Miracle-dz/UniFAR-FTUs huggingface dataset:
An Extended MHD Study of the 16 October 2015 MMS Diffusion Region Crossing

The Magnetospheric Multiscale (MMS) mission has given us unprecedented access to high cadence particle and field data of magnetic reconnection at Earth's magnetopause. MMS first passed very near an X‐line on 16 October 2015, the Burch event, and has since observed multiple X‐line crossings. Subsequent 3‐D particle‐in‐cell (PIC) modeling efforts of and comparison with the Burch event have revealed a host of novel physical insights concerning magnetic reconnection, turbulence‐induced particle mixing, and secondary instabilities. In this study, we employ the Gkeyll simulation framework to study the Burch event with different classes of extended, multifluid magnetohydrodynamics (MHD), including models that incorporate important kinetic effects, such as the electron pressure tensor, with physics‐based closure relations designed to capture linear Landau damping. Such fluid modeling approaches are able to capture different levels of kinetic physics in global simulations and are generally less costly than fully kinetic PIC. We focus on the additional physics one can capture with increasing levels of fluid closure refinement via comparison with MMS data and existing PIC simulations. In particular, we find that the ten‐moment model well captures the agyrotropic structure of the pressure tensor in the vicinity of the X‐line and the magnitude of anisotropic electron heating observed in MMS and PIC simulations. However, the ten‐moment model is found to have difficulty resolving the lower hybrid drift instability, which plays a fundamental role in heating and mixing electrons in the current layer.
Linear Observer‐Based Active Disturbance Rejection Control of the Omnidirectional Mobile Robot

This article describes the design of a linear observer‐linear controller‐based robust output feedback scheme for output reference trajectory tracking tasks in an omnidirectional mobile robot. The unknown, possibly state‐dependent, additive nonlinearities influencing the input‐output tracking error dynamics are modeled as an absolutely bounded, additive, unknown “time‐varying disturbance” input signal. This procedure simplifies the system tracking error description to that of three independent chains of second order integrators with, known, position‐dependent control gains. These simplified systems are additively perturbed by the unknown, smooth, time‐varying signal which is proven to be trivially observable. Generalized proportional integral (GPI) observers, are shown to naturally estimate, in an arbitrarily close manner, the unknown perturbation input of the simplified system and a certain number of its time derivatives. This information is used to advantage on the linear, observer‐based, feedback controller design via a simple cancellation effort. The results are implemented on a laboratory prototype of an omnidirectional mobile robot.
Generalized quasiperiodic Rauzy tilings

We present a geometrical description of new canonical $d$-dimensional codimension one quasiperiodic tilings based on generalized Fibonacci sequences. These tilings are made up of rhombi in 2d and rhombohedra in 3d as the usual Penrose and icosahedral tilings. Thanks to a natural indexing of the sites according to their local environment, we easily write down, for any approximant, the sites coordinates, the connectivity matrix and we compute the structure factor.
Imaging the inner structure of a nuclear reactor by cosmic muon radiography

We studied the inner structure of the nuclear reactor of the Japan Atomic Power Company (JAPC) at Tokai, Japan, by the muon radiography. In this study, muon detectors were placed outside of the reactor building. By detecting cosmic muons penetrating through the wall of the reactor building, we could successfully identify the objects such as the containment vessel, pressure vessel, and other structures of the reactor. We also observed a concentration of heavy material which can be attributed to the nuclear fuel assemblies stored in the nuclear fuel storage pool.
Topology-Aware Flow-Based Point Cloud Generation

Point clouds have attracted attention as a representation of an object’s surface. Deep generative models have typically used a continuous map from a dense set in a latent space to express their variations. However, a continuous map cannot adequately express the varying numbers of holes. That is, previous approaches disregarded the topological structure of point clouds. Furthermore, a point cloud comprises several subparts, making it difficult to express it using a continuous map. This paper proposes ChartPointFlow, a flow-based deep generative model that forms a map conditioned on a label. Similar to a manifold chart, a map conditioned on a label is assigned to a continuous subset of a point cloud. Thus, ChartPointFlow is able to maintain the topological structure with clear boundaries and holes, whereas previous approaches generated blurry point clouds with fuzzy holes. The experimental results show that ChartPointFlow achieves state-of-the-art performance in various tasks, including generation, reconstruction, upsampling, and segmentation.
AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions

