Calibration Data Research Articles

Verification of uranium composite materials using active interrogation techniques for safeguards purposes

Abstract The uranium isotope characterization for nuclear safeguards and security purposes was conducted using both passive and active non-destructive techniques. The interrogated samples were natural uranium (NU), low-enriched uranium (LEU), and depleted uranium (DU) all in the form of uranium dioxide. The obtained spectra using both techniques were accumulated using a high-efficiency HPGe detector. Improving detector efficiency was developed using the neural network (NN) method based on the obtained intrinsic calibration data. The obtained enrichment results of passive measurements showed good agreement with certified samples to within 1.12% in the case of LEU, 1.03% in the case of NU, and 1.04% in the case of DU.
Active interrogation of the samples was done using the neutron spectrum of a 5 Ci 241Am-Be neutron source. Seven short-lived fission products (FPs) (101Tc, 97Nb, 105Ru, 92Y, 91Sr, 92Sr, and 88Kr) were chosen as indicators to provide a complete characterization of isotopic mass determination, uranium content, and enrichment. Isotopic masses of the samples were calculated using thermal and fast interrogations. A comparison of the obtained results of 238U mass was found to agree within 0.2% with certified value based on fast interrogation, and within 0.3% of 235U mass based on thermal interrogation.

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.

Comparing K-Means, Nearest Neighbor, and Lloyd's Clustering Algorithms

Clustering Organizing items into groups based on their properties such that the items in the same group are similar and those in other groups are distinct is known as clustering and is one method of unsupervised learning. The primary benefit of clustering is that, with little or no prior information, fascinating patterns and structures can be discovered directly from very large data sets. The most representative algorithms, the K-Means algorithm, the nearest neighbor algorithm, and Lloyd's algorithm, were explored and evaluated in this study based on their basic strategies. The proposed algorithms proved highly efficient in classifying data, as k-means results were high by creating data points and classifying that data into 10 groups, while the nearest neighbor algorithm proved highly effective in predicting new groups in light of pre-existing groups for new data points, and finally Lloyd's algorithm achieved high results through several iterations to reach the target groups. Using the random function, 100 random values for inputs as x and 100 for outputs as y are generated, and these values are grouped as points. Calculations of the mean and standard deviation for the data set indicate that 68% of the data points will fall within one standard deviation, 95% within two standard deviations, and 99.7% within three standard deviations. In the nearest neighbor algorithm, confidence in data points within a specified standard deviation number is determined by the coverage factor, or k value. For k = 10, 97% of the data points are expected to fall within one standard deviation. At Lloyd's, a normal distribution curve appears when generating calibration or measurement data.

Total Suspended Solids in Teluk Awur, Jepara using Red Reflectance from Landsat-8

The coastal waters of Teluk Awur, Jepara are affected by human activities such as aquaculture, tourism, agriculture, and settlements. The number of human activities on land will contribute to high suspended solids (TSS) entering coastal waters by run-off. The high TSS in coastal areas will affect the entry of light intensity, disrupting the photosynthesis process and ecosystem. This study compares TSS values based on Landsat images using several TSS algorithms such as Wirasatriya, Budhiman, Ajiperwata, and Parwati algorithms and the algorithm resulting from the July 2023 field data calibration against red reflectance. The algorithm's performance from calibration has a smaller error value with RMSE = 8.51, MAPE = 25.77%, and Bias = 7.52. TSS estimation from Landsat 8 satellite imagery has a range of values of 30.56 - 62.55 mg/L (average 35.60 mg/L), while field measurements of TSS values are 22.40 - 64.52 mg/L (average 32.69 mg/L). This research will be valuable information for using the right Landsat-8 algorithm for temporal and sustainable TSS monitoring. It can be used in abrasion monitoring and management in the Teluk Awur coastal waters of Jepara.

Lever arm measurement precision and its impact on exterior orientation parameters in GNSS/IMU integration

Abstract Airborne mobile mapping systems are crucial in various geodetic applications. A key aspect of these systems is the accurate estimation of exterior orientation parameters (EOPs), which is achieved through the integration of global navigation satellite systems (GNSSs) and inertial measurement unit (IMU) technologies. One critical component in this integration is the lever arm (LA), the vector that connects the GNSS antenna and the IMU center. The uncertainty (standard deviation) in LA measurements can introduce errors in the EOP estimation, thereby affecting the overall system performance. However, how much the EOP estimation is affected by LA measurement uncertainty is examined in this study based on calibration data (test flight) using the TerrainMapper 2 system collected by Lantmäteriet in Sweden. The findings reveal that LA uncertainties have minimal influence on attitude and negligible impacts on position in terms of standard deviation (SD) if the LA is measured with an accuracy of better than 2–3 cm. Additionally, the research explores the combined effects of virtual reference station-rover baseline length and dilution of precision on positioning accuracy and their correlation with LA uncertainty, providing further insights into the complexities of EOP estimation. By advancing GNSS/IMU integration techniques, this study contributes to the enhancement of geodetic technologies customized for airborne mobile mapping applications.

