Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Groundwater Potential Zones Using Remote Sensing and GIS Techniques: A Case Study of the Shatt Al-Arab Basin

Rapid population growth has raised the groundwater resources demand for socio-economic development in the Shatt Al-Arab basin. The sustainable management of groundwater resources requires precise quantitative evaluation, which can be achieved by applying scientific principles and modern techniques. An integrated concept has been used in the current study to identify the groundwater potential zones (GWPZs) in the Shatt Al-Arab basin using remote sensing (RS), geographic information system (GIS), and analytic hierarchy process (AHP). For this purpose, nine groundwater occurrence and movement controlling parameters (i.e., lithology, rainfall, geomorphology, slope, drainage density, soil, land use/land cover, distance to river, and lineament density) were prepared and transformed into raster data using ArcGIS software. These nine parameters (thematic layers) were allocated weights proportional to their importance. Furthermore, the hierarchical ranking was conducted using a pairwise comparison matrix of the AHP in order to estimate the final normalized weights of these layers. We used the overlay weighted sum technique to integrate the layers for the creation of the GWPZs map of the study area. The map has been categorized into five zones (viz., very good, good, moderate, poor, and very poor) representing 4, 51, 35, 9, and 1% of the study area, respectively. Finally, for assessing the effectiveness of the model, the GWPZs map was validated using depth to groundwater data for 99 wells distributed over the basin. The validation results confirm that the applied approach provides significantly solid results that can help in perspective planning and sustainable utilization of the groundwater resources in this water-stressed region.
Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 Time Series in Austria

<p>Worldwide, forests provide natural resources and ecosystem services. However, forest ecosystems are threatened by increasing forest disturbance dynamics, caused by direct human activities or by altering environmental conditions. It is decisive to reconstruct and trace the intra- to transannual dynamics of forest ecosystems. Therefore, the monitoring of large and small scale vegetation changes such as those caused by natural events (e.g., pest infestation, higher mortality due to altering site conditions) or forest management practices (e.g., thinning or selective timber extraction) becomes more and more crucial. National to local forest authorities and other stakeholders request detailed area-wide maps that delineate forest disturbance dynamics at various spatial scales. </p><p>We developed a time series analysis (TSA) framework that comprises data download, data management, image preprocessing and an advanced but flexible TSA. We use dense Sentinel-2 time series and a dynamic Savitzky&#8211;Golay-filtering approach to model robust but sensitive phenology courses. Deviations from the phenology models are used to derive detailed spatiotemporal information on forest disturbances. In a first case study, we apply the TSA to map forest disturbances directly or indirectly linked to recurring bark beetle infestation in Northern Austria. </p><p>In addition to spatiotemporal disturbance maps, we produce zonal statistics on different spatial scales that provide aggregated information on the extent of forest disturbances between 2018 and 2019. The outcomes are (a) area-wide consistent data of individual phenology models and deduced phenology metrics for Austrian forests and (b) operational forest disturbance maps, useful to investigate and monitor forest disturbances, for example to facilitate sustainable forest management. </p><p>At a forest stand level, we reconstruct the origin date of forest disturbances (FDD &#8211; Forest Disturbance Date). Theses FDD outputs show the spatiotemporal patterns and the development of damages and indicate that most dynamics are caused by recurring and spreading bark beetle infestation. The validation results based on field data confirm a high detection rate and show that the derived temporal information is reliable. In total, 23400 hectares, i.e., on average 2.8% of the forest area in the study area, are found to be affected by forest disturbance. The zonal statistic maps point out hotspots of significant forest disturbances, where adequate forest management measures are highly needed. Furthermore, this study highlights the TSA&#8217;s potential to also depict and monitor minor human impacts on forests, such as thinning, selective timber extraction or other moderate forest management practices. </p><p><strong>Keywords:&#160; </strong><em>forest disturbance; forest monitoring; bark beetle infestation; forest management; time series analysis; phenology modelling; remote sensing; satellite imagery; Sentinel-2</em></p>
Decadal Urban Land Use/Land Cover Changes and Its Impact on Surface Runoff Potential for the Dhaka City and Surroundings Using Remote Sensing