Wireless IoT networks have seen an unprecedented rise in number of devices, heterogeneity and emerging use cases which led to diverse throughput, reliability and latency (Quality of Service) requirements. Fulfilling these diverse requirements in a rapidly changing and dynamic wireless environment is a complex and challenging task. On top of including new technologies and wireless standards, one solution is to deploy cross-layer Design (CLD) and multiple Radio Access Technologies (Multi-RAT) to develop scalable QoS-aware IoT networks. However, the complexity of such solutions is high as it involves complex inter-layer interactions and dependencies and inter-application dependencies in multi-RAT networks. Moreover, the wireless environment is very dynamic, so having an optimal constellation of parameters is a challenging task. In this paper, we address the possibilities of using Artificial Intelligence (AI) and Machine Learning (ML) to address these high dimensional and dynamic problems. Based on our findings, we have proposed a distributed network management framework employing AI & ML for studying inter-layer dependencies and developing cross-layer design, traffic classification and traffic prediction at the edge devices for reliable QoS in multi-RAT IoT networks. A thorough discussion on future directions and emerging challenges related to our proposed framework has also been provided for further research in this field.
Regularity of high energy photon events from gamma ray bursts

The effect of Quantum Gravity (QG) may bring a tiny light speed variation as v(E)=c(1−E/ELV), where E is the photon energy and ELV is a Lorentz violation scale. A remarkable regularity was suggested in previous studies to look for the light speed variation from high energy photon events of Gamma Ray Bursts (GRBs). We provide a general analysis on the data of 25 bright GRBs observed by the Fermi Gamma-ray Space Telescope (FGST). Such method allows a completed scan over all possibilities in a more clean and impartial way without any bias compared to previous intuitive analysis. The results show that with the increase in the intrinsic energies of photons, such regularity truly emerges and gradually becomes significant. For photons with intrinsic energies higher than 40 GeV, the regularity exists at a significance of 3–5 σ with ELV=3.6× 1017 GeV determined by the GRB data.
Identifying Paintwork Deterioration for Image-based Monitoring of Distribution Towers

Electrical overhead line towers are painted to pro-tect their metal surfaces from direct interaction with the en-vironment. Subsequently, paint is applied to refurbish exposed towers. On a vast network, it is difficult to identify which line segments or towers require refurbishment. Industry practice involves taking aerial images of towers and classifying the level of paint defects, albeit manually. This process is labour-intensive and subjective. In this paper, we propose a prototype system based on deep learning to automatically identify towers at risk due to paint deterioration. We use a representative tower inspection data set from the industry with 343k images of 6,333 towers for development and evaluation. Each tower is classified as being within normal operating conditions or at high risk. This is achieved by aggregating class predictions from each of the multiple images of the tower. Supervised learning used only tower-level condition labels; no annotation of individual images or image regions was used. Prototype systems based on EfficientNets achieved a test area under the ROC curve of 0.97. A true positive rate of 0.98 was obtained for a corresponding false positive rate of 0.14. Thus, we demonstrate that towers at risk from significant paintwork deterioration can be identified effectively, and that tower-level labels are adequate for training, eliminating the need for the costly annotation of sub-tower parts.
Rail Break Prediction and Cause Analysis Using Imbalanced In-Service Train Data

Timely detection and identification of rail breaks are crucial for safety and reliability of railway networks. This article proposes a new deep learning-based approach using the daily monitoring data from in-service trains. A time-series generative adversarial network (TimeGAN) is employed to mitigate the problem of data imbalance and preserve the temporal dynamics for generating synthetic rail breaks. A feature-level attention-based bidirectional recurrent neural network (AM-BRNN) is proposed to enhance feature extraction and capture two-direction dependencies in sequential data for accurate prediction. The proposed approach is implemented on a three-year dataset collected from a section of railroads (up to 350 km) in Australia. A real-life validation is carried out to evaluate the prediction performance of the proposed model, where historical data are used to train the model and future “unseen” rail breaks along the whole track section are used for testing. The results show that the model can successfully predict nine out of 11 rail breaks three months ahead of time with a false prediction of nonbreak of 8.2%. Predicting rail breaks three months ahead of time will provide railroads enough time for maintenance planning. Given the prediction results, a Shapley additive explanations (SHAP) method is employed to perform a cause analysis for individual rail break. The results of cause analysis can assist railroads to plan appropriate maintenance to prevent rail breaks.
Towards Online Socially Acceptable Robot Navigation

