bluuebunny/crossref_metadata_2025 huggingface dataset:
Squeezing Light via Levitated Cavity Optomechanics
Authors: Li Guoyao , Yin Zhang-Qi
Published: January 2022

Squeezing light is a critical resource in both fundamental physics and precision measurement. Squeezing light has been generated through optical-parametric amplification inside an optical resonator. However, preparing the squeezing light in an optomechanical system is still a challenge for the thermal noise inevitably coupled to the system. We consider an optically levitated nano-particle in a bichromatic cavity, in which two cavity modes could be excited by the scattering photons of the dual tweezers, respectively. Based on the coherent scattering mechanism, the ultra-strong coupling between the cavity field and the torsional motion of nano-particle could be achieved for the current experimental conditions. With the back-action of the optically levitated nano-particle, the broad single-mode squeezing light can be realized in the bad cavity regime. Even at room temperature, the single-mode light can be squeezed for more than 17 dB, which is far beyond the 3 dB limit. The two-mode squeezing light can also be generated, if the optical tweezers contain two frequencies, one is on the red sideband of the cavity mode, the other is on the blue sideband. The two-mode squeezing can be maximized near the boundary of the system stable regime and is sensitive to both the cavity decay rate and the power of the optical tweezers.
Effect of Cooling Medium on LDPE Dielectric Properties
Authors: Cheng Yujia , Yu Guang , Duan Zhuohua
Published: January 2022

Polyethylene, with its excellent mechanical and dielectric properties is used as an insulator for high-voltage direct current (HVDC) transmission. In ultra-high-voltage direct current (UHVDC) transmission, the ageing of insulation materials caused by space charge under high DC voltage becomes serious. Therefore, restraining the space charge and improving the dielectric properties of HVDC cables is important. In this study, low-density polyethylene (LDPE) was used as the raw material and combined with cooling media in a vulcanizing press. A polarizing microscope was used to observe the samples’ crystal morphologies. The space charge accumulation and dispersion were detected using pulsed electro-acoustic. Additionally, dielectric properties such as electrical conductivity and dielectric frequency spectrum were tested. The grain size in the air-cooled LDPE samples was found to be large and unevenly dispersed. However, the grain sizes in the water and oil cooling LDPE samples were small. The mean charge density of the oil cooling samples was the lowest. Under a short circuit measurement, more space charges were found in the natural and rapid air cooling samples. The mean charge densities of these two samples were high, with a fast decay rate. With an 8 and 50 kV/mm electric field strength, the oil cooling samples’ conductivity was the highest and lowest, respectively.
Olefin-Metathesis-Derived Norbornene–Ethylene–Vinyl Acetate/Vinyl Alcohol Multiblock Copolymers: Impact of the Copolymer Structure on the Gas Permeation Properties
Authors: Roenko Alexey V. , Nikiforov Roman Y. , Gringolts Maria L. , Belov Nikolay A. , Denisova Yulia I. , Shandryuk Georgiy A. , Bondarenko Galina N. , Kudryavtsev Yaroslav V. , Finkelshtein Eugene S.
Published: January 2022

Commercial metathesis polynorbornene is used for the fabrication of high-damping coatings and bulk materials that dissipate vibration and impact energies. Functionalization of this non-polar polymer can improve its adhesive, gas barrier, and other properties, thereby potentially expanding its application area. With this aim, the post-modification of polynorbornene was carried out by inserting ethylene–vinyl acetate–vinyl alcohol blocks into its backbone via the cross-metathesis of polynorbornene with poly(5-acetoxy-1-octenylene) and subsequent deacetylation and hydrogenation of the obtained multiblock copolymers. For the first time, epoxy groups were introduced into the main chains of these copolymers, followed by the oxirane ring opening reaction. The influence of post-modification on the thermal, gas separation, and mechanical properties of the new copolymers was studied. It was shown that the gas permeability of the copolymer significantly depends on its composition, as well as on the amounts of hydroxyl and epoxy groups. The developed methods efficiently improve the barrier properties, reducing the oxygen permeability by 15–33 times in comparison with polynorbornene. The obtained results are promising for various applications and can be extended to a broader family of polydienes and other polymers containing backbone double bonds.
Rheology of Poly(glycidyl methacrylate) Macromolecular Nano Assemblies
Authors: Cardil Andrés , Palenzuela Miguel , Vega Juan F. , Mosquera Marta E. G.
Published: January 2022

