Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulations of core formation for frequent dark matter self-interactions

We present the first $N$-body simulations that adapt the equations of smoothed particle hydrodynamics to capture the effect of dark matter self-interactions which are too frequent to be resolved explicitly. The relevant energy transfer equations are derived, the appropriate thermal conductivity is determined and the effects of different smoothing kernels are studied. We apply our framework to simulate the formation of isothermal cores in isolated dark matter haloes and determine the core growth rate as a function of the self-scattering cross section. Our approach may be combined with explicit simulations of rare scatterings in order to simulate accurately the effects of arbitrary dark matter self-interactions in future cosmological simulations.
Handling-Design Method by Multi-primitive Recognition of Object Shape

This study proposes a method for generating the optimum grasp position of a robotic hand. First, the shape of the object was approximated as multi-primitive to simplified. Subsequently, two parallel planes are extracted from the multi-primitive object, various grasping positions were computed for the two planes, and grasping evaluations were performed for the grasping direction to generate the optimum grasping position. As a result of the experiment, we found that 10 grasps were successfully obtained with seven types of object and posture.
Feature Extraction and Classification Stage on Facial Expression: A Review

Human facial expression becomes an important technology in recent years. As information technology and networks have grown, identification and authentication have become more frequent in people's daily lives, especially using biometric technology. Human facial recognition involves face detection, feature extraction, and classification. A lot of experiments showed that there are various techniques for extracting facial features and classifying facial expressions. This paper reviews and analyze the various optimization techniques on extract feature and classification stage for human facial expression recognition. This review will compare two kinds of extract features methods and one classification method. The first technique of extracting features is the optimization technique using the K-Mean algorithm, which helps to increase recognition accuracy. The second extract feature is an optimization technique using improved Gradient Local Ternary Pattern (GLTP) which is beneficial for computational resources efficiency. Lastly, the optimization technique for image classification using a three-staged Support Vector Machine (SVM) is very helpful for increasing accuracy and eliminating error. The modified GLTP is able to obtain an accuracy of 97%.
Analysis and Comparison of Marchand and Transformer Baluns Applied in GaAs

Analysis and measurement validation of Marchand and Transformer baluns is presented in this brief that compares key performance metrics including amplitude and phase balance, circuit size, bandwidth and impedance match. Whilst novel variants of these circuits have been published in the open literature to improve their performance by direct compensation, there yet remains a fundamental comparison of the core coupled-line structures to reveal a systematic design procedure for baluns. Simple analytical equations are derived for both the baluns to reveal a deep analytical insight and a basis for performance comparison. It is then validated by measurement on the same gallium arsenide wafer, which is demonstrated for the first time. A systematic design approach is summarized that can be followed by other designers that has resulted in state-of-the-art performance for both balun types without the need for additional compensation and or tuning techniques at the output ports. The Marchand balun covers a frequency range of 30-65 GHz with a measured amplitude and phase imbalance less than 0.7 dB and 1.2° respectively. The transformer balun covers a frequency range of 30-45 GHz with a measured amplitude and phase imbalance less than 0.2 dB and 0.9° respectively.
A combined HST and XMM-Newton campaign for the magnetic O9.7 V star HD 54879: towards constraining the weak-wind problem of massive stars

Context: HD 54879 (O9.7 V) is one of a dozen O-stars for which an organized atmospheric magnetic field has been detected. To gain insights into the interplay between atmospheres, winds, and magnetic fields of massive stars, we acquired UV and X-ray data of HD 54879 using the Hubble Space Telescope and the XMM-Newton satellite. In addition, 35 optical amateur spectra were secured to study the variability of HD 54879. A multiwavelength (X-ray to optical) spectral analysis is performed using the Potsdam Wolf-Rayet (PoWR) model atmosphere code and the xspec software. Results: The photospheric parameters are typical for an O9.7 V star. The microturbulent, macroturbulent, and projected rotational velocities are lower than previously suggested ( $12\,R_\odot$, implying that its true mass-loss rate is $\log \dot{M}< -10.2\,[{M_\odot}/{\rm yr}]$. Hence, density enhancements around magnetic stars can be exploited to estimate mass-loss rates of non-magnetic stars of similar spectral types, essential for resolving the weak wind problem. Conclusions: Our study confirms that strongly magnetized stars lose little or no mass, and supplies important constraints on the weak-wind problem of massive main sequence stars.
Opinion Divergence Reveals the Hierarchical and Overlapping Community Structure in Networks

