Calibration Data Research Articles

129Xe Image Processing Pipeline: An open-source, graphical user interface application for the analysis of hyperpolarized 129Xe MRI.

Hyperpolarized 129Xe MRI presents opportunities to assess regional pulmonary microstructure and function. Ongoing advancements in hardware, sequences, and image processing have helped it become increasingly adopted for both research and clinical use. As the number of applications and users increase, standardization becomes crucial. To that end, this study developed an executable, open-source 129Xe image processing pipeline (XIPline) to provide a user-friendly, graphical user interface-based analysis pipeline to analyze and visualize 129Xe MR data, including scanner calibration, ventilation, diffusion-weighted, and gas exchange images. The customizable XIPline is designed in MATLAB to analyze data from all three major scanner platforms. Calibration data is processed to calculate optimal flip angle and determine129Xe frequency offset. Data processing includes loading, reconstructing, registering, segmenting, and post-processing images. Ventilation analysis incorporates three common algorithms to calculate ventilation defect percentage and novel techniques to assess defect distribution and ventilation texture. Diffusion analysis features ADC mapping, modified linear binning to account for ADC age-dependence, and common diffusion morphometry methods. Gas exchange processing uses a generalized linear binning for data acquired using 1-point Dixon imaging. The XIPline workflow is demonstrated using analysis from representative calibration, ventilation, diffusion, and gas exchange data. The application will reduce redundant effort when implementing new techniques across research sites by providing an open-source framework for developers. In its current form, it offers a robust and adaptable platform for 129Xe MRI analysis to ensure methodological consistency, transparency, and support for collaborative research across multiple sites and MRI manufacturers.

Online Optical Axis Parallelism Measurement Method for Continuous Zoom Camera Based on High-Precision Spot Center Positioning Algorithm

Ensuring precise alignment of the optical axis is critical for achieving high-quality imaging in continuous zoom cameras. However, existing methods for measuring optical axis parallelism often lack accuracy and fail to assess parallelism across the entire focal range. This study introduces an online measurement method designed to address these limitations by incorporating two enhancements. First, image processing methodologies enable sub-pixel-level extraction of the spot center, achieved through improved morphological processing and the incorporation of an edge tracing algorithm. Second, measurement software developed using Qt Creator can output real-time data on optical axis parallelism across the full focal range post-measurement. This software features a multi-threaded architecture that facilitates the concurrent execution of image acquisition, data processing, and serial communication. Experimental results derived from simulations and real data indicate that the maximum average error in extracting the center of the spot is 0.13 pixels. The proposed system provides critical data for optical axis calibration during camera adjustment and inspection.

Robust Support for Semi-automated Reductions of Keck/NIRSPEC Data Using PypeIt

We present a data reduction pipeline (DRP) for Keck/Near InfraRed SPECtrograph (NIRSPEC) built as an addition to the PypeIt Python package. The DRP is capable of reducing multi-order echelle data taken both before and after the detector upgrade in 2018. As part of developing the pipeline, we implemented major improvements to the capabilities of the PypeIt package, including manual wavelength calibration for multi-order data and new output product that returns a coadded spectrum order-by-order. We also provide a procedure for correcting telluric absorption in NIRSPEC data by using the spectra of telluric standard stars taken near the time of the science spectra. At high resolutions, this is often more accurate than modeling-based approaches.

ESR Essentials: radiomics-practice recommendations by the European Society of Medical Imaging Informatics.

