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Based on the MOD09GA/MYD09GA 500-m surface reflectance, a new MODIS snow-cover-extent (SCE) product over China has been produced by the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER MODIS SCE product contains two preliminary clear-sky SCE datasets — Terra-MODIS and Aqua-MODIS SCE datasets, and a final daily cloud-gap-filled (CGF) SCE dataset. The formers are generated mainly through optimizing snow-cover discriminating rules over different land-cover types, and the latter is produced after a series of gap-filling processes such as aggregating the two preliminary datasets, reducing cloud gaps with adjacent information in space and time, and eliminating all gaps with auxiliary data. Validation against 362 China Meteorological Administration (CMA) stations shows during snow seasons the overall accuracies (OA) of the three datasets are all larger than 93 %, the omission errors (OE) are all constrained within 9 %, and the commission errors (CE) are all constrained within 10 %. Biases ranging from the lowest 0.98 to the medium 1.02, to the largest 1.03 demonstrate on a whole the SCEs given by the new product are neither overestimated nor underestimated significantly. Based on the same ground reference data, we found the new product’s accuracies are clearly higher than those of standard MODIS snow products, especially for Aqua-MODIS and CGF SCE. For examples, compared with the CE of 23.78 % that the standard MYD10A1 product shows, the CE of the new Aqua-MODIS SCE dataset is 6.78 %; the OA of the new CGF SCE dataset is up to 93.15 %, versus 89.54 % of the standard MOD10A1F product and 84.36 % of the standard MYD10A1F product. Besides, as expected snow discrimination in forest areas is also improved significantly. An isolated validation at four forest CMA stations demonstrates the OA has increased by 3–10 percentage points, the OE has dropped by 1–8 percentage points, and the CE has dropped by 4–21 percentage points. Therefore, our product has virtually provided more reliable snow knowledge over China, and thereby can better serve for hydrological, climatic, environmental, and other related studies there.

This paper presents outcome of a research which investigates the fatigue behaviour of duplex stainless steel grade (EN 1.4162) via cyclic tests on welded cruciform joints and base plates. A database of experimental tests for similar high-strength steel details was also collated from the literature. The obtained experimental S-N curve was then compared to the current European design ones and re-evaluated according to the hot spot stress method. A good agreement was observed between the studied detail performance and the collated reference data. Improvement to the fatigue design rules for the studied duplex and similar high-strength steel details is then proposed. ispartof: Engineering Structures vol:247 status: Published online

Snow water equivalent (SWE) can be measured using low-cost Global Navigation Satellite System (GNSS) sensors with one antenna placed below the snowpack and another one serving as a reference above the snow. The underlying GNSS signal-based algorithm for SWE determination for dry- and wet-snow conditions processes the carrier phases and signal strengths and additionally derives liquid water content (LWC) and snow depth (HS). So far, the algorithm was tested intensively for high-alpine conditions with distinct seasonal accumulation and ablation phases. In general, snow occurrence, snow amount, snow density and LWC can vary considerably with climatic conditions and elevation. Regarding alpine regions, lower elevations mean generally earlier and faster melting, more rain-on-snow events, and shallower snowpack. Therefore, we assessed the applicability of the GNSS-based SWE measurement at four stations along a steep elevation gradient (820, 1185, 1510 and 2540 m a.s.l.) in the eastern Swiss Alps during two winter seasons (2018–2020). Reference data of SWE, LWC and HS were collected manually and with additional automated sensors at all locations. The GNSS-derived SWE estimates agreed very well with manual reference measurements along the elevation gradient, and the accuracy (RMSE = 34 mm, RMSRE = 11 %) was similar under wet- and dry-snow conditions, although significant differences in snow density and meteorological conditions existed between the locations. The GNSS-derived SWE was more accurate than measured with other automated SWE sensors. However, with the current version of the GNSS algorithm, the determination of daily changes of SWE was found to be less suitable compared to manual measurements or pluviometer recordings and needs further refinement. The values of the GNSS-derived LWC were robust and within the precision of the manual and radar measurements. The additionally derived HS correlated well with the validation data. We conclude that SWE can reliably be determined using low-cost GNSS sensors under a broad range of climatic conditions, and LWC and HS are valuable add-ons.

Radiometric calibration of the medium-resolution satellite data is critical for monitoring and quantifying changes in the Earth's environment and resources. Many medium-resolution satellite sensors have irregular revisits and, sometimes, have a large difference in illumination viewing geometry compared with a reference sensor, posing a great challenge for routine cross-calibration practices. To overcome these issues, this study proposed a cross-calibration method to calibrate medium-resolution multispectral data. The Chinese Gaofen-4 (GF-4) panchromatic and multispectral sensor (PMS) data with large viewing angles were used as the test data, and Landsat-8 operational land imager (OLI) data were used as the reference data. A bidirectional reflectance distribution function (BRDF) correction method was proposed to eliminate the effects of differences in illumination viewing geometry between GF-4 and Landsat-8. The validation using concurrent image shows that the mean relative error (MRE) of cross calibration is less than 6.65%. Validation using ground measurements shows that our calibration results have an improvement of around 14.8% compared with the official released calibration coefficients. The time series cross calibration reveals that, without the requirements of simultaneous nadir observations (SNOs), our calibration activities can be carried out more often in practice. Gradual and continuous radiometric sensor degradation is identified with the monthly updated calibration coefficients, demonstrating the reliability and importance of the timely cross calibration. Besides, the cross-calibration approach does not rely on any specific calibration site, and the difference in illumination viewing geometry can be well considered. Thus, it can be easily adapted and applied to other optical satellite data.

Rainfall detection (RD) and rainfall intensity (RI) retrieval are hot topics in the field of ocean remote sensing. In the past, the sea surface RD and RI retrieval were usually based on X-band ocean radar image data. In this study, we aim to investigate the potential of Global Navigation Satellite System-Reflectometry (GNSS-R) for sea surface RD and RI retrieval based on delay Doppler maps (DDMs) data collected by the Cyclone GNSS (CYGNSS) mission. Firstly, the block-matching and 3D filtering (BM3D) algorithm is proposed to improve the quality of DDM data. In addition, 12 GNSS-R observables derived from DDM are calculated, and a RD method based on the threshold of 12 GNSS-R observables is proposed based on the probability density function (PDF). When rainfall DDM data are detected, these data are used to develop and verify the sea surface RI retrieval model. The Integrated Multi-satellite Retrievals of Global Precipitation Measurements (GPM-IMERG) data product is used as reference data to evaluate the performance of RD and RI retrieval model. The experimental results show that under very low wind speed (< 5 m/s) the proposed trailing edge waveform summation of normalized integral delay waveform (TEWS-NIDW), TEWS of normalized center delay waveform (TEWS-NCDW), and TEWS of differential delay waveform (TEWS-DDW) observables are the best for RD, and the probability of detection of rainfall (PDr) is better than 75 %. In the aspect of model retrieval performance, the Mean Square Error (RMSE) of the 12 observables are less than 4.66 mm/hr. Among them, the model based on TEWS-NCDW observable has the highest accuracy, better than 3.74 mm/hr.

