Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Fungible Token Donation Platform Delivers Continuous Goodwill without Daunting Donor Commitment

The phenomenon of impulsive donation has been encouraged by the rapid growth of internet news and social media. On the one hand, tragic events can be known to the world, necessitating timely donations, whereas on the other hand, it does supplant the committed donations for ones that need long-term support. In this study, a blockchain-based non-fungible token charitable donation platform was developed that allowed people to donate the royalties of transaction of digital assets and therefore, generate continuous support for charitable organizations or suffering individuals.
Long-wave infrared digital holographic interferometry with diffuser or point source illuminations for measuring deformations of aspheric mirrors

Abstract. Long-wave infrared digital holographic interferometry with CO2 laser and microbolometer arrays has been developed for testing the large deformations of space reflectors. The setup considered is a Mach–Zehnder, associated to the digital holography reconstruction of the wavefront in the inline configuration with phase shifting. Two possibilities exist for illuminating the tested reflector: either with a point source (similarly to classical interferometry) or an extended source (with a diffuser). This paper presents the development of a modular setup which allows comparing both in the case of a parabolic mirror.
Self-avoiding random walks as a probe of large-scale structure in the Universe

We present a new random walk technique for probing large-scale structure in the Universe. The method is based upon a growth model used in condensed matter physics. We have applied our algorithm to point distributions in two and three dimensions. We find that our method can distinguish between distributions that have similar two-point correlation functions on small scales, but which have different amounts of large-scale power
Estimating the Incubation Period of AIDS by Comparing Population Infection and Diagnosis Patterns

Abstract The incidence of AIDS virus infections over time among gay men in San Francisco is nonparametrically estimated from interval-censored data by using an EM algorithm to maximize a roughness-penalized likelihood. Because the distribution of AIDS diagnoses is the convolution of the infection and incubation distributions, the incubation distribution can be estimated by comparing the estimated infection distribution and the observed pattern of diagnoses. This is again accomplished by nonparametrically maximizing a roughness-penalized likelihood using the EM algorithm. The optimal degree of smoothness for the estimates is chosen using external data and subjective assessments of plausibility. Three prospective studies of initially uninfected men produce comparable estimated infection rates and are merged to produce an overall estimate, which shows rates increasing until late 1981 and then falling sharply. The estimated incubation period hazard function is near 0 for two years following infection and then.
Frequency Oscillation Control Using Tilt Cascaded Controller Cosidering Renewable Generation

Damping of frequency in unified system because of load uncertainty is a major concern for power engineer. Oscillation of tie-line power and frequency of control area are mainly damped with the help of secondary controller. Here a cascaded PI - PTI (proportional integral- proportional tilt integral) controller based on fractional mathematics is projected for frequency deviation control in a unified system integrated with PV and thermal generating units. Efficient Sine-cosine algorithm is employed to get the suitable values of PI-PTI controller's gains. To observe the outcomes of PI-PTI controller implemented in the thermal-PV integrated system a disturbance in terms of load perturbation (1% per unit) is applied in the control area. The oscillation of tie-line power and frequency in control areas are observed in terms of negative peak, positive peak and settling time. The outcomes of PI-PTI controller is compared with the PI controller and the dominance of PI-PTI controller is established over PI controller. Later by incrementing the demand of control area 1 the efficacy of the suggested PI - PTI controller is witnessed.
Optimized Design and Application of GaN Based Power Amplifier for C-Ku Band of AESA RADAR

This paper presents the optimized design and application of a C-Ku band for GaN (Gallium Nitrate) based PA (Power Amplifier). PA and LNA are most important sub components of Transmitting and Receiving Module (TRM) for Active Electronically Scan Array (AESA) RADAR. As AESA has n number of patch antenna elements, multiple fabrication techniques, so it is mandatory to explained the updated technologies of design and up-gradation according to new era. According to taken references, can say that, the Self Oscillation (SO) is a effect which cause due to the closed feedback loop in TRM. In the Rx end of the TRM have more losses, to overcome this losses and to achieve greater efficiency of power amplifier, GaN based technology has explained. AESA has the great efficiency of the scanning and seeking as automation of the steering is given and commanded by the chip called FPGA. The reduction of fabrication error according to the theoretical values and practical testing results has updated by the 6 bit phase shiftier, 6 bit attenuator, these working process namely called Collimation. The performance of the 50 W output power, GaN technology based power amplifier with more than 35dBm gain has Power added Efficiency (PAE) is greater than 30 %. The average Fabricated Monolithic Micro Integrated Circuit (MMIC) size is 15 * 15 * 3 mm3 used for Air-born applications. Designing with thermal effect also plays a major role and effects on the components has explained in this paper. The explained GaN technology-based operate up to 180°C with 1500 W/cm2 and thermal packing design will help to increase the gain of PA by 33%.
Building and Monitoring a Smart System by using IoT Technology