Rapid urban growth processes give rise to impervious surfaces and are regarded as the primary cause of urban flooding or waterlogging in urban areas. The high rate of urbanization has caused waterlogging and urban flooding in many parts of Dhaka city. Therefore, the study is undertaken to quantify the changes in land use/land cover (LULC) and urban runoff extent based on the Natural Resources Conservation Service (NRCS) Curve Number (CN) during 1978–2018. The five-decadal LULC has been analyzed using three-generation Landsat time-series data considering six different classes, namely agriculture, built-up, wetland, open land, green spaces, and water bodies for the years 1978, 1988, 1998, 2007, and 2018. Significant changes in LULC for the study area from 1978–2018 are observed as 13.1%, 4.8%, and 7.8% reduction in agricultural land, green spaces, and water bodies, respectively, and a 22.1% increase in the built-up area is estimated. Within Dhaka city, 14.6%, 16.0%, and 12.3% reduction in agricultural land, green spaces, and water bodies, respectively, and a radical increase of 41.9% in built-up area are reckoned. The decadal runoff assessment has been carried out using the NRCS-CN method, considering an extreme rainfall event of 341 mm/day (13 September 2004). The catchment area under very high runoff category is observed as 159.5 km2 (1978) and 318.3 km2 (2018), whereas, for Dhaka city, the setting is dynamic as the area under the very high runoff category has increased from 74.24 km2 (24.44%) to 174.23 km2 (57.36%) in years 1978 and 2018, respectively, and, mostly, the very high runoff potential areas correspond to the dense built-up surfaces.
Weed Identification in Maize, Sunflower, and Potatoes with the Aid of Convolutional Neural Networks

The increasing public concern about food security and the stricter rules applied worldwide concerning herbicide use in the agri-food chain, reduce consumer acceptance of chemical plant protection. Site-Specific Weed Management can be achieved by applying a treatment only on the weed patches. Crop plants and weeds identification is a necessary component for various aspects of precision farming in order to perform on the spot herbicide spraying or robotic weeding and precision mechanical weed control. During the last years, a lot of different methods have been proposed, yet more improvements need to be made on this problem, concerning speed, robustness, and accuracy of the algorithms and the recognition systems. Digital cameras and Artificial Neural Networks (ANNs) have been rapidly developed in the past few years, providing new methods and tools also in agriculture and weed management. In the current work, images gathered by an RGB camera of Zea mays, Helianthus annuus, Solanum tuberosum, Alopecurus myosuroides, Amaranthus retroflexus, Avena fatua, Chenopodium album, Lamium purpureum, Matricaria chamomila, Setaria spp. , Solanum nigrum and Stellaria media were provided to train Convolutional Neural Networks (CNNs). Three different CNNs, namely VGG16, ResNet–50, and Xception, were adapted and trained on a pool of 93,000 images. The training images consisted of images with plant material with only one species per image. A Top-1 accuracy between 77% and 98% was obtained in plant detection and weed species discrimination, on the testing of the images.
Ground-Based Radar Interferometry of Sea Ice

In light of recent Arctic change, there is a need to better understand sea ice dynamic processes at the floe scale to evaluate sea ice stability, deformation, and fracturing. This work investigates the use of the Gamma portable radar interferometer (GPRI) to characterize sea ice displacement and surface topography. We find that the GPRI is best suited to derive lateral surface deformation due to mm-scale horizontal accuracy. We model interferometric phase signatures from sea ice displacement and evaluate possible errors related to noise and antenna motion. We compare the analysis with observations acquired during a drifting ice camp in the Beaufort Sea. We used repeat-scan and stare-mode interferometry to identify two-dimensional shear and to track continuous uni-directional convergence. This paper demonstrates the capacity of the GPRI to derive surface strain on the order of 10−7 and identify different dynamic regions based on sub-mm changes in displacement. The GPRI is thus a promising tool for sea ice applications due to its high accuracy that can potentially resolve pre- and post-fracture deformation relevant to sea ice stability and modeling.
Clustering-Based Plane Refitting of Non-planar Patches for Voxel-Based 3D Point Cloud Segmentation Using K-Means Clustering

