Miracle-dz/UniFAR-FTUs huggingface dataset:
SWALKIT: A generic augmented walker kit to provide haptic feedback navigation assistance to people with both visual and motor impairments

Feedback solutions are a privileged form of assistance in order to increase mobility and independence of people with both motor and visual impairments. Indeed, it empowers the ability of the person to make decisions and take actions based on the provided information. Moreover, it maintains the use of the walker, and thus the residual locomotor skills. We here propose the SWALKIT, an open-source, cost-efficient, lightweight, easy to install and generic augmented walker kit. The SWALKIT can be equipped on any walker without requiring modifications of the structure or advanced technical knowledge. Vibrotactile feedback is provided through the handles to indicate the proximity of obstacles on the way of the user. The open source project is reproducible thanks to the online repository https://github.com/IH2A/Swalkit. In this paper, we present the design of the SWALKIT based on a user-centered approach following target users and therapists guidelines. Then, we present a technical validation study performed with 14 able-bodied blindfolded participants on a cardboard circuit. They were asked to use a standard walker with and without activation of the SWALKIT system. Results of this pilot study showed the efficiency and reliability of the proposed solution. Finally, we provide feedback after 2 months of daily life use by a target user.
Proof-of-Concept: A Hands-Free Interface for Robot-Assisted Self-Feeding

Eating and drinking is an essential part of every-day life. And yet, there are many people in the world today who rely on others to feed them. In this work, we present a prototype robot-assisted self-feeding system for individuals with movement disorders. The system is capable of perceiving, localizing, grasping, and delivering non-compliant food items to an individual. We trained an object recognition network to detect specific food items, and we compute the grasp pose for each item. Human input is obtained through an interface consisting of an eye-tracker and a display screen. The human selects options on the monitor with their eye and head movements and triggers responses with mouth movements. We performed a pilot study with four able-bodied participants and one participant with a spinal cord injury (SCI) to evaluate the performance of our prototype system. Participants selected food items with their eye movements, which were then delivered by the robot. We observed an average overall feeding success rate of 89.1% and an average overall task time of $31.4 \pm 2.4$ seconds per food item. The SCI participant gave scores of 90.0 and 8.3 on the System Usability Scale and NASA Task Load Index, respectively. We also conducted a custom, post-study interview to gather participant feedback to drive future design decisions. The quantitative results and qualitative user feedback demonstrate the feasibility of robot-assisted self-feeding and justify continued research into mealtime-related assistive devices.
The Role of Hydrogen Electrolysers in the Frequency Containment Reserve: A Case Study in the Iberian Peninsula up to 2040

This paper investigates the contribution of hydrogen electrolysers (HEs) as highly controllable loads in the context of the Frequency Containment Reserve (FCR), in future operation scenarios on the Iberian Peninsula (IP). The research question is whether HEs can mitigate system insecurity regarding frequency or Rate of Change of Frequency (RoCoF) in critical periods of high renewable energy penetration (i.e. low system inertia), due to the fact that these periods will coincide with high volume of green hydrogen production. The proposed simulation platform for analysis consists of a simplified dynamic model developed in MATLAB/Simulink. The results obtained illustrate how HEs can outperform conventional generators on the provision of FCR. It is seen that the reference incident of 1GW loss in the IP in a 2040 low inertia scenario does not lead to insecure values of either frequency or Rate of Change of Frequency (RoCoF). On the other hand, an instantaneous loss of inverter-based resources (IBR) generation following a short-circuit may result in RoCoF violating security thresholds. The obtained results suggest that the HEs expected to be installed in the IP in 2040 may contribute to reduce RoCoF in this case, although this mitigation may be insufficient. The existing FCR mechanism does not fully exploit the fast-ramping capability of HEs; reducing measurement acquisiton delay would not improve results.
A phase-compensated harmonic suppression method for virtual synchronous generator system in distorted grid

Power quality issues are challenging to deal with by conventional harmonic suppression methods in a virtual synchronous generator-based system connected to the distorted grid. To address this issue, a phase-compensated harmonic suppression method is proposed to effectively suppress the grid harmonic current for the virtual synchronous generator in the distorted grid. The phase-compensated harmonic suppression method is developed according to the Nyquist diagram to improve the harmonic control bandwidth and the system stability. With this method, the grid harmonic component is effectively suppressed, the Nyquist sensitivity distance of the system is maximized, the total harmonic distortion (THD) of the grid current is reduced from 8.53% to 2.97%, and the harmonic suppression response time is reduced from 10 cycles to 2 cycles, showing a better harmonic suppression performance.
THE $q$-LAGRANGE POLYNOMIALS IN SEVERAL VARIABLES

