dataesr/scientific-paragraphs-categorization huggingface dataset:
Geolocation Data Enhancement with RTK In an effort to acquire more accurate geolocation data than that provided by mobile devices' embedded GNSS modules, we propose to use external hardware modules for satellite navigation with Real-Time Kinematic Positioning (RTK). RTK is a type of differential positioning system that works with a base station whose actual and accurate position is known. The difference between the known position and the one continuously measured by the base station is known as the range error. The station broadcasts the range error over a standard internet protocol (NTRIP caster), which makes it accessible by any mobile device equipped with a multi-GNSS receiver and an internet connection (also known as "rover station"). The receiving mobile device needs to run a third-party open-source NTRIP client application (i.
Ardusimple RTK Surveyor Kit After considering various options, we decided to use a low-cost (399€) ready-made GNSS/RTK module manufactured by Ardusimple for Android mobile devices  [53]. It includes a Funduino processor with a u-blox ZED-F9P multi-GNSS receiver, a helical GNSS antenna (SY-301) and a Bluetooth module. Once the kit is powered and connected to a mobile device by Bluetooth or USB, the NTRIP client application retrieves the correction data from the base station and streams it to the kit's multi-GNSS receiver, which additionally measures satellite distances on its own. The correction data and measured data are integrated into the navigation engine, and the adjusted geolocation data are sent by the processor and its Bluetooth module to the NTRIP client application. The application exposes that data to the entire mobile device through the "mock location" feature.
Because any small-scale microelectromechanical systems (MEMS) magnetic field sensor (magnetometer) will suffer the same perturbations, the use of a more accurate, external magnometer module does not seem to be an option. Instead, we are laying down a concept for data fusion using SLAM data and geolocation data to improve all at once the positioning and anchoring of the virtual layer. SLAM is the process of simultaneously mapping moving objects with optical sensors, achieving self-localization and modeling the surrounding environment while in motion. When a user moves while holding a device running a WebXR-powered system in AR mode, they keep track of their displacement, relative to a local origin (the place where AR mode was initiated) with great accuracy. Simultaneously, they are able to estimate their absolute geolocation with the Geolocation API, based on the device's GNSS sensors or an external RTK module.
If there is any difference between our two groups, they may still be statistically non-significant, because the anchoring of the POIs in the AR interface may also be impacted by the orientation data. Because we think the use of location-based AR in combination with standard GNSS data can cause usability problems and is the main limiting factor for broad adoption, our future efforts will focus on developing solutions to these. There are encouraging signs, such as the recent publication of Google's Geospatial API and Visual Positioning System (VPS), which is based on trillions of point clouds harvested over the last 15 years for the Google Street View service  [58]. It retrieves accurate positioning by comparing the device's camera view with this huge database, after initial filtering based on the user's approximate geolocation.
Thanks to the use of interoperable GeoJSON files, participants can further valorize the data they collected by uploading it to existing CS databases such as the Global Biodiversity Information Facility https:// www. gbif. org/ (accessed on 28 December 2022) or Infoflora (in Switzerland) https:// www. infoflora. ch/fr/ (accessed on 28 December 2022) projects. Because the objectives of this age group are aligned with-and better known as-those of CS, they have been modeled according to this nomination in the application's use cases. The workshop led to the inclusion of the following features that allow users to achieve CS goals:.
Thanks to the use of interoperable GeoJSON files, participants can further valorize the data they collected by uploading it to existing CS databases such as the Global Biodiversity Information Facility https:// www. gbif. org/ (accessed on 28 December 2022) or Infoflora (in Switzerland) https:// www. infoflora. ch/fr/ (accessed on 28 December 2022) projects. Because the objectives of this age group are aligned with-and better known as-those of CS, they have been modeled according to this nomination in the application's use cases. The workshop led to the inclusion of the following features that allow users to achieve CS goals: (a) A feature for creating custom data attributes; (b) The possibility to create/edit POIs from the mobile interface; (c) The ability to import/export data (interoperability with other geoportals); (d)The ability to make ARLEs available/editable by everyone (wiki); (e).
