Original Calibration Research Articles

Segmented Two-Dimensional Progressive Polynomial Calibration Method for Nonlinear Sensors.

Nonlinearity in sensor measurements reduces the sensor's accuracy. Therefore, accurate calibration is necessary for reliable sensor operation. This study proposes a segmented calibration method that divides the input range into multiple sections and calculates the optimized calibration functions for each one. This approach reduces the overall error rate and improves the calibration accuracy by isolating distinctive regions. The modified progressive polynomial calibration technique is used to calculate the calibration function. This algorithm addresses the computational complexity, allowing for reduced polynomial degrees and improving the accuracy. The segmented calibration method achieves a significantly lower error rate of 0.000006% compared to the original single calibration method, which has an error rate of 0.0823%, when using the same six calibration points and a fifth-degree polynomial function. This method maintains improved accuracy with fewer calibration points, and its ability to reduce the computational complexity and calculation time while using lower polynomial degrees is confirmed. Additionally, it can be extended to two dimensions to reduce the errors caused by cross-sensitivity. The results from a two-dimensional simulation show a reduction in the error rate ranging from 15.84% to 2.07% in an 8-bit signed fixed-point system. These results indicate that the segmented calibration method is an effective and scalable solution for various typical sensors.

Unraveling Plasmonic Tilted Fiber Bragg Gratings (TFBG): A Journey From “Anomalous Resonances” to Refined Refractometry

AbstractPlasmonic tilted fiber Bragg gratings (TFBGs) have emerged as versatile tools for refractometric analyses and biochemical sensing. Their applications have significantly blossomed these last years, from proteins and cellular bioassays to operando monitoring in batteries, to cite just a few. They are widely recognized for their cutting‐edge performance and low limits of detection, arising from their dense multimodal spectral nature featuring tens of narrowband cladding mode resonances. Their comb‐like spectrum is so rich that numerous demodulation techniques have been reported, without benchmark of their relative performance while they possess important distinctions. This review highlights developments in detangling techniques from the pioneering works based on single‐peak analysis up to the most recent approaches involving Fourier analysis, the implementation of machine learning, and cascaded spectral decomposition processes. To fairly compare the different techniques of the literature, we implemented each analysis on original experimental refractometric calibrations, revealing the assets of the most updated methods. This paper therefore reviews these demodulation techniques based on the same datasets, obtained under the same conditions. We show and discuss the results obtained from bioassays and pinpoint the importance of advanced analytical methodologies to maximize the reproducibility, reliability and performance of plasmonic‐based TFBGs biosensors.

Extending the Asteroseismic Calibration of the Stellar Rossby Number

The stellar Rossby number (Ro) is a dimensionless quantity that is used in the description of fluid flows. It characterizes the relative importance of Coriolis forces on convective motions, which is central to understanding magnetic stellar evolution. Here we present an expanded sample of Kepler asteroseismic targets to help calibrate the relation between Ro and Gaia color, and we extend the relation to redder colors using observations of the mean activity levels and rotation periods for a sample of brighter stars from the Mount Wilson survey. Our quadratic fit to the combined sample is nearly linear between 0.55 < G BP − G RP < 1.2, and can be used to estimate Ro for stars with spectral types between F5 and K3. The strong deviation from linearity in the original calibration may reflect an observational bias against the detection of solar-like oscillations at higher activity levels for the coolest stars.

Redefining Spectral Data Analysis with Immersive Analytics: Exploring Domain-Shifted Model Spaces for Optimal Model Selection.

Modern developments in autonomous chemometric machine learning technology strive to relinquish the need for human intervention. However, such algorithms developed and used in chemometric multivariate calibration and classification applications exclude crucial expert insight when difficult and safety-critical analysis situations arise, e.g., spectral-based medical decisions such as noninvasively determining if a biopsy is cancerous. The prediction accuracy and interpolation capabilities of autonomous methods for new samples depend on the quality and scope of their training (calibration) data. Specifically, analysis patterns within target data not captured by the training data will produce undesirable outcomes. Alternatively, using an immersive analytic approach allows insertion of human expert judgment at key machine learning algorithm junctures forming a sensemaking process performed in cooperation with a computer. The capacity of immersive virtual reality (IVR) environments to render human comprehensible three-dimensional space simulating real-world encounters, suggests its suitability as a hybrid immersive human-computer interface for data analysis tasks. Using IVR maximizes human senses to capitalize on our instinctual perception of the physical environment, thereby leveraging our innate ability to recognize patterns and visualize thresholds crucial to reducing erroneous outcomes. In this first use of IVR as an immersive analytic tool for spectral data, we examine an integrated IVR real-time model selection algorithm for a recent model updating method that adapts a model from the original calibration domain to predict samples from shifted target domains. Using near-infrared data, analyte prediction errors from IVR-selected models are reduced compared to errors using an established autonomous model selection approach. Results demonstrate the viability of IVR as a human data analysis interface for spectral data analysis including classification problems.

