Miracle-dz/UniFAR-FTUs huggingface dataset:
Flexible Consensus Mechanism for Current Allocation in DC Microgrid

Renewable energy sources (RESs) have become the predominant trend in the development of energy systems in many countries. To coordinate RESs, microgrids have emerged as an efficient and optimal solution. However, the microgrid is facing the problem of inaccurate power sharing between the generators. To address the issue, this paper proposes a control technique that combines a dynamic consensus with droop control algorithms. The proposed method calculates current sharing error by utilizing current values and consensus algorithm. Then, an external current-sharing controller is adopted to regulate the droop parameter in an inner controller for improving the current sharing among RESs. By applying the proposed control method, the current sharing accuracy is achieved in the microgrid system under different load conditions. The proposed technique is analyzed and evaluated with the scale-down microgrid consisting of three distributed generators. The simulation results of the scale-down microgrid are obtained from the PLECS simulation platform.
Poisoning Programs by Un-Repairing Code: Security Concerns of AI-generated Code

AI-based code generators have gained a fundamental role in assisting developers in writing software starting from natural language (NL). However, since these large language models are trained on massive volumes of data collected from unreliable online sources (e.g., GitHub, Hugging Face), AI models become an easy target for data poisoning attacks, in which an attacker corrupts the training data by injecting a small amount of poison into it, i.e., astutely crafted malicious samples. In this position paper, we address the security of AI code generators by identifying a novel data poisoning attack that results in the generation of vulnerable code. Next, we devise an extensive evaluation of how these attacks impact state-of-the-art models for code generation. Lastly, we discuss potential solutions to overcome this threat.
A Solution for Wireless Precision Synchronization

In this manuscript we present a wireless time synchronization method based on wireless two-way interferometry (Wi-Wi). The need for precision time synchronization is growing as more systems are seeking more efficiency gains. Traditionally, synchronization has been done via wire (copper or fiber) due to the simplicity and determinism. This becomes an issue for applications where using a wire is not feasible due to the mobility or accessibility to the infrastructure. Wireless methods such as Wi-Fi(802.11), Bluetooth or Zigbee are unable to get precision time synchronization because the system architecture and the lack of using phase information. Our method utilizes the phase information between the transmitted signal and received signal. This provides the necessary synchronization. With the use of a unique packet analysis, synchronization is provided. We plan to implement this technology of the receiver carrier board(RCB) to be used on the Time Card.
Walker Branch Throughfall Displacement Experiment Data Report: Site Characterization, System Performance, Weather, Species Composition, and Growth

This numeric data package provides data sets, and accompanying documentation, on site characterization, system performance, weather, species composition, and growth for the Throughfall Displacement Experiment, which was established in the Walker Branch Watershed of East Tennessee to provide data on the responses of forests to altered precipitation regimes. The specific data sets include soil water content and potential, coarse fraction of the soil profile, litter layer temperature, soil temperature, monthly weather, daily weather, hourly weather, species composition of trees and saplings, mature tree and sapling annual growth, and relative leaf area index. Fortran and SAS{trademark} access codes are provided to read the ASCII data files. The data files and this documentation are available without charge on a variety of media and via the Internet from the Carbon Dioxide Information Analysis Center (CDIAC).
Formalization and Verification of the ICC Mechanism in Android System Using CSP

With the rapid development of mobile computing technology, Android System is widely used in smart devices, and the quantity of Android Apps continuously grows. The Inter-Component Communication (ICC) mechanism in the Android framework allows communication between components (inside the same App or on different Apps). However, some security issues are caused by this mechanism, especially in the case of Inter-App Communication (IAC). To ensure the security of the communication in Android System, we formally model the ICC mechanism using Communication Sequential Process (CSP). After that, we verify four properties of the model using the Process Analysis Toolkit (PAT) with the help of C#, including Deadlock Freedom, Data Reachability, Data Security, and Data Reliability.
Novel Range Wise Optimization of the Exponential Bounds on the Gaussian Q Function and its Applications in Communications Theory

This paper presents a novel and highly effective method for improving the accuracy of approximations for the Gaussian Q function. By rigorously optimizing the coefficients of the approximations using the interior point optimization technique, significantly tighter bounds are achieved with simplicity intact. The proposed approach, which is applicable to a wide range of scenarios, focuses on enhancing the simple exponential bounds proposed in the literature. Through a comprehensive analysis based on the relative error, the superiority of the optimized coefficients compared to the existing bounds and approximations available in the open literature is demonstrated. Moreover, an insight into the generic applicability of the optimized coefficients is provided, which exhibits excellent performance in terms of the absolute error as well. The Gaussian Q function plays a crucial role in evaluating the performance of diverse wireless communication systems under various challenging fading distributions. Therefore, the proposed research significantly contributes to advancing the accuracy of the approximations of the Gaussian Q function, enabling improved error performance for coherent digital modulation techniques. The findings presented herein offer valuable contributions to the state-of-the-art and set a new standard for accuracy in the work related to Gaussian Q function approximations.
Improved Bus Contention Analysis for 3-Phase Tasks