When robots move through social spaces (i.e., environments shared with people) such as museums and shopping centers, they must navigate in a safe and socially acceptable manner to facilitate their inclusion and adoption. Therefore, robots operating in such settings must be able not only to avoid colliding with nearby obstacles, but also to show socially accepted behaviors, e.g., by minimizing the disruption in the comfort zone of nearby people. While there are well known approaches for social robot navigation, they are mostly based on social force models, which suffer from local minima. Meanwhile, other robot navigation frameworks do not consider social aspects. In this paper, we present an online social robot navigation framework, which is capable of generating collision free and socially acceptable paths online in uncontrolled crowded environments. Our proposed framework employs a modified sampling-based planner together with a new social relevance validity checking strategy. To evaluate our approach, we have designed a simulated social space in which the Pepper robot can safely navigate in a socially accepted manner. We compare our approach with other two alternative solutions while measuring specific social navigation metrics.
On the Impact of Probability Distributions of Stochastic Processes on Power System Dynamics

The paper originates from the observation that, when fitting stochastic processes based on data measurements, it is often possible to fit various probability distribution functions (PDFs) to the same data. This paper evaluates the impact of modeling stochastic processes with different PDFs on the dynamic behavior of the power system. The case study uses the well-known two-area system, which is modified to include stochastic processes through wind generation. Simulation results show that modeling stochastic processes such as wind speeds through different PDFs impacts the behavior of the power system differently, despite exhibiting similar statistical properties. The case study also shows that some PDFs worsen the effects of contingencies.
Classification Based Hard Disk Drive Failure Prediction: Methodologies, Performance Evaluation and Comparison

Considering the reliability of the data storage system, it is essential to accurately and timely predict impending failures of hard disk drives (HDDs) so as to prevent data loss and reduce recovery cost. Over the past decades, taking as input the SMART (Self-Monitoring, Analysis and Reporting Technology) attributes, many supervised machine learning based methods have been proposed for HDD failure prediction. However, these methods are conducted on different datasets or different preprocessing treatments and thus lack comparative analysis. To fill this gap, we provide a systematic study in this paper on three key steps of the failure prediction, i.e., feature selection strategies, data preprocessing treatments and classification models. A feature selection strategy is proposed by testing the significance of difference between healthy and failed samples. Data relabeling, together with some other data preprocessing treatments are applied and proven to be effective in the case study. The performance of seven classification models are compared, among which the Random Forest model achieves the best performance with 53.95% failure detection rate (FDR) and 6.0% false alarm rate (FAR). Moreover, the Gini importance of SMART attributes is calculated, where two attributes, SMART 197 and SMART 187 are found closely related to the HDD failures.
Towards Distributed Real-Time capable Robotic Control using ROS2

While controllers for modern robots often appear to users as monolithic black-boxes, approaches exist to write custom controllers and tune them with tools from community based frameworks. Using ROS2 as such a framework, single components and parameters can be exchanged and customized easily. Especially in research institutions but also in industrial environments this framework is used increasingly to fulfill the demanding requirements of robotic control. While ROS1 had no real-time capable method of communication, ROS2 promises to give this advantage. Matching the trend to use many distributed, specialized and small systems instead of one higly system-specific, inflexible system, this study assesses tuning of DDS parameters in order to improve communication speed.
Robot Learning of Assembly Tasks from Non-expert Demonstrations using Functional Object-Oriented Network

Robot Learning from Demonstration (RLfD) is a research field that focuses on how robots can learn new tasks by observing human performances. Existing RLfD approaches mainly enable robots to repeat the demonstrated tasks by mimicking human activities, which usually requires efficient demonstrations for human experts. This paper proposes a new Function Object-Oriented Network (FOON) based approach to make robots learn and optimize assembly tasks from non-expert demonstrations. It first proposes an assembly FOON construction approach with automatic subgraph creation and merging algorithms to extract information from multiple non-expert demonstrations. It then proposes an assembly task tree retrieving approach with a robot execution optimization process to make the robot learn and generate the best possible task execution plan from the constructed FOON. The proposed approaches are validated through experiments with a dual-arm YuMi robot and the experimental results illustrate the effectiveness and advantages of the proposed approach.
Feature Extraction Methods for Depression Detection Through Social Media Text

The high number of depressed people and the fatal effect it can cause raise the urgency to detect a depressed person as soon as possible. Social media as a platform to express oneself can help us do this job. By properly extracting user-created content in social media, we can detect those who are depressed. This paper compares four feature extraction methods to find the best one. The combinations of TF-IDF, LIWC, Word2Vec, and weighted Word2Vec paired with Naïve Bayes or Linear Support Vector Machine classifiers are used on the Reddit Mental Health dataset. Word2Vec paired with the SVM classifier proved to be the best combination with 95.68% accuracy, 92.58% precision, 93.10% recall, and 92.84% F1- score. However, the weighted Word2Vec failed to improve the performance of averaging the basic Word2Vec, obtaining only 95.15% accuracy. Another finding is that SVM performed better than NB for classification though it takes significantly longer to train. This experiment shows that choosing suitable feature extraction methods will benefit the model's performance.
Evaluation of the Smoked Machine with Dual Heating Sources