Radiomics is a method to extract detailed information from diagnostic images that cannot be perceived by the naked eye. Although radiomics research carries great potential to improve clinical decision-making, its inherent methodological complexities make it difficult to comprehend every step of the analysis, often causing reproducibility and generalizability issues that hinder clinical adoption. Critical steps in the radiomics analysis and model development pipeline-such as image, application of image filters, and selection of feature extraction parameters-can greatly affect the values of radiomic features. Moreover, common errors in data partitioning, model comparison, fine-tuning, assessment, and calibration can reduce reproducibility and impede clinical translation. Clinical adoption of radiomics also requires a deep understanding of model explainability and the development of intuitive interpretations of radiomic features. To address these challenges, it is essential for radiomics model developers and clinicians to be well-versed in current best practices. Proper knowledge and application of these practices is crucial for accurate radiomics feature extraction, robust model development, and thorough assessment, ultimately increasing reproducibility, generalizability, and the likelihood of successful clinical translation. In this article, we have provided researchers with our recommendations along with practical examples to facilitate good research practices in radiomics. KEY POINTS: Radiomics' inherent methodological complexity should be understood to ensure rigorous radiomic model development to improve clinical decision-making. Adherence to radiomics-specific checklists and quality assessment tools ensures methodological rigor. Use of standardized radiomics tools and best practices enhances clinical translation of radiomics models.

The SRG/eROSITA All-Sky Survey. Constraints on f(R) gravity from cluster abundances

The evolution of the cluster mass function traces the growth of the linear density perturbations and can be utilized to constrain the parameters of cosmological and alternative gravity models. In this context, we present new constraints on potential deviations from general relativity by investigating the Hu-Sawicki parametrization of the $f(R)$ gravity with the first Spectrum Roentgen Gamma (SRG)/ All-Sky Survey ( cluster catalog in the western Galactic hemisphere in combination with the overlapping Dark Energy Survey Year-3, KiloDegree Survey, and Hyper Suprime-Cam data for weak lensing mass calibration. For the first time, we present constraints obtained from cluster abundances only. When we consider massless neutrinos, we find a strict upper limit of $ f_ R0 | < -4.31$ at a 95<!PCT!> confidence level. Massive neutrinos suppress structure growth at small scales, and thus have the opposite effect of $f(R)$ gravity. We consequently investigate the joint fit of the mass of the neutrinos with the modified gravity parameter. We obtain $ f_ R0 | < -4.08$ jointly with $ m_ eV $ at a 95<!PCT!> confidence level, which is tighter than the limits in the literature utilizing cluster counts only. At $ f_ R0 |= - 6$, the number of clusters is not significantly changed by the theory. Consequently, we do not find any statistical deviation from general relativity in the study of eRASS1 cluster abundance. Deeper surveys with eROSITA, increasing the number of detected clusters, will further improve constraints on $ |f_ R0 |$ and investigate alternative gravity theories.

Self-Calibrating Stress Measurement System Based on Multidirectional Barkhausen Noise Measurements

The system presented in this paper enables automatization of the two-dimensional calibration process (determination of Barkhausen noise (BN) intensity dependence on in-plane components of strain). Then, using dedicated software created by the authors in LabVIEW environment, and with the help of two dimensional calibration data one can effectively determine strain and stress distribution i.e. magnitude and orientation of main strain/stress components relative to measurement direction. BN signal measurements are performed using an advanced, multidirectional Barkhausen noise (BN) measuring sensor and a measurement system dedicated for cooperation with it. The system uses a robust algorithm for the strain components determination based on calibration surfaces, instead of usually applied curves, thus taking the influence of normal strain component directly into account instead of treating it as a correction factor (if not completely neglecting). The originality of the system arises also from the fact that this is the first BN measurement system that is self-calibrating (i.e. automatically loads the calibration sample in a pre-programmed way, performs BN signal measurements and calculates calibration planes), provided that the user possesses enough of the investigated material for calibration sample preparation.

