bluuebunny/crossref_metadata_2025 huggingface dataset:
Scenarios for Optical Encryption Using Quantum Keys
Authors: Velasco Luis , Ahmadian Morteza , Ortiz Laura , Brito Juan P. , Pastor Antonio , Rivas Jose M. , Barzegar Sima , Comellas Jaume , Martin Vicente , Ruiz Marc
Published: October 2024

Optical communications providing huge capacity and low latency remain vulnerable to a range of attacks. In consequence, encryption at the optical layer is needed to ensure secure data transmission. In our previous work, we proposed LightPath SECurity (LPSec), a secure cryptographic solution for optical transmission that leverages stream ciphers and Diffie–Hellman (DH) key exchange for high-speed optical encryption. Still, LPSec faces limitations related to key generation and key distribution. To address these limitations, in this paper, we rely on Quantum Random Number Generators (QRNG) and Quantum Key Distribution (QKD) networks. Specifically, we focus on three meaningful scenarios: In Scenario A, the two optical transponders (Tp) involved in the optical transmission are within the security perimeter of the QKD network. In Scenario B, only one Tp is within the QKD network, so keys are retrieved from a QRNG and distributed using LPSec. Finally, Scenario C extends Scenario B by employing Post-Quantum Cryptography (PQC) by implementing a Key Encapsulation Mechanism (KEM) to secure key exchanges. The scenarios are analyzed based on their security, efficiency, and applicability, demonstrating the potential of quantum-enhanced LPSec to provide secure, low-latency encryption for current optical communications. The experimental assessment, conducted on the Madrid Quantum Infrastructure, validates the feasibility of the proposed solutions.
Parallel Lossless Compression of Raw Bayer Images on FPGA-Based High-Speed Camera
Authors: Regoršek Žan , Gorkič Aleš , Trost Andrej
Published: October 2024

Digital image compression is applied to reduce camera bandwidth and storage requirements, but real-time lossless compression on a high-speed high-resolution camera is a challenging task. The article presents hardware implementation of a Bayer colour filter array lossless image compression algorithm on an FPGA-based camera. The compression algorithm reduces colour and spatial redundancy and employs Golomb–Rice entropy coding. A rule limiting the maximum code length is introduced for the edge cases. The proposed algorithm is based on integer operators for efficient hardware implementation. The algorithm is first verified as a C++ model and later implemented on AMD-Xilinx Zynq UltraScale+ device using VHDL. An effective tree-like pipeline structure is proposed to concatenate codes of compressed pixel data to generate a bitstream representing data of 16 parallel pixels. The proposed parallel compression achieves up to 56% reduction in image size for high-resolution images. Pipelined implementation without any state machine ensures operating frequencies up to 320 MHz. Parallelised operation on 16 pixels effectively increases data throughput to 40 Gbit/s while keeping the total memory requirements low due to real-time processing.
Deep Learning-Based Electric Field Enhancement Imaging Method for Brain Stroke
Authors: Zuo Tong , Jiang Lihui , Cheng Yuhan , Yu Xiaolong , Tao Xiaohui , Zhang Yan , Cao Rui
Published: October 2024

In clinical settings, computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) are commonly employed in brain imaging to assist clinicians in determining the type of stroke in patients. However, these modalities are associated with potential hazards or limitations. In contrast, microwave imaging emerges as a promising technique, offering advantages such as non-ionizing radiation, low cost, lightweight, and portability. The primary challenges faced by microwave tomography include the severe ill-posedness of the electromagnetic inverse scattering problem and the time-consuming nature and unsatisfactory resolution of iterative quantitative algorithms. This paper proposes a learning electric field enhancement imaging method (LEFEIM) to achieve quantitative brain imaging based on a microwave tomography system. LEFEIM comprises two cascaded networks. The first, based on a convolutional neural network, utilizes the electric field from the receiving antenna to predict the electric field distribution within the imaging domain. The second network employs the electric field distribution as input to learn the dielectric constant distribution, thereby realizing quantitative brain imaging. Compared to the Born Iterative Method (BIM), LEFEIM significantly improves imaging time, while enhancing imaging quality and goodness-of-fit to a certain extent. Simultaneously, LEFEIM exhibits anti-noise capabilities.
Machine Learning-Based Approach towards Identification of Pharmaceutical Suspensions Exploiting Speckle Pattern Images
Authors: Bello Valentina , Coghe Luca , Gerbasi Alessia , Figus Elena , Dagliati Arianna , Merlo Sabina
Published: October 2024

