Miracle-dz/UniFAR-FTUs huggingface dataset:
Exchange Data Augmentation for Change Detection

Change detection is developed to automatically identify semantic changes between remote sensing (RS) images captured at different points of time in a specific geographic location. Due to the limited availability of annotated data and the high complexity of the change detection problem, the performance of change detection models can not meet our expectations. To address this issue, many methods are proposed to solve this issue. However, ignoring the characteristics of the change detection task limits their performance. Therefore, we introduce a novel exchange data enhancement method (EDEM) strategy to generate image pairs to help the neural network to capture the temporal consistency in the data. We evaluate the proposed approach on two publicly available datasets and compare it with several state-of-the-art methods. The experimental results demonstrate that our proposed approach can effectively improve the performance of change detection models, achieving state-of-the-art performance on both datasets.
On the Number of Graphs With a Given Histogram

Let <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> be a large (simple, unlabeled) dense graph on <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> vertices. Suppose that we only know, or can estimate, the empirical distribution of the number of subgraphs <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> that each vertex in <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> participates in, for some fixed small graph <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>. How many other graphs would look essentially the same to us, i.e., would have a similar local structure? In this paper, we derive upper and lower bounds on the number of graphs whose empirical distribution lies close (in the Kolmogorov-Smirnov distance) to that of <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>. Our bounds are given as solutions to a maximum entropy problem on random graphs of a fixed size <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> that does not depend on <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>, under <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> global density constraints. The bounds are asymptotically close, with a gap that vanishes with <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> at a rate that depends on the concentration function of the distribution at the center of the Kolmogorov-Smirnov ball.
Flood Depth Estimation Using Synthetic Aperture Radar (SAR) Imagery and Topography: A Case Study of the 2021 and 2022 Floods in Hawkesbury Valley, Australia

Floods are among the most common and devastating extreme weather events that cause damage to infrastructure, agricultural lands, transportation and communication systems. To effectively respond to floods, it is important to have accurate information about the depth of floodwater across the affected area, and ground surveys are not always possible in such scenarios. This information can be estimated remotely through the use of spaceborne synthetic aperture radar (SAR) systems, which are equipped to operate under nearly all weather and time conditions. Here, we present a case study of estimating the flood depth using inferred flood extents from the very high resolution (VHR) Capella Space X-band constellation. We have used a publicly available digital elevation model (DEM) with a Python-based implementation of the Floodwater Depth Estimation Tool (FwDET) to determine flood depth from the SAR imagery. We estimated the depth of floods in two major flood events (March 2021 and March 2022) in Hawkesbury Valley, New South Wales, Australia. Furthermore, we explored the utility of these depth maps for rapid flood damage assessments.
Hydrology of a Karst Area in east‐central West Virginia

Movement of groundwater through a maturely karsted limestone aquifer in Pocahontas County, West Virginia, was studied by dye-tracing and extensive underground mapping techniques. The aquifer consists of 400 feet of flat-lying Mississippian Greenbrier limestone. The drainage basin of Hills Creek is completely enclosed by clastic rocks, and discharge is by subterranean flow. The Swago Creek underground drainage is quite unrelated to surface drainage, and drainage lines cross major surface divides. Underground divides have been mapped and may occur under valleys as well as under ridges. The water table is remarkably flat, with abrupt breaks in slope only near the margins of the area under a rugged surface topography whose relief exceeds 2000 feet. The surface topography is not reflected in the karst water table, and free-surface flow often occurs as much as 1000 feet below the land surface. Even minor insoluble beds are capable of perching tributary underground streams as much as several hundred feet above base level. Underground flow takes place along hydraulic gradients that point to the major base-leveling stream of the area, the Greenbrier River, rather than to surface valleys. (Key words: Drainage basin characteristics; geomorphology; groundwater; West Virginia)
An Interpretation Study on the ML Models for Landslide Susceptibility Mapping

Natural hazards frequently threaten human life, ecosystem, and economy all around the world, while landslides are the most destructive ones. Landslide susceptibility mapping (LSM) is an effective strategy to determine the probability of future landslide events by involving a comprehensive analysis of various factors, including geological environmental, historical landslides and the landslide physical laws. As artificial intelligence techniques are becoming more popular in LSM, it is important to understand how decisions are made by these models. This study aims to use representative ML (ML) and deep learning (DL) models (include random forest (RF), support vector machine (SVM), residual neural networks (ResNet) and Densely connected convolutional networks (DenseNet)) to map the landslide susceptibility of the study area in Zigui, and then visualize the decision-making process of the model through an explainable artificial intelligence (XAI) technology, so as to provide more transparency and reliability about the occurrence of landslides.
Adaptive Sparse Iterative Reweigthed Super-Resolution Method for Scanning Radar Imaging

