Calibration Step Research Articles

A protocol for model intercomparison of impacts of marine cloud brightening climate intervention

Abstract. A modeling protocol (defined by a series of climate model simulations with specified model output) is introduced. Studies using these simulations are designed to improve the understanding of climate impacts using a strategy for climate intervention (CI) known as marine cloud brightening (MCB) in specific regions; therefore, the protocol is called MCB-REG (where REG stands for region). The model simulations are not intended to assess consequences of a realistic MCB deployment intended to achieve specific climate targets but instead to expose responses to interventions in six regions with pervasive cloud systems that are often considered candidates for such a deployment. A calibration step involving simulations with fixed sea surface temperatures (SSTs) is first used to identify a common forcing, and then coupled simulations with forcing in individual regions and combinations of regions are used to examine climate impacts. Synthetic estimates constructed by superposing responses from simulations with forcing in individual regions are considered a means of approximating the climate impacts produced when MCB interventions are introduced in multiple regions. A few results comparing simulations from three modern climate models (CESM2, E3SMv2, and UKESM1) are used to illustrate the similarities and differences between model behavior and the utility of estimates of MCB climate responses that were synthesized by summing responses introduced in individual regions. Cloud responses to aerosol injections differ substantially between models (CESM2 clouds appear much more susceptible to aerosol emissions than the other models), but patterns in precipitation and surface temperature responses were similar when forcing is imposed with similar amplitudes in the same regions. A previously identified La Niña-like response to forcing introduced in the Southeast Pacific is evident in this study, but the amplitude of the response was shown to markedly differ across the three models. Other common response patterns were also found and are discussed. Forcing in the Southeast Atlantic consistently (across all three models) produces weaker global cooling than that in other regions, and the Southeast Pacific and South Pacific show the strongest cooling. This indicates that the efficiency of a given intervention depends on not only the susceptibility of the clouds to aerosol perturbations, but also the strength of the underlying radiative feedbacks and ocean responses operating within each region. These responses were generally robust across models, but more studies and an examination of responses with ensembles would be beneficial.

Nitrogen status of durum wheat derived from Sentinel-2 satellite data in Central Italy

In agriculture, nitrogen (N) is a key element in plant nutrition that affects, both positively and negatively, the productive and qualitative results of the crop. Accurate quantification of nitrogen levels is crucial for devising effective plant nutrition strategies. The objective of this study was to validate a novel method to estimate the N content at different phenological stages of durum wheat (Triticum durum Desf. cv. Iride) under different N management strategies (chemical synthetic fertilizer - SYN and organic fertilizer - ORG) in Italy, using the Nitrogen Nutrition Index (NNI)as a diagnostic tool for improving nitrogen fertilization timing and doses. The NNI is the ratio between the actual crop nitrogen content (Na) and the optimal level (Nc) required for ideal growth conditions.On ground level, Leaf Area Index (LAI) , Canopy reflectance, leaf chlorophyll content (LCC) and N concentration in leaves were measured. At landscape level, LAI and Canopy Chlorophyll Indices (CIs) were derived from Sentinel 2 (S2) multispectral images captured on the same days as the ground measurements: Chlorophyll Indexes were used for estimating the canopy chlorophyll content, CCC. Na in leaves and canopy were calculated from LCC and CCC respectively.In the study area, Nc is Nc=4.65LAI–0.35, R2=0.92. Among the tested Chlorophyll Indices (CIs) regression models, the linear regression was the more accurate to predict Na content, even though most of the tested Chlorophyll Indices (CIs) showed an R2 > 0.8,a. The best-performing spectral index in both calibration and validation steps resulted from the IRECI, with R2=0.90 and RMSE=0.31. The developed NNI well-captured the seasonal N dynamic for durum wheat, under different N management and meteorological conditions. The NNI calculated from S2 data for crop N status assessment, showed to be an accurate estimation of the Nitrogen Nutrition Index and can be used for the fertilization plans without costly on ground measurements.

