Miracle-dz/UniFAR-FTUs huggingface dataset:
An integrated GIS application system for soil moisture data assimilation

The gaps in knowledge and existing challenges in precisely describing the land surface process make it critical to represent the massive soil moisture data visually and mine the data for further research.This article introduces a comprehensive soil moisture assimilation data analysis system, which is instructed by tools of C#, IDL, ArcSDE, Visual Studio 2008 and SQL Server 2005. The system provides integrated service, management of efficient graphics visualization and analysis of land surface data assimilation. The system is not only able to improve the efficiency of data assimilation management, but also comprehensively integrate the data processing and analysis tools into GIS development environment. So analyzing the soil moisture assimilation data and accomplishing GIS spatial analysis can be realized in the same system. This system provides basic GIS map functions, massive data process and soil moisture products analysis etc. Besides,it takes full advantage of a spatial data engine called ArcSDE to effeciently manage, retrieve and store all kinds of data. In the system, characteristics of temporal and spatial pattern of soil moiture will be plotted. By analyzing the soil moisture impact factors, it is possible to acquire the correlation coefficients between soil moisture value and its every single impact factor. Daily and monthly comparative analysis of soil moisture products among observations, simulation results and assimilations can be made in this system to display the different trends of these products. Furthermore, soil moisture map production function is realized for business application.
Quantifying the energy dissipation of overriding plate deformation in three‐dimensional subduction models

In a subduction system the force and the energy required to deform the overriding plate are generally thought to come from the negative buoyancy of the subducted slab and its potential energy, respectively. Such deformation might involve extension and back‐arc basin formation or shortening and mountain building. How much of the slab's potential energy is consumed during overriding plate deformation remains unknown. In this work, we present dynamic three‐dimensional laboratory experiments of progressive subduction with an overriding plate to quantify the force (FOPD) that drives overriding plate deformation and the associated energy dissipation rate (ΦOPD), and we compare them with the negative buoyancy (FBU) of the subducted slab and its total potential energy release rate (ΦBU), respectively. We varied the viscosity ratio between the plates and the sublithospheric upper mantle with ηSP/ηUM = 157–560 and the thickness of the overriding plate with TOP = 0.5–2.5 cm (scaling to 25–125 km in nature). The results show that FOPD/FBU has average values of 0.5–2.0%, with a maximum of 5.3%, and ΦOPD/ΦBU has average values of 0.05–0.30%, with a maximum of 0.41%. The results indicate that only a small portion of the negative buoyancy of the slab and its potential energy are used to deform the overriding plate. Our models also suggest that the force required to deform the overriding plate is of comparable magnitude as the ridge push force. Furthermore, we show that in subduction models with an overriding plate bending dissipation at the subduction zone hinge remains low (3–15% during steady state subduction).
Formal Methods-Based Optimization of Dataflow Models with Translation to Synchronous Models

Graphical dataflow models are widely used for industrial control system implementation and are accepted by users with different backgrounds due to their intuitive visualization of the dataflow. As real-world applications have evolved, the complexity of these diagrams has increased, posing a challenge in the verification and maintenance of software models. In this paper, we argue that the perceived complexity of real-world applications can be simplified and transformed into formal models for reuse in software engineering or formal verification. To overcome the limitations of previous research on optimizing graphical dataflow models in an industrial context, we identify potentially optimizable submodels and optimize them, leaving the non-modifiable components unchanged. The optimized software model is then reconstructed and optionally translated into formal models for reuse or verification purposes. The paper describes the identification of submodels in real-world applications and demonstrates the need for configurability of the optimization. We present an open-source-based implementation using graphical dataflow models of programmable logic controllers and demonstrate the impact of our optimization approach using real-world applications from the literature and vendor-provided examples. The results help to reduce the complexity of existing dataflow models and to reuse existing real-world applications as formal software models.
EMI Radiated Emission Prediction of Full Bridge Inverter

In recent trends, modular multilevel converters are widely used in Flexible AC Transmission Systems (FACTS). These converters consist of numerous sub-modules that perform nonlinear switching operations, resulting in electromagnetic interference (EMI) issues. However, due to high voltage, grid connected system, and safety considerations, troubleshooting EMI problems at the testing level is not feasible. In this paper, we have modeled the parasitic components of the Full Bridge Inverter used in modular multi-level converter (MMC) and verify its operational state through transient analysis in the time domain. The results of the time domain analysis are compared with EMI measurements obtained through 3D analysis, and the methodology is described.
Research On Method Of User Preference Analysis Based on Entity Similarity and Semantic Assessment

