bluuebunny/crossref_metadata_2025 huggingface dataset:
Identifying the Coupling Coordination Relationship between Urbanization and Ecosystem Services Supply–Demand and Its Driving Forces: Case Study in Shaanxi Province, China
Authors: Liu Jiamin , Wang Hao , Hui Le , Tang Butian , Zhang Liwei , Jiao Lei
Published: July 2024

Exploring the relationship and driving forces between supply–demand of ecosystem services (ESs) and urbanization can help solve the environmental problems and promote regional sustainable development. This study analyzed the spatio-temporal distribution characteristics of supply–demand of ESs and comprehensive urbanization level (CUL) in Shaanxi Province from 2010 to 2019 and assessed the coupling relationship between ecosystem service supply–demand ratio (ESSDR) and CUL using the coupling coordination degree (CCD) model. Random forests and geographically weighted regression methods were utilized to characterize the contribution and spatial distribution of the drivers of CCD. The results showed that: (1) except for habitat quality, the ESSDR of the other three types of services as well as the comprehensive services showed a decreasing trend, CUL exhibited increasing trend; (2) Although CCD was generally increasing, a significant portion (78.51%) of regions still remained uncoordinated, with relatively better coordination shown around the Guanzhong urban agglomeration, which has a higher urbanization level; (3) The CCD in Shaanxi Province was primarily influenced by local financial income, the secondary industry, and temperature forces. In regions with high and increasing CCD, the tertiary industry was the decisive force. In other areas, there were significant spatial variations in the driving forces. These findings provide a coupled and coordinated perspective for urban ecological management, which can provide scientific reference and practical guidance for cities with different development modes.
A Power Combiner–Splitter Based on a Rat-Race Coupler for an IQ Mixer in Synthetic Aperture Radar Applications
Authors: Ruhiyat Abdurrasyid , Kurniawan Farohaji , Apriono Catur
Published: July 2024

Synthetic aperture radar (SAR) is a powerful tool in remote sensing applications that can produce high-resolution images and operate in any weather condition. It is composed of many RF components, such as the IQ mixer, which mixes the base chirp signal (IF) with the carrier signal (LO) and increases the bandwidth of the transmitted signal to twice the maximum frequency of the base chirp signal, reducing the workload of Programmable Field Gate Arrays (FPGA) and increasing the resolution of the SAR system. This research proposes a power combiner–splitter design that will be used as a supporting component to construct the IQ mixer in SAR applications based on a rat-race coupler. The measurement results show that the coupler has good S-parameter values. S11, S22, and S33 have a low reflection value below −17 dB, S13 has a high isolation value below −22 dB, and S21 and S31 have a low attenuation value below −4 dB with amplitude unbalance below 0.1 dB and phase unbalance below 1°. The 150 MHz requirement bandwidth for the RF signal is also achieved.
Spaceborne Synthetic Aperture Radar Aerial Moving Target Detection Based on Two-Dimensional Velocity Search
Authors: Hao Jialin , Yan He , Liu Hui , Xu Wenshuo , Min Zhou , Zhu Daiyin
Published: July 2024

Synthetic aperture radar (SAR) can detect moving targets on the ground/sea, and high-resolution imaging on the ground/sea has critical applications in both military and civilian fields. This paper attempts to use a spaceborne SAR system to detect and image moving targets in the air for the first time. Due to the high velocity of aerial targets, they usually appear as two-dimensional range and azimuth direction defocus in SAR images, and clutter will also have a profound impact on target detection. To solve the above problems, a method of detecting and focusing on a spaceborne SAR target based on a two-dimensional velocity search is proposed by combining the BP algorithm. According to the current environment of the aerial target and the number of system channels, the clutter suppression methods are set and combined with two-dimensional velocity search with different precision, the Shannon entropy under different search velocity groups is used to obtain the search velocity group closest to the actual velocity and realize the integrated processing of moving target detection–focused imaging parameter estimation. Combined with simulation data, the effectiveness of the proposed method is verified.
Impact of Fragmentation on Carbon Uptake in Subtropical Forest Landscapes in Zhejiang Province, China
Authors: Jiao Jiejie , Cheng Yan , Hong Pinghua , Ma Jun , Yao Liangjin , Jiang Bo , Xu Xia , Wu Chuping
Published: July 2024

