Miracle-dz/UniFAR-FTUs huggingface dataset:
DUSTY CRADLES IN A TURBULENT NURSERY: THE SGR A EAST H ii REGION COMPLEX AT THE GALACTIC CENTER

We present imaging at 19, 25, 31, and 37 μm of the compact H ii region complex G-0.02-0.07 located 6 pc in projection from the center of the Galaxy obtained with SOFIA using FORCAST. G-0.02-0.07 contains three compact H ii regions (A, B, and C) and one ultra-compact H ii region (D). Our observations reveal the presence of two faint, infrared sources located 23″ and 35″ to the east of region C (FIRS 1 and 2) and detect dust emission in two of the three “ridges” of ionized gas west of region A. The 19/37 color temperature and 37 μm optical depth maps of regions A–C are used to characterize the dust energetics and morphology. Regions A and B exhibit average 19/37 color temperatures of ∼105 K, and regions C and D exhibit color temperatures of ∼115 K and ∼130 K, respectively. Using the DustEM code, we model the SEDs of regions A–D and FIRS 1, all of which require populations of very small, transiently heated grains and large, equilibrium-heated grains. We also require the presence of polycyclic aromatic hydrocarbons in regions A–C in order to fit the 3.6, 4.5, 5.8, and 8.0 μm fluxes observed by Spitzer/IRAC. The location of the heating source for region A is determined by triangulation from distances and temperatures derived from DustEM models fit to SEDs of three different points around the region, and it is found to be displaced to the northeast of the center of curvature near the color temperature peak. Based on total luminosity, expected 1.90 μm fluxes, and proximity to the mid-IR color temperature peaks, we identify heating source candidates for regions A, B, and C. However, for region D, the observed fluxes at 1.87 and 1.90 μm of the previously proposed ionizing star are a factor of ∼40 times too bright to be the heating source and hence is likely just a star lying along the line of sight toward region D.
Active Manipulation of Elastic Rods using Optimization-based Shape Perception and Sensorimotor Model Approximation

Deformable object manipulation (DOM) remains challenging in robotics since the physical characteristics of deformable linear objects (DLO) are generally strongly nonlinear and unknown. This paper presents an active deformation frame-work for robot manipulating the elastic rod. The optimization-based curve fitting method is designed to extract a low-dimensional feature vector representing the three-dimensional centerline of the elastic rod. Deformation Jacobian Matrix (DJM) connecting feature changes and robot motion changes is estimated online by Adaptive Kalman Filter (AKF). The model-free velocity controller is designed based on the optimization criterion to accelerate the deformation speed of the elastic rod. The stability of the proposed manipulation framework and the boundedness of all signals in the closed-loop system are proved by Lyapunov theory. Detailed simulations are given to evaluate the performance of the proposed approach.
A Point-Source Survey of M31 with the Spitzer Space Telescope

We explore the stellar population of M31 in a Spitzer Space Telescope survey utilizing IRAC and MIPS observations. Red supergiants are the brightest objects seen in the infrared; they are a prominent evolutionary phase. Due to their circumstellar envelopes, many of these radiate the bulk of their luminosity at IRAC wavelengths and do not stand out in the near-infrared or optically. Going fainter, we see large numbers of luminous asymptotic giant branch stars (AGB), many of which are known long-period variables. Relative to M33 the AGB carbon star population of M31 appears sparse, but this needs to be spectroscopically confirmed.
Drag-based composite continuous nonsingular terminal sliding mode control method design for Mars entry trajectory tracking

This paper investigates the drag-based trajectory tracking control problem for Mars entry vehicles. A composite continuous nonsingular terminal sliding mode controller based on finite-time disturbance observer is proposed for Mars entry trajectory tracking. The proposed controller not only eliminates the effects of disturbances due to the uncertainties of atmospheric models and vehicle aerodynamics, but also guarantees the continuity of control action. Simulation results show that the proposed method improves the disturbance rejection performance and tracking precision significantly.
Intelligent Vehicle Lane-Change Trajectory Planning on Slippery Road Surface Using Nonlinear Programming

As a dynamic driving process, lane change behavior involves the longitudinal and lateral motion of lane changing vehicles. There is a strong coupling effect between longitudinal and lateral motion, so it is difficult to achieve ideal control effect by single control. This paper constructs a scene in which vehicles accelerate to change lanes on slippery roads, and longitudinal safety model is introduced to measure the longitudinal safety of vehicle following lane changing process. Considering the side-slip phenomenon of lane change vehicles on slippery roads, the classical vehicle “bicycle” model and fixed tire cornering-stiffness are used to evaluate the tire force of vehicles on slippery roads to ensure the lateral safety of lane change vehicle. The nonlinear programing problem of lane-change trajectory planning is established, and the comparison of three solutions is given to plan the optimal lateral and longitudinal lane change acceleration for the vehicle. The simulation results show that the planned lane change trajectory meet the longitudinal and lateral safety objective. By considering the lateral and longitudinal coupling effects, the safety and rationality of lane changing on slippery pavement can be improved, and the deficiency of the existing lane changing trajectory planning model that does not consider the risk of sideslip can be made up.
Formation Tracking Control of Quadrotor Unmanned Aerial Vehicle Swarm with Double Formation Structure

