Miracle-dz/UniFAR-FTUs huggingface dataset:
Load-Adaptive and Energy-Efficient Topology Control in LEO Mega-Constellation Networks

The Low-Earth-Orbit (LEO) mega-constellation networks, by providing low-latency and high-speed communications, are becoming indispensable infrastructures for the future six-generation (6G) architecture. Consequently, the topology, with thousands of satellites equipped with batteries of limited life, has to be adaptively controlled with high energy efficiency. However, existing work lacks the joint consideration of energy efficiency and load adaptation. In this paper, we first propose the line-of-sight condition to determine the candidate ISL set. Next, we model the energy consumption of the LEO mega-constellation networks. Along this direction, we formulate the Load-Adaptive and Energy-Efficient (LAEE) topology control problem in LEO mega-constellation networks and prove its NP-hardness. Finally, we propose the Amortized Energy based Topology Control (AETC) algorithm to solve the LAEE problem, with good adaptation to the fluctuating load and guarantees connectivities between any two satellites. Extensive simulation results demonstrate that the AETC algorithm outperforms related schemes in terms of energy consumption and results in good topology stability.
An Adaptive Neuro-Fuzzy PID Controller Approach for thermal Systems: An Experimental Validation

The current work includes an experimental evaluation using a device known as TCLAB as a benchmark. A NARX neural network is used to identify the thermal process in a broad operation region and can be considered the model of reference. The control scheme employs a neuro-fuzzy adaptive control. The controller systems work by comparing the neural netwok model to the actual process, and from this comparison a model error can be obtained; if the modeling error is different from zero, a fuzzy system adjusts the PID tuning parameters to get the best performance in the operating point. The proposed approach implementation is compared to a typical PID controller are noticeable the improving of settling time for disturbances rejection.
Autonomous Decentralized Control of Distributed Generation using Multi-Agent Reinforcement Learning

With the large-scale integration of distributed generations (DGs), a centralized control approach is being challenged concerning communication efficiency, resiliency to communication failure, privacy, and scalability. An autonomous decentralized control for DGs in distribution networks is proposed to ensure privacy while also reducing the computational burden. Meanwhile,a multi-agent reinforcement learning method considering privacy protection constraints is developed to solve the autonomous decentralized control problem. The effectiveness of the proposed control method is verified by the numerical example of the modified 141-bus distribution system test_feeder.
Estimation of Time-Varying Parameters Defining Contact of a Planar Manipulator with a Surface

The knowledge of parameters, defining interaction of a robotic manipulator with environment, is crucial when robots execute contact-tasks involving tracking of trajectories while desired forces are applied on the environment. For contact tasks on planar surfaces, the inclination and stiffness of the surface are key parameters since the first one defines the direction of the desired force and trajectory, which are typically defined relative to a frame attached to the environment, and the second one is required to compute the control signal. There exist methods for estimation of inclination and stiffness, whenever they are constant. The estimation of time-varying stiffness and inclination is less studied. In this paper, we propose a method to estimate on-line the stiffness and inclination of the planar surface, when they are varying during the task execution. The method is based on adaptive observers that ensure asymptotic or finite-time convergence of the estimates to the real values of the parameters.
Turbine Efficiency Measurement System for the QinetiQ Turbine Test Facility

A turbine efficiency measurement system has been developed and installed on the turbine test facility (TTF) at QinetiQ Farnborough. The TTF is an engine-scale short-duration (0.5s run time) rotating transonic facility, which can operate as either single stage (HP vane and rotor) or 112 stage (HP stage with IP or LP vane). The current MT1 HP stage is highly loaded and unshrouded and is therefore relevant to current design trends. Implementation of the efficiency measurement system forms part of the EU Turbine Aero-Thermal External Flows (TATEF II) program. The following aspects of the efficiency measurement system are discussed in this paper: mass-flow rate measurement, power measurement by direct torque measurement, turbine inlet and exit area traverse measurement systems, computation of efficiency by mass weighting, and uncertainty analysis of the experimentally determined turbine efficiency. The calibration of the mass-flow rate and torque measurement systems are also discussed. Emphasis was placed on the need for a low efficiency precision uncertainty, so that changes in efficiency associated with turbine inlet temperature distortion and swirl can be resolved with good accuracy. Measurements with inlet flow distortion form part of the TATEF II program and will be the subject of forthcoming publications.
An Overview of Recent Advances in Energy Storage for Solar Power Systems

