bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Boundary Layer of the Haze at Xianyang Airport Based on Multi-Source Detection Data
Authors: Ming Hu , Wang Minzhong , Gao Lianhui , Qian Yijia , Gao Mingliang , Chehri Abdellah
Published: January 2023

To reveal the high-resolution atmospheric and statistical characteristics of haze events within the boundary layer (BL) in different months, this study conducted a combined detection experiment using a wind-profiling radar, a microwave radiometer, and an ambient particulate monitor on 1230 haze events occurring at Xianyang Airport from 2016 to 2021. First, the boundary layer heights (BLHs) of the haze events were calculated using the atmospheric refractive index structure constant, wind direction and speed, and these were verified against reanalysis data from ERA-Interim. Spatial–temporal evolution and statistical characteristics of temperature, and relative humidity and horizontal wind during haze events, were then analyzed. Finally, the relationships between the BLH and AQI (air quality index) and PM2.5 during the haze events were analyzed. The results indicate that the average BLHs during haze events at Xianyang Airport were generally lower than 1000 m. Moreover, the average BLHs in December and January were distributed in the range of 200–600 m, and lower than that in June and July, in a range of 500–1100 m. Furthermore, the maximum value of the average BLH appears at 13:00–15:00. When the temperature was low in the morning, the stratification difference was small and the sensible heat flux between ground and air was still weak, leading to a low BLH value. Meanwhile, when the air quality was poor, the relative humidity was relatively large, and the corresponding AQI and PM2.5 were very large. Subsequently, when the temperature gradually increased with time, the heat flux and the average BLH also gradually increased. Moreover, the relative humidity within the BL decreased, and the corresponding AQI and PM2.5 also gradually decreased, with the corresponding air quality improving accordingly. The results obtained herein provide a key reference for the preparedness of haze events.
The Habitat Map of Switzerland: A Remote Sensing, Composite Approach for a High Spatial and Thematic Resolution Product
Authors: Price Bronwyn , Huber Nica , Nussbaumer Anita , Ginzler Christian
Published: January 2023

Habitat maps at high thematic and spatial resolution and broad extents are fundamental tools for biodiversity conservation, the planning of ecological networks and the management of ecosystem services. To derive a habitat map for Switzerland, we used a composite methodology bringing together the best available spatial data and distribution models. The approach relies on the segmentation and classification of high spatial resolution (1 m) aerial imagery. Land cover data, as well as habitat and species distribution models built on Earth observation data from Sentinel 1 and 2, Landsat, Planetscope and LiDAR, inform the rule-based classification to habitats defined by the hierarchical Swiss Habitat Typology (TypoCH). A total of 84 habitats in 32 groups and 9 overarching classes are mapped in a spatially explicit manner across Switzerland. Validation and plausibility analysis with four independent datasets show that the mapping is broadly plausible, with good accuracy for most habitats, although with lower performance for fine-scale and linear habitats, habitats with restricted geographical distributions and those predominantly characterised by understorey species, especially forest habitats. The resulting map is a vector dataset available for interactive viewing and download from open EnviDat data sharing platform. The methodology is semi-automated to allow for updates over time.
Digital Mapping of Root-Zone Soil Moisture Using UAV-Based Multispectral Data in a Kiwifruit Orchard of Northwest China
Authors: Zhu Shidan , Cui Ningbo , Zhou Ji , Xue Jingyuan , Wang Zhihui , Wu Zongjun , Wang Mingjun , Deng Qingling
Published: January 2023

Accurate estimation of root-zone soil moisture (SM) is of great significance for accurate irrigation management. This study was purposed to identify planted-by-planted mapping of root-zone SM on three critical fruit growth periods based on UAV multispectral images using three machine learning (ML) algorithms in a kiwifruit orchard in Shaanxi, China. Several spectral variables were selected based on variable importance (VIP) rankings, including reflectance Ri at wavelengths 560, 668, 740, and 842 nm. Results indicated that the VIP method effectively reduced 42 vegetation indexes (VIs) to less than 7 with an evaluation accuracy of root-zone SM models. Compared with deep root-zone SM models (SM40 and SM60), shallow root-zone SM models (SM10, SM20, and SM30) have better performance (R2 from 0.65 to 0.82, RRMSE from 0.02 to 0.03, MAE from 0.20 to 0.54) in the three fruit growth stages. Among three ML algorithms, random forest models were recommended for simulating kiwi root-zone SM during the critical fruit growth period. Overall, the proposed planted-by-planted root-zone SM estimation approach can be considered a great tool to upgrade the toolbox of the growers in site-specific field management for the high spatiotemporal resolution of SM maps.
Seismic-Q Compensation by Iterative Time-Domain Deconvolution
Authors: Deng Wubing , Cao Qingsong , Morozov Igor B. , Fu Li-Yun
Published: January 2023