We analyse ambient noise seismic data from 23 three-component seismic nodes to study firn velocity structure and seismic anisotropy near the EastGRIP camp along the Northeast Greenland Ice Stream (NEGIS). Using 9-component correlation tensors, we derive dispersion curves of Rayleigh and Love wave group velocities from 3 Hz to 40 Hz. These velocity distributions exhibit anisotropy along and across the flow. To assess these variations, we invert dispersion curves for shear wave velocities (V sh and V sv ) in the top 150 m of NEGIS using a Markov Chain Monte Carlo approach. The reconstructed 1-D shear velocity model reveals radial anisotropy in the firn, with V sh 12%-15% greater than V sv , peaking at the critical density (550 kg m -3 ). We combine density data from firn cores drilled in 2016 and 2018 to create a new density parameterisation for NEGIS, serving as a reference for our results.
In addition, given the fragility of firn cores, anisotropic properties of firn, for instance, obtained by standard thin-section fabric analyses, are rare. In 2023, a new standardised densification model of EastGRIP has been created using core data from 2016 and 2018. These data are presented here, and used for comparison with our ambient seismic noise dispersion analysis results. Seismic waves offer a means to image firn and ice structures that complement the capabilities of other techniques. For example, in glaciology, seismic methods have been shown to be an effective tool to determine firn structure and velocity profiles, from the use of refraction data (e. g. Schlegel et al. , 2019; Hollmann et al. , 2021; Pearce et al. , 2023; Picotti et al. , 2015) , distributed acoustic sensing methods  (Zhou et al.
Here we present passive seismic data acquired over 29 days on the NEGIS. The corresponding noise-based Love and Rayleigh wave dispersion curves are inverted to estimate the S-wave velocity structure of the top 150 m of the NEGIS and the radial anisotropy within the firn (variations between horizontally and vertically polarised shear wave velocities). Our findings contribute novel insights into the internal structure of the NEGIS and advance our understanding of the complex dynamics characterising firn and ice formation and show that environmental noise seismic acquisition is a useful and simple tool to obtain local firn properties.
Cores Nodes EastGRIPBorehole Nodes In addition to the main EastGRIP core density data obtained in 2016, in 2018, core S6 was acquired close to the borehole (1. 2 km away). We combined the available density data, and include the first interpretation of a standardised EastGRIP density parameterisation, which we will use as a reference for our results inferred from passive seismic.
Data, Noise Processing and Cross-Correlations We use continuous noise data from 23 stations recorded during 2022 from July 21 until August 21 with IGU-16HR 3C 5Hz SmartSolo nodes. The network recorded at a sampling rate of 1000 Hz, with inter-station distances ranging from ≈ 50 m to 2900 m. We process the ambient noise data in the following order; (1) single station data preparation, (2) cross-correlation and temporal stacking, (3) measurement of dispersion curves and (4) quality control, including error analysis and selection of group velocity measurements. Processing of ambient noise data follows a typical processing chain  (Bensen et al. , 2007; Poli et al. , 2013)  but modified for higher frequency content (see  Zigone et al. , 2019).
Density data and parameterisation A density parameterisation of EastGRIP has been created and is used for the first time in our discussion. The data used to create the parameterisation were obtained from various locations close to the EastGRIP main core. A visual inspection of three-dimensional reconstructions of pore and ice structure along the firn column from core S6, measured by X-ray tomography  (Freitag et al. , 2013) , reveals no significant geometrical anisotropy in pore and ice structures. Enclosed air bubbles below the firn ice transition at around 65 m depth show no indications for systematic deviations from spherical shapes (Fig. 6 ). Layering from a few centimetre thick layers of different densities in the core is strong down to about 50 m followed by a decreasing trend downwards, with the addition of 1 mm sized wind crust layers occurring one to two times per meter.
