Miracle-dz/UniFAR-FTUs huggingface dataset:
Wireless Communication System

In this study, we proposed an indoor broadband dual-polarized 2 × 2 MIMO (multipleinput and multiple-output) antenna having dimensions of 240 mm × 200 mm × 40 mm, for application in 5G wireless communication systems. The proposed antenna comprised two vertically polarized circular monopole antennas (CMAs), two horizontally polarized modified rectangular dipole antennas (MRDAs), and a ground plane. The distance between the two MRDAs (MRDA1 and MRDA2) was 70.5 mm and 109.5 mm in the horizontal (x-direction) and 109.5 mm vertical (y-direction) directions, respectively. Conversely, the distance between the two CMAs (CMA1 and CMA2) was 109.5 mm and 70.5 mm in the horizontal (x-direction) and vertical (y-direction) directions, respectively. While the CMAs achieved broadband characteristics owing to the optimal gap between the dielectric and the driven radiator using a parasitic element, the MRDAs achieved broadband owing to the optimal distance between the dipole antennas. The observations in this experiment confirmed that the proposed could operate in the 5G NR n46 (5.15–5.925 GHz), n47 (5.855–5.925 GHz), n77 (3.3–4.2 GHz), n78 (3.3–3.8 GHz), and the n79 (4.4–5 GHz) bands. Moreover, it exhibited a wide impedance bandwidth (dB magnitude of S11) of 101% in the 2.3–7 GHz frequency range, high isolation (dB magnitude of S21), low envelope coefficient correlation (ECC), gain of over 5 dB, and average radiation efficiency of 87.19%, which verified its suitability for application in sub-6 GHz 5G wireless communication systems.
Insights on carbapenem-resistant Acinetobacter baumannii

Acinetobacter baumannii (A. baumannii) is an important nosocomial pathogen, which may be a causative agent in a wide-range of human pathologies. Carbapenems are usually considered the last safe and eﬀective choice of drugs for the treatment of Gram-negative infections. The emergence of carbapenem-resistant A. baumannii (CRAB) is a critical public health issue as they leave clinicians with limited therapeutic options. In this study, phenotypic methods were used to characterize sixty-two (n = 62) A. baumannii isolates, which were included based on their suspected non-susceptibility to meropenem. Minimum inhibitory concentrations (MICs) of meropenem, levofloxacin, gentamicin, sulfamethoxazole/trimethoprim, tigecycline were determined using E-tests, while colistin MICs were determined using broth microdilution. The isolates were subjected to the modiﬁed Hodge test (MHT), the modiﬁed carbapenem-inactivation method (mCIM) and the imipenem/EDTA combined disk test (CDT). Eﬄux pump overexpression was studied using agar plates containing phenylalanine-arginine β-naphthylamide (PAβN). Assessment of bioﬁlm-formation was carried out using the crystal violet tube-adherence method. 64.5% of the strains showed meropenem MICs in the resistant range (>8 mg/L), resistance rates were similarly high to the other tested antibiotics. The MHT and mCIM assay were positive in 79.0% and 67.7% of cases, respectively; the presence of an MBL was suggested for 29.0% of isolates. Eﬄux-pump overexpression was seen in 12.9% of isolates. 54.8% of the isolates were characterized as strong bioﬁlm-producers. Microbiology laboratories have an important role in diﬀerentiating the distinct mechanisms by which these pathogens develop the CRAB phenotype, as plasmid-borne carbapenemases are signiﬁcant from the standpoint of public health microbiology.
Substrate Integrated Waveguide Cavity Backed Wideband Slot Antenna for 5G Applications