A recently reported combined polymerization process of glycidyl methacrylate, mediated by homometallic and heterobimetallic aluminium complexes, naturally produces nano-sized macromolecular assemblies. In this work, the morphological features and the rheological properties of these novel nanoassemblies are studied. The hydrodynamic sizes of the nanoparticles in the solution range from 10 to 40 nm (in numbers), but on a flat surface they adopt a characteristic thin disk shape. The dynamic moduli have been determined in a broad range of temperatures, and the time—temperature superposition applied to obtain master curves of the whole viscoelastic response from the glassy to the terminal regions. The fragility values obtained from the temperature dependence are of m ~40, typical of van de Waals liquids, suggesting a very effective packing of the macromolecular assemblies. The rheological master curves feature a characteristic viscoelastic relaxation with the absence of elastic intermediate plateau, indicating that the systems behaved as un-entangled polymers. The analysis of the linear viscoelastic fingerprint reveals a Zimm-like dynamics at intermediate frequencies typical of unentangled systems. This behaviour resembles that observed in highly functionalized stars, dendrimers, soft colloids and microgels.
Fault Feature Extraction Method of a Permanent Magnet Synchronous Motor Based on VAE-WGAN
Authors: Zhan Liu , Xu Xiaowei , Qiao Xue , Qian Feng , Luo Qiong
Published: January 2022

This paper focuses on the difficulties that appear when the number of fault samples collected by a permanent magnet synchronous motor is too low and seriously unbalanced compared with the normal data. In order to effectively extract the fault characteristics of the motor and provide the basis for the subsequent fault mechanism and diagnosis method research, a permanent magnet synchronous motor fault feature extraction method based on variational auto-encoder (VAE) and improved generative adversarial network (GAN) is proposed in this paper. The VAE is used to extract fault features, combined with the GAN to extended data samples, and the two-dimensional features are extracted by means of mean and variance for visual analysis to measure the classification effect of the model on the features. Experimental results show that the method has good classification and generation capabilities to effectively extract the fault features of the motor and its accuracy is as high as 98.26%.
Preparation of 2D Periodic Nanopatterned Arrays through Vertical Vibration-Assisted Convective Deposition for Application in Metal-Enhanced Fluorescence
Authors: Dhanasiwawong Kittidhaj , Thamaphat Kheamrutai , Horprathum Mati , Klamchuen Annop , Phetsahai Apiwat , Limsuwan Pichet
Published: January 2022

The performance of a metal-enhanced fluorescence (MEF) substrate is fundamentally based on the orientation of the metal nanostructures on a solid substrate. In particular, two-dimensional (2D) periodic metallic nanostructures exhibit a strong confinement of the electric field between adjacent nanopatterns due to localized surface plasmon resonance (LSPR), leading to stronger fluorescence intensity enhancement. The use of vertical vibration-assisted convective deposition, a novel, simple, and highly cost-effective technique for preparing the 2D periodic nanostructure of colloidal particles with high uniformity, was therefore proposed in this work. The influences of vertical vibration amplitude and frequency on the structure of thin colloidal film, especially its uniformity, monolayer, and hexagonal close-packed (HCP) arrangement, were also investigated. It was found that the vibration amplitude affected film uniformity, whereas the vibration frequency promoted the colloidal particles to align themselves into defect-free HCP nanostructures. Furthermore, the results showed that the self-assembled 2D periodic arrays of monodisperse colloidal particles were employed as an excellent template for a Au thin-film coating in order to fabricate an efficient MEF substrate. The developed MEF substrate provided a strong plasmonic fluorescence enhancement, with a detection limit for rhodamine 6G as low as 10−9 M. This novel approach could be advantageous in further applications in the area of plasmonic sensing platforms.
Assessing the Sustainability of Industrial Equipment Life Extension Strategies through a Life Cycle Approach: Methodology and Practical Guidelines
Authors: Rossi Ludovica , Leone Deborah , Barni Andrea , Fontana Alessandro
Published: January 2022

Nowadays, Circular Economy (CE) is shifting from a nice-to-have marketing operation to a business development strategy that is central to the restoring and preservation of our natural ecosystem. However, despite motivated promotion efforts, the concrete application of this model at the micro-level is still in its infancy due to several challenges that companies face. One of these challenges is the uncertainty that businesses have in the estimation of the actual impact that adopting CE strategies can have on their activities from an environmental and economic perspective. This work aims to define a conceptual framework to be applied to the Life Cycle Extension Strategies (LCES) of products, which is meant to understand whether the application of a strategy is beneficial from an environmental and economic point of view. In order to address the identified need, a list of indicators to be exploited in the LCES performance analysis has been designed and divided into the two categories of environmental and economic indicators. On the top of this, a calculation methodology that is based on life cycle perspective approaches is applied, which exploits a gap-based approach and thus, compares the effects generated by the linear strategy and those created by the LCES analysis. The evaluation approach is meant to highlight the possible advantages and disadvantages offered by the CE model for product lifecycles. The conceptual framework is tested through an industrial case study and further analysis leading to the improvement of the calculation methodology is discussed.
Bearing Fault Diagnosis Based on a Novel Adaptive ADSD-gcForest Model
Authors: Zhai Shuo , Wang Zhenghua , Gao Dong
Published: January 2022

