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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]

In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σb), and temperature, is examined for a number of different soils, different θ and different levels of the electrical conductivity of the soil solution (ECW) under laboratory conditions. For the above reason, a prototype device was developed including a low-cost microcontroller and suitable adaptation circuits for the aforementioned sensor. Six characteristic porous media were examined in a θ range from air drying to saturation, while four different solutions of increasing Electrical Conductivity (ECw) from 0.28 dS/m to approximately 10 dS/m were used in four of these porous media. It was found that TEROS 12 apparent dielectric permittivity (εa) readings were lower than that of Topp's permittivity–water content relationship, especially at higher soil water content values in the coarse porous bodies. The differences are observed in sand (S), sandy loam (SL) and loam (L), at this order. The results suggested that the relationship between experimentally measured soil water content (θm) and εa0.5 was strongly linear (0.869 < R2 < 0.989), but the linearity of the relation θm-εa0.5 decreases with the increase in bulk EC (σb) of the soil. The most accurate results were provided by the multipoint calibration method (CAL), as evaluated with the root mean square error (RMSE). Also, it was found that εa degrades substantially at values of σb less than 2.5 dS/m while εa returns to near 80 at higher values. Regarding the relation εa-σb, it seems that it is strongly linear and that its slope depends on the pore water electrical conductivity (σp) and the soil type. [ABSTRACT FROM AUTHOR]

This work addresses the challenge of calibrating multiple solid-state LIDAR systems. The study focuses on three different solid-state LIDAR sensors that implement different hardware designs, leading to distinct scanning patterns for each system. Consequently, detecting corresponding points between the point clouds generated by these LIDAR systems—as required for calibration—is a complex task. To overcome this challenge, this paper proposes a method that involves several steps. First, the measurement data are preprocessed to enhance its quality. Next, features are extracted from the acquired point clouds using the Fast Point Feature Histogram method, which categorizes important characteristics of the data. Finally, the extrinsic parameters are computed using the Fast Global Registration technique. The best set of parameters for the pipeline and the calibration success are evaluated using the normalized root mean square error. In a static real-world indoor scenario, a minimum root mean square error of 7 c m was achieved. Importantly, the paper demonstrates that the presented approach is suitable for online use, indicating its potential for real-time applications. By effectively calibrating the solid-state LIDAR systems and establishing point correspondences, this research contributes to the advancement of multi-LIDAR fusion and facilitates accurate perception and mapping in various fields such as autonomous driving, robotics, and environmental monitoring. [ABSTRACT FROM AUTHOR]

Mangosteens can develop a postharvest physiological disorder, called "hardening", which affects their marketability and is not detectable using visual inspection. The hardening disorder of mangosteens was determined by firmness value using the texture analyzer. Near-infrared hyperspectral imaging (NIR-HSI) in the region of 935–1720 nm was tested as a possible rapid and non-destructive method to detect this disorder. The spectra from a region of interest of mangosteens were acquired and used for analysis. Calibration models for firmness of a similarly sized group and a mixed-size group were established using partial least squares regression (PLSR) and support vector machine regression (SVMR). Chemometric algorithms were investigated in order to determine the optimal conditions for establishing the models for firmness. The optimum model was obtained when the fruit were graded into similarly sized groups. Using partial least squares regression (PLSR), the correlation coefficient of prediction (Rp) was 0.87 and the root mean square error of prediction (RMSEP) was 6.25 N. The predictive images for firmness of the fruit were created by interpreting predicted firmness visualized as colors in every pixel. From the data, it was concluded that NIR-HSI can potentially be used to visualize hardening of individual mangosteens based on their predictive images. [ABSTRACT FROM AUTHOR]

Optical 3D scanning applications are increasingly used in various medical fields. Setups involving multiple adjustable systems require repeated extrinsic calibration between patients. Existing calibration solutions are either not applicable to the medical field or require a time-consuming process with multiple captures and target poses. Here, we present an application with a 3D checkerboard (3Dcb) for extrinsic calibration with a single capture. The 3Dcb application can register captures with a reference to validate measurement quality. Furthermore, it can register captures from camera pairs for point-cloud stitching of static and dynamic scenes. Registering static captures from TIDA-00254 to its reference from a Photoneo MotionCam-3D resulted in an error (root mean square error ± standard deviation) of 0.02 mm ± 2.9 mm. Registering a pair of Photoneo MotionCam-3D cameras for dynamic captures resulted in an error of 2.2 mm ± 1.4 mm. These results show that our 3Dcb implementation provides registration for static and dynamic captures that is sufficiently accurate for clinical use. The implementation is also robust and can be used with cameras with comparatively low accuracy. In addition, we provide an extended overview of extrinsic calibration approaches and the application's code for completeness and service to fellow researchers. [ABSTRACT FROM AUTHOR]

In the evolving field of digital soil mapping (DSM), the determination of sample size remains a pivotal challenge, particularly for large-scale regional projects. We introduced the Jensen-Shannon Divergence (DJS), a novel tool recently applied to DSM, to determine optimal sample sizes for a 2790 km2 area in Ontario, Canada. Utilizing 1791 observations, we generated maps for cation exchange capacity (CEC), clay content, pH, and soil organic carbon (SOC). We then assessed sample sets ranging from 50 to 4000 through conditioned Latin hypercube sampling (cLHS), feature space coverage sampling (FSCS), and simple random sampling (SRS) to calibrate random forest models, analyzing performance via concordance correlation coefficient and root mean square error. Findings reveal DJS as a robust estimator for optimal sample sizes—865 for cLHS, 874 for FSCS, and 869 for SRS, with property-specific optimal sizes indicating the potential for enhanced DSM accuracy. This methodology facilitates a strategic approach to sample size determination, significantly improving the precision of large-scale soil mapping. Conclusively, our research validates the utility of DJS in DSM, offering a scalable solution. This advancement holds considerable promise for improving soil management and sustainability practices, underpinning the critical role of precise soil data in agricultural productivity and environmental conservation. [ABSTRACT FROM AUTHOR]

Given the critical importance of addressing effluent quality concerns, the present study was dedicated to developing a dynamic simulation model based on the Activated Sludge Model 1 (ASM1) of a wastewater treatment plant located in Maghnia City, Algeria. The model calibration process involved collecting and analyzing 56 samples from the plant over a period of 18 months (from July 2021 to January 2023). Thirteen physicochemical parameters were analyzed to identify the variations in their water quality over time. Stoichiometric and kinetic parameters were adjusted during the plant calibration process. These modifications resulted in a reasonable alignment with the investigated variables, enabling the accurate prediction of the wastewater treatment plants (WWTPs)' steady-state behavior regarding the removal measurements of chemical oxygen demand (COD), total suspended solids (TSS), and ammonium (NH4-N). The model was validated using 14-day measurements spanning a 4-month duration, and the results indicated good agreement between the observed and simulated effluent variable of chemical oxygen demand (COD) with a root mean square error (RMSE) of 23%. These findings highlight the utility of the ASM1 Model in comprehending and managing the intricate dynamics of the activated sludge process in wastewater treatment plants. [ABSTRACT FROM AUTHOR]

