dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction The interest in understanding the role that the ocean plays in the global climate system has led to the creation of the Global Ocean Observing System (GOOS), a multidisciplinary observation system that aims to monitor the essential physical and biogeochemical variables of the oceans before the turn of the 21st century  (Moltmann and Coauthors, 2019). One of the main in-situ components of GOOS is the Argo International Program. Argo is a global observation network that collects, through its Core mission, data on pressure, salinity, temperature in the top 2000 meters of the ocean. This network is made up of approximately 4,000 autonomous floats in the global ocean, high latitudes, ice-covered areas and marginal seas. More than two million vertical profiles have been collected by the Argo network during the past two decades  (Wong and Coauthors, 2020).
Its constellation of satellites are positioned in Low Earth Orbit (LEO) and provides a pole-to-pole coverage. This allows requests to be transmitted from one satellite to another until they reach the satellite above the float, thus transmitting the data to Earth. At present, all transmission systems mentioned above offer this service. Before this two-way communication system got introduced, floats were pre-programmed before being deployed and because its settings could not be modified from a distance, it would keep doing its cycles until the battery died. The new system makes it possible to modify the float settings in real-time. In this way, it is possible for the floats to be placed on the surface when required (recovery mode), waiting for a possible recovery. Satellite communications providers have a wide range of services, some of which meet the requirements of the Argo network itself.
Among the services offered by IRIDIUM, the Short-Burst data (SBD) and the Router-based Unrestricted Digital Interworking Connectivity Solution (RUDICS) are the solutions that best fit the requirements of the Argo network, as both guarantee two-way communication for low data volume. For both solutions, two types of connections are generated: Mobile Originated (MO), meaning a message originated in a mobile phone, and Mobile Terminated (MT), meaning a message ended in a mobile phone. SBD allows messages to be transmitted to (MT) and from (MO) an Argo float, so the message sending protocol can be simply initiated through an email from the deployers' system. RUDICS works in a similar way, since it facilitates two-way communication but it is based on an optimized circuit-switched data channel. AIS is an automatic tracking system developed in the 1990s that operates in the VHF (Very High Frequency, 30-300 MHz) band assigned to shipping mobile services  through the use of transceivers and receivers to transmit real-time information of shipping traffic.
By increasing the float's GPS position frequency to intervals of minutes or seconds, the searching becomes more accurate, although GPS errors or unfavorable weather conditions can severely affect search times. A series of processing levels have been applied that aim to draw as much as possible the real conditions in which potential encounters would occur. The level (L1) is made up of a global raw data matrix that houses all kinds of alphanumeric data from the Argo and AIS datasets. Level (L2) filters encounters by ship size (Figure  2 ). Passenger, cargo and tanker (PCT) ships have been discarded due to their low maneuverability and tight schedules. All those ships greater than 60 meters have also been ruled out regardless of type, except NGO and oceanographic research ships. In level (L3) we included only the encounters that occurred in locations with favorable weather conditions for potential recoveries.
Argo and AIS data The global Argo dataset  (Argo, 2022)  for years 2019 and 2020 used in this study was obtained on November 11 th , 2021 through the Euro-Argo data selection tool (Euro-Argo 2021). No distinction was made between Core, Deep or BGC floats. Two-way communications was the only requirement to fulfill. These inputs were set on the Euro-Argo Data Selection Tool main menu and the resulting dataset was exported in Argo Original NetCDF format. The last 10 surfacing of all floats with Iridium transmission were selected for the analysis. On the other hand, all the surfacing of the floats (>10) contained in the Argo greylist  (Argo Data Management Team, 2022)  were also included in the dataset for the same time range. The recovery of these floats is desirable regardless of how much longer the battery may last, since their measurements are suspect or unreliable, mainly due to different technical issues related to the sensors.
