bluuebunny/crossref_metadata_2025 huggingface dataset:
CISTO ADENOCARCINOMA MUCIONOSO ENDOCERVICAL-LIKE EM OVÁRIO ESQUERDO E CISTO ADENOMA MUCINOSO EM OVÁRIO DIREITO: RELATO DE CASO
Authors: Moura Natália Mathias Barbosa Avelino de , Kelly Paiva Guimarães
Published: October 2024

Os cistos de origem neoplásicas em ovários podem coexistir em sua forma maligno ou benigna, sendo ambas, ocasionando em sinais de compressão em órgãos vizinhos, como bexiga ou cólon, em suas formas gigantes. Este relato descreve o caso de uma paciente do sexo feminino, 39 anos, G2P2A0, apresentando quadro álgico evolutivo em região hipogastrica, sem sinais de alterações intestinais. Apresentou volumosa expansão cística à tomografia computadorizada de pelve, sem alterações em hemograma, CA 125,  CA 19-9 e CEA. Posteriormente, indicado laparotomia exploratória, evidenciando cisto volumoso ovariano bilateralmente, sendo optado por anexectomia bilateral  e histerectomia total abdominal. Por fim, dado o diagnóstico histopatológico de cistoadenocarcinoma mucinoso endocervical “like” em ovário esquerdo e cistoadenoma mucinoso em ovário direito. Apresentou  boa evolução clínica, tendo alta hospitalar e seguindo com acompanhamento ambulatorial pelo serviço de oncologia. O presente relato tem como importância elucidar a apresentação clínica de uma patologia rara e de sintomas insidiosos. Sendo assim, devido suas formas assintomáticas, é necessário ter seu diagnóstico e conduta disseminados para o amplo conhecimento da equipe clínica de primeiro atendimento, principalmente ginecologista e oncologista.
DIFICULDADE DE DEPENDENTES QUÍMICOS EM SEU TRATAMENTO: UMA ANÁLISE PROFUNDA
Authors: Oliveira Ramon Alves de , Lopes Júnior Hélio Marco Pereira , Silva Luana Guimaraes da
Published: October 2024

Introdução: A sociedade frequentemente estigmatiza os dependentes químicos com preconceito e discriminação, o que cria barreiras substanciais para a busca de tratamento. O medo de ser julgado ou marginalizado pode fazer com que esses indivíduos se isolem e evitem o suporte essencial para sua recuperação. Objetivo: Visa identificar os fatores que dificultam a adesão ao tratamento, incluindo aspectos relacionados ao estigma social, fatores psicológicos e sociais, e a eficácia dos programas de tratamento disponíveis. Metodologia: Trata-se de um estudo de revisão integrativa, com abordagem qualitativa utilizando-se de busca de produções científicas nas bases de dados Scientific Eletronic Library Online (Scielo) e Biblioteca Virtual da Saúde (BVS), no período de 2019 a 2023. Resultados e Discussão: Observa-se que a prevenção de recaídas e a manutenção da abstinência requerem um suporte contínuo e estratégias de enfrentamento eficazes e a ausência de uma rede de apoio social sólida pode ser um grande obstáculo para os dependentes químicos durante o tratamento e a recuperação. Considerações finais: Nota-se que, o estigma social, o acesso limitado a recursos, as comorbidades, a recaída e a falta de apoio social são apenas algumas das dificuldades enfrentadas por aqueles que lutam contra o vício. No entanto, com abordagens integradas, apoio adequado e compreensão empática, é possível superar esses desafios e promover uma recuperação bem-sucedida e duradoura para os dependentes químicos.
O TRATAMENTO DO BULLYING CONFORME A LEGISLAÇÃO BRASILEIRA
Authors: Rodrigues Diene Cristina Araújo , Carvalho Josineide Ribeiro de Oliveira , Azevedo Delner do Carmo
Published: October 2024

O estudo em questão investiga o tratamento jurídico do bullying sob as óticas penais e civis no contexto do direito brasileiro. Inicialmente, o trabalho oferece uma definição de bullying, ressaltando as dificuldades de conceituar essa forma de violência na realidade brasileira e na língua portuguesa. O desafio de definir o termo correto de sua natureza complexa, que envolve atos repetitivos de violência física, verbal ou psicológica, muitas vezes sutis, mas profundamente impactantes. Em seguida, uma pesquisa identifica as infrações que podem ser equivalentes ao bullying segundo o Estatuto da Criança e do Adolescente (ECA), estabelecendo uma conexão entre as práticas de bullying e as disposições legais existentes. O estudo também analisa como o Código Penal e o Código Civil brasileiro podem ser aplicados a situações de bullying, considerando tanto as consequências criminais quanto as reparações civis que podem ser relevantes para as partes envolvidas. A justificativa para a realização deste estudo é baseada na crescente preocupação com a qualidade de vida, um tema amplamente debatido na sociedade atual, onde o bullying se apresenta como um problema antagônico que merece atenção. A pesquisa é de natureza bibliográfica, centrando-se na análise de doutrina e instrução para fornecer uma base teórica sólida sobre o tema. Ao final, o estudo não apenas examina criticamente o tratamento atual do bullying no contexto jurídico brasileiro, mas também sugere novas abordagens para o enfrentamento penal e civil desse problema, propondo ideias que possam servir de base para futuras discussões e reflexões no âmbito jurídico.
A RESPONSABILIDADE CIVIL POR VIOLAÇÃO DE DIREITOS AUTORAIS EM CONTEÚDO DIGITAL NA INTERNET
Authors: Martins Gabriela Trindade , Furlan Fernando Palma Pimenta
Published: October 2024