Raman spectroscopy is a useful tool for polymorphic form-monitoring during the crystallization process. However, its application to solute concentration estimation in two-phase systems like crystallization is rare, as the Raman signal is influenced by various changing factors in the crystallization process. The development of a robust calibration model that covers all variations is complex and represents a major challenge for the implementation of Raman spectroscopy for in-line monitoring and control of the solution crystallization process. This paper describes the development of a Raman-based calibration model for estimating the solute concentration of the active pharmaceutical ingredient ceritinib. Several different calibration approaches were tested, which included both temperature and spectra of clear solutions and slurries/suspensions. It was found that the concentration of the ceritinib solution could not be accurately predicted when suspended crystals were present. To overcome this challenge, the approach was enhanced by including additional variables related to crystal size and solid concentration obtained via in-line process microscopy (chord-length distribution percentiles D10, D50 and D90) and turbidity. Partial least squares regression (PLSR) and artificial neural network (ANN) models were developed and compared based on root mean square error (RMSE). ANN models estimated the solute concentration with high accuracy, with the prediction error not exceeding 1% of the nominal solute concentration. [ABSTRACT FROM AUTHOR]

The amplitude and phase errors in array elements lead to uncertainties in the estimation of arrival angles (DOA), which, in turn, affect the accuracy of current measurements for high-frequency surface wave radar (HFSWR). To address this issue, this paper proposes a passive amplitude-phase self-calibration method for a wide-beam HFSWR system with a nonlinear array. This method utilizes the aggregation of the amplitude ratio among array elements to screen reception matrices of single DOA sources. Based on the differences in reception matrices, the amplitude error is calibrated. Moreover, the cost function is calculated using the multiple signal classification (MUSIC) algorithm, and the initial phase error is first obtained after triangular array dimensionality reduction. Then, the phase error is further calibrated through quadratic-form iterative optimization. This method has been validated through simulations and real measurements. An approximately 4-day dataset obtained via HFSWR is reanalyzed in this paper. After data calibration, the radar-estimated currents were in good agreement with the buoy-measured results, with a root mean square error (RMSE) as low as 0.06 m/s and a correlation coefficient (CC) of up to 0.88. The results indicate that this method is suitable for the amplitude and phase error calibration of nonlinear arrays in wide-beam HFSWR systems. [ABSTRACT FROM AUTHOR]

Array calibration can effectively ensure the positioning accuracy of the ultra-short baseline (USBL) system. Traditional USBL array calibration methods focus on measuring the geometric position of the array elements. However, directional phase differences on the receive path are often ignored in the current calibration process, which can also cause array mismatch, especially when using the superdirective beamforming (SDB) technique. To further improve the calibration accuracy and convenience of the USBL using the SDB technique, a fast calibration method is proposed in this paper. In the new method, the hydrophone geometry error and the receiver path phase error are jointly considered. Then, two calibration models with different complexity are presented, and the conventional beamforming (CBF) beam output is deconvoluted with the calibrated beam pattern. The results of anechoic tank experiments show that the bearing root mean square error (RMSE) can be reduced from 1.663° to 0.081°, and the calibration time can be reduced from hours to tens of minutes. [ABSTRACT FROM AUTHOR]

Background: For the reconstruction of Cone-Beam CT volumes, the exact position of each projection is needed; however, in some situations, this information is missing. Purpose: The development of a self-calibration algorithm for arbitrary CBCT trajectories that does not need initial positions. Methods: Projections are simulated in a spherical grid around the center of rotation. Through using feature detection and matching, an acquired projection is compared to each simulated image in this grid. The position with the most matched features was used as a starting point for a fine calibration with a state-of-the-art algorithm. Evaluation: This approach is compared with the calibration of nearly correct starting positions when using FORCASTER and CMA-ES minimization with a normalized gradient information (NGI) objective function. The comparison metrics were the normalized root mean squared error, structural similarity index, and the dice coefficient, which were evaluated on the segmentation of a metal object. Results: The parameter estimation for a regular Cone-Beam CT with a 496 projection took 1:26 h with the following metric values: NRMSE = 0.0669; SSIM = 0.992; NGI = 0.75; and Dice = 0.96. FORCASTER with parameter estimation took 3:28 h with the following metrics: NRMSE = 0.0190; SSIM = 0.999; NGI = 0.92; and Dice = 0.99. CMA-ES with parameter estimation took 5:39 h with the following metrics: NRMSE = 0.0037; SSIM = 1.0; NGI = 0.98; and Dice = 1.0. Conclusions: The proposed algorithm can determine the parameters of the projection orientations for arbitrary trajectories with enough accuracy to reconstruct a 3D volume with low errors. [ABSTRACT FROM AUTHOR]

Sodium nitrite is a food additive commonly used in sausages, but legally, the unsafe levels of nitrite in sausage should be less than 80 mg/kg, since higher levels can be harmful to consumers. Consumers must rely on processors to conform to these levels. Therefore, the determination of nitrite content in chicken sausages using near infrared hyperspectral imaging (NIR-HSI) was investigated. A total of 140 chicken sausage samples were produced by adding sodium nitrite in various levels. The samples were divided into a calibration set (n = 94) and a prediction set (n = 46). Quantitative analysis, to detect nitrate in the sausages, and qualitative analysis, to classify nitrite levels, were undertaken in order to evaluate whether individual sausages had safe levels or non-safe levels of nitrite. NIR-HSI was preprocessed to obtain the optimum conditions for establishing the models. The results showed that the model from the partial least squares regression (PLSR) gave the most reliable performance, with a coefficient of determination of prediction (Rp) of 0.92 and a root mean square error of prediction (RMSEP) of 15.603 mg/kg. The results of the classification using the partial least square-discriminant analysis (PLS-DA) showed a satisfied accuracy for prediction of 91.30%. It was therefore concluded that they were sufficiently accurate for screening and that NIR-HSI has the potential to be used for the fast, accurate and reliable assessment of nitrite content in chicken sausages. [ABSTRACT FROM AUTHOR]

An in-depth analysis of the flow patterns and mixing dynamics in a twin-paddle blender with bi-disperse non-spherical particles was investigated using the discrete element method (DEM) and experiments. This study aimed to explore the mixing efficiency of a twin-paddle blender containing two different shapes of non-spherical particles. The study focussed on the demonstration of the applicability of the GPU-based DEM model. To achieve this, calibration tests were performed using a classical rotary drum to validate the accuracy of the DEM model. The next step was to examine the impact of various operating parameters on the mixing performance, such as impeller rotational speed. The relative standard deviation (RSD) was employed as a measure of mixing performance. Results revealed that the rotational speed of the impellers had a significant impact on the mixing performance. [ABSTRACT FROM AUTHOR]

Models based on Numerical Weather Prediction (NWP) are widely used for the day-ahead forecast of solar resources. This study is focused on the calibration of the hourly global solar irradiation forecasts provided by the Global Forecast System (GFS), a model from the NWP class. Since the evaluation of GFS raw forecasts sometimes shows a high degree of uncertainty (the relative error exceeding 100%), a procedure for reducing the errors is needed as a prerequisite for engineering applications. In this study, a deep analysis of the error sources in relation to the state of the atmosphere is reported. Of special note is the use of sky imagery in the identification process. Generally, it has been found that the largest errors are determined by the underestimation of cloud coverage. For calibration, a new ensemble forecast is proposed. It combines two machine learning approaches, Support Vector Regression and Multi-Layer Perceptron. In contrast to a typical calibration, the objective function is constructed based on the absolute error instead of the traditional root mean squared error. In terms of normalized root mean squared error, the calibration roughly reduces the uncertainty in hourly global solar irradiation by 16%. The study was conducted with high-quality ground-measured data from the Solar Platform of the West University of Timisoara, Romania. To ensure high accessibility, all the parameters required to run the proposed calibration procedures are provided. [ABSTRACT FROM AUTHOR]