Recently, Chan, Chyan and Srivastava [1] introduced and systematically investigated the Lagrange polynomials in several variables. The main object of this paper is to study a basic (or $q-$) analogue of the Chan-Chyan-Srivastava multivariable polynomials, which we define here. Several results involving generating functions are presented in this paper for the multivariable $q-$Lagrange polynomials.
A multi-objective swarm approach with pedestrians spotlight in traffic urban optimisation

The way that people moves is changing. Froma sustainability point of view is necessary to put the focus on pedestrians. To reduce pollution and congestion in urban areas, it is necessary moves people with not necessary moves vehicles. This work introduces a particle swarm multi-objective approach that optimizes vehicles and pedestrians traffic urban flow, considering the traffic lights timing. Traffic lights and their scheduling significantly impact vehicles and pedestrian flow in metropolitan cities. From the point of view of the scenario, a large-scale congested urban area is used to test the proposed methodology. The strategy is compared with five state-of-the-art algorithms with satisfactory results.
Combine Histogram Matching and Domain Adaptation to Cope with Temporal Transfer Learning for the Semantic Segmentation of VHR Images

Very High spatial Resolution (VHR) imagery is a standard in-put to derive fine grain land cover maps (LCM) to support pol-icy makers in many application domains like urban planning and biodiversity. The generation of LCM mainly relies on available ground truth (GT) data to calibrate machine learning methods. Unfortunately, this data is not always accessi-ble. In this scenario, the possibility to transfer a model learnt on a certain period (source domain), where GT data is avail-able, to another period (target domain) without the necessity to collect new GT data would be a cost-effective strategy. To cope with this issue, in this paper, we present a re-search study on temporal transfer learning for the semantic segmentation of VHR imagery. To this end, we propose a case study in which a lightweight procedure such as histogram matching and a recent domain adaptation technique are com-bined together to cope with possible distribution shifts affecting VHR imagery acquired on the same area but at different period of time.
Very Short to Medium-term Demand and Generation Forecast: A Study Case in Austria

Modern electricity networks are increasing in complexity due to the integration of variable renewable energy sources at both transmission and distribution levels. As part of the daily operation and scheduling of such systems, the very short to medium-term forecasts are a crucial element, providing the expected demand and generation output over a requested period. Artificial neural networks techniques promise to deliver adaptability which overcome the source's variability if the inputs are properly selected. This paper proposes demand and generation forecasting models for distribution systems, each employing a different artificial neural networks architecture. The models are demonstrated on real data from Energie Güssing distribution system. The adaptability of the models to changes in demand during the Covid-19 lockdown is investigated.
Pansharpening with Spatial Hyper-Laplacian and Spectral Sparse Constraints

This paper proposed a new pansharpening model with spatial hyper- Laplacian and spectral sparse constraints (PSHSS), which finally generated the high resolution multispectral (HR MS) images. Based on the panchromatic (PAN) and low resolution multispectral (LR MS) images, an enhanced PAN image was first constructed. To-gether with the local spectral consistency constraint, then the pro-posed PSHSS model particularly exploited the spatial gradient hyper-Laplacian sparse constraint between enhanced PAN and HR MS for spatial prior modeling, and the spectral gradient sparse constraint of HR MS for spectral prior modeling. Then, the proposed PSHSS model was optimized under the alternating direction method of multipliers (ADMM) framework. Finally, the fusion experiment demonstrated the superiority of PSHSS method.
Estimating PV Curtailed Power as a Voltage Support Service using Data-Driven Approaches

To guarantee a successful deployment of a droop-based control strategy to mitigate overvoltage problems caused by solar photovoltaic (PV) generation, Distribution System Operators (DSOs) will need to estimate the amount of active power curtailed by the PV inverters for billing purposes. This paper provides a structural elaboration on the development of data-driven approaches in Python to estimate the PV curtailed power as a provision of voltage support services by residential users using droop-based voltage control strategies. The use of the total input data, available for a DSO, would be impractical for an all-regression approach for the estimation of the PV curtailed power. Since in the majority of the data no active power is curtailed, the data-driven models would in this case partly be trained and fitted for situations where there is no active power curtailment. The regression models for the curtailed power prediction are therefore preceded by a classification model. The developed combined classification-regression model was able to estimate the PV curtailed power with an error of less than 4%, for test data from the network on which the model was trained.
Comparative analysis of inverse kinematics methodologies to improve the controllability of rehabilitative robotic devices