Self-traceable angle standards with simplified traceability chain for dimensional metrology

Traceability is a fundamental issue for ensuring accuracy of nanometrology. A shortened traceability chain is advantageous for reducing calibration steps, thus reducing errors and raising application efficiency. A novel kind of two-dimensional grating is manufactured by atom lithography, whose pitch and (orthogonal) angle values are directly determined by natural constants, offering a feasible approach for effectively shortening the traceability chain. The PTB’s calibration results show that the two-dimensional grating has excellent orthogonality with a deviation of only 0.001°. The application of two-dimensional grating is demonstrated for the characterization of the angular distortion of a SEM, showing its great application potential.

An analytical heat transfer model for transient Raman thermometry analysis.

Transient Raman thermometry improves on its steady-state counterpart by eliminating the error-prone steps of temperature calibration and laser absorption measurement. However, the accompanying complex heat transfer process often requires numerical analysis, such as the finite element method, to decipher the measured data. This step can be time-consuming, inconvenient, and difficult to derive a physical understanding of the heat transfer process involved. In this work, the finite element method is replaced by fitting the measured data to an analytical three-dimensional heat transfer model. This process can be completed in a few seconds. Using this approach, the in-plane thermal conductivity of two bulk layered materials and the interfacial thermal conductance between two-dimensional materials and quartz have been successfully measured. Based on our model, we performed an analytical quantitative sensitivity analysis for transient Raman thermometry to discover new physical insights. The sensitivity of the in-plane thermal conductivity of bulk layered materials is dictated by the ratio between the spot radius and heat spreading distance. The sensitivity of the interfacial thermal conductance between two-dimensional materials and quartz is determined by its conductance value. In addition, the uncertainty of the measured value contributed by the uncertainty of the input parameters can be efficiently estimated using our model. Our model provides an efficient data and sensitivity analysis method for the transient Raman thermometry technique to enable high throughput measurements, facilitate designing experiments, and derive physical interpretations of the heat transfer process.

Deep learning methods for 2D in-vivo dose reconstruction with EPID detector

Over the past two decades, radiotherapy has seen a steep increase in technological complexity of treatment preparation and treatment execution, calling for the development of more and better quality assurance tools and procedures. In-vivo dosimetry has emerged as a very powerful tool for treatment verification in conjunction with Electronic Portal Imaging Devices (EPIDs). However, EPIDs present several drawbacks like non-water equivalence and cumbersome calibration procedures. Artificial intelligence, specifically Deep Learning (DL), plays a crucial role in this context. A critical step is modeling the EPID response to estimate 2D dose distributions (Portal Dose, PD). This work introduces a DL-based methodology aimed at converting EPID responses into PD images. The proposed procedure is fully data driven, being based on advanced DL methods. This approach will allow bypassing the complex calibration steps needed to overcome the non-water equivalent panel.

A mixed-effects height-diameter model for Pinus douglasiana Martínez in temperate forests of Jalisco, Mexico

predicting tree-height of Pinus douglasiana in the natural forests of Jalisco, Mexico. Design/methodology/approach: For this study, we utilized 2,921 pairs of tree-height measurements collected from 65 permanent plots (each 50x50 m) in the study area. For each plot, we estimated the tree density as the number of trees per hectare (N, ha-1), the basal area per hectare (G, m2 ha-1) and the quadratic mean diameter (dg, cm). Subsequently, we tested several models from the literature and developed a generalized mixed-effects model for tree height prediction. Results: The Gompertz base model outperformed the other local models, achieving an R² of 0.75 and an RMSE of 3.13. Including the stand variables (N, G, and dg) and incorporating random effects significantly improved the model fit, resulting in an R² of 0.89 and an RMSE of 2.09 m. Calibration and validation steps revealed that selecting the three thickest trees is effective for estimating random effects in new plots or stands, with the RMSE and R² being 2.59 and 0.83, respectively. Limitations on study/implications: The present model can be applied in areas where P. douglasiana is naturally distributed. However, it is advisable to calibrate the model using a sub-sample to achieve more accurate predictions of tree heights in new plots or areas. Findings/conclusions: The Gompertz function was used as the base function to develop the final generalized mixed-effects model for predicting the height of Pinus douglasiana. The inclusion of stand variables (N, G, and dg) along with random effects improved the model fit. This model represents a new and more accurate tool for predicting the height of P. douglasiana in areas where it is naturally distributed.

