dataesr/scientific-paragraphs-categorization huggingface dataset:
Dry Matter Intake There is minimal literature on the DMI of grazing cows milked OAD in pasture-based systems. This research is unique in this respect and data indicate that cows milked OAD had reduced pasture DMI in early lactation (≤49 d), there was on average a 22% reduction in DMI between OAD and TAD up to d 49 into lactation. Although there were 5 intake estimation periods in the experiment, is clear the difference in DMI was large in early lactation and reduced as lactation progressed. Grala et al. (2016)  reported cows milked OAD tended to have a lower DMI (-0. 4 kg of DM/cow per day) in late lactation when pasture was cut and fed to cows indoors, rather than grazed in situ. Rémond et al. (2004)  collected DMI data from fully fed cows in the first 14 WOL, unlike our experiment these authors found no difference between cows milked OAD and TAD during the first 6 WOL.
ACKNOWLEDGMENTS This research is funded by  Teagasc Core Funding and Dairy Research Ireland. The authors thank the  Moorepark farm  staff for their care of the animals and assistance with this experiment. The technical help of  Teagasc Moorepark  staff;  Michelle Liddane ,  Andy Mc-Grath , and  Pat O'Connor  is also greatly appreciated. The authors have not stated any conflicts of interest.
Model description We use a numerical model based on the three-dimensional z-coordinate Massachusetts Institute of Technology general circulation model (MITgcm;  Marshall et al. , 1997a,b)  adapted to the Mediterranean region. The model solves the fully non-linear hydrostatic Navier-Stokes equations under the Boussinesq approximation using a spatial finite-volume discretization. It is run with partial step topography, rescaled vertical height (z*) coordinate  (Adcroft (Adcroft et al. , 1997) , with an implicit non-linear surface based on  Campin et al. (2004). The source code and documentation are available at the following website: https://github. com/MITgcm/MITgcm (last access: 10 October 2022). The model domain extends over the whole Mediterranean Sea and part of the Atlantic Ocean. The horizontal grid is a non-uniform and curvilinear Arakawa-C grid composed of 1040 × 320 points, reaching a resolution of approximately 1/200 • × 1/200 • (∼ 500m) at the SoG (Figure  1 ) and the Bosphorus Strait, and 1/50 • × 1/50 • (∼ 2km) at the Straits of Dardanelles.
It is smoothly degraded down to a regular 1/16 • × 1/16 • grid (∼ 6km) in the Mediterranean Sea. The vertical grid comprises 100 unevenly spaced z*-levels, rescaled as suggested by  Adcroft and Campin (2004). The (vertical) cell thicknesses vary between 2m at the top to 100m at the bottom. We use a time step of 20 seconds. Three datasets compose the bathymetry: the European Marine Observation and Data Network (EMODnet) 2016 dataset (https://www. emodnet-bathymetry. eu) for the Mediterranean Basin and the Black Sea, the very high-resolution digitalized chart of  Sanz et al. (1991)  for the SoG, and a high-resolution bathymetry for the Bosphorus and Dardanelles Straits made available by Erkan Gökas ¸an  (Gökas ¸an et al. , 2005 (Gökas ¸an et al. , , 2007)) , with the permission of the Turkish Navy, Navigation, Hydrography, and Oceanography Office.
The datasets were combined through a bilinear interpolation on the computational grid. In addition, a handmade check has been made for isolated points, islands, and narrow passages (see  Sannino et al. , 2015 Sannino et al. , , 2017)). We use a third-order direct space-time flux-limited advection scheme for tracers (temperature, salinity), with a constant horizontal Laplacian diffusivity coefficient of 2m 2. s -1. The vertical eddy viscosity and diffusivity coefficients are computed from the 1. 5 order Turbulent Kinetic Energy (TKE) closure scheme  (Gaspar et al. , 1990) , adapted from the atmospheric case developed by  Bougeault and Lacarrere (1989). The TKE scheme was chosen for its straightforward physical interpretation and its few parameters to configure. We set the background vertical eddy viscosity and diffusivity coefficients to their molecular values of 1.