Attenuation is often significant during seismic wave propagation in the subsurface, leading to the reduced resolution and narrower bandwidth of seismic images. Traditional corrections for such effects are inverse-Q filtering and deconvolution, which require a high signal-to-noise ratio (SNR) to avoid noise boost-up. Here, we propose a time-domain method offering advantages in the resolution and interpretational quality of the resulting images. Similar to wavelet transforms, the iterative time-domain deconvolution (ITD) represents the seismogram by a superposition of non-stationary source wavelets modeled in the appropriate attenuation model. Arbitrary frequency-dependent Q and velocity dispersion laws can be used and non-Q type attenuation can be caused by focusing, defocusing, scattering, effects of fine layering, and fluctuations of the wavefield. Compared to inverse-Q filtering and some deconvolution methods, the method does not boost high-frequency noise and is less sensitive to the accuracy of the Q model. We illustrate and compare this method to inverse-Q filtering by using several synthetic and real data examples. The tests include noise-contaminated data, inaccurate Q models, and variable source wavelets. The examples show that the ITD is a practical and effective tool for Q-compensation with a broad scope of potential applications, albeit with some defects. An important benefit of ITD that other methods may not possess could be the ability to utilize geological information, such as locations and sparseness of major reflectors or the presence of interpreted Q contrasts, which might be able to further improve the performance of ITD.
Gaussian Process and Deep Learning Atmospheric Correction
Authors: Basener Bill , Basener Abigail
Published: January 2023

Atmospheric correction is the processes of converting radiance values measured at a spectral sensor to the reflectance values of the materials in a multispectral or hyperspectral image. This is an important step for detecting or identifying the materials present in the pixel spectra. We present two machine learning models for atmospheric correction trained and tested on 100,000 batches of 40 reflectance spectra converted to radiance using MODTRAN, so the machine learning model learns the radiative transfer physics from MODTRAN. We created a theoretically interpretable Bayesian Gaussian process model and a deep learning autoencoder treating the atmosphere as noise. We compare both methods for estimating gain in the correction model to process for estimating gain within the well-know QUAC method which assumes a constant mean endmember reflectance. Prediction of reflectance using the Gaussian process model outperforms the other methods in terms of both accuracy and reliability.
Attention Fusion of Transformer-Based and Scale-Based Method for Hyperspectral and LiDAR Joint Classification
Authors: Zhang Maqun , Gao Feng , Zhang Tiange , Gan Yanhai , Dong Junyu , Yu Hui
Published: January 2023

In recent years, there have been many multimodal works in the field of remote sensing, and most of them have achieved good results in the task of land-cover classification. However, multi-scale information is seldom considered in the multi-modal fusion process. Secondly, the multimodal fusion task rarely considers the application of attention mechanism, resulting in a weak representation of the fused feature. In order to better use the multimodal data and reduce the losses caused by the fusion of different modalities, we proposed a TRMSF (Transformer and Multi-scale fusion) network for land-cover classification based on HSI (hyperspectral images) and LiDAR (Light Detection and Ranging) images joint classification. The network enhances multimodal information fusion ability by the method of attention mechanism from Transformer and enhancement using multi-scale information to fuse features from different modal structures. The network consists of three parts: multi-scale attention enhancement module (MSAE), multimodality fusion module (MMF) and multi-output module (MOM). MSAE enhances the ability of feature representation from extracting different multi-scale features of HSI, which are used to fuse with LiDAR feature, respectively. MMF integrates the data of different modalities through attention mechanism, thereby reducing the loss caused by the data fusion of different modal structures. MOM optimizes the network by controlling different outputs and enhances the stability of the results. The experimental results show that the proposed network is effective in multimodality joint classification.
Space-Time Cascaded Processing-Based Adaptive Transient Interference Mitigation for Compact HFSWR
Authors: Yao Di , Chen Qiushi , Tian Qiyan
Published: January 2023

In high-frequency (HF) radar systems, transient interference is a common phenomenon that dramatically degrades the performance of target detection and remote sensing. Up until now, various suppression algorithms of transient interference have been proposed. They mainly concentrate on the skywave over-the-horizon radar on the basis of the assumption that the interference is sparse in a coherent processing interval (CPI). However, HF surface wave radar (HFSWR) often faces more complex transient interference due to various extreme types of weather, such as thunderstorm and typhoon, etc. The above algorithms usually suffer dramatic performance loss when transient interference contaminates the enormously continuous parts of a CPI. Especially for the compact HFSWR, which suffers from severe beam broadening and fewer array degrees of freedom. In order to solve the above problem, this study developed a two-dimensional interference suppression algorithm based on space-time cascaded processing. First, according to the spatial correlation of the compact array, the statistical samples of the main-lobe transient interference are estimated using a rotating spatial beam method. Next, an adaptive selection strategy is developed to obtain the optimal secondary samples based on information geometry distance. Finally, based on a quadratic constraint approximation, a precise estimation method of the optimal weight is developed when the interference covariance matrix is singular. The experimental results of simulation and measured data demonstrate that the proposed approach provides far superior suppression performance.
An Influence of Snow Covers on the Radar Interferometry Observations of Industrial Infrastructure: Norilsk Thermal Power Plant Case
Authors: Zakharov Alexander , Zakharova Liudmila
Published: January 2023

