dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodology Adopted The methodology used in this study consists of two major parts (Figure  2 ): The first one aims to predict the different characteristics of storms (wind speed, pressure, humidity, temperature, and wave height), which represents a univariate time-series prediction, and the second part focuses on predicting the occurrence of storms based on their characteristics. To achieve these goals; the following steps were taken: First, data collection: a storm database containing all storm events that have occurred in the study area over the last 25 years was used, in conjunction with buoy data that represents hourly records of wind speed, pressure, humidity, temperature, and wave height. Daily values for each characteristic were calculated from these records and then combined with the storm database to create a final timeseries data that represents a day-by-day record of weather and marine data, in addition to the exact days that have known the occurrence of a storm.
Prediction of Storm Characteristics Brittany buoy data, which contains historical records of wind speed, pressure, humidity, temperature, and wave height from 1 January 1996 to 31 May 2021, was used to predict storm characteristics. Since the task is a univariate time series prediction, the values for each storm characteristic should be predicted separately. Therefore, a unique model was created for each of them. Taking wind speed prediction as an example, a GRU model was created based on the univariate dataset of past wind speed, and all other characteristics were predicted in the same manner. Finally, five GRU models were developed, each designed to predict a particular characteristic (wind speed, pressure, humidity, temperature, and wave height). The data was split into training and testing sets, with a ratio of 80 to 20.
Prediction of storms In this second part, a binary SVM classifier was developed to predict storm occurrence based on their characteristics. To achieve this goal, multivariate time series data was created by combining the storm database and the buoy data to predict storm characteristics. The multivariate time series data represent historical measurements of wind speed, temperature, pressure, humidity, and wave height from 1 January 1996 to 31 May 2021, in addition to the target variable to be predicted, which is in the range of 0 and 1, where 1 represents the occurrence of a storm and 0 indicates that there is no storm. This time series data was divided into training and testing at the ratio of 80 to 20%; 80% for training and 20% for testing the model's performance. In this study, there were two classes; storms and no storms.
(Lu et al. , 2020)  also found that the number of identified cyclones increased by 8. 29% compared to the results obtained by the traditional method. Consequently, we also examine the efficiency of an artificial intelligence approach in the context of storm prediction. Therefore, this study targeted the building of time series prediction models to predict storm occurrence and characteristics, which could be used globally to mitigate the disastrous consequences of these extreme events by taking the necessary precautions. The paper's main contribution is applying a new artificial intelligence approach using two of the most advanced machine learning and deep learning algorithms, Support Vector Machine (SVM) and Gated Recurrent Unit (GRU) Neural Network. Experiments were carried out using a database of storm events in the western regions of France over the past 25 years and buoy data representing historical measurements of wave height, wind speed, temperature, pressure, and humidity since 1996.
As a first step, the GRU model was used to predict the different characteristics of storms (wind speed, pressure, humidity, temperature, and wave height). Each storm characteristic was predicted separately, and a GRU model was created for each of them based on past data. (Khosravi et al. , 2018 ) also developed a time-series prediction model using machine learning algorithms to predict wind speed over Brazil based on past measured wind speed data values. The results demonstrated that the adopted method could successfully predict wind speed with high accuracy. Wind speed prediction is one of the most critical aspects of storm events in recent years. (Yang et al. , 2017 (Yang et al. , , 2019) )  have recently proposed the Bayesian linear regression to predict wind speed for storms that impacted the northeast United States based on a database composed of historical storms.
Metal Additives Adding nano-or micron-sized particles to the solid fuel grain can significantly increase the performance of the hybrid engine. While the additives do not directly increase the regression rate, in general, additives in the fuel increase the combustion temperature, specific impulse, density and introduce a more dominant radiative heat transfer. Because of the aforementioned effects, the regression rate increases  [33, 34]. The US Air Force Sandpiper Target Missile and Lockheed Martin's HYSER program are prominent examples of flight-tested engines with additives  [34]. The list of potential metal candidates as additives is very extensive and out of the scope for this review. For a more detailed overview, the reader is referred to Risha et al. [34]  or more recently Karabeyoglu  [33].
