dataesr/scientific-paragraphs-categorization huggingface dataset:
The case The main element of the application will be the case where all the relevant information of the patient, the diagnosis, and the treatment will be registered and recorded in the application database. Particularly, a case will be composed of five blocks: • Patient data. This block will contain a form with different fields to enter the relevant data. To describe the fields for each one of them the information that has to be entered by the user is presented in brackets. These fields are: the patient's name and surname (text); the birth date (date); the medical identifier (number); the EMS identifier assigned when the EMS takes the patient control of (number), and the patient sex (choose option). Note that depending on the status of the patient it may be difficult to fill some of the fields.
• Diagnostic. This block will contain the information of the first diagnosis. This will be entered in a form with the following fields: the image of the pre-hospital electrocardiogram (image); the place from where the first contact has been done (choose option from home or public space, primary care, or hospital with no hemodynamic unit); time of electrocardiogram diagnostic (date); the infarct location (choose option from anterior, posterior, inferior, lateral or no located); the Killip level at admission (choose option from I to V to classify the heart failure degree); previous bypass surgery (mark option); and sudden death (mark option). In this block there will also be an access to the chat of the case where the personnel involved in the case can text. The text of this chat will be recorded in the application database with the case.
The case management Since the case is the key element of the application, the provided actions will be related to the cases. To manage the cases the following actions will be supported (see Fig. 2 ): • Open case. This action will create and register a new case in the application database. To enter the information of the case, as it was described, different forms will be presented to enter patient data, treatment, and diagnosis. This Open action will be carried out by personnel from the primary care center, from the hospital with no catheterization laboratory, or from the EMS. The user that opens the case will have the control of the case until another user takes it off. At each moment, only one owner of the case will be possible. • List cases. This action will list the cases where the user has participated and also the unfollowed cases.
; take the control; consult the information; and participate in the chat. Finally, if the case is closed the user will be able to consult the information of the case. Fig. 3. The three-level architecture of ODISEA application with its main components. Fig. 4. The schema for the ODISEA data base. • Take Control. This action will give the control of the case to another user. Remind that there will be a unique owner of the case, the unique that will be able to modify it. • Close case. This action will be carried out by the cardiologist from the catheterization laboratory of the reference hospital to close the case. In this case the final report of the case will also be generated. In addition, the cardiologist and nurses from the reference hospital can set the alarm to know when the patient is at some minutes far from the hospital.
The second level is the core of the application where the modules to manage the cases, and the data, the treatment, and the diagnosis information of the patient are codified. In addition. there are the modules to control the GPS satellite to indicate at every moment the position of the patient. There is also a report manager to control the different reports that can be generated. Note that the GPS is controlled via an external module that provides the Openroute Service [14] via an https connection. The third level maintains all the data of the application in a database. The schema of this database is illustrated in Fig. 4. The main tables of the database are: the User table which registers all the users that can access to the application. The users are classified according to their profile that determines the functions that they can carry out.
Pilot trial and application evaluation The first prototype, which supports Catalan, Spanish and English languages, has been tested simulating different cases and situations to ensure the robustness and the correct performance of the application and to guarantee that all identified requirements are met. Experts covering all the profiles involved in the STEMI procedure have participated in this test phase. Once the correct performance has been proved the deployment and pilot trial in a real scenario has started. ODISEA was deployed in a pilot trial conducted in Girona's health area. This has six hospitals without catheterization laboratory, a Medical Emergency System to transfer patients, and one reference hospital capable to perform primary angioplasty, the Hospital Universitari de Girona Dr.
Results. ODISEA app screenshots of English version corresponding to a simulated case are presented in Fig. 5. From left to right, Image (a) is the identification screen where the user has to enter name, password and select the language. Previously, the administrator has registered and assigned a profile to this user. Image (b) illustrates a map with active cases and its locations on a map. For each case, the application shows the code and for cases that are on road the distance in minutes to the hospital. A code of colors is used to show followed cases (in blue) and the unfollowed ones (in red). When the user selectsa case by clicking on the map the menu of Image (c) to access case options appears. Images (d) and (e) correspond to information of the patients and the treatment, respectively.
