bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Amplification in Compliant Mechanisms: Optimization of Plastic Types and Injection Conditions
Authors: Minh Pham Son , Nguyen Van-Thuc , Uyen Tran Minh The , Huy Vu Quang , Le Dang Hai Nguyen , Nguyen Van Thanh Tien
Published: January 2024

This study surveys the impacts of injection parameters on the deformation rate of the injected flexure hinge made from ABS, PP, and HDPE. The flexure hinges are generated with different filling time, filling pressure, filling speed, packing time, packing pressure, cooling time, and melt temperature. The amplification ratio of the samples between different injection parameters and different plastic types is measured and compared to figure out the optimal one with a high amplification ratio. The results show that the relationship between the input and output data of the ABS, PP, and HDPE flexure hinges at different injection molding parameters is a linear relation. Changing the material or many injection molding parameters of the hinge could lead to a great impact on the hinge’s performance. However, changing each parameter does not lead to a sudden change in the input and output values. Each plastic material has different optimal injection parameters and displacement behaviors. With the ABS flexure hinge, the filling pressure case has the greatest amplification ratio of 8.81, while the filling speed case has the lowest value of 4.81. With the optimal injection parameter and the input value of 105 µm, the ABS flexure hinge could create a maximum average output value of 736.6 µm. With the PP flexure hinge, the melt temperature case achieves the greatest amplification ratio of 6.73, while the filling speed case has the lowest value of 4.1. With the optimal injection parameter and the input value of 128 µm, the PP flexure hinge could create a maximum average output value of 964.8 µm. The average amplification ratio values of all injection molding parameters are 6.85, 5.41, and 4.01, corresponding to ABS, PP, and HDPE flexure hinges. Generally, the ABS flexure hinge has the highest amplification ratios, followed by the PP flexure hinge. The HDPE flexure hinge has the lowest amplification ratios among these plastic types. With the optimal injection parameter and the input value of 218 µm, the HDPE flexure hinge could create a maximum average output value of 699.8 µm. The results provide more insight into plastic flexure hinges and broaden their applications by finding the optimal injection parameters and plastic types.
Epoxy-Based Copper (Cu) Sintering Pastes for Enhanced Bonding Strength and Preventing Cu Oxidation after Sintering
Authors: Han Seong-ju , Lee Seungyeon , Jang Keon-Soo
Published: January 2024

The investigation of interconnection technologies is crucial for advancing semiconductor packaging technology. This study delved into the various methods of achieving electrical interconnections, focusing on the sintering process and composition of the epoxy. Although silver (Ag) has traditionally been utilized in the sintering process, its high cost often precludes widespread commercial applications. Copper (Cu) is a promising alternative that offers advantages, such as cost-effectiveness and high thermal and electrical conductivities. However, the mechanical robustness of the oxide layers formed on Cu surfaces results in several challenges. This research addresses these challenges by integrating epoxy, which has advantages such as adhesive capabilities, chemical resistance, and robust mechanical properties. The chemical reactivity of the epoxy was harnessed to both fortify adhesion and inhibit oxide layer formation. However, the optimal sintering performance required considering both the composite composition (20 wt% epoxy) and the specific sintering conditions (pre-heating at 200 °C and sintering at 250 °C). The experimental findings reveal a balance in the incorporation of epoxy (20 wt%) for the desired electrical and mechanical properties. In particular, the bisphenol A epoxy (Da)-containing sintered Cu chip exhibited the highest lab shear strength (35.9 MPa), whereas the sintered Cu chip without epoxy represented the lowest lab shear strength of 2.7 MPa. Additionally, the introduction of epoxy effectively curtailed the onset of oxidation in the sintered Cu chips, further enhancing their durability. For instance, 30 days after sintering, the percentage of oxygen atoms in the Da-containing sintered Cu chip (4.5%) was significantly lower than that in the sintered Cu chip without epoxy (37.6%), emphasizing the role of epoxy in improving Cu oxidation resistance. Similarly, the samples sintered with bisphenol-based epoxy binders exhibited the highest electrical and thermal conductivities after 1 month. This study provides insights into interactions between epoxy, carboxylic acid, solvents, and Cu during sintering and offers a foundation for refining the sintering conditions.
Sage-King Naming Theory: A New Perspective on Understanding Xunzi’s Doctrine of the Rectification of Names
Authors: Hao Yingting
Published: January 2024

By focusing on Xunzi’s concept of the sage-king and the sage-king naming theory, a new interpretive perspective can be applied to Xunzi’s doctrine of the rectification of names. During the Warring States period, mainstream views among Confucian and Daoist scholars tended to interpret the sage-king as the bearer of Heaven or Dao. However, Xunzi follows the principle of the division between Heaven and humanity, asserting that the sage-king is entirely subordinate to the realm of humans, embodying the ideal order of Confucian community. In light of this concept of the sage-king, this paper aims to address two questions: first, where the legitimacy of the sage-king naming prerogative comes from, and second, how the sage-king, as the master-signifier, quilts all floating signifiers to ensure the publicity and normativity of names. Moral norms emerge from the sage-king’s projection of intentions and emotions onto external objects. Sage-king naming theory provides the prerequisite for the superassertibility of moral norms, limiting the potential arbitrariness and private interpretations of names and norms, which ensures the public and authoritative nature of the social discourse system, thereby endowing the sage-kings with legitimacy. As the quilting point and stopping point, the sage-king performs the totalization and prohibition by means of which the floating names and norms are fixed and become parts of the structured Confucian discourse of meaning.
Force-Sensor-Free Implementation of a Hybrid Position–Force Control for Overconstrained Cable-Driven Parallel Robots
Authors: Guagliumi Luca , Berti Alessandro , Monti Eros , Fabritius Marc , Martin Christoph , Carricato Marco
Published: January 2024