Meteorological data To evaluate the possible risks related to recovery operations due to weather conditions in locations where encounters occurred, a meteorological data analysis was carried out every hour for years 2019 and 2020. The ERA5  (Hersbach and Coauthors, 2020)  product generated by the Copernicus Climate Change Service, which is part of the European Union Earth monitoring program and implemented by the ECMWF (European Centre for Medium-Range Weather Forecasts), was selected to develop the meteorological analysis. The ERA5 dataset is a new analysis of hourly weather conditions back to 1979, although for our study only the years 2019 and 2020 were selected. It combines a weather model with observational data from satellites and ground-based sensors to create a consistent long-term record of our climate.
The global gridded datasets were obtained through the Copernicus Climate Data Store (Copernicus, 2021) and their coverage is global, with a resolution of 0. 25°x 0. 25°for wind data and 0. 5°x 0. 5°for ocean waves. The parameters selected for this analysis included the u and v components of the wind at a height of ten meters above the surface (m/s) and the significant height of combined wind waves and swell (m). The analysis simply crosses the Argo surfacing data with both meteorological sets in time and space, in such a way that we obtain the closest wind and wave values for each surfacing.
Encounters analysis A total of 18,615 encounters between ships and Argo floats occurred in 2019 and 28,488 in 2020. These figures decreased by 75% when levels of data processing were applied, meaning a total of 4,719 and 6,951 encounters respectively for both years (Figures  3A-D ). The float 6902968 comprised 52% of all encounters globally for the year 2019. A similar scenario occurred in 2020 with floats 6903014 and 6902954; together they comprised 54% of the total encounters. Greylisted floats represented approximately 61% and 74% of the encounters (Table  1 ). The geographical areas that concentrated the most global encounters in both years were the Mediterranean Sea, the Indian Ocean, the Pacific Ocean and the Atlantic Ocean (Figures  3A-D ). In 2019 and 2020, 66% and 53% of the total encounters happened with sailing-pleasure ships, while 25% (2019) and 35% (2020) happened with fishing ships (Figure  4 ).
The distances given by the difference between both the floats and the ships' GPS positions, were divided into 4 groups (Figure  5 ), from 0 nm to 20 nm. Roughly 4% of the encounters happened in distances of 0-5 nm and around 35% in distances of 10-15 nm in average for both years. A total of 19 and 26 encounters, respectively in 2019 and 2020, happened in distances shorter than 1nm. The highest figure of encounters (39. 24%) happened for distances between 15-20 miles. The global shipping fleet was also filtered by This classification corresponds to the size of the global fleet on average that interacted with Argo floats (encounters) in 2019 and 2020. The first stacked bar corresponds to the raw data from GFW (L1). The second stacked bar is obtained after applying the L2 processing level, discarding the PCT ships and all those ships > 60 m in length, except NGO and oceanographic ships.
Abrupt salinity drift affected floats from an encounters analysis perspective Through an extensive audit of the global network conducted by Euro-Argo, it was found that at least 677 of Sea-Bird Electronics  Distance and time difference between ships and floats for each encounter in 2019 and 2020 (averaged). The L4 processing level dataset has been used to generate this plot. Cumulative diagrams of wind speed (A) and significant height of combined wind waves and swell (B) for both years. Both plots correspond to the L3 processing level dataset. The total number of encounters and the number of floats is displayed based on the different processing levels. The percentage of total encounters is 100% for level 1 and is used as a reference for the rest of the levels. The number of floats shows individual floats regardless of how many times they have interacted with ships of opportunity.
Initiatives such as the one developed by Euro-Argo's Fleet Monitoring Tool, constitute a step forward in EOL prediction. An alert trigger based on technical parameters collected by the float has been designed when the battery voltage drops below a certain threshold  (Arduini and Coauthors, 2021) , obtaining a window of opportunity to identify potential float's last cycles. This information would be extremely valuable, as it could work as the primary source of data input to run an accurate encounters analysis. However, the objective of this analysis was not to determine the Argo floats life expectancy, but these considerations should be taken in account in the assessment of potential for float recovery.
