Miracle-dz/UniFAR-FTUs huggingface dataset:
Passive WiFi CSI Sensing Based Machine Learning Framework for COVID-Safe Occupancy Monitoring

The COVID-19 pandemic requires social distancing to prevent transmission of the virus. Monitoring social distancing is difficult and expensive, especially in "travel corridors" such as elevators and commercial spaces. This paper describes a low-cost and non-intrusive method to monitor social distancing within a given space, using Channel State Information (CSI) from passive WiFi sensing. By exploiting the frequency selective behaviour of CSI with a cubic SVM classifier, we count the number of people in an elevator with an accuracy of 92%, and count the occupancy of an office to 97%. As opposed to using a multi-class counting approach, this paper aggregates CSI for the occupancies below and above a COVID-Safe limit. We show that this binary classification approach to the COVID safe decision problem has similar or better accuracy outcomes with much lower computational complexity, allowing for real-world implementation on IoT embedded devices. Robustness and scalability is demonstrated through experimental validation in practical scenarios with varying occupants, different environment settings and interference from other WiFi devices.
Needles in a Haystack: How Pooling Can Control Error Rates in Needles in a Haystack: How Pooling Can Control Error Rates in Noisy Tests Noisy Tests

: Testing many individuals for a reasonably rare condition using imperfect, time consuming, and expensive tests can be facilitated by pooling. Pooling groups samples from different individuals that are then tested for the existence of a pathogen. An individual is diagnosed as a carrier if a threshold of the tests to which the individual contributed samples is positive. Our assumptions dictate a testing strategy that is not adaptive, with the
Tropospheric Volcanic SO2 Mass and Flux Retrievals from Satellite. The Etna December 2018 Eruption

The presence of volcanic clouds in the atmosphere affects air quality, the environment, climate, human health and aviation safety. The importance of the detection and retrieval of volcanic SO2 lies with risk mitigation as well as with the possibility of providing insights into the mechanisms that cause eruptions. Due to their intrinsic characteristics, satellite measurements have become an essential tool for volcanic monitoring. In recent years, several sensors, with different spectral, spatial and temporal resolutions, have been launched into orbit, significantly increasing the effectiveness of the estimation of the various parameters related to the state of volcanic activity. In this work, the SO2 total masses and fluxes were obtained from several satellite sounders—the geostationary (GEO) MSG-SEVIRI and the polar (LEO) Aqua/Terra-MODIS, NPP/NOAA20-VIIRS, Sentinel5p-TROPOMI, MetopA/MetopB-IASI and Aqua-AIRS—and compared to one another. As a test case, the Christmas 2018 Etna eruption was considered. The characteristics of the eruption (tropospheric with low ash content), the large amount of (simultaneously) available data and the different instrument types and SO2 columnar abundance retrieval strategies make this cross-comparison particularly relevant. Results show the higher sensitivity of TROPOMI and IASI and a general good agreement between the SO2 total masses and fluxes obtained from all the satellite instruments. The differences found are either related to inherent instrumental sensitivity or the assumed and/or calculated SO2 cloud height considered as input for the satellite retrievals. Results indicate also that, despite their low revisit time, the LEO sensors are able to provide information on SO2 flux over large time intervals. Finally, a complete error assessment on SO2 flux retrievals using SEVIRI data was realized by considering uncertainties in wind speed and SO2 abundance.
Hybrid Model Based on Maxwell-Slip Model and Relevance Vector Machine

This article proposes a hybrid model based on Maxwell-slip (MS) model and relevance vector machine (RVM) for piezoelectric actuators (PEAs). The MS model maps the input voltage into the internal state space and provides an estimation of the output displacement. The output function of the MS model provides a linear relationship between the deformation vector and the output displacement. Replacing the regression function of the RVM by the output function of the MS model enables the RVM to update parameters of the MS model. The RVM is chosen since it is also able to provide the distribution of the estimated output displacement. Integration of the MS model with the RVM also reduces the dimension of the RVM from the number of support data points to the number of MS elements, which will reduce the computational time. Results demonstrate that the hybrid model is capable of describing rate-dependent and asymmetric hysteresis, is robust to excitations, enhances the accuracy of the MS model, and requires much shorter computational time than the standalone RVM. The modeling error under random excitation is 0.54% and for asymmetric hysteresis in a reluctance actuator is 0.31%. Compensation for hysteresis is carried out by connecting the inverse model in serial with a PEA and achieves tracking error of less than 0.81%.
Reduced Graphene Oxide-Poly(3,4-ethylenedioxythiophene) Polystyrenesulfonate Based Dual-Selective Sensor for Iron in Different Oxidation States