The rapidly evolving landscape of Internet technology has led to an increasing interest in smart terminal devices, particularly smartphones, among users. As users interact with these devices, they encounter a range of applications (APPs) with diverse functionalities and consume services according to their individual needs. The service consumption process elicits different emotional experiences from users, which serve as an instinctive reflection of their satisfaction with the service consumption experience. Thus, there is an urgent need to address the challenge of accurately identifying and analyzing users’ emotional experiences, assessing their service experience during service consumption, and exploring the factors that influence their service experience. This research addresses the challenge of accurately identifying and analyzing user emotions during service consumption on smart terminal devices. In the ontology alignment work, we integrate existing methods for edit distance similarity, WordNet similarity, and shared nearest neighbor similarity calculation. We achieve entity alignment in the requirement ontology based on both element and structure. Furthermore, we propose an improved method for calculating WordNet similarity.
Features of Preparing a Data Set for Training a Neural Network of an Object Tracking System in a Stream

This article is devoted to discussing the influence of the content of a data set and the features of its markup on the quality of the resulting neural network. To perform the tasks of detecting and localizing objects, the YOLOv8 neural network is used. A brief motivation for using this particular neural network with appropriate links is given. This network is trained on three similar data sets, after which the results obtained are compared and analyzed regarding the real problem. In conclusion, general recommendations are given for compiling a data set for training a neural network, as well as suggestions for the further development of this work.
Research on Disturbance Immunity Balance Control of Biped Robot Based on ESO-DDPG

The current research on biped robots is mostly concentrated on ideal conditions, while in reality, the robot system is always subject to many external disturbances and internal parameter perturbations, which will lead to such defects as weak anti-interference ability, low real-time performance, low accuracy of controller trajectory tracking, weak balance control ability, and solving its standing posture balance problem is the premise to solve the robot walking stability. Combining the advanced depth deterministic strategy gradient algorithm (DDPG) and extended state observer (ESO), a control strategy for biped robot upright disturbance rejection balance is designed, which can quickly and timely adjust the pitch angle of the robot hip joint to restore the balance state. The DDPG controller adjusts the joint angle in real time through the difference between the actual zero torque point (ZMP) and the expected value to suppress the disturbance; ESO estimates and compensates the disturbance of unmodeled dynamics and internal parameter perturbation, and outputs joint angle compensation to improve control accuracy and rapidity. In order to verify the effectiveness of ESO-DDPG controller, it is applied to the interference suppression experiment of a stationary NAO robot. When the biped robot is disturbed by external disturbances and internal parameter perturbations, it can quickly restore its standing posture balance in real time.
Prediction of Accuracy in Wireless Networks by Power Control Algorithm with Multi Agent Q-Learning Method

The design of power control algorithms for wireless networks has long been a challenging problem for researchers. Previous research has focused mainly on optimizing the delivery of data in order to increase network throughput and reduce interference. However. , prediction of the accuracy of these algorithms in different scenarios is an open problem which has received little attention in the literature. This paper proposes the use of a Support Vector Machine (SVM) for predicting the accuracy of a given power control algorithm's performance in a wireless network. The algorithm was trained using data from a large pool of transmissions over many different wireless networks. , and a cross-validation procedure was used to ensure that the results are not biased. The performance of the SVM model is compared to traditional algorithms and shows significant improvements in accuracy. Additionally. , the results illustrate that different scenarios and networks may require different power control algorithms and that the SVM can accurately predict which algorithm is best suited for each scenario. The results suggest that the improved accuracy of the SVM model should be taken into account when designing power control algorithms for wireless networks.
Correction of the Beam Hardening Artifacts in CT Images Using Pix2pixGAN Network

Due to the noncoincidence between the mono energetic rays-based reconstruction algorithms and the multi-energy projection data, there always exit beam hardening artifacts destroying the reconstructed CT images. A correction of the beam hardening artifacts can effectively enhance the accuracy and reliability of the scan results. In this preliminary study, we propose a pix2pixGAN generative adversarial network to do this work. Compared to the traditional approaches, this method can increase the structural similarity index measure by 11.81%, improve the decrease the peak signal-to-noise ratio by 25.59% and decrease the root-mean-square error by 62.02%. These results demonstrate that the pix2pixGAN generative adversarial network can simultaneously correct the artifacts and improve the recovery of details.
The Performance Analysis of Various Parameters of Underwater WSN via ZRP Routing Protocol