A Novel Platform and Workflow for Underwater Photogrammetry Surveys

Abstract. Underwater photogrammetry has gained popularity for creating three-dimensional (3D) maps and ortho-images of marine environments as compared to traditional echosounder surveys, they can be more cost-effective at creating high-resolution 3D models, and orthoimages are usually more informative than acoustic backscatter maps. This paper builds on previous work by the authors that developed an underwater image-capturing platform with several additional sensors, including GNSS, IMU, pressure depth sensor and single beam echosounder. This study aims to analyse the impact of calibration and sensor data integration into the photogrammetric processing workflow. The tests were performed using two underwater sites. The low-cost device's pressure sensor and tide station data outperform GNSS PPK-derived heights. Furthermore, it was observed that incorporating IMU motion sensor data did not improve the processing results. Additionally, utilising the echosounder point cloud proves valuable for enhancing the overall quality of the survey. Despite its lower density, it serves a dual purpose by validating the photogrammetry dataset and, more importantly, can be employed for correcting DSM height. This study further underlined the importance of reliable camera calibration for accurate 3D reconstruction.

Temperature correction of near-infrared spectra of raw milk

Accurate milk composition analysis is crucial for improving product quality, economic efficiency, and animal health in the dairy industry. Near-infrared (NIR) spectroscopy can quantify milk composition quickly and nondestructively. However, external factors, such as temperature fluctuations, can alter the molecular vibrations and hydrogen bonding in milk, altering the NIR spectra and leading to errors in predicting key constituents such as fat, protein, and lactose. This study compares the effectiveness of Piecewise Direct Standardization (PDS), Continuous PDS (CPDS), External Parameter Orthogonalization (EPO), and Dynamic Orthogonal Projection (DOP in correcting the impact of temperature-induced variations on predictions in milk long-wave NIR spectra (LW-NIR, 1000–1700 nm).A total of 270 raw milk samples were analyzed, collecting both reflectance and transmittance spectra at five different temperatures (20 °C, 25 °C, 30 °C, 35 °C, and 40 °C). The experimental setup ensured precise temperature control and accurate spectral measurements. PLSR models were calibrated at 30 °C to predict milk fat, protein, and lactose content. The performance of these models was assessed before and after applying the temperature correction methods, with a primary focus on reflectance spectra.Results indicate that EPO and DOP significantly enhance model robustness and prediction accuracy across all temperatures, outperforming PDS and CPDS, especially for lactose prediction. These orthogonalization methods were compared against PLSR models calibrated with spectra from all temperatures. EPO and DOP showed comparable or superior performance, highlighting their effectiveness without requiring extensive temperature-specific calibration data. These findings suggest that orthogonalization methods are particularly suitable for in-line milk quality measurements under farm conditions where temperature control is challenging. This study highlights the potential of advanced chemometric techniques to improve real-time, on-farm milk composition analysis, facilitating better farm management and enhanced dairy product quality.

Evaluating antenna phase center variation effects on tropospheric delay retrieval using a low-cost dual-frequency GNSS receiver

Abstract This study examines the impact of Phase Center Variation (PCV) corrections on Zenith Wet Delay (ZWD) accuracy using a low-cost U-blox ZED-F9P receiver paired with three different antenna configurations: the high-grade TRM57971 antenna, the moderate-grade AS-ANT3BCAL antenna, and the low-cost ANN-MB-00 antenna. Among the three antennas evaluated, the low-cost antenna exhibited the largest PCV magnitude and a pronounced elevation angle dependence. In contrast, the other two antennas demonstrated lower levels of PCV variation. Without PCV corrections, the low-cost antenna showed significant ZWD biases compared to reference values. Applying PCV corrections significantly improved its accuracy, reducing bias and RMS by 88% and 79%, respectively. Moderate- and high-grade antennas experienced minimal improvement with correction. All antennas exhibited remarkable day-to-day repeatability in their residual patterns, despite variations observed in the RMS of phase residuals. This observed repeatability is likely attributable to the presence of unmodeled multipath contributions. The variations in RMS, in turn, can be primarily ascribed to inherent differences in multipath resistance among the antenna designs. This study highlights the critical role of PCV corrections for accurate ZWD estimation with low-cost GNSS receivers. Future research should prioritize the acquisition of manufacturer-provided calibration data for low-cost antennas to streamline and enhance the accuracy of PCV correction applications. Moreover, efforts should be directed toward developing innovative solutions, such as low-cost, multipath-resistant antennas or advanced signal processing algorithms, to mitigate the impact of multipath errors. By addressing these areas, low-cost GNSS solutions can become more reliable and cost-effective tools for tropospheric delay estimation.

