bluuebunny/crossref_metadata_2025 huggingface dataset:
APPLICATION OF THE INTERNATIONAL NEUROBLASTOMA RISK GROUP STAGING SYSTEM 2009 (INRGSS 2009) IN DIAGNOSIS AND PROGNOSIS OF CHILDHOOD NEUROBLASTOMA
Authors: Nguyen Van Tuy , Chau Van Ha , Nguyen Thi Kim Hoa , Nguyen Thi My Linh , Dang Thi Tam
Published: May 2024

Background: Neuroblastoma is the most common extracranial solid tumor of childhood. INRGSS 2009 divides patients into stages L1, L2, M, Ms, from which, in combination with histopathology, molecular biology, each patient will be divided into risk groups from which to guide prognosis and suitable treatment. Objectives: Describe the clinical and laboratory features of neuroblastoma in children; distribution of pediatric neuroblastoma stages according to the INRGSS 2009 classification and its value in predicting survival outcomes. Methods: This was a cross-sectional study combined with retrospective and prospective. Patients were diagnosed with neuroblastoma and classified according to INRGSS 2009 at the Pediatric Center, Hue Central Hospital. Results: The age at the time of diagnosis ranged from 1 day old to 9 years old, with a mean of 35.4 ± 25.5 months. Regarding primary tumor, 73.5% of cases were found in the abdomen, the mediastinum region (22.4%), an undetermined region (4.1%). Systemic symptoms, local symptoms and symptoms caused by metastasis were frequently reported. At the time of diagnosis, 73.5% of the patients had distant metastases (stage M or MS). The EFS of the whole group at 12 and 24 months old were 86.3 % and 64.7% respectively. The OS was 77.9% at 24 months old. Most of these patients had a progressive disease: 6.1% were in stage L1; 20.4% were in stage L2; 69.4% were in stage M; and 4.1% were in stage MS. Estimated mean of event-free survival for L1 was 38.7 ± 3.5 months, L2 was 40.5 ± 4.2 months and M was 28.0 ± 2.5 months (p < 0.05). Conclusions: Most neuroblastoma tumors are abdominal and have distant metastases. LDH and ferritin elevations are prevalent. Neuroblastoma outcomes can be predicted using the INRGSS 2009 classification.
CLINICAL FACTORS PREDECTING T790M MUTATION IN NON-SMALL CELL LUNG CANCER PROGRESSED AFTER FIRST- AND SECOND-GENERATION TKI TREATMENT
Authors: Hoang Huy Hung , Truong Cong Minh , Nguyen Thi Thai Hoa , Vu Ha Thanh
Published: May 2024

Objective: We aimed to identify the T790M mutation rate and clinical factors predicting the occurrence of acquired T790M mutations in advanced non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations after first- and second-generation tyrosine kinase inhibitor (TKI) treatment. Method: The EGFR-mutant NSCLC patients who experienced progression after first- and second-generation TKI treatment and underwent testing for EGFR using plasma samples between January 2021 and April 2023 were enrolled. The T90M mutation rate and related clinical factors were evaluated. We utilized the Chi-square test or Fisher’s exact test for univariate analysis and multivariable logistic binary regression for multivariate analysis. Results: A total of 247 patients were eligible for the study. Patients harbored the exon 19 deletion mutation (Del19), exon 21 L858R point mutation, and uncommon mutations in 63.2%, 34.4%, and 2.4% of cases, respectively. The overall T790M mutation rate was 33.6%. In multivariate analyses, the Del19 mutation versus L858R mutation, duration of first-line TKI exceeding 12 months versus below 12 months, and solitary lesion progression versus multiple lesion progression were identified as favorable prognostic factors for the occurrence of T790M mutation. Conclusion: Patients with EGFR exon 19 deletion, a prolonged treatment duration of first-line EGFR-TKI, and solitary lesion progression pattern are more likely to acquire the T790M mutation, as detected by the PCR method in plasma samples.
EFFECTS OF SAGYDI (SAD) ON ENDOGENOUS DYSLIPIDEMIA MODEL IN MICE
Authors: Pham Thi Van Anh , Dau Thuy Duong , Nguyen Thi Thuy
Published: May 2024

Dyslipidemia  is one of the popular issues in developed countries and an increasing trend in developing countries. It is  a  major  risk  factor  for  cardiovascular  disease, hypertension and diabetes. Polyherbal formulations have been used to treat dyslipidemia for many years. This study was designed to evaluate the effects of SAGYDI (SAD) on endogenous dyslipidemia experimental animal model. In endogenous hyperlipidemia model, mice were previously treated by SAD and intraperitoneally injected by poloxamer - 407 to induce hyperlipidemia. They were divided into groups and intraperitoneally injected with a 2% P-407 solution, followed by oral administration of varying doses of SAD. The results showed that SAD at the daily doses of 0.72g dried extract/kg and 2.16g dried extract/kg significantly reduced the serum total cholesterol (TC) levels and serum non-HDL cholesterol levels by 13.56%, 14.49%, and 16.40%, 11.10%, respectively (p<0.05). So, SAD has demonstrated an impact on serum lipid modulations in endogenous dyslipidemia model in mice.
ASSESSMENT OF NT-PROBNP LEVELS IN PATIENTS WITH CARDIAC ARRHYTHMIAS
Authors: Duong Linh Ha Khanh , Doan Sang , Lam Nien Vinh , Tran Vinh Thanh , Le Thanh Van , Kieu Dung Ngoc , Tran Phuong Le Uyen , Nguyen Thuc Tri
Published: May 2024