Underwater Wireless Sensor Networks (UWSNs) have become increasingly popular in recent years to solve complex and critical problems in many fields such as oceanography, aquaculture, surveillance, etc. In order to provide efficient communication and routing in UWSNs, many new routing protocols have been proposed in recent years. One of the recently proposed routing protocols is the Zone Routing Protocol (ZRP), which is a hybrid of proactive and reactive routing techniques. ZRP divides the ocean into a number of different zones and each node stores the routing information within those zones enabling efficient routing between the sensor nodes. ZRP provides efficient routing in UWSNs by preventing the flooding of routing messages throughout the network and by avoiding non-optimal paths. In addition, the use of the Zone Routing Table (ZRT) allows for rapid convergence of network topologies as well as easy modification of the routing path. This paper presents an overview of how ZRP works, the advantages it has over other routing protocols, and the performance it can provide in UWSNs. Finally, some future research directions for using ZRP in UWSNs are explored.
Exploring Student Persistence with Automatically Generated Practice through Interaction Patterns

Incorporating formative practice with text content in a learning by doing approach has been proven to be a highly effective learning method, and therefore questions generated using artificial intelligence (AI) were developed to scale this approach as broadly as possible. Millions of AI-generated questions were incorporated into thousands of digital textbooks as a study tool available to learners using those books for any learning context. While this advancement of AI for learning tools is itself significant, the volume of microlevel data collected from the digital environment makes it possible to investigate how learners are interacting with this learning tool in detail. In this study, we leverage this large data set to investigate student interaction patterns with AI-generated questions, focusing specifically on how students persist when their first attempt at a question is incorrect. The aggregated data shows differences in interaction patterns by question type, while comparison to a single course in which the questions were assigned finds differences in patterns likely due to context. The implications of these interaction patterns and future research avenues are discussed.
Stock Price Prediction Model Using LSTM: A Comparative Study

Due to its potential use in financial markets, stock price prediction is a difficult issue that has drawn considerable interest from academics and business executives. Recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) have demonstrated promising results in time series prediction applications, such as stock price prediction. In this paper, we examine both univariate and multivariate LSTM-based stock price prediction models. We compare the forecasting accuracy of these models using a variety of assessment criteria in order to measure their performance on a real-world stock price dataset. Additionally, we look into how different hyperparameters, such as batch size, learning rate, and the quantity of hidden layers, affect how well LSTM models perform. Finally, we talk about the interpretability, limitations, and prospective uses of LSTM-based stock price prediction models in practical financial decision-making scenarios.
An Assessment of Multilevel Converters Submodules EMI Emissions Considering Three Configurations Including Parasitic Parameters

With the expanding use of modular multilevel converters in power installations it became more important to lower the total conducted emissions. One of the most important key factors in controlling electromagnetic interference emissions is the type of submodules used and the utilized switches. This paper compares three well-known submodules which are half bridge, full bridge and double-clamped half bridge effect on the electromagnetic interference emissions. For making the model more accurate the parasitic components were included in all the circuits Additionally, the same configurations are carried out and compared under two switches which are insulated gate bipolar transistors and metal oxide semiconductor field effect transistors operating at two different switching frequencies. The results were obtained by a simulation in a Matlab/Simulink environment.
SemCom for Metaverse: Challenges, Opportunities and Future Trends

Metaverse is a new paradigm yet to come, gaining popularity in many applications and being developed using a combination of technologies, including Virtual Reality (VR), Augmented Reality (AR), blockchain, and other immersive technologies. One key technology critical to the success of the Metaverse is Semantic Communication (SemCom), which enables efficient and effective communication between humans and intelligent agents. This paper discusses the challenges and opportunities associated with SemCom in the Metaverse and potential future trends. We identify the critical challenges in SemCom, such as context awareness, scalability, and interoperability, and discuss potential solutions to address these challenges. Furthermore, we explore the various opportunities that SemCom can bring to the Metaverse, such as improved user experiences, interactions with virtual assistants, and enhanced data analysis capabilities. Moreover, we examine the emerging trends in SemCom for the Metaverse and highlight some key research directions that will be important in the future.
Enhancing Road Pothole Detection Algorithm of YOLOv5

To address the issues of low accuracy and difficult deployment in road pothole detection, an improved algorithm for road pothole detection based on YOLOv5s is proposed in this paper. The algorithm incorporates a GhostNet backbone feature extraction network to reduce model complexity. Additionally, GSConv (Ghost Shuffle Convolution) is introduced in the Neck module to ensure model accuracy while reducing model weight. Moreover, a weighted bidirectional feature pyramid network (BiFPN) is employed to capture more feature information and enhance the information representation capability of feature maps, thereby increasing the detection accuracy of the network.Experimental results demonstrate that the improved model achieves a 30% reduction in algorithm complexity and a 33.3% decrease in computational workload. The mAP0.5:0.95 is improved by 4%, and the precision is increased by 5.3%. The improved algorithm achieves a balance between lightweight design and accuracy. Overall, the proposed algorithm achieves a significant reduction in complexity while maintaining high accuracy. This improvement contributes to the effective detection of road potholes, addressing the challenges of accuracy and deployment in practical applications.
Traffic Spatial-Temporal Transformer for Traffic Prediction

