Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Test Case Generation for Safety-Critical Software in Scade

Software systems for automating safety-critical tasks in application domains like, for example, avionics, railways, automotive, industry 4.0 and healthcare, must be highly reliable. In this paper, we focus on safety-critical software written in Scade, a model-based programming language largely adopted in industry, and we specifically draw on our own experience in a joint industry-university project aimed at developing safety-critical Scade programs for the railways domain. We investigate automated test case generation for Scade programs. We leverage on state-of-the-art test generators based on either symbolic execution, bounded model checking or search-based testing, in order to define an original toolchain for generating test cases for Scade programs. We rely on the toolchain to explore the absolute and relative effectiveness of those mainstream test generation approaches on a benchmark of 37 Scade programs developed as part of an on-board signaling unit for high-speed railway systems.
Three-Dimensional Structure Inversion Through Wide Fractional Bandwidth, UAV-Based SAR Interferometry

This paper presents a novel method to retrieve the three-dimensional structure of semi-transparent media using wide fractional bandwidth interferometric synthetic aperture radar (SAR). Interferometric SAR (InSAR) is a well-established remote sensing technique capable of providing accurate topographic information of the illuminated area. According to the InSAR principle, multiple scattering within a single resolution cell cannot be resolved using a single baseline and results in a degradation of the height accuracy. Techniques such as polarimetric InSAR and SAR tomography tackle this problem using additional measurements to obtain the three-dimensional structure of the volume. However, it requires numerous observations, which are challenging to be performed in a single-pass. The method proposed in this paper utilizes instead the frequency dependency of the InSAR coherence to invert the three-dimensional structure. The dependency can be exploited for signals with wide fractional bandwidth available on unmanned aerial vehicles (UAVs). As the extinction is in general frequency-dependent, a third observation may be needed for the inversion. In this paper, simulated radar data with wide fractional bandwidth were used to demonstrate the frequency dependency of the InSAR coherence. According to the simulation results, the frequency profile can be inverted to obtain information about the three-dimensional scattering structure. This method allows a swarm of two or three UAVs to be used for frequent and accurate monitoring of semi-transparent targets, e.g., forests and vegetation.
An Improved Triple-Vector Modulated Model Predictive Control Method for Permanent Magnet Synchronous Motor

Model predictive control (MPC) has been widely used in motor control, and modulated model predictive control (MMPC) has been derived. However, in the conventional MMPC, the action time of the basic voltage vector is calculated by the inverse ratio of the cost function, which lacks a strictly theoretical basis. To enhance the theoretical basis of the MMPC, an improved triple-vector MMPC is proposed. First, two adjacent dual-vectors are synthesized by the conventional method. Then, the Helen’s formula and geometric relationship between the two dual-vectors are used to scale and synthesize the triple-vectors. Finally, the simulation verifies the effectiveness of the proposed method.
Event-Triggered single-network Control for Nonlinear Continuous-Time Zero-Sum Games with Partially Unknown Dynamics

This paper proposes an event-triggered integral reinforcement learning algorithm to solve the Hamilton-JacobiIsaacs (HJI) equation related to the zero-sum game problem. Then, we design a triggering rule to constrain the control updates. Secondly, the HJI partially differential equation is approximately solved using a single-critic neural network structure. The control law and disturbance law are modified when an event occurs. The system state and the network weights error are uniformly ultimately bounded (UUB) by Lyapunov theory. Finally, an engineering example is utilized to confirm the performance of the algorithm.
Investigation of Plasma Material Erosion Under Mechanical Stress

The operational lifetime of traditional Hall effect thrusters is limited by the erosion of the discharge channel walls. Erosion during long-duration life testing of Hall effect thrusters produces surface features that are unexplained by present models. Anomalous erosion ridges are one such unexplained feature. Hall effect thrusters are also expected to be under moderate (100 kPa to 6 MPa) thermomechanical stresses when operating, due to heat loads from the plasma. Thus, there may be a relationship between mechanical stress and the erosion features observed in Hall effect thrusters. This work investigates the plasma erosion of materials placed under mechanical loads. An experiment is designed to detect any changes to the erosion of material samples due to stress. Amorphous fused-silica samples are investigated in this paper. Fused silica erodes quickly and has no material heterogeneity. Three pairs of fused-silica samples, each pair with one control sample and one sample loaded to 6.0, 14.4, and 25.0±1.1 .
Three-Dimensional Deformation Monitoring Technology Based on Ground-Based Synthetic Aperture Radar and Optical Images