Global changes cause widespread forest fragmentation, which, in turn, has given rise to many ecological problems; this is especially true if the forest carbon stock is profoundly impacted by fragmentation levels. However, the way in which forest carbon uptake changes with different fragmentation levels and the main pathway through which fragmentation affects forest carbon uptake are still unclear. Remote sensing data, vegetation photosynthesis models, and fragmentation models were employed to generate a time series GPP (gross primary productivity) dataset, as well as forest fragmentation levels for forest landscapes in Zhejiang province, China. We analyzed GPP variation with forest fragmentation levels and identified the relative importance of the phenology (carbon uptake period—CUP) and physiology (maximum daily GPP—GPPmax) control pathways of GPP under different fragmentation levels. The results showed that the normalized mean annual GPP data of highly fragmented forests during the period from 2000 to 2018 were significantly higher than those of other fragmentation levels, while there was almost no significant difference in the annual GPP trend of forest landscapes with all fragmentation levels. Moreover, the percentage area of the control variable, GPPmax, gradually increased with fragmentation levels; the mean GPPmax between 2000 and 2018 of high-level fragmentation was higher than that of other fragmentation levels. Our results demonstrate that the carbon uptake capacity per unit area was enhanced in highly fragmented forest areas, and the maximum photosynthetic capacity (physiology-based process) played an important role in controlling carbon uptake, especially in highly fragmented forest landscapes. Our study calls for a better and deeper understanding of the potential of forest carbon uptake, and it is necessary to explore the mechanism by which forest fragmentation changes the vegetation photosynthetic process.
Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors
Authors: Kettelgerdes Marcel , Sarmiento Nicolas , Erdogan Hüseyin , Wunderle Bernhard , Elger Gordon
Published: July 2024

With current advances in automated driving, optical sensors like cameras and LiDARs are playing an increasingly important role in modern driver assistance systems. However, these sensors face challenges from adverse weather effects like fog and precipitation, which significantly degrade the sensor performance due to scattering effects in its optical path. Consequently, major efforts are being made to understand, model, and mitigate these effects. In this work, the reverse research question is investigated, demonstrating that these measurement effects can be exploited to predict occurring weather conditions by using state-of-the-art deep learning mechanisms. In order to do so, a variety of models have been developed and trained on a recorded multiseason dataset and benchmarked with respect to performance, model size, and required computational resources, showing that especially modern vision transformers achieve remarkable results in distinguishing up to 15 precipitation classes with an accuracy of 84.41% and predicting the corresponding precipitation rate with a mean absolute error of less than 0.47 mm/h, solely based on measurement noise. Therefore, this research may contribute to a cost-effective solution for characterizing precipitation with a commercial Flash LiDAR sensor, which can be implemented as a lightweight vehicle software feature to issue advanced driver warnings, adapt driving dynamics, or serve as a data quality measure for adaptive data preprocessing and fusion.
Deblurring of Beamformed Images in the Ocean Acoustic Waveguide Using Deep Learning-Based Deconvolution
Authors: Zha Zijie , Yan Xi , Ping Xiaobin , Wang Shilong , Wang Delin
Published: July 2024

A horizontal towed linear coherent hydrophone array is often employed to estimate the spatial intensity distribution of incident plane waves scattered from the geological and biological features in an ocean acoustic waveguide using conventional beamforming. However, due to the physical limitations of the array aperture, the spatial resolution after conventional beamforming is often limited by the fat main lobe and the high sidelobes. Here, we propose a method originated from computer vision deblurring based on deep learning to enhance the spatial resolution of beamformed images. The effect of image blurring after conventional beamforming can be considered a convolution of beam pattern, which acts as a point spread function (PSF), and the original spatial intensity distributions of incident plane waves. A modified U-Net-like network is trained on a simulated dataset. The instantaneous acoustic complex amplitude is assumed following circular complex Gaussian random (CCGR) statistics. Both synthetic data and experimental data collected from the South China Sea Experiment in 2021 are used to illustrate the effectiveness of this approach, showing a maximum 700% reduction in a 3 dB width over conventional beamforming. A lower normalized mean square error (NMSE) is provided compared with other deconvolution-based algorithms, such as the Richardson–Lucy algorithm and the approximate likelihood model-based deconvolution algorithm. The method is applicable in various acoustic imaging applications that employ linear coherent hydrophone arrays with one-dimensional conventional beamforming, such as ocean acoustic waveguide remote sensing (OAWRS).
Integrated Remote Sensing Investigation of Suspected Landslides: A Case Study of the Genie Slope on the Tibetan Plateau, China
Authors: Yu Wenlong , Li Weile , Wu Zhanglei , Lu Huiyan , Xu Zhengxuan , Wang Dong , Dong Xiujun , Li Pengfei
Published: July 2024

The current deformation and stable state of slopes with historical shatter signs is a concern for engineering construction. Suspected landslide scarps were discovered at the rear edge of the Genie slope on the Tibetan Plateau during a field investigation. To qualitatively determine the current status of the surface deformation of this slope, this study used high-resolution optical remote sensing, airborne light detection and ranging (LiDAR), and interferometric synthetic aperture radar (InSAR) technologies for comprehensive analysis. The interpretation of high-resolution optical and airborne LiDAR data revealed that the rear edge of the slope exhibits three levels of scarps. However, no deformation was detected with differential InSAR (D-InSAR) analysis of ALOS-1 radar images from 2007 to 2008 or with Stacking-InSAR and small baseline subset InSAR (SBAS-InSAR) processing of Sentinel-1A radar images from 2017 to 2020. This study verified the credibility of the InSAR results using the standard deviation of the phase residuals, as well as in-borehole displacement monitoring data. A conceptual model of the slope was developed by combining field investigation, borehole coring, and horizontal exploratory tunnel data, and the results indicated that the slope is composed of steep anti-dip layered dolomite limestone and that the scarps at the trailing edges of the slope were caused by historical shallow toppling. Unlike previous remote sensing studies of deformed landslides, this paper argues that remote sensing results with reliable accuracy are also applicable to the study of undeformed slopes and can help make preliminary judgments about the stability of unexplored slopes. The study demonstrates that the long-term consistency of InSAR results in integrated remote sensing can serve as an indicator for assessing slope stability.
Anomaly Detection of Sensor Arrays of Underwater Methane Remote Sensing by Explainable Sparse Spatio-Temporal Transformer
Authors: Zhang Kai , Ni Wangze , Zhu Yudi , Wang Tao , Jiang Wenkai , Zeng Min , Yang Zhi
Published: July 2024

