Miracle-dz/UniFAR-FTUs huggingface dataset:
Scientific data products and the data pre-processing subsystem of the Chang'e-3 mission

The Chang'e-3 (CE-3) mission is China's first exploration mission on the surface of the Moon that uses a lander and a rover. Eight instruments that form the scientific payloads have the following objectives: (1) investigate the morphological features and geological structures at the landing site; (2) integrated in-situ analysis of minerals and chemical compositions; (3) integrated exploration of the structure of the lunar interior; (4) exploration of the lunar-terrestrial space environment, lunar surface environment and acquire Moon-based ultraviolet astronomical observations. The Ground Research and Application System (GRAS) is in charge of data acquisition and pre-processing, management of the payload in orbit, and managing the data products and their applications. The Data Pre-processing Subsystem (DPS) is a part of GRAS. The task of DPS is the pre-processing of raw data from the eight instruments that are part of CE-3, including channel processing, unpacking, package sorting, calibration and correction, identification of geographical location, calculation of probe azimuth angle, probe zenith angle, solar azimuth angle, and solar zenith angle and so on, and conducting quality checks. These processes produce Level 0, Level 1 and Level 2 data. The computing platform of this subsystem is comprised of a high-performance computing cluster, including a real-time subsystem used for processing Level 0 data and a post-time subsystem for generating Level 1 and Level 2 data. This paper describes the CE-3 data pre-processing method, the data pre-processing subsystem, data classification, data validity and data products that are used for scientific studies.
Optimal Distribution Network Reconfiguration Using Ant Lion Optimizer

With the leaning towards improving the distribution systems performance, network reconfiguration process has emerged as one of the promising strategies. The current study aims to solve the distribution network reconfiguration (DNR) problem by applying an Ant Lion Optimizer (ALO) algorithm to dwindle the total active power losses and to enhance the reliability of an IEEE 33-bus test system. To this end, a set of constraints including the nodes voltages, lines' currents and radial topology constraints are considered. So as to evaluate the real power losses, a Backward/Forward algorithm was used in this study and for network reliability assessment, the Energy Not-Supplied (ENS) index is used. Moreover, to check the radiality of the optimal system topology, the union-find with path compression approach is developed in this investigation. For a thorough evaluation of the proposed method's performance in exploring the global optimum, a comparative study with 3 other algorithms (GA (Genetic Algorithm), CFPSO (Constriction Factor- Particle Swarm of Optimization), and GWO (Grey Wolf Optimizer)) was presented in this manuscript. Furthermore, the proposed algorithm was validated against other existing methods in the recent literature. The research findings revealed a significant total power losses minimization by 31.14%, an important total ENS index reduction by 33.35% and an enhancement of the voltage profile as well after the DNR process.
Mission Critical Resiliency with IRON

The Intrinsically Resilient Overlay Network (IRON) architecture and implementation demonstrates that backpressure forwarding is a viable and beneficial technique for increasing capacity and resilience in real-world networks. IRON brings together numerous academic research papers into a working system that provides latency-bounded, prioritized, resilient network forwarding. IRON was previously demonstrated on wired IP networks in a bare-metal testbed. This work focuses on porting IRON into an emulated heterogeneous network spanning space, air, and land domains using a variety of transport networks (RF, SATCOM, and IP-over-fiber). Experimentation demonstrates that IRON improves the packet receive rate by as much as 164% and successfully ensures delivery of the most critical flows, even during link outages. Reactions to changes in the network (including link outages or capacity changes due to attacks or congestion) occur within milliseconds with no human involvement.
Differential Detection Based Deterministic Linear Processing for Single-User MIMO Systems

In this paper, we study deterministic linear precoding and combining for a single-user multiple-input multiple-output communication system in flat Rayleigh fading. We consider differential encoding at the transmitter and differential detection using a maximum likelihood decision rule at the receiver. The precoding matrix is designed to maximize the bound on the average received signal-to-noise ratio, whereas the combining matrix is structured to minimize the union bound on the symbol error probability (SEP). In addition, we present a comprehensive evaluation of the error performance in terms of the pairwise error probability and the SEP.
Analytical Solutions for Two-Contact Whole-Arm Manipulation Inverse Kinematics for Manipulators with Link Offsets

Whole-arm manipulation (WAM) is an ideal way to manipulate large and heavy loads. Whole-arm configuration generation algorithms are always constructed for a specific manipulator type. However, through increased transferability between types, advances in whole-arm manipulation could be applied more easily and developed to other manipulators. We therefore present two algorithms for transferring a whole-arm configuration from a manipulator without link offsets to one with link offsets in the elbow and wrist in this work. Both are analytical, where one can be solved algebraically, the other one only numerically. We show the advantages and disadvantages of these two variants and compare them with a memetic evolution algorithm as a baseline. It is shown that our algorithms require only about one thousandth of the computational time and achieve a significantly smaller variance in the joint position solutions.
Show and Listen: Generating Music Based on Images using Machine Learning

