bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Analysis of Pixel-Level Fusion Algorithms and a New High-Resolution Dataset for SAR and Optical Image Fusion
Authors: Li Jinjin , Zhang Jiacheng , Yang Chao , Liu Huiyu , Zhao Yangang , Ye Yuanxin
Published: November 2023

Synthetic aperture radar (SAR) and optical images often present different geometric structures and texture features for the same ground object. Through the fusion of SAR and optical images, it can effectively integrate their complementary information, thus better meeting the requirements of remote sensing applications, such as target recognition, classification, and change detection, so as to realize the collaborative utilization of multi-modal images. In order to select appropriate methods to achieve high-quality fusion of SAR and optical images, this paper conducts a systematic review of current pixel-level fusion algorithms for SAR and optical image fusion. Subsequently, eleven representative fusion methods, including component substitution methods (CS), multiscale decomposition methods (MSD), and model-based methods, are chosen for a comparative analysis. In the experiment, we produce a high-resolution SAR and optical image fusion dataset (named YYX-OPT-SAR) covering three different types of scenes, including urban, suburban, and mountain. This dataset and a publicly available medium-resolution dataset are used to evaluate these fusion methods based on three different kinds of evaluation criteria: visual evaluation, objective image quality metrics, and classification accuracy. In terms of the evaluation using image quality metrics, the experimental results show that MSD methods can effectively avoid the negative effects of SAR image shadows on the corresponding area of the fusion result compared with CS methods, while model-based methods exhibit relatively poor performance. Among all of the fusion methods involved in the comparison, the non-subsampled contourlet transform method (NSCT) presents the best fusion results. In the evaluation using image classification, most experimental results show that the overall classification accuracy after fusion is better than that before fusion. This indicates that optical-SAR fusion can improve land classification, with the gradient transfer fusion method (GTF) yielding the best classification results among all of these fusion methods.
Susceptibility Mapping of Unhealthy Trees in Jiuzhaigou Valley Biosphere Reserve
Authors: Gao Sheng , Chen Fulong , Wang Qin , Shi Pilong , Zhou Wei , Zhu Meng
Published: November 2023

Jiuzhaigou Valley is recognized as both a world natural heritage site and a biosphere reserve. Conducting research on vegetation health within its scope can provide a demonstration role for sustainable development research. In this study, we proposed a technology integration approach that combined remote sensing intelligent identification and quantitative retrieval, and achieved vegetation health monitoring and susceptibility mapping of unhealthy trees. Leveraging WorldView-2 high-resolution satellite images, unhealthy trees were elaborately identified through the object-oriented classification method employing spectral and texture features, with F1 Score exceeding 75%. By applying fuzzy operations on indices related to leaf pigment and canopy architecture, we ultimately generated susceptibility maps of unhealthy trees on Sentinel-2 satellite images, with Area Under the Curve (AUC) exceeding 0.85. Our findings underscore that the vegetation health in Jiuzhaigou Valley is predominantly influenced by human activities and geological hazards. The forests of Jiuzhaigou Valley exhibit a certain degree of resilience to geological disasters, while human activities have been continuously exerting adverse effects on forest health in recent years, necessitating heightened attention. The methodology proposed in this study for mapping unhealthy trees susceptibility presents a cost-effective solution that can be readily applied for vegetation health monitoring and early warning in analogous biosphere reserves.
Simulation of Synthetic Aperture Radar Images for Ocean Ship Wakes
Authors: Wu Shuya , Wang Yunhua , Li Qian , Zhang Yanmin , Bai Yining , Zheng Honglei
Published: November 2023

To assist in the detection of ship targets in complex sea conditions, a numerical simulation method is proposed to obtain synthetic aperture radar (SAR) images of time-varying ocean ship wakes under various radar, ship, and sea surface parameters. This method addresses the limitations of recent simulations, which failed to simultaneously incorporate different types of time-varying ship wakes, simulate based on the echo data, and discuss the velocity bunching (VB) effect on the image results. To address these issues, firstly, the time-varying wave height and velocity fields of the sea surface, Kelvin wakes, and turbulence wakes are simulated using the linear filtering method, classic fluid dynamics models, and attenuation function method, respectively. Secondly, raw data of the ocean ship wakes are obtained by calculating the backscattering fields using geophysical model functions (GMFs), as well as by determining the changing slant range varying with the elevation and velocity fields. Thirdly, by applying the Range-Doppler algorithm (RDA) for pulse compression and range cell migration correction (RCMC) on the echo data, SAR images with and without the VB effect are generated. Our simulation also accounts for the influence of speckle noise. The SAR imaging results indicate that whether the VB effect is considered or not, the radar electromagnetic wavebands, polarization modes, wind speeds, and the relative wind directions have distinct impacts on the SAR image intensity, and the texture and morphology of ship wakes vary significantly with the wind speeds, ship speeds, and the relative radar looking directions. When considering the VB effect, the azimuthal offset and blur in the images caused by the more intense wave motion also increase with the wave speeds.
An Efficient BP Algorithm Based on TSU-ICSI Combined with GPU Parallel Computing
Authors: Li Ziya , Qiu Xiaolan , Yang Jun , Meng Dadi , Huang Lijia , Song Shujie
Published: November 2023

