Search

Crop simulation models are valuable tools for decision making regarding evaluation and crop improvement under different field conditions. CSM-CROPGRO model integrates genotype, environment and crop management portfolios to simulate growth, development and yield. Modeling the safflower response to varied climate regimes are needed to strengthen its productivity dynamics. The main objective of the study was to evaluate the performance of DSSAT-CSM-CROPGRO-Safflower (Version 4.8.2) under diverse climatic conditions. The model was calibrated using the field observations for phenology, biomass and safflower grain yield (SGY) of the year 2016–17. Estimation of genetic coefficients was performed using GLUE (Genetic Likelihood Uncertainty Estimation) program. Simulated results for days to flowering, maturity, biomass at flowering and maturity and SGY were predicted reasonably with good statistical indices. Model evaluation results elucidate phenological events with low root mean square error (6.32 and 6.52) and high d-index (0.95 and 0.96) for days to flowering and maturity respectively for all genotypes and climate conditions. Fair prediction of safflower biomass at flowering and maturity showed low RMSE (887.3 and 564.3 kg ha−1) and high d-index (0.67 and 0.93) for the studied genotypes across the environments. RMSE for validated safflower grain yield (101.8 kg ha−1) and d-index (0.95) depicted that model outperformed for all genotypes and growing conditions. Longer appropriate growing conditions at NARC-Islamabad took optimal duration to assimilate photosynthetic products lead to higher grain yield. Safflower resilience to different environments showed that it can be used as an alternate crop for different agroecological regions. Furthermore, CROPGRO-Safflower model can be used as tool to further evaluate inclusion of safflower in the existing cropping systems of studied regions. [ABSTRACT FROM AUTHOR]

Background: To minimize measurement uncertainties and create seamless procedures between tests and simulations for the characterization and prediction of damage in large scale structures, a system capable of monitoring the quantities of interest at different scales throughout the test is required. Objective: The aim of this work is to develop a multiview DIC framework at varying scales in which kinematic fields are expressed on a unique mesh. Methods: A three-point flexural test was performed on a concrete beam and the images acquired by three different cameras were used to perform DIC calculations. Results: Displacement and strain fields were measured using mono and multiview implementations; their associated uncertainties were assessed. Damage initiation and growth within the sample was quantified based on the standard displacement uncertainty. Conclusion: The reported results show that the proposed method reduced the associated displacement uncertainties. The onset and propagation of damage was successfully quantified. [ABSTRACT FROM AUTHOR]

• The calibration problem of the general acupuncture robot is analyzed. • A simultaneous offline calibration method for problems defined with multiple closed kinematic chains is proposed. • A multi-objective optimization model covering all coupling components and constraints is constructed and solved. • A real-world platform with a customized calibration board is built. • The proposed method has higher calibration accuracy and convergence speed. Acupuncture robot is a new-era product combining traditional acupuncture and cutting-edge technology. The calibration of the vision system and the acupuncture mechanism is a crucial prerequisite for humanoid acupuncture control, which has not yet been explored. In this paper, a simultaneous offline calibration method is proposed for acupuncture robots. Analysis reveals that its calibration problem is defined with three closed kinematic chains, while the typical problems cover only a single chain. Decoupling them, a Kronecker product-based method is deduced to access the closed-form rotation component. As opposed to quaternion-based methods, it only experiences linear complexity in sign ambiguity, which can produce a more precise solution in finite time. Further, a simultaneous optimization model that encompasses all components is established, which can be solved with stochastic gradient descent-based methods. It is free of truncation errors and thus has higher calibration accuracy and convergence speed. Besides, the impairment in error propagation between different closed kinematic chains is mitigated compared to step-by-step methods. Finally, simulations and experiments are carried out. Notably, the proposed method can be easily extended to other robot calibration problems with multiple closed kinematic chains. [ABSTRACT FROM AUTHOR]

In this study, we assess the homogeneity of aerosol optical depth (AOD) between the sun photometer networks Global Atmospheric Watch-Precision Filter Radiometer (GAW-PFR) and European Skynet Radiometers network (ESR) at the 2 common wavelengths of their main instruments 500 nm and 870 nm. The main focus of the work is on evaluating the effect of the Improved Langley calibration method, (ILP) used by SKYNET and investigating the factors affecting its performance. We used data from three intercomparison campaigns that took place in the period 2017–2021. Each campaign has two phases in two locations. One is mountainous rural (Davos, Switzerland) and the other urban (Rome, Italy). Our analysis shows that the AOD differences due to post processing and instrument differences are minor. The major factor leading to AOD differences is the calibration method where we found a systematic underestimation of AOD compared to GAW-PFR due to an underestimation in the ILP calibration. The calibration and AOD differences are smaller in Davos where at 870 nm the traceability criteria are satisfied and at 500 nm the median differences are below 0.01. In Rome at 500 nm the AOD median differences per campaign are between 0.015–0.035. Attempting to explain the differences we found no association between the calibration performance and the level or the variability of the aerosol properties. We also conducted a sensitivity study, which shows that part of the difference can be potentially explained by errors in the assumed surface albedo and instrument solid view angle provided as inputs to the ILP code (based on Skyrad pack 4.2). Our findings suggest that the ILP method is mainly sensitive to the measured sky radiance. The calibration underestimation is probably caused by an error on the retrieved scattering aerosol optical depth (sc-AOD) through the sky radiance inversion. Using an alternative retrieval method (Skyrad MRI pack version 2) to derive sc-AOD and repeat ILP calibration, we found no significant differences between the retrieved sc-AOD nor systematic increase of the calibrations. The potential error may be a result of the forward model assumptions, To conclude, calibration of sun photometers on site offers the advantage of avoiding instrument shipments and data gaps. However, ILP shows larger uncertainty and significant systematic difference compared to the traditional Langley calibration performed under low and constant AOD conditions at high altitude sites, due to the uncertainties of the calibration method and the input parameters needed for it. In the following sections we report on results on the AOD retrievals of several instruments in different environments using different principles in their calibration methods. We also perform an investigation to explain the causes of differences. [ABSTRACT FROM AUTHOR]

Accurate calibration of a Stewart platform is important for their precise and efficient operation. However, the calibration of these platforms using forward kinematics is a challenge for researchers because forward kinematics normally generates multiple feasible and unfeasible solutions for any pose of the moving platform. The complex kinematic relations among the six actuator paths connecting the fixed base to the moving platform further compound the difficulty in establishing a straightforward and efficient calibration method. The authors developed a new forward kinematics-based calibration method using Denavit-Hartenberg (DH) convention and used the Stewart platform "Tiger 66.1" developed in their lab for experimenting with the photogrammetry-based calibration strategies described in this paper. This system became operational upon completion of construction, marking its inaugural use. The authors used their calibration model for estimating the errors in the system and adopted three compensation options or strategies as per least-square method to improve the accuracy of the system. These strategies leveraged a high-resolution digital camera and off-the-shelf software to capture the poses of the moving platform's center. This process is non-invasive and does not need any additional equipment to be attached to the hexapod or any alteration of the hexapod hardware. This photogrammetry-based calibration process involves multiple high-resolution images from different angles to measure the position and orientation of the platform center in the three-dimensional space. The target poses and actual poses are then compared, and the error compensations are estimated using the least-square methods to calculate the predicted poses. Results from each of the three compensation approaches demonstrated noticeable enhancements in platform pose accuracies, suggesting room for further improvements. Given that "Tiger 66.1" is based on the general Stewart platform structure, the proposed calibration method holds promise for extension to machines operating on similar principles where non-invasive calibration is desirable. This study contributes to advancing the field of Stewart platform calibration, paving the way for more precise and efficient applications in various domains. [ABSTRACT FROM AUTHOR]