192 Prediction of fecal starch content of fattening cattle using near-infrared spectroscopy and machine learning

Abstract Starch content in cattle feces can be used to predict starch digestibility. Near-infrared spectroscopy (NIRS) can be used to rapidly and non-chemically predict fecal starch content after a calibration model has been consequentially developed. Recent advancements in machine learning have introduced potent methods for developing optimized models. In addition, diverse preprocessing methods, such as scatter correction and smoothing, are available to eliminate irrelevant variance sources. This study aimed to identify optimal spectral preprocessing and regression methods for predicting fecal starch content using NIR spectra. NIR spectra were measured for 196 fecal samples collected from 9 fattening cattle farms. Fecal starch content on a DM basis was measured using enzymatic reactions and colorimetry. The 196 samples were divided into a calibration set (n = 123), a validation set (n = 29) comprising four farms, and an external validation set (n = 44) comprising five farms that differed from the calibration set. Based on the calibration set, two preprocessing methods were selected using the partial least squares (PLS) model. In method 1, the calibration models were developed by a grid search 5-fold cross validation on the 1,152 preprocessed calibration sets, and preprocessing with the lowest root mean square error (RMSE) of cross-validation among these calibration sets was selected. In method 2, the original calibration set (n = 123) was divided into a calibration set (n = 116) and a preprocessing selection set (n = 7) with different neutral detergent fiber and similar starch contents. After preprocessing, calibration models were developed as described in method 1. The preprocessing selection sets were then predicted by the developed models, and preprocessing with the lowest RMSE among these preprocessing selection sets was selected. Using the preprocessing methods selected in methods 1 and 2, the validation and external validation sets were predicted using seven regression models. As a result, the preprocessing selected by method 1 was in the wavelength range of 400–2500 nm, with robust normal variate (RNV) scatter correction and first-order derivative Savitzky-Golay filtering (2nd order polynomial, 2-nm window size). The preprocessing selected by method 2 had a wavelength range of 1500–2500 nm, RNV scatter correction, and 2nd-order derivative Savitzky-Golay filtering (3rd order polynomial, 25-nm window size). Among the seven regression models, the Lasso model was the most accurate for prediction. The Lasso models based on the preprocessing methods selected by methods 1 and 2 had RMSE of external validation = 2.054% of DM and 1.540% of DM, respectively. The feature importance of the Lasso model based on preprocessing method selected by methods 2 was higher at wavelengths of 1775 nm and 2325 nm. In conclusion, seven models were developed and evaluated based on diverse preprocessing methods using NIRS. The Lasso model exhibited the highest accuracy.

Local Modeling by Adapting Source Calibration Models to Analyte Shifted Target Domain Samples Without Reference Values.

Spectral multivariate calibration aims to derive models characterizing mathematical relationships between sample analyte amounts and corresponding spectral responses. These models are effective at predicting target domain sample analyte amounts when target samples are within the analyte and spectral calibration source domain. Models fail when target samples shift (analyte amounts and/or spectra) from the original calibration domain model. A total recalibration solution requires acquisition of new sample reference values and spectra. However, obtaining enough reference values to distinguish the target domain may be challenging or expensive. A simpler approach adapts the original model to the target domain using target sample spectra without analyte reference values (unlabeled). Analytical chemists have developed several machine learning algorithms using unlabeled regression domain adaptation processes. Unfortunately, prediction accuracy declines for these methods depending on how much the target domain analyte distribution has shifted from the calibration distribution, and regression transfer learning methods are instead needed. Regression domain adaptation and transfer learning are often referred to as model updating in analytical chemistry, but regression domain adaptation only applies to spectral shifts. The regression transfer learning method presented in this paper named null augmentation regression constant analyte (NARCA) leverages unlabeled repeat spectra of a single target sample to update an original calibration model to the shifted target domain sample. With sample repeat spectra, the analyte amount can be assumed constant or nearly constant for NARCA and because models are formed for one sample, NARCA operates as a local modeling method. The performance of NARCA as a regression transfer learning method is evaluated using five near-infrared data sets.

