Reviewerly/exHarmony huggingface dataset:
Requirements for Using Digital Agricultural Extension in Providing Extension Service from Viewpoint of Agricultural Extension Agents

Abstract The research has aimed to determine the requirements for using digital agricultural extension in providing an extension service from the viewpoint of agricultural extension agents. The research population has included all agricultural extension agents who work in the Agricultural Extension and Training Department, the Training and Rehabilitation Center, the Agricultural Extension Center for Training, Al-Tarmiya Farm, as well as Al-Rashidiya Farm in Baghdad Governorate. Thus, a random sample has been taken from the research population at a rate of (83%). Accordingly, the proposed sample size has included (83) agricultural extension agents (from male and female). To achieve the goal of the research, a questionnaire has been prepared that included four axes: administrative requirements, technical and financial requirements, human requirements, and requirements of integrating the target audience to use digital agricultural extension. The agreement of extension agents on the requirements has been measured by using a five-point Likert scale (strongly agree, agree, somewhat agree, disagree, strongly disagree), and the following scores have determined for the scale expressions (5, 4, 3, 2, 1) respectively, hence, the general results (for the five axes) have shown that the majority of agricultural extension agents have had a high level of agreement with all requirements of using digital agricultural extension in providing agricultural extension services, with an average of (213.45) degrees, which is higher than the general average, and this may reflect the realism and objectivity of the requirements that have addressed in the research, which will provide an appropriate ground and achieve a satisfactory level of efficiency and effectiveness in the process of using digital agricultural extension in providing the agricultural extension service, if they implement and activate by the decision makers (authorities) in the Iraqi Ministry of Agriculture.
Sedimentary Circumstances and Physiographic Sites Impact on the Morphological Properties of Feldspar Minerals of Iraqi Soils

Abstract The current research aimed to investigate the effect of sedimentary conditions, physiographic locations, proximity, and distance from the source on the morphological characteristics of feldspar minerals diagnosed by Scanning Electron Microscope (SEM) for some selected soils from northern and southern Iraq. The results of SEM present the emergence of a series of surface changes, corrosion, and the absence of healthy surfaces on the surfaces of orthoclase metal. It is dominated by roundness or sphericity, with the polishing of the metal surfaces coinciding with the phenomenon of roundness and sphericity. The edges of the orthoclase mineral were less affected due to being transported over long distances with transported river deposits. The presence of long lines indicates that the metal is subject to weathering and change. One of the most critical manifestations of change occurring on the surfaces of feldspars is the emergence of long parallel lines, which are called linear parallels, which reflect the severity of weathering to which the feldspar minerals were subjected. The morphological characteristics of the surfaces of the mineral microcline appeared under the SEM similar to those shown by the surfaces of the mineral orthoclase with clarity of the phenomenon of linear parallelism and a decrease in the phenomena of sphericity and polishing compared to the surfaces of the mineral orthoclase, which is less stable and resistant to weathering than the microcline. There was a phenomenon of accumulated plates on the surface of the metal, with the edges of the metal being subjected to weathering processes with greater intensity than the surface areas, despite the expansion of the surface area of the metal particles. The appearance of plagioclase of albite type and the edges of its particles were less affected by the weathering processes compared to its surfaces. The reason is attributed to the fact that the edges area is more susceptible to erosion and grinding during transport. This discrepancy in the morphological characteristics of feldspar minerals is attributed to their influence on the processes of weathering, transport, and sedimentation in the soils of the study areas and to the variation in their physiographic locations and their distance and proximity to the Iraqi-Iranian border strip in southern Iraq within the sedimentary plain affected by sediments coming from the heights of Iran in addition to being affected by the sediments of the Tigris River.
Optical Anisotropy of GRIN Lenses: Modeling Based on the Integrated Photoelasticity

The conditions of applicability of the quasi-isotropic approximation of geometrical optics (single-trajectory approximation) for the case of coaxial incidence are discussed. For this case, the reconstruction of residual stresses distributions (the inverse problem of transversal translucency) is carried out for a number of GRIN lenses. Using these data as input for calculations within the framework of the “direct” problem in coaxial incidence, the radial distributions of optical phase difference (birefringence) are determined for the case of longitudinal ray propagation in a GRIN rods.
Quantitative Analysis of Biomolecular Films: Automated Detection and Characterization of Leaves and Spiral Structures

Biomolecular films, pivotal in various scientific and technological domains, harbor intricate patterns that necessitate accurate analysis. In this study, we introduce a systematic methodology for the automated detection and characterization of leaves and spiral structures within biomolecular films. The method encompasses a series of meticulously designed steps, including preprocessing via grayscale conversion, edge enhancement, denoising, and contrast adj ustment. Leveraging watershed segmentation, we adeptly isolate leaves within the films. The algorithm's capability to detect spiral cores is realized through the Hough Transform and Canny edge detection. Experimental results exhibit the method's efficacy, elucidating the process visually.
A Two-Stage Attention Based Efficient Second-Order Minimization Network for Planar Object Tracking

