Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilization of 5G Technologies in IoT Applications: Current Limitations by Interference and Network Optimization Difficulties—A Review

5G (fifth-generation technology) technologies are becoming more mainstream thanks to great efforts from telecommunication companies, research facilities, and governments. This technology is often associated with the Internet of Things to improve the quality of life for citizens by automating and gathering data recollection processes. This paper presents the 5G and IoT technologies, explaining common architectures, typical IoT implementations, and recurring problems. This work also presents a detailed and explained overview of interference in general wireless applications, interference unique to 5G and IoT, and possible optimization techniques to overcome these challenges. This manuscript highlights the importance of addressing interference and optimizing network performance in 5G networks to ensure reliable and efficient connectivity for IoT devices, which is essential for adequately functioning business processes. This insight can be helpful for businesses that rely on these technologies to improve their productivity, reduce downtime, and enhance customer satisfaction. We also highlight the potential of the convergence of networks and services in increasing the availability and speed of access to the internet, enabling a range of new and innovative applications and services.
Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

ABSTRACT Scandium is the best alloying element to improve the mechanical properties of industrial Al-Si-Mg casting alloys. Most literature reports devote to exploring/designing optimal Sc additions in different commercial Al-Si-Mg casting alloys with well-defined compositions. However, no attempt to optimize the contents of Si, Mg, and Sc has been made due to the great challenge of simultaneous screening in high-dimensional composition space with limited experimental data. In this paper, a novel alloy design strategy was proposed and successfully applied to accelerate the discovery of hypoeutectic Al-Si-Mg-Sc casting alloys over high-dimensional composition space. Firstly, high-throughput CALculation of PHAse Diagrams (CALPHAD) solidification simulations of ocean of hypoeutectic Al-Si-Mg-Sc casting alloys over a wide composition range were performed to establish the quantitative relation ‘composition-process-microstructure’. Secondly, the relation ‘microstructure-mechanical properties’ of Al-Si-Mg-Sc hypoeutectic casting alloys was acquired using the active learning technique supported by key experiments designed by CALPHAD and Bayesian optimization samplings. After a benchmark in A356-xSc alloys, such a strategy was utilized to design the high-performance hypoeutectic Al-xSi-yMg alloys with optimal Sc additions that were later experimentally validated. Finally, the present strategy was successfully extended to screen the optimal contents of Si, Mg, and Sc over high-dimensional hypoeutectic Al-xSi-yMg-zSc composition space. It is anticipated that the proposed strategy integrating active learning with high-throughput CALPHAD simulations and key experiments should be generally applicable to the efficient design of high-performance multi-component materials over high-dimensional composition space. GRAPHICAL ABSTRACT
Deep Reinforcement Learning of UAV Tracking Control Under Wind Disturbances Environments

Aiming at the problems of strong nonlinearity, strong coupling, and unknown interference encountered in the flight control process of unmanned aerial vehicles (UAVs) in a complex dynamic environment and reinforcement-learning-based algorithm generalization, this study presents an innovative incremental reinforcement-learning-based algorithm for UAV tracking control in a dynamic environment. The main goal is to make a UAV able to adjust its control policy in a dynamic environment. The UAV tracking control task is transformed into a Markov decision process (MDP) and further investigated using an incremental reinforcement-learning-based method. First, a policy relief (PR) method is used to make UAVs capable of performing an appropriate exploration in a new environment. In this way, a UAV controller can mitigate the conflict between a new environment and the current knowledge to ensure better adaptability to a dynamic environment. In addition, a significance weighting (SW) method is developed to improve the utilization of episodes with higher importance and richer information. In the proposed method, learning episodes that include more useful information are assigned with higher importance weights. The numerical simulation, hardware-in-the-loop (HITL) experiments, and real-world flight experiments are conducted to evaluate the performance of the proposed method. The results demonstrate high accuracy and effectiveness and good robustness of the proposed control algorithm in a dynamic flight environment.
Pengaruh Kombinasi dan Urutan Pre-Processing pada Tweets Bahasa Indonesia