This paper proposes a hybrid position–force control strategy for overconstrained cable-driven parallel robots (CDPRs). Overconstrained CDPRs have more cables (m) than degrees of freedom (n), and the idea of the proposed controller is to control n cables in length and the other m−n ones in force. Two controller implementations are developed, one using the motor torque and one using the motor following-error in the feedback loop for cable force control. A friction model of the robot kinematic chain is introduced to improve the accuracy of the cable force estimation. Compared to similar approaches available in the literature, the novelty of the proposed control strategy is that it does not rely on force sensors, which reduces the hardware complexity and cost. The developed control scheme is compared to classical methods that exploit force sensors and to a pure inverse kinematic controller. The experimental results show that the new controller provides good tracking of the desired cable forces, maintaining them within the given bounds. The positioning accuracy and repeatability are similar those obtained with the other controllers. The new approach also allows an online switch between position and force control of cables.
Optimizing Geo-Hazard Response: LBE-YOLO’s Innovative Lightweight Framework for Enhanced Real-Time Landslide Detection and Risk Mitigation
Authors: Du Yingjie , Xu Xiangyang , He Xuhui
Published: January 2024

Prompt detection of landslides is crucial for reducing the disaster risk and preventing landslides. However, landslide detection in practical applications still faces many challenges, such as the complexity of environmental backgrounds, the diversity of target scales, and the enormity of model weights. To address these issues, this paper proposes a lightweight LBE-YOLO model for real-time landslide detection. Firstly, a lightweight model is designed by integrating the GhostConv lightweight network with the YOLOv8n model. Inspired by GhostConv, this study innovatively designed the GhostC2f structure, which leverages linear thinking to further reduce the model parameters and computational burden. Additionally, the newly designed EGC2f structure, incorporating an attention mechanism, not only maintains the model’s lightweight characteristics but also enhances the network’s capability to extract valid information. Subsequently, the Path Aggregation Network (PAN) was optimized by introducing a bidirectional feature propagation mechanism to improve the model’s feature fusion ability. Additionally, the Bijie landslide dataset was expanded through data augmentation strategies, thereby further improving the model’s generalization capability. The experimental results indicate that, compared to the YOLOv8n model, the proposed model increased accuracy by 4.2%, while the model’s weight and computational load were reduced by 32.0% and 35.5%, respectively. This verifies the superiority of the LBE-YOLO model in landslide target detection, which will help mitigate the impacts of natural disasters.
Pentagram Arrays: A New Paradigm for DOA Estimation of Wideband Sources Based on Triangular Geometry
Authors: Khalafalla Mohammed , Jiang Kaili , Tian Kailun , Feng Hancong , Xiong Ying , Tang Bin
Published: January 2024

Antenna arrays are used for signal processing in sonar and radar direction of arrival (DOA) estimation. The well-known array geometries used in DOA estimation are uniform linear array (ULA), uniform circular array (UCA), and rectangular grid array (RGA). In these geometries, the neighboring elements are separated by a fixed distance λ/2 (λ is the wavelength), which does not perform well for d greater than λ/2. Uniform rectangular arrays introduce grating lobes, which cause poor DOA estimation performance, especially for wideband sources. Random sampling arrays are sometimes practically not realizable. Periodic geometries require numerous sensors. Based on the minimization of the number of sensors, this paper developed a novel pentagram array to address the problem of DOA estimation of wideband sources. The array has a fixed number of elements with variable element spacing and is abbreviated as (FNEVES), which offers a new idea for array design. In this study, the geometric structure is designed and mathematically analyzed. Also, a DOA signal model is designed based on a spherical radar coordinate system to derive its steering manifold matrix. The DOA estimation performance comparison with ULA and UCA geometries under the multiple signal classification (MUSIC) algorithm using different wideband scenarios is presented. For further investigation, more simulations are realized using the minimum variance distortionless (MVDR) technique (CAPON) and the subtracting signal subspace (SSS) algorithm. Simulation results demonstrate the effectiveness of the proposed geometry compared to its counterparts. In addition, the SSS, through the simulations, provided better results than the MUSIC and CAPON methods.
Automation in Middle- and Upper-Atmosphere LIDAR Operations: A Maximum Rayleigh Altitude Prediction System Based on Night Sky Imagery
Authors: Wei Junfeng , Liu Linmei , Cheng Xuewu , Fan Yi , Zhan Weiqiang , Du Lifang , Xiong Wei , Lin Zhaoxiang , Yang Guotao
Published: January 2024

