Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the Dynamic Filters' Characteristics for the Analysis of Random Signals During Data Transmission

The paper analyzes methods for optimizing the parameters of the distribution function of the dynamic filters” characteristics. Such methods are used in the spectral analysis of random signals during data transmission. A feature of the work is the proposed functional, which is the starting point for solving the problem of optimizing the parameters of the distribution function of the characteristics of narrow-band dynamic filters for the spectral analysis of ergodic random signals using the correlation-filter method. Three problems of optimizing the parameters of the distribution function of the characteristics of narrow-band dynamic filters are considered and methods for their solution are proposed. It is shown that the first two optimization problems have only an asymptotic solution, which significantly limits their practical application. A simplified problem and a method for optimizing the parameters of the distribution function of the characteristics of narrow-band dynamic filters are proposed. The results of solving the optimization problem obtained by the Levenberg-Marquardt method using a software package for mathematical calculations are presented. Promising methods for solving such a problem are neural network, genetic and other intelligent algorithms for solving optimization problems. The results obtained make it possible to set optimal characteristics when designing narrow-band dynamic filters for random signal spectrum analyzers. The results of the study are proposed to be used in communication systems for filtering interference, radar to improve the quality of identifying airborne objects against the background of interference, and in measuring technology when building spectrum analyzers.
A 30-GHz CMOS LC VCO with Optimal Tail Filter Considering Tail FET's Output Resistance

A low phase-noise and wide tuning-range 30-GHz CMOS LC VCO based on the tail-current-biased cross-coupled nFET structure is designed for Ka-band applications. An RC low-pass filter at the tail FET's gate node boosts the output resistance over a wide range of VDS, as well as suppresses the output noise of the tail FET. It consequently improves the phase noise when combined with an LC tail filter. An inter-stage LC filter at VCO-buffer interface boosts the swing at the buffer input to avoid unwanted phase noise degradation. An optimal placement and routing are designed to compactly embed the proposed LC and RC filters into the entire layout. Realized in 65 nm CMOS, the VCO covers 29.9-34.5 GHz with four sub-bands, while consuming 14.1 mA from 1.2 V. It exhibits the phase noise of -108.7 dBc/Hz at 1 MHz offset with 31.3 GHz output frequency, resulting in FoM of -187.5 dBc/Hz.
The Robotic Platform for Measuring Linear Velocity Using Computer Vision Algorithms

This article presents approach to integrate algorithms of computer vision to control system of traction electric drives in rail transport. Shows how to use computer vision algorithms to calculate line velocity instead of other sensors such as wheel odometers, GPS, DGPS and inertial sensors that do not work on slippery surfaces and at low speeds. Therefore, this article deals with the application of linear velocity as feedback in the control system to optimize power consumption at the start and stop and to eliminate wheel slip. System of control of electric drive implemented and tested by robotics platform. Shown the process of development robotics platform that uses for imitation dynamic modes behavior in real situations for rail transports.
Schwinger-Keldysh formalism on the lattice: A faster algorithm and its application to field theory

A new algorithm is developed allowing the Monte Carlo study of a 1 + 1 dimensional theory in real time. The main algorithmic development is to avoid the explicit calculation of the Jacobian matrix and its determinant in the update process. This improvement has a wide applicability and reduces the cost of the update in thimble-inspired calculations from O(N^3) to less than O(N^2). As an additional feature, the algorithm leads to improved Monte Carlo proposals. We exemplify the use of the algorithm to the real time dynamics of a scalar {\phi}^4 theory with weak and strong couplings.
Merging Similar Patterns for Hardware Prefetching