Music and images can be interpreted as arts of communicating emotions and feelings. Thereby, one can observe that there are instances where music and images are in a close association, such as an instance where background music is used to enhance the emotion depicted in a picture or video. A human composer would be able to compose such music by analyzing an image with the intention of sparking emotion in the subject within the context of the image. Thereby, the goal of this research is to develop a machine learning model which can perform the same task of composing a novel meaningful melody given an image. Inspired by image captioning using machine learning, the proposed architecture for the model involves using a Convolutional neural network (CNN) to extract image features and a Long short-term memory (LSTM) model to generate melodies. Following the model training, a subjective and objective evaluation was conducted and the obtained results indicated that the model performed well in accomplishing its goal. Naturally, though the model does not achieve perfection, it takes us one step closer to opening a wide range of possibilities for generating music from images using machine learning.
Autonomous Robotic Map Refinement for Targeted Resolution and Local Accuracy

This paper presents a multistage approach to refining the map of an environment and satisfying the targeted resolution and local accuracy by an autonomous mobile robot. The proposed approach consists of two steps. Having a globally accurate coarse map of the environment developed using a conventional technique such as SLAM or SfM with bundle adjustment, the proposed first step plans a path for the robot to revisit the environment while maintaining a desired distance to all occupied regions of interest since the resolution and the local accuracy of the map typically depends on the distance from which objects in the environment are observed. An Unoccupancy Distance Map (UDM) and a reduced-order Travelling Salesman Problem (TSP) techniques are newly proposed to solve this class of problems. In the final step, an online path replanning and map refinement technique is proposed to achieve the targeted resolution and local accuracy of the map. Parametric studies have firstly validated the effectiveness of the proposed two steps. The autonomous capability of the proposed approach has then been demonstrated successfully in its use for a practical mission.
Analysis of Predictive Control Strategies for High-Speed Operation of PMSM Drive

This work presents an analysis of predictive control strategies for a permanent magnet synchronous machine and designed initially at the nominal operating regimen. The moti-vation of this work is based on applications where high-speed requirement and limited supply voltage are needed such as pow-ered by battery-pack. Three control strategies are analyzed and compared based on the following characteristics: Model-Based Predictive Control, avoided weight factor, and fixed frequency operation. The main contribution of this work yields in the behavior verification of the mentioned control strategies and the availability determination to work along to the field-weakening approach. Moreover, a combination of two strategies is presented generating a new contribution; this combination reduces the operating time interval while reduces the computational burden. The effectiveness of the analysis is experimentally validated.
A Fault Diagnosis Comparative Approach for a Quadrotor UAV

This work presents a comparative between a Fault Detection and Isolation (FDI) approach, and a Fault Detection and Estimation scheme (FDE) applied to a quadrotor UAV gyroscopic sensor. The system is modeled using a convex Takagi-Sugeno strategy and a descriptor representation for the vehicle rotational dynamics. The proposed schemes are based upon convex observer design, using a linear matrix inequality (LMI) approach. Simulations on the nonlinear model are presented in order to validate the schemes.
A Distributed Deep Learning Approach for A Team of Unmanned Aerial Vehicles for Wildfire Tracking and Coverage

Recent large wildfires in the United States and the subsequent damage that they have caused have increased the importance of wildfire monitoring and tracking. However, human monitoring on the ground or in the air may be too dangerous and therefore, there need to be alternatives to monitoring wildfires. Unmanned Aerial Vehicles (UAVs) have been previously used in this problem domain to track and monitor wildfires with approaches such as artificial potential fields and reinforcement learning. Our work aims to look at a team of UAVs, in a distributed approach, over an area to maximize the sensor coverage in dynamic wildfire environments. We proposed and implemented the Deep Q-Network (DQN) with a state estimator (auto-encoder), then compared it to existing methods including a Q-learning, a Q-learning with experience replay, and a DQN. The proposed DQN with a state estimator outperformed existing deep learning methods in terms of reward maximization and convergence.
Visual Sensor System Applied to Trajectory Generation for UAVs

In this work, a visual sensor system is applied to generate free collision trajectories using a new algorithm called Trajectory Generation Based on Remote Object Detection (TGBROD). The TGBROD algorithm is based on remote object detection through a video received from a drone to a station work. The video analysis is done by the TGBROD algorithm, and as a result, free and collision regions are defined. Using information on the regions and the drone position, free and collision vector trajectories have been generated. The trajectories were mathematically represented as the vector, parametric and symmetric equations. The free vectors find practical applications for UAVs since these can be used as references for the drone controller.
A NONCONFORMING MIXED FINITE ELEMENT METHOD FOR MAXWELL'S EQUATIONS