This study proposes a method for 3D deformation monitoring of surface targets. Firstly, the radial deformation information is obtained by radar, and the 2D deformation information in the vertical plane of radar line of sight is obtained by optical equipment. Then, based on multi-source image matching, the information fusion of radar deformation monitoring data and optical deformation monitoring data is realized. Finally, the deformation of the fused data is solved to obtain the 3D deformation of the target. The experimental results show that when there is a certain angle between the actual deformation direction of the target and the radial direction of the radar, the relative error of the deformation monitored by this method is obviously small, which can be reduced to less than 1/3 of the traditional radar monitoring error.
TECHSUMBOT: A Stack Overflow Answer Summarization Tool for Technical Query

Stack Overflow is a popular platform for developers to seek solutions to programming-related problems. However, prior studies identified that developers may suffer from the redundant, useless, and incomplete information retrieved by the Stack Overflow search engine. To help developers better utilize the Stack Overflow knowledge, researchers proposed tools to summarize answers to a Stack Overflow question. However, existing tools use hand-craft features to assess the usefulness of each answer sentence and fail to remove semantically redundant information in the result. Besides, existing tools only focus on a certain programming language and cannot retrieve up-to-date new posted knowledge from Stack Overflow. In this paper, we propose TECHSUMBOT, an automatic answer summary generation tool for a technical problem. Given a question, TECH-SUMBOT first retrieves answers using the Stack Overflow search engine, then TECHSUMBOT 1) ranks each answers sentence based on the sentence's usefulness, 2) estimates the centrality of each sentence to all candidates, and 3) removes the semantic redundant information. Finally, TECHSUMBOT returns the top 5 ranked answer sentences as the answer summary. We implement TECHSUMBOT in the form of a search engine website. To evalu-ate TECHSUMBOT in both automatic and manual manners, we construct the first Stack Overflow multi-answer summarization benchmark and design a manual evaluation study to assess the effectiveness of TECHSUMBOT and state-of-the-art baselines from the NLP and SE domain. Both results indicate that the summaries generated by TECHSUMBOT are more diverse, useful, and similar to the ground truth summaries. Tool Link: www.techsumbot. com Video Link: https://youtube.com/watchvozuJOp_vILMReplicationPackage: https://github.com/TechSumBot/TechSumBot
Cascaded Deadbeat Predictive Power Control for High-speed PMSG under Wide Speed Range

In order to improve the dynamic and steady-state performance of the traditional PI control for high-speed surface-mounted PMSG, a cascaded model predictive control consisted of deadbeat predictive power control (DPPC) and deadbeat predictive current control (DPCC) is proposed in this paper. Firstly, the model between PMSG and load is obtained by the small-signal analysis. Then, according to the above discretized model and the idea of deadbeat model predictive control, the DPCC inner loop and DPPC outer loop is designed. In addition, the operating constraints of PMSG are analyzed. To adapt to wide speed range, field weaking control based on voltage feedback is introduced to the above cascaded control. The simulation results verify the effectiveness of field weakening control and improvement of the dynamic and steady-state performance of the cascaded deadbeat predictive control compared with the traditional PI control strategy.
Calibration of Large Coherent MIMO Radar Arrays: Channel Imbalances and 3D Antenna Positions

