bluuebunny/crossref_metadata_2025 huggingface dataset:
A Contrastive-Augmented Memory Network for Anti-UAV Tracking in TIR Videos
Authors: Wang Ziming , Hu Yuxin , Yang Jianwei , Zhou Guangyao , Liu Fangjian , Liu Yuhan
Published: December 2024

With the development of unmanned aerial vehicle (UAV) technology, the threat of UAV intrusion is no longer negligible. Therefore, drone perception, especially anti-UAV tracking technology, has gathered considerable attention. However, both traditional Siamese and transformer-based trackers struggle in anti-UAV tasks due to the small target size, clutter backgrounds and model degradation. To alleviate these challenges, a novel contrastive-augmented memory network (CAMTracker) is proposed for anti-UAV tracking tasks in thermal infrared (TIR) videos. The proposed CAMTracker conducts tracking through a two-stage scheme, searching for possible candidates in the first stage and matching the candidates with the template for final prediction. In the first stage, an instance-guided region proposal network (IG-RPN) is employed to calculate the correlation features between the templates and the searching images and further generate candidate proposals. In the second stage, a contrastive-augmented matching module (CAM), along with a refined contrastive loss function, is designed to enhance the discrimination ability of the tracker under the instruction of contrastive learning strategy. Moreover, to avoid model degradation, an adaptive dynamic memory module (ADM) is proposed to maintain a dynamic template to cope with the feature variation of the target in long sequences. Comprehensive experiments have been conducted on the Anti-UAV410 dataset, where the proposed CAMTracker achieves the best performance compared to advanced tracking algorithms, with significant advantages on all the evaluation metrics, including at least 2.40%, 4.12%, 5.43% and 5.48% on precision, success rate, success AUC and state accuracy, respectively.
Distributed Target Detection with Coherent Fusion in Tracking Based on Phase Prediction
Authors: Wang Aoya , Lu Jing , Zhou Shenghua , Wang Linhai
Published: December 2024

In distributed radar, a coherent system often gains attention for its higher detection potential in contrast to its non-coherent counterpart. However, even for a distributed coherent radar, it is difficult to coherently accumulate local observations in the searching mode if target returns in local channels are decorrelated. In order to obtain the superiority of coherent processing while overcoming the real implementation difficulties of a coherent framework, this paper studies a distributed coherent detection algorithm for fusion detection. It is utilized in detecting a target during tracking while a target is searched for in a non-coherent manner. From historic observations on target tracking, relative phase delays in different channels are predicted by a phase lock loop and then used to compensate phases for observations in the current frame. Moreover, to enhance the detection performance of distributed radar during tracking, a switching rule between phase prediction-based coherent and non-coherent processing is proposed based on their detection performance. Numerical results indicate that the switching operation can improve the detection probability during tracking, and the non-coherent operation can still provide a moderate detection performance if the phase prediction is unreliable.
Multi-Dimensional and Multi-Scale Physical Dehazing Network for Remote Sensing Images
Authors: Zhou Hao , Wang Le , Li Qiao , Guan Xin , Tao Tao
Published: December 2024

Haze obscures remote sensing images, making it difficult to extract valuable information. To address this problem, we propose a fine detail extraction network that aims to restore image details and improve image quality. Specifically, to capture fine details, we design multi-scale and multi-dimensional extraction blocks and then fuse them to optimize feature extraction. The multi-scale extraction block adopts multi-scale pixel attention and channel attention to extract and combine global and local information from the image. Meanwhile, the multi-dimensional extraction block uses depthwise separable convolutional layers to capture additional dimensional information. Additionally, we integrate an atmospheric scattering model unit into the network to enhance both the dehazing effectiveness and stability. Our experiments on the SateHaze1k and HRSD datasets demonstrate that the proposed method efficiently handles remote sensing images with varying levels of haze, successfully recovers fine details, and achieves superior results compared to existing state-of-the-art dehazing techniques.
Research on 2-D Direction of Arrival (DOA) Estimation for an L-Shaped Array
Authors: Ye Kun , Zhou Lang , Hong Shaohua , Zhang Xuebo , Sun Haixin
Published: December 2024

Lately, there has been a significant increase in interest in coprime array configurations, as they offer the advantage of generating more extensive array apertures and enhanced degrees of freedom when contrasted with standard linear arrays. This document introduces an innovative two-dimensional direction-of-arrival (2-D DOA) estimation technique founded on the zero-completion principle. In particular, our initial step involves interpolating the synthetic co-array signals to achieve completion, followed by the regeneration of the covariance matrix utilizing the interpolated synthetic signals. Then, the 2-D angle estimation is realized based on the complemented matrix using the auto-pairing method. The computational modeling outcomes indicate that the suggested method demonstrates superior angular discriminability. Moreover, this method excels in estimation accuracy when contrasted with its algorithmic counterparts.
Quantitative Genetic Aspects of Accuracy of Tree Biomass Measurement Using LiDAR
Authors: Sano Haruka , Miura Naoko , Inamori Minoru , Unno Yamato , Guo Wei , Isobe Sachiko , Kusunoki Kazutaka , Iwata Hiroyoshi
Published: December 2024