Considering the significant influence of clouds on thermal infrared (TIR) data sea surface temperature (SST) retrieval, this study focuses on the reduction of cloud influence on Moderate Resolution Imaging Spectroradiometer (MODIS) longwave SST retrieval. First, the quality level (QL) 3 MODIS SSTs are classified into cloudy or clear-sky pixels based on MODIS cloud mask products. The cloudy SST pixels, flagged as confident cloudy or probably cloudy in the cloud masks, are further classified into three groups according to their associated daytime cloud-top and optical properties. Taking the SSTs measured by 11 buoys over one year as the reference data, those three groups of cloudy SSTs are significantly underestimated, with biases of -33.45 °C, -6.35 °C, and -4.72 °C. The QL 3 clear-sky SSTs are identified as probably and confident clear in the cloud masks and are not influenced by clouds, with a bias of nearly 0 °C. Then, three support vector regression (SVR) models are individually proposed for the three groups of cloudy SSTs. Taking the cloud-top and optical parameters as inputs of the proposed SVR models, we can obtain SSTs with a bias of 0 °C and a root-mean-square error (RMSE) of less than 1.6 °C for the three groups of cloudy SSTs. The method proposed in this study shows the potential for TIR SST estimation under some cloudy conditions using satellite remote sensing cloud products. Considering the RMSE of 0.4 °C in operational sea surface temperature and sea ice analysis, further study is needed before actual applying the method proposed in this study.

Images from the high-resolution stereo camera (HRSC) is an important data source for retrieving 3-D information of the Martian surface. Photogrammetry has been widely applied to HRSC images to generate digital elevation models (DEMs). However, in circumstances of insufficient textures or severe atmospheric influences on Mars, photogrammetry may fail to generate DEMs of favorable quality. Besides, photogrammetry is not able to recover pixelwise geometrical information. Meanwhile, photoclinometry is a promising method to retrieve pixelwise 3-D information from image intensities. Few works in the past have explored the appropriate use of photoclinometry for HRSC data. This article presents an innovative approach of integrating photogrammetry and photoclinometry for pixelwise 3-D mapping of the Martian surface using multiple HRSC images. First, an effective photogrammetric strategy is developed to process multiple HRSC images. For images influenced by haze or aerosol problems, an automatic object-based matching algorithm is formulated to compensate for obvious flaws on the disparity map. Second, multiview photoclinometry considering atmospheric effect is developed to refine the photogrammetric DEM to pixel resolution. Qualitative and quantitative validations are conducted using two representative sets of HRSC images and compared with the reference data. The results show that the generated DEMs have good geometric consistency with the reference data, but offer subtle topographic details conforming to the original images. The proposed method provides a promising solution to exploit the HRSC images for 3-D mapping of the Martian surface with better resolution and coverage, which is of significance for future Mars exploration missions and scientific research.

The subject of this work is to propose a new method based on the ANFIS system and PSO algorithm to conceive a model for estimating the solubility of solid drugs in supercritical CO2 (sc-CO2). The high nonlinear process was modeled by the neuro-fuzzy approach (NFS). The PSO algorithm was used for two purposes: replacing the standard backpropagation in training the NFS and optimizing the process. The validation strategy has been carried out using a linear regression analysis of the predicted versus experimental outputs. The ANFIS approach is compared to the ANN in terms of accuracy. Statistical analysis of the predictability of the optimized model trained with a PSO algorithm (ANFIS-PSO) shows a better agreement with the reference data than the ANN method. Furthermore, the comparison in terms of the AARD deviation (%) between the predicted results, the results predicted by the density-based models, and a set of equations of state demonstrates that the ANFIS-PSO model correlates far better with the solubility of the solid drugs in scCO2. A control strategy was also developed for the first time in the field of phase equilibrium by using the neuro-fuzzy inverse approach (ANFISi) to estimate pure component properties from the solubility data without passing through the GCM methods.

The paper presents the Simulator for Investigation of Solar Temperature Error of Radiosondes (SISTER), a setup that was developed to quantify the solar heating of the temperature sensor of radiosondes under laboratory conditions by recreating as closely as possible the atmospheric and illumination conditions that are encountered during a daytime radiosounding ascent. SISTER controls the pressure (3 to 1020 hPa) and ventilation speed of the air inside the wind-tunnel-like setup to simulate the conditions between the surface and 35 km altitude, to determine the dependence of the radiation temperature error on the irradiance and the convective cooling. The radiosonde is mounted inside a quartz tube, while the complete sensor boom is illuminated by an external light source to include the conductive heat transfer between sensor and boom. A special feature of SISTER is that the radiosonde is rotated around its axis to imitate the spinning of the radiosonde in flight. The characterisation of the radiation temperature error is performed for various pressures, ventilation speeds, and illumination angles, yielding a 2D parameterisation of the radiation error for each illumination angle, with an uncertainty smaller than 0.2 K (k=2) for typical ascend speeds. This parameterisation is applied in the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) processing for radiosonde data, which relies on the extensive characterisation of the sensor properties to produce a traceable reference data product which is free of manufacturer-dependent effects. The GRUAN radiation correction model combines the laboratory characterisation with model calculations of the actual radiation field during the sounding to estimate the correction profile. In the second part of this paper it is described how this procedure was applied in the development of the GRUAN data product for the Vaisala RS41 radiosonde (version 1, RS41-GDP.1). The magnitude of the averaged correction profile increases gradually from 0.1 K at the surface to approximately 0.8 K at 35 km altitude. Comparisons between sounding data (N=154) that were GRUAN-processed and Vaisala-processed reveal that the daytime differences (GRUAN−Vaisala) are smaller than +0.1 K in the troposphere and increase above the tropopause steadily with altitude to +0.35 K at 35 km. These differences are just within the limits of the combined uncertainties (with coverage factor k=2) of both data products, meaning that the GRUAN processing and the Vaisala processing are in agreement.

This paper evaluates the dimensional accuracy of industrial X-ray computed tomography (CT) measurements as a function of the geometric magnification (Mg) used for CT scanning. Two plates of square-shaped geometry with 28 drilled holes, one made of aluminum (48 mm × 48 mm × 8 mm) and a second one made of steel (6 mm × 6 mm × 1 mm), are used as measuring workpieces to test fourteen different magnification axis positions in a metrology-grade X-ray CT system. In addition to measuring the diameter and roundness of each hole, distance measurements between the holes are evaluated—both as uni-directional (center-to-center) and bi-directional (point-to-point or edge-to-edge) lengths. The variance of CT dimensional measurements, with respect to reference data obtained from tactile coordinate measurement machines (CMMs), is studied. It is found that the measurement deviations for uni-directional distances are approximately independent of Mg and mostly within a ±4 μm range. In contrast, for measurements of diameter, roundness, and bi-directional lengths, the deviations between CT and CMM data extend outside the ±4 μm limits and strongly depend on Mg (which is a variable inversely proportional to the voxel size of CT scan, VX). More specifically, decreasing Mg or, equivalently, increasing VX, generally leads to larger deviations in the dimensional data. To include the magnification dependence on the estimations of CT measurement deviations, based on the experimental data presented throughout this paper, the authors propose that the maximum error of CT length measurements can be expressed as E m a x = ( A + X / B + L / K ) μm, with A , B , and K being constant factors (determined for a particular CT measuring setup), L the dimension of the measuring length (in mm), and X the magnification axis position of the CT scanner (also in mm). Whereas the term “ L / K ” considers error influences from the size of the sample, the term “ X / B ” accounts for influences from magnification (and voxel size) selections during CT measurement.