AbstractThis article proposes a new kriging that has a rational form. It is shown that the generalized least squares estimator of the mean from rational kriging is much more well behaved than that of ordinary kriging. Parameter estimation and uncertainty quantification for rational kriging are proposed using a Gaussian process framework. A generalized version of rational kriging is also proposed, which includes ordinary and rational kriging as special cases. Extensive simulations carried out over a wide class of functions show that the generalized rational kriging performs on par or better than both ordinary and rational kriging in terms of prediction and uncertainty quantification. The only extra step needed for generalized rational kriging over ordinary kriging is the computation of Perron eigenvector of an augmented correlation matrix which can be computed in near linear time and therefore, its overall computational complexity is no more than that of ordinary kriging. The potential applications of the new kriging methods in the emulation of computationally expensive models and model calibration problems are illustrated with real and simulated examples. [ABSTRACT FROM AUTHOR]

Electrical tomography sensors have been widely used for pipeline parameter detection and estimation. Before they can be used in formal applications, the sensors must be calibrated using enough labeled data. However, due to the high complexity of actual measuring environments, the calibrated sensors are inaccurate since the labeling data may be uncertain, inconsistent, incomplete, or even invalid. Alternatively, it is always possible to obtain partial data with accurate labels, which can form mandatory constraints to correct errors in other labeling data. In this paper, a semi-supervised fuzzy clustering algorithm is proposed, and the fuzzy membership degree in the algorithm leads to a set of mandatory constraints to correct these inaccurate labels. Experiments in a dredger validate the proposed algorithm in terms of its accuracy and stability. This new fuzzy clustering algorithm can generally decrease the error of labeling data in any sensor calibration process. [ABSTRACT FROM AUTHOR]

Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added. [ABSTRACT FROM AUTHOR]

A phenomenological material model has been developed to facilitate the efficient numerical analysis of fiber-reinforced high-performance concrete (HPC). The formulation integrates an elasto-plastic phase-field model for simulating fractures within the HPC matrix, along with a superimposed one-dimensional elasto-plasticity model that represents the behavior of the embedded fibers. The Drucker–Prager plasticity and one-dimensional von-Mises plasticity formulations are incorporated to describe the nonlinear material behavior of both the HPC matrix and the fibers, respectively. Specific steps are undertaken during the development and calibration of the phenomenological material model. In the initial step, an experimental and numerical analysis of the pullout test of steel fibers embedded in an HPC matrix is conducted. This process is used to calibrate the micro-mechanical model based on the elasto-plastic phase-field formulation for fracture. In the subsequent step, virtual experiments based on an ellipsoidal unit cell, also with the resolution of fibers (used as a representative volume element, RVE), are simulated to analyze the impact of fiber–matrix interactions and their physical properties on the effective material behavior of fiber-reinforced HPC. In the final step, macroscopic boundary value problems (BVPs) based on a cuboid are simulated on a single scale using the developed phenomenological material model. The resulting macroscopic stress–strain characteristics obtained from both types of simulations, namely simulations of virtual experiments and macroscopic BVPs, are compared. This comparison is utilized for the calibration of material parameters to obtain a regularized solution and to assess the effectiveness of the presented phenomenological material model. [ABSTRACT FROM AUTHOR]

The GaoFen (GF)-5-02 satellite is one of the new generations of hyperspectral observation satellites launched by China in 2021. The directional polarimetric camera (DPC) is an optical sensor onboard the GF-5-02 satellite. The precipitable water vapor (PWV) is a key detection parameter of DPC. However, the existing PWV data developed using DPC data have significant errors due to the lack of the timely calibration of the two bands (865, 910 nm) of DPC used for PWV retrieval. In order to acquire DPC PWV data with smaller errors, a calibration method is developed for these two bands. The method consists of two parts: (1) calibrate the 865 nm band of the DPC using the cross-calibration method, (2) calibrate the 910 nm band of the DPC according to the calibrated 865 nm band of the DPC. This method effectively addresses the issue of the absence of a calibration method for the water vapor absorption band (910 nm) of the DPC. Regardless of whether AERONET PWV data or SuomiNET PWV data are used as the reference data, the accuracy of the DPC PWV data developed using calibrated DPC data is significantly superior to that of the DPC PWV data retrieved using data before recalibration. This means that the calibration method for the NIR bands of the DPC can effectively enhance the quality of DPC PWV data. [ABSTRACT FROM AUTHOR]

In this paper, a matching method for altimeter and transponder signals in Sub-optimal Maximum Likelihood Estimate (SMLE) tracking mode is proposed. In the in-orbit calibration of the altimeter in SMLE tracking mode using the reconstructive transponder, it is necessary to separate the forwarding signal from the ground echo signal. At the same time, the fluctuations in the received signal of the altimeter, which are caused by the forwarding signal of the transponder, can be eliminated. The transponder generates a bias when measuring the arrival time of the transmitting signal from the altimeter and embeds this bias in both the transponder-recorded data and the altimeter-recorded data. Therefore, the two sets of data have one-to-one correspondence, and they are superimposed using the sliding sum method. Moreover, the distance between the altimeter and the transponder is a parabolic geometric relationship, and the outliers are eliminated by the fitting error minimization decision, and the transponder signal is separated from the ground echo. The final altimeter transmitting–receiving signal path is obtained. Furthermore, the principles underlying this method can be used for any transponder that can adjust the response signal delay during calibration. [ABSTRACT FROM AUTHOR]

High-resolution melting (HRM) is a cost-efficient tool for targeted DNA methylation analysis. HRM yields the average methylation status across all CpGs in PCR products. Moreover, it provides information on the methylation pattern, e.g., the occurrence of monoallelic methylation. HRM assays have to be calibrated by analyzing DNA methylation standards of known methylation status and mixtures thereof. In general, DNA methylation levels determined by the classical calibration approach, including the whole temperature range in between normalization intervals, are in good agreement with the mean of the DNA methylation status of individual CpGs determined by pyrosequencing (PSQ), the gold standard of targeted DNA methylation analysis. However, the classical calibration approach leads to highly inaccurate results for samples with heterogeneous DNA methylation since they result in more complex melt curves, differing in their shape compared to those of DNA standards and mixtures thereof. Here, we present a novel calibration approach, i.e., temperature-wise calibration. By temperature-wise calibration, methylation profiles over temperature are obtained, which help in finding the optimal calibration range and thus increase the accuracy of HRM data, particularly for heterogeneous DNA methylation. For explaining the principle and demonstrating the potential of the novel calibration approach, we selected the promoter and two enhancers of MGMT, a gene encoding the repair protein MGMT. [ABSTRACT FROM AUTHOR]