Recently, a sparse super-resolution method relying on L1 iterative reweighted norm (IRN) has been proposed to improve the imaging resolution of scanning radar. However, the method has poor adaptability due to the noise-sensitive user-parameter. To this end, an adaptive L1 iterative reweighted sparse super-resolution method with no user-parameter is derived. Firstly, the scanning radar super-resolution model is established. Secondly, the user-parameter selection in the L1-IRN method is analyzed. Finally, the adaptive iteration weights are derived by transforming the sparse estimation problem into a maximum posterior (MAP) estimation problem. Compared with the existing L1-IRN method, the proposed method does not have any user-parameter, so it has adaptability to different signal-to-noise ratios (SNR) and is more robust. Simulation verifies the superiority of the proposed method.
EXPLORING STRATEGIES FOR OVERCOMING ISSUES OF USER INVOLVEMENT IN AGILE SOFTWARE DEVELOPMENT: A SYSTEMATIC LITERATURE REVIEW

The present systematic literature review (SLR) explores the challenges and strategies associated with managing users during requirement elicitation within agile software development. Drawing insights from an analysis of 24 relevant studies, this study comprehensively examines the issues that arise and the effective approaches to overcome them. The findings reveal five prominent challenges of user involvement during requirement elicitation. The most dominant issues identified are the lack of user involvement, insufficient user knowledge, and a deficit in the expertise of the Product Owner. These challenges can hinder the effective integration of user perspectives and needs into the development process. To address these challenges, the study identifies seven strategies that Product Owners can adopt to facilitate effective user involvement. These strategies include Mind Maps, User Interface Mockups, Workshops, Hybridism (combining agile and non-agile techniques), Face-to-Face Meetings, Continuous Delivery, and Training and Learning initiatives. The application of these strategies empowers Product Owners and software practitioners to enhance user involvement, improve communication, and streamline the requirement elicitation process in agile software development. The outcomes of this SLR provide valuable insights for both researchers and software practitioners, exploring the complex dynamics of user involvement in agile contexts. By recognizing these challenges and deploying effective strategies, software development teams can ensure more successful requirement elicitation processes, leading to the creation of software products that better align with user needs and expectations. This review contributes to a deeper understanding of user involvement challenges and offers actionable guidance for optimizing the requirement elicitation within agile software development paradigm.
Robust Road Detection on High-Resolution Remote Sensing Images with Occlusion by a Dual-Decoded UNet

It is challenging to perform robust road detection on remote sensing images in a complex scene with occlusions by plants and buildings. In this paper, an elaborate dual-decoded U-Net combined with atrous spatial pyramid pooling is proposed to tackle this scenario. In the proposed network, a dual-decoder structure is designed, where a small decoder aims to extract the attention information and it is delivered to the other decoder to enhance the context. Finally, the proposed method is verified on the DeepGlobe dataset. The experiment results demonstrate that the proposed method outperforms other compared methods.
Tomographic Imaging with Enhanced Spatial Structures Via a Physics-Aware 3D Reconstruction Network

TomoSAR imaging is a classical inverse problem. Learning to optimize is a newly emerging technique for solving such problems in a deep-learning way. This technique may facilitate the efficiency and accuracy of the inverse process. In this research, we apply this framework to TomoSAR imaging and aim to enhance spatial structures within the framework. We establish a two-part imaging optimization model. One part is a regularization term for the forward observation process, and the other part is for constraining the sparsity of structures in the gradient domain. Using the methodology of learning to optimize, we design basic neural network modules and stack them in a cascaded manner to solve the model. We validate the proposed network using a public TomoSAR dataset. The results show that the proposed method obtains buildings with much more complete overall surfaces and more apparent edges.
Multispectral Pansharpening Based on High-Pass Modulation Injection Model with Difference Factor

This paper proposes a multispectral pansharpening based on high-pass modulation injection model with difference factor (HPM-DF), which is dedicated to solving the problem of difference between multispectral image and panchromatic image acquired at different moments. In the high-pass modulation injection model, we introduce a difference factor and use the alternating direction method of multipliers (ADMM) to fully analyze the difference variability to derive the final fusion product. Experiments assessed at both reduced and full resolution show that the proposed method can acquire better performance than the traditional pansharpening methods.
Using Phase-Delay Approaches to Estimate Snow Properties: A Comparison of Airborne L-Band InSAR and Ground-Based 6-18 GHz FMCW Radar Observations During the NASA SnowEx 2020 Grand Mesa Campaign

