Miracle-dz/UniFAR-FTUs huggingface dataset:
Polarization-Selective Bifunctional Metasurface for High-Efficiency Millimeter-wave Folded Transmitarray Antenna with Circular Polarization

A novel high-efficiency polarization-selective bifunctional metasurface (MS) is proposed which can realize cross-polarized focusing transmission for left-handed circular polarization (LHCP) incidence while co-polarized specular reflection for right-handed circular polarization (RHCP) incidence. As an example of its practical application, the designed MS is applied to form a RHCP folded transmitarray antenna (FTA) whose feed source is integrated with the bottom metallic surface. To prove the concept, a prototype of millimeter-wave FTA based on the proposed MS are numerically simulated, physically implemented, and experimentally measured. The measured results coincide well with the simulated ones, showing a peak gain of 23.6 dBi at 27.7 GHz with the peak aperture efficiency of 43.1%, a 3 dB gain bandwidth of 15.8% (25 -29.3 GHz), and a 3 dB axial-ratio (AR) bandwidth over 29.6%. The proposed high-performance antenna has the advantages of low profile, high efficiency, integration design, broad bandwidth, which may have promising applications in the fifth-generation millimeter-wave communication system. The proposed MS and its application to high-gain antenna may promote the uses of MS to real-world applications in satellite communication, radar detection, and wireless communication.
Robust Linear Unmixing for Hyperspectral Remote Sensing Imagery Based on Enhanced Constraint of Classification

Hyperspectral remote sensing image is rich in spectral information. Due to the limitations of sensors and the complexity of the scene, a large number of mixed pixels exist in the scene. Therefore, it is very necessary to develop the unmixing technology. The linear unmixing model and its derived algorithms have made some progress. The existing unmixing methods treat all pixels in the scene as mixed pixels for operation, but the real scene is often a complex scene with pure pixels and mixed pixels. Considering the unmixing of complex scenes, the robust linear unmixing model based on enhanced constraint of classification (ECRLU) is proposed in this paper. The model combines unmixing and classification. After extracting endmembers, the number of endmembers is expanded by using local similarity and spatial similarity to obtain hard classification items, so as to provide sparsity constraints for the model. In this paper, synthetic data set and real data set are used to verify the effectiveness of the model.
Multistatic Synthetic Aperture Radar Baseline Design for 3-D Imaging

Multistatic synthetic aperture radar (SAR) can realize 3D imaging of observation scenes by single navigation using distributed aperture. Meanwhile, due to the flexible baseline configuration of unmanned aerial vehicle (UAV), and has broad application prospects in remote sensing, surveying and mapping fields. However, the 3D reconstruction performance of multistatic SAR is closely related to its multistatic baseline. In this paper, a multistatic baseline design method is proposed to achieve optimal 3D reconstruction performance. Firstly, the quantitative relationship model between multistatic baseline and 3D imaging measurement matrix is established, and the cross-correlation value of measurement matrix is introduced as the evaluation index of reconstruction performance. Then, the multistatic baseline design problem is modeled as an optimization problem with an optimal crossrelation number. Finally, the differential evolution algorithm is used to obtain the multistatic baseline of the optimal design. Simulation results show that compared with the unoptimized multistatic baseline, the optimized multistatic baseline can obtain better reconstruction performance when the typical sparse recovery method is used for 3D imaging.
Robust Distribution-Shift Aware Sar-Optical data Fusion for Multi-Label Scene Classification

Out-of-distribution (OOD) detection is an emerging research topic in remote sensing where existing works focus on single sensor analysis. However, many remote sensing works use multi-modal data to benefit from different characteristics of the sensors. Data that is in-domain for one sensor may be OOD for another sensor. In this work, we address such a scenario focusing on Synthetic Aperture Radar (SAR) and optical data fusion for multi-label scene classification. Besides data distribution shifts caused by unknown classes and snow, we also consider cases where only one modality is affected. Optical images acquired with significant cloud coverage are considered as OOD, while their corresponding SAR images can be in-distribution. We propose a weighted feature propagation strategy based on the in-distribution probabilities of the single modalities. We show, that we not only improve the prediction performance on the cloudy samples but also receive a higher predictive uncertainty when both modalities are OOD.
Spectral Super-Resolution for Hyperspectral Image Reconstruction Using Dictionary and Machine Learning

