dataesr/scientific-paragraphs-categorization huggingface dataset:
A large eddy was identified formed by the thermal plume induced by the fire, its impact on the ceiling and then on the sidewall, allowing smoke to flow initially under the ceiling and then be partially transported to the lower part of the room. This downward flow, which is rarely considered in the literature, has been highlighted thanks to PIV. These new data allow us to propose explanations for the vertical temperature profiles observed in mechanically-ventilated enclosures. The influence of fire HRR on the velocity fields was evaluated by experiments with several pool sizes. The increase in HRR leads to an increase in the amplitude of the velocities without modifying the structure of the flows. An attempt to normalise the velocity by the power is proposed and shows the generic feature of the global flow field based on the ratio U * /Q * n.
This was to continuously record variations in the main physico-chemical variables and relate them to surface processes. To assess the impact of dam removal on these variables, two measurement sites were chosen: one upstream of the dams where flow conditions re-mained stable, and another downstream of the dams where a large amount of fine sediment has been released. This original data can be used to determine the biogeochemical functioning of the hyporheic zone and its coupling with dynamical flow and sedimentary processes. © 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ).
Subject Environmental Science Specific subject area Hydrology and Water Quality, Ecology Type of data Tables: time series and geospatial data Graphs Figures How the data were acquired The data was acquired during the removal of two dams (Roche-Qui-Boit (RQB) and Vezins) to restore hydro-sedimentary continuity in the Selune River (France). We used high-frequency autonomous probes to continuously monitor physico-chemical variables in several sectors of the river over a 12-month period. Two measurement sites were chosen. The first site, referred to as the "Virey" site, is located far enough upstream of the dams to not be directly affected by the dam removal. The second site, referred to as the "Signy" site, is located downstream of the dams. During the measurement period, it was thus directly impacted by a large-scale release of fine sediments.
A pressure differential sensor is installed in the direct vicinity of the temperature system and measures the differential in hydraulic head between the stream and the sediment at 30cm. The LOMOS-mini data can be used to estimate advective and diffusive thermal fluxes, as well as the permeability and thermal conductivity of the riverbed at a sub-daily resolution  [2]. In addition to these sensors, two INRAE (the French National Research Institute for Agriculture, Food and the Environment) permanent gauging stations continuously record the water level, river conductivity and turbidity at both the Virey and Signy sites, plus river dissolved oxygen at Virey only. Data format Raw: CSV file of time-series of river and hyporheic zone physico-chemical variables.
Foam plugs (ethylene vinyl acetate) seal the tubes and preclude the infiltration of stream water. All tubes were installed in the riverbed within an area with a 1m radius. ( continued on next page ) The LOMOS-mini data was monitored between 14 th April 2022 and 16 th September 2022. Other physico-chemical variables were monitored over a full year (October 2021 to September 2022). All data was registered at a high frequency (15 min). This period equates to ten months before and four months after the withdrawal of the Roche-Qui-Boit dam (May 2022). The data from the autonomous probes was retrieved every few months, when stream discharge was sufficiently low to walk in the river. Data from the wired probes was constantly accessible via the Campbell station on the riverbank. • The links above lead directly to the data.
Value of the Data • This data constitutes a new time database of significant physico-chemical parameters for the river water and hyporheic zone. Notably, the data provides the first simultaneous and high-frequency observations of vertical gradients in the sediment in dissolved oxygen, conductivity and temperature. This gradient data can be used to determine the nature of reactive transport processes in other riverbeds. • This data was obtained in the unique context of dam removal in the Selune river. Data comparison between the Signy site, directly impacted by sediment release, and the Virey site, located upstream of the dams, provides a robust way of assessing the consequences of dam removal for the hyporheic zone. • The long and high frequency (e. g. every 15 min) measurement period makes it possible to determine the impact of dynamical forcing such as flood waves on the functioning of the hyporheic zone.
• This database is also useful for monitoring spawning ground and clogging processes. The variability of dissolved oxygen is key for the survival of fish eggs in the sediments. • Vertically resolved data provides new opportunities for predicting oxygen distribution in the hyporheic zone and its dependence on geomorphological processes  [3]. • This data can be used to compare reactive transport models (oxygen uptake, denitrification, precipitation, etc. ) and to assess biogeochemical function hotspots and key times in the hyporheic zone  [4].