The growing focus on the role of forests in carbon sequestration highlights the importance of accurately and efficiently measuring biophysical traits, such as diameter at breast height (DBH) and tree height. Understanding genetic contributions to trait variation is crucial for enhancing carbon storage through the genetic improvement of forest trees. Light detection and ranging (LiDAR) has been used to estimate DBH and tree height; however, few studies have explored the heritability of these traits or assessed the accuracy of biomass increment selection based on them. Therefore, this study aimed to leverage LiDAR to measure DBH and tree height, estimate tree heritability, and evaluate the accuracy of timber volume selection based on these traits, using 60-year-old larch as the study material. Unmanned aerial vehicle laser scanning (ULS) and backpack laser scanning (BLS) were compared against hand-measured values. The accuracy of DBH estimations using BLS resulted in a root mean square error (RMSE) of 2.7 cm and a coefficient of determination of 0.67. Conversely, the accuracy achieved with ULS was 4.0 cm in RMSE and a 0.24 coefficient of determination. The heritability of DBH was higher with BLS than with ULS and even exceeded that of hand measurements. Comparisons of timber volume selection accuracy based on the measured traits demonstrated comparable performance between BLS and ULS. These findings underscore the potential of using LiDAR remote sensing to quantitatively measure forest tree biomass and facilitate their genetic improvement of carbon-sequestration ability based on these measurements.
Assessing the Effectiveness of Estimating the Construction Years of Cambodia Road Bridges Using an Improved Satellite-Based Method
Authors: Hamunzala Bennie , Goto Hiroto , Matsumoto Koji
Published: December 2024

This study evaluates the effectiveness of an enhanced satellite-based method for estimating the construction years of Cambodian road bridges. By leveraging Landsat satellite imagery and the Normalized Difference Water Index 2 (NDWI_2), in combination with the sequential t-test analysis of regime shifts (STARS) method, this research identifies significant shifts in NDWI_2 values indicative of bridge construction activities. A total of 423 road bridges were analyzed, with the accuracy of the method assessed using the Mean Absolute Error (MAE). The improved method demonstrated considerable accuracy, with 64.3% of bridges classified as high-accuracy (MAE of 0–5), 20.8% as moderate-accuracy (MAE of 6–10), and 14.9% as low-accuracy (MAE of 11–30). Factors such as the bridge length and data quality were found to influence accuracy. A regression analysis comparing the actual bridge age to damage ratings closely aligned with the model using the estimated bridge age to damage ratings, suggesting that the the estimated bridge age obtained from the estimated construction years, in conjunction with damage ratings, provide a reliable basis for evaluating the damage trends of road bridges. The findings underscore the method’s potential for broader applications in regions with limited infrastructure data. Although effective for medium to large bridges, challenges remain for smaller bridges due to spatial resolution limitations. Recommendations for further enhancement include refining data preprocessing, segmenting bridges by size, and incorporating auxiliary datasets. This research highlights the critical role of accurate construction year estimation in effective bridge infrastructure monitoring and maintenance planning.
Spatial-Temporal Evolution and Cooling Effect of Irrigated Cropland in Inner Mongolia Region
Authors: Li Long , Wang Shudong , Bo Yuewei , Yang Banghui , Li Xueke , Liu Kai
Published: December 2024

Monitoring the dynamic distribution of irrigated cropland and assessing its cooling effects are essential for advancing sustainable agriculture amid climate change. This study presents an integrated framework for irrigated cropland monitoring and cooling effect assessment. Leveraging dense time series vegetation indices with Google Earth Engine (GEE), we evaluated multiple machine learning algorithms within to identify the most robust approach (random forest algorithm) for mapping irrigated cropland in Inner Mongolia from 2010 to 2020. Furthermore, we developed an effective method to quantify the diurnal, seasonal, and interannual cooling effects of irrigation. Our generated irrigated cropland maps demonstrate high accuracy, with overall accuracy ranging from 0.85 to 0.89. This framework effectively captures regional cropland expansion patterns, revealing a substantial increase in irrigated cropland across Inner Mongolia by 27,466.09 km2 (about +64%) between 2010 and 2020, with particularly pronounced growth occurring after 2014. Analysis reveals that irrigated cropland lowered average daily land surface temperature (LST) by 0.25 °C compared to rain-fed cropland, with the strongest cooling effect observed between July and August by approximately 0.64 °C, closely associated with increased evapotranspiration. Our work highlights the potential of satellite-based irrigation monitoring and climate impact analysis, offering a valuable tool for supporting climate-resilient agriculture practices.
Multi-Global Navigation Satellite System for Earth Observation: Recent Developments and New Progress
Authors: Jin Shuanggen , Meng Xuyang , Dardanelli Gino , Zhu Yunlong
Published: December 2024