Often exhibiting hierarchical and overlapping structures, communities or modular groups are fundamental and complex in network science. One of the most exploited tools to detect the mesoscopic structure is synchronization. Several phenomena including convergence rate and local convergence under constraints are studied to uncover the existence and features of communities. Here, employing a background of opinion dynamics, we study the divergence of agents' opinions, i.e. the state differences of nodes, in the progress of reaching global consensus and then reveal its connections with hierarchy and overlap in the modular structure. Furthermore, based on the obtained close relationships, a new method is proposed to identify hierarchical and overlapping communities, whose robustness and efficiency are validated via experiments on real and artificial networks. Both the connections and approaches provide a novel insight on the features of modular structures in networks.
Experimental Comparison of Hybrid Sampling Methods for an Efficient NIDS

The recent tremendous development of the Internet and network technology has made our lives more convenient. However, problems such as cyberattacks and privacy concerns are also emerging. Cyberattacks such as DDoS, flooding attacks, and zero-day attacks are causing economic damage not only to enterprises and organizations but also to many users. Therefore, the importance of cyber security is increasing, and various devices are used to provide network security functions. The most representative network security device, the Network Intrusion Detection System (NIDS), analyzes the existing public dataset and applies it to intrusion detection. However, most public datasets for network security are unbalanced data with more benign data than malicious data. In order to design an effective NIDS, it is necessary to solve such data imbalance, and various sampling techniques are applied to solve the data imbalance problem. In this paper, a hybrid sampling method that combines undersampling and oversampling is applied to solve the data imbalance problem. Each sampling method generates sampled data and transforms each data into grayscale images to train a CNN-based detection model. As a result of the experiment, the hybrid sampling method outperforms the oversampling method. In particular, OSS-BSMOTE achieved the highest performance of 94.58% accuracy, 94.58% F1-Score (micro), and 91.36% F1-Score (macro).
Nondestructive Testing and Evaluation of Smart Textile Sensors using Embedded Systems

The integration and coating with conductive elements allows the use of textiles as flexible sensors for physical and chemical quantities. These conductive textiles combine the mechanical properties of the carrier material with the electrical characteristics of the conductive elements. Due to inhomogeneities in manufacturing as well as the influence of ageing factors, such as mechanical or chemical stress, the accuracy of these sensor systems suffers over the product lifespan. In this paper, we present a comprehensive evaluation system to detect these inhomogeneities in the textile material and measure the influence of the most common ageing factors. We compare several measuring methods from electrical engineering, medicine and chemistry in order to select the best methods for wear detection in conductive textiles. Using test stands for accelerated ageing, the ability to measure these effects was also successfully demonstrated on embedded systems. This means that the next generation of smart textile sensors systems are able to compensate for inhomogeneities in the production process as well as wear and tear due to ageing by itself. Thus, a reduction of waste, a longer lifetime of the sensor systems as well as improved accuracy can be achieved.
Home Energy Management with Clothing Integrated Thermal Comfort and EV SOC Concern

This paper proposes a home energy management system (HEMS) while considering the residential occupant's clothing integrated thermal comfort and electrical vehicles (EV) state-of-charge (SOC) concern. An adaptive dynamic program-ming (ADP) based HEMS model is proposed to optimally determine the setpoints of heating, ventilation, air conditioning (HVAC), the donning/doffing decisions for the clothing conditions and charging/discharging of EV while taking into account the uncertainties in outside temperature and EV arrival SOC. We use model predictive control (MPC) to simulate a multi-day energy management of a residential house equipped with the proposed HEMS. The proposed HEMS is compared with a baseline case without the HEMS. The simulation results show that a 47.5% of energy cost saving can be achieved by the proposed HEMS while maintaining satisfactory occupant thermal comfort and negligible EV SOC concerns.
Transmit diversity using rotated constellations with Hadamard transform

We introduce a general framework to build block space-time (ST) codes by the combination of rotated constellations and the Hadamard transform. We prove that these codes have a maximum transmit diversity over a quasi-static and fast fading channels. Furthermore, we prove that the conjunction of the rotated constellations and the Hadamard transform is quasi-optimum in the sense of maximizing the coding gain. The main modulated data stream is split into n substreams which, by the aid of a rotation and the corresponding Hadamard transform, are encoded into a ST n/spl times/n matrix. Given m receive antennas, the received signal at each one is the superposition of n symbols affected by independent fades and disturbed by noise. The generalized algebraic space-time (GAST) codes transmit at a rate of 1 symbol/sec for any number n such that n is 1, 2 or a multiple of 4. They maintain their rate, diversity and coding gains whether the used constellation is real or complex. Using the GAST codes with complex constellations largely outperforms the codes from orthogonal design for high spectral efficiency since the latter transmit at 1/2 symbol/sec, and the GAST codes are available for a much larger spectrum of dimensions. Maximum likelihood (ML) decoding is performed by the sphere decoder (SD) and has a moderate complexity (up to n=32).
Optimization of a state feedback controller using a PSO algorithm