This paper presents a design for a smoked machine that combines an insulated chamber with a heating filament, blowers, and a biomass smoking furnace. A combination of energy sources from heating filament and wood which was fed into the furnace at predetermined amounts were utilized to achieve the desired temperature. Temperature and airflow velocity in the chamber of the machine were simulated to elucidate the behaviour of hot air flow and observed the temperature difference at every point in the machine. The finite element analyses showed that there was a stagnant air pocket in the upper rear region of the chamber due difference in pressure at the inlet and outlet. Therefore, the design was optimized, and a rotating tray design was proposed for the pilot test machine. Then, a pilot test was carried out to verify the proposed design. The pilot tests were conducted using banana fish, which is a type of Albula vulpes species, weighing from 0. 3-0.4kg/head. It was observed that the temperature of the smoking chamber varied from 85ºC and 115ºC. The results showed that the smoking system was able to perform the smoking process of the fish in 3 hours with 50% moisture reduction and no coliform or e-coli were found in the samples. Based on the test results, the dual heating smoking system developed and evaluated in this study is predicted to be able to complete the smoking process of the fish in 4-5 hours with 70% moisture reduction which complies with food safety regulations. In addition, it has the capacity to produce hygienic smoked fisheries.
An Analytical Approach to Energy Storage Sizing for Inertia and Frequency Reserve Support

Large-scale integration of renewable energy resources (RER) in the power system can lead to the displacement of conventional generation systems, consequently reducing the system inertia response (IR) and primary frequency reserve (PFR). The energy storage systems (ESS) can help improve the frequency stability of RER integrated systems by proving IR and PFR support. In this paper, an analytical method to size the ESS for IR and PFR support of RER integrated systems is proposed. The proposed method presents an analytical model to reevaluate the system parameters considering the reduction in system inertia and equivalent frequency regulation due to RER integration. The reevaluated parameters are then solely used for ESS size estimation, resulting in a computationally inexpensive ESS sizing algorithm. The proposed method is applied to the WSCC 9-bus system where the ESS sizes are estimated with varying RER integration levels and a sensitivity analysis is performed to evaluate the performance of estimated ESS sizes in IR and PFR support modes.
Quantitative Power System Resilience Metrics and Evaluation Approach

Power system resilience is an emerging topic and plays an essential role in helping power industry understand and respond to the increasing threats of extreme weather events. The first step of power system resilience analysis is to introduce metrics to quantity the resilience reasonably. Existing resilience metrics are typically restrained by the limited data for extreme event modeling and fall short in terms of physical interpretation and comparability. This paper develops novel quantitative metrics to evaluate power system resilience in pre- and post-event contexts. The developed metrics illustrate clear physical meanings and can be effectively used to compare resilience across different systems under different extreme events. Moreover, the developed metrics can be applied to both transmission and distribution systems. Simulation on a distribution system is employed to validate the effectiveness of the proposed resilience metrics and resilience evaluation approach.
What drives patients to choose a physician online? A study based on tree models and SHAP values

Smart healthcare is changing our lives. As an emerging medical pattern, online medical platform is arising from the combination of traditional medical resources and Internet platform, which largely resolve the disequilibrium of offline medical resources in China. Compared with offline healthcare, online platforms shorten the distance between patients and medical resources and give patients more options to seek medical treatment during the COVID-19 epidemic. In order to better help and guide patients in making decisions, the platform provides physicians’ treatment information for patients’ reference. This information describes the physician’s diagnostic capability and service level from different dimensions, such as the physician’s specialty, the number of gifts received from patients, etc., which are important basis for patients to choose a physician. For the platform and physicians, it is crucial to understand patients’ preferences for different characteristics of physicians in the consultation process, in order to manage data more targeted. This paper use machine learning methods to build a prediction model of physician’s characteristics data on incremental volume of consultation to study patients’ preferences in medical consultation. Most existing studies use linear models, but given the complexity of patient preferences, they may have greater limitations in reflecting patients’ choice logic. Therefore, this paper turn to more complex models on the training data. For the lack of interpretation of complex models, this paper uses a Shapley Value-based approach to parse the model’s feature contributions to obtain patients’ preferences for physician information. From the perspectives of local interpretation, global interpretation and interaction effect, this paper obtains regular conclusions on patients’ preferences for physicians’ information, and discusses the management insights in the context of online platform management and physicians’ word-of-mouth maintenance.