Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault Prediction of Phased Array Radar T/R Components Based on Data Mining -- Method Study

Based on the processing flow and key techniques of data mining, a fault quantity prediction method of phased array radar T/R components is studied. The method mainly consists of two parts: fault data pre-processing and prediction model building, in which the fault data pre-processing mainly performs missing value processing, outlier analysis and data smoothing on T/ R component fault count data to obtain high-quality training data; while the prediction model building is to build a prediction model for T/ R component fault count data and analyse the model performance to meet the prediction requirements. Finally, the effectiveness of the prediction method is verified by using the actual T/ R component failure count data of a certain type of radar.
Enhanced charge-carrier mobility in β -phase polyfluorene

Alkyl substituted polyfluorenes are promising candidates for use in organic display applications due to efficient, pure blue (polarized) luminescence, high charge carrier mobility, and good processabilty. Poly(9,9? -dioctylfluorene) (PFO) is an especially interesting polyfluorene derivative, because of its self-organization into distinct supramolecular structures at room temperature. In addition to the amorphous glassy phase, PFO exhibits a unique packing behavior, the so-called ? -phase formation, which leads to a higher degree of organization. We show that the ? -phase is an energetically favorable environment for charge carriers. We look into the migration of charges from glassy polyfluorene to areas where PFO exhibits ? -phase organization and find that the charge carrier mobility is higher in ? -phase polyfluorene than in glassy polyfluorene. Our results illustrate that the order on a supramolecular scale determines the conductive properties of conjugated polymers to a large extent. We conclude that the performance of devices based on polyfluorene can be significantly improved by the enhancement of the supramolecular order.
A Finger Vein Template Protection Algorithm Based on Multi-level Feature Fusion

The existing finger vein template protection algorithms are difficult to make full use of pixel layer and feature layer elements, and are not robust to the change of feature contrast. We proposed a new vein feature template protection algorithm based on Multi-level Feature Fusion (MFF). By introducing random token and local permutation strategy, the pixel layer and feature layer elements are encrypted and fused in parallel to ensure the irreversibility and revocability of the template. Firstly, the orthogonal matrix is generated by inner product operation between the real-valued eigenvectors of pixel layer and random matrix vectors. Secondly, a Stepwise Decision Diagram (SDD) is used to generate the permutation seed, which converts the real-valued orthogonal matrix into a binary hash sequence. Then, the hash template is obtained by XOR operation between the pixel permutation result and the partial permutation hash sequence of the feature layer and stored in the database. We tested and validated the proposed algorithm with four current representative algorithms on three different publicly available finger vein datasets. Experimental results show that the algorithm proposed in this paper achieves excellent EER performance in both token security and leakage, satisfying the requirements of revocable template protection.
Satellite-Observed Southern Hemisphere Cloud Vortices in Relation to Conventional Observations

Abstract A classification scheme for cloud vortices recorded on hemispheric digital mosaics southward of 20S during a period of some 550 days is used to construct models based on observations of associated atmospheric structure. Such models for various vortex types are expressed in terms of anomaly patterns of surface pressure, and 500- and 300-mb geopotential. Hopefully such models may find application in the development of analysis techniques over the data-void regions of the hemisphere.
On the Use of Conventional Neural Networks for COVID-19 Detection in CT-Scan Images: A Comparative Study and Performance Analysis

This paper presents a comprehensive study on deep learning for COVID-19 detection using CT-scan images. The proposed study investigates several Conventional Neural Networks (CNN) architectures such as AlexNet, ZFNet, VGGNet, and ResNet, and thus proposed a hybrid methodology base on merging the relevant optimized architectures considered for detecting COVID-19 from CT-scan images. The proposed methods have been assessed on real datasets, and the experimental results conducted have shown the effectiveness of the proposed methods, allowing achieving a higher accuracy up to 99%.
Characterization of biaxial stress and its effect on optical properties of ZnO thin films