The increasing discovery of underwater methane leakage underscores the importance of monitoring methane emissions for environmental protection. Underwater remote sensing of methane leakage is critical and meaningful to protect the environment. The construction of sensor arrays is recognized as the most effective technique to increase the accuracy and sensitivity of underwater remote sensing of methane leakage. With the aim of improving the reliability of underwater methane remote-sensing sensor arrays, in this work, a deep learning method, specifically an explainable sparse spatio-temporal transformer, is proposed for detecting the failures of the underwater methane remote-sensing sensor arrays. The data input into the explainable sparse block could decrease the time complexity and the computational complexity (O (n)). Spatio-temporal features are extracted on various time scales by a spatio-temporal block automatically. In order to implement the data-driven early warning system, the data-driven warning return mechanism contains a warning threshold that is associated with physically disturbing information. Results show that the explainable sparse spatio-temporal transformer improves the performance of the underwater methane remote-sensing sensor array. A balanced F score (F1 score) of the model is put forward, and the anomaly accuracy is 0.92, which is superior to other reconstructed models such as convolutional_autoencoder (CAE) (0.81) and long-short term memory_autoencoder (LSTM-AE) (0.66).
Predicting China’s Maize Yield Using Multi-Source Datasets and Machine Learning Algorithms
Authors: Miao Lijuan , Zou Yangfeng , Cui Xuefeng , Kattel Giri Raj , Shang Yi , Zhu Jingwen
Published: July 2024

A timely and accurately predicted grain yield can ensure regional and global food security. The scientific community is gradually advancing the prediction of regional-scale maize yield. However, the combination of various datasets while predicting the regional-scale maize yield using simple and accurate methods is still relatively rare. Here, we have used multi-source datasets (climate dataset, satellite dataset, and soil dataset), lasso algorithm, and machine learning methods (random forest, support vector, extreme gradient boosting, BP neural network, long short-term memory network, and K-nearest neighbor regression) to predict China’s county-level maize yield. The use of multi-sourced datasets advanced the predicting accuracy of maize yield significantly compared to the single-sourced dataset. We found that the machine learning methods were superior to the lasso algorithm, while random forest, extreme gradient boosting, and support vector machine represented the most preferable methods for maize yield prediction in China (R2 ≥ 0.75, RMSE = 824–875 kg/ha, MAE = 626–651 kg/ha). The climate dataset contributed more to the prediction of maize yield, while the satellite dataset contributed to tracking the maize growth process. However, the methods’ accuracies and the dominant variables affecting maize growth varied with agricultural regions across different geographic locations. Our research serves as an important effort to examine the feasibility of multi-source datasets and machine learning techniques for regional-scale maize yield prediction. In addition, the methodology we have proposed here provides guidance for reliable yield prediction of different crops.
Multi-Decadal Trends in Aerosol Optical Depth of the Main Aerosol Species Based on MERRA-2 Reanalysis: A Case Study in the Baltic Sea Basin
Authors: Mancinelli Enrico , Passerini Giorgio , Virgili Simone , Rizza Umberto
Published: July 2024

This study analyses the trends of total aerosol and the main aerosol species over nine capitals in the Baltic Sea basin from 1989 to 2019 based on the Modern-Era Retrospective Analysis for Research and Applications, Version 2 Reanalysis. Aerosol speciation includes mineral dust, sea salt, sulphate (SO4), organic carbon (OC), and black carbon (BC). The mean total aerosol optical depth (AOD) values were the highest (up to 0.216) over the continental capitals (i.e., Warsaw, Berlin, and Vilnius). For each capital, the mean SO4 AOD was the main aerosol species, with a trend specular to total AOD. Apart from Warsaw, the mean BC AOD was the aerosol species with the lowest level. The composition of aerosols changed with respect to the species of anthropogenic origins (i.e., SO4, OC, and BC), with the percentage contribution to the total AOD decreasing for the SO4 AOD and increasing for the BC AOD. Also, the OC AOD showed an increase in the percentage contribution to total AOD for Copenhagen, Oslo, Stockholm, and the continental capitals. Anthropogenic aerosols contributed up to 90.3% of the total AOD, with the highest values over the continental capitals. For each capital, the minimum in the percentage contribution of anthropogenic AOD was between 2007 and 2008, likely due to the global financial crisis. Anthropogenic AOD as a percentage of the total AOD decreased from 1989 to 2008. Both the total and the SO4 AODs decreased over each capital. By contrast, the BC AOD increased over Stockholm, and both the OC and BC AODs increased over Berlin, Copenhagen, and Oslo. The decoupling of carbonaceous aerosols and the SO4 AOD trends was likely due to concurrent factors such as biomass burning and low-sulphur fuel policies. From 2000 to 2019, the inverse relationships between gross domestic products and SO4 AODs suggest a relative decoupling of economic growth from fossil fuels for Oslo, Stockholm, Tallinn, and Vilnius.
A GRNN-Based Model for ERA5 PWV Adjustment with GNSS Observations Considering Seasonal and Geographic Variations
Authors: Pang Haoyun , Zhang Lulu , Liu Wen , Wang Xin , Wang Yuefeng , Huang Liangke
Published: July 2024