The Global Navigation Satellite System (GNSS) has made important progress in Earth observation and applications. With the successful design of the BeiDou Navigation Satellite System (BDS), four global navigation satellite systems are available worldwide, together with Galileo, GLONASS, and GPS. These systems have been widely employed in positioning, navigation, and timing (PNT). Furthermore, GNSS refraction, reflection, and scattering signals can remotely sense the Earth’s surface and atmosphere with powerful implications for environmental remote sensing. In this paper, the recent developments and new application progress of multi-GNSS in Earth observation are presented and reviewed, including the methods of BDS/GNSS for Earth observations, GNSS navigation and positioning performance (e.g., GNSS-PPP and GNSS-NRTK), GNSS ionospheric modelling and space weather monitoring, GNSS meteorology, and GNSS-reflectometry and its applications. For instance, the static Precise Point Positioning (PPP) precision of most MGEX stations was improved by 35.1%, 18.7%, and 8.7% in the east, north, and upward directions, respectively, with PPP ambiguity resolution (AR) based on factor graph optimization. A two-layer ionospheric model was constructed using IGS station data through three-dimensional ionospheric model constraints and TEC accuracy was increased by about 20–27% with the GIM model. Ten-minute water level change with centimeter-level accuracy was estimated with ground-based multiple GNSS-R data based on a weighted iterative least-squares method. Furthermore, a cyclone and its positions were detected by utilizing the GNSS-reflectometry from the space-borne Cyclone GNSS (CYGNSS) mission. Over the years, GNSS has become a dominant technology among Earth observation with powerful applications, not only for conventional positioning, navigation and timing techniques, but also for integrated remote sensing solutions, such as monitoring typhoons, river water level changes, geological geohazard warnings, low-altitude UAV navigation, etc., due to its high performance, low cost, all time and all weather.
Annual Cropping Intensity Dynamics in China from 2001 to 2023
Authors: Ren Jie , Shao Yang , Wang Yufei
Published: December 2024

Spatial and temporal information about cropping patterns of single and multiple crops is important for monitoring crop production and land-use intensity. We used time-series MODIS NDVI 8-day composite data to develop annual cropping pattern products at a 250 m spatial resolution for China, covering the period from 2001 to 2023. To address the potential impacts of varying parameters in both data pre-processing and the peak detection algorithm on the accuracy of cropping pattern mapping, we employed a grid-search method to fine-tune these parameters. This process focused on optimizing the Savitzky–Golay smoothing window size and the peak width parameters using a calibration dataset. The results highlighted that an optimal combination of a five to seven MODIS composite window size in Savitzky–Golay smoothing and a peak width of four MODIS composites achieved good overall mapping accuracy. Pixel-wise accuracy assessments were conducted for the selected mapping years of 2001, 2011, and 2021. Overall accuracies were between 89.7% and 92.0%, with F1 scores ranging from 0.921 to 0.943. Nationally, this study observed a fluctuating trend in multiple cropping percentages, with a notable increase after 2013, suggesting shifts toward more intensive agricultural practices in recent years. At a finer spatial scale, the combination of Mann–Kendall and Sen’s slope analyses revealed that approximately 12.9% of 3 km analytical windows exhibited significant changes in cropping intensity. We observed spatial clusters of increasing and decreasing crop intensity trends across provinces such as Hebei, Shandong, Shaanxi, and Gansu. This study underscores the importance of data smoothing and peak detection methods in analyzing high temporal resolution remote sensing data. The generation of annual single/multiple cropping pattern maps at a 250 m spatial resolution enhances our comprehension of agricultural dynamics through time and across different regions.
Combination of Remote Sensing and Artificial Intelligence in Fruit Growing: Progress, Challenges, and Potential Applications
Authors: Furuya Danielle Elis Garcia , Bolfe Édson Luis , Parreiras Taya Cristo , Barbedo Jayme Garcia Arnal , Santos Thiago Teixeira , Gebler Luciano
Published: December 2024

Fruit growing is important in the global agricultural economy, contributing significantly to food security, job creation, and rural development. With the advancement of technologies, mapping fruits using remote sensing and machine learning (ML) and deep learning (DL) techniques has become an essential tool to optimize production, monitor crop health, and predict harvests with greater accuracy. This study was developed in four main stages. In the first stage, a comprehensive review of the existing literature was made from July 2018 (first article found) to June 2024, totaling 117 articles. In the second stage, a general analysis of the data obtained was made, such as the identification of the most studied fruits with the techniques of interest. In the third stage, a more in-depth analysis was made focusing on apples and grapes, with 27 and 30 articles, respectively. The analysis included the use of remote sensing (orbital and proximal) imagery and ML/DL algorithms to map crop areas, detect diseases, and monitor crop development, among other analyses. The fourth stage shows the data’s potential application in a Southern Brazilian region, known for apple and grape production. This study demonstrates how the integration of modern technologies can transform fruit farming, promoting more sustainable and efficient agriculture through remote sensing and artificial intelligence technologies.
Time-Series InSAR Technology for Monitoring and Analyzing Surface Deformations in Mining Areas Affected by Fault Disturbances
Authors: He Kuan , Zou Youfeng , Han Zhigang , Huang Jilei
Published: December 2024