The model is also forced with atmospheric pressure, taken from the atmospheric model. The explicit tidal forcing used in ExpT is the same as in  Palma et al. (2020). It includes an equilibrium tide, acting as a potential in the momentum equations, and the Atlantic tide propagating from the western open boundary, where it is prescribed as an open boundary condition on the dynamic sea level. The equilibrium tide is composed of four tidal components : M2, O1, S2, K1, namely, the semidiurnal and diurnal principal lunar tides, the principal semidiurnal solar tide, and diurnal lunisolar declination tide. The tidal values used to prescribe the Atlantic tide are derived from the Oregon State University Tidal inversion software (OTIS hereinafter,  Egbert and Erofeeva, 2002). In ExpNT, the equilibrium and Atlantic tides are removed.
Model Validation The tidal forcing used in ExpT has been validated over the Mediterranean Sea by  Palma et al. (2020)  and  Sannino et al. (2015)  with similar model configurations. The authors performed a one-year simulation with a barotropic version of the model and separated the tidal constituents through a harmonic analysis of the surface elevation  (Foreman, 1977; Pawlowicz et al. , 2002). The amplitude and phase of the modeled tides were validated against the OTIS global tide inverse model, and the simulation correctly reproduced the main zero amplitude points for the M2 tidal component in the Mediterranean Sea  (Tsimplis et al. , 1995). Palma et al. (2020)  also validated the tidal forcing against maps from more recent tidal simulations  (Ferrarin et al. , 2018). In this study, we carry out further validations specific to the SoG by comparing the simulated tides to reference tidal models and observations, as well as transports and velocities against observational estimates and datasets.
Regarding transports and velocities, when available, we indicate the interannual variability of the estimates. Otherwise, we provide their uncertainties, which are larger than the interannual variability of the estimated quantities. To begin with, we will first assess the realism of the simulated tidal elevations against the observation-based charts of  Candela et al. (1990)  and  Lafuente et al. (1990) (CA90 and GL90 hereinafter). We also refer to the global tidal atlas FES2014 (produced by Noveltis, Legos, and CLS and distributed by Aviso+, with support from CNES: https://www. aviso. altimetry. fr/) as a reference numerical dataset. In the following, differences between the charts of CA90 and GL90 provide an observational estimate of the tidal amplitude and phase. They display very similar amplitude patterns, mainly differing at the center of the strait near the Tarifa Narrow (between 5.
Regarding the tidal phase, ExpT is in better agreement with CA90 and GL90 than FES2014. The main differences with respect to CA90 and GL90 fall within the uncertainty of the observation-based estimates. To assess the baroclinic structure of the simulated tides, we refer to in situ Doppler current profiler measurements collected at the ES in the framework of the INGRES projects (more details in  Sánchez-Román et al. , 2008 , 2009; Sammartino et al. , 2015). We display in Figure  3  the vertical profile of M2 tidal ellipses extracted from the INGRES in situ data and the simulated velocity fields at the closest point to the measurement station. The tidal ellipses synthesize the evolution of horizontal tidal currents along the tidal cycle. Simulation-based and observation-based ellipses are in overall good agreement.
Thus, the rotating direction has a low impact on the associated tidal Figure  2 : In shades, M2 tidal elevation amplitude, in contours, M2 tidal elevation. On the left is the MITgcm simulation, and on the right is the global tidal atlas FES2014. For ExpT, tidal amplitudes and phases are estimated from hourly outputs of instantaneous (not averaged) sea level elevation over January 1981 using the "Utide" Python package (version 0. 2. 5). FES2014 fields are interpolated on the MITgcm model grid through bilinear interpolation, following  Xu (2018). currents. To conclude on the tidal validation, in light of the reasonable biases highlighted by the former analyses and the consistency with available references, we consider the simulated tides relevant to study the processes involved in tidal mixing at the SoG.