Background: NT-proBNP is a valuable biomarker for the diagnosis and treatment of cardiovascular diseases, including arrhythmias. Objective: To evaluate NT-proBNP levels in patients with arrhythmias and analyze the correlation between NT-proBNP levels and several variables. Subjects and Methods: A cross-sectional descriptive study was conducted on 158 patients with arrhythmias from December 2023 to March 2024 at the Arrhythmias Treatment Department of Cho Ray Hospital. Clinical and paraclinical characteristics were analyzed; NT-proBNP levels were compared between patients with arrhythmias with and without heart failure (HF); and the correlation between NT-proBNP levels and several factors was analyzed using R software version 4.3.1. Statistical significance was considered at P < 0.05. Results: The NT-proBNP level (Median [IQR]) in patients with arrhythmias (n = 158) was 367 (61.9-1297) pg/ml. NT-proBNP levels were higher in patients with arrhythmias with HF (n = 41) than in those without HF (n = 117), at 1273 (498-4173) vs. 210 (51-875) pg/ml (P < 0.001). Univariate analysis showed that age, weight, HGB, EF, creatinine, and eGFR were statistically significantly correlated with NT-proBNP. Conclusions: The NT-proBNP level in patients with arrhythmias was 367.3 (61.9-1297) pg/ml; it was higher in patients with arrhythmias with HF than in those without HF. NT-proBNP level was statistically significantly correlated with age, weight, HGB, EF, creatinine, and eGFR.
THE DISTRIBUTION OF TRADITIONAL MEDICINE BODY CONSTITUTIONS AND THE FACTORS ASSOCIATED WITH DEPRESSION AMONG ELDERLY IN PATIENTS IN TRADITIONAL MEDICINE HOSPITAL OF THUA THIEN HUE
Authors: Chau Van Hao , Le Thuc Trinh , Doan Van Minh , Nguyen Quang Quynh Nhu , Tran Nhu Minh Hang
Published: May 2024

Objectives: The purpose of this study is to explore the distribution of Traditional Medicine (TM) body constitutions, examine the factors associated with depression among elderly inpatients in Traditional Medicine Hospital of Thua Thien Hue, and further contribute to enhancing the value of traditional medicine theory's quintessence in preventing depression in medical facilities and in the community. Subjects and methods: A cross-sectional study of 420 elderly inpatients in Traditional Medicine Hospital of Thua Thien Hue from July 2022 to March 2024, screening for depression, using the Geriatric Depression Scale 30 items (GDS-30) and determining the TM body constitutions, using the Constitution in Traditional Medicine questionnaire (CCMQ). The multiple logistic regression model was applied to examine the factors associated with depression. Results: The proportion of the balanced constitution was the lowest (0%). Among the unbalanced constitutions, the highest proportion was the Phlegm-dampness constitution (70.2%), followed by Qi stagnation constitution (66.9%), Yin deficiency constitution (62.6%), Blood-stasis constitution (58.3%), Qi deficiency constitution (56.6%), Dampness-heat constitution (46.9%), Inherited-special constitution (38.8%) and the lowest was Yang deficiency constitution (31.9%). The higher odds of depression were observed in Qi stagnation constitution (p < 0.001, OR = 5.787, 95%CI: 3.275 – 10.227), Qi deficiency constitution (p < 0.001, OR = 4.369, 95%CI: 2.643 – 7.222), Yang deficiency constitution (p < 0.001, OR = 3.682, 95%CI: 2.097 – 6.277), Gender (p < 0.001, OR = 3.451, 95%CI: 1.980 – 6.017), Dampness-heat constitution (p < 0.001, OR = 2.534, 95%CI: 1.545 – 4.156), Inherited-special constitution (p = 0.003, OR = 2.160, 95%CI: 1.302 – 3.584) and Yin deficiency constitution (p = 0.028, OR = 1.771, 95%CI: 1.064 – 2.948). Conclusion: This study indicated the distributions of TM body constitutions and the factors associated with depression among elderly inpatients in Traditional Medicine Hospital of Thua Thien Hue. Therefore, the assessment of the TM body constitutions contributes to the prevention strategy and early detection of depression in the health facilities as well as in the community.
CLINICAL CHARACTERISTICS AND OUTCOMES AMONG ADULT PATIENTS WITH MEASLES AT HOSPITAL FOR TROPICAL DISEASES
Authors: Vo Trieu Ly , Bui Thi Nhat Hanh , Nguyen Hoang Phi , Tran Dang Khoa , Vuong Minh Nhut , Vu Thi Hieu
Published: May 2024

Introduction: Measles is an acute infectious disease which occurs in children and adults who are not immune to measles. However, adult patients with measles often have more severe symptoms than children. Objectives: Description of the clinical and subclinical features, complications, and some related factors to complications of adult patients with measles. Methods: A cross-sectional study was conducted on patients aged ≥ 16 years with clinical symptoms suspected of measles and positive test result of serological immunoglobulin M for measles. Results: From October 2018 to May 2020, 294 patients meeting the inclusion criteria were enrolled. The average age was 29 ± 6. The sex ratio from male to female was 1:1.06. The most common clinical manifestations were high temperature, rash, and severe cough. About three-fourths of patients had elevated liver enzymes. Nearly one-third of adult patients with measles developed complications, including bronchitis (57.6%) and pneumonia (27.2%). Having a fever when the rash disappeared, a productive cough, and increased leukocytes were associated with the complication rate of measles (p < 0.001). Recommendation: Adult patients with measles had a high rate of complications, which were commonly bronchitis, pneumonia, and preterm birth in pregnant women. The proportion of complications in patients with persistent fever, productive cough, and increased leukocytes was higher than in the remaining groups.
Meningitis a Listeria monocytogenes in a young, immunocompetent male
Authors: Dafir A. , Faris Y. , Kettani C. El , Barrou H.
Published: May 2024