Radar imaging SATellite (RISAT-1A), also known as Earth observation satellite-04 (EOS-04), is a follow-on mission of India’s first indigenously developed spaceborne C-band synthetic aperture radar (SAR) on-board RISAT-1 satellite. This article provides a description of the post-launch calibration and data quality evaluation of EOS-04 launched on 14 February 2022. Calibration devices (corner reflectors) of different shapes and sizes were deployed at Ahmedabad and Amrapur calibration sites in Gujarat, India from 2 to 24 April 2022, and their response in the EOS-04 data was used to assess the radiometric and polarimetric calibration. The results of the analysis showed satisfactory radiometric and polarimetric data quality. Geolocation accuracy was assessed using the ground-surveyed position of the corner reflectors and was found to be in accordance with the specified values of less than 50 m. [ABSTRACT FROM AUTHOR]

AbstractProbability predictions are essential to inform decision making across many fields. Ideally, probability predictions are (i) well calibrated, (ii) accurate, and (iii) bold, that is, spread out enough to be informative for decision making. However, there is a fundamental tension between calibration and boldness, since calibration metrics can be high when predictions are overly cautious, that is, non-bold. The purpose of this work is to develop a Bayesian model selection-based approach to assess calibration, and a strategy for boldness-recalibration that enables practitioners to responsibly embolden predictions subject to their required level of calibration. Specifically, we allow the user to pre-specify their desired posterior probability of calibration, then maximally embolden predictions subject to this constraint. We demonstrate the method with a case study on hockey home team win probabilities and then verify the performance of our procedures via simulation. We find that very slight relaxation of calibration probability (e.g., from 0.99 to 0.95) can often substantially embolden predictions when they are well calibrated and accurate (e.g., widening hockey predictions’ range from 26%–78% to 10%–91%). [ABSTRACT FROM AUTHOR]

The tip of the red giant branch (TRGB) based distance method in the I band is one of the most efficient and precise techniques for measuring distances to nearby galaxies (D ≲ 15 Mpc). The TRGB in the near-infrared (NIR) is 1–2 mag brighter relative to the I band, and has the potential to expand the range over which distance measurements to nearby galaxies are feasible. Using Hubble Space Telescope (HST) imaging of 12 fields in eight nearby galaxies, we determine color-based corrections and zero-points of the TRGB in the Wide Field Camera 3 IR (WFC3/IR) F110W and F160W filters. First, we measure TRGB distances in the I band equivalent Advanced Camera System (ACS) F814W filter from resolved stellar populations with the HST. The TRGB in the ACS F814W filter is used for our distance anchor and to place the WFC3/IR magnitudes on an absolute scale. We then determine the color dependence (a proxy for metallicity/age) and zero-point of the NIR TRGB from photometry of WFC3/IR fields that overlap with the ACS fields. The new calibration is accurate to ∼1% in distance relative to the F814W TRGB. Validating the accuracy of the calibrations, we find that the distance modulus for each field using the NIR TRGB calibration agrees with the distance modulus of the same field as determined from the F814W TRGB. This is a JWST preparatory program, and the work done here will directly inform our approach to calibrating the TRGB in JWST NIRCam and NIRISS photometric filters. [ABSTRACT FROM AUTHOR]

Automated radio telemetry systems have become a popular and invaluable tool in tracking the activity and movement of wild animals. However, many environmental conditions can hinder accuracy when tracking with this technology. For instance, study sites may contain multiple habitat types, each habitat uniquely affecting the signal strength received from tagged species. To investigate the influence of a structurally diverse study site on an automated radio telemetry system, we conducted this project at a restored and managed pine barren habitat that consisted of a mix of mature pitch pine, treated pitch pine, scrub oak, and hardwood forests. This site, Montague Plains Wildlife Management Area, Montague, Massachusetts, is also a known breeding ground for Eastern whip-poor-will (Antrostomus vociferus). To measure the relationship of radio signal strength with distance across each habitat, we used radio telemetry equipment manufactured by Cellular Tracking Technologies. We produced negative exponential decay functions measuring radio signal strength over distance and tested for differences among habitat types on radio signal strength (RSS). We found that decay function parameters significantly differed by habitat type, prompting us to investigate if accounting for these differences improved location estimate accuracy. To test this, we estimated known locations using trilateration methods with and without habitat calibration. Comparing these tests indicates that habitat-specific adjustments significantly improved location accuracy. Lastly, we visualized estimated RSS-based locations of 1 week of whip-poor-will data and compared them to GPS data generated from the same individual. Previous studies have accounted for types of environmental interference (like elevation) in the field but have avoided incorporating habitat-specific factors by working with node networks covering a relatively small area, but in this study, we examined the potential to scale up for larger areas and in more complex habitats. [ABSTRACT FROM AUTHOR]

The autonomous navigation of mobile robots contains three parts: map building, global localization, and path planning. Precise pose data directly affect the accuracy of global localization. However, the cumulative error problems of sensors and various estimation strategies cause the pose to have a large gap in data accuracy. To address these problems, this paper proposes a pose calibration method based on localization and point cloud registration, which is called L-PCM. Firstly, the method obtains the odometer and IMU (inertial measurement unit) data through the sensors mounted on the mobile robot and uses the UKF (unscented Kalman filter) algorithm to filter and fuse the odometer data and IMU data to obtain the estimated pose of the mobile robot. Secondly, the AMCL (adaptive Monte Carlo localization) is improved by combining the UKF fusion model of the IMU and odometer to obtain the modified global initial pose of the mobile robot. Finally, PL-ICP (point to line-iterative closest point) point cloud registration is used to calibrate the modified global initial pose to obtain the global pose of the mobile robot. Through simulation experiments, it is verified that the UKF fusion algorithm can reduce the influence of cumulative errors and the improved AMCL algorithm can optimize the pose trajectory. The average value of the position error is about 0.0447 m, and the average value of the angle error is stabilized at about 0.0049 degrees. Meanwhile, it has been verified that the L-PCM is significantly better than the existing AMCL algorithm, with a position error of about 0.01726 m and an average angle error of about 0.00302 degrees, effectively improving the accuracy of the pose. [ABSTRACT FROM AUTHOR]

Ransomware poses a significant threat by encrypting files or systems demanding a ransom be paid. Early detection is essential to mitigate its impact. This paper presents an Uncertainty-Aware Dynamic Early Stopping (UA-DES) technique for optimizing Deep Belief Networks (DBNs) in ransomware detection. UA-DES leverages Bayesian methods, dropout techniques, and an active learning framework to dynamically adjust the number of epochs during the training of the detection model, preventing overfitting while enhancing model accuracy and reliability. Our solution takes a set of Application Programming Interfaces (APIs), representing ransomware behavior as input we call "UA-DES-DBN". The method incorporates uncertainty and calibration quality measures, optimizing the training process for better more accurate ransomware detection. Experiments demonstrate the effectiveness of UA-DES-DBN compared to more conventional models. The proposed model improved accuracy from 94% to 98% across various input sizes, surpassing other models. UA-DES-DBN also decreased the false positive rate from 0.18 to 0.10, making it more useful in real-world cybersecurity applications. [ABSTRACT FROM AUTHOR]