Tidal ellipses parameters are computed using the "Utide" Python package (version 0. 2. 5) from hourly-averages of horizontal currents, taken the closest point to the measurement site (≃ 35. 86 • N, 5. 97 • W; black dot in Figure  1 ), over January 1981 for ExpT, and instantaneous 30-minute measurements over January 2004-2015 for the INGRES dataset. For ExpT, horizontal velocities are obtained by projecting the model outputs onto the zonal and meridional directions. Measurements are linearly interpolated onto the model vertical grid. flow through the SoG, as well as the intense vertical shear that characterizes it. After crossing the CS, the Atlantic and Mediterranean layers become narrower, forcing the water masses to accelerate up to 1 m. s -1. Overall, tidal forcing increases both eastward and westward advection (colored contours in Figure  5 (c)  and (d) ), reflecting a periodic reversal of the inflowing and outflowing layers  (Candela et al.
The light-colored lines show the value of these terms in ExpNT. On the middle panel, we display the total tendency of TKE (in orange), the buoyancy flux (in green), and the sum of dynamic shear production and implicit TKE tendency (in purple). The correlation coefficient with the total tendency is given in the legend. The lower panel illustrates the pseudo-zonal (following the model grid) barotropic tidal currents, estimated from the simulated pseudo-zonal and pseudo-meridional velocities, using the "Utide" Python package (version 0. 2. 5). The four black dots represent the time of the hourly averages displayed in Figures  9  and 10  over the domain and period examined. It indicates that TKE dissipation is the main contributor to I. Overall, the inclusion of tidal forcing substantially enhances S and I.
The deteriorating air quality in urban areas, particularly in developing countries, has led to increased attention being paid to the issue. Daily reports of air pollution are essential to effectively manage public health risks. Pollution estimation has become crucial to expanding spatial and temporal coverage and estimating pollution levels at different locations. The emergence of low-cost sensors has enabled high-resolution data collection, either in fixed or mobile settings, and various approaches have been proposed to estimate air pollution using this technology. The objective of this study is to enhance the data from fixed stations by incorporating opportunistic mobile participatory monitoring (MPM) data. The main research question we are dealing with is: How can we augment fixed station data through MPM? In order to address the challenge of limited MPM data availability, we leverage existing data collected during periods when the pollution maps align with those observed by the fixed stations.
Using deep learning methods, we will then use these enriched maps to quantify air pollution concentrations in uncovered spots. Deep learning methods have shown promise in predicting air pollution concentrations by learning the underlying patterns and relationships. In order to address the challenge of limited MPM data availability, we merge the MPM data corresponding to similar general pollution conditions. Once the MPM data is aligned with the fixed station map in the same time interval, we look for similar conditions at different periods and harness the MPM data collected in these periods. To do so, we identify clusters of different fixed station data, match them with MPM data at corresponding times, and combine them to generate more data samples and improve the pollution map. This method results in enhanced pollution estimation.
Our contributions are as follows: -Propose a method to combine fixed station data with mobile participatory monitoring data. We can create enriched maps that capture the spatiotemporal variability of air pollution in uncovered spots. This approach provides a more comprehensive understanding of air pollution levels than individual monitoring approaches and can be used to identify pollution hotspots and sources. -Using deep learning methods to estimate air pollution levels in uncovered spots, we can expand the spatiotemporal coverage of air pollution monitoring. -Validating our approach on top of real-life datasets from two cities in France and the USA: Versailles and Chicago. The remainder of this paper is organized as follows: Section 2 reviews related work and different approaches discussed in the literature.
The approach implemented several regression methods, such as SVR, DTR, and RFR. In  [17] , authors adapted mobile sampling low-cost sensors and machine learning to map urban air quality in Seoul, Korea. They collected data by conducting three weeks of campaigns across five routes with ten volunteers sharing seven AirBeams, a low-cost, smartphone-based particle counter. In contrast, geospatial data were extracted from OpenStreetMap. They applied three statistical approaches to constructing the LUR model: linear regression, random forest, and stacked ensemble. The collected air pollution data and the openly available and crowd-sourced geographical data source OpenStreetMap (OSM), were then used to construct LUR models using linear regression and machine learning methods. Notable differences between morning, evening, and night were also observed across the five routes, and the LUR model was sensitive to different segment lengths and buffer radiuses.