Effect of online training on the reliability of assessing sacroiliac joint radiographs in axial spondyloarthritis - a randomized, controlled study.

Radiographic assessment of sacroiliac joints (SIJs) according to the modified New York (mNY) criteria is key in classification of axSpA but has moderate inter-reader agreement. We aimed to investigate the reliability improvements scoring SIJ radiographs after applying an online real-time iterative calibration (RETIC) module, in addition to a slideshow and video alone. Nineteen readers, randomized to 2 groups (A/B), completed 3 calibration steps: I) review of manuscripts, II) review of slideshow and video and group A completed RETIC, III) re-review of slideshow+video and group B completed RETIC . The RETIC module gave instant feedback on reader's gradings and continued until predefined reliability (kappa) targets for mNY positivity/negativity were met. Each step was followed by scoring different batches of 25 radiographs (Exercises I-III). Agreement (kappa) with an expert radiologist was assessed for mNY+/mNY- and individual lesions. Improvements by training strategies were tested by linear mixed models. In exercises I/II/III, mNY kappas were 0.61/0.76/0.84 in group A, and 0.70/0.68/0.86 in group B, respectively, i.e. increasing, mainly after RETIC completion. Improvements were observed for both grading mNY+/mNY- and for individual pathologies, both in experienced and, particularly, inexperienced readers. Completion of the RETIC module in addition to slideshow and video caused a significant kappa increase of 0.17 (CI: 0.07-0.27, P=0.002) for mNY+/mNY- grading, while completion of slideshow and video alone did not (0.0; CI -0.10-+0.10, P=0.99). Agreement on scoring radiographs according to the mNY criteria significantly improved when adding an online RETIC module, but not by slideshow and video alone.

Systematical and universal calibration scheme for division-of-aperture polarimetric camera

Division-of-aperture polarimetric (DoAP) camera has been widely used to obtain polarization images of scene owing to its feature in simultaneous acquisition of polarization information, real-time measurement and compact system. However, practical applications of DoAP imaging inevitably involve calibrating the camera from several aspects, which is very essential but the related reports are relatively lacking in current research. In this paper, we proposed a systematical and universal calibration scheme for the DoAP camera, which includes the lens distortion correction, the image registration, the polarization channel calibration and the Stokes parameters modification. Accordingly, a software for calibration and imaging processing, which consists of four modules, is designed to collectively facilitate the efficient and accurate execution of all calibration steps. In order to achieve real-time polarimetric imaging, the lens distortion correction and the image registration were done by means of lookup tables. Experimental results by using a full-Stokes DoAP camera we fabricated indicate that, via the lens distortion correction and the image registration, the alignment error between two images with a mean absolute error (MAE) less than 0.16 pixels can be obtained, resulting in the aligned images having an average increase of 64.53 % for the structural similarity index (SSIM) and that of 84.97 % for the peak signal to noise ratio (PSNR), respectively; and further via the polarization channel calibration and the Stokes parameters modification, the MAE of 0.0146 for the degree of polarization (DoP) and that of 0.3544° for the angle of polarization (AoP) are obtained, respectively. This proposed calibration scheme for DoAP camera is systematic, automatic and robust, which is benefit of improving the polarization detection accuracy, enhancing the camera calibration efficiency and thus promoting its polarimetric imaging performance.

Full-reference calibration-free image quality assessment

Objective Image Quality Assessment (IQA) methods often lack of linearity of their quality estimates with respect to scores expressed by human subjects and therefore IQA metrics undergo a calibration process based on subjective quality examples. However, example-based training presents a challenge in terms of generalization hampering result comparison across different applications and operative conditions. In this paper, new Full Reference (FR) techniques, providing estimates linearly correlated with human scores without using calibration are introduced. We show that on natural images, application of estimation theory and psychophysical principles to images degraded by Gaussian blur leads to a so-called canonical IQA method, whose estimates are linearly correlated to both the subjective scores and the viewing distance. Then, we show that any mainstream IQA methods can be reconducted to the canonical method by converting its metric based on a unique specimen image. The proposed scheme is extended to wide classes of degraded images, e.g. noisy and compressed images. The resulting calibration-free FR IQA methods allows for comparability and interoperability across different imaging systems and on different viewing distances. A comparison of their statistical performance with respect to state-of-the-art calibration prone methods is finally provided, showing that the presented model is a valid alternative to the final 5-parameter calibration step of IQA methods, and the two parameters of the model have a clear operational meaning and are simply determined in practical applications. The enhanced performance are achieved across multiple viewing distance databases by independently realigning the blur values associated with each distance.

