Miracle-dz/UniFAR-FTUs huggingface dataset:
Carrier Frequency Offset Estimation in 5G NR: Introducing Gradient Boosting Machines

The Beyond fifth Generation (B5G) communication systems imposed several challenges on radio designers. For example, a machine is required to set up a call at a low Signal-to-Noise Ratio (SNR), as low as −10 dB, in the extended coverage mode. Moreover, only one receive antenna will be available, and virtually no frequency diversity. Such requirements present major challenges to maintaining timing and frequency synchronization. Carrier Frequency Offset (CFO) estimation is at the heart of these challenges. Different approaches have been proposed for CFO estimation such as maximum likelihood based on a cyclic prefix. Nevertheless, these methods remain limited in various ways. At the same time, Machine Learning (ML) techniques showed outstanding performance in several wireless communication problems. In this work, we propose an ML-based approach for CFO estimation in OFDM systems. Specifically, we propose a Gradient-Boosting Machine (GBM)-based solution to predict the CFO given the received Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS). Furthermore, we make our dataset available for public access to encourage other researchers to pursue this promising direction. We compare our results with different baseline models (i.e., artificial neural networks and support vector machines). The experimental results show that our model outperforms other baseline models due to its ensemble nature which enables ensemble models to obtain a better generalization behavior.
PARAMETER ESTIMATION AND HYPOTHESES TESTING FOR NONHOMOGENEOUS POISSON PROCESS

We consider a nonhomogeneous Poisson process (NHPP) with intensity function ) (t λ . This function is parameterized in two forms: log-linear with ) exp( ) ( t t β α λ + = and Weibull-type 1 ) ( − = β β β α λ t t . Parameter estimation is carried out by the well-known maximum likelihood procedure. We present its modus operandi and show that there is one and the only one solution to the maximum likelihood equation for the log-linear form of ) (t λ . For the case of the known intensity function, testing the hypothesis that the given sample path is a realization of NHPP can be carried out on the basis of the following well-known fact. Let n t t
FieldPlant: A Dataset of Field Plant Images for Plant Disease Detection and Classification With Deep Learning

The Food and Agriculture Organization of the United Nations suggests increasing the food supply by 70% to feed the world population by 2050, although approximately one third of all food is wasted because of plant diseases or disorders. To achieve this goal, researchers have proposed many deep learning models to help farmers detect diseases in their crops as efficiently as possible to avoid yield declines. These models are usually trained on personal or public plant disease datasets such as PlantVillage or PlantDoc. PlantVillage is composed of laboratory images captured under laboratory conditions, with one leaf each and a uniform background. The models trained on this dataset have very low accuracies when running on field images with complex backgrounds and multiple leaves per image. To solve this problem, PlantDoc was built using 2,569 field images downloaded from the Internet and annotated to identify the individual leaves. However, this dataset includes some laboratory images and the absence of plant pathologists during the annotation process may have resulted in misclassification. In this study, FieldPlant is suggested as a dataset that includes 5,170 plant disease images collected directly from plantations. Manual annotation of individual leaves on each image was performed under the supervision of plant pathologists to ensure process quality. This resulted in 8,629 individual annotated leaves across the 27 disease classes. We ran various benchmarks on this dataset to evaluate state-of-the-art classification and object detection models and found that classification tasks on FieldPlant outperformed those on PlantDoc.
Two-dimensional digital processing of one-dimensional signal

It is of importance to find the necessary and sufficient conditions under which the one-dimensional and two-dimensional processing of any general transform should be equivalent. These conditions are found. It is known that the Fourier transform does not possess the described property. It is shown in this paper that a one-dimensional Fourier transform cannot be equivalent to any two-dimensional transform. On the other hand it is shown that the two-dimensional Fourier transform is equivalent to a one-dimensional transform of another kind and that the processing can be performed by a fast algorithm.
Estimation of Lower Extremity Joint Moments and 3D Ground Reaction Forces Using IMU Sensors in Multiple Walking Conditions: A Deep Learning Approach