The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests

Abstract. Process-based ecosystem models are increasingly important for predicting forest dynamics under future environmental conditions, which may encompass non-analogous climate coupled with unprecedented disturbance regimes. However, challenges persist due to the extensive number of model parameters, scarce calibration data, and trade-offs between the local precision and the applicability of the model over a wide range of environmental conditions. In this paper, we describe a protocol that allows a modeller to collect transferable ecosystem properties based on ecosystem characteristic criteria and to compile the parameters that need to be described in the field. We applied the procedure to develop a new parameterisation for European beech (Fagus sylvatica L.) for the Biome-BGCMuSo model, the most advanced member of the Biome-BGC family. For model calibration and testing, we utilised multiyear forest carbon data from 87 plots distributed across five European countries. The initial values of 48 new ecophysiological parameters were defined based on a literature review. The final values of six calibrated parameters were optimised for single sites as well as for multiple sites using generalised likelihood uncertainty estimation (GLUE) and model output conditioning that ensured plausible simulations based on user-defined ranges of carbon stock output variables (carbon stock in aboveground wood biomass, soil, and litter) and finding the intersections of site-specific plausible parameter hyperspaces. To support the model use, we tested the model performance by simulating aboveground tree wood, soil, and litter carbon across a large geographical gradient of central Europe and evaluated the trade-offs between parameters tailored to single plots and parameters estimated using multiple sites. Our findings indicated that parameter sets derived from single sites provided an improved local accuracy of simulations of aboveground wood, soil, and litter carbon stocks by 35 %, 55 %, and 11 % in comparison to the a priori parameter set. However, their broader applicability was very limited. A multi-site optimised parameter set, on the other hand, performed satisfactorily across the entire geographical domain studied here, including on sites not involved in the parameter estimation, but the errors were, on average, 26 %, 35 % and 9 % greater for the aboveground wood, soil, and litter carbon stocks than those obtained with the site-specific parameter sets. Importantly, model simulations demonstrated plausible responses across large-scale environmental gradients, featuring a clear production optimum of beech that aligns with empirical studies. These findings suggest that the model is capable of accurately simulating the dynamics of European beech across its range and can be used for more comprehensive experimentations.

Design and Implementation of Digital Calibration Certificate for RFID Tag Storage.

The digital transformation of metrology is one of the most active activities in the field of metrology at present, where the digital calibration certificate (DCC) being developed is the topic receiving the most concern in the current digital transformation. In practical industrial applications, the issue of storage carrier for the DCC plays a crucial role in its promotion and implementation. To address this issue, a DCC meta-model called DCC-Lite schema has been designed along with a corresponding processing method. This solution involves compressing and segmenting the DCC to make it suitable for storage using RFID tags. These RFID tags are affixed to the instruments and accompany them throughout their usage. Additionally, the DCC-RFID processing system has been developed to validate the effectiveness of the DCC meta-model and its corresponding processing method within a wireless temperature acquisition system. Experimental results demonstrate that the system successfully reads and writes the DCC stored within the RFID tag group. Furthermore, it enables automated parsing of the DCC calibration data by the machine and real-time compensation of measurement data to reduce measurement errors.