With the continuous improvement of industrial production requirements, bearings work significantly under strong noise interference, which makes it difficult to extract fault features. Deep Learning-based approaches are promising for bearing diagnosis. They can extract fault information efficiently and conduct accurate diagnosis. However, the structure of deep learning is often determined by trial and error, which is time-consuming and lacks theoretical support. To address the above problems, an adaptive (Adaptive Depthwise Separable Dilated Convolution and multi-grained cascade forest) ADSD-gcForest fault diagnosis model is proposed in this paper. Multiscale convolution combined with convolutional attention mechanism (CBAM) concentrates on effectively extracting fault information under strong noise, and the Meta-Activate or Not (Meta-ACON) activation function is integrated to adaptively optimize the model structure according to the characteristics of input samples, then gcForest outputs the final diagnosis result as the classifier. The experiment compares the effects of three bearings failure diagnoses under various noise and load conditions. The experimental results show the effectiveness and practicability of the proposed method.
Effect of Mammalian Tissue Source on the Molecular and Macroscopic Characteristics of UV-Cured Type I Collagen Hydrogel Networks
Authors: Brooker Charles , Tronci Giuseppe
Published: January 2022

The tissue source of type I collagen is critical to ensure scalability and regulation-friendly clinical translation of new medical device prototypes. However, the selection of a commercial source of collagen that fulfils both aforementioned requirements and is compliant with new manufacturing routes is challenging. This study investigates the effect that type I collagen extracted from three different mammalian tissues has on the molecular and macroscopic characteristics of a new UV-cured collagen hydrogel. Pepsin-solubilised bovine atelocollagen (BA) and pepsin-solubilised porcine atelocollagen (PA) were selected as commercially available raw materials associated with varying safety risks and compared with in-house acid-extracted type I collagen from rat tails (CRT). All raw materials displayed the typical dichroic and electrophoretic characteristics of type I collagen, while significantly decreased lysine content was measured on samples of PA. Following covalent functionalisation with 4-vinylbenzyl chloride (4VBC), BA and CRT products generated comparable UV-cured hydrogels with significantly increased averaged gel content (G ≥ 97 wt.%), while the porcine variants revealed the highest swelling ratio (SR = 2224 ± 242 wt.%) and an order of magnitude reduction in compression modulus (Ec = 6 ± 2 kPa). Collectively, these results support the use of bovine tissues as a chemically viable source of type I collagen for the realisation of UV-cured hydrogels with competitive mechanical properties and covalent network architectures.
Shinran as Global Philosopher
Authors: Mattice Sarah
Published: January 2022

Gutoku Shinran 愚禿親鸞 (1173–1263) is one of Japan’s most creative and influential thinkers. He is the (posthumous) founder of what ultimately became Jōdo Shinshū, better known today as Shin Buddhism, the most widely practiced form of Buddhism in Japan. Despite this, his work has not received the global attention of other historical Japanese philosophical figures such as Kūkai 空海 (774–835) or Dōgen 道元 (1200–1253). The relationships of influence between Shin Buddhism in general—or Shinran’s work more specifically—and earlier Chinese sources, especially non-Buddhist sources, are complex, rarely examined in much detail, and often buried under layers of interpretive difficulties, made all the more challenging for contemporary Anglophone scholars by the ways in which Shin Buddhism has been marginalized in much of the philosophical scholarship on East Asian traditions. Exploring his work through a lens of connection to the broader Chinese philosophical landscape reveals new insights, both for our understanding of Shinran’s philosophical project, and for contemporary comparative engagement across East Asian traditions, helping to resituate Shinran as a globally significant philosopher.
Multi-Fidelity Information Fusion to Model the Position-Dependent Modal Properties of Milling Robots
Authors: Busch Maximilian , Zaeh Michael F.
Published: January 2022

Robotic machining is a promising technology for post-processing large additively manufactured parts. However, the applicability and efficiency of robot-based machining processes are restricted by dynamic instabilities (e.g., due to external excitation or regenerative chatter). To prevent such instabilities, the pose-dependent structural dynamics of the robot must be accurately modeled. To do so, a novel data-driven information fusion approach is proposed: the spatial behavior of the robot’s modal parameters is modeled in a horizontal plane using probabilistic machine learning techniques. A probabilistic formulation allows an estimation of the model uncertainties as well, which increases the model reliability and robustness. To increase the predictive performance, an information fusion scheme is leveraged: information from a rigid body model of the fundamental behavior of the robot’s structural dynamics is fused with a limited number of estimated modal properties from experimental modal analysis. The results indicate that such an approach enables a user-friendly and efficient modeling method and provides reliable predictions of the directional robot dynamics within a large modeling domain.
A Dual Attention Convolutional Neural Network for Crop Classification Using Time-Series Sentinel-2 Imagery
Authors: Seydi Seyd Teymoor , Amani Meisam , Ghorbanian Arsalan
Published: January 2022