Listeria Monocytogenes (LM) is a gram-positive intracellular pathogen usually transmitted to humans by ingesting contaminated food. It typically infects immunocompromised individuals, such as pregnant women, newborns, alcoholics, and the elderly. LM is a rare cause of meningitis in immunocompetent, healthy adults. However, its clinical presentation is similar to that of other viral or bacterial central nervous system infections and its course can be rapid and aggressive. Physicians should therefore always consider LM as a possible etiologic agent of meningitis, especially in cases that do not respond to first-line empiric antibiotic therapy.

We report a case of Listeria Monocytogenes meningitis in a previously healthy 16-year-old.
Evaluating and Improving the Robustness of Web Crawlers Against IP Blocking and Captchas
Authors: Onyenagubom Victor
Published: May 2024

In this work, we demonstrate the capability of a JavaScript-based web crawler to overcome anti-crawling measures such as CAPTCHAs and IP blocking. we delved into the ethical and legal dimensions of web crawling and provide recommendations for future research endeavors in this domain. A web crawler, being an automated software program, can navigate through websites and extract information. While it serves purposes like website analysis and indexing, it can also be misused for extracting personal data, scraping content, and overloading servers. Website administrators often employ anti-crawling techniques, such as CAPTCHAs, Robot.txt, and IP blocking, to thwart malicious web crawlers from accessing their content. These techniques aim to curtail the ability of a web crawler to scrape, access, or overload website resources, without impeding legitimate users from accessing the necessary content. The objective of this study is to demonstrate and enhance the resilience of legitimate web crawlers against anti-crawling techniques like CAPTCHAs and IP blocking, challenging the notion that these measures are universally effective against all types of web crawlers.
PERDA E LUTO NA PANDEMIA: IMPACTO PSICOLÓGICO DA COVID-19 NAS FAMÍLIAS ENLUTADAS
Authors: Santos Andreia Lima Silva dos , Lopes Júnior Hélio Marco Pereira , Nunes Jenina Ferreira
Published: May 2024

A pandemia de Covid-19 foi um evento surpreendente que ceifou muitas vidas em todo o mundo e causou importante impacto na saúde mental de parte considerável da população, principalmente dos que perderam amigos e familiares em razão dessa pandemia. O objetivo é compreender o impacto da pandemia de Covid-19 sobre a saúde mental da  população enlutada. O método utilizado neste artigo foi revisão de literatura a partir de artigos científicos sobre essa temática, disponíveis na Web. Os resultados mostraram que a pandemia de Covid-19 impactou negativamente a saúde mental dos enlutados, especialmente mulheres, aumentando ansiedade, estresse e dor psíquica. Neste sentido, os psicólogos desempenharam papel fundamental no apoio às famílias, ajudando na aceitação do luto e na gestão emocional durante esse período desafiador. A pandemia de Covid-19 causou um impacto profundo e global, afetando os enlutados e evidenciando nossa vulnerabilidade diante de crises pandêmicas. Os psicoterapeutas enfrentaram o desafio de lidar com essa dor psíquica inédita, buscando contribuir para a cura emocional daqueles que sofrem com o luto.
ATENÇÃO FARMACÊUTICA NO CUIDADO AO PACIENTE IDOSO
Authors: Alves Maycon de Grimalde , Andrade Leonardo Guimarães de
Published: May 2024

Introdução: Os farmacêuticos são capazes de discutirem e compreenderem questões terapêuticas e clínicas devido a sua formação profissional abrangente e ao seu entendimento sobre diversas áreas da saúde. Objetivo: Investigar o papel da atenção farmacêutica no cuidado ao paciente idoso, avaliando sua eficácia na otimização da terapia medicamentosa, na prevenção de eventos adversos relacionados a medicamentos e na melhoria da qualidade de vida nessa população. Metodologia: Para a realização deste trabalho, será primeiramente desenvolvido uma coleta de informações advindas de fontes bibliográficas, sobre “atenção farmacêutica no cuidado ao paciente idoso”, nas normas e resoluções aplicadas no Brasil, através de artigos, teses, livros, sites, entre outros. Conclusão: Ao longo deste trabalho, foi possível destacar a importância da atuação do farmacêutico na promoção do uso racional de medicamentos, na prevenção de eventos adversos relacionados à farmacoterapia e na melhoria da qualidade de vida dos idosos.
EVOLUÇÃO JURÍDICA DO ABANDONO AFETIVO NO BRASIL
Authors: Rezende Maria Júlia Andrade , Costa Vanuza Pires da
Published: May 2024