Planar object tracking, which detects the posture change of the 2D objects in a frame-by-frame manner, plays a critical role in various practical applications including visual simultaneous localization and mapping, visual servoing, and augmented reality. However, in real world, current methods suffer from ubiquitous challenges such as motion blur, texture degradation, illumination change, and partial occlusion. In this article, we propose a new tracking pipeline to handle the preceding challenges. The main novelty of our method lies in two new modules. Concretely, we first propose a Perspective Matrix Selection Module (PMSM) to rectify the search range error caused by large motion displacement. Second, we design an efficient attention mask module to highlight the target object while mitigating the disturbance caused by scene occlusion. The proposed method is evaluated on the challenging dataset POT. Experimental results show that the proposed method outperforms the state-of-the-art methods by a large margin in terms of accuracy and robustness over various tracking challenges, such as rotation, scale change, occlusion, motion blur, and perspective degradation.
Line-Post Insulator Fault Classification Model Using Deep Convolutional GAN-Based Synthetic Images

Due to thermal, electrical, mechanical, and chemical stresses, line-post insulators in the power system may degrade over time. The degradation process continuously gets exacerbated by the above-mentioned factors. Therefore, condition monitoring of line insulators must be frequently carried out. Optical cameras are considered the most accurate among existing technologies for detecting such defects. Computer vision techniques aided by optical cameras could automate faulty insulator identification. However, there is a limited size of the training data set obtained from real-world optical camera images. In this paper, we propose a generative approach to creating a massive amount of line-post insulator fault images through Deep Convolutional Generative Adversarial Networks (DCGAN). The additional training data obtained from DCGAN-based approach is shown to improve the accuracy of the insulator fault classification. In the case study, we show that with an increasing number of synthetic images created by DCGAN, the accuracy of the fault classification continuously improves. The ability to classify true faulty insulators has increased from 56% to 94%. The performance of the DCGAN-based approach is also compared with the random oversampling approach. The numerical results suggest that the DCGAN-based approach has the advantage of detection accuracy and a lower false positive rate.
DiME and AGVis: A Distributed Messaging Environment and Geographical Visualizer for Large-Scale Power System Simulation

This paper introduces the messaging environment and the geographical visualization tools of the CURENT Large-scale Testbed (LTB) that can be used for large-scale power system closed-loop simulation. First, Distributed Messaging Environment (DiME) is presented as an asynchronous shared workspace to enable high-concurrent data exchange. Second, Another Grid Visualizer (AGVis) is presented as a geovisualization tool that facilitates the visualization of real-time power system simulation. Third, case studies show the use of DiME and AGVis in power system research. The results demonstrate that, with the modular structure of DiME and AGVis, the LTB is capable of not only federal use for real-time, large-scale power system simulation, but also independent use for customized power system research.
Nearest-Neighbor Gaussian Process to Downscale Solar Forecasting at the Grid-Edge for Increased Situational Awareness

Accurate solar irradiance prediction is essential for energy-efficient building heating/cooling systems with rooftop solar panels. Planning electricity usage in the face of outages, such as those caused by natural disasters like hurricanes, is difficult in regions with high solar irradiance variability, like Puerto Rico. This project aims to design a computationally accelerated method to downscale nationally available climate data, particularly solar irradiance, to forecast microclimate data for buildings in western Puerto Rico. Downscaling links low-resolution climate models to local-scale data. This project uses spatiotemporal data from nearby weather stations and global weather forecasts to design highly accurate building-local models in Puerto Rico. Low-power sensors, local computing resources, and an empirical downscaling method are used to quantify the relationship between global and local data. An effective (and parallelizable) nearest neighbor Gaussian process (NNGP), accelerated by a graphics processing unit (GPU), is used to achieve this at the grid-edge. Models developed from local and global climate data, each for a different season in Puerto Rico, forecast solar irradiance given the location, time, and date, resulting in more accurate microclimate data. The applicability of NNGP to embedded computers was validated using an NVIDIA Jetson Nano.
Campus Photovoltaic Integration for Carbon Emission Reduction Compliance