Parenteral artificial nutrition (PAN) is a lifesaving medical treatment for many patients worldwide. Administration of the wrong PAN drug can lead to severe consequences on patients’ health, including death in the worst cases. Thus, their correct identification, just before injection, is of crucial importance. Since most of these drugs appear as turbid liquids, they cannot be easily discriminated simply by means of basic optical analyses. To overcome this limitation, in this work, we demonstrate that the combination of speckle pattern (SP) imaging and artificial intelligence can provide precise classifications of commercial pharmaceutical suspensions for PAN. Towards this aim, we acquired SP images of each sample and extracted several statistical parameters from them. By training two machine learning algorithms (a Random Forest and a Multi-Layer Perceptron Network), we were able to identify the drugs with accurate performances. The novelty of this work lies in the smart combination of SP imaging and machine learning for realizing an optical sensing platform. For the first time, to our knowledge, this approach is exploited to identify PAN drugs.
Isotropic Brain MRI Reconstruction from Orthogonal Scans Using 3D Convolutional Neural Network
Authors: Tian Jinsha , Xiao Canjun , Zhu Hongjin
Published: October 2024

As an alternative to true isotropic 3D imaging, image super-resolution (SR) has been applied to reconstruct an isotropic 3D volume from multiple anisotropic scans. However, traditional SR methods struggle with inadequate performance, prolonged processing times, and the necessity for manual feature extraction. Motivated by the exceptional representational ability and automatic feature extraction of convolutional neural networks (CNNs), in this work, we present an end-to-end isotropic MRI reconstruction strategy based on deep learning. The proposed method is based on 3D convolutional neural networks (3D CNNs), which can effectively capture the 3D structural features of MRI volumes and accurately predict potential structure. In addition, the proposed method takes multiple orthogonal scans as input and thus enables the model to use more complementary information from different dimensions for precise inference. Experimental results show that the proposed algorithm achieves promising performance in terms of both quantitative and qualitative assessments. In addition, it can process a 3D volume with a size of 256 × 256 × 256 in less than 1 min with the support of an NVIDIA GeForce GTX 1080Ti GPU, which suggests that it is not only a quantitatively superior method but also a practical one.
Development and Validation of a Portable EIT System for Real-Time Respiratory Monitoring
Authors: Alvarado-Arriagada Fabian , Fernández-Arroyo Bruno , Rebolledo Samuel , Pino Esteban J.
Published: October 2024

This work contributes to the improvement of novel medical technologies for the prevention and treatment of diseases. Electrical impedance tomography (EIT) has gained attention as a valuable tool for non-invasive monitoring providing real-time insights. The purpose of this work is to develop and validate a novel portable EIT system with a small form factor for respiratory monitoring. The device uses a 16-electrode architecture with adjacent stimulation and measurement patterns, an integrated circuit current source and a single high-speed ADC operating with multiplexers to stimulate and measure across all electrodes. Tests were conducted on 25 healthy subjects who performed a pulmonary function test with a flowmeter while using the EIT device. The results showed a good performance of the device, which was able to recognize all respirations correctly, and from the EIT signals and images, correlations of 96.7% were obtained for instantaneous respiratory rate and 96.1% for tidal volume prediction. These results validate the preliminary technical feasibility of the EIT system and demonstrates its potential as a reliable tool for non-invasive respiratory assessment. The significance of this work lies in its potential to democratize advanced respiratory monitoring technologies, making them accessible to a wider population, including those in remote or underserved areas.
Effective Acoustic Model-Based Beamforming Training for Static and Dynamic Hri Applications
Authors: Luzanto Alejandro , Bohmer Nicolás , Mahu Rodrigo , Alvarado Eduardo , Stern Richard M. , Becerra Yoma Néstor
Published: October 2024

Human–robot collaboration will play an important role in the fourth industrial revolution in applications related to hostile environments, mining, industry, forestry, education, natural disaster and defense. Effective collaboration requires robots to understand human intentions and tasks, which involves advanced user profiling. Voice-based communication, rich in complex information, is key to this. Beamforming, a technology that enhances speech signals, can help robots extract semantic, emotional, or health-related information from speech. This paper describes the implementation of a system that provides substantially improved signal-to-noise ratio (SNR) and speech recognition accuracy to a moving robotic platform for use in human–robot interaction (HRI) applications in static and dynamic contexts. This study focuses on training deep learning-based beamformers using acoustic model-based multi-style training with measured room impulse responses (RIRs). The results show that this approach outperforms training with simulated RIRs or matched measured RIRs, especially in dynamic conditions involving robot motion. The findings suggest that training with a broad range of measured RIRs is sufficient for effective HRI in various environments, making additional data recording or augmentation unnecessary. This research demonstrates that deep learning-based beamforming can significantly improve HRI performance, particularly in challenging acoustic environments, surpassing traditional beamforming methods.
Detection and Estimation of Diffuse Signal Components Using the Periodogram
Authors: Selva Jesus
Published: October 2024