In the artificial neural networks domain, the Levenberg-Marquardt technique is novel with convergent stability and generates a numerical solution of the wire coating system for Sisko fluid flow (WCS-SFF) through regression plots, histogram representations, state transition measures, and means squared errors. In this paper, the analysis of fluid flow problem based on WCS-SFF is studied with a new application of intelligent computing system via supervised learning mechanism using the efficacy of neural networks trained by Levenberg-Marquardt algorithm (NN-TLMA). The original mathematical formulation in terms of PDEs for WCS-SFF is converted into dimensionless nonlinear ODEs. The data collection for the projected NN-TLMA is produced for parameters associated with the system model WCS-SFF influencing the velocity using the explicit Runge-Kutta technique. The training, validation, and testing processes of NN-TLMA are utilized to evaluate the obtained results of WCS-SFF for various cases, and a comparison of the obtained results is performed with reference data set to check the accuracy and effectiveness of the proposed algorithm NN-TLMA for the analysis of non-Newtonian fluid problem-related WCS-SFF. The proposed NN-TLMA for solving the WCS-SFF is effectively confirmed through state transition dynamics, mean square error, regression analyses, and error histogram studies. The powerful consistency of suggested outcomes with reference solutions indicates the validity of the framework, and the accuracy of 10 - 8 to 10 - 6 is also achieved.

The main objective of this research is to evaluate change detection techniques to monitoring land-cover changes that occurred between 2016 and 2017 in the study area located in new administrative capital region in Cairo Governorate, Egypt. The Study area is 77.350 km2. Image data is two Satellite images covering the study area, both are by Landsat_8 OLI/TIRS satellite with a low spatial resolution 30 m * 30 m. Reference data is two Satellite images covering the study area, both are by Nanosat satellite with a high spatial resolution 3 m * 3 m. The Study area was classified into roads, sand, rocks, bare soil, routes and buildings using Maximum Likelihood Classifier. Three change detection techniques namely; post-classification, independent component analysis and principal component analysis were applied. Quantitative evaluations for the results of these techniques were performed to determine the most appropriate change detection technique which will provide the highest accuracy for identifying the nature and extent of land-cover changes in New capital. The results indicated that the post classification change detection technique provided the highest accuracy while the principal component analysis technique gave the least accuracy. And the results show that the post classification change detection technique is straightforward because it is direct and its accuracy is mainly dependent upon the accuracy of the initial classifications of the two images. In the other two change detection techniques, more processing stages are required such as transformation stage

First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming an essential non-invasive imaging method for detecting deficits of myocardial blood flow, allowing the assessment of coronary heart disease. Nevertheless, acquisitions suffer from relatively low spatial resolution and limited heart coverage. Compressed sensing (CS) methods have been proposed to accelerate FPP-CMR and achieve higher spatial resolution. However, the long reconstruction times have limited the widespread clinical use of CS in FPP-CMR. Deep learning techniques based on supervised learning have emerged as alternatives for speeding up reconstructions. However, these approaches require fully sampled data for training, which is not possible to obtain, particularly high-resolution FPP-CMR images. Here, we propose a physics-informed self-supervised deep learning FPP-CMR reconstruction approach for accelerating FPP-CMR scans and hence facilitate high spatial resolution imaging. The proposed method provides high-quality FPP-CMR images from 10x undersampled data without using fully sampled reference data., Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2021, held in Conjunction with MICCAI 2021

Occupational exposure assessments are dominated by small sample sizes and low spatial and temporal resolution with a focus on conducting Occupational Safety and Health Administration regulatory compliance sampling. However, this style of exposure assessment is likely to underestimate true exposures and their variability in sampled areas, and entirely fail to characterize exposures in unsampled areas. The American Industrial Hygiene Association (AIHA) has developed a more realistic system of exposure ratings based on estimating the 95th percentiles of the exposures that can be used to better represent exposure uncertainty and exposure variability for decision-making; however, the ratings can still fail to capture realistic exposure with small sample sizes. Therefore, low-cost sensor networks consisting of numerous lower-quality sensors have been used to measure occupational exposures at a high spatiotemporal scale. However, the sensors must be calibrated in the laboratory or field to a reference standard. Using data from carbon monoxide (CO) sensors deployed in a heavy equipment manufacturing facility for eight months from August 2017 to March 2018, we demonstrate that machine learning with probabilistic gradient boosted decision trees (GBDT) can model raw sensor readings to reference data highly accurately, entirely removing the need for laboratory calibration. Further, we indicate how the machine learning models can produce probabilistic hazard maps of the manufacturing floor, creating a visual tool for assessing facility-wide exposures. Additionally, the ability to have a fully modeled prediction distribution for each measurement enables the use of the AIHA exposure ratings, which provide an enhanced industrial decision-making framework as opposed to simply determining if a small number of measurements were above or below a pertinent occupational exposure limit. Lastly, we show how a probabilistic modeling exposure assessment with high spatiotemporal resolution data can prevent exposure misclassifications associated with traditional models that rely exclusively on mean or point predictions.

A simpler method is proposed for determining the resistances of an induction motor (total inductive resistance, active resistance of the stator and reduced active resistance of the rotor) according to reference data. Three algebraic equations are obtained from the equations of reactive power dissipation and electromagnetic power in the nominal mode and the equation of electromagnetic power in the critical mode: the first is relative to three resistances, the second is the equation of the dependence of the active resistance of the stator relative to the total inductive resistance, and the third is the active resistance of the rotor relative to the total inductive resistance. An iterative method is proposed for solving this system of equations, which gives a small error already at the second step of the calculations. When assessing the error of the method, the specified values ​​of the electromagnetic power and the multiplicity of the maximum torque were used, which are expressed relative to the reference resistances of the control motors. An estimate is given of the total error from the imperfection of the method and the discrepancy between the reference values ​​of the multiplicity of the maximum torque and power on the motor shaft with their refined values ​​calculated from the resistances from the reference book. Based on the calculated resistances in the nominal mode, the dependences of the active and inductive resistances of the motor are constructed using an accurate and approximate method.

The effectiveness of the forest height inversion methods using polarimetric SAR interferometry data is affected by the estimation accuracy of ground phase, volume only coherence and model prediction for forest canopy layer. Finding the forest model that considers the effects of the signal penetration in the forest medium as well as optimum volume coherence, which is an important purpose of the forest inversion method. This paper suggests a novel inversion algorithm to increase the accuracy of forest height estimation as well as ground phase estimation based on mean of coherence set. For this purpose, an adaptive total least square line fit is proposed to estimate the ground phase and coherence line. Then, the combination between the VE-RVoG model and an optimization volume only coherence algorithm based on mean coherence set and polarization signature is developed to find more accurate forest parameter values. The proposed algorithm applies forest height estimation using L-band PolInSAR data acquired by PolSARProSim software, spaceborne SIR-C and UAVSAR system. The method was further validated by using UAV L-band PolInSARata and the reference data of LiDAR canopy height model over rainforest Lope National Park, Gabon. Results showed that RMSE of results approximate 2.91 m and R2 is 0.909. The obtained results demonstrate the potential of proposed method in forest parameters estimation.