A prediction system was developed to determine the maximum Rayleigh altitude (MRA) by improving the automated detection of LIDAR power-on conditions and adapting to advancements in middle- and upper-atmosphere LIDAR technology. The proposed system was developed using observational data and nighttime sky imagery collected from multiple LIDAR stations. To assess the accuracy of predictions, three key parameters were employed: mean square error, root mean square error, and mean absolute error. Among the three prediction models created through multivariate regression and autoregressive integrated moving average (ARIMA) analyses, the most suitable model was selected for predicting the MRA. One-month predictions demonstrated the accuracy of the MRA with a maximum error of no more than 5 km and an average error of less than 2 km. This technology has been successfully implemented in numerous LIDAR stations, enhancing their automation capabilities and providing key technical support for large-scale, unmanned, and operational deployments in the middle- and upper-atmosphere LIDAR systems.
High-Quality Radar Pulse Signal Acquisition and Deinterleaving under a Low Signal-to-Noise Ratio with Multi-Layer Particle Swarm Optimization
Authors: Wei Song , Fang Yuyuan , He Chao , Zhang Lei
Published: January 2024

Acquiring pulse signals of radar source is an essential component in implementing electronic support measures (ESM). The conventional signal detection or deinterleaving method are mainly applied in relatively simple environments. Currently, radar electronic reconnaissance signal processing capability is severely constrained by poor signal-to-noise ratio (SNR) and the interleaving of signals from various radar sources. This research develops a multi-layer particle swarm optimization (PSO) pulse extraction and deinterleaving technique to improve ESM’s efficacy further. First, coherent accumulation of the received signals is performed using PSO to obtain higher SNR pulse. Second, the signals from this radar source are deinterleaved using the obtained pulse. Ultimately, the aforementioned procedures are combined into a multi-layer PSO architecture to capture radar source signals and deinterleaving them at low SNRs. The suggested algorithm’s efficacy and robustness are confirmed through simulation experiments.
Lidar Optical and Microphysical Characterization of Tropospheric and Stratospheric Fire Smoke Layers Due to Canadian Wildfires Passing over Naples (Italy)
Authors: Damiano Riccardo , Amoruso Salvatore , Sannino Alessia , Boselli Antonella
Published: January 2024

In the summer of 2017, huge wildfires in the British Columbia region (Canada) led to the injection of a remarkably high concentration of biomass burning aerosol in the atmosphere. These aerosol masses reached the city of Naples, Italy, at the end of August 2017, where they were characterized by means of a multiwavelength lidar and a sun–sky–lunar photometer. Here we report on the optical and microphysical properties of this aerosol in an intriguing condition, occurring on 4 September 2017, which is characterized by an interesting multi-layered vertical distribution of the aerosol. The Lidar profiles highlighted the presence of four aerosol layers, with two located in the lower troposphere and the other two at stratospheric altitudes. A rather thorough characterization of the biomass burning aerosol was carried out. The aerosol depolarization ratio showed an increasing dependence on the altitude with averaged values of 2–4% for the tropospheric layers, which are indicative of almost spherical smoke particles, and larger values in the stratospheric layers, suggestive of aspheric particles. Lidar-derived size distributions were retrieved for the first three aerosol layers, highlighting a higher particle concentration in the fine-mode fraction for the layers observed at higher altitudes. A dominance of fine particles in the atmosphere (fine-mode fraction > 0.8) with low absorption properties (absorption AOD < 0.0025 and SSA > 0.97) was also observed over the whole atmospheric column by sun photometer data. The space-resolved results provided by the lidar data are consistent with the columnar features retrieved by the AERONET sun photometer, thus evidencing the reliability and capability of lidar characterization of atmospheric aerosol in a very interesting condition of multiple aerosol layers originating from Canadian fires overpassing the observation station.
Spatial-Spectral BERT for Hyperspectral Image Classification
Authors: Ashraf Mahmood , Zhou Xichuan , Vivone Gemine , Chen Lihui , Chen Rong , Majdard Reza Seifi
Published: January 2024

Several deep learning and transformer models have been recommended in previous research to deal with the classification of hyperspectral images (HSIs). Among them, one of the most innovative is the bidirectional encoder representation from transformers (BERT), which applies a distance-independent approach to capture the global dependency among all pixels in a selected region. However, this model does not consider the local spatial-spectral and spectral sequential relations. In this paper, a dual-dimensional (i.e., spatial and spectral) BERT (the so-called D2BERT) is proposed, which improves the existing BERT model by capturing more global and local dependencies between sequential spectral bands regardless of distance. In the proposed model, two BERT branches work in parallel to investigate relations among pixels and spectral bands, respectively. In addition, the layer intermediate information is used for supervision during the training phase to enhance the performance. We used two widely employed datasets for our experimental analysis. The proposed D2BERT shows superior classification accuracy and computational efficiency with respect to some state-of-the-art neural networks and the previously developed BERT model for this task.
Automated Workflow for High-Resolution 4D Vegetation Monitoring Using Stereo Vision
Authors: Kobe Martin , Elias Melanie , Merbach Ines , Schädler Martin , Bumberger Jan , Pause Marion , Mollenhauer Hannes
Published: January 2024