Into the depths: Unveiling ELAIS-N1 with LOFAR’s deepest sub-arcsecond wide-field images

We present the deepest wide-field 115–166 MHz image at sub-arcsecond resolution spanning an area of 2.5° × 2.5° centred at the ELAIS-N1 deep field. To achieve this, we improved the direction-independent (DI) and direction-dependent (DD) calibrations for the International LOw Frequency ARray (LOFAR) Telescope. This enhancement enabled us to efficiently process 32 h of data from four different 8-h observations using the high-band antennas (HBAs) of all 52 stations, covering baselines up to approximately 2000 km across Europe. The DI calibration was improved by using an accurate sky model and refining the series of calibration steps on the in-field calibrator, while the DD calibration was improved by adopting a more automated approach for selecting the DD calibrators and inspecting the self-calibration on these sources. For our brightest calibrators, we also added an additional round of self-calibration for the Dutch core and remote stations in order to refine the solutions for shorter baselines. To complement our highest resolution at 0.3″, we also made intermediate resolution wide-field images at 0.6″ and 1.2″. Our resulting wide-field images achieve a central noise level of 14 μJy beam−1 at 0.3″, doubling the depth and uncovering four times more objects than the Lockman Hole deep field image at comparable resolution but with only 8 h of data. Compared to LOFAR imaging without the international stations, we note that due to the increased collecting area and the absence of confusion noise, we reached a point-source sensitivity comparable to a 500-h ELAIS-N1 6″ image with 16 times less observing time. Importantly, we have found that the computing costs for the same amount of data are almost halved (to about 139 000 CPU h per 8 h of data) compared to previous efforts, though they remain high. Our work underscores the value and feasibility of exploiting all Dutch and international LOFAR stations to make deep wide-field images at sub-arcsecond resolution.

Prediction of pea composites physicochemical traits and techno-functionalities using FTIR spectroscopy

This study aimed to test the feasibility of applying FTIR spectroscopy in order to create models that can predict quality attributes of pea composites. FTIR spectroscopy data were captured from three types of samples namely pea flour (PF), pea concentrate (PC) and pea isolate (PI). The FTIR spectral data along with partial least square (PLS) regression were used to build predictive models for physicochemical (moisture content, protein content, starch content, fiber content, bulk density, color), structural (particle size distribution, surface openings, fractal dimension), thermal (denaturization temperature, enthalpy), and techno-functional traits (water holding capacity, foaming capacity, foam stability, solubility, least gelation concentration) of pea composites. The FTIR spectra were subjected to different spectral pretreatment where 2nd derivative pretreatment provided the most suitable model for prediction of the studied parameters. The values of pea composite's traits determined through non-destructive FTIR spectroscopy coupled with partial least squares regression analysis, were very closed to the values obtained by the destructive standard methods. Performance (correlation, root mean square error) of the developed models were attribute-specific. For most of the studied attributes, correlation coefficient (r) were higher than 0.82 in the calibration step, and 0.71 in the prediction step. Pea composites have shown distinctive functionalities both as individual entity and in formulating meat analogs. Overall, it could be recommended that FTIR spectroscopy could be used in pea processing industry for developing robust models, to non-destructively assess the pea composite's properties and techno-functionalities.

Electromagnetical and ultrasonic characterizations of concretes subjected to internal swelling reactions

Among pathologies of reinforced concrete structures, internal swelling reactions (ISR), including alkali-aggregate reaction and delayed ettringite formation, are at the origin of cracks and major disorders due to rebar corrosion. Visual evaluation of crack density combined to non-destructive testing techniques can be used to characterize the global swelling and then give some structural diagnosis. For the last ones, an intermediate step (assimilated to a calibration step) can be performed at laboratory to evaluate the sensitivity of electromagnetic, electrical and ultrasonic properties of concretes subject to ISR.This present study focuses on such characterizations of concrete samples presenting different levels of ISR and for several water content. Numerous samples have been extracted from mock-ups representative of two massive concrete structures, affected one by alkali-aggregate reaction and the other by delayed ettringite formation, and conditioned in homogeneous and controlled conditions. After describing the experimental campaign, results are shown and commented.