Two measurement sites were chosen. The first was upstream of the dams where flow conditions remained stable, known as the Virey site, and the second was downstream of the dams where a large quantity of fine sediment was released, known as the Signy site ( Fig. 1  ). At these two sites, a number of probes were installed to monitor physical and chemical gradients in the sediment riverbed layer. The data from these probes will help to fulfil a more fundamental objective, namely that of better understanding hyporheic physico-chemical flows. The Signy site is characterized by the presence of fish spawning grounds, including lamprey and Atlantic salmon  [7]. This site very sensitive to the presence of fine sediments and to riverbed clogging, both of which reduce oxygen penetration inside the riverbed.
Before May 2022, the dams prevented   most of the sediment from reaching the Signy site. Since the removal of the second dam in May 2022, high concentrations of suspended load are being transported to the Signy site. This sediment had been accumulating in the reservoir behind the dams for the last hundred years and is now being stirred up by the higher stream velocities. It is in this particular context that the LEARN Project (Impact of dam removal on the hyporheic flows and river exchanges of the Selune) is being carried out. This project is part of a global exosystemic program managed by the INRAE ( https://programme-selune. com/en/ ). In all files, the first column is the date set out in YYYY/MM/DD HH:MM:SS format. Data is registered at a high frequency (15 min).
Data at the Virey site At the Virey site, the physico-chemical variables are measured using autonomous probes installed in the hyporheic zone. Data on the water level, conductivity and turbidity of the river is also recorded at an INRAE gauging station located 300m upstream. All data from the Virey site is registered in the following order in each file: √ The csv file "data_oxygen_upstream-virey-zh. csv" contains the times series of oxygen concentration in mg/l at 15cm and 30cm depths below the riverbed; √ The csv file "data_conduc_upstream-virey-zh. csv" contains the time series of conductivity in micro-siemens/meter (μs/m) and temperature in degrees Celsius ( °C) at 30cm below the riverbed; √ The csv file "data_oxygen_upstream-virey-river. csv" contains the time series of oxygen concentration in mg/l and temperature data of the river in °C; √ The csv file "data_station_oxygen_upstream-virey-river.
Data at the Signy site At the Signy site, the physico-chemical variables are measured using wire probes installed in the hyporheic zone. Data on the water level, conductivity and turbidity of the river is also collected at an INRAE station located 200 meters upstream. Data is saved in the following order in each file: √ The csv file "data_downstream-signy-zh. csv" contains data on battery voltage in Volts, battery temperature in °C, temperature in °C, oxygen saturation in %, oxygen concentration in mg/l (firstly at 30cm and secondly at 15cm below the riverbed), conductivity in μs/m, pressure in mm and temperature in °C (all at 15cm below the riverbed), and water level in mm; √ The csv file "data_mini-lomos_downstream-signy. csv" contains the time series of pressure differentials in meters between the bottom of the hyporheic zone and the river, and water temperatures at depths of 10, 20, 30 and 40cm in the hyporheic zone; √ The csv file "data_oxygen_downstream-signy-river.
csv" contains data on temperature in °C, oxygen saturation in %, and oxygen concentration in mg/l; √ The csv file "data_station_downstream-signy-river. csv" contains the time series of conductivity in μs/m, turbidity in FNU and river water levels ( INRAE station ) in mm. For the Signy site, it is to be noted that some of the data is missing from the file "data_station-downtream-signy-river. csv" for a one-month period (12 th April to 12 th May 2022). The abrupt restoration of the hydro sedimentary continuity induced a massive release of fine sediment downstream that buried the INRAE station conductivity and turbidity probes. Missing values are replaced by NaN characters for these dates. There is also data missing from the file "data_oxygen_downstream-signy-river. csv" for the period between 11 th May 2022 and 18 th August 2022.