The high complexity of the parameter–simulation problem in land surface models over semiarid areas makes it difficult to reasonably estimate the surface simulation conditions that are important for both weather and climate in different regions. In this study, using the dense site datasets of a typical semiarid region over Tibet and the Noah land surface model with the constrained land parameters of multiple sites, an enhanced Kling–Gupta efficiency criterion comprising multiple objectives, including variable and layer dimensions, was obtained, which was then applied to calibration schemes based on two global search algorithms (particle swarm optimization and shuffled complex evaluation) to investigate the site-scale spatial complexities in soil temperature simulations. The calibrations were then compared and further validated. The results show that the Noah land surface model obtained reasonable simulations of soil moisture against the observations with fine consistency, but the negative fit and huge spatial errors compared with the observations indicated its weak ability to simulate the soil temperature over regional semiarid land. Both calibration schemes significantly improved the soil moisture and temperature simulations, but particle swarm optimization generally converged to a better objective than shuffled complex evaluation, although with more parameter uncertainties and less heterogeneity. Moreover, simulations initialized with the optimal parameter tables for the calibrations obtained similarly sustainable improvements for soil moisture and temperature, as well as good consistency with the existing soil reanalysis. In particular, the soil temperature simulation errors for particle swarm optimization were unbiased, while those for the other method were found to be biased around −3 K. Overall, particle swarm optimization was preferable when conducting soil temperature simulations, and it may help mitigate the efforts in surface forecast improvement over semiarid regions. [ABSTRACT FROM AUTHOR]

The traditional stratification weight is widely used in survey sampling for estimation under stratified random sampling (StRS). A neutrosophic calibration approach is proposed under neutrosophic statistics for the first time with the aim of improving conventional stratification weight. This addresses the challenge of estimating the empirical cumulative distribution function (CDF) of a finite population using the neutrosophic technique. The neutrosophic technique extends traditional statistics, dealing with indeterminate, vague, and uncertain values. Thus, using additional information, we are able to obtain an effective estimate of the neutrosophic CDF. The suggested estimator yields an interval range in which the population empirical CDF is likely to exist rather than a single numerical value. The proposed family of neutrosophic estimators will be defined under suitable calibration constraints. A simulation study is also computed in order to assess the effectiveness of the suggested and adapted neutrosophic estimators using real-life symmetric and asymmetric datasets. [ABSTRACT FROM AUTHOR]

Performance adaptation is an effective way to improve the accuracy of gas turbine performance models. Although current performance adaptation methods, such as those using genetic algorithms or evolutionary computation to modify component characteristic maps, are useful for finding good solutions, they are essentially searching methods and suffer from long computation time. This paper presents a novel approach that can achieve good performance adaptation with low time complexity and without using any searching method. In this method, the actual component performance parameters are first estimated using engine measurements at different operating conditions. For each operating condition, some scaling factors are introduced and calculated to indicate the difference between the actual and predicted component performance parameters. Afterward, an interpolating algorithm is adopted to synthesize the scaling factors for modifying all major component maps. The adapted component maps are then able to make the engine model match all the gas path measurements and achieve the required accuracy of the engine performance model. The proposed approach has been tested with a model high-bypass turbofan engine using simulated data. The results show that the proposed performance adaptation approach can effectively improve the model's accuracy. Specifically, the prediction errors can be reduced from about 9% to about 0.6%. In addition, this approach has much less computational complexity compared to other optimization-based counterparts. [ABSTRACT FROM AUTHOR]

Calibration and regionalization are two key tasks in hydrological modelling, and the availability of increased data and computing resources has enhanced our ability to complete these tasks. This paper addresses both tasks, i.e. (i) it proposes novel model structure-based calibration strategies with the goal to improve estimation of high streamflows, and (ii) it explores the association of soil hydraulic properties with optimal parameters to estimate streamflows in ungauged catchments. Both tasks are demonstrated by employing a conceptual hydrological model, Sacramento Soil Moisture Accounting (SAC-SMA), on multiple catchments in the Narmada River Basin. The initial calibrated model exhibited good model performance, with high flows being underestimated. Seven calibration strategies via revising parameter space are explored, and improvement in high flow performance is observed at nine out of 12 catchments. Estimated streamflows based on soil hydraulic parameters demonstrate regionalization potential. The results indicate successful application of the proposed methods which are transferable to other basins. [ABSTRACT FROM AUTHOR]

We analyze the VIX futures market with a focus on the exchange-traded notes written on such contracts, in particular we investigate the VXX notes tracking the short-end part of the futures term structure. Inspired by recent developments in commodity smile modelling, we present a multi-factor stochastic-local volatility model that is able to jointly calibrate plain-vanilla options both on VIX futures and VXX notes, thus going beyond the failure of purely stochastic or simply local-volatility models. We discuss numerical results on real market data by highlighting the impact of model parameters on implied volatilities. [ABSTRACT FROM AUTHOR]

Empirical studies have emphasized that the equity implied volatility is characterized by a negative skew inversely proportional to the square root of the time-to-maturity. We examine the short-time-to-maturity behavior of the implied volatility smile for pure jump exponential additive processes. An excellent calibration of the equity volatility surfaces has been achieved by a class of these additive processes with power-law scaling. The two power-law scaling parameters are β , related to the variance of jumps, and δ , related to the smile asymmetry. It has been observed, in option market data, that β = 1 and δ = - 1 / 2 . In this paper, we prove that the implied volatility of these additive processes is consistent, in the short-time, with the equity market empirical characteristics if and only if β = 1 and δ = - 1 / 2 . [ABSTRACT FROM AUTHOR]

Simple Summary: Use of cancer biomarkers for tumor aggressiveness is an unmet clinical need. Differentiation of high-risk versus low-risk tumors may guide physicians to select appropriate treatment strategies tailored to the risk level of individual patients. This study aimed to evaluate the value of an optical redox imaging technique for differentiating a human melanoma mouse xenograft model with a high risk of metastasis from a low-risk mouse model. Several imaging indices were found to be significantly different between the two models. The high-risk model was found to be in a more oxidized status and to have a higher intratumor redox heterogeneity. These findings may inform further research development of the optical redox imaging approach into translatable cancer biomarkers in the future. To develop imaging biomarkers for tumors aggressiveness, our previous optical redox imaging (ORI) studies of the reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavoproteins (Fp, containing flavin adenine dinucleotide, i.e., FAD) in tumor xenografts of human melanoma associated the high optical redox ratio (ORR = Fp/(Fp + NADH)) and its heterogeneity to the high invasive/metastatic potential, without having reported quantitative results for NADH and Fp. Here, we implemented a calibration procedure to facilitate imaging the nominal concentrations of tissue NADH and Fp in the mouse xenografts of two human melanoma lines, an indolent less metastatic A375P and a more metastatic C8161. Images of the redox indices (NADH, Fp, ORR) revealed the existence of more oxidized areas (OAs) and more reduced areas (RAs) within individual tumors. ORR was found to be higher and NADH lower in C8161 compared to that of A375P xenografts, both globally for the whole tumors and locally in OAs. The ORR in the OA can differentiate xenografts with a higher statistical significance than the global averaged ORR. H&E staining of the tumors indicated that the redox differences we identified were more likely due to intrinsically different cell metabolism, rather than variations in cell density. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Suite-Nadir (OMS-N) instrument is the first hyperspectral remote sensor in the ultraviolet band of China's Fengyun series satellites. It can be used to detect several kinds of atmospheric constituents. This paper describes the prelaunch spectral calibration of the OMS-N onboard FengYun 3F. Several critical spectral parameters including the spectral resolution, spectral dispersion, and the instrument spectral response function were determined through laser-based measurements. A secondary peak of the instrument spectral response function from the short wavelength side of the ultraviolet band was found, and the possible influence on data applications was analyzed using a reference solar model and radiative transfer model. The results indicate that the spectral resolution and spectral accuracy of OMS-N meet the mission requirements. However, the asymmetries in the instrument spectral response function in the ultraviolet band were found near nadir rows, which are expressed as the "asymmetric central peak" and "secondary peak". The analysis results show that if the influences of the instrument spectral response function "asymmetric central peak" and "secondary peak" in the ultraviolet band are ignored, they will bring an error as large as 5% at the center of the absorption line. [ABSTRACT FROM AUTHOR]