Este artigo analisa a evolução jurídica do conceito de abandono afetivo no contexto brasileiro, destacando sua importância no direito de família. Inicialmente, são apresentadas as origens históricas do abandono afetivo e seu reconhecimento como uma violação dos direitos fundamentais da criança e do adolescente. Em seguida, são discutidas as principais mudanças legislativas e jurisprudenciais que ocorreram ao longo do tempo, incluindo a influência de tratados internacionais e a interpretação dos tribunais superiores. O artigo aborda também os desafios enfrentados pelos operadores do direito na caracterização e quantificação do dano moral decorrente do abandono afetivo, bem como as diferentes abordagens adotadas pelos tribunais brasileiros para lidar com essa questão delicada. Além disso, são analisadas as possíveis consequências jurídicas para os responsáveis pelo abandono afetivo, incluindo a aplicação de medidas coercitivas e indenizações. Adotaram-se como métodos de abordagem indutiva; de procedimento pesquisa bibliográfica, de cunho teórico-prático.
CONFLITO ENTRE O PRINCÍPIO DA PUBLICIDADE TRATADO NA LEI 6.015/73 E OS DADOS SENSÍVEIS TRATADOS PELA LEI GERAL DE PROTEÇÃO DE DADOS - LGPD NAS SERVENTIAS EXTRAJUDICIAIS
Authors: Silva Kaian Notier Gomes da , Oliveira Rômulo de Morais e
Published: May 2024

A temática do trabalho possui como questão central o objetivo de analisar a implementação da Lei Geral de Proteção de Dados Pessoais no contexto da atividade notarial e registral, Este estudo norteou-se a partir de um objetivo geral em demonstrar como a Constituição da República Federativa do Brasil de 1988 trata das competências dos entes federados, munindo-os de autonomias para lidar com a gestão pública visando assegurar a transparência dos procedimentos extrajudiciais, ao mesmo tempo em que resguarda a privacidade dos indivíduos. A metodologia empregada foi através da pesquisa jurídica, de cunho exploratória, sendo bibliográfica e documental, com análises qualitativas, considerando que foram observados entendimentos doutrinários e jurisprudenciais acerca do direito a publicidade dos atos. Evidenciando a importância do princípio da publicidade na proteção dos direitos e na eficácia dos atos cartorários, além de ressaltar a importância de proteger a privacidade e os dados pessoais dos usuários dos cartórios. A partir das competências federativas constitucionais, cujo método de abordagem teórica foi o dedutivo. Dentre os resultados alcançados tem-se que ficou demonstrado que a publicidade dos atos é de suma importância. Sendo que diante desta situação recomenda-se aplicar o princípio da ponderação ou da proporcionalidade no sentido de evitar-se lesão a direito fundamental essencial.
Shoreline Extraction Methods from Sentinel-2 and PlanetScope Images
Authors: Angelini Riccardo , Angelats Eduard , Luzi Guido , Ribas Francesca , Masiero Andrea
Published: May 2024

Abstract. This work aims to compare and assess the performance of certain methodologies for shoreline mapping based on the use of medium (10 m) and high resolution (3 m) multi-spectral imagery, provided by Sentinel-2 (S2) and PlanetScope (PS), respectively. Being Sentinel-2 part of the Copernicus missions, its data are freely available. PS imagery are also freely available for scientific research, upon approval by the European Space Agency of a related project proposal. Several spectral indices, including Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Automated Water Extraction Index (AWEI), and Water Index (WI), were used for shoreline detection. In particular, two unsupervised classification techniques, the Gaussian Mixture Model (GMM) and K-means clustering were deployed as shoreline extraction methods. The outcomes of such approaches were validated using reference shorelines derived from aerial orthomosaics, generated from images acquired as close as possible to the satellite imagery dates, and the ”baseline and transect” approach for accuracy verification. Three tide-less Mediterranean beaches were used as study cases for comparison: the beach between Castelldefels and Gava in Spain, Feniglia and Marina di Grosseto in Italy. The results demonstrated sub-pixel accuracy in shoreline extraction, with Mean Absolute Distances ranging from 2 m to 5 m for S2 data and 1.5 m to 2 m for PS data. These findings highlight the potential of freely available satellite data for semi-automatic shoreline detection. Results obtained by using the combination of different indices and methodologies show that the best option may change depending on the considered context, hence future investigations should be dedicated to the development of a procedure for automatically determining the context-based (close to) optimal index-classifier combination.
Detecting and Monitoring Artisanal Mining Operations in Semi-Arid Terrain Using Multitemporal SAR Data for InSAR Coherence Estimation and Unsupervised Classification
Authors: Chike Ifeanyi , Balz Timo
Published: May 2024

Abstract. The impact of artisanal mining in sub-Saharan Africa and other developing countries are often huge as they leave trails of environmental and socio-economic effects on the people and the environment. 123 Multi-temporal SAR data acquired between 2019 and 2023 was used. SARPROZ was used to co-register the images and other preprocessing steps like radiometric and geometric correction to transform the images from SAR coordinates to geographic coordinates after generating all the interferograms. A stack of the 122 coherence map was created. Unsupervised classification was implemented on the stack. Principal component analysis of dimensionality reductions method yielded a far better result than the other unsupervised cluster methods attempted, it showed a very high classification accuracy of the terrain. The principal component analysis worked by computing the covariance matrix of the stacked coherence map then performed eigen-decomposition on it to yield eigenvectors and eigenvalues, The eigenvectors corresponding to the largest eigenvalues represent the principal components. These principal components capture the directions of maximum variance, and the eigenvectors provides a reduced-dimensional representation of the image stack which is then used to reconstruct an approximation of the original image that captures the essential features of the original SAR data. Backscatter intensity of the SAR images processed for this study period for unsupervised change detection and land cover classification, delineated the different features and classes based on long term coherence values.
Research on Dynamic Updating Technology of Digital Maps for Mobile Communications Based on Multi source Remote Sensing Images
Authors: Bai Jin , Lu Chenni , Dong Shuai , Liu Chang , Yan Qingqing , Wang Chenyang , Gao Jingnan
Published: May 2024