In this article, a study of wideband substrate integrated waveguide (SIW) cavity based slot antenna is presented. The proposed antenna consists of a U-shaped slot etched in the ground plane which helps in achieving the wideband behaviour. A detailed analysis of the SIW cavity and prediction of various modes propagating inside it using accurate design equations are discussed. An equivalent circuit modeling of the proposed antenna along with surface current distributions at various resonating frequencies is also performed. Parametric study on various parameters to improve the performance in terms of wide impedance bandwidth, gain and efficiency levels are also discussed in detail. The fabricated prototype of the proposed SIW antenna shows the reflection coefficient |S11 | ≤ 10 dB in frequency range from 26.20–30.30GHz (14.51%) which are in good agreement with the simulated results. A peak gain and maximum radiation efficiency of 7.65 dBi and 91.29%, respectively. A low variation in peak gain (±0.26 dBi) and radiation efficiency (±0.09%) within the operating frequency range is seen. A good matching characteristics along with good level of gain, radiation efficiency levels and stable radiation patterns makes the proposed antenna a suitable candidate for 5G applications.
The Effect of Firm Profitability on Expected Stock Return in ASEAN Stock Market

This research discusses and analyzes the company's profitability related to the company's stock return performance Profitability of the firm is related to the firm's performance of stock return. This study uses time-series data with a total sample of 1,010 firms from five countries in ASEAN (Indonesia, Thailand, Malaysia, Philippines, and Vietnam) from January 2010 to December 2019. Fama-French 3 factor model based on two different profitability showed that profitability positively affects the stock return in ASEAN markets. Fama-MacBeth's (1973) regression confirms that firm profitability scaled by operating profit-to-equity or operating profit-to-assets positively influences expected stock returns in the ASEAN market.DOI: 10.26905/jkdp.v25i3. 5598
ROBUST PARAMETER ESTIMATION FOR RANDOM EFFECT PANEL DATA MODEL IN THE PRESENCE OF HETEROSCEDASTICITY AND INFLUENTIAL OBSERVATIONS

Panel data estimators can strongly be biased and inconsistent in the presence of heteroscedasticity and anomalous observations called influential observations (IOs) in Random effect (RE) panel data model. The existing methods (LWS, WLSF, WLSDRGP) address only the problem of IO but fail to remedy the combine problem of heteroscedasticity and IOs. Therefore, in this research we develop a method that will remedy the combine problem of heteroscedasticity and IOs based on robust heteroscedasticity consistent covariance matrix (RHCCM) estimator and fast improvised influential distance (FIID) weighting method denoted by WLSFIID. The simulation and numerical evidences show that our proposed estimation method is more efficient than the existing methods by providing smallest bias, and smallest standard error of HC4 and HC5.
Path Tracking Control of Autonomous Vehicle Based on Nonlinear Tire Model

The tire forces of vehicles will fall into the non-linear region under extreme handling conditions, which cause poor path tracking performance. In this paper, a model predictive controller based on a nonlinear tire model is designed. The tire forces are characterized with nonlinear composite functions of the magic formula instead of a simple linear relation model. Taylor expansion is used to linearize the controller, the first-order difference quotient method is used for discretization, and the partial derivative of the composite function is used for matrix transformation. Constant velocity and variable velocity conditions are selected to compare the designed controller with the conventional controller in Carsim/Simulink. The results show that when the tire forces fall in the nonlinear region, two controllers have good stability, and the tracking accuracy of the controller designed in this paper is slightly better. However, after the tire forces become nonlinear, the controller with linear tire force becomes worse, the tracking accuracy is far worse than the controller with the nonlinear tire model, and the vehicle stability is also degraded. In addition, an active steering test platform based on LabVIEW-RT is established, and hardware-in-the-loop tests are carried out. The effectiveness of the designed controller is verified.
Generative Adversarial Networks for Electron Miscroscope Image Denoising

Image denoising has always been a problem of interest in experimental sciences. Due to hardware limitations, images obtained from equipment need to be cleaned and denoised before further scientific analysis can be conducted. This issue becomes even more of a problem when working on the microscopic scale. For our project, we aim to denoise electronic microscope images of atomic structures. Traditional denoising methods use mean-squared error as a loss function, but due to its averaging nature, the resulting images often have problems such as blurriness or exhibiting phantom artifacts [11]. To overcome these issues, we propose a generative adversarial network (GAN) based denoising method in which we first train a GAN to generate realistic microscope images, then optimize over the latent space to find a clean image that most resembles the noisy image we were given. Since well-trained GANs could only generate reasonable images, we hypothesize that our method can help avoid the problems brought by traditional denoising methods.
LAND-COVER CLASSIFICATION USING FREELY AVAILABLE MULTITEMPORAL SAR DATA (WORK IN PROGRESS)