O presente artigo examina a questão da responsabilidade civil por violação de direitos autorais em conteúdo digital na internet. Aborda as complexidades legais, tecnológicas e sociais envolvidas, destacando o papel das plataformas online e as medidas de proteção de conteúdo. Analisa casos jurisprudenciais relevantes e propõe diretrizes para promover uma melhor proteção dos direitos autorais na era digital. O artigo também aborda a disseminação de conteúdo digital e as peculiaridades da sua proteção na internet, evidenciando as dificuldades em monitorar e controlar o uso não autorizado de obras digitais. Discute-se, ainda, a responsabilidade de diferentes atores no ambiente online, incluindo provedores de serviço e plataformas de compartilhamento de conteúdo, analisando decisões judiciais e a evolução da jurisprudência sobre o tema. Ademais, a legislação aplicável é discutida em detalhes, incluindo tratados internacionais e normativas específicas para o meio digital, como o Marco Civil da Internet (Lei nº 12.965/14), que estabelece diretrizes para o uso da internet no Brasil e impõe obrigações aos provedores de serviços. Por fim, através de uma pesquisa bibliográfica e descritiva são apresentadas considerações sobre a necessidade de harmonização legislativa e cooperação internacional para efetivar a proteção dos direitos autorais na internet, propondo soluções para os desafios enfrentados nesse campo, como a adoção de tecnologias de proteção de conteúdo e a promoção de práticas de educação digital entre usuários e provedores.
ESTADIAMENTO DO CÂNCER INFANTO-JUVENIL NO OESTE DO PARANÁ: CARACTERIZAÇÃO A PARTIR DA CLASSIFICAÇÃO INTERNACIONAL DE TORONTO
Authors: Silva Lidia Soares da , Fiori Carmem Maria Costa Mendonça
Published: October 2024

Este projeto teve como objetivo permitir uma nova avaliação no sistema de estadiamento do câncer infanto-juvenil, seguindo os critérios da diretriz TORONTO CHILDHOOD CANCER STAGE GUIDELINES (TCCSG) do câncer pediátrico e proporcionar uma melhor visibilidade quanto a extensão anatômica da doença ao diagnóstico, comparando com os sistemas de estadiamento preconizados nas diferentes patologias. Foram avaliados 179 prontuários de pacientes em acompanhamento no Hospital de Câncer de Cascavel – PR (UOPECCAN). Desses, 178 (99,45%) tinham informações suficientes em prontuário médico para serem estadiados, utilizando o Nível 1 das Diretrizes de Câncer Pediátrico de Toronto. Observamos que o tumor de Wilms, Sarcomas e tumores germinativos foram as patologias que houve pequenas modificações nas comparações entre os estadiamentosj já os demais tumores foram semelhantes na comparação. Foi possível classificar grande parte dos tumores malignos infantis usando esse estadiamento. O TCCSG não tem a proposta de substituir os estadiamentos utilizados na clínica e sim de avaliar a extensão anatômica da doença ao diagnóstico com dados encontrados em prontuários e com isso facilitar uma uniformidade dos estadiamentos. Isso facilitará a avaliação dos aspectos relacionados ao diagnóstico precoce ou tardio, assim como das características biológicas dos tumores das diversas regiões do mundo.
LASERTERAPIA UTILIZADA NA ODONTOLOGIA CONTEMPORÂNEA COMO MECANISMO DE TRATAMENTO DA PARESTESIA E OUTRAS APLICAÇÕES ORAIS
Authors: Bernardes Aiko Camille Sato , Cunha Tereza Cristina Rodrigues da , Vilela Júnior Rafael de Aguiar
Published: October 2024

Esse artigo buscou falar sobre a parestesia, sendo uma alteração temporária/permanente da sensibilidade em uma região inervada, em que há sensações de formigamento, dormência ou queimação na região dos lábios, boca e bochecha, estando associada a danos em nervos periféricos (trigêmeo, glossofaríngeo e facial). As causas mais comuns de parestesia na odontologia são após procedimentos de extração de terceiros molares inferiores, anestesia local, implantes prematuros, procedimentos cirúrgicos na mandíbula e traumas mecânicos. Com o avanço da odontologia, a laserterapia vem ganhando espaço significativo para terapias complementares, principalmente em casos de parestesias. A laserterapia envolve o uso de radiação eletromagnética na faixa de luz visível ou infravermelha, gerada por dispositivos de laser com diferentes comprimentos de onda, esta interação com os tecidos biológicos produz efeito terapêutico, sendo capaz de resultados como analgesia, efeito anti-inflamatório e efeito cicatrizantes. O objetivo deste trabalho foi realizar uma revisão de literatura e discutir a ação, aplicabilidade da laserterapia na prática odontológica. Constatou-se que a laserterapia é uma abordagem de destaque e promissora na odontologia contemporânea.
Ketentuan Pertanggungjawaban Pidana Terhadap Usia Anak DalamPerspektif Hukum Pidana Dengan Hukum Islam
Authors: Reski Anwar
Published: October 2024