The system will provide high-quality measurements and estimations, relying upon an underlying sensor network consisting of static and mobile sensors. The proposed system will collect data from various subjective and objective air quality and noise monitoring inputs. They proposed a spatial and temporal air-pollutant concentration estimation model based on environmental features. Existing approaches in the literature that use fixed and/or mobile data have typically conducted targeted data collection campaigns on specific roads or outdoor places. However, this work uses MPM data to enhance fixed stations' data without relying on directed or outdoor data collection campaigns.
Methodology This section will present our proposed methodology for enhancing fixed station measures with data obtained through mobile crowd sensing or low-cost sensors. We may have very few samples from various outdoor locations when collecting opportunistic mobile crowd-sensing data. Our proposed solution addresses this data scarcity, allowing us to utilize MPM data and fixed station measures to estimate air pollution. Air pollution levels can vary significantly from one place to another and may change rapidly due to various factors such as meteorological conditions, traffic, and land use. Despite these differences, it is possible to group these changes into clusters that reflect pollution levels during specific periods. This intuition guided our method to overcome MPM data scarcity. Hence, we hypothesize that fixed station measures within the same pollution cluster could share similar MPM data.
To test this hypothesis, we will cluster fixed station measurements and use the matching dates and times to identify relevant MPM data. We will then use the union of these MPM data to enrich the pollution maps and estimate pollution using an interpolation or a prediction algorithm with a larger input sample. The approach involves clustering the air pollution levels based on fixed station data, merging these clusters with MPM data, and applying interpolation using Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) to estimate the values at uncovered places. The methodology for enriching air pollution fixed station data with data collected from mobile crowd sensing or low-cost fixed stations involves clustering, data enrichment, and interpolation. Our approach is detailed in Algorithm 1, which outlines the following steps.
In the next step, we keep track of timestamps to merge them with MPM data to produce enriched maps (Step 4). Table  1  shows an example of the snapshots representing the measurements of different fixed stations at a timestamp T i. For instance, the first snapshot corresponds to the pollution map at period T 1 with sensor values (13. 3, 16. 2,. , 12. 1, 10. 9) for the fixed sensors in order S 1 , S 2 ,. , S k-1 , and S k. The values here represent the same type of measures (e. g. , PM2. 5); thus, they have the same scale and range. Therefore, we simply used the Euclidean distance between the vectors of sensor values in K-means. The algorithm output is K clusters where each cluster groups together pollution maps based on their similarity (i. e. , the similarity of their fixed sensor vector values).
The corresponding timestamps are also returned to match the timestamps of mobile sensors. These steps are described in figure  1. The next step in the methodology is to enrich the data collected from fixed stations with MPM data collected. To do this, we use the date-time values of the measures in each cluster to merge those measures with the mobile crowd sensing or low-cost fixed stations data. We begin by calculating the hourly average of MPM data to have unified timestamps. Then, merging is performed using the date-time values as the common identifier. We add available MPM data to unmonitored cells. Cells containing fixed station measures are not changed. The result is enriched clusters containing data from fixed stations and MPM measures (steps 5 -7). Figure  2  describes the previous steps.
Data Collection This subsection will focus on data collection from two study areas, Versailles and Chicago. We collect data from fixed, low-cost, and mobile sensors. In Versailles city, we collected data from 14 June until 16 July 2023. The collection includes data from 6 fixed stations spread over different spots Fig. 4 : Implementation Pipeline in Versailles. We used the AtmoTube Pro sensor for opportunistic MPM data to collect air pollution measures. With the help of ten volunteers, we collected around 4000 minutes of outdoor records. For fixed stations, we had around 700 hourly average records. The study area in this experiment only covers Versailles city with an area around 27 km 2. Fixed sensors were deployed by eLichens 3  as part of the GoGreen Routes project in Versailles. These sensors monitor air quality and provide data for analysis.