Systematic measurement of pasture biomass (kg DM/ha) is crucial for optimising pasture utilisation and increasing dairy farm profitability. On-farm pasture monitoring can be conducted using various sensors, but calibrations are necessary to convert the measured variable into pasture biomass. In this study, we conducted three experiments in New South Wales (Australia) to evaluate the use of the rising plate meter (RPM), pasture reader (PR), unmanned aerial vehicles (UAV) and satellites as pasture monitoring tools. We tested various calibration methods that can improve the accuracy of the estimations and be implemented more easily on-farm. The results indicate that UAV and satellite-derived reflectance indices (e.g., Normalised Difference Vegetation Index) can be indirectly calibrated with height measurements obtained from an RPM or PR. Height measurements can be then converted into pasture biomass ideally by conducting site-specific sporadic calibrations cuts. For satellites, using the average of the entire paddock, root mean square error (RMSE) = 226 kg DM/ha for kikuyu (Pennisetum clandestinum Hochst. ex Chiov) and 347 kg DM/ha for ryegrass (Lolium multiflorum L.) is as effective as but easier than matching NDVI pixels with height measurement using a Global Navigation Satellite System (RMSE = 227 kg DM/ha for kikuyu and 406 kg DM/ha for ryegrass). For situations where no satellite images are available for the same date, the average of all images available within a range of up to four days from the day ground measurements were taken could be used (RMSE = 225 kg DM/ha for kikuyu and 402 kg DM/ha for ryegrass). These methodologies aim to develop more practical and easier-to-implement calibrations to improve the accuracy of the predictive models in commercial farms. However, more research is still needed to test these hypotheses under extended periods, locations, and pasture species. [ABSTRACT FROM AUTHOR]

Battery life management is critical for plug-in hybrid electric vehicles (PHEVs) to prevent dangerous situations such as overcharging and over-discharging, which could cause thermal runaway. PHEVs have more complex operating conditions than EVs due to their dual energy sources. Therefore, the SOH estimation for PHEV vehicles needs to consider the specific operating characteristics of the PHEV and make calibrations accordingly. Firstly, we estimated the initial SOH by combining data-driven and empirical models. The data-driven method used was the incremental state of charge (SOC)-capacity method, and the empirical model was the Arrhenius model. This method can obtain the battery degradation trend and predict the SOH well in realistic applications. Then, according to the multiple characteristics of PHEV, we conducted a correlation analysis and selected the UF as the calibration factor because the UF has the highest correlation with SOH. Finally, we calibrated the parameters of the Arrhenius model using the UF in a fuzzy logic way, so that the calibrated fitting degradation trends could be closer to the true SOH. The proposed calibration method was verified by a PHEV dataset that included 11 vehicles. The experiment results show that the root mean square error (RMSE) of the SOH fitting after UF calibration can be decreased by 0.2–14% and that the coefficient of determination ( R 2 ) for the calibrated fitting trends can be improved by 0.5–32%. This provides more reliable guidance for the safe management and operation of PHEV batteries. [ABSTRACT FROM AUTHOR]

Accurate sky identification is one of the most important functions of an automated telescope mount. The more accurately the robotic telescope is navigated to the investigated part of the sky, the better the observations and discoveries made. In this paper, we present mathematical methods for accurate sky identification (celestial coordinates determination). They include the automatic selection of the reference stars, preliminary and full sky identification, as well as an interaction with international databases, which are a part of the astrometric calibration. All described methods help to receive accurately calculated astrometric data and use it for the positional calibration and better navigation of the automated telescope mount. The developed methods were successfully implemented in the Collection Light Technology (CoLiTec) software. Through its use, more than 1600 small solar system objects were discovered. It has been used in more than 700,000 observations and successful sky identifications, during which, five comets were discovered. Additionally, the accuracy indicators of the processing results of the CoLiTec software are provided in the paper, which shows benefits of the CoLiTec software and lower standard deviation of the sky identification in the case of low signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

The accuracy of X-ray fluorescence spectrometry in quantitative element analysis depends on the particular sample composition (so-called matrix effects). Counteracting these effects requires a large number of calibration samples similar in composition to those under analysis. Application of the model constructed for a particular type of samples is not possible for the analysis of samples having a different matrix composition. A possible solution for this problem can be found in the construction of universal calibration models. We propose the development of these universal models using chemometric tools: influence coefficients—partial least squares regression (IC-PLS) and nonlinear kernel regularized least squares regression. We hypothesize that the application of these methods for constructing calibration models would allow embracing the samples of different types in the framework of a single model. We explored this approach for the case of two substantially different types of samples: ores and steels. The performance of these methods was compared with the fundamental parameters (FP) method, which takes into account matrix effects using theoretical equations and allows handling samples of different elemental composition. IC-PLS significantly outperforms traditional FP in terms of accuracy for predicting the content of Al (root mean squared error of prediction 0.96% vs. 3.87%) and Ti (0.05% vs. 0.09%) and yields comparable results for Si and Mn quantification in ores and steels. [ABSTRACT FROM AUTHOR]

The two-parameter logistic (2PL) item response model is likely the most frequently applied item response model for analyzing dichotomous data. Linking errors quantify the variability in means or standard deviations due to the choice of items. Previous research presented analytical work for linking errors in the one-parameter logistic model. In this article, we present linking errors for the 2PL model using the general theory of M-estimation. Linking errors are derived in the case of log-mean-mean linking for linking two groups. The performance of the newly proposed formulas is evaluated in a simulation study. Furthermore, the linking error estimation in the 2PL model is also treated in more complex settings, such as chain linking, trend estimation, fixed item parameter calibration, and concurrent calibration. [ABSTRACT FROM AUTHOR]

Model-based optimization is an important means by which to analyze the energy-saving potential of chiller plants. To obtain reliable energy-saving results, model calibration is essential, which strongly depends on operating data. However, sufficient data cannot always be satisfied in reality. To improve the prediction accuracy of the model with limited data, a model calibration method based on error reverse correction was investigated. A traditional optimization-based calibration method was first used for preliminary model calibration to obtain simulation data and simulation errors. Then, the sources of the simulation errors were analyzed to determine the distribution characteristics of the corresponding operating conditions of the model. Finally, the performance of the model was reversely corrected by adding a correction term to the original model. The proposed calibration method was tested on a chiller plant in Xiamen, China. The results showed that the proposed calibration method improved prediction accuracy by 2.61% (the coefficient of variation of the root mean square error (CV (RMSE)) was reduced from 3.96% to 1.35%) compared to the traditional method. The maximum mean bias error (MBE) for monthly chiller energy consumption was 2.66% with the proposed calibration method, while it was 10.42% with the traditional method. Overall, in scenarios with limited data, the proposed calibration method can effectively improve the accuracy of simulation results. [ABSTRACT FROM AUTHOR]