High resolution remains a primary goal in the advancement of synthetic aperture radar (SAR) technology. The backprojection (BP) algorithm, which does not introduce any approximation throughout the imaging process, is broadly applicable and effectively meets the demands for high-resolution imaging. Nonetheless, the BP algorithm necessitates substantial interpolation during point-by-point processing, and the precision and effectiveness of current interpolation methods limit the imaging performance of the BP algorithm. This paper proposes a TSU-ICSI (Time-shift Upsampling-Improved Cubic Spline Interpolation) interpolation method that integrates time-shift upsampling with improved cubic spline interpolation. This method is applied to the BP algorithm and presents an efficient implementation method in conjunction with the GPU architecture. TSU-ICSI not only maintains the accuracy of BP imaging processing but also significantly boosts performance. The effectiveness of the BP algorithm based on TSU-ICSI is confirmed through simulation experiments and by processing measured data collected from both airborne SAR and spaceborne SAR.
Consistent Healthcare Safety Recommendation System for Preventing Contagious Disease Infections in Human Crowds
Authors: Amoon Mohammed , Altameem Torki , Hashem Mohammed
Published: November 2023

The recent impact of COVID-19, as a contagious disease, led researchers to focus on designing and fabricating personal healthcare devices and systems. With the help of wearable sensors, sensing and communication technologies, and recommendation modules, personal healthcare systems were designed for ease of use. More specifically, personal healthcare systems were designed to provide recommendations for maintaining a safe distance and avoiding contagious disease spread after the COVID-19 pandemic. The personal recommendations are analyzed based on the wearable sensor signals and their consistency in sensing. This consistency varies with human movements or other activities that hike/cease the sensor values abruptly for a short period. Therefore, a consistency-focused recommendation system (CRS) for personal healthcare (PH) was designed in this research. The hardware sensing intervals for the system are calibrated per the conventional specifications from which abrupt changes can be observed. The changes are analyzed for their saturation and fluctuations observed from neighbors within the threshold distance. The saturation and fluctuation classifications are performed using random forest learning to differentiate the above data from the previously sensed healthy data. In this process, the saturated data and consistency data provide safety recommendations for the moving user. The consistency is verified for a series of intervals for the fluctuating sensed data. This alerts the user if the threshold distance for a contagious disease is violated. The proposed system was validated using a prototype model and experimental analysis through false rates, data analysis rates, and fluctuations.
Joint Object Detection and Re-Identification for 3D Obstacle Multi-Camera Systems
Authors: Cortés Irene , Beltrán Jorge , de la Escalera Arturo , García Fernando
Published: November 2023

The growing on-board processing capabilities have led to more complex sensor configurations, enabling autonomous car prototypes to expand their operational scope. Nowadays, the joint use of LiDAR data and multiple cameras is almost a standard and poses new challenges for existing multi-modal perception pipelines, such as dealing with contradictory or redundant detections caused by inference on overlapping images. In this paper, we address this last issue in the context of sequential schemes like F-PointNets, where object candidates are obtained in the image space, and the final 3D bounding box is then inferred from point cloud information. To this end, we propose the inclusion of a re-identification branch into the 2D detector, i.e., Faster R-CNN, so that objects seen from adjacent cameras can be handled before the 3D box estimation takes place, removing duplicates and completing the object’s cloud. Extensive experimental evaluations covering both the 2D and 3D domains affirm the effectiveness of the suggested methodology. The findings indicate that our approach outperforms conventional Non-Maximum Suppression (NMS) methods. Particularly, we observed a significant gain of over 5% in terms of accuracy for cars in camera overlap regions. These results highlight the potential of our upgraded detection and re-identification system in practical scenarios for autonomous driving.
Intensive and Efficient Design of a Two-dimensional 8 × 8 Silicon-Based Optical Phased Array Transceiver
Authors: Zhang Yaoyuan , Wang Rui , Liu Haibo , Zhao Guobin , Wei Ming , Jiang Ruitao , Du Kunyang
Published: November 2023

In recent years, the silicon-based optical phased array has been widely used in the field of light detection and ranging (LIDAR) due to its great solid-state steering ability. At the same time, the optical phased array transceiver integration scheme provides a feasible solution for low-cost information exchange of small devices in the future. Based on this, this paper designs a two-dimensional optical phased array transceiver with high efficiency and a large field of view, which can realize a dense array with antenna spacing of 5.5 μm × 5.5 μm by using low crosstalk waveguide wiring. Additionally, it can realize the conversion between the receiving mode and the transmitting mode by using the optical switch. The simulation results show that the scanning range of 16.3° × 16.3° can be achieved in the transmitting mode, and the overall loss is lower than 10dB. In the receiving mode, we can achieve a collection efficiency of more than 27%, and the antenna array receiving loss is lower than 12.1 dB.
Visible Light Communications-Based Assistance System for the Blind and Visually Impaired: Design, Implementation, and Intensive Experimental Evaluation in a Real-Life Situation
Authors: Căilean Alin-Mihai , Avătămăniței Sebastian-Andrei , Beguni Cătălin , Zadobrischi Eduard , Dimian Mihai , Popa Valentin
Published: November 2023