Precipitation water vapor (PWV) is an important parameter in numerical weather forecasting and climate research. However, existing PWV adjustment models lack comprehensive consideration of seasonal and geographic factors. This study utilized the General Regression Neural Network (GRNN) algorithm and Global Navigation Satellite System (GNSS) PWV in China to construct and evaluate European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5) PWV adjustment models for various seasons and subregions based on meteorological parameters (GMPW model) and non-meteorological parameters (GFPW model). A linear model (GLPW model) was established for model accuracy comparison. The results show that: (1) taking GNSS PWV as a reference, the Bias and root mean square error (RMSE) of the GLPW, GFPW, and GMPW models are about 0/1 mm, which better weakens the systematic error of ERA5 PWV. The overall Bias of the GLPW, GFPW, and GMPW models in the Northwest (NWC), North China (NC), Tibetan Plateau (TP), and South China (SC) subregions is approximately 0 mm after adjustment. The adjusted overall RMSE of the GLPW, GFPW, and GMPW models of the four subregions are 0.81/0.71/0.62 mm, 1.15/0.95/0.77 mm, 1.66/1.26/1.05 mm, and 2.11/1.35/0.96 mm, respectively. (2) The accuracy of the three models is tested using GNSS PWV, which is not involved in the modeling. The adjusted overall RMSE of the GLPW, GFPW, and GMPW models in the four subregions are 0.89/0.85/0.83 mm, 1.61/1.58/1.27 mm, 2.11/1.75/1.68 mm and 3.65/2.48/1.79 mm, respectively. As a result, the GFPW and GMPW models have better accuracy in adjusting ERA5 PWV than the linear model GLPW. Therefore, the GFPW and GMPW models can effectively contribute to water vapor monitoring and the integration of multiple PWV datasets.
Resonant Eddy Current Sensor Design for Corrosion Detection of Reinforcing Steel
Authors: Thibbotuwa Upeksha Chathurani , Cortés Ainhoa , Casado Aurora María , Irizar Andoni
Published: July 2024

This paper introduces an LC resonator-based single-frequency eddy current (EC) sensor designed for corrosion detection in reinforcing bars (rebars) embedded within concrete structures. The work addresses the challenges of the limited detection ranges and reduced sensitivity over longer distances, prevalent in current EC sensor applications. The sensor development process involved a systematic experimental approach to carefully selecting each parameter in the LC resonator. The sensor design aimed to assess the condition of the rebar from a distance of up to 5–6 cm outside the concrete and provide insights into different corrosion levels. By examining the characteristics of the inductors, the parallel resistance Rp of the eddy current coil was identified as a key parameter reflecting the corrosion conditions in the rebar. The relationship between the Rp fluctuations and temperature variations was investigated, with the data indicating that an approximately 155 Ω variation can be expected per 1 °C change within the temperature range of 20–25 °C, allowing for temperature compensation if necessary. Subsequently, the sensor’s performance was evaluated by placing a rebar within a concrete block, where controlled mechanical degradation cycles were applied to simulate uniform corrosion in the rebar. The experimental results show that our EC sensor can detect material loss around the rebar with accuracy of approximately 0.17 mm.
Knee Angle Estimation from Surface EMG during Walking Using Attention-Based Deep Recurrent Neural Networks: Feasibility and Initial Demonstration in Cerebral Palsy
Authors: Abdelhady Mohamed , Damiano Diane L. , Bulea Thomas C.
Published: July 2024

Accurately estimating knee joint angle during walking from surface electromyography (sEMG) signals can enable more natural control of wearable robotics like exoskeletons. However, challenges exist due to variability across individuals and sessions. This study evaluates an attention-based deep recurrent neural network combining gated recurrent units (GRUs) and an attention mechanism (AM) for knee angle estimation. Three experiments were conducted. First, the GRU-AM model was tested on four healthy adolescents, demonstrating improved estimation compared to GRU alone. A sensitivity analysis revealed that the key contributing muscles were the knee flexor and extensors, highlighting the ability of the AM to focus on the most salient inputs. Second, transfer learning was shown by pretraining the model on an open source dataset before additional training and testing on the four adolescents. Third, the model was progressively adapted over three sessions for one child with cerebral palsy (CP). The GRU-AM model demonstrated robust knee angle estimation across participants with healthy participants (mean RMSE 7 degrees) and participants with CP (RMSE 37 degrees). Further, estimation accuracy improved by 14 degrees on average across successive sessions of walking in the child with CP. These results demonstrate the feasibility of using attention-based deep networks for joint angle estimation in adolescents and clinical populations and support their further development for deployment in wearable robotics.
Normal-Incidence Germanium Photodetectors Integrated with Polymer Microlenses for Optical Fiber Communication Applications
Authors: Liu Yu-Hsuan , Lin Chia-Peng , Chen Po-Wei , Tsao Chia-Tai , Lin Chun-Chi , Wu Tsung-Ting , Wang Likarn , Na Neil
Published: July 2024

