Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Provisioning of Random Access Capacity in mMTC Slice Based on Beam Splitting/Merging

In massive Machine Type Communication (mMTC), an enormous number of user equipments (UEs) try to access the network in a short period. It is usually challenging to support such massive access with limited resources. Fortunately, directional beams can offer spatial degrees of freedom for random access (RA). In this article, a novel technique called beam splitting/merging is proposed to improve the performance of RA in mMTC network slice with limited resources such as preambles. We describe the framework of beam splitting/merging with the preambles reused, derive the RA throughput of the split beams which is approximately linearly increased with the number of beams, and give a method to obtain beam configuration ensuring the global RA success probability. With the derived analytic results, we further give an analysis of beam configuration in a typical scenario of beam splitting. Moreover, we design an RA control algorithm based on beam splitting/merging technique, by which the mMTC slice can dynamically provision RA capacity according to current access intensity. The updated RA configuration of each beam is broadcast by system information [e.g., system information block 1 (SIB1)]. Detailed analysis and simulation results show that the proposed beam splitting/merging algorithm can effectively improve system efficiency and guarantee RA performance of mMTC network slice with little increase of signaling overhead and delay. The proposed beam splitting/merging framework is applicable to both grant-based and grant-free RA procedures.
The Machine Learning Methods in Non-Destructive Testing of Dynamic Properties of Vacuum Insulated Glazing Type Composite Panels

The VIG (Vacuum Insulated Glazing) unit, composite glazing in which the space between glass panes is filled with vacuum, is one of the most advanced technologies. The key elements of the construction of VIG plates are the support pillars. Therefore, an important issue is the analysis of their mechanical properties, such as Young’s modulus and their variability over a long period of time. Machine learning (ML) methods are undergoing tremendous development these days. Among the many different techniques included in AI, neural networks (NN) and extreme gradient boosting (XGB) algorithms deserve special attention. In this study, to train selected methods of machine learning, numerical data developed in the VIG plate modelling process using Abaqus program were used. The test method proposed in this article is based on the VIG plate subjected to forced vibrations of specific frequencies and then the reading of the dynamic response of the composite plate. Such collected and pre-developed experimental data were used to obtain the mechanical parameters of the steel elements located inside the analysed vacuum glazing. In the future, the proposed research methods can be used to analyse the mechanical properties of other types of composite panels.
A new calcareous nannofossil species of the genus Helicolithus from the Santonian and its biostratigraphic significance in the Cretaceous Western Interior Seaway.

A high-resolution biostratigraphic study of Santonian calcareous nannofossils from the type area of the Smoky Hill Member of the Niobrara Formation in western Kansas identified a new nannofossil species, Helicolithus blairiae sp. nov. This species has a lozenge-shaped longitudinal bar which separates two very short, but diagnostic, disjunct segments along the minor axis of the ellipse. The longitudinal bar has a distinct ‘s’-shaped extinction pattern. The range and distribution of H. blairiae is limited to the Late Santonian in the Smoky Hill type area and is verified in sections from South Dakota and Texas. Helicolithus blairiae may have been limited in its paleogeographic range, as it is present in the Western Interior Seaway but it was not observed in Santonian sediments from the Gulf of Mexico or the western coast of Africa. Helicolithus blairiae could prove to be a useful biostratigraphic marker in the Niobrara Formation because of the absence of holococcolith marker species.
Observer‐based formation tracking control for leader–follower multi‐agent systems

This study investigates the formation tracking control for second-order leader-follower multi-agent systems. To estimate the velocity and acceleration of the leader, distributed observers are constructed for each follower. Based on the observed states, the authors propose a novel distributed formation tracking control protocol and analyse two communication networks with and without communication delays. Then, they prove that each agent can follow the active leader closely and form the desired spatial pattern. Finally, simulation examples are provided to show the validity of the theoretical results.
Pattern of choroidal thickness in early-onset high myopia