Renewable energy sources and technologies have the potential to bring answers to energy-related challenges in developing nations such as Ethiopia. PV systems generate clean, dependable electricity without the need of fossil fuels and are employed in a broad range of applications. Clean electricity is one approach for increasing capacity, improving energy security, and addressing environmental problems. Since, energy storage is one of the components of standalone system to holds all energy and electrical energy storage is advantageous to balance between fluctuating generation and varying consumption. This paper analysis about standalone solar power system, advantage of DC, charge controller, various storage systems (stationary and nonstationary) its advantages, applicability and comparison between them. Finally, conclusion and recommendation drawn from used papers which indicate best and sensitive research area for future works.
A Study on the Design and Analysis of a Bidirectional IPT System for EV wireless charging by Using Switch-Controlled Capacitor

For loosely coupled transformers, the leakage and magnetizing inductances vary due to the changing positions for the primary and the secondary sides of the transformer windings in inductive power transfer (IPT) systems. For reducing the effects of these varying inductances, various compensation networks have been proposed. These compensation networks are used to obtain a unity power factor and constant output voltage for power electronic applications. However, most of these compensation networks have a fixed compensation network designed for only a specific coupling coefficient. Therefore, a compensation network with variable components needs to be implemented to compensate for the changing inductances of the network. In this paper switched capacitor-based compensation network is proposed to match the resonant frequency of the network with the switching frequency of the converter. The proposed network compensates the magnetizing inductance for coupling coefficient in the range of 0.23 to 0,35 using a switched capacitor. Moreover, the proposed converter is symmetrical so that bidirectional power flow is possible while maintaining constant output voltage and unity power factor under zero voltage switching condition (ZVS).
Angular Distance-Based Performance Analysis of mmWave Cellular Networks

In millimeter wave (mmWave) networks, the interference characteristics are heavily dependent on the directional transceiver beams and the corresponding half power beamwidths. The angular distance of the desired transmitter and the interferers as seen by a reference receiver, plays a key role on the interference levels. In this paper, stochastic geometry tools are utilized to capture the effect of a reference receiver's gain at azimuthal angle $\phi$, on the performance of a mmWave network. Accordingly, performance analysis in terms of coverage probability is conducted under three interference scenarios: i) a worst-case dominant interference scenario, in which the dominant interfering base station (BS) is the BS that is both the nearest in azimuthal angle, $\phi$, measured from the direction of the serving BS, and the second nearest in Euclidean distance, $r$, from the receiver, ii) a dominant interference scenario, in which the dominant interferer is the nearest BS in azimuthal angle, $n$, and uniformly distributed in an annulus formed by the the serving distance and a ball radius $R$, and iii) the aggregate interference scenario. By defining the signal-to-interference-plus-noise ratio (SINR) for the three interference scenarios, the coverage probability is derived in exact form. Numerical results show that azimuthal angles play a key role in the definition of dominant interference and consequently in the coverage performance in mmWave cellular networks.
Circuit Driving of RC Scale Cars using Reinforcement Learning

This paper presents control of an RC scale car in a scale circuit using reinforcement learning. Experimental environment has been constructed with 1/27 scale remote controlled car, motion tracking system, and a computer that sends steering and thrust commands to the RC car based on feedback from the motion tracking system. The control consists of two layers. Low-level controller receives a desired velocity vector as a reference and do a basic PI control for thrust and P control for steering. High-level controller is trained by reinforcement learning that receives the car state and outputs the velocity command vector. The state include position, velocity, heading of the RC car, distances to surrounding boundaries of the circuit. The high-level controller takes the form of a recursive neural network, which is trained entirely in virtual environment. The car dynamics in the virtual environment is a bicycle model that includes tire slip force from the literature. With the resulting policy (high-level controller) the RC car successfully completes 10 laps in the actual environment of the circuit without colliding to the boundaries.
A Practical Position/Velocity Estimation for Multicopter Guidance and Control

: In this work, a practical algorithm to estimate the position and velocity which can be used for feedback control of multicopter UAV, is proposed. For the purpose of practical applicability to multicopter, a straight forward algorithm which can be constructed with MEMS AHRS, low-cost GPS, and pressure sensor is considered. The data obtained from flight experiment with the proposed algorithm are analyzed.
Achieving Traceable RFCMOS FT and FMAX Wafer Measurements

This paper investigates the impacts of RF probe aging, how RF probe contact resistances on test pads of calibration standards and devices affect the Ft and Fmax measurements of RFCMOS transistors. It is found that poor probe contact on calibration standards lead to large extracted load standard inductance when eLRRM calibration is used, degrading transistor Fmax. A RF wafer test strategy to help device and test engineers obtain accurate, repeatable and traceable Ft and Fmax measurements is proposed at the end of this paper.
Asymptotic stability of continuous-time switched affine systems with unknown equilibrium points*