Faults, as unique geological structures, disrupt the mechanical connections between rock masses. During coal mining, faults in the overlying strata can disturb the original stress balance, leading to fault activation and altering the typical subsidence patterns. This can result in abnormal ground deformation and significant damage to surface structures, posing a serious geological hazard in mining areas. This study examines the influence of a known fault (F13 fault) on ground subsidence in the Wannian Mine of the Fengfeng Mining Area. We utilized 12 Sentinel-1A images and applied SBAS-InSAR, StaMPS-InSAR, and DS-InSAR time-series InSAR methods, alongside the D-InSAR method, to investigate surface deformations caused by the F13 fault. The monitoring accuracy of these methods was evaluated using leveling measurements from 28 surface movement observation stations. In addition, the density of effective monitoring points and the relative strengths and limitations of the three time-series methods were compared. The findings indicate that, in low deformation areas, DS-InSAR has a monitoring accuracy of 7.7 mm, StaMPS-InSAR has a monitoring accuracy of 16.4 mm, and SBAS-InSAR has an accuracy of 19.3 mm.
High-Resolution Single-Pixel Imaging of Spatially Sparse Objects: Real-Time Imaging in the Near-Infrared and Visible Wavelength Ranges Enhanced with Iterative Processing or Deep Learning
Authors: Stojek Rafał , Pastuszczak Anna , Wróbel Piotr , Cwojdzińska Magdalena , Sobczak Kacper , Kotyński Rafał
Published: December 2024

We demonstrate high-resolution single-pixel imaging (SPI) in the visible and near-infrared wavelength ranges using an SPI framework that incorporates a novel, dedicated sampling scheme and a reconstruction algorithm optimized for the rapid imaging of highly sparse scenes at the native digital micromirror device (DMD) resolution of 1024 × 768. The reconstruction algorithm consists of two stages. In the first stage, the vector of SPI measurements is multiplied by the generalized inverse of the measurement matrix. In the second stage, we compare two reconstruction approaches: one based on an iterative algorithm and the other on a trained neural network. The neural network outperforms the iterative method when the object resembles the training set, though it lacks the generality of the iterative approach. For images captured at a compression of 0.41 percent, corresponding to a measurement rate of 6.8 Hz with a DMD operating at 22 kHz, the typical reconstruction time on a desktop with a medium-performance GPU is comparable to the image acquisition rate. This allows the proposed SPI method to support high-resolution dynamic SPI in a variety of applications, using a standard SPI architecture with a DMD modulator operating at its native resolution and bandwidth, and enabling the real-time processing of the measured data with no additional delay on a standard desktop PC.
Enhancing Weigh-in-Motion Systems Accuracy by Considering Camera-Captured Wheel Oscillations
Authors: Hagmanns Moritz P. M. , Lamberty Serge , Fazekas Adrian , Oeser Markus
Published: December 2024

Weigh-in-motion (WIM) systems aim to estimate a vehicle’s weight by measuring static wheel loads as it passes at highway speed over roadway-embedded sensors. Vehicle oscillations and the resulting dynamic load components are critical factors affecting measurements and limiting accuracy. As of now, a satisfactory solution has yet to be found. This paper discusses a novel correction approach that fuses WIM sensor data with wheel oscillation captured by cameras. In an experiment, a hard plastic speed bump was placed ahead of a piezoelectric WIM sensor to induce oscillation in trucks crossing the WIM sensor. Three high-speed cameras captured the motion of the wheels. The results show that the proposed method improved the accuracy of the measured gross weight for significant wheel oscillations, while no improvement is observed for smaller oscillation amplitudes.
Attention-Based PSO-LSTM for Emotion Estimation Using EEG
Authors: Oka Hayato , Ono Keiko , Panagiotis Adamidis
Published: December 2024

Recent advances in emotion recognition through Artificial Intelligence (AI) have demonstrated potential applications in various fields (e.g., healthcare, advertising, and driving technology), with electroencephalogram (EEG)-based approaches demonstrating superior accuracy compared to facial or vocal methods due to their resistance to intentional manipulation. This study presents a novel approach to enhance EEG-based emotion estimation accuracy by emphasizing temporal features and efficient parameter space exploration. We propose a model combining Long Short-Term Memory (LSTM) with an attention mechanism to highlight temporal features in EEG data while optimizing LSTM parameters through Particle Swarm Optimization (PSO). The attention mechanism assigned weights to LSTM hidden states, and PSO dynamically optimizes the vital parameters, including units, batch size, and dropout rate. Using the DEAP and SEED datasets, which serve as benchmark datasets for emotion estimation research using EEG, we evaluate the model’s performance. For the DEAP dataset, we conduct a four-class classification of combinations of high and low valence and arousal states. We perform a three-class classification of negative, neutral, and positive emotions for the SEED dataset. The proposed model achieves an accuracy of 0.9409 on the DEAP dataset, surpassing the previous state-of-the-art accuracy of 0.9100 reported by Lin et al. The model attains an accuracy of 0.9732 on the SEED dataset, recording one of the highest accuracies among the related research. These results demonstrate that integrating the attention mechanism with PSO significantly improves the accuracy of EEG-based emotion estimation, contributing to the advancement of emotion recognition technology.
Communication Link Analysis of a Low-Earth Orbit Satellites Considering Interference Sources Moving Along Various Parabola-Curved Paths
Authors: Kang Hyunmu , Kang Eunjung , Choo Hosung
Published: December 2024