We present a novel photon-acid diffusion method to integrate polymer microlenses (MLs) on a four-channel, high-speed photo-receiver consisting of normal-incidence germanium (Ge) p-i-n photodiodes (PDs) fabricated on a 200 mm Si substrate. For a 29 µm diameter PD capped with a 54 µm diameter ML, its dark current, responsivity, 3 dB bandwidth (BW), and effective aperture size at −3 V bias and 850 nm wavelength are measured to be 138 nA, 0.6 A/W, 21.4 GHz, and 54 µm, respectively. The enlarged aperture size significantly decouples the tradeoff between aperture size and BW and enhances the optical fiber misalignment tolerance from ±5 µm to ±15 µm to ease the module packaging precision. The sensitivity of the photo-receiver is measured to be −9.2 dBm at 25.78 Gb/s with a bit error rate of 10−12 using non-return-to-zero (NRZ) transmission. Reliability tests are performed, and the results show that the fabricated Ge PDs integrated with polymer MLs pass the GR-468 reliability assurance standard. The demonstrated photo-receiver, a first of its kind to the best of our knowledge, features decent performance, high yield, high throughput, low cost, and compatibility with complementary metal-oxide-semiconductor (CMOS) fabrication processes, and may be further applied to 400 Gb/s pulse-amplitude modulation four-level (PAM4) communication.
Automated Imaging to Evaluate the Exogenous Gibberellin (Ga3) Impact on Seedlings from Salt-Stressed Lettuce Seeds
Authors: Iradukunda Mark , van Iersel Marc W. , Seymour Lynne , Lu Guoyu , Ferrarezi Rhuanito Soranz
Published: July 2024

Salinity stress is a common challenge in plant growth, impacting seed quality, germination, and general plant health. Sodium chloride (NaCl) ions disrupt membranes, causing ion leakage and reducing seed viability. Gibberellic acid (GA3) treatments have been found to promote germination and mitigate salinity stress on germination and plant growth. ‘Bauer’ and ‘Muir’ lettuce (Lactuca sativa) seeds were soaked in distilled water (control), 100 mM NaCl, 100 mM NaCl + 50 mg/L GA3, and 100 mM NaCl + 150 mg/L GA3 in Petri dishes and kept in a dark growth chamber at 25 °C for 24 h. After germination, seedlings were monitored using embedded cameras, capturing red, green, and blue (RGB) images from seeding to final harvest. Despite consistent germination rates, ‘Bauer’ seeds treated with NaCl showed reduced germination. Surprisingly, the ‘Muir’ cultivar’s final dry weight differed across treatments, with the NaCl and high GA3 concentration combination yielding the poorest results (p < 0.05). This study highlights the efficacy of GA3 applications in improving germination rates. However, at elevated concentrations, it induced excessive hypocotyl elongation and pale seedlings, posing challenges for two-dimensional imaging. Nonetheless, a sigmoidal regression model using projected canopy size accurately predicted dry weight across growth stages and cultivars, emphasizing its reliability despite treatment variations (R2 = 0.96, RMSE = 0.11, p < 0.001).
Multisensory Fusion for Unsupervised Spatiotemporal Speaker Diarization
Authors: Xylogiannis Paris , Vryzas Nikolaos , Vrysis Lazaros , Dimoulas Charalampos
Published: July 2024

Speaker diarization consists of answering the question of “who spoke when” in audio recordings. In meeting scenarios, the task of labeling audio with the corresponding speaker identities can be further assisted by the exploitation of spatial features. This work proposes a framework designed to assess the effectiveness of combining speaker embeddings with Time Difference of Arrival (TDOA) values from available microphone sensor arrays in meetings. We extract speaker embeddings using two popular and robust pre-trained models, ECAPA-TDNN and X-vectors, and calculate the TDOA values via the Generalized Cross-Correlation (GCC) method with Phase Transform (PHAT) weighting. Although ECAPA-TDNN outperforms the Xvectors model, we utilize both speaker embedding models to explore the potential of employing a computationally lighter model when spatial information is exploited. Various techniques for combining the spatial–temporal information are examined in order to determine the best clustering method. The proposed framework is evaluated on two multichannel datasets: the AVLab Speaker Localization dataset and a multichannel dataset (SpeaD-M3C) enriched in the context of the present work with supplementary information from smartphone recordings. Our results strongly indicate that the integration of spatial information can significantly improve the performance of state-of-the-art deep learning diarization models, presenting a 2–3% reduction in DER compared to the baseline approach on the evaluated datasets.
Determination of Carbohydrate Composition in Lentils Using Near-Infrared Spectroscopy
Authors: López-Calabozo Rocío , Liberal Ângela , Fernandes Ângela , Revilla Isabel , Ferreira Isabel C. F. R. , Barros Lillian , Vivar-Quintana Ana M.
Published: July 2024