Calibration transfer between NIR instruments using optimally predictive calibration subsets.

In this study, a new approach for the selection of informative standardization samples from the original calibration set for the transfer of a calibration model between NIR instruments is proposed and evaluated. First, a calibration model is developed, after variable selection by the Final Complexity Adapted Models (FCAM) method, using the significance of the PLS regression coefficients (FCAM-SIG) as selection criterion. Then, the resulting model is used for the selection of the best fitting subset of calibration samples with optimally predictive ability, called the optimally predictive calibration subset (OPCS). Next, the standardization samples are selected from the OPCS. The spectra on the slave instruments are transferred to corresponding spectra on the master instrument by the widely used Piecewise Direct Standardization (PDS) method. Thereafter, for the test set on the slave instrument, a 3D response surface plot is drawn for the root mean squared error of prediction (RMSEP) as a function of the number of OPCS samples and window sizes used for the PDS method. Finally, the smallest set of calibration samples, in combination with the optimal window size, providing the optimal RMSEP, is selected as standardization set. The proposed OPCS approach for the selection of standardization samples is tested on two real-life NIR data sets providing 13 X-y combinations to model. The results show that the obtained numbers of OPCS-based standardization samples are statistically significantly lower than those obtained with the widely used representative sample selection method of Kennard and Stone, while the predictive performances are similar.

Third Order Polynomial Bundle Adjustment For Optical Calibration Refinement

We propose a refinement and validity region expansion process of a 3rd order polynomial calibration based on bundle adjustment. The present methodology can be expanded to any model as long as the mapping is a smooth function. Both methods are tested with a tomographic Particle Tracking Velocimetry measurement whose results are compared in terms of the smoothness and length of the retrieved tracks. The refinement method shows a significant improvement in both the associated PDFs. Similarly, the expansion shows a corresponding behaviour to the original calibration even though a higher degree of smoothness of the tracks is achieved.

A dataset of land use types in the Lijiang River Basin from 2016 to 2020

Land use monitoring is an important basis for land resource management. On the basis of Sentinel-2 remote sensing image data, we used multiple preprocessing methods such as original data calibration, Mosaic and tailoring, and established remote sensing interpretation markers as classification training samples. Then we adopted the maximum likelihood method to classify the land use situation of the Lijiang River Basin in Guilin City from 2016 to 2020. Moreover, in combined with the original random test samples, we used the confusion matrix analysis method to evaluate the accuracy of the dataset. The classification results show a relatively good classification effect of land use types in the Lijiang River Basin, with an overall classification accuracy above 95%, and the kappa coefficient above 0.8. On the whole, the land use types in the Lijiang River basin changed little, and the main land use type is woodland, accounting for about 65.2%. The spatial heterogeneity show that the construction land area expanded from 4.17% to 5.19% from 2016 to 2020. This dataset can provide data support for the study of spatio-temporal changes of land pattern and land use transfer and evolution.

Use of Constant-Analyte Samples in Updating Quantitative Near-Infrared Calibration Models for Physiological Levels of Glucose

Updating procedures were investigated for multivariate calibration models for physiological levels of glucose in aqueous samples based on near-infrared spectroscopy. The methodology evaluated used samples with constant glucose concentrations collected on the prediction day to augment previously collected calibration spectra for the purpose of computing an updated model for use only on that day. Models were based on partial-least squares (PLS) regression in conjunction with spectral preprocessing using direct orthogonal signal correction (DOSC). The use of constant-analyte samples allowed the procedure to be compatible with samples collected at the beginning of a chemical reaction or from a fasting patient in a blood glucose monitoring application. The methodology was tested with nine sets of prediction spectra collected from 368 to 617 days after the collection of the calibration data. The updated PLS models based on four augmented samples exhibited an approximately 50% improvement in prediction accuracy relative to models that were based solely on the original calibration data. The use of DOSC in conjunction with updating was found to be beneficial overall, but only led on average to less than 10% improvement in prediction performance.

