Calibration Data Research Articles

Oxygen optodes on oceanographic moorings: recommendations for deployment and in situ calibration

Increasing interest in the deployment of optical oxygen sensors, or optodes, on oceanographic moorings reflects the value of dissolved oxygen (DO) measurements in studies of physical and biogeochemical processes. Optodes are well-suited for moored applications but require careful, multi-step calibrations in the field to ensure data accuracy. Without a standardized set of protocols, this can be an obstacle for science teams lacking expertise in optode data processing and calibration. Here, we provide a set of recommendations for the deployment and in situ calibration of data from moored optodes, developed from our experience working with a set of 60 optodes deployed as part of the Gases in the Overturning and Horizontal circulation of the Subpolar North Atlantic Program (GOHSNAP). In particular, we detail the correction of drift in moored optodes, which occurs in two forms: (i) an irreversible, time-dependent drift that occurs during both optode storage and deployment and (ii) a reversible and pressure-and-time-dependent drift that is detectable in some optodes deployed at depths greater than 1,000 m. The latter is virtually unidentified in the literature yet appears to cause a low-bias in measured DO on the order of 1 to 3 µmol kg−1 per 1,000 m of depth, appearing as an exponential decay over the first days to months of deployment. Comparisons of our calibrated DO time series against serendipitous mid-deployment conductivity-temperature-depth (CTD)-DO profiles, as well as biogeochemical (BGC)-ARGO float profiles, suggest the protocols described here yield an accuracy in optode-DO of ∼1%, or approximately 2.5 to 3 µmol kg−1. We intend this paper to serve as both documentation of the current best practices in the deployment of moored optodes as well as a guide for science teams seeking to collect high-quality moored oxygen data, regardless of expertise.

A Novel Calibration Scheme of Gas Sensor Array for a More Accurate Measurement Model of Mixed Gases.

Gas sensor arrays (GSAs) usually encounter challenges due to the cross-contamination of mixed gases, leading to reduced accuracy in measuring gas mixtures. However, with the advent of artificial intelligence, there is a promising avenue for addressing this issue effectively. In pursuit of more accurate mixed gas measurements, we proposed a measurement model leveraging neural networks. Our approach involved employing the encoder of an autoencoder network (AEN) to extract features from experimental data, while fully connected layers were utilized for predicting concentrations of mixed gases. To refine the neural network parameters, we employed a variational autoencoder to generate additional data resembling the distribution of experimental data. Subsequently, we designed a domain difference maximum entropy technique to identify optimal concentration points for the calibration data. These calibration points were instrumental in training the fully connected layers, enhancing the model's accuracy. During practical usage, with the AEN configuration fixed, the model can be fine-tuned by using a small subset of test points across large-scale GSA deployments. Simulation and practical measurement results demonstrated the efficacy of our proposed measurement model, boasting high accuracy, with confidence intervals for relative errors of the four gas measurements below 3% at the 95% confidence level. Besides, the calibration scheme reduced the number of test points compared with traditional methods, reducing the cost of labor and equipment.

Brackish water parameters monitoring dashboard using Internet of things and industry 4.0

INTRODUCTION: Brackish water aquaculture plays a crucial role in meeting the growing global demand for seafood. It offers an opportunity to diversify aquaculture production and reduce pressure on overexploited marine resources. OBJECTIVES: By harnessing the unique properties of brackish ecosystems, this practice contributes to food security, economic growth, and sustainable resource management, while also promoting the conservation of valuable marine habitats. The development of a cutting-edge Indigenous Water Quality Monitoring Prototype named "Aqua BuoySis" for precision brackish water aquaculture utilizing machine intelligence. METHODS: The prototype integrates sensors for Dissolved Oxygen (DO), pH, Temperature, Turbidity, and Total Dissolved Solids (TDS). These sensors are calibrated using a dynamic temperature-based machine-learning approach to ensure accuracy in real-time environments. Sensor calibration constants are uploaded to a server for comprehensive data calibration. RESULTS: The system collects data at 20-second intervals, associating it with specific pond IDs. Data refinement is achieved through Long Short-Term Memory (LSTM) processing. An Android and Web application, available in native languages such as Tamil and Telugu, has been developed to provide live updates to aqua farmers, facilitating informed decision-making. CONCLUSION: This technology represents a significant step towards enhancing precision in brackish water aquaculture through the fusion of machine intelligence and water quality management.