Pertanggungjawaban pidana anak bagi anak yangmelakukan tindak pidana diatur dalam hukum pidana di IndonesiaMemiliki beberapa ketentuan yang berpatokan pada regulasi yang ada.Batasan Usia Anak menjadi faktor penentu anak dikenakan hukuman ataulepas dari pertanggungjawaban. Bagaimana konsep ideal usia anak padapertanggungjawaban pidana dan hukum islam. Pendekatan menggunakanyuridis normatif, data kepustakaan dan analisis induktif-komparatif. Darihasil penelitian dapat disimpulkan. Menurut hukum pidana, anak yangdapat dikenakan pidana yaitu berusia 12-18 tahun. Selanjutnya menuruthukum Islam anak yang dikatakan telah Baligh yaitu berusia 15 tahun.Konsep usia ideal anak yang dikenakan pidana menurut Undang-undangNo 11 Tahun 2012 mengatakan bahwa anak yang bisa dikenakan pidanaberusia 12 Tahun sampai 18 Tahun dan belum pernah kawin. Karenaapabila anak melakukan suatu tindak pidana, seperti membunuh,mencuri, atau memperkosa, maka anak tersebut akan bersangkutandengan hukum, dan dikenakan hukuman tindak pidanaa. Hal lain idealnyamenurut hukum islam yang dimana batasan-batasan ini tidakberdasarkan atas hitungan usia, akan tetapi dimulai sejak munculnyatanda biologis atau perubahan secara fisik pada anak tersebut, baik priamaupun wanita.
Characterization of surface clutter signal in presence of orography for a spaceborne conically scanning W-band Doppler radar
Authors: Manconi Francesco , Battaglia Alessandro , Kollias Pavlos
Published: October 2024

Abstract. The Earth's surface radar reflection is one of the most important signals received by spaceborne radar systems. It is used in several scientific applications including geolocation, terrain classification, and path-integrated attenuation estimation. A simulator based on the ray tracing approach has been developed to reproduce the clutter reflectivity and the Doppler velocity signal for a conically scanning spaceborne Doppler radar system. The simulator exploits topographic information through a raster digital elevation model, land types from a regional classification database, and a normalized radar surface cross‐section look-up table. The simulator is applied to the WInd VElocity Radar Nephoscop (WIVERN) mission, which proposes a conically scanning W-band Doppler radar to study in-cloud winds. Using an orbital model, detailed simulations for conical scans over the Piedmont region of Italy that offers a variety of landscape conditions are presented. The results highlight the strong departure of the reflectivity and Doppler velocity profiles in the presence of marked orography and the significant gradient in the surface radar backscattering properties. The simulations demonstrate the limitations and advantages of using the surface Doppler velocity over land as an antenna-pointing characterization technique. The simulations represent the full strength range of the surface radar clutter over land surfaces for the WIVERN radar. The surface clutter tool applies to other spaceborne radar missions such as the nadir pointing EarthCARE and CloudSat cloud profiling radars, or the cross-track scanning GPM precipitation radars.
Simple analytical–statistical models (ASMs) for mean annual permafrost table temperature and active-layer thickness estimates
Authors: Uxa Tomáš , Hrbáček Filip , Kňažková Michaela
Published: October 2024

Abstract. A variety of numerical, analytical and statistical models have been developed for estimating the mean annual permafrost table temperature (MAPT) and active-layer thickness (ALT). These tools typically require at least a few ground physical properties, such as thermal conductivity, heat capacity, water content or bulk density, as input parameters in addition to temperature variables, which are, however, unavailable or unrepresentative at most sites. Ground physical properties are therefore commonly estimated, which may yield model outputs of unknown validity. Hence, we devised two simple analytical–statistical models (ASMs) for estimating MAPT and ALT, which are driven solely by pairwise combinations of thawing and freezing indices in the active layer; no ground physical properties are required. ASMs reproduced MAPT and ALT well in most numerical validations, which corroborated their theoretical assumptions under idealized scenarios. Under field conditions of Antarctica and Alaska, the mean ASMs deviations in MAPT and ALT were less than 0.03 °C and 5 %, respectively, which is similar or better than other analytical or statistical models. This suggests that ASMs can be useful tools for estimating MAPT and ALT under a wide range of climates and ground physical conditions.
Explaining the period fluctuation of the quasi-biennial oscillation
Authors: Kim Young-Ha
Published: October 2024

Abstract. The tropical stratosphere is characterized by a periodic oscillation of wind direction between westerly and easterly, known as the quasi-biennial oscillation, which modulates middle atmospheric circulations and surface climate on interannual time scales. The oscillation period fluctuates irregularly between 20 and 35 months. The causes of this fluctuation have long been hypothesized but lack observational evidence. This study shows that the period fluctuation is primarily driven by variability in small-scale wave (gravity wave) activity. Using an atmospheric reanalysis dataset, we capture temporal variations in small-scale wave activity that are coherent with the varying speed of the oscillation. This wave activity variation stems from the seasonality of tropical convection and tropopause-layer wind, revealing their fundamental role in modulating the quasi-biennial period. Our findings suggest that better representing these multi-scale interactions in models can enhance the accuracy of seasonal forecasts and the reliability of future climate projections.
A Practice of City-scale 3D Geographic Entity Representation and Application in China: The Smart Chongqing City Centre
Authors: He Yuqi , Sheng Jiahui , Li Xiang , Chen Guang , Zhang Xiang , Chen Yiqun
Published: October 2024