Considering their fleet density and remarkably global distribution (even in remote areas of the ocean where the Argo floats operate), our study suggests that fishing ships may be the most feasible option for retrieving Argo floats, since they can perform float recovery maneuvers in better conditions than large or gross tonnage ships. A total of 1,182 and 2,430 encounters (Figure  4 ) for fishing ships may seem much lower compared to the initial figures (around 10,500 and 16,000 encounters in 2019 and 2020) before applying the processing levels. Also, AIS data includes only a small fraction (approximately 70,000 ships) of the world's estimated 2. 8 million fishing ships  (Global Fishing Watch, 2021b). The lack of data (from many fishing boats i. e. ), the noninterpolation of the boats' tracks and the Argo floats that do not stay on the surface suggest that many more encounters occurred in a real scenario than those found in the analysis, but they are difficult to trace.
Other factors such as the distance between the ship's deck and the waterline (freeboard) for direct-recoveries or accounting with an auxiliary boat, are key in terms of fishing ship's maneuverability. On the other hand, tug-type ships were mainly constrained to coastal areas, being an interesting option for floats located in boundary regions. In a potential recovery scenario, the weather forecast is an essential tool to guarantee the safety of the maneuver and crew's integrity. Factors such as the number of crew members, type of ship or having an auxiliary boat, when necessary, may help dealing with Relative averaged abundance of encounters between ASD-affected floats and world shipping traffic by distance. The L4 processing level dataset has been used to generate this plot. weather conditions and therefore, determine if a recovery is feasible.
Encounters analysis considerations The assumptions made at each level of data processing have been taken to obtain a figure that is closest to reality. Although the final number of encounters resulted in 1/4 of the original dataset, it is intended not to lose sight of the fact that the encounters are events that take place with ships of opportunity on a daily basis in oceanic and coastal areas around the planet. Due to their high frequency and wide spatial spectrum, we have shown that it could be a valuable resource in the recovery of Argo floats. Despite its global scope, the encounter analysis has shown some sensitivity in certain geographical areas such as the Mediterranean Sea. The results showed by far that it was the most represented ocean basin by number of encounters globally. It could be due to being one of the busiest traffic lanes in the world (United Nations Environment Programme/Mediterranean Action Plan and Plan Bleu, 2020), but it is mainly due to two factors.
These figures have a direct relationship with Argo float configurations based on certain specific scientific requirements. Especially those floats that worked with a period of cycles lower than the standard (5 or 10 days), causing an increase in encounter frequency. The greater the number of surfacings, the greater the chances of encountering a ship. The float number 6902968 was a clear example, which Relative abundance of types of ships of opportunity that interacted with Argo floats in both years. The L4 processing level dataset has been used to generate this plot. represented 52% of the encounters worldwide in 2019, despite having been operating on the high seas for less than a month and having performed 30 daily cycles. The float 6902954 showed to be a similar case, which accounted for 20.
Acknowledgments We would like to thank the  European Commission  and  Euro-Argo ERIC  for the implementation of the  EA-RISE (Euro-Argo Research Infrastructure Sustainability and Enhancement)  project. Special thanks to  Rubeń Marrero Goḿez  for his fruitful feedback whenever it was required and  Clàudia Peŕez Barrancos  for her recommendations. Finally, we would like to thank the  Global Fishing Watch organization , for working in partnership with us and without whom this work could not have gone ahead.
Parameter estimates obtained from forest inventory data We used SNFI data from the whole Spanish territory to find suitable values for twelve parameters. A number of quantitative traits, such as maximum and median plant heights and tree diameter-height relationships under shade/sunlit conditions, were directly calculated from SNFI shrub/tree records  (Morin et al. , 2021). We also used permanent forest inventory plot data (SNFI2-3 and SNFI3-4) to estimate tree mortality and recruitment parameters. Initial values for basal mortality rates were estimated using observed tree mortality rates in permanent inventory plots, excluding those where management effects (i. e. stumps) were detected. Since the model incorporates mortality due to carbon starvation and desiccation explicitly, using observed mortality rates as basal rates leads to overestimation of mortality.