Accurate and timely mapping of crop types and having reliable information about the cultivation pattern/area play a key role in various applications, including food security and sustainable agriculture management. Remote sensing (RS) has extensively been employed for crop type classification. However, accurate mapping of crop types and extents is still a challenge, especially using traditional machine learning methods. Therefore, in this study, a novel framework based on a deep convolutional neural network (CNN) and a dual attention module (DAM) and using Sentinel-2 time-series datasets was proposed to classify crops. A new DAM was implemented to extract informative deep features by taking advantage of both spectral and spatial characteristics of Sentinel-2 datasets. The spectral and spatial attention modules (AMs) were respectively applied to investigate the behavior of crops during the growing season and their neighborhood properties (e.g., textural characteristics and spatial relation to surrounding crops). The proposed network contained two streams: (1) convolution blocks for deep feature extraction and (2) several DAMs, which were employed after each convolution block. The first stream included three multi-scale residual convolution blocks, where the spectral attention blocks were mainly applied to extract deep spectral features. The second stream was built using four multi-scale convolution blocks with a spatial AM. In this study, over 200,000 samples from six different crop types (i.e., alfalfa, broad bean, wheat, barley, canola, and garden) and three non-crop classes (i.e., built-up, barren, and water) were collected to train and validate the proposed framework. The results demonstrated that the proposed method achieved high overall accuracy and a Kappa coefficient of 98.54% and 0.981, respectively. It also outperformed other state-of-the-art classification methods, including RF, XGBOOST, R-CNN, 2D-CNN, 3D-CNN, and CBAM, indicating its high potential to discriminate different crop types.
Estimating Individual Tree Above-Ground Biomass of Chinese Fir Plantation: Exploring the Combination of Multi-Dimensional Features from UAV Oblique Photos
Authors: Lei Lingting , Chai Guoqi , Wang Yueting , Jia Xiang , Yin Tian , Zhang Xiaoli
Published: January 2022

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is one of the important tree species in plantation in southern China. Rapid and accurate acquisition of individual tree above-ground biomass (IT-AGB) information is of vital importance for precise monitoring and scientific management of Chinese fir forest resources. Unmanned Aerial Vehicle (UAV) oblique photogrammetry technology can simultaneously obtain high-density point cloud data and high spatial resolution spectral information, which has been a main remote sensing source for obtaining forest fine three-dimensional structure information and provided possibility for estimating IT-AGB. In this study, we proposed a novel approach to estimate IT-AGB by introducing the color space intensity information into a regression-based model that incorporates three-dimensional point cloud and two-dimensional spectrum feature variables, and the accuracy was evaluated using a leave-one-out cross-validation approach. The results demonstrated that the intensity variables derived from the color space were strongly correlated with the IT-AGB and obviously improved the estimation accuracy. The model constructed by the combination of point cloud variables, vegetation index and RGB spatial intensity variables had high accuracy (R2 = 0.79; RMSECV = 44.77 kg; and rRMSECV = 0.25). Comparing the performance of estimating IT-AGB models with different spatial resolution images (0.05, 0.1, 0.2, 0.5 and 1 m), the model was the best at the spatial resolution of 0.2 m, which was significantly better than that of the other four. Moreover, we also divided the individual tree canopy into four directions (East, West, South and North) to develop estimation models respectively. The result showed that the IT-AGB estimation capacity varied significantly in different directions, and the West-model had better performance, with the estimation accuracy of 67%. This study indicates the potential of using oblique photogrammetry technology to estimate AGB at an individual tree scale, which can support carbon stock estimation as well as precision forestry application.
Research of Distance-Intensity Imaging Algorithm for Pulsed LiDAR Based on Pulse Width Correction
Authors: Yan Shiyu , Yang Guohui , Li Qingyan , Wang Yue , Wang Chunhui
Published: January 2022

Walking error has been problematic for pulsed LiDAR based on a single threshold comparator. Traditionally, walk error must be suppressed by some time discrimination methods with extremely complex electronic circuits and high costs. In this paper, we propose a compact and flexible method for reducing walk error and achieving distance-intensity imaging. A single threshold comparator and commercial time digital converter chip are designed to measure the laser pulse’s time of flight and pulse width. In order to obtain first-class measurement accuracy, we designed a specific pulse width correction method based on the Kalman filter to correct the laser recording time, significantly reducing the ranging walk error by echo intensity fluctuation. In addition, the pulse width obtained by our method, which is a recording of the laser intensity, is conducive to target identification. The experiment results verified plane point clouds of various targets obtained by the proposed method with a plane flatness less than 0.34. The novel contribution of the study is to provide a highly integrated and cost-effective solution for the realization of high-precision ranging and multi-dimensional detection by pulsed LiDAR. It is valuable for realizing multi-dimension, outstanding performance, and low-cost LiDAR.
Entropy-Based Non-Local Means Filter for Single-Look SAR Speckle Reduction
Authors: Chan Debora , Gambini Juliana , Frery Alejandro C.
Published: January 2022