Carbohydrates are the main components of lentils, accounting for more than 60% of their composition. Their content is influenced by genetic factors, with different contents depending on the variety. These compounds have not only been linked to interesting health benefits, but they also have a significant influence on the techno-functional properties of lentil-derived products. In this study, the use of near-infrared spectroscopy (NIRS) to predict the concentration of total carbohydrate, fibre, starch, total sugars, fructose, sucrose and raffinose was investigated. For this purpose, six different cultivars of macrosperm (n = 37) and microsperm (n = 43) lentils have been analysed, the samples were recorded whole and ground and the suitability of both recording methods were compared. Different spectral and mathematical pre-treatments were evaluated before developing the calibration models using the Modified Partial Least Squares regression method, with a cross-validation and an external validation. The predictive models developed show excellent coefficients of determination (RSQ > 0.9) for the total sugars and fructose, sucrose, and raffinose. The recording of ground samples allowed for obtaining better models for the calibration of starch content (R > 0.8), total sugars and sucrose (R > 0.93), and raffinose (R > 0.91). The results obtained confirm that there is sufficient information in the NIRS spectral region for the development of predictive models for the quantification of the carbohydrate content in lentils.
Advanced Monitoring of Manufacturing Process through Video Analytics
Authors: Hakam Nisar , Benfriha Khaled , Meyrueis Vincent , Liotard Cyril
Published: July 2024

The digitization of production systems has revolutionized industrial monitoring. Analyzing real-time bottom-up data enables the dynamic monitoring of industrial processes. Data are collected in various types, like video frames and time signals. This article focuses on leveraging images from a vision system to monitor the manufacturing process on a computer numerical control (CNC) lathe machine. We propose a method for designing and integrating these video modules on the edge of a production line. This approach detects the presence of raw parts, measures process parameters, assesses tool status, and checks roughness in real time using image processing techniques. The efficiency is evaluated by checking the deployment, the accuracy, the responsiveness, and the limitations. Finally, a perspective is offered to use the metadata off the edge in a more complex artificial-intelligence (AI) method for predictive maintenance.
A Region-Monitoring-Type Slitless Imaging Spectrometer
Authors: Ouyang Rui , Wang Duo , Jin Longxu , Fu Tianjiao , Zhao Zhenzhang , Zhang Xingxiang
Published: July 2024

In modern scientific practice, it is necessary to consistently observe predetermined zones, with the expectation of detecting and identifying emerging targets or events inside such areas. This research presents an innovative imaging spectrometer system for the continuous monitoring of specific areas. This study begins by providing detailed information on the features and optical structure of the constructed instrument. This is then followed by simulations using optical design tools. The device has an F-number of 5, a focal length of 100 mm, a field of view of 3 × 7, and a wavelength range spanning from 400 nm to 600 nm. The optical path diagram demonstrates that the system’s dispersion and imaging pictures can be distinguished, hence fulfilling the system’s specifications. Furthermore, the utilization of a Modulation Transfer Function (MTF) graph has substantiated that the image quality indeed satisfies the specified criteria. To evaluate the instrument’s performance in the spectrum observation of fixed regions, a region-monitoring-type slitless imaging spectrometer was built. The equipment has the capability to identify a specific region and rapidly capture the spectra of objects or events that are present inside that region. The spectral data were collected effectively by the implementation of image processing techniques on the captured photos. The correlation coefficient between these data and the reference data was 0.9226, showing that the device successfully measured the target’s spectrum. Therefore, the instrument that was created successfully demonstrated its ability to capture images of the observed areas and collect spectral data from the targets located within those regions.
Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions
Authors: Vladov Serhii , Scislo Lukasz , Sokurenko Valerii , Muzychuk Oleksandr , Vysotska Victoria , Osadchy Serhii , Sachenko Anatoliy
Published: July 2024

The article’s main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network has been developed that integrates closed loops for the helicopter turboshaft engine parameters, which are regulated based on the filtering method. This made achieving almost 100% (0.995 or 99.5%) accuracy possible and reduced the loss function to 0.005 (0.5%) after 280 training epochs. An algorithm has been developed for neural network training based on the errors in backpropagation for closed loops, integrating the helicopter turboshaft engine parameters regulated based on the filtering method. It combines increasing the validation set accuracy and controlling overfitting, considering error dynamics, which preserves the model generalization ability. The adaptive training rate improves adaptation to the data changes and training conditions, improving performance. It has been mathematically proven that the helicopter turboshaft engine parameters regulating neural network closed-loop integration using the filtering method, in comparison with traditional filters (median-recursive, recursive and median), significantly improve efficiency. Moreover, that enables reduction of the errors of the 1st and 2nd types: 2.11 times compared to the median-recursive filter, 2.89 times compared to the recursive filter, and 4.18 times compared to the median filter. The achieved results significantly increase the helicopter turboshaft engine sensor readings accuracy (up to 99.5%) and reliability, ensuring aircraft efficient and safe operations thanks to improved filtering methods and neural network data integration. These advances open up new prospects for the aviation industry, improving operational efficiency and overall helicopter flight safety through advanced data processing technologies.
Domain Adaptation for Bearing Fault Diagnosis Based on SimAM and Adaptive Weighting Strategy
Authors: Tang Ziyi , Hou Xinhao , Huang Xinheng , Wang Xin , Zou Jifeng
Published: July 2024