Purpose To explore the etiology and choroidal thickness (ChT) pattern in children with early-onset high myopia (eoHM). Methods Sixty children with eoHM and 20 healthy controls were enrolled in this study between January 2019 and December 2021. All children underwent comprehensive ophthalmologic examinations including swept-source optical coherence tomography. ChT was measured in the subfoveal region and at 1000 μm and 2,500 μm nasal, temporal, superior, and inferior to the fovea. Results Overall, 120 eyes of 60 children with eoHM were examined (mean spherical equivalent, −8.88 ± 3.05 D; mean axial length, 26.07 ± 1.59 mm). Simple high myopia (SHM), familial exudative vitreoretinopathy (FEVR), and Stickler syndrome (STL) were the most frequent etiologies of eoHM and were included in further ChT analysis. Adjusted the effect of SE, multivariate regression analysis showed that children with SHM had thinnest ChT at N2500 and I2500 among the subgroups (p = 0.039, p = 0.013). FEVR group showed thinner ChT at T2500 (p = 0.023), while STL patients exhibited thin ChT at all locations. Conclusion This study revealed that SHM, STL and FEVR was the most frequent etiology, and showed a distinctive pattern of ChT. Asymmetric nasal ChT thinning is a distinctive biomarker for SHM, asymmetric temporal ChT thinning might serve as a biomarker for FEVR, and symmetric diffuse thinning is more common in STL. These ChT patterns may provide a convenient, fast, and noninvasive strategy to differentiate the potential etiology of eoHM.
Real‐time mosaic of multiple fisheye surveillance videos based on geo‐registration and rectification

A distributed fisheye video surveillance system (DFVSS) can monitor a wide area without blind spots, but it is often affected by the viewpoint discontinuity and space inconsistency of multiple videos in the area. This paper proposes a novel real‐time fisheye video mosaic algorithm for wide‐area surveillance. First, by extending the line photogrammetry theory under central projection to spherical projection, a fisheye video geo‐registration model is established and estimated using orthogonal parallel lines on the ground, so that all videos of DFVSS are in the unified reference system to eliminate the space inconsistency between them. Second, by combining the photogrammetry orthorectification technique with thin‐plate spline transformation, a fisheye video rectification model is established to eliminate serious distortion in geo‐registered fisheye videos and align them accurately. Third, the viewport‐dependent video selection strategy and video look‐up table computation technique are adopted to create a high‐resolution panorama from input fisheye videos in real time. A parking lot of about 0.4 km2 monitored by eight fisheye cameras was selected as the test area. The experimental result shows the line re‐projection error in fisheye videos is about 0.5 pixels, and the overall efficiency, including panorama creation and mapping to the ground as texture, is not <30 fps. It indicates that the proposed algorithm can achieve a good balance between the limitation of video transmission bandwidth and the smooth observation requirement of computer equipment for the panorama, which is of great value for the construction and application of DFVSS.
Current Evidence and Future Perspectives about the Role of PARP Inhibitors in the Treatment of Thoracic Cancers

Abstract In recent years, poly (ADP-ribose) polymerase (PARP) inhibition has become a promising therapeutic option for several tumors, especially for those harboring a BRCA 1–2 mutation or a deficit in the homologous recombination repair (HRR) pathway. Nevertheless, to date, PARP inhibitors are still not largely used for thoracic malignancies neither as a single agent nor in combination with other treatments. Recently, a deeper understanding of HRR mechanisms, alongside the development of new targeted and immunotherapy agents, particularly against HRR-deficient tumors, traced the path to new treatment strategies for many tumor types including lung cancer and malignant pleural mesothelioma. The aim of this review is to sum up the current knowledge about cancer-DNA damage response pathways inhibition and to update the status of recent clinical trials investigating the use of PARP inhibitors, either as monotherapy or in combination with other agents for the treatment of thoracic malignancies. We will also briefly discuss available evidence on Poly(ADP-Ribose) Glycohydrolase (PARG) inhibitors, a novel promising therapeutic option in oncology.
Tectonics, Base‐Level Fluctuations, and Climate Impact on the Eocene to Present‐Day Erosional Pattern of the Arabia‐Eurasia Collision Zone (NNW Iranian Plateau and West Alborz Mountains)

The NNW Iranian Plateau and west Alborz within the Arabia‐Eurasia collision zone are characterized by three main tectono‐stratigraphic zones, crosscut by the Qezel‐Owzan River (QOR) Basin. The interplay between present‐day deformation and climate, which control the landscape evolution of the region, is still poorly constrained. We addressed this gap by measuring millennial‐scale erosion rates from 10Be‐concentration in the QOR sands along with topographic/climatic metrics analyses. Results reveal low erosion rates in the Plateau and relatively high in the west Alborz. The regional consistency of topographic parameters with geomorphology suggests that they control sediment fluxes in the Plateau, while the surface uplift, active thrust‐faulting, and shallow crustal seismicity in the west Alborz are the main controlling factors. Climate has a secondary role on erosion rates. Furthermore, we calculated exhumation rates from published thermochronometric AFT/AHe ages to determine their relationship with 10Be short‐term data. Results imply that the exhumation rates increased slightly in the Plateau and west Alborz from ∼26 to ∼10 Ma, simultaneous with hard collision processes between the Arabia‐Eurasia. This trend accelerated from ∼10 to ∼2.8 Ma due to the isolation of the Caspian Sea and extreme base‐level fall. From ∼2.8 to ∼2 Ma, base‐level rise occurred under climate influence, and erosion rates decreased. Millennial‐scale data show the erosion rate decreased from ∼2 Ma to the Present‐day, which is attributed to the change in deformation style and fault kinematics from fold/thrusting to mainly strike‐slip faulting. The significantly lower erosion rates in the Plateau compared to west Alborz suggest a relatively stable plateau surface.
Flexible Learning Environments for a Sustainable Lifelong Learning Process for Teachers in the School Context