One basic limitation of using the periodogram as a frequency estimator is that any of its significant peaks may result from a diffuse (or spread) frequency component rather than a pure one. Diffuse components are common in applications such as channel estimation, in which a given periodogram peak reveals the presence of a complex multipath distribution (unresolvable propagation paths or diffuse scattering, for example). We present a method to detect the presence of a diffuse component in a given peak based on analyzing the projection of the data vector onto the span of the signature’s derivatives up to a given order. Fundamentally, a diffuse component is detected if the energy in the derivatives’ subspace is too high at the peak’s frequency, and its spread is estimated as the ratio between this last energy and the peak’s energy. The method is based on exploiting the signature’s Vandermonde structure through the properties of discrete Chebyshev polynomials. We also present an efficient numerical procedure for computing the data component in the derivatives’ span based on barycentric interpolation. The paper contains a numerical assessment of the proposed estimator and detector.
Integrated Estimation of Stress and Damage in Concrete Structure Using 2D Convolutional Neural Network Model Learned Impedance Responses of Capsule-like Smart Aggregate Sensor
Authors: Ta Quoc-Bao , Pham Ngoc-Lan , Kim Jeong-Tae
Published: October 2024

Stress and damage estimation is essential to ensure the safety and performance of concrete structures. The capsule-like smart aggregate (CSA) technique has demonstrated its potential for detecting early-stage internal damage. In this study, a 2 dimensional convolutional neural network (2D CNN) model that learned the EMI responses of a CSA sensor to integrally estimate stress and damage in concrete structures is proposed. Firstly, the overall scheme of this study is described. The CSA-based EMI damage technique method is theoretically presented by describing the behaviors of a CSA sensor embedded in a concrete structure under compressive loadings. The 2D CNN model is designed to learn and extract damage-sensitive features from a CSA’s EMI responses to estimate stress and identify damage levels in a concrete structure. Secondly, a compression experiment on a CSA-embedded concrete cylinder is carried out, and the stress–damage EMI responses of a cylinder are recorded under different applied stress levels. Finally, the feasibility of the developed model is further investigated under the effect of noises and untrained data cases. The obtained results indicate that the developed 2D CNN model can simultaneously estimate stress and damage status in the concrete structure.
Unsupervised Denoising in Spectral CT: Multi-Dimensional U-Net for Energy Channel Regularisation
Authors: Kumrular Raziye Kubra , Blumensath Thomas
Published: October 2024

Spectral Computed Tomography (CT) is a versatile imaging technique widely utilized in industry, medicine, and scientific research. This technique allows us to observe the energy-dependent X-ray attenuation throughout an object by using Photon Counting Detector (PCD) technology. However, a major drawback of spectral CT is the increase in noise due to a lower achievable photon count when using more energy channels. This challenge often complicates quantitative material identification, which is a major application of the technology. In this study, we investigate the Noise2Inverse image denoising approach for noise removal in spectral computed tomography. Our unsupervised deep learning-based model uses a multi-dimensional U-Net paired with a block-based training approach modified for additional energy-channel regularization. We conducted experiments using two simulated spectral CT phantoms, each with a unique shape and material composition, and a real scan of a biological sample containing a characteristic K-edge. Measuring the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) for the simulated data and the contrast-to-noise ratio (CNR) for the real-world data, our approach not only outperforms previously used methods—namely the unsupervised Low2High method and the total variation-constrained iterative reconstruction method—but also does not require complex parameter tuning.
Dynamic Spatial-Temporal Memory Augmentation Network for Traffic Prediction
Authors: Zhang Huibing , Xie Qianxin , Shou Zhaoyu , Gao Yunhao
Published: October 2024

Traffic flow prediction plays a crucial role in the development of smart cities. However, existing studies face challenges in effectively capturing spatio-temporal contexts, handling hierarchical temporal features, and understanding spatial heterogeneity. To better manage the spatio-temporal correlations inherent in traffic flow, we present a novel model called Dynamic Spatio-Temporal Memory-Augmented Network (DSTMAN). Firstly, we design three spatial–temporal embeddings to capture dynamic spatial–temporal contexts and encode the unique characteristics of time units and spatial states. Secondly, these three spatial–temporal components are integrated to form a multi-scale spatial–temporal block, which effectively extracts hierarchical spatial–temporal dependencies. Finally, we introduce a meta-memory node bank to construct an adaptive neighborhood graph, implicitly representing spatial relationships and enhancing the learning of spatial heterogeneity through a secondary memory mechanism. Evaluation on four public datasets, including METR-LA and PEMS-BAY, demonstrates that the proposed model outperforms benchmark models such as MTGNN, DCRNN, and AGCRN. On the METR-LA dataset, our model reduces the MAE by 4% compared to MTGNN, 6.9% compared to DCRNN, and 5.8% compared to AGCRN, confirming its efficacy in traffic flow prediction.
Measurement of Hydraulic Fracture Aperture by Electromagnetic Induction
Authors: Talebkeikhah Mohsen , Moradi Alireza , Lecampion Brice
Published: October 2024

