Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Approach in Understanding Growth and Decay of the Sunspots

From the previous study (Hiremath 2009b; Hiremath 2010), on the genesis of solar cycle and activity phenomena, it is understood that sunspots are formed at different depths by superposition of Alfven wave perturbations of a strong toroidal field structure in the convective envelope and after attaining a critical strength, due to buoyancy, raise toward the surface along the rotational isocontours that have positive (0.7-0.935 $R_{\odot}$) and negative (0.935-1.0 $R_{\odot}$) rotational gradients. Owing to physical conditions in these two rotational gradients, from the equation of magnetic induction, sunspot's area growth and decay problem is solved separately. It is found that rate of growth of sunspot's area during its evolution at different depths is function of steady and fluctuating parts of Lorentzian force of the ambient medium, fluctuations in meridional flow velocity, radial variation of rotational gradient and $cot(\vartheta)$ (where $\vartheta$ is co-latitude). While rate of decay of sunspot's area at different depths during its evolution mainly depends upon magnetic diffusivity, rotational gradient and $sin^{2}(\vartheta)$. Gist of this study is that growth and decay of area of the sunspot mainly depends upon whether sunspot is originated in the region of either positive or negative rotational gradient. For different latitudes and life spans of the sunspots on the surface during their evolutionary history, both the analytically derived theoretical area growth and decay curves match reasonably well with the observed area growth and decay curves.
A Fully-Printed 3D Antenna with 92% Quasi-Isotropic and 85% CP Coverage

Internet of things (IoT) applications require orientation insensitive wireless devices to maintain stable and reliable communication. For those reasons, antennas providing a wide quasi-isotropic and circular polarization (CP) coverage are very attractive. However, achieving a wide quasi-isotropic and CP coverage simultaneously is challenging. In this work, we show that properly designed sloped dipoles on a 3D structure can maximize the CP coverage (theoretically up to 100%) even with equal-phased feed to the dipole elements. We derive the conditions and present the design graphs for the optimum slope angle for the dipole elements on a 3D hexagonal-shaped package to achieve a wide quasi-isotropic and CP coverage simultaneously. Based on the proposed theory, a practical antenna has been designed and fabricated using additive manufacturing. The measured results demonstrate a 7dB-isotropy of 92% and a CP coverage of 85%, which matches well with the predicted results from the theoretical analysis and full-wave simulations.
Marine Oil Spills Detection and Classification from Polsar Images Based on Complex-Valued Convolutional Neural Network

The recent development of marine transportation and offshore oil exploration and exploitation has increased the risk of marine oil spill accidents. Oil pollution is one of the most complex marine pollutions, it will seriously threaten the marine ecological environment. Synthetic aperture radar (SAR) has been widely used in marine monitoring for its all-day and all-weather imaging capability. Polarimetric SAR can obtain polarimetric scattering information of the ground targets, which provides a new means for marine oil spill detection. Besides, with the recent development of machine learning algorithms, especially convolutional neural networks, higher oil spill classification accuracy can be obtained given more training datasets. However, currently most neural network models are applied to real-valued input and cannot fully exploit the phase information contained in complex-valued data of polarimetric SAR images. In this study, a classification approach of marine oil spills in polarimetric SAR images is presented based on the complex-valued convolutional neural network (CVCNN). The experimental results show that the proposed approach outperforms the real-valued convolutional neural network (RVCNN) in both the detection of oil spills from sea surface and the classification of crude oil films and biogenic oil films.
A Review of Point Clouds Deep Learning Fusion for Unmanned Aerial Data Systems

Use of unmanned aerial systems (UAS) for surveillance are likely to expand into new areas such as aerial logistics and night time operations. Thus, there are foreseeable needs for autonomous perception capabilities to detect unwanted objects, to provide safety alerts and safe navigation. This review paper provides an overview on the subject of camera image and LiDAR point cloud fusion between sensor data, as well as fusion of deep learning (DL) methods for aerial applications. This review compares various DL methods for point clouds and fusion methods, which could guide future research work on this subject.
VISUAL PERCEPTION BASED SEMANTIC SAR CHANGE DETECTION