Abstract. With the rapid development of 5G networks in China, mobile communication network simulation technology has gradually become a widely studied direction in the academic and application fields. This study focuses on three-dimensional digital geographic information data for mobile communication, including a large amount of geographic information data that significantly affects the propagation of mobile communication radio waves, such as terrain height and ground use types. These data play a crucial role in coverage prediction, interference analysis, and frequency planning in various telecommunications planning software. This article starts from the perspective of mobile communication network management, and combines typical large-scale terrain data production practice projects for mobile communication to deeply study the process and system function design of combining geographic information systems with multi-source remote sensing data in mobile communication network monitoring and management. On this basis, this study designed and implemented a dynamic updated terrain database for mobile communication. This database can update geographic information data in real-time, ensuring the timeliness and accuracy of the data. At the same time, by optimizing the database structure and query algorithms, the speed and efficiency of data processing have been improved, providing efficient and reliable solutions for coverage prediction, optimization analysis, and planning management of large-scale mobile communication networks.In summary, dynamic updating database of three-dimensional digital geographic information has important application value in mobile communication network simulation technology. Through in-depth research and application of this database, the performance and service quality of mobile communication networks can be effectively improved, laying a solid foundation for future communication technology development.
Method for Generating Indoor 3D Scene Graphs Based on Instance Features and Relationship Encoding
Authors: Du Han , Cai Benhe , Li Xiaoming , Wang Weixi , Tang Shengjun
Published: May 2024

Abstract. A 3D scene graph is a compact and explicit representation in scene analysis. In today’s 3D scene graph prediction methods, the feature encoding method of nodes and edges is relatively simple, which essentially hinders the network from fully learning 3D point cloud features. In this paper, we propose a 3D scene graph task framework that fully expresses node and edge features, trying to meet the requirements of fully utilizing point cloud features to achieve high-precision prediction. Experimental results show that with the help of the new representation method, the prediction performance of 3D scene graphs has been significantly improved.
AI-Driven Dim-Light Adaptive Camera (DimCam) for Lunar Robots
Authors: Duan Ran , Wu Bo , Chen Long , Zhou Hao , Fan Qichen
Published: May 2024

Abstract. The past decade has been a boom in lunar exploration. China, India, Japan and other countries have successfully landed landers or rovers on the lunar surface (Wu et al., 2014, 2018, 2020; Prasad et al., 2023). Future missions to explore the Moon are focusing on the lunar south pole (Peña-Asensio et al., 2024). The solar altitude angle at the lunar south pole is extremely low, resulting in low solar irradiance and large areas often in dim light or shadows. The permanently shadowed regions (PSRs) at the lunar south pole are also likely containing substantial amounts of water ice (Li et al., 2018). Future lunar robots exploring the lunar south pole will need to operate in low light or shadowed regions, making camera sensors sensitive to the dim-light environments necessary for these robots. Common night vision sensors usually use near-infrared cameras. However, sensors based on passive infrared technology have image resolution limited by several factors, including the intensity of infrared radiation emitted by the object, the sensitivity of the camera, and the performance of the optical system. For instance, thermal imagers typically have a resolution of 388*284 pixels only. We have developed a dim-light adaptive camera (DimCam) that is ultra-sensitive to the varying illumination conditions driven by AI to achieve high-definition imaging of 1080P or above, for future lunar robots operating in shadows or dim-light regions. The DimCam integrates two starlight-level ultra-sensitive imaging sensors connected by a rigid base to provide stereo vision in low illumination environment. An AI edge computing unit is embedded inside the DimCam to adaptively denoise and enhance image quality. The AI module uses an end-to-end image denoising network to identify and remove noises in the images more accurately by utilizing depth information from the stereo sensors. Compared with traditional monocular denoising algorithms, the denoising network based on stereo vision can significantly improve denoising effects and efficiency by enhancing the signal-to-noise ratio of the data input in the front end. The superposition of overlapping scenes can be regarded as a delayed exposure. Concurrently, the residual analysis of the aligned images aids in noise identification. In addition, for pixels obscured by noise, more accurate pixel values can be restored through interpolation or replacement using depth information obtained from the stereo sensors. Subsequently, a pre-trained lightweight deep network modified from Zero-DCE (Guo et al., 2020) is used for image quality enhancement in terms of brightness and contrast, providing high-quality images even in low-light environments for subsequent applications, such as positioning and navigation of robots, 3D mapping of the surrounding environment, and autonomous driven. We have tested the DimCam in a simulated environment in the laboratory, and the results show that the DimCam has promising performances and great potential for various applications.
Efficient Feature Matching and Pose-graph Initialization for SfM
Authors: Gan Wen Tian , Zhan Zong Qian , Wang Xin
Published: May 2024

