Miracle-dz/UniFAR-FTUs huggingface dataset:
Vorticity and Vertical Motions Diagnosed from Satellite Deep-Layer Temperatures

Abstract Spatial fields of satellite-measured deep-layer temperatures are examined in the context of quasigeostrophic theory. It is found that midtropospheric geostrophic vorticity and quasigeostrophic vertical motions can be diagnosed from microwave temperature measurements of only two deep layers. The lower- (1000–400 hPa) and upper- (400–50 hPa) layer temperatures are estimated from limb-corrected TIROS-N Microwave Sounding Units (MSU) channel 2 and 3 data, spatial fields of which can be used to estimate the midtropospheric thermal wind and geostrophic vorticity fields. Together with Trenberth's simplification of the quasigeostrophic omega equation, these two quantities can be then used to estimate the geostrophic vorticity advection by the thermal wind, which is related to the quasigeostrophic vertical velocity in the midtroposphere. Critical to the technique is the observation that geostrophic vorticity fields calculated from the channel 3 temperature features are very similar to those calculated fro.
An Analysis of CMOS Latched Comparators

Using suitable types of CMOS latched comparators for specific conditions is vital –because each comparator has different pros and cons. Also, it is important to determine which comparators to utilize for specific conditions. A need remains for the practical comparison of various types of comparators with applicable criteria. Within the present study, we performed comparisons between StrongArm latch, modified double-tail latch, and track-and-regenerate slicer with three different types of criteria. We analyzed each comparator by controlling three types of variables: power supply, input common mode voltage, and differential signal input. Then, in keeping with these three types of criteria, the simulation results, kickback noise, power consumption, and clock-to-Q delay were evaluated. We designed three kinds of CMOS latched comparators with 40-nm CMOS technology. In post-layout simulations, using a StrongArm latch resulted in low power consumption. Modified double-tail latch showed strength in kickback noise and is applicable for low power supply. Track-and-regenerate slicer showed better performance in clock-to-Q delay. Our findings expand on the performance of each comparator that would be applied to mixed-signal circuits and systems.
Control of series-cascaded grid-forming microgrid configuration with a storage device

With the increase of inverter-based energy resources in power grids, it is imperative that the frequency stability is maintained and inverters can be controlled to improve frequency damping by using grid-forming inverters (GFM). The microgrid that is formed by several DGs that are connected in series is called a series topology microgrid. The series topology with individual LC output filters benefits from utilizing lower and different dc-link voltages. One of the drawbacks in series topologies is that the series cascaded DG microgrids have rarely been discussed when it comes to a different configuration combinations of DGs such as dispatchable and non-dispatchable DGs. In this paper, a comprehensive study of series-cascaded microgrid configuration consisting of master and slave DGs with a storage device is done. The power circuit, control circuit, and results are shown.
Fault Ride-Through Control of Cascaded H-Bridge Converter-Based Battery Energy Storage Systems with Redundant Submodules

Cascaded H-bridge (CHB) converter is a suitable topology to integrate battery energy storage systems (BESSs) into the power system. Redundant submodules (SMs) are implemented in the CHB converter for higher reliability, and those redundant SMs may not be integrated with batteries. This paper proposes a control scheme for the CHB-based BESS with redundant SMs. When there is no fault, the redundant SMs are discharged and idle, while the SMs with batteries support the BESS operation. In the faulty condition, the redundant SMs will be charged and can support the BESS operation if needed. Simulation results are provided to verify the effectiveness of the proposed control scheme.
Multi-Ratio Operation of Flying Capacitor Multilevel Converters At and Above Resonance