Twitter merupakan jaringan microblog online yang dijadikan gaya hidup baru di kalangan masyarakat sebagai wadah pengganti untuk mencari dan menyebarkan informasi, sebagai tempat mencurahkan perasaan, ataupun menjalankan bisnis, dengan cara menuliskan tweet. Permasalahannya adalah tweet yang dituliskan mayoritas oleh remaja berumur 18-24 tahun, sehingga kata-kata yang dituliskan masih banyak mengandung karakter pengganggu, ejaan, kata gaul, atau kata yang bersifat non-baku. Data yang tidak bersih dan akurat akan berdampak buruk bagi hasil analisis. Pre-processing data dalam hal ini berperan penting untuk memperbaiki data agar menjadi lebih bersih dan akurat sebelum diproses. Penelitian ini fokus membahas mengenai beberapa skenario kombinasi pre-processing, serta dengan mengubah urutan proses cleaning, normalisasi, stemming, dan stop-word, untuk mendapatkan akurasi paling baik dan meningkatkan performa dalam klasifikasi. Hasil testing pada tweet menunjukkan akurasi tertinggi ada pada data yang melewati tahapan penuh pre-processing data dengan urutan kombinasi pre-processing adalah menaruh proses normalisasi sebelum melakukan proses stemming, yaitu sebesar 89.2%.
A novel wideband dual‐polarized heptagonal magnetoelectric dipole antenna loaded with inverted L‐shaped structures

A novel dual linearly‐polarized magnetoelectric (ME) dipole antenna with wide bandwidth is presented in this letter, where two orthogonally placed Г probes are utilized to realize dual polarizations. In particular, four heptagonal patches are first adopted in lieu of the four conventional rectangular patches to obtain a wider bandwidth. In the sequel, four inverted L‐shaped parasitic elements are loaded on the periphery of the four patches to further enhance the bandwidth of ME dipole antenna. A prototype is fabricated and measured to verify the properties of the proposed antenna. The simulated and measured results show that the overlapped 10 dB bandwidth of 99.2% can be achieved ranging from 1.2 to 3.56 GHz, and the available peak gain can be as high as 12 dBi. Meanwhile, its cross‐polarization level (CRPL) is lower than −22 dB, and the isolation between the two ports is larger than 20 dB across the operating frequency.
Application of Parametric-Based Framework for Regionalisation of Flow Duration Curves

It is common knowledge that the end user of stream flow data may necessarily not have any prior knowledge of the quality control measures applied in their generation, therefore, conclusions drawn most often times may not be effective as desired. Thus, this study is an attempt at providing an independent quality construct to boost the confidence in the use of stream flow data by developing regional flow duration curves for selected ungauged stations of the upper Niger River Basin, Nigeria. Toward this end, stream flow data for seven gauging stations cover some sub basins in the Basin were obtained; precisely, monthly stream flow data covering a range of eleven to fifty-three years period. The flow duration curves from the gauging stations were fitted with three probability distribution models; i.e., logarithmic, power and exponential regression models. For the regionalisation, parameterisation was carried out in terms of the drainage area alone to allow for simplicity of models. Results obtained showed that the exponential regression model, in terms of Coefficient of Determination (R2) had the best fit. Though the regionalised model was simple, measurable agreement was obtained during the calibration and validation phases. However, considering the length of data used and probable variability in the stream flow regime, it is not possible to objectively generalise on the quality of the results. Against this backdrop, it suffices to take into cognisance the need to use an ensemble of catchment characteristics in the development of the flow duration curves and the overall regional models; this is important considering the implications of anthropogenic activities and hydro-climatic variations.
Identifying Wet and Dry Snow With Dual-Polarized C-Band SAR Data Based on Markov Random Field Model