We analyze the communication link of an LEO satellite considering interference sources moving along various parabola-curved paths. In this situation, the location of the ground station, airborne interference source paths, and the satellite’s trajectory were expressed in the East-North-Up (ENU) coordinate system. The airborne interference source path is designed using a parabola equation with a directrix parallel to the satellite’s trajectory to analyze the interference situation for more diverse interference source paths, rather than using a straight path. To investigate critical interference situations where the J/S ratio is maintained above −20 dB with a small deviation during the communication time, we investigate interference situations by changing the parameters of the interference source path. The genetic algorithm (GA) is used to easily find an airborne interference source path that maintains the J/S ratio above −20 dB with a small deviation. A cost function for the GA is then defined as the average difference between the J/S ratio and the reference value (−10 dB and −20 dB) during the communication time. The optimum parameters of the interference source path are determined at a minimum cost in the GA. These results demonstrate that more significant interference situations for the communication link can be easily found by using parabola-curved paths and the GA. As a result, previous studies investigated the basic tendency of the J/S ratio using straight paths. However, this study provides a database for operating an anti-jamming system based on the obtained optimized path.
UAV Trajectory Control and Power Optimization for Low-Latency C-V2X Communications in a Federated Learning Environment
Authors: Fernando Xavier , Gupta Abhishek
Published: December 2024

Unmanned aerial vehicle (UAV)-enabled vehicular communications in the sixth generation (6G) are characterized by line-of-sight (LoS) and dynamically varying channel conditions. However, the presence of obstacles in the LoS path leads to shadowed fading environments. In UAV-assisted cellular vehicle-to-everything (C-V2X) communication, vehicle and UAV mobility and shadowing adversely impact latency and throughput. Moreover, 6G vehicular communications comprise data-intensive applications such as augmented reality, mixed reality, virtual reality, intelligent transportation, and autonomous vehicles. Since vehicles’ sensors generate immense amount of data, the latency in processing these applications also increases, particularly when the data are not independently identically distributed (non-i.i.d.). Furthermore, when the sensors’ data are heterogeneous in size and distribution, the incoming packets demand substantial computing resources, energy efficiency at the UAV servers and intelligent mechanisms to queue the incoming packets. Due to the limited battery power and coverage range of UAV, the quality of service (QoS) requirements such as coverage rate, UAV flying time, and fairness of vehicle selection are adversely impacted. Controlling the UAV trajectory so that it serves a maximum number of vehicles while maximizing battery power usage is a potential solution to enhance QoS. This paper investigates the system performance and communication disruption between vehicles and UAV due to Doppler effect in the orthogonal time–frequency space (OTFS) modulated channel. Moreover, a low-complexity UAV trajectory prediction and vehicle selection method is proposed using federated learning, which exploits related information from past trajectories. The weighted total energy consumption of a UAV is minimized by jointly optimizing the transmission window (Lw), transmit power and UAV trajectory considering Doppler spread. The simulation results reveal that the weighted total energy consumption of the OTFS-based system decreases up to 10% when combined with federated learning to locally process the sensor data at the vehicles and communicate the processed local models to the UAV. The weighted total energy consumption of the proposed federated learning algorithm decreases by 10–15% compared with convex optimization, heuristic, and meta-heuristic algorithms.
Blockchain and Internet of Things Technologies for Food Traceability in Olive Oil Supply Chains
Authors: Vitaskos Vassilios , Demestichas Konstantinos , Karetsos Sotirios , Costopoulou Constantina
Published: December 2024

This study presents a blockchain-based traceability system designed specifically for the olive oil supply chain, addressing key challenges in transparency, quality assurance, and fraud prevention. The system integrates Internet of Things (IoT) technology with a decentralized blockchain framework to provide real-time monitoring of critical quality metrics. A practical web application, linked to the Ethereum blockchain, enables stakeholders to track each stage of the supply chain via tamper-proof records. Key functionalities include smart contracts that automate quality checks, ensuring data integrity and providing immediate verification of product authenticity. Initial user feedback highlights the system’s potential to enhance transparency and reduce fraud risks in the olive oil market, supporting consumer trust and regulatory compliance. This approach offers a scalable solution adaptable to other high-value agricultural products, demonstrating the blockchain’s transformative potential for secure and transparent food traceability.
Initial Pose Estimation Method for Robust LiDAR-Inertial Calibration and Mapping
Authors: Park  Eun-Seok , Arshad Saba , Park Tae-Hyoung
Published: December 2024

