Miracle-dz/UniFAR-FTUs huggingface dataset:
Space and Time Scales in Ambient Ozone Data

Abstract This paper describes the characteristic space and time scales in time series of ambient ozone data. The authors discuss the need and a methodology for cleanly separating the various scales of motion embedded in ozone time series data, namely, short-term (weather related) variations, seasonal (solar induced) variations, and long-term (climate–policy related) trends, in order to provide a better understanding of the underlying physical processes that affect ambient ozone levels. Spatial and temporal information in ozone time series data, obscure prior to separation, is clearly displayed by simple laws afterward. In addition, process changes due to policy or climate changes may be very small and invisible unless they are separated from weather and seasonality. Successful analysis of the ozone problem, therefore, requires a careful separation of seasonal and synoptic components. The authors show that baseline ozone retains global information on the scale of more than 2 months in time and about 300 km.
Excitation of the 5-day Wave by Antarctica

Abstract The 5-day wave has been detected in composite maps of geopotential height fields of European Centre for Medium-Range Weather Forecasts dataset for 7 years (1984–91, except 1985) and shows a characteristic northwest–southeast meridional phase tilt in the Southern Hemisphere. A numerical integration of Laplace’s tidal equations with periodic forcing of zonal wavenumber 1 only produced the meridional phase tilt when the forcing is located in high latitudes. Such a forcing is created through coupling of the time-fluctuating westerlies with the topography of Antarctica. Numerical simulations that incorporated this mechanism reproduced the observed meridional phase tilt of the 5-day wave, which suggests that Antarctica is responsible for the observed phase tilt through the process of resonance. A linear theory on the meridional phase tilt is given with a nondivergent barotropic model that includes both forcing and dissipation.
Ensemble Cloud Model Applications to Forecasting Thunderstorms

Abstract A cloud model ensemble forecasting approach is developed to create forecasts that describe the range and distribution of thunderstorm lifetimes that may be expected to occur on a particular day. Such forecasts are crucial for anticipating severe weather, because long-lasting storms tend to produce more significant weather and have a greater impact on public safety than do storms with brief lifetimes. Eighteen days distributed over two warm seasons with 1481 observed thunderstorms are used to assess the ensemble approach. Forecast soundings valid at 1800, 2100, and 0000 UTC provided by the 0300 UTC run of the operational Meso Eta Model from the National Centers for Environmental Prediction are used to provide horizontally homogeneous initial conditions for a cloud model ensemble made up from separate runs of the fully three-dimensional Collaborative Model for Mesoscale Atmospheric Simulation. These soundings are acquired from a 160 km × 160 km square centered over the location of interest; they ar.
Time to Coalescence for a Class of Nonuniform Allocation Processes

We study a so-called coalescent process that can be described as follows: start with a set of n boxes and b0 balls. Let p = (p1, p2, . . . , pn) be any probability vector. Throw each ball into box j with probability pj, independently for each ball. Any balls that land in the same box are fused into a single ball, and the process is repeated with this (possibly smaller) number of balls. Continue this process until there is only one ball left; the time at which this happens is called the coalescence time, denoted T . This problem can also be phrased in the context of population genetics, where it is referred to as the Generalized Wright-Fisher Model. In that formulation, the balls represent ancestral lineages, and T is the the number of generations back in time one has to go to find a common ancestor for b0 individuals from the current generation. We shall mainly study the expected coalescence time E[T ]. For b0 = n, and p nonuniform, little is known about the expected time spent when the number of balls is relatively large. We show that for vectors p satisfying a mild uniformity condition, this quantity is negligible compared to the expected time spent when the number of balls is “small”, which is asymptotically 2( ∑ j p 2 j) −1. We further show that this condition is sharp, in that if it is not met, there are vectors p which give rise to processes which do not have this qualitative behavior, and thus where the expected coalescence time far exceeds 2( ∑ j p 2 j) −1.
Modern Techniques of Power System Analysis with Intelligent System