The flying capacitor multilevel (FCML) converter is an attractive candidate for dc/dc step-up and step-down applications due to its relatively low switch stress and greatly reduced inductor volume. It can be operated in either a regulating pulse width modulated (PWM) or fixed-ratio resonant mode. For fixed-ratio operation, the FCML converter is capable of achieving multiple rational conversion ratios (up to a maximum of $N:1$) with the same switch-capacitor network simply by changing the gating signals of its switches. In addition, the inductance requirement can be further reduced compared to regulated operation, while switching losses at resonance can be mitigated due to the presence of soft-switching. However, there is limited prior analysis of higher level-count $(N\geq 3)$ fixed-ratio FCML converters operating at resonance or above resonance, where the derivation of appropriate phase durations becomes significantly more involved. This work presents a full analytical method to calculate optimal phase durations for the fixed-ratio FCML converter across all rational conversion ratios while operating at or above resonance. Hardware results for an $N=5$ FCML converter are presented for conversion ratios 5: 1, 5: 2, 5:3 and 5:4, illustrating the ability to use above-resonance operation to optimize the distribution of switching losses and conduction losses in the converter.
Distributed Control Scheme for a 11-Level Modular Multilevel Converter in a Grid Connected Photovoltaic Power Conversion System

In recent years multilevel modular cascade converters have become a promising technique to connect photovoltaic cells to the grid. The reason for this is that they allow for plug and play modularity (allowing expansion and reduction), can have multiple local maximum power points (MPPs) allowing the system to produce the maximum possible power even when the PV cells are partially shaded, and allow for a reduction in the output filter. While current control systems can produce individual maximum power point tracking (MPPT) for each module they do not allow for plug and play modularity. This paper proposes a module voltage balancing technique coupled with a global maximum power point tracker to achieve this dual goal. This was then tested on a 11-level modular multilevel converter at several different irradiance levels between different modules. These tests show that if all the modules have an irradiances of $1 kW/m^{2}$ then a total MPPT efficiency of 98.8-100% and an individual MPPT efficiency of 98.8-100% was achieved. It also showed that as long as the number of modules with irradiance difference is minimal and the location of these modules with irradiance differences is optimal, then the global efficiency is maintained with only slight difference in individual MPPT efficiency.
Prediction of EV Energy consumption Using Random Forest And XGBoost

As climatic crisis increasing in the world due to the increasing pollution day by day, in which one of the important contributor is the increasing demand for energy,it has been found that 25 percent of the global energy consumption is only due to transportation sector, so in order to minimize the effect due to transportation sector we have to shift from Internal Combustion Engine (ICE) to battery based Electric Vehicle (EVs), there are several issues that need to be addressed to encourage the adoption of EVs on large scale, one of the severe effect due to large scale adoption of EVs is on the grid, large scale deployment of EVs causes overloading on the power grid due to the unscheduled charging of the EVs, in order to reduce this overloading of the grid proper scheduling algorithm are needed by which we can scheduled the charging of EVs and growing public charging demand for EVs. For this data-driven tools can be utilized which can predict the various parameters like energy consumption, time of charging, whether the EVs use charging stationtomorrow, use of DC fast charging etc, in this paper we are focusing on the prediction of energy consumption by using the historical charging data of the EVs by using various popular machine learning (ML) algorithm such as Random Forest, XGboost, linear Regression, ANN and DNN. The best predictive results are obtained by Random Forest and XGboost, and various result of past work is also discussed.
On the limit distributions of the degrees of vertices in configuration graphs with a bounded number of edges

A model of a configuration graph on vertices is considered in which the degrees of the vertices are distributed identically and independently according to the law , , 0$? >, and the number of edges is no greater than . Limit theorems for the number of vertices of a particular degree and for the maximum vertex degree as are established. Bibliography: 18 titles.
Evaluation of the image quality of telescopes using the star test