We present a new method for accurately measuring the aperture of a fluid-driven fracture. This method uses an eddy current probe located within a completion tool specifically designed to obtain the fracture aperture in the wellbore at the location where the fluid is injected into the fracture. The probe induces an eddy current in a target object, producing a magnetic field that affects the overall magnetic field. It does not have any limitations with respect to fluid pressure and temperature within a large range, making it unlike other methods. We demonstrate the accuracy and performance of the sensor under laboratory conditions. A hydraulic fracture experiment in a porous sandstone is conducted and discussed. The obtained measurement of the evolution of the fracture inlet aperture by the eddy current probe during the multiple injection cycles performed provided robust information. The residual fracture aperture (after the test) measured by the probe is in line with estimations from image processing of X-ray CT scan images as well as a thin-section analysis of sub-parts of the fractured specimen. The robustness and accuracy of this electromagnetic induction probe demonstrated herein under laboratory conditions indicate an interesting potential for field deployment.
High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks
Authors: Shi Yuzhuo , Xue Xufeng , Wang Beibei , Hao Kun , Chai Haoyi
Published: October 2024

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission.
Microwave Digital Twin Prototype for Shoulder Injury Detection
Authors: Borzooei Sahar , Tournier Pierre-Henri , Dolean Victorita , Migliaccio Claire
Published: October 2024

One of the most common shoulder injuries is the rotator cuff tear (RCT). The risk of RCTs increases with age, with a prevalence of 9.7% in those under 20 years old and up to 62% in individuals aged 80 years and older. In this article, we present first a microwave digital twin prototype (MDTP) for RCT detection, based on machine learning (ML) and advanced numerical modeling of the system. We generate a generalizable dataset of scattering parameters through flexible numerical modeling in order to bypass real-world data collection challenges. This involves solving the linear system as a result of finite element discretization of the forward problem with use of the domain decomposition method to accelerate the computations. We use a support vector machine (SVM) to differentiate between injured and healthy shoulder models. This approach is more efficient in terms of required memory resources and computing time compared with traditional imaging methods.
Multi-Source Remote Sensing Data for Wetland Information Extraction: A Case Study of the Nanweng River National Wetland Reserve
Authors: Yu Hao , Li Shicheng , Liang Zhimin , Xu Shengnan , Yang Xin , Li Xiaoyan
Published: October 2024

Wetlands play a vital role in regulating the global carbon cycle, providing biodiversity, and reducing flood risks. These functions maintain ecological balance and ensure human well-being. Timely, accurate monitoring of wetlands is essential, not only for conservation efforts, but also for achieving Sustainable Development Goals (SDGs). In this study, we combined Sentinel-1/2 images, terrain data, and field observation data collected in 2020 to better understand wetland distribution. A total of 22 feature variables were extracted from multi-source data, including spectral bands, spectral indices (especially red edge indices), terrain features, and radar features. To avoid high correlations between variables and reduce data redundancy, we selected a subset of features based on recursive feature elimination (RFE) and Pearson correlation analysis methods. We adopted the random forest (RF) method to construct six wetland delineation schemes and incorporated multiple types of characteristic variables. These variables were based on remote sensing image pixels and objects. Combining red-edge features, terrain data, and radar data significantly improved the accuracy of land cover information extracted in low-mountain and hilly areas. Moreover, the accuracy of object-oriented schemes surpassed that of pixel-level methods when applied to wetland classification. Among the three pixel-based schemes, the addition of terrain and radar data increased the overall classification accuracy by 7.26%. In the object-based schemes, the inclusion of radar and terrain data improved classification accuracy by 4.34%. The object-based classification method achieved the best results for swamps, water bodies, and built-up land, with relative accuracies of 96.00%, 90.91%, and 96.67%, respectively. Even higher accuracies were observed in the pixel-based schemes for marshes, forests, and bare land, with relative accuracies of 98.67%, 97.53%, and 80.00%, respectively. This study’s methodology can provide valuable reference information for wetland data extraction research and can be applied to a wide range of future research studies.
A Recurrent Deep Network for Gait Phase Identification from EMG Signals During Exoskeleton-Assisted Walking
Authors: Guerra Bruna Maria Vittoria , Schmid Micaela , Sozzi Stefania , Pizzocaro Serena , De Nunzio Alessandro Marco , Ramat Stefano
Published: October 2024

