Miracle-dz/UniFAR-FTUs huggingface dataset:
Ion movement in thin Nafion films under an applied electric field

The electromechanical response of Nafion films with and without an ionic liquid (1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide) ([emim]Tf2N) additive were characterized under an applied electric field in situ using neutron reflectometry (NR) and voltage modulated atomic force microscopy (VM-AFM). NR showed that pure Nafion films exhibited no response under field strengths of ∼0.18 V/μm, while VM-AFM measurements showed a minimal response at higher field strengths (∼200 V/μm), which is ascribed to the residual water presence in the films. The addition of ionic liquid resulted in clear electroresponsiveness seen in both NR and VM-AFM. NR results indicated mass migration away from the cathodic interface driven by the [emim]+ movement in the direction of the electric field. The lack of ionic liquid accumulation at the electrode interfaces contrasts the bulk electromechanical behavior of similar systems reported in the literature. VM-AFM measurements were able to resolve the relative contributions of the [emim]+ cation and Tf2N− anion to film deformation by alternating the direction of the applied field and support the [emim]+ dominant migration seen in the NR results. The findings presented here emphasize the need for the nanoscale analysis of material properties of electroresponsive thin film systems and demonstrate the potential for probing electric field effects using in situ techniques.The electromechanical response of Nafion films with and without an ionic liquid (1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide) ([emim]Tf2N) additive were characterized under an applied electric field in situ using neutron reflectometry (NR) and voltage modulated atomic force microscopy (VM-AFM). NR showed that pure Nafion films exhibited no response under field strengths of ∼0.18 V/μm, while VM-AFM measurements showed a minimal response at higher field strengths (∼200 V/μm), which is ascribed to the residual water presence in the films. The addition of ionic liquid resulted in clear electroresponsiveness seen in both NR and VM-AFM. NR results indicated mass migration away from the cathodic interface driven by the [emim]+ movement in the direction of the electric field. The lack of ionic liquid accumulation at the electrode interfaces contrasts the bulk electromechanical behavior of similar systems reported in the literature. VM-AFM measurements were able to resolve the relative contributi.
150 GHz Radar Imagery using Doppler Beam Sharpening for Marine Sensing

The application of the Doppler Beam Sharpening (DBS) technique is shown for a free moving maritime target using a 150 GHz FMCW radar equipped with 10° horn antenna. The methodology of synchronizing the radar data with the spatial and inertial data is presented with the resulting DBS imagery from the scene at Coniston Water, UK. DBS with sub-THz (above 0.1 THz) radar was found to be an effective tool for producing images with range resolutions on the order of cm and azimuth resolutions of near 0.1°. This resolution was found to be more than 100 times more refined than the 150 GHz horn antenna azimuth beamwidth.
Towards Automatic Processing of Georeferenced Information Within Airport Services

Many airports, particularly military and smaller ones, still employ outdated and cumbersome procedures that negatively affect their service processes. This research aims to address this issue by focusing on the inspection and fault observation procedures on the airfield. Specifically, it assesses the current situation in the Czech Republic military and proposes an advanced approach that utilizes Python and Flask frameworks to create a user-friendly online application for monitoring airport service operations. The proposed tool is free and can be tailored to the specific needs of each airport. Furthermore, the research explores the potential for further development of the application to enhance handling services. The paper highlights the significance of maintaining a perfect airport condition and analyzes the current practices for recording inspection results and disseminating information to users and superior units like A TC and pilots, particularly in the Czech Air Force. It also outlines the frequency and content of daily inspections as required by the existing legislation. The research also categorizes inspections based on the regulations of the ACR and defines the requirements for the application, taking into account available commercial solutions and the regulations of the American FAA agency. The proposed application is divided into two main parts, namely inspections and defects, which will streamline and simplify the process of recording inspections, enhance searchability of inspection history and improve air traffic safety by reducing human error in defect logging. In summary, this research provides an advanced and customized solution for monitoring airport service operations, which can enhance the efficiency, safety, and reliability of airport service processes.
On Diversity and Complementarity of Pedestrian Detection Models

Pedestrian Detection (PD) is now almost a mature field of signal processing in which numerous application areas exists. Emerging one of these applications is driving autonomy that requires advanced driver assistance systems (ADAS) for PD. Although various benchmark data sets are collected and several methods are already developed, the reported results show that current systems lack generalization capacity due to strong dependence of system performance on PD training set. In other words, the systems trained with a pedestrian data set usually perform considerably well on a test set taken from the same benchmark, but their performance drop drastically when tested on a different benchmark data set. In order to overcome this limitation, one of the approaches might be fusing outcomes of different systems. Here, “different systems” can refer either to train the same model with different data sets or using entirely different models. In both cases, the outcomes of different systems should be diverse enough to cover the entire solution space. Up to our knowledge, the performance of existing well-established models for PD is frequently compared to each other but their diversity is not analysed in detail yet. In this study, the feasibility of PD systems, whose effectiveness on PD is shown by earlier studies, was examined by systematically training and testing them with different benchmark data sets and their combinations. For this purpose, pairwise and non-pairwise measures of diversity have been employed and several conclusions are driven about the complementarity of existing PD models.
The Distribution and Disengagement of Women Contributors in Open-Source: 2008–2021