Characterizing ephemeral karstic wetlands through hydrological modelling is key for sustainable protection of their ecosystems and to understand and mitigate the impact of flooding events. UISCEmod is a new open-source software for modelling water level time series, focused on ephemeral karstic wetlands, that requires minimal input information. UISCEmod contains both experimental and lumped hydrological models, and the calibration process is automated following a Bayesian approach. The main outputs of UISCEmod include volume, stage, inflow and outflow model time series, calibrated model parameters, and the associated uncertainties. UISCEmod was evaluated at 16 representative sites in Ireland obtaining Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) above 0. 85 for both stage and volume time series for most of the sites, showing its potential for covering the need for a simple, pragmatic, and flexible framework for modelling water levels in ephemeral karstic wetlands with relatively limited input data requirements.
, 2014). In the particular case of groundwater dependent wetlands in karst systems the location and size of conduits or fissures are usually unknown, which hinders the use of distributed hydrological models in these types of environment  (Borghi et al. , 2016). Although complex lumped hydrological models have been successfully applied to specific ephemeral karstic wetlands  (Gill et al. , 2013 (Gill et al. , , 2021;; Morrissey et al. , 2020; Schuler et al. , 2020) , there is the need for a simple, pragmatic and flexible framework that can be applied to large scale studies. In this paper the UISCEmod software for modelling and characterizing ephemeral karstic wetlands is presented. UISCEmod includes two hydrological models, empirical and lumped, that requires minimal input information (meteorological, water levels, and digital terrain models (DTM) data) to calibrate the models and to generate water level time series.
Software design UISCEmod was developed using the high-level and object-oriented Python programming language, v3. 8. It includes two modelling    (Nash, 1960) , there are differences between them. The EM treats the system as a whole, providing information about the behaviour of the hydrological system but without providing direct information about the individual components of the system. In contrast, the LM approach distinguishes between inflow and outflow mechanisms, providing inflow and outflow model time series, considers the dependency of the outflow with the depth of the flood, accounts for direct interaction with the atmosphere (direct rainfall and evaporation), and has the potential to incorporate specific information about the site (e. g. elevation of potential outflow mechanisms) into the model parameters.
Experimental model (EM) The EM model is a mathematical operator that relates two or more datasets, such as ER time series (input) and volume time series (output). In this case the EM approach is based the combination of linear store models, where ER is transformed into flood volume within the wetland by routing it through a series of linear storages or Nash reservoirs  (Nash, 1960) , with the relationship between the storage in each reservoir (S) and the outflow (Q) given by (eq. (  1 )): S = kQ eq. where k is a time constant or mean residence time for the reservoir  (Nash, 1959; Shaw, 1994). When n linear reservoirs are combined in series, the unit hydrograph, f (t), is of the form of a gamma function (eq. (  2 )). f (t) = ( t k ) n-1 e -t/k k(n -1)! eq. Mathematically the value of n does not have to be an integer but can be fractional.
Implementation UISCEmod is divided in three modules: Inputs, Calibration, and Outputs (Fig. 4 ). The Inputs module combines meteorological datasets, R and ET o , to generate ER time series, which are then considered for the hydrological models. The calibration module is used to calibrate the hydrological models and can be ignored if model parameters are already defined. The third module, Outputs, generates the products including: forward solution for stage and volume time series, probability density function (pdf) of the calibrated model parameters, and a set of products to help evaluating convergence of the calibration process as well as the fit of the data.
Inputs. The input module uses three main datasets: 1) continuous meteorological time series at daily steps with no missing data, including R, ET o and Evap t time series, 2) water level time series at daily steps, in which missing data is allowed and considered as NaN, and 3) stage-volume conversion curves. Within this module meteorological data is converted to ER using a soil moisture deficit (SMD) model based on  Schulte et al. (2005). The SMD model simplifies the system as a reservoir, which water level deficit at time t (Hs t ) is calculated by a mass balance for each time step (eq. 9) assuming that actual evapotranspiration, ET A , decreases linearly to zero as Hs t approaches a maximum deficit Hs max (eq. 10). All variables are water depths in mm.