Severe visual impairment and blindness significantly affect a person’s quality of life, leading sometimes to social anxiety. Nevertheless, instead of concentrating on a person’s inability, we could focus on their capacities and on their other senses, which in many cases are more developed. On the other hand, the technical evolution that we are witnessing is able to provide practical means that can reduce the effects that blindness and severe visual impairment have on a person’s life. In this context, this article proposes a novel wearable solution that has the potential to significantly improve blind person’s quality of life by providing personal assistance with the help of Visible Light Communications (VLC) technology. To prevent the wearable device from drawing attention and to not further emphasize the user’s deficiency, the prototype has been integrated into a smart backpack that has multiple functions, from localization to obstacle detection. To demonstrate the viability of the concept, the prototype has been evaluated in a complex scenario where it is used to receive the location of a certain object and to safely travel towards it. The experimental results have: i. confirmed the prototype’s ability to receive data at a Bit-Error Rate (BER) lower than 10−7; ii. established the prototype’s ability to provide support for a 3 m radius around a standard 65 × 65 cm luminaire; iii. demonstrated the concept’s compatibility with light dimming in the 1–99% interval while maintaining the low BER; and, most importantly, iv. proved that the use of the concept can enable a person to obtain information and guidance, enabling safer and faster way of traveling to a certain unknown location. As far as we know, this work is the first one to report the implementation and the experimental evaluation of such a concept.
Enhancing Security of Web-Based IoT Services via XSS Vulnerability Detection
Authors: Kim Jemin , Park Joonseok
Published: November 2023

The Internet of Things (IoT) technology is experiencing significant growth and integration into various aspects of daily life. With the rising number of connected devices, diverse security challenges are emerging as substantial threats to IoT. Cross-Site Scripting (XSS) is one of the major security risks in web services and so is within the application layer of IoT. Many existing web applications remain susceptible to XSS vulnerabilities. In this paper, we propose an XSS detection scheme aimed at enhancing the security of IoT, particularly concerning web application services. To achieve this, we developed a framework for combining symbolic execution and dynamic taint analysis to provide a comprehensive security assessment. Our objective is to increase the ratio of vulnerability detection while avoiding false alarms and keeping the required analysis time as minimal. To realize our idea, we have defined an instrumentation scheme for taint analysis and concolic executions and automated the process of vulnerability detection for a web application. Our framework is capable of pinpointing the precise locations of security vulnerabilities and the exact input datasets at risk of XSS threats. Subsequently, the detected flaws can be easily removed. The experimental results demonstrate the validity of the proposed scheme. We achieved a detection rate of XSS threats of 90.62% using a test set of SecuriBench Micro and 69.11% using OWASP while showing 0% false positives.
Development of a Wideband Precision Electric Field Measuring Sensor
Authors: Long Zhaozhi , Zhou Feng , Lin Fuchang , Fan Jiawei , Li Wenting , Diao Yinglong , Hu Kangmin
Published: November 2023

High-voltage electric field measurement technology has certain applications in electric field measurement of power systems, but due to the limitation of its measurement accuracy and bandwidth, it cannot be used for the measurement of lightning-impulse voltage. In order to calibrate the nonlinearity of the MV-level lightning-impulse voltage measurement system, this paper proposes the design and implementation of a high-precision inductive wideband electric field measuring sensor (EFMS). The influence of the metal shell on the electric field distribution was simulated, and the influence of the electric field non-uniformity coefficient was studied. The characteristics of the EFMS were tested, and the results showed that the EFMS can accurately reproduce the waveform of lightning-impulse voltage and power-frequency voltage, with a proportionality coefficient of 0.05664 V/(kV/m). In mostly uniform and extremely non-uniform fields, the nonlinearity of the EFMS for impulse voltage is less than ±0.25%, and the nonlinearity of the EFMS for power-frequency voltage is less than 0.1%. It is shown that the EFMS can be used for the nonlinearity calibration of ultra-high voltage impulse measurement devices.
Polarization-Insensitive Transmissive Metasurfaces Using Pancharatnam–Berry and Resonant Phases in Microwave Band
Authors: Wang Ling , Yang Yang , Gao Feng , Teng Shuhua , Zhang Jinggui , Deng Li , Hong Weijun , Li Zhuofang
Published: November 2023