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

Abstract This work presents procedures to implement machine learning methods in the existing algorithms for multi-hole probe calibrations and data reduction. It is shown here, that utilizing artificial neural networks (ANNs) can reduce the amount of calibration data that needs to be acquired in order to obtain a specific calibration uncertainty, by more than 50% while simultaneously reducing data reduction times significantly. ANNs were used instead of the surface fitting methods, where first, the directional calibration coefficients related to the flow angles are calculated based on the pressure measurements, and then the flow angles are used as a set of the input parameters for the following ANNs to define Mach number and static and total pressure iteratively. In a second approach, novel calibration coefficients were used to directly relate the pressure measurements from five-hole probe to the quantities of interest thus, eliminating the need for iterative algorithms used in the conventional surface fitting methods. The advantageous features of this method are an average increase of less than 1% in the calibration uncertainty for flow angles and significant reduction of the data reduction times (few seconds). In addition, we confirmed the methodology to avoid over-fitting and under-fitting.

Exploring the Relationship Between Very-High-Resolution Satellite Imagery Data and Fruit Count for Predicting Mango Yield at Multiple Scales

Tree- and block-level prediction of mango yield is important for farm operations, but current manual methods are inefficient. Previous research has identified the accuracies of mango yield forecasting using very-high-resolution (VHR) satellite imagery and an ’18-tree’ stratified sampling method. However, this approach still requires infield sampling to calibrate canopy reflectance and the derived block-level algorithms are unable to translate to other orchards due to the influences of abiotic and biotic conditions. To better appreciate these influences, individual tree yields and corresponding canopy reflectance properties were collected from 2015 to 2021 for 1958 individual mango trees from 55 orchard blocks across 14 farms located in three mango growing regions of Australia. A linear regression analysis of the block-level data revealed the non-existence of a universal relationship between the 24 vegetation indices (VIs) derived from VHR satellite data and fruit count per tree, an outcome likely due to the influence of location, season, management and cultivar. The tree-level fruit count predicted using a random forest (RF) model trained on all calibration data produced a percentage root mean squared error (PRMSE) of 26.5% and a mean absolute error (MAE) of 48 fruits/tree. The lowest PRMSEs produced from RF-based models developed from location, season and cultivar subsets at the individual tree level ranged from 19.3% to 32.6%. At the block level, the PRMSE for the combined model was 10.1% and the lowest values for the location, seasonal and cultivar subset models varied between 7.2% and 10.0% upon validation. Generally, the block-level predictions outperformed the individual tree-level models. Maps were produced to provide mango growers with a visual representation of yield variability across orchards. This enables better identification and management of the influence of abiotic and biotic constraints on production. Future research could investigate the causes of spatial yield variability in mango orchards.

A comparison of stochastic and deterministic dynamics of tuberculosis model

Tuberculosis is a deadly infectious disease leading to a major health concern in Southern India while there are major obstacles to controlling its spread. Here, we present a mathematical model with five compartments. We categorize the infected compartment into two subcategories: latent TB-infected individuals and active TB-infected individuals. By introducing white noise in a deterministic system, we formulate a stochastic system. We investigate the model in deterministic and stochastic framework, followed by data calibration using TB infection data from Kanyakumari District in South India from 2019 to 2023. In the deterministic model, we derive the disease-free and endemic equilibrium points, compute the basic reproduction number, and examine their stability. Moreover, we perform sensitivity analysis to evaluate how variations in model parameters affect TB prevalence. In addition, we study the uniqueness of solutions of stochastic model. After that we derive the conditions for disease extinction and stochastic permanence, and execute extensive simulations to capture the variability and randomness in TB transmission dynamics. This study signifies that stochastic dynamics is richer than deterministic one in the way of mitigation TB transmission.

SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters

The growing use of sensors in fresh waters for water quality measurements generates an increasingly large amount of data that requires quality assurance (QA)/quality control (QC) before the results can be exploited. Such a process is often resource-intensive and may not be consistent across users and sensors. SentemQC (QA-QC of high temporal resolution sensor data) is a cost-efficient, and open-source Python approach developed to ensure the quality of sensor data by performing data QA and QC on large volumes of high-frequency (HF) sensor data. The SentemQC method is computationally efficient and features a six-step user-friendly setup for anomaly detection. The method marks anomalies in data using five moving windows. These windows connect each data point to neighboring points, including those further away in the moving window. As a result, the method can mark not only individual outliers but also clusters of anomalies. Our analysis shows that the method is robust for detecting anomalies in HF sensor data from multiple water quality sensors measuring nitrate, turbidity, oxygen, and pH. The sensors were installed in three different freshwater ecosystems (two streams and one lake) and experimental lake mesocosms. Sensor data from the stream stations yielded anomaly percentages of 0.1%, 0.1%, and 0.2%, which were lower than the anomaly percentages of 0.5%, 0.6%, and 0.8% for the sensors in Lake and mesocosms, respectively. While the sensors in this study contained relatively few anomalies (<2%), they may represent a best-case scenario in terms of use and maintenance. SentemQC allows the user to include the individual sensor uncertainty/accuracy when performing QA-QC. However, SentemQC cannot function independently. Additional QA-QC steps are crucial, including calibration of the sensor data to correct for zero offsets and implementation of gap-filling methods prior to the use of the sensor data for determination of final real-time concentrations and load calculations.