Domain adaptation techniques are crucial for addressing the discrepancies between training and testing data distributions caused by varying operational conditions in practical bearing fault diagnosis. However, transfer fault diagnosis faces significant challenges under complex conditions with dispersed data and distinct distribution differences. Hence, this paper proposes CWT-SimAM-DAMS, a domain adaptation method for bearing fault diagnosis based on SimAM and an adaptive weighting strategy. The proposed scheme first uses Continuous Wavelet Transform (CWT) and Unsharp Masking (USM) for data preprocessing, and then feature extraction is performed using the Residual Network (ResNet) integrated with the SimAM module. This is combined with the proposed adaptive weighting strategy based on Joint Maximum Mean Discrepancy (JMMD) and Conditional Adversarial Domain Adaption Network (CDAN) domain adaptation algorithms, which minimizes the distribution differences between the source and target domains more effectively, thus enhancing domain adaptability. The proposed method is validated on two datasets, and experimental results show that it improves the accuracy of bearing fault diagnosis.
CSMC: A Secure and Efficient Visualized Malware Classification Method Inspired by Compressed Sensing
Authors: Wu Wei , Peng Haipeng , Zhu Haotian , Zhang Derun
Published: July 2024

With the rapid development of the Internet of Things (IoT), the sophistication and intelligence of sensors are continually evolving, playing increasingly important roles in smart homes, industrial automation, and remote healthcare. However, these intelligent sensors face many security threats, particularly from malware attacks. Identifying and classifying malware is crucial for preventing such attacks. As the number of sensors and their applications grow, malware targeting sensors proliferates. Processing massive malware samples is challenging due to limited bandwidth and resources in IoT environments. Therefore, compressing malware samples before transmission and classification can improve efficiency. Additionally, sharing malware samples between classification participants poses security risks, necessitating methods that prevent sample exploitation. Moreover, the complex network environments also necessitate robust classification methods. To address these challenges, this paper proposes CSMC (Compressed Sensing Malware Classification), an efficient malware classification method based on compressed sensing. This method compresses malware samples before sharing and classification, thus facilitating more effective sharing and processing. By introducing deep learning, the method can extract malware family features during compression, which classical methods cannot achieve. Furthermore, the irreversibility of the method enhances security by preventing classification participants from exploiting malware samples. Experimental results demonstrate that for malware targeting Windows and Android operating systems, CSMC outperforms many existing methods based on compressed sensing and machine or deep learning. Additionally, experiments on sample reconstruction and noise demonstrate CSMC’s capabilities in terms of security and robustness.
Receptive Field Space for Point Cloud Analysis
Authors: Jiang Zhongbin , Tao Hai , Liu Ye
Published: July 2024

Similar to convolutional neural networks for image processing, existing analysis methods for 3D point clouds often require the designation of a local neighborhood to describe the local features of the point cloud. This local neighborhood is typically manually specified, which makes it impossible for the network to dynamically adjust the receptive field’s range. If the range is too large, it tends to overlook local details, and if it is too small, it cannot establish global dependencies. To address this issue, we introduce in this paper a new concept: receptive field space (RFS). With a minor computational cost, we extract features from multiple consecutive receptive field ranges to form this new receptive field space. On this basis, we further propose a receptive field space attention mechanism, enabling the network to adaptively select the most effective receptive field range from RFS, thus equipping the network with the ability to adjust granularity adaptively. Our approach achieved state-of-the-art performance in both point cloud classification, with an overall accuracy (OA) of 94.2%, and part segmentation, achieving an mIoU of 86.0%, demonstrating the effectiveness of our method.
Ferulic Acid—A Brief Review of Its Extraction, Bioavailability and Biological Activity
Authors: Pyrzynska Krystyna
Published: July 2024

Ferulic acid is a widespread phenolic compound that occurs in seeds and leaves, both in its free form and conjugated to polysaccharides, carbohydrates, glycoproteins and lignins in the plant cell walls. It exhibits various biological activities, like antioxidant, anticarcinogenic, anti-inflammatory, hepatoprotective, antimicrobial, and antiviral activity, and it modulates enzyme activity. Given these wide potential health benefits, ferulic acid has attracted considerable research interest and may be considered a biomolecule with strong prospects as a functional food ingredient. Great attempts have been made to enhance its extraction process and recovery from natural matrices and agro-industrial wastes for its various applications relating to human health and nutrition. This review presents the recently available information on the extraction methods for quantifying ferulic acid in different samples, along with its bioavailability and stability in processing foods and biological activities.
Analysis of the Energy-Saving Effect of a Novel Central Air-Conditioning System with an Internal Heat Exchanger in Summer
Authors: Deng Sensen , Wang Dong , Zhang Kangkang , Li Mengxue , Lu Yuehong
Published: July 2024