Abstract. The Chinese photogrammetry industry is currently experiencing a significant revolution driven by digital intelligence technologies. Traditional city-scale 3D modelling, typically reconstructed from oblique photogrammetry or laser points, is facing increasing limitations in refined expression, real-time rendering, machine recognition, data sharing, and interoperability due to its large data volume and mesh structure. These limitations make it difficult to meet the substantial demands of spatial analysis, simulation, and prediction. The concept of geographic entities, which are machine-recognizable spatial data structures, is anticipated to provide a competitive solution for these challenges. This work employs 3D scene modelling and geographic knowledge graphs to construct geographic entities with semantic relationships at city-scale, addressing the limitations encountered in the application of traditional mesh/point-based 3D models. A case study in Chongqing city centre demonstrates the feasibility and effectiveness of the proposed approach and its potential applications in simulation, prediction, early warning, and feedback for urban planning and smart city management.
Exploring Spatiotemporal Interaction Patterns of Dockless Shared Bicycle Networks: A Case Study of Shenzhen, China
Authors: Hu Xiaoqian , Chen Yang , Xu Shishuo , Li Songnian
Published: October 2024

Abstract. The explosive development of shared bicycles has changed the way people travel. In parallel with the urbanization process, shared bicycles have emerged as a significant mode of transportation, especially for the first and last mile transit. With the aim of obtaining a comprehensive understanding of the spatiotemporal patterns underlying shared bicycle usage, this study takes Shenzhen, China as a case study. Drawing on the principles and techniques of complex network analysis, the research adopts a spatial grid framework, treating each grid as a node, the flow of shared bicycles as an edge, and the flow frequency as the corresponding weight. Subsequently, the spatiotemporal interaction networks of different periods of working days and weekends are constructed respectively, based on which the topological characteristics are analysed. The results show that the spatiotemporal interaction networks exhibit high regional connectivity and node interdependence, suggesting the presence of a small-world phenomenon.
Research on Semantic Driven Urban Pipeline Dataspace Construction Method
Authors: Li Binghan , Huo Liang , Yang Yaodong , Bao Peng , Zhang Maolin , Li Yucai
Published: October 2024

Abstract. Urban pipeline data is heterogeneous in multiple sources and rich in data volume, and there are problems such as data conflict and difficult organization and management due to the heterogeneity of multiple sources when accessing the data in large-scale concurrently. To address this problem, this paper proposes a semantics-driven urban pipeline dataspace construction method, which aims to realize the efficient organization of pipeline data. Firstly, this method combines the classification and characteristics of urban pipelines, and expresses the semantic information of pipeline geographic entities from four dimensions: semantic description, spatial location, attribute characteristics and time evolution. Then, the four expression sets are embedded into the dataspace RDF model as predicates, and the associated description mechanism of pipeline geographic entities is established by means of genus classes and so on, so as to construct the pipeline dataspace RDF model. Finally, the model is stored and graphically visualized using neo4j to achieve fast retrieval of data within the pipeline dataspace. The research results show that this method provides a unified expression of pipeline entities, solves the problem of pipeline multi-source heterogeneous data conflict and organization difficulties, and improves the efficiency of multi-source heterogeneous pipeline data organization while ensuring the integrity of pipeline information to the maximum extent.
Prediction of Potential Urban Hot Spots in Urban Environments Using Convolutional Neural Networks and Remote Sensing Techniques
Authors: Li Jiongye , Stouffs Rudi
Published: October 2024

Abstract. With the increase in built-up areas and rising urban populations, Land Surface Temperature (LST) has significantly increased, leading to the proliferation of Urban Hot Spots (UHS) in urban environments. To mitigate UHS proactively, researchers have conducted studies using various models to predict LST. However, current predictions are primarily based on data samples from isolated stations, making them unfeasible for continuous LST prediction on larger scales, such as regional levels. Therefore, this research aims to use Singapore as a case study to predict UHS on a regional scale using machine learning based on essential variables. Specifically, this research proposes training a Convolutional Neural Network (CNN) model using identified independent variables, including elevation, Normalized Difference Built-up Index (NDBI), Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), population, and Land Use and Land Cover (LULC), along with the target variable, UHS. After training, the model achieves high test accuracy and is fed projected data, subsequently producing projected UHS locations. The findings indicate that new UHS primarily appear in southwestern areas, Marina Bay, and the northwest regions of the country. This research predicts that 2.24 percent of the site could be classified as UHS by 2025, compared to the current percentage of 0.95 percent. Based on these projections, the research proposes preventative measures to proactively mitigate UHS. This research fills the gap by constructing a prediction model that can predict UHS locations on a regional scale.
Estimating Soil Salinity Using HISUI Hyperspectral Data in the Western Australian Wheatbelt
Authors: Ariyasu Emiko , Kakuta Satomi
Published: October 2024