During the launch and path to its final orbit, the Landsat 9 satellite performed a once in a mission lifetime maneuver as it passed beneath Landsat 8, resulting in near coincident data collection. This maneuver provided ground validation teams from across the globe the opportunity of collecting surface in situ data to compare directly to Landsat 8 and Landsat 9 data. Ground validation teams identified surface targets that would yield reflectance and/or thermal values that could be used in Landsat Level 2 product validation and set out to collect at these locations using surface validation methodologies the teams developed. The values were collected from each team and compared directly with each other across each of the different bands of both Landsat 8 and 9. The results proved consistency across the Landsat 8 and 9 platforms and also agreed well in surface reflectance underestimation of the Coastal Aerosol, Blue, and SWIR2 bands. [ABSTRACT FROM AUTHOR]

Passive differential optical absorption spectroscopy (DOAS) is widely used to monitor the three-dimensional distribution of atmospheric pollutants. However, the observational and retrieval accuracy of this technique is significantly influenced by the precise wavelength calibration of solar spectra. Current calibration methods face challenges in automation when dealing with complex remote-sensing conditions. We introduce a novel automatic wavelength calibration algorithm for passive DOAS based on sequence-matching technology to estimate the spectral parameters of the spectrometer channels, integrating advanced processing measures such as feature structure enhancement and sub-pixel interpolation. These measures significantly reduce the dependency on reference spectrum resolution and accurately correct even minor spectral shifts. We perform sensitivity experiments using synthetic spectra to determine optimal retrieval configurations, followed by field tests at four cities on the Yangtze River Delta, China, to calibrate and compare passive DOAS instruments of various resolutions. Comparative verification in these field studies demonstrated that our algorithm was suitable for rapid spectral calibration within a wider resolution range of 0.03 nm to 0.1 nm with a wavelength inversion error < 0.01 nm. This highlights the applicability and calibration precision of our algorithm. [ABSTRACT FROM AUTHOR]

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory. [ABSTRACT FROM AUTHOR]

In this paper, a six-degree-of-freedom analog tactile probe with a new, simple, and robust mechanical design is presented. Its design is based on the use of one elastomeric ring that supports the stylus carrier and allows its movement inside a cubic measuring range of ±3 mm. The position of the probe tip is determined by three low-cost, noncontact, 2D PSD (position-sensitive detector) sensors, facilitating a wider application of this probe to different measuring systems compared to commercial ones. However, several software corrections, regarding the size and orientation of the three LED light beams, must be carried out when using these 2D sensors for this application due to the lack of additional focusing or collimating lenses and the very wide measuring range. The development process, simulation results, correction models, experimental tests, and calibration of this probe are presented. The results demonstrate high repeatability along the X-, Y-, and Z-axes (2.0 µm, 2.0 µm, and 2.1 µm, respectively) and overall accuracies of 6.7 µm, 7.0 µm, and 8.0 µm, respectively, which could be minimized by more complex correction models. [ABSTRACT FROM AUTHOR]

The precision requirements for aeroengine blade machining are exceedingly stringent. This study aims to improve the accuracy of existing aeroengine blade measurement methods while achieving comprehensive measurement. Therefore, this study proposes a new concentric ring calibration method and designs a multi-layer concentric ring calibration plate. The effectiveness of this calibration method was verified through actual testing of standard ball gauges. Compared with the checkerboard-grid calibration method, the average deviation of the multilayer concentric ring calibration method for measuring the center distance of the standard sphere is 0.02352, which improves the measurement accuracy by 3–4 times. On the basis of multi-layer concentric ring calibration, this study builds a fringe projection profiler based on the three-frequency twelve-step phase shift method. Compared with the CMM, the average deviation of the blade chord length measured by this solution is 0.064, which meets the measurement index requirements of aeroengine fan blades. [ABSTRACT FROM AUTHOR]

Most finite element model updating (FEMU) studies on bridges are acceleration-based due to their lower cost and ease of use compared to strain- or displacement-based methods, which entail costly experiments and traffic disruptions. This leads to a scarcity of comprehensive studies incorporating strain measurements. This study employed the strain- and acceleration-based FEMU analyses performed on a more than 50-year-old multi-span concrete highway viaduct. Mid-span strains under heavy vehicles were considered for the strain-based FEMU, and frequencies and mode shapes for the acceleration-based FEMU. The analyses were performed separately for up to three variables, representing Young's modulus adjustment factors for different groups of structural elements. FEMU studies considered residual minimisation and the error-domain model falsification (EDMF) methodology. The residual minimisation utilised four different single-objective optimisations focusing on strains, frequencies, and mode shapes. Strain- and frequency-based FEMU analyses resulted in an approximately 20% increase in the overall superstructure's design stiffness. This study shows the benefits of the intuitive EDMF over residual minimisation for FEMU, where information gained from the strain data, in addition to the acceleration data, manifests more sensible updated variables. EDMF finally resulted in a 25–50% overestimated design stiffness of internal main girders. [ABSTRACT FROM AUTHOR]