The flexibility of digital learning environments allows for personalized content delivery tailored to individual teachers’ needs, fostering active and engaged learning. The opportunities offered by these digital technologies can help teachers adopt a lifelong learning attitude, which has become necessary to maintaining high educational standards in line with international guidelines and policy. However, teachers often struggle to leverage these digital technologies and integrate them in their daily activities. To overcome this problem, we developed a custom-built webinar training course focused on enhancing distance learning teaching in a flexible environment. We tested this training course on a group of 197 primary school teachers and examine the relationship between learning goal orientation, motivation, and intention to transfer and how they related to teachers’ personality traits. We found that our webinar training course is easily implementable and valued by teachers, who highlight the importance of allowing the choice between different training levels. The data analysis indicates that intention to transfer is predicted by learning goal orientation and motivation. In conclusion, the study emphasizes the importance of flexible learning environments and tailored training programs that meet teachers’ needs and interests. From a sustainable perspective, such approaches foster teachers’ lifelong learning, enhance their professional development, nurture a growth mindset, and facilitate adaptability to change.
Fingerprint Identification Using FPGA with a Newly Designed Architecture

The Aadhaar project was recently introduced by the Unique Identification Authority of India (UIDAI) to provide a Unique Identification (UID) number to every resident of India. Biometric and demographic information are used to create a unique identification number (Aadhaar). You may use your Aadhaar number to get a subsidy on LPG cylinders, a driving licence, a PAN card and a bank account. The software industry is crucial to the success of the Aadhaar system. The suggested work, including the pre-processing stage, fingerprint recognition process, and fingerprint recognition system, is carried out across three platforms, including MATLAB, field programmable gate array (FPGA), and application specific integrated circuit (ASIC). In order to improve the processing speed, resource utilisation, power consumption, and recognition rate of the proposed job, the FPGA is programmed with two specialised hardware methods: the CORDIC algorithm and IP cores. The suggested work largely focuses on fixing the processing time and accuracy issues. The concept is converted into an ASIC using FPGA technology with the goal of reducing power consumption and resource utilisation without sacrificing performance. In contrast to FPGA implementation, which makes use of look-up tables (LUTs) and customizable logic blocks (CLBs), ASIC implementation makes use of the basic cells directly. When compared to FPGA design, ASIC development speeds up recognition by 20%.
Design and Analysis of a Hybrid Circularly Polarized Multi-Band MIMO Antenna for Sub 6 GHz Applications

1 Abstract — In this article, a hybrid circularly polarized Multiple-Input Multiple-Output (MIMO) antenna for multi-band operation from 2.3 to 9.2 GHz with an impedance bandwidth of 7 GHz is proposed and investigated experimentally. The designed MIMO antenna model has a compact size of 20mm×40mm×1.6mm on the FR-4 substrate. The microstrip feed of the proposed slot antenna consists of a tapered structure, and the radiating element consists of the inverted L-shaped slots, which were opened on both sides of the radiating elements to introduce notches at the sub-6 GHz frequencies. L-shaped stubs are also introduced on another side of the substrate in the common ground plane to attain high isolation between the radiating elements of the proposed antenna. In the operating band from 2.3 to 9.2 GHz, isolation of less than -20 dB is achieved by the proposed model. The performance of the circularly polarized MIMO antenna in terms of RHCP and LHCP radiation patterns, axial ratio, surface current distributions, isolation between the ports, diversity gain (DG), envelope correlation coefficient (ECC), total active reflection coefficient (TARC), and peak gain are studied and presented in this work. The obtained characteristics of the proposed antenna make it suitable for sub-6-GHz frequency applications.
Repurposing a Peer-Reviewed Publication to Engage Students in Statistics: An Illustration of Study Design, Data Collection, and Analysis

Abstract Engaging and motivating students in undergraduate statistics courses can be enhanced by using topical peer-reviewed publications for analyses as part of course assignments. Given the popularity of on-campus therapy dog stress-reduction programs, this topic fosters buy-in from students whilst providing information regarding the importance of mental health and well-being as it impacts learning. This article describes how instructors can use a study on the benefits of human–dog interactions to teach students about study design, data collection and ethics, and hypothesis testing. The data and research questions are accessible to students without requiring detailed subject-area knowledge. Students can think carefully about how to collect and analyze data from a randomized controlled trial with two-sample hypothesis tests. Instructors can use these data for short in-class examples or longer assignments and assessments, and throughout this article, we suggest activities and discussion questions. Supplementary materials for this article are available online.
Image demosaicing using Deep Image Prior