Northwards, near the Ancón and Pacoa area (see  Aizprua et al. , [2019]  for a more detailed description),  Alemán et al. , [2021]  present a structural map (Fig. S-2 ) suggesting a variety of shortening directions. They conclude on a generalized N-S vergence by combining: 1) a map resulting from an over-interpreted seismic dataset, considering the low quality of the seismic profiles across this area (see seismic profiles presented by  Aizprua et al. , [2019] ); and 2) a fault kinematic analysis along the Estancia Hills, which is an area very likely subject to a localized deformation, as we discuss in the next section. In our opinion, proposing a model based on the kinematic analysis along the Estancia Hills and associating it to an area bounded to the south and north by the westward extension of the suggested "Rodeo" and "Dorado" Riedels, respectively (Fig.
, 2019 ; Figure  14-A ]. The resulting local geometry, following the re-establishment of a stable subduction system, might have been subject to a localized strain partitioning south of the CCH. A composed structural map indicating the main lineaments observed in the area based on seismic data (black) and a vertical derivative of the gravity anomaly (blue) are shown in Fig. 1-B. The strain partitioning across the SEH is evident by the existence of folded structures oriented NNW-SSE (red), with large variations near the La Cruz Fault  [Alemán et al. , 2021] ; together with transtensional structures along the Carrizal Fault System (CFS), (Figs. 1  and 2 ). Towards the complex onshore sector of the SEH, partly discussed in  Aizprua et al. , [2019]. We can observe a clear truncation of the gravity anomalies and an apparent displacement that suggest a sinistral movement around the western side of the Peninsula (near the Ancs.
Chronological and petrographical data Another issue throughout the article is when referring to the Zapotal formation,  Alemán et al. , [2021]  do not cite the source of the U-Pb detrital zircon ages. To mention some examples, we found the following: "… toward the top U/Pb detrital zircon from an ashfall tuff unit interbedded with conglomeratic sandstones reported 14. 3 Ma suggesting an important diachronous deposition"; or a bit further "Detrital zircons ages (32. 5 Ma to 14. 3 Ma) for this section indicate broader age than reported by  Witt et al. , [2019b] ". Moreover, the youngest age (ca. 14 Ma) shown in figure  S3 , which claim to compile 222 zircon grains dating from 5 ashfall tuffs from the Zapotal Formation, lack to indicate the source(s) for these analyses, like who measured these ages? Furthermore, key information is lacking in their analyses, for instance: 1) What type of ages are reported (206 Pb/238U ages? 207Pb/206 Pb ages? Something else?); 2) Were these rare (apparently less than 5) 14 Ma old analyses concordant? If so, how many grains were yielding the same age within error? Detrital zircon geochronology requires to be very rigorous when interpreting the dataset (see for example  Gehrels [2014 ]Gehrels [2014] ).
Acknowledgements The authors wish to thank  editor-in-chief Taylor Schildgen from Tectonics  and reviewer  Adrian Pfiffner  for their suggestions to improve the quality of an unpublished manuscript intended to Reply to a comment (unpublished) from Alemán et al. to Aizprua et al. , [2019]. We are also thankful to Editor-in-Chief  Dr. Andrés Folguera  for reviewing the final version of the manuscript and for incentivizing the scientific discussion. Most of the data used for this study is accessible at the  NTNU Open Research data repository  (https://doi. org/10. 18710/BYZIZX).
This simplification is helpful in implementing the equations set into a fire propagation simulator or a coupled fire-atmosphere simulator. It needs a complete change in the structure of the model and the predicted ROS was tested at the field scale against 179 shrubland fires (burnt in Australia, South Africa, Turkey, Portugal, Spain, New Zealand) and 178 Australian grassland fires with a very good agreement with the observed ROS. Two statistical tools are used to check this agreement (Normalized Mean Square Error, NMSE and Mean Absolute Percentage Error, MAPE) and the Fractional Bias (FB) aims at understanding when the model over-predicts or under-predicts the ROS. The proposed model is accurate and its model parameters are calibrated against a small training dataset which makes it fully predictive whatever the environmental and topographic conditions and the fuel bed characteristics.