Standard beams are mainly used for the calibration of strain sensors using their load reconstruction models. However, as an ill-posed inverse problem, the solution to these models often fails to converge, especially when dealing with dynamic loads of different frequencies. To overcome this problem, a piecewise Tikhonov regularization method (PTR) is proposed to reconstruct dynamic loads. The transfer function matrix is built both using the denoised excitations and the corresponding responses. After singular value decomposition (SVD), the singular values are divided into submatrices of different sizes by utilizing a piecewise function. The regularization parameters are solved by optimizing the piecewise submatrices. The experimental result shows that the MREs of the PTR method are 6.20% at 70 Hz and 5.86% at 80 Hz. The traditional Tikhonov regularization method based on GCV exhibits MREs of 28.44% and 29.61% at frequencies of 70 Hz and 80 Hz, respectively, whereas the L-curve-based approach demonstrates MREs of 29.98% and 18.42% at the same frequencies. Furthermore, the PREs of the PTR method are 3.54% at 70 Hz and 3.73% at 80 Hz. The traditional Tikhonov regularization method based on GCV exhibits PREs of 27.01% and 26.88% at frequencies of 70 Hz and 80 Hz, respectively, whereas the L-curve-based approach demonstrates PREs of 29.50% and 15.56% at the same frequencies. All in all, the method proposed in this paper can be extensively applied to load reconstruction across different frequencies. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Palaeoecological investigations are crucial in understanding millennial to centennial land use and land cover change. By analysing the modern pollen rain from four main vegetation types across Madagascar, this research provides baselines for improving the interpretation of pollen records in palaeoecological studies. This study determines the modern pollen‐vegetation relationship in Madagascar and gives a better understanding of the island's landscapes. Despite some spatial limitations, this approach contributes to resolving the debated topic related to the origin of Madagascar's open ecosystems. Knowing the vegetation history prior to and after human settlement would help guide biodiversity management and its associated ecosystem services. Summary: Modern pollen rain contributes to calibrating and defining palaeo‐records relative to the current vegetation of diverse landscapes. Quantifying past vegetation change is important to better frame sustainable and resilient management methods of biodiversity considering human needs. In this paper, we aim to assess modern pollen assemblages in different vegetation types in Madagascar and inform how these could contribute to the interpretation of palaeo‐records.We identified modern pollen assemblages from 21 samples across four major vegetation types in Madagascar, including seven (N = 7) newly generated samples from the tropical dry forest in the Northwest region and 14 datasets downloaded from the African Pollen Database website. The downloaded datasets are from core tops representing modern vegetation previously analysed in the tropical dry forests and spiny thickets, the wooded grassland–bushland mosaic and the littoral humid forest. We evaluated the frequency of different taxa in the samples and conducted multivariate analyses to evaluate the similarities and ecological affinities between samples.Pollen rain reflected the main vegetation types across Madagascar and provided information on the current structure and state of the landscape. Regarding the interpretation of pollen records, pollen rain samples revealed that: (i) Open mosaic ecosystems should contain a percentage of at least 40% grass pollen, informing on the origin of the vegetation in older samples and the state of degradation in more recent vegetation reconstructions; and (ii) Anthropogenic vegetation such as plantations are marked by the abundance of pioneer taxa such as Pinus and/or Eucalyptus spp. with a frequency >20%.Our findings improve the interpretations of pollen records and help differentiate past distribution of forest, open and mosaic vegetation, in Madagascar. It will contribute to further research on human practices, land use and into scenarios of conservation planning not only in Madagascar but also across the tropics. [ABSTRACT FROM AUTHOR]

Achieving a truly accurate wavelength calibration of high-dispersion echelle spectrographs is a challenging task but crucially needed for certain science cases, e.g. to test for a possible variation of the fine-structure constant in quasar spectra. One of the spectrographs best suited for this mission is Very Large Telescope/Echelle SPectrograph for Rocky Exoplanets and Stable Spectroscopic Observation (ESPRESSO). Nevertheless, previous studies have identified significant discrepancies between the classical wavelength solutions and the one derived independently from the laser frequency comb. The dominant parts of these systematics were intra-order distortions, most-likely related to a deviation of the instrumental line-spread function from the assumed Gaussian shape. Here, we therefore present a study focused on a detailed modelling of the ESPRESSO instrumental line-spread function. We demonstrate that it is strongly asymmetric, non-Gaussian, different for the two slices and fibres, and varies significantly along the spectral orders. Incorporating the determined non-parametric model in the wavelength calibration process drastically improves the wavelength calibration accuracy, reducing the discrepancies between the two independent wavelength solutions from |$50\,\rm{m\,s^{-1}}$| to about |$10\, \rm{m\,s^{-1}}$|⁠. The most striking success is, however, that the different fibres and slices now provide fully consistent measurements with a scatter of just a couple m s−1. This demonstrates that the instrument-related systematics can be nearly eliminated over most of the spectral range by properly taking into account the complex shape of the instrumental line-spread function and paves the way for further optimizations of the wavelength calibration process. [ABSTRACT FROM AUTHOR]

An increasing number of measurement electrodes have been designed to satisfy the demand for high-resolution detection using galvanic logging technology in complex formations. The forward modeling response analysis of logging tools has important guiding significance in the design of galvanic logging tools. Based on a three-dimensional finite element numerical simulation method, we established a forward model of galvanic multi-electrodes in a complex formation. We also designed a symmetrical resistance network model of the formation with equivalent resistance between two electrodes. A symmetrical resistance network was derived using the balanced bridge method. The asymmetrical admittance matrix was extended to a symmetrical extended admittance matrix to realize a convenient calculation of the equivalent symmetrical resistance network in complex formations. Verification of the microcolumn-focused logging tool, with nine electrodes in a simulated standard well, and an evaluation of the degree of invasion in an actual oil well indicate that this calibration method can improve the measurement accuracy of galvanic logging instruments. [ABSTRACT FROM AUTHOR]

Background: Nanoindentation experiments require the calibration of the tip area function, which involves up to 9 fitting coefficients following classical method. These coefficients are determined from indentation tests on a reference material. However, their values may vary from one test batch to another. Consequently, these coefficients cannot describe the amplitude of the indenter tip defect. Objective: The main objective of this study is to propose a contact area function that uses only one fitting coefficient to represent the indenter tip defect. This coefficient corresponds to the distance between the blunt and ideal indenter tip. Methodology: To demonstrate the efficiency of the proposed contact area function, we reanalyzed nearly 40 calibration procedures, while keeping the same experimental protocol, performed between 2014 and today. A novel two-step calibration methodology is advanced. We compared the results of the proposed method to those obtained with the classic methodology. Results: This two-step methodology was applied to a fused silica calibration sample. The values of the Young's modulus and instrumented hardness are equals to 71 and 10 GPa, respectively. The length of the indenter tip defect increases gradually from 5 to 30 nm accordingly to the frequency of use of the indenter. The values of the mechanical properties calculated by this methodology are in good agreement with those obtained using the classical contact area function. Conclusion: The methodology presented in this paper demonstrates its ability to accurately calibrate the tip area function. This new calibration procedure considers both the Young's modulus and the tip defect parameter as free parameters. Furthermore, the calibration parameters have a clear physical meaning and their values remain stables from one batch to another. [ABSTRACT FROM AUTHOR]

Landsat 9 Operational Land Imager (L9-OLI) was launched on 27 September 2021, after completing a successful radiometric pre-launch calibration and characterization phase. The radiometric math model that governs the ground system—the data processing and analysis system (DPAS)—uses various calibration parameters that had been derived based on the pre-launch tests and analysis. During the on-orbit commissioning phase, the OLI system acquired specific sets of data collects, which enabled the revalidation of the pre-launch absolute calibration scale and other associated instrument performance characteristics. The analysis results shown in this paper focus on the activities and results related to the transfer-to-orbit analysis for the SI-traceable pre-launch radiometric scale. Key topics discussed in this paper include: radiance and reflectance calibration parameters for OLI; solar diffuser collects; stimulation-lamp collects; dark response; signal-to-noise ratios; and noise characteristics; radiometric response stability and the on-orbit update to the radiance to reflectance conversion factors. It will be shown that the OLI response during the early on-orbit operation matched pre-launch results and therefore this re-validates the absolute radiometric scaling at the predicted pre-launch level within the expected level of uncertainties. The launch did not cause any significant changes to the OLI system from the perspective of the absolute radiometric calibration performance. Once the transfer to orbit of the absolute calibration was confirmed, it created a solid basis for further on-orbit refinements of the radiance calibration parameters. As such, follow-on calibration refinements are discussed in other articles within this special issue, and they address issues such as uniformity as well as cross-calibration activities. [ABSTRACT FROM AUTHOR]

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing number of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9 and Sentinel-2, and planned future missions, like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA)'s Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from reference systems to client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO. [ABSTRACT FROM AUTHOR]

