Dataset Of Measurements Research Articles

Multisource elevations strategy obtaining robust seed points and reference surfaces for ground points extraction in complex terrain area

The appearance of unmanned aerial vehicle photogrammetry and airborne lidar makes it possible to obtain measurement data for complex terrains such as gullies and mountainous regions. However, extracting ground points from these abundant and massive measurement datasets is challenging. In traditional extractions, their essence is to determine the surfaces that can describe the terrain from the seed points in the grid and use them as the basis for separating non-ground points. For effective extraction, this study proposes a multisource elevations strategy (MES) obtaining robust seed points and reference surfaces. First, two-level extended grids were constructed as the basic units. Then, to select more robust values between measurement and interpolation elevations, an elevation-determination rule was established for seed points. After, based fitting and interpolation elevations of grid nodes, the correction range is determined and the elevation is corrected for reference surfaces. In two representative complex terrain areas, when non-ground points were marked as seed points, the MES effectively reduced the phenomenon of seed points moving away from the ground. Reference surfaces can also accurately represent the global change trend and local elevation of the ground in areas where the terrain changes rapidly. This strategy provides a new thinking for ground point extraction from point cloud.

A machine learning classifier for automated fault detection and diagnosis (AFDD) of rooftop units, addressing practical challenges of application

There have been many recent developments to take advantage of machine learning for the task of automated fault detection and diagnosis in air-conditioning equipment. A common approach is to develop a machine learning model to classify fault status, using simulated or measurement data, and test the model’s fault diagnostic capability on the same system that was used for training. However, such models don’t generalize well to different systems. Compounding this challenge, machine learning requires a rich dataset to train the models, and many of the measurement features require additional sensors, all making the potential for practical machine learning AFDD questionable. The current paper focuses on these challenges by: (i) training a model using simulation data from three rooftop units (RTU); (ii) rebalancing the training dataset to reduce false alarms; (iii) developing a feature set that minimizes sensor cost without significantly impacting performance; (iv) testing the model’s performance on a set of laboratory measurement data from the same RTUs that were simulated for training data; (v) testing the model on a faulted RTU in the field. The results demonstrate good progress toward practical application of machine learning based AFDD for RTU, but generalization to different systems remains a challenge.

Evaluation and Refinement of Chlorophyll-a Algorithms for High-Biomass Blooms in San Francisco Bay (USA)

A massive bloom of the raphidophyte Heterosigma akashiwo occurred in summer 2022 in San Francisco Bay, causing widespread ecological impacts including events of low dissolved oxygen and mass fish kills. The rapidly evolving bloom required equally rapid management response, leading to the use of near-real-time image analysis of chlorophyll from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3. Standard algorithms failed to adequately capture the bloom, signifying a need to refine a two-band algorithm developed for coastal and inland waters that relates the red-edge part of the remote sensing reflectance spectrum to chlorophyll. While the bloom was the initial motivation for optimizing this algorithm, an extensive dataset of in-water validation measurements from both bloom and non-bloom periods was used to evaluate performance over a range of concentrations and community composition. The modified red-edge algorithm with a simplified atmospheric correction scheme outperformed existing standard products across diverse conditions, and given the modest computational requirements, was found suitable for operational use and near-real-time product generation. The final version of the algorithm successfully minimizes error for non-bloom periods when chlorophyll a is typically <30 mg m−3, while also capturing bloom periods of >100 mg m−3 chlorophyll a.

Optimal microphone selection for aero-engine acoustic measurement

Acoustic testing is crucial in the aero-engine design process, providing valuable information about the sound field in terms of acoustic modes. The conventional use of a uniform sampling array limits the achievable resolution for a given number of sensors, necessitating the use of a large number of sensors to achieve high-resolution estimates. This limitation may be overcome using a non-uniform sensing array. In this work, we propose an optimal microphone selection scheme to determine which microphone placements that are preferable given the typically available a priori knowledge of the relevant acoustic modes. Using a weighted A-optimality criterion, we formulate the sensor selection problem as the one minimizing the expected variance of the resulting parameter estimates. Using both numerical and measured data sets, we illustrate how the proposed scheme allows for improved performance as compared to random and uniform sensor placement schemes.