Biaxial stress of ZnO film deposited on quartz was measured by side-inclination x-ray diffraction technique, indicating that the film is subjected to a tensile stress. One part of the stress is induced by thermal mismatch between the ZnO and the quartz and increases with annealing temperature, while another part results from lattice mismatch and is about 1.03GPa. The optical band gap of the ZnO film shows a blueshift with increasing biaxial tensile stress, opposed to the change of the band gap with biaxial tensile stress for GaN. The mechanism of the stress-dependent band gap is suggested in the present work.
ASDCLAIMS: Twitter Dataset of Claims on Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder found in 2.3% of children. Despite its prevalence, fraudulent science and information on autism are pervasive and entrenched in our society. To comprehend and analyze misinformation on autism, we provide the ASDCLAIMS dataset, the first dataset of claims on ASD with topic labels. The claims on Twitter during the week of World Autism Awareness Day are collected using keywords and hashtags pertinent to autism. They are labeled with one of the nine topics, defined based on a keyword extraction method. The dataset and source codes are available at https://github.com/cozymichelle/AsdClaims.
A Semisupervised Approach to Predicting a Twitch Streamer’s Growth based on the Game Streamed

Games live streaming is growing rapidly as a form of entertainment. A game streamer will like to know what game to stream in order to attract huge number of viewers and followers which in turn will generate sizable income for the streamer. Using streamer’s metrics, the main goal of this research work is to design and develop a set of resources that a streamer can use to maximize the number of viewers and followers for a particular game and when to play the game. This research develops two models using machine learning techniques that can be used by game streamers to maximum the returns on investment. When both model predictions are presented as percentage, Model 1 using regression algorithms provides a MAE of 5.48 meaning the prediction has an error within 5.48% of the streamer’s total follower count. Also, Model 1 has 85.46% of its predictions’ absolute error less than or equal to 5. Similarly Model 2 with 2.53 MAE and 87.68% of its predictions’ absolute error less than or equal to 5.
A pencil-MUSIC algorithm for finding two-dimensional angles and polarizations using crossed dipoles

The authors study the problem of finding two-dimensional (2-D) angles of wave arrival and wave polarizations using a uniform rectangular array of crossed dipoles. The method presented effectively exploits the redundancy in this array via 2-D moving-window smoothing to handle coherent sources and to achieve optimum noise sensitivity. The method combines the computational advantages of the MUSIC and matrix pencil approaches. The method is shown in simulation to be nearly optimum compared with the Cramer-Rao bound. >
Emergence of an Autonomous Vehicle Secondary Data Market for Breakthrough Applications

The prophesied circulation of fleets of autonomous vehicles (AVs) in urban and rural environments promises unprecedented opportunities to remotely sense streetscapes at fine-grain spatial and temporal resolution. AVs employ a variety of on-board sensors to capture information about the local environs for the primary purpose of vehicular navigation. However, we propose that these data may find further secondary use in a broad array of breakthrough applications: technologies and use cases that are enabled through the fine-grain spatio-temporal sensing of the lived environment. Consequently, a market for the secondary use of AV-collected data is emergent and a cloud-based architecture to manage the collection, processing, and communication of AV-derived data is required. Excitingly, the application of machine learning models to extract desirable secondary information from these fine-grain spatio-temporal data will enable unprecedented global-scale and time-series studies. Herein, we outline our vision for the utility of a Remote sensing AV-based Informatics Layer (RAIL) and the breakthrough applications it would enable. We define our vision based on recent and relevant trends in AV technology, discuss anticipated applications, discuss key technical considerations, and explore theoretical economic models for the exposed API. We conclude with discussion of the socio-technical ramifications of this system.
Migration Destination Selection Algorithm for Spot Instances using SPS

Amazon Web Services (AWS) provides its excess computing resources as spot instances. Although spot instances are cheaper than on-demand instances, they may be removed by AWS when demand increases for computing resources. Consumers need to judiciously select and prioritize those resources whose operations must avoid being interrupted. Although recently a spot placement score (SPS) is being provided as a measure of spot instance availability, few researches are using SPS. In this study, we propose an instance selection algorithm using it to select spot instances that reduce interruptions.
Novel EUV resist materials for 16nm half pitch and EUV resist defects