The 3-phase task execution model has shown to be a good candidate to tackle the memory bus contention problem. It divides the execution of tasks into computation and memory phases that enable a fine-grained memory bus contention analysis. However, existing works that focus on the bus contention analysis for 3-phase tasks, neglect the fact that memory bus contention strongly relates to the number of bus/memory requests generated by tasks, which, in turn, depends on the content of the cache memories during the execution of those tasks. These existing works assume that the worst-case number of bus/memory requests will be generated during all the memory phases of all tasks, irrespective of the already existing content in the cache memory. This overestimates the memory bus contention of tasks, leading to pessimistic worst-case response time (WCRT) bounds. This work proposes a holistic approach towards bus contention analysis for 3-phase tasks by (1) deriving an upper bound on the actual cache misses of tasks that lead to bus/memory requests; (2) improving State-of-the-Art (SOTA) bus contention analysis of two bus arbitration schemes that dominate all existing works on the bus contention analysis for 3-phase tasks; and (3) performing an extensive experimental evaluation under different settings to compare the proposed analysis against the SOTA. Results show that incorporating a tighter bound on the number of cache misses of tasks into the bus contention analysis can lead to a significant improvement in the task set schedulability.
Towards Developer-Centered Secure Coding Training

Software security continues to be a matter of concern for both end-users and developers, with the cost of potential lapses expected to become larger as software plays a larger role in society. Despite investments in secure coding training programmes, organisations are not achieving the expected success rate. An often overlooked reason for this among many others is that current training programmes are not tailored to consider the diversity among software developers as it relates to human aspects. In this research, data was gathered from software developers of various backgrounds on their perceptions of secure coding training, their expectations from and challenges with such a training program. The findings suggest that developers with personality traits of agreeableness tend to ignore secure coding standards. Additionally, developers with higher work experience tend to demand storage management, responsible use of privileges, security and privacy laws and testing topics to be included in the secure coding training. Furthermore, in terms of training structure, developers with higher openness tend to demand hands-on training to be included. The study's findings seek to inform future researchers and organisations on factors to consider when designing adaptive secure coding programs that would address the needs of developers from different backgrounds.
Generic EMT Modelling of PMSG Wind Turbine for Digital-Analog Hybrid Bulk Grid Simulation

In order to investigate the impact of integrating large-scale wind energy into the power grid, the electromagnetic transient (EMT) simulation is required. The EMT wind turbine model is essential to conduct corresponding research since the manufacturer-specific model limits the study of internal dynamic mechanisms. On the other hand, the digital-analog hybrid simulation is a powerful technology to conduct bulk grid simulation, which has few unique requirements for the EMT model such as comprehensive function, long-duration stable operation, generality, real-time simulation, etc. This paper presents a generic permanent magnet synchronous generator (PMSG) wind turbine EMT model, which can be used for digital-analog hybrid bulk grid simulation. The developed model has been validated by a black box model through extensive simulation. The case study for wind turbine integrated into the bulk grid simulation is presented.
Back-stepping Technique-based Distributed Formation Control of Multiple Under-actuated Autonomous Underwater Vehicles

This paper focuses on a distributed formation control for multiple under-actuated autonomous underwater vehicles (AUVs) with prescribed performance subject to external disturbance. First, distributed formation tracking errors are calculated using graph theory and state transformation. The errors are dexterously transformed into new error variables to satisfy the predefined performance. Based on the back-stepping procedure, the distributed formation controller is then developed and combined with a nonlinear disturbance observer (NDO) to enhance the robustness against external disturbances. It is mathematically proven that the proposed formation controller guarantees that all signals in the whole control system are uniformly ultimately bounded (UUB) and the distributed formation tracking errors always remain within the predefined regions. Finally, numerical simulations verify the proposed controller's effectiveness and feasibility.
Experimental evidence for seismic nucleation phase

The friction experiments on macroscopically homogeneous and non-homogeneous faults were performed by using a double-shear friction rig, and the waveforms of acoustic emission and the fault slip corresponding to the stick-slip instability events were analyzed. The results indicate that there exist two types of nucleation phase for stick-slip instability of non-homogeneous faults, and one is coincident with the preslip model, the other with the cascade model. It is concluded that the seismic nucleation phase exists objectively, and its generation is related to heterogeneity of fault.
Progress in near-infrared spectroscopy for baijiu quality and safety detection