FOCA: a new quality-controlled database of floods and catchment descriptors in Italy

Abstract. Here we present FOCA (Italian FlOod and Catchment Atlas), the first systematic collection of data on Italian river catchments for which historical discharge time series are available. Hydrometric information, including the annual maximum peak discharge and average daily annual maximum discharge, is complemented by several geomorphological, climatological, extreme rainfall, land-cover and soil-related catchment attributes. All hydrological information derives from the most recently released datasets of discharge and rainfall measurements. To enhance the reproducibility and transferability of the analysis, this paper provides a description of all the raw data and the algorithms used to build the basin attribute dataset. We also describe the approaches adopted to solve problems encountered during the digital elevation model elaboration in areas characterized by a complex morphology. Details about the data quality-control procedure developed to detect and correct errors are also reported. One of the main novelties of FOCA with respect to other national-scale datasets is the inclusion of a rich set of geomorphological attributes and extreme rainfall features for a large set of basins covering a wide range of elevations and areas. Using this first nationwide data collection (available at https://doi.org/10.5281/zenodo.10446258, Claps et al., 2023), a wide range of environmental applications, with a particular focus on flood studies, can be undertaken within the Italian territory.

Comparing and combining data-driven and model-driven approaches to monitor wheat green area index with high spatio-temporal resolution satellites

Monitoring crops with high spatio-temporal resolution satellites provides valuable observations to ensure food security in the global change context. This study focuses on estimating the Green Area Index (GAI) to monitor wheat crops with a spatial resolution of 3 m and daily satellite observations from the SuperDove constellation. With an easier access to large training datasets of ground GAI measurements, and the improvement of the realism of radiative transfer model simulations, the choice of the optimal approach (data-driven or model-driven) constitutes a key question when retrieving GAI from satellite observations.This study compares a data-driven and a model-driven approach to estimate GAI from the SuperDove satellites. Both approaches are based on Gaussian Process Regression (GPR) machine learning techniques. The data-driven approach uses over 300 ground GAI measurements collected from 12 sites in China and France, each with 20 to 51 contrasting plots. The model-driven approach uses 10,000 simulations of top of canopy reflectance and the corresponding GAI values generated by the LESS radiative transfer model applied to 3D scenes built with the ADEL-Wheat (Architectural model of Development based on L-systems) model.Results confirm that the SuperDove reflectance are reliable and consistent with Sentinel-2 values. When estimating GAI using GPR with SuperDove top of canopy reflectance, the model-driven approach (R2 = 0.83, RMSE = 0.80, Accuracy = 0.01 and Precision = 0.80) generally outperforms the data-driven approach (R2 = 0.80, RMSE = 0.88, Accuracy = −0.13 and Precision = 0.87), except for small GAI values. In-silico experiments show that the uncertainties in the ground-measured GAI and the size and diversity of the training datasets limit the data-driven approach. In contrast, the model-driven approach is mostly constrained by the realism of the reflectance simulations, particularly for low GAI values.Two ensemble solutions based on the weighted average of the two previous approaches are then proposed: the global ensemble solution (R2 = 0.86, RMSE = 0.75, A = −0.06 and P = 0.74) where the weight is assumed independent from the GAI values, and the adaptive ensemble solution (R2 = 0.85, RMSE = 0.76, A = −0.08 and P = 0.76) where the weight depends on the GAI values. Both solutions perform similarly, improving both data-driven and model-driven approaches. Finally, applying both solutions to monitor wheat plots along the growth cycle allows clear differentiation of nitrogen modalities and cultivar effects. However, a minimum plot size of 12 m × 12 m (4 × 4 pixels) is recommended to minimize the co-registration errors and increase estimate precision.