Conclusions and future work ODISEA is a new application created to complement the infarct code protocol of our country. It has been tested in a pilot study and it has been seen that professionals, specially from the hospitals with no catheterization laboratory, feel more confident with the application. Although an evaluation with more patients and professionals is required the first obtained results are very satisfactory intervention times are reduced. • Professionals, specially from the hospitals with no catheterization laboratory, feel more confident with the support of mobile applications.
In Figure  1 , we can see that this criterion divides our star sample into one larger accreted group and two smaller possible in situ groups in the [Fe/ H]-[Mg/Fe] panel. The accreted group has a clear sausage-like pattern in the v rv f diagram. Values of [Al/Fe], [Mg/Fe], and S/N are provided by a value added catalog of the LAMOST survey using a deep convolutional neural network  (Li et al. 2022)  The orbits are integrated by a python package galpy and a Milky Way potential MWPotential2014  (Bovy 2015). We use a bigger dark matter halo mass of M vir = 1. 0 × 10 12 M e rather than 0. 8 × 10 12 M e. In Galactocentric Cartesian coordinates, we use the values of the solar Galactocentric distance r gc,e = 8. 122 kpc  (Gravity Collaboration et al. 2018) , and height Z e = 20. 8 pc  (Bennett & Bovy 2019).
We adopt a solar motion of (+12. 9, +245. 6, +7. 78) km s -1  (Reid & Brunthaler 2004; Drimmel & Poggio 2018; Gravity Collaboration et al. 2018). The uncertainties in distance, line-of-sight radial velocity, and proper motion (including the measurement error and covariance of proper motions) are propagated using Monte Carlo sampling in order to estimate the median and standard error of the orbital period P. We remove stars with large uncertainties in the orbit integration by requiring ΔP/ P < 0. 3. The final data sample contains 5949 GSE-related K giants. For each star, we integrate 1 Gyr and store 1000 particles at all equal time intervals. Following  Liu et al. (2017) , the selection function S in which the probability of a K giant being included in the observation is given by their Galactic coordinates (l, b), colors (c), and apparent magnitudes (m).
2014; Hattori et al. 2016 Hattori et al. , 2019)). A recent simulation work of  Davies et al. (2023a)  showed that a rotating bar could accelerate the phase mixing of these chevron-like substructures found in  Belokurov et al. (2023). We add a steadily rotating bar to the modified MWPoten-tial2014 with M 200 = 10 12 M e by a python package Agama  (Vasiliev 2019). To represent the bar, we choose a model following  Chiba & Schönrich (2022)  as r t r m t , , , sin cos , 1 b b r 2 b ( ) () ( ) ( ) q f q f F =F -W where (r, θ, f) are the spherical coordinates and m = 2 is set to only consider the quadrupole term. The radial dependence of the bar potential Φ br is ⎜ ⎟ ⎜ ⎟ ⎛ ⎝ ⎞ ⎠ ⎛ ⎝ ⎞ ⎠ r Av r r b b r r 2 1 , 2 br c 2 CR 2 CR 5 ) ( ( ) F =- + + where A is the strength of the bar, v c is the circular velocity in the solar vicinity, and b is the ratio between the bar scale length and the value of corotation radius r CR.
Acknowledgments This study is supported by the  National Natural Science Foundation of China  under grant Nos. 11988101 ,  11890694 ,  11873052 ,  12273027 , the  National Key R&D Program of China  No. 2019YFA0405500 , and the  International Partnership Program of CAS  Grant No. 178GJHZ2022040GC. W. W. and X. Y. acknowledge the support from the  China Scholarship Council. We thank the anonymous reviewer for the valuable comments. We thank  Prof. Rafael Rebolo ,  Prof. Carlos Allende ,  Dr. Jonay Gonzalez ,  Dr. Wenyu Xin , and  Dr. Zhicun Liu  for useful discussion. Guoshoujing Telescope (the Large Sky Area Multi-Object  Fiber Spectroscopic Telescope LAMOST ) is a National Major Scientific Project built by the  Chinese Academy of Sciences. Funding for the project has been provided by the  National Development and Reform Commission.