[Purpose] In this study, we verified the validity of the step time and walking speed obtained from the smartphone gait analysis application CareCoaching. [Participants and Methods] The participants were 66 independent, community-dwelling adults aged 65 years or older who performed a 10-m walking test twice each under preferred- and slow-speed conditions. We concurrently measured gait motions using CareCoaching and the OptoGait system for reference data. Both systems compute walking speed and step time as gait parameters. We examined the concurrent validity of these parameters by using intra-class correlation coefficients (ICCs) and limits of agreement (LOAs) with Bland-Altman analyses. [Results] In the preferred walking speed condition, the ICCs of walking speed and step times between the CareCoaching and the OptoGait system were 0.67 and 0.93, respectively. In the slow walking speed condition, the ICCs for walking speed and step time were 0.78 and 0.97, respectively. In addition, the LOAs for step time were -0.0941 to 0.1160 for preferred walking speed and -0.0596 to 0.0883 for slow walking speed. The LOAs for walking speed were -0.4158 to 0.0568 for preferred walking speed and -0.3348 to 0.0523 for slow walking speed. [Conclusion] CareCoaching showed excellent agreement for step time and moderate-to-good agreement for walking speed in independent, community-dwelling older adults.

In support of cropland monitoring, operational Copernicus Sentinel-2 (S2) data became available globally and can be explored for the retrieval of important crop traits. Based on a hybrid workflow, retrieval models for six essential biochemical and biophysical crop traits were developed for both S2 bottom-of-atmosphere (BOA) L2A and S2 top-of-atmosphere (TOA) L1C data. A variational heteroscedastic Gaussian process regression (VHGPR) algorithm was trained with simulations generated by the combined leaf-canopy reflectance model PROSAILat the BOA scale and further combined with the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmosphere model at the TOA scale. Established VHGPR models were then applied to S2 L1C and L2A reflectance data for mapping: leaf chlorophyll content (Cab), leaf water content (Cw), fractional vegetation coverage (FVC), leaf area index (LAI), and upscaled leaf biochemical compounds, i.e., LAI ∗ Cab (laiCab) and LAI ∗ Cw (laiCw). Estimated variables were validated using in situ reference data collected during the Munich-North-Isar field campaigns within growing seasons of maize and winter wheat in the years 2017 and 2018. For leaf biochemicals, retrieval from BOA reflectance slightly outperformed results from TOA reflectance, e.g., obtaining a root mean squared error (RMSE) of 6.5 μμg/cm2 (BOA) vs. 8 μμg/cm2 (TOA) in the case of Cab. For the majority of canopy-level variables, instead, estimation accuracy was higher when using TOA reflectance data, e.g., with an RMSE of 139 g/m2 (BOA) vs. 113 g/m2 (TOA) for laiCw. Derived maps were further compared against reference products obtained from the ESA Sentinel Application Platform (SNAP) Biophysical Processor. Altogether, the consistency between L1C and L2A retrievals confirmed that crop traits can potentially be estimated directly from TOA reflectance data. Successful mapping of canopy-level crop traits including information about prediction confidence suggests that the models can be transferred over spatial and temporal scales and, therefore, can contribute to decision-making processes for cropland management.

We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.

Introduction. One of the traditionally relevant problems of the theoretical framework for production and technology is the description, parameterization, and prediction of the properties of the mix depending on the parameters of the mix components. One of the most significant problems that hinder the effective use of granularmaterials, for example, in the construction industry, is the difficulty of ensuring their maximum compaction to increase the efficiency of their practical application.Problem Statement. The understanding of the principles due to which the basic parameters of such systems are formed is based on theoretical models that allow the parameterization of the measurement data in terms of parameters that characterize the individual pure components (reference data). The construction of such models is a very difficult task that requires phenomenological information from alternative sources.Purpose. Based on the Kirkwood-Buff theory and the data of analysis of experimental data on the study of macroscopic parameters of bi-dispersed granular mix we have developed a theoretical algorithm for describing and parameterizing its physical and mechanical characteristics in terms of its macroscopic and partial properties.Materials and Methods. The methods of theoretical statistical physics for bi-component model systems, in particular the Kirkwood-Buff theory, the model equation of the state (the Carnahan-Starling equation), and phenomenological information on the dynamics of compaction of binary granular mixes have been used in the research.Results. Using the Kirkwood-Buff and Carnahan-Starling theories and phenomenological data, we have developed a continuous description of the macroscopic properties of binary granular systems, which operates on the partial parameters of its components.Conclusions. The obtained data have confirmed the influence of multi-dispersion on the dynamics of compaction, i.e. the mix ability to change its local structure of packing under external impact.

A new approach is presented to improve the performance of semiempirical quantum mechanical (SQM) methods in the description of noncovalent interactions. To show the strategy, the PM6 Hamiltonian was selected, although, in general, the procedure can be applied to other semiempirical Hamiltonians and to different methodologies. A set of small molecules were selected as representative of various functional groups, and intermolecular potential energy curves (IPECs) were evaluated for the most relevant orientations of interacting molecular pairs. Then, analytical corrections to PM6 were derived from fits to B3LYP-D3/def2-TZVP reference–PM6 interaction energy differences. IPECs provided by the B3LYP-D3/def2-TZVP combination of the electronic structure method and basis set were chosen as the reference because they are in excellent agreement with CCSD(T)/aug-cc-pVTZ curves for the studied systems. The resulting method, called PM6-FGC (from functional group corrections), significantly improves the performance of PM6 and shows the importance of including a sufficient number of orientations of the interacting molecules in the reference data set in order to obtain well-balanced descriptions The authors thank financial support from Ministerio de Ciencia e Innovación (grant # PID2019-107307RB-I00) and Xunta de Galicia (ED431C 2021/40). We also thank CESGA for computational facilities SI

Seismic exploration is a complex process that depends on different sources of information. An essential one is seismic imaging, and much of its interpretation performance relies on high-quality processing, which is currently still very dependent on prone-to-error human mediation. Automation of such processing steps is necessary to reduce the amount of time to treat seismic data—usually months—and improve the outcome overall quality by reducing the inherent subjectivity in the process. One of the most critical steps in seismic processing is noise suppression, and ground roll is one of the most challenging and everyday noises observed in seismic prestack data. In this article, we propose a self-supervised two-step approach to attenuate ground-roll noise in seismic prestack images. First, we detect ground-roll-affected area using convolutional neural networks, and then, we filter ground-roll noise in the detected area using conditional generative adversarial networks (cGANs). For each of these steps, we propose to build paired noisy/noise-free training sets with no supervision or reference data, hence creating a self-supervised pipeline for filtering ground-roll noise. Our two-stage approach enables noise suppression in the affected area while preserving the signal in unaffected areas. In addition, we propose to refactor conventional qualitative metrics in the industry into quantitative scores disregarding any reference data to evaluate ground-roll suppression for different geologies and report reliable results compared with expert filtering.

This paper proposes an integrated navigation algorithm based on the deep learning method using lidar and inertial measurements. The proposed method develops a new synthetic structure of neural networks for implementing the Lidar-inertial odometry to generate a 6 degree of freedom pose estimation. The proposed network consists of component neural networks that reflect each sensor’s characteristics, then an integrating network for combining estimates from heterogeneous sensors at the terminal stage. To secure an efficient estimation performance, a compound loss function design is exploited. The performance of the proposed deep learning-based LIO algorithm was verified through artificially generated data sets based on a high fidelity dynamics simulator. Instead of using the well-known reference data set of ground vehicles, the employed data set reflects the full 3D dynamic characteristics of the drone as well as low-cost sensor characteristics considering onboard implementation. Through the flight simulator data set, the estimation performance of the proposed synthetic network was demonstrated.