Precision agriculture relies on understanding crop growth dynamics and plant responses to short-term changes in abiotic factors. In this technical note, we present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, such as biomass, from 3D object information obtained via stereo images in the red, green, and blue (RGB) color space. The novelty of our approach lies in the automated workflow, which includes a reliable automated data pipeline for 3D point cloud reconstruction from dynamic scenes of RGB images with high spatio-temporal resolution. The setup is based on a permanent rigid and calibrated stereo camera installation and was tested over an entire growing season of winter barley at the Global Change Experimental Facility (GCEF) in Bad Lauchstädt, Germany. For this study, radiometrically aligned image pairs were captured several times per day from 3 November 2021 to 28 June 2022. We performed image preselection using a random forest (RF) classifier with a prediction accuracy of 94.2% to eliminate unsuitable, e.g., shadowed, images in advance and obtained 3D object information for 86 records of the time series using the 4D processing option of the Agisoft Metashape software package, achieving mean standard deviations (STDs) of 17.3–30.4 mm. Finally, we determined vegetation heights by calculating cloud-to-cloud (C2C) distances between a reference point cloud, computed at the beginning of the time-lapse observation, and the respective point clouds measured in succession with an absolute error of 24.9–35.6 mm in depth direction. The calculated growth rates derived from RGB stereo images match the corresponding reference measurements, demonstrating the adequacy of our method in monitoring geometric plant traits, such as vegetation heights and growth spurts during the stand development using automated workflows.
Integrating SAR, Optical, and Machine Learning for Enhanced Coastal Mangrove Monitoring in Guyana
Authors: Chan-Bagot Kim , Herndon Kelsey E. , Puzzi Nicolau Andréa , Martín-Arias Vanesa , Evans Christine , Parache Helen , Mosely Kene , Narine Zola , Zutta Brian
Published: January 2024

Mangrove forests are a biodiverse ecosystem known for a wide variety of crucial ecological services, including carbon sequestration, coastal erosion control, and prevention of saltwater intrusion. Given the ecological importance of mangrove forests, a comprehensive and up-to-date mangrove extent mapping at broad geographic scales is needed to define mangrove forest changes, assess their implications, and support restoration activities and decision making. The main objective of this study is to evaluate mangrove classifications derived from a combination of Landsat-8 OLI, Sentinel-2, and Sentinel-1 observations using a random forest (RF) machine learning (ML) algorithm to identify the best approach for monitoring Guyana’s mangrove forests on an annual basis. Algorithm accuracy was tested using high-resolution planet imagery in Collect Earth Online. Results varied widely across the different combinations of input data (overall accuracy, 88–95%; producer’s accuracy for mangroves, 50–87%; user’s accuracy for mangroves, 13–69%). The combined optical–radar classification demonstrated the best performance with an overall accuracy of 95%. Area estimates of mangrove extent ranged from 908.4 to 3645.0 hectares. A ground-based validation exercise confirmed the extent of several large, previously undocumented areas of mangrove forest loss. The results establish that a data fusion approach combining optical and radar data performs marginally better than optical-only approaches to mangrove classification. This ML approach, which leverages free and open data and a cloud-based analytics platform, can be applied to mapping other areas of mangrove forests in Guyana. This approach can also support the operational monitoring of mangrove restoration areas managed by Guyana’s National Agricultural and Research Extension Institute (NAREI).
Methodology and Modeling of UAV Push-Broom Hyperspectral BRDF Observation Considering Illumination Correction
Authors: Wang Zhuo , Li Haiwei , Wang Shuang , Song Liyao , Chen Junyu
Published: January 2024

The Bidirectional Reflectance Distribution Function (BRDF) is a critical spatial distribution parameter in the quantitative research of remote sensing and has a wide range of applications in radiometric correction, elemental inversion, and surface feature estimation. As a new means of BRDF modeling, UAV push-broom hyperspectral imaging is limited by the push-broom imaging method, and the multi-angle information is often difficult to obtain. In addition, the random variation of solar illumination during UAV low-altitude flight makes the irradiance between different push-broom hyperspectral rows and different airstrips inconsistent, which significantly affects the radiometric consistency of BRDF modeling and results in the difficulty of accurately portraying the three-dimensional spatial reflectance distribution in the UAV model. These problems largely impede the application of outdoor BRDF. Based on this, this paper proposes a fast multi-angle information acquisition scheme with a high-accuracy BRDF modeling method considering illumination variations, which mainly involves a lightweight system for BRDF acquisition and three improved BRDF models considering illumination corrections. We adopt multi-rectangular nested flight paths for multi-gray level targets, use multi-mode equipment to acquire spatial illumination changes and multi-angle reflectivity information in real-time, and introduce the illumination correction factor K through data coupling to improve the kernel, Hapke, and RPV models, and, overall, the accuracy of the improved model is increased by 20.83%, 11.11%, and 31.48%, respectively. The results show that our proposed method can acquire multi-angle information quickly and accurately using push-broom hyperspectral imaging, and the improved model eliminates the negative effect of illumination on BRDF modeling. This work is vital for expanding the multi-angle information acquisition pathway and high-efficiency and high-precision outdoor BRDF modeling.
Validation of ERA5 Boundary Layer Meteorological Variables by Remote-Sensing Measurements in the Southeast China Mountains
Authors: Wei Yiming , Peng Kecheng , Ma Yongjing , Sun Yankun , Zhao Dandan , Ren Xinbing , Yang Simin , Ahmad Masroor , Pan Xiaole , Wang Zifa , Xin Jinyuan
Published: January 2024