Speckle is an interference phenomenon that contaminates images captured by coherent illumination systems. Due to its multiplicative and non-Gaussian nature, it is challenging to eliminate. The non-local means approach to noise reduction has proven flexible and provided good results. We propose in this work a new non-local means filter for single-look speckled data using the Shannon and Rényi entropies under the G0 model. We obtain the necessary mathematical apparatus (the Fisher information matrix and asymptotic variance of maximum likelihood estimators). The similarity between samples of the patches relies on a parametric statistical test that verifies the evidence whether two samples have the same entropy or not. Then, we build the convolution mask by transforming the p-value into weights with a smooth activation function. The results are encouraging, as the filtered images have a better signal-to-noise ratio, they preserve the mean, and the edges are not severely blurred. The proposed algorithm is compared with three successful filters: SRAD (Speckle Reducing Anisotropic Diffusion), Lee, and FANS (Fast Adaptive Nonlocal SAR Despeckling), showing the new method’s competitiveness.
Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China
Authors: Wei Qinyu , Nurmemet Ilyas , Gao Minhua , Xie Boqiang
Published: January 2022

Soil salinization is a global problem that damages soil ecology and affects agricultural development. Timely management and monitoring of soil salinity are essential to achieve the most sustainable development goals in arid and semi-arid regions. It has been demonstrated that Polarimetric Synthetic Aperture Radar (PolSAR) data have a high sensitivity to the soil dielectric constant and soil surface roughness, thus having great potential for the detection of soil salinity. However, studies combining PALSAR-2 data and Landsat 8 data to invert soil salinity information are less common. The particle swarm optimization (PSO) algorithm is characterized by simple operation, fast computation, and good adaptability, but there are relatively few studies applying it to soil salinity as well. This paper takes the Keriya Oasis as an example, proposing the PSO-SVR and PSO-BPNN models by combining PSO with support vector machine regression (SVR) and back-propagation neural network (BPNN) models. Then, PALSAR-2 data, Landsat 8 data, evapotranspiration data, groundwater burial depth data, and DEM data were combined to conduct the inversion study of soil salinity in the study area. The results showed that the introduction of PSO generated a satisfactory estimating performance. The SVR model accuracy (R2) improved by 0.07 (PALSAR-2 data), 0.20 (Landsat 8 data), and 0.19 (PALSAR + Landsat data); the BP model accuracy (R2) improved by 0.03 (PALSAR-2 data), 0.24 (Landsat 8 data), and 0.12 (PALSAR + Landsat data), and then combined with the model inversion plots, we found that PALSAR + Landsat data combined with the PSO-SVR model could achieve better inversion results. The fine texture information of PALSAR-2 data can be used to better invert the soil salinity in the study area by combining it with the rich spectral information of Landsat 8 data. This study complements the research ideas and methods for soil salinization using multi-source remote sensing data to provide scientific support for salinity monitoring in the study area.
Long Term Indian Ocean Dipole (IOD) Index Prediction Used Deep Learning by convLSTM
Authors: Li Chen , Feng Yuan , Sun Tianying , Zhang Xingzhi
Published: January 2022

Indian Ocean Dipole (IOD) is a large-scale physical ocean phenomenon in the Indian Ocean that plays an important role in predicting the El Nino Southern Oscillation in the tropical Pacific. Predicting the occurrence of IOD is of great significance to the study of climate change and other marine phenomena. Generally, the IOD index is calculated to judge whether the IOD occurs. In this paper, a convolutional LSTM (convLSTM) neural network is used to build the deep learning model to predict the sea surface temperature in the next seven months and calculate the IOD index. Through the analysis of marine atmospheric data with complex temporal and spatial relationships, the wind field signal knowledge of the physical ocean is introduced to predict IOD phenomenon by combining the prior knowledge of the physical ocean and deep learning. The experimental results show that the average correlation of IOD index time series to the true IOD index time series is 82.87% from 2015 to 2018, seven months ahead for IOD prediction. IOD manifests as sea surface temperature (SST) anomaly changes, and this thesis verifies that the wind field signal information has a positive impact on the prediction of IOD changes. Moreover, the convLSTM can predict this anomaly better. The IOD index line graph can generally fit the real IOD index variation trend, which has a profound impact on the study of the IOD phenomenon.
Decoupling Relationship between Urbanization and Carbon Sequestration in the Pearl River Delta from 2000 to 2020
Authors: Li Xuechen , Luo Yuhang , Wu Jiansheng
Published: January 2022

Rapid urbanization has a significant impact on the ecological environment. Net primary productivity (NPP) can effectively reflect the growth of urban vegetation and the carbon sequestration capacity of an ecosystem. Taking the rapidly growing Pearl River Delta (PRD) as our study area, the relative contributions of human activities and climate change to NPP were analyzed using an improved two-step method based on residual trend analysis. The decoupling index was used to compare the coordinated development of socioeconomic factors and the NPP in different time periods. This study lays the foundation for formulating comprehensive and reasonable urban low-carbon development measures. The results showed that (1) NPP decreased significantly before 2010, but by 2019, NPP in most regions of the PRD showed a slight increase. The NPP of new urban land was better than that of original urban land. (2) The negative contribution of climatic factors to NPP was clearer than that of human activities, and human activities contributed positively to NPP outside urban land. (3) The decoupling status of socioeconomic factors and NPP is improving, and the degree of decoupling in 2010–2019 was higher than that in 2000–2010. In conclusion, as the first forest urban agglomeration in China, the PRD has shown a good implementation of carbon sequestration policies, which can provide a reference for the coordinated development of urbanization and carbon sequestration.
Construction of Regional Ecological Security Patterns Based on Multi-Criteria Decision Making and Circuit Theory
Authors: Yu Haoran , Gu Xinchen , Liu Guihua , Fan Xin , Zhao Qi , Zhang Qiang
Published: January 2022