This manuscript presents the results of the study of snow covers’ influence on the interferometric measurements of the stability of industrial infrastructure in the vicinity of Norilsk city, Russia. Fuel tanks of the Norilsk thermal power plant (TPP) were selected as an object of study due to a well-known accident when about 20,000 tons of diesel fuel spilled from one of the tanks. Sentinel-1 synthetic aperture radar data acquired over the territory of Norilsk TPP were used in the DInSAR study of the possible displacements of the tanks that could be the cause of the tank’s damage. For twelve days, radar interferograms that were generated in the study covered the cold and warm seasons of 2018–2020, including the catastrophic event—the rupture of the tank with diesel fuel—in order to shed light on the possible impact of the area subsidence because of permafrost thaw under the tanks. As the tank walls and adjacent concrete base constituted the virtual dihedral corner reflector, the accumulation of snow on the surface near the tanks created a distorting effect on the results of monitoring the stability of the tank’s location. Three models of snow layer within the dihedral proposed could help explain the deviations in the signal amplitude and phase in the case of snowfalls occurring between radar observations. We propose three ways to minimize the influence of snow on interferometric measurements. One of them, the selection of the radar data acquired in proper observation conditions, made it possible to assess the stability of the mutual location of the tanks. Among the most important processing and analysis results in the paper is a conclusion about the high stability of the fuel tank’s location on the yearly time interval, including the troubleshooting tank.
Study on Regional Eco-Environmental Quality Evaluation Considering Land Surface and Season Differences: A Case Study of Zhaotong City
Authors: Ji Jianwan , Tang Zhanzhong , Jiang Linlin , Sheng Tian , Zhao Fei , Zhang Rui , Shifaw Eshetu , Liu Wenliang , Li Huan , Liu Xinhan , Lu Huiyuan
Published: January 2023

Timely and quantitatively evaluating regional eco-environmental quality (EEQ) is of great significance for realizing regional sustainable development goals. Especially for cloudy areas, it was a great challenge to construct a regional EEQ dataset with high quality and high resolution. However, existing studies failed to consider the influence of land surface and season elements in evaluating regional EEQ. Therefore, this study aimed to promote an accurate EEQ-evaluating framework for cloudy areas. Zhaotong city, a typical karst and cloudy region, was chosen as the study area. First, we integrated multi-source spatiotemporal datasets and constructed a novel eco-environmental comprehensive evaluation index (ECEI) to assess its EEQ from 2000 to 2020. Next, standard deviation ellipse (SDE) and trend analysis methods were applied to investigate regional EEQ’s change trends. Finally, ecological index (EI) values for different years were calculated to validate the effectivity of the ECEI. The main findings were as follows: (1) The EEQ of Zhaotong showed an upward-fluctuating trend (0.0058 a−1), with average ECEI values of 0.729, 0.693, 0.722, 0.749, and 0.730. (2) The spatial distribution pattern of the EEQ showed high values in the north and low values in the south, with Zhaoyang district having the lowest ECEI value. (3) From 2000 to 2020, the standard deviation of the major axis of the ellipse moved northeast of Zhaotong city with θ of SDE changing from 57.06° to 62.90°, thus, indicating the improvement of northeastern regions’ EEQ. (4) The coefficients of the determinant (R2) between the EI and ECEI were 0.84, which was higher than that of EI–RSEI (R2 = 0.56). This indicated that our promoted framework and the ECEI could acquire more accurate EEQ results and provide suggestions for relevant policymakers.
Topographic Changes, Surface Deformation and Movement Process before, during and after a Rotational Landslide
Authors: Ma Shuyue , Qiu Haijun , Zhu Yaru , Yang Dongdong , Tang Bingzhe , Wang Daozheng , Wang Luyao , Cao Mingming
Published: January 2023

The deformation characteristics and instability patterns of rotational landslides are complicated. Such landslides are large and occur continuously, seriously threatening people’s lives. We used interferometry synthetic aperture radar (InSAR), digital elevation models of difference (DODs), numerical simulations, and other techniques for analyzing the topographic changes, surface deformation and movement process before, during and after a landslide. Based on the high-resolution terrain data before and after the landslide, the topographic changes were analyzed, and the active zone of the landslide was identified. The areas of the topographic changes were mainly located on the main scarp, toe and secondary landslides. The topographic changes were influenced by rainfall and rill erosion. The geomorphologically-guided InSAR interpretation method was applied to explore the displacement pattern. The deformation area in the middle of the landslide coincided with the secondary landslides. A time-series InSAR analysis revealed the dynamic evolution of the deformation before and after the landslide. Based on its evolution, the simulated landslide process included the main landslide and three secondary landslides. Based on the displacement of the longitudinal ground surface profiles, the displacement characteristics and kinematic behavior were summarized and compared with those of a single rotational landslide and multiple rotational landslides. The single rotational landslide had obvious secondary and progressive characteristics, developing into multiple rotational landslides triggered by conditions such as rainfall.
Assessing the Vertical Structure of Forests Using Airborne and Spaceborne LiDAR Data in the Austrian Alps
Authors: Hirschmugl Manuela , Lippl Florian , Sobe Carina
Published: January 2023