Hyperspectral sensors measure the radiance spectrum across hundreds of wavelength channels with a resolution typically on the order of 10 nm represented by the full-width-half-maximum (FWHM). The spectra are used in the study of surface materials in the biological, geological and oceanographic sciences to name a few, utilizing quantitative spectroscopic techniques. The instruments developed to measure such data are expensive due to the increased number of bands, and create large datasets that can be difficult to downlink for a given instance. Repeat cycle of space-borne hyperspectral observations of the earth surface is also less than those of multi-spectral sensors. It becomes incumbent to develop mechanisms that could be cost-effective and give desired results. With this aim, spectral Super-Resolution (SR) is attempted on the Airborne Visible and Infra-Red Imaging Spectrometer (AVIRIS) data to reconstruct the hyperspectral band radiance from equally-spaced narrow multi-spectral bands using dictionary learning, followed by denoising using machine learning. The hyperspectral band radiance are first estimated from 30 selected input multi-spectral bands using dictionary trained through K-Singular Value Decomposition (K-SVD), followed by denoising using Random Forest Regression. An overall Signal-to-Noise Ratio (SNR) of 31.58dB is observed from reconstruction after denoising using Random Forest.
Comparaison morpho-biométrique de chèvres au Nord Cameroun et au Tchad

Deux populations de chevres adultes du Nord Cameroun et du Tchad ont ete comparees en utilisant une mesure biometrique simple (la hauteur au garrot : HG) et deux mesures combinees [l'indice de gracilite (IGS) et l'indice auriculaire thorax (IAT). L'analyse des hauteurs au garrot a permis de distinguer 3 sous-populations, une au Nord Cameroun (de moyenne m1 = 50,20 +/- 0,30 cm) et deux au Tchad ( de moyenne m2=63,00 +/- 060 cm et m3=70,00 /- 0,40 cm. L'IGS etait resp. de 1,14 +/- 0,01 pour la population camerounaise et de 1,48 +/- 0,02 et 1,56 +/- 0,02 pour les deux sous-populations de plus grande taille du Tchad. L'indice auriculaire thorax (IAT) etait quasiment identique pour les deux populations tchadiennes (IAT=0,75 +/- 0,01) alors qu'il etait de 0,52 +/- 0,01 au Nord Cameroun. On peut penser que la sous-population intermediaire observee au Tchad provient du croisement des 2 populations extremes. L'augmentation lineaire de ces trois parametres avec les degres de latitude nord serait due a la pression de selection naturelle exercee par des conditions de secheresse et de chaleur de plus en plus severes lorsque l'on monte vers le nord
Estudi de l'herbivorisme del Senecio mitjançant models zero-inflats

Els estudis ecologics que involucren recomptes d'abundancia o de presencia-absencia sovint produeixen dades amb un exces de zeros. Una forma de tractar amb l'exces de zeros es considerar els models zeros inflats. Aquests models assumeixen que l'exces de zeros prove d'un proces amb dues parts que es poden modelar de forma conjunta emprant una mixtura de distribucions. Per una banda, es modela el proces de recompte mitjancant una distribucio de Poisson o Binomial Negativa on queden recollits una part dels zeros observats. Per altra banda, es modelen els zeros addicionals a traves d'un model logit. En aquest treball aquesta metodologia es compara amb un model de regressio amb distribucio Zipfian. Aquesta distribucio de probabilitat exhibeix una elevada probabilitat al primer valor, te una cua pesada i mostra un comportament lineal en l'escala log-log.
Adaptive Hybrid FES-Force Controller for Arm Exosuit