One critical challenge of designing an efficient prefetcher is to strike a balance between performance and hardware overhead. Some state-of-the-art prefetchers achieve very high performance at the price of a very large storage requirement, which makes them not amenable to hardware implementations in commercial processors. We argue that merging memory access patterns can be a feasible solution to reducing storage overhead while obtaining high performance, although no existing prefetchers, to the best of our knowledge, have succeeded in doing so because of the difficulty of designing an effective merging strategy. After analysis of a large number of patterns, we find that the address offset of the first access in a certain memory region is a good feature for clustering highly similar patterns. Based on this observation, we propose a novel hardware data prefetcher, named Pattern Merging Prefetcher (PMP), which achieves high performance at a low cost. The storage requirement for storing patterns is largely reduced and, at the same time, the prefetch accuracy is guaranteed by merging similar patterns in the training process. In the prefetching process, a strategy based on access frequencies of prefetch candidates is applied to accurately extract prefetch targets from merged patterns. According to the experimental results on a wide range of various workloads, PMP outperforms the enhanced Bingo by 2.6% with 30× lesser storage overhead and Pythia by 8.2% with 6× lesser storage overhead.
CORUSCANT: Fast Efficient Processing-in-Racetrack Memories

The growth in data needs of modern applications has created significant challenges for modern systems leading to a “memory wall. ” Spintronic Domain-Wall Memory (DWM), provides near-SRAM read/write performance, energy savings and non-volatility, potential for extremely high storage density, and does not have significant endurance limitations. However, DWM’s benefits cannot directly address data access latency and throughput limitations of memory bus bandwidth. Processing-inmemory (PIM) is a popular solution to reduce the demands of memory-to-processor communication by offloading computation directly to the memory. PIM has been proposed in multiple technologies including DRAM, Phase-change memory (PCM), resistive memory (ReRAM), and Spin-Transfer Torque Memory (STT-MRAM). DRAM PIM provides solutions for a restricted set of two operand bulk-bitwise operations. PIM in PCM and ReRAM raise concerns about their effective endurance and PIM in STT-MRAM has insufficient density for main-memory applications. We propose CORUSCANT, a DWM-based in-memory computing solution that leverages the properties of DWM nanowires and allows them to serve as polymorphic gates. While normally DWM is accessed by applying spin polarized currents orthogonal to the nanowire at access points to read individual bits, transverse access along the DWM nanowire allows the differentiation of the aggregate resistance of multiple bits in the nanowire, akin to a multi-level cell. CORUSCANT leverages this transverse reading to directly provide multi-operand bulk-bitwise logic. Leveraging this multi-operand concept enabled by transverse access, CORUSCANT provides techniques to conduct multi-operand addition and two operand multiplication much more efficiently than prior digital PIM solutions. CORUSCANT provides a 1.6 × speedup compared to the leading DRAM PIM technique for query applications that leverage bulk bitwise operations. Compared to the leading PIM technique for DWM, CORUSCANT improves performance by 6.9 ×, 2.3 × and energy by 5.5 ×, 3.4 × for 8-bit addition and multiplication, respectively. For arithmetic heavy benchmarks, CORUSCANT reduces access latency by 2.1 ×, while decreasing energy consumption by 25.2 × for a 10% area overhead versus non-PIM DWM.
Clustering and voids in the Lyman-alpha forest

In a statistical study of quasar absorption lines, no evidence is found for large voids in the Ly-alpha forest, contrary to some recent suggestions. The 28 Mpc gap found by Crotts (1987) in the spectrum of Q0420-388 is not statistically significant, and it is possible, from a sample of 18 quasar spectra, to reject at the 2 sigma level the hypothesis that comoving voids of 30 Mpc fill as much as 15 percent of the universe. However, a significant (at the 99 percent level) excess of small line intervals of velocity width 200-600 km/s is found. This provides evidence either for the gravitationally induced correlations proposed in some models or for the fragmented shells proposed in others.
A Data Quality-Aware Framework to Reliably Forecast Photovoltaic Generation and Consumer Load for an Improved Resilience of Microgrids