Lower limb exoskeletons represent a relevant tool for rehabilitating gait in patients with lower limb movement disorders. Partial assistance exoskeletons adaptively provide the joint torque needed, on top of that produced by the patient, for a correct and stable gait, helping the patient to recover an autonomous gait. Thus, the device needs to identify the different phases of the gait cycle to produce precisely timed commands that drive its joint motors appropriately. In this study, EMG signals have been used for gait phase detection considering that EMG activations lead limb kinematics by at least 120 ms. We propose a deep learning model based on bidirectional LSTM to identify stance and swing gait phases from EMG data. We built a dataset of EMG signals recorded at 1500 Hz from four muscles from the dominant leg in a population of 26 healthy subjects walking overground (WO) and walking on a treadmill (WT) using a lower limb exoskeleton. The data were labeled with the corresponding stance or swing gait phase based on limb kinematics provided by inertial motion sensors. The model was studied in three different scenarios, and we explored its generalization abilities and evaluated its applicability to the online processing of EMG data. The training was always conducted on 500-sample sequences from WO recordings of 23 subjects. Testing always involved WO and WT sequences from the remaining three subjects. First, the model was trained and tested on 500 Hz EMG data, obtaining an overall accuracy on the WO and WT test datasets of 92.43% and 91.16%, respectively. The simulation of online operation required 127 ms to preprocess and classify one sequence. Second, the trained model was evaluated against a test set built on 1500 Hz EMG data. The accuracies were lower, yet the processing times were 11 ms faster. Third, we partially retrained the model on a subset of the 1500 Hz training dataset, achieving 87.17% and 89.64% accuracy on the 1500 Hz WO and WT test sets, respectively. Overall, the proposed deep learning model appears to be a valuable candidate for entering the control pipeline of a lower limb rehabilitation exoskeleton in terms of both the achieved accuracy and processing times.
Methods for Evaluating Tibial Accelerations and Spatiotemporal Gait Parameters during Unsupervised Outdoor Movement
Authors: Silder Amy , Wong Ethan J. , Green Brian , McCloughan Nicole H. , Hoch Matthew C.
Published: October 2024

The purpose of this paper is to introduce a method of measuring spatiotemporal gait patterns, tibial accelerations, and heart rate that are matched with high resolution geographical terrain features using publicly available data. These methods were demonstrated using data from 218 Marines, who completed loaded outdoor ruck hikes between 5–20 km over varying terrain. Each participant was instrumented with two inertial measurement units (IMUs) and a GPS watch. Custom code synchronized accelerometer and positional data without a priori sensor synchronization, calibrated orientation of the IMUs in the tibial reference frame, detected and separated only periods of walking or running, and computed acceleration and spatiotemporal outcomes. GPS positional data were georeferenced with geographic information system (GIS) maps to extract terrain features such as slope, altitude, and surface conditions. This paper reveals the ease at which similar data can be gathered among relatively large groups of people with minimal setup and automated data processing. The methods described here can be adapted to other populations and similar ground-based activities such as skiing or trail running.
Muscle Oxygen Saturation Dynamics During Upper-Body Resistance Exercise
Authors: Gonzalez Adam M. , Mangine Gerald T. , Pinzone Anthony G. , Beyer Kyle S. , Townsend Jeremy R.
Published: October 2024

Research examining the changes in muscle oxygen saturation across multiple sets of resistance exercise is limited. The purpose of this study was to describe the physiological response of muscle oxygenation parameters during upper-body resistance exercise and examine the differential effects of relevant participant characteristics on resistance training performance and muscle oxygen saturation dynamics. Sixty-one recreationally trained men (n = 44; 21.8 ± 2.6 years) and women (n = 17; 20.2 ± 1.8 years) completed five-repetition maximum sets of barbell bench presses at a load equal to 75% 1-RM with a 2 min rest interval. Muscle oxygen saturation (SmO2) dynamics within the anterior deltoid were monitored using a portable near-infrared spectroscopy sensor. The percent change in SmO2 (∆%SmO2), the muscle oxygen re-saturation rate (SmO2RecSlope), and the highest measured SmO2 value during recovery periods (SmO2Peak) were measured. Two-way (sex [men, women] x time [sets 1–5]) repeated measures analyses of variance (ANOVA) were performed on muscle saturation variables. To examine the effect of relevant controlling variables, separate analyses of covariance (ANCOVA) with repeated measures were also performed. No differences were seen with ∆%SmO2 across sets. The main effects for sets occurred for SmO2RecSlope, whereby a decline was noted on sets 4 and 5 (p = 0.001) compared to set 1. Additionally, SmO2Peak was the lowest on set 5 (p < 0.001) compared to all other sets. Moreover, body mass (p = 0.013), diastolic blood pressure (p = 0.044), and mean arterial pressure (p = 0.033) for ∆%SmO2 were the only significant covariates noted amongst the muscle oxygenation variables. In conclusion, no sex differences and only a few set differences in muscle oxygen saturation dynamics were seen without employing any covariates. Body mass, diastolic blood pressure, and mean arterial pressure were identified as factors that could influence observed responses.
Research on Precise Attitude Measurement Technology for Satellite Extension Booms Based on the Star Tracker
Authors: Sang Peng , Liu Wenbo , Cao Yang , Xue Hongbo , Li Baoquan
Published: October 2024