Physical Interactions between WRF and SUEWS The coupling between WRF and SUEWS occurs via the biophysical interactions between the land surface and other physics modules in WRF (Fig. 1 ): 1. The radiation module provides radiative forcing variables, incoming short-K ↓ and longwave radiation (L ↓ ), to the land surface module. The land surface module returns outgoing short-and long-wave radiation (K ↑ and L ↑ ). 2. Atmospheric variables needed for SUEWS, including air temperature T a , relative humidity RH, barometric pressure p a and wind speed U are supplied by the boundary layer (BL) module. These are influenced by turbulent transport (i. e. , momentum τ , sensible heat Q H and latent heat Q E fluxes) from the land surface. 3. Precipitation P , is generated by the microphysics and cumulus modules that parameterise precipitation-related processes at different scales.
Technical implementation of the WRF-SUEWS coupling The following are considered in the design of coupled WRF-SUEWS system: -Performance: Given coupling with file-based IO (input-output) exchanges has unacceptable computational performance, we use the SuMinmodule under the WRF framework (Figs. 2  and 3 ). -Extendibility: As SUEWS is regularly enhanced (Table  1 ), it is desirable or even essential, for the coupled system to use the full capacity of the standalone SUEWS. -Sustainability: Given the vast community effort to build and improve sophisticated software systems coupling should not be limited to one version. Instead a highly standardised coupling procedure is required to be sustainable  (Meyer et al. , 2020). To address these, the coupled WRF-SUEWS system uses an adaptive intermediate layer SuMin (SUEWS in a Minimum mode) to link both models (Fig.
5 ). From SUEWS, SuMin calls the main SUEWS calculator to conduct all core SUEWS physics calculations. Whereas from WRF, SuMin is linked to the module_sf_suews via suews_1d as a complete land surface model that can be used by WRF dynamics solver (i. e. , ARW solver) via the surface driver (Fig. 5 ). By coupling SUEWS and WRF this way, fast prototyping of new functionalities is possible on the SUEWS side while maintaining a stable coupling to the more complex WRF. When new SUEWS features are available to be fully coupled, appropriate switches can be activated to incorporate them within the whole WRF system. This intermediate-layer-based approach allows efficient communication between SUEWS and other models (e. g. , SuPy, Sun and Grimmond, 2019) through an explicit, unified interface. Thus, SUEWS can be potentially coupled to other weather/climate modelling systems (e.
g. , openIFS, ECMWF, 2021). A Python-based WRF-SUEWS pre-processor system (WSPS, Fig. 5 ) formats the data to allow the additional parameters not in standard WRF input files (e. g. input variables in wrfinput. nc , namelist based configurations; cf. IO workflow in Fig. 3 ) to be incorporated, rather than modifying the WRF Preprocessing System (WPS). The WSPS can be used for offline model spin-up runs to obtain the appropriate required initial conditions for WRF-SUEWS. The files prepared are: i wrfinput. nc: a modified version of WRF inputs with initial model states and other static properties; and ii namelist. suews: the global configuration for SUEWS model. To generate these, four types of inputs are needed (Fig. 3 ): i1 Standard WRF input files for WPS: The geographic and meteorological data are processed by WPS to produce wrfinput.
nc (i1), to produce the wrfinput. nc files for WRF-  The seven land cover (LC) types can be assigned different parameter values (   , 2000) , to the seven LC classes (Fig. 4 ). Each SUEWS LC may combine fractions from multiple IGBP LU. Through this reclassification, WRF-SUEWS can use existing LU data for SUEWS simulations while allowing the other parameters to vary between grids. Note a flexible number of WRF LUs (up to 100 in this release) can be specified to compose a SUEWS LC so an extremely heterogeneous LU composition can be accounted for.
5b ), we replace it with a high resolution (i. e. 2. 5 m ) land cover map (Fig. 5c ) derived from an earth observation (EO) VHR SPOT imagery  (Mitraka et al. , 2016; Marconcini et al. , 2017)  with more realistic surface information. This high resolution dataset is processed using UMEP  (Lindberg et al. , 2018)  to derive both land cover fractions for the seven SUEWS classes and other morphological parameters of roughness elements (e. g. building and vegetation heights, frontal area index; Table  2 ). The resulting dataset is upscaled to obtain 1 km resolution data for d03. As the equivalent detailed land cover information are unavailable for d04, the land cover (plus building and vegetation height) for the single grid where Swindon site is located is modified based on values in  Ward et al.