MATLAB script We provide a MATLAB script (read_data_"site-name". m) to automatically read and plot data time series from a generic folder named "data_formatted," in which all files should be saved. The resulting plots are saved in a "Plot" folder. Fig. 4. is an example of the MATLAB script output plot for physical and chemical parameters measured at the Signy station for 18/10/2021 to 4/02/2022.
Wire probes setup (Signy site) At the Signy site, we installed wire probes controlled by a Campbell CR 10 0 0X data logger, which centralizes and applies the same reference times to all measurements at this site. The Campbell CR 10 0 0X controls: • The STS sensor TM/N that records water levels. • The Wisens CTD 50 data logger that records conductivity, temperature and pressure at a depth of 15cm in the hyporheic zone. • The Ponsel OPTOD optical digital oxygen sensor that records dissolved oxygen in the river at depths of 15 and 30cm in the riverbed.
Calibration of the probes The dissolved oxygen sensor (Prosensor HOBO, U26-RDOB-1) was calibrated before each deployment at barometric pressure with 100% and 0% saturation solutions. Calibration to 0% was carried out using Prosensor U26-CAL-SOL Sodium Sulfite Solution, after the probe had been cleaned with distilled water. Prosensor HOBO, U26-RDOB-1 was replaced once (after 6 months of deployment) following a Prosensor recommendation. For the Ponsel OPTOD optical digital oxygen sensor, factory calibration was controlled in the laboratory before deployment with a reference portable probe, Hach HQ300. To control drift, all of the oxygen sensors were checked during each field data collection campaign (every two months), and the dissolved oxygen concentration in the water of the river was measured with both probes: the in-situ probe sensor and the reference probe Hach HQ300.
No drift was observed during the monitoring period. CTD conductivity sensors were calibrated in the factory. Each field data collection campaign, the conductivity of the water in the river was measured with a reference Hach HQ300 portable probe. This conductivity value is used to recalibrate and correct any drift, either through the HOBOwarePro software for the in-situ Prosensor HOBO U24 or the Campbell PC400 software for the Wisens CTD sensor device configuration. In-Situ Inc. TROLL 100 and STS TM/N probes were checked in the laboratory before deployment with a device containing a water column. The water level in the river was measured man-  -The second HOBO data logger records the differential pressure between the bottom of the hyporheic zone and the river. This box also contains the equipment required for its operation: six 1.
5V AA batteries; the batteries are connected in series to provide a voltage greater than or equal to 7V. In the center of the diagram, there are mini-piezometers containing conductivity sensors (pink), or oxygen sensors (black) set at depths of 15 and 30cm. The mini-piezometers are perforated (screened) at 15cm and 30cm to ensure water flow circulates from the mini-piezometers. On the left of the mini-piezometers, there are pressure (orange), conductivity (pink) and oxygen probes (black) that are used to measure conductivity, oxygen and water levels. ually at each site and during each field data collection campaign. These values help to control any drifting. None of the water level sensors showed any noticeable drift during the monitoring period. LOMOS-mini differential pressure measurements are based on a sensor that converts the pressure difference on an electrical voltage.
Acknowledgments This work was supported by the  Agence de l'Eau Seine Normandie  ( Selune Project ,  LEARN  Project), the  Centre National de la Recherche Scientifique  ( 80|Prime ) and the region of Bretagne [ARED 2019 SUCHY]. We would like to thank the  ANR JCJC  for its SUCHY grant. We would also like to thank  Alban Thomas  and  François MERIC of INRAE , the  French National Research Institute for Agriculture, Food  and the Environment, for their data management.
Leaf galls have previously been observed on veins (either the primary, secondary, or tertiary veins), between veins, near the lamina apex or base, and dispersed throughout the lamina and petiole. In fact, to the best of our knowledge, none of the fossil galls described in previous studies resemble the galls on the leaf margin presented here. Labandeira et al. (  2007 )  21  and extended unpublished updates which are used as the main reference in current studies of plant-arthropod interactions in the fossil record, described and classified all known fossil types of galls based on a large worldwide dataset. In addition to gross morphological characteristics and whether galls are solitary or in groups, they also recognized different types of galls based on their position on the leaves (Supplementary Fig.