Human kinetics, specifically joint moments and ground reaction forces (GRFs) can provide important clinical information and can be used to control assistive devices. Traditionally, collection of kinetics is mostly limited to the lab environment because it relies on data that are measured from a motion capture system and floor-embedded force plates to calculate the dynamics via musculoskeletal models. This spatially limited method makes it extremely challenging to measure kinetics outside the laboratory in a variety of walking conditions due to the expensive device setup and large space required. Recently, employing machine learning with IMU sensors are suggested as an alternative method for biomechanical analyses. Although these methods enable estimating human kinetic data outside the laboratory by linking IMU sensor data with kinetics dataset, they are limited to show inaccurate kinetic estimates even in highly repeatable single walking conditions due to the employment of generic deep learning algorithms. Thus, this paper proposes a novel deep learning model, Kinetics-FM-DLR-Ensemble-Net for single limb prediction of hip, knee, and ankle joint moments and 3-dimensional GRFs using three IMU sensors on the thigh, shank, and foot under several representatives walking conditions in daily living, such as treadmill, level-ground, stair, and ramp. This is the first study that implements both joint moments and GRFs in multiple walking conditions using IMU sensors via deep learning. Our deep learning model is versatile and accurate for identifying human kinetics across diverse subjects and walking conditions and outperforms state-of-the-art deep learning model for kinetics estimation by a large margin.
Respiration, Production, and Growth Efficiency of Marine Pelagic Fungal Isolates

Despite recent studies suggesting that marine fungi are ubiquitous in oceanic systems and involved in organic matter degradation, their role in the carbon cycle of the oceans is still not characterized and fungal respiration and production are understudied. This study focused on determining fungal growth efficiencies and its susceptibility to temperature differences and nutrient concentration. Hence, respiration and biomass production of three fungal isolates (Rhodotorula mucilaginosa, Rhodotorula sphaerocarpa, Sakaguchia dacryoidea) were measured in laboratory experiments at two temperatures and two nutrient concentrations. We found that fungal respiration and production rates differed among species, temperature, and nutrient concentration. Fungal respiration and production were higher at higher temperatures, but higher fungal growth efficiencies were observed at lower temperatures. Nutrient concentration affected fungal respiration, production, and growth efficiency, but its influence differed among species. Altogether, this study provides the first growth efficiency estimates of pelagic fungi, providing novel insights into the role of fungi as source/sink of carbon during organic matter remineralization. Further research is now needed to unravel the role of pelagic fungi in the marine carbon cycle, a topic that gains even more importance in times of increasing CO2 concentrations and global warming.
A Dilation and Norm in Several Variable Operator Theory

Abstract For every m-tuple of operators acting on a Hilbert space, it is shown that there exists a common dilation of these operators to mcommuting normal operators on some larger Hilbert space. We then introduce a norm on the m-fold cartesian product of ℬ(ℋ) that is defined to be, for a given w-tuple, the infimum of the joint spectral radii of all joint normal dilations of the m operators. This norm has several good features, one of which is that it is invariant under the passage to adjoints.
Changes in Vegetation and Rainfall over West Africa during the Last Three Decades (1981-2010)