Extreme ultraviolet (EUV) lithography is a candidate for the manufacturing of semiconductor devices at the 22 nm half pitch node and below. EUV lithography requires high performance resist with limited outgassing property. The key challenge for EUV resist is the simultaneous requirement of ultrahigh resolution (R), low line edge roughness (L) and high sensitivity (S) for lines and spaces (LS) features. To achieve high resist sensitivity EUV resist absorbance should be increased. Resin containing fluorine atom is one of the most attractive methods to improve absorbance level of EUV resist because the fluorine atom absorbs EUV light strongly. However, resist hydrophobicity (or high contact angle) also increase due to presence of fluorine atoms in the resist polymer. It is difficult to rinse high CA resist during development process so the resist containing polymer with fluorine atom may produce additional defects. In this paper, we will report the relationship between line edge roughness and acid diffusion length. We will also show the method to diminish defects caused by high contact angle (CA) resist. We achieved good resolution and LER improvement by controlling acid diffusion length. Moreover, we found the relationship of the number of defects and the structure of the monomers containing fluorine units.
Some Approximations for the Noncentral-F Distribution

Among the various approximations for the noncentral-F distribution. the best known are the computationally simpler approximation due to Severo and Zelen [18] and Laubscher [9], which requires only the normal distribution, and a more accurate approximation due to Tiku [21], which requires the F distribution. Thcsr approximations are systematically evaluated. Some variations of these approximations are considered and found to he of comparable accuracy. An Edgeworth-acries approximation, which requires only the normal distribution, is developed and sren to be superior in accuracy. A FORTRAN-subroutine for the Edgeworth-series approximation is given.
Towards Dynamic Capture-The-Flag Training Environments For Reinforcement Learning Offensive Security Agents

In this paper, we propose a formalised process for the generation of dynamically generated SQL queries that are vulnerable to SQL injection attacks for the training of reinforcement learning offensive security agents. These queries are presented as capture-the-flag style challenges and deployed as a scalable, realistic and virtualised infrastructure using the Docker framework. These challenges are deployed for integration with OpenAI Gym environments for the implementation of state of the art reinforcement learning models. We analyse the needs and requirements of recent research that utilises reinforcement learning for offensive security and propose a method of generating dynamic CTF environments with limited domain knowledge on CTF challenges. Our solution can generate vulnerable SQL queries with any provided SQL dataset and deliver a front-end interface for both agent and human interaction. We analyse the performance of the environment when deployed on an end user device to demonstrate the feasibility of such an environment for training reinforcement learning agents. Our work fits in the wider research on the development of training scenarios for reinforcement learning agents focused on conducting offensive security tasks. Our results aim to contribute to enhancing the challenges facing reinforcement learning in offensive security tasks by providing variable realistic and dynamic environments for training.
Improvements in piezoelectric and energy harvesting properties with a slight change in depolarization temperature in modified BNKT ceramics by a simple technique

For many BNT-based ceramics, an attempt to increase the piezoelectric properties usually results in a decrease in depolarization temperature (Td). This trend limits the applications of the materials. Many previous experiments have used different methods to enhance the piezoelectric properties and improve the Td characteristic. In this study, we demonstrated a simple technique (thermal annealing) to enhance the piezoelectric properties with a very slight decrease in Td by ∼2 °C for a modified BNKT ceramic (BNKT doped with ZnO). Other phase transition characteristic temperatures of the studied ceramics were also slightly changed. The optimum dielectric (εr = 651, tan δ = 0.0503, TF–R = 167.38 °C, Tm = 305.41 °C, εmax = 5551, TB = 367.15 °C, Td = 155.98 °C, and γ = 1.43), ferroelectric (Pmax = 41.28 μC cm−2, Pr = 35.85 μC cm−2, Ec = 42.60 kV cm−1 and Rsq = 1.42), piezoelectric (d33 = 198 pC N−1, kp = 0.598, and g33 = 34.35 × 10−3 Vm N−1), and energy harvesting (FoM = 6.80 pm2 N−1) were obtained for the 8 h annealed ceramic. Furthermore, higher energy harvesting properties (which were 32% higher than that of the unannealed ceramic) were obtained after employing this technique.
Colossal electroresistance mechanism in a Au ∕ Pr 0.7 Ca 0.3 Mn O 3 ∕ Pt sandwich structure: Evidence for a Mott transition