Imazalil and prochloraz toxicokinetics in fish probed by a physiologically based kinetic (PBK) model.

Azole fungicides are highly suspected endocrine disruptors (EDs) and are frequently detected in surface water. Among them, there are prochloraz (PCZ), a commonly used moleculefor ED studies, and imazalil (IMZ), a highly suspected ED. Little is known about their toxicokinetic (TK) behavior in fish. Hence, research suggested that an improved risk assessment could be achieved by gaining insight into their TK behavior. The aim of this study is to understand and model the TK of both substances in different fish species, irrespective of the scheme of exposure. TK data from the literature were retrieved including different modes of exposure (per os and waterborne). In addition, two experiments on zebrafish exposed to either IMZ or PCZ were performed to address the lack of in vivo TK data. A physiologically based kinetic (PBK) model applied to IMZ and PCZ was developed, capable of modeling different exposure scenarios. The parameters of the PBK model were simultaneously calibrated on datasets reporting internal concentration in several organs in three fish species (original and literature datasets) by Bayesian methods (Monte Carlo Markov Chain). Model predictions were then compared to other experimental data (i.e., excluded from the calibration step) to assess the predictive performance of the model. The results strongly suggest that PCZ and IMZ are actively transported across the gills, resulting in a small fraction being effectively absorbed by the fish. The model's results also confirm that both molecules are extensively metabolized by the liver into mainly glucuronate conjugates. Overall, the model performances were satisfying, predicting internal concentrations in several key organs. On average, 90% of experimental data were predicted within a two-fold range. The PBK model allows the understanding of IMZ and PCZ kinetics profiles by accurately predicting internal concentrations in three different fish species regardless of the exposure scenario. This enables a proper understanding of the mechanism of action of EDs at the molecular initiating event (MIE) by predicting bioaccumulation in target organs, thus linking this MIE to a possible adverse outcome.

Discrimination and Quantification of Cotton and Polyester Textile Samples Using Near-Infrared and Mid-Infrared Spectroscopies.

In the textile industry, cotton and polyester (PES) are among the most used fibres to produce clothes. The correct identification and accurate composition estimate of fibres are mandatory, and environmentally friendly and precise techniques are welcome. In this context, the use of near-infrared (NIR) and mid-infrared (MIR) spectroscopies to distinguish between cotton and PES samples and further estimate the cotton content of blended samples were evaluated. Infrared spectra were acquired and modelled through diverse chemometric models: principal component analysis; partial least squares discriminant analysis; and partial least squares (PLS) regression. Both techniques (NIR and MIR) presented good potential for cotton and PES sample discrimination, although the results obtained with NIR spectroscopy were slightly better. Regarding cotton content estimates, the calibration errors of the PLS models were 3.3% and 6.5% for NIR and MIR spectroscopy, respectively. The PLS models were validated with two different sets of samples: prediction set 1, containing blended cotton + PES samples (like those used in the calibration step), and prediction set 2, containing cotton + PES + distinct fibre samples. Prediction set 2 was included to address one of the biggest known drawbacks of such chemometric models, which is the prediction of sample types that are not used in the calibration. Despite the poorer results obtained for prediction set 2, all the errors were lower than 8%, proving the suitability of the techniques for cotton content estimation. It should be stressed that the textile samples used in this work came from different geographic origins (cotton) and were of distinct presentations (raw, yarn, knitted/woven fabric), which strengthens our findings.