The Point Spread Function (PSF) or star test is one of the main criteria to be considered in the quality of the image formed by a telescope. In a real system the distribution of irradiance in the image of a point source is given by the PSF, a function which is highly sensitive to aberrations. The PSF of a telescope may be determined by measuring the intensity distribution in the image of a star. Alternatively, if we already know the aberrations present in the optical system, then we may use diffraction theory to calculate the function. In this paper we propose a method for determining the wavefront aberrations from the PSF, using Genetic Algorithms to perform an optimization process starting from the PSF instead of the more traditional method of adjusting an aberration polynomial. We show that this method of phase recuperation is immune to noise-induced errors arising during image aquisition and registration. Some practical results are shown.
EyeRis: Visual Image Recognition using Machine Learning for the Visually-Impaired

Visually impaired people struggle daily and have difficulty recognizing and distinguishing objects around them. Thus, they mainly depend on supervision from other people to assist them. Since smartphones have become a necessity in this modern world, the researchers formulated a solution to help the visually impaired through a machine learning-based mobile application for object recognition. Nowadays, software applications can provide accurate findings in picture classification and processing processes thanks to machine learning techniques and algorithms. In this study, the researchers use a convolutional neural network (CNN)-based system on TensorFlow Lite to create a mobile version of a visual information system employing a machine learning strategy and deep learning framework. The main objectives of the smartphone application, EyeRis, is to recognize and categorize items in real-time and to separate photographs from the user-selected scenarios. Results were analyzed and contrasted based on the app’s obtained recognition accuracy data. It demonstrated the utility of CNN as a model for image recognition algorithms.
Second-Order Statistics-Aided Channel Estimation for Multipath Massive MIMO-OFDM Systems

This paper develops an efficient channel estimation algorithm based on second-order statistics of time division duplex (TDD) multiuser massive multiple-input multiple-output (MIMO) systems. The algorithm uses the received signal correlation to determine the most significant lags (MSLs) of the received signal. We first employ these MSLs to propose a novel set containing the channel’s four most significant taps (MSTs). Then, by using them, we propose an efficient semi-blind iterative algorithm called enhanced modified-subspace pursuit (EM-SP). It uses the set mentioned above and two theoretical results (Lemma 1 and Theorem 1) to estimate an arbitrary number of MSTs efficiently. Simulation results show that the normalized mean square error (NMSE) of the proposed EM-SP algorithm is much smaller than that of the subspace pursuit (SP) and orthogonal matching pursuit (OMP) algorithms at the cost of 0.3 % and 2 % more computational complexity for the channels with three and six nonzero paths, respectively. Moreover, the NMSE of it is very close to that of the optimal genie-aided least square algorithm.
Design of 15 kW, 440 V Three Phase Induction Motor for Electrical Vehicle Applications with Improved Efficiency and Wide Speed Range

Designing of a 3-phase squirrel cage induction motor (IM) for high performance applications with high efficiency, high power factor and torque is a challenging task for a machine designer. This work proposes an efficient 15 kW induction motor with high speed operation for electric vehicle applications. The rotor and stator slot dimensions of IM are analyzed which affects the performance parameters. The IM is designed using JMAG software and the results are presented.
A Review on Current-Steering DAC Design

This article discusses the design considerations of a current-steering digital-to-analogue converter (CSDAC) and reviews some techniques that addresses non-ideal behaviors of a CSDAC. To understand the design considerations and how non-idealities affect the performance of a CSDAC, a 12-bit CSDAC is designed in TSMC 40nm technology node and the simulation results are provided.
Avaliacao da Curva de Juros Empregando Extensoes do Modelo de Diebold e & LI com Tres Fatores

This paper evaluates methods that employ Kalman Filter to estimate Diebold & Li (2006) extensions in a state-space representation, applying the Nelson & Siegel (1987) function as measure equation and different specifications for the transition equation that determines level, slope and curvature dynamics. The models that were analyzed have the following structures in transition equation: (1) AR(1) specification, employing a diagonal covariance matrix for the residuals; (2) VAR(1) specification, employing a covariance matrix calculated with Cholesky decomposition; (3) VAR(1) extension, inserting variables related to the Covered Interest Rate Parity (CIRP); (4) VAR(1) extension, including stochastic volatility components. The major findings of this paper were: (1) evaluating the latent variables dynamics, the curvature was the factor that fitted better to the stochastic volatility component; (2) in a broad sense, even though the simplest VAR(1) model was the one that provided the best out-of-sample performance in the most part of maturities and forecasting horizons, the extension inserting variables related to the CIRP was able to overcome the former specification in some of these simulations.
On media with negative dispersion