Fixed stations provide reliable data over a longer period and are suitable for long-term air quality monitoring. These sensors measure of particulate matter (P M 1. 0, P M 2. 5, and P M 10), as well as estimates of Nitrogen dioxide N O 2 , Ozone O 3 , temperature, and humidity. They provide hourly aggregations, resulting in one representative record per station for each timestamp. MPM data is collected with the help of ten volunteers. They used At-moTube Pro sensor which collects P M 1. 0, P M 10, P M 2. 5, V OCs , temperature, and relative humidity. The collection was performed opportunistically, as the participants conducted real-life activities. The data collected from mobile crowdsensing is valuable as it provides a high spatial resolution of air quality data. To generalize our approach, in the study's second phase, we seek out public datasets with community-based data collection from Aircasting 4  and Ope-nAQ 5  specifically in Chicago city.
OpenAQ is a platform that provides data from various sources, including fixed stations and low-cost sensors. The data from OpenAQ provides a more comprehensive picture of air quality by combining data from different sources. This platform helps compare data from different sources and identify patterns in air quality over time. Using the provided API 6  , we collected reference grade measures (fixed stations) and low-cost fixed station measures. On the other side, Aircasting is a platform that provides data from low-cost sensors, which are small, portable, and easy to install. These sensors measure carbon monoxide, nitrogen dioxide, and particulate matter. The data from Aircasting provides valuable information for monitoring air quality in real time. Using AirCasting API  7  , we were able to acquire mobile participatory data.
We collected fixed stations, low-cost sensors, and mobile sensor data within a bounding box of 288 km 2 in Chicago. The data collection took place between October and December. The fixed stations produced roughly 1304 hourly average records. For the low-cost fixed sensors, we have 40575 minutes of data. At the same time, the length of MPM data was originally 368276 records at the seconds' timescale, which results in 2515 minutes of data after averaging the measures. For the sake of simplicity, we restricted our experiments to PM2. 5, which is the most available in both fixed and mobile sensors. However, our method can be applied to any environmental measure.
GPS data often contain noise and outliers that can affect the accuracy of the data. To remove noise and spikes, we used the scikit mobility library. This library is designed for mobile data analysis and provides various data cleaning and preprocessing functions. The MPM data and low-cost sensor data usually have high-frequency sampling rates. On the contrary, the fixed stations provide an hourly average of pollution levels. Thus, we calculated the hourly averages for mobile participatory monitoring data and low-cost sensor data to have a unified sampling rate. This step allowed us to identify air quality patterns over time and compare air quality across different times of day or days of the week. Finally, after ensuring all the data was cleaned and relevant, we assigned the collected data to their proper cells in the map using the GPS coordinates.
Data Enrichment Data enrichment is a crucial step in air pollution analysis. This section provides a more detailed description of the data enrichment process used in our analysis. We used the data collected from the fixed stations to create clusters of different pollution levels. We grouped the pollution levels based on the fixed stations' data, which allowed us to better understand the spatial distribution of pollution levels over time. This clustering was done using unsupervised machine-learning techniques such as k-means. For each timestamp, we used the vector of air pollution levels from all available fixed stations as the input to the clustering model. The output of this model is the different clusters, where each cluster represents different periods with the same pollution conditions.
After clustering the fixed-station pollution maps, MPM data are merged with the cluster that matches their acquisition time and propagated to the whole validity periods in that cluster. This increases the enrichment power of the MPM data while maintaining their relevance. The output of this step is to augment the data spatial coverage in the same way in each cluster. The resulting coverage can be different from one cluster to another. We can then integrate this data with the air quality data using timestamp and location as the common variables. This provides valuable insights into the factors contributing to air pollution and informs the development of effective air quality management strategies.