The underrepresentation of women contributors in the open-source software (OSS) community has been a widely recognized problem. Past research has found that, in OSS collaboration, a gender-diverse team can enhance productivity and lower community smell [1]–[3]. However, these benefits will be hindered when a team lacks gender diversity. To better address this gender imbalance, we need to first understand the overall gender representation.
Real-Time and Uniform Feature Filtering for Stereo Images Based on Spatial Indexing

For mobile robots' or vehicles' navigation in dynamic environments, visual odometry is an effective technique to get precise locations at a lower cost. The feature-based method now becomes the typical solution, but it requires heavy computational power in filtering good features and matches. This paper presents an efficient pipeline to detect, filter, and match the features for stereo cameras based on AGAST, BEBLID descriptor, and spatial indexing. We use two quadtrees to manage features and filter good matches by stereo line searching and quadtree querying. The algorithm is evaluated on the KITTI dataset and greatly outperforms a mainstream feature filtering method using an image grid, KNN, and Lowe ratio test in terms of inlier ratio, uniformity, and real-time processing. The improved pipeline is well suited for a full stereo visual odometry or visual SLAM system.
Extreme Hydrometeorological Events In The Březina Military Training Area

This paper deals with the variability of extreme hydrometeorological events in the Březina MTA (military training area) and its surroundings from 1961–2020 in order to determine if there were more such extremes during the latter part of the period in connection with climate change impacts. The work focuses on various meteorological elements (air temperature, precipitation, wind speed, and wind direction) and inferred extreme events. Regarding the fact, that on the territory of the Březina MTA there is not any meteorological station, it was worked with the data of those meteorological stations of the CHMI (Czech Hydrometeorological Institute) located within 50 km far from the MTA. The classification of the extreme hydrometeorological events is based on the national IESS (Integrated Emergency Services System), which divides meteorological events by intensity into three categories according to the specific meteorological variable - (1) high, very high, extreme, (2) low, very low, extreme, (3) strong, very strong and extreme. In the framework of the analysis of the extreme hydrometeorological events, their development in the 1961–2020 period and spatial extent were analyzed. The results were visualized by spatial interpolation methods. We determined that high temperature extremes were the most pronounced impact of climate change with significant increase of 20.6 days/decade. Furthermore, low temperature extremes significantly decreased by 5.6 days/decade. There was no change in high precipitation and strong winds categories, respectively.
Understanding Permanent Hardware Failures in Deep Learning Training Accelerator Systems

Hardware failures pose critical threats to deep neural network (DNN) training workloads, and the urgency of tackling this challenge (known as the Silent Data Corruption challenge in a broader context) has been raised widely by the industry. Based on industry reports, a large number of the failures observed in real systems are permanent hardware failures in logic. However, there is a very limited understanding of the effects that these failures can impose on DNN training workloads. In this paper, we present the first resilience study on this subject, focusing on deep learning (DL) training accelerator systems. We developed a fault injection framework to accurately simulate the effects of permanent faults, and conducted 100K fault injection experiments. Our results provide the fundamental understanding on how logic permanent hardware failures affect training workloads and eventually generate unexpected training outcomes. Based on this new knowledge, we developed efficient software-based detection and recovery techniques to mitigate logic permanent hardware failures that are likely to generate unexpected outcomes. Evaluation on Google Cloud TPUs shows that our techniques are effective and practical: they require 15−25 lines of code change, and introduce 0.004%−0.025% performance/energy overhead for various representative neural network models.
Analysis and Design of Refractive Index Sensor Based on Waveguide Coupling

This paper presents a new refractive index sensor based on the Fano resonance in optical phenomena. The sensor has two main parts: a metal-insulator-metal (MIM) as the base waveguide and a V -shaped ring resonant cavity with a convex outer ring (VRROR) as the coupling structure. The sensing characteristics of the structure are rationalized by applying the finite element method, and the specific effects of the various parameters in the structure on the sensitivity(S) and superiority of the VRROR are analyzed. The final sensitivity of the VRROR type sensor reaches 2960 nm/RIU, and the figure of merit (FOM) under the corresponding system reaches 51.3.
Cryptocurrency meets CAP Theorem