The main contribution of this work is to solve the optimization problem associated to the gain tuning of a state feedback controller using a PSO algorithm through a new cost function. The latter is built by adding a term to the classic quadratic cost function used in the Linear Quadratic Regulator (LQR) method, which depends on the quadratic value of the time derivative of the control signal. The added term weights the amount of chattering, and the sharpness and level of the peaks appearing in the control signal. High levels of chattering may produce undesired oscillations and actuator wear, and reducing this behavior is paramount in practice. On the other hand, large peaks could generate actuator saturation which may lead to instability. An experimental comparative study is made between the proposed and the standard LQR method by means of an experimental platform. It is observed that the closed-loop system with the gains obtained through the proposed method produces a control signal with reduced peaks and less chattering compared with the signal obtained with the gain generated by the LQR method.
Bio-Inspired Obstacle Avoidance using Wavelet-Based Element Analysis

The paper proposes a bio-inspired approach to the obstacle avoidance problem using analytic wavelet transform to extract perception cues for the reactive control. The small and large scale environment features are perceived using laser scans. Wavelets are then used as the bases of projection of the spatial signals, in order to decompose and observe the environment features of interest. The wavelet-based element analysis is leveraged for this purpose, using Morse wavelets as the analytic wavelet bases. The inspiration of the approach is derived from the principle of sensorimotor convergence that has been studied in many small living organisms. A steering potential function, based on a human walking model, is finally used to close the control loop. The safety of the method is analyzed by computing appropriate barrier certificates. The algorithm is programmed and released as a ROS/C++ package and is thoroughly tested using physical hardware experiments.
Distant monsters: high redshift AGN predictions from a $\Lambda$CDM cosmological model

The next decade promises to revolutionise the study of the high redshift Universe: upcoming missions such as JWST, EUCLID and ATHENA will greatly advance our understanding of the early stages of galaxy formation. We use an updated model for the evolution of masses and spins of supermassive black holes (SMBHs), coupled to the latest version of the semi-analytical model of galaxy formation GALFORM using the Planck cosmology, and a new high resolution Millennium style dark matter simulation to make predictions for multiwavelength Active Galactic Nuclei (AGN) luminosity functions in the redshift range $0 6$ in the broadband filters of JWST, EUCLID and ATHENA. We predict that these three surveys will select three somewhat different samples of SMBHs, with EUCLID unveiling the more massive, high accretion rate SMBHs, JWST the less massive, lower accretion rate SMBHs and ATHENA covering objects inbetween. Overall, typical detected SMBHs will have masses, $M_{\mathrm{BH}} \sim 10^{6-7} M_{\odot}$, with dimensionless mass accretion rates, $\dot{M}/\dot{M}_{\mathrm{Edd}} \sim 1-10$, in host galaxies of stellar mass, $M_{\star} \sim 10^{8-9} M_{\odot}$. The future comparison of these predictions to observations will provide insights into our understanding of SMBHs in galaxy formation.
Numerical Models of Irrotational Binary Neutron Stars in General Relativity

We report on general relativistic calculations of quasiequilibrium configurations of binary neutron stars in circular orbits with zero vorticity. These configurations are expected to represent realistic situations as opposed to corotating configurations. The Einstein equations are solved under the assumption of a conformally flat spatial 3-metric (Wilson-Mathews approximation). The velocity field inside the stars is computed by solving an elliptical equation for the velocity scalar potential. Results are presented for sequences of constant baryon number (evolutionary sequences). Although the central density decreases much less with the binary separation than in the corotating case, it still decreases. Thus, no tendency is found for the stars to individually collapse to black hole prior to merger.
Load-Haul Cycle Segmentation with Hidden Semi-Markov Models

Precise tracking of haul truck activities in mining aids automation and data analysis and allows for accurate material tracking. Fleet Management Systems (FMS) record information about haul truck activities, however, today’s mines frequently employ legacy systems with limited sensing leading to poor data quality. In this paper we present a novel method for load-haul cycle segmentation using vehicle telemetry data to determine precise load and dump times. Central to this is a Hidden Semi-Markov Model (HSMM) which infers a truck’s state from discrete observations generated from GPS positional data. The HSMM is trained unsupervised from historic data. Evaluation of segmented state estimates made from real-world data showed over 98% of loads and 91% of dumps were correctly identified, demonstrating the effectiveness of the proposed method.
The Task of Identifying Morphological Errors of Words in the Kazakh Language in Social Networks