Functional form selection and calibration of macroscopic fundamental diagrams

Macroscopic fundamental diagram (MFD) is widely applied in network-level traffic control and management with most applications necessitating a well-calibrated MFD. With various data sources, more and more empirical MFDs are documented, while the MFD functional form is predetermined by traffic engineers based on their prior experiences. To our best, no generally accepted functional form has been identified. An automatic functional form selection method is yet to be devised. To meet this, a two-step MFD calibration framework is proposed to enable both the functional form selection and the estimation of parameters in this paper. A math program problem is first developed to identify a proper functional form from a set of candidate functions via random sampling of the measurement data. A mean-field variational Bayesian (MFVB) algorithm is then proposed to estimate the parameters of the selected MFD functions using the full measurement dataset. Both calibrations with and without the MFD dynamics are evaluated. The comparison between these calibration results highlights that the calibration considering the MFD dynamics can better characterize network traffic dynamics subject to dynamic travel demand and traffic control measures. Leveraging functional form selection and the computational advantages of the MFVB method, the two-step framework can significantly reduce the computational burden. Results using simulated data and empirical data validate the effectiveness and efficiency of the two-step framework. Furthermore, different functional forms are identified for different cities, highlighting the importance of functional form selection in the MFD calibration.

WEA-Acceptance Data—A Dataset of Acoustic, Meteorological, and Operational Wind Turbine Measurements

In this article, a dataset is described which combines wind turbine supervisory control and data acquisition (SCADA), meteorological and acoustical data and thus gives a detailed description of a wind farm and its atmospheric and acoustic environment. The data were collected during different seasons for several weeks at a time, such that a multitude of environmental and operational conditions are covered. In five measurement campaigns, in total three different locations with similar surroundings were captured. The raw data were enhanced with derived values such as atmospheric stability or direction of sound propagation. Data of one month including all time series measurements as well as monophonic audio recordings are now published. The dataset also contains three exemplary use cases along with documents that describe the data pre-processing.

The Potential of Tidal Energy Production in a Narrow Channel: The Gulf of Morbihan

The tidal currents of the Gulf of Morbihan reach up to 3.5 m/s within a narrow (200 m large) channel connecting the sea to the inner part of the gulf. In this study, a Telemac2D model validated with a large dataset of field measurements is used to assess the resources of the gulf. The results show that two sites have the potential to host up to 48 turbines (diameter of 8 m). If the entire width of the channel is occupied by turbines, significant increases in current speed are expected to occur on each side of the main channel. Simulations also show that flow changes differ between ebbing and flooding tides. During ebbing tide, the changes are limited in amplitude and remain localised within the channel. During flooding tide, the changes are more significant, especially in the vicinity of one of the two sites where the water passing through the site is flushed into a large and shallow basin. In this area, energy extraction significantly modifies the spatial distribution of the current velocities. We consider different scenarios of tidal energy extraction. The results show that flow perturbation can be significantly reduced using a lower density of turbines, that extracting tidal energy at one site slightly reduces the resource of the other, and that the deployment of two turbines (testing conditions) has a negligible effect on ambient current speeds.

Indoor environmental comfort in an antarctic research station: A case study

The indoor environmental quality of research stations requires attention and constant improvement due to the harsh polar climatic conditions. We conducted a case study at an Antarctic research station from March 2021 to November 2022. Indoor and outdoor condition monitoring and questionnaire surveys with the occupants were conducted to obtain environmental parameters and subjective evaluations. Twenty-six participants were surveyed, and 355 sets of measurement data and subjective questionnaires were obtained. An analysis of environmental evaluations and adaptation behaviors demonstrated that more than 80% of the participants expected the indoor temperature to remain unchanged, and over 40% desired improvements in humidity, air quality, and overall environmental quality. Opening windows was the most common environmental adaptation behavior made by participants. However, they had high expectations for the overall environment, indicating the demand for diverse and effective environmental adaptive options in interior design. Moreover, we analyzed the weighting coefficients of the influence of various environmental evaluations on overall environmental perceptions and the correlations between them. The results revealed the importance of the thermal environment and air quality on overall evaluations. Regressions yielded thermal neutral temperature ranges of 18.4–21.3 °C and acceptable temperature ranges, at which 90% of participants were satisfied, of 21.6–26.7 °C. These results could guide future assessments of the indoor environmental comfort at Antarctic stations and help create polar building environments suitable for work and life.