There is a pressing demand for data and understanding of the technological capabilities of the need for total access to a structure at any point in time and from any location, regardless of the restrictions involved, will develop with the “Internet of Things” (IoT) for buildings remote control. “Smart” buildings (IB) Optimizing their performance throughout their lifetime is the goal of this technology. A solution to the problem of an experimental testing bench for one of the initial phases leading to IB's IoT network architecture to develop the IoT for integrating IB components and subsystems and IB networks is briefly described in this article. The first section of the report provides an outline of IB and the sector's future tendencies. Paper. The Internet of Things (IoT) is further discussed. IoT architecture and how it may be implemented afterward are shown in the context of how they've been integrated into intelligent building systems. A test of the solution's implementation and performance. for a dispersed network of IB innovative components and an Android-compatible monitoring device to communicate The following section of the paper is covered by the gadget. Efforts to implement the notion have been successful. The piece of writing comes to an end. Provides the conclusion and the execution strategy for the idea.
Diagnostic Models of Sensitivity of Automatic Control Systems

An approach to the construction of a simplified diagnostic model for analyzing the distinguishability of single block defects in continuous dynamic objects, based on a sensitivity model using integral signal transformations, is considered. The influence of the parameter of the integral transformation of the signals of the object of diagnosis on the a priori and actual distinguishability of single defects is studied. A good agreement between the values of a priori and actual distinguishability of single defects was found when using a single stepwise input action on the object. All experiments and simulations were carried out using specialized software complex created in Scilab/SciNotes.
Measurement and Prediction of Micronewton Class Thrust of Electric Propulsion Based on the Torsional Pendulum and Machine Learning Technique

Accurate and direct measurement of thrust is the most fundamental requirement in the evaluation of electric thruster performance. This article performed the measurement and prediction of the micronewton class thrust of micro gridded ion thrusters (μGITs) based on a novel design of torsional pendulum and high precision neural network models. The developed torsional pendulum adopts specially designed liquid metal connectors to eliminate the interference caused by the power supply cables and propellant feeding lines, realizing an accurate thrust measurement of radio frequency (RF)- and dc-μGIT from micronewton level to millinewton level with a measuring range of 0–10 mN and a resolution of $6.4~\mu \text{N}$ . Results show that the RF-μGIT generates a thrust of 0.46–2.43 mN for the RF power from 100 to 120 W and the xenon flow rate from 0.75 to 1.50 sccm. The dc-μGIT generates a thrust of 0.016–0.310 mN for the acceleration voltage from 700 to 1050 V and the flow rate from 0.2 to 2.0 sccm. Both the artificial neural network (ANN) and the radial basis function neural network (RBF-NN) are adopted to predict the relationship between the thrust and input parameters of RF-μGIT. The predicted thrusts by ANN deviate about 10% from the measured data, and the maximum relative deviation using RBF-NN model is about 2%. Furthermore, the RBF-NN model is used to obtain the distribution maps of thrust and specific impulse under 600 different working conditions and provides a solution for intelligent regulation of thruster performance. For the first time, the combination of thrust measurement and machine learning (ML) provides a new approach for the fast performance evaluation, prediction and regulation of electric thrusters.
On Pre-chewing Compression Degradation for Learned Video Compression

Artificial Intelligence (AI) needs huge amounts of data, and so does Learned Restoration for Video Compression. There are two main problems regarding training data. 1) Preparing training compression degradation using a video codec (e.g., Versatile Video Coding - VVC) costs a considerable resource. Significantly, the more Quantization Parameters (QPs) we compress with, the more coding time and storage are required. 2) The common way of training a newly initialized Restoration Network on pure compression degradation at the beginning is not effective. To solve these problems, we propose a Degradation Network to pre-chew (generalize and learn to synthesize) the real compression degradation, then present a hybrid training scheme that allows a Restoration Network to be trained on unlimited videos without compression. Concretely, we propose a QP-wise Degradation Network to learn how to compress video frames like VVC in real-time and can transform the degradation output between QPs linearly. The real compression degradation is thus pre-chewed as our Degradation Network can synthesize the more generalized degradation for a newly initialized Restoration Network to learn easier. To diversify training video content without compression and avoid overfitting, we design a Training Framework for Semi-Compression Degradation (TF-SCD) to train our model on many fake compressed videos together with real compressed videos. As a result, the Restoration Network can quickly jump to the near-best optimum at the beginning of training, proving our promising scheme of using pre-chewed data for the very first steps of training. In other words, a newly initialized Learned Video Compression can be warmed up efficiently but effectively with our pre-trained Degradation Network. Besides, our proposed TF-SCD can further enhance the restoration performance in a specific range of QPs and provide a better generalization about QPs compared with the common way of training a restoration model. Our work is available at https://minhmanho.github. io/prechewing_degradation.
Buckwheat Grain Image Segmentation Based on U-Net Model