The research in this paper is related to the accuracy of the tilt compensation function of the GNSS/IMU receivers, which were examined in an open sky environment. The purpose of the paper is to point out to geodesists the conditions and limitations of using GNSS/IMU technology in precise measurements to not jeopardize the coordinate's accuracy. The environment in which the measurement is made affects the quality of the GNSS signal and can limit the visibility of the satellite, leading to larger errors in the measurement. In this experiment, the current performance of the GNSS/IMU receivers was checked. Seven GNSS/IMU receivers were used for the realization of the experiment. For six receivers the compensation angle was α = 30°, while for one receiver, the compensation angle was α = 45°. The standard uncertainty of GNSS coordinates of the antenna phase center has values less than 9 mm. The standard uncertainty of the IMU component has values less than 31 mm. The measurement uncertainty of the position of the used GNSS receivers is in the range of 18.1 mm to 31.7 mm. The limit values for the differences along the coordinate axes x and y were determined, and their values are from 26 mm to 44 mm. In the conducted experiment, it was confirmed that three GNSS/IMU receivers have a "Satisfactory" result. The results show that GNSS/IMU measurements with a slope greater than 30° significantly affect the accuracy and reliability of GNSS/IMU technology. A slope greater than 45° has a deviation along the coordinate axes of 121.3 mm. The conducted research is particularly important for geodetic works that require high positioning performance. The testing method of the GNSS/IMU receiver presented in this paper can help its users to make correct conclusions regarding the coordinate accuracy of the measured point of interest. [ABSTRACT FROM AUTHOR]

Energy efficiency is an important issue in industry, especially with the ever-increasing consumption of electrical energy. The power quality and the traceability of metering devices are essential when integrating energy metering systems for energy efficiency. This management requires an understanding of electrical current events such as pulse and transient currents. Current transducers are widely used to measure these electrical current events up to a few megahertz. Their use makes it possible to measure not only the main current flowing through the transducer, but also the bypass current that affects electrical equipment. Calibration of these sensors up to a few megahertz then becomes an essential step. Currently, most calibration methods are limited to 100 kHz frequency for a current of 10 A. This paper presents an improvement of a traceable calibration methodology for current transducers up to 10 A and 1 MHz, thus increasing, by 10 times, the current level for such high frequency applications. This calibration methodology is based on a metrological traceability chain (uninterrupted link to the International System of Units) with respect to a calculable current shunt and is currently the only traceable method for calibrating current transducers at 10 A and up to 1 MHz. The uncertainty obtained for the transimpedance ratio is less than 0.2%, which is considerably reduced with respect to the existing capabilities. [ABSTRACT FROM AUTHOR]

Currently, 3D reconstruction methods in structured light are generally implemented in a pre-calibrated area. To realize a full-field reconstruction, the calibration plate can be moved to multiple positions in a time-consuming manner, or the whole field can be calibrated with the help of a large calibration plate, which is more costly. In this paper, we address this problem by proposing a method for obtaining a global phase-angle model under a locally calibrated region, and based on this relationship, we investigate and analyze the reconstruction inside and outside of the calibrated zone. The results show that the method can reconstruct the object outside of the calibration zone completely, and can keep the planarity error around 0.1 mm and the sphericity error below 0.06 mm. The method only requires local calibration of the projected fringes at the two calibration positions to realize the 3D reconstruction of the full-field, which makes the method more advantageous. [ABSTRACT FROM AUTHOR]

Among the common applications of plenoptic cameras are depth reconstruction and post-shot refocusing. These require a calibration relating the camera-side light field to that of the scene. Numerous methods with this goal have been developed based on thin lens models for the plenoptic camera's main lens and microlenses. Our work addresses the often-overlooked role of the main lens exit pupil in these models, specifically in the decoding process of standard plenoptic camera (SPC) images. We formally deduce the connection between the refocusing distance and the resampling parameter for the decoded light field and provide an analysis of the errors that arise when the exit pupil is not considered. In addition, previous work is revisited with respect to the exit pupil's role, and all theoretical results are validated through a ray tracing-based simulation. With the public release of the evaluated SPC designs alongside our simulation and experimental data, we aim to contribute to a more accurate and nuanced understanding of plenoptic camera optics. [ABSTRACT FROM AUTHOR]

This paper investigates the manufacturing uncertainties at a 60 GHz millimeter-wave band for the monolithic hybrid microwave integrated circuits (MHMIC) fabrication process. It specifically deals with the implementation tolerances of thin-film gold microstrip transmission lines, titanium oxide thin-layer resistors, microstrip quarter-wavelength radial stubs, and active device implementation using the gold-bonding ribbons. The impacts of these manufacturing tolerances are assessed and experimentally quantified through prototyped MHMIC circuits. This allows us, on one hand, to identify the acceptable amount of dimensional variation enabling reasonable performances. On the other hand, it aims to establish a relationship between the manufacturing tolerances and the circuit parameters to provide more flexibility for the tolerance compensation and accuracy enhancement of the MHMIC fabrication processes. [ABSTRACT FROM AUTHOR]

Background: To identify independent factors of cognitive frailty (CF) and construct a nomogram to predict cognitive frailty risk in patients with lung cancer receiving drug therapy. Methods: In this cross‐sectional study, patients with lung cancer undergoing drug therapy from October 2022 to July 2023 were enrolled. The data collected includes general demographic characteristics, clinical data characteristics and assessment of tools for cognitive frailty and other factors. Logistic regression was harnessed to determine the influencing factors, R software was used to establish a nomogram model to predict the risk of cognitive frailty. The enhanced bootstrap method was employed for internal verification of the model. The performance of the nomogram was evaluated by using calibration curves, the area under the receiver operating characteristic curve, and decision curve analysis. Results: A total of 372 patients were recruited, with a cognitive frailty prevalence of 56.2%. Age, education background, diabetes mellitus, insomnia, sarcopenia, and nutrition status were identified as independent factors. Then, a nomogram model was constructed and patients were classified into high‐ and low‐risk groups with a cutoff value of 0.552. The internal validation results revealed good concordance, calibration and discrimination. The decision curve analysis presented prominent clinical utility. Conclusions: The prevalence of cognitive frailty was higher in lung cancer patients receiving drug therapy. The nomogram could identify the risk of cognitive frailty intuitively and simply in patients with lung cancer, so as to provide references for early screening and intervention for cognitive frailty at the early phases of drug treatment. [ABSTRACT FROM AUTHOR]

The paleo-temperature (Th) data from fluid inclusions are utilized for thermal history modelling using PetroMod software. Generally, bottom hole temperature (BHT) and vitrinite reflectance (Ro) measurements are widely used in petroleum system modelling (PSM) in the oil industry for calibration purposes. Th representing the minimum temperature of fluid entrapment estimated from fluid-inclusion study provides extra support to build the thermal models for PSM. Fluid inclusion parameters along with Rock-Eval pyrolysis analysis have been used to predict the maturity of oil in terms of API gravity as well as the maturity of source rocks respectively. Two exploratory wells RV-1 (Mumbai Offshore Basin) and KK4C-A-1 (Kerala-Konkan Offshore Basin), India were examined and the Th from most of the fluid inclusions of wells RV-1 and KK4C-A-1 fell in the oil window range of 60e140 °C suggesting thermal conditions favourable for oil generation in both of the wells. Th of coeval aqueous inclusions along with the Hydrocarbon Fluid inclusions (HCFIs) was used to calibrate PSM. Vital parameters show that source rocks of well RV-1 are mature and that of well KK4C-A-1 are immature. Two sets of PSM are created in terms of generation and expulsion for the dry wells RV-1 and KK4C-A-1 and calibrated each well using fluid inclusion Th and BHT. From the fluid inclusion analysis method, it is evident that hydrocarbon generation happened in both wells and the paleo-temperature indicates that the formations of both wells were subjected to temperatures in the oil window range, even though it was designated as dry wells in the present scenario. The present study highlights the application of fluid inclusion paleo-temperature (Th) during calibration instead of commonly used methods. We could obtain desirable and accurate data output from PSM using Th calibration. [ABSTRACT FROM AUTHOR]