Vertical structure is an important parameter not only for assessment of the naturalness of a forest and several functional parameters, such as biodiversity or protection from avalanches or rockfall, but also for estimating biomass/carbon content. This study analyses the options for assessing vertical forest structure by using airborne (ALS) and spaceborne LiDAR data (GEDI) in a mountainous near-natural forest in the Austrian Alps. Use of the GEDI waveform data (L1B) is still heavily underexploited for vertical forest structure assessments. Two indicators for explaining forest vertical structure are investigated in this study: foliage height diversity (FHD) and number of layers (NoL). For estimation of NoL, two different approaches were tested: break-detection algorithm (BDA) and expert-based assessment (EBA). The results showed that FHD can be used to separate three structural classes; separability is only slightly better for ALS than for GEDI data on a 25 m diameter plot level. For NoL, EBA clearly outperformed BDA in terms of overall accuracy (OA) by almost 20%. A better OA for NoL was achieved using ALS (49.5%) rather than GEDI data (44.2%). In general, OA is limited by difficult terrain and near-natural forests with high vertical structure. The usability of waveform-based structure parameters is, nonetheless, promising and should be further tested on larger areas, including managed forests and simpler stands.
A Novel Deep Nearest Neighbor Neural Network for Few-Shot Remote Sensing Image Scene Classification
Authors: Chen Yanqiao , Li Yangyang , Mao Heting , Chai Xinghua , Jiao Licheng
Published: January 2023

Remote sensing image scene classification has become more and more popular in recent years. As we all know, it is very difficult and time-consuming to obtain a large number of manually labeled remote sensing images. Therefore, few-shot scene classification of remote sensing images has become an urgent and important research task. Fortunately, the recently proposed deep nearest neighbor neural network (DN4) has made a breakthrough in few-shot classification. However, due to the complex background in remote sensing images, DN4 is easily affected by irrelevant local features, so DN4 cannot be directly applied in remote sensing images. For this reason, a deep nearest neighbor neural network based on attention mechanism (DN4AM) is proposed to solve the few-shot scene classification task of remote sensing images in this paper. Scene class-related attention maps are used in our method to reduce interference from scene-semantic irrelevant objects to improve the classification accuracy. Three remote sensing image datasets are used to verify the performance of our method. Compared with several state-of-the-art methods, including MatchingNet, RelationNet, MAML, Meta-SGD and DN4, our method achieves promising results in the few-shot scene classification of remote sensing images.
Prediction of Grassland Biodiversity Using Measures of Spectral Variance: A Meta-Analytical Review
Authors: Thornley Rachael H. , Gerard France F. , White Kevin , Verhoef Anne
Published: January 2023

Over the last 20 years, there has been a surge of interest in the use of reflectance data collected using satellites and aerial vehicles to monitor vegetation diversity. One methodological option to monitor these systems involves developing empirical relationships between spectral heterogeneity in space (spectral variation) and plant or habitat diversity. This approach is commonly termed the ‘Spectral Variation Hypothesis’. Although increasingly used, it is controversial and can be unreliable in some contexts. Here, we review the literature and apply three-level meta-analytical models to assess the test results of the hypothesis across studies using several moderating variables relating to the botanical and spectral sampling strategies and the types of sites evaluated. We focus on the literature relating to grasslands, which are less well studied compared to forests and are likely to require separate treatments due to their dynamic phenology and the taxonomic complexity of their canopies on a small scale. Across studies, the results suggest an overall positive relationship between spectral variation and species diversity (mean correlation coefficient = 0.36). However, high levels of both within-study and between-study heterogeneity were found. Whether data was collected at the leaf or canopy level had the most impact on the mean effect size, with leaf-level studies displaying a stronger relationship compared to canopy-level studies. We highlight the challenges facing the synthesis of these kinds of experiments, the lack of studies carried out in arid or tropical systems and the need for scalable, multitemporal assessments to resolve the controversy in this field.
Detecting the Effects of Extreme Events on Estuarine Suspended Particulate Matter Using Satellite Remote Sensing (Scheldt Estuary): Challenges and Opportunities
Authors: Tavora Juliana , Salama Mhd. Suhyb , Penning de Vries Marloes , Mannaerts Chris M. , van der Wal Daphne
Published: January 2023