Geometric Wide-Angle Camera Calibration: A Review and Comparative Study

Wide-angle cameras are widely used in photogrammetry and autonomous systems which rely on the accurate metric measurements derived from images. To find the geometric relationship between incoming rays and image pixels, geometric camera calibration (GCC) has been actively developed. Aiming to provide practical calibration guidelines, this work surveys the existing GCC tools and evaluates the representative ones for wide-angle cameras. The survey covers the camera models, calibration targets, and algorithms used in these tools, highlighting their properties and the trends in GCC development. The evaluation compares six target-based GCC tools, namely BabelCalib, Basalt, Camodocal, Kalibr, the MATLAB calibrator, and the OpenCV-based ROS calibrator, with simulated and real data for wide-angle cameras described by four parametric projection models. These tests reveal the strengths and weaknesses of these camera models, as well as the repeatability of these GCC tools. In view of the survey and evaluation, future research directions of wide-angle GCC are also discussed.

How uncertainty in calibration data affects the modeling of non-point source pollutant loads in baseflow

Baseflow is a major transport pathway for non-point source (NPS) pollutants. Watershed water quality (WWQ) models calibrated by low-quality data may produce misleading predictions of baseflow NPS pollutant loads, resulting in poor management decisions. We evaluated how models of the baseflow nitrate loads in the Huron River basin, southwest of Lake Erie, were affected by uncertainty in the calibration data. Based on a five-year time series of daily streamflow, nitrate concentration, and specific conductance, two sets of “observed” baseflow nitrate load data that include uncertainty were estimated using various tracer-based and non-tracer-based hydrograph separation methods, in conjunction with assumptions regarding baseflow nitrate concentrations. We calibrated the Soil and Water Assessment Tool plus (SWAT+) model with the two “observed” data sets and used the Generalized Likelihood Uncertainty Estimation (GLUE) approach to quantify parameter and predictive uncertainties. The results showed that baseflow accounted for 26 %–34 % of the mean annual total streamflow (11.8 m3/s) and 8 %–37 % of the mean annual total nitrate load (14.3 kg·ha−1·year−1) in the Huron River basin. The baseflow and nitrate load estimates from the non-tracer-based methods resembled those from the tracer-based method but had greater uncertainty. The posterior parameter distributions, as well as the weighted means and 90 % prediction intervals of the simulated baseflow nitrate loads, exhibited minimal variation when different calibration data sets for SWAT+ and different threshold likelihood values for GLUE were used. Our analysis emphasizes the necessity of calibrating WWQ models with baseflow pollutant loads/concentrations when addressing water quality issues related to baseflow. It also demonstrates the feasibility of utilizing multiple non-tracer-based hydrograph separation methods to estimate baseflow NPS pollutant loads. These non-tracer-based methods offer a simplicity and broader applicability compared to tracer-based methods. This study has provided insights into how calibration data uncertainty impacts the modeling of NPS pollution in baseflow and highlights the practical value of non-tracer-based hydrograph separation methods.

Sampling Confined Fission Tracks for Constraining Geological Thermal Histories

Fission-track modeling rests on etching, counting and measuring the lattice damage trails from uranium fission. The tools for interpreting fission-track data are advanced but the results are never better than the data. Confined-track samples must be an adequate size for statistical analysis, representative of the track population and consistent with the model assumptions and with the calibration data. Geometrical and measurement biases are understood and can be dealt with up to a point. However, the interrelated issues of etching protocol and track selection are more difficult to untangle. Our investigation favors a two-step protocol. The duration of the first step is inversely proportional to the apatite etch rate so that different apatites etch to the same Dpar. A long immersion reveals many more confined tracks, terminated by basal and prism faces. This allows consistent length measurements and permits orienting each track relative to the c-axis. Long immersion times combined with deep ion irradiation reveal confined tracks deep inside the grains. Provided it is long enough, the precise immersion time is not important if the effective etch times of the selected tracks are calculated from their measured widths. Then, whether the sample is mono- or multi-compositional, we can, post hoc, select tracks with the desired properties. The second part of the protocol has to do with the fact that fossil tracks in geological samples appear to be under-etched compared to induced tracks etched under the same conditions. This should be assumed if the semi-axes of a fitted ellipse plot above the induced-track line. In that case, an additional etch can increase the track lengths to a point where they are consistent with the model based on lab-annealing of induced tracks, a condition for valid thermal histories. Here too, it is possible to select a subset of tracks with effective etch times consistent with the model if the widths of confined tracks are measured along with their lengths and orientations.