A dual-selective sensor platform for detection of iron in ferrous (Fe2+) and ferric (Fe3+) oxidation states was developed. Upon dispersing reduced graphene oxide (rGO) sheets into poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS) polymer, we deposited a hybrid film of rGO-PEDOT:PSS onto a glassy carbon electrode (GCE) surface. Effective intercalation of rGO sheets in PEDOT:PSS film was observed by Raman spectroscopy, UV–vis-NIR spectroscopy, and scanning electron microscopy. The rGO-PEDOT:PSS/GCE sensor showed high electrocatalytic activity for Fe2+/Fe3+ redox reaction. Using amperometry with controlled applied potential, we demonstrated selective detection of both Fe2+ and Fe3+ with rGO-PEDOT:PSS/GCE. The sensor responded linearly to Fe2+ from 20–833 μM (at 0.6 V) and Fe3+ from 1–833 μM (at 0.4 V) in 0.5 M KCl + 0.05 M HCl. The presence of several common metal and organic interferents such as Cu2+, Co2+, Ag+, Pb2+, Cd2+, Zn2+, Mn2+, Ni2+, Hg2+, l-glycine, l-cysteine, l-tyrosine, glucose, .
How Much Can We See from a UAV-Mounted Regular Camera? Remote Sensing-Based Estimation of Forest Attributes in South American Native Forests

Data collection from large areas of native forests poses a challenge. The present study aims at assessing the use of UAV for forest inventory on native forests in Southern Chile, and seeks to retrieve both stand and tree level attributes from forest canopy data. Data were collected from 14 plots (45 × 45 m) established at four locations representing unmanaged Chilean temperate forests: seven plots on secondary forests and seven plots on old-growth forests, including a total of 17 different native species. The imagery was captured using a fixed-wing airframe equipped with a regular RGB camera. We used the structure from motion and digital aerial photogrammetry techniques for data processing and combined machine learning methods based on boosted regression trees and mixed models. In total, 2136 trees were measured on the ground, from which 858 trees were visualized from the UAV imagery of the canopy, ranging from 26% to 88% of the measured trees in the field (mean = 45.7%, SD = 17.3), which represented between 70.6% and 96% of the total basal area of the plots (mean = 80.28%, SD = 7.7). Individual-tree diameter models based on remote sensing data were constructed with R2 = 0.85 and R2 = 0.66 based on BRT and mixed models, respectively. We found a strong relationship between canopy and ground data; however, we suggest that the best alternative was combining the use of both field-based and remotely sensed methods to achieve high accuracy estimations, particularly in complex structure forests (e.g., old-growth forests). Field inventories and UAV surveys provide accurate information at local scales and allow validation of large-scale applications of satellite imagery. Finally, in the future, increasing the accuracy of aerial surveys and monitoring is necessary to advance the development of local and regional allometric crown and DBH equations at the species level.
Fusion of China ZY-1 02D Hyperspectral Data and Multispectral Data: Which Methods Should Be Used?

ZY-1 02D is China’s first civil hyperspectral (HS) operational satellite, developed independently and successfully launched in 2019. It can collect HS data with a spatial resolution of 30 m, 166 spectral bands, a spectral range of 400~2500 nm, and a swath width of 60 km. Its competitive advantages over other on-orbit or planned satellites are its high spectral resolution and large swath width. Unfortunately, the relatively low spatial resolution may limit its applications. As a result, fusing ZY-1 02D HS data with high-spatial-resolution multispectral (MS) data is required to improve spatial resolution while maintaining spectral fidelity. This paper conducted a comprehensive evaluation study on the fusion of ZY-1 02D HS data with ZY-1 02D MS data (10-m spatial resolution), based on visual interpretation and quantitative metrics. Datasets from Hebei, China, were used in this experiment, and the performances of six common data fusion methods, namely Gram-Schmidt (GS), High Pass Filter (HPF), Nearest-Neighbor Diffusion (NND), Modified Intensity-Hue-Saturation (IHS), Wavelet Transform (Wavelet), and Color Normalized Sharping (Brovey), were compared. The experimental results show that: (1) HPF and GS methods are better suited for the fusion of ZY-1 02D HS Data and MS Data, (2) IHS and Brovey methods can well improve the spatial resolution of ZY-1 02D HS data but introduce spectral distortion, and (3) Wavelet and NND results have high spectral fidelity but poor spatial detail representation. The findings of this study could serve as a good reference for the practical application of ZY-1 02D HS data fusion.
Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments

This paper designs a cascading vector tracking loop based on the Unscented Kalman Filter (UKF) for high dynamic environment. Constant improvement in dynamic performance is an enormous challenge to the traditional receiver. Due to the doppler effect, the satellite signals received by these vehicles contain fast changing doppler frequency shifts and the first and second derivatives of doppler frequency, which will directly cause a negative impact on the receiver’s stable tracking of the signals. In order to guarantee the dynamic performance and the tracking accuracy, this paper designs a vector carrier structure to estimate the doppler component of a signal. Firstly, after the coherence integral, the IQ values are reorganized into new observations. Secondly, the phase error and frequency of the carrier are estimated through the pre-filter. Then, the pseudorange and carrier frequency are used as the observations of the main filter to estimate the motion state of the aircraft. Finally, the current state is fed back to the carrier Numerical Controlled Oscillator (NCO) as a complete closed loop. In the whole structure, the cascading vector loop replaces the original carrier tracking loop, and the stable signal tracking of code loop is guaranteed by carrier assisted pseudo-code method. In this paper, with the high dynamic signals generated by the GNSS signal simulator, this designed algorithm is validated by a software receiver. The results show that this loop has a wider dynamic tracking range and lower tracking error than the second-order frequency locked loop assisted third-order phase locked loop in high dynamic circumstances. When the acceleration of carrier is 100 g, the convergence time of vector structure is about 100 ms, and the carrier phase error is lower than 0.6 mm.
Beam Selection for Ultra-Reliable Low-Latency Communication in Industrial Environment with Beamforming Repeaters

This paper investigates the use of beamforming millimeter-wave (mmWave) repeaters in wireless industrial control systems. We study deployment of mmWave in a factory floor with several production lines and a multitude of devices that periodically receive packets from the controller via a hub access point (AP). We propose to use beamforming repeaters as an alternative to the so-called multi-transmission reception point (multi-TRP) technology, where wireless fronthaul links from the hub TRP to the repeaters substitute the bulky wired links to the different TRPs. The proposed wireless TRP is then demonstrated to extend communication coverage across the factory floor, while improving end-to-end link reliability via over-the-air combining of the signal from the repeater and the hub TRP. We formulate the optimization problem of associating TRPs and beams to each user for the objective of minimizing the overall scheduling latency while satisfying reliable communication to all users. To tackle such a problem, we propose a low-complexity greedy algorithm, which through extensive simulations is shown to significantly reduce system- level scheduling latency compared to the existing schemes in the literature. In our simulations, the effects of most objects on radio wave propagation are accurately modeled using a ray-tracing tool.
Evaluation of Current Trends of Climatic Actions in Europe Based on Observations and Regional Reanalysis

Since extreme values of climatic actions are commonly derived assuming the climate being stationary over time, engineering structures and infrastructures are designed considering design actions derived under this assumption. Owing to the increased relevance of the expected climate change effects and the correlated variations of climate actions extremes, ad hoc strategies for future adaption of design loads are needed. Moreover, as current European maps for climatic actions are generally based on observations collected more than 20 years ago, they should be updated. By a suitable elaboration of the projections of future climate changes, the evolution over time of climatic actions can be assessed; this basic and crucial information allows us to facilitate future adaptations of climatic load maps, thus improving the climate resilience of structures and infrastructures. In this paper, current trends of climatic actions in Europe, daily maximum and minimum temperatures, daily precipitation, and ground snow loads, are investigated based on available gridded datasets of observations (E-OBS) and regional reanalysis (Uncertainties in Ensembles of Regional Re-Analyses, UERRA), to assess their suitability to be used in the elaboration of maps for climatic actions. The results indicate that the E-OBS gridded datasets reproduce trends in extreme temperatures and precipitation well in the investigated regions, while reanalysis data, which include snow water equivalent, show biases in the assessment of ground snow load modifications over the years in comparison with measurements. As far as climate change effects are concerned, trends of variation of climatic actions are estimated considering subsequent time windows, 40 years in duration, covering the period 1950–2020. Results, in terms of factors of change, are critically discussed, also in comparison with the elaborations of reliable datasets of real observations, considering a case study covering Germany and Switzerland.
High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

: Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the ﬁeld of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime trafﬁc control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, signiﬁcantly reducing the model size, detection time, number of computational parameters, and memory consumption, and reﬁning the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.
Tropospheric Correction of Sentinel-1 Synthetic Aperture Radar Interferograms Using a High-Resolution Weather Model Validated by GNSS Measurements