With the developing technologies, the use of unmanned aerial vehicles’s (UAV) is increasing in all areas. Compared with the conventional photogrammetry and remote sensing sensors, UAVs are more convenient to collect data for small areas. In this study, the accuracy of UAV products was investigated in the archeological area of Eskişehir Şarhöyük. In order to produce reference data for the orthophoto and DTM accuracy analysis, a digital map from the test area was produced using in-situ measurements. Also, for the comparison of the point cloud, a small test area was determined and reference point cloud data was collected with terrestrial laser scanner. The comparison of the results showed significant difference between the UAV images and images collected by conventional methods. Thus, while there was 1 m difference between the data without the use of control points, and the use of control points significantly improved the results.

The validation of online perception algorithms in automotive systems requires a large amount of ground-truth data. Since manual labeling is inefficient and error-prone, an automatic generation of accurate and reliable reference data is desirable. We present a post-processing approach based on a particle-based dynamic occupancy grid representation of the environment. In contrast to existing online dynamic grid algorithms, our estimation additionally utilizes future measurements by applying offline smoothing algorithms. Our proposed concept uses a two-filter procedure for smoothing the occupancy states of the grid cells. We further introduce two methods based on particle reweighting and two-filter smoothing to improve the velocity estimates. We show that our approach enhances the situational awareness and thus provides a more precise environment model. We demonstrate these benefits using lidar data from real-world experiments.

The prediction of average Material Removal Rate (MRR) in Chemical Mechanical Planarization (CMP) process is regarded as a crucial research objective of Virtual Metrology (VM) for semiconductor manufacturing. In this paper, a novel VM model is proposed to predict MRR in CMP process based on the integration of Gaussian Process Regression (GPR) with a reference-based strategy. The proposed method estimates the similarity of the changing trends of the sensor traces using Maximum Mean Discrepancy (MMD) as a metric to extract reliable references for further prediction. The temporal information is also combined in the strategy by filtering the data samples by timestamps. Afterwards, GPR is used to fuse the reference data samples to predict the metrology with a confidence interval. Compared with the benchmarks in the recent literature, the proposed method, referred as MMD-GPR model, gives better prediction performance than ensemble learning methods and equivalent accuracy compared with Deep Neural Networks (DNN) but with a more efficient framework.

The coastline of the German Wadden Sea is constantly subjected to the tides and the tidal-induced environmental changes like erosion and accumulation of sediments need to be monitored constantly. This task requires digital elevation models (DEMs), which are derived from remote sensing data. To model those DEMs, a separation of data collected over landmasses and water bodies is required. In the GeoWAM project the potential of airborne SAR-data (F-SAR) is investigated for monitoring purposes in the Wadden Sea. As part of the project, this paper focuses on the suitability of F-SAR data regarding the derivation of water-land-boundaries (WLBs). Therefore, water-land-boundaries based on independent data sets are compared and evaluated. Analyzed data sets include data collected via F-SAR, airborne laserscanning (ALS), on site GNSS measured WLB points and sea-level data from two acoustic gauges. The algorithms were tested on a study site on Spiekeroog island. Our results show, that the accuracies of the derived WLBs mostly depend on the on-site topography and sediments. The spatial deviation between the reference data and the approximated WLBs is mostly less than 2 m horizontally and 0.15 m vertically. Identified challenges to overcome are mostly related to processing of F-SAR data in areas with highly water saturated sediments. Our results suggest, that F-SAR data in tidal flats is not necessarily dependent on further supplementing surveys, as one of the main advantages of the F-SAR data is the potential to derive DEMs and WLBs from the same data set.

Uncontrolled refraction of optical rays in underwater photogrammetry is known to reduce its accuracy potential. Several strategies have been proposed aiming at restoring the accuracy to levels comparable with photogrammetry applied in air. These methods are mainly based on rigours modelling of the refraction phenomenon or empirical iterative refraction corrections. The authors of this contribution have proposed two mitigation strategies of image residuals systematic patterns in the image plane: (i) empirical weighting of image observations as function of their radial position; (ii) iterative look-up table corrections computed in a squared grid. Here, a novel approach is developed. It explicitly takes into account the object point-to-camera distance dependent error introduced by refraction in multimedia photogrammetry. A polynomial correction function is iteratively computed to correct the image residuals clustered in radial slices in the image plane as function of the point-to-camera distance. The effectiveness of the proposed method is demonstrated by simulations that allow to: (i) separate the geometric error under investigation from other effects not easily modellable and (ii) have reliable reference data against which to assess the accuracy of the result., International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2021, ISSN:1682-1750, ISSN:2194-9034, ISSN:1682-1777

Recently, deep learning approaches have been successfully used for ultrasound (US) image artifact removal. However, paired high-quality images for supervised training are difficult to obtain in many practical situations. Inspired by the recent theory of unsupervised learning using optimal transport driven CycleGAN (OT-CycleGAN), here, we investigate the applicability of unsupervised deep learning for US artifact removal problems without matched reference data. Two types of OT-CycleGAN approaches are employed: one with the partial knowledge of the image degradation physics and the other with the lack of such knowledge. Various US artifact removal problems are then addressed using the two types of OT-CycleGAN. Experimental results for various unsupervised US artifact removal tasks confirmed that our unsupervised learning method delivers results comparable to supervised learning in many practical applications.

Based on the analysis of geometric and hydraulic characteristics of pressureless water channels cross sections, recommendations for parameters calculation of these systems with an arbitrary shape of their cross section were elaborated in this article. Elements of hydraulic modeling and analogy were used in the preparation of the presented material. The considered pressureless channels are widely used in rainwater disposal systems designing and can also be used in the designing of other special water disposal networks. Well-known theoretical and empirical hydraulic dependences, relevant reference data, in particular, discharge and velocity characteristics, local resistance coefficients were used in this article. Necessary information on selection of water disposal wells is given. The proposed method of calculation the parameters of water disposal channels can be used in different fluid flow modes: both pressureless and pressure, steady and unsteady. It is recommended for use in the water disposal networks designing, regardless of the pipelines material.