Comparative clumped isotope temperature relationships in freshwater carbonates

AbstractLacustrine, riverine and spring carbonates represent archives of terrestrial climates and their geochemistry has been used to study palaeoenvironments. Clumped isotope thermometry is an emerging tool that has been applied to freshwater carbonates. Limited work has been done to evaluate comparative relationships between clumped isotopes and temperature in different types of modern freshwater carbonates. This study assembles an extensive calibration data set with 135 samples of modern freshwater carbonates from 96 sites and constrains the relationship between independent observations of water temperature and the clumped isotopic composition of carbonates (denoted by Δ47), including new measurements, and recalculates published data in accordance with current community‐defined standard values. For temperature reconstruction, the study reports a composite freshwater calibration and material‐specific calibrations for biogenic carbonates (freshwater gastropods and bivalves), fine‐grained carbonate (e.g. micrites), biologically mediated carbonates (microbialites and tufas) and travertines. Material‐specific calibration trends show a convergence of slopes that are in agreement with recently published syntheses, but statistically significant differences in intercepts occur between some materials (e.g. some biogenics, fine‐grained carbonates). These differences may arise due to unresolved seasonal biases, kinetic isotope effects and/or varying degrees of biological influence. The impact of different calibrations is shown through application to new data for glacial and deglacial age travertines from Austria and published data sets. While material‐specific calibrations may yield more accurate results for biogenic and fine‐grained carbonate samples, the use of material‐specific and the composite freshwater calibrations generally produces values within 1.0–1.5°C of each other, and typically fall within calibration uncertainty given limitations of precision.

Assessing the feasibility of mapping channel bathymetry of long river corridors from hyperspectral data across a range of flow conditions

ABSTRACT Hyperspectral imagery is a promising tool for bathymetric retrieval because that might be more robust than traditional imagery. However, in fluvial sciences, this approach has only been applied to short reaches for which sparse bathymetric information is available and studies extrapolating such models to longer reaches are lacking. In this paper, we use linear regression and random forest (RF) regression to derive bathymetric estimates from hyperspectral data acquired under two different flow conditions: an unmanned aerial vehicle (UAV) acquisition at 27 m3.s−1 (base flow) and an airplane acquisition at 127 m3.s−1 (mean flow) for reaches of 0.35 km and 20 km, respectively. Using a 2D bathymetric model based on a green LiDAR acquisition, we generate calibration and validation data for the 20 km reach and the two flow conditions. First, we show that the airplane dataset is able to retrieve base flow bathymetry with a similar accuracy to the UAV acquisition (5–10 cm) despite having been acquired at a higher discharge. Then, we show that we can extrapolate bathymetric models calibrated on a smaller reach to the 20 km study reach with a loss in accuracy compared to an RF model calibrated on the study reach (20–30 cm vs. 5–10 cm). By analysing the errors from our models, we show that the accuracy of the extrapolated models dropped due to an underprediction of deep pool areas and changes in the optical properties of the water column and substratum across the study reach, with some spectral bands performing more poorly. The better performance of the random forest calibrated on the study reach led us to suggest using multi-band machine learning models when attempting bathymetric predictions over long river corridors, and highlights the need to cover the range of environmental conditions in the target reach when acquiring field data.

