Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepGrip: Cricket Bowling Delivery Detection with Superior CNN Architectures

Delivery in cricket is the sole action of bowling a cricket ball towards the batsman. The outcome of the ball is immensely pivoted on the grip of the bowler. An instance when whether the ball is going to take a sharp turn or keeps straight through with the arm depends entirely upon the grip. And to the batsmen, the grip of the cricket bowl is one of the biggest enigmas. Without acknowledging the grip of the bowl and having any clue of the behavior of the ball, the mis-hit of a ball is the most likely outcome due to the variety in bowling present in modern-day cricket. The paper proposed a novel strategy to identify the type of delivery from the finger grip of a bowler while the bowler makes a delivery. The main purpose of this research is to utilize the preliminary CNN architecture and the transfer learning models to perfectly classify the grips of bowlers. A new dataset of 5573 images from Real-Time videos in offline mode were prepared for this research, named GRIP DATASET, consisted of grip images of 13 different classes. Hence the preliminary CNN model and the pre-trained transfer learning models - Vgg16, Vgg19, ResNet101, ResNet52, DenseNet, MobileNet, AlexNet, Inception V3, and NasNet were used to train with GRIP DATASET and analyze the outcome of grips. The training and validation accuracies of the models are noteworthy with the maximum validation accuracy of the preliminary model reaching 98.75%. This study is expected to be yet another steppingstone in the use of deep learning for the game of cricket.
Spin Polarized Photoemission

Spin-polarized photoemission has developed into a versatile tool for the study of surface and thin film magnetism. In this review, we examine the methodology of the technique and its application to a number of different problems, including both valence band and core level studies. After a detailed review of spin-polarization measurement techniques and the related experimental requirements we consider in detail studies of the bulk properties both above and below the Curie temperature. This section also includes a discussion of observations relating to unique metastable phases obtained via epitaxial growth. The application of the technique to the study of surfaces, both clean and adsorbate covered, is reviewed. The report then examines, in detail, studies of the spin-polarized electronic structure of thin films and the related interfacial magnetism. Finally, observations of spin-polarized quantum well states in non-magnetic thin films are discussed with particular reference to their mediation of the oscillatory exchange coupling in related magnetic multilayers.
Generation of Printed Electronics on Thermal Sensitive Substrates by Laser Assisted Sintering of Nanoparticle Inks

Direct printing technologies enable the generation of arbitrary electrical conductive structures on 3D surfaces and thus the fabrication of new and innovative mechatronic integrated devices (MID). In order to fabricate electrical circuits, a new approach using a laser beam for sintering a silverbased nanoparticle ink is investigated, reducing the thermal load on the device by a selective energy input compared to a standardized furnace process. Thus, processing of temperature sensitive and low-cost substrate materials for applications such as covering or trim parts is enabled. However, laser assisted generation of highly conductive printed structures is often accompanied by defects such as line deformations and requires a deeper understanding regarding the influence of the laser process on the production of defect-free circuits. This paper explores the benefits and limits of laser assisted sintering of printed electronics promising a fast and flexible production of complex conductive circuits on a small time scale in contrast to furnace sintering processes. For an evaluation of characteristic properties, four point measurements and adhesion tests are carried out on the conductive structures. In order to guarantee safe and reliable operation of the printed structures, the long-term characteristics are determined by means of a temperature change test.
Deep Reinforcement Learning for Trustworthy and Time-Varying Connection Scheduling in a Coupled UAV-Based Femtocaching Architecture

The paper is motivated by the urgent need, imposed by the COVID-19 pandemic, for trustworthy access to secure communication systems with the highest achievable availability and minimum latency. In this regard, we focus on an ultra-dense wireless network consisting of Femto Access Points (FAPs) and Unmanned Aerial Vehicles (UAVs), known as caching nodes, where there are more than one possible caching node to handle user’s request. To efficiently cope with the dynamic topology of wireless networks and time-varying behavior of ground users, our focus is to develop an efficient connection scheduling framework, where ground users are autonomously trained to determine the optimal caching node, i.e., UAV or FAP. Our aim is to minimize users’ access delay by maintaining a trade-off between the energy consumption of UAVs and the occurrence of handovers. To achieve these objectives, we formulate a multi-objective optimization problem and propose the Convolutional Neural Network (CNN) and Q-Network-based Connection Scheduling (CQN-CS) framework. More specifically, to solve the constructed multi-objective connection scheduling problem, a deep Q-Network model is developed as an efficient Reinforcement Learning (RL) approach to train ground users to handle their requests in an optimal and trustworthy fashion within the coupled UAV-based femtocaching network. The effectiveness of the proposed CQN-CS framework is evaluated in terms of the cache-hit ratio, user’s access delay, energy consumption of UAVs, handover, lifetime of the network, and cumulative rewards. Based on the simulation results, the proposed CQN-CS framework illustrates significant performance improvements in companion to Q-learning and Deep Q-Network (DQN) schemes across all the aforementioned aspects.
Crystal structure of N-(2-(trifluoromethyl)phenyl)hexanamide, C13H16F3NO