The decadal variability of rainfall and vegetation over West Africa have been studied over the last three decades, 1981-1990, 1991-2000 and 2001-2010 denoted as 1980s, 1990s and 2000s, respectively. Climate Research Unit (CRU) monthly precipitation and Normalized Difference Vegetation Index (NDVI) from the National Oceanic and Atmosphere Administration (NOAA), all covering the period 1981-2010 have been used. This study aimed to assess the changes in the land surface condition and the spatio-temporal distribution of rainfall over West Africa region. The relationship between rainfall and vegetation indices over this region was determined using Pearson’s correlation. Also, the decadal comparison between rainfall and NDVI over the region was based on the significant t-test and the Pearson’s correlation. Results showed that significant return to wet conditions is observed between decade 1980s and decade 1990s over West Africa, and also during decade 2000s with the exception of central Benin and the western Nigeria. Meanwhile, a regreening of the central Sahel and Sudano-Sahel regions is noted. From 1990s to 2000s, this regreening belt is located in the South and the coastal areas: the Guinea Coast, Sudano-Guinea and western Sahel regions. A northward displacement of this re-greening belt is also detected. Thus, a linear relationship occurs between rainfall and NDVI in the Sudanian savannah region, but it is not the case in the rest of West Africa. This may suggest that the re-growth of vegetation in the Sudanian savannah region may be linked to rainfall supplies. Therefore, re-greening over Sahel region in 1990s is related to rainfall recovery. However, this re-greening was not sustained in the decade 2000s due to a slight decrease in rainfall.
The Opioid-Sparing Effect of Acupuncture After Abdominal Surgery: A Systematic Review and Meta-Analysis Protocol

Purpose Routine overprescribing of postoperative opioid analgesics may induce side effects and correlate with chronic opioid use following surgery. This review aims to evaluate the effectiveness and safety of acupuncture for opioid-sparing effects in patients who underwent abdominal surgery. Methods Eleven databases in different languages, including English (Ovid MEDLINE, CENTRAL, EMBASE, CINAHL), Chinese, Korean, and Japanese, will be searched. Randomized controlled trials using acupuncture for postoperative pain control in adult patients undergoing abdominal surgery will be screened. All randomized controlled trials comparing acupuncture with no treatment, sham acupuncture, and conventional treatments will be included. The Cochrane risk of bias tool will be used to assess the risk of bias. The primary outcome will consist of a cumulative opioid consumption. Additionally, the number of cumulative opioid analgesic demands/requests, the time to initial opioid analgesic usage, postoperative pain, opioid-related side effects, and adverse events of acupuncture will be assessed. The mean differences or risk ratios with a 95% confidence interval will be calculated to estimate the pooled effect of acupuncture when it is possible to conduct a meta-analysis. Results This study could confirm the effect of opioid-sparing on acupuncture after abdominal surgery. Conclusion This study would evaluate the evidence on the effectiveness of acupuncture after abdominal surgery with a focus on opioid intake. It provides evidence to support decision-making on applying acupuncture for postoperative management. Registration Number CRD42022311155.
Students’ mathematical beliefs and motivation in the context of inquiry-based mathematics teaching

In this paper, we investigate the learning experiences, beliefs and motivations of students in classes where the mathematics teachers have received support for using inquiry-based learning activities. Data were collected from 248 students in the age-range 11–16 using electronic questionnaires. Our results show that key features of inquiry-based mathematics were only moderately reflected in these students’ beliefs about the subject, their dispositions towards mathematics were less positive across the transition from primary to secondary school, and with respect to motivation this decline was stronger for girls than for boys. Furthermore, medium to strong correlations between belief- and motivation subdomains were found, for instance, students who view mathematics as a creative subject and/or have a growth mindset of mathematics also tend to find this subject enjoyable and perceive it as useful. Finally, our results indicate that inquiry-based teaching has a potential for fostering positive dispositions towards mathematics, as students who often experience inquiry-related activities in class also tend to see mathematics as a creative and interesting subject that will be useful for them in the future.
BCTNet: Bi-Branch Cross-Fusion Transformer for Building Footprint Extraction