Photovoltaic (PV) power and consumer load forecasting plays a critical role to ensure operational resilience of the electric grid. Most data-driven forecasting algorithms rely heavily on the continuous availability of good quality data for periodic training and validation. When deployed at the grid’s edge, prolonged disruptions to communications during extreme events degrade data quality. Factors such as missing observations, epistemic uncertainties, data drift, and concept drift are manifestations of data quality that impact the generalization of such field-deployed forecasting models. Currently, there exists no mechanism in the literature to dynamically switch between models under varying degrees of data quality as quantified by certain metrics for each factor highlighted above. This paper addresses this shortcoming by conceptually introducing a data quality-aware framework for reliable PV generation and consumer load forecasting. The framework’s design incorporates components of missing values, divergence tests, and continuous monitoring of generalization performance to detect changes in data quality caused by communications disruptions and trigger specific classes of forecasting models grouped under three use cases (UC1-UC3). As a first step towards validating this framework, real data collected from an actual field microgrid system is used to demonstrate the viability of the three use cases. Results show that the performance is the best in UC1 with an unadjusted R-square value of 0.954, followed by 0.939 for UC2 and 0.757 for UC3.
5G RAN Slicing to Support Reliability in Industrial Applications

Industry 4.0 and 5.0 applications will contribute towards safer, zero-defect and customized production environments. Such applications (e.g. digital twins, collaborative robotics and extended reality) require communication networks capable to satisfy stringent latency, bandwidth, and reliability requirements. Such requirements can be sustained with 5G networks and their evolution that offer unprecedented communications performance and flexibility thanks to the softwarization of networks and the use of network slicing. Network slicing creates different logical partitions or slices of the common network infrastructure and configures each slice to the requirements of the applications it will support. RAN (Radio Access Network) slicing is a fundamental part of network slicing in 5G as the radio channel is prone to errors and this impacts the capacity to support stringent reliability requirements. To date, RAN slices have been created considering the number of radio resources that must be reserved to guarantee the transmission rate or bandwidth demanded by the applications they will serve. This study demonstrates that this design approach cannot guarantee satisfying the reliability requirements of industrial applications and proposes a novel RAN slice descriptor that takes into account both the reliability and transmission rate requirements of the applications.
Systematic calibration error requirements for gravitational-wave detectors via the Cramér–Rao bound

Gravitational-wave (GW) laser interferometers such as Advanced LIGO (The LIGO Scientific Collaboration 2015 Class. Quantum Grav. 32 074001) transduce spacetime strain into optical power fluctuation. Converting this optical power fluctuation back into an estimated spacetime strain requires a calibration process that accounts for both the interferometer’s optomechanical response and the feedback control loop used to control the interferometer test masses. Systematic errors in the calibration parameters lead to systematic errors in the GW strain estimate, and hence to systematic errors in the astrophysical parameter estimates in a particular GW signal. In this work we examine this effect for a GW signal similar to GW150914, both for a low-power detector operation similar to the first and second Advanced LIGO observing runs and for a higher-power operation with detuned signal extraction. We set requirements on the accuracy of the calibration such that the astrophysical parameter estimation is limited by errors introduced by random detector noise, rather than calibration systematics. We also examine the impact of systematic calibration errors on the possible detection of a massive graviton.
Sentiment Analysis of Depression Illness during Covid-19 in Social Media: A Preliminary Study

Depression is now a common mental health condition that must be treated in people, particularly adolescents. Although the issue of mental illness has received more attention in recent years, many cases of depression remain undiagnosed. Traditionally, the diagnosis process involved face-to-face communication between doctors and patients, with the doctors referring to clinical depression criteria. However, there are few and limited resources and facilities for the early detection and treatment of depression and other mental illnesses. Therefore, to reduce the negative effect on public health, it is important to provide a model to classify depression by using sentiment analysis. Sentiment analysis has recently emerged as a hot topic in classifying the opinions expressed in a piece of writing to determine the information insight and the writer's point of view on a specific issue. This paper investigates the method to classify sentiments in the mental health domain. The classical features and sentiment analysis approach is conducted in this preliminary study to detect depression using a sample of data collected from Twitter. By using a combination of the lexical approach and the Naive Bayes model, the highest accuracy is achieved above 69%. The results indicate a combination of lexical and machine learning outperforms several machine learning approaches for classifying depression in individual tweets.
Optical and electrical properties of MoS2 and Fe-doped MoS2