The scientific identification of key areas for regional ecological protection represents an important basis for maintaining regional ecological stability and enhancing regional development. It is also a developing methodology of landscape ecology (LE) research. The strategic ecological protection and integrated development of the Yangtze River Delta region is of critical significance for promoting the construction of ecological civilization in the watershed and improving societal health. Here, we analyze Anhui Province and select important ecosystem service functions (biodiversity, carbon fixation, soil conservation function, and water retention) in the study area. We construct a spatial pattern of ecosystem services for multiple scenarios based on the multi-criteria decision-making method ordered weighted averaging (OWA), calculate the trade-off degree and conservation efficiency of each scenario, identify ecological priority conservation areas, use circuit theory to explore ecological corridors and key areas and to construct regional ecological corridors, and identify regional pinch points and barrier points which may impede the construction of regional ecological security patterns. The study area is divided into an ecological protection zone, ecological buffer zone, ecological transition zone, ecological optimization zone, and ecological governance zone. Our results show that: (1) the four types of ecosystems in the study area have similar spatial distribution patterns, with an overall spatial characteristic of “high in the southwest and low in the northeast”; (2) the four types of ecosystem services in the study area show synergistic relationships at the provincial scale, but in areas with different natural and geographical characteristics, there are both synergistic and trade-off relationships between different ecosystem services; (3) a total of 121 ecological source sites were identified in the study area based on multi-criteria decision making which cover an area of 43,222.26 km2, accounting for 30.87% of the total area of the study area; and (4) a total of 250 ecological corridors, 54 pinch points, and 24 barrier points were identified in the study area based on circuit theory. Our results may provide decision support for ecological restoration and land use structure optimization in Anhui Province and other regions.
Semantic Segmentation of Very-High-Resolution Remote Sensing Images via Deep Multi-Feature Learning
Authors: Su Yanzhou , Cheng Jian , Bai Haiwei , Liu Haijun , He Changtao
Published: January 2022

Currently, an increasing number of convolutional neural networks (CNNs) focus specifically on capturing contextual features (con. feat) to improve performance in semantic segmentation tasks. However, high-level con. feat are biased towards encoding features of large objects, disregard spatial details, and have a limited capacity to discriminate between easily confused classes (e.g., trees and grasses). As a result, we incorporate low-level features (low. feat) and class-specific discriminative features (dis. feat) to boost model performance further, with low. feat helping the model in recovering spatial information and dis. feat effectively reducing class confusion during segmentation. To this end, we propose a novel deep multi-feature learning framework for the semantic segmentation of VHR RSIs, dubbed MFNet. The proposed MFNet adopts a multi-feature learning mechanism to learn more complete features, including con. feat, low. feat, and dis. feat. More specifically, aside from a widely used context aggregation module for capturing con. feat, we additionally append two branches for learning low. feat and dis. feat. One focuses on learning low. feat at a shallow layer in the backbone network through local contrast processing, while the other groups con. feat and then optimizes each class individually to generate dis. feat with better inter-class discriminative capability. Extensive quantitative and qualitative evaluations demonstrate that the proposed MFNet outperforms most state-of-the-art models on the ISPRS Vaihingen and Potsdam datasets. In particular, thanks to the mechanism of multi-feature learning, our model achieves an overall accuracy score of 91.91% on the Potsdam test set with VGG16 as a backbone, performing favorably against advanced models with ResNet101.
A Semi-Empirical Anisotropy Correction Model for UAS-Based Multispectral Images of Bare Soil
Authors: Crucil Giacomo , Zhang He , Pauly Klaas , Van Oost Kristof
Published: January 2022