Mountainous terrains are typical over southeast China, with complex and diverse topography, large terrain undulations, rich geographic features, and meteorological variations. Previous studies show that ERA5 meteorological variables are generally accurate with respect to large plains or urban agglomerations, while their applicability to mountainous areas remains inconclusive. In this paper, using high-precision measurements probed by ground-based remote sensing instruments in May–July 2023 at a typical mountainous Shanghuang site in southeast China, the vertical accuracy of the ERA5 reanalysis datasets were comparatively evaluated. Our findings depict that the horizontal wind speeds of the ERA5 reanalysis data show a good performance compared to the Doppler lidar observations. In quantitative terms, ERA5 horizontal wind speeds are about 8% higher than the observed values below a height of 400 m, while above 400 m, an increasing negative bias is observed along as altitude increases. Differing from the horizontal wind speeds, there is a large discrepancy in the vertical wind speeds between the ERA5 and the observations, with a deviation of −150% to 40%. In terms of the thermal variables, the temperature extracted from ERA5 are consistent with the measurements in the low troposphere. Nevertheless, large systematic errors occur at 2000–3000 m, and the overall presentation shows that the errors gradually increase with the increase in altitude. Concerning the relative humidity, the general trend in ERA5 is similar to that observed by the microwave radiometer, but the relative errors from 500 to 2500 m range from 40% to 100%. This study also reveals that ERA5 is poorly representative and requires further improvements during extreme weather events such as rainstorms and typhoons. In particular, the horizontal wind speeds at the middle and lower levels deviate strongly from the observations. Given the importance of atmospheric thermodynamic stratifications in terms of both environmental and climatic issues, the results expand the application of the ERA5 reanalysis datasets in the mountainous areas of southeast China. More importantly, it provides credible reference data for the meteorological predictions and climate modelings in the southeast China mountainous region.
Determining Effective Temporal Windows for Rapeseed Detection Using Sentinel-1 Time Series and Machine Learning Algorithms
Authors: Maleki Saeideh , Baghdadi Nicolas , Najem Sami , Dantas Cassio Fraga , Bazzi Hassan , Ienco Dino
Published: January 2024

This study investigates the potential of Sentinel-1 (S1) multi-temporal data for the early-season mapping of the rapeseed crop. Additionally, we explore the effectiveness of limiting the portion of a considered time series to map rapeseed fields. To this end, we conducted a quantitative analysis to assess several temporal windows (periods) spanning different phases of the rapeseed phenological cycle in the following two scenarios relating to the availability or constraints of providing ground samples for different years: (i) involving the same year for both training and the test, assuming the availability of ground samples for each year; and (ii) evaluating the temporal transferability of the classifier, considering the constraints of ground sampling. We employed two different classification methods that are renowned for their high performance in land cover mapping: the widely adopted random forest (RF) approach and a deep learning-based convolutional neural network, specifically the InceptionTime algorithm. To assess the classification outcomes, four evaluation metrics (recall, precision, F1 score, and Kappa) were employed. Using S1 time series data covering the entire rapeseed growth cycle, the tested algorithms achieved F1 scores close to 95% on same-year training and testing, and 92.0% when different years were used, both algorithms demonstrated robust performance. For early rapeseed detection within a two-month window post-sowing, RF and InceptionTime achieved F1 scores of 67.5% and 77.2%, respectively, and 79.8% and 88.9% when extended to six months. However, in the context of temporal transferability, both classifiers exhibited mean F1 scores below 50%. Notably, a 5-month time series, covering key growth stages such as stem elongation, inflorescence emergence, and fruit development, yielded a mean F1 score close to 95% for both algorithms when trained and tested in the same year. In the temporal transferability scenario, RF and InceptionTime achieved mean F1 scores of 92.0% and 90.0%, respectively, using a 5-month time series. Our findings underscore the importance of a concise S1 time series for effective rapeseed mapping, offering advantages in data storage and processing time. Overall, the study establishes the robustness of RF and InceptionTime in rapeseed detection scenarios, providing valuable insights for agricultural applications.
Estimates of the Land Surface Hydrology from the Community Land Model Version 5 (CLM5) with Three Meteorological Forcing Datasets over China
Authors: Wang Dayang , Wang Dagang , Mei Yiwen , Yang Qing , Ji Mingfei , Li Yuying , Liu Shaobo , Li Bailian , Huang Ya , Mo Chongxun
Published: January 2024