Analysis of power system consisting of analysis of power distribution, power transmission and power generation. It is very important for efficient operation of power system. Different modern techniques are used for power system analysis by using intelligent system, artificial intelligence, machine learning and optimization techniques. Intelligent system is useful for different businesses and industries. While analyzing the power system, different parameters are considered such as arc resistance, fault current and fault voltage. Analysis of power system with modern intelligent electronic devices, distributed generation and hybrid type superconducting fault current limiter is very important. While calculating fault current and arc resistance for different fault conditions in power system, business intelligence tool is used for data integration, data analysis and data visualization.
3D mesh pose transfer based on skeletal deformation

For 3D mesh pose transfer, the target model is obtained by transferring the pose of the reference mesh to the source mesh, where the shape and pose of the source are usually different from that of the reference. In this paper, pose transfer is considered as a deformation process of the source mesh, and we propose a 3D mesh pose transfer method based on skeletal deformation. First, we design a neural network based on the edge convolution operator to extract the skeleton of the 3D mesh and bind the rigid weights; then, we calculate the bone transformations between the two skeletons with different poses and use the diffusion equation to smooth the rigid weights; finally, the source mesh is deformed according to the bone transformations and the smooth weights to get the target mesh. Experiment results on different datasets show that the pose of the reference mesh can be effectively transferred to the source one while maintaining the shape and high‐quality geometric details of the source mesh by using our method.
The Theory of Risk

SUMMARY The classical theory of risk is reviewed. It is shown to be a static theory and this is adduced to be its main disadvantage. A dynamic theory was introduced by Lundberg in 1909. This is discussed and Lundberg's achievement is reaffirmed. However, it is pointed out that this theory, in spite of extensive mathematical development, has found virtually no application in practice. The major difficulty lies in the formulation of insurance problems in a convincing way. In the last section a new approach is called for. A model incorporating dynamic features of Lunberg's collective risk theory with more realistic constraints is investigated. Possible directions for future research are suggested.
AGN with faint broad line regions: Some ‘True’-Seyfert 2s might be Narrow Line Seyfert 1s

Abstract The intriguing existence of ‘True’-Seyfert 2s has opened a debate on the validity of the unified model of active galactic nuclei (AGN). These objects, also called ‘Naked’-AGN, seem to lack a broad line region. In some cases, their X-ray emission is unabsorbed, typical of Seyfert 1 galaxies, indicating a clear view toward the nucleus, but no broad lines are seen in the optical. In ‘True’-Seyfert 2s with higher column densities, observations of polarized light have failed to reveal the hidden broad emission. We performed high-resolution near-infrared integral-field observations of two ‘True’-Seyfert 2 candidates: IRAS 01072+4954 and NGC 7496. We found that the AGN in these sources might power very faint and narrow broad emission lines (FWHMbroad < 1500km s−1). Here, we discuss the properties of the broad components estimated from published X-ray and/or optical measurements and present their near-infrared candidate identification. Both galaxies host intermediate-mass black holes, with masses ~ 105−106 M⊙. Our results favor the unified model in the cases of high accretion rates, and stress the necessity of a multiwavelength approach to unveil the nature of ‘Naked’-AGN.
The ZIMPOL high contrast imaging polarimeter for SPHERE: polarimetric high contrast commissioning results