One of the main challenges for next generation automotive radars is the improvement of angular resolution to a sub-degree level. In this context, wide aperture automotive radars of 1m length or more and resolution close to 0.1° in azimuth and 0.5° in elevation could be beneficial. To enable coherent processing of arrays with such large aperture, prior (i.e offline) and online calibration are necessary: channel imbalances (gains and phases) and three dimensional coordinates of transmit and receive elements need to be determined. We propose a calibration strategy based on alternating steps between the two subtasks of i) channel imbalance estimation with ‘known’ array positions, by applying a singular value decomposition to the resulting tensor calculus problem; and ii) antenna position estimation with ’known’ channel imbalances, by numerically maximizing the Bayesian posterior probability; in both cases operating on range/Doppler snapshots of disjoint targets (with potentially unknown locations). Simulation studies based on the parameters of a MIMO 8x6 linear sparse array show promising results as long as the initial position errors do not exceed half a wavelength (2mm), beyond which we observe strong effects of ambiguity. Experimental results with real measurements show that after calibration in laboratory conditions, our MIMO 8x6 demonstrator with 50cm aperture is able to resolve two targets at the same range with angular separation at least as close as 0.4°.
What Would You do? An Ethical AI Quiz

The resurgence of Artificial Intelligence (AI) has been accompanied by a rise in ethical issues. AI practitioners either face challenges in making ethical choices when designing AI-based systems or are not aware of such challenges in the first place. Increasing the level of awareness and understanding of the perceptions of those who develop AI systems is a critical step toward mitigating ethical issues in AI development. Motivated by these challenges, needs, and the lack of engaging approaches to address these, we developed an interactive, scenario-based ethical AI quiz. It allows AI practitioners, including software engineers who develop AI systems, to self-assess their awareness and perceptions about AI ethics. The experience of taking the quiz, and the feedback it provides, will help AI practitioners understand the gap areas, and improve their overall ethical practice in everyday development scenarios. To demonstrate these expected outcomes and the relevance of our tool, we also share a preliminary user study. The video demo can be found at https://zenodo.org/record/7601169#. Y9xgA-xBxhF.
Fusion Detection Algorithm of Maritime Radar and Electro-Optical Pod for Complex Sea Conditions

In order to improve the perceptual capability of Unmanned Surface Vessels (USVs) in complex sea conditions, a fusion detection algorithm has been developed that combines maritime radar and electro-optical (EO) pod. This algorithm integrates the positional state of the USV, class information of the maritime target obtained through a vision-based target detection algorithm, and processed maritime radar information. By using Gaussian and morphological filtering, the fusion of position state information from the USV, EO pod data, and maritime radar data is achieved. This effectively mitigates the negative impact of environmental factors, such as waves and water mist, on target detection. A cooperative sensing process between the maritime radar and EO pod is proposed to fuse the data obtained from both sources, resulting in accurate comprehensive target information. Experimental results show that the fusion algorithm achieves an average accuracy of 80.24% and a detection rate of 74.7%, enabling precise detection of maritime targets, including their distance, speed, and category. This enhanced perceptual capability is essential for USVs operating in complex maritime environments, enabling better decision-making and navigation performance.
A Novel and Pragmatic Scenario Modeling Framework with Verification-in-the-loop for Autonomous Driving Systems

Scenario modeling for Autonomous Driving Systems (ADS) enables scenario-based simulation and verification which are critical for the development of safe ADS. However, with the increasing complexity and uncertainty of ADS, it becomes increasingly challenging to manually model driving scenarios and conduct verification analysis. To tackle these challenges, we propose a novel and pragmatic framework for scenario modeling, simulation and verification. The novelty is that it’s a verification-in-the-loop scenario modeling framework. The scenario modeling language with formal semantics is proposed based on the domain knowledge of ADS. It facilitates scenario verification to analyze the safety of scenario models. Moreover, the scenario simulation is implemented based on the scenario executor. Compared with existing works, our framework can simplify the description of scenarios in a non-programming, user-friendly manner, model stochastic behavior of vehicles, support safe verification of scenario models with UPPAAL-SMC and generate executable scenario in some open-source simulators such as CARLA. To preliminarily demonstrate the effectiveness and feasibility of our approach, we build a prototype tool and apply our approach in several typical scenarios for ADS.
DeepScenario: An Open Driving Scenario Dataset for Autonomous Driving System Testing