Accurate traffic prediction can help administrators better plan and manage urban traffic, alleviating the traffic pressure. Local spatial-temporal dependency is the strongest and most direct dependency within traffic data. However, recent researches in traffic prediction using stacked or coupled fusion methods to combine temporal and spatial learning networks have not fully captured local spatial-temporal dependency in traffic data. This paper introduces a recurrent neural network structure that captures local spatial-temporal dependency by considering the spatial relationship of each time with its current, past, and future time simultaneously. Additionally, a period enhanced attention mechanism is introduced to capture long-term temporal dependency. Finally, the two modules are combined to construct a Traffic Spatial-Temporal Transformer for traffic prediction. Experimental results demonstrate that the proposed transformer outperforms baselines in terms of prediction accuracy.
Learning to Be Green: Carbon-Aware Online Control for Edge Intelligence with Colocated Learning and Inference

Edge intelligence is an emerging paradigm that leverages edge computing to pave the last mile delivery of artificial intelligence. While pilot efforts on edge intelligence have mostly focused on the performance and power issues, the sustainability dilemma along with the upcoming carbon peaking and neutrality era has largely been overlooked. To green edge intelligence, we propose a carbon-aware online control framework (CARE) in this paper. CARE colocates learning and inference tasks within an edge node and dynamically adapts their configurations based on the temporal variation of carbon intensity and renewable energy availability. With such a colocation setup, CARE aims to minimize the long-term inference accuracy loss under the long-term carbon emission cap. The underlying long-term optimization problem is nontrivial since it involves uncertain information (e.g., renewable energy availability) and is NP-hard. To address these dual challenges, CARE first designs an online learning module to make fractional decisions by learning from previous system dynamics and configuration adaptation results. Then, CARE further designs a randomized rounding module, which converts the fractional decision into integer without violating the long-term carbon emission cap. The effectiveness of CARE is verified by rigorous theoretical analysis and extensive trace-driven simulations.
A Dynamic Trust Chain Measurement Method Based on Mixed Granularity of Call Sequence

Trusted computing technology aims to protect the computer or embedded system from tampering. Based on the idea of trust chain establishment, it establishes a trust chain for the whole process from the embedded system power-on to the application running in the operating system. However, the trust chain uses the static measurement scheme before starting each component, so the system state information recorded by the trust chain can only reflect the state of the program in the system when it is started, but not the state at run time. In view of the above problems, by analyzing the credibility theorem of process operation defined in the non-interference model, a dynamic measurement model design idea using the synchronous comparison between the hypothetical system and the real system is proposed. Based on this design idea, this paper analyzes the running model of the program through the application call sequence, and improves the model from the perspective of measurement granularity to improve the running efficiency of measurement. Based on this, a dynamic integrity measurement method that can measure the program dynamically in real time is proposed. Experiment results demonstrate that the abnormal state of the process can be well detected by the proposed method at a small cost.
Intertidal biogenic reefs built by the polychaete Boccardia proboscidea in sewage-impacted areas of Argentina, SW Atlantic

The rocky intertidal zone around the city of Mar del Plata (SW Atlantic, 38° S–57° W) is characterized by dense mussel beds of Brachidontes rodriguezii. This intertidal community develops on natural and artificial hard substrates, including abrasion platforms in sewage-impacted areas. A monitoring program, designed to assess the effect of sewage discharge on intertidal macrobenthic communities, has been conducted since 1997. During the spring season of 2008, a new spionid polychaete (Boccardia proboscidea) was found near the sewage outfall, forming large biogenic reefs. This is the first report of biogenic reefs being built by a non reef-forming spionid polychaete in areas organically impacted by sewage discharges. The aim of this work was to evaluate the spatial–temporal dynamics (% cover and density of B. proboscidea) of these reefs. These biogenic reefs covered almost the entire impacted site, reaching a density of 650,000 ind·m−2. This phenomenon is unique in that there is no other record available worldwide of any other biogenic polychaete reefs that could be sewage-induced. The presence and stability of these biogenic reefs is discussed in relation to increased organic contamination as a structuring factor.