Abstract. Soil salinity, caused by natural and anthropogenic factors, significantly impacts agricultural productivity, ecosystems, and global biodiversity. To mitigate severe salinity levels, it is necessary to detect early the salt-affected land for implementing solutions, such as adequate irrigation and cultivation of salt-tolerant crops. Before launching spaceborne hyperspectral sensor, Hyperspectral Imager Suite, (HISUI) developed by Japanese Ministry of Economy Trade and Industry, Kobayashi et al. (2010 and 2013) developed methods utilizing airborne hyperspectral data from HyMap to estimate soil salinity in wheatbelt region of Western Australia as a previous study. This paper aims to assess the feasibility of estimating soil salinity with HISUI in the same study area, following the past approach. Also, the goal is to estimate low soil salinity levels as same as the past objectives. As a result, past approach could not be fully applied in spaceborne hyperspectral sensor, caused by atmospheric effect in SWIR region. However, absorption of soil salinity in SWIR regions could be detected by HISUI. In the soil index, the NDSI (Normalized Difference Soil Index) showed higher accuracy than the Soil Index (SI) developed in previous studies. Then, the lowest values of RMSE were less than 100 mS/m in NDSI. As a result, HISUI data could not leach to map soil salinity at lower levels in this study. However, it showed the potential to estimate it with higher accuracy using hyperspectral data.
Remote Sensing Image Segmentation Based on Probabilistic Fuzzy Local Information Clustering
Authors: Li Xiaoli , Zhao Longlong , Chen Jinsong
Published: October 2024

Abstract. In this paper, a remote sensing image segmentation based on probabilistic fuzzy local information clustering algorithm is proposed. First, assuming that the spectral measure within the same ground object follows the same probability distribution. The dissimilarity between pixel and object is modeled by the negative logarithm of the Gaussian probability density function. It can improve the noise sensitive problem caused by the Euclidean distance which can only describe the data with isotropic distribution. Then, in order to consider the effect of local spatial constraint, on the one hand, the probability measure is used to modify the local fuzzy factor to establish the dissimilarity measure with spatial constraints. On the other hand, the hidden Markov random field is used to model the prior probability model of pixel membership. Next, the entropy regularization term of the objective function is built by combining the Kullback-Leibler(KL) maximum entropy model to further improve the robustness and noise resistance. The qualitative and quantitative analysis of simulated image and different types of real remote sensing images show that the proposed algorithm can effectively overcome the above problems and further improve the accuracy of image segmentation to over 95%.
Integrating Surface-related Indicators of Coverage, Distance and Distribution for Quantifying Scan-to-BIM Confidence Level
Authors: Malihi Shirin , Bosché Frédéric
Published: October 2024

Abstract. Scan-to-BIM is a widely-used approach to generate Building Information Modelling and by extension Digital Twin models in the architecture, engineering, and construction sector. The resulting models need to be as accurate as possible to ensure subsequent activities that make use of them can do so effectively. Quality assessment of point clouds and occlusion assessment of BIM outputted from Scan-to-BIM has been investigated. However, Scan-to-BIM systems currently do not provide proper metrics as to the confidence the user can have in the quality, in particular geometric quality of the outputted model. This paper addresses this gap by introducing a confidence index, for analysing the reliability of the generated 3D models and thereby quantifying the confidence the user can have in them. Index of confidence is itself derived from three more specific indices: index of coverage estimates the portion of the surface of the modelled element that is explained by the input point cloud. Index of distribution estimates how well the points explaining the modelled surfaces are distributed around the overall object’s surface. Index of distance defines the closeness of the generated element models to the input point cloud. The proposed indices are assessed using three real examples, demonstrating their adequacy.
Acquiring Semantic Information of Precast Concrete Pipe Using Geometric Feature Extraction from Mobile Laser Scanning Point Cloud
Authors: Putri Kartika Nur Rahma , Shen Xuesong , Aleksandrov Mitko , Barati Khalegh , Linke James
Published: October 2024

Abstract. In a construction project, the need to conduct effective and efficient material management is urgent since it takes up to 60% of the project budget. Most of the material tracking uses tag-based technology by attaching RFID, GPS, or UWB to the materials. This method is found to be effective in tracking the material in construction projects. However, there is still a manual job of putting the tag into each of the materials and problems related to the signal quality and infrastructure requirements. As an alternative way to do material tracking, point cloud processing can be used. This paper aims to develop an efficient approach to accurately detect concrete pipe precast material, calculate its numbers, and produce valuable data for material inventory management. The data was taken by a mobile laser scanner of a site-specific infrastructure project in Sydney. The resulting data was sparse and occluded because it was taken only from one side to replicate the actual scanning process in a construction project. The automated process has been conducted by matching the material based on its geometric feature of the 3D material model. The proposed approach can provide some spatial information such as location (x, y, z global coordinate), orientation of each material, and the number of materials. The result can detect up to 78.5% of the material. The difference between actual and predicted global coordinates is 0.75m which is acceptable for material location in a large infrastructure project. This data can be reconstructed in a 3D detail of the site project in the Building Information Modeling (BIM) platform in its actual location. The implementation of this method serves as an initial stage toward achieving synchronization between the physical construction and its corresponding digital twin in the field of construction.
A Virtual Reality Tool for Accuracy Assessment of 3D Models in an Immersive Virtual Environment
Authors: Subramaniyam Bala , Shylesh Dharshan , Ramasamy Jaganathan , Kumar Navin
Published: October 2024