Patients suffering from neuromuscular diseases experience motor disabilities which hinder their independence during activities of daily living (ADLs). For such impaired subjects, robotic devices and Functional Electrical Stimulation (FES) are technologies commonly used to rehabilitate lost functions. Nevertheless, both systems present some limitations, and merging FES and robots in Hybrid Robotic Rehabilitation Systems allows to overcome these boundaries. Here we propose for the first time a hybrid cooperative controller involving FES and a soft wearable upper arm exosuit to rehabilitate elbow movements. We tested the designed hybrid controller on six healthy participants. The results showed how the proposed hybrid controller allowed the wearers to perform flexion movements with no significant decrease in accuracy and precision with respect to the exosuit alone, while significantly decreasing the fatigue level by about 63% and delaying its onset with respect to the FES action alone.
Multipass InSAR with Multiple Bands: Application to Landslides Mapping and Monitoring

Among the several applications of multipass InSAR, monitoring of landslides represents one of the most challenging case because of the topographic characteristics, the heterogeneous displacement rates and the frequent presence of vegetation. In this study, we consider the use of multi-frequency InSAR data for the application to case studies located in the North and South Italy.
Snow-Mantle Remote Sensing from Spaceborne Sar Interferometry Using a Model-Based Synergetic Retrieval Approach in Central Apennines

Using Sentinel-1 satellite data, differential interferometric synthetic-aperture-radar (DInSAR) retrieval techniques at C band are presented to estimate snowpack depth, combined with SAR backscattered data for wet snow discrimination and a physically based snowpack model. Optical satellite data from satellite multispectral imagers are used for snow extent mapping. The processing chain is tested in central Apennines (Italy), using several validation sites where in-situ snow measurements are daily available during the winter 2018–19. The potential of using analytical and statistical inversion algorithms, trained by forward SAR and snowpack model simulations of the same area, is discussed. Results, in terms of error bias, standard deviation and correlation between estimated and in situ snow data, are illustrated pointing out critical issues due to coherence loss.
A Backbone-Tracking Passive Exoskeleton to Reduce the Stress on the Low-Back: Proof of Concept Study*

Exoskeletons for the low-back have great potential as tools to both prevent low-back pain for healthy subjects and limit its impact for chronic patients. Here, we show a proof-of-concept evaluation of our low-back exoskeleton. Its peculiar feature is the backbone-tracking kinematic structure that allows tracking the motion of the human spine while bending the trunk. This mechanism is implemented with a rigid-yet-elongating structure that does not hinder nor constrain the motion of the wearer while providing assistance. In this work, we show the first prototype we manufactured. It is equipped with a traction spring to assist the wearer during trunk flexion/extension. Then, we report the results of a preliminary test with healthy subjects. We measured a reduction of the mean absolute value for some target muscles – including the erector spinae – when using the exoskeleton for payload manipulation tasks. This was achieved without affecting task performance, measured as task time and joints range of motion. We believe these preliminary results are encouraging, paving the way for a broader experimental campaign to evaluate our exoskeleton.
GNSS-based End-of-Train Device Performance Evaluation and Improvement by Means of Particle Filter Algorithm

The Train Integrity Monitoring System (TIMS) function is becoming more and more demanding for the next generation for train control (NGTC) in both China and European train control systems. Traditional train integrity detection provides the integrity status of the train by monitoring the status of brake pipe pressure without adding equipment a major the train, but not possible to provide redundancy to meet the SIL 4 (Safety Integrity Level 4) requirement of the train control system[1]. GNSS-based train localization provides essential location and velocity data for each wagon, the information transmitted to head-of-train (HOT) device, then the integrity can be determined. End-of-Train (EOT) device is normally mounted on the coupler of the last wagon, the observation condition for GNSS is not good, normally “half-sky”. In view of the above conditions, this paper constructs three different constraint scenarios: open sky, general constraint and severe constraint based on the laboratory GNSS simulator by setting the EOT user antenna directivity pattern, and evaluates the positioning accuracy in the constraint scenarios. On this basis, the state equation and observation equation of the system are established with a simulation example, and the positioning results are optimized by using the particle filter algorithm. The results show that the average location error in the unobstructed scenario is 0.12m; The average value of general constraint error is 3.93m, and the average value of error after pf processing is 3.02m; The mean value of severe constraint location error is 11.94m, and 8.83m after PF filtering. This filtering algorithm can improve the location accuracy to a certain extent.
Prediction of Single Trial Somatosensory Evoked Potentials From Mechanical Stimulation Intensity