Abstract. Synthetic Aperture Radar (SAR) images are a valuable tool for wetlands monitoring since they are able to detect water below the vegetation. Furthermore, SAR images can be acquired regardless of the weather conditions. The monitoring and study of wetlands have become increasingly important due to the social and ecological benefits they provide and the constant pressures they are subject to. The Sentinel-1 mission from the European Space Agency enables the possibility of having free access to multitemporal SAR data. This study aims to investigate the use of multitemporal Sentinel-1 data for wetlands land-cover classification. To perform this assessment, we acquired 76 Sentinel-1 images from a portion of the Lower Delta of the Paraná River, and considering different seasons, texture measurements, and polarization, 30 datasets were created. For each dataset, a Random Forest classifier was trained. Our experiments show that datasets that included the winter dates achieved kappa index values (κ) higher than 0.8. Including textures measurements showed improvements in the classifications: for the summer datasets, the κ increased more than 14%, whereas, for Winter datasets in the VH and Dual polarization, the improvements were lower than 4%. Our results suggest that for the analyzed land-cover classes, winter is the most informative season. Moreover, for Summer datasets, the textures measurements provide complementary information.
Modular Teleoperation Framework

Teleoperation is a growing mode of communication between master and slave devices especially in applications such as minimally invasive surgery. There are a variety of available slaves and masters each having different DH parameters and Degrees of freedom(DOFs). This paper aims at devising a modular teleoperation framework which can merge various different masters and slaves. The framework can take velocities from one robot and feed that to the slave and return a force feedback if necessary to the master. This would be very helpful in surgeries where haptic feedback is valuable.
Imaging Using Machine Learning for the LDMX Electromagnetic Calorimeter

LDMX is a fixed target experiment designed to search for light dark matter. The experiment will search for dark matter signatures using missing momentum and energy in events with electrons scattering in the target. Part of the experimental setup in this search is a high granularity electromagnetic calorimeter to accurately measure the energies of recoil electrons. In this thesis artificial neural networks are trained on simulated events in the electromagnetic calorimeter to classify events based on their particle contents, and to separate two showers from each other in the calorimeter. For the first task two sets of events are generated, one with a single electron that either undergoes bremsstrahlung or not, and one with multiple primary electrons. Two different neural networks models are used to classify these events. A basic convolutional neural network model achieves a classification accuracy of 96% on the first set of data, with an AUC of 0.99, and an accuracy 99.8% on the second set. A graph neural network model achieves a classification accuracy of 83% on the first set, with an AUC of 0.91, and an accuracy 99.3% on the second set. Another graph neural network model is trained on a subset of the first data set to label the individual hits in the calorimeter by whether they come from and electron shower or a photon shower. This network achieves an average accuracy of 86.6% over all events and correctly labels 90.5% of the energy in these events. The neural network models are compared to each other and evaluated based on their performance, limitations, and computing requirements.
Analysis of oxidation degree of graphite oxide and chemical structure of corresponding reduced graphite oxide by selecting different-sized original graphite

The thermal exfoliation and reduction of graphite oxide (GO) is the most commonly used strategy for large-scale preparation of graphene, and the oxidation degree of GO would influence the chemical structure of prepared graphene, thereby affecting its final physical and chemical properties. In addition to serving as the precursor for synthesizing graphene, GO also possesses great potential for various important applications owing to its abundant oxygen-containing groups and hybrid electronic structure. Therefore, systematically studying the influencing factors on the oxidation degree of GO and clarifying the effect of oxidation degree on the corresponding graphene is particularly important. Herein, we have studied the effect of the lateral size of the original graphite on the oxidation degree of GO in order to control the oxidation degree of GO. GOs with different degrees of oxidation were synthesized using a modified Hummers method. The results of X-ray diffraction (XRD), X-ray photoelectron spectra (XPS) and Raman spectroscopy revealed that decreased lateral size of the original graphite would lead to increased oxidation degree of GO. Furthermore, the interlayer spacing of the GO samples achieved 0.9–1.0 nm, which indicated that the modified Hummers method could make well oxidized graphite. The corresponding reduced graphite oxide (rGO) was also prepared by low-temperature exfoliation of GO at 140 °C under ambient atmosphere. It was found that a larger lateral size of GO resulted in rGO with fewer oxygen-containing functional groups, but a smaller lateral size of graphite possessed a higher exfoliation degree with a larger specific surface area. More importantly, the relationship between binding energy (EB) of photoelectron of C atom in oxygen-containing groups and the number of oxygen-containing groups in GO and rGO samples was analyzed theoretically.
Growth variation of Atlantic salmon Salmo salar at sea affects their population-specific reproductive potential