The presence of contaminants in Baijiu, a type of Chinese liquor, can have adverse effects on human health, highlighting the importance of comprehensive analysis and detection. This paper focuses on the research progress of Near-Infrared Spectroscopy (NIRS) in the quantitative and qualitative analysis of harmful substances in Baijiu. NIRS, as an efficient analytical method, offers advantages such as non-invasiveness, rapidity, accuracy, and environmental friendliness, and holds significant potential for application in the Baijiu industry. The article primarily discusses the application of NIRS in the detection of external pollutants in Baijiu, including contaminants from raw materials, contact materials, and food additives. By measuring the absorption and scattering characteristics of Baijiu samples in the near-infrared spectral range, NIRS enables rapid and accurate analysis of the content and types of harmful substances, ensuring the quality and safety of Baijiu. Additionally, the paper also focuses on the analysis of microbial metabolites in Baijiu using NIRS. Microbial metabolites are important indicators of internal contamination, directly affecting the quality of Baijiu. NIRS demonstrates potential in the detection of microbial metabolites, providing an effective means for quality control during the brewing process. In conclusion, NIRS technology allows for rapid and accurate quantitative and qualitative analysis of harmful contaminants in Baijiu, including external pollutants and microbial metabolites, ensuring the quality and consumer health safety of Baijiu. This research is of significant importance to quality control and food safety in the Baijiu industry.
EvoScenario: Integrating Road Structures into Critical Scenario Generation for Autonomous Driving System Testing

Autonomous Driving Systems (ADS) are safety-critical and require comprehensive testing before their deployment on public roads. Most existing testing approaches consist in generating scenarios that vary the behaviors of dynamic objects, while leaving a predefined road environment unchanged. Consequently, these approaches overlook the influence of different road structures on ADS safety, e.g., collisions can happen more frequently than usual on a merging road, because of the specific road structure. In this paper, we propose EvoScenario, a novel approach that integrates road structures into the generation of critical scenarios for exposing safety risks of ADS. Specifically, EvoScenario models a driving road as a sequence of road segments characterized in different aspects, such as their shapes and widths. Then, a test case is defined by concatenating the sequence of road segments and the sequence of dynamic object maneuvers. Inspired by EvoSuite that generates sequential method calls for Java unit testing, EvoScenario leverages the sequential models of test cases and constructs a multi-objective optimization framework to search for critical scenarios. We implement and demonstrate EvoScenario on an ADS provided by our industrial partner. Evaluation results show that EvoScenario can identify 6 types of safety violations, and outperform existing baseline testing approaches.
Beam Blockage Identification for Weather Radars on board the R/V Mirai Using Archived Data

The Doppler weather radar on board the research vessel Mirai, which was installed in 1998, was replaced by a dual-polarization Doppler weather radar in 2014. This study explores the beam blockage for both radars on board the Mirai. Reflectivity data from long-term observations were used to calculate the probability of detection (POD) at a given range, azimuth, and elevation relative to the Mirai. Azimuthal sectors suffering from beam blockage caused by the infrastructure of the Mirai were characterized by pronounced minima of the POD in their centers and significant discontinuities of the POD on their edges. An objective method was developed to ascertain the boundaries of the blocked sectors based on the rate of change of the POD gradient along the azimuthal direction. According to the distinct signatures of the POD, detailed beam blockage information for both radars was identified in different ship-relative elevations. Although beam blockage for both radars occurred from low to high elevations, the total azimuths of the low-level blocked sectors for the dual-polarization Doppler radar decreased by more than half. This improvement was attributed to the higher location of the antenna for the dual-polarization Doppler radar. This accurate beam blockage information will facilitate the quality control of the Mirai radar data.
An improved time-frequency signal decomposition method and its application in bearing incipient fault diagnosis

Aiming at the problem that incipient fault signals of rolling bearings are often drowned by strong noise, a rolling bearing incipient fault diagnosis method is proposed by combing an ARIMA based empirical mode decomposition algorithm and an improved variational mode decomposition algorithm (AEMD-IVMD). Firstly, the so-called AEMD algorithm was introduced to deal with the end effect problem of traditional EMD by extending the length at both ends of the decomposed IMF components, and a denoising vibration signal could be obtained by selecting and restructuring these selected sensitive IMF components. Secondly, an improved particle swarm optimization (IPSO) algorithm was proposed to solve the problem of difficult parameter selection of VMD algorithm, and which was the above IVMD algorithm. Then, the IVMD algorithm was used to decompose these de-noising vibration signal by AEMD and several new IMF components could be obtained to get secondary denoising vibration signals by restructuring these selected new sensitive IMF components once again. Finally, the experimental verification of the proposed method was carried out by using the collected vibration signals, and the Hilbert envelope analysis was compared with the traditional EMD and EEMD. The experimental results showed that the proposed method is more suitable for incipient fault diagnosis of rolling bearings than the traditional EMD and EEMD.
Seasonal thermal fronts on the northern South China Sea shelf: Satellite measurements and three repeated field surveys