We present a nonconforming mixed finite element scheme for the approximate solution of the time-harmonic Maxwell's equations in a three-dimensional, bounded domain with absorbing boundary conditions on artificial boundaries. The numerical procedures are employed to solve the direct problem in magnetotellurics consisting in determining a scattered electromagnetic field in a model of the earth having bounded conductivity anomalies of arbitrary shapes. A domain-decomposition iterative algorithm which is naturally parallelizable and is based on a hybridization of the mixed method allows the solution of large three-dimensional models. Convergence of the approximation by the mixed method is proved, as well as the convergence of the iteration.
FRACTIONAL WHITE NOISE CALCULUS AND APPLICATIONS TO FINANCE

The purpose of this paper is to develop a fractional white noise calculus and to apply this to markets modeled by (Wick–) Ito type of stochastic differential equations driven by fractional Brownian motion BH(t); 1/2 < H < 1. We show that if we use an Ito type of stochastic integration with respect to BH(t) (as developed in Ref. 8), then the corresponding Ito fractional Black–Scholes market has no arbitrage, contrary to the situation when the pathwise integration is used. Moreover, we prove that our Ito fractional Black–Scholes market is complete and we compute explicitly the price and replicating portfolio of a European option in this market. The results are compared to the classical results based on standard Brownian motion B(t).
Modification of the diamond cell for measuring strain and the strength of materials at pressures up to 300 kilobar

A diamond anvil cell has been adapted for determination of the strain ellipsoid in a polycrystalline sample. An x‐ray beam (Mo Kα) is directed through the sample perpendicular to the loading axis. Since the diamond anvils are transparent to Mo Kα radiation, a complete cone of diffraction is available from each set of crystallographic planes (hkl). These cones produce rings on a flat film placed in front reflection geometry. From the ellipticity of these rings it is possible to calculate directly the strain ellipsoid. Material strengths as a function of pressure can then be calculated.
Fluorescence lifetime of Mn-doped ZnSe quantum dots with size dependence

Radiative lifetimes of high quality Mn:ZnSe nanocrystals synthesized by nucleation-doping method are experimentally measured at wavelength near 580nm. The slow decay rate in millisecond time scale is identified as the radiative decay from the T14 metastable excited state of Mn2+ ions embedded in the ZnSe nanocrystals. Also, two fast decay components are measured at this wavelength with much lower intensities, which can be attributed to the emission tails from the host ZnSe nanocrystals and from the surface-trap states or the self-activated luminescence due to Mn ion pairs, respectively. Size dependences of the radiative decay rates for the Mn:ZnSe samples are measured.
Experimental analysis of vibrations damping due to magnetostrictive based energy harvesting

The paper deals with the mechanical vibrations damping effect induced by a magnetostrictive energy harvesting device properly working. The proposed experimental setup allows us to investigate, through time-domain measurements, the dependence of the mechanical damping from parameters like magnetic bias and external load, affecting the global harvested power. This result would underline the promising possibility to reduce mechanical vibrations and obtain power harvesting at the same time.
Fractional quantum Hall states in higher Landau levels

The authors discuss the existence of fractional quantised Hall states in other than the lowest (n=0) Landau level. They present the results of numerical studies of systems with up to nine particles in the n=1 Landau level. They find that a fractional quantised Hall state is likely for filling fractions of the n=1 Landau level, nu 1, with nu 1-1=5. At nu 1-1=3 they find that the Laughlin trial wavefunctions is not a good candidate ground-state wavefunction. If there is a gap at nu 1-1 it is predicted to be small. At nu 1-1=7/2 they predict the existence of a fractional quantised Hall state despite the small or non-existent gap at nu 1-1=3. They also show that introducing truncated pseudopotential interactions is not always a physical procedure outside of the lowest Landau level.
Outdoor visual SLAM and Path Planning for Mobile-Robot

This paper proposes a robust visual SLAM and a path planning algorithm for autonomous vehicles in the outdoor environment. The consideration of the outdoor characteristics was essential in both SLAM and path planning processes. This study can be used when it is necessary to know the exact appearance of the environment due to the impossibility of observing the environment through a satellite map, e.g., inside a forest. The visual SLAM system was developed using GPS data in consideration of the deterioration of camera recognition performance outdoors. The GPS data was inserted into every multi-thread of visual SLAM, which are Camera Tracking, Local Mapping, and Loop Closing. It enhanced the accuracy of the map and saved computational power by preventing useless calculations. In the path planning part, our method divided the path based on the stability of the roads. When determining the optimal path, the stability of the road and the driving time were considered, and the weight was assigned based on the GPS data.
New Finite-Time Stability Criterion for Piecewise Linear Systems through a Finsler's Lemma Approach