Most of the existing metasurfaces are effective for the incident wave with the specific circularly polarized (CP) or linearly polarized (LP) state, that is the polarization-sensitive metasurface. This drawback dramatically hinders the practical use of the metasurface. Herein, this paper presents a strategy of polarization-insensitive transmissive microwave metasurfaces to manipulate the incident wave with arbitrary CP and LP states. The metasurface consists of polarization-insensitive unit cells. For the left circularly polarized (LCP) and right circularly polarized (RCP) incident waves, the same abrupt phase covering 0° to 360° can be realized by combining the Pancharatnam–Berry (PB) and resonant phases. As the arbitrary LP wave can decompose into the LCP and RCP waves, metasurfaces consisting of designed unit cells are valid for any polarization states. The polarization-insensitive transmissive microwave metalens and orbital angular momentum multiplexing metasurface working at 23 GHz are devised for verification. Simulation and measurement results verify the availability of the approach. The proposed method is suitable for designing microwave-transmissive metasurfaces capable of polarization insensitivity.
Bypassing Heaven’s Gate Technique Using Black-Box Testing
Authors: Hwang Seon-Jin , Utaliyeva Assem , Kim Jae-Seok , Choi Yoon-Ho
Published: November 2023

In recent years, the number and sophistication of malware attacks on computer systems have increased significantly. One technique employed by malware authors to evade detection and analysis, known as Heaven’s Gate, enables 64-bit code to run within a 32-bit process. Heaven’s Gate exploits a feature in the operating system that allows the transition from a 32-bit mode to a 64-bit mode during execution, enabling the malware to evade detection by security software designed to monitor only 32-bit processes. Heaven’s Gate poses significant challenges for existing security tools, including dynamic binary instrumentation (DBI) tools, widely used for program analysis, unpacking, and de-virtualization. In this paper, we provide a comprehensive analysis of the Heaven’s Gate technique. We also propose a novel approach to bypass the Heaven’s Gate technique using black-box testing. Our experimental results show that the proposed approach effectively bypasses and prevents the Heaven’s Gate technique and strengthens the capabilities of DBI tools in combating advanced malware threats.
Autostereoscopic 3D Measurement Based on Adaptive Focus Volume Aggregation
Authors: Gao Sanshan , Cheung Chi Fai
Published: November 2023

Autostereoscopic three-dimensional measuring systems are a kind of portable and fast precision metrology instrument. The systems are based on integral imaging that makes use of a micro-lens array before an image sensor to observe measured parts from multiple perspectives. Since autostereoscopic measuring systems can obtain longitudinal and lateral information within single snapshots rapidly, the three-dimensional profiles of the measured parts can be reconstructed by shape from focus. In general, the reconstruction process consists of data acquisition, pre-processing, digital refocusing, focus measures, and depth estimation. The accuracy of depth estimation is determined by the focus volume generated by focus measure operators which could be sensitive to the noise during digital refocusing. Without prior knowledge and surface information, directly estimated depth maps usually contain severe noise and incorrect representation of continuous surfaces. To eliminate the effects of refocusing noise and take advantage of traditional focus measure methods with robustness, an adaptive focus volume aggregation method based on convolutional neural networks is presented to optimize the focus volume for more accurate depth estimation. Since a large amount of data and ground truth are costly to acquire for model convergence, backpropagation is performed for every sample under an unsupervised strategy. The training strategy makes use of a smoothness constraint and an identical distribution constraint that restricts the difference between the distribution of the network output and the distribution of ideal depth estimation. Experimental results show that the proposed adaptive aggregation method significantly reduces the noise during depth estimation and retains more accurate surface profiles. As a result, the autostereoscopic measuring system can directly recover surface profiles from raw data without any prior information.
Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors
Authors: Jin Xu , Wang Xuhu , Hou Yujun , Hao Siyuan , Wang Xinjie , Xu Zhenhua , Zhang Qunfei
Published: November 2023

To reduce the influence of gain-phase errors and improve the performance of direction-of-arrival (DOA) estimation, a robust sparse Bayesian two-dimensional (2D) DOA estimation method with gain-phase errors is proposed for L-shaped sensor arrays. The proposed method introduces an auxiliary angle to transform the 2D DOA estimation problem into two 1D angle estimation problems. A sparse representation model with gain-phase errors is constructed using the diagonal element vector of the cross-correlation covariance matrix of two submatrices of the L-shaped sensor array. The expectation maximization algorithm derives unknown parameter expression, which is used for iterative operations to obtain off-grid and signal precision. Using these parameters, a new spatial spectral function is constructed to estimate the auxiliary angle. The obtained auxiliary angle is substituted into a sparse representation model with gain and phase errors, and then the sparse Bayesian learning method is used to estimate the elevation angle of the incident signal. Finally, according to the relationship of the three angles, the azimuth angle can be estimated. The simulation results show that the proposed method can effectively realize the automatic matching of the azimuth and elevation angles of the incident signal, and improves the accuracy of DOA estimation and angular resolution.
Correction of Spacecraft Magnetic Field Noise: Initial Korean Pathfinder Lunar Orbiter MAGnetometer Observation in Solar Wind
Authors: Lee Junhyun , Jin Ho , Kim Khan-Hyuk , Park Hyeonhu , Jo Wooin , Jang Yunho , Kang Hyeonji , Kim Eunhyeuk , Choi Young-Jun
Published: November 2023