Data analysis procedures to improve energy resolution in silicon detectors using GET electronics

This study primarily focuses on improving the energy resolution of Double-Sided Silicon Strip Detectors (DSSSD) through optimized data analysis procedures using General Electronics for Time Projection Chambers (GET). We introduce two edge identification algorithms that demonstrate comparable efficacy. These methods are subsequently integrated with various techniques for measuring pulse amplitude, including sample smoothing and pole-zero cancellation. Notably, sample smoothing significantly enhances performances, achieving a resolution of 0.33% with DSSSD α calibration data. Additionally, we describe and apply the trapezoidal filter, examining its impact on resolution improvement and obtaining comparable results. The study also evaluates how internal GET parameters, specifically the sampling frequency and the shaping time of the Sallen-Key (SK) low-pass filter, affect signal resolution. Higher sampling frequencies and lower values of the SK filter were found to increase performance.

Leveraging textured flickers: a leap toward practical, visually comfortable, and high-performance dry EEG code-VEP BCI.

Reactive Brain-Computer Interfaces (rBCIs) typically rely on repetitive visual stimuli, which can strain the eyes and cause attentional distraction. To address these challenges, we propose a novel approach rooted in visual neuroscience to design visual Stimuli for Augmented Response (StAR). The StAR stimuli consist of small, randomly-oriented Gabor or Ricker patches that optimize foveal neural response while reducing peripheral distraction.

Methods: In a factorial design study, 24 participants equipped with an 8-dry electrode EEG system focused on series of target flickers presented under three formats: traditional Plain flickers, Gabor-based, or Ricker-based flickers. These flickers were part of a five-class Code Visually Evoked Potentials (c-VEP) paradigm featuring low-frequency, short, and aperiodic visual flashes.

Results: Subjective ratings revealed that Gabor and Ricker stimuli were visually comfortable and nearly invisible in peripheral vision compared to plain flickers. Moreover, Gabor and Ricker-based textures achieved higher accuracy (93.6% and 96.3%, respectively) with only 88 seconds of calibration data, compared to plain flickers (65.6%). A follow-up online implementation of this experiment was conducted to validate our findings in naturalistic operations. During this trial, remarkable accuracies of 97.5% in a cued task and 94.3% in an asynchronous digicode task were achieved, with a mean decoding time as low as 1.68 seconds.

Conclusion: This work demonstrates the potential to expand BCI applications beyond the lab by integrating visually unobtrusive systems with gel-free, low-density EEG technology, thereby making BCIs more accessible and efficient. The datasets, algorithms, and BCI implementations are shared through open-access repositories.

Toward Modeling Continental‐Scale Inland Water Carbon Dioxide Emissions

AbstractInland waters emit significant amounts of carbon dioxide (CO2) to the atmosphere; however, the global magnitude and source distribution of inland water CO2 emissions remain uncertain. These fluxes have previously been “statistically upscaled” by independently estimating dissolved CO2 concentrations and gas exchange velocities to calculate fluxes. This scaling, while robust and defensible, has known limitations in representing carbon source limitations and spatial variability. Here, we develop and calibrate a CO2 transport model for the continental United States, simulating carbon transport and transformation in >22 million hydraulically connected rivers, lakes, and reservoirs. We estimate 25% lower CO2 fluxes compared to upscaling estimates forced by the same observational calibration data. While precise CO2 source distribution estimates are limited by the resolution of model parameterizations, our model suggests that stream corridor CO2 production dominates over groundwater inputs at the continental scale. Our results further suggest that the lack of observational networks for groundwater CO2 and scalable metabolic models of aquatic CO2 production remain the most salient barriers to further coupling of our model with other Earth system components.