SPHERE (Spectro-Polarimetric High-contrast Exoplanet Research) is a second generation VLT instrument aimed at the direct detection of exo-planets. It has received its first light in May 2014. ZIMPOL (Zurich Imaging Polarimeter) is the imaging polarimeter subsystem of the SPHERE instrument. It's capable of both high accuracy and high sensitivity polarimetry but can also be used as a classical imager. It is located behind an extreme AO system and a stellar coronagraph. ZIMPOL operates at visible wavelengths which is best suited to detect the very faint reflected and hence polarized visible light from extra solar planets. During the SPHERE fourth commissioning period (October 2014) we have made deep coronagraphic observations of the bright star alpha Gru (mR = 1.75) to assess the high contrast polarimetric performance of SPHERE-ZIMPOL. We have integrated on the target for a total time of about 45 minutes during the meridian transit in the Very Broad Band filter (600 - 900 nm) with a classical Lyot coronagraph with 3 λ/D radius focal mask. We reduce the data by a combination of Polarized Background subtraction, Polarimetric Differential Imaging (PDI) and Angular Differential Imaging (ADI). We reach contrasts of 10-6 and 10-7 at a radial distances of respectively 7 and 14 lambda/D from the PSF core. At these radial distances we are respectively a factor of 10 and 2 above the photon noise limit. We discuss our results by considering the temporal and spatial speckle behavior close to the PSF core in combination with low order polarimetric aberrations.
Postpartum ovarian vein thrombosis: a case report

Introduction: Ovarian vein thrombosis (OVT) is a rare condition most frequently seen in the immediate postpartum period. Typical symptoms include pelvic pain, fever, and abdominal mass. Although a rare diagnosis, OVT can potentially cause fatal complications; thus, early recognition and prompt treatment is important. Case presentation: We present a case of a 30-year-old lady, G4P3A0, previously healthy, who presented at 29+3 weeks to the hospital for preterm premature rupture of membrane. The patient had a Cesarean section (C-section), which was complicated with uterine atony and massive bleeding controlled by emergent intrauterine balloon tamponade and uterine artery embolization. The next day, the patient complained of new-onset right lower quadrant abdominal pain, for which she had an abdominal and pelvic computed tomography (CT) scan that showed OVT. Thus, she was started on anticoagulants, and discharged home. Conclusion: OVT is a rare disorder that has been described as occurring mainly during the postpartum period, after pelvic surgery, or in women with gynecological malignancies. Clinical features of OVT include fever, abdominal pain and tenderness, and a palpable abdominal mass. Diagnosis can be obtained using CT, magnetic resonance (MR), or ultrasound (US) Doppler. Treatment includes a combination of anticoagulants and antibiotics. Mortality is low nowadays.
Optimization of multi-arm robot locomotion to reduce satellite disturbances during in-orbit assembly

Traditionally, manufacturing and assembly of space assets is performed on ground before sending them into orbit. However, this monolithic approach involves high launch costs due to increasing asset sizes, e.g., large telescopes for space observation. Alternatively, in-orbit assembly of space structures after launching the raw materials to orbit opens wider possi-bilities at a reduced cost. Mobile robotics, such as walking manipulators or multi-arm robots, are a critical component for this approach due to their mobility in orbit. However, unlike ter-restrial assembly tasks, the continuous motion of the robot and materials, coupled with the change of inertial properties of the structure, results in a rotational deviation of the platform due to conservation of angular momentum in orbit. This might violate the tolerance limits of the platform antenna's cone angle for communication with the ground stations. Although exploiting the attitude control system of the platform is a straightforward solution, it might lead to issues related to the associated actu-ators like reaction wheels saturation, high-frequency vibration, or high fuel consumption. To deal with this problem, in this paper we formulate the attitude disturbance problem as a minimization of the effects created by the gait of the walking manipulator. Investigating the dynamic coupling between the robot system and the space structure gives a deeper understanding of the spacecraft's be-havior depending on the robot gaits. The paper proposes a controller that optimizes the forces that the robotic arm applies to the structure, hence minimizing the base rotation. As an application, we use a space structure composed of identical elements, namely the mirrors of a segmented telescope, endowed with standard interfaces to allow the robot locomotion. We show the effects of optimizing these interaction forces in various sce-narios and positions on the structure through multiple dynamic simulations.
Stress Recognition from Speech by Combining Image-based Deep Spectrum and Text-based Features

