay. Using the joint constraints of the corrected deformation fields, the geometric parameters of the seismogenic fault and the distribution of coseismic slip are then inverted. The findings show that the coseismic deformation field (long axis strike was nearly east–west) was distributed along the Kalpingtag fault and the Ozgertaou fault, and the earthquake was found to occur in the low dip thrust nappe structural belt at the subduction interface of the block. Correspondingly, the slip model further revealed that the slips were concentrated at depths between 10 and 20 km, with a maximum slip of 0.34 m. Accordingly, the seismic magnitude of the earthquake was estimated to be Ms 6.06. Considering the geological structure in the earthquake region and the fault source parameters, we infer that the Kepingtag reverse fault is responsible for the earthquake, and the improved IDW interpolation tropospheric decomposition model can perform atmospheric correction more effectively, which is also beneficial for the source parameter inversion of the Jiashi earthquake.
Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey
Authors: Poudel Sabitri , Arafat Muhammad Yeasir , Moh Sangman
Published: March 2023

Advancements in electronics and software have enabled the rapid development of unmanned aerial vehicles (UAVs) and UAV-assisted applications. Although the mobility of UAVs allows for flexible deployment of networks, it introduces challenges regarding throughput, delay, cost, and energy. Therefore, path planning is an important aspect of UAV communications. Bio-inspired algorithms rely on the inspiration and principles of the biological evolution of nature to achieve robust survival techniques. However, the issues have many nonlinear constraints, which pose a number of problems such as time restrictions and high dimensionality. Recent trends tend to employ bio-inspired optimization algorithms, which are a potential method for handling difficult optimization problems, to address the issues associated with standard optimization algorithms. Focusing on these points, we investigate various bio-inspired algorithms for UAV path planning over the past decade. To the best of our knowledge, no survey on existing bio-inspired algorithms for UAV path planning has been reported in the literature. In this study, we investigate the prevailing bio-inspired algorithms extensively from the perspective of key features, working principles, advantages, and limitations. Subsequently, path planning algorithms are compared with each other in terms of their major features, characteristics, and performance factors. Furthermore, the challenges and future research trends in UAV path planning are summarized and discussed.
Structure, Functionality, Compatibility with Pesticides and Beneficial Microbes, and Potential Applications of a New Delivery System Based on Ink-Jet Technology
Authors: Idbella Mohamed , Giusti Domenico , Gulli Gianluca , Bonanomi Giuliano
Published: March 2023

Accurate application of agrochemicals is an important way to achieve efficient use of chemicals and to combine limited pollution with effective control of weeds, pests, and diseases. In this context, we investigate the potential application of a new delivery system based on ink-jet technology. First, we describe the structure and functionality of ink-jet technology for agrochemical delivery. We then evaluate the compatibility of ink-jet technology with a range of pesticides (four herbicides, eight fungicides, and eight insecticides) and beneficial microbes, including fungi and bacteria. Finally, we investigated the feasibility of using ink-jet technology in a microgreens production system. The ink-jet technology was compatible with herbicides, fungicides, insecticides, and beneficial microbes that remained functional after passing through the system. In addition, ink-jet technology demonstrated higher area performance compared to standard nozzles under laboratory conditions. Finally, the application of ink-jet technology to microgreens, which are characterized by small plants, was successful and opened the possibility of full automation of the pesticide application system. The ink-jet system proved to be compatible with the main classes of agrochemicals and showed significant potential for application in protected cropping systems.
Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery
Authors: Xiong Yifan , Wu Guangyuan , Li Xiaozhou
Published: March 2023

The similarity between samples is an important factor for spectral reflectance recovery. The current way of selecting samples after dividing dataset does not take subspace merging into account. An optimized method based on subspace merging for spectral recovery is proposed from single RGB trichromatic values in this paper. Each training sample is equivalent to a separate subspace, and the subspaces are merged according to the Euclidean distance. The merged center point for each subspace is obtained through many iterations, and subspace tracking is used to determine the subspace where each testing sample is located for spectral recovery. After obtaining the center points, these center points are not the actual points in the training samples. The nearest distance principle is used to replace the center points with the point in the training samples, which is the process of representative sample selection. Finally, these representative samples are used for spectral recovery. The effectiveness of the proposed method is tested by comparing it with the existing methods under different illuminants and cameras. Through the experiments, the results show that the proposed method not only shows good results in terms of spectral and colorimetric accuracy, but also in the selection representative samples.
Nested DWT–Based CNN Architecture for Monocular Depth Estimation
Authors: Paul Sandip , Mishra Deepak , Marimuthu Senthil Kumar
Published: March 2023

Applications such as medical diagnosis, navigation, robotics, etc., require 3D images. Recently, deep learning networks have been extensively applied to estimate depth. Depth prediction from 2D images poses a problem that is both ill–posed and non–linear. Such networks are computationally and time–wise expensive as they have dense configurations. Further, the network performance depends on the trained model configuration, the loss functions used, and the dataset applied for training. We propose a moderately dense encoder–decoder network based on discrete wavelet decomposition and trainable coefficients (LL, LH, HL, HH). Our Nested Wavelet–Net (NDWTN) preserves the high–frequency information that is otherwise lost during the downsampling process in the encoder. Furthermore, we study the effect of activation functions, batch normalization, convolution layers, skip, etc., in our models. The network is trained with NYU datasets. Our network trains faster with good results.