Point cloud processing is a struggled field because the points in the clouds are three-dimensional and irregular distributed signals. For this reason, the points in the point clouds are mostly sampled into regularly distributed voxels in the literature. Voxelization as a pretreatment significantly accelerates the process of segmenting surfaces. The geometric cues such as plane directions (normals) in the voxels are mostly used to segment the local surfaces. However, the sampling process may include a non-planar point group (patch), which is mostly on the edges and corners, in a voxel. These voxels can cause misleading the segmentation process. In this paper, we separate the non-planar patches into planar sub-patches using k-means clustering. The largest one among the planar sub-patches replaces the normal and barycenter properties of the voxel with those of itself. We have tested this process in a successful point cloud segmentation method and measure the effects of the proposed method on two point cloud segmentation datasets (Mosque and Train Station). The method increases the accuracy success of the Mosque dataset from 83.84% to 87.86% and that of the Train Station dataset from 85.36% to 87.07%.
An Effective Solution to Unit Commitment Problem in Presence of Sustainable Energy Using Hybrid Harris Hawk's Optimizer

In the present scenario of power system, complexity increases with the large penetration of intermittent energy sources. Harris hawk optimizer (HHO) is recently developed optimizer which efficiently solves engineering and optimization problems. Local minima entrapment is common and major issue of these optimization techniques. In this research search capability of Harris Hawks optimization is enhance by incorporating improved grey wolf optimizer (IGWO) for solving the UC in presence of sustainable energy source. The newly developed technique is tested for a 10 unit generating unit system with 5 % and 10 % spinning reserve and also compared with solar and wind power.
Potentially Invalid: Stable Matching with Outside Option

Matching is based on the order of selection given by the participants. We found that in real life, there are indeed other ideas when matching. We want to reject the matching when there is no suitable object in the matching. We abstract all non-opposite choices as “outside option”. This means that each applicant and the hospital have a total order of opposites and an “outside option”. In this paper, we use virtual objects as “outside option” to implement extended deferred acceptance algorithm. The extended deferred acceptance algorithm does not destroy the original structure, and has proven to retain the same properties. In addition, we proved that when there are “outside option”, potentially stable matches may have invalid matches.
Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks

Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems’ potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output—future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution—which is an inherently much more difficult problem.
Cloud-based Deception against Network Reconnaissance Attacks using SDN and NFV

An attacker's success crucially depends on the reconnaissance phase of Distributed Denial of Service (DDoS) attacks, which is the first step to gather intelligence. Although several solutions have been proposed against network reconnaissance attacks, they fail to address the needs of legitimate users' requests. Thus, we propose a cloud-based deception framework which aims to confuse the attacker with reconnaissance replies while allowing legitimate uses. The deception is based on for-warding the reconnaissance packets to a cloud infrastructure through tunneling and SDN so that the returned IP addresses to the attacker will not be genuine. For handling legitimate requests, we create a reflected virtual topology in the cloud to match any changes in the original physical network to the cloud topology using SDN. Through experimentations on GENI platform, we show that our framework can provide reconnaissance responses with negligible delays to the network clients while also reducing the management costs significantly.
What Motivates Shariah Compliant Companies in Applying Accounting Conservatism?

This study seeks to investigate the potential of corporate governance attributes (board independence, board leadership, ownership concentration, and audit committee) and bankruptcy prediction in the use of accounting conservatism by shariah compliant companies listed at Amana Income Investor as the first Islamic index in the world. The samples consist of the Top 10 Companies listed at Amana Income Investor from 2010- 2019. The 98 total observation data comprising of annual reports of the selected companies are analyzed using multiple linear regression. The findings indicate that some of corporate governance attributes like board independence, board leadership, and ownership concentration have no association with accounting conservatism. By contrast, audit committee and bankruptcy prediction have a significant effect accounting conservatism. This study implies that financial reporting environment for Shariah compliant businesses is relatively unique and indicate a support for a dedicated accounting principles and financial accounting standards for Shariah compliant businesses.
Pandemic Stabilizer using Smartwatch

An outburst of Covid-19, a new disease by coronavirus has been noted by December 2019 in China and subsequently this Covid-19 spread throughout the world. The serious effect of this disease causes death due to the failure of respiratory system of human. Nowadays the spreading of the coronavirus is uncontrollable especially in crowded places as well as due to avoiding the protocols given by world health organization (WHO) to reduce the spread of COVID-19. Many researchers have involved in identifying vaccine for giving proper treatment to the affected people and to avoid further spreading. In addition, with this some other researchers have also involved in using modern technologies to prevent the spreading of Covid-19 as well as to identify the disease in very earlier stage and to reduce the rate of death. One of the modern systems recommended by the researchers is that smartwatch. By using smartwatches, the user's body temperature, heart rate and blood pressure can be measured, and the smartwatch can be automatically charged by body heat. This kind of smartwatches can be controlled by chatbot Google Assistant. This paper discusses the design, principle of operation and features of different smartwatches and their usage. Based on the analysis, it is identified that the availability of recent cost-effective developments of smartwatches are available on reducing spread of Covid-19.
Independently Tunable Dual-Band Bandpass Filter