Quasi-optical properties of three artificial dielectrics (cubic lattices formed of perfectly conducting elements) are studied theoretically. It is shown that all three dielectrics have negative dispersion of the zero-order spatial harmonic in the second passband. The first two structures exhibit this property in all three coordinates, and the third one exhibits it in one coordinate only. It is found that this dispersion causes an anomalous refraction of waves.
A Simple Reconfigurable FSS Structure for Antenna Beam Steering Applications

This paper presents a simple and novel active reconfigurable frequency selective surface (FSS) layer for antenna beam steering applications. The FSS layer is made up of two unit cells arranged adjacent to each other. Each FSS unit cell consist of a discontinuous metallic square-ring incorporated with PIN diodes. By controlling the states of the PIN diodes, the FSS layer can transmit or reflect the impinging electromagnetic waves. To verify the feasibility of the proposed FSS for beam steering, a monopole rod antenna is designed with the proposed FSS incorporated. The experimental results demonstrate that the proposed FSS structure can achieve beam steering for different boresight beam directions.
Power Reserve Control Methods for Grid-Connected Photovoltaic Power Plants: A Review

Solar penetration is steadily increasing to provide power generation as the world turns to clean and sustainable solutions to meet the rising energy demand. Consequently, grid codes and standards are constantly evolving with new requirements such as constant power injection into the grid and power reserve control (PRC) to provide grid support. As a result, existing control techniques in photovoltaic (PV) systems, such maximum power point tracking (MPPT) and flexible power point tracking (FPPT) algorithms, need to be modified to provide such functionalities. Constant power injection, achieved by FPPT, mitigates the problem of solar intermittency, whereas PRC can provide frequency response capability to the grid-connected PV power plants. PRC methods are able to determine the maximum available power which indicates the dispatchable power reserve magnitude, while performing FPPT. PRC methods can be broadly classified as estimation and measurement-based ones. In this paper, a review of PRC methods followed by a comparative discussion on the basis of complexity, ease of implementation, accuracy, and additional hardware requirement of these methods is done.
Iterative Interactive Modeling for Knotting Plastic Bags

: Deformable object manipulation has great research signiﬁcance for the robotic community and numerous applications in daily life. In this work, we study how to knot plastic bags that are randomly dropped from the air with a dual arm robot and image input. The complex initial conﬁguration and intricate physical properties of plastic bags pose challenges for reliable perception and planning. Directly knotting it from random initial states is difﬁcult. In this work, we propose Iterative Interactive Modeling (IIM) to ﬁrst adjust the plastic bag to a standing pose with imitation learning to establish a high-conﬁdence keypoint skeleton model, then perform a set of learned motion primitives to knot it. We leverage spatial action maps to accomplish the iterative pick-and-place action and a graph convolutional network to evaluate the adjusted pose during the IIM process. In experiments, we achieve an 85.0% success rate in knotting 4 different plastic bags including one that has no demonstration
A SLiM-dependent conformational change in baculovirus IE1 controls its focus formation ability