The city of Boston has set a goal for all buildings to achieve net zero carbon emissions by 2050. To meet this goal, urban universities could take the following steps, such as increasing renewable energy generation, adding energy storage, converting heating and cooling systems to electric power, and reducing energy consumption. This project aims to analyze the feasibility of incorporating photovoltaic (PV) systems on the campus of Wentworth Institute of Technology, located in an urban area of Boston. Though the university currently emits less greenhouse gas (GHG) than the target for 2025, GHG emissions would need to be reduced by 30% to meet its 2030 goals. PV systems could contribute to this reduction. Rooftop PV systems are suitable for urban universities as the building structures cover much of the available land. The design and analysis were done using industry-standard software packages of Helioscope and National Renewable Energy Laboratory (NREL) System Advisor Model (SAM). The analysis included considerations for shade, weather, module and equipment selection, and campus energy consumption. Two PV designs were made for the campus. The first design resulted in a maximum power production of 1 MW and an energy output of 1,396 MWh/year. The second design yielded a power production of 0.887 MW and energy output of 1,135 MWh/year.
Multiple Unmanned Ground Vehicles Cooperative Trajectory Planning with Flexible Safety Guarantees

The growing demand for unmanned ground vehicles (UGVs) in various applications necessitates the development of efficient trajectory planning techniques that can ensure both safety and cooperative behavior. This paper presents a novel approach for multi-UGV cooperative trajectory planning with flexible safety guarantees. The proposed method addresses the challenge of finding safe trajectories for multiple UGVs by incorporating risk assessment into the planning process. A field-based risk assessment model is used to quantify the risk levels between UGVs, and a predefined threshold is adopted to define risk conflicts. To solve the cooperative trajectory planning problem, an interactive trajectory planning approach is introduced. It combines path planning with trajectory validation to ensure safety and address the risk conflicts. Vehicle characteristics are considered in the planning process. The simulation results show that the proposed interactive trajectory planning algorithm achieves a high success rate while maintaining a maximal risk level below a predefined threshold. It also outperforms the benchmark algorithms in terms of safety performance, with reduced risk levels and improved adaptability to varying conditions.
Cross-Domain Remote Sensing Image Object Detection Based on Multi-Scale Domain Adaptive Teacher Network

Cross-domain remote sensing image object detection is highly dependent on the annotation of data and can be difficult to label. In addressing this problem, numerous researchers have proposed the utilization of pseudo-labeling approaches to mitigate domain shift, especially in two-stage detectors, which have shown significant effectiveness. However, the application of using a classical mean teacher network to generate pseudo-labels in single-stage detection models poses challenges. To address this problem, we propose a single-stage teacher model based on adversarial learning. Specifically, the teacher network is divided into classification and location sub-task branches, with an emphasis on optimizing pseudo-labeling. Compared to the mean teacher model, we pay more attention to the reliability of the boundary box. In the student network, this paper uses a multi-scale domain adaptive mechanism to extract domain invariant features of cross-domain images and uses an exponential sliding average to update the teacher network parameters, thereby improving pseudo labels and further narrowing the distribution differences. Experimental results confirm that compared to other single-stage detection methods for cross-domain object detection, this paper showcases a superior performance improvement in the field of remote sensing images.
Space Object Tracking Using Orbit Element Constraints with Uncertainties

Tracking of a space object (SO) plays a key role in the space situational awareness. Research on the SO mainly focuses on the space object tracking (SOT) problem without incorporating the orbit information into estimation. However, the orbit contains much information about the SO since the object without maneuvering moves along a fixed elliptical orbit around the center of the Earth. Using the orbit information is beneficial to obtain an accurate tracking result. Thus, a SOT approach using orbit element constraints is proposed, in which the object location is constrained on the orbit. The uncertainties of the orbit element are also considered in the constraints. Then we present two tracking algorithms with the uncertainties considered and not considered, respectively. The effectiveness of the proposed approaches is demonstrated by simulation results for a SO located at a low earth orbit.
Argumentative Explanation for Deep Learning: A Survey

Neural Networks (NNs) are often referred to as “black box” models, which has sparked increasing interest among researchers in understanding their internal workings. Computational argumentation (CA), a subfield of symbolic Artificial Intelligence (AI), has demonstrated notable advantages in the field of Explainable Deep Learning (XDL) recently. This is primarily due to the inherent compatibility between the dialectical nature of argumentation and the key elements of interpretation. In this paper, we provide an introduction to several Argumentation Frameworks (AFs) that serve as important connections between NNs and explanation. Furthermore, we present a comprehensive overview of existing argumentation-based explanatory approaches, covering both ante-hoc explanation and post-hoc explanation. Finally, current issues and future directions that can be explored in depth are discussed, from both academic research and business application perspectives.
An Efficient Planning Framework for Ground Vehicles Navigating on Uneven Terrain