In relation to the computation of the likelihood, UISCEmod assumes that the distance between the observed and modelled time series, the error residuals, are normally distributed around zero (eq. (  14 )). p(D|θ) = 1 ̅̅̅̅̅̅̅̅̅̅ 2πσ 2 √ e - (D-g(θ)) 2 2σ 2 eq. 14 where g(θ) is the modelled time series and σ is the standard deviation on the data and is unknown. Note that because σ is not known a priori it can also be considered as a model parameter and estimated during the calibration process  (Malinverno and Briggs, 2004). During the calculation of the likelihood, only volumes of water above a defined threshold (i. e. above the data logger elevation) are considered. This discrimination is due to the nature of water level time series, as they may have flat sections when the dataloggers are dry.
When working with measured data, it was observed that the constrained posterior data errors, σ, were significantly larger than the errors expected from the dataloggers (few millimetres), and that the measured time series were most of the time outside the 95% confidence interval from computing the forward solution using the accepted model parameters. This epistemic uncertainty of the hydrological models can be an issue when using UISCEmod as it can significantly underestimate the confidence intervals of the model time series. With the aim of providing model time series that include the measured water level time series within the confidence intervals, the quantiles 0. 025, 0. 16, 0. 84 and 0. 975 of the error residuals from the calibration process are considered to define 68% and 95% confidence intervals.
The validity of this approach was evaluated during the validation process. 3. 1. 3. 3. Outputs. The outputs module performs the forward calculations and assesses the confidence in the results by providing relevant information related to: 1) adequate sampling of the prior p(θ), 2) convergence of the calibration process and correlation between model parameters, 3) distribution of error residuals, and 4) data fit. The sampling of the prior is used to corroborate that the sampling process follows the expected distribution of the prior, and it should be performed before starting the calibration process. This test is performed by running the calibration process setting the acceptance probability to 1 (i. e. all candidates are accepted). If the sampling process is well implemented, the pdf of the model parameters returned by the framework, p(θ|D), should be the same as the pdf of the prior p(θ).
Error residuals following a nonnormal distribution may be an indication that certain parts of the data disagree with the initial assumptions, which may add a bias to the constrained model parameters and the confidence intervals. Finally, the ability of the model parameters to reproduce the data is evaluated. Data fit is evaluated by considering Nash-Sutcliffe Efficiency (NSE)  (Nash and Sutcliffe, 1970) , Kling-Gupta Efficiency (KGE)  (Gupta et al. , 2009; Kling et al. , 2012)  parameters, and model bias (BIAS). BIAS is computed using normalized time series between 0 and 1 (min-max normalization), using the measured time series to define the normalization parameters, to facilitate comparison between sites and between volume and stage datasets. Following this approach, the BIAS is presented as a percentage of the amplitude of the signal (eq.
R data was extracted from a gridded dataset of 1 × 1 km resolution resulted from the interpolation of data from 500 meteorological stations, approx. (www. met. ie, Daily Rainfall and Temperature Grids). Evap and ET o time series from the nearest Met Éireann station with available data were considered for each site. Distance between the considered meteorological stations ranges between 45 and 75 km, with distance between the sites to the closest meteorological station ranging between 9 and 40 km. Water level time series were measured during two separate time periods. The first dataset includes 8 sites with continuous hourly measurement recorded between September 2006 and August 2009 using Schlumberger Diver™ pressure transducers and compensated for barometric pressure using a BaroDiver™  (Naughton et al.
, 2012). The second dataset includes data from all sites with hourly measurements recorded between November 2016 and January 2022  (McCormack et al. , 2020). Water levels of the second dataset were monitored using Van Essen TD-Divers™ pressure transducer data loggers, and logger data were compensated with barometric data to yield water levels above the logger. In both cases vertical resolution of the dataloggers was less than 1 cm. Conversion from stage to volume was performed using stage-volume curves (SVC) created for each site from a national DTM for Ireland  (McCormack et al. , 2020) , with horizontal resolution of 5 × 5 m and vertical resolution ranging between 0. 1 m and 0. 7 m. For all of the sites considered in this study the vertical resolution of the DTM was close to 0. 1 m as Lidar datasets were used to improve the resolution of the national DTM in these regions  (McCormack et al.