The baculovirus IE1 gene encodes a multifunctional protein that is essential for both DNA replication and RNA transcription of the virus. Prior to viral DNA replication, IE1 promotes early gene transcription when localized in hr-dependent foci. During viral DNA replication, the IE1 foci expand and fuse to generate the virogenic stroma (VS) with IE1 forming the VS reticulum. To explore the IE1 structural features essential for this coordinated localization, we constructed various IE1 mutants based on three putative domains (N, I, and C). We determined that a BDI motif located in the intrinsic disorder region (IDR) between the N and I domains acts as a nuclear localization signal, whereas BDII and HLH in the C domain are required for VS localization in infected cells or for chromosomal association in uninfected mitotic cells. Deletion of the SLiM (short linear motif) located in the I domain restrains both nuclear- and VS localization. Intra-molecular fluorescence resonance energy transfer (FRET) probes of IE1 mutants revealed a conformational change of the I-C two-domain fragment during infection, which was inhibited by aphidicolin, suggesting that IE1 undergoes a stage-dependent conformational change. Further, homo-dimerization of the I domain and stage-dependent conformational changes require an intact SLiM. Mutational analysis of SLiM revealed that VS localization and chromosomal association were retained following S291A and S291E substitutions, but hr-dependent focus formation differed between the two mutations. These results suggest that coordinated IE1 localization is controlled by SLiM-dependent conformational changes and that are dependent on the SLiM phosphorylation state. Importance SLiMs (short linear motifs) are compact non-globular protein interaction interfaces that mediate various cellular functions and as such are often mimicked by viruses to rewire cellular pathways. Here, we found that an unusual type of viral SLiM acts as a conformational switch rather than a cellular mimic. Prior to viral DNA replication, the baculovirus IE1 protein promotes early gene transcription within its focus. During viral DNA replication, the IE1 foci expand and fuse to form the virogenic stroma where late virus replication events occur. Our results indicate that IE1 undergoes a conformational change that is dependent on the infection stage and SLiM phosphorylation. This work provides new insights into the role SLiMs play in heterotypic and homotypic protein-protein interactions.
Solving Complex Manipulation Tasks with Model-Assisted Model-Free Reinforcement Learning

: In this paper, we propose a novel deep reinforcement learning approach for improving the sample efﬁciency of a model-free actor-critic method by using a learned model to encourage exploration. The basic idea consists in generating imaginary transitions with noisy actions, which can be used to update the critic. To counteract the model bias, we introduce a high initialization for the critic and two ﬁlters for the imaginary transitions. Finally, we evaluate our approach with the TD3 algorithm on different robotic tasks and demonstrate that it achieves a better performance with higher sample efﬁciency than several other model-based and model-free methods.
Hail in the Vicinity of Organized Updrafts

Abstract Several encounters with hail and graupel (often inadvertent) have occurred while flying an aircraft through the organized updrafts at the base of thunderstorms in the High Plains area. These encounters normally occurred while entering or exiting the organized updrafts and while flying in the vicinity of the strong horizontal reflectivity gradients which commonly border organized updrafts. On several occasions mobile ground crews were beneath the organized updrafts and confirmed the observations from the aircraft. These crews also noted the sequence of precipitation events in this region. The hail which falls in this particular region has the following characteristics: it has a narrow size range, it is large hail (often the largest which falls from the storm), it is commonly not accompanied by rain, and it has a small range of concentrations for a given diameter. The precipitation sequence at the ground over which an organized updraft passes is large hail followed by smaller hail and rain.
Fusing Priori and Posteriori Metrics for Automatic Dataset Annotation of Planar Grasping

: Grasp detection based on deep learning has been a research hot spot in recent years. The performance of grasping detection models relies on high-quality, large-scale grasp datasets. Taking comprehensive consideration of quality, extendability, and annotation cost, metric-based simulation methodology is the most promising way to generate grasp annotation. As experts in grasping, human intuitively tends to make grasp decision based both on priori and posteriori knowledge. Inspired by that, a combination of priori and posteriori grasp metrics is intuitively helpful to improve annotation quality. In this paper, we build a hybrid metric group involving both priori and posteriori metrics and propose a grasp evaluator to merge those metrics to approximate human grasp decision capability. Centered on the evaluator, we have constructed an automatic grasp annotation framework, through which a large-scale, high-quality, low annotation cost planar grasp dataset GMD is automatically generated.