The recent developments in the performance and miniaturization of uncrewed aircraft systems (UAS) and multispectral imaging sensors provide new tools for the assessment of the spatial and temporal variability of soil properties at sub-meter resolution and at relatively low costs, in comparison to traditional chemical analysis. The accuracy of multispectral data is nevertheless influenced by the anisotropic behaviour of natural surfaces, framed in the general theory of the bidirectional reflectance distribution function (BRDF). Accounting for BRDF effects in multispectral data is paramount before formulating any scientific interpretation. This study presents a semi-empirical spectral normalization methodology for UAS-based multispectral imaging datasets of bare soils to account for the effects of the BRDF, based on the application of an anisotropy factor (ANIF). A dataset of images from 15 flights over bare soil fields in the Belgian loam belt was used to calibrate a model relating the ANIF to a wide range of illumination geometry conditions by using only two angles: relative sensor-pixel-sun zenith and relative sensor-pixel-sun azimuth. The employment of ANIF-corrected images for multispectral orthomosaic generation with photogrammetric software provided spectral maps free of anisotropic-related artefacts in most cases, as assessed by several ad hoc indexes, and was also tested on an independent validation set. Most notably, the standard deviation in the measured reflectance of the same georeferenced point by different pictures decreased from 0.032 to 0.023 (p < 0.05) in the calibration dataset and from 0.037 to 0.030 in the validation dataset. The validation dataset, however, showed the presence of some systematic errors, the causes of which require further investigation.
Consistency Analysis of the GNSS Antenna Phase Center Correction Models
Authors: Zhou Renyu , Hu Zhigang , Zhao Qile , Cai Hongliang , Liu Xuanzuo , Liu Chengyi , Wang Guangxing , Kan Haoyu , Chen Liang
Published: January 2022

For the same antenna type, numerical differences among the phase center correction (PCC) models released by different institutes can reach several millimeters, which is far beyond the nominal calibration accuracy. This contribution introduces a new method to evaluate the consistency of these PCC models. We first investigated the coupling of phase center offset (PCO) and phase center variation (PCV) through simulation experiments, and the results show that the calibrated PCO values under different strategies may result in large differences, and so do PCV values due to strong coupling with PCO. This is further confirmed by field calibration experiments. Moreover, a new datum parameter is introduced to equivalently transform the PCC models to assess the consistency of PCC models of the same antenna type calibrated under different strategies or by different facilities. It is also essential to perform consistency analysis of PCC models in the coordinate domain. We further investigated these PCC models through a simulated positioning experiment. The results show that millimeter-level consistency of PCC models will lead to the same level of positioning precision in the coordinate domain. Moreover, as a comparison, both baseline positioning and PPP were performed with an antenna-type JAV_RINGANT_G3T NONE based on real observations. Multiday results showed that the average RMS of the positioning differences between PCC models from robot and anechoic chamber calibration is less than 1 mm for the baseline solution and 4 mm for the PPP solution, although the PCC model differences can reach 6 mm in L1 and 4 mm in L2, respectively. Finally, we also investigated the distribution of position biases without PCC or with inaccurate PCC. Considering the actual GPS constellation, we found that position biases have a strong correlation with latitude, if PCV values fluctuate greatly with the users’ elevation angle.
CNT/Graphite/SBS Conductive Fibers for Strain Sensing in Wearable Telerehabilitation Devices
Authors: Walter Piotr , Podsiadły Bartłomiej , Zych Marcin , Kamiński Michał , Skalski Andrzej , Raczyński Tomasz , Janczak Daniel , Jakubowska Małgorzata
Published: January 2022

Rapid growth of personal electronics with concurrent research into telerehabilitation solutions discovers opportunities to redefine the future of orthopedic rehabilitation. After joint injury or operation, convalescence includes free active range of movement exercises, such as joints bending and straightening under medical supervision. Flexion detection through wearable textile sensors provides numerous potential benefits such as: (1) reduced cost; (2) continuous monitoring; (3) remote telerehabilitation; (4) gamification; and (5) detection of risk-inducing activities in daily routine. To address this issue, novel piezoresistive multi-walled carbon nanotubes/graphite/styrene–butadiene–styrene copolymer (CNT/Gr/SBS) fiber was developed. The extrusion process allowed adjustable diameter fiber production, while being a scalable, industrially adapted method of manufacturing textile electronics. Composite fibers were highly stretchable, withstanding strains up to 285%, and exhibited exceptional piezoresistive parameters with a gauge factor of 91.64 for 0–100% strain range and 2955 for the full scope. Considering the composite’s flexibility and sensitivity during a series of cyclic loading, it was concluded that developed Gr/CNT/SBS fibers were suitable for application in wearable piezoresistive sensors for telerehabilitation application.
Degradation Study of Thin-Film Silicon Structures in a Cell Culture Medium
Authors: Wang Huachun , Tian Jingjing , Lu Bingwei , Xie Yang , Sun Pengcheng , Yin Lan , Wang Yuguang , Sheng Xing
Published: January 2022

Thin-film silicon (Si)-based transient electronics represents an emerging technology that enables spontaneous dissolution, absorption and, finally, physical disappearance in a controlled manner under physiological conditions, and has attracted increasing attention in pertinent clinical applications such as biomedical implants for on-body sensing, disease diagnostics, and therapeutics. The degradation behavior of thin-film Si materials and devices is critically dependent on the device structure as well as the environment. In this work, we experimentally investigated the dissolution of planar Si thin films and micropatterned Si pillar arrays in a cell culture medium, and systematically analyzed the evolution of their topographical, physical, and chemical properties during the hydrolysis. We discovered that the cell culture medium significantly accelerates the degradation process, and Si pillar arrays present more prominent degradation effects by creating rougher surfaces, complicating surface states, and decreasing the electrochemical impedance. Additionally, the dissolution process leads to greatly reduced mechanical strength. Finally, in vitro cell culture studies demonstrate desirable biocompatibility of corroded Si pillars. The results provide a guideline for the use of thin-film Si materials and devices as transient implants in biomedicine.
W-Band Photonic Receiver for Compact Cloud Radars
Authors: Strekalov Dmitry , Majurec Ninoslav , Matsko Andrey , Ilchenko Vladimir , Tanelli Simone , Ahmed Razi
Published: January 2022