Understanding the processes shaping the dynamics of anadromous fish populations is essential for their management and conservation. Yet, little is known about how variation in performance at sea affects their population dynamics. Here we show that variation in body growth at sea contributes to explaining variation in the reproductive potential for 2 Atlantic salmon Salmo salar populations, but to a varying extent. To this end, we assembled data collected during 50 yr for 2 Baltic salmon populations of hatchery origin, including annually released smolts, survival at sea estimates, size-specific growth at sea, annual length distributions of returning adult females and their reproductive potential. The regression models fitted to explain the reproductive potential of our 2 study populations improved when growth at sea was included as an explanatory variable, in addition to smolt year class abundance and estimates of their survival at sea. This link between body growth at sea and population-level reproductive potential suggests that growth at sea can be important to consider when resolving variation in recovery and dynamics among salmon populations sharing the same sea.
Multistage development of a hydrothermal tungsten deposit during the Variscan late-orogenic evolution: the Puy-les-Vignes W breccia pipe (Massif Central, France)

Multistage development of a hydrothermal tungsten deposit during the 1 Variscan late-orogenic evolution: the Puy-les-Vignes W breccia pipe (Massif 2 Central, France) 3 4 Développement multiphasé d’un gîte hydrothermal à tungstène au cours de 5 l’évolution tardi-orogénique varisque : le conduit bréchique à W de Puy-les6 Vignes (Massif Central, France) 7 8 Matthieu Harlaux1,2*, Christian Marignac1, Julien Mercadier1, Marc Poujol3, Marie-Christine 9 Boiron1, Kalin Kouzmanov4, Alfredo Camacho5, Saïda Alikouss6, Benjamin Roméo7, Bernard 10 Mouthier8, and Michel Cuney1 11 12 1 Université de Lorraine, CNRS, GeoRessources, F-54000 Nancy, France 13 2 Nevada Bureau of Mines and Geology, University of Nevada, Reno, NV 89557-0178, USA 14 3 Univ Rennes, CNRS, Géosciences Rennes – UMR 6118, F-35000 Rennes, France 15 4 Department of Earth Sciences, University of Geneva, CH-1205 Geneva, Switzerland 16 5 Department of Geological Sciences, University of Manitoba, Winnipeg, Manitoba R3T 2N2, 17 Canada 18 6 Department of Geology, Hassan II University of Casablanca, Geodynamics of old Chains 19 Laboratory, Casablanca, Morocco 20 7 SOQUEM INC., 1740 Chemin Sullivan, Val-d’Or, Québec J9P 7H1, Canada 21 8 5 Quai André Lassagne, 69001 Lyon, France 22 23 * Corresponding author: mharlaux@unr.edu 24 25 26
cerf: A Python package to evaluate the feasibility and costs of power plant siting for alternative futures

Long-term planning for the electric grid, also referred to as resource adequacy planning, informs decisions about investments in infrastructure associated with policy goals with the ultimate objective to maintain or enhance resilience to potential future vulnerabilities under various natural and human stressors (Alova, 2020; Craig et al., 2018; Jayadev et al., 2020; Khan et al., 2021; Sridharan et al., 2019). Future power plant siting costs will depend on a number of factors including the characteristics of the electricity capacity expansion and electricity demand (e.g., fuel mix of future electric power capacity, and the magnitude and geographic distribution of electricity demand growth) as well as the geographic location of power plants (U. S. Energy Information Administration, 2021). Electricity technology capacity expansion plans modeled to represent alternate future conditions meeting a set of scenario assumptions are traditionally compared against historical trends which may not be consistent with current and future conditions (Iyer et al., 2015; Sluisveld et al., 2015; Wilson et al., 2013).
Visual Exploration of Text Co - Occurrences