Somatosensory evoked potentials (SSEPs) are important for both scientific research and the evaluation of treatment efficacy in neurorehabilitation. SSEPs measure the response in the sensorimotor cortex to a peripheral stimulation. Individual responses are often noisy, so SSEPs have typically required hundreds of stimulation-response trials to produce a single measurement. This work presents a method to estimate single trial SSEPs from tendon hammer accelerations. While predictions from the input side can not completely replace actual measurements of SSEPs, the results produced may help to provide clinicians insight where full scale SSEP measurement is not practical.
RESONANT INTERACTIONS BETWEEN PROTONS AND OBLIQUE ALFVÉN/ION-CYCLOTRON WAVES IN THE SOLAR CORONA AND SOLAR FLARES

We consider interactions between protons and Alfvén/ion-cyclotron (A/IC) waves in collisionless low-β plasmas in which the proton distribution function f is strongly modified by wave pitch-angle scattering. If the angle θ between the wave vector and background magnetic field is zero for all the waves, then strong scattering causes f to become approximately constant on surfaces of constant η, where η ≃ v2⊥ + 1.5 v2/3A|v∥|4/3. Here, v⊥ and v∥ are the velocity components perpendicular and parallel to the background magnetic field, and vA is the Alfvén speed. If f = f(η), then A/IC waves with θ = 0 are neither damped nor amplified by resonant interactions with protons. In this paper, we argue that if some mechanism generates high-frequency A/IC waves with a range of θ values, then wave–particle interactions initially cause the proton distribution function to become so anisotropic that the plasma becomes unstable to the growth of waves with θ = 0. The resulting amplification of θ = 0 waves leads to an angular distribution of A/IC waves that is sharply peaked around θ = 0 at the large wavenumbers at which A/IC waves resonate with protons. Scattering by this angular distribution of A/IC waves subsequently causes f to become approximately constant along surfaces of constant η, which in turn causes oblique A/IC waves to be damped by protons. We calculate the proton and electron contributions to the damping rate analytically, assuming Maxwellian electrons and f = f(η). Because the plasma does not relax to a state in which proton damping of oblique A/IC waves ceases, oblique A/IC waves can be significantly more effective at heating protons than A/IC waves with θ = 0.
Optimization-Based Multimodal Characterization of Waveguide Transitions

A multimodal optimization technique is presented to determine the full multi-modal S-parameters for a three-port waveguide. The technique consists of measuring at one port only with a sliding short at one other port, and a short or perfect load at a third port. The reflection coefficient at the measured port is used to mathematically formulate the full set of S-parameters. An optimization algorithm is implemented to calculate the full set of S-parameters. The technique is demonstrated on a coaxial-rectangular waveguide transition with three propagating TE modes at frequencies up to 20 GHz.
A Unified Gait Phase Estimation and Control of Exoskeleton using Virtual Energy Regulator (VER)

Virtual Energy Regulator (VER) is a time independent controller that can generate stable limit cycles in lower-limb exoskeleton devices. In this work, we apply VER to control a lower-limb exoskeleton for assistive walking. We design two different limit cycles for hip and knee joints to assist the user during overground walking with the Indego explorer lower-limb exoskeleton. We tested the designed VER on a single participant for overground walking at a self-selected speed. Interestingly, due to VER time-independent nature, it can properly coordinate with the user’s motions and produce mechanically stable overground walking in which the user can walk overground without a walker or crutches. The resultant gait is also more similar to a normal gait with improved range of motion compared to cases without controller; range of motion improved from $42.9 \pm 4.8{deg}$ and $44.9 \pm 4.9 {deg}$ to $46.6 \pm 1.3 {deg}$ and $63.0 \pm 6.8 {deg}$ at hip and knee joints, respectively. Especially, for the knee joint, the user is able to fully extend her knee during stance phase only when the VER is in the loop. In VER, the radius of each desired limit cycle is a function of phase. Accordingly, during walking, the internal phase of the VER is a monotonically increasing parameter that can be considered as a candidate for real-time gait phase estimation and heel-strike event detection. Hence, for gait phase estimation, VER relies only on a single joint position provided by the exoskeleton.
An Online Truthful Algorithm for Menu-Based Scheduling in Electric Vehicle Charging Networks