Abstract. Accurate validation and assessment techniques are essential for ensuring the reliability of spatial reconstructions derived from photogrammetry, enabling well-informed decision-making across diverse domains. This study presents a Virtual Reality (VR) based accuracy assessment tool tailored for evaluating the accuracy and quality of 3D models generated by Unmanned Aerial Vehicles (UAVs). Leveraging the Unity game engine platform, our workflow entails three key steps: aligning real-world coordinates with an arbitrary Unity coordinate system, transforming the positions of Ground Control Points (GCPs) from field survey to the arbitrary system using a reference GCP, and marking observed points on the 3D models. Absolute and Relative Root Mean Square Errors (RMSE), Mean Errors (ME), and Standard Deviation of errors (SD) are computed within the virtual environment via the game object transform properties. The error distributions around each GCP are visually depicted using Unity game engine components for enhanced interaction and comprehension. The efficacy of the tool is validated through experimentation on four 3D models generated from varying camera angles during UAV data capture. The tool provides the opportunity to directly interact with the 3D models and visualize the errors, which is quite distinct from traditional methods. Using the developed tool, results were obtained to indicate that configurations employing camera angles of 60° + 75º exhibit notable performance in terms of relative and absolute accuracy.
Temporal and Spatial Dynamics of Subsidence in Eastern Jharia, India
Authors: Thakur Aditya Kumar , Attri Luvkesh , Garg Rahul Dev , Jain Kamal , Kumar Dheeraj , Chowdhury Arnab
Published: October 2024

Abstract. Jharia, one of the oldest and most significant mining areas, is facing the issue of subsidence for different reasons, such as coal fires, underground mining, and soil sensitivity towards seasonal change. This paper concentrates on the subsidence analysis of the Eastern region of the Jharia Coalfield from January 2018 to January 2021. Sentinal-1 SLC SAR imagery is used for multi-image sparse point processing and generating time series. Data processing is done using SARPROZ software. Further, the PS points inside the study area are filtered, and a classification map is drawn using ArcGIS software. The yearly percentage change of different classified zones is examined. The percentage change is significant for subsiding and uplifting zones but minimal for stable zones. Different critically and highly subsiding areas are identified by overlaying the classification zones on the topographic map. The seasonal variation of cumulative displacement is also examined, and patterns are analyzed. Different regions have different sensitivity towards seasonal variation. Overall, this paper provides insightful deformation results over Eastern Jharia for 2018–2021.
Exploring the Effect of Road Network Structure on Inter-Regional Accessibility in a Diverse Road Network
Authors: Thiede Renate , Fabris-Rotelli Inger , Debba Pravesh , Cleghorn Christopher
Published: October 2024

Abstract. Accessibility analyses quantify the level of access to certain areas or opportunities, such as employment and healthcare facilities. Since public data is often aggregated at the level of regions, such as administrative units, it is useful to quantify accessibility between regions. Many factors influence inter-regional accessibility, most notably the accessibility metric used, and the way in which regions are chosen. This paper investigates the effects of road network structure on accessibility, using a previously developed inter-regional accessibility model that bases its accessibility metric on travel distance via the road network. This paper considers an area within the City of Tshwane municipality in South Africa. We investigate the effects of road structure in two ways. Firstly, regions are chosen based on the road network structure, which is done by extending a previously developed road network clustering algorithm for this novel use. Different spatial scales of regionalisation are considered, and the accessibility between these regions is compared to the accessibility between administrative units within the study area. Secondly, the effect of road network homogeneity on accessibility is investigated, where homogeneity corresponds to a uniform concentration of roads across a region. The results show that although road network homogeneity does not significantly correlate with accessibility, the way in which regions are chosen and their spatial scale has a strong effect on the results of the accessibility model. Our novel method of obtaining regions thus provides fresh insights into road-based accessibility within the City of Tshwane.
Near Real-Time Responsive Flood Event Representation: An Open-Source Interactive Web Application Architecture
Authors: Turner Ryan , Sun Chayn
Published: October 2024

Abstract. The adoption of accessible Digital Twin technology in flood applications has been impeded by a notable lack of practical examples. Arguably, the most distinguishing feature of Digital Twin technology is the integration of near real-time data analytics through IoT sensor connectivity, which has remained underutilised in flood applications and therefore indicates a critical research gap. This project endeavoured to develop a comprehensive and replicable open-source architecture framework, specifically tailored for near real-time responsive flood event representation. The resultant interactive web application integrated near real-time river height and rain fall data streams and performed on-demand data analytics. Key results include the establishment of a back-end spatial database, a coupled physical city and digital space model, and a functional holistic front-end user interface. Additionally, this research aligned with the Gemini Principles by emphasising data interoperability and maintaining an information feedback loop. The study also aimed to configure near real-time IoT sensor connections, implement event triggers, and deliver interactive visualisations in an easily accessible format. The research addressed key questions surrounding the effective integration of IoT sensor data, identification of crucial flood indicators and parameters, and rapid quantification of flood event impacts. Ultimately, this research project demonstrates how Digital Twin technology can swiftly provide decision-makers with crucial insights during flood disaster events.
Lightweight Indoor Positioning System Based on Multiple Self-Learning Features and Key Frame Classification
Authors: Wang Chenzhe , Bi Kai , Zhao Bianli , Li Ming , Chen Yujia , Tao Shiliang , Yang Juntao
Published: October 2024