The new-generation photon-counting laser altimeter aboard the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has acquired unprecedented high-density laser data on the global surface. The continuous analysis and calibration of potential systematic biases in laser data are important for generating highly accurate data products. Current studies mainly calibrate the absolute systematic bias of laser altimeters based on external reference data. There are few studies that focus on the analysis and calibration of relative systematic biases in long-term laser data. This paper explores a method for systematic biases analysis and calibration of ICESat-2 laser data based on track crossovers for the first time. In the experiment, the simulated data and ICESat-2 data were used to verify the algorithm. The results show that, during the three-year period in orbit, the standard deviation (STD) and bias of the crossover differences of the ICESat-2 terrain data were 0.82 m and −0.03 m, respectively. The simulation validation well demonstrate that the crossover adjustment can calibrate the relative bias between different beams. For ICESat-2 data, the STD of the estimated systematic bias after crossover adjustment was 0.09 m, and the mean absolute error (MAE) was 0.07 m. Compared with airborne lidar data, the bias and root mean square error (RMSE) of the ICESat-2 data remained basically unchanged after adjustment, i.e., −0.04 m and 0.38 m, respectively. This shows that the current ICESat-2 data products possess excellent internal and external accuracy. This study shows the potential of crossover for evaluating and calibrating the accuracy of spaceborne photon-counting laser altimeter data products, in terms of providing a technical approach to generate global/regional high-accuracy point cloud data with consistent accuracy. [ABSTRACT FROM AUTHOR]

With the advantages of high measurement accuracy and wide detection range, LiDARs have been widely used in information perception research to develop agricultural robots. However, the internal configuration of the laser transmitter layout changes with increasing sensor working duration, which makes it difficult to obtain accurate measurement with calibration files based on factory settings. To solve this problem, we investigate the intrinsic calibration of multi-beam laser sensors. Specifically, we calibrate the five intrinsic parameters of LiDAR with a nonlinear optimization strategy based on static planar models, which include measured distance, rotation angle, pitch angle, horizontal distance, and vertical distance. Firstly, we establish a mathematical model based on the physical structure of LiDAR. Secondly, we calibrate the internal parameters according to the mathematical model and evaluate the measurement accuracy after calibration. Here, we illustrate the parameter calibration with three steps: planar model estimation, objective function construction, and nonlinear optimization. We also introduce the ranging accuracy evaluation metrics, including the standard deviation of the distance from the laser scanning points to the planar models and the 3σ criterion. Finally, the experimental results show that the ranging error of calibrated sensors can be maintained within 3 cm, which verifies the effectiveness of the laser intrinsic calibration. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) is often used for merging observations to improve the predictions of the current and future states of characteristics of interest. In forest inventory, DA has so far found limited use, although dense time series of remotely sensed (RS) data have become available for estimating forest characteristics. A problem in forest inventory applications based on RS data is that errors from subsequent predictions tend to be strongly correlated, which limits the efficiency of DA. One reason for such a correlation is that model-based predictions, using techniques such as parametric or non-parametric regression, are normally biased conditional on the actual ground conditions, although they are unbiased conditional on the RS predictor variables. A typical case is that predictions are shifted towards the mean, i.e., small true values are overestimated, and large true values are underestimated. In this study, we evaluated if the classical calibration of RS-based predictions could remove this type of bias and improve DA results. Through a simulation study, we mimicked growing stock volume predictions from two different sensors: one from a metric strongly correlated with growing stock volume, mimicking airborne laser scanning, and one from a metric slightly less correlated with growing stock volume, mimicking data obtained from 3D digital photogrammetry. Consistent with previous findings, in areas such as chemistry, we found that classical calibration made the predictions approximately unbiased. Further, in most cases, calibration improved the DA results, evaluated in terms of the root mean square error of predicted volumes, evaluated at the end of a series of ten RS-based predictions. [ABSTRACT FROM AUTHOR]

The use of inertial measurement units (IMUs) to compute gait outputs, such as the 3D lower-limb kinematics is of huge potential, but no consensus on the procedures and algorithms exists. This study aimed at evaluating the validity of a 7-IMUs system against the optoelectronic system. Ten asymptomatic subjects were included. They wore IMUs on their feet, shanks, thighs and pelvis. The IMUs were embedded in clusters with reflective markers. Reference kinematics was computed from anatomical markers. Gait kinematics was obtained from accelerometer and gyroscope data after sensor orientation estimation and sensor-to-segment (S2S) calibration steps. The S2S calibration steps were also applied to the cluster data. IMU-based and cluster-based kinematics were compared to the reference through root mean square errors (RMSEs), centered RMSEs (after mean removal), correlation coefficients (CCs) and differences in amplitude. The mean RMSE and centered RMSE were, respectively, 7.5° and 4.0° for IMU-kinematics, and 7.9° and 3.8° for cluster-kinematics. Very good CCs were found in the sagittal plane for both IMUs and cluster-based kinematics at the hip, knee and ankle levels (CCs > 0.85). The overall mean amplitude difference was about 7°. These results reflected good accordance in our system with the reference, especially in the sagittal plane, but the presence of offsets requires caution for clinical use. [ABSTRACT FROM AUTHOR]

Currently, more and more emphasis is being placed on reducing energy consumption in buildings to reduce greenhouse gases in the atmosphere. Building performance simulation is very useful to predict energy demand and indoor environment quality. An indispensable element of the simulation is the validation and calibration of the model, which is an arduous process. The aim of the study was to present a four-level validation (using measurement results) and calibration of a thermal model of a naturally ventilated single-family house. Numerical calculations using co-simulation between EnergyPlus and Contam were performed. The results of the one-year simulation measurements of the indoor temperature and ventilation airflows were compared. After the calibration was performed, a high convergence of the results was found. The normalized mean bias error for hourly and monthly values did not exceed 1% and the coefficient of variation of the root mean squared error was a maximum of 7% with a simultaneous high correlation of the results in the range from 0.85 to 0.89. It was found that the final results were significantly influenced by the appropriate modeling of air exchange in the building, including the opening of windows. [ABSTRACT FROM AUTHOR]

The reflection of Global Navigation Satellite Systems (GNSS) signals, namely GNSS-Reflectometry (GNSS-R), has recently proven to be able to monitor land surface properties in the microwave spectrum, at a global scale, and with very low revisiting time. Moreover, this new technique has numerous additional advantages, including low cost, low power consumption, lightweight and small payloads, and near real-time massive data availability, as compared to conventional monostatic microwave remote sensing. However, the GNSS-R surface reflectivity values estimated through the bistatic radar equation, and the Fresnel coefficients have shown a lack of coincidence with real surface reflectivity data, mostly due to calibration issues. Previous studies have attempted to avoid this matter with direct regression methods between uncalibrated GNSS-R reflectivity data and external soil moisture content (SMC) products. However, calibration of GNSS-R reflectivity used in traditional inversion models is still a challenge, such as those to estimate SMC, freeze/thaw, or biomass. In this paper, a successful procedure for GNSS-R reflectivity calibration is established using data from the CYGNSS (Cyclone GNSS) constellation. The scale and bias parameters are estimated from the theoretical dielectric properties of water and dry sand, which are well-known and empirically validated values. We employ four calibration areas that provide maximum range limits of reflectivity, such as deserts and wetlands. The CYGNSS scale factor and the bias parameter resulted in a = 3.77 and b = 0.018, respectively. The derived scale and bias parameters are applied to the CYGNSS dataset, and the retrieved SMC values through the Fresnel reflection coefficients are in excellent agreement with the Soil Moisture Active Passive (SMAP) SMC product. Then, the SMAP SMC is used as a reference true value, and provides a standard linear regression with an R-square coefficient of 0.803, a root mean square error (RMSE) of 0.084, and a Pearson's correlation coefficient of 0.896. [ABSTRACT FROM AUTHOR]