It consists in a set of non-linear algebraic equations depending on the triangle of fire (fuel, wind and slope) and built over three heat transfer mechanisms: (1) radiation from the flame base, (2) radiation from the free flame and (3) convection inside the vegetal stratum. The model parameters are calibrated upon a small training dataset of experimental fires found in the literature. The Balbi model is physics-based, fully predictive and faster than real time; it is then designed for an implementation in decision making tools for people involved in firefighting or in fire management. This work deals with a change in the structure of the Balbi model in order to obtain a brand-new version which aims at simplifying the resolution method for an easier implementation. The predicted ROS is compared to the ROS measured in 179 shrubland fires and 178 grassland fires using some usual statistical tools: (1) Normalized Mean Square Error (NMSE) and Mean Absolute Percentage Error (MAPE) to measure the model's accuracy and (2) Fractional Bias (FB) to check if the observed ROS is overestimated or underestimated.
Depending on the objectives set, generic questionnaires (quality of hospital catering services, evaluation of hygiene measures or reception on arrival in the establishment) or questionnaires that are specific to certain care pathways or pathologies are used. The Picker Institute Europe database offers validated questionnaires covering seven general themes (sharing of information, coordination of care, physical comfort during hospitalisation, emotional support and respect, respect for patients' preferences, involvement of friends and family, continuity of care) as well as tools specific to certain pathways such as the transition to adult care or pathologies such as sickle-cell anaemia. 9 However, various criticisms have emerged about these tools, particularly in the case of chronic diseases.
Acknowledgments to: •  Kathy Sabadosa ,  Karen Homa ,  Pamela Mertz  from the  US Cystic Fibrosis Foundation  for their support in the preparation of the study, their participation in project meetings and discussions about the results from the  US Patient and Family Experience survey. • Vaincre la Mucoviscidose  for their support, including financial subsidies and their contribution to the recruitment of patients and parents as co-researchers.
• Funding was provided by the  Institute of Research in Public Health (IReSP)  for the entire project in the frame of the call for project " AAP General 2018 " ( Decision IReSP   16/07/2019 ):  €197 , 320. • Funding was provided by the patient association  Vaincre la Mucoviscidose (VLM)  for the training of patient and parent co-researchers (Decision  VLM 22/06/2019 ):  €9 , 360. PL is responsible for the project and participated in writing the manuscript. DPB is the study coordinator and was a major contributor in the manuscript. , CF, MS, FM conducted interviews with the study participants. All authors read and approved the final manuscript.
Acknowledgements The authors wish to thank all the interviewees who accepted to participate in this work. The authors express their gratitude to the referees for their insightful comments. Funding: The data collection, analysis conducted and drafting of this paper was made in the course of the  CoCliServ  project. Project  CoCliServ  is part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate , with co-funding by the  European Union  (Grant  690462 ). Further worktime has been funded through the  RREFlood  project. The  RREFlood  project is funded through the  Belmont Forum International Opportunity Fund Pathways 2020 call.
The Observatoire de Haute Provence (OHP) weekly electrochemical concentration cell (ECC) ozonesonde data have been homogenized for the period 1991-2021 according to the recommendations of the Ozonesonde Data Quality Assessment (O3S-DQA) panel. The assessment of the ECC homogenization benefit has been carried out using comparisons with other ozone-measuring ground-based instruments at the same station (lidar, surface measurements) and with colocated satellite observations of the O 3 vertical profile by Microwave Limb Sounder (MLS). The major differences between uncorrected and homogenized ECC data are related to a change of ozonesonde type in 1997, removal of the pressure dependency of the ECC background current and correction of internal pump temperature. The original 3-4 ppbv positive bias between ECC and lidar in the troposphere is corrected with the homogenization.
, 2017). This correction is larger than -10 % at altitudes above 30 km and on the order of -4 % in the troposphere. -When I b > 0. 1 µA (less than 6 % of the dataset), I b is replaced by 0. 05 µA, the average of the measured background current for our dataset, while the uncertainty of I b becomes 0. 1 µA. -The pressure dependency of the background current was removed for the homogenized version since the O 2 concentration does not play a significant role in the residual current when ozone is removed  (Thornton and Niazy, 1983; Vömel and Diaz, 2010). -No vertical smoothing of the ozone partial pressure. Smoothing over 100 m was applied in the uncorrected data, i. e. , before homogenization. -Correction of measured T i to account for changes in the position of the thermistor and for differences with the true air sample temperature (the thermistor was taped to the pump before July 2007 and inserted in the pump hole since that time).