Abstract. Traditional indoor positioning technologies mostly require advanced installation of hardware devices, resulting in high costs and long-term maintenance. With advancements in image recognition and deep learning technologies, indoor visual positioning based on image recognition has become increasingly mature. This method offers the benefits of low cost and does not require additional hardware installation. However, it still has inherent defects, such as cumbersome data collection, complex algorithms, and universality. To minimize indoor information pre-collection cost, improve versatility, and enable rapid deployment in low-performance mobile devices, this paper proposes a lightweight indoor positioning system based on multiple self-learning features and key frame classification. The system is divided into two stages: preprocessing and real-time positioning. In the preprocessing stage, image information is collected for the entire indoor environment, and a key-frame recognizer is trained based on the image information. Simultaneously, an environmental feature information database is established. In the real-time positioning stage, the system first uses mobile devices such as smartphones to obtain real-time video streams. A key frame recognizer based on convolutional neural networks identifies key frames in each video stream frame, thereby obtaining approximate positions for rough positioning. Second, feature points are identified in each frame of the video stream and matched with feature points with location information in the built environmental feature information database to calculate precise positions for fine positioning. It has significant optimizations compared with conventional visual solutions in terms of preprocessing data collection, algorithm performance consumption, and versatility.
Spectral Unmixing of Pigments on Surface of Painted Artefacts Considering Spectral Variability
Authors: Wang Yu , Lyu Shuqiang , Ning Bo , Yan Ding , Hou Miaole , Sun Pengyu , Li Lihong
Published: October 2024

Abstract. Painted artefacts, such as murals and paintings, are the treasures of human civilization. Pigment is an important component of their surfaces. It is crucial to study the composition and proportion of pigments on the surface of painted artefacts for the field of heritage conservation. In this study, hyperspectral remote sensing was used to invert the abundance of mixed pigments by spectral unmixing. Spectral variability effects are often present in hyperspectral images. Hyperspectral images of cultural artefacts also suffer from spectral variability due to factors such as particle size and impurities within the pigment, and acquisition conditions. Therefore, spectral variability was incorporated into the Linear Mixed Model of the pigment spectral unmixing. The unmixing methods are classified into two categories based on whether or not spectral libraries are used. In the experiment, computer synthesized data and laboratory-made sample blocks of mixed pigments, which mixed with different ratios of Azurite and Malachite, were selected as validation data. Five commonly used algorithms for solving the spectral variability problem, namely MESMA, Fractional Sparse SU, ELMM, RUSAL, and BCM, are used for pigment unmixing and compared with FCLS, which does not consider spectral variability. The results show that ELMM has the highest unmixing accuracy of aRMSE and xRMSE compared to other methods. At the same time, it can be seen from the metric of SAM that ELMM is able to extract reliable endmember variability spectral, which is more suitable for solving the problem of spectral variability in pigment unmixing. Finally, we apply ELMM to real artefacts Yungang Grottoes mural paintings, and obtain a lower xRMSE than FCLS, which improves the unmixing accuracy.
A LiDAR-Based Digital Twinning Workflow for Traffic Monitoring and Simulation
Authors: Wibisana Maulana Ikram , Koeva Mila , Nourian Pirouz , Petrova-Antonova Dessislava , Karamitov Kaloyan
Published: October 2024

Abstract. The process of ensuring efficient and safe urban transportation is closely linked to urban planning, particularly through the aspects of transportation planning. Transportation planning is a pivotal concern for urban regions worldwide, reflecting the growing need to increase mobility while ensuring safety and sustainability in densely populated areas. This research focuses on developing a novel digital-twin-based approach for micro-traffic simulation to support data-driven decision-making for increasing traffic safety through scenario planning. Leveraging the traffic data obtained through monitoring one of the busiest intersections in Sofia city, this research workflow shows the effective integration of LiDAR data and the urban digital twin concept in intelligent transportation systems (ITS). The research addresses problems related to moving object classification, trajectory analysis, and reclassification of unrecognised objects by processing the LiDAR data, pre-processed in a .osef format, thereby transforming it to make it suitable for simulation. The proposed solution for the monitoring of urban traffic is demonstrated by the usage of SUMO (Simulation of Urban MObility) for performing simulations and a Random Forest model for unrecognized object reclassification to pre-existing vehicles and pedestrian classes. The architecture of the proposed workflow can possibly be applied in other similar urban settings, providing a scalable solution for both traffic management and urban planning. The study’s results support the wider use of urban digital twin principles in ITS by highlighting the value of advanced modelling tools and high-quality data in addressing today's urban transportation challenges.
An Efficient Attentive-GRU Structure for Uncertainty Modelling of Crowdsourced Human Trajectories Under Building-obscured Urban Scenes
Authors: Yu Yue , Liu Zhewei , Tang Kexin , Sun Yunjuan , Zhang Shuyu , Chen Liang , Chen Ruizhi
Published: October 2024