During 2020 and 2021, the NASA SnowEx Mission performed a time series with UAVSAR, and L-band InSAR, in the Western U.S. Small field efforts were performed throughout the time series, and in addition, SnowEx carried out an intensive campaign on Grand Mesa, involving five aircraft with seven different airborne instruments, and a large field campaign. Snow properties can vary significantly over distances of 50-200 meters, and therefore rapid techniques for measuring bulk snow properties are valuable for calibration and validation of snow remote sensing efforts. We developed and deployed a ground-based microwave radar from a snowmobile, during the 2020 NASA SnowEx campaign on Grand Mesa. These observations provide information about the spatial distribution of snow depth, snow water equivalent, and stratigraphy, and were performed coincident with many different in-situ and airborne snow remote sensing observations.
Optimizing Computational Complexity: Real-Time Speech Enhancement Using an Efficient Convolutional Recurrent Dense Neural Network

Real-time communication through cell phones and telephones often involves challenging acoustic environments where the original speech signal is contaminated by environmental noise, known as the cocktail party problem. Audio source separation can be an effective solution for isolating the voice in a noisy environment, by suppressing undesired noise without distorting speech components, which can improve speech quality and intelligibility. Deep Neural Network (DNN) models, despite their excellent performance in speech enhancement, require a substantial computational effort during the inference process. This characteristic makes them less than ideal for addressing this specific problem. The high computational complexity of deep models can further impede regression latency, which is crucial for real-time applications that require minimized complexity. Considering these assumptions in this paper, a novel neural network for speech enhancement is presented, which incorporates phase information into the loss function. The proposed method utilizes a convolutional Recurrent Dense (CRD) network, which not only achieves notable computational efficiency but also demonstrates superior performance compared to other existing networks. Experimental results are provided to highlight the advantages and distinctions of the CRD network when compared with alternative state of the art approaches.
Interpretable Disentangled Adversarial Auto-Encoder for SAR-ATR with Sparse Training Samples

Lack of interpretability and weak generalization ability have become the major problems with data-driven intelligent Synthetic Aperture Radar-Automatic Target Recognition (SAR-ATR) technology, especially with sparse training samples. A novel insight into the typical target representation with neural networks from a causal perspective is presented in this paper. Specifically, a set of SAR images is causally modeled by intrinsic, diverse, and random attributes, which is consistent with the forward imaging process of SAR. Consequently, an Interpretable Disentangled Adversarial auto-Encoder (IDAE) is proposed based on the disentangled representations. A Symmetrically Conditional Encoding (SCE) module is established to constrain the semantic consistency of low-dimensional features. Besides, a hybrid loss function is designed for iterative training. Experiments conducted on the MSTAR dataset show that the proposed model improves both representation and generalization abilities. IDAE is able to achieve a classification accuracy of 93.1% using only 12 samples per class.
Synthetic Daily 20-Meter Surface Temperature Measurements from Space: Hydrosat Daily Surface Temperature (HDST)

The Hydrosat Fusion Hub generates daily 20 m resolution Land Surface Temperature (LST) anywhere on Earth from spatiotemporal data fusion of Landsat, MODIS, and Sentinel-2. The fused data, source data, and intermediate products are served through a spatiotemporal asset catalog (STAC) API (https://fusion-stac.hydrosat.com). Spatiotemporal data fusion is used to first improve the temporal resolution of the LST data and then to predict high spatial resolution LST values for dates where no higher spatial resolution data are available. The resulting fusion data product and source data are then cataloged in the API for easy access by users. RMSE compared to ground sites ranged from 2.89K to 8.34K at individual sites and is less than 5.4K when considering all sites. These data provide the requisite resolutions and information needed for monitoring and managing critical temperature-related changes such as agricultural crop heat and water stress, and water use.
Analysis of Intelligent User Chat Interaction Behavior Based on Hadoop and Clustering Algorithm

With the advantages of content generation and information diffusion speed, social media has become an information sharing and online interaction tool used by more and more people every day. Different from the traditional instant interactive application, the online interaction of users in social media takes messages as the carrier, which makes the content and form of user interaction data extremely rich. Nowadays, social networks have become the main means for people to keep in touch and have fun. Users are the core of social networks, and their behavior is the starting point for understanding the operating mechanism of social networks, which is of great value to the analysis and research of user behavior in social networks. In this article, an analysis model of social media users' chat interaction behavior based on Hadoop and clustering algorithm is proposed to model social media users' interaction behavior and predict users' preferences, so as to provide users with various individual services. The analysis and modeling of high-quality user data can reveal the user preferences and behavior patterns behind users' online behavior, and provide more abundant supporting knowledge for personalized recommendation of social media.
A Novel MIMO SAR Transmission Scheme for Restoring Repeated Equivalent Phase Centers