Abstract C13H16F3NO, triclinic, P1‾$‾{1}$ (no. 2), a = 4.7889(4) Å, b = 11.7867(10) Å, c = 12.5323(11) Å, α = 100.762(5)°, β = 100.232(4)°, γ = 100.683(4)°, V = 666.19(10) Å3, Z = 2, Rgt(F) = 0.0531, wRref(F2) = 0.1760, T = 200 K.
Mass attenuation coefficients of the elements Ti, V, Fe, Co, Ni, Cu and Zn for the K emission lines between 4.51 and 10.98 keV

Mass attenuation coefficients of seven elements between atomic numbers 22 and 30 were measured at 20 X-ray energies between 4.5 and 11 keV. These energies correspond to fluorescence radiation excited by monochromatic Mo Kα radiation. The measurements were made on thin metal foils, which were spread flat and perpendicular to the beam scattered from the fluorescent sample. In this arrangement, the measured attenuation includes photoelectric absorption plus average elastic and inelastic scattering. The mass per unit area of a foil was determined by weighing and the impurity concentration by fluorescence analysis. The spectral lines corresponding to the components of the fluorescent radiation and those of the secondary radiation were resolved by the so-called singular value decomposition (SVD). The probable errors of the attenuation coefficients were typically 1%. The general agreement with previous experimental and theoretical values is good, but at low energies and at energies just above the absorption edges the present values are about 5% larger than theoretical values based on relativistic HFS (Hartree–Fock–Slater) calculations.
Comparison of Simulation Tools for Load Flow Analysis

The paper analyses power system in its initial operating conditions by load flow study using various open source and commercial software tools. The IEEE 9-bus and 14-bus system are considered for analysis. The voltage and phase angle of the buses obtained from the software tools were compared with standard results of the test cases. The final results provide us with a qualitative comparison of the software packages taken into account for the test systems considered.
Saturated Flow Boiling of Water in a Narrow Channel: Experimental Investigation of Local Phenomena

This paper describes some observations of flow boiling of water at near-atmospheric pressure in a single vertical channel of rectangular cross-section 2 mm × l mm, heated on three sides over a length of 248 mm, with a window on the fourth side. Measurements have been made of the fluctuating wall temperatures and pressures at several stations along the channel, with simultaneous high-speed video recordings of the flow patterns at a mass flux of 134 kg m −2 s −1 and heat fluxes of 50–100 kW m −2 . The frequency spectra of the fluctuations depend on the compressibility of the inlet flow to the channel. It is shown that the processes of initiation of boiling and of heat transfer differ from flow boiling in large channels. The acceleration of liquid slugs by confined bubbles causes pressure pulses to propagate the full length of the channel. Heat transfer is distributed in time between convection and nucleate boiling. The experimental limitations are discussed.
A Whittle Index Approach to Minimizing Age of Multi-Packet Information in IoT Network

Age of information (AoI) captures the freshness of information and has been used broadly as an important performance metric in big data analytics in the Internet of Things (IoT). We consider a general scenario where a meaningful piece of information consists of multiple packets and the information is not complete until all related packets have been correctly received. Minimizing AoI in this general scenario is challenging in both scheduling algorithm design and theoretical analysis, because we need to track the history of received packets before a complete piece of information can be updated. We first analyse the necessary condition for optimal scheduling, based on which we present an optimal scheduling method. The optimal solution, however, has high time complexity. To address the problem, we investigate the problem with a special type of learning, i.e., learning in restless multi-armed bandits (RMAB), and propose a Whittle index-based scheduling method. We also propose a new transmission strategy based on erasure codes to improve the performance of scheduling policies in lossy networks. Performance evaluation results demonstrate that our solution outperforms other baseline policies such as greedy policy and naïve Whittle index policy in both lossless and lossy networks.
Risk Trading in Energy Communities

Local energy communities are proposed as a regulatory framework to enable the market participation of end-consumers. However, volatile local market-clearing prices, and consequently, volatile cost give rise to local market participants with generally heterogeneous risk attitudes. To prevent the increased operational cost of communities due to conservative trading decisions in the forward stage, e.g., a day-ahead market, we propose risk trading in energy communities via financial hedging products, the so-called Arrow-Debreu securities. The conditional value-at-risk serves as our risk measure for players to study different degrees of market completeness for risk. We define a risk-averse Nash game with risk trading and solve the Nash equilibrium problem for an incomplete market for risk as a mixed complementarity problem. We show that such a Nash equilibrium problem reduces to a single optimization problem if the market is complete for risk. Numerical findings indicate that a significant community cost saving can be realized when players engage in risk trading and sufficient financial hedging products are available. Moreover, risk trading efficiently protects less risk-averse players from highly risk-averse decision-making of rival players.
Identification Method of Typical Dynamic Time Series Weather Scenes in Terminal Area