Handheld LiDAR scanners, which typically consist of a LiDAR sensor, Inertial Measurement Unit, and processor, enable data capture while moving, offering flexibility for various applications, including indoor and outdoor 3D mapping in fields such as architecture and civil engineering. Unlike fixed LiDAR systems, handheld devices allow data collection from different angles, but this mobility introduces challenges in data quality, particularly when initial calibration between sensors is not precise. Accurate LiDAR-IMU calibration, essential for mapping accuracy in Simultaneous Localization and Mapping applications, involves precise alignment of the sensors’ extrinsic parameters. This research presents a robust initial pose calibration method for LiDAR-IMU systems in handheld devices, specifically designed for indoor environments. The research contributions are twofold. Firstly, we present a robust plane detection method for LiDAR data. This plane detection method removes the noise caused by mobility of scanning device and provides accurate planes for precise LiDAR initial pose estimation. Secondly, we present a robust planes-aided LiDAR calibration method that estimates the initial pose. By employing this LiDAR calibration method, an efficient LiDAR-IMU calibration is achieved for accurate mapping. Experimental results demonstrate that the proposed method achieves lower calibration errors and improved computational efficiency compared to existing methods.
Silicon Drift Detectors for the Measurement and Reconstruction of Beta Spectra
Authors: Nava Andrea , Bernardini Leonardo , Biassoni Matteo , Bradanini Tommaso , Carminati Marco , De Gregorio Giovanni , Fiorini Carlo , Gagliardi Giulio , Lechner Peter , Mancino Riccardo , Brofferio Chiara
Published: December 2024

The ASPECT-BET project, or An sdd-SPECTrometer for BETa decay studies, aims to develop a novel technique for the precise measurement of forbidden beta spectra in the 10 keV–1 MeV range. This technique employs a Silicon Drift Detector (SDD) as the main spectrometer with the option of a veto system to reject events exhibiting only partial energy deposition in the SDD. A precise understanding of the spectrometer’s response to electrons is crucial for accurately reconstructing the theoretical shape of the beta spectrum. To compute this response, GEANT4 simulations optimized for low-energy electron interactions are used and validated with a custom-made electron gun. In this article we present the performance of these simulations in reconstructing the electron spectra measured with SDDs of a 109Cd monochromatic source, both in vacuum and in air. The allowed beta spectrum of a 14C source was also measured and analyzed, proving that this system is suitable for the application in ASPECT-BET.
Track Deflection Monitoring for Railway Construction Based on Dynamic Brillouin Optical Time-Domain Reflectometry
Authors: Zhang Tianfang , Zhou Liming , Liu Weimin , Cheng Linghao
Published: December 2024

Real-time online monitoring of track deformation during railway construction is crucial for ensuring the safe operation of trains. However, existing monitoring technologies struggle to effectively monitor both static and dynamic events, often resulting in high false alarm rates. This paper presents a monitoring technology for track deformation during railway construction based on dynamic Brillouin optical time-domain reflectometry (Dy-BOTDR), which effectively meets requirements in the monitoring of both static and dynamic events of track deformation. Dy-BOTDR can provide a two-dimensional spatial–temporal distribution map of track strain changes to characterize various events for better monitoring accuracy and lower false alarm rates.
A Scene Knowledge Integrating Network for Transmission Line Multi-Fitting Detection
Authors: Chen Xinhang , Xu Xinsheng , Xu Jing , Zheng Wenjie , Wang Qianming
Published: December 2024

Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings. So, the scene knowledge will include global context information, fitting fine-grained visual information and scene structure information. Then, a scene filter module is designed to learn the global context information and fitting fine-grained visual information, and a scene structure module is designed to learn the scene structure information. Finally, the scene semantic features are used as the carrier to integrate three categories of information into the relative scene features, which can assist in the recognition of the occluded fittings and the tiny-scale fittings after feature mining and feature integration. The experiments show that the proposed network can effectively improve the performance of the multi-fitting detection task compared with the Faster R-CNN and other state-of-the-art models. In particular, the detection performances of the occluded and tiny-scale fittings are significantly improved.
Comparative Analysis of Low-Cost Portable Spectrophotometers for Colorimetric Accuracy on the RAL Design System Plus Color Calibration Target
Authors: Samec Jaša , Štruc Eva , Berzina Inese , Naglič Peter , Cugmas Blaž
Published: December 2024

Novel low-cost portable spectrophotometers could be an alternative to traditional spectrophotometers and calibrated RGB cameras by offering lower prices and convenient measurements but retaining high colorimetric accuracy. This study evaluated the colorimetric accuracy of low-cost, portable spectrophotometers on the established color calibration target—RAL Design System Plus (RAL+). Four spectrophotometers with a listed price between USD 100–1200 (Nix Spectro 2, Spectro 1 Pro, ColorReader, and Pico) and a smartphone RGB camera were tested on a representative subset of 183 RAL+ colors. Key performance metrics included the devices’ ability to match and measure RAL+ colors in the CIELAB color space using the color difference CIEDE2000 ΔE. The results showed that Nix Spectro 2 had the best performance, matching 99% of RAL+ colors with an estimated ΔE of 0.5–1.05. Spectro 1 Pro and ColorReader matched approximately 85% of colors with ΔE values between 1.07 and 1.39, while Pico and the Asus 8 smartphone matched 54–77% of colors, with ΔE of around 1.85. Our findings showed that low-cost, portable spectrophotometers offered excellent colorimetric measurements. They mostly outperformed existing RGB camera-based colorimetric systems, making them valuable tools in science and industry.
Evaluating Communication Performance in Rotating Electrical Machines Using RSSI Measurements and Artificial Intelligence
Authors: Brahim Sonia Ben , Dardouri Samia , Lajnef Hanen , Slimane Amel Ben , Bouallegue Ridha , Vuong Tan-Hoa
Published: December 2024