This paper investigates the design of visualizations for exploring co-occurrences. Co-occurrences are defined in terms of ordered, discrete events. Examples of such corpora are Twitter (unit = tweet, order = time, event = appearance of hash tag) or the King James Bible (unit = line, order = book, event = appearance of word). Standard approaches to exploring co-occurrence are simple frequency counts. This paper presents a new, intuitive approach to displaying co-occurrences that combines elements of area-proportional (Venn) diagrams and stacked bar graphs. The technique enables human analysts to better understand the nature of co-occurring events. Detailed design considerations are presented with justification. The technique is integrated into an application for exploring real-world data.
First vegetation-plot database of woody species from Huíla province, SW Angola

Angola is a country in south-central Africa, particularly rich in biodiversity. Despite the efforts recently made to document its biodiversity, there is a need for standardized sampling methods to document and compare the variety of ecosystems and plants occurring in the country. With this database report we aim to document the abundance and diversity of woody species in the woodlands of Huíla province. The database hosts the results of a standardised plot-based vegetation survey, consisting of 448 vegetation plots distributed throughout the 14 municipalities and Bicuar National Park. In total, 40,009 individuals belonging to 44 plant families were recorded and measured, belonging to 193 woody species. Species richness per municipality ranged from 32 to 126. The mean stem diameter (DBH) was 10.9 cm ± 7.5 cm. Small-size classes are increasingly dominated by few species, while the largest trees come from a wider range of species; miombo key-species dominated almost all size classes. Our study represents the first plot-based vegetation survey of any Angolan province and constitutes a useful source of information for conservation and management. Additionally, may constitute a powerful dataset to support future studies on biodiversity patterns and vegetation change over time and facilitate the elaboration of vegetation maps. Taxonomic reference: Checklist of Angolan Plants (Figueiredo and Smith 2008), The African Plant Database (version 3.4.0) and A new classification of Leguminosae (LPWG 2017). Abbreviations: DBH = Diameter at Breast Height; GIVD = Global Index of Vegetation-Plot Databases; LUBA = Acronym of the Herbarium of Lubango
Predicting Undergraduate Level Students’ Performance Using Regression

Students’ academic performance in the university environment changes from one academic year to another as they climb up the ladder of their academic programme. Predicting students’ academic performance in higher educational institutions is challenging due to the lack of a central database of students’ performance records. The other challenge is the lack of standard methods for predicting students’ performance and other moderating factors like physical, economic and health that affect students’ progress. In this work, we predicted students’ performance based on previous academic results. A model to predict students’ performance based on their Cumulative Grade Point Average (CGPA) was developed using Linear Regression Algorithm. A dataset of 70 undergraduate students studying Computer Science was analyzed and the results show that the model was able to predict the 4th year CGPA of the Students using the previous Cumulative Grade Point of the past three years with an accuracy of 87.84%, and a correlation of 0.9338. This study also identified students’ second semester CGPA in the first year and their first semester CGPA in the second year as the most important CGPAs that affect the accuracy
Stakeholder Participation in a Company's Success and Resilience Against Shocks

This paper investigates how several of a company's stakeholders (labor, capital, and the government) share in a company's success and how this division affects firms' resilience to shocks. The analysis draws on detailed microdata of Japanese manufacturing companies (listed and unlisted). The labor share (the share of wages in operating income before depreciation and wages) is larger in smaller, less profitable, and older firms; in fact, labor share has fallen substantially over the last 20 years. The opposite holds for the residual share, potentially accruing to shareholders. In theory, a higher labor share either acts as operating leverage, amplifying exogenous shocks, or helps to mitigate such shocks by inducing higher effort and loyalty from workers. Empirically, firms' profit growth with higher labor shares turns out to be more resilient to macroeconomic shocks. These results have implications for understanding the consequences of societal changes such as technological progress that reduce the labor share.