Abstract. Over the last decades, Structure from Motion (or image orientation) has been widely studied in the fields of photogrammetry and computer vision, mainly thanks to its feasibility for dealing with various image datasets (such as crowdsourced or UAV images). However, due to the fact that images are becoming easy to obtain, nowadays, it is challenging to deal with large scale of datasets, wherein the feature matching and pose-graph generation are the key limitations in terms of time efficiency. In this work, we proposed an efficient method to accelerate the generation of correspondences and two-view geometries. Specifically, based on some already estimated two-view geometries, unknown two-view geometries can be computed via A* algorithm. Then, the corresponding feature matching can be perform in a guided way using an epiploar-hash bins that is derived from the estimated two-view epipolar geometries. The experimental results demonstrated that, our method can improve the speed of generating pose-graph by 3–4 times comparing to two popular packages (colmap and OpenMVG) and is also faster than one SOTA method of Barath et al., (2021), yet the results of SfM are typically on par with them and reprojection error of our works are even better.
Evaluating the Stability of the Jianchuan Ancient Town with TerraSAR-X images
Authors: Gao Xinning , Li Menghua , Yang Mengshi , Tang Li , Tang Bo-Hui
Published: May 2024

Abstract. Architectural heritage holds significant historical and cultural value. However, it faces substantial risks of deterioration stemming from natural aging and human activities. Traditional methods rely on manual surveys or ground-based measurements, which are time-consuming and labor-intensive, making it challenging to meet the demand for large-scale stability assessments of architectural heritage. In this study, we designed a methodology based on the analysis of differential deformation and angular distortion of buildings using InSAR line-of-sight (LOS) displacements. Then, the stability of buildings is categorized into three groups: stable, moderate, and unstable, using these stability indicators. A total of 74 TerraSAR-X images acquired from August 2017 to November 2019 in ascending orbit were used to monitor the Jianchuan Ancient Town, which has a history of over 650 years. Our evaluation reveals that among the 1891 architectures in Jianchuan Ancient Town, 1191 are rated as stable, 696 as moderate, and 4 as unstable. Time-series results indicate that one of the four unstable buildings exhibited a severe accelerating trend that requires attention. In conclusion, this methodology can enhance the capability to assess the stability of large-scale architectural heritages.
Comparison of Extraction accuracy of Sugarcane from different resolution satellite images using Deep lab V3+ Mode
Authors: Gao Xinyuan , Chen Chen , Gan Yuhang , Liu Yu
Published: May 2024

Abstract. Sugarcane is an annual or perennial persistent rooted tropical and subtropical herb that grows in tropical and subtropical regions. As China's production ranks among the world's leading, sugarcane industry is an important part of agricultural economy in China. As the largest sugarcane production center in China, Guangxi is one of the most suitable areas for sugarcane cultivation in China and even in the world. Sugarcane industry, as an agricultural advantageous industry in Guangxi, not only has a significant image to the national economy of the region, but also is closely related to the issue of security of national sugar supply. Continuous cropping of sugarcane is very common in Guangxi, which is very helpful for the concentration selection of sugarcane samples. The wide application of satellite remote sensing monitoring technology has become an indispensable means of natural resources monitoring. Using optical satellite remote sensing image to identify and extract sugarcane planting areas is of great significance to quickly and conveniently grasp the information of sugarcane distribution and yield. In this paper, the precision of sugarcane extraction from GF1 and GF2 satellite images is analyzed by using deeplab V3 + model, the effect of optical remote sensing images with different resolution on sugarcane extraction accuracy was studied to provide better data support for dynamic monitoring of sugarcane planting.
Accurate Pointing Determination of Space Targets under the Stars Background
Authors: Guan Zhichao , Hou Chongyuan , Zhong Xing , Jiang Yonghua , Zhang Guo , Gong Jing
Published: May 2024

Abstract. This study proposes a method for determining the direction of space targets using existing satellite constellations. Usually, when optical satellites point towards deep space for imaging, stars inevitably appear in the image. This study conducts all targets extraction, star identification, camera calibration. Ultimately obtaining high-precision space targets longitude and latitude information. One of the Jilin-1 GF06A series satellite was used to conduct observation experiments on space targets of this study. Each scene has an exposure time of 10 seconds, and a total of 90 seconds of observation data was obtained. This study analysed the results of star extraction and the identification result which indicate that GF06A can capture a maximum magnitude of 15.6. After using the extraction star coordinate information for calibration, the interior accuracy of camera was better than 0.48 arcseconds. Then, the precise longitude and latitude information of 5 space targets were obtained. Finally, cataloguing information of these stars is provided, which is beneficial for determining the orbit of the target in the future.
Deep Convolutional Network Based on Attention Mechanism for Matching Optical and SAR Images
Authors: He Haiqing , Yu Shixun , Zhou Fuyang , Zhang Hai , Chen Longyu
Published: May 2024

Abstract. Complex geometric distortions and nonlinear radiation differences between optical and synthetic aperture radar (SAR) images present challenges for the matching of sufficient and evenly distributed corresponding points. To address this problem, this paper proposes a deep convolutional network based on an attention mechanism for matching optical and SAR images. In order to obtain robust feature points, we employ phase consistency instead of image intensity and gradient information for feature detection. A deep convolutional network (DCN) is designed to extract high-level semantic features between optical and SAR images, providing robustness to geometric distortion and nonlinear radiation changes. Notably, incorporating multiple inverted residual structures in the DCN facilitates efficient extraction of local and global features, promoting feature reuse, and reducing the loss of key features. Furthermore, a dense feature fusion module based on coordinate attention is designed, focusing on the spatial positional information of effective features, integrating key features into deep descriptors to enhance the robustness of deep descriptors to nonlinear radiometric differences. A coarse-to-fine strategy is then employed to enhance accuracy by eliminating mismatches. Experimental results demonstrate that the proposed network performs better than the manually designed descriptors-based methods and the stateof- the-art deep learning networks in both matching effectiveness and accuracy. Specifically, the number of matches achieved is approximately 2 times greater than that of other methods, with a 10% improvement in F-measure.
Light-weight Fusion Network for UAV Visible-light and Infrared Images based on Real-time Brain-like Intelligent Computing
Authors: Hu Kun , Qu Jiayi , Zheng Wenhao , Zhou Haoyang , Cai Tianyu , Zhang Qingle , Wang Shichao , Li Bo
Published: May 2024