Quad-pol synthetic aperture radar (SAR) is one of the most effective approaches for dry and wet snow identification data, but its applicability is limited by the high cost of quad-pol SAR data. Dual-pol SAR such as Sentinel-1 has larger spatial coverage, longer time sequences, and freely accessible data, but there is still a highly uncertainty in dual-pol SAR to distinguish dry and wet snow due to limited polarimetric information. In this study, a pixel neighborhood-based snow identification algorithm was developed and verified using dual-pol C-band SAR data in Northern Xinjiang, China. A total of six decomposed parameters were obtained to characterize the polarimetric information of dual-pol SAR data by modifying the H- $\alpha $ decomposition applicable to dual-pol SAR data. In the case of limited training samples, polarimetric features that were most sensitive to snow identification were selected as the optimal features for support vector machine (SVM), and the result derived from SVM was employed as the initial labels of Markov random field (MRF) model to separate dry and wet snow using iterative conditional mode (ICM). Then, the proposed algorithm, dual-pol SVM-MRF (DSVM-MRF), was validated and compared with previously published methods. The results show that the DSVM-MRF acquires the superior snow recognition with the overall accuracy (OA) and Kappa coefficient of 84.5% and 0.58%, respectively.
Development of Imeglimin Electrospun Nanofibers as a Potential Buccal Antidiabetic Therapeutic Approach

The prevalence of type 2 diabetes (T2D) has been growing worldwide; hence, safe and effective antidiabetics are critically warranted. Recently, imeglimin, a novel tetrahydrotriazene compound, has been approved for use in T2D patients in Japan. It has shown promising glucose-lowering properties by improving pancreatic beta-cell function and peripheral insulin sensitivity. Nevertheless, it has several drawbacks, including suboptimal oral absorption and gastrointestinal (GI) discomfort. Therefore, this study aimed to fabricate a novel formulation of imeglimin loaded into electrospun nanofibers to be delivered through the buccal cavity to overcome the current GI-related adverse events and to provide a convenient route of administration. The fabricated nanofibers were characterized for diameter, drug-loading (DL), disintegration, and drug release profiles. The data demonstrated that the imeglimin nanofibers had a diameter of 361 ± 54 nm and DL of 23.5 ± 0.2 μg/mg of fibers. The X-ray diffraction (XRD) data confirmed the solid dispersion of imeglimin, favoring drug solubility, and release with improved bioavailability. The rate of drug-loaded nanofibers disintegration was recorded at 2 ± 1 s, indicating the rapid disintegration ability of this dosage form and its suitability for buccal delivery, with a complete drug release after 30 min. The findings of this study suggest that the developed imeglimin nanofibers have the potential to be given via the buccal route, thereby achieving optimal therapeutic outcomes and improving patient compliance.
Alternating Current Asynchronous Motor Control System based on Vector Control

Alternating current asynchronous motor has the advantages of simple structure, reliable operation, and low cost. It is especially suitable for the wheel drive system of large electric drive dump truck, and the nonlinear characteristics put forward higher requirements for the control system. According to the operation characteristics of a vehicle AC asynchronous motor, the characteristic parameter description equation was obtained. Based on the principle of vector control, the control system was designed, the analysis model of the control system was built based on Simulink, and the model speed and torque control were verified. The single and double step speed, slow acceleration and load operation was selected to test, and the changes of speed, output torque and other parameters were obtained. With reference to the actual vehicle characteristics, the test bench of asynchronous AC motor control system was built, and the characteristics of the control system under different load conditions were selected for verification. The analysis results show that the motor runs well in the control system built by vector control. Under different operating conditions, the system responds quickly, the parameter fluctuation is small, and the overall stability is maintained. Under the action of the control system, when operating below the fundamental frequency, the constant speed regulation operation is realized through voltage compensation. When the fundamental frequency is exceeded and the load condition is met, the system realizes constant power speed regulation. The maximum efficiency of the motor is more than 90%, which increases with the increase of load. The control system makes the motor show good advantages. It shows the reliability of the control model and provides a reference for this kind of design research.
Computation complexity of the euler algorithms for the roots of complex polynomials