The land surface model (LSM) is extensively utilized to simulate terrestrial processes between land surface and atmosphere in the Earth system. Hydrology simulation is the key component of the model, which can directly reflect the capability of LSM. In this study, three offline LSM simulations were conducted over China using the Community Land Model version 5.0 (CLM5) driven by different meteorological forcing datasets, namely China Meteorological Forcing Dataset (CMFD), Global Soil Wetness Project Phase 3 (GSWP3), and bias-adjusted ERA5 reanalysis (WFDE5), respectively. Both gridded and in situ reference data, including evapotranspiration (ET), soil moisture (SM), and runoff, were employed to evaluate the performance levels of three CLM5-based simulations across China and its ten basins. In general, all simulations realistically replicate the magnitudes, spatial patterns, and seasonal cycles of ET over China when compared with remote-sensing-based ET observations. Among ten basins, Yellow River Basin (YRB) is the basin where simulations are the best, supported by the higher KGE value of 0.79. However, substantial biases occur in Northwest Rivers Basin (NWRB) with significant overestimation for CMFD and WFDE5 and underestimation for GSWP3. In addition, both grid-based or site-based evaluations of SM indicate that systematic wet biases exist in all three CLM5 simulations for shallower soil layer over nine basins of China. Comparatively, the performance levels in simulating SM for deeper soil layer are slightly better. Moreover, all three types of CLM5 simulate reasonable runoff spatial patterns, among which CMFD can capture more detailed information, but GSWP3 presents more comparable change trends of runoff when compared to the reference data. In summary, this study explored the capacity of CLM5 driven by different meteorological forcing data, and the assessment results may provide important insights for the future developments and applications of LSM.
Research on the Ionospheric Delay of Long-Range Short-Wave Propagation Based on a Regression Analysis
Authors: Jiang Xiaoli , Li Huimin , Guo Lixin , Ye Dalin , Yang Kehu , Li Jiawen
Published: January 2024

The ionospheric delay effect is inevitable when a radio signal propagates through the ionosphere. It has been extensively studied in the L-band. Typically, the influence of the ionosphere on the code phase and carrier phase is regarded as the same. In this study, a numerical ratio between the group delay time and phase advance time with reference to the study of the L-band is investigated in the short-wave band. The variation in the numerical ratio with frequency, critical frequency, and elevation angle is discussed in detail. There appears an interesting phenomenon wherein the elevation angle has a greater effect on the ratio than the frequency and critical frequency. The numerical ratio increases with an increasing elevation angle. On the other hand, a regression analysis is used to establish the mapping relation between the ratio and the three factors of frequency, critical frequency, and elevation angle, based on the simulation results obtained by a ray-tracing algorithm. The little relative errors between the fitted values and simulation values under different ionospheric models prove the relationship between the ratio and the three factors of frequency, critical frequency, and elevation angle.
The Relationship of Gross Primary Productivity with NDVI Rather than Solar-Induced Chlorophyll Fluorescence Is Weakened under the Stress of Drought
Authors: Zhao Wenhui , Rong Yuping , Zhou Yangzhen , Zhang Yanrong , Li Sheng , Liu Leizhen
Published: January 2024

Grasslands cover approximately one-fourth of the land in the world and play a crucial role in the carbon cycle. Therefore, quantifying the gross primary productivity (GPP) of grasslands is crucial to assess the sustainable development of terrestrial ecosystems. Drought is a widespread and damaging natural disaster worldwide, which introduces uncertainties in estimating GPP. Solar-induced chlorophyll fluorescence (SIF) is considered as an effective indicator of vegetation photosynthesis and provides new opportunities for monitoring vegetation growth under drought conditions. In this study, using downscaled GOME-2 SIF satellite products and focusing on the drought event in the Xilingol grasslands in 2009, the ability of SIF to evaluate the variations in GPP due to drought was explored. The results showed that the anomalies of SIF in July–August exhibited spatiotemporal characteristics similar to drought indicators, indicating the capability of SIF in monitoring drought. Moreover, the determination coefficient (R2) between SIF and GPP reached 0.95, indicating that SIF is a good indicator for estimating GPP. Particularly under drought conditions, the relationship between SIF and GPP (R2 = 0.90) was significantly higher than NDVI and GPP (R2 = 0.62), demonstrating the superior capability of SIF in tracking changes in grassland photosynthesis caused by drought compared to NDVI. Drought reduces the ability of NDVI to monitor GPP but does not affect that of SIF to monitor GPP. Our study provides a new approach for accurately estimating changes in GPP under drought conditions and is of significant importance for assessing the carbon dynamics of ecosystems.
BackMov: Individualized Motion Capture-Based Test to Assess Low Back Pain Mobility Recovery after Treatment
Authors: Villalba-Meneses Fernando , Guevara Cesar , Velásquez-López Paolo A. , Arias-Serrano Isaac , Guerrero-Ligña Stephanie A. , Valencia-Cevallos Camila M. , Almeida-Galárraga Diego , Cadena-Morejón Carolina , Marín Javier , Marín José J.
Published: January 2024