Satellite observations provide spatially resolved global estimates of column-averaged mixing ratios of CO 2 (XCO 2 ) over the Earth's surface. The accuracy of these datasets can be validated against reliable standards in some areas, but other areas remain inaccessible. To date, limited reference data over oceans hinder successful uncertainty quantification or bias correction efforts and preclude reliable conclusions about changes in the carbon cycle in some regions. Here, we propose a new approach to analyze and evaluate seasonal, interannual, and latitudinal variations of XCO 2 over oceans by integrating cargo-ship (Ship Of Opportunity – SOOP) and commercial aircraft (Comprehensive Observation Network for Trace gases by Airliner – CONTRAIL) observations with the aid of state-of-the art atmospheric chemistry-transport model calculations. The consistency of the “observation-based column-averaged CO 2 ” dataset (obs. XCO 2 ) with satellite estimates was analyzed over the western Pacific between 2014 and 2017, and its utility as a reference dataset evaluated. Our results demonstrate that the new dataset accurately captures seasonal and interannual variations of CO 2 . Retrievals of XCO 2 over the ocean from GOSAT (Greenhouse Gases Observing Satellite: National Institute for Environmental Studies – NIES v02.75; Atmospheric CO 2 Observation from Space – ACOS v7.3) and OCO-2 (Orbiting Carbon Observatory, v9r) observations show a negative bias of about 1 part per million (ppm) in northern midlatitudes, which was attributed to measurement uncertainties of the satellite observations. The NIES retrieval had higher consistency with obs. XCO 2 at midlatitudes as compared to the other retrievals. At low latitudes, it shows many fewer valid data and high scatter, such that ACOS and OCO-2 appear to provide a better representation of the carbon cycle. At different times, the seasonal cycles of all three retrievals show positive phase shifts of 1 month relative to the observation-based data. The study indicates that even if the retrievals complement each other, remaining uncertainties limit the accurate interpretation of spatiotemporal changes in CO 2 fluxes. A continuous long-term XCO 2 dataset with wide latitudinal coverage based on the new approach has great potential as a robust reference dataset for XCO 2 and can help to better understand changes in the carbon cycle in response to climate change using satellite observations.

Accurate prediction of the wall temperature downstream of the trailing-edge slot is crucial to designing turbine blades that can withstand the harsh aerothermal environment in a modern gas turbine. Because of their computational efficiency, industry relies on low-fidelity tools like RANS for momentum and thermal field calculations, despite their known underprediction of wall temperature. In this paper, a novel framework using a branch of machine learning, geneexpression programming (GEP) [Zhao et al. 2020, J. Comp. Physics, 411:109413] is used to develop closures for the turbulent heat-flux to improve upon this underprediction. In the original use of GEP (“frozen” approach), the turbulent heat-flux from a high-fidelity database was used to evaluate the fitness of the candidate closures during the symbolic regression, however, the resulting closure had no information of the temperature field during the optimisation process. In this work, the regression process of the GEP instead incorporates RANS calculations to evaluate the fitness of the candidate closures. This allows the inclusion of the temperature field from RANS to advance the iterative regression, leading to a more integrated heat-flux closure development, and consequently more accurate and robust models. The GEP-based CFD-driven framework is demonstrated on a trailing edge slot configuration with three blowing ratios. Full a posteriori predictions from the new closures are compared to high-fidelity reference data and both conventional RANS closures and closures obtained from the “frozen” approach.

PURPOSE To develop a simulation model for GammaMed Plus high dose rate 192 Ir brachytherapy source in TOPAS Monte Carlo software and validate it by calculating the TG-43 dosimetry parameters and comparing them with published data. METHODS We built a model for GammaMed Plus high dose rate brachytherapy source in TOPAS. The TG-43 dosimetry parameters including air-kerma strength SK , dose-rate constant Λ, radial dose function gL (r), and 2D anisotropy function F(r,θ) were calculated using Monte Carlo simulation with Geant4 physics models and NNDC 192 Ir spectrum. Calculations using an old 192 Ir spectrum were also carried out to evaluate the impact of incident spectrum and cross sections. The results were compared with published data. RESULTS For calculations using the NNDC spectrum, the air-kerma strength per unit source activity SK /A and Λ were 1.0139 × 10-7 U/Bq and 1.1101 cGy.h-1 .U-1 , which were 3.56% higher and 0.62% lower than the reference values, respectively. The gL (r) agreed with reference values within 1% for radial distances from 2 mm to 20 cm. For radial distances of 1, 3, 5, and 10 cm, the agreements between F(r,θ) from this work and the reference data were within 1.5% for 15°

The Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) is an open-source community model designed to address research questions that explore the role of the land surface in the global climate system. Here, we evaluate how well CLASSIC reproduces the energy, water, and carbon cycle when forced with quasi-observed meteorological data. Model skill scores summarize how well model output agrees with observation-based reference data across multiple statistical metrics. A lack of agreement may be due to deficiencies in the model, its forcing data, and/or reference data. To address uncertainties in the forcing, we evaluate an ensemble of CLASSIC runs that is based on three meteorological data sets. To account for observational uncertainty, we compute benchmark skill scores that quantify the level of agreement among independent reference data sets. The benchmark scores demonstrate what score values a model may realistically achieve given the uncertainties in the observations. Our results show that uncertainties associated with the forcing and observations are considerably large. For instance, for 10 out of 19 variables assessed in this study, the sign of the bias changes depending on what forcing and reference data are used. Benchmark scores are much lower than expected, implying large observational uncertainties. Model and benchmark score values are mostly similar, indicating that CLASSIC performs well when considering observational uncertainty. Future model development should address (i) a positive albedo bias and resulting shortwave radiation bias in parts of the Northern Hemisphere (NH) extratropics and Tibetan Plateau, (ii) an out-of-phase seasonal gross primary productivity cycle in the humid tropics of South America and Africa, (iii) a lacking spatial correlation of annual mean net ecosystem exchange with site-level measurements, (iv) an underestimation of fractional area burned and corresponding emissions in the boreal forests, (v) a negative soil organic carbon bias in high latitudes, and (vi) a time lag in seasonal leaf area index maxima in parts of the NH extratropics. Our results will serve as a baseline for guiding and monitoring future CLASSIC development.

The complex geological conditions of drilling, the difficulty of formation collapse and fracture pressure prediction in South Sichuan work area lead to the complex drilling and frequent failure, which seriously restricts the safe and efficient development of shale gas. In view of this problem, this paper has carried out relevant research. First of all, the existing calculation model of formation collapse and fracture pressure is established and improved; on this basis, the sources of uncertainty in the calculation model of collapse and fracture pressure are analyzed, mainly the in-situ stress and rock mechanics parameters, which have a lot of uncertainties; then, the uncertainty of rock mechanics parameters and in-situ stress is analyzed, and its probability is determined. Finally, based on Monte Carlo simulation, the quantitative characterization method of formation collapse and fracture pressure uncertainty is established. The prediction result of collapse and fracture pressure is no longer a single curve or value, but an interval, which is more practical for drilling in complex geological environment. The results of this study are helpful to better describe the collapse and fracture pressure of complex formation and can provide more valuable reference data for drilling design.

Grey correlation analysis uses grey correlations to describe the strength, magnitude, and order of relationships among factors. The data sequence of the drmined data is used as a reference data string, and the data sequence matrix of each influence factor is used as a control data matrix to calculate the correlation between the data sequence and the reference of each factor in the control data matrix. The basic idea of quantitatively measuring the correlation between each factor and the object is to determine the similarity between the reference data sequence and the shape of multiple comparison data sequences and to determine whether the connection is strong. It reflects the degree of correlation between curves. In this paper, we examine the problem of evaluating the quality of undergraduate education by multiple indicators and establish a comprehensive evaluation model of the quality of undergraduate education in the 13 prefecture level cities by using the grey correlation analysis. The correlation coefficient of each index was obtained by the grey correlation analysis, the correlation number of each index was analyzed and ranked, and finally the improvement of the number of “advanced teachers” and the education of 13 prefectures were reduced as much as possible.