We introduce an RF-photonics receiver concept enabling the next generation of ultra-compact millimeter wave radars suitable for cloud and precipitation profiling, planetary boundary layer observations, altimetry and surface scattering measurements. The RF-photonics receiver architecture offers some compelling advantages over traditional electronic implementations, including a reduced number of components and interfaces, leading to reduced size, weight and power (SWaP), as well as lower system noise, leading to improved sensitivity. Low instrument SWaP with increased sensitivity makes this approach particularly attractive for compact space-borne radars. We study the photonic receiver front-end both analytically and numerically and predict the feasibility of the greater than unity photonic gain and lower than ambient effective noise temperature of the device. The receiver design is optimized for W-band (94 GHz) radars, which are generally assessed to be the primary means for observing clouds in the free troposphere as well as planetary boundary layer from space.
Nonlinear Optimization of Light Field Point Cloud
Authors: Anisimov Yuriy , Rambach Jason Raphael , Stricker Didier
Published: January 2022

The problem of accurate three-dimensional reconstruction is important for many research and industrial applications. Light field depth estimation utilizes many observations of the scene and hence can provide accurate reconstruction. We present a method, which enhances existing reconstruction algorithm with per-layer disparity filtering and consistency-based holes filling. Together with that we reformulate the reconstruction result to a form of point cloud from different light field viewpoints and propose a non-linear optimization of it. The capability of our method to reconstruct scenes with acceptable quality was verified by evaluation on a publicly available dataset.
Machine Learning for Multimedia Communications
Authors: Thomos Nikolaos , Maugey Thomas , Toni Laura
Published: January 2022

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise.
Imitating Emergencies: Generating Thermal Surveillance Fall Data Using Low-Cost Human-like Dolls
Authors: Nikolov Ivan , Liu Jinsong , Moeslund Thomas
Published: January 2022

Outdoor fall detection, in the context of accidents, such as falling from heights or in water, is a research area that has not received as much attention as other automated surveillance areas. Gathering sufficient data for developing deep-learning models for such applications has also proven to be not a straight-forward task. Normally, footage of volunteer people falling is used for providing data, but that can be a complicated and dangerous process. In this paper, we propose an application for thermal images of a low-cost rubber doll falling in a harbor, for simulating real emergencies. We achieve thermal signatures similar to a human on different parts of the doll’s body. The change of these thermal signatures over time is measured, and its stability is verified. We demonstrate that, even with the size and weight differences of the doll, the produced videos of falls have a similar motion and appearance to what is expected from real people. We show that the captured thermal doll data can be used for the real-world application of pedestrian detection by running the captured data through a state-of-the-art object detector trained on real people. An average confidence score of 0.730 is achieved, compared to a confidence score of 0.761 when using footage of real people falling. The captured fall sequences using the doll can be used as a substitute to sequences of people.
Magnetoelastic Sensor Optimization for Improving Mass Monitoring
Authors: Skinner William S. , Zhang Sunny , Guldberg Robert E. , Ong Keat Ghee
Published: January 2022

Magnetoelastic sensors, typically made of magnetostrictive and magnetically-soft materials, can be fabricated from commercially available materials into a variety of shapes and sizes for their intended applications. Since these sensors are wirelessly interrogated via magnetic fields, they are good candidates for use in both research and industry, where detection of environmental parameters in closed and controlled systems is necessary. Common applications for these sensors include the investigation of physical, chemical, and biological parameters based on changes in mass loading at the sensor surface which affect the sensor’s behavior at resonance. To improve the performance of these sensors, optimization of sensor geometry, size, and detection conditions are critical to increasing their mass sensitivity and detectible range. This work focuses on investigating how the geometry of the sensor influences its resonance spectrum, including the sensor’s shape, size, and aspect ratio. In addition to these factors, heterogeneity in resonance magnitude was mapped for the sensor surface and the effect of the magnetic bias field strength on the resonance spectrum was investigated. Analysis of the results indicates that the shape of the sensor has a strong influence on the emergent resonant modes. Reducing the size of the sensor decreased the sensor’s magnitude of resonance. The aspect ratio of the sensor, along with the bias field strength, was also observed to affect the magnitude of the signal; over or under biasing and aspect ratio extremes were observed to decrease the magnitude of resonance, indicating that these parameters can be optimized for a given shape and size of magnetoelastic sensor.