Before infrared hyperspectral data are used in satellite data assimilation systems or retrieval systems, the quantitative analysis of data deviation is necessary. Based on RTTOV's (Radiative Transfer for TOVS) simulation data of FY-3E/HIRAS-II (Hyperspectral Infrared Atmospheric Sounder) and the observation data of HIRAS-II, we counted the bias of observation minus simulation (OMB) during an on-orbit test; analyzed the characteristics and reasons for the bias from the perspective of the FOV (field of view), the scanning angle of the instrument, the day and night, and the target temperature change; and analyzed the stability of the radiometric calibration accuracy. We also combined the results of the MetOp-C/IASI (infrared atmospheric sounding interferometer), a similar high-precision instrument, with the bias of OMB to compare and evaluate the FY-3E/HIRAS-II radiometric calibration accuracy. In the end, we found that the mean OMB bias of the long-wave and medium-wave infrared bands is within ±2 K, and the bias standard deviation is better than 2 K; the bias of each FOV is consistent and the bias of most channels is better than 2 K. The OMB bias of each channel is consistent with the changes in the angle of the instrument. The bias trend of long-wave and medium-wave infrared channels is more consistent with the deviation of the day and night; the bias of the short-wave infrared channel at night is lower than in the daytime. When counting the bias as the target temperature changed, the results showed that there are no obvious temperature dependencies in the long-wave and medium-wave infrared channels. This reflects that the instrument's non-linear effect is well ordered. We further evaluated the stability of the radiometric calibration accuracy through statistics from the OMB standard deviation of each channel of FY-3E/HIRAS-II. Most channel accuracy stability values were better than 0.1 K. We calculated that IASI and HIRAS-II OMB have double differences, and the results show that the double difference in most channels is better than 1 K. It shows that the HIRAS-II and IASI observations are highly consistent. Through the statistics of the OMB bias during the on-orbit test period of FY-3E/HIRAS-II, we fully evaluated its radiometric calibration accuracy and laid the foundation for FY-3E/HIRAS-II data to be used in the retrieval application and assimilation system. [ABSTRACT FROM AUTHOR]

Featured Application: This work is used for calibrating intrinsic parameters of camera and extrinsic parameters between camera and flash LiDAR. Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Passive microwave satellite brightness temperatures (TB) that were observed by the F13 Special Sensor Microwave/Imager (SSM/I) and the subsequent F17 Special Sensor Microwave Imager/Sounder (SSMIS) were inter-calibrated using empirical relationship models during their overlap period. Snow depth (SD) on the Arctic first-year sea ice was further retrieved. The SDs derived from F17 TB and F13C TB which were calibrated F17 TB using F13 TB as the baseline were then compared and evaluated against in situ SD measurements based on the Operational IceBridge (OIB) airborne observations from 2009 to 2013. Results show that Cavalieri inter-calibration models (CA models) perform smaller root mean square error (RMSE) than Dai inter-calibration models (DA models), and the standard deviation of OIB SDs in the 25 km pixels is around 6 cm on first-year sea ice. Moreover, the SDs derived from the calibrated F17 TB using F13 TB as the baseline were in better agreement than the F17 SDs as compared with OIB SDs, with the biases of -2 cm (RMSE of 5 cm) and -9 cm (RMSE of 10 cm), respectively. We conclude that TB observations from F17 SSMIS calibrated to F13 SSM/I as the baseline should be recommended when performing the sensors' biases correction for SD purpose based on the existing algorithm. These findings could serve as a reference for generating more consistent and reliable TB, which could help to improve the retrieval and analysis of long-term snow depth on the Arctic first-year sea ice. [ABSTRACT FROM AUTHOR]

To determine the planar motion of a 6-DOF precision stage, a measurement system based on three Hall sensors is adopted to obtain the X, Y, Rz motions of the stage. The machining and assembly errors in the actual mechanical system, which are difficult to measure directly, cause the parameters in the model of the Hall measurement system to deviate from their designed values. Additionally, the vertical movement of the stage will render the measurement model nonlinear. To guarantee the accuracy of the measurement, the parameters in the measurement model should be estimated and the nonlinearity compensated. In this paper, a novel approach based on self-adaptive hybrid TLBO (teaching-learning-based-optimization) is proposed to estimate the parameters in the Hall measurement model. The influences of zero deviations and vertical movements on the measurement accuracy are analyzed and compensated. The effectiveness of the proposed method is validated by experimental results obtained on a 6-DOF precision stage. Thanks to parameter estimation and calibration, the measurement error of the Hall sensor array is reduced to 6 micrometers. [ABSTRACT FROM AUTHOR]

In modern industrial geodesy, high demands are placed on the final accuracy, with expectations currently falling below 1 mm. The measurement methodology and surveying instruments used have to be adjusted to meet these stringent requirements, especially the total stations as the most often used instruments. A standard deviation of the measured distance is the accuracy parameter, commonly between 1 and 2 mm. This parameter is often discussed in conjunction with the determination of the real accuracy of measurements at very short distances (5-50 m) because it is generally known that this accuracy cannot be increased by simply repeating the measurement because a considerable part of the error is systematic. This article describes the detailed testing of electronic distance meters to determine the absolute size of their systematic errors, their stability over time, their repeatability and the real accuracy of their distance measurement. Twenty instruments (total stations) have been tested, and more than 60,000 distances in total were measured to determine the accuracy and precision parameters of the distance meters. Based on the experiments' results, calibration procedures were designed, including a special correction function for each instrument, whose usage reduces the standard deviation of the measurement of distance by at least 50%. [ABSTRACT FROM AUTHOR]

The first airborne hyperspectral campaign in the Antarctic Peninsula region was carried out by the British Antarctic Survey and partners in February 2011. This paper presents an insight into the applicability of currently available radiative transfer modelling and atmospheric correction techniques for processing airborne hyperspectral data in this unique coastal Antarctic environment. Results from the Atmospheric and Topographic Correction version 4 (ATCOR-4) package reveal absolute reflectance values somewhat in line with laboratory measured spectra, with Root Mean Square Error (RMSE) values of 5% in the visible near infrared (0.4-1 µm) and 8% in the shortwave infrared (1-2.5 µm). Residual noise remains present due to the absorption by atmospheric gases and aerosols, but certain parts of the spectrum match laboratory measured features very well. This study demonstrates that commercially available packages for carrying out atmospheric correction are capable of correcting airborne hyperspectral data in the challenging environment present in Antarctica. However, it is anticipated that future results from atmospheric correction could be improved by measuring in situ atmospheric data to generate atmospheric profiles and aerosol models, or with the use of multiple ground targets for calibration and validation. [ABSTRACT FROM AUTHOR]