The removal of the pressure dependency of the background current leads to significant relative differences (> 5 %) in the upper troposphere where the ECC current is smallest. In February 2004, the UHF receiver and ground calibration tools were changed, and a new processing software, STRATO, developed by Holger Vömel at NOAA (https: //cires1. colorado. edu/~voemel/strato/strato. html, last access: 9 May 2022) was implemented for the O 3 partial pressure retrieval. The raw ECC current data files for between February 2004 and July 2007 no longer exist, and the ECC currents have been retrieved from the uncorrected ozone partial pressure using Eq. (  1 ). The recorded lab temperature and relative humidity are used for the pump flow rate correction of ozonesondes launched after June 1999  (except in 2002 and 2003) ; for the other dates, the monthly means of the lab temperature and relative humidity terms in the pump flow rate equation  (Smit et al.
The UV-visible SAOZ (Système d'Analyse par Observation Zénithale) and the UV Dobson spectrophotometer total ozone column (TOC) measurements are available at OHP. The Dobson spectrophotometer was used from 1991 to 2004, and SAOZ data were used from 2004 up to now. The socalled normalization factor (N T ) is calculated as the ratio of the spectrophotometer TOC and the ECC TOC. Following the methodology used in  Smit and Thompson (2021)  or  Stauffer et al. (2020) , the TOC corresponding to the ECC soundings is calculated using the integration of the ozone concentrations up to 10 hPa or the burst altitude, provided it is higher than 25 km. The residual ozone above 10 hPa or the burst altitude has been calculated using the monthly mean climatology of  McPeters and Labow (2012)  at pressures smaller than 10 hPa.
Data and homogenization assessment In this work, the benefit from homogenization of the ECC ozonesonde time series is assessed by comparison of homogenized and non-homogenized ECC ozone concentrations with other ozone measurements carried out at OHP. First, these comparisons are made as a function of altitude using either Ultraviolet DIfferential Absorption Lidar (UV-DIAL) or Microwave Limb Sounder (MLS) satellite observations in the stratosphere  (Froidevaux et al. , 2008). Two UV-DIAL instruments have been operating at OHP. The first one, LiO3St, is optimized for stratospheric O 3 profiling between 10 and 50 km a. s. l. using an absorbed wavelength at 308 nm and a reference wavelength at 355 nm  (Godin-Beekmann et al. , 2002). The second one, LiO3Tr, is optimized for tropospheric ozone monitoring between 2.
The time distribution of the number of lidar profiles per year for optimal comparison with ECC is shown for LiO3Tr and LiO3St in Fig. 3. The 2005-2021 stratospheric O 3 profiles from AURA MLS v5 level-2 files have been also retrieved from 56. 23 to 6. 81 hPa with a vertical resolution on the order of 2 km at these levels  (Schwartz et al. , 2020). The overpass criteria are ±5 • latitude and ±8 • longitude, and all MLS profiles meeting this distance criteria within 1 d of the sonde are averaged to make the comparison with the ozonesonde. Although the spatiotemporal differences between ECC soundings and satellite overpasses will be greater using these criteria, we obtain many more comparisons than by restricting ourselves to nighttime soundings. For the sake of a more complete discussion of the two types of comparisons made in the stratosphere, we also considered a lidar dataset of 366 profiles from 2005 to 2021 with less restrictive measurement time difference with the ECC launches (< 12 h).