Abstract. Multiple sensors equipped on the Unmanned Aerial Vehicle (UAV) enables the acquisition of multi-modal and multi-source remote sensing data. UAV remote sensing usually faces with real-time or near-real-time tasks in complex and highly dynamic environments, such as disaster monitoring, traffic management, border patrol and so on. Under these conditions, the image fusion algorithm needs to be high efficiency, precision and reliability. In this paper, we proposed an intelligent real-time fusion network for UAV multi-source remote sensing data based on AI brain-like chips, and deployed the algorithm on the UAV platform to achieve online high-efficiency computing. Firstly, we have developed a novel image fusion algorithm named SFNet for infrared and visible image fusion based on ShuffleNetv2. Then, we use ZCA and l1-norm to process the remodeled deep feature. The weight maps are generated by bi-cubic interpolation and soft-max operation. Finally, the fused image is reconstructed by weighted-average operation. The proposed SFNet is deployed on the Lynxi KA200 brain computing chip, and a comprehensive inference test is carried out with UAV remote sensing data. Several State-Of-The-Art (SOTA) data fusion algorithms are deployed on the same chip for experimental comparison. The proposed SFNet is proved to have faster inference speed and better feature extraction results on brain-like chips. It is more suitable for real-time UAV remote sensing image fusion tasks.
Research and application of land illegal behavior monitoring based on video image recognition
Authors: Hu Min
Published: May 2024

Abstract. Our country had increasingly high requirements for land management, especially for arable land, which was the most precious resource. Strict policies for protecting arable land are being implemented. The enforcement of satellite imagery in mega cities had certain limitations in terms of timeliness and accuracy. To meet the requirements of refined management, a high-altitude camera was constructed to form a near ground monitoring network covering the cultivated land area. Multi temporal image groups were obtained through video frame extraction. A monitoring sample library for typical behaviors of natural resources was established. Land objects closely related to illegal activities such as construction machinery and bulldozing areas were identified based on deep learning algorithms. By using the technology of bidirectional conversion between video spatial location and geographic coordinates, a fusion of spatial information video monitoring patterns was formed. Taking the 20000 acre permanent basic farmland area as an example, the recognition rate of illegal land related behaviors was about 81%. By combining approval information and conducting spatial analysis on the identified patterns, real-time warning information could be pushed. This application can effectively improve the timeliness of supervision and enhance the level of supervision.
Crop Height Estimation Based on a Novel Semi-Empirical Model Considering Double-Bounce Scattering Using RADARSAT-2 PolSAR Data
Authors: Hu Shuaifeng , Xie Qinghua , Peng Xing , Wang Jinfei , Fu Haiqiang , Zhu Jianjun , Liu Xiaotong
Published: May 2024

Abstract. Obtaining precise and rapid crop height is essential to facilitate agricultural production services, field management, disaster monitoring, and yield assessment. With the capability to penetrate vegetation and record vertical structure information, Polarimetric Synthetic Aperture Radar (PolSAR) holds significant potential for application in vegetation height inversion. The Water Cloud Model (WCM) and its enhanced versions are extensively utilized for estimating crop heights from PolSAR data owing to their physical significance and simplicity. However, the method is not practical for stalk crops due to the neglect of double-bounce scattering considerations. Therefore, according to the growth characteristics of stalk crops, a three-component polarimetric coherent backscattering model considering crop target double-bounce scattering is established by simplifying the Random Volume over Ground (RVoG) coherent scattering model. The empirical coefficient is introduced to simplify the model into a semi-empirical for crop height inversion. The suitability of applying the RVoG-B three-component model for crop height inversion at the early stage in corn fields was assessed using Multi-temporal C-band PolSAR RADARSAT-2 data in three polarimetric channels. The results show that the HV channel exhibits superior potential in inverting the height of corn compared with the HH and the VV channels. The results of corn height inversion demonstrate that the RVoG-B three-component semi-empirical model performs effectively in estimating corn height, with its inversion accuracy having an RMSE ranging from 11.66cm to 24.51cm. This study demonstrates the potential application of the RVoG-B three-component semi-empirical model for inverting crop height at the early stage dominated by double-bounce scattering.
Estimating Forest Canopy Height based on GEDI Lidar Data and Multi-source Remote Sensing Images
Authors: Lei Yuqi , Wang Yuanjia , Wang Guilong , Song Chengwen , Cao Hui , Xiao Wen
Published: May 2024