Due to the considerable depth of the salt layers and the lack of calibration by exploratory drilling, the interpretation of the Middle and Lower Cambrian salt formations in the central Tarim Basin poses a challenge. In this paper, we apply the coupling and decoupling deformation theory in salt tectonics to analyze the No.7 fault mapped in the seismic datasets by the response characteristics of the Middle and Lower Cambrian layers. By quantifying the stratigraphic framework of the Middle and Lower Cambrian strata, we define the position of the salt layer with the seismic data. Structural decoupling is observed in the Middle and Lower Cambrian sequences in the Shuntuoguole Low Uplift, while deformation coupling is observed in these two sequences in the Shaya Uplift. [ABSTRACT FROM AUTHOR]

Global Climate Model tuning (calibration) is a tedious and time‐consuming process, with high‐dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand‐picked values of tuning parameters. Many, in both the statistical and climate literature, have proposed alternative calibration methods, but most are impractical or difficult to implement. We present a practical, robust, and rigorous calibration approach on the atmosphere‐only model of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can be summarized into two main parts: (a) the training of a surrogate that predicts E3SM output in a fraction of the time compared to running E3SM, and (b) gradient‐based parameter optimization. To train the surrogate, we generate a set of designed ensemble runs that span our input parameter space and use polynomial chaos expansions on a reduced output space to fit the E3SM output. We use this surrogate in an optimization scheme to identify values of the input parameters for which our model best matches gridded spatial fields of climate observations. To validate our choice of parameters, we run E3SMv2 with the optimal parameter values and compare prediction results to expertly‐tuned simulations across 45 different output fields. This flexible, robust, and automated approach is straightforward to implement, and we demonstrate that the resulting model output matches present day climate observations as well or better than the corresponding output from expert tuned parameter values, while considering high‐dimensional output and operating in a fraction of the time. Plain Language Summary: The systems governing the atmosphere and other physical processes on Earth are complex, and climate scientists use Earth Systems Models (ESMs) based on physical and computational representations of these processes to create simulations that mimic the behavior of Earth's climate. Since ESMs are defined at a lower resolution than some processes require, parameters are introduced to address the uncertainty in the approximation. The traditional approach is to hand‐tune these parameters using expert knowledge, a time‐consuming process that involves matching climate model output to observational data on Earth, as closely as possible. Instead, we introduce an alternative "automated calibration" framework that chooses such parameters automatically and in a fraction of the time. This is done by building a very fast machine‐learning‐based substitute (surrogate) for the ESM to predict variability in the climatology for 11 different spatial fields over four seasons, and then using this surrogate to search for the parameters that best match climate observations. We apply our framework to the atmospheric component of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2, and demonstrate an improvement of 2.7% in root‐mean‐squared‐error on average across the different variables and seasons, compared to the hand‐tuned solution. Key Points: We apply an autocalibration method to uncertain parameters in the Energy Exascale Earth System Model version 2 (E3SMv2) atmosphere modelWe use a 250‐member perturbed parameter E3SMv2 ensemble to train a polynomial chaos surrogate model and optimize input parametersThe autocalibrated parameter set improves E3SMv2's root‐mean‐squared‐error against observations by 2.7% [ABSTRACT FROM AUTHOR]

Breaking atmospheric gravity waves (GWs) in the tropical stratosphere are essential in driving the roughly 2‐year oscillation of zonal winds in this region known as the Quasi‐Biennial Oscillation (QBO). As Global Climate Models (GCM)s are not typically able to directly resolve the spectrum of waves required to drive the QBO, parameterizations are necessary. Such parameterizations often require knowledge of poorly constrained physical parameters. In the case of the spectral gravity parameterization used in this work, these parameters are the total equatorial GW stress and the half width of phase speed distribution. Radiosonde observations are used to obtain the period and amplitude of the QBO, which are compared against values obtained from a GCM. We utilize two established calibration techniques to obtain estimates of the range of plausible parameter values: History matching & Ensemble Kalman Inversion (EKI). History matching is found to reduce the size of the initial range of plausible parameters by a factor of 98%, requiring only 60 model integrations. EKI cannot natively provide any uncertainty quantification but is able to produce a single best estimate of the calibrated values in 25 integrations. When directly comparing the approaches using the Calibrate, Emulate, Sample method to produce a posterior estimate from EKI, history matching produces more compact posteriors with fewer model integrations at lower ensemble sizes compared to EKI; however, these differences become less apparent at higher ensemble sizes. Plain Language Summary: Atmospheric gravity waves (GWs) are buoyancy driven oscillations which propagate through the atmosphere and deposit momentum where they break. This momentum exchange plays a significant role in setting various large‐scale atmospheric phenomena, of which a prominent example is the Quasi‐Biennial Oscillation (QBO), a roughly 2‐year oscillation of winds in the tropical stratosphere. Many of the waves responsible for creating these large scale patterns are too small to be simulated by climate models. Thus, we use parameterizations to estimate their impact on the large scale. These parameterizations have settings that require tuning, to enable the model to produce variability that matches the observed climate. In this work, we utilize and compare two techniques: History matching and Ensemble Kalman Inversion. These methods are combined with observations of the QBO to tune the settings for the GW parameterization. Key Points: History matching and Ensemble Kalman Inversion (EKI) were used to calibrate two parameters in a gravity wave parameterization to Quasi‐Biennial Oscillation observationsHistory matching was found to rapidly and compactly produce an estimate of the plausible space of parameters when compared to EKIEKI was found to be strong at single best estimates of the calibrated parameters at low ensemble sizes requiring few iterations [ABSTRACT FROM AUTHOR]

Particle size measurement is crucial in various applications, be it sizing droplets in inkjet printing or respiratory events, tracking particulate ejection in hypersonic impacts or detecting floating target markers in free-surface flows. Such systems are characterised by extracting quantitative information like size, position, velocity and number density of the dispersed particles, which is typically non-trivial. The existing methods like phase Doppler or digital holography offer precise estimates at the expense of complicated systems, demanding significant expertise. We present a novel volumetric measurement approach for estimating the size and position of dispersed spherical particles that utilises a unique 'Depth from Defocus' (DFD) technique with a single camera. The calibration-free sizing enables in situ examination of hard to measure systems, including naturally occurring phenomena like pathogenic aerosols, pollen dispersion or raindrops. The efficacy of the technique is demonstrated for diverse sparse dispersions, including dots, glass beads and spray droplets. The simple optical configuration and semi-autonomous calibration procedure make the method readily deployable and accessible, with a scope of applicability across vast research horizons. [ABSTRACT FROM AUTHOR]