On the Internet and on Instagram, Vkontakte, Twitter and other social networks, applications are very attractive in terms of receiving and analyzing information in messages, because the information in these systems is real. However, text posts, user comments in social. networks often differs from the generally accepted norms of the language. There are mistakes in it, deliberate distortions of words. Unfortunately, such inaccurate data cannot be taken into account in content analysis, statistical or sentiment analysis of data. Words with such errors, i.e. incorrect words can be processed and analyzed, identifying and correcting them into the correct form. This paper presents research and implementation of a model for determining incorrect words of the Kazakh language in semi-structured data, using the example of comments and posts on social networks. To solve and analyze the task, the following was done: a comparative analysis of text correction systems was carried out; explored various spell-checking technologies; the most frequently used errors are identified and the classification of errors in words is presented. A dictionary of the basics of the Kazakh language from the electronic corpus has been developed. The authors have developed an approach to identify incorrect words and auto-replace with a suitable candidate object (word) for the Kazakh language. To solve the problem, a approach is presented to detect incorrect words in the Kazakh language. An approach has been developed for identifying incorrect words from semi-structured data, which is based on the stemming algorithm with lexicon stems according to the CSE (Complete Set of Endings) Experimental calculations and evaluation of the results have been carried out.
An Event-Driven Finite-Time Distributed Optimization Algorithm for Economic Dispatch in Islanded Microgrids

This paper considers the economic dispatch problem (EDP) with general convex cost functions in islanded microgrids. First, the EDP is equivalently modeled as a distributed uncon-strained optimization problem. Then, a distributed optimization algorithm with finite-time convergence is proposed. Furthermore, an asynchronous distributed event-triggered sampling scheme is established to reduce the information exchange required. Our convergence analysis shows that the proposed algorithm can solve the EDP in finite time, and events cannot be triggered an infinite number of times in any finite time period, that is, it does not have Zeno behavior. Both algorithms are initialization-free, which means that they do not require the sum of the initial output power of dispatchable distributed energy resources (DDERs) to equal the total demand. Finally, simulation results illustrate the effectiveness and scalability of our algorithms.
Ellipsoid SLAM with Novel Object Initialization

Object-based SLAM has been widely studied in recent years. While many research works focus on representing objects as ellipsoids, the initialization is still an open problem. The traditional approach using 2D detected bounding boxes from multiple views fails in straight motion scenarios, because the object observation is limited to a few frames. In this work, we present a novel approach to recover object pose from a single RGB image by decoupling the translation, orientation and dimension. Under the assumption that the object dimension is already known, the translation can be approximately computed by back-projection of 2D bounding box center. Finally, the optimal rotation angle is calculated to satisfy the bounding-box constraints and image lines alignment. The proposed method shows an efficient solution to leverage the 2D information for 3D object initialization. Besides, the initialized object is integrated into a simultaneous localization and mapping system (SLAM) for a further optimization. We evaluate our novel initialization method and object-based SLAM system on public indoor and outdoor dataset, the results show that our system reconstructs the object with a higher accuracy.
A Novel Robot Skill Learning Framework Based on Bilateral Teleoperation

In this paper, a bilateral teleoperation-based robot skill learning framework is developed to transfer multi-step and contact manipulation skills from humans to robots. Robot skill acquisition via bilateral teleoperation provides a solution for human teacher to transfer the manipulation skills to robots in a remotely feasible manner. Besides, the bilateral teleoperation with force feedback allows humans in the loop to monitor and interface with the robot behaviour, hence improving the safety of the robot execution. The dynamic movement primitive (DMP) model is first employed to encode primitive skills, including those for both the translation and orientation. We have been utilized the behaviour tree (BT) to model the sequence of primitive skills. Since each node of the BT represents a single primitive skill, we can reproduce the BT nodes by employing different controllers based on the task requirements. We have evaluated the approach through two robot manipulation tasks, (i) grasping irregular objects with a customized soft suction cup and (ii) wiping whiteboard by a 7-DoF Frank Emika Panda. Results and performance analysis of the experiments are presented subsequently.
Coarse graining theories with gauge symmetries

Discretizations of continuum theories often do not preserve the gauge symmetry content. This occurs in particular for diffeomorphism symmetry in general relativity, which leads to severe difficulties both in canonical and covariant quantization approaches. We discuss here the method of perfect actions, which attempts to restore gauge symmetries by mirroring exactly continuum physics on a lattice via a coarse graining process. Analytical results can only be obtained via a perturbative approach, for which we consider the first steps, namely the coarse graining of the linearized theory. The linearized gauge symmetries are exact also in the discretized theory, hence we develop a formalism to deal with gauge systems. Finally we provide a discretization of linearized gravity as well as a coarse graining map and show that with this choice the 3D linearized gravity action is invariant under coarse graining.