System code validation in periodic two-phase flow from low-pressure oscillations

Low-pressure two-phase flow instabilities can potentially challenge start-up transients of water-cooled nuclear reactors. Predicting oscillations caused by flow instability is therefore paramount to reactor safety. While many thermal hydraulics system codes are developed as general-purpose tools, their performance is not guaranteed outside their optimized application ranges. The current study therefore performs validations of two thermal hydraulics system analysis codes, the Adaptive SYStem Thermal-hydraulics Version 3 (ASYST VER3) and Reactor Excursion and Leak Analysis Program MOD3.3 (RELAP5/MOD3.3), in reproducing oscillatory two-phase flow induced by low-pressure flashing instability. Benchmark conditions are selected from a novel dataset resolving void fraction with radial resolution and quantifying periodic behavior with ensemble averaging. Focusing on the prediction of transient two-phase phenomena rather than the determination of stability, validations are conducted by simulating a single channel under prescribed periodic boundary conditions. ASYST exhibits a systematic underprediction of void fraction in an adiabatic chimney downstream of a heated section. The prominent causal discrepancy is identified as a lost travelling void wave due to overpredicted condensation. RELAP5 noticeably overpredicts subcooled boiling and underpredicts flashing, which is consistent with existing validations against steady-state separate-effect tests under low pressure. This degraded code performance also suggests that in low-pressure transients prone to flashing instability, the confidence in RELAP5 derived from existing validations against integral-effect tests shall be carefully limited to validated capabilities in integral systems. In general, the current study fills the previous gap of knowledge about the performance of ASYST and RELAP5 in predicting detailed transient two-phase phenomena in low-pressure flashing-induced oscillations. The identified code defects reveal future directions for code improvements eventually towards reliable application of ASYST and RELAP5 under low pressure beyond their original calibration.

Improving the Temporal Accuracy of Eye Gaze Tracking for the da Vinci Surgical System through Automatic Detection of Decalibration Events and Recalibration

Robust and accurate eye gaze tracking can advance medical telerobotics by providing complementary data for surgical training, interactive instrument control, and augmented human–robot interactions. However, current gaze tracking solutions for systems such as the da Vinci Surgical System (dVSS) are limited to complex hardware installations. Additionally, existing methods do not account for operator head movement inside the surgeon console, invalidating the original calibration. This work provides an initial solution to these challenges that can seamlessly integrate into console devices beyond the dVSS. Our approach relies on simple and unobtrusive wearable eye tracking glasses and provides calibration routines that can contend with operator-head movements. An external camera measures movement of the glasses through trackers mounted on the glasses to detect invalidation of the prior calibration from head movement and slippage. Movements beyond a threshold of 5 cm or 9ˆ∘ prompt another calibration sequence. In a study where users moved freely in the surgeon console after an initial calibration procedure, we show that our system tracks the eye tracking glasses to initiate recalibration procedures. Recalibration can reduce the mean tracking error up to 89% compared to the current prevailing approach which relies on the initial calibration only. This work is an important first step towards incorporating user movement into gaze-based applications for the dVSS.

Semi-empirical calibration of remote microphone probes using Bayesian inference

The empirical calibration of remote microphone probes (RMP), used to acquire wall-pressure fluctuations, can introduce spurious resonance into the sensor transfer function due to the difference in the pressure field inside the calibrator geometry over multiple calibration steps. Such spurious resonance subsequently propagates into the unsteady-pressure data at which the calibration is applied, hindering the accuracy of the measurements. Current post-processing methods for tackling these issues are often manual and strongly dependent on the operator’s expertise. In this study, we propose an original semi-empirical calibration method to remove spurious resonance in a less operator-reliant manner. The approach is based on fitting an existing analytical fluid-dynamical model for the propagation of pressure waves in the probe to the empirical calibration data using Bayesian inference. The proposed method is successfully applied to three datasets, from a simple probe recessed behind a pinhole to a more complex branching RMP. For all the configurations, spurious resonance is eliminated from the transfer function with a strongly reduced impact of the operator intervention while retaining the resonant features that are characteristic of the RMP. The affected frequency bands are then replaced using the underlying physical model. In this way, the detrimental impact of spurious resonance is removed from the measured wall-pressure spectra. Furthermore, the RMP parameters retrieved by the fit can also be used as inputs to corrective models, specifically to account for averaging effects due to the probe sensing area or for the impact of grazing flow or temperature variations on the transfer function.