In this paper, a time-varying formation tracking control method of quadrotor unmanned aerial vehicle (UAV) swarm with multiple leaders is proposed to realize the double formation structure. First, time-varying formation tracking protocols are constructed based on the neighboring information of UAVs. Then, sufficient conditions of time-varying formation tracking with the double formation structure are proposed. It is shown that both followers and leaders can form desired formations, and followers track to a formation center determined by all the leaders. Besides, an explicit presentation of the formation center function is given. Finally, a numerical simulation is presented to verify the effectiveness of the theoretical results.
Research on Electric Vehicle Charging Network Planning Method Considering Photovoltaic Coupling

This paper studies the construction needs of charging stations and charging network planning brought about by the promotion and application of electric vehicle (EV). In this paper, a planning method for EV charging network coupled with photovoltaic power generation is proposed. In this method, the stochastic characteristics of EV charging behavior are quantified in the form of probability distribution function by comprehensively considering EV types, endurance and temporal and spatial distribution of charging behavior. On this basis, firstly, the site layout of EV charging network is determined through cluster analysis of charging station location based on endurance capacity and comparative analysis of convenience of access to power grid. Secondly, the capacity optimization of EV charging facilities based on queuing algorithm was used to determine the installed capacity of EV charging facilities in charging stations. Finally, the optimal configuration model of photovoltaic power generation system supporting EV charging network was established considering the geographical space constraints, resource conditions and expansion and renovation costs of each charging station site. In this paper, a traffic network system is taken as an example to analyze the EV charging network planning, and the correctness and effectiveness of the above methods are verified. The research shows that the planning and layout of EV charging network should not only fully consider the application and development needs of local EV, but also adapt to local resource conditions and power grid planning. Finally, EV charging stations are taken as coupling nodes to realize the deep integration and collaborative development of transportation network and power network.
Planetary companions in K giants β Cancri, μ Leonis, and β Ursae Minoris

Aims. The aim of our paper is to investigate the low-amplitude and long-period variations in evolved stars with a precise radial velocity survey. Methods. The high-resolution, the fiber-fed Bohyunsan Observatory Echelle Spectrograph (BOES) was used from 2003 to 2013 for a radial velocity survey of giant stars as part of the exoplanet search program at Bohyunsan Optical Astronomy Observatory (BOAO). Results. We report the detection of three new planetary companions orbiting the K giants β Cnc, μ Leo, and β UMi. The planetary nature of the radial velocity variations is supported by analyzes of ancillary data. The Hipparcos photometry shows no variations with periods close to those in radial velocity variations and there is no strong correlation between the bisector velocity span (BVS) and the radial velocities for each star. Furthermore, the stars show weak or no core reversal in Ca II H lines indicating that they are inactive stars. The companion to β Cnc has a minimum mass of 7.8 MJup in a 605-day orbit with an eccentricity of 0.08. The giant μ Leo is orbited by a companion of minimum mass of 2.4 MJup having a period of 357 days and an eccentricity of 0.09. The giant β UMi is a known barium star and is suspected of harboring a white dwarf or substellar mass companion. Its companion has a minimum mass of 6.1 MJup, a period of 522 days, and an eccentricity e = 0.19.
Apollonius Partitions Based Pursuit-evasion Game Strategies by Q-Learning Approach

This paper studies a classical single pursuer and single evader pursuit-evasion game. The pursuer attempts to capture the slower evader who aims to extend its lifetime during the game. To simplify this question, requiring the evader to take fixation strategy which is choosing the farthest point in its current dominant region as aimpoint and moving at a constant speed. Assuming the pursuer is faster than the evader. Then, the speed ratio is a constant. The instaneous state space will be partitioned into pursuer's dominant zone and evader's dominant zone by the generalized Apollonius circle. The pursuit strategy is based on minimizing the area of the evader's dominant region. Furthermore, we propose a supermodular game for this game. Thus, the existence of the Nash equilibrium is guaranteed. Simulation results based on Q-learning are presented to solve the problem, which shows the effectiveness of this method.
AN HST/COS SURVEY OF THE LOW-REDSHIFT INTERGALACTIC MEDIUM. I. SURVEY, METHODOLOGY, AND OVERALL RESULTS