This paper investigates how stress can be expressed by speech not only with the acoustic part but also semantic information. This investigation is established in order to recognize the different intensities of stress in surveillance applications. This research aims to recognize stress in human-human interactions at service desk by analyzing their behavioral patterns when interacting with each other's. More specifically, this paper focuses on stress recognition from speech with its verbal (acoustic) and non-verbal (semantic) parts. For this purpose, we combine image-based deep spectrum with text-based features using neural networks. In the acoustic part, we use pre-trained Convolutional Neural Networks (CNNs) to extract descriptors from audio spectrograms. These descriptors are defined as deep spectrum features. Indeed, these features are the activations of fully connected layers from VGG16 which is an image classification CNN. In the semantic part, we adopt text-based features including linguistic, word affect and indications of spontaneous speech. To obtain the final feature set for both deep spectrum and textual features, we apply Multilayer Perceptrons (MLPs) to learn the representations before twinning them with a neural network. Our fusion method achieves accuracies for weighted and unweighted average of 83.57% and 82.46%, respectively.
Aleatoric and Epistemic Uncertainty Quantification in Bayesian Dirichlet Cost Rules of Thumb

The total cost of any project is the sum of the costs of its components. At NASA, these components are called the Work Breakdown Structure (WBS). There are eleven numbered elements at the top level of the NASA WBS, including 1: Project Management, 2: Systems Engineering, 4: Science/Technology, 5: Payload(s), and 6: Spacecraft. During the earliest phases of the project life cycle, the costs of only one or a few of these components are well constrained, and the cost of the other elements must be estimated in order to submit a proposal with a reasonable chance of success. In the absence of time, money, or the level of detail to produce grassroots estimates, cost estimators look to past missions to gain insight; they have traditionally averaged the percentage allocations for each WBS breakdown from previously flown missions and used them to predict allocations for the new project that they are trying to cost. At IEEE 2022, we presented a novel costing method to project costs for future MIDEX class missions; rather than relying on average percentages, we proposed a Dirichlet-distributed model, which aptly describes the uncertainty of resource allocation by capturing the correlation between components. Here, we expand on our work from last year by (a) applying similar methodologies to more mission classes other than MIDEX (Flagship, New Frontiers, Discovery, SMEX, etc.), (b) imputing missing data records using expert knowledge of how costs are bookkept, (c) expanding our communication to align with up-to-date Uncertainty Quantification standards (categorizing uncertainty as aleatoric or epistemic), and (d) generating an interactive web tool easily accessible and usable by systems engineers.
Impact of Design and Process on Alpha-Induced SER in 4 nm Bulk-FinFET SRAM

This paper evaluates alpha-induced soft error rate $\boldsymbol{(\alpha \text{SER})}$ by alpha irradiation test in four different SRAMs and simulation. The test result shows the impact of three factors on $\boldsymbol{\alpha \text{SER}}$: process technology, the number of fins, and fin-pitch. The process technology advancing from 7 nm to 4 nm increases the $\boldsymbol{\alpha \text{SER}}$ by 33%, the #fin change (2-fin to 1-fin) decreases the $\boldsymbol{\alpha \text{SER}}$ by 54%, and the fin-pitch shrinking increases the $\boldsymbol{\alpha \text{SER}}$ by 17%. The simulation results show that the process variation does not contribute to the $\boldsymbol{\alpha \text{SER}}$. The BEOL thickness change can increase the $\boldsymbol{\alpha \text{SER}}$ by 1.24X. This paper also discusses the $\boldsymbol{\alpha \text{SER}}$ trend in SRAM from 130 nm to 4 nm technologies. Overall, the study aims to investigate the impact of process technology and design parameters on $\boldsymbol{\alpha \text{SER}}$ in SRAMs.
Evaluation of different deep learning approaches for EEG classification

EEG classification is a promising approach to facilitate the life of handicapped people and to generate future human-computer-interfaces. In this paper we want to compare the effectiveness of current state of the art deep learning techniques for EEG classification. Therefore, we applied different approaches on various datasets and did a crosscomparison of the results in order to get more knowledge on the generalization capabilities. Additionally, we created a new EEG dataset and made it available for further research.
Intelligent Situation Sensing and Multi-dimensional Presentation for Power Emergency Communications