Building footprint extraction in remote sensing remains challenging due to the diverse appearances of buildings and confusing scenarios. Recently, researchers have revealed that both the globality and locality are vitally important in building footprint extraction tasks and proposed to incorporate the local context and global long-range dependency in the segmentation models. However, inadequate integration of the globality and locality still leads to incomplete, fake, or missing extraction results. To alleviate these problems, a novel segmentation method named bi-branch cross-fusion transformer network (BCTNet) is proposed in this study. Two parallel branches of the convolutional encoder branch (CB) and the transformer encoder branch (TB) are designed to extract multiscale feature maps. A concatenation-then-cross-fusion transformer block (CCTB) is put forward to integrate the locality from the CB and globality from the TB in a cross-fusion way at each stage of the encoding process. Then, an adaptive gating module (AGM) is proposed to gate the feature maps from the CCTB to strengthen the important features while suppressing irrelevant interference information. After that, the segmentation results can be obtained through a simple decoding process. Comprehensive experiments on two benchmark datasets demonstrate that the proposed BCTNet can achieve superior performance compared with the current state-of-the-art (SOTA) segmentation methods.
Stretchable and compressible thin films of stiff materials on compliant wavy substrates

This letter presents experimental, numerical, and analytical studies of Au thin films on elastomeric substrates of poly(dimethylsiloxane) that are designed with sinusoidal, “wavy” features of surface relief. Such systems can be useful as stretchable conductors in electronic or sensory devices. The maximum film strain is obtained in terms of film and substrate elastic moduli, film thickness, amplitude and wavelength of the wavy profile, and the applied strain. These analytical solutions agree well with both finite element analysis and experimentally measured changes in the sinusoidal profile under small, uniaxial strains. A simple expression for the system stretchability and compressibility is established.
Secondary electron emission characteristics of single-crystal and polycrystalline diamond

Secondary electron emission spectroscopy (SEES) is used to examine the transport and emission of low-energy electrons in diamond. In particular, SEES measurements from single-crystal (100) and (111) diamond and polycrystalline chemical vapor deposited (CVD) diamond are compared in order to examine the effect of crystallographic orientation on the emission characteristics. Crystal orientation is found to influence the surface properties of the samples but not the low-energy transport properties. Specifically, very high yields are obtained from negative-electron-affinity (NEA) surfaces of all three samples, indicating that low-energy electrons are transported and emitted very efficiently regardless of crystal orientation. However, the energy distributions measured from adsorbate-covered C(111) surfaces are broader and shifted lower in energy than those measured from corresponding C(100) surfaces. In fact, the energy distributions measured from polycrystalline CVD diamond surfaces appear to be a superpositio.
Radiation boundary conditions for wave-like equations

In the numerical computation of hyperbolic equations it is not practical to use infinite domains; instead, the domain is truncated with an artificial boundary. In the present study, a sequence of radiating boundary conditions is constructed for wave-like equations. It is proved that as the artificial boundary is moved to infinity the solution approaches the solution of the infinite domain as O(r exp -m-1/2) for the m-th boundary condition. Numerical experiments with problems in jet acoustics verify the practical nature of the boundary conditions.
MICROENCAPSULATION AND ITS VARIOUS ASPECTS: A REVIEW.

: Microencapsulation is the process which involves wrapping core parties in a continuous layer of coating material that isolates them from the surrounding environment. Microcapsules can be up to 800 μm in size and it is a promising technique for controlled drug delivery. The review will discuss various terms related to microencapsulation, including benefits, need for microencapsulation, particle types, materials required to create microcapsules, drugs released from microcapsules, and factors affecting microencapsulation. Different techniques of the microencapsulation such as physical, chemical, and physiochemical method and characteristics and parameter evaluation of microencapsulation are also included in this review article. This article also provides us with information regarding its application in real life and give better understanding about the different aspects of said topic.
Regulation of Cardiac Cav1.2 Channels by Calmodulin