Regionalized multiple-point statistical simulation for calibrating process-based geologic models to seismic data

Calibrating process-based geologic models to seismic data is critical and has been challenging for decades. The traditional approach to data calibration involves tuning the model input parameters by trial-and-error or through an automated inverse procedure. This can improve the model calibration to data but can hardly reach a fully satisfactory result. We adopt a multiple-point statistics (MPS) approach where a process-based geologic model is used as a training image for statistical pattern recognition. First, we define a rock-physics model from the process-based geologic model and derive its seismic attributes through seismic forward modeling. Then, we use the process-based model and its seismic attributes as coupled training images for geologic pattern recognition and regeneration under seismic data constraints. The method differs from the conventional MPS method in several ways: (1) the training image is a process-based geologic model of the reservoir of interest, thus defined on the same grid of the reservoir model; (2) the training image is generally nonstationary but there is no need to partition the nonstationary training image into pseudostationary ones; (3) the geologic facies and seismic constraint are related through seismic forward modeling instead of statistical inference, thus there is no need to convert seismic data to facies proportion or probability; (4) multiple-point statistics are based on Bayes’ law and Gaussian kernel approximation of conditional probability instead of a somehow arbitrary probability combination scheme or a heuristic rule; and (5) the method does not involve an iterative optimization procedure. Therefore, it also differs from the neural-network-based machine-learning approach, where the data conditioning is achieved through an iterative optimization procedure. These differences make our method advantageous for calibrating process-based geologic models. The two examples with synthetic data illustrate the effectiveness of the method. The first one is derived from a satellite image of a river delta, and the second one from the Stanford VI reservoir model.

Development and deployment of a mid-cost CO2 sensor monitoring network to support atmospheric inverse modeling for quantifying urban CO2 emissions in Paris

Abstract. To effectively monitor highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. These dense measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or to separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm for hourly mean measurements. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision cavity ring-down spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12:00–17:00 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects for integrating mid-cost measurements along with high-precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area.

Distribution-free prediction intervals with conformal prediction for acoustical estimation.

Acoustical parameter estimation is a routine task in many domains. The performance of existing estimation methods is affected by external uncertainty, yet the methods provide no measure of confidence in the estimates. Hence, it is crucial to quantify estimate uncertainty before real-world deployment. Conformal prediction (CP) generates statistically valid prediction intervals for any estimation model using calibration data; a limitation is that calibration data needed by CP must come from the same distribution as the test-time data. In this work, we propose to use CP to obtain statistically valid uncertainty intervals for acoustical parameter estimation using a data-driven model or an analytical model without training data. We consider direction-of-arrival estimation and localization of sources. The performance is validated on plane wave data with different sources of uncertainty, including ambient noise, interference, and sensor location uncertainty. The application of CP for data-driven and traditional propagation models is demonstrated. Results show that CP can be used for statistically valid uncertainty quantification with proper calibration data.

Kalman filter-driven state observer for thermal error compensation in machine tool digital twins

Sustainable reduction of thermal errors during production is the key challenge in modern high-precision manufacturing. Numerical compensation models provide an energy-efficient solution, but in the case of data-driven models, high-quality experimental data must be time-consuming and expensive to produce, negatively impacting overall productivity. Furthermore, robustness concerns arise in the case of new operating conditions, which were not contained in the training data. This paper presents a novel use of a Kalman filter together with model order reduced finite element models to observe the entire thermal state, which allows the subsequent solution of the mechanical model and computation of the thermal errors in real-time without requiring any training data but instead purely based on the physical system model. The effectiveness of this approach is evaluated using experiments on a thermal test bench with 16 out of 40 temperature sensors employed for observation and demonstrated on a 5-axis machine tool (MT) with 13 out of 25 temperature sensors used. Due to the combination of the reduced order model and Kalman filter these 13 temperature sensors are sufficient to represent a MT mesh of more than 350’000 elements. The entire temperature profile of the thermal test bench is reconstructed to achieve a root mean square error (RMSE) of the unobserved temperature sensors of only 2.7 °C, which accounts for more than 83% of all temperature variations and 1.3 °C for the 5-axis MT. For the thermal error of the thermal test bench, the RMSE could be reduced from 67.4μm to 33.3μm, corresponding to a reduction of 52.7 %. This could be achieved without the need for experimental data for model calibration, in a real-time capable physics-based model.