Such a criterion is valid as long as the rapid O 3 variations typically encountered below 18 km are not present. Secondly, comparisons of total ozone column (TOC) are also useful to check the benefit of the homogenization in the stratosphere. In addition to the TOC provided by the OHP spectrophotometer  (Hendrick et al. , 2011; Van Roozendael et al. , 1998) , satellite TOC level-2 (L2) overpass measurements by AURA Ozone Monitoring Instrument (OMI)  (Bhartia, 2012) , Suomi-NPP Ozone Mapping and Profiler Suite (OMPS)  (Jaross, 2017) , and Global Ozone Monitoring Experiment (GOME) 2A and B have been selected when they are within 12 h of the ozonesonde and selecting the closest pixel in time and space to the ozonesonde station. The vast majority of L2 TOC data are within 100 km of OHP. The corresponding ECC sounding TOC used for the satellite comparison is calculated using the integration of the ozone concentrations up to 10 hPa and the McPeters and Labow (2012) climatology above 10 hPa.
Thirdly, the benefit of homogenization on long-term ozone trends for several altitude ranges in the troposphere and the stratosphere has been studied using all the lidar and ECC measurements made at OHP. The lidar monitoring period is indeed as long as the ozonesonde dataset and includes the major ozonesonde preparation or ozonesonde type changes in  1997, 2004 and 2007. Only simple linear trends of the ozone concentrations corrected for the mean seasonal variation at OHP will be considered in this study. The trend uncertainties are calculated using the 95 % confidence limit of the slope of the linear regression, assuming that the residuals  are not correlated for weekly (ECC) or 2/3 per week (lidar) observations. A more comprehensive trend analysis for the OHP would need either a multiple linear regression model as described in  Nair et al.
(2013)  or  Thompson et al. (2021)  for the stratosphere or a statistical regularization method as described in  Chang et al. (2020)  for the troposphere when data sampling is sparse. For the stratospheric trend, the period 1990-1995 is removed to minimize interferences by the eruption of Mt. Pinatubo. Lidar and satellite ozone observations cannot be retrieved in the lowest atmospheric layer below 2 km a. s. l. ; O 3 surface observations are then included using measurements of a TEI-49C instrument with an air intake on the roof of the lidar building. Data are recorded continuously since December 1997, except between June 2010 and August 2012 when the responsibility for surface ozone measurements was entrusted to the ATMOS-SUD air quality network at the same location. Data between July 2002 and July 2003 have also been removed because of a contamination problem in the air intake.
Tropospheric trend In this section, the interannual variation of homogenized and uncorrected ECC ozone data is compared in the free troposphere for three layers of 2 km thickness at 5 and 7 km and just below the dynamical tropopause taken at 2 Potential Vorticity units (PVu) (Fig. 11 ). The three layers were selected in order to compare the ozone trends of the ECC sondes with those of the LiO3tr lidar. The altitude Z tp of the dynamical tropopause is calculated using ECMWF meteorological analysis with 1 • horizontal resolution and 137 vertical levels. The mean value of Z tp is 10. 5 km at OHP (10 km in winter and 11. 5 km in summer), so the upper layer approximately corresponds to the 8-10 km altitude range. As for the surface trend retrieval, the mean ozone concentrations of the layers are deseasonalized before calculating the trends of mixing ratios from the regression lines across all the 2 km ozone mixing ratio averages available in the 30year database.
Conclusions The 30-year ozone dataset from weekly ECC ozone soundings has been homogenized according to the recommendations of  Smit et al. (2012). The major changes are related to the change of ECC manufacturer in 1997 (SPC-SST 1 % to EnSci-SST 1 %), the background current and internal sonde Table  1. Tropospheric trends and their confidence limits for lidar, uncorrected ECC and homogenized ECC. The last column corresponds to the 2 km layer just below the altitude of the dynamical tropopause (Z tp ). Altitude range 4 to 6 km 6 to 8 km Z tp -2 km to Z tp LiO3Tr, ppbv yr -1 0. 10 ± 0. 05 0. 11 ± 0. 06 0. 31 ± 0. 09 Uncorrected ECC, ppbv yr -1 0. 16 ± 0. 05 0. 24 ± 0. 08 0. 44 ± 0. 08 Homogenized ECC, ppbv yr -1 0. 07 ± 0. 05 0. 14 ± 0. 08 0. 32 ± 0. 08 temperature corrections. The assessment of the OHP ECC homogenization benefit has been carried out using comparisons with ground-based instruments located at the same station (lidar, surface measurements) and satellite overpass observations (MLS in the stratosphere and GOME/OMI/OMPS for the total ozone column, TOC).