The solution of the inverse kinematics problem in multi-degrees of freedom robots has been tackled, through the last three decades, by several different approaches including analytical, geometrical, differential and numerical methods. All these techniques present their own advantages and drawbacks. However, a guideline on which approach is better to follow, depending on the kind of task to perform and the type of robotic device used, is still missing. In this work, a quantitative comparative analysis of three different inverse kinematics methodologies for the control of rehabilitative robotic devices is proposed, with aim of devising best practices and guidelines for the selection of the most suitable approach. The analyzed methodologies are implemented and numerically tested on two actual devices, specifically an upper-limb exoskeleton and an upper-limb prosthetic arm.
Relating Underlying Performance Objectives of Overground Walking to Observable Walking Mechanics using Predictive Musculoskeletal Simulations

There exists motor redundancy during human gait that allows individuals to perform the same task in different observable ways (i.e., with varied styles). However, how differences in observable walking mechanics depend on unique and underlying biomechanical objectives is unclear. As an example, these objectives could include metabolic energy consumption, sum of muscle activations, limb mechanical loading, balance and combinations thereof. In this study, we develop predictive neuromuscular simulations to investigate the relationships between these biomechanical objectives and observable mechanics during level walking. We simulated 3D normal walking of five healthy subjects, while optimizing each of the aforementioned objectives-resulting in 25 forward dynamics simulations for analysis. We compared the resulting joint kinematics and moments of different simulations. One of main findings suggests that decreased hip abduction angle is tightly related to when the regulation of dynamic balance (computed as whole-body angular momentum) is included in a movement cost function. We also find that increased joint moments are related to including metabolic cost (i.e., objectives associated with improving the energy economy of movement). Further, the timing of joint kinematics is adjusted for different performance objectives. These findings could guide the development of rehabilitation training and assistive devices that target specific individuals, tasks, and specific styles of movement.
Development of Face Recognition Algorithm System for Verification of Qualification of Functional Personnel in Construction Sites

To stabilize the power system, asset management of the distribution system is essential. There is a legal basis for restricting authorized personnel to perform their assigned duties at the power distribution construction site, but sometimes illegal activities are carried out by unqualified people. This disguised subcontracting is an important issue that should be prevented not only for the lives of workers but also the aspect of managing the company's assets. In this paper, we propose a qualification verification system using deep learning-based face recognition technology. Face recognition is a technique that distinguishes individuals by extracting shapes and characteristics of their face with an automated device. This paper addresses following three major contributions: First, we introduce how to collect and learn Asian face datasets to improve Asian face recognition performance. Second, we developed and provided algorithms as REST APIs so that they can be used for various purposes and regions. Third, we provide guidelines for improving recognition performance in a noisy environment. Our face recognition system showed very good recognition performance.
Probing axions with the neutrino signal from the next Galactic supernova

We study the impact of axion emission in simulations of massive star explosions, as an additional source of energy loss complementary to the standard neutrino emission. The inclusion of this channel shortens the cooling time of the nascent protoneutron star and hence the duration of the neutrino signal. We treat the axion-matter coupling strength as a free parameter to study its impact on the protoneutron star evolution as well as on the neutrino signal. We furthermore analyze the observability of the enhanced cooling in current and next-generation underground neutrino detectors, showing that values of the axion mass ${m}_{a}\ensuremath{\gtrsim}8\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}\text{ }\text{ }\mathrm{eV}$ can be probed. Therefore a galactic supernova neutrino observation would provide a valuable possibility to probe axion masses in a range within reach of the planned helioscope experiment, the International Axion Observatory.
Optical and Acoustic Fusion in Borehole Imaging Logging

Optical Televiewer (OTV) and Acoustic Televiewer (ATV) are cornerstones of establishing borehole imaging logging systems. OTV provides the intuitive analysis of lithology and textures, while ATV has advantages in presenting fractures at deep formation. Combining both imaging methods with integrated interpretation is conducive for geophysicists to characterize structures, and therefore the synthesis based on image fusion is studied in this paper. The self-supervised autoencoder with convolutional neural networks (CNN) and transformer modules is established for feature extraction and reconstruction, where the fusion strategy is implemented in the independent stage. The introduction of the self-attention mechanism makes up for the lack of long-range relationships in CNN. The nested networks capture detailed information in convolutional layers, and the semantic features are conveyed with fine-grained decomposition simultaneously. Besides, the introduction of the self-attention mechanism makes up long-range dependencies for intrinsic information. Quali-tative and quantitative experiments prove that the proposed scheme outperforms competitive methods over textured and structural fidelity.
Early-Season Industry-Wide Rice Maps Using Sentinel-2 Time Series