The weather scenes identification in the terminal area is a data analysis method that can effectively identify the historical operating environment and improve the consistency of the controller's strategy. Aiming at the problem that the existing air transportation meteorological image feature extraction methods are easy to lose the spatial information in the image, and the clustering technology is not high in the identification accuracy of meteorological scenes, this paper proposes a simple and effective dimensionality reduction method-rasterized weather severe index (WSI), and considers the time series of weather information can cluster and identify the weather in the airport terminal area. Rasterized WSI takes into account the spatial structure characteristics of the terminal area and the actual needs of airport control for meteorological information. This dimensionality reduction method not only extracts important spatial information, but also achieves a high degree of dimensionality reduction simply and effectively. Then this paper uses the dynamic time warping (DTW) after optimizing the search path to calculate the time series similarity measure, and uses spectral clustering to identify different weather patterns. In the experiment, we compared different image dimensionality reduction methods. The experimental results proved the effectiveness of our method. At the same time, we visualized the clustering results and analyzed the results.
Transmission of Encrypted data in WSN: An Implementation of Hybridized RSA-TDES Algorithm

Data security and transmission are always be the concern with its importance in wireless sensor network. Today, most of the research works are going on the improvement of security and transmission as well. Transmission of secured data is very essential to maintain data integrity between the sender and the receiver. As data flows in a wireless traffic, it is to be protected from the unauthenticated users by some means. We have proposed a new algorithm for transmitting the encrypted data. Hybridization of existing RSA and Triple DES is executed to obtain the encrypted version of the data to be sent. For decryption too we have used both the algorithms. We have achieved data integrity, confidentiality, and security by means of encryption. NS3 simulator is used to execute the proposed approach. The results obtained is compared with other standard algorithms. Our approach outperforms over existing algorithms.
A Surrogate Based Computationally Efficient Method to Coordinate Damping Controllers for Enhancement of Probabilistic Small-Signal Stability

This paper proposes a computationally efficient method based on deep neural network and a meta-heuristic optimization algorithm known as bat algorithm to coordinate power oscillation damping controllers incorporated in renewable energy stations to enhance system small signal stability considering uncertainties. The proposed method consists of three main stages: database generation, supervised learning, and optimization using the created model. A database is first created of Probabilistic small-signal stability margin calculated using the combined cumulant and Gram-Charlier expansion and the parameters of damping controllers, which is then given to different supervised machine learning algorithms such as linear regression, support vector machine, random forest, and chiefly deep neural network to create a surrogate model. The surrogate model provides an approximate relationship between the probabilistic small-signal instability margin and damping controller parameters. An optimization problem is then formulated to minimize the surrogate probabilistic instability margin with damping controller parameters acting as constraints. Finally, this optimization problem is solved using the bat algorithm to obtain the optimized parameters for power oscillation damping controllers. Our study results tested on a large IEEE 16 machines, 68 bus system show that the power oscillation damping controllers optimized using the proposed method can largely improve the system low frequency oscillatory stability margin in a very low computational time (around 19 times faster than the conventional method). This study can be used by the power system operators to tune the parameters of damping controller in a fast manner.
Analysis Based on Recent Deep Learning Approaches Applied in Real-Time Multi-Object Tracking: A Review

The deep learning technique has proven to be effective in the classification and localization of objects on the image or ground plane over time. The strength of the technique’s features has enabled researchers to analyze object trajectories across multiple cameras for online multi-object tracking (MOT) systems. In the past five years, these technical features have gained a reputation in handling several real-time multiple object tracking challenges. This contributed to the increasing number of proposed deep learning methods (DLMs) and networks seen by the computer vision community. The technique efficiently handled various challenges in real-time MOT systems and improved overall tracking performance. However, it experienced difficulties in the detection and tracking of objects in overcrowded scenes and motion variations and confused appearance variations. Therefore, in this paper, we summarize and analyze the 95 contributions made in the past five years on deep learning-based online MOT methods and networks that rank highest in the public benchmark. We review their expedition, performance, advantages, and challenges under different experimental setups and tracking conditions. We also further categorize these methods and networks into four main themes: Online MOT Based Detection Quality and Associations, Real-Time MOT with High-Speed Tracking and Low Computational Costs, Modeling Target Uncertainty in Online MOT, and Deep Convolutional Neural Network (DCNN), Affinity and Data Association. Finally, we discuss the ongoing challenges and directions for future research.
Comparison of Transmission Control Protocol Variants for Two Way Transfer and Propagation Model with WiMAX Network Bandwidth Asymmetry