Suspended Particulate Matter (SPM) plays an important role in controlling water quality, particularly in turbid estuaries. SPM may be impacted by changes in weather and climate, including potential changes in the frequency or intensity of extremes. Yet, the linkages between extreme events in wind and river discharge, and particularly the role these have on local dynamics and spatial patterns of estuarine SPM are, to date, largely unknown. This study investigates the effects that wind and river discharge have on SPM in a turbid estuary. It uses atmospherically corrected remotely sensed reflectances from Landsat-5, -7, and -8. From these data, we establish a 37-year-long time series, from 1984 to 2020, of satellite-derived SPM for the Scheldt Estuary. SPM was estimated using an algorithm applied to the near-infrared band and subsequently compared to in situ SPM data. Additionally, a time series of river discharge and wind speed were used to assess the frequency and severity of extreme events of wind speed and river discharge. In general, statistically significant but weak relationships between SPM and river discharge and between SPM and wind speed were observed. SPM correlated with river discharge and wind in large parts of the estuary, indicating the important role of these drivers in the entire estuarine system. Our study demonstrates how synoptic satellite snapshots can be combined with in situ time series of drivers, such as river discharge and wind, to capture where these drivers relate to (and likely affect) SPM within an estuary. However, our study demonstrates an inability to capture SPM during windstorms both from in situ and satellite data. We discuss the challenges and limitations of assessing the effects of extreme events from satellite and in situ SPM. We recommend the deployment of complementary moored turbidimeters for continuous observations at two strategic locations, as indicated by our spatial study, to improve the ability to capture the effects of extreme events in both space and time.
Arctic Heritage at Risk: Insights into How Remote Sensing, Robotics and Simulation Can Improve Risk Analysis and Enhance Safety
Authors: Lintott Bryan , Rees Gareth
Published: January 2023

Increased and enhanced utilisation of remote sensing and robotics in the Arctic can further enhance cultural safety and well-being and reduce the risks posed to archaeologists, heritage workers and others in the field. In this preliminary scoping survey, the authors review the current use of these technologies and consider a range of related issues, from cultural safety to nefarious use by criminals. Initial discussions with experts have informed areas of concern; and the potential for further integration. In the future, the University of Tromsø’s new Tromsø Arctic Simulation Integration Centre (TASIC) will be utilised to evaluate a range of scenarios to inform risk analysis and contribute towards safety enhancement in the Arctic Heritage at Risk Project (Arctic-HARP). The following is an overview of the significant state-of-the-art technologies and related matters.
Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques
Authors: ELKarazle Khaled , Raman Valliappan , Then Patrick , Chua Caslon
Published: January 2023

Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.
Convolutional Networks and Transformers for Mammography Classification: An Experimental Study
Authors: Cantone Marco , Marrocco Claudio , Tortorella Francesco , Bria Alessandro
Published: January 2023

Convolutional Neural Networks (CNN) have received a large share of research in mammography image analysis due to their capability of extracting hierarchical features directly from raw data. Recently, Vision Transformers are emerging as viable alternative to CNNs in medical imaging, in some cases performing on par or better than their convolutional counterparts. In this work, we conduct an extensive experimental study to compare the most recent CNN and Vision Transformer architectures for whole mammograms classification. We selected, trained and tested 33 different models, 19 convolutional- and 14 transformer-based, on the largest publicly available mammography image database OMI-DB. We also performed an analysis of the performance at eight different image resolutions and considering all the individual lesion categories in isolation (masses, calcifications, focal asymmetries, architectural distortions). Our findings confirm the potential of visual transformers, which performed on par with traditional CNNs like ResNet, but at the same time show a superiority of modern convolutional networks like EfficientNet.
ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning
Authors: Hernandez-Suarez Aldo , Sanchez-Perez Gabriel , Toscano-Medina Linda K. , Perez-Meana Hector , Olivares-Mercado Jesus , Portillo-Portillo Jose , Benitez-Garcia Gibran , Sandoval Orozco Ana Lucila , García Villalba Luis Javier
Published: January 2023

In recent years, cybersecurity has been strengthened through the adoption of processes, mechanisms and rapid sources of indicators of compromise in critical areas. Among the most latent challenges are the detection, classification and eradication of malware and Denial of Service Cyber-Attacks (DoS). The literature has presented different ways to obtain and evaluate malware- and DoS-cyber-attack-related instances, either from a technical point of view or by offering ready-to-use datasets. However, acquiring fresh, up-to-date samples requires an arduous process of exploration, sandbox configuration and mass storage, which may ultimately result in an unbalanced or under-represented set. Synthetic sample generation has shown that the cost associated with setting up controlled environments and time spent on sample evaluation can be reduced. Nevertheless, the process is performed when the observations already belong to a characterized set, totally detached from a real environment. In order to solve the aforementioned, this work proposes a methodology for the generation of synthetic samples of malicious Portable Executable binaries and DoS cyber-attacks. The task is performed via a Reinforcement Learning engine, which learns from a baseline of different malware families and DoS cyber-attack network properties, resulting in new, mutated and highly functional samples. Experimental results demonstrate the high adaptability of the outputs as new input datasets for different Machine Learning algorithms.
Orientation Control System: Enhancing Aerial Maneuvers for Quadruped Robots
Authors: Roscia Francesco , Cumerlotti Andrea , Del Prete Andrea , Semini Claudio , Focchi Michele
Published: January 2023