With the rapid development of autonomous driving systems (ADSs), testing ADSs under various environmental conditions has become a key method to ensure the successful deployment of ADS in the real world. However, it is impossible to test all the scenarios due to the inherent complexity and uncertainty of ADSs and the driving tasks. Further, testing of ADSs is expensive regarding time and computational resources. Therefore, a large-scale driving scenario dataset consisting of various driving conditions is needed. To this end, we present an open driving scenario dataset DeepScenario, containing over 30K executable driving scenarios, which are collected by 2880 test executions of three driving scenario generation strategies. Each scenario in the dataset is labeled with six attributes characterizing test results. We further show the attribute statistics and distribution of driving scenarios. For example, there are 1050 collision scenarios, in 917 scenarios there were collisions with other vehicles, 105 and 28 with pedestrians and static obstacles, respectively. Target users include ADS developers who need to validate their systems under various environmental conditions.
KINEMATIC STRATEGIES TO KEEP AN UNCHANGED MARGIN OF STABILITY DURING TREADMILL RUNNING ON AN EVEN AND UNEVEN SURFACE

Understanding how to control stability when running, particularly when being exposed to uneven terrain, is vital to prevent falls and to get an insight into compensatory strategies while running on uneven terrain. The purpose of this study was to assess surface related differences of the margin of stability, kinematics of hip and knee and upper body acceleration which may affect the control of running stability. Eighteen healthy younger adults ran on an even and an uneven surfaced treadmill for two minutes at fixed speeds of 2.0 m/s (female) and 2.2 m/s (male), respectively. Results showed an unchanged margin of stability in both conditions. Further, lower limb kinematics, step width variability and upper body acceleration increased on the uneven surface meaningfully to keep the extrapolated centre of mass within the base of support.
Work Environment and Compensation on Teacher Performance: The Mediating Role of Work Professionalism

Purpose: This research was conducted at SMA (Senior High School) and SMK (Vocational High School) Kawung 2 Surabaya with the aim of analyzing the influence of the work environment on teacher performance, analyzing the effect of compensation on teacher performance, and analyzing the effect of work professionalism on teacher performance, analyzing the influence of work environment mediated by work professionalism on teacher performance, analyzing the effect of compensation mediated by work professionalism on teacher performance. Design/methodology/approach: The population of this study were teachers of SMA and SMK Kawung 2 Surabaya with a total of 52 teachers. Data analysis using multiple linear regression using SPSS software. Findings: The results showed that work environment has a significant effect on teacher performance, compensation has a significant effect on teacher performance, work professionalism has a significant effect on teacher performance, work professionalism is able to mediate the effect of work environment on teacher performance, and work professionalism is able to mediate the effect of compensation on performance teacher.
THE EFFECTIVENESS OF USING PADLET IN ESL CLASSROOM.

MunirahHaris, Melor Md. Yunus and Jamaludin Hj Badusah. Faculty of Education, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia. Manuscript Info Abstract . Manuscript History
Isolation and Characterization of Thermotolerant Mungbean (Vigna Radiata L.) Rhizobial Isolate As Biofertilizer

Rhizobia act as primary nitrogen fixing microorganisms in the root of leguminous plants. Efficient rhizobia are helpful in increasing productivity of legumes. In the present investigation a total of twenty rhizobial isolates were retrieved from root nodules of mungbean plants growing in field during summer season. On evaluation of plant growth promoting traits of all rhizobial isolates it was found that rhizobial isolate HSR1 had maximum indole acetic acid (IAA) production (58.83 μg/ml), ammonia excretion (4.78 μg/ml) and phosphate solubilization potential. Other plant growth promoting traits like aminocyclopropane- 1-carboxylic acid (ACC) deaminase activity, siderophore production and hydrogen cyanide (HCN) production were also exhibited by isolate HSR1. This isolate was able to survive 45ºC and exhibited plant growth promoting traits at high temperature. Genomic DNA extracted from isolate HSR1 was subjected to amplification of 16S rRNA gene for identification. The similarity searching of the sequence obtained after 16S rRNA gene sequencing showed 99% similarity with Sinorhizobium sp. T25, which was confirmed by morphological and biochemical tests. Presence of good plant growth promoting traits makes HSR1 a promising strain to be developed as biofertilizer to improve soil fertility and plant growth. Bangladesh J. Bot. 52(2): 217-224, 2023 (June)
Peru Mining: Analysis and Forecast of Mining Production in Peru Using Time Series and Data Science Techniques