This paper introduces a novel methodology for evaluating communication performance in rotating electric machines using Received Signal Strength Indication (RSSI) measurements coupled with artificial intelligence. The proposed approach focuses on assessing the quality of wireless signals in the complex, dynamic environment inside these machines, where factors like reflections, metallic surfaces, and rotational movements can significantly impact communication. RSSI is used as a key parameter to monitor real-time signal behavior, enabling a detailed analysis of communication reliability. The methodology comprises several stages, including data collection, preprocessing, feature extraction, and model training. Various machine learning models are implemented and evaluated. Among these, the SVM model with a Radial Basis Function (RBF) kernel outperforms others, achieving an accuracy of 97%, with high precision and recall scores, confirming its robustness in classifying RSSI data and handling complex signal behavior. The confusion matrix further supports the SVM model’s accuracy, showing minimal misclassification.
Disturbance Robust Attitude Stabilization of Multirotors with Control Moment Gyros
Authors: Yang Youyoung , Kim Sungsu , Lee Kwanghyun , Leeghim Henzeh
Published: December 2024

This paper presents a novel control framework for enhancing the attitude stabilization of multirotor UAVs using Control Moment Gyros (CMGs) and a Disturbance Robust Drive Law (DRDL). Due to their lightweight and compact structure, multirotor UAVs are highly susceptible to disturbances such as wind, making it challenging to achieve stable attitude control using rotor thrust alone. To address this issue, we employ CMGs to provide robust attitude control and apply Fast Terminal Sliding Mode Control (FTSMC) to ensure fast and accurate convergence within a finite time. The combination of CMGs’ torque amplification capability with the DRDL enables the system to effectively avoid singularities and maintain stable control performance in the presence of disturbances. Simulation results demonstrate that the CMG-equipped hexarotor utilizing the DRDL rapidly converges to the target attitude despite external disturbances, while minimizing oscillations in both motor speed and gimbal movement. Additionally, compared to the pseudo-inverse control method, the proposed approach significantly improves singularity avoidance and disturbance mitigation. The proposed control framework enhances the stability and reliability of UAV operations and demonstrates its potential for high-performance control in challenging disturbance environments.
Fusion of Visible and Infrared Aerial Images from Uncalibrated Sensors Using Wavelet Decomposition and Deep Learning
Authors: Vipparla Chandrakanth , Krock Timothy , Nouduri Koundinya , Fraser Joshua , AliAkbarpour Hadi , Sagan Vasit , Cheng Jing-Ru C. , Kannappan Palaniappan
Published: December 2024

Multi-modal systems extract information about the environment using specialized sensors that are optimized based on the wavelength of the phenomenology and material interactions. To maximize the entropy, complementary systems operating in regions of non-overlapping wavelengths are optimal. VIS-IR (Visible-Infrared) systems have been at the forefront of multi-modal fusion research and are used extensively to represent information in all-day all-weather applications. Prior to image fusion, the image pairs have to be properly registered and mapped to a common resolution palette. However, due to differences in the device physics of image capture, information from VIS-IR sensors cannot be directly correlated, which is a major bottleneck for this area of research. In the absence of camera metadata, image registration is performed manually, which is not practical for large datasets. Most of the work published in this area assumes calibrated sensors and the availability of camera metadata providing registered image pairs, which limits the generalization capability of these systems. In this work, we propose a novel end-to-end pipeline termed DeepFusion for image registration and fusion. Firstly, we design a recursive crop and scale wavelet spectral decomposition (WSD) algorithm for automatically extracting the patch of visible data representing the thermal information. After data extraction, both the images are registered to a common resolution palette and forwarded to the DNN for image fusion. The fusion performance of the proposed pipeline is compared and quantified with state-of-the-art classical and DNN architectures for open-source and custom datasets demonstrating the efficacy of the pipeline. Furthermore, we also propose a novel keypoint-based metric for quantifying the quality of fused output.
Metal Sulfide Nanoparticles as Sphalerite Surface Activators to Improve Zinc Recovery Through Flotation Process
Authors: Ávila-Márquez Delia Monserrat , Blanco-Flores Alien , Toledo-Jaldin Helen Paola , González Torres Maribel , Vilchis-Nestor Alfredo Rafael , Reyes Domínguez Iván Alejandro , de Aquino García Ramiro
Published: December 2024