In this paper, a novel central air-conditioning system with an internal heat exchanger is proposed and analyzed for its energy-saving effect. Two frequently used systems are chosen as the reference systems, i.e., a conventional system with a sensible heat exchanger (Reference System I) and a conventional system with a total heat exchanger (Reference System II). Analysis models are built to simulate the performance of the system. The energy-saving effects of the proposed system and the two reference systems under different conditions are theoretically calculated using a case-studied shopping mall in Nanjing, China. The results show that the influence of indoor relative humidity (RH) on the energy-saving effect is much greater than that of the indoor design temperature. Indoor design parameters have a greater impact on energy saving than outdoor design parameters. Under the studied conditions, the maximum energy-saving rates of the proposed system, Reference System I, and Reference System II, are 52.7%, 2.3%, and 12.1%, respectively. With the decrease in fresh-air ratio from 70% to 20%, the difference in energy-saving rates between the proposed system and Reference System II (Reference System I) can increase from 5% to 23% (15% to 30%). Therefore, the proposed system has obvious energy-saving potential and advantage, especially under the condition of a lower fresh-air ratio.
Water Hyacinth Biochar: A Sustainable Approach for Enhancing Soil Resistance to Acidification Stress and Nutrient Dynamics in an Acidic Nitisol of the Northwest Highlands of Ethiopia
Authors: Lewoyehu Mekuanint , Kohira Yudai , Fentie Desalew , Addisu Solomon , Sato Shinjiro
Published: July 2024

Soil acidity impacted over 43% of Ethiopia’s arable land and debilitated agricultural productivity. Due to reacidification susceptibility, high costs, and inadequate availability of lime, biochar has emerged as an alternative soil acidity ameliorator. However, biochar application, particularly from invasive plants such as water hyacinth, as a soil acidity amendment and waste management strategy has not fully expanded in Ethiopia. Therefore, this study investigated the potential of water hyacinth biochar (WHB) to enhance soil resistance to acidification stresses and nutrient dynamics in an acidic Nitisol of the northwest highlands of Ethiopia. An incubation study was conducted using nine treatments viz. control (soil only), biochar produced using a furnace (WHBf), and a grounding method (WHBg) each at 1% and 2% application rates, lime (L), fertilizer (F), 2% WHBf combined with fertilizer (2WHBfF), and lime combined with fertilizer (LF). Soil samples, except the control, underwent simulated acidification with HNO3. The results showed that WHB decelerated nitrification, reduced H+ ions released into the soil, and enhanced available phosphorus and nitrogen dynamics. After incubation, 1% and 2% WHB applications increased soil pH in the range of 0.30–0.35 and 0.72–0.86 units, respectively, compared to the limed soil. Conversely, exchangeable acidity decreased by 26.5% to 28.8% and 58.4% to 63%, respectively. The 2WHBfF treatment led to soil pH increases of 0.71 and 0.90 units, with exchangeable acidity reductions of 49.8% and 64.7% compared to the LF and F treatments, respectively. Compared to lime, WHB treatments demonstrated more effective resistance against soil acidification from nitrification and simulated acidification with HNO3. Therefore, WHB can be used as a sustainable approach to increase soil resistance against various acidification stresses while aiding in soil nutrient management. The study’s findings can offer valuable insights to change environmental challenges into sustainable soil acidity management approaches.
Digital Service Quality Measurement Model Proposal and Prototype Development
Authors: Sur Erhan , Çakır Hüseyin
Published: July 2024

Traditional service quality models, which are survey-based methods, have been noted by researchers to contain operational errors in their application. Researchers criticize service quality models such as SERVQUAL and SERVPERF for containing operational errors, high implementation costs, and the issue of response recall. Additionally, these models face difficulties when applied to different sectors, as they were developed for the retail industry. The adaptation of the model, data collection, and processing have become outdated in comparison to current information processing technologies. With the rise in the use of social media, new communication paradigms have emerged. In this new paradigm, direct communication is established between people and institutions through social media. Institutions analyze social media data using text mining and sentiment analysis methods to keep up with this change. There are studies in the literature proposing new methods for measuring service quality by separately using text mining and sentiment analysis techniques. In this study, these two techniques have been combined. It is believed that combining these two techniques will result in a more robust service quality measurement model. Additionally, an application has been developed to demonstrate the functionality of the model. A municipality was specifically chosen as the application area because social media allows for fast, efficient, and inclusive participation between citizens and the municipality. The proposed model will enable the better identification of service quality deficiencies, leading to a more efficient use of municipal resources and fostering a more sustainable understanding of the municipality. With the implementation of the model, 463,886 tweets sent to the @ankarabbld and @mavimasa accounts were analyzed to identify 10 service quality dimensions and 106 keywords representing these dimensions, which would reveal the municipality’s service quality. The sentiment analysis technique was applied to 187,084 tweets containing the identified keywords. Thus, an attempt was made to uncover the municipality’s service quality.