, 2020)  and dGPS measurements were used to correct for potential regional vertical shifts of the DTM at each site. The implementation process was carried out with two synthetic water level datasets, one for each modelling approach, to verify the correct performance of the calibration process. Synthetic water level time series were generated using the parameters from Table  3 , with values based on previous hydrological studies of karst systems in Ireland (i. e. McCormack et al. , 2020; Naughton, 2011). Historic meteorological data, and stage-volume curve for Termon South (Co. Clare, ID:15) were considered to generate the synthetic water level time series. Gaussian noise was added to the synthetic time series to include uncertainties using the true σ parameter. Synthetic water level times series of fourand three-years duration were considered for calibration and validation, respectively.
Calibration and validation datasets for both implementation and evaluation process were defined based on the following criteria: 1) the calibration period should include the largest and smallest observed volumes, 2) a complete rising and recession phase must be included in both datasets, 3) the length of the calibration time series should be equal or longer than validation time series, and 4) if possible after considering points 1 to 3, the newest data should be considered for calibration and the oldest data for validation with the aim of calibrating the models with closest data to current hydrogeological conditions and with the most accurate datasets. Higher quality control standards were followed for the newest datasets, such as multiple dGPS measurements, with virtual reference station (VRS), along the flood seasons to corroborate the quality of the measurements.
Implementation The capacity of UISCEmod to converge on model parameters and accurately reproduce the synthetically generated water level series was first explored to validate the underlying modelling approach. Trace plots and histograms of the posterior pdf for each variable (Fig. 5 ) corroborated convergence of the calibration process and that the true parameters are within the posterior pdf. The capacity of the calibration process at recovering the data errors is presented in Fig. 6 , comparing: 1) the distribution of the error residuals (blue), 2) the normal distributions generated with the accepted values for σ (black), and 3) the "true" errors added to the synthetic data (orange dashed line). In relation to data fit, NSE and KGE values above 0. 98 were obtained for calibration and validation datasets for both modelling approaches, BIAS was centred at zero with interquartile range (IQR) being smaller than 1%, and the percentage of measured data points within the 68% and 95% confidence intervals was 68% and 95% for calibration and 70% and 94% for validation datasets for EM model, and 68% and 95% for calibration and 67% and 95% for validation datasets for LM model.
Evaluation Performance metrics for the 16 sites used for the evaluation are presented in Table  4  and summarized in Fig. 7. Both modelling approaches were capable at modelling the water level time series with model performance NSE and KGE values above 0. 85 for most sites for calibration and validation datasets, for volume and stage time series (Fig. 7a ). In terms of BIAS, both calibration and validation datasets showed relatively small values for most of the sites, between -2% and 5% (Table  4 ). BIAS analysis for volume and stage time series for different flood levels (low ([0. 0,0. 25]), medium  ([0. 25,0. 75] ) and high ([0. 75,1. 0])) is presented in Fig. 7b. Finally, to illustrate the performance of UISCEmod across the spectrum of recorded flood behaviours, three sites representative of the range of flood dynamics are shown in Fig.
8  for validation datasets. The empirically defined confidence intervals were validated by looking at the percentage of measured data points within the defined confidence intervals for the modelled time series, for the validation dataset, with the results suggesting that the presented approach is reasonable. For the EM approach, most of the sites had between 60% and 77% of the points within the 68% confidence interval, and between 87% and 98% within the 95% confidence interval. For the LM approach most of the sites had between 51% and 75% of the points within the 68% confidence interval, and between 80% and 99% for the 95% confidence interval. In relation to computational time, based on computer with 31. 7 GB of useable RAM with a processor of 11th Gen Intel® Core™ i7-1185G7 @3 GHz @1. 80 GHz the EM was shown to be significantly faster than the LM approach.