Low back pain (LBP) is a common issue that negatively affects a person’s quality of life and imposes substantial healthcare expenses. In this study, we introduce the (Back-pain Movement) BackMov test, using inertial motion capture (MoCap) to assess lumbar movement changes in LBP patients. The test includes flexion–extension, rotation, and lateralization movements focused on the lumbar spine. To validate its reproducibility, we conducted a test-retest involving 37 healthy volunteers, yielding results to build a minimal detectable change (MDC) graph map that would allow us to see if changes in certain variables of LBP patients are significant in relation to their recovery. Subsequently, we evaluated its applicability by having 30 LBP patients perform the movement’s test before and after treatment (15 received deep oscillation therapy; 15 underwent conventional therapy) and compared the outcomes with a specialist’s evaluations. The test-retest results demonstrated high reproducibility, especially in variables such as range of motion, flexion and extension ranges, as well as velocities of lumbar movements, which stand as the more important variables that are correlated with LBP disability, thus changes in them may be important for patient recovery. Among the 30 patients, the specialist’s evaluations were confirmed using a low-back-specific Short Form (SF)-36 Physical Functioning scale, and agreement was observed, in which all patients improved their well-being after both treatments. The results from the specialist analysis coincided with changes exceeding MDC values in the expected variables. In conclusion, the BackMov test offers sensitive variables for tracking mobility recovery from LBP, enabling objective assessments of improvement. This test has the potential to enhance decision-making and personalized patient monitoring in LBP management.
Sensitive Electrochemical Detection of Ammonia Nitrogen via a Platinum–Zinc Alloy Nanoflower-Modified Carbon Cloth Electrode
Authors: Wang Guanda , Zhou Guangfeng , Zhang Qingze , He Dong , Zhao Chun , Suo Hui
Published: January 2024

As a common water pollutant, ammonia nitrogen poses a serious risk to human health and the ecological environment. Therefore, it is important to develop a simple and efficient sensing scheme to achieve accurate detection of ammonia nitrogen. Here, we report a simple fabrication electrode for the electrochemical synthesis of platinum–zinc alloy nanoflowers (PtZn NFs) on the surface of carbon cloth. The obtained PtZn NFs/CC electrode was applied to the electrochemical detection of ammonia nitrogen by differential pulse voltammetry (DPV). The enhanced electrocatalytic activity of PtZn NFs and the larger electrochemical active area of the self-supported PtZn NFs/CC electrode are conducive to improving the ammonia nitrogen detection performance of the sensitive electrode. Under optimized conditions, the PtZn NFs/CC electrode exhibits excellent electrochemical performance with a wide linear range from 1 to 1000 µM, a sensitivity of 21.5 μA μM−1 (from 1 μM to 100 μM) and a lower detection limit of 27.81 nM, respectively. PtZn NFs/CC electrodes show excellent stability and anti-interference. In addition, the fabricated electrochemical sensor can be used to detect ammonia nitrogen in tap water and lake water samples.
IOTA-Based Distributed Ledger in the Mining Industry: Efficiency, Sustainability and Transparency
Authors: Gligoric Nenad , Escuín David , Polo Lorena , Amditis Angelos , Georgakopoulos Tasos , Fraile Alberto
Published: January 2024

The paper presents a traceability framework founded upon a methodological approach specifically designed for the integration of the IOTA-based distributed ledger within the mining industry. This framework constitutes an initial stride towards the certification and labelling of sustainable material production. The efficacy of this methodology is subject to real-world evaluation within the framework of the European Commission funded project DIG_IT. Within the architectural framework, the integration of decentralized identifiers (DIDs) and the IOTA network are instrumental in effecting the encryption of data records, with associated hashes securely anchored on the explorer. Recorded environmental parameters, encompassing metrics such as pH level, turbidity, electrical conductivity, and emissions, serve as tangible evidence affirming their adherence to prevailing regulatory standards. The overarching system architecture encompasses a sophisticated Industrial Internet of Things platform (IIoTp), facilitating the seamless connection of data from a diverse array of sensors. End users, including governmental entities, mining managers, and the general public, stand to derive substantial benefits from tailored dashboards designed to facilitate the validation of data for emission compliance.
Evaluating the Use of Vibrational Spectroscopy to Detect the Level of Adulteration of Cricket Powder in Plant Flours: The Effect of the Matrix
Authors: Alagappan Shanmugam , Ma Siyu , Nastasi Joseph Robert , Hoffman Louwrens C. , Cozzolino Daniel
Published: January 2024