Relationships and statistics between various vibration metrics, derived from a large set of measured train pass-bys

The impacts of groundborne vibration (GBV) and sound (GBS) induced by railway infrastructure are assessed across the world through different metrics. Many national and international standards can be found for assessing GBV, based either on acceleration (UK, Spain, etc.) or velocity metrics (Germany, USA, etc.). Various frequency and/or time-weightings can be used, and different quantities (running RMS or highest levels) are also considered. Direct comparison between the various assessment criteria and measured metrics is, therefore, difficult. Although no international standard defines GBS criteria, many guidelines suggest such a criterion through a relationship between GBS levels and vibration velocity levels on room surfaces. Different quantities are often used, even for neighboring projects: London’s Crossrail (now Elizabeth Line) and Northern Line extension impact assessments both used maximum vibration velocity levels but with different time weighting (slow and fast, respectively). Arup has amassed a large dataset of vibration measurements through its involvement in many railway schemes over the years. For each of the thousands of train pass-bys, measured at different distances, infrastructure types, operation, and ground conditions, the most common European and North American GBS and GBV metrics are derived and compared to each other to develop statistical relationships and associated uncertainties.

Contactless Respiration Monitoring Using Wi-Fi and Artificial Neural Network Detection Method.

Detecting respiration in a non-intrusive manner is beneficial not only for convenience but also for cases where the traditional ways cannot be applied. This paper presents a novel simple low-cost system where ambient Wi-Fi signals are acquired by a third-party tool (Nexmon) installed in a Raspberry Pi and is able to detect the respiration time domain waveform of a person. This tool was selected as it uses 80 MHz bandwidth of the Wi-Fi signal and supports the latest implementations that are widely used, such as 802.11ac. A neural network is developed to detect the respiration frequency of the waveform. Generated waves emulating respiration waveforms were used for training, validating, and testing the model. The model can be applied to unseen real measurement data and successfully determine the breathing frequency with a very low average error of 4.7% tested in 20 measurement datasets.

Probing with Each Step: How a Walking Crab-like Robot Classifies Buried Cylinders in Sand with Hall-Effect Sensors.

Shallow underwater environments around the world are contaminated with unexploded ordnances (UXOs). Current state-of-the-art methods for UXO detection and localization use remote sensing systems. Furthermore, human divers are often tasked with confirming UXO existence and retrieval which poses health and safety hazards. In this paper, we describe the application of a crab robot with leg-embedded Hall effect-based sensors to detect and distinguish between UXOs and non-magnetic objects partially buried in sand. The sensors consist of Hall-effect magnetometers and permanent magnets embedded in load bearing compliant segments. The magnetometers are sensitive to magnetic objects in close proximity to the legs and their movement relative to embedded magnets, allowing for both proximity and force-related feedback in dynamically obtained measurements. A dataset of three-axis measurements is collected as the robot steps near and over different UXOs and UXO-like objects, and a convolutional neural network is trained on time domain inputs and evaluated by 5-fold cross validation. Additionally, we propose a novel method for interpreting the importance of measurements in the time domain for the trained classifier. The results demonstrate the potential for accurate and efficient UXO and non-UXO discrimination in the field.

품질이 관리된 스트레스 측정용 데이터셋 구축을 위한 제언

The construction of a stress measurement dataset plays a crucial role in various modern applications. In particular, for the efficient training of artificial intelligence models for stress measurement, it is essential to compare various biases and construct a quality-controlled dataset. In this paper, we propose the construction of a stress measurement dataset with quality management through the comparison of various biases. To achieve this, we introduce stress definitions and measurement tools, the process of building an artificial intelligence stress dataset, strategies to overcome biases for quality improvement, and considerations for stress data collection. Specifically, to manage dataset quality, we discuss various biases such as selection bias, measurement bias, causal bias, confirmation bias, and artificial intelligence bias that may arise during stress data collection. Through this paper, we aim to systematically understand considerations for stress data collection and various biases that may occur during the construction of a stress dataset, contributing to the construction of a dataset with guaranteed quality by overcoming these biases.