This paper reports the successful application of a self-developed, miniaturized, low-power nano-star tracker for precise attitude measurement of a 5-m-long satellite extension boom. Such extension booms are widely used in space science missions to extend and support payloads like magnetometers. The nano-star tracker, based on a CMOS image sensor, weighs 150 g (including the baffle), has a total power consumption of approximately 0.85 W, and achieves a pointing accuracy of about 5 arcseconds. It is paired with a low-cost, commercial lens and utilizes automated calibration techniques for measurement correction of the collected data. This system has been successfully applied to the precise attitude measurement of the 5-m magnetometer boom on the Chinese Advanced Space Technology Demonstration Satellite (SATech-01). Analysis of the in-orbit measurement data shows that within shadowed regions, the extension boom remains stable relative to the satellite, with a standard deviation of 30′′ (1σ). The average Euler angles for the “X-Y-Z” rotation sequence from the extension boom to the satellite are [−89.49°, 0.08°, 90.11°]. In the transition zone from shadow to sunlight, influenced by vibrations and thermal factors during satellite attitude adjustments, the maximum angular fluctuation of the extension boom relative to the satellite is approximately ±2°. These data and the accuracy of the measurements can effectively correct magnetic field vector measurements.
Rural Road Extraction in Xiong’an New Area of China Based on the RC-MSFNet Network Model
Authors: Yang Nanjie , Di Weimeng , Wang Qingyu , Liu Wansi , Feng Teng , Tian Xiaomin
Published: October 2024

High-resolution remote sensing imagery, reaching meter or sub-meter levels, provides essential data for extracting and identifying road information. However, rural roads are often narrow, elongated, and have blurred boundaries, with textures that resemble surrounding environments such as construction sites, vegetation, and farmland. These features often lead to incomplete extraction and low extraction accuracy of rural roads. To address these challenges, this study introduces the RC-MSFNet model, based on the U-Net architecture, to enhance rural road extraction performance. The RC-MSFNet model mitigates the vanishing gradient problem in deep networks by incorporating residual neural networks in the downsampling stage. In the upsampling stage, a connectivity attention mechanism is added after dual convolution layers to improve the model’s ability to capture road completeness and connectivity. Additionally, the bottleneck section replaces the traditional dual convolution layers with a multi-scale fusion atrous convolution module to capture features at various scales. The study focuses on rural roads in the Xiong’an New Area, China, using high-resolution imagery from China’s Gaofen-2 satellite to construct the XARoads rural road dataset. Roads were extracted from the XARoads dataset and DeepGlobe public dataset using the RC-MSFNet model and compared with some models such as U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. Experimental results showed that: (1) The proposed method achieved precision (P), intersection over union (IOU), and completeness (COM) scores of 0.8350, 0.6523, and 0.7489, respectively, for rural road extraction in Xiong’an New Area, representing precision improvements of 3.8%, 6.78%, 7.85%, 2.14%, 0.58%, and 2.53% over U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. (2) The method excelled at extracting narrow roads and muddy roads with unclear boundaries, with fewer instances of omission or false extraction, demonstrating advantages in complex rural terrain and areas with indistinct road boundaries. Accurate rural road extraction can provide valuable reference data for urban development and planning in the Xiong’an New Area.
Implementation of Nature-Based Solutions in Urban Water Management in Viet Nam, a Comparison among European and Asian Countries
Authors: Huynh Thi Ngoc Chau , Back Yannick , Funke Fabian , Hauser Martina , Kleidorfer Manfred
Published: October 2024

Climate change is severely affecting all regions of the world, and urban water management has become a major urban challenge. Although nature-based solutions (NBSs) have been widely implemented in developed countries in the Global North to address stormwater-related challenges in urban areas, implementation of such approaches in Viet Nam and other Asian countries remains limited. In addition, comprehensive and critical reviews of NBS adoption and development processes in Viet Nam are scarce. This study aims to clarify several aspects through a literature-based review: to understand the development of urban water management in Europe and Asia (China and Southeast Asian countries) along with the drivers for NBS implementation in Viet Nam, to explain the barriers to NBS adoption in Viet Nam, to present feasible solutions for promoting NBS adoption, and to explore future perspectives for NBS development in the context of Viet Nam. Although significant barriers exist, opportunities for NBS implementation are evident. The findings of this study can be used to promote NBS in other municipalities in developing countries.
Dose Effect of Drinking Water Nitrate on Health, Feed Intake, Rumen Fermentation and Microbiota, and Nitrogen Excretion in Holstein Heifers for a Sustainable Water Use
Authors: Llonch Lourdes , Verdú Marçal , Guivernau Miriam , Viñas Marc , Martí Sonia , Medinyà Carles , Riera Joan , Cucurull Jordi , Devant Maria
Published: October 2024