Code and data availability. The snapshots of input data and source code for WRF-SUEWS used in this paper have been archived on Zenodo at https://doi. org/10. 5281/zenodo. 7957903 (Sun et al. , 2023a)  and https://zenodo. org/record/8137708 (Sun et al. , 2023b), respectively. The up-to-date version of WRF-SUEWS is available at https://github. com/Urban-Meteorology-Reading/WRF-SUEWS (last access: 16 May.
In this paper, we present a Light CNN-based model for screening the presence of type 2 diabetes using a single raw pulse extracted from photoplethysmographic signals. In addition to the baseline architecture, we evaluate different model architectures that take as input age and biological sex or PPG handcrafted features. Furthermore, we apply transfer learning to all the tested architectures to evaluate the effectiveness of harnessing pre-trained models in detecting diabetes. We tested a model pre-trained on a general PPG shape dataset and another model pre-trained on a dataset containing hypertension PPG signals. Our model scored an AUC of 75. 5 when trained with raw PPG waves, age, and biological sex without applying transfer learning, which is competitive with current state of the art.
Devices for diabetes test, by contrast, need to be specifically bought and this usually becomes the case when there is a suspicion of diabetes. Recently, several studies have investigated the possibility of detecting the diabetes through PPG signals with traditional, machine learning or deep learning approaches  [7]. Since the availability of data is one of the main issues in biomedical research  [8] , we have focused on searching for a method that could succeed without the need of a large amount of data. For this reason, we propose a light CNN-based model trained over a small dataset in terms of size and signal length. Our dataset is composed of 100 subjects (15% with diabetes) and consists of single PPG waves. Considering the size of our dataset, we have investigated the effect of transfer learning.
We trained two models with two larger datasets: the first contains generic PPG waves from 500 subjects while the second dataset contains PPG waves from 181 hypertension (HT) and healthy subjects. The goal is to assess if utilizing the model weights and biases from a generic PPG shape classification model or from a specific pathology classification model can enhance the DT2 detection through transfer learning. Our contributions can be summarized as follows: • Firstly, we have designed a baseline CNN model for detecting DT2 using PPG signals. Our model takes as input much shorter PPG segments compared to the previous studies, as it operates with single pulses normalized to a length of 1 second. This segment length enables the model to operate with a variety of signal lengths without requiring a long PPG acquisition.
• We propose a Light CNN trained over a small dataset. Our results are comparable to those reported by Avram et al. [9]  that use much larger nets and larger datasets. • Lastly, we utilize transfer learning to investigate the possible benefits of using firstly a pre-trained model over a larger dataset and then fine tuning it with a very small dataset containing DT2 PPG signals, and secondly to investigate the relation between hypertension and diabetes. The rest of the paper is organized as follows: In Section II, we present related works. In Section III, we briefly outline the background of PPG and its correlation with diabetes. Then, we describe the datasets used, the extracted features, the model architecture, and the process we have followed to investigate the transfer learning effect. In Section IV, we present and discuss the results.
II. RELATED WORKS Several approaches have recently been employed in detecting modifications in PPG waveforms  [10] ,  [11]. These approaches can be categorized into three types: traditional, machine learning-based, and deep learning-based. Bagus et al. [12]  proposed a traditional approach in which the Poincaré plot of finger PPG signals was used to significantly discriminate (p<0. 05) between healthy subjects (glycohemoglobin<6. 5%), diabetic subjects with good control (6. 5%<glycohemoglobin<8%), and diabetic subjects with poor control (glycohemoglobin<8%). Their dataset consisted of 22 healthy subjects, 23 diabetic subjects with good control, and 17 subjects with poor control. The length of the acquired signal was not specified. In  [13] , Pilt et al. proposed a new parameter, the PPGAI (PPG augmented index), to assess arterial stiffness in elderly patients.