In order to improve the segmentation effect of buckwheat grain image, the improved U-Net network was used to increase the network depth and the Batch Normalization layer was added; Transfer learning is introduced and Pascal VOC pre training model is adopted to improve the performance of the model; A new loss function combining Dice loss and Cross Entropy loss (CEloss) is used to mitigate the sample imbalance. The buckwheat grain mixture images collected by the image acquisition experimental platform were annotated, 175 512 pixel images were selected as the initial samples, and data enhancement was used to expand the data to improve the robustness and generalization ability of the model. The test results showed that the comprehensive evaluation index value of the segmentation of non shelled buckwheat was 97. 99%, the comprehensive evaluation index value of the segmentation of intact buckwheat rice was 89. 62%, and the comprehensive evaluation index value of the segmentation of broken buckwheat rice was 72. 70%. The buckwheat grain image segmentation algorithm based on the U-Net model proposed in this paper can effectively segment the unwrapped buckwheat, intact buckwheat rice and broken buckwheat rice in the buckwheat grain image, with higher accuracy, and is of great significance to the realization of adaptive optimal control of the buckwheat husker.
Electromagnetic scattering by a lossy dielectric-coated elliptic cylinder

Utilisant des ondes elliptiques exprimees en terme de fonctions de Mathieu complexes, nous analysons la diffusion d'ondes electromagnetiques par un cylindre elliptique recouvert de dielectrique avec pertes. Nous obtenons numeriquement le champ diffuse dans la region lointaine pour differents rapports axiaux, constantes de perte dielectrique et angle d'incidence. Le resultat numerique montre un changement significatif dans l'echo de diffusion arriere due a la couche dielectrique avec perte.
Applying Deep Learning in Forecasting Stock Index: Evidence from RTS Index

This paper compares a deep learning method with parametric models with respect to their performance in forecasting the Moscow exchange stock index – RTS Index. We use deep learning-based algorithms namely, Long Short-Term Memory (LSTM) model to measure if it outperforms univariate Autoregressive Integrated Moving Average (ARIMA) process. RTSI is a capitalization-weighted composite US dollars index calculated based on prices of the most liquid 50 Russian stocks. We question the accuracy and precision of traditional forecasting techniques compared to deep learning-based forecasting algorithms. We find that the deep learning LSTM model is more successful in forecasting RTSI index prices in the test data. This result shows that deep learning has significant power in forecasting the RTS index prices. And it has an implication for the market efficiency in Moscow Exchange. Portfolio managers may use deep learning methods to analyze the market and take appropriate positions.
A New Slight Step-Up Quadratic Boost Converter

A novel single-switch quadratic boost converter is presented in this paper. Its static conversion ratio indicates that the proposed topology is well suited in applications requiring a slight step-up of the input voltage compared to the classical boost. Static characteristics are derived and the semiconductor stresses proved to be lower than in the classical boost. For the considered design example, a measured efficiency of 93% was obtained. According to the design relationships, a prototype was breadboarded. First the designed converter was simulated, then subjected to practical experiments. Finally, the theoretical aspects were confirmed by the simulations and the experimental results, thus proving the feasibility of the proposed converter.
Performance Comparison of Error-Control Schemes in Collaborative Multiple-Input Multiple-Output Systems

Multiple-input multiple-output (MIMO) collaborative reception is an emerging technology in wireless communications. In this reception system, received signals from a base station are shared among neighboring multiple mobile stations (MSs). Multiple received signals can be used for decoding the MIMO signal, so the detection performance is enhanced. In particular, we consider collaborative detection schemes in order to improve the detection performance further while keeping the collaboration traffic as small as possible. Collaborative detection schemes utilize multiple decision results of multiple detection MSs. Through computer simulations, we show that a selection scheme based on decision reliability information outperforms soft combining schemes.
Timing is Everything! A Test and Production Class View of Self-Admitted Technical Debt