This paper presents a visual perception based framework for semantic change detection between multi-temporal SAR images. Gabor texture features have been incorporated into accurately and effectively extracting differences for generating saliency difference image. Then we have proposed to build a visual perception based multi-class change indicator to describe various types of changes. In final classification stage, samples with high confidences have been selected and fed into PCANet to predict pixel classes for semantic SAR change detection. Comprehensive experiments have been conducted on the area of Nanhaizi park. Comparisons with other change detection methods have been reported to verify the usefulness of the proposed method in accurately detecting and describing various types of changes.
Centroidal Moment Pivot for ambulatory estimation of relative feet and CoM movement post stroke: Portable Gait Lab*

Measuring gait and balance recovery is necessary post stroke. In an earlier study, we developed a minimal three Inertial Measurement Units (IMUs) system called Portable Gait Lab (PGL). The PGL used the Centroidal Moment Pivot (CMP) assumption to estimate relative foot and centre of mass (CoM) positions, and thereby estimate gait parameters in healthy participants. In this study, we validate the feasibility of the PGL to track foot and CoM trajectory during gait in four persons with chronic stroke. Spatiotemporal gait and balance measures were estimated from the foot and CoM trajectories, and compared with the reference ForceShoes™. Each participant made at least 20 steps, and the PGL was able to track foot and CoM trajectories with a root mean square of the differences with the reference of 2.9 ± 0.2 cm and 4.6 ± 3.6 cm. The distances between either foot at the end of the walking task, and step lengths were estimated by PGL with an average error with the reference of 1.98 ± 2.2 cm and 7.8 ± 0.1 cm respectively across participants. We show that our approach was able to estimate spatiotemporal and balance parameters related to gait quality in a clinically useful manner. We recommend conducting further studies to study the feasibility of using the PGL system for variable gait patterns measured post stroke.
Chemical evolution of neutron capture elements in our Galaxy and in the dwarf spheroidal galaxies of the Local Group

By adopting a chemical evolution model for the Milky Way already reproducing the evolution of several chemical elements, we compare our theoretical results with accurate and new stellar data of neutron capture elements and we are able to impose strong constraints on the nucleosynthesis of the studied elements. We can suggest the stellar sites of production for each element. In particular, the r-process component of each element (if any) is produced in the mass range from 10 to 30 Msun, whereas the s-process component arises from stars in the range from 1 to 3 Msun. Using the same chemical evolution model, extended to different galactocentric distances, we obtain results on the radial gradients of the Milky Way. We compare the results of the model not only for the neutron capture elements but also for alpha-elements and iron peak elements with new data of Cepheids stars. We give a possible explanation to the considerable scatter of neutron capture elements observed in low metallicity stars in the solar vicinity, compared to the small star to star scatter observed for the alpha-elements. In fact, we have developed a stochastic chemical evolution model, in which the main assumption is a random formation of new stars, subject to the condition that the cumulative mass distribution follows a given initial mass function. With our model we are able to reproduce the different features of neutron capture elements and alpha-elements. Finally, we test the prescriptions for neutron capture elements also for the dwarf spheroidal galaxies of the Local Group. We predict that the chemical evolution of these elements in dwarf spheroidal galaxies is different from the evolution in the solar vicinity and indicates that dwarf spheroidal galaxies (we see nowadays) cannot be the building blocks of our Galaxy.
DC-GAN for Fracture Data Generation Based on Segmented Outcrop Images Acquired from UAV