For legged robots, aerial motions are the only option to overpass obstacles that cannot be circumvented with standard locomotion gaits. In these cases, the robot must perform a leap to either jump onto the obstacle or fly over it. However, these movements represent a challenge, because, during the flight phase, the Center of Mass (CoM) cannot be controlled, and there is limited controllability over the orientation of the robot. This paper focuses on the latter issue and proposes an Orientation Control System (OCS), consisting of two rotating and actuated masses (flywheels or reaction wheels), to gain control authority on the orientation of the robot. Due to the conservation of angular momentum, the rotational velocity if the robot can be adjusted to steer the robot’s orientation, even when the robot has no contact with the ground. The axes of rotation of the flywheels are designed to be incident, leading to a compact orientation control system that is capable of controlling both roll and pitch angles, considering the different moments of inertia in the two directions. The concept was tested by means of simulations on the robot Solo12.
Analysis of Spatial Decorrelation of Small-Scale Tropospheric Delay Using High-Resolution NWP Data
Authors: Håkegård Jan Erik , Sokolova Nadezda , Morrison Aiden
Published: January 2023

This paper contains results from a study where numerical weather product (NWP) data provided by MET Norway were used to estimate the differential zenith tropospheric delay (dZTD) for an area including Scandinavia, Finland and the Baltic countries. The NWP data have a high spatial resolution of 2.5×2.5 km, and the estimated dZTD for the grid positions allows for calculation of the tropospheric gradient on short baselines. The results give an indication of how large dZTD values for baselines of up to 20 km can be, and of where the largest events are located within the coverage area. One year of data were processed, and dZTD values up to 18 cm with baselines were detected. Preliminary results comparing the NWP-based results with GNSS-based results are included. The motivation for this investigation was to better understand the characteristics of this phenomenon as a preamble to a later investigation of how it might impact GNSS-based navigation systems with integrity support in these regions.
A Velocity Measurement Method Based on Charge Induction
Authors: Chi Yangbin , Fan Ziyu , Wang Shufan , Zhang Limin
Published: January 2023

In this paper, based on the principle of charge induction, a new velocity measurement method is proposed. A moving target generates a low-frequency electric field, which can be induced with an electrode and detection frontend. Velocity measurements are achieved by placing two electrodes at a fixed distance to detect the characteristic times. Firstly, the electric field generated by the moving target is modeled, and the theoretical output of the detection frontend is obtained via a simulation of the target passing by a single electrode. Then, according to the theoretical output, the velocity measurement simulation results of double electrodes are given for various driving conditions, such as a single vehicle driving in a single lane, a single vehicle changing lanes, two vehicles driving close together, and a multiple-vehicle situation. Finally, the above driving conditions are experimentally verified in sunny weather, windy and rainy weather, and a night environment.
Optimizing Resources and Increasing the Coverage of Internet-of-Things (IoT) Networks: An Approach Based on LoRaWAN
Authors: Gava Matheus Araujo , Rocha Helder Roberto Oliveira , Faber Menno Jan , Segatto Marcelo Eduardo Vieira , Wörtche Heinrich , Silva Jair Adriano Lima
Published: January 2023

A resource optimization methodology is proposed for application in long range wide area networks (LoRaWANs). Using variable neighborhood search (VNS) and a minimum-cost spanning tree algorithm, it reduces the implementation and the maintenance costs of such low power networks. Performance evaluations were conducted in LoRaWANs with LoRa repeaters to increase coverage, in scenario where the number and the location of the repeaters are determined by the VNS metaheuristic. Parameters such as spread factor (SF), bandwidth and transmission power were adjusted to minimize the network’s total energy per useful bit (Ebit) and the total data collection time. The importance of the SF in the trade-off between (Ebit) and time on-air is evaluated, considering a device scaling factor. Simulation results, obtained after model adjustments with experimental data, show that, in networks with few associated devices, there is a preference for small values of SF aiming at reduction of Ebit. The usage of large SF’s becomes relevant when reach extensions are required. The results also demonstrate that, for networks with high number of nodes, the scaling of devices over time become relevant in the fitness function, forcing an equal distribution of time slots per SF to avoid discrepancies in the time data collection.
The Gas Sensing Drone with the Lowered and Lifted Measurement Platform
Authors: Szczurek Andrzej , Gonstał Dawid , Maciejewska Monika
Published: January 2023