We prepared undoped and Fe-doped MoS2 layered crystals using a chemical vapor transport method to compare their optical and electrical properties. Optical behaviors of carrier transitions were observed successfully in both undoped and Fe-doped MoS2 samples using reflectance and piezoreflectance. Frequency-dependent photoconductivity (PC) measurements reveal an additional deep Fe doping level for the Fe-doped MoS2 sample. In addition, a longer carrier lifetime was calculated for the Fe-doped MoS2 sample than for the undoped MoS2 sample through PC analysis. Hall measurements were also performed for both samples and indicated that the Fe-doped MoS2 sample exhibited a higher carrier concentration and a lower mobility owing to the effect of Fe dopants. Furthermore, both samples were confirmed to have n-type carriers.
Supersymmetric electrovacs in gauged supergravities

We show that the D = 6 SU(2) gauged supergravity of van Nieuwenhuizen et al, obtained by dimensional reduction of the D = 7 topologically massive gauged supergravity and previously thought not to be dimensionally reducible, can be further reduced to five and four dimensions. On reduction to D = 4 one recovers the special case of the SU(2)SU(2) gauged supergravity of Freedman and Schwarz for which one of the SU(2) coupling constants vanishes. Previously known supersymmetric electrovacs of this model then imply new ground states in 7D. We construct a supersymmetric electrovac solution of N = 2 SU(2) gauged supergravity in 7D. We also investigate the domain wall solutions of these theories and show that they preserve half of the supersymmetry.
Application of Uniformly Deployed Sets

Uniformly deployed sets (UDS) are combinatorial objects that contain n-bit binary words of weight $p$ and each pair of words from such a set has a prescribed minimal Hamming distance. In our previous works, we used UDS in heuristics for a p-median problem. In this contribution, we introduce a more effective algorithm for the construction of UDS with given parameters. We also show their application in a covering problem and two special problems from reliability analysis and artificial neural networks that involve work with n-bit binary words of various weights.
A Novel Enhanced VGG16 Model to Tackle Grapevine Leaves Diseases With Automatic Method

Diseases of grape leaves are destructive because they spread very quickly. Therefore, they should be detected and tackled fast. This study proposes a novel Enhanced VGG16 model for detecting, identifying, and classifying three widespread diseases in grape leaves. These diseases include Downey Mildew, Anthracnose, and Powdery Mildew. The data are automatically gathered with the assistance of photos taken with a quadcopter from the grapevine garden. The main central system analyzes the images received from the quadcopter to identify diseases. If it finds any of the three diseases, it sends the command to the hexacopter to spray pesticide into the grapevine garden location where the disease is found. The proposed method using the Enhanced VGG16 model with Faster Region-based Convolutional Neural Networks (R-CNN) is compared with different networks, including VGG16, GoogLeNet, ResNet50, and AlexNet. The experimental results on the grape leaf diseases demonstrated that the proposed method achieves the mean Average Precision) mAP (criterion improvements of 0.53%, 0.912%, 2.759%, and 7.268% compared with the ResNet50, VGG16, GoogLeNet, and AlexNet networks, respectively. Also, the average accuracy of the proposed Enhanced VGG16 model is 99.6%, which is 0.437–1.91% higher than other models. The Enhanced VGG16 has the best precision, and the number of layers is acceptable, although it is not less.
Web-Based Movie Recommendation System using Content-Based Filtering and KNN Algorithm