The Korean Pathfinder Lunar Orbiter (KPLO)-MAGnetometer (KMAG) consists of three triaxial fluxgate sensors (MAG1, MAG2, and MAG3) that measure the magnetic field around the Moon. The three sensors are mounted in the order MAG3, MAG2, and MAG1 inside a 1.2 m long boom, away from the satellite body. Before it arrived on the Moon, we compared the magnetic field measurements taken by DSCOVR and KPLO in solar wind to verify the measurement performance of the KMAG instrument. We found that there were artificial disturbances in the KMAG measurement data, such as step-like and spike-like disturbances, which were produced by the spacecraft body. To remove spacecraft-generated disturbances, we applied a multi-sensor method, employing the gradiometer technique and principal component analysis, using KMAG magnetic field data, and confirmed the successful elimination of spacecraft-generated disturbances. In the future, the proposed multi-sensor method is expected to clean the magnetic field data measured onboard the KPLO from the lunar orbit.
Emerging Microfluidic Tools for Simultaneous Exosomes and Cargo Biosensing in Liquid Biopsy: New Integrated Miniaturized FFF-Assisted Approach for Colon Cancer Diagnosis
Authors: Marassi Valentina , Giordani Stefano , Placci Anna , Punzo Angela , Caliceti Cristiana , Zattoni Andrea , Reschiglian Pierluigi , Roda Barbara , Roda Aldo
Published: November 2023

The early-stage diagnosis of cancer is a crucial clinical need. The inadequacies of surgery tissue biopsy have prompted a transition to a less invasive profiling of molecular biomarkers from biofluids, known as liquid biopsy. Exosomes are phospholipid bilayer vesicles present in many biofluids with a biologically active cargo, being responsible for cell-to-cell communication in biological systems. An increase in their excretion and changes in their cargo are potential diagnostic biomarkers for an array of diseases, including cancer, and they constitute a promising analyte for liquid biopsy. The number of exosomes released, the morphological properties, the membrane composition, and their content are highly related to the physiological and pathological states. The main analytical challenge to establishing liquid biopsy in clinical practice is the development of biosensors able to detect intact exosomes concentration and simultaneously analyze specific membrane biomarkers and those contained in their cargo. Before analysis, exosomes also need to be isolated from biological fluids. Microfluidic systems can address several issues present in conventional methods (i.e., ultracentrifugation, size-exclusion chromatography, ultrafiltration, and immunoaffinity capture), which are time-consuming and require a relatively high amount of sample; in addition, they can be easily integrated with biosensing systems. A critical review of emerging microfluidic-based devices for integrated biosensing approaches and following the major analytical need for accurate diagnostics is presented here. The design of a new miniaturized biosensing system is also reported. A device based on hollow-fiber flow field-flow fractionation followed by luminescence-based immunoassay is applied to isolate intact exosomes and characterize their cargo as a proof of concept for colon cancer diagnosis.
High Gain Flat-Panel mmWave Antenna Array
Authors: Moon Seong-Mo , Cho Junhyuk , Lee Han Lim
Published: November 2023

In the realm of mmWave communication and connectivity, integrating chips and antennas into a cohesive system is paramount. Given this, planar antenna arrays have become indispensable. In this article, we introduce a novel antenna array tailored for mmWave applications, characterized by its high directivity. Distinctively, this new array employs a flat-panel radiator, ensuring an augmented gain without necessitating additional superstrate layers. To validate its potency, a 4 × 4 flat-panel array with dimensions of 3.74 λ0 × 3.74 λ0 × 0.106 λ0 at 28 GHz including a ground plane was designed and tested for n257 band. The standalone array element exhibited a bandwidth of 20.6%, centered at 28.5 GHz. Furthermore, a 1 × 16 mmWave feed network was designed and amalgamated with the array elements to assess the comprehensive antenna performance. The measured peak gain of 21.3 dBi at 28.5 GHz was observed with the measured half power beamwidth of 15° while the gain variation within the operation band was less than 3 dB.
Laser Heterodyne Detection Based on Photon Time–Domain Differential Detection Avoiding the Effect of Decoherence Phase Noise
Authors: Guan Ce , Zhang Zijing , Jia Fan , Zhao Yuan
Published: November 2023