A serious factor that limits the environmental applications of drones is the disturbance of the air pollution concentration field by the drone propulsion system. This work presents a gas-sensing drone offering measurements that are unaffected by this phenomenon. The novel development was based on the idea that, during measurements, the sensing device should be spatially separated from a zone influenced by the drone’s rotors. To attain this goal, special equipment was designed that allows one to undock and lower the sensing device for measurement, lift it and dock for flight. The field experiments demonstrated the full functionality of the developed system and its superiority compared to a sensing platform mounted at the bottom of the drone. Higher measurement sensitivity and resolution were attained by lowering the sensing platform to the measurement point. This solution minimizes the rotor flow effect, ground effect, and pollution concentration field flattening. The test in real conditions confirmed that the designed construction assures drone stability. The presented technology may be an important step in developing effective mobile measurement tools that allow one to reach poorly accessible or dangerous places and perform measurements at a low cost and with high efficiency.
Method of Building Detection in Optical Remote Sensing Images Based on SegFormer
Authors: Li Meilin , Rui Jie , Yang Songkun , Liu Zhi , Ren Liqiu , Ma Li , Li Qing , Su Xu , Zuo Xibing
Published: January 2023

An appropriate detection network is required to extract building information in remote sensing images and to relieve the issue of poor detection effects resulting from the deficiency of detailed features. Firstly, we embed a transposed convolution sampling module fusing multiple normalization activation layers in the decoder based on the SegFormer network. This step alleviates the issue of missing feature semantics by adding holes and fillings, cascading multiple normalizations and activation layers to hold back over-fitting regularization expression and guarantee steady feature parameter classification. Secondly, the atrous spatial pyramid pooling decoding module is fused to explore multi-scale contextual information and to overcome issues such as the loss of detailed information on local buildings and the lack of long-distance information. Ablation experiments and comparison experiments are performed on the remote sensing image AISD, MBD, and WHU dataset. The robustness and validity of the improved mechanism are demonstrated by control groups of ablation experiments. In comparative experiments with the HRnet, PSPNet, U-Net, DeepLabv3+ networks, and the original detection algorithm, the mIoU of the AISD, the MBD, and the WHU dataset is enhanced by 17.68%, 30.44%, and 15.26%, respectively. The results of the experiments show that the method of this paper is superior to comparative methods such as U-Net. Furthermore, it is better for integrity detection of building edges and reduces the number of missing and false detections.
Using Machine Learning on V2X Communications Data for VRU Collision Prediction
Authors: Ribeiro Bruno , Nicolau Maria João , Santos Alexandre
Published: January 2023

Intelligent Transportation Systems (ITSs) are systems that aim to provide innovative services for road users in order to improve traffic efficiency, mobility and safety. This aspect of safety is of utmost importance for Vulnerable Road Users (VRUs), as these users are typically more exposed to dangerous situations, and their vehicles also possess poorer safety mechanisms when in comparison to regular vehicles on the road. Implementing automatic safety solutions for VRU vehicles is challenging since they have high agility and it can be difficult to anticipate their behavior. However, if equipped with communication capabilities, the generated Vehicle-to-Anything (V2X) data can be leveraged by Machine Learning (ML) mechanisms in order to implement such automatic systems. This work proposes a VRU (motorcyclist) collision prediction system, utilizing stacked unidirectional Long Short-Term Memorys (LSTMs) on top of communication data that is generated using the VEINS simulation framework (coupling the Simulation of Urban MObility (SUMO) and Network Simulator 3 (ns-3) tools). The proposed system performed well in two different scenarios: in Scenario A, it predicted 96% of the collisions, averaging 4.53 s for Average Prediction Time (s) (APT) and with a Correct Decision Percentage (CDP) of 41% and 78 False Positives (FPs); in Scenario B, it predicted 95% of the collisions, with a 4.44 s APT, while the CDP was 43% with 68 FPs. The results show the effectiveness of the approach: using ML methods on V2X data allowed the prediction of most of the simulated accidents. Nonetheless, the presence of a relatively high number of FPs does not allow for the usage of automatic safety features (e.g., emergency breaking in the passenger vehicles); thus, collision avoidance must be achieved manually by the drivers.
Optimization and Modeling of Citrobacter freundii AD119 Growth and 1,3-Propanediol Production Using Two-Step Statistical Experimental Design and Artificial Neural Networks
Authors: Drożdżyńska Agnieszka , Wawrzyniak Jolanta , Kubiak Piotr , Przybylak Martyna , Białas Wojciech , Czaczyk Katarzyna
Published: January 2023

1,3-propanediol (1,3-PD) has a wide range of industrial applications. The most studied natural producers capable of fermenting glycerol to 1,3-PD belong to the genera Klebsiella, Citrobacter, and Clostridium. In this study, the optimization of medium composition for the biosynthesis of 1,3-PD by Citrobacter freundii AD119 was performed using the one-factor-at-a-time method (OFAT) and a two-step statistical experimental design. Eleven mineral components were tested for their impact on the process using the Plackett–Burman design. MgSO4 and CoCl2 were found to have the most pronounced effect. Consequently, a central composite design was used to optimize the concentration of these mineral components. Besides minerals, carbon and nitrogen sources were also optimized. Partial glycerol substitution with other carbon sources was found not to improve the bioconversion process. Moreover, although yeast extract was found to be the best nitrogen source, it was possible to replace it in part with (NH4)2SO4 without a negative impact on 1,3-PD production. As a part of the optimization procedure, an artificial neural network model of the growth of C. freundii and 1,3-PD production was developed as a predictive tool supporting the design and control of the bioprocess under study.
A Smart System for an Assessment of the Remaining Useful Life of Ball Bearings by Applying Chaos-Based Health Indicators and a Self-Selective Regression Model
Authors: Li Shih-Yu , Li Hao-An , Tam Lap-Mou , Chen Chin-Sheng
Published: January 2023