Cav1.2 Ca2+ channels, a type of voltage-gated L-type Ca2+ channel, are ubiquitously expressed, and the predominant Ca2+ channel type, in working cardiac myocytes. Cav1.2 channels are regulated by the direct interactions with calmodulin (CaM), a Ca2+-binding protein that causes Ca2+-dependent facilitation (CDF) and inactivation (CDI). Ca2+-free CaM (apoCaM) also contributes to the regulation of Cav1.2 channels. Furthermore, CaM indirectly affects channel activity by activating CaM-dependent enzymes, such as CaM-dependent protein kinase II and calcineurin (a CaM-dependent protein phosphatase). In this article, we review the recent progress in identifying the role of apoCaM in the channel ‘rundown’ phenomena and related repriming of channels, and CDF, as well as the role of Ca2+/CaM in CDI. In addition, the role of CaM in channel clustering is reviewed.
GROUND STATE OF THE HELIUM ATOM. II

A further attempt is made to improve the theoretical prediction of ihe energy of the ground state of atomic helium. The nonrelativistic part is treated by the variational method of Stevenson and Crawford which is useful for improving the lower bound for the ground-state energy. Linear combinations of up to 80 terms of generalized Hylleraas type are employed in the numerical computation. The best trial function gives --2.9037237 atomic units as an upper bound and -- 2.9037467 atomic units as a lower bound for the ground-state energy. It ia estimated from the calculated results that the exact nonrelativistic energy of He ground state will be found in the neighborhood of --2.9037247 atomic units. Rigorous formulas are derived which can be used for calculating the upper limits to the errors in the expectation values of mass polarization and relativistic corrections. Although these formulas give very broad limits of error, they are useful in estimating the order of magnitude of actual errors in a semiempirical manner. With mass polarization and relativistic corrections as well as electrodynamical corrections, the theoretical ionization potential becemes 198310.77 cm/sup -1/ which is in good agreement with the latest observed value
Template-Based 2D High-Precision Model Reconstruction of Car Body

Car body design includes the construction of car sketches, 2D models and 3D models. The establishment of high-quality car body models usually relies on CAD software, which requires the proficient operating skills, and the efficiency is relatively low compared with automatic modeling. The time cost of communication between the designer and the model engineer will also affect the modeling efficiency. How to quickly and automatically generate high quality parametric models from car sketches or images efficiently has become a development direction for the automotive design. In this paper, we propose a template-based reconstruction method of 2D high-precision car body model. On the basis of the established 2D model database, a coarse model is achieved by the improved Orthogonal Matching Pursuit algorithm with few key points which are obtained by deep neural network; further, according to the proposed auto-fitting optimization algorithm of cubic Bezier curve, the modeling process of “from coarse to fine” is realized combining edge information. The proposed template-based 2D high-precision model reconstruction algorithm of the car body can greatly reduce the modeling time under the given modeling accuracy.
Fast classification scheme for VQ

In this paper, a new classification scheme using concentric circles is derived for Vector Quantization (VQ) coder. Two DCT coefficients of each image block are used to compute the radius (r) and angle (θ) in the polar coordinate. A classification scheme consisting of a total of 65 classes is implemented according to the values of r and θ, and is called 65-classes Concentric Circle Classified VQ (65-CCCVQ). Simulation results show that using 65-CCCVQ, the compression speed is much faster than the original VQ algorithm while maintaining similar image quality.
Design and Fabrication of Concentric Tube Robots: A Survey

Concentric tube robots (CTRs) have drawn significant research attention over the years, particularly due to their applications in minimally invasive surgery (MIS). Indeed, their small size, flexibility, and high dexterity enable several potential benefits for MIS. The research has led to an increasing number of discoveries and scientific breakthroughs in CTR design, fabrication, control, and applications. Numerous prototypes have emerged from different research groups, each with its own design and specifications. This survey paper provides an overview of the state of the art of the mechatronics aspects of CTRs, including approaches for the design and fabrication of the tubes, actuation unit, and end effector. In addition to the various hardware and associated fabrication methods, we propose to the research community, a unifying way of classifying CTRs based on their actuation unit architecture, as well as a set of specification details for the evaluation of future CTR prototypes. Finally, we also aim to highlight the current advancements, challenges, and perspectives of CTR design and fabrication.