The resistive switching characteristics of $\mathrm{Au}∕{\mathrm{Pr}}_{0.7}{\mathrm{Ca}}_{0.3}\mathrm{Mn}{\mathrm{O}}_{3}(\mathrm{PCMO})∕\mathrm{Pt}$ sandwich structure were investigated by changing growth temperature of the PCMO film, adding an oxygen annealing process, and modifying the Au/PCMO/Pt sandwich structure by inserting a $\mathrm{Pr}\mathrm{Mn}{\mathrm{O}}_{3}$ (PMO) or $\mathrm{Ca}\mathrm{Mn}{\mathrm{O}}_{3}$ (CMO) layer at the Au/PCMO interface. From these experiments, we obtained the following results. First, only crystalline PCMO films exhibited reversible resistive switching behavior in Au/PCMO/Pt sandwich structure. Secondly, the ${\mathrm{Mn}}^{4+}∕{\mathrm{Mn}}^{3+}$ ratio at the PCMO surface was changed by oxygen annealing of the PCMO film, resulting in an increase of the resistance ratio of high resistance state and low resistance state. Lastly, we could not observe the resistive switching behavior in Au/PMO/PCMO/Pt and Au/CMO/PCMO/Pt sandwich structures. The resistive switching behavior could be observed only in Au/PCMO/PMO(or CMO)/PCMO/Pt sandwich structure. These results indicate that the resistive switching of Au/PCMO/Pt sandwich structure depends on the mixed valence state ${\mathrm{Mn}}^{4+}∕{\mathrm{Mn}}^{3+}$ of Mn ions in the metal/PCMO interface domains. This result can be regarded as evidence for a Mott transition.
Next-generation sequencing shows the genomic features of ovarian clear cell cancer and compares the genetic architectures of high-grade serous ovarian cancer and clear cell carcinoma in ovarian and endometrial tissues

Ovarian clear cell carcinoma (OCCC) is a special histological type of epithelial ovarian cancer (EOC) that is not derived from epithelial cells of the ovarian or fallopian tube as the most common type of ovarian cancer, high-grade serous ovarian carcinoma (HGSOC), but is closely related to endometriosis and similar to endometrial clear cell carcinoma (ECCC) at morphologic and phenotypic features. However, limited data was shown in OCCC genomic features and compared with that in OCCC, HGSOC and ECCC. Herein, we utilized next-generation sequencing analysis of a panel of 1,021 genes to profile the mutational alterations in 34 OCCC and compared them to those from HGSOC (402 cases) and ECCC (30 cases). In result, the ARID1A and PIK3CA are high-frequency mutations of OCCC. Clonal architectures showed that all the mutations of genes occur in the later stage in the OCCC progress, whereas KRAS mutation is the earlier event compared with mutation of ARID1A or PIK3CA, which usually occurs in a group of ARID1A or PIK3CA mutations. The mutation frequency of main driver genes is similar between OCCC and ECCC, while TP53 is the main mutation in HGSOC and ECCC. Shared mutational signatures between OCCC and ECCC tissues with commonly observed a C>T change indicated a common carcinogens-exposed between these two carcinomas, but HGSOC and ECCC have common and distinct mutational signatures across cohorts respectively. In addition, we identified some novel CNV gains in NF1, ASXL1, TCF7L2, CREBBP and LRP1B and loss in ATM, FANCM, RB1 and FLT in OCCC. Our study offered a new perspective for OCCC tumorigenesis from two organs, the ovary and uterus, at genomic architectures and revealed novel CNV events for helping to provide theoretical support for OCCC treatment.
DETECTION OF THE METASTABLE STATE OF THE EL2 DEFECT IN GAAS