In this short paper, we investigate whether the “time of day” when recognised changes are made to code influences the self-admission of technical debt (SATD). We look at this question from a test and production class perspective. We examine if there is a specific time of day when technical debt is “self-admitted” more frequently and whether there are any similarities in this sense between test and production classes. We also analyse whether class complexity makes a difference to SATD occurrence. To facilitate our analysis, we used a data set of over 300k changes developed by Riquet et al., as a basis. Results suggest that a lower proportion of SATD occur in afternoons as opposed to mornings and that class complexity has a significant say in the role and application of SATD.
Thermal modelling, characterization and optimization of 2.5D heterogeneous integrated platform for RF front end

This paper studies the thermal characteristics of a compact 2.5D heterogeneous integrated platform by numerical simulation. The platform contains a silicon interposer carrying high-density power amplifiers made of gallium nitride on silicon carbide (GaN-on-SiC) and temperature sensitive passive filter dies. Comparing with traditional wire bonding method, the flip chip method suspended by metal stub enjoys greater benefits on RF performance. However, thermal issue becomes more challenging in this configuration due to the improper thermal transferring path. This work numerically studies the metal casing as a cooling solution and explores the potential capabilities in 2.5D heterogeneous integrated platform based on a full-scale 3D finite element model. Temperature distributions across the package in different conditions are presented to reveal the effect of the casing. And the parametric study results guide the thermal design of such casing in practice. The information in this paper could be helpful in the thermal design of 2.5D heterogeneous integrated platform for RF front end.
PointNet++GPD:6-DOF grasping pose detection method based on object point cloud

To improve the accuracy and reliability of the grasp detection model for generating grasp poses, this paper proposes an end-to-end grasp pose detection network based on PointNet++ to address the shortcoming that PointNetGPD is challenging to learn local features in point clouds. Firstly, a dataset with a grasping quality score label is generated based on the BigBIRD dataset for model training. Then a grasping pose evaluation network based on PointN et++ is designed to classify the candidate grasping poses, to obtain the optimal grasping pose. Experimental results show that the classification accuracy of the grasping pose of the proposed model is higher than that of PointNetGPD and GPD, and the success rate of grasping objects in the real environment is higher than that of the above two models.
Error Analysis of an Optimal Rotated M-PSK Constellation in a SOMA-Based Wireless Communication System

In this work, we consider a two-user receive diversity wireless communication system with a rotated M-ary phase-shift keying (M-PSK) modulation, also known as semi-orthogonal multiple access (SOMA), for data transmission at the transmitter. At the receiver of the considered wireless communication system, each user utilizes a maximum ratio combining (MRC) technique and an optimal maximum-likelihood (ML) rule for transmitted data detection. We provide an analytical framework for the error analysis of the receive diversity wireless communication system and derive the closed-form expressions of the symbol error probability (SEP) for the users. Further, we also obtain the asymptotic expressions of the SEP at a high signal-to-noise ratio (SNR). We provide a numerical solution for the problem of finding the optimal rotation angle for the M-PSK constellation minimizing the SEP. The effects of the system parameters, namely, the number of diversity branches, SNR, and M, on the optimal rotation angle are demonstrated via numerical results. Furthermore, the simulation results corroborate the presented analytical results.
Path Planning and Control for Modular Self-assembling Robots on Rough Terrain

Some insects are born socially, such as bees and ants. They could combine multiple individuals into special shape for other inserts to pass the complex or dangerous natural environment. In this paper, referring to the swarm characteristics in insects, a path optimization algorithm for modular self-assembling robots is designed. Modular self-assembling robots are composed of multiple independent motion modules that can be connected to each other. When one of them are facing complex environment that is difficult to pass though, they will combine themselves into a shape that adapts the complex environment, and smoothly pass the obstacles ahead.
Cubic Buck-Boost Converter with High Step-Up Capability

The paper presents a cubic buck-boost topology that has one active switch and five diodes. Examining the static conversion ratio, it results that this converter can provide a very high step-up of the input voltage compared to the classical buck-boost and classical boost topologies. A DC analysis of the proposed converter is presented, together with its semiconductor stresses calculation. In order to prove the theoretical considerations, a simulation is performed based on the component values obtained from the design equations.
2-D Control of Payload on an Offshore Crane during Ship-to-ship Load Transfer Operation

Offshore load transfer in rough sea conditions, especially when Ship-to-Ship, is risky due to the increasing demand for the acceleration system requirements and the restricted unloading area. This study proposes an efficient solution by integrating the combined Fuzzy and PID control systems for the vertical and horizontal control of payload movement, the sea environment with realistic irregular waves, and the crane model. With the Trajectory Route Control System, safe unloading is ensured by advancing in the desired vertical position, and the operation's speed route with horizontal positioning is realized. The simulation results revealed that the system model is a valid and successful solution.