Seasonal thermal fronts associated with wind-driven coastal downwelling/upwelling in the northern South China Sea are investigated using satellite measurements and three repeated fine-resolution mapping surveys in winter, spring, and summer. The results show that vigorous thermal fronts develop over the broad shelf with variable widths and intensities in different seasons, which tend to be approximately aligned with the 20-100 m isobaths. Driven by the prevailing winter/summer monsoon, the band-shaped fronts were observed with a magnitude exceeding 0.1 degrees C/km in the subsurface, and accompanied by energetic coastal downwelling/upwelling due to shoreward/offshore Ekman transport. The downward/upward tilting of seasonal thermoclines across the shelf exceeds 20 m, significantly contributing to the development of thermal fronts over the shelf. In addition, the diagnostic analysis of Potential Vorticity (PV) suggests that the summer frontal activities induced by the coastal upwelling are more stable to convection and symmetric instabilities in comparison to the winter fronts associated with downwelling-favorable monsoon forcing. This is primarily due to their essential differences in the upper ocean stratification and horizontal buoyancy gradients arising from wind forcing. At the same time, the coastal currents are substantially regulated by the seasonal winds. An expected lag correlation between the velocity from mooring measurements and alongshore wind stress is detected near the frontal region. These results indicate that seasonal wind forcing plays an important role in the frontal activities and coastal water transport over the shelf.
Enhancing Robustness in Multi-modal CT Image Segmentation with a Clustering-based Deep Active Learning Approach

Supervised deep learning techniques have become a powerful tool in many applications, including medical image segmentation. However, their efficacy depends heavily on the availability of large and uniformly annotated datasets. The need for high-quality annotations presents a significant challenge as it requires significant cost, time, and effort. This challenge is particularly relevant in medical imaging, where segmentation tasks require a high degree of accuracy and precision. To address this challenge, we propose a novel framework for active learning in multi-modal medical image segmentation. Our approach involves selecting optimal training samples from multi-modal Spectral CT medical images during the initial sample selection phases. Our experimental results demonstrate the effectiveness of our proposed framework, which exhibits higher precision and more robust outcomes than conventional active learning methods. Specifically, our proposed image training sample selection methods can achieve stable model performance for lesion segmentation using less than ten multi-modal medical image samples, compared to conventional single modal methods. Furthermore, as the number of annotated training samples increases, our approach continues to display superior accuracy and robustness compared to other initial sample selection methods. In summary, by leveraging the advantages of both representativeness and model uncertainty, our approach demonstrates improved precision and robustness, highlighting its potential as a valuable tool for lesion segmentation applications.
The Integrated Compensation Structure Design of Broadband Mixed Frequency Response Mismatch in High-Resolution Quadrature Signal Reception System

This research will conduct a joint analysis on the propagation mechanism of phase/quadrature (I/Q) imbalance error and time-interleaved analog to digital converter (TIADC) mismatch error in the broadband quadrature reception system, establish a unified broadband mixed frequency response mismatch error model, and propose a digital integrated compensation strategy based on flower pollination algorithm to jointly eliminate the error, aiming to minimize the comprehensive impact of the two errors on the system and ensure the best performance of high-speed and high-precision sampling of the system, and ensure the system with stronger capture and analysis capabilities for broadband complex signals. The simulation verified that the compensation structure can eliminate the mixed frequency response mismatch error and enhance the key indicators effectively.
Precise Dynamic Data Race Prediction for Interrupt-Driven Embedded Software

Data races are common in interrupt-driven embedded software and have caused serious safety accidents. Unfortunately, existing dynamic race detection techniques for these software suffer from high false positives and overhead, mainly due to the negligence of widely existing user-defined synchronizations and excessively simulating interrupt triggering during execution. This paper proposes intRace, a precise dynamic data race detection technique for interrupt-driven embedded software. Based on an industrial full-system simulation platform VTest, intRace can detect races on-the-fly during dynamic testing of embedded software. Guided by existing test cases with high coverage, intRace uses a pattern-based hybrid method to identify user-defined synchronizations and detect data races based on accurate partial order relationships precisely and efficiently.We evaluated intRace on 4 real-world interrupt-driven aerospace embedded software. The results showed that intRace identified all user-defined synchronizations with no false positives, and found 40 data races with an average FP rate of 11.1% at low overhead.