Using a classical photocapacitance technique, we have transformed the well-known EL2 defects, related to the As antisite in GaAs, into their metastable states. Using the capacitance, we have monitored the temperature dependence of the electron occupancy of these metastable states at thermal equilibrium. From this study, we deduce that a level located at 40 meV below the conduction band is associated with electron ionization from the metastable EL2 states.
The Regional Analysis and Forecast System of the National Meteorological Center

Abstract The three components of the Regional Analysis and Forecast System (RAFS) of the National Meteorological Center (NMC) are described. This system was implemented in March 1985 to supplement guidance from NMC's limited-area fine-mesh model (LFM), especially for precipitation forecasting. The three components of the RAFS are the regional optimum interpolation analysis, the Baer–Tribbia nonlinear normal mode initialization, and the nested grid model—a grid point, primitive-equation model in sigma coordinates. Postprocessing of model forecasts and plans for system improvement are also discussed.
A Piezoelectric Micro Machined Ultrasonic Transducer Based Hybrid-Morph AlScN Film

Al1-xScxN has much higher piezoelectric coefficient compared with traditional AlN, thus is promising to be used in piezoelectric micro machined ultrasonic transducer (pMUT). However, high doping of Sc introduces the precipitation of the Sc at the grain boundaries, which makes it difficult to prepare AlScN membranes with good piezoelectric performance. This paper reports a method fabricating AlScN composite membranes on the 8-inch wafer. Sc with content of 20% and 9.6% are separately doped in the top and bottom of the composite membrane. The hybrid membrane shows better surface morphology and less Sc precipitations compared with pure Al0.8Sc0. 2N film. Meanwhile, the process of the hybrid membrane is compatible with CMOS and facilitates the batch process. The pMUT prepared by the membrane has high $\text{k}_{\mathrm {t}}^{2}$ of 2.9%, which is about twice as large as that of pMUT made by Al0.904Sc0. 096N (1.5%). The proposed hybrid-morph structure with highly doped AlScN thin film contributes a novel solution to new generation of high-quality film for both research and commercial application. [2022-0141]
Wavelet‐based data compression for flow simulation on block‐structured Cartesian mesh

A data compression method based on image encoding techniques is presented for a flow simulation data set. An input flow field data set is converted into the octree structure by discrete wavelet transform, and then quantized finely or coarsely depending on its importance in the flow field. Embedded zerotree wavelet encoding as the image encoding technique and entropy encoding reduce the data size by making use of the octree structure created previously. The present compression method is incorporated in a block‐structured Cartesian mesh method called Building‐Cube method. The Building‐Cube method gives not only good performance in the flow simulation but also consistency with the embedded zerotree wavelet encoding in the data compression. Three compression cases for incompressible and compressible flow simulations, including a large‐scale simulation with O(10 8) mesh points, demonstrate that the present compression method gives both high compression ratios and good qualities of compressed data. Copyright © 2013 John Wiley & Sons, Ltd.
Analytical expressions of collective excitations for trapped superfluid Fermi gases in a BCS-BEC crossover

We investigate the collective excitations of a harmonically trapped superfluid Fermi gas at varying coupling strengths across a BCS-BEC crossover. Using a hydrodynamic approach, we solve analytically the eigenvalue problem of collective modes and provide explicit expressions for all eigenvalues and eigenfunctions, which are valid for both BCS and BEC limits and also for the whole crossover regime. Both spherical- and axial-symmetric traps are taken into account, and the features of these collective modes in the BCS-BEC crossover are discussed and compared with available experimental and numerical data.
Jeans instability in a plasma with positive–negative charged and neutral dust components

A new dispersion relation for electrostatic perturbations is derived by employing the hydrodynamic and Poisson equations for an unmagnetized, partially ionized, self-gravitating plasma whose constituents are positive–negative charged and neutral dust particles. Collisions caused by the dust-neutral and neutral-dust particulates are incorporated. Two new modes are found without collisional effects. The frequency spectra are numerically analysed to study the effects of Jeans frequencies due to positive, negative and neutral dust particulates and neutral temperature on the real angular frequencies and the growth rates.