A calibration method based on convex optimization (CVX) and neural networks is proposed for the large planar arrays of phased array three-dimensional imaging sonar systems. The method only needs an acoustic calibration source at an unknown position in the far field, and the direction of arrival (DOA) and gain and phase error are jointly estimated. The method uses a CVX algorithm to solve an optimization problem and initially estimates the DOA of the calibration source robustly. Subsequently, according to the estimation results, a neural network is used for fitting to obtain off-grid DOA estimation of the calibration source. Thereafter, spatial matched filtering is performed to obtain the gain and phase residual estimations. The root mean square error (RMSE) of the beam pattern calibrated by the method for uniform planar arrays can reach a value of 4.9542 × 10−5. The experimental results demonstrate the efficiency of the proposed method for gain and phase calibration. [ABSTRACT FROM AUTHOR]

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET), a key component of the hydrological cycle, has a direct impact on runoff and water balance. Various global satellite-based and numerical datasets provide continuous and high spatiotemporal resolution data, which makes it possible to calibrate hydrological parameters against ET. However, the accuracy of ET datasets varies with region and algorithm, introducing uncertainties in hydrological parameter calibration. This study focused on evaluating the potential of different ET datasets in the calibration of distributed hydrological model parameters. Five different ET datasets (PML, SEBAL, EB-ET, GLASS, REA-ET) were evaluated using the water balance method to explore the effect of intrinsic dataset accuracy on applications. The benchmark calibration scheme calibrated parameters by using observed streamflow data from the outlet. Two calibration schemes were proposed to take advantage of the temporal dynamics and spatial patterns of the raw ET datasets. The results show that the model parameters calibrated by all selected ET datasets produced satisfactory results in streamflow simulations. These results were dependent on the calibration schemes and accuracy of ET datasets. Overall, the scheme calibrated by using temporal dynamics of ET at the grid scale provided better streamflow simulations at the basin outlet than the scheme calibrated by using spatial patterns of ET at the basin scale. Three metrics (bias, root mean square error [RMSE], and correlation coefficient [R]) showed that there is a high potential for selected ET datasets to improve soil moisture simulations, as compared to the benchmark scheme. Parameters calibrated by EB-ET and PML datasets provided the best performance in the simulation of streamflow at the outlet and the sub-basin scale. The calibration case with the SEBAL dataset showed the highest potential to improve soil moisture simulation. The annual average ET estimates of these three datasets were closest to the water balance-based ET values. [ABSTRACT FROM AUTHOR]

A plane-based dynamic calibration method had been proposed by the previous study for the GeoSLAM ZEB Horizon handheld LiDAR scanner. Only one preliminary test was presented. Therefore, three datasets in a calibration field were collected in this study on different dates and at different times on the same date to investigate the efficiency of the proposed calibration approach and calibration results. The calibration results for these three datasets showed that all average residuals were closer to 0, and all a posterior unit weight standard deviations of the adjustment were also significantly reduced after calibration. Moreover, the RMSE (root mean square error) of the check planes was improved by about an average of 32.61%, 28.44%, and 14.7%, respectively, for the three datasets. The improvement was highly correlated with the quality of the calibration data. The RMSE differences of all check planes using calibration data collected on different dates and at different times on the same date for calibration was about 1–2 cm and less than 1 mm, respectively. There was no difference in the calibration results, demonstrating the efficiency of the proposed calibration approach and the calibration results during the two different dates. [ABSTRACT FROM AUTHOR]

Infrared thermographs (IRTs) implemented according to standardized best practices have shown strong potential for detecting elevated body temperatures (EBT), which may be useful in clinical settings and during infectious disease epidemics. However, optimal IRT calibration methods have not been established and the clinical performance of these devices relative to the more common non-contact infrared thermometers (NCITs) remains unclear. In addition to confirming the findings of our preliminary analysis of clinical study results, the primary intent of this study was to compare methods for IRT calibration and identify best practices for assessing the performance of IRTs intended to detect EBT. A key secondary aim was to compare IRT clinical accuracy to that of NCITs. We performed a clinical thermographic imaging study of more than 1000 subjects, acquiring temperature data from several facial locations that, along with reference oral temperatures, were used to calibrate two IRT systems based on seven different regression methods. Oral temperatures imputed from facial data were used to evaluate IRT clinical accuracy based on metrics such as clinical bias ( Δ c b ), repeatability, root-mean-square difference, and sensitivity/specificity. We proposed several calibration approaches designed to account for the non-uniform data density across the temperature range and a constant offset approach tended to show better ability to detect EBT. As in our prior study, inner canthi or full-face maximum temperatures provided the highest clinical accuracy. With an optimal calibration approach, these methods achieved a Δ c b between ±0.03 °C with standard deviation ( σ Δ c b ) less than 0.3 °C, and sensitivity/specificity between 84% and 94%. Results of forehead-center measurements with NCITs or IRTs indicated reduced performance. An analysis of the complete clinical data set confirms the essential findings of our preliminary evaluation, with minor differences. Our findings provide novel insights into methods and metrics for the clinical accuracy assessment of IRTs. Furthermore, our results indicate that calibration approaches providing the highest clinical accuracy in the 37–38.5 °C range may be most effective for measuring EBT. While device performance depends on many factors, IRTs can provide superior performance to NCITs. [ABSTRACT FROM AUTHOR]

So far, no studies of material deformations (e.g., bending of sports equipment) have been performed to measure the curvature (w″) using an optoelectronic measurement system OMS. To test the accuracy of the w″ measurement with an OMS (Qualisys), a calibration profile which allowed to: (i) differentiates between three w″ ( 0.1 3 ˙ m−1, 0.2 m−1, and 0.4 m−1) and (ii) to explore the influence of the chosen infrared marker distances (50 mm, 110 mm, and 170 mm) was used. The profile was moved three-dimensional at three different mean velocities ( v zero = 0 ms−1, v slow = 0.2 ms−1, v fast   = 0.4 ms−1) by an industrial robot. For the accuracy assessment, the average difference between the known w″ of the calibration profile and the detected w″ from the OMS system, the associated standard deviation (SD) and the measuring point with the largest difference compared to the defined w″ (=maximum error) were calculated. It was demonstrated that no valid w″ can be measured at marker distances of 50 mm and only to a limited extent at 110 mm. For the 170 mm marker distance, the average difference (±SD) between defined and detected w″ was less than 1.1 ± 0.1 mm−1 in the static and not greater than −3.8 ± 13.1 mm−1 in the dynamic situations. The maximum error in the static situation was small (4.0 mm−1), while in the dynamic situations there were single interfering peaks causing the maximum error to be larger (−30.2 mm−1 at a known w″ of 0.4 m−1). However, the Qualisys system measures sufficiently accurately to detect curvatures up to 0.1 3 ˙ m−1 at a marker distance of 170 mm, but signal fluctuations due to marker overlapping can occur depending on the direction of movement of the robot arm, which have to be taken into account. [ABSTRACT FROM AUTHOR]