A reconfigurable multiband band-pass filter is proposed in this paper, using the structures and techniques of the Stub-loaded Stepped-Impedance Resonators dual-mode configuration. This design uses the source-load link and the shorted T-shaped stub lines. The proposed structure has the advantages of compact size, high selectivity, and a single control bias voltage applied. Four adjustable transmission zeroes were attained by the suggested filter, with more than 40 dB rejection between the two passbands. Employing the coupling paths effectively yielded the independent control of the two passbands. For the purpose of tuning, two identical varactor diodes were loaded onto the open ends of the resonators. A frequency tuning range of 1.08 GHz to 1.5 GHz and 1.99 GHz to 2.7 GHz is achieved in the two passbands respectively. The designed filter realized an insertion of 1.2 dB to 1.32 and 0.86 dB to 1.11 dB respectively in the tuning process. The overall filter size of 0. 13λ0 × 0. 03λ0is achieved.
Risk-Aware Leak Detection at Binary Level

Memory leak is a problematic bug that a heap object will never be deallocated after it is last accessed. Memory leaks can be classified into two categories in terms of their risk: high-risk leaks and low-risk ones. High-risk leaks eventually induce failures such as program clashes and performance degradation, whereas low-risk ones may not necessarily cause failures. As promising leak detectors, there have been proposed growth-sensitive or staleness-sensitive leak detectors. However, they have at least one of the major drawbacks: (1) they are not applicable to executable binaries of C/C++ programs, (2) they cannot quickly detect both high-risk and low-risk leaks in distinction, or (3) their run-time overheads are prohibitively high.This paper proposes BIGLeak, a risk-aware, efficient leak detector based on dynamic binary analysis. BIGLeak consists of three components: (1) the BIGLeak algorithm, which enables the accurate detection of high-risk leaks at binary level, (2) the intermittency analysis, which allows for the quick prediction of both high-risk and low-risk leaks in a short period of time, and (3) context-aware execution sampling, which effectively reduces the run-time overheads incurred by the BIGLeak algorithm and the intermittency analysis.Experiments with several synthetic and real programs, including lighttpd and postgres, show that BIGLeak succeeded in accurately detecting both high-risk and low-risk leaks in distinction at binary level in a short period of time. Especially, BIGLeak’s precision and recall of high-risk leak detection were much better than those of an existing staleness detector, SWAT, which works at binary level. Experimental results also indicate that BIGLeak’s run-time overheads were comparable to SWAT, despite BIGLeak’s accuracy outperformed SWAT’s.
Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method

Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors; (2) adding geographical factors can significantly improve the performance of SM downscaling; (3) integrating LST, LEE, and geographical factors got the best performance; (4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.
Distributed Control of Multi-Functional Grid-Tied Inverters for Power Quality Improvement

Multi-functional grid-tied inverters (MFGTIs) have been investigated recently for improving the power quality (PQ) of microgrids (MGs) by exploiting the residual capacity (RC) of distributed generators. Several centralized and decentralized methods have been proposed to coordinate the MFGTIs. However, with the increasing number of the MFGTIs, it demands a method with improved reliability and flexibility, which are characteristics of distributed framework that has not been introduced into the PQ improvement (PQI) field before. In this paper, we propose a distributed consensus method to undertake the PQI task. The task is proportionally shared among the MFGTIs according to their instant RCs. Besides, most of the existing methods assume that the RCs of the MFGTIs are sufficient for tackling the PQ problem (PQP), which is not always true. In the case of insufficient RC, the active power output of each MFGTI is scaled down by the same factor determined by a proposed leader-follower protocol to make room for the task. In summary, the PQP is dealt with in both cases of sufficient and insufficient RC under the distributed control framework. Finally, simulations and hardware-in-the-loop experiments of an MG consisting of three 10kVA MFGTIs are presented to verify the effectiveness of the proposed methods.