Power emergency communications are very important to smart grids when some natural disasters happen. In this case, situation sensing can provide key scenario information to decision makers and disaster rescuers. Furthermore, the situation information should be presented from different dimensions. This paper studies intelligent situation sensing issues and provides key solutions from theoretical and practice aspects. The tensor theory is used to model multidimensional situation information, and a new definition of situation sensing is provided for power emergency communications. Some artificial intelligent (AI) schemes such as multi-head attention mechanism and federated learning (FL) are used to construct situation sensing schemes. Simulation results indicate that the proposed situation sensing schemes can achieve better sensing performance.
A comprehensive IoT-Based Home Automation Solution: Analyzing Security Threats, Maintenance and Proposal

Every aspect of life in this 4th industrial revolution era is influenced by technology. IoT is a key component of this technology since it enables the integration of multiple sensors into a network that is globally connected and where communication and sharing of information are associated. IoT is an effective evidence holder that provides real-time data updates and preserves any outdated information. Analyzing the security threats and maintenance concerns and future scope, we presented a solution in this paper. This research aims to design a biometric technology coupled with a sensor-integrated framework for defense. Our Prototype is a combination of multiple subsystems: i. Authentication Mechanism ii. Remote Maintenance. The first mechanism guarantees safety enhancement by prohibiting unauthorized visits by scanning fingerprints at the entrance. The other one is an advanced sensor module that takes immediate action whenever it detects an occurrence of an accident. Finally, there is a web panel server interconnected, through which we can keep track of all the real-time updates of the residence. In order to maintain our loved ones' safety, we are advancing technology in this way with the certainty of security.
Robust topology optimization of thin plate structure under concentrated load with uncertain load position

1. Abstract This study investigates the robust topology optimization of the thin plate under concentrated load with uncertain load point. Several researches investigated the effect of uncertain load direction, load magnitude or load distribution on the topology optimization. However, the robust topology optimization considering uncertainty of the load point has not been studied yet. In this study, the load point uncertainty is modelled through the convex hull model. The nominal concentrated load in out-of-plane direction is applied at the center of the plate modeled based on Reissner-Mindlin plate theory. The load point uncertainty is limited in a circle centered at the nominal load point. The worst load condition is defined as the applied load at the worst point in the convex hull that gives the worst value of the mean compliance. The worst point is easily obtained from the convex hull approach. Then, the robust objective function is formulated as a weighted sum of the mean compliance obtained from the mean load condition and the worst compliance obtained from the worst load condition. This robust topology optimization is constructed using the level set-based topology optimization method. Through numerical examples, the robust optimum configuration is compared with the deterministic optimum configuration. Then, validity of the proposed robust design method is discussed.
Pedestrian Localization with Stride-Wise Error Estimation and Compensation by Fusion of UWB and IMU Data

Indoor positioning enables mobile machines to perform tasks (semi-)automatically, such as following an operator. However, the usefulness and safety of these applications depends on the reliability of the estimated operator localization. Thus, quantifying the accuracy of positioning at runtime is critical for the application in real-world industrial contexts. In this paper, we present a method that produces an estimate of the current positioning error for each user stride. To accomplish this, we construct a virtual stride vector from Ultra-Wideband (UWB) position measurements. The virtual vectors are then compared to stride vectors from a foot-mounted Inertial Measurement Unit (IMU). Using these independent measurements, we estimate the current reliability of the UWB measurements. Positioning errors are mitigated through loosely coupled filtering of both vector types. We evaluate our method in three environments, showing that it improves positioning accuracy, especially in challenging conditions with obstructed line of sight and sparse UWB infrastructure. Additionally, we demonstrate the mitigation of simulated spoofing attacks on UWB positioning. Our findings indicate that positioning quality can be judged at runtime by comparing user strides reconstructed from UWB and IMU measurements. Our method is independent of situation- or environment-specific parameter tuning, and as such represents a promising approach for detecting both known and unknown positioning error states.