Characterization of Hydrotreated Vegetable Oil (HVO) in a Euro 6 Diesel Engine as a Drop-In Fuel and With a Dedicated Calibration

Renewable fuels can play an important role in achieving future goals of energy sustainability and CO2 reduction. In particular, hydrotreated vegetable oil (HVO) represents one of the most promising alternatives to petroleum-derived diesel fuels. Several studies have shown that conventional diesel engines can run on 100% HVO without significant modifications to the hardware and control strategies. The current activity has experimentally evaluated the potential of HVO as a “drop-in” fuel, i.e., without changes to the original baseline calibration, comparing it to conventional diesel fuel on a 2.3-litre Euro 6 compression ignition engine.Tests revealed that HVO can significantly reduce engine-out soot (by more than 60%), HC and CO emissions (by about 40%), compared to diesel, while NOx levels and fuel conversion efficiency remain relatively unchanged under steady-state warmed-up conditions. The advantages of HVO proved to be further enhanced when the engine has not yet warmed up.Using statistical techniques of design of experiments (DoE) at three warmed-up steady-state operating points, the main engine control parameters were recalibrated to demonstrate that engine-out emissions can be further optimized with a dedicated calibration.

Volumetric and Simultaneous Photoacoustic and Ultrasound Imaging with a Conventional Linear Array in a Multiview Scanning Scheme.

Volumetric, multimodal imaging with precise spatial and temporal co-registration can provide valuable and complementary information for diagnosis and monitoring. Considerable research has sought to combine 3D photoacoustic (PA) and ultrasound (US) imaging in clinically translatable configurations. However, technical compromises currently result in poor image quality either for photoacoustic or ultrasonic modes. This work aims to provide translatable, high quality, simultaneously co-registered dual-mode PA/US 3D tomography. Volumetric imaging based on a synthetic aperture approach was implemented by interlacing PA and US acquisitions during a rotate-translate scan with a 5-MHz linear array (12 angles and 30-mm translation to image a 21-mm diameter, 19 mm long cylindrical volume within 21 seconds). For co-registration, an original calibration method using a specifically designed thread phantom was developed to estimate 6 geometrical parameters and 1 temporal off-set through global optimization of the reconstructed sharpness and superposition of calibration phantom structures. Phantom design and cost function metrics were selected based on analysis of a numerical phantom, and resulted in a high estimation accuracy for the 7 parameters. Experimental estimations validated the calibration repeatability. The estimated parameters were used for bimodal reconstruction of additional phantoms with either identical or distinct spatial distributions of US and PA contrasts. Superposition distance of the two modes was within < 10% of the acoustic wavelength and a wavelength-order uniform spatial resolution was obtained. This dual-mode PA/US tomography should contribute to more sensitive and robust detection and follow-up of biological changes or the monitoring slower-kinetic phenomena in living systems such as the accumulation of nanoagents.

Five Years of Accurate PM2.5 Measurements Demonstrate the Value of Low-Cost PurpleAir Monitors in Areas Affected by Woodsmoke