Detection of ChatGPT fake science with the xFakeSci learning algorithm

Generative AI tools exemplified by ChatGPT are becoming a new reality. This study is motivated by the premise that “AI generated content may exhibit a distinctive behavior that can be separated from scientific articles”. In this study, we show how articles can be generated using means of prompt engineering for various diseases and conditions. We then show how we tested this premise in two phases and prove its validity. Subsequently, we introduce xFakeSci, a novel learning algorithm, that is capable of distinguishing ChatGPT-generated articles from publications produced by scientists. The algorithm is trained using network models driven from both sources. To mitigate overfitting issues, we incorporated a calibration step that is built upon data-driven heuristics, including proximity and ratios. Specifically, from a total of a 3952 fake articles for three different medical conditions, the algorithm was trained using only 100 articles, but calibrated using folds of 100 articles. As for the classification step, it was performed using 300 articles per condition. The actual label steps took place against an equal mix of 50 generated articles and 50 authentic PubMed abstracts. The testing also spanned publication periods from 2010 to 2024 and encompassed research on three distinct diseases: cancer, depression, and Alzheimer’s. Further, we evaluated the accuracy of the xFakeSci algorithm against some of the classical data mining algorithms (e.g., Support Vector Machines, Regression, and Naive Bayes). The xFakeSci algorithm achieved F1 scores ranging from 80 to 94%, outperforming common data mining algorithms, which scored F1 values between 38 and 52%. We attribute the noticeable difference to the introduction of calibration and a proximity distance heuristic, which underscores this promising performance. Indeed, the prediction of fake science generated by ChatGPT presents a considerable challenge. Nonetheless, the introduction of the xFakeSci algorithm is a significant step on the way to combating fake science.

Striving towards robust phase diversity on-sky

Context. The recent IRLOS upgrade for VLT/MUSE narrow field mode (NFM) introduced a full-pupil mode to enhance sensitivity and sky coverage. This involved replacing the 2 × 2 Shack-Hartmann sensor with a single lens for full-aperture photon collection, which also enabled the engagement of the linearized focal-plane technique (LIFT) wavefront sensor instead. However, initial on-sky LIFT experiments have highlighted a complex point spread function (PSF) structure due to strong and polychromatic non-common path aberrations (NCPAs), complicating the accurate retrieval of tip-tilt and focus using LIFT. Aims. This study aims to conduct the first on-sky validation of LIFT on VLT/UT4, outline challenges encountered during the tests, and propose solutions for increasing the robustness of LIFT in on-sky operations. Methods. We developed a two-stage approach to focal-plane wavefront sensing, where tip-tilt and focus retrieval done with LIFT is preceded by the NCPA calibration step. The resulting NCPA estimate is subsequently used by LIFT. To perform the calibration, we proposed a method capable of retrieving the information about NCPAs directly from on-sky focal-plane PSFs. Results. We verified the efficacy of this approach in simulated and on-sky tests. Our results demonstrate that adopting the two-stage approach has led to a significant improvement in the accuracy of the defocus estimation performed by LIFT, even under challenging low-flux conditions. Conclusions. The efficacy of LIFT as a slow and truth focus sensor in practical scenarios has been demonstrated. However, integrating NCPA calibration with LIFT is essential to verifying its practical application in the real system. Additionally, the proposed calibration step can serve as an independent and minimally invasive approach to evaluate NCPA on-sky.

Avoiding heat source calibration for finite element modeling of the laser powder bed fusion process

The melt pool characteristics govern process stability and microstructure development during Laser Powder Bed Fusion (LBPF). The melt pool is strongly affected by the laser parameters and based on the melt pool depth-to-width ratio, three melting modes can be distinguished: conduction, transition, and keyhole mode. Finite element (FE) simulations have been extensively used to investigate the cyclic thermal history of the LPBF process and the effects of different process parameters on the part quality. However, the heat sources applied in such simulations can only accurately predict the melt pool behavior, and thus the temperature profile, when processing in conduction mode. As for the transition and keyhole regime modeling, heat source parameters or material properties are usually adjusted to make the simulated melt pool dimensions match the real values. This study proposes a novel approach to bypass the conventional heat source calibration strategy to simulate the LBPF process across different melting regimes. The predicted melt pool dimensions are validated for a wide range of process parameters applied to three different alloys: Ti-6Al-4 V, 316 L austenitic stainless steel (316 L), and 2507 super duplex stainless steel (SDSS). The average errors obtained on the melt pool width, depth, length, and aspect ratio are 9.7 %, 12.4 %, 14.5 %, and 14.3 % respectively. These results demonstrate how the proposed approach can capture the melt pool dimensions throughout different materials and melting modes eliminating the time-consuming heat source calibration steps and facilitating the LPBF modeling workflow.