Applying Conformal Prediction to a Deep Learning Model for Intracranial Hemorrhage Detection to Improve Trustworthiness.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To apply conformal prediction to a deep learning (DL) model for intracranial hemorrhage (ICH) detection and evaluate model performance in detection as well as model accuracy in identifying challenging cases. Materials and Methods This was a retrospective (November 2017 through December 2017) study of 491 noncontrast head CT volumes from the CQ500 dataset in which three senior radiologists annotated sections containing ICH. The dataset was split into definite and challenging (uncertain) subsets, where challenging images were defined as those in which there was disagreement among readers. A DL model was trained on 146 patients (mean age = 45.7, 70 females, 76 males) from the definite data (training dataset) to perform ICH localization and classification into five classes. To develop an uncertainty-aware DL model, 1,546 sections of the definite data (calibration dataset) was used for Mondrian conformal prediction (MCP). The uncertainty-aware DL model was tested on 8,401 definite and challenging sections to assess its ability to identify challenging sections. The difference in predictive performance (P value) and ability to identify challenging sections (accuracy) were reported. Results After the MCP procedure, the model achieved an F1 score of 0.920 for ICH classification on the test dataset. Additionally, it correctly identified 6,837 of the 6,856 total challenging sections as challenging (99.7% accuracy). It did not incorrectly label any definite sections as challenging. Conclusion The uncertainty-aware MCP-augmented DL model achieved high performance in ICH detection and high accuracy in identifying challenging sections, suggesting its usefulness in automated ICH detection and potential to increase trustworthiness of DL models in radiology. ©RSNA, 2024.

Use of process-based coupled ecological-hydrodynamic models to support lake water ecosystem service protection planning at the regional scale

Protection plans of lake waters are based on ecological and/or chemical targets, often simplified in terms of total phosphorus (TP) concentrations, customarily the depth-averaged ones at spring mixing for temperate environments. These target lake TP concentrations are then commonly employed to determine target external loading through reverse use of Vollenweider-OECD-type steady-state empirical models. Such models are also adopted in their direct form to estimate lake TP concentrations following hypothetical external load reductions. However, such approaches suffer from extreme parameterisation and often give inaccurate results. Process-based coupled ecological-hydrodynamic models offer a much wider flexibility and produce an extensive set of information, solving many of the issues of Vollenweider-OECD-type models. However, their application has been up to now restricted to single lakes due to calibration effort and data availability burdens. To overcome these obstacles, in this study we developed a simplified application of the process-based coupled model QWET over 9 lakes in Northern Italy, making use of the ParSAC automatic calibration tool and feeding the models only with general data available from public monitoring. QWET models were calibrated over past observations, simulating nutrient reduction scenarios for the near-future decades. The advantages over traditionally employed models for lake water protection planning at the regional scale were hence identified through a practical application, determining the strengths and limits of the herein-adopted simplified process-based approach over lakes with different features. Obtained results were also analysed considering the specific case study.

Preoperative risk prediction models for noncardiac surgery patients : Interpret and use risk scores correctly

Risk prediction models are an established component of the preoperative evaluation. In its guidelines the European Society for Cardiology proposes several risk scores but the benefit of these is mostly unclear for clinicians. This article describes the individual steps in the preparation of a valid prediction model with a focus on the parameters, discrimination, calibration and external validation. The clinical benefits of the risk scores proposed in the guidelines with respect to these parameters was investigated. All proposed risk scores appear to show a good discrimination in the validation cohorts. Only a few reliable data for a good calibration could be compiled. The external validity of the individual models is unclear. The general benefit of the risk scores cannot be recommended as data for calibration or discrimination in external cohorts are lacking. A precise estimation of the risk cannot be expected.