Laser heterodyne detection (LHD) is a key velocimetry technique that provides better accuracy and sensitivity than direct laser detection. However, random phase noise can be introduced by the surface topography of the moving target undulation or atmospheric turbulence during transmission. The random phase noise causes the target echo to undergo decoherence, resulting in degradation of the signal-to-noise ratio (SNR). Here, we propose a novel LHD method based on photon time–domain differential detection. It can infer the heterodyne spectrum of the target echo and the local oscillator light from the time intervals of the photon arrival. The time interval statistic is a relative quantity, which can effectively avoid the effect of random phase noise in LHD. With our method, the SNR of LHD can be improved in application scenarios where the target echo is decoherent. We developed a complete solution model for acquiring the heterodyne spectrum based on photon time–domain differential detection and performed proof-of-principle experiments. The experimental results show that in the presence of random phase noise, the SNR and velocity measurement error of our method are significantly better than that of the conventional method, and the larger the phase noise is, the more the SNR and velocity measurement error of our method are improved. Moreover, along with the increase in phase noise, the SNR of our method is basically unchanged, which also indicates that our method is not affected by random phase noise. This advantage is significant for photon-level weak echoes that require long detection times to be detected.
Correlation Analysis of Large-Span Cable-Stayed Bridge Structural Frequencies with Environmental Factors Based on Support Vector Regression
Authors: Xu Jingye , Xiao Tugang , Liu Yu , Hong Yu , Pu Qianhui , Wen Xuguang
Published: November 2023

The dynamic characteristics of bridge structures are influenced by various environmental factors, and exploring the impact of environmental temperature and humidity on structural modal parameters is of great significance for structural health assessment. This paper utilized the Covariance-Driven Stochastic Subspace Identification method (SSI-COV) and clustering algorithms to identify modal frequencies from four months of acceleration data collected from the health monitoring system of the Jintang Hantan Twin-Island Bridge. Furthermore, a correlation analysis is conducted to examine the relationship between higher-order frequency and environmental factors, including temperature and humidity. Subsequently, a Support Vector Machine Regression (SVR) model is employed to analyze the effects of environmental temperature on structural modal frequencies. This study has obtained the following conclusions: 1. Correlation analysis revealed that temperature is the primary influencing factor in frequency variations. Frequency exhibited a strong linear correlation with temperature and little correlation with humidity. 2. SVR regression analysis was performed on frequency and temperature, and an evaluation of the fitting residuals was conducted. The model effectively fit the sample data and provided reliable predictive results. 3. The original structural frequencies underwent smoothing, eliminating the influence of temperature-induced frequency data generated by the SVR model. After eliminating the temperature effects, the fluctuations in frequency within a 24 h period significantly decreased. The data presented in this paper can serve as a reference for further health assessments of similar bridge structures.
First-Arrival Differential Counting for SPAD Array Design
Authors: White Mel , Zhang Tianyi , Dave Akshat , Ghajari Shahaboddin , Molnar Alyosha , Veeraraghavan Ashok
Published: November 2023

We present a novel architecture for the design of single-photon detecting arrays that captures relative intensity or timing information from a scene, rather than absolute. The proposed method for capturing relative information between pixels or groups of pixels requires very little circuitry, and thus allows for a significantly higher pixel packing factor than is possible with per-pixel TDC approaches. The inherently compressive nature of the differential measurements also reduces data throughput and lends itself to physical implementations of compressed sensing, such as Haar wavelets. We demonstrate this technique for HDR imaging and LiDAR, and describe possible future applications.
AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin
Authors: Casari Martina , Po Laura , Zini Leonardo
Published: November 2023

In recent times, pollution has emerged as a significant global concern, with European regulations stipulating limits on PM 2.5 particle levels. Addressing this challenge necessitates innovative approaches. Smart low-cost sensors suffer from imprecision, and can not replace legal stations in terms of accuracy, however, their potential to amplify the capillarity of air quality evaluation on the territory is not under discussion. In this paper, we propose an AI system to correct PM 2.5 levels in low-cost sensor data. Our research focuses on data from Turin, Italy, emphasizing the impact of humidity on low-cost sensor accuracy. In this study, different Neural Network architectures that vary the number of neurons per layer, consecutive records and batch sizes were used and compared to gain a deeper understanding of the network’s performance under various conditions. The AirMLP7-1500 model, with an impressive R-squared score of 0.932, stands out for its ability to correct PM 2.5 measurements. While our approach is tailored to the city of Turin, it offers a systematic methodology for the definition of those models and holds the promise to significantly improve the accuracy of air quality data collected from low-cost sensors, increasing the awareness of citizens and municipalities about this critical environmental information.
Multipath Detection and Mitigation of Random Noise Signals Propagated through Naturally Lossy Dispersive Media for Radar Applications
Authors: Vazquez Alejos Ana , Dawood Muhammad
Published: November 2023