This note deals with the Stability in Finite Time (FTS) of a special class of hybrid dynamic system (HDS) which are the Piecewise Linear (PWL) systems. New condition for FTS of this type of systems has been investigated. The new conditions proposed in this notice are on the one hand the result of the Finsler's lemma and on the other hand a combination between this lemma and the S-procedure. These conditions are given in terms of Linear Matrix Inequalities (LMI). A Numerical example is stated to illustrate the proposed approaches and to compare its effectiveness with the previous results, based on an S-procedure approach.
TCN-based Lightweight Log Anomaly Detection in Cloud-edge Collaborative Environment

In the cloud-edge collaboration system, logs help developers diagnose system failures, and make abnormal diagnosis decisions as soon as possible for edge devices with a low tolerance for failure recovery time. As the computing power and storage of the edge devices are limited, the existing log-based anomaly detection approaches are difficult to do well in model size, computing power requirements, and accuracy at the same time. For example, the approach based on PCA has low anomaly detection accuracy, while Transformer-based models have huge parameters. In this paper, we propose a lightweight log-based anomaly detection model based on a compressed Temporal Convolutional Network (TCN), called EdgeLog. Edgelog replaces the fully connected layer of TCN with a Global Average Pool(GAP), and uses a sparsification strategy to compress the TCN network. In addition, a tree structure-based anomaly detection model deployment strategy for the cloud-edge collaborative system is proposed. Experimental results on 4 public datasets demonstrate the effectiveness and efficiency of Edgelog. Compared with the baseline, Edgelog’s model size is reduced by around 75%, FLOPS is reduced by around 97%, and it surpasses the baseline in average Precision, F-value, and Recall.
Introduction to Stochastic Integration

1 Preliminaries. - 2 Definition of the Stochastic Integral. - 3 Extension of the Predictable Integrands. - 4 Quadratic Variation Process. - 5 The Ito Formula. - 6 Applications of the Ito Formula. - 7 Local Time and Tanaka's Formula. - 8 Reflected Brownian Motions. - 9 Generalization Ito Formula, Change of Time and Measure. - 10 Stochastic Differential Equations. - References. - Index.
Practical Validation of Autonomous Source Localization with Ground Robots

A source localization experiment with a group of ground robots is presented in this paper. The process implemented on the robots is shown together with its building blocks which include methods from robotics and control. The results of the experiments show that the source localization is successful in the presented environment. The workings of the process are explained and an implication about the performance in dependence on the number of robots used is given. A way to use measurements from the mapping phase for source localization is presented which speeds up the localization process and the effect of the tuning parameters is investigated.
Standard Confidence Limits for Linear Functions of the Normal Mean and Variance

Abstract Tables of standard confidence limits for linear functions of the mean and variance of a normal distribution are presented. Algorithms are presented by which confidence limits for an arbitrary linear function μ + λσ 2 can be obtained using the tables given observed values of Y distributed as N(μ,σ 2 /γ 2) and S 2 distributed independently as σ2/v times χ2 with v degrees of freedom, where μ and σ2 are unknown parameters and v, γ, and λ are known but arbitrary numbers.
Blockchain: A Review from the Perspective of Operations Researchers

Blockchain is a distributed append-only digital ledger. The technology has caught much attention since the emergence of cryptocurrency, and there is an increasing number of blockchain applications in various businesses. The concept, however, is still novel to many members of the simulation and operations research community. In this tutorial, we introduce the blockchain technology and review its frontier related research. There are exciting opportunities for researchers in simulation, system analysis, and data science.
MPPT Optimization using Ant Colony Algorithm: Solar PV applications

Solar photovoltaic systems' power output depends on the local solar radiation levels among other prevailing climatic parameters such as temperature, dust, and humidity. In fact, the same radiation levels at low temperatures would increase the solar panel output compared to higher ambient temperatures. In this case, the voltage, the current, and the power levels would fluctuate accordingly. Among the three quantities, the voltage and current levels are monitored and often controlled to give regulated voltage and optimal power at the same time. To do so, various techniques are used to enhance the efficiency of solar PV by means of maximum power point tracking (MPPT). The main aim is to set the PV array to operate at optimal power or close to it even when environmental conditions fluctuate. The conventional incremental conductance (INC) algorithm is one of the MPPT approaches, but its fixed step size response speed is low under rapidly changing in the solar irradiation level and or load. To improve response time, a new MPPT technique using the Ant Colony (ACO) approach for photovoltaic (PV) systems is developed and discussed in this work. Artificial ants are released into the problem space to forage, and those that discover a better source of food are kept while the number of ants that are unable to successfully forage is deleted.