Abstract. Uncertainty modelling is regarded as one of the core components in the field of human mobility analysis and urban navigation, that can affect the performance of human behaviour modelling and location information acquisition. Existing uncertainty modelling algorithms towards the human movement trajectory are subjected to random and highly dynamic human motion characteristics and sampling and observation errors of Global Navigation Satellite System (GNSS) signals caused by the occlusion of buildings in urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of uncertainty modelling. To fill in this gap, this paper proposes an efficient attentive-GRU structure for uncertainty modelling of crowdsourced human trajectories under building-obscured urban scenes, that takes into account both temporal correlation and spatial correlation of human-originated GNSS trajectories and related motion features. A period of human motion data is modelled instead of only one or adjacent location points to avoid interference factors caused by the obstruction of urban buildings, and time-varying measurement and sampling errors are further estimated and combined with comprehensive human motion features to improve the accuracy of final uncertainty modelling. Comprehensive experiments indicate that compared with existing uncertainty modelling methods including physical models and deep-learning models, the proposed attentive-GRU structure realizes much better performance under different accuracy indexes.
Machine Learning-Based Soil Moisture Retrieval Using CYGNSS and Interpolated SMAP Data
Authors: Zhang Kun , Yu Kegen , Xu Nianfu , Wang Changyang , Lin Yiruo
Published: October 2024

Abstract. Soil moisture is a crucial component of the global terrestrial ecosystem water vapor cycle, and higher spatial-temporal soil moisture product has significant impacts on research in agriculture, hydrology, and ecology. Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology which can be used to retrieve soil moisture. Cyclone Global Navigation Satellite System (CYGNSS), launched by NASA in 2016 to observe ocean surface hurricanes, has also collected a substantial amount of data of GNSS signals reflected over the land. This paper focuses on soil moisture retrieval using CYGNSS and SMAP dataset. In this study, three machine learning methods (GSVM, BP and RF) are used for spatial interpolation of SMAP soil moisture data. Result shows RF interpolation is the best with the RMSE of 0.044 cm3/cm3 and the CC of 0.959 compared with SMAP truly SM. Then soil moisture data after RF interpolation and conventional linear interpolation are respectively used as response variables to train the retrieval model by ELM, BP and RF machine learning methods respectively. Results show that, among the six regression models, the RF-RF regression model performs the best with the RMSE of 0.012 cm3/cm3 and the CC of 0.986. Using this model, we get a global soil moisture product and have higher temporal and spatial resolution.
Open Geospatial Engine: A Cloud-based Spatiotemporal Computing Platform
Authors: Zhang Xianyuan , Xiang Longgang , Yue Peng , Gong Jianya , Wu Huayi
Published: October 2024

Abstract. The quantity and diversity of Earth spatiotemporal big data have significantly increased in recent years, providing the potential to comprehensively analyse complex spatiotemporal problems from new perspectives. However, integrating, managing, and applying multi-source heterogeneous Earth spatiotemporal big data remains a challenge. To address this issue, this study proposes a cloud-based spatiotemporal computing platform, the Open Geospatial Engine (OGE), for the unified organization and joint analysis of Earth spatiotemporal big data in multiple dimensions and scales. The framework of this platform comprises three modules: data management, computing engine, and service interface. The data management module adopts the GeoCube model to effectively integrate and manage multi-source heterogeneous spatiotemporal data through multi-dimensional aligned tiles. The computing engine module seamlessly maps the GeoCube model to the cloud environment by extending Spark RDD, making the GeoCube model capable of high-performance distributed computing. Following OGC specifications, the service interface module integrates and shares data, operators, and spatiotemporal analysis models through extensible APIs in an interactive development environment. Combined with a series of high-performance optimization techniques, OGE simplifies data queries and the construction of complex analytical applications by integrating these three modules. The applicability of OGE is demonstrated by case studies involving multi-dimensional queries and joint analysis of long-time-series and heterogeneous spatiotemporal data.
Optimized Factor Graph for Tightly-Coupled LiDAR/IMU Localization in Underground Parking Garages
Authors: Bao Wangyusha , Wang Jian , Han Houzeng , Zhao Yiwen , Liu Menghan , Wu He
Published: October 2024

Abstract. To address the challenge of intelligent vehicle localization in underground parking structures due to the loss of GNSS signals, this paper introduces a method to address this issue by developing a novel localization framework known as GF-LIO, which denotes a tightly-coupled fusion of LiDAR and IMU data, innovatively combining the Interactive Extended State Kalman Filter (IESKF) with a factor graph to enhance the localization process and solve the problem of GNSS signal loss in underground parking lots. The GF-LIO model commences with a strategic feature selection process, facilitated by a greedy algorithm that prioritizes environmental cues within the point cloud data. This method effectively filters out redundant features, thereby enhancing the saliency of retained features and subsequently improving the robustness of the localization process. Following feature selection, the model integrates LiDAR and IMU measurements utilizing the IESKF algorithm, ensuring a cohesive fusion of sensor data and bolstering attitude estimation accuracy. The culmination of the GF-LIO framework involves factor graph optimization, a sophisticated technique that synthesizes LiDAR odometry, IMU pre-integration factors, and loop closure detection factors. This optimization step enhances the overall precision and consistency of the localization process, resulting in superior performance compared to existing methodologies. Experimental evaluations conducted within underground parking environments corroborate the efficacy of the GF-LIO model. Comparative analyses against established approaches such as A-LOAM, LeGO-LOAM, LIO-LOAM, and FAST-LIO demonstrate a notable performance improvement exceeding 12.53%. The proposed model adeptly integrates domain-specific environmental characteristics with multi-sensor data, thereby facilitating precise localization and map construction tasks for intelligent vehicle navigating within the intricate confines of subterranean parking structures.