We use high-quality, medium-resolution Hubble Space Telescope/Cosmic Origins Spectrograph (HST/COS) observations of 82 UV-bright active galactic nuclei (AGNs) at redshifts zAGN < 0.85 to construct the largest survey of the low-redshift intergalactic medium (IGM) to date: 5138 individual extragalactic absorption lines in H i and 25 different metal-ion species grouped into 2611 distinct redshift systems at zabs < 0.75 covering total redshift pathlengths ΔzH i = 21.7 and ΔzO vi = 14.5. Our semi-automated line-finding and measurement technique renders the catalog as objectively defined as possible. The cumulative column density distribution of H i systems can be parametrized d  ( > N ) / dz ? > = C 14 ( N / 10 14 cm − 2 ) − ( β − 1 ) ? > , with C14 = 25 ± 1 and β = 1.65 ± 0.02. This distribution is seen to evolve both in amplitude, C 14 ∝ ( 1 + z ) 2.3 ± 0.1 ? > , and slope β(z) = 1.75–0.31 z for z ≤ 0.47. We observe metal lines in 418 systems, and find that the fraction of IGM absorbers detected in metals is strongly dependent on N H I ? > . The distribution of O vi absorbers appears to evolve in the same sense as the Lyα forest. We calculate contributions to Ωb from different components of the low-z IGM and determine the Lyα decrement as a function of redshift. IGM absorbers are analyzed via a two-point correlation function in velocity space. We find substantial clustering of H i absorbers on scales of Δv = 50–300 km s−1 with no significant clustering at Δv ≳ 1000 km s−1. Splitting the sample into strong and weak absorbers, we see that most of the clustering occurs in strong, NH i ≳ 1013.5 cm−2, metal-bearing IGM systems. The full catalog of absorption lines and fully reduced spectra is available via the Mikulski Archive for Space Telescopes (MAST) as a high-level science product at http://archive.stsci. edu/prepds/igm/.
Multi-platform Cooperative Target Assignment Method Base on Receding Horizon Control Heuristic

Multi-platform collaborative target allocation needs to fully consider the different tasks and capabilities of each platform, consider the performance constraints and usage constraints of platform equipment, and rationally call the resources of each platform by designing an allocation algorithm to maximize the killing of incoming targets and play the overall combat effectiveness of the system. Aiming at the problem of multi-platform collaborative target assignment, this paper propose an effective collaborative air defence system, and construct the mathematical model of air defence operation. Also we have given a heuristic algorithm which combines with Receding Horizon Control (RHC) to solve the DWTA problem. The proposed algorithm does not require a large number of iterative calculations and meets the requirements of real-time decision-making. Simulation results also demonstrate the effectiveness of the mathematical model and allocation algorithm.
Collisions with other Universes: the Optimal Analysis of the WMAP data

An appealing theory is that our current patch of universe was born as a nucleation bubble from a phase of false vacuum eternal inflation. We search for evidence for this theory by looking for the signal imprinted on the CMB that is generated when another bubble "universe" collides with our own. We create an efficient and optimal estimator for the signal in the WMAP 7-year data. We find no detectable signal, and constrain the amplitude, a, of the initial curvature perturbation that would be generated by a collision: -4.66 \times 10^{-8} < a (\sin{\thetabubble})^{4/3} < 4.73 \times 10^{-8} [Mpc^{-1}] at 95% confidence where \thetabubble is the angular radius of the bubble signal.
Community Capacity Building: A Renewable Energy Workshop as an Investment in Sustainable Businesses in the Galapagos Islands

With renewable energy penetration rapidly increasing worldwide in response to climate change concerns, efforts must also consider teaching and educating communities about its principles and how they can take advantage of these resources. This paper aims to show how building local capacity in renewable energy and developing a renewable energy program can provide education and training to students and youth in the Galapagos Islands. The goal will be to train the youth, starting with basic electrical principles and culminating with how to design and install renewable energy systems on the islands and build productive enterprises using these resources. The program was designed to reach the youth in the Galapagos Archipelago. High school students and young people interested in renewable energy were the primary target recruitment for the program. Twenty Santa Cruz Island students participated in the program’s first iteration. The design and implementation of workshops are described in this paper. Two surveys were conducted before and after the workshop to evaluate the knowledge improvement related to renewable energy and off-grid PV solar systems. The survey results show that the participants initially had a limited understanding of renewable energy technologies. Thus, educating the community about renewable energy and solar PV systems contributed to promoting environmental awareness and building capacity towards clean and sustainable electricity generation and solar energy resources in the Galapagos Islands.
A Deep Neural Network in a Web-based Career Track Recommender System for Lower Secondary Education