Multi-input and multi-output (MIMO) radar has drawn much attention in synthetic aperture radar (SAR) due to the possession of more degrees of freedom (DOFs). However, there are some repeated equivalent phase centers (EPCs) caused by the same wave path have no contribution to the improvement of DOFs. To this end, a novel interpulse phase coding and multi-carrier (IPCMC) transmission scheme is investigated to restored repeated EPCs. Furthermore, an advanced range ambiguity separation method is proposed based on the increased efficient EPCs. Finally, distributed targets simulation experiments are performed and the experiment results illustrate that the range ambiguity suppression performance is significantly improved due to the restored EPCs.
Accuracy Assessment of Stem Classification Obtained from Forest Point Cloud Using FSCT Algorithm

The recent advancements in close-range remote sensing techniques are creating opportunities to explore more about the forest ecosystem more precisely. The Light Detection and Ranging (LiDAR) scanners are a boon to forest applications because they provide detailed and precise information about the forest structure from ground to canopy top level. The point cloud data collected with Terrestrial Laser Scanner (TLS) must be processed to get exact information about the trees, including topographic terrain information in the forests. Tree parameters are essential to calculate the total productivity of the forests and their monitoring. In this research, two forest plots were considered in the Zvolen district within central Slovakia; one forest plot (TLS_Plot1) consists of 49 trees, and the other (TLS_Plot2) consists of 102 trees. A total of nine TLS scans were performed in each of the forest plots. It is also crucial to segregate the unstructured 3D point cloud data into various classes for accurate information extraction of each feature in the forest, such as stem, canopy, terrain, dead wood, etc. Forest Structural Complexity Tool (FSCT) algorithm is one such algorithm that has been recently developed for point cloud classification. So, we are focusing on the classification function of this tool. The classification function is classifying the forest point cloud data into stems, leaves, some branches, wood debris, lying dead wood, etc. We tried to investigate the accuracy of stem classification obtained for the forest point cloud using the FSCT algorithm. But a qualitative assessment must be done to evaluate the accuracy of the point cloud classification obtained. For this reason, stems from both the forest point cloud were manually extracted and compared with the stems point extracted from the FSCT algorithm classification, and the accuracy was evaluated. The FSCT algorithm-based stem classification achieved accuracy for TLS_Plot1 and TLS_Plot2 is 94.80% and 96.28%.
AN AUTHENTICATION MODEL FOR CLOUD STORAGE SERVICES BASED ON FINGERPRINT RECOGNITION

Over the past several years, cloud service becomes one of the major subjects of IT, and the major topic is cloud data storage, due to the advantages of cloud service such as flexibility, mobility, costs saving, and easy to use. Cloud services provide multiple services to users over the internet. These advantages drove individuals and organizations moved their applications, data, and services to the cloud storage server. The main concerns of these services are security and privacy when the individuals and organizations are saved private data or information (sensitive data or information) in untrusted servers because of the traditional methods that used to authenticate users such as passwords, tokens and digital certificate these credentials may be often be stolen, wrongly revealed or difficult to remember, thus companies and individuals require a secure method to authenticate their users in order to ensure the functionality of their services and data saved in the cloud storages are working in a secure environment, and prevent leakage any information for users and individuals to any untrusted party. In this paper, propose a biometric-based security and authentication paradigm to help user's authentication in the cloud storage environment,used fingerprint as a biometric to an untrusted user to login to the cloud services.
A B S T R A C T

Choosing the best form of domestic sewage treatment requires analyses that allow decision-making on the ideal solutions for implementation, according to the particularities of each regionality, especially when it comes to rural and isolated areas dependent on decentralized solutions. Thus, through this study, the Analytic Hierarchy Process (AHP) was used from a data collection on simplified treatment systems existing in temperate municipalities without a dry season, to evaluate priority systems in different scenarios considering environmental, social, and technical-economic indicators. Among the results for the scenarios performed, the compact upflow anaerobic reactor, the septic tank and anaerobic filter, and the septic tank and built-in flooded system of vertical subsurface flow were the solutions that proved to be the most indicated following the indicators used and the existing information. It is noteworthy that the results refer to the study area in question, and new applications of the method are needed in different climatic regions. From the application of the methodology, the AHP tool indicated that it is a viable method to assist in decision-making regarding the selection of sewage treatment systems in rural areas, which, being tied to municipal planning in basic sanitation, assists in the optimization of existing resources. The sensitivity of the method showed the importance of its application with data collected on site, in addition to the incorporation of public and experts opinions in the contribution of the degree of importance of the criteria and indicators used.