Reservoir rocks have intrinsic properties of porosity and per-meability that make them natural hosts for hydrocarbons, and the estimation of these properties is key to simulating reser-voir models. Additionally, fractures can greatly influence the fluid flow within the reservoir. However, fracture data from reservoirs is often disregarded due to its sub-seismic scale. In this matter, analogue outcrops play an important role in Dis-crete Fracture Network (DFN) modeling which represents the fracture data in terms of their attribute distribution allowing the scaling of the fracture data to reservoir scale which is done mostly by stochastic methods. Recent works have used Gen-erative Adversarial Networks (GANs) to scale porosity data from images, and we propose its use in fracture data from images acquired from Unmanned Aerial Vehicles (UAV) em-ploying a Deep Convolutional GAN (DC-GAN). We obtained similar attribute distribution for fracture aperture showing the viability to replicate statistical behavior.
Generation of a Digital Elevation Model Using Capella High-Resolution SAR Data: First Results Over La Palma Island

Generation of Digital Elevation Models (DEMs) using Synthetic Aperture Radar (SAR) data is a valid flexible alternative to other surveying techniques. In this work we adopt radargrammetry on two SAR images recorded by the Capella Space X-band spaceborne sensor on two consecutive days over La Palma Island (Canary Islands, Spain) during the Cumbre Vieja volcanic eruption, with the aim of investigating the potential of these high-resolution (up to 0.7 by 0.5 m2) SAR images in generating precise DEMs. The adopted method employs a multilook-based spatial filter followed by an iterative region-growing algorithm to identify matching pixels between the two images. The resultant radargrammetric DEM is compared with a Lidar-based DEM, showing a good agreement in the areas less affected by lava flow.
Prediction of the Estimated Time of Arrival of container ships on short-sea shipping: A pragmatical analysis

Fighting against climate change and global warming is one of the biggest challenges faced by the Maritime Industry nowadays to make the supply chain greener and environmentally sustainable. Cutting greenhouse gases (GHG) emissions and decarbonizing the international shipping has been a paramount activity for the International Maritime Organization (IMO) since the first set of international mandatory measures to improve ships' energy efficiency and reduce CO2 emissions per transport work, as part of the International Convention for the Prevention of Pollution from Ships (MARPOL) released in 2011. Besides that, changes in consumption habits around the globe (i.e., digitalization and growth of e-commerce) plus disruptive events like the COVID-19 or the blocking of the Suez Canal, to name only a few, have also highlighted the need for building more resilient maritime transport networks. In this work, a pragmatical analysis of the principal machine learning algorithms has been carried out to provide a qualitative prediction of the Estimate Time of Arrival (ETA) of container vessels applied to short-sea shipping where the distance between ports is reduced. By exploiting both, the Automatic Identification System (AIS) and meteorological data gathered over a desired area of interest, the developed approach delivers a model capable of predicting the ETA of ships where the reaction time of the stakeholders involved in the management of the Port Call is very reduced (i.e., less than two hours of sailing between ports) and therefore, tolerance for error is low. Very positive results were obtained for the training dataset collected under real conditions for more than a year. The best results were obtained by the RF model with a Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 11.31 and 19.56 minutes respectively.
Accurate Real-time Phase Estimation for Normal and Asymmetric Gait

An accurate real-time gait phase estimator for normal and asymmetric gait is developed by training and testing a time-delay neural network on gait data collected from six participants during treadmill walking. The trained model can generate smooth and highly accurate predictions of the gait phase with a root mean square error of less than 3.48% and 4.31% in normal and asymmetric gait, respectively. The coefficient of determination between the estimated and target phase is greater than 99% for all subjects with both normal and asymmetric gait. The proposed gait estimator also exhibits precise heel-strike event detection with an RMSE of 2.56% and 3.70% in normal and asymmetric gait, respectively. A spatial impedance controller is then employed and tested based on the estimated gait phase of a new participant. Obtained results confirm that the controller provided assistance in coordination with the user’s motion both in normal and asymmetric gait conditions. The estimated gait phase is compared in the case of walking without and with the exoskeleton in passive and active modes, indicating persistent accuracy of the gait phase estimator regardless of the walking conditions.
Evaluation of the implementation of Armeo®Spring in a specialized neurorehabilitation center