Guerraoui et al. [PODC, 2019] implement a cryptocurrency (in a permissioned setting) using an asset transfer object. Their main result implies that consensus is not necessary for implementing a cryptocurrency, since computationally speaking, an asset transfer object is equivalent to an atomic read/write register. In this work, we take a step further to understand fundamental limitations of cryptocurrency under the CAP framework. Particularly, we show that no cryptocurrency that tolerates Byzantine adversary works in a partitioned network. We point out future directions that can circumvent the impossibility.
Long Range Noise Radar Concept

The classical noise radar is most often considered to be a kind of continuous wave sensor, transmitting the sounding waveforms and receiving the target echoes simultaneously. Thus it suffer from the near-far problem and the detection range is limited mainly by antenna crosstalk and receiver dynamic range. The paper presents the limitation caused by this problem and the alternative solutions leading to almost unlimited distance of noise radar working in pulsed waveform mode.
Test-Point Insertion for Power-Safe Testing of Monolithic 3D ICs using Reinforcement Learning*

Monolithic 3D (M3D) integration for integrated circuits (ICs) offers the promise of higher performance and lower power consumption over stacked-3D ICs. However, M3D suffers from large power supply noise (PSN) in the power distribution network due to high current demand and long conduction paths from voltage sources to local receivers. Excessive switching activities during the capture cycles in at-speed delay testing exacerbate the PSN-induced voltage droop problem. Therefore, PSN reduction is necessary for M3D ICs during testing to prevent the failure of good chips on the tester (i.e., yield loss). In this paper, we first develop an analysis flow for M3D designs to compute the PSN-induced voltage droop. Based on the analysis results, we extract the test patterns that are likely to cause yield loss. Next, we propose a reinforcement learning (RL)-based framework to insert test points and generate low-switching patterns that help in mitigating PSN without degrading the test coverage. Simulation results for benchmark M3D designs demonstrate the effectiveness of the proposed power-safe testing approach, compared to baseline cases that utilize commercial tools.
State Space Predictive Control for Quadrotors Cooperative Flight

This paper investigates the cooperative control of quadrotor formation. A state space predictive control strategy is proposed for the leader-following quadrotor cooperative flight. Specifically, based on the state predictive control method, a tracking controller is firstly designed for quadrotor leader to achieve the stable trajectory tracking. Secondly, a distributed state space predictive formation control law is established for quadrotor followers to keep the desired relative position with respect to the quadrotor leader. The proposed control scheme guarantees the overall stability of quadrotors cooperative control. Experiment results are provided, which verify the effectiveness of proposed control scheme.
An Efficient FCS-MPC Using Virtual Voltage Vectors for Three-Phase T-Type Inverters

In this paper, a finite-control-set model predictive control (FCS-MPC) base on virtual voltage vectors is proposed for three-phase three-level (3P-3L) T-type voltage source converters (VSCs). Different from traditional method, a set of 51 virtual voltage vectors (VVs) is used in this method. Thus, the performance of the VSC is improved and the harmonic of output voltage decreased significantly. At the same time, the VVs used in this method have no effect on the neutral point (NP) voltage of VSCs. Furthermore, the multi-objective cost function including the NP voltage balance term in the traditional method is optimized, and the design process is simplified. In order to prove the effectiveness and validity of the proposed FCS-MPC and show its advantages over typical strategy, simulation results are presented.
Stochastic Recursive Algorithms For Optimization Simultaneous Perturbation Methods

Thank you for reading stochastic recursive algorithms for optimization simultaneous perturbation methods. Maybe you have knowledge that, people have look hundreds times for their chosen readings like this stochastic recursive algorithms for optimization simultaneous perturbation methods, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious virus inside their desktop computer.
Test-Driven Development Benefits Beyond Design Quality: Flow State and Developer Experience

Test-driven development (TDD) is a coding technique that combines design and testing in an iterative and incremental fashion. It prescribes that tests written before the production code help the developer to find good interfaces and to evolve the design safely and incrementally. Improvements on the design of code produced by the test-driven development approach have been extensively evaluated in the literature; in this research, we focus on seeking explanations on the benefits of TDD in another dimension which we believe has been undervalued – developer experience. We identified that there is a natural connection between the TDD approach and flow state, a well-known mental state characterized by total immersion, focus, and involvement in a task that promotes increased enjoyment and productivity. We present evidence that the continuous stream of mini-scope, short-lived, red-green-refactor cycles of TDD frame the development task as a structure that creates the pre-conditions reported by neuroscience research to produce flow state, namely (1) clear goals, (2) unambiguous feedback, (3) challenge-skill balance and (4) sense of control. Our work contributes to increase the understanding on the reasons why adopting practices such as TDD can benefit the software development process as a whole and can support its adoption in software development projects.
Randomized Differential Testing of RDF Stores