Synthetic Aperture Radar Interferometry (InSAR) is a space geodetic technique used for mapping deformations of the Earth’s surface. It has been developed and used increasingly during the last thirty years to measure displacements produced by earthquakes, volcanic activity and other crustal deformations. A limiting factor to this technique is the effect of the troposphere, as spatial and temporal variations in temperature, pressure, and relative humidity introduce significant phase delays in the microwave imagery, thus “masking” surface displacements due to tectonic or other geophysical processes. The use of Numerical Weather Prediction (NWP) models as a tropospheric correction method in InSAR can tackle several of the problems faced with other correction techniques (such as timing, spatial coverage and data availability issues). High-resolution tropospheric modelling is particularly useful in the case of single interferograms, where the removal of the atmospheric phase screen (and especially the highly variable turbulent component) can reveal large-amplitude deformation signals (as in the case of an earthquake). In the western Gulf of Corinth, prominent topography makes the removal of both the stratified and turbulent atmospheric phase screens a challenging task. Here, we investigate the extent to which a high-resolution WRF 1-km re-analysis can produce detailed tropospheric delay maps of the required accuracy by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is operated with varying physical parameterization in order to identify the best configuration, and validated with GNSS zenithal tropospheric delays, providing a benchmark of real atmospheric conditions. We correct sixteen Sentinel-1A interferograms with differential delay maps at the line-of-sight (LOS) produced by WRF re-analysis. In most cases, corrections lead to a decrease in the phase gradient, with average root-mean-square (RMS) and standard deviation (SD) reductions in the wrapped phase of 6.0% and 19.3%, respectively. Results suggest a high potential of the model to reproduce both the long-wavelength stratified atmospheric signal and the short-wave turbulent atmospheric component which are evident in the interferograms.
Forest Canopy Changes in the Southern Amazon during the 2019 Fire Season Based on Passive Microwave and Optical Satellite Observations

Canopy dynamics associated with fires in tropical forests play a critical role in the terrestrial carbon cycle and climate feedbacks. The aim of this study was to characterize forest canopy dynamics in the southern Amazon during the 2019 fire season (July–October) using passive microwave-based vegetation optical depth (VOD) and three optical-based indices. First, we found that precipitation during July–October 2019 was close to the climatic means, suggesting that there were no extreme hydrometeorological events in 2019 and that fire was the dominant factor causing forest canopy anomalies. Second, based on the active fire product (MCD14ML), the total number of active fires over each grid cell was calculated for each month. The number of active fires during the fire season in 2019 was above average, particularly in August and September. Third, we compared the anomalies of VOD and optical-based indices (the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the normalized burn ratio (NBR)) against the spatiotemporal distribution of fires during July–October 2019. Spatially, the location with a concentrated distribution of significant negative VOD anomalies was matched with the grid cells with fire activities, whereas the concentrated distribution of strong negative anomalies in optical-based indices were found in both burned and unburned grid cells. When we focused on the temporal pattern over the grid cells with fire activity, the VOD and the optical-based indices behaved similarly from July to October 2019, i.e., the magnitude of negative anomalies became stronger with increased fire occurrences and reached the peak of negative anomalies in September before decreasing in October. A discrepancy was observed in the magnitude of negative anomalies of the optical-based indices and the VOD; the magnitude of optical-based indices was larger than the VOD in August–September and recovered much faster than the VOD over the grid cells with relatively low fire activity in October. The most likely reason for their different responses is that the VOD represents the dynamics of both photosynthetic (leaf) and nonphotosynthetic (branches) biomass, whereas optical-based indices are only sensitive to photosynthetic (leaf) active biomass, which recovers faster. Our results demonstrate that VOD can detect the spatiotemporal of canopy dynamics caused by fire and postfire canopy biomass recovery over high-biomass rainforest, which enables more comprehensive assessments, together with classic optical remote sensing approaches.
International Conference on Medicine and Nursing & Pharmaceutical and Natural Sciences

NIR spectroscopy provides the spectral signatures (i.e. “fingerprints”) of living human muscles, which represent specific, accurate, and reproducible measures of their overall biological status. We showed that chemometric analysis applied to NIR spectra acquired from the upper limb distinguishes the biceps from the triceps. We acquired muscles reflectance spectra in the Vis-SWIR regions (350-2500 nm), utilizing an ASD FieldSpec 4TM Standard-Res Spectroradiometer with a spectral sampling capability of 1.4 nm at 350-1000 nm and 1.1 nm at 1001-2500 nm. Optical spectroscopy proves effective for studying human muscles in vivo and contribute to non-invasive more thorough evaluation of the muscular system.