Autonomous navigation on uneven terrain is significant for ground vehicles to perform complex tasks. This paper proposes an efficient planning framework that achieves safe and fast navigation on 3D uneven terrain. The framework utilizes probabilistic elevation mapping method and SLAM (Simultaneous Localization and Mapping) techniques to construct a global elevation map, while using terrain normal vectors and roughness measures to capture the undulations and roughness of the ground vehicle's path. Subsequently, a terrain-kinematics interaction-based path search method (TK A *) is proposed. This method fully leverages the interaction between the vehicle model and the terrain to effectively expand 3D path nodes on the elevation map. Additionally, a terrain-based traversal cost is designed to reflect the vehicle's ability to travel on non-planar surfaces, while an efficient heuristic cost is devised to improve search efficiency.
Adaptive Multi-Swarm Differential Evolution Algorithm for UAV Path Planning

Path planning for unmanned aerial vehicles (UAVs) remains a crucial prerequisite for UAV application in various fields. However, due to the complexity of the model, several state-of-the-art methods may encounter challenges in finding feasible solutions or be prone to getting stuck in local optima, especially in the complex 3D battlefield environment. An adaptive multi-swarm differential evolution algorithm (AMSDE) is put forward to address these problems. First, we employ the ε-level comparison to ensure the feasibility of the solution. Second, we design an adaptive swarm partitioning technique to avoid crossover evolution between sub-swarms caused by random partitioning. Third, the hierarchical update mechanism is implemented to guide each sub-swarm's search. It facilitates effective communication between sub-swarms and keeps the balance of exploration and exploitation. Experiment results have shown that AMSDE is competitive compared with other excellent algorithms, proving its capability to generate higher-quality paths for UAVs.
AcquainLay: An Efficient Blockchain Block Relay Method Based on Acquaintance Immunization

The propagation of information in blockchain plays a key role in its scalability improvement, and it is difficult to maintain the original decentralized characteristics and security in the existing methods of accelerating block propagation. In this paper, we combine blockchain and complex network epidemic immunization strategies and then propose an approach (Acquain-Lay) based on acquaintance immunization to find suitable nodes to disseminate information without the need for global topolog-ical information. The evaluation results show that AcquainLay improves transaction throughput by 43.53 % at best and reduces the orphan block rate significantly. In addition, AcquainLay is easy to deploy because it does not change the key data structures and network topology of the blockchain.
Analysis of Alternatives for Scout and Strike UAV Demonstration

Analysis of Alternatives (AoA) is an important analysis in scout and strike UAV system demonstration, which directly determines the quality of UAV research and development. This paper presents a method for scout and strike UAV demonstration AoA method. At first, it puts forward the drone demonstration AoA framework, and elaborates drone demonstration AoA measure system. Then, it establishes their quantitative models. At last, an AoA model based on Data Envelopment Analysis (DEA) and an application example are given to illustrate the feasibility and effectiveness of the purposed method.
An Optimization-based Trajectory Planning Method with Polynomial Curves

Trajectory planning is an essential module of autonomous vehicles, and its planning efficiency and robustness directly affect driving safety and ride comfort. The sampling-based polynomial curve trajectory planning method on Frenet Frame decouples the trajectory, which reduces the complexity of the problem-solving. However, the sampling method's limitations on the solution space, along with the collision detection, reduce the real-time performance and robustness. This paper proposes an efficient method that integrates optimization techniques with polynomial curve planning on Frenet Frame. The trajectory planning problem based on the polynomial curve is transformed into a quadratic programming problem. By taking the lateral and longitudinal distances between the ego vehicle and obstacles as part of the cost function, the optimization-based method avoids time-consuming collision detection. Active set method is employed to solve coefficients of the trajectory polynomial, thereby obtaining a safer and more comfortable trajectory. Simulation results illustrate that the proposed method outperforms the sampling-based polynomial curve trajectory planning method in terms of real-time performance and robustness.
Improved Multi-Strategy Salp Swarm Algorithm for UAV Path Planning

In response to the unmanned aerial vehicle (UAV) path planning in a three-dimensional environment map, an improved multi-strategy salp swarm algorithm (IMSSA) is proposed under the condition of the environmental state. Based on the original salp swarm algorithm, introduce a Piecewise chaotic map to initialize the population, and use the Levy flight strategy to update the leader's position preventing the algorithm from falling into local optimum and enhancing the search capabilities; add a non-uniform Gaussian mutation operator in the follower's position update stage, that not only improves the search accuracy but also avoids individuals falling into local optimum. This paper's improved multi-strategy salp swarm algorithm is applied to solve the UAV path planning problem in a three-dimensional space environment. First of all, a three-dimensional environment model is established according to the grid method. Furthermore, considering UAVs' mobility efficiency, safety avoidance, and movement coherence, the mathematical model of UAV motion is determined, and the path planning problem is transformed into optimizing the cost functions. At last, the IMSSA algorithm applies to find the optimal flight path. Verified by simulation experiments, the improved multi-strategy salp swarm algorithm has a robust global search ability in the given simulation scenario, the convergence speed is obviously accelerated, and a better path is obtained compared with the given algorithm.