Miracle-dz/UniFAR-FTUs huggingface dataset:
GPS as a solar observational instrument: Real‐time estimation of EUV photons flux rate during strong, medium, and weak solar flares

In this manuscript, the authors show how the Global Navigation Satellite Systems, GNSS (exemplified in the Global Positioning System, GPS), can be efficiently used for a very different purpose from that for which it was designed as an accurate Solar observational tool, already operational from the open global GPS measurements available in real‐time, and with some advantages regarding dedicated instruments onboard spacecraft. The very high correlation of the solar extreme ultraviolet (EUV) photon flux rate in the 26–34 mm spectral band, obtained from the solar EUV monitor instrument onboard the SOHO spacecraft during Solar flares, is shown with the GNSS solar flare activity indicator (GSFLAI). The GSFLAI is defined as the gradient of the ionospheric vertical total electron content rate versus the cosine of the Solar zenith angle in the day hemisphere (which filters out nonsolar over ionization), and it is measured from data collected by a global network of dual frequency GPS receivers (giving in this way continuous coverage). GSFLAI for 60 X class flares, 320 M class flares, and 300 C class flares, occurred since 2001, were directly compared with the EUV solar flux rate data to show existing correlations. It was found that the GSFLAI and EUV flux rate present the same linear relationship for all classes of flares, not only the strong and medium intensity ones, X and M class, as in previous works, but also for the weakest C class solar flares, which is a remarkable result.
of Multiscale Gas Transport in Shale-Core Samples

This work introduces an experimental technique to probe simultaneously ﬂow and diffusion of gas through shale. A core-scale pressure-pulse-decay experiment is used to study the upstream-and downstream-pressure responses of Eagle Ford and Haynes- ville shale samples. With the aid of numerical models, the pressure histories obtained from the experiments are matched and gas and rock properties are obtained. The experiments are conducted at varying pore pressure and net effective stress to understand the sensitivity of the rock porosity and permeability as well as the gas diffusivity. A dual-porosity model is constructed to examine gas transport through a system of micropores and microcracks. In this sense, the role of the two different-sized pore systems is decon- volved. In some cases, the micropore system carries roughly one-third of the gas ﬂow. The porosity, permeability, and diffusivity obtained assign physical properties to the macroscales and micro- scales simultaneously. Results bridge the gap between these scales and improve our understanding of how to assign transport physics to the correct pore scale.
MULTI RESPONSE OPTIMIZATION APPLICATION ON A MANUFACTURING FACTORY

The purpose of real-life problems is often to be able to find less expensive and more effective ways of production without compromising product quality because companies must provide competitive advantage to maintain existence. In order to improve quality, design of experiment techniques is employed. RSM is a widely used technique thanks to its minimum number of experiment requirement. Hence it is used especially with continuous solution spaces and high-cost experimentations. Moreover, in most cases there is more than one response that firms must optimize simultaneously. For instance companies want to reduce the costs while improving product quality. Decision making is more difficult when conflicting objectives exist. For this reason multi response optimization is an important field to study. In this study, optimization of a manufacturing problem with two responses was carried out by the application of response surface methodology (RSM) and desirability function.
Behavioural Repeatability and Behavioural Syndrome in the Dung Beetle Copris umbilicatus (Coleoptera, Scarabaeidae)

Simple Summary Not only vertebrates but also invertebrates can display personalities and behavioural syndromes. Here, three multiple behaviours (activity, thanatosis and distress call emission) were investigated in Copris umbilicatus. Moderate to excellent levels of repeatability were found in all behavioural traits considered. Results suggest the existence of a behavioural syndrome involving thanatosis and activity, with ‘bolder’ individuals exhibiting shorter thanatosis and higher locomotor activity, in contrast with ‘fearful’ individuals which display longer thanatosis and poor locomotor activity. Noticeable differences between individuals, which were not due to differences in sex or body size, could be attributable to differences in personality among individuals. Abstract Although personality studies have primarily focused on vertebrates, the evidence showing invertebrates to be capable of displaying personalities has been steadily growing in recent years. In this study, we investigated the behavioural repeatability (repetition of a behaviour over time) and behavioural syndromes (a set of correlated behaviours) in Copris umbilicatus, which is a dung beetle species showing complex sub-social behaviour. We analysed three behaviours (activity, thanatosis and distress call emission) by measuring seven distinct behavioural traits (i.e., three activity-, one thanatosis- and three distress call-related traits). We found moderate to high levels of individual repeatability in all behavioural traits considered. The duration of thanatosis was inversely correlated with two activity traits, hinting a behavioural syndrome for thanatosis and activity, with bolder individuals exhibiting shorter thanatosis and higher locomotor activity in contrast with fearful individuals, which display longer thanatosis and poor locomotor activity. No relationships were found between the behavioural traits and body size or sex. Results of the principal component analysis (PCA) suggested personality differences among individuals. Dung beetles provide an impressive variety of ecosystem services. Since the provision of these services may depend on the personalities represented in local populations and communities, studies on the ecology of personality in dung beetles should be encouraged in future research.
Design of UHF-Overvoltage Sensor and Measurement Under Superimposed Voltage