In this thesis we show that the Euler type algorithms find (i) a complex number z such that (VBAR)f(z)(VBAR) 0, approximately, with 6672(d(d+log(VBAR)log (epsilon)(VBAR))M(26(d+(VBAR)log (epsilon)(VBAR)) binary bit operations, (ii) an approximate zero for a polynomial of degree d without multiple roots with average bit complexity approximately, 6672(d M(26d))) binary bit operations, where M(n) denotes the bit complexity for the multiplication of two n-digit numbers.
Fractionally Delayed Bayesian Approximation Filtering Under Non-Gaussian Noisy Environment

Gaussian filtering traditionally suffers from two major drawbacks: 1) Gaussian approximation of the intrinsic non-Gaussian measurement noises and 2) ignoring delay in measurements. This article designs an advanced Gaussian filtering algorithm for addressing the two drawbacks and improving the accuracy. The proposed method is abbreviated as GFMCFD, indicating Gaussian filtering under the maximum correntropy (MC) criterion for fractionally delayed measurements. The MC-criterion-based design enables the proposed GFMCFD to handle the non-Gaussian noises. Moreover, to deal with the delay, the proposed GFMCFD stochastically identifies the delay and uses the current measurement to estimate the desired state at a past instant, depending on the preidentified delay. Thereafter, it later updates the estimated state till the current time instant using state dynamics to perform real-time estimation. Interestingly, the proposed GFMCFD considers the delay as a fractional multiple of the sampling interval. The improved accuracy of the proposed GFMCFD is validated for two nonlinear filtering problems.
How Polynomial Regression Improves DeNATing

The ubiquity of Network Address Translation (NAT) and mobile hotspots that aggregate source IP addresses of connected devices to a single IP address makes it difficult for an observer in the Internet to learn anything about the internal network. The IP Identification header field of Domain Name System requests and the TCP Timestamp (TCP TS) header field of TCP SYN packets are the main features for counting devices in the internal network and association of packets to these devices, also known as DeNATing. This paper introduces a new method that relies on polynomial least-squares curve fitting for DeNATing. Evaluation of our model is performed on multiple real-world datasets containing Windows and Unix devices behind a router using NAT and a mobile hotspot. The proposed method outperforms other state-of-the-art methods for all of the used datasets on all types of devices. Successful DeNATing may help in cybersecurity, anti-fraud, and other use cases.
Planar Graphs and Partial k-Trees

It is well-known that many NP-hard problems can be solved efficiently on graphs of bounded treewidth. We begin by showing that Knuth’s results on nested satisfiability are easily derived from this fact since nested satisfiability graphs have treewidth at most three. Noting that nested satisfiability graphs have a particular form of planar drawing, we define a more general form of graph drawing while maintaining planarity and bounded treewidth. We name all graphs with such drawings partial Matryoshka graphs—a reference to the nesting Russian dolls—and demonstrate that they encompass other important graph classes of treewidth at most three, such as Halin and IO-graphs. Based on decompositions derived for partial Matryoshka graphs, we then proceed to explore edge-decompositions into graphs of bounded treewidth. We prove that any Hamiltonian planar graph on n vertices can be decomposed into a forest and a graph of O(log n) treewidth, and provide an efficient algorithm for constructing this decomposition. Similarly, we show that graphs of maximum degree three can be decomposed into a matching and an SP-graph (i.e. a graph of treewidth two). Furthermore, we prove that this manner of decomposition cannot be extended to graphs of higher degree by giving a method for constructing a graph of maximum degree four that cannot be decomposed into a matching and a graph of treewidth at most k, for any constant k. Lastly, we motivate such decompositions by producing an approximation algorithm for weighted vertex cover and weighted independent set on all graphs that can be decomposed into a forest and a graph of bounded treewidth.
GR-AMRVAC code applications: accretion onto compact objects, boson stars versus black holes