One method of measuring precipitation and wind over the ocean is through analysis of the underwater ambient acoustics. In this study, the ambient ocean noises recorded by a passive aquatic listener (PAL) in the Mediterranean are used to compare the effectiveness of the machine learning techniques for measuring the wind speed and precipitation rate with the empirical methods from previous works. The data were collected over the timeframe of June 2011 to May 2012 and included two storms that caused severe coastal flooding in Genoa, Italy. A spar buoy at the surface above the PAL provided high-quality in situ measurements to act as the reference data for model training and validation. The results using the machine learning models show correlation coefficients of 0.95 between the acoustic data and wind speed and a reduction in unexplained variance by over a third from previous methods. For precipitation, CatBoost and random forest models are used to measure the total precipitation for 12 events using leave-one-event-out cross-validation, demonstrating mean errors of 28% and 34% and median errors of 18% and 17%, respectively. The ability to measure wind and precipitation by applying machine learning on data from underwater acoustic recorders shows potential to help improve in situ measurements over oceans globally.

The estimation of soil permittivity under fully covered grassland is a challenging task that can be approached by either model-based polarimetric decomposition techniques or data-driven machine-learning (ML) methods. In this study, we test the benefits and limitations of those techniques when individually or jointly applied to estimate the permittivity of the top soil (lower than 5-cm depth) from the L-band full-polarimetric SAR data. Training (needed for the ML approach) and reference data for accuracy assessment are based on the soil-permittivity measurements from an $in\,\, situ$ sensor network. The applied polarimetric decomposition approaches are unable to estimate high soil-permittivity ranges (permittivity higher than 25–30) under the full-cover grassland leading to an underestimation compared with the $in\,\, situ$ values. Purely data-driven ML techniques, here a case-adapted Random Forest (RF) architecture, applied directly to the SAR data achieve similar results as the decomposition approaches, where estimation quality mostly depends on the quality of the training set. The combination of both techniques works best and is able to represent high soil-permittivity ranges. The joint estimation decreases the mean absolute error drastically compared with applying any of the two approaches alone (i.e., from 4.88 and 4.99 of an informed physical model and ML on SAR only to 3.35).

There is a paucity of studies on the relationship between the control parameters and kinetic parameters in binary-size granular segregation models. In this study, the machine vision method and particle image velocimetry (PIV) were used to measure the shape index of segregation pattern and streamwise velocity, respectively. Two scaled parameters, control parameters and kinetic parameters, were calculated based on the continuous flow model and segregation model. The control parameters include volume flow rate, and the kinetic parameters include the ratio of segregation effects to diffusion effects. By analyzing the relationship between the two parameters under the core and streak patterns, the configuration of segregation pattern at steady-state changes with the control parameters wherein the ratio of segregation effects to diffusion effects decreases approximately linearly as the volume flow rate decreases. These experimental results aid in revealing the mechanism of segregation and providing the reference data for improving the theoretical models.

We investigate the main challenges to prediction of turbulent external flows of practical interest with Reynolds-Averaged Navier–Stokes equations (RANS) and Scale-Resolving Simulation (SRS) models. This represents a crucial step toward further developing and establishing these formulations so they can be confidently utilized in engineering problems without reference data. The study initiates by identifying the major challenges to prediction. A literature review is performed to illustrate their effects in RANS and SRS computations. Afterward, we evaluate the impact of the challenges to prediction by analyzing representative statistically steady and unsteady flows with prominent RANS and SRS methods. These include multiple turbulent viscosity and second-moment RANS closures, and hybrid and bridging SRS models. The results demonstrate the potential of the selected SRS models to predict engineering flows. Yet, they also show the importance of considering the challenges to prediction during the setup and conduction of numerical experiments. These can suppress the advantages of using SRS formulations. The data also indicate that only SRS models can confidently predict statistically unsteady flows. In contrast, the results demonstrate that mean-flow quantities of statistically steady flows can be efficiently calculated with RANS closures, especially second-moment closures. Among the selected SRS methods, bridging models reveal better suited for prediction due to their ability to prevent commutation errors and enable the robust evaluation of numerical and modeling errors. This last property allows the use of a new validation technique that does not require reference data.

The synthesis of CuCrO2 crystals by mixing CuO and Cr2O3 has been carried out using the solid reaction method at a temperature of 1200 0C. The characterization of the structure used XRD and analyzed using GSAS software. The results of characterization using XRD showed that no other phase occurred. This is evidenced by the absence of other phases from the results of refinement of measurement data with reference data and a value of χ 2 which is 1.222. The lattice parameter values resulting from the refinement of the CuCrO2 X-ray diffraction pattern are a = b = 2.9715 Å and c = 17.1104 Å with a cell volume of 130.584 Å 3. In addition to the lattice parameter values, the distance between atoms was also obtained, both Cu - O, Cr - Cr, and Cr - O.

To further progress the validation of global leaf area index (LAI) products, temporally continuous reference data are a key requirement, as periodic field campaigns fail to adequately characterize temporal dynamics. Progress in cost-effective automated measurement techniques has been made in recent years, but appropriate upscaling methodologies are less mature. Recently, the use of multitemporal transfer functions has been proposed as a potential solution. Using data collected during an independent field campaign, we evaluated the performance of both vegetation index-based multitemporal transfer functions and a radiative transfer model (RTM)-based upscaling approach. Whether assessed using cross validation or data from the independent field campaign, the RTM-based approach provided the best performance (r2 ≥ 0.88, RMSE ≤ 0.41, NRMSE

In this paper, a detailed combined 1-D model of HVAC systems of a vehicle were developed by using the LMS imagine LAB AMESIM software package. The numerical simulations were considered for soaking and cool down analysis under different environmental conditions. The thermal performance of different refrigerants as R-134a and R-1234yf were evaluated in terms of thermal performance and energy consumption. According to the soaking simulation results, the cabin air temperature values ranged from 49?C to 57?C in general. The maximum increase in cabin air temperature value was about 22?C obtained for 1000 W/m2 solar load. The total time until reaching the steady-state conditions for a target temperature value (23.5?C) was different for all simulations. The total time was calculated as 910 seconds for 1000 W/m2 solar load by using R-134a refrigerant loop. The results also showed that although the thermal performance of R-134a was slightly better, R-1234yf can be used due to its environmental properties with acceptable performance. The calculated COP values during cooldown analysis were ranged from 1.71 to 4.52 in general. The minimum value was obtained for the cases which had a maximum solar load and higher cabin interior temperature values. The calculated temperature data for soaking and cool down analysis were in good agreement with the reference data presented in this study. These numerical results are very important for reducing the thermal load of the vehicle cabin considering energy consumption of the HVAC system for different thermal conditions.

To monitor the insulation deterioration of power equipment and realize prompt fault warning systems in air-insulated substations, in this study, we propose a multiple signal classification (MUSIC) algorithm-based partial discharge (PD) localization method with an angle of arrival (AOA) and ultrahigh frequency (UHF)-received signal strength indicator (RSSI). Compared with traditional UHF time-difference-based techniques, this RSSI-based AOA localization method is a more economical solution. In addition, by comparing the measured RSSI vector to a prebuilt reliable reference data set, the MUSIC method can effectively locate the direction of the PD source with high accuracy. Compared with the method that directly determines the smallest RSSI values by several sensors, this method can accomplish localization by fewer sensors without impeding accuracy. Furthermore, the interpolation method was adopted to improve the precision of the relationship curve of AOA/RSSI, which it did with a limited number of sensors. Laboratory tests were conducted to verify the accuracy of the proposed method, and most of the localization errors were less than 1°, which indicates its potential application in the prompt identification of faults regarding the insulation deterioration of power equipment in substations.