Low-cost optical sensors are used in many countries to monitor fine particulate (PM2.5) air pollution, especially in cities and towns with large spatial and temporal variation due to woodsmoke pollution. Previous peer-reviewed research derived calibration equations for PurpleAir (PA) sensors by co-locating PA units at a government regulatory air pollution monitoring site in Armidale, NSW, Australia, a town where woodsmoke is the main source of PM2.5 pollution. The calibrations enabled the PA sensors to provide accurate estimates of PM2.5 that were almost identical to those from the NSW Government reference equipment and allowed the high levels of wintertime PM2.5 pollution and the substantial spatial and temporal variation from wood heaters to be quantified, as well as the estimated costs of premature mortality exceeding $10,000 per wood heater per year. This follow-up study evaluates eight PA sensors co-located at the same government site to check their accuracy over the following four years, using either the original calibrations, the default woodsmoke equation on the PA website for uncalibrated sensors, or the ALT-34 conversion equation (see text). Minimal calibration drift was observed, with year-round correlations, r = 0.98 ± 0.01, and root mean square error (RMSE) = 2.0 μg/m3 for daily average PA PM2.5 vs. reference equipment. The utitilty of the PA sensors without prior calibration at locations affected by woodsmoke was also demonstrated by the year-round correlations of 0.94 and low RMSE between PA (woodsmoke and ALT-34 conversions) and reference PM2.5 at the NSW Government monitoring sites in Orange and Gunnedah. To ensure the reliability of the PA data, basic quality checks are recommended, including the agreement of the two laser sensors in each PA unit and removing any transient spikes affecting only one sensor. In Armidale, from 2019 to 2022, the continuing high spatial variation in the PM2.5 levels observed during the colder months was many times higher than any discrepancies between the PA and reference measurements. Particularly unhealthy PM2.5 levels were noted in southern and eastern central Armidale. The measurements inside two older weatherboard houses in Armidale showed that high outdoor pollution resulted in high pollution inside the houses within 1–2 h. Daily average PM2.5 concentrations available on the PA website allow air pollution at different sites across regions (and countries) to be compared. Such comparisons revealed major elevations in PA PM2.5 at Gunnedah, Orange, Monash (Australian Capital Territory), and Christchurch (New Zealand) during the wood heating season. The data for Gunnedah and Muswellbrook suggest a slight underestimation of PM2.5 at other times of the year when there are proportionately more dust and other larger particles. A network of appropriately calibrated PA sensors can provide valuable information on the spatial and temporal variation in the air pollution that can be used to identify pollution hotspots, improve estimates of population exposure and health costs, and inform public policy.

Evaluating Landsat-9 TIRS-2 calibrations and land surface temperature retrievals against ground measurements using multi-instrument spatial and temporal sampling along transects

High-resolution TIR data are essential for a wide range of studies, e.g., in environmental monitoring, urban effects, water stress, agricultural productivity, and land/water resources. Landsat series provides high-resolution TIR data, but data accuracy must be ensured to contribute to these fields. After the calibration problems of Landsat 8 (L8) TIRS due to a stray light effect, efforts were made in the design of Landsat 9 (L9) TIRS-2 to mitigate this effect. In any case, field calibration and validation activities are necessary to evaluate the accuracy of recently launched sensors. These activities require high-quality ground TIR radiance data that are representative at satellite spatial resolution and that quantify spatial–temporal fluctuations at the experimental sites concurrently to satellite overpasses. However, these requirements are not always accomplished in the literature.This paper provides vicarious calibrations of the L9 TIRS-2 data with accurate ground measurements acquired using multi-instrument spatial and temporal sampling in a rice paddy area from December 2021 to August 2023, in order to contribute to the calibration and validation of L9 TIRS-data.In March 1, 2023 L9 TIRS-2 scenes started to be reprocessed and calibration was updated. Both the original calibration and that after reprocessing were evaluated. Close results were obtained for band 10 (b10). When comparing satellite brightness temperatures and those determined from in situ LSTs, systematic differences of −0.3 K were shown, with root-mean-square differences (RMSDs) of 0.5 K. Results for band 11 showed a bias of −0.1 K for the original data and of 0.2 K after the reprocessing, with RMSDs of 0.6 K.The L2 LST product generated operationally from b10 data was also evaluated, together with alternative single-channel (SC) algorithms. A bias (RMSD) of 0.4 K (1.1 K) was obtained for the product. However, negative biases were shown both with the radiative transfer equation used on b10 radiances and the alternative SC algorithm.Finally, we validated six types of split-window (SW) algorithms applied to L9 TIRS-2 data before and after the reprocessing. A systematic difference of about −0.6 K between SW LST biases when using the reprocessed and original data was observed. In any case, for most of the algorithms with original and reprocessed data, LST biases and standard deviations were within the recommended threshold of 1 K, which proves the good performance of L9 TIRS-2 data to monitor LST at high spatial resolution.