—The paper focuses on the problem of image demo-saicing using the deep image prior. The deep image prior (DIP) is an uncommon concept that uses a generative neural network which, however, utilizes only the degraded image as the input for training. A novel method for image demosaicing is proposed, based on DIP, and it is compared with common demosaicing methods. In terms of the objective PSNR and SSIM values, the proposed method proved to be comparable with a widely used Malvar’s demosaicing method. Nevertheless, subjectively, DIP produces demosaiced images comparable with the superior Menon’s algorithm. Unfortunately, the proposed method turned out to be computationally immensely challenging.
Learning the scale of image features in Convolutional Neural Networks

The millions of filter weights in Convolutional Neural Networks (CNNs), all have a well-defined and analytical expression for the partial derivative to the loss function. Therefor these weights can be learned from data with a technique called gradient descent optimization. While the filter weights have a well-defined derivative, the filter size has not. There is currently no other way to optimize filter sizes, then with an exhaustive search over multiple CNNs trained with different filter sizes. In this report, we propose a new filter called Structured Receptive Field of which the filter size can be optimized during the training stage. This new filter constitutes a parameterization of a normal filter as a linear combination of all 2D Gaussian derivatives up to a certain order. Instead of learning the weights of the resulting filter directly, we learn the weights of the linear combination and the sigma of the Gaussian derivatives that implicitly define the filter weights. The advantage of parameterizing normal filters in this way, is that gradient descent optimization of the continuous sigma parameter which has a well-defined derivative, can be used as a proxy for optimizing the discrete filter size which has no well-defined derivative. The basic idea for this parameterization comes from scale-space theory in which the scale of image features is studied by parameterizing the image features with a continuous scale parameter. Thereby explicitly decoupling the spatial structure of image feature from the scale at which it occurs in the image. Structured receptive fields have several compelling advantages: they can learn bigger filters without increasing the number of learnable parameters and without increasing the complexity of the structure of the filter. In this report, we both provide theoretical and empirical evidence that structured receptive fields can approximate any filter learned by normal CNNs. I.e. we show that structured receptive fields of order 4 can approximate the filters in all layers of a pre-trained AlexNet. Furthermore, we demonstrate empirically that structured receptive fields can indeed learn their own filter size during training, and that this extra ability over normal filters seems to give them an advantage over normal filters when used for classification tasks. I.e. by replacing normal filters in a DenseNet architecture and keeping every- thing else the same, a ~1% higher test accuracy was obtained on the highly competitive CIFAR-10 benchmark dataset. It would not be wise to draw hard conclusions from a single training run on a single dataset, but this result definitely looks promising. In future research, we hope to demonstrate that, because of their extra ability to learn their filter size, replacing normal filters with structured receptive fields will always lead to strictly better or equal performance.
State and parameter estimation of physics-based lithium-ion battery models

This thesis investigates novel algorithms for enabling the use of first-principle electrochemical models for battery monitoring and control in advanced battery management systems (BMSs). Specifically, the fast solution and state estimation of a high-fidelity spatially resolved thermal-electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional (P2D) model are investigated. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the P2D model is then used in combination with an extended Kalman filter (EKF) algorithm modified for differential-algebraic equations (DAEs) to estimate the states of the model, e.g. lithium concentrations, overpotential. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1p in less than 200s despite a 30p error on battery initial state-of-charge (SoC) and additive measurement noise with 10mV and 0.5°C standard deviations. The parameter accuracy of such first-principle models is of utmost importance for the trustworthy estimation of internal battery electrochemical states. Therefore, the identifiability of the simpler single particle (SP) electrochemical model is investigated both in principle and in practice. Grouping parameters and partially non-dimensionalising the SP model equations in order to understand the maximum expected degrees of freedom in the problem reveals that there are only six unique parameters in the SP model. The structural identifiability is then examined by asking whether the transfer function of the linearised SP model is unique. It is found that the model is unique provided that the electrode open circuit voltage curves have a non-zero gradient, the parameters are ordered, and that the behaviour of the kinetics of each electrode is lumped together into a single parameter which is the charge transfer resistance. The practical estimation of the SP model parameters from frequency-domain experimental data obtained by electrochemical impedance spectroscopy (EIS) is then investigated and shows that estimation at a single SoC is insufficient to obtain satisfactory results and EIS data at multiple SoCs must be combined.