Peruvian mining plays a crucial role in the country's economy, being one of the main producers and exporters of minerals worldwide. In this project, an application was developed in RStudio that utilizes statistical analysis and time series modeling techniques to understand and forecast mineral extraction in different departments of Peru. The application includes an interactive map that allows users to explore Peruvian geography and obtain detailed statistics by clicking on each department. Additionally, bar charts, pie charts, and frequency polygons were implemented to visualize and analyze the data. Using the ARIMA model, predictions were made on the future extraction of minerals, enabling informed decision-making in planning and resource management within the mining sector. The application provides an interactive and accessible tool to explore the Peruvian mining industry, comprehend trends, and make accurate forecasts. These predictions for 2027 in total annual production are as follows: Copper = 2,694,957 MT, Gold = 72,817.47 kg Fine, Zinc = 1,369,649 MT, Silver = 3,083,036 MT, Lead = 255,443 MT, Iron = 15,776,609 MT, Tin = 29,542 MT, Molybdenum = 35,044.66 MT, and Cadmium = 724 MT. These predictions, based on historical data, provide valuable information for strategic decision-making and contribute to the sustainable development of the mining industry in Peru.
Conformal and Contact Kinetic Dynamics and Their Geometrization

We propose a conformal generalization of the reversible Vlasov equation of kinetic plasma dynamics, called conformal kinetic theory. In order to arrive at this formalism, we start with the conformal Hamiltonian dynamics of particles and lift it to the dynamical formulation of the associated kinetic theory. The resulting theory represents a simple example of a geometric pathway from dissipative particle motion to dissipative kinetic motion. We also derive the kinetic equations of a continuum of particles governed by the contact Hamiltonian dynamics, which may be interpreted in the context of relativistic mechanics. Once again we start with the contact Hamiltonian dynamics and lift it to a kinetic theory, called contact kinetic dynamics. Finally, we project the contact kinetic theory to conformal kinetic theory so that they form a geometric hierarchy.
Subtyping Context-Free Session Types

Context-free session types describe structured patterns of communication on heterogeneously-typed channels, allowing the specification of protocols unconstrained by tail recursion. The enhanced expressive power provided by non-regular recursion comes, however, at the cost of the decidability of subtyping, even if equivalence is still decidable. We present an approach to subtyping context-free session types based on a novel kind of observational preorder we call $\mathcal{XYZW}$-simulation, which generalizes $\mathcal{XY}$-simulation (also known as covariant-contravariant simulation) and therefore also bisimulation and plain simulation. We further propose a subtyping algorithm that we prove to be sound, and present an empirical evaluation in the context of a compiler for a programming language. Due to the general nature of the simulation relation upon which it is built, this algorithm may also find applications in other domains.
Learning Stochastic Dynamical Systems as an Implicit Regularization with Graph Neural Networks

Stochastic Gumbel graph networks are proposed to learn high-dimensional time series, where the observed dimensions are often spatially correlated. To that end, the observed randomness and spatial-correlations are captured by learning the drift and diffusion terms of the stochastic differential equation with a Gumble matrix embedding, respectively. In particular, this novel framework enables us to investigate the implicit regularization effect of the noise terms in S-GGNs. We provide a theoretical guarantee for the proposed S-GGNs by deriving the difference between the two corresponding loss functions in a small neighborhood of weight. Then, we employ Kuramoto's model to generate data for comparing the spectral density from the Hessian Matrix of the two loss functions. Experimental results on real-world data, demonstrate that S-GGNs exhibit superior convergence, robustness, and generalization, compared with state-of-the-arts.