Abstract-Insulation performance of epoxy surface in GIS systems is influenced by the applied superimposed voltage of AC and overvoltage. The simultaneous measurement of the partial discharges (PDs) and applied voltage can help to evaluate the condition of GIS equipment more accurately. A novel composite sensor for PD UHF signals and overvoltage measurement is proposed to realize the simultaneous detection. The effective height of the composite sensor is calculated with FDTD model and the preliminary optimization is carried based on it. The effectiveness of the composite sensor is tested on an epoxy surface discharge model under of AC voltage, lightning impulse and superimposed voltage. Experiment results show that the extra PDs are excited by the superimposed LI and the proposed sensor is capable to detect the voltage waveform and PD signals under the superimposed condition.
The Artificial Intelligence Learning Anxiety and Self-Efficacy of In-Service Teachers Taking AI Training Courses

This research explored the degree of learning self-efficacy of machine learning experience (MLSE) and artificial intelligence learning anxiety (AILA) of elementary and junior high school teachers. The participants were in-service teachers in the technology domain. This research applied the AI2 Robot City, which is a computational thinking board game, to in-service teacher education. The learning content was image classification for AI application. Elementary and junior high school teachers operated the MIT App Inventor(MAI) and Personal Image Classifier(PIC) platform, and trained the model for practicing AI to implement supervised machine learning on the PIC platform. The learners then inserted the model they had trained into the block-based programming environment of MAI. They completed the smart phone app and used the app to recognize the board game cards so as to control the movement of the smart cars on the table map to meet the requirements of the task in the AI2 Robot City board game. In order to understand affective factors such as the self-efficacy and learning anxiety of the elementary and middle school teachers participating in the AI teacher training workshop, the MLSE and AILA scales were administered before and after the classes. A total of 28 samples were collected. The results showed that there was no significant difference between the MLSE of the elementary and junior high school teachers. However, the average AILA degree of the junior high school teachers was significantly higher than that of the elementary school teachers. It was found that AILA was significantly negatively correlated with MLSE. The elementary and junior high school teachers were confident that they could study AI-related courses. However, the AILA of the junior high school teachers was higher than that of the elementary school teachers. Therefore, more teacher training workshops on AI application can be conducted for junior high school teachers to generally improve their familiarity with AI application. Future research can further explore whether teachers will gradually improve their AILA and MLSE with time and as training courses increase.
Robust Network-Secure Operating Envelope for Aggregators Bidding in Wholesale Market

As aggregators (AGs) bid in the wholesale market, the delivery of their bids may jeopardize distribution network security. A network-secure operating envelope approach was recently developed to limit an AG’s active power when bidding. However, the existing relevant research in this regard either imposes high communication burdens or has the risk of failure against uncertainty. This paper proposes a new robust network-secure envelope approach, which considers distribution network security constraints, the power injection of all buses, and the uncertainty in the distribution network, so the resulting robust network-secure envelope can avoid distribution network security issues against uncertainty, which will facilitate AGs participating in the wholesale market. This new robust network-secure envelope is evaluated via two-stage robust optimization, which can be effectively solved by a column-and-constraint generation algorithm. Numerical tests validate the merits and significance of the proposed approach.
The Seasonal Variation of the Propagating Diurnal Tide in the Mesosphere and Lower Thermosphere. Part II: The Role of Tidal Heating and Zonal Mean Winds

A linear mechanistic tidal model is used to understand the mechanisms responsible for the seasonal variation of the propagating diurnal tide in the mesosphere and lower thermosphere simulated in the Canadian Middle Atmosphere Model (CMAM). The linear model uses a spectral approach to represent the horizontal structure of the tidal perturbations and employs dissipative processes that do not depend on season. By constraining the model with the zonal mean zonal winds, zonal mean temperatures, and tidal heating from the CMAM, the relative role of each of these terms is assessed. The linear model is able to reproduce all of the important tidal features found in the CMAM, in particular the semiannual amplitude variation in the lower thermosphere at low latitudes that is seen in observations. From this analysis the effects of both heating and mean winds are found to be responsible for the seasonal variation of the tidal amplitude, while variations in the tidal phase are attributed solely to changes in the mean winds. The strong sensitivity of the tide to the mean winds is the novel result of this study. This sensitivity is attributed to latitudinal shears in the zonal mean easterlies in the summer mesosphere. Although these shears occur on an annual basis, their impact on tidal amplitudes in the lower thermosphere is semiannual as a result of the 6-month shift in seasons between the two hemispheres. Simulations using observational datasets from the Committee on Space Research (COSPAR) International Reference Atmosphere (CIRA) and the High Resolution Doppler Imager (HRDI) reveal significant differences in the resulting tidal structure from that obtained using the CMAM winds, and point to possible deficiencies in these datasets.
WiFi and BLE Fingerprinting for Smartphone Proximity Detection