Cross-Radiometric Calibration and NDVI Application Comparison of FY-4A/AGRI Based on Aqua-MODIS

To enhance the accuracy and stability of FY-4A/AGRI detection data, the MODIS, with highly accurate onboard calibration, is selected as the reference sensor for cross-radiation calibration calculations. The following are the data selection conditions: full considered time, observation geometries, field angles, cloud cover, etc. FY-4A/AGRI and Aqua-MODIS image data are selected as matching sample region locations, where the time difference between the observations for the same ground object is less than 15 min, the satellite zenith angle is less than 30°, and the field angle difference is less than 0.01. The 245 collected reflectance spectral curves are convolved with the spectral response functions of the two sensors, and the spectral band adjustment factors of the corresponding bands are calculated for spectral correction purposes. The cross-calibration coefficients for the red and near-infrared bands are calculated by linearly fitting the simulated top of the atmosphere reflectance values and digital number values from the AGRI sensor in a homogeneous area. In this paper, 16 cross-calibration calculations are performed on FY-4A/AGRI image data from August 2018 to September 2020, and the results are compared with the original calibration coefficients to test the feasibility of the proposed method. Additionally, 31 cross-calibration calculations are performed on image data from October 2020 to December 2022 to study the resulting AGRI sensor quality and performance changes. The NDVI of the FY-4A/AGRI image data was calculated before and after the cross-radiometric calibration using the maximum synthesis method. Additionally, the NDVI of the MODIS image data was compared and analyzed from three aspects: time, space, and the change trend. The results show that the spectral band adjustment factor calculated using the reflectance spectral curves of the ground objects in this paper can effectively correct for the spectral differences between the two sensors. Sixteen cross-calibration coefficients are less than 5.2% different from the original calibration coefficients, which fully proves the feasibility of the method used in this paper. All of the cross-calibration results show that the AGRI sensors have a certain degree of attenuation in the red and near-infrared bands, and the annual attenuation rates are approximately 1.37% and 2.55%, respectively. Cross-radiometric calibration has further improved the quality of the NDVI in FY-4A/AGRI imagery, enhancing the precision of its data application.

Influence of coaxial cable resistance on impedance measurements at low frequency and guidelines for phase determination of ac electrical signals.

We present an experimental study on ac measurements at low frequencies (below 1MHz) when the coaxial cables used for the measurement are resistive, as in cryogenic conditions. More precisely, we are interested in admittance or impedance measurements and the accurate determination of the phase. Our experiments were completed using an auto-balancing bridge impedance analyzer and various standard coaxial cables, as well as their serial combinations. We characterize the experimental setup, and then, we analyze and measure the phase shift introduced in impedance measurements by lead resistance. Using basic equations for electrical signal propagation in coaxial cables, we calculate phase shift in the whole frequency range and explain our data in the low frequency limit of our model. We propose a quantitative criterion to determine whether the experimental context is appropriate. If not, we show that by using an original calibration procedure, the imaginary part can be accurately recovered, avoiding artifacts. The calibration procedure can be applied to any ac voltage or current measurement, whatever the detection technique is, with known accuracy.

Dark Energy Survey Year 3 results: redshift calibration of the MagLim lens sample from the combination of SOMPZ and clustering and its impact on cosmology

ABSTRACT We present an alternative calibration of the MagLim lens sample redshift distributions from the Dark Energy Survey (DES) first 3 yr of data (Y3). The new calibration is based on a combination of a self-organizing-map-based scheme and clustering redshifts to estimate redshift distributions and inherent uncertainties, which is expected to be more accurate than the original DES Y3 redshift calibration of the lens sample. We describe in detail the methodology, and validate it on simulations and discuss the main effects dominating our error budget. The new calibration is in fair agreement with the fiducial DES Y3 n(z) calibration, with only mild differences (&amp;lt;3σ) in the means and widths of the distributions. We study the impact of this new calibration on cosmological constraints, analysing DES Y3 galaxy clustering and galaxy–galaxy lensing measurements, assuming a Lambda cold dark matter cosmology. We obtain Ωm = 0.30 ± 0.04, σ8 = 0.81 ± 0.07, and S8 = 0.81 ± 0.04, which implies a ∼0.4σ shift in the Ω − S8 plane compared to the fiducial DES Y3 results, highlighting the importance of the redshift calibration of the lens sample in multiprobe cosmological analyses.