In the close vicinity of a compact object strong gravity imprints its signature onto matter. Systems that contain at least one compact object are observed to exhibit extreme physical properties and typically emit highly energetic radiation. The nature of the compact objects that produce the strongest gravitational fields is to date not settled. General relativistic numerical simulations of fluid dynamics around black holes, neutron stars, and other compact objects such as boson stars (BSs) may give invaluable insights into this fundamental question. In order to study the behavior of fluid in the strong gravity regime of an arbitrary compact object we develop a new general relativistic hydrodynamics code. To this end we extend the existing versatile adaptive mesh refinement code MPI-AMRVAC into a general relativistic hydrodynamics framework and adapt it for the use of numerically given spacetime metrics. In the present article we study accretion flows in the vicinity of various types of BSs whose numerical metrics are calculated by the KADATH spectral solver library. We design specific tests to check the reliability of any code intending to study BSs and compare the solutions with those obtained in the context of Schwarzschild black holes. We perform the first ever general relativistic hydrodynamical simulations of gas accretion by a BS. The behavior of matter at small distances from the center of a BS differs notably from the black hole case. In particular we demonstrate that in the context of Bondi spherical accretion the mass accretion rate onto non-rotating BSs remains constant whereas it increases for Schwarzschild black holes. We also address the scenario of non-spherical accretion onto BSs and show that this may trigger mass ejection from the interior of the BS. This striking feature opens the door to forthcoming investigations regarding accretion-ejection flows around such types of compact objects.
The Maximum Number of Strongly Connected Subtournaments*

In the ranking of a collection of p objects by the method of paired comparisons, a measure of consistency is provided by the relative number of transitive (or consistent) triples and cyclic (or inconsistent) triples. This point of view was introduced by Kendall and Babington Smith [4]. They found a formula for the maximum number of cyclic triples, thereby determining the greatest inconsistency possible. The purpose of this note is to extend the result to obtain the maximum number of "strongly connected" collections of n objects among the given p objects.
Modal particles in Italian

In the last decades, the linguistic studies on German (and other Germanic languages) have led to the identification of a small class of words, which are generally referred to as Modalpartikeln (‘modal particles’), Abtönungspartikeln (‘gradating, shading particles’), etc. Their name varies in the technical literature according to the aspect each author wants to stress. For the present purposes, I will use the term ‘modal particles’ (henceforth MPs) because, as I pointed out in previous works (cf. Coniglio 2005), I find the name fully legitimated by their syntactic behavior. 1 They constitute a specific class of elements, which are similar to speaker-oriented adverbs, but compared to these they display a higher degree of grammaticalization and other peculiar characteristics clearly distinguishing them from this class of adverbs. It is difficult to provide a definition describing the features of the whole class which at the same time applies to each word belonging to it. The lexemes of this closed word class fulfill very different functions. Let us think of words such as ja, denn, wohl, etc. 2
Non Local Detection of quantum dynamical processes in spin chains

We study the dynamics of a one dimensional quantum spin chain evolving from unentangled or entangled initial state. At a given instant of time a quantum dynamical process (ex. measurement) is performed on a single spin at one end of the chain. The aim is to detect if the process occurred or not from a measurement on a far away spin at a later time. From the dynamical evolution of the state from the epoch time it is possible to detect the occurrence of the dynamical process. The propagation of the signal for the dynamical process, and the speed of the signal are investigated for various spin models, viz. the Ising, Heisenberg, and the transverse-XY dynamics.
Finite impulse response filters

A finite impulse response (FIR) filter has a unit impulse response that has a limited number of terms, as opposed to an infinite impulse response (IIR) filter which produces an infinite number of output terms when a unit impulse is applied to its input. FIR filters are generally realized nonrecursively, which means that there is no feedback involved in computation of the output data. The output of the filter depends only on the present and past inputs. This quality has several important implications for digital filter design and applications. This chapter discusses several FIR filters typically used for real-time ECG processing, and also gives an overview of some general FIR design techniques.