Exploration and prediction of high pressure dynamic water hidden collapse column in coal mines

Hidden collapse column associated with high pressure dynamic water is a main cause of major water inrush accidents in North China type coal fields. Taking the structural abnormality area discovered in 11603 working face of Daizhuang Coal Mine as an example, underground three-dimensional high-density electrical method, advanced exploration of underground drilling and curtain grouting were used to detect the existence of collapse column, and analyzed the water conductivity of collapse columns based on the hydraulic connection analysis of the 13th limestone and Ordovician limestone aquifers. Finally, it is determined that this abnormal area is a strong water filling collapse column originating from the upper Ordovician strata runoff zone (inferred to be within a range of 30 to 100 m below the Ordovician limestone top interface), developed to a height of 12th limestone. Based on the fact that the water yield and water pressure of underground directional drilling, the grouting pressure of curtain grouting, and the amount of cement injected are external quantitative factors that reflect the existence of hidden karst collapse columns during the process of detecting hidden karst collapse columns, and in combination with the feature that deep learning can fully independently learn abstract knowledge expression, a prediction model based on convolutional neural networks is constructed. According to the established network model, it was found that among the 12 sets of actual measurement data, only one data point indicated the absence of a collapse column. The prediction accuracy reached 91.6%, which meets the practical needs.

A branch-and-bound algorithm with growing datasets for large-scale parameter estimation

The solution of nonconvex parameter estimation problems with deterministic global optimization methods is desirable but challenging, especially if large measurement datasets are considered. We propose to exploit the structure of this class of optimization problems to enable their solution with the spatial branch-and-bound algorithm. In detail, we start with a reduced dataset in the root node and progressively augment it, converging to the full dataset. We show for nonlinear programs (NLPs) that our algorithm converges to the global solution of the original problem considering the full dataset. The implementation of the algorithm extends our open-source solver MAiNGO. A numerical case study with a mixed-integer nonlinear program (MINLP) from chemical engineering and a dynamic optimization problem from biochemistry both using noise-free measurement data emphasizes the potential for savings of computational effort with our proposed approach.

Positioning in Global Value Chains: World Map and Indicators, a New Dataset Available for GVC Analyses

Abstract This work reviews and computes the commonly used Global Value Chains (GVC) positioning indicators found in the empirical literature, providing scholars with a novel and comprehensive global dataset of upstreamness and downstreamness measures. This dataset covers a wide range of countries, including many developing nations, and industries, and spans an extensive timeframe. Specifically, it offers GVC positioning indicators for all economies and industries included in prominent Inter-Country Input-Output tables, such as ADB, EORA, OECD TiVA, WIOD, and Long-run WIOD. This work also delves into the degree of comparability across the different datasets, offering informative comparisons of the GVC positioning measures encompassing overlapping countries and periods, sectors, geographical regions, and income levels. Notably, these indicators are “ready-to-use” and open access, presenting an exceptional opportunity for qualitative and quantitative analyses of various economic dimensions on GVCs and for informing policymaking.

Beyond [formula omitted]CDM with [formula omitted]CDM: Criticalities and solutions of Padé Cosmography