This paper describes a methodological analysis of the Brillouin precursor formation to understand the impairments undergone by like-noise and random noise waveforms propagating through naturally dispersive media commonly found in radar applications. By means of a frequency-domain methodology based on considering the frequency response of the medium under study, the effect of these dispersive media on the evolution of an input signal can be seen as frequency filtering. The simulations were performed at a center frequency of 1.5 GHz and for a signal bandwidth of 3 GHz. Four random noise signals were considered: Barker codes, PRBS codes, Frank codes, Costas codes and additive white Gaussian noise. The experienced impairments were assessed in terms of cross-correlation function (CCF) degradation. The differences in the behavior of each type of phase and frequency coded signal to face the dispersive propagation have been demonstrated in terms of parameters used for information retrieval: peak amplitude decay, CCF secondary sidelobe level and multipath detectability. Finally, a frequency filtering approach is proposed to mitigate the impairments due to dispersive propagation under multipath conditions.
Recycling of Coal Combustion Waste through Production of Foamed Geopolymers with Improved Strength
Authors: Yatsenko Elena Alfredovna , Goltsman Boris Mikhailovich , Novikov Yuri Vladimirovich , Trofimov Sergey Vyacheslavovich , Ryabova Anna Vladimirovna , Smoliy Victoria Alexandrovna , Klimova Lyudmila Vasilievna
Published: November 2023

Recycling of industrial waste into useful materials is a crucial aim for achieving sustainable development in materials science. The use of production waste in the manufacture of construction materials contributes to improving the environmental situation and reducing the cost of the final product. This article examines the utilization of coal combustion waste recycled into foamed geopolymers and ways of enhancing their strength properties through the introduction of strengthening additives. Eight compositions of foamed geopolymers containing different strengthening additives were synthesized. Inorganic substances (CaO, MgO, ZnO, TiO2, Al2O3, SiC, and ZrO2) were chosen as strengthening additives that were introduced in an amount of 3% (over 100%). The physical and mechanical properties (density, compressive strength, porosity, and pore size distribution) of the obtained samples were studied and compared. Magnesium oxide MgO and aluminum oxide Al2O3 were chosen as the best strengthening additives. Magnesium oxide allows geopolymer materials with the lowest density to be obtained. Aluminum oxide can significantly increase the strength of geopolymers. The phase composition of the samples contains new crystalline phases in the form of α-alumina and periclase. The porous structure is homogeneous and meets the requirements for foamed thermal insulation materials. The strengthening effect of the chosen additives was verified using geopolymers based on different waste from Novocherkassk SDPP.
A Comprehensive Overview of Photovoltaic Technologies and Their Efficiency for Climate Neutrality
Authors: Lazaroiu Alexandra Catalina , Gmal Osman Mohammed , Strejoiu Cristian-Valentin , Lazaroiu Gheorghe
Published: November 2023

Solar photovoltaic (PV) technology is a cornerstone of the global effort to transition towards cleaner and more sustainable energy systems. This paper explores the pivotal role of PV technology in reducing greenhouse gas emissions and combatting the pressing issue of climate change. At the heart of its efficacy lies the efficiency of PV materials, which dictates the extent to which sunlight is transformed into electricity. Over the last decade, substantial advancements in PV efficiency have propelled the widespread adoption of solar PV technology on a global scale. The efficiency of PV materials is a critical factor, determining how effectively sunlight is transformed into electricity. Enhanced efficiency, achieved through a decade of progress, has driven the global expansion of solar PV. Multi-junction photovoltaic materials have now exceeded 40% efficiency in lab tests. China leads the world in solar PV installations, boasting over 253 GW of installed capacity by the end of 2021. Other prominent countries in this sector are the United States, Japan, Germany, and India. Photovoltaic (PV) cell technologies are rapidly improving, with efficiencies reaching up to 30% and costs falling below $0.50/W, making PV a competitive source of energy in many countries around the world. Solar PV technology holds immense potential for creating a cleaner, reliable, scalable, and cost-effective electricity system. To expedite its deployment and foster a more sustainable energy future, continued investment in research and development along with supportive policies and market mechanisms is essential. This paper underscores the pivotal role of solar PV technology in the global energy transition and advocates for a concerted effort to unlock its full potential in achieving a more sustainable and resilient energy future.
Data Management for Environmentally Sustainable and Profitable Business: Evidence from the Food Retail Industry
Authors: Dibsi Joudi , Cho Jaeyoung
Published: November 2023

Climatic changes are now a global reality with widespread repercussions for individuals and businesses worldwide. The responsibility to adapt behaviors and protect the environment is shared by all of humanity. Business leaders can play a pivotal role in expediting this transformation by shifting their focus from pure profit-seeking to becoming value-driven entities that actively contribute to societal betterment while championing environmental conservation and sustainable profitability. This transition, however, poses certain challenges, notably the higher costs associated with ecofriendly materials, which can strain a company’s financial equilibrium. Furthermore, a significant number of organizations overlook the untapped potential of their data, a valuable asset that could significantly bolster their competitive position. To address these challenges, this study aims to provide actionable guidance for business leaders by using a notable data framework to reassess their capabilities and dynamically adapt to external environments. Our method relies on data mining algorithms and business intelligence tools to report data. The results pinpoint which products are greatly in demand, so leaders can be assured their investment in redesigning products yields profit, improves product freshness, and eliminates waste. By bridging the gap between profitability and environmental responsibility, this study endeavors to empower businesses to thrive in a sustainable, value-driven future.
A Semi-Analytical Model of Contaminant Transport in Barrier Systems with Arbitrary Numbers of Layers
Authors: Lemos Moisés A. C. , Baran Camilla T. , Cavalcante André L. B. , Palmeira Ennio M.
Published: November 2023