Therefore, the overlying matrix was removed with fine needles, scalpels, and brushes. After cleaning, photographs were taken using a digital camera (Canon EOS 1500D) mounted on a stereo zoom microscope, and edited with CorelDraw and Adobe Photoshop software (Figs. 2, 3 ). The details of the margin galls were drawn using CorelDraw ver. 2021 (Fig. 4 ; Supplementary Fig. S6, S7 ). We also surveyed modern-day forests adjacent to the fossil exposures for similar types of galling on modern leaves and photographed and collected diverse gall-bearing modern leaves. The terminology adopted by Mani (1973)   48  was followed for the general description of the margin galls. The fossil specimens (SKBUH/ PPL/JH/324A, SKBUH/PPL/JH/324B, SKBUH/PPL/JH/325A, SKBUH/PPL/JH/325B, SKBUH/PPL/JH/325C, SKBUH/PPL/JH/332) reported here are housed at the Department of Botany, Palaeobotany and Palynology Laboratory, Sidho-Kanho-Birsha University, Purulia, India.
Acknowledgements TH and MAK gratefully acknowledge the  Department of Botany, Sidho-Kanho-Birsha University  for providing the infrastructure to accomplish this work. BA and TD acknowledge support from a  Carl Tryggers Stiftelse postdoctoral grant  (project no. CTS21 :  1585 ) and a  Swedish Research Council  grant (grant number  2021-05849  to TD). All co-authors thank  Prof. Carlos HW Flechtmann  and one anonymous reviewer for their constructive comments on our manuscript.
Streamflow forecasting is crucial in water planning and management. Physically-based hydrological models have been used for a long time in these fields, but improving forecast quality is still an active area of research. Recently, some artificial neural networks have been found to be effective in simulating and predicting short-term streamflow. In this study, we examine the reliability of Long Short-Term Memory (LSTM) deep learning model in predicting streamflow for lead times of up to ten days over a Canadian catchment. The performance of the LSTM model is compared to that of a process-based distributed hydrological model, with both models using the same weather ensemble forecasts. Furthermore, the LSTM's ability to integrate observed streamflow on the forecast issue date is compared to the data assimilation process required for the hydrological model to reduce initial state biases.
, 2018; Zhang et al. , 2018; Sahoo et al. , 2019; Hunt et al. , 2022; Arsenault et al. , 2023). For example,  Zhang et al. (2018)  evaluated the performance of various RNN architectures in simulating water levels in Norway and found that the LSTM is better suited for multi-step-ahead forecasts than other architectures without cell memory. Kratzert et al. (2018)  compared the performance of the LSTM and the SAC-SMA hydrological model  (Sorooshian et al. , 1993)  for simulating long-term streamflow over 241 catchments in North America and found that the LSTM outperformed the SAC-SMA model, highlighting the potential of LSTMs as regional hydrological models. Tounsi et al. (2023)  corroborated these findings by showing the LSTM model performance over the MOPEX dataset in the United States. Kratzert et al.
Observed hydrometeorological data (used for calibration of hydrologic model) The observed hydrometeorological data were provided by Rio Tinto, the operator of the hydropower generating station and owner of water rights for the LSJ system. The data covered the period from January 1954 to December 2019 and included daily minimum and maximum temperatures and precipitation from a network containing 16 weather stations distributed throughout the catchment. In addition to the weather data, Rio Tinto also provided inflows to the main reservoir, which were derived through mass balance calculations by evaluating changes in the reservoir level at various locations along with known outflows from turbines and spillways. This was necessary as multiple rivers and tributaries flow into the reservoir, making a significant portion of the flows ungauged.
Ensemble weather forecast (used for forecasting with hydrologic and LSTM model) To assess the performance of the distributed hydrological model and the LSTM model in forecasting mode, the 50-member operational ensemble weather forecast data was obtained from the European Center for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) using the Meteorological Archival and Retrieval System (MARS) archive (https://www. ecmwf. int/en/forecasts/dataset/opera tional-archive;  Grawinkel et al. , 2015). The variables of total precipitation, maximum and minimum temperature were available at a spatial resolution of 0. 2 • x 0. 2 • on a 6-hourly time step twice per day (0Z and 12Z). For this study, only the 0Z-emitted forecasts were utilized and the four 6-hour timesteps were aggregated to a daily time step for each day.