Rehabilitation of the upper extremity (UE) is an essential part of the process following an acquired brain injury, where robot technologies have the potential of making the rehabilitation more intensive and effective. However, the implementation of robot technologies in a clinical setting can be complicated and not always successful. The aim of this study was to evaluate the implementation of the robot technology Armeo®Spring (Hocoma, Volketswil, Switzerland) at a specialized neurorehabilitation center, and to propose a list of actions for further implementation of the technology.The Study, Act and Plan phases of the Plan-Do-Study-Act (PDSA) model for structuring technology implementations was applied as the methodological framework in this study. In the Study-phase, nine semi-structured interviews with therapists, using Armeo®Spring, were conducted to evaluate the current implementation. In the Act-phase, a workshop was held with 13 participants to discuss the findings of the Study-phase and to find possible solutions to the identified problems. The results were incorporated into a list of actions (Plan-phase) for further implementation of Armeo®Spring.Facilitating and inhibiting factors for the implementation of Armeo®Spring were identified. Facilitating factors were the practical “hands-on” approach during training, support from the management, support and sparring with colleagues as well as a positive work culture. The inhibiting factors were related to the retention of the therapists’ acquired competencies, the identification of the type of patient that can benefit from this form of training, challenges due to the technical use of Armeo®Spring, and prioritization of the rehabilitation needs of the patients.Several solutions were proposed in the Act-phase, which subsequently resulted in eight concrete actions to facilitate the further implementation of Armeo®Spring. It is expected that these actions will contribute to the further implementation of Armeo®Spring at the neurorehabilitation center. As a first step, an Armeo®Spring group with therapists from all relevant wards was established.
Identifying Lyman α emitters powered by AGNs

Lyman Alpha Emitters (LAEs) are usually thought to be powered by star formation. It has been recently reported that a fraction of LAEs at redshift z~3-4 hosts an Active Galactic Nuclei (AGN). If an AGN is present it could be obscured and undetectable in X-rays, but yet dominate the Lya luminosity. We examine the properties of these AGN-powered LAEs at high redshift (z>=6) using radiative transfer cosmological simulations and obtain a reliable criterion to identify them from their observed \Lya line and surface brightness. We find that these sources should have: (a) negative line weighted skewness, S_w = 1.5". This parameter space cannot be populated by starburst LAEs. Thus, LAEs satisfying this criterion would be strong candidates for the presence of a hidden AGN powering their luminosity.
Automation of Modular Multilevel Converter Topology Evaluation including Thermal Monitoring

Modular Multilevel converter systems are subject to current research in the field of power electronics. Although several different topologies are developed and in use, topology evaluation is usually performed purely simulatively. In this paper, the basics and setup of an automated test system for multilevel converter hardware are presented. Its focus is on flexibility, accuracy and data acquisition in the context of rapidly changing research initiatives.An exemplary test setup is built, demonstrating the usage of such a system to find the optimal gate voltage in a battery powered multilevel application depending on the load current provided by the system. In addition to efficiency measurements, monitoring via thermal imaging is integrated into the system. The results show a sweet spot in the evaluated multilevel converter’s efficiency at a load current of 15A. Furthermore, the optimal gate voltage is strongly dependent on the load current since the auxiliary power consumption impacts the overall efficiency.
Regularity, closedness and spectral dimensions of the Dirichlet forms on P.C.F. self-similar sets

Today, there has been many works about the self-similar sets. One well known framework for describing them is due to Hutchinson ([21): Let F1(1 i <N ) be contractions on a complete metric space X . Then there exists a unique compact set K which satisfies K =U 1:1--1F(K ). K is called a self-similar set with respect to {F1}7=1. K is said to be finitely ramified if #( U *,(F,(K)(1 F.,(K)))< c o . The Sierpinski gasket (Example 2.1) and nested fractals (e.g. Example 2.3) introduced by LindstrOm ([8]) are the examples of finitely ramified fractals. In [3], Kigami defined a n d studied Laplace operators on the finitely ramified fractals systematically. There he defined post critically finite (P.C.F. for short) self-similar sets, which are almost the same concept as finitely ramified fractals. H e introduced the notion of the "harmonic structure" and the "regularity of the harmonic structure" on them to define qua-
Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Chlorophyll-A Retrieval in an Eutrophic Reservoir