HiFAST: An HI Data Calibration and Imaging Pipeline for FAST III. Standing Wave Removal

HiFAST: An HI Data Calibration and Imaging Pipeline for FAST III. Standing Wave Removal

Hall probe calibration in high-precision magnetic field mapping system of superconducting cyclotron

Abstract Introduction Superconducting cyclotron can generate high-energy proton beams and are mainly used for radiation therapy of tumors and cancers. In the superconducting cyclotron SC200, the maximum magnetic induction intensity can typically reach up to 4.6 T, and the magnetic field accuracy is 1e-4. Hall probes are commonly used tools for measuring high-intensity magnetic fields. Objective Through comprehensive consideration, this study selects the SENIS Low-Noise Teslameter 3MH5 and Hall probe C to measure the magnetic field. When the magnetic field exceeds the range of 2 T, the measurement accuracy of the Hall probe is less than 1e-4, and the Hall probe needs to be calibrated to improve its measurement accuracy. Methods The Hall probes are calibrated using Swiss METROLAB PT2025 nuclear magnetic resonance (NMR) Tesla instrument and 1062 probe. Based on the calibration principle, a calibration system platform was built, test data were collected, and calibration curves were obtained. At the same time, the calibration data were analyzed through cross-validation experiments using the cubic polynomial fitting method. Results The results indicate that the test deviation range is from −0.1 g to 0.1 g, and the measurement accuracy can reach 1e-4. Conclusion In summary, the Hall probe can accurately measure the magnetic field distribution of the superconducting cyclotron. It can provide accurate and important data for the calculation and analysis of particle beam dynamics.

Robust handling of extremes in quantile mapping – “Murder your darlings”

Abstract. Quantile mapping is a method often used for the bias adjustment of climate model data toward a reference, i.e. to construct a transformation of the model's distribution to that of the reference. The main moments of the distributions are typically well transformed by quantile mapping, but statistical uncertainty increases towards the extreme tails, making robust transformations challenging. Because of the limited data at the extreme tails, an empirical quantile mapping also needs to make some estimation or fit a parameterised function for data beyond the calibration data range. Here, the MIdAS bias adjustment platform is employed to explore different methods for handling the extreme tail; these approaches are evaluated using an indicator of extreme precipitation – the maximum daily precipitation amount per year. Different methodologies are evaluated for a large ensemble of regional climate model projections over Scandinavia. The sensitivity of the empirical quantile mapping to the tails of the distribution is demonstrated, and it is found that the behaviour is significantly different within and outside of the calibration period, causing severe issues with the temporal consistency of the time series. The sensitivity is identified to be due to differences in the activated features of the bias adjustment within the calibration period (where the empirical transfer function is applied) and outside of that period (where the extrapolation method is likely applied). This means that the bias adjustment method is, in a sense, different between different time periods. Furthermore, finding a robust parameterisation for the tail is not straightforward. We identify a two-step solution that works well for this problem: We refer to the first step as “Murder your darlings”. By excluding data from the tail data in the calibration period, the extrapolation feature is activated for all time periods, even the calibration period. In the second step, applying an outlier-insensitive method for linear regression works well for finding an extrapolation parameterisation for the tail.

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.

Predictions for the abundance and clustering of H α emitting galaxies

ABSTRACT We predict the surface density and clustering bias of H $\alpha$ emitting galaxies for the Euclid and Nancy Grace Roman Space Telescope redshift surveys using a new calibration of the galform galaxy formation model. We generate 3000 galform models to train an ensemble of deep learning algorithms to create an emulator. We then use this emulator in a Markov Chain Monte Carlo (MCMC) parameter search of an eleven-dimensional parameter space, to find a best-fitting model to a calibration data set that includes local luminosity function data, and, for the first time, higher redshift data, namely the number counts of H $\alpha$ emitters. We discover tensions when exploring fits for the observational data when applying a heuristic weighting scheme in the MCMC framework. We find improved fits to the H $\alpha$ number counts while maintaining appropriate predictions for the local universe luminosity function. For a flux limited Euclid-like survey to a depth of $2\times 10^{-16}~\textrm {erg}^{-1}~\textrm {s}^{-1}~\textrm {cm}^{-2}$ for sources in the redshift range $0.9< z< 1.8$, we estimate 2962–4331 H $\alpha$ emission-line sources deg$^{-2}$. For a Nancy Grace Roman survey, with a flux limit of $1\times 10^{-16}~\textrm {erg}^{-1}~\textrm {s}^{-1}~\textrm {cm}^{-2}$ and a redshift range $1.0< z< 2.0$, we predict 6786–10 322 H $\alpha$ emission-line sources deg$^{-2}$.

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.

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.

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.