The present study aimed to evaluate the potential hazardous effects of NO3− concentration in drinking water on health, feed intake, rumen fermentation and microbiota, and nitrogen excretion of Holstein heifers fed a high-concentrate diet for a sustainable water use. Twenty-four Holstein heifers were individually allocated and assigned to one of four treatments with increasing drinking water NO3− concentration: CTR, without NO3−; LOW, with 44 mg NO3−/L; MOD, with 110 mg NO3−/L; and HIGH, with 220 mg NO3−/L. The entire study lasted 168 days. Fortnightly water NO3− concentration and daily feed and water intake were recorded. Blood parameters, rumen pH, volatile fatty acids, NO3− and NO2− concentration, microbiota, and apparent total tract digestibility were determined at the beginning and at the end of the study. Most of the analyzed parameters were similar among treatments. Denitrifying bacteria population, estimated as nosZ gene copies, were greater in HIGH animals than in CTR animals at the end of the study. In conclusion, drinking water NO3− concentration up to 220 mg/L has no detrimental effect on health, feed intake, rumen fermentation, nor N excretion in dairy beef cattle for periods up to 168 days; moreover, denitrifying bacteria population increased, which are related with the neutralization of the greenhouse gas N2O.
Life Cycle Assessment of Resource-Oriented Sanitation Based on Vacuum Toilet and Vacuum Kitchen Waste Shredder: A Case Study in Rural Southeastern China
Authors: Zhang Yu , Shi Yunpeng , Shu Shangyi , Zhu Shikun , Fan Bin
Published: October 2024

The resource value of feces and kitchen waste has not been fully emphasized and utilized in rural sanitation management in China. In this paper, we propose a new ecological sanitation model with the core technology of “vacuum toilet and vacuum kitchen waste shredder—vacuum collection—resource treatment”, i.e., the modern home–farm cycle sanitation (MHFCS) system. We compared the environmental performance of the MHFCS system with that of a typical end-pipe treatment sanitation (EPTS) system (Johkasou—small onsite wastewater treatment system) in rural China using a life cycle assessment (LCA) approach. The results showed that the main source of environmental impacts of the MHFCS system was the collection and treatment process of domestic organic liquid wastes; the greenhouse gas emissions were 64.543 kg CO2eq·PE−1·year−1, and the MHFCS system indirectly gained a fertilizer substitution benefit of 65.960 kg CO2eq·PE−1·year−1 through nutrient element recycling. The MHFCS system has significant advantages in terms of net GHG emissions. Sensitivity analyses showed that resource consumption of vacuum facilities was a key factor for the MHFCS system. This system offers the potential to break down the barriers of the EPTS system in order to meet environmental sustainability and market demands for systemic diversity.
A Product’s Kansei Appearance Design Method Based on Conditional-Controlled AI Image Generation
Authors: Du Yuanjian , Liu Xiaoxue , Cai Mobing , Park Kyungjin
Published: October 2024

Accurately grasping users’ Kansei needs and rapidly transforming them into product design solutions are key factors in enhancing product competitiveness and sustainability. This paper proposes a product appearance design method based on Kansei engineering and AI image generation technology, integrating other approaches, with household indoor hydroponics as the research subject. First, the web crawler is used to obtain product image samples and user online reviews, and factor analysis (FA) is applied to quickly extract users’ Kansei needs. Second, product morphology is used to deconstruct and encode product appearances. Partial least squares regression (PLSR) is then employed to map and quantify the relationships between Kansei needs and design elements, yielding optimal design solutions and one-dimensional sketches. These sketches are subsequently used as controlled conditions in Stable Diffusion (SD), combined with a team-trained Lora model, to generate two-dimensional colored sketches in batches. Finally, evaluations verify that the generated design solutions are satisfactory and meet users’ Kansei needs. The results indicate that the proposed product appearance design method not only holds significant implications for the sustainable development of Kansei engineering in product design but also greatly enhances the efficiency of the design process, providing new insights into integrating new technologies and scientific research methods in the field of product design.
Sustainable Stabilizer Derived from Calcium- and Phosphorus-Rich Biowaste for Remediation of Heavy Metal-Contaminated Soil: A Critical Review
Authors: Datta Dibya Kanti , Biswas Tandra , Castonguay Elli , Ni Pan
Published: October 2024

Soil pollution by heavy metals (HMs) is a major environmental problem around the world. The addition of biowaste-based stabilizers for HM remediation has recently gained attention due to its relatively low cost and eco-risk, abundance, ease of operation, and quick remediation results. Among these stabilizers, shell (crustacean shell, bivalve shell, and eggshell), starfish, and bone-based stabilizers are particularly attractive because of their high Ca and P contents, allowing for highly efficient HM immobilization and simultaneous supplement of nutrients to the soil. However, a comprehensive review focusing on these stabilizers is currently missing. Therefore, this review attempts to summarize the HM immobilization efficiency of these stabilizers and the mechanisms associated with HM stabilization, and perform an operation cost estimation and cost comparison. Cost comparisons among different stabilizers are widely ignored in reviews due to the lack of reliable cost estimation tools or methods. However, for practical application in soil remediation, cost is one of the most important factors to consider. Thus, a simple but reasonable cost estimation method is developed and discussed in this review. Bivalve shell-based stabilizers demonstrated the most promising results for the immobilization of soil HMs in terms of higher performance and lower cost. Current research limitations, challenges, and recommendations regarding possible future research directions are also provided.
A Serial Mediation Model of the Relationship between Digital Entrepreneurial Education, Alertness, Motivation, and Intentions
Authors: Alzahrani Saeed , Bhunia Anish Kumar
Published: October 2024