This paper deals with switched affine systems control ensuring asymptotic stability of an unknown equilibrium point. More specifically, our study is focused on the class of systems with the affine term depending linearly on an uncertain vector, which can be used to model input and load variations of dc-dc power converters. The main goal is to design a switching function that governs the state trajectories towards an unknown equilibrium point containing in one of its components the desired value specified by the designer. The other components are adapted in real time based on the uncertainty estimation. The design conditions are solved by a two-steps procedure expressed in terms of linear matrix inequalities, and they do not require any stability property of the individual subsystems. The control design of a dc-dc flyback converter is used for illustration and comparison together with an academic example composed of two third order unstable subsystems.
GLEE: GPR-based Latent Effect Elimination for Performance Prediction of Big Data Transfer

A wide spectrum of large-scale applications in science, industry, and business domains generate colossal amounts of data that need to be moved across geographical locations for various purposes. Such big data transfer is increasingly carried out over connections with guaranteed bandwidth provisioned in high-performance networks, as exemplified by ESnet via advance bandwidth reservation agents such as OSCARS. Accurate performance prediction of big data transfer is vital for resource management to reserve appropriate bandwidth and optimize resource utilization. Data-driven methods using machine learning offer a promising solution to such prediction capabilities. However, most of these methods highly rely on the quality of training data and might suffer from low prediction accuracy when performance measurements are collected in environments with high dynamics or under latent effects. We propose a performance prediction method to help end users obtain a good estimate of achievable throughput, which could also be used by network managers to infer expected bandwidth usage and hence optimize bandwidth reservation and utilization. Based on Gaussian Process Regression (GPR), our approach is able to automatically detect intrinsic noise and identify latent effects to enhance prediction accuracy. We implement and evaluate the proposed method using performance measurements collected in real-life networks and the results demonstrate its effectiveness.
Energy spectra of 43 galactic X-ray sources observed by Uhuru

X-ray observations from the Uhuru satellite have been used to obtain the energy spectra from 2 to 20 keV for 43 galactic X-ray sources, which then were used to classify the X-ray sources. This survey has shown that galactic sources exhibit different spectra with a temperature range from 10 to the 7th to 2 x 10 to the 8th K. For the sources included in this survey no significant increase was observed in the number of sources with very cool temperatures or with very hot temperatures. Ten of the 11 X-ray sources which have been reported to pulsate regularly on time scales of seconds to minutes have temperatures exceeding about 10 to the 8th K. The four globular-cluster X-ray sources 3U 1820-30, 3U 1746-37, A1850 + 09, and MX 1746-20 have spectra similar to the strong galactic center sources with temperatures between 4 x 10 to the 7th and 9 x 10 to the 7th K.
A Reconfigurable 60-GHz VCO with −103.2 dBc/Hz Phase Noise in a 0.13-µm SiGe BiCMOS Technology

In this work, a reconfigurable voltage controlled oscillator (VCO) with low phase noise at 60GHz in a 0.13-µm SiGe BiCMOS technology is investigated. This VCO is tailored for use in a non-hierarchical synchronization layer in which reconfiguration is intended to affect the sensitivity of the VCO, thus adaptively adjusting synchronization accuracy to the coupling channel used. A switchable capacitor bank enables reconfiguration of the sensitivity between 719MHz/V and 270MHz/V. The lowest phase noise is -103.2dBc/Hz and the output power is 5.46dBm at a power consumption of 69.59mW resulting in a figure of merit of -184.43dB.
ℒ2 Observer Design for Singular Lypschitz Linear Parameter-Varying (S-LPV) Systems

The main contribution of this paper is an observer design based on ℒ2 optimization for a class of Singular Lypschitz Linear Parameter-Varying (S-LPV). Thanks to a strict LMI solution, the observer not only relaxes the existing UI-decoupling constraint in the conventional UI observer but also ensures the stability of estimation dynamics under the presence of Lipschitz nonlinearity and time-varying parameters. This proposed synthesis allows to extend the results of the UI Observer for a large class of system (singular, nonlinearity, parameter variant) and by relaxing the UI decoupling condition. Finally, a numerical example is illustrated to highlight the advantages and drawbacks of the proposed design.
Battery based renewable energy source grid-connected generation system with Improved Power Quality Feature

The proposed paper has objective to show the solar power has poor quality inverted AC output, if it is merely using power conversion approach from DC to AC. If it is not regulated and controlled properly, the poor quality power would be supplied to the grid as well as to the load. The paper presents a battery-based renewable energy source grid connected generation system (RES-GGS) with improved power quality feature. The power quality is improved by using two stage power conversion approaches. The solar power is extracted at maximum power point tracking (MPPT) using conventional approach and further regulated by DC-DC converter. The cascaded H-bridge inverter is used to give AC output for non-linear load as well as to the grid. The performance of the proposed system is evaluated under varying irradiation condition. The simulated results show improvement of power quality features.
Planar Beam Switched Antenna Module for 5G New-Radio FR2 Applications