Regional maps of rice fields provided early in each growing season facilitate production estimates, planning around harvest logistics, marketing and targeted agronomic recommendations. This work develops maps of all irrigated rice fields in New South Wales, Australia. Classification models were trained on reference maps from the 2019 and 2020 harvest seasons. Model predictions were tested against a reference rice map from the 2021 harvest season, covering 60,000 km2. The random forest algorithm was used, with features from aggregated time-series of Sentinel-2 imagery. A sequence of maps were generated at intervals of 15 days, from early to late in the growing season, with accuracy assessed at each time. The maps achieved 95% overall accuracy against point samples at 16 January 2021 ($\approx 80$ days after sowing). Pixel-based F1-scores against the reference map were above 80% for the 1, 16 and 31 January classified maps.
Building a Network of Overmature Forests Using Airborne Laser Scanning and Landscape Graphs

Building a network of interconnected overmature forests is crucial to provide habitat and promote the dispersal of a multitude of species that depend on several forest structural maturity attributes such as very large living trees and deadwood. The use of Airborne Laser Scanning (ALS) to locate patches of overmature forests is still in progress. However, few studies have paid attention to the spatial distribution of overmature forests and its ecological consequences on landscape connectivity for species depending on forest structural overmaturity. In this study, we investigated the potential of ALS-derived maturity and connectivity indices to identify overmature forests that most contribute to connectivity and that could be integrated into a network of forest protected areas.
Using Deep Learning for Detection and Classification of Insects on Traps

Insect pests are the main cause of loss of productivity and quality in crops worldwide. Insect monitoring becomes necessary for the early detection of pests and thus avoiding the excessive use of pesticides. Automatic detection of insects attracted by traps is a form of monitoring. Modern data-driven methods present great results for object detection when representative datasets are available, but public datasets for insect detection are few and small. Pest24 public dataset is extensive, but noisy resulting in a poor detection rate. In this work, we aim to improve insect detection in the Pest24 dataset. We propose the creation of three sub-datasets selecting the highest represented classes, the highest colour discrepancy, and the one with the highest relative scale, respectively. Several Faster R-CNN and YOLOv5 architectures are explored, and the best results are achieved with the YOLOv5 with an mAP of 95.5%.
Low-Profile and Shared Aperture Dual-Polarized Omnidirectional Antenna by Reusing Structure of Annular Quasi-Dipole Array

A low-profile and shared aperture dual-polarized (DP) omnidirectional antenna by reusing the structure of an annular quasi-dipole array is presented. The proposed antenna consists of a center-fed circular patch antenna, an annular quasi-dipole array, a wideband one-to-four power divider integrated with the center-fed circular patch antenna, and two annular strips. The center-fed circular patch antenna is utilized as the driven element for the vertical polarization (VP) radiation. The annular quasi-dipole array fed by the one-to-four power divider produces horizontal polarization (HP) radiation. Simultaneously, it also acts as the VP director to improve VP gain and bandwidth. Besides, the two annular strips enhance the HP omni-directivity and gain. In this way, the VP and HP share the same aperture to improve the profile utilization rate effectively. Thanks to the symmetry of the basic antenna structure, stable DP donut patterns are obtained within the entire operating bandwidth. For demonstration, a 4.1-5.0 GHz prototype is designed, fabricated, and measured. Under a low profile of 0. 13λ0, the prototype realizes a -10-dB overlapped bandwidth of 20%, high port isolation of above 35 dB, and stable DP donut patterns with horizontal gain larger than 1.8 dBi.
Improving SAR and Optical Image Fusion for Lulc Classification with Domain Knowledge

Fusing SAR and multi-spectral images to generate a precise land cover map in a weakly supervised setting is a challenging yet essential problem. The inaccurate, noisy, and inexact ground truth labels pose difficulty training any machine learning models. In this paper, we make a fundamental and pivotal contribution towards improving the ground truth label quality using domain knowledge. We present a simple yet effective mechanism to refine the low-resolution noisy ground truth labels. The proposed approach is trained and tested on a publicly available DFC2020 dataset. Through experiments, we show the effectiveness of our method by training a deep learning model on the refined labels that outperform even the models trained with clean ground truth.
Winter Wheat Yield Estimation by a New Way Coupling Markov Chain Monte Carlo and Ensemble Kalman Filtering

In this work, we compare the impact of two ways of representing the crop growth model uncertainty (the crop growth model was calibrated by Markov Chain Monte Carlo (MCMC)) on the subsequent assimilation of the Sentinel-2 LAI through Ensemble Kalman Filter (EnKF): generating N sets of crop parameters based on the Optimal-posterior parameter value (reference method, we name it OMCMC-EnKF) and Randomly selecting N sets of crop parameters from the most frequent parameters (our proposed method, RMCMC-EnKF). The results show that our proposed method is better than the reference model, when compared with the field measured winter wheat LAI and yield, its performance in the R2, RMSE, and uncertainty estimation is better. Furthermore, our proposed method is more robust to feedback from remote sensing data and more resistant to lower quality observations encountered during assimilation.