The Full Duplex Data Transfer, Propagation Model and other working parameters influence the performance of World Wide Interoperability for Microwave Access (WiMAX) with various Transmission Control Protocol (TCP) Variants with bandwidth asymmetry. This paper illustrates the comparison of performances for different TCP variants in WiMAX network assuming bandwidth asymmetry condition. Comparison is done for propagation model and data transfer in full duplex mode for varying number of wireless stations. The performance is calculated using Throughput, Goodput and number of Packets Dropped. This study suggests that TCP Linux performs better when the number of uploading wireless nodes are more having lower DL:UL ratio as well as when number of downloading wireless nodes are more having higher DL: UL ratio. TCP New Reno performs better in case of Two Ray Ground propagation model with higher DL:UL.
An IoT based Solution to Avoid Venting in Lithium Ion Battery Due to Overcharging

Protecting the battery pack from the various user, electrical and physical abuse is necessary. Overcharging of the battery is always an issue with the eventual condition of venting gases and possibly an explosion. This destroys the battery module that constitutes the battery pack and in severe cases the entire pack. Also, the C-rate and battery state-of-charge is mostly neglected by the user while charging the battery. The method proposed in this paper, to avoid venting is based on the emission rate of carbon dioxide in an overcharged battery. Apart from high amounts of hydrogen gas, lithium cells with different cathode chemistry produce carbon-dioxide in a great proportion and its emission rate is also high. Different carbon-dioxide detection modules that can establish a connection with Arduino and can be incorporated into the battery management system are available in market. These modules will be useful in alarming during the condition of venting.
Controlled-Release Theophylline Tablet Formulations Containing Acrylic Resins, II. Combination Resin Formulations

AbstractTheophylline tablet formulations containing a combination of cationic and anionic acrylic resins were prepared and evaluated. Equal amounts of Eudragit RSPM (cationic resin) and Eudragit L100 (anionic resin) were included at the 15% level (total polymer content) into the tablet formulations. Pressure-hardness profiles with theophylline-resin compacts (4:1) demonstrated that compacts containing the RSPM resin were the most compressible. The dissolution profiles for theophylline in acidic media showed slower release rates from tablets containing the combined resins than from those containing each of the single resins. It was proposed that this decrease in drug release rate was a result of a solid state interaction between the oppositely charged polymers. As the amount of retardant in the matrix increased, the release rates in acidic media decreased. In pH 7.4 phosphate buffer, much faster release was seen due to the higher solubility of the Eudragit L-100 resin at this pH level. Tablet hardness betw.
Performance characteristics of ultra-violet femtosecond laser ablation inductively coupled plasma mass spectrometry at ∼265 and ∼200 nm

The analytical figures of merit of ultra-violet femtosecond laser ablation inductively coupled plasma mass spectrometry (UV-fs-LA-ICP-MS) using the 3rd and 4th harmonics of Ti:Sapphire (∼265 and ∼200 nm, respectively) were explored. For this purpose, elemental ratios of aerosols produced by LA of silicate glass (SRM NIST 610) were studied under varying fluence conditions ranging from moderate values of 2 J cm−2 up to 30 J cm−2, taking into account e.g. laser-induced (66Zn/65Cu) and particle size-related (238U/232Th) phenomena. It could, for instance, be shown that signal ratios were less dependent on the wavelength or laser repetition rate chosen. Furthermore, fractionation indices defined using the temporal drift of elemental ratios over two equal parts of the acquired signal were subject to systematic changes for threshold-close fluences. As a consequence, corresponding 42Ca-normalized values were found to deviate by more than 20% from unity. In contrast, LA at higher fluences resulted in less pronounced discrepancies, falling below 5% even for the most critical elements such as 66Zn, 111Cd, and 208Pb. The complete suppression of particle size-related fractionation quantified on the basis of the 238U/232Th-system turned out to be highly consistent with the absence of μ-sized particles which were measured by optical particle counting (OPC). The relative fraction of particles >0.5 μm was determined to be less than 5%, independent on the wavelength, fluence, or laser repetition rate chosen. Moreover, our results indicate the occurrence of ICP-induced elemental fractionation during analysis due to an increased mass loading of the plasma source if medium or high fluences are applied. Using the 66Zn/65Cu-ratio as a thermometric probe, the change in plasma ionization temperature among low and high mass loading conditions was estimated to be −900 K. Nevertheless, UV-fs-LA-ICP-MS analysis of different matrices (silicate glass SRM NIST 610 and brass (Zn ∼20%)) performed within the high fluence range was found to enhance the accuracy for non-matrix-matched calibration. Evidence is given that the enhancement observed mainly depends upon the suppression of laser- and/or transport-induced fractionation.