As a special kind of graph database systems, RDF stores have been widely used in many applications, e.g., knowl-edge graphs and semantic web. RDF stores utilize SPARQL as their standardized query language to store and retrieve RDF graphs. Incorrect implementations of RDF stores can introduce logic bugs that cause RDF stores to return incorrect query results. These logic bugs can lead to severe consequences and are likely to go unnoticed by developers. However, no available tools can detect logic bugs in RDF stores. In this paper, we propose RD2, a Randomized Differential testing approach of RDF stores, to reveal discrepancies among RDF stores, which indicate potential logic bugs in RDF stores. The core idea of RD2 is to build an equivalent RDF graph for multiple RDF stores, and verify whether they can return the same query result for a given SPARQL query. Guided by the SPARQL syntax and the generated RDF graph, we automatically generate syntactically valid SPARQL queries, which can return non-empty query results with high probability. We further unify the formats of SPARQL query results from different RDF stores and find discrepancies among them. We evaluate RD2 on three popular and widely-used RDF stores. In total, we have detected 5 logic bugs in them. A video demonstration of RD2 is available at httos://youtu.be/da7XlsdbRR4.
YOLOv5-SC: A Lightweight Detection Model for Railway Catenary Dropper

With the development of artificial intelligence, railway enterprises hope to add intelligent detection functions to their detection equipment, realizing the intelligence of railway detection to solve problems such as low efficiency in traditional manual detection methods. However, due to limitations in the performance of mobile devices, it is difficult to deploy network models with large amounts of computation and weight parameters on mobile devices. Based on the YOLOv5 network model, this article proposes the YOLOv5-SC model for railway catenary dropper location detection, which has less computation and smaller network model size while still maintaining good detection accuracy. Through experimental comparison, the lightweighted network model has a small decrease in detection accuracy, and YOLOv5-SC network model size and computation are half that of the original YOLOv5 network model, with an improved detection speed by14.5%.
A Single Input Algorithm for Transmission Synchronization of Multiple Memristor Chaotic Systems

Chaos is a very complex phenomenon in deterministic nonlinear systems. In the development of just a few decades, the concept of chaos has been extended to the fields of natural and social sciences. With the deepening of chaos research, people begin to study how to simply control chaos. The theory of chaotic synchronization and control makes people realize that chaotic system has complex dynamic characteristics and can be applied in practice. In this work, we focus on the transmission synchronization of multiple memristor systems via a single input method. First, a chaotic system based on memristor is presented to show the internal chaotic behavior. Then, by using Routh-Hurwitz criterion, a sufficient condition is established to guarantee asymptotic synchronization of state trajectories of multiple memristor chaos. Finally, the numerical results reveal that the single input algorithm designed in this work is valid for transmission chaotic synchronization of multiple memristor chaotic systems.
Evaluating the Trade-offs of Text-based Diversity in Test Prioritisation

Diversity-based techniques (DBT) have been cost-effective by prioritizing the most dissimilar test cases to detect faults at earlier stages of test execution. Diversity is measured on test specifications to convey how different test cases are from one another. However, there is little research on the trade-off of diversity measures based on different types of text-based specification (lexicographical or semantics). Particularly because the text content in test scripts vary widely from unit (e.g., code) to system-level (e.g., natural language). This paper compares and evaluates the cost-effectiveness in coverage and failures of different text-based diversity measures for different levels of tests. We perform an experiment on the test suites of 7 open source projects on the unit level, and 2 industry projects on the integration and system level. Our results show that test suites prioritised using semantic-based diversity measures causes a small improvement in requirements coverage, as opposed to lexical diversity that showed less coverage than random for system-level artefacts. In contrast, using lexical-based measures such as Jaccard or Levenshtein to prioritise code artefacts yield better failure coverage across all levels of tests. We summarise our findings into a list of recommendations for using semantic or lexical diversity on different levels of testing.
Model-Free Predictive Control: A Method to Always Improve the Performance Robustness of Power Electronic Systems?

Model predictive control (MPC) typically suffers from performance degradation due to model uncertainties. To address this issue, model-free predictive control (MFPC) based on the ultra-local model has been proposed, which promises perfect performance robustness against model uncertainties compared to conventional MPC. However, simulation and experimental results show that this advantage does not always hold in practice. In this paper, we discuss the question of whether MFPC is always more performance robust than MPC, based on classical control theories. Two representative MFPC methods are analyzed and compared with MPC. And a criterion to improve their performance robustness is proposed. Such a criterion will provide a powerful tool for the development of more robust MFPC methods in the future. The criterion is validated using a buck converter with a Pareto Fronts analysis in simulations and experiments simultaneously.