The high productivity of biomass registered in eutrophic water bodies, leads to qualitative and quantitative changes in the phytoplankton community, resulting in massive algae blooms. This research evaluates different atmospheric cor-rection methods, Sen2cor and Acolite, over Sentinel-2 (S2) images and its effects for monitoring algae blooms in an eu-trophic reservoir. Specifically, we analyze two dates com-paring the corrected reflectance to field reflectance data and laboratory results to study the turbid and productive water of San Roque reservoir, Argentina.
Cross-Frequency Detail Compensation Network for Pansharpening

Pansharpening is a fusion technique aiming at improving the spatial resolution of multispectral images while preserving spectral information. Previous attempts to adopt CNNs have led to significant progress in pansharpening, but always with a cumbersome network structure, and there exists redundancy in both spatial and channel of feature maps learned by CNNs. Considering the distinct properties of components with different frequencies in the feature map, we propose a cross-frequency detail compensation network (CFDCNet) by processing low, medium, and high frequency separately. Specifically, a cross-frequency convolution block is designed to produce a representation that captures the different frequency classes while achieving the more efficient detail extraction. Overall pipeline is progressive, and the learned features are fused in an interactive compensation manner to obtain the final output. Experimental results demonstrate the superiority of CFDCNet over state-of-the-art pansharpening methods in terms of visual quality and quantitative metrics.
Design of a compliant, stabilizing wrist mechanism for a pediatric hand exoskeleton

Children affected by hand impairment due to cerebral palsy or stroke experience serious difficulties when performing activities of daily life (ADL), which reduces their quality of life and development. Wearable robots such as hand exoskeletons have been proposed to support people with hand impairment in therapy as well as daily tasks. While numerous actuated wearable robots have been developed, few designs support both fingers and wrist function, despite being mutually relevant for reach-to-grasp tasks. A recent feasibility study investigating the use of PEXO, a lightweight and fully wearable pediatric hand exoskeleton, showed that a wrist fixed in a slightly extended position may limit the user’s ability to reach and grasp during ADL and restrict the user group. These insights and further interactions with clinicians inspired a novel design of PEXO that features an additional degree of freedom in the wrist. In this paper, we present a compliant wrist mechanism extending the existing leaf spring finger mechanism of the device. The novel design provides both wrist motion capability of 60° in flexion and extension and wrist stabilization at the same time while actively supporting finger motion. Preliminary results suggest that the adjustability in the wrist enables a larger variety of grasping gestures. The implemented wrist support has the potential to allow for a more versatile use of PEXO and increase the potential target user group.
Fe-LinkNet: Enhanced D-LinkNet with Attention and Dense Connection for Road Extraction in High-Resolution Remote Sensing Images

Extracting roads from high-resolution remote sensing images automatically is more efficient than field acquisition and manual annotation by experts. However, most road extraction methods based on deep-learning have problems of poor connectivity, due to occlusion of buildings and trees or confusion of backgrounds that have similar texture. In this paper, we proposed a novel feature-enhanced D-LinkNet (FE-LinkNet) to deal with the problem that road information is vulnerable to loss. Firstly, we introduced the idea of dense connection in the down-sampling stage to provide enhanced information for subsequent modules. Then we redesigned the D-Block to DP-Block in another cascade way to extract densely multi-scale contexts for road extraction. Finally, we adopted the self-attention mechanism in the up-sampling stage to learn long-distance pixel dependence to improve the connectivity of roads. Experimental results on CHN6-CUG Road Dataset prove that our FE-LinkNet performs better in accuracy and connectivity than D-LinkNet.