CuS nanoparticles (Np) were synthesized and deposited on synthetic sphalerite (SP) using two different methods. Two nanoparticle products were obtained on the surface of SP, Np1 and Np2, resulting in two active materials (Np1-SP and Np2-SP) with specific characteristics. Nanoparticles and active materials were characterized by TEM, XRD, SEM, and XPS. The collectors PAX and SIPX were adsorbed on Np1-SP and Np2-SP to determine the adsorption capacity. Method 1 provides a higher quantity of nanoparticles on SP, which allows for the adsorption of a higher amount of SIPX. Method 1 was used to deposit nanoparticles on two natural sphalerites (SN) with different iron contents. SN, unlike SP, can be used to test nanoparticle activation results in microflotation experiments. SN was activated with nanoparticles (Np1-SN) and using the traditional method (Cu-SN). The recovery of 75% of zinc using the microflotation process suggests that the hydrophobicity of Np1-SN is higher than that of Cu-SN. Nanoparticles improve the hydrophobicity of SN compared to the traditional activation used in the mining industry. These results suggest that using nanoparticles is an excellent option to activate minerals in flotation processes, decreasing the consumption of reagents and helping to mitigate negative impacts on the environment.
Phaseolus vulgaris (L.) Growth Promotion by Cladosporium halotolerans Inoculation Under Salinity Stress
Authors: Alzahrani Samah , Najjar Azhar , Noor Samah , Zabermawi Nidal M. , Qattan Shaza Y. , Hassoubah Shahira A. , Makki Rania M. , El-Zohri Manal
Published: December 2024

Salinization induced by salt stress is a critical environmental factor, and limits the expansion of agricultural areas and population distribution in continental regions, including Saudi Arabia. Common beans, a vital source of protein, energy, and dietary fibers, are negatively affected by salt stress. In this research, the endophytic fungus Cladosporium halotolerans was utilized to remediate saline soils and enhance common bean growth and resilience. The results of this study demonstrated that soil treatment with C. halotolerans enhances the soil properties by decreasing soil pH and increasing soil organic matter content under saline conditions. Inoculation by C. halotolerans also significantly improved plant growth parameters, induced systemic resistance to salinity, and increased the levels of chlorophyll b and carotenoids. Fungal inoculation also causes stress relief as indicated by reducing malondialdehyde concentration by 27.4% lower than stressed plants. Microscopic images revealed the active association and colonization of C. halotolerans within the roots of the Phaseolus vulgaris both under control and saline conditions. Therefore, utilizing endophytic fungi C. halotolerans for saline soil remediation appears to be a promising alternative in plant treatments, highlighting their potential as valuable resources for both research and commercial applications under salinity stress.
Impact of Climate Change on Green Technology Innovation—An Examination Based on Microfirm Data
Authors: Wang Bin , Liu Jiaxin
Published: December 2024

Against the pressing backdrop of global climate change, various environmental issues are becoming increasingly prominent, posing unprecedented challenges to both the global economic system and business operations. Green technology innovation, as a critical response to climate change, is vital not only for the sustainable development of firms, but also for fostering the harmonious coexistence of the economy and environment. However, whether climate change itself affects green technology innovation activities is still a topic that needs to be explored in depth. This paper utilizes data from the China Meteorological Administration (CMA), State Intellectual Property Office of China (SIPO), and CRNDS database to empirically examine the impact of climate change on green technology innovation of Chinese A-share listed firms from 2011 to 2020. The findings indicate the following: (1) Climate change significantly inhibits green technology innovation. (2) Entrepreneurs’ green human capital can mitigate the negative impact of climate change on green technology innovation. (3) When faced with higher investor attention or stronger environmental regulation, firms will pay more attention to their green technology innovation when adversely affected by climate change. (4) Resource-based cities show greater resilience against the negative impacts of climate change on green technology innovation compared to non-resource-based cities, suggesting that climate change-induced adaptive behavior may break the resource curse. (5) Mature, non-polluting, high-tech, and non-manufacturing firms are more effective in resisting the inhibitory effects. This research contributes to understanding climate risks and managing them effectively.
Integrating Ecosystem Service Assessment, Human Activity Impacts, and Priority Conservation Area Delineation into Ecological Management Frameworks
Authors: Wang Zhongxu , Chen Shengbo , Xu Junqiang , Ren Chao , Yu Yafeng , Wang Zibo , Wang Lei , Xu Yucheng
Published: December 2024

The comprehensive protection and restoration of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts is critical for enhancing ecological environmental quality and fulfilling the aspirations of ecological civilization in the modern era. Centered on the key project area of the Mountain-River Project within the Luohe River Basin of the Eastern Qinling Mountains, this study employs the InVEST model to assess spatiotemporal variations in habitat quality (HQ), water yield (WY), carbon sequestration (CS), and soil retention (SR) for the years 2000, 2010, and 2020. This study further examines the trade-offs and synergies among these ecosystem services, integrates the Ordered Weighted Averaging (OWA) and GIS methodology with human activity patterns, determines the optimal management scenario, and offers targeted recommendations for optimization. The findings reveal that areas of high habitat quality, carbon sequestration, and soil retention are predominantly concentrated in the western and southwestern regions of the basin, whereas high-value zones of water yield are primarily situated in the southern and southwestern sectors. Habitat quality demonstrates significant synergies with other ecosystem services, whereas water yield presents a notable trade-off with soil retention. By conducting a comparative analysis of protection efficiency, we identified priority conservation areas predominantly located in the southern and southwestern regions of the basin. Moreover, through overlaying the priority conservation zones with the Human Footprint Index (HFI), the priority conservation area was precisely delineated to encompass 5.41 × 105 hectares. This methodology provides critical guidance for the implementation of the Mountain-River Project and offers substantial value in scientifically advancing ecological restoration initiatives.