Functional near-infrared spectroscopy (fNIRS) in brain imaging needs to be robust to subject-wise variability. The use of a fixed differential pathlength factor (DPF) per wavelength for the entire brain will degrade the accuracy of hemodynamic responses. Since the tissue composition varies within the brain, correct DPF values should be used for various emitter-detector distances and brain regions. In this article, a DPF estimation method combining a state-space model of the modified Beer-Lambert law (mBLL), a parameter model for estimating the reduced scattering coefficients, and dual square-root cubature Kalman filters (SCKFs) is proposed. To validate the proposed method, known light intensities (six channels, two wavelengths) and reference DPFs are generated using NIRFAST (a Matlab toolbox) using a presumed paradigm, known tissue properties, a Balloon model, and a finite element head model consisting of 58,818 mesh elements. Then, the DPF values are estimated using a Jacobian matrix from the head model and the mBLL. The results show that the estimated concentration changes correlate well with the reference data. Also, the estimated DPFs showed relative errors less than 1.33% maximum and 0.75% on average. A one-tailed $t$ -test revealed that the estimated DPFs matched the reference DPFs with more than 99.9% confidence. The developed method can efficiently access the actual DPFs even if emitter-detector distances vary significantly and the tissue properties are not uniform. With the developed state-space models for dual SCKFs, real-time estimation of the DPFs from one experiment to another has become plausible.

Walking/gait speed is a key measure for daily mobility characterization. To date, various studies have attempted to design algorithms to estimate walking speed using an inertial sensor worn on the lower back, which is considered as a proper location for activity monitoring in daily life. However, these algorithms were rarely compared and validated on the same datasets, including people with different preferred walking speed. This study implemented several original, improved, and new algorithms for estimating cadence, step length and eventually speed. We designed comprehensive cross-validation to compare the algorithms for walking slow, normal, fast, and using walking aids. We used two datasets, including reference data for algorithm validation from an instrumented mat (40 subjects) and shanks-worn inertial sensors (88 subjects), with normal and impaired walking patterns. The results showed up to 50% performance improvements. Training of algorithms on data from people with different preferred speeds led to better performance. For the slow walkers, an average RMSE of 2.5 steps/min, 0.04 m, and 0.10 m/s were respectively achieved for cadence, step length, and speed estimation. For normal walkers, the errors were 3.5 steps/min, 0.08 m, and 0.12 m/s. An average RMSE of 1.3 steps/min, 0.05 m, and 0.10 m/s were also observed on fast walkers. For people using walking aids, the error significantly increased up to an RMSE of 14 steps/min, 0.18 m, and 0.27 m/s. The results demonstrated the robustness of the proposed combined speed estimation approach for different speed ranges. It achieved an RMSE of 0.10, 0.18, 0.15, and 0.32 m/s for slow, normal, fast, and using walking aids, respectively. This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Content in this publication reflects the authors’ view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.

Nighttime light image (NTL) is an effective data for urban built-up area. Due to the shortcomings of low resolution and the blooming effect of NTL, the extraction result of the original NTL image is not ideal. We combined point of interest (POI), road network, and land surface temperature (LST) data to propose the POI, road density, and LST-adjusted NTL Index (PRLANI) to correct the luminance value of the NTL image. The proposed index was verified by NTL images of different developed areas and different spatial resolutions. The threshold segmentation method was used to obtain the result of the built-up area extraction from the original NTL and the image modified by the PRLANI. Then, we generated the vector boundary from the built-up area. Taking Changchun urban area as an example, the kappa coefficient based on PRLANI-VIIRS is 0.802, which is 0.293 higher than of NTL-VIIRS. The kappa coefficient based on PRLANI-LJ is 0.838, which is 0.179 higher than that of NTL-LJ. The results show that: the PRLANI can effectively suppress the blooming effect and improve the overestimation of the built-up area caused by light dimming; the PRLANI can enhance the weak light signal in the original NTL image and compensate for the missing information of the built-up area; and the fusion of road network data improves the extraction accuracy and refines the internal structure of built-up area, especially the NPP/VIIRS. The vector boundary was consistent with the built-up area range of reference data, and the visualization effect was good.

The aims of this study were to compare the outcomes and provide reference data for a set of barbell mechanical parameters collected via a linear velocity transducer in 126 male sprinters (n = 62), rugby players (n = 32), and soccer players (n = 32). Bar-velocity, bar-force, and bar-power outputs were assessed in the jump-squat exercise with jump-squat height determined from bar-peak velocity. The test started at a load of 40% of the athletes’ body mass (BM), and a load of 10% of BM was gradually added until a clear decrement in the bar power was observed. Comparisons of bar variables among the three sports were performed using a one-way analysis of variance. Relative measures of bar velocity, force, and power, and jump-squat height were significantly higher in sprinters than in rugby (difference ranging between 5 and 35%) and soccer (difference ranging between 5 and 60%) players across all loads (40–110% of BM). Rugby players exhibited higher absolute bar-power (mean difference = 22%) and bar-force (mean difference = 16%) values than soccer players, but these differences no longer existed when the data were adjusted for BM (mean difference = 2.5%). Sprinters optimized their bar-power production at significantly greater relative loads (%BM) than rugby (mean difference = 22%) and soccer players (mean difference = 25%); nonetheless, all groups generated their maximum bar-power outputs at similar bar velocities. For the first time, we provided reference values for the jump-squat exercise for three different bar-velocity measures (i.e., mean, mean propulsive, and peak velocity) for sprinters, rugby players, and soccer players, over a wide range of relative loads. Practitioners can use these reference values to monitor their athletes and compare them with top-level sprinters and team-sport players.

This article compares the performance of four different interferometric synthetic aperture radar (SAR) techniques for the estimation of rice crop height by means of bistatic TanDEM-X data. Methods based on the interferometric phase alone, on the coherence amplitude alone, on the complex coherence value, and on polarimetric SAR interferometry (PolInSAR) are analyzed. Validation is conducted with reference data acquired over rice fields in Spain during the Science Phase of the TanDEM-X mission. Single- and dual-polarized data are exploited to also provide further insights into the polarization influence on these approaches. Vegetation height estimates from methodologies based on the interferometric phase show a general underestimation for the HH channel (with a bias that reaches around 25 cm in mid-July for some fields), whereas the VV channel is strongly influenced by noisy phases, especially at large incidences [root-mean-square error (RMSE) = 31 cm]. Results show that these approaches perform better at shallower incidences than the methodologies based on coherence amplitude and on PolInSAR, which obtain the most suitable results at steep incidences, with RMSE values of 17 and 23 cm. On the contrary, at shallower incidences, they are highly affected by very low input coherence levels. Hence, they tend to overestimate vegetation height. This work was supported by the Spanish Ministry of Science and Innovation, in part by the State Agency of Research, and in part by the European Funds for Regional Development under Project TEC2017-85244-C2-1-P. The work of Noelia Romero-Puig was supported in part by the Generalitat Valenciana and in part by the European Social Fund under Grant ACIF/2018/204.