In regions with sanitary landfills, unsuitable liner designs can result in significant soil and groundwater contamination, leading to substantial environmental remediation costs. Addressing this challenge, we propose a semi-analytical model for solute transport that uses the advection–dispersion–reaction equation in a multi-layered liner system. A distinctive feature of our model is its ability to account for infiltration velocity, arbitrary numbers of layers, thin layers such as geomembranes, and mass flow. We validated our model against existing published models and applied it to a case study of a real sanitary landfill in the capital of Brazil. Through parametric analyses, we simulated contaminant transport across various layers, including the geomembrane (GM), geosynthetic clay liner (GCL), soil liner (SL), and compacted clay liner (CCL). The analyses showed the importance of choosing the most appropriate construction system based on the location and availability of materials. Considering toluene contamination, a GM molecular diffusion coefficient (DGM) greater than 10−13 m2 s−1 exhibited similar efficiency when compared with CCL (60 cm thick). In addition, the results showed that the liner system may have the same efficiency in changing SL (60 cm thick) for a GCL (1 cm thick).
A Review of Trade-Offs in Low ILUC-Risk Certification for Biofuels—Towards an Integrated Assessment Framework
Authors: Sumfleth Beike , Majer Stefan , Thrän Daniela
Published: November 2023

Indirect land use change (ILUC) is considered a significant challenge, resulting from an increasing demand for biomass and bioenergy. On a political level sustainability certification of biomass-derived products is discussed as one potential instrument to manage the risk of ILUC. However, extending existing schemes towards a credible and reliable certification approach to account for ILUC-risks is still an open challenge. To develop such a certification instrument, so-called “additionality practices” are gaining relevance. Such practices include measures that an individual producer can adopt to provide an amount of biomass in addition to the business-as-usual feedstock production. This applies in particular to the certification of low ILUC-risk biofuels through voluntary certification schemes recognised by the European Commission. To date, however, no studies have been conducted that examine how such schemes account for potential trade-offs that may arise from the use of additionality practices. In preparation of an integrated assessment framework for low ILUC-risk certification, this study presents a gap analysis that examines whether such trade-offs are considered already in existing sustainability certification schemes for biofuels. In this way, we have found trade-offs that are preferentially addressed by the schemes, e.g., biodiversity loss, on the one hand, and considerable gaps for certain trade-offs, e.g., resource depletion, on the other. In addition, we identified biomass cultivation on unused land as the most promising additionality practice. Most schemes already have certification instruments in place to verify the large number of trade-offs that could be identified as preferentially addressed for this additionality practice. Moreover, only a few new criteria and indicators need to be developed for the small number of gaps found for biomass cultivation on unused land. Finally, this paper recommends future work to verify the scientific evidence of existing certification instruments for the trade-offs addressed and to develop assessment approaches for the identified gaps.
Just Transition Policies, Power Plant Workers and Green Entrepreneurs in Greece, Cyprus and Bulgaria: Can Education and Retraining Meet the Challenge?
Authors: Apostolopoulos Nikolaos , Kakouris Alexandros , Liargovas Panagiotis , Borisov Petar , Radev Teodor , Apostolopoulos Sotiris , Daskou Sofia , Anastasopoulou Eleni Ε.
Published: November 2023

This study seeks to shed light on the changes currently occurring in Greece, Cyprus and Bulgaria on the energy transition process by examining the relevant consequences for power plant workers in the green energy market. The main aim of this paper is to explore the impacts of the implemented policies on the labour force of electricity generation stations and to identify learning needs for new skills and competences. Thus, it explores the possibility to achieve a just transition by employing workers from the high-carbon energy sector to the renewable energy sector. Qualitative research was carried out to explore the views and expectancies of the above-mentioned people as well as to trace the perceptions of the stakeholders of the green energy industry. A total of 30 semi-structured interviews were conducted in the previously mentioned three countries, which also investigated relevant training and upskilling programmes. The findings revealed various types of educational needs and techniques that can be applied by training providers in accordance to the power plant worker’s views as well as the disciplines and specialities of the workforce that are suitable to receive the further fit for attending such programmes. Workers in Greece and Cyprus appeared more optimistic for this possibility compared to Bulgarian workers. Green entrepreneurs were also optimistic but worried for the rate of investments in the green sector. The current findings describe the backdrop that challenges future retraining programmes with regard to achieving just transition policies for this workforce.