Edible insects have been recognised as an alternative food or feed ingredient due to their protein value for both humans and domestic animals. The objective of this study was to evaluate the ability of both near- (NIR) and mid-infrared (MIR) spectroscopy to identify and quantify the level of adulteration of cricket powder added into two plant proteins: chickpea and flaxseed meal flour. Cricket flour (CKF) was added to either commercial chickpea (CPF) or flaxseed meal flour (FxMF) at different ratios of 95:5% w/w, 90:10% w/w, 85:15% w/w, 80:20% w/w, 75:25% w/w, 70:30% w/w, 65:35% w/w, 60:40% w/w, or 50:50% w/w. The mixture samples were analysed using an attenuated total reflectance (ATR) MIR instrument and a Fourier transform (FT) NIR instrument. The partial least squares (PLS) cross-validation statistics based on the MIR spectra showed that the coefficient of determination (R2CV) and the standard error in cross-validation (SECV) were 0.94 and 6.68%, 0.91 and 8.04%, and 0.92 and 4.33% for the ALL, CPF vs. CKF, and FxMF vs. CKF mixtures, respectively. The results based on NIR showed that the cross-validation statistics R2CV and SECV were 0.95 and 3.16%, 0.98 and 1.74%, and 0.94 and 3.27% using all the samples analyzed together (ALL), the CPF vs. CKF mixture, and the FxMF vs. CKF mixture, respectively. The results of this study showed the effect of the matrix (type of flour) on the PLS-DA data in both the classification results and the PLS loadings used by the models. The different combination of flours (mixtures) showed differences in the absorbance values at specific wavenumbers in the NIR range that can be used to classify the presence of CKF. Research in this field is valuable in advancing the application of vibrational spectroscopy as routine tools in food analysis and quality control.
Voltammetric Sensor Based on the Combination of Tin and Cerium Dioxide Nanoparticles with Surfactants for Quantification of Sunset Yellow FCF
Authors: Gimadutdinova Liliya , Ziyatdinova Guzel , Davletshin Rustam
Published: January 2024

Sunset Yellow FCF (SY FCF) is one of the widely used synthetic azo dyes in the food industry whose content has to be controlled for safety reasons. Electrochemical sensors are a promising tool for this type of task. A voltammetric sensor based on a combination of tin and cerium dioxide nanoparticles (SnO2–CeO2 NPs) with surfactants has been developed for SY FCF determination. The synergetic effect of both types of NPs has been confirmed. Surfactants of various natures (sodium lauryl sulfate (SLS), Brij® 35, and hexadecylpyridinium bromide (HDPB)) have been tested as dispersive media. The best effects, i.e., the highest oxidation currents of SY FCF, have been observed in the case of HDPB. The sensor demonstrates a 4.5-fold-higher electroactive surface area and a 38-fold-higher electron transfer rate compared to the bare glassy carbon electrode (GCE). The electrooxidation of SY FCF is an irreversible, two-electron, diffusion-driven process involving proton transfer. In differential pulse mode in Britton–Robinson buffer (BRB) pH 2.0, the sensor gives a linear response to SY FCF from 0.010 to 1.0 μM and from 1.0 to 100 μM with an 8.0 nM detection limit. The absence of an interferent effect from other typical food components and colorants has been shown. The sensor has been tested on soft drinks and validated with the standard chromatographic method.
Enhanced Perception for Autonomous Vehicles at Obstructed Intersections: An Implementation of Vehicle to Infrastructure (V2I) Collaboration
Authors: Mo Yanghui , Vijay Roshan , Rufus Raphael , Boer Niels de , Kim Jungdae , Yu Minsang
Published: January 2024

In urban intersections, the sensory capabilities of autonomous vehicles (AVs) are often hindered by visual obstructions, posing significant challenges to their robust and safe operation. This paper presents an implementation study focused on enhancing the safety and robustness of Connected Automated Vehicles (CAVs) in scenarios with occluded visibility at urban intersections. A novel LiDAR Infrastructure System is established for roadside sensing, combined with Baidu Apollo’s Automated Driving System (ADS) and Cohda Wireless V2X communication hardware, and an integrated platform is established for roadside perception enhancement in autonomous driving. The field tests were conducted at the Singapore CETRAN (Centre of Excellence for Testing & Research of Autonomous Vehicles—NTU) autonomous vehicle test track, with the communication protocol adhering to SAE J2735 V2X communication standards. Communication latency and packet delivery ratio were analyzed as the evaluation metrics. The test results showed that the system can help CAV detect obstacles in advance under urban occluded scenarios.
A Data-Mining Interpretation Method of Pavement Dynamic Response Signal by Combining DBSCAN and Findpeaks Function
Authors: Liu Hailong , Yao Ruqing , Cui Chunyi , Zhao Jiuye
Published: January 2024

During a heavy traffic flow featuring a substantial number of vehicles, the data reflecting the strain response of asphalt pavement under the vehicle load exhibit notable fluctuations with abnormal values, which can be attributed to the complex operating environment. Thus, there is a need to create a real-time anomalous-data diagnosis system which could effectively extract dynamic strain features, such as peak values and peak separation from the large amount of data. This paper presents a dynamic response signal data analysis method that utilizes the DBSCAN clustering algorithm and the findpeaks function. This method is designed to analyze data collected by sensors installed within the pavement. The first step involves denoising the data using low-pass filters and other techniques. Subsequently, the DBSCAN algorithm, which has been improved using the K-Dist method, is used to diagnose abnormal data after denoising. The refined findpeaks function is further implemented to carry out the adaptive feature extraction of the denoised data which is free from anomalies. The enhanced DBSCAN algorithm is tested via simulation and illustrates its effectiveness while detecting abnormal data in the road dynamic response signal. The findpeaks function enables the relatively accurate identification of peak values, thus leading to the identification of strain signal peaks of complex multi-axle lorries. This study is valuable for efficient data processing and effective information utilization in pavement monitoring.