Wireless devices such as smartphones, wireless bracelets or smartwatches are now often used by a single person. They can therefore be used to model human contact, proximity to each other, or social networking. In light of events such as the recent pandemic interactions or proximity between people have become increasingly important. In this context, this paper explores the limitations of a machine learning-based approach that detects the proximity of two devices (up to about two metres) based on WiFi and BLE (Bluetooth Low Energy) fingerprints of their radio environments. Specifically, we compare the use of a rudimentary set of two features and an extended, more complex set of features, exploring the use of separate classifiers that treat WiFi and BLE features separately. In addition, we investigate whether using only one of the two communication technologies for detection could lead to better results and the importance of radio propagation expertise in feature extraction. We found that using a more complex set of features that can be subjected to further feature selection procedures can provide a performance benefit of about 4.6 percentage points. In terms of the communication technologies used, our results also show that using BLE alone always leads to significantly worse results than using WiFi alone or WiFi and BLE together. The evaluation was carried out in three different radio environments, namely indoors in companies, outdoors on the street and in the staircase of residential building with many apartments.
Performance Comparison of Different Visual Odometry Architectures to Improve ATDN vSLAM

In this paper an incremental development of an Optical Flow-based Visual Odometry Neural Network is introduced. This solution was developed as part of the ATDN vSLAM project which aims to solve the Visual Simultaneous Localization And Mapping (vSLAM) task using All-Through Deep Neural networks (ATDN). In our current work we propose a performance comparison study of odometry network structures. For input, we use optical flow data instead of RGB as the latter provides low level information in comparison with the former and we aim to build a SLAM framework with building blocks that accept and produce data on different abstraction levels. We present the main developments made in the network structure and network training technique since the ones described in the original ATDN vSLAM paper. These changes are made to encourage the odometry module to properly generalize and provide more precise estimations.
CNN Based Disease Detection in Apple Leaf via Transfer Learning

The broad and regular use of pesticides is justified by the tremendous economic relevance of plant diseases that harm field crops. In some regions, plant diseases may make it impossible to cultivate or grow food plants; alternatively, plant diseases may make it feasible but attack the plants, kill some or all of them, and reduce much of their yield, or food, before it can be harvested or consumed. Early detection of plant diseases is advocated since it is essential to ensuring that the entire population has access to food. It is bad to anticipate illnesses when crops are young, nevertheless. Agriculture is a major source of income for more than 80% of individuals. The work of identifying leaf diseases is crucial since the crop's overall production is decreased by a variety of illnesses. It can be challenging for farmers to diagnose a specific disease. We looked at different sorts of vegetable and fruit leaf diseases and how to spot them. Hence, Image Processing and Deep Learning are the two main methods for detecting leaf disease. The preferred strategy now emphasizes the use of deep learning ideas, particularly Convolutional Neural Networks.
Phasor Analysis of the Symmetric Crisscrossed-assisted Coupled-Ring Reflector

We investigate the general phasor characteristics of the recently reported crisscrossed coupled-ring reflector (X-CRR) and compare it with the typical coupled-ring reflector (CRR) configuration. We observe that they distinctly have different phasor features under arbitrary parameter conditions. We also (i) discuss the importance of normalized frequency range in generating the complete phasor picture of these configurations and (iii) show that in most cases the X-CRR appears to be a spread-out, rotated version of the phasor diagram of the CRR. These phasor features imply that X-CRR possesses more interesting functionalities for various potential telecom applications.
Semi-coherent Heat Conduction in Bulk and Single-Nanowire Twinning Superlattices