Assessing the reliability of species distribution models under changing environments: a case study on cetaceans in the North-East Atlantic

1.Species Distribution Models (SDMs) assume stable relationships between species and their environment from which predictions are made. These relationships are likely to vary with changing environments, and predictions might depend more on modelling choices than on empirical data. Reliability assessments of predictions are necessary to support policy-making.2.We identified environmental extrapolations among potential predictions of cetaceans’ distribution from 2005 to 2020 in the North-East Atlantic and calculated the percentages of calibration data with similar environments (nearby data), supporting these predictions. Thus, the assessment of reliability is generic, as evaluated before model fitting.3.Predictions on continental shelves were extensively supported by the calibration data and were more reliable throughout the year than predictions on continental slopes and abyssal plains, which were more supported in summer. Predictions off Portugal were particularly uncertain due to the lack of surveys in this region of deep, warmer waters with seamounts.4.The high effort between May and July led to a southern winter shift of nearby data, following the decrease in temperatures. A large part of the predictions between December and April was extrapolated due to the low coverage of the winter primary productivity drops, spring peaks and cold waters. They were based on data collected during other seasons and regions, and given the large spatial extent of the area, and the seasonality and regionality of the cetacean distributions, reliable winter predictions might be restricted to geographic areas where winter surveys took place. These predictions are more uncertain and warrant caution.5.Synthesis and applications: extrapolations and nearby data highlighted environmental gaps to predict cetacean distributions in the North-East Atlantic, which could be covered by future surveys. This informs model users of regions and periods when predictions reliability becomes uncertain. SDMs are invaluable tools for supporting conservation applications and, despite the warnings that have been issued, the degree of information available for predicting distribution is still rarely reported. We recommend adding this assessment as routine information on the reliability of predictions.

Spectra data calibration based on deep residual modeling of independent component regression

Independent component regression (ICR) has recently become quite popular in spectra data calibration, due to its advantages in non-Gaussian data modeling and high-order statistics feature extraction. Inspired by the idea of deep learning, this paper extends the basic ICR model to the deep form by introducing a layer-wise residual learning strategy. Based on the residual information generated from last layer of the deep learning model, more and more different patterns of independent components can be extracted layer-by-layer. Then, a further information compression step is taken to combine and also to condense those independent components obtained from different layers of the deep model. Two detailed benchmark case studies are implemented to evaluate the calibration performance of the develop model, based on which the effectiveness of both layer-by-layer component extraction and further information compression are well confirmed.

Calibration and characterization of the RED-100 detector at the Kalinin Nuclear Power Plant

RED-100 is a two-phase Xe detector designed and built for the study of coherent elastic neutrino-nucleus scattering (CEνNS) of reactor antineutrinos. A comprehensive calibration was performed in order to obtain important parameters of the detector during its operation at the Kalinin Nuclear Power Plant (Tver region, Russia). This paper outlines the analysis of calibration data, position and energy reconstruction procedures, and calculation of the efficiency of electron extraction from the liquid xenon to the gas phase.

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.

Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark.

Intracortical brain-computer interfaces (iBCIs) can restore movement and communication abilities to individuals with paralysis by decoding their intended behavior from neural activity recorded with an implanted device. While this activity yields high-performance decoding over short timescales, neural data are often nonstationary, which can lead to decoder failure if not accounted for. To maintain performance, users must frequently recalibrate decoders, which requires the arduous collection of new neural and behavioral data. Aiming to reduce this burden, several approaches have been developed that either limit recalibration data requirements (few-shot approaches) or eliminate explicit recalibration entirely (zero-shot approaches). However, progress is limited by a lack of standardized datasets and comparison metrics, causing methods to be compared in an ad hoc manner. Here we introduce the FALCON benchmark suite (Few-shot Algorithms for COnsistent Neural decoding) to standardize evaluation of iBCI robustness. FALCON curates five datasets of neural and behavioral data that span movement and communication tasks to focus on behaviors of interest to modern-day iBCIs. Each dataset includes calibration data, optional few-shot recalibration data, and private evaluation data. We implement a flexible evaluation platform which only requires user-submitted code to return behavioral predictions on unseen data. We also seed the benchmark by applying baseline methods spanning several classes of possible approaches. FALCON aims to provide rigorous selection criteria for robust iBCI decoders, easing their translation to real-world devices.

Long-Distance Measurements Using a Chromatic Confocal Sensor

In this work, we demonstrate the use of a chromatic confocal sensor for long-distance measurements. The sensor increases the working distance of state-of-the-art confocal sensors by a factor of 10, reaching a working distance of 620 mm. The chromatic aberration exhibited by a lens was utilized to establish the working range. The chromatic dispersion of the optics led to images of the different wavelength components at different longitudinal points along the optical axis. The sensor employs a robust algorithm to measure relative displacements of the sample’s motion. The calibration process simplifies data analysis and improves the accuracy of displacement measurements in experimental setups. To facilitate the design process, a simulator was developed specifically for this purpose. The calibration data obtained in both the experimental and the simulated data show that the simulator was able to predict the sensitivity with an error of 5%. We also describe the effect on the sensitivity of oversampling the spectrum. In addition, the superiority of low-pass filtering over Gaussian fitting over the detected spectrum is shown.

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.