This research leverages insights from both social cognitive theory (SCT) and stimulus-organism-response theory (S-O-R) to investigate how digital entrepreneurship education (DEE) influences undergraduate students’ digital entrepreneurial alertness (DEA), motivation (DEM), and intentions (DEI). The main objective of the study was to examine whether individual levels of DEA and DEM independently and sequentially mediate the relationship between DEE and DEI. To collect the data, convenience sampling was utilized, involving 221 students from a single public university in Saudi Arabia, and a theoretical model was examined utilizing structural equation modelling (SEM) techniques in SPSS AMOS (Version 27). The results found that DEE had a significant positive impact on students’ DEIs, DEA had a significant positive impact on students’ DEI, and DEM had a significant positive impact on students’ DEI. Moreover, the results of the serial mediation analysis indicated that DEA and DEM served as independent and sequential mediators in the relationship between DEE and DEI. These findings provide further insight into the association between DEE and DEI, offering valuable implications for both entrepreneurship education curriculum developers and government policymakers. This study adds substantial contributions to the existing literature on entrepreneurship education and DEI.
Effects of Compost Application of Green Waste on Soil Properties: A Meta-Analysis
Authors: Wang Di , Li Suyan , Sun Xiangyang , Hao Dan , Li Yalin , Wang Hui
Published: October 2024

Objective: With the accelerating urbanization process, the garden area is gradually expanding, and the production of green waste is also increasing. Composting green waste can not only reduce environmental pollution caused by incineration and landfill and improve the utilization efficiency of resources but also improve the soil and increase soil productivity. The study aims to investigate the comprehensive impact of green waste compost (GWC) application on soil nutrient conditions. Through comprehensive data analysis, the impact of compost application of green waste on soil properties was revealed as a reference for compost application and fertilizer reduction. Methods: Based on meta-analysis, we quantitatively investigated the response of soil properties to the application of green waste, collected published experimental data, and integrated 25 domestic and international literature to analyze the effects of different soil properties on soil nutrients. Literature was used to analyze the impact of different application rates of GWC on the physicochemical properties of soils with varying pH levels. The results were compared to control conditions with no GWC application. Results: The application of significantly improved soil quality by reducing soil bulk density and increasing the levels of soil organic matter, organic carbon, total nitrogen, available phosphorus, available potassium, and dehydrogenase activity. The increases in available potassium and soil organic carbon were consistently significant across all subgroups. However, the effects on available phosphorus and organic matter varied in significance depending on initial soil pH. Soil bulk density was influenced by the GWC content, while dehydrogenase activity showed significant effects only when initial soil pH was ≥8. Total nitrogen levels were significantly impacted by GWC application in soil with an initial pH of <8 and at GWC contents of ≥50%. Conclusion: The application of green waste compost demonstrates a positive effect on soil improvement. This study provides a comprehensive database that supports the use of GWC in enhancing soil quality and reducing the need for chemical fertilizers.
Appraising Education 4.0 in Nigeria’s Higher Education Institutions: A Case Study of Built Environment Programmes
Authors: Ebekozien Andrew , Hafez Mohamed Ahmed , Aigbavboa Clinton , Samsurijan Mohamad Shaharudin , Al-Hasan Abubakar Zakariyya , Nwaole Angeline Ngozika Chibuike
Published: October 2024

In the era of digitalisation, the construction industry is fast embracing digital technology, which evolved from Industry 4.0 (fourth industrial revolution [4IR]). Built environment programmes (BEPs) are expected to meet the needs of the digitalisation era via Education 4.0. Education 4.0 aims to transform education’s future with diverse digital automation and innovative paedagogical procedures. Studies concerning Education 4.0 in Nigeria’s BEPs are scarce. Hence, this study aims to appraise Education 4.0 and investigate the perceived issues facing implementing Education 4.0 in Higher Education Institutions (HEIs), using BEPs as a case study. The findings intend to improve Education 4.0 implementation practices in BEPs. Data were sourced from 40 participants across Nigeria for better coverage and representation via a semi-structured interview approach. The participants were knowledgeable about Education 4.0 and Nigeria’s BEPs. This study adopted a thematic analysis of the virtually collected data and presented the findings in themes. This study shows that Education 4.0 implementation in Nigeria’s BEPs is lax and should be overhauled to improve achieving Sustainable Development Goal 4 (SDG 4)—quality education—and other related SDGs. The findings reveal that improved Education 4.0 can enhance the achievement of SDG 4. The findings cluster the perceived 18 hindrances facing Education 4.0 implementation into three main groups. Also, the findings proffer feasible measures to improve Education 4.0 implementation in Nigeria’s HEIs, using BEPs as a case study, via improved transformative competencies, technological advancement, innovative paedagogical procedures, and stakeholders’ collaboration to improve achieving SDG 4. The proposed framework could assist in creating new values and transforming the students’ BEP competencies via stakeholder collaboration and Education 4.0 for the private sector (future talents’ beneficiary), thus fostering their employability.