Coherent twin boundaries, which form periodic lamellar twinning in a wide variety of semiconductor nanowires, are often viewed as near-perfect interfaces with reduced phonon and electron scattering behaviors. Such unique characteristics are of practical interest for high-performance thermoelectrics and optoelectronics; however, insufficient evidence for the existence of coherent heat conduction in nanotwinned materials poses significant limitations for potential applications. Here, using atomistic simulations and ab-initio calculations, we report direct computational observations showing intrinsic nanotwin effects on thermal conductivity of twinning superlattices in prototypical bulk and nanowire Si examples. Incoherent phonon transport is identified for twin periods $\geq$ 15.1 nm and coherent transport for twin periods $\leq$ 3.8 nm. Remarkably, a regime of semi-coherent phonon transport is unveiled for twin periods spreading between these two limits, described by quasi-ballistic phonons traveling through the interfaces with a Kapitza thermal conductance 3 times higher than for incoherent heat conduction. Detailed analysis of vibrational modes shows that modeling twin boundaries as atomically-thin 6H-Si layers, rather than phonon scattering interfaces, provides an accurate description of effective cross-plane and in-plane thermal conductivities in semi-coherent twinning superlattices, as a function of the twin period thickness.
Multiple solutions for a nonlinear scalar field equation involving the Fractional Laplacian

We investigate the existence of multiple solutions for the following nonlinear fractional equation (-\Delta)^{s} u = g(u) in \mathbb{R}^{N} where s\in (0,1), N \geq 2, (-\Delta)^{s} is the fractional Laplacian and g: \mathbb{R} \rightarrow \mathbb{R} is an odd \mathcal{C}^{1, \alpha} function satisfying Berestycki-Lions type assumptions. The proof is based on the symmetric mountain pass approach developed by Hirata, Ikoma and Tanaka in \cite{HIT}. Moreover, by combining the mountain pass approach and an approximation argument, we also prove the existence of a positive radially symmetric solution for the above problem when g satisfies suitable growth conditions which make our problem fall in the so called "zero mass" case.
Lesson Study as a Means to Change Secondary Preservice Physics Teachers’ Practice in the Use of Multiple Representations in Teaching

: Multiple representations, such as experimental data, schemas, tables, and graphs, are an essential resource in science teaching. However, their use in the classroom typically poses a challenge for preservice teachers. The aim of this research is to examine changes in the practices of a group of preservice teachers regarding the use of multiple representations in the teaching of kinetic energy to 9th grade students, when this training is included in their initial teacher education program. For this purpose, a collaborative, reﬂexive, and student-learning centered approach, namely, a lesson study with three cycles, was implemented. A descriptive and content analysis for qualitative data collected showed improvement in the practices of the preservice teachers, namely on the representations both of the event that represents the “real” world, as well as of the scientiﬁc concepts. The results obtained contribute to deepening the knowledge on the use of multiple representations by preservice teachers, as well as to increasing the knowledge on using lesson study to develop the ability to use multiple representations during initial teacher education.
Patched approximations and their convergence

Abstract Patched approximations of copulas unify ordinal sums, shuffles of Min, checkerboard, and checkmin approximations. We give a characterization of patched approximations and an error bound of the approximations in Sobolev norm. Patched approximations with uniform marginal conditional distributions are shown to arise naturally. We prove that these uniform patched approximations converge uniformly and in the Sobolev norm. The latter convergence is settled by showing the convergence almost everywhere of the first partial derivatives. We also show that the independence copula can be approximated by conditional mutual complete copulas in the Sobolev norm.
Applications of Mathematics

For nonparametric estimation of a smooth regression function, local linear fitting is a widely-used method. The goal of this paper is to briefly review how to use this method when the unknown curve possibly has some irregularities, such as jumps or peaks, at unknown locations. It is then explained how the same basic method can be used when estimating unsmooth probability densities and conditional variance functions.
On the robustness of receding-horizon control with terminal constraints

Robustness properties of nonlinear receding-horizon controllers with zero terminal state constraints are investigated with respect to gain and additive perturbations. Some robustness margins are derived by extending to the receding-horizon case the analysis originally proposed by Geromel and da Cruz for infinite-horizon controllers. In the linear case, it is shown that the zero terminal state receding-horizon controller exhibits worse robustness margins compared to standard infinite-horizon LQ control.
Learning Activities that Influence Deep Active Learning in Reading Circles Learning

Deep active learning is active learning techniques and strategies that develop advanced thinking ability. To achieve deep active learning, it is necessary to promote internalization out of class and externalization in class. In order to realize deep active learning, this study improved face-to-face reading circles learning through empirical research by creating the flipped reading I and II. Students' evaluation of the learning activities was used to judge the effect and whether the depth of learning had changed. The results found that learning activities in face-to-face, flipped reading I and II were similar, and half or more of the students felt that the discussion and question contributed to deep learning. The reasons for the discussion and the question revealed that the depth of learning in the flipped II was higher than that of the face-to-face and flipped reading I, because discussion -externalization in class, and question -internalization out of class, both of which were improved in flipped reading II. The improvement was triggered by the use of visualization support, which also ensured the fairness of participation in externalization.