Bearings are the most commonly used components in rotating machines and the ability to diagnose their faults and predict their remaining useful life (RUL) is critical for system maintenance. This paper proposes a smart system combined with a regression model to predict the RUL of bearings. The method converts the azimuth signal through low-pass filtering (LPF) and a chaotic mapping system, and uses Euclidean feature values (EFVs) to extract features in order to construct useful health indicators (HIs). In fault detection, the iterative cumulative moving average (ICMA) is used to smooth the HIs, and the Euclidean norm is used to find the time-to-start prediction (TSP). In terms of prediction, this paper uses a self-selective regression model to select the most suitable regression model to predict the RUL of the bearing. The dataset provided by the Center for Intelligent Maintenance Systems (IMS) is applied for performance evaluation; in comparison with previous research, better prediction results can be achieved by applying the proposed smart assessment system. The proposed system is also applied to the PRONOSTIA (also called FEMTO-ST) bearing dataset in this paper, demonstrating that acceptable prediction performance can be obtained.
High-Temperature Sensing Based on GAWBS In Silica Single-Mode Fiber
Authors: Ma Shaonian , Pang Yuxi , Ji Qiang , Zhao Xian , Li Yongfu , Qin Zengguang , Liu Zhaojun , Xu Yanping
Published: January 2023

High temperature detection is a constant challenge for condition monitoring under harsh environments in optical fiber sensors research. In this study, the temperature response characteristics of guided acoustic wave Brillouin scattering (GAWBS) spectra in silica single-mode fiber (SMF) up to 800 °C are experimentally investigated, demonstrating the feasibility of the method for high-temperature monitoring. With increasing temperature, the resonance frequency of GAWBS spectra increases in a nearly linear manner, with linearly fitted temperature-dependent frequency shift coefficients of 8.19 kHz/°C for TR2,7 mode and 16.74 kHz/°C for R0,4 mode. More importantly, the linewidth of the GAWBS spectra is observed to narrow down with increasing temperature with a linearly fitted rate of −6.91 × 10−4/°C for TR2,7 modes and −8.56 × 10−4/°C for R0,4 modes. The signal-to-noise ratio of the GAWBS spectra induced by both modes increase by more than 3 dB when the temperature rises from 22 °C to 800 °C, which indicates that the proposed sensing scheme has better performance in high-temperature environments, and are particularly suitable for sensing applications in extreme environments. This study confirms the potential of high-temperature sensing using only GAWBS in silica fibers without any complex micromachining process, which has the advantages of strong mechanical strength, simple structure, easy operation, and low cost.
Temperature and Humidity Stability of Fibre Optic Sensor Cables for High Resolution Measurements
Authors: Maier Marcus , Kechavarzi Cedric , Xu Xiaomin , Lees Janet M.
Published: January 2023

Fibre optic sensors offer a means for the real-time continuous measurement of temperature or strain in concrete structures. Backscattered light along a fibre optic sensing (FOS) cable is interrogated to record a frequency shift and this shift is typically translated into a physical parameter such as strain or temperature using a calibration factor. However, when the measured frequency shift is a response to a combination of mechanical, thermal or hygral (humidity) loadings it is difficult to decouple individual influences. This presents a challenge in complex materials such as concrete where the strain, temperature and moisture levels change concurrently during the fresh and hardened states. Furthermore, depending on the application, both short- and longer-term measurements are required. As such, not only is the influence of these physical factors of interest but also the time and spatial stability of the measured frequency, which is highly dependent on the FOS cable composition. To investigate this aspect, fibre optic cables commonly used for strain (three tight-buffered cables) or temperature (two loose-buffered cables) measurement were considered. The cables were subjected to mechanical or environmental exposure and interrogated using a high-resolution optical backscatter reflectometer. The exposure regimes included three temperature cycles with sustained steps from 10 °C to 60 °C and back to 10 °C and an increasing and decreasing humidity cycle with steps between 30 to 90% relH. These ranges were selected to be indicative of typical environments for concrete. The results showed that the calibration factors back-calculated from increasing and decreasing temperature or humidity cycles differed. The third temperature cycle results were found to exhibit the smallest differences between heating and cooling suggesting that temperature pre-conditioning prior to installation could be advantageous. For all the cables, a drift in the readings was observed over the duration (2.5 h for temperature and 30 h for moisture) of the sustained steps. The magnitude of the drift depended on the cable type and exposure condition. In addition, local frequency fluctuations along the cable were observed which would need to be taken into account if only a single point along the cable length was used for analysis. The obtained results highlight the importance of the cable selection to maximise the FOS measurement fidelity for a given parameter of interest.