Hydrologic research is a very demanding application of fiber-optic distributed temperature sensing (DTS) in terms of precision, accuracy and calibration. The physics behind the most frequently used DTS instruments are considered as they apply to four calibration methods for single-ended DTS installations. The new methods presented are more accurate than the instrument-calibrated data, achieving accuracies on the order of tenths of a degree root mean square error (RMSE) and mean bias. Effects of localized non-uniformities that violate the assumptions of single-ended calibration data are explored and quantified. Experimental design considerations such as selection of integration times or selection of the length of the reference sections are discussed, and the impacts of these considerations on calibrated temperatures are explored in two case studies. [ABSTRACT FROM AUTHOR]

Infrared tympanic thermometers (ITTs) are easy to use and have a quick response time. They are widely used for temperature measurement of the human body. The accuracy and uncertainty of measurement is the importance performance indicator for these meters. The performance of two infrared tympanic thermometers, Braun THT-3020 and OMRON MC-510, were evaluated in this study. The cell of a temperature calibrator was modified to serve as the standard temperature of the blackbody. The errors of measurement for the two meters were reduced by the calibration equation. The predictive values could meet the requirements of the ASTM standard. The sources of uncertainty include the standard deviations of replication at fixed temperature or the predicted values of calibration equation, reference standard values and resolution. The uncertainty analysis shows that the uncertainty of calibration equation is the main source for combined uncertainty. Ambient temperature did not have the significant effects on the measured performance. The calibration equations could improve the accuracy of ITTs. However, these equations did not improve the uncertainty of ITTs. [ABSTRACT FROM AUTHOR]

A portable X-ray fluorescence probe (pXRF) is a tool that is used to measure many elements quickly and efficiently in various samples, without any pretreatment. However, each type of sample generally requires different calibrations to be accurate. To overcome this, our work evaluated the efficacy of determining several elements in forage plant samples using the 'Soil Nutrient and Metal' calibration in a commercially available pXRF probe, envisioning that a single calibration can be used to measure samples of different matrixes. For this, the net intensity of the pXRF probe was determined in place of the concentration values that are obtained directly from measurements. Elemental concentrations (P, K, Ca, Mg, S, Cu, Fe, Zn, and Mn) from forage plant samples, collected across Oklahoma, US, were assessed in a representative number of 'modeling' and 'validation' (independent dataset) samples. Linear regression (LR) associated with the d-index, polynomial regression (PR), and power regression (PwR) were tested for predictions, producing many statistical parameters associated with the models that were used for comparison goals. The pXRF elemental data provided highly reliable predictions of K, S, Zn, and Mn regardless of the regression model. Although all models can be reliable in prediction of Ca and Fe concentrations, the PwR provided better root mean square error (RMSE) values. The predictions of Mg concentrations were less reliable, although highly significant; however, the P and Cu predictions were not acceptable. Our work successfully showed that, once established, a single calibration curve that covers a wide range of concentrations of several elements in soils and plant tissues enables both soil and plant samples to be analyzed. This suggests that manufacturers can develop a new calibration model for a commercially available pXRF probe that covers a wide variety of heterogeneous samples. [ABSTRACT FROM AUTHOR]

Satellite-based observations of sea wind are useful for forecasting marine weather and performing marine disaster management. Meteorological Operational Satellite-B (MetOp-B) is one of the satellites that provide wind products through a scatterometer named the Advanced Scatterometer (ASCAT). Since the linear regression method has been conventionally employed for calibrating remotely-sensed wind data, deviations and biases remain un-resolved to some degree. For coastal applications, these remotely-sensed wind data need to be calibrated again using local in-situ measurements in order to provide more accurate and realistic information. Thus, this study proposed a new method to calibrate ASCAT-based wind speed estimates using artificial neural networks. Herein, a deep neural network (DNN) model was applied. Wind databases collected during a period from 2012 to 2019 by the MetOp-B ASCAT and ten buoy stations in Korean seas were considered for deep learning-based calibration. ASCAT-based wind data and in-situ measurements were collocated in space and time. They were then separated into training and validation sets. A DNN model was designed and trained using multiple input variables such as observation location, sensing date and time, wind speed, and wind direction of the training set. The DNN-based best fit calibration model was evaluated using the validation set. The mean of biases between ASCAT-based and in-situ wind speeds was found to be decreased from 0.41 to 0.05 m/s on average for all buoy locations. The root mean squared error (RMSE) of wind speed was reduced from 1.38 m/s to 0.93 m/s. Moreover, the DNN-based calibration considerably improved the quality of wind speeds of less than 4 m/s, and of high wind speeds of 11–25 m/s. These results suggest that ASCAT-based observations can accurately represent real wind fields if a DNN-based calibration approach is applied. [ABSTRACT FROM AUTHOR]

The directional polarimetric camera (DPC) on-board the GF-5A satellite is designed for atmospheric or water color detection, which requires high radiometric accuracy. Therefore, in-flight calibration is a prerequisite for its inversion application. For large field optical sensors, it is very challenging to ensure the consistency of radiation detection in the whole field of view in the space environment. Our work proposes a vicarious in-flight calibration method based on sea non-equipment sites (visible bands) and land non-equipment sites (all bands). Combined with environmental parameters and radiation transmission calculations, we evaluated the radiation detection accuracy of the 0° to 60° view zenith angle of the DPC in each band. Our calibration method is based on the single-day normalized radiance data measured by the DPC. Through data selection, enough calibration samples can be obtained in a single day (the number of desert samples is more than 5000, and the number of calibration samples of the ocean is more than 2.8 × 10 6 ). The measurements are compared with the simulation of 6SV VRT code or look-up tables. The massive amount of data averages the uncertainty of a single-point calculation. Although the uncertainty of a single sample is significant, the final fitting of the curve of the variation in the radiometric calibration coefficient with the observation angle can still keep the root mean squared error at approximately 2–3% or even lower, and for visible bands, the calibration results for both ocean sites and desert sites are in good agreement regarding the non-uniformity of the sensor. [ABSTRACT FROM AUTHOR]

Historically stable areas across North Africa, known as pseudo invariant calibration sites (PICS), have been used as targets for the calibration and monitoring of optical satellite sensors. However, two major drawbacks exist for these sites: first is the dependency on a single location to be always invariant, and second is the limited amount of observation achieved using these sites. As a result, longer time periods are necessary to construct a dense dataset to assess the radiometric performance of on-orbit optical sensors and confirm that the change detected is sensor-specific rather than site-specific. This work presents a global land cover classification to obtain an extended pseudo invariant calibration site (EPICS) on a global scale using Landsat-8 Operational Land Imager (OLI) data. This technique provides multiple calibration sites across the globe, allowing for the building of richer datasets in a shorter time frame compared to the traditional approach (PICS), with the advantage of assessing the calibration and stability of the sensors faster, detecting possible changes sooner and correcting them accordingly. This work identified 23 World Reference System two (WRS-2) path/row locations around the globe as part of the global EPICS. These EPICS have the advantage of achieving multiple observations per day, with similar spectral characteristics compared to traditional PICS, while still producing a temporal coefficient of variation (ratio of temporal standard deviation and temporal mean) less than 4% for all bands, with some as low as 2.7%. [ABSTRACT FROM AUTHOR]