, 2009; Cusack et al. , 2012; in 't Veld et al. , 2023; in 't Veld et al. , 2021; Pandolfi et al. , 2016; Pandolfi et al. , 2014; Pérez et al. , 2008; Pey et al. , 2009; Querol et al. , 2014 , Querol et al. , 2004a, b; Ripoll et al. , 2015). At both measurement sites, an aethalometer (Magee Scientific, USA, model AE33) was used to quantify the aerosol light absorption coefficient at seven different wavelengths  (370, 470, 520, 590, 660, 880, and 950 nm)  and the mass concentration of black carbon (BC)  (Drinovec et al. , 2015). Additionally, the AQMN used instrumentation to measure hourly concentrations of O 3 , CO, NO, NO 2 , and NO x (Table  S6 ). The results of the gas data are shown in Fig. 2  and discussed in Section 2 of the SI. Finally, ambient temperature and solar radiation were measured at the station in MSY, and at the Department of Physics building of the Barcelona University for the BCN station.
, 2003; Dunne et al. , 2018; Ionicon Analytik GmbH, 2014; Warneke et al. , 2011). The parameters of each PTR-MS are described in Table  1. For the 2022 measurements, a periodic calibration was made during the measurements, with automatic blanks taken every hour for MSY and every 6 h for BCN. For the measurement in 2017, a calibration was performed at the start of the measurement. Since a blank measurement was missing for the measurement period, an approximation of the blank was made using blanks from the previous campaign using the same apparatus. Table  1  presents the measurement periods at both stations, the apparatus used, and the measurement frequency. The periods were selected as approximately 1 month in duration when data were available at both BCN and MSY. Two gravimetrically prepared multicomponent standards created by Apel-Riemer (Apel-Riemer Environmental Inc.
, Miami, USA) and Restek (Restek Corporation, Bellefonte, PA, USA) contained all VOC compounds discussed here, which were humidity-dependent calibrated at various dilution steps. These calibrations used bubbled zero air to dilute the standards and were regulated as closely as possible to the ambient humidity. A list of the compounds is presented in Table  2. Notably, since PTR-MS measures the exact masses of compounds, it is not compound- specific since compounds with the same masses may interfere with the measured signal. The compounds were assigned to the masses based on their exact mass, the compounds included in the calibration canister, the GLOVOCs database  (Yáñez-Serrano et al. , 2021b) , and previous measurements in the study area  (Peñuelas et al. , 2009; Seco et al. , 2013; Seco et al.
PMF source apportionment The source apportionment analysis was performed on the VOC dataset via PMF using the U. S. Environmental Protection Agency's PMF v5. 0 software  (Norris et al. , 2014). PMF is a multivariate factor analysis tool that decomposes time trends in chemical composition into factor contributions and chemical profiles by performing chemical mass balance between measured species concentrations and the sum of source contributions for those species: X ij = ∑ p k=1 g ik *f kj + e ij Here, X is the data matrix with i number of samples and j number of chemical species. p is the number of sources, f is the chemical profile of each source with mass contribution g, and e ij is the residual for each sample. To obtain better factor profiles, certain VOCs were set as "weak" species, which means that the uncertainty is tripled.
These species were selected based on the signal-to-noise ratio (0. 5 > S/N > 1. 5) and visual anomalies in the factor profiles. Strong species included acetone, isoprene, benzene, toluene, and monoterpenes. Weak species included methanol, acetonitrile, acetaldehyde, MVK + MACR, methyl ethyl