We consider online spatial-temporal scheduling of Electric Vehicles (EVs) in a network of Charging Stations (CSs), aiming to maximize the total value of EVs reaching their destinations within their deadlines. We present an efficient algorithmic framework for computing, in an online fashion, a menu of service-price options, with a different trip completion time and energy cost, for each EV. EVs accept their most preferable option, if it is affordable and meets their deadline. Our algorithm is prompt, in the sense that it fully commits to the EV’s charging schedule, upon acceptance of an option, and truthful, because EVs act in their best interest. We present an general analysis of the competitive ratio of our algorithmic framework, not depending on any statistical assumptions e.g. on the population or the arrival times of EVs. We determine the dependence of competitive ratio on the maximum value of EVs and on the pricing scheme adopted at the CSs. Our experimental evaluation demonstrates its good performance in practically relevant scenarios.
Distribution System Planning Considering Non-Utility-Owned Electric Vehicle Charging Stations

This article proposes a new mixed-integer linear programming formulation for the planning of active distribution networks and non-utility-owned electric vehicle charging stations (EVCSs). The approach uses multi-objective optimization to consider both the utility’s and the EVCSs owner’s economic interests. In this context, the utility decides on investments in network assets, such as replacing overloaded conductors and installing capacitor banks and voltage regulators, whereas the EVCSs owner decides on EVCSs infrastructure such as land size and location, as well as the number of chargers to be constructed. The model is designed to reduce the total expected cost for both owners. A travel simulation algorithm provides the EVCSs’ load profiles. Scenario-based optimization is used to address uncertainties related to the energy price at the substation, wind speed, solar irradiation, electricity demand, EVCSs load profiles, and plug-in electric vehicle adoption rate. The effectiveness of the proposed model has been proved on a 69-node network. Results show that the objectives of both owners are in conflict, both depending on the location of the EVCSs.
Stable outcomes for contract choice problems

In this paper, we consider the problem of choosing a set of multi-party contracts, where each coalition of agents has a non-empty finite set of feasible contracts to choose from. We call such problems, cntract choic problems. We provide conditions under which a contract choice problem has a non-empty set of "stable" outcomes. There are two types of stability concepts we study in this paper:cooperative stability and non- cooperative stability. The cooperative stability concept that we invoke here is the core. We also show, that a simple generalization of the Deferred Acceptance Procedure with men proposing due to Gale and Shapley(1962), yeilds outcomes for a generalized marriage problem, which necessarily belong to the core. The non-cooperative stability concept that we study here is individual stability. The final result of this paper states that every contract choice problem has a non-empty weak bargaining st.
Harmonic Mitigation in Low-Voltage Distribution Networks: Sensitivity Analysis

The increased penetration of converter-interfaced distributed renewable energy sources (CI-DRES) along with the existence of non-linear loads (NLLs) might cause severe power quality problems in distribution networks (DNs). Currently, various methods have been proposed so that voltage source converters (VSCs) of the CI-DRES act also as harmonic filters, however, most of them mitigate the problem locally without examining which CI-DRES within the DN would be most suitable to provide such service. In this paper the mathematical framework and a sensitivity analysis is performed in order to evaluate the contribution of each CI-DRES in the decentralized harmonic filtering process, with the presence of NLLs within a low-voltage (LV) DN based on the CI-DRES location and available capacity. The available capacity is an important parameter, since in the future, the CI-DRES might provide simultaneously several services, e.g. reactive power or virtual inertia. The mathematical framework for the quantification of the CI-DRES contribution is validated via simulations in the time domain and via harmonic power flows in the benchmark CIGRE LV network under various mixtures of CI-DRES/NLLs. The derived conclusions will pave the path, so that the active filtering can be treated as a new ancillary service to be provided by CI-DRES and introduced in respective markets.