CE-QUAL-W2 is widely used for simulating hydrodynamics and water quality of the aquatic environments. Currently, the model calibration is mainly based on trial and error, and therefore it is subject to the knowledge and experience of users. The Particle Swarm Optimization (PSO) algorithm has been tested for automatic calibration of CE-QUAL-W2, but it has an issue of prematurely converging to a local optimum. In this study, we proposed an Improved Global-Best Harmony Search (IGHS) algorithm to automatically calibrate the CE-QUAL-W2 model to overcome these shortcomings. We tested the performance of the IGHS calibration method by simulating water temperature of Devils Lake, North Dakota, which agreed with field observations with R2 = 0.98, and RMSE = 1.23 and 0.77 °C for calibration (2008–2011) and validation (2011–2016) periods, respectively. The same comparison, but with the PSO-calibrated CE-QUAL-W2 model, produced R2 = 0.98 and Root Mean Squared Error (RMSE) = 1.33 and 0.91 °C. Between the two calibration methods, the CE-QUAL-W2 model calibrated by the IGHS method could lower the RMSE in water temperature simulation by approximately 7–15%. [ABSTRACT FROM AUTHOR]

To realize high-precision and high-frequency unattended site calibration and detection of satellites, automatic direction adjustment must be implemented in mirror arrays. This paper proposes a high-precision automatic calibration model based on a novel point light source tracking system for mirror arrays. A camera automatically observes the solar vector, and an observation equation coupling the image space and local coordinate systems is established. High-precision calibration of the system is realized through geometric error calculation of multipoint observation data. Moreover, model error analysis and solar tracking verification experiments are conducted. The standard deviations of the pitch angle and azimuth angle errors are 0.0176° and 0.0305°, respectively. The root mean square errors of the image centroid contrast are 2.0995 and 0.8689 pixels along the x- and y-axes, respectively. The corresponding pixel angular resolution errors are 0.0377° and 0.0144°, and the comprehensive angle resolution error is 0.0403°. The calculated model values are consistent with the measured data, validating the model. The proposed point light source tracking system can satisfy the requirements of high-resolution, high-precision, high-frequency on-orbit satellite radiometric calibration and modulation transfer function detection. [ABSTRACT FROM AUTHOR]

Similar to the Reflective Solar Band (RSB) calibration, Suomi-National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) on-board calibration of Day Night Band (DNB) is based on the Solar Diffuser (SD) observations in the Low Gain State (LGS). DNB has a broad spectral response covering a wavelength range roughly from 500 nm to 900 nm with a large dynamic range from three different gain states called High Gain State (HGS), Mid Gain State (MGS), and LGS. The calibration of MGS and HGS is also dependent on the LGS gain estimation with the gain ratios for each gain state. Over the lifetime of S-NPP VIIRS operations, the LGS gains have been derived from the on-board SD observations since its launch in October 2011. In this study, the lifetime LGS gains are validated by the lunar calibration coefficients (defined as F-factors) using a lunar irradiance model called Global Space-based Inter-Calibration System (GSICS) Implementation of RObotic Lunar Observatory (ROLO) (GIRO). Using the moon as an independent on-orbit calibration source, the S-NPP VIIRS DNB on-board SD based radiometric calibration is validated by the lunar F-factors within two percent of the lunar F-factors in terms of the standard deviation in the long-term trends over nine years of the S-NPP VIIRS operation. [ABSTRACT FROM AUTHOR]

Soil water content or soil moisture content is considered one of the most critical properties of the soil for crop production, irrigation, and environmental studies. The technical development of soil moisture measurement devices is swift, but calibration among field conditions is still not entirely resolved. Accurate calibration requires samples taken right next to the sensor that disturbs the site and changes the soil conditions. Real field operation requires the probe to represent larger areas that have undisturbed soils around the probe. These would describe the parcel's general soil conditions and start providing data from the time of installation. This study aimed to compare several potential solutions for off-site calibration of an operational EnviroScan sensor (Sentek Technologies, Stepney South, Australia). The performances of the default and soil texture-specific equations provided by the manufacturer were compared with the field and laboratory calibration approaches. Two statistical parameters, coefficient of determination (R2) and root square mean error (RMSE) was used to determine logarithmic model results. The results show that the default calibration equations in all three classes have relatively low performances with RMSE values of around 10–15 and R2 values ranging from 0.4 to 0.8. However, significant refinement was achieved by selecting texture-specific equations from the manufacturer's libraries. The soil texture-specific equations of the EnviroScan often yielded quite satisfactory results, with RMSEs ranging between 2 and 4. Similar RMSE values were achieved from the laboratory calibration exercises, but the reapplication potential of these equations was often questionable due to the severely changed soil conditions of the laboratory processed soil compared to the field soil conditions. [ABSTRACT FROM AUTHOR]

In Thailand, Napier grass is expected to play an important role as an energy resource for future power generation. To accomplish this goal, numerous areas are required for Napier grass plantations. Before introducing crops, the land potential of the country and the impact of crops on the environment should be assessed. The soil and water assessment tool (SWAT) model is very useful in investigating crop impacts and land potential. Unfortunately, the crop growth parameters of Napier grass are yet to be identified and, thus, conducting effective analysis has not been possible. Accordingly, in this study, parameter calibration and SWAT model validation of Napier grass production in Thailand was carried out using datasets from eight sites with 93 samples. Parameter sensitivity analysis was performed prior to parameter calibration, the results of which suggest that the radiation use efficiency and potential harvested index are both highly sensitive. The crop growth parameters were calibrated in order of their sensitivity index ranking, and the final values were obtained by reducing the root mean square error from 10.77 to 1.38 t·ha−1. The validation provides satisfactory results with coefficient of determination of 0.951 and a mean error of 0.321 t·ha−1. Using the developed model and calibrated parameters, local Napier grass dry matter yield can be evaluated accurately. The results reveal that, if only abandoned area in Thailand is used, then Napier grass can provide roughly 33,600–44,900 GWh of annual electricity, and power plant carbon dioxide (CO2) emissions can be reduced by approximately 21.2–28.3 Mt-CO2. The spatial distribution of estimated yield obtained in this work can be further utilized for land suitability analysis to help identify locations for Napier grass plantations, anaerobic digesters, and biogas power plants. [ABSTRACT FROM AUTHOR]

Hard-shell thoracolumbar sacral orthoses (TLSOs) are used for treating idiopathic scoliosis, a deformation of the spine with a sideways curvature. The pressure required inside the TLSO for ideal corrective results remains unclear. Retrofitting TLSOs with commercially available pressure measurement systems is expensive and can only be performed in a laboratory. The aim of this study was to develop a cost-effective but accurate pressure sensor system for TLSOs. The sensor was built from a piezoresistive polymer, placed between two closed-cell foam liners, and evaluated with a material testing machine. Because foams are energy absorbers, the pressure-conductance curve was affected by hysteresis. The sensor was calibrated on a force plate with the transitions from loading to unloading used to establish the calibration curve. The root mean square error was 12% on average within the required pressure range of 0.01–0.13 MPa. The sensor reacted to the changing pressure during breathing and different activities when tested underneath a chest belt at different tensions. The peak pressure reached 0.135 MPa. The sensor was further tested inside the scoliosis brace during different activities. The measured pressure was 0.014–0.124 MPa. The results from this study enable cheaper and mobile systems to be used for clinical studies on the comfort and pressure of braces during daily activities. [ABSTRACT FROM AUTHOR]