The number of epochs, or the number of times the model sees the full dataset during the training, and batch size, or the size of the dataset sub-sample used to estimate the gradient descent, were adjusted to speed up the training and ensure its convergence. This led to a larger number of epochs for longer lead-times. The LSTM models were trained and tested using multiple meteorological variables from the ERA5 reanalysis dataset, including precipitation, temperature, wind, surface pressure, net solar radiation and dewpoint temperature, (Section 2. 2. 3) and daily observed streamflow provided by Rio Tinto (Section 2. 2. 1) for the period from January 1979 to December 2014. The LSTM models were trained on sequences of  [365-k]  days of hydrometeorological data prior to a single observed streamflow target, where k is the lead-time for which the model will be used to forecast flows (see Fig.
2a ). Additionally, streamflow data up to the forecast date were used as inputs to the LSTM model, allowing the network to access recent information about the current hydrological state at the time of forecast, similar to how CEQUEAU has access to assimilated initial states. However, this also means that for each forecast lead-time, the LSTM model needs to be retrained with streamflow observations being lagged by the same number of days as the forecast leadtime. For example, for a 3-day lead-time forecast, the LSTM inputs would be a combination of 362 days of observed weather data prior to the forecast day as well as 362 days of streamflow observations lagged by 3 days (see Fig. 2a  for an example of data and periods used to train a 1-day and a 3-day lead-time forecasting model). This ensures that no observed streamflow from the forecast period is used during model training.
To train these models, observed data (both weather and hydrometric) are used for all time steps; however, in forecasting, the forecast lead-time days are replaced with actual forecast data and the observed streamflow time series ends on the day of the forecast issue (see Fig. 2b  for an example of application of a 1-day and a 3-day forecasting model). Nine different LSTM models were thus trained, corresponding to each lead-time (one to nine days) of the forecast. It is important to note that in this study, ERA5 reanalysis data was used as the study is performed in hindcasting mode. However, in a real-world application, ERA5 data would not be available for the 3-4 more recent days as the ERA5 data are released with a few days of lag. These data would thus need to be taken elsewhere in an operational setting.
The network weights were optimized to maximize model performance over all training sequences. The training set was composed of 70 % of the years selected at random from the training period, 15 % of the years were kept as the validation dataset to prevent the LSTM from overfitting during training, and the remaining 15 % of the dataset is used as testing data which are used to evaluate the LSTM's robustness on an independent period prior to forecasting. Data normalization was applied to all hydrometeorological data using a scaling model calibrated on the training period data only, to ensure no contamination of the training dataset was accidentally introduced. Training and validation were performed using the Tensorflow and Keras libraries in Python, with the Adam optimizer and the KGE metric as the objective (loss) function.
The model was trained by generating one streamflow value for each [365-k]-day window of training data, and repeated for all days in the dataset, generating a hydrograph one day at a time in multiple batches. The trained model was then used to perform forecasts for the period from January 2015 to December 2019 by combining historical (ERA5 reanalysis dataset) and forecast (ECMWF) data into sequences of [365-k] days, ending on the desired lead-time's meteorological data. One LSTM model was trained for each forecast lead-time due to lagged streamflow observations. The LSTM forecasts were also generated every three days and on the same dates as the CEQUEAU-DA data assimilation and forecasts were performed.
2. Example implementation of a training sample for a 1-day and a 3-day lead-time forecasting model (a) and an example implementation of such forecasting models for 1-day and 3-day lead-time streamflow forecasts (b). Panel (a) presents a single sample, but the process is repeated on the entire dataset available for the training period and the same process is repeated on the validation and testing periods. Panel (b) shows an example for a single issue date, but the process is repeated for other forecast issue dates, combining the outputs of the 1-day to 9-day forecasts for each forecast issue date. first two days, and a similar trend can be observed for MAE. This is supported by a non-parametric Wilcoxon test for median equality, which shows the results on an annual basis for both skill scores, as depicted in Table  2a.