Beamforming technique has been the major trend for the next-generation wireless communication system to overcome the high propagation loss transmitting through the air. This paper presents a highly integrated planar switched beam antenna module for 5G new-radio (NR) application. The module consisted of 1×4 array with a Butler matrix and a single-pole-quadruple-throw (SP4T) switch. Implemented with Rogers RO3010 as the substrate, the antenna module has shown the four beam steering directions of 14°, -40°, 49°, and -16° with a 10-dB impedance bandwidth from 35 to 41 GHz. The experimental results demonstrate a great potential for the 5G NR wireless applications.
Optimal Design for Acoustic Wave SAW Extractor with Simple Matching Circuits

This study applies the optimization approach to design a SAW extractor in a broadband RF module, which demonstrates bandpass and bandstop features in the in-band to two output ports, while bandstop and bandpass features, respectively, in the out of bands. The extractor is formed by eight SAW resonators based on mBVD model and simple matching circuits of two inductors. Compared with the commercially available product, the designed SAW extractor can meet the stringent product specifications in the frequency range of 0.7GHz ~ 2.7GHz
Polarization Agile Circular Polarized Crossed Dipole Antenna For Sub-6 GHz Band

A circular polarized crossed-dipole antenna (CPDA) with reconfigurable polarization is presented. To generate circular polarization, two dipole antennas are placed orthogonally, and fed by quarter-vacant rings that produce 90° phase delay. PIN diodes are inserted in between CPDA and quarter-vacant rings for polarization switching. Either left-hand circular polarization (LHCP) or right-hand circular polarization (RHCP) can be selected by applying bias voltage to the diodes. To validate LHCP/RHCP switching and far-fields characteristics, the proposed CPDA is fabricated and measured. Experimental results show that the antenna has fractional impedance bandwidth covering 3-dB AR of 8.8% (3.57-3.9 GHz). Measured peak gain of the antenna has maximum gain of 5.9 dBiC at 3.5 GHz.
Distributed Hierarchical Control for VSC-Based DC Microgrids with AC-DC Coupled Strategy

This paper proposes an ac-dc coupled distributed hierarchical control strategy for VSC-based de microgrids (MGs), which directly links the load sharing performance of the MG with the ac inner current (id) control of VSCs. In the primary layer of the hierarchical control, an ac-dc coupled droop method is used, which does not require any outer dc voltage/current PI loops. In the secondary layer, the dc bus voltage is restored while maintaining the precise load sharing performance among sources. Moreover, the feedback current of the secondary layer is obtained from the nonlinear disturbance observer without additional current measurements. Stability analysis of the ac-dc coupled hierarchical control is performed by the established state space model. Case studies are conducted by simulations to demonstrate the performance of the proposed control strategy.
Design of fog computing infrastructures with rotary-wing UAVs

Fog nodes usually belong to fixed infrastructures that, once deployed, cannot be easily moved. Despite the easy management of these infrastructures, they are limited in dealing with variations in the demand of mobile users. To cope with that, fog nodes can be mounted on unmanned aerial vehicles (UAVs), which can be quickly deployed, staying close to those users requiring low latency services. The location and period of operation of UAVs must be determined to maximize the number of requests processed and reduce the latency in accessing the infrastructure. This paper proposes a solution to the fog node location problem with rotary-wing UAVs that considers variable demands. We have modeled the problem with an integer linear programming formulation and have proposed the Sequential UAV Fog Node Location algorithm to deal with large scenarios. The results derived using real traces show that low delay applications can be supported. It is, however, essential to plan the deployment of UAVs so that the operational time of battery-constrained UAVs can be extended.
Detection of Bad Data and False Data Injection Based on Back-Propagation Neural Network

Power system state estimation is an essential tool for monitoring the operating conditions of the grid. However, the collected measurements may not always be reliable due to bad data from various faults as well as the increasing potential of being exposed to cyber-attacks, particularly from data injection attacks. To enhance the accuracy of state estimation, this paper presents a back-propagation neural network to detect and identify bad data and false data injections. A variety of training data exhibiting different statistical properties were used for training. The developed strategy was tested on the IEEE 30-bus and 118-bus power systems using MATLAB. Simulation results revealed the feasibility of the method for the detection and differentiation of bad data and false data injections in various operating scenarios.
Approximation of Koopman Operators: Irregular Domains and Positive Orbits

In this paper, we derive rates of convergence of a class of approximations of the Koopman operator that are obtained using the extended domain decomposition (EDMD) method. The paper extends the previous approaches that consider the case in which samples become dense in or approach a smooth Riemannian manifold or a regular subset of ℝd. Here, we extend these results to the case where samples become dense in some compact subset of the state space without requiring the common regularity properties. Rates of convergence of approximations of the Koopman operator are described in terms of approximation spaces, spectral approximation spaces, and in terms of the power function of the kernel of a reproducing kernel Hilbert space $\mathcal{H}$. A numerical example is presented to demonstrate the results.