Recently, cosmography emerged as a valuable tool to effectively describe the vast amount of astrophysical observations without relying on a specific cosmological model. Its model-independent nature ensures a faithful representation of data, free from theoretical biases. Indeed, the commonly assumed fiducial model, the ΛCDM, shows some shortcomings and tensions between data at late and early times that need to be further investigated. In this paper, we explore an extension of the standard cosmological model by adopting the f(z)CDM approach, where f(z) represents the cosmographic series characterizing the evolution of recent universe driven by dark energy. To construct f(z), we take into account the Padé series, since this rational polynomial approximation offers a better convergence at high redshifts than the standard Taylor series expansion. Several orders of such an approximant have been proposed in previous works, here we want to answer the questions: What is the impact of the cosmographic series choice on the parameter constraints? Which series is the best for the analysis? So, we analyze the most promising ones by identifying which order is preferred in terms of stability and goodness of fit. Theoretical predictions of the f(z)CDM model are obtained by the Boltzmann solver code and the posterior distributions of the cosmological and cosmographic parameters are constrained by a Monte Carlo Markov Chains analysis. We consider a joint data set of cosmic microwave background temperature measurements from the Planck collaboration, type Ia supernovae data from the latest Pantheon+ sample, baryonic acoustic oscillations and cosmic chronometers data. In conclusions, we state which series can be used when only late time data are used, while which orders has to be considered in order to achieve the necessary stability when large redshifts are considered.

Increase in precipitation scavenging contributes to long-term reductions of light-absorbing aerosol in the Arctic

Abstract. We investigated long-term changes using a harmonised 22-year data set of aerosol light absorption measurements, in conjunction with air mass history and aerosol source analysis. The measurements were performed at Zeppelin Observatory, Svalbard, from 2002 to 2023. We report a statistically significant decreasing long-term trend for the light absorption coefficient. However, the last 8 years of 2016–2023 showed a slight increase in the magnitude of the light absorption coefficient for the Arctic haze season. In addition, we observed an increasing trend in the single-scattering albedo from 2002 to 2023. Five distinct source regions, representing different transport pathways, were identified. The trends involving air masses from the five regions showed decreasing absorption coefficients, except for the air masses from Eurasia. We show that the changes in the occurrences of each transport pathway cannot explain the reductions in the absorption coefficient observed at the Zeppelin station. An increase in contributions of air masses from more marine regions, with lower absorption coefficients, is compensated for by an influence from high-emission regions. The proportion of air masses en route to Zeppelin, which have been influenced by active fires, has undergone a noticeable increase starting in 2015. However, this increase has not impacted the long-term trends in the concentration of light-absorbing aerosol. Along with aerosol optical properties, we also show an increasing trend in accumulated surface precipitation experienced by air masses en route to the Zeppelin Observatory. We argue that the increase in precipitation, as experienced by air masses arriving at the station, can explain a quarter of the long-term reduction in the light absorption coefficient. We emphasise that meteorological conditions en route to the Zeppelin Observatory are critical for understanding the observed trends.

Exploring symptom clusters in mild cognitive impairment and dementia with the NIH Toolbox.

Symptom clustering research provides a unique opportunity for understanding complex medical conditions. The objective of this study was to apply a variable-centered analytic approach to understand how symptoms may cluster together, within and across domains of functioning in mild cognitive impairment (MCI) and dementia, to better understand these conditions and potential etiological, prevention, and intervention considerations. Cognitive, motor, sensory, emotional, and social measures from the NIH Toolbox were analyzed using exploratory factor analysis (EFA) from a dataset of 165 individuals with a research diagnosis of either amnestic MCI or dementia of the Alzheimer's type. The six-factor EFA solution described here primarily replicated the intended structure of the NIH Toolbox with a few deviations, notably sensory and motor scores loading onto factors with measures of cognition, emotional, and social health. These findings suggest the presence of cross-domain symptom clusters in these populations. In particular, negative affect, stress, loneliness, and pain formed one unique symptom cluster that bridged the NIH Toolbox domains of physical, social, and emotional health. Olfaction and dexterity formed a second unique cluster with measures of executive functioning, working memory, episodic memory, and processing speed. A third novel cluster was detected for mobility, strength, and vision, which was considered to reflect a physical functioning factor. Somewhat unexpectedly, the hearing test included did not load strongly onto any factor. This research presents a preliminary effort to detect symptom clusters in amnestic MCI and dementia using an existing dataset of outcome measures from the NIH Toolbox.