In this paper, web-based career track recommender system was used to guide guidance counselor in assisting their students in choosing an appropriate career track. Many junior high school students struggled with track uncertainty and were perplexed when it came to deciding whether senior high school career track was appropriate and suitable for them. Increased in drop-out rate is also a bigger concern in the country, and students switching to another program can be a waste of government funds intended for free tuition at state universities. Given the current state of K-12 evaluation, adequate counseling of guidance counselor in the selection of relevant career tracks should be undertaken. This study included 1500 students from the first to third grades of the K-12 curriculum, and their grades and socio-demographic profiles were used as factors in determining their academic strand in Senior High School with the utilization of Deep Neural Network. The study's findings suggest that the DNN algorithm predicts the academic strand of students quite well with a prediction accuracy of 83.11%. Using the devised approach, guidance counselors' work became more efficient in dealing with student concerns. With the use of the DNN technique, it is concluded that the recommender system acts as a decision tool for counselors in advising their students to select which Senior High School track is appropriate for them. The web-based career track recommender system has effectively integrated the DNN predictive model.
Nonnegative Matrices and Applications

Preface 1. Perron-Frobenius theory and matrix games 2. Doubly stochastic matrices 3. Inequalities 4. Conditionally positive definite matrices 5. Topics in combinatorial theory 6. Scaling problems and their applications 7. Special matrices in economic models References Index Author index.
Multi-view Spectral Clustering Based on Graph Learning

Multi-view clustering is based on the exploration of complementary information from different views. The graph-based approach is a representative method among existing multi-view clustering methods. However, how to effectively obtain complementary information in multi-view data and how to perform effective clustering in the face of the low quality of the initially constructed data graph is still a challenge. To address the aforementioned issues, we propose a new multi-view spectral clustering model, namely multi-view spectral clustering based on graph learning (GLSC), which utilizes the relevant knowledge of self-representing attributes to construct similarity graphs that can represent the relationship between data, uses the constrained Laplacian rank method to construct an affinity graph for the similarity graphs, and applies the classical spectral clustering method for clustering. We propose an iterative algorithm to solve the optimization problem. Finally, we conduct extensive experiments on four real benchmark datasets, and the experimental results show the superiority of GLSC clustering performance.
Model Predictive Flux Control-Based Open-Circuit Fault Diagnosis Method for Two-Level Three-Phase Inverter Induction Motor Drive

Traditional open-circuit fault diagnosis methods for two-level three-phase inverter induction motor drive are mainly for field-oriented control or directly torque control. With the wide application of model predictive control due to its simple structure and excellent dynamic performance, fault diagnosis based on model predictive control has attracted researchers' attention. However, the existing researches focus on the model predictive current control (MPCC), few research has discussed the model predictive flux control (MPFC). In this paper, a MPFC-based fault diagnosis method is proposed, which only adopts the error of direct control variable (stator flux vector) of MPFC as the diagnosis basis. No additional data processing or variable observer is required. This proposed method occupies very few computing resources, and has excellent dynamic robustness. It is even effective when the motor is stopped. Besides, the diagnosis delay is minimal. Experiment results verify the effectiveness of proposed fault diagnosis method for single and double switch fault.
Simple feed-through for coupling optical fibres into high pressure and temperature systems

A best practice guide for assembling and testing a simple and inexpensive system feeding an optical fibre into a high pressure and temperature environment is presented. A standard Swagelok-type connector is tested together with different ferrule materials and a PEEK capillary tube as feed-through. The system proved to seal an optical fibre during several pressure and temperature cycling experiments up to 500 bar and 180 °C.
Upcoming trend prediction from historical time series using Bayesian estimation

Prediction of an abnormal condition is absolutely critical to avoid both human injuries and equipment damage. A new data-driven predictive technique is proposed for predicting the upcoming trend in the current time series. The proposed method has two important features: (i) trends can be predicted at the beginning of trend variation, which can help industrial operators take timely actions; (ii) the prediction of trends is given with quantitative probabilities and credibility intervals. By contrast, existing prediction methods either focus on predicting specific values of time series with short predictive step size, or cannot provide a quantitative measurement of prediction results. The proposed method firstly captures qualitative trends in the current and historical time series, and divides these trends into several clusters. Secondly, the current and historical time series are transformed into trend sequences according to the trend clusters. Finally, the proposed method choose some subsequence of recent trends in the current trend sequence and predicts the probability of the alarm states based on the identical subsequences in the historical trend sequence. A Bayesian estimator is exploited to calculate the posterior probabilities and their credibility intervals for the alarm states. The optimal predicted alarm state is the one with the largest lower bound of posterior probability as a quantitative measurement of prediction reliabilities. The effectiveness of the proposed method is illustrated via numerical examples based on a well-accepted benchmark of the Tennessee Eastman process.
The tableau method for temporal logic: an overview

An overview of the tableau decision method for propositional temporal logic is presented. The method is described in detail for linear time temporal logic. It is then discussed how it can be applied to other variants of temporal logic like branching time temporal logic and extensions o f linear time temporal logic. Finally, applications o f temporal logics to computer science are reviewed.