Feature Engineering for Physics-informed Evaluation of Electromechanical Impedance Measurements for Sandwich Face Layer Debonding Identification

The present research exploits various physically explainable features for the evaluation of electromechanical impedance (EMI) measurements for sandwich face layer debonding identification using a recently published two-step physics- and machine-learning (ML)-based approach. In the previous work, the ML approach used employs a One-Class Support Vector Machine and a K-Nearest Neighbor model for debonding detection and size estimation, respectively. Feature engineering was used to compensate for the simplifications of the finite element (FE)-based physical model and a simple data calibration step was used to adapt to distribution shifts. This enabled to train both ML models exclusively with FE simulation-based synthetic EMI spectra data. The efficacy of the method was demonstrated on real-world EMI spectra measurements of a circular aluminum sandwich panel by a piezoelectric transducer. The considered face layer debonding damage was idealized and stepwise increased by a milling process. In the present work, we explore opportunities for further optimization of our method with respect to data distillation, where we reduce the computational requirements of both training and inference while at the same time preserving essential information. Furthermore, we investigate the usefulness of a separate approach for debonding detection, formulated as an anomaly detection problem. The data preprocessing and ML models are validated by unseen real-world EMI measurement data and benchmarked with the previously published damage evaluation results, considering both reliability and accuracy. The data and the code used in this study are provided in their entirety to enable reproducibility, enhance comprehensibility, and encourage future research.

Statistical image reconstruction with beam-hardening compensation for X-ray CT by a calibration step (2DIterBH).

The beam-hardening effect due to the polychromatic nature of the X-ray spectra results in two main artifacts in CT images: cupping in homogeneous areas and dark bands between dense parts in heterogeneous samples. Post-processing methods have been proposed in the literature to compensate for these artifacts, but these methods may introduce additional noise in low-dose acquisitions. Iterative methods are an alternative to compensate noise and beam-hardening artifacts simultaneously. However, they usually rely on the knowledge of the spectrum or the selection of empirical parameters. We propose an iterative reconstruction method with beam hardening compensation for small animal scanners that is robust against low-dose acquisitions and that does not require knowledge of the spectrum, overcoming the limitations of current beam-hardening correction algorithms. The proposed method includes an empirical characterization of the beam-hardening function based on a simple phantom in a polychromatic statistical reconstruction method. Evaluation was carried out on simulated data with different noise levels and step angles and on limited-view rodent data acquired with the ARGUS/CT system. Results in small animal studies showed a proper correction of the beam-hardening artifacts in the whole sample, independently of the quantity of bone present on each slice. The proposed approach also reduced noise in the low-dose acquisitions and reduced streaks in the limited-view acquisitions. Using an empirical model for the beam-hardening effect, obtained through calibration, in an iterative reconstruction method enables a robust correction of beam-hardening artifacts in low-dose small animal studies independently of the bone distribution.

Filling data gaps in long-term solar UV monitoring by statistical imputation methods

Knowledge of long-term time trends of solar ultraviolet (UV) radiation on ground level is of high scientific interest. For this purpose, precise measurements over a long time are necessary. One of the challenges solar UV monitoring faces is the permanent and gap-free data collection over several decades. Data gaps hamper the formation and comparison of monthly or annual means, and, in the worst case, lead to incorrect conclusions in further data evaluation and trend analysis of UV data. For estimating data to fill gaps in long-term UV data series (daily radiant exposure and highest daily irradiance), we developed three statistical imputation methods: a model-based imputation, considering actual local solar radiation conditions using predictors correlated to the local UV values in an empirical model; an average-based imputation based on a statistical approach of averaging available local UV measurement data without predictors; and a mixture of these two imputation methods. A detailed validation demonstrates the superiority of the model-based imputation method. The combined method can be considered the best one in practice. Furthermore, it has been shown that the model-based imputation method can be used as an useful tool to identify systematic errors at and between calibration steps in long-term erythemal UV data series.