Improving the effectiveness of approaches has been a significant obstacle for recommended systems. It has also been crucial to address the trade-off between accuracy and calculation time when promoting goods via recommendation systems, as they must deliver accurate and real-time recommendations. This article discusses the development of a web-based system for recommending movies based on content-based filtering techniques. The system can recommend movies based on any combination of these two or more characteristics. This study has created a recommendation system based on genre preferences. To achieve this, content-based filtering based on genre connection is employed using cosine similarity. In order to improve the accuracy of this system, the KNN algorithm and the cosine similarity concept were implemented. The system employed a Movie Lens dataset, and the accuracy reached more than 87%. Moreover, this research successfully developed a user-friendly movie recommendation system (RS) with Python data analytic tools and the Django web framework. Moreover, this system can be accessed using a web browser and has more than 87% accuracy for giving movie recommendations based on some features or characteristics of the movie.
Thin and Flexible mm-wave Chipless RFID Tags

MM-wave chipless RFID tags are designed and tested within 30 GHz frequency band. The designed tags consist of a periodic surface printed on a thin grounded dielectric substrate. The structure can provide multiresonant cross polar peaks exhibiting a significant Radar Cross Section (RCS). The unit cell of employed tag consists of dipole resonators. The effect of losses and different potential manufacturing procedures for their integration into packaging are discussed.
Helium Reionization Simulations. III. The Helium Lyα Forest

In a previous paper we presented a new suite of hydrodynamic simulations with the aim of accurately capturing the process of helium ii reionization. In this paper, we discuss the observational signatures present in the He ii Lyα forest. We show that the effective optical depth of the volume-averaged τeff alone is not sufficient for capturing the ionization state of helium ii, due to the large variance inherent in sightlines. However, the He ii flux probability density function (PDF) can be used to determine the timing of helium ii reionization. The amplitude of the one-dimensional flux power spectrum can also determine the ionization state of helium ii. We show that, even given the currently limited number of observations (∼50 sightlines), measurements of the flux PDF can yield information about helium ii reionization. Further, measurements using the one-dimensional power spectrum can provide clear indications of the timing of reionization, as well as the relative bias of sources of ionizing radiation.
Optical Structure and Proper-Motion Age of the Oxygen-rich Supernova Remnant 1E 0102–7219 in the Small Magellanic Cloud

We present new optical emission-line images of the young SNR 1E 0102-7219 in the SMC obtained with the ACS on HST. This object is a member of the oxygen-rich class of SNRs showing strong oxygen, neon, and other metal-line emissions in its optical and X-ray spectra, and an absence of hydrogen and helium. The progenitor of 1E 0102-7219 may have been a Wolf-Rayet star that underwent considerable mass loss prior to exploding as a Type Ib/c or IIL/b supernova. The ejecta in this SNR are generally fast-moving (V > 1000 km s-1) and emit as they are compressed and heated in the reverse shock. In 2003 we obtained optical [O III], Hα, and continuum images with the ACS Wide Field Camera. The [O III] image through the F475W filter captures the full velocity range of the ejecta and shows considerable high-velocity emission projected in the middle of the SNR that was Doppler-shifted out of the narrow F502N bandpass of a previous WFPC2 image from 1995. Using these two epochs separated by ~8.5 yr, we measure the transverse expansion of the ejecta around the outer rim in this SNR for the first time at visible wavelengths. From proper-motion measurements of 12 ejecta filaments, we estimate a mean expansion velocity for the bright ejecta of ~2000 km s-1 and an inferred kinematic age for the SNR of ~2050 ± 600 yr. The age we derive from HST data is about twice that inferred by Hughes et al. from X-ray data, although our 1 σ error bars overlap. Our proper-motion age is consistent with an independent optical kinematic age derived by Eriksen et al. in 2003 using spatially resolved [O III] radial-velocity data. We derive an expansion center that lies very close to conspicuous X-ray and radio hot spots, which could indicate the presence of a compact remnant (neutron star or black hole).