Abstract. Estimating forest canopy height is crucial for assessing aboveground biomass and carbon sequestration. Light detection and ranging (lidar) is an important technology for its ability in capturing vertical structural information. However, due to instrument limitations and cost constraints, acquiring large-scale and continuous forest data solely through lidar is challenging. To compensate this, remote sensing images can be used to cover wide regions. Therefore, leveraging multi-source data for constructing canopy height models (CHMs) holds great promise in this field. The objective of this study is to evaluate and compare the contributions of multi-source remote sensing data and methods in estimating forest canopy height. In constructing the CHM, the commonly used random forest (RF) and fully convolutional network (FCN) are assessed. The canopy height obtained from GEDI was used as the reference data, and Landsat 8 and Sentinel-2 data were used for prediction. Multiple CHMs were constructed for the Dabie Mountains, Central China, in 2019 based on different data sources and methods, respectively, which are then comparatively analysed. The results showed that (1) the accuracy of the CHM using Sentinel-2 as input is marginally better than that using Landsat 8 based on RF, where the difference is insignificant; and (2) FCN is less accurate than RF despite domain-specific fine-tuning, although further improvement in accuracy is expected by weighing in more FCN models.
Assessing Looting Holes by Using SAR Simulation
Authors: Boyoğlu Cem Sönmez , Balz Timo , Sultanbekova Aigerim
Published: May 2024

Abstract. Looting is one of the biggest threats to archaeology and cultural heritage and archaeologists had been struggling with this problem for decades. Help to deal with this threat, we carried out an experiment in Wuhan / China and created our own artifical looting holes by digging two holes and shaping them in different measurements while collecting TerraSAR-X images from the experiment area. Our study aims creating simulated SAR images of looting holes, in the light of the information we obtained from our experiment. Parameters of collected SAR images and 3D models of the artificial holes will be foundation of the work with SAR simulation. Since field work is money and time consuming duty, simulating SAR images of looting hole is advantageous and benefitial since we can play with the 3D models and change their geometry. In this study we obtain SAR simulation images by using our 3D models of the looting holes. By using simulation, we are able to change the geometry of the hole and even possible to implement natural effects to the 3D model which is effective on results. Thus, assessment of the looting holes will become much more simple work and it will help to protection of cultural heritage.
Qinghai-Tibet Plateau Glacier-DEM product derived from the Chinese stereo mapping satellites
Authors: Li Guoyuan , Guo Jinquan , Shang Jing , Zhu Siao , Chen Jie
Published: May 2024

Abstract. The Digital Elevation Model (DEM) is an important basic data for glacier research on the Qinghai-Tibet Plateau. With the rapid development of China 's stereo mapping satellite, it is possible to obtain high-precision DEM in the glacier area of the Qinghai-Tibet Plateau. In this study, the stereo images and laser altimetry data of ZY-3 and GF-7 satellites were comprehensively used, and the matching and joint adjustment methods of laser elevation control points and stereo images were proposed. A high-precision DEM product in glacier area was established, and the elevation accuracy of DEM was verified by using UAV data and ICESat-2 data. The results show that the accuracy of GF-7 DEM is better than 1.5 m when the slope is less than 6 degrees, and the overall accuracy is better than 2.8 m. The accuracy of ZY-3 DEM is better than 3.5 m when the slope is less than 6 degrees, and the overall accuracy is better than 5.0 m. Compared with the open source DEM with medium spatial resolution, the DEM elevation accuracy based on domestic stereo mapping satellite images is better, and the grid is finer and more detailed to describe the texture characteristics of the end of the glaciers. Compared with the high spatial resolution data set HMA DEM, the ZY-3 DEM is slightly worse and the GF-7 DEM is better, and both the ZY-3 DEM and GF-7 DEM are better than the HMA DEM in terms of coverage integrity in the Qinghai- Tibet Plateau. Chinese stereo mapping satellites can generate high-precision DEM in the glacier area, and can provide more accurate and reliable terrain reference data than the existing open free data for the study of glaciers in the Qinghai-Tibet Plateau.
A comparative study of mass balance in the Lambert Glacier and Amery Ice Shelf along the Chinese inland traverse during 2019–2023 using altimetry, gravity, and in-situ observations
Authors: Li Hongwei , He Youquan , Gu Yuanyuan , Qiao Gang
Published: May 2024

Abstract. The new photon-counting laser altimetry satellite ICESat-2 was successfully launched on September 15, 2018 with an unprecedented ice surface elevation measurement accuracy of 2–4 cm. It is meaningful for accurately estimating volumetric changes in the Antarctic Ice Sheet. Cross-validation of different types of data, especially comparison with in-situ data, is important for the ice sheet mass balance results of a new satellite. This paper proposes an elevation-difference method of grid elevation change rate model, based on ICESat-2 ATL11 elevation time series data, to distinguish the linear change trend of ice sheet surface elevation from the elevation change resulting from periodic precipitation. In order to compare the estimated elevation-change rate with the flux computed from in-situ snow stake velocity measurements and GRACE-FO gravity survey data, we made corrections for firn air content, elastic, and glacier isostatic adjustment. Based on the ATL11 data from 2019 to 2023, our results show that the ice sheet change in Basin 11 along the CHINARE traverse is from ~0.019 m yr−1 to ~0.121 m yr−1, and the mass balance in the upstream of the traverse in Basin 11 is ~1.9±0.2 Gt yr−1. It is comparable to ~2.4±1.2 Gt yr−1 from GRACE-FO during the same time period. Furthermore, the flux across traverse-11.9±1.1 Gt yr−1 is comparable to that of ~-9.7±0.9 Gt yr−1 across the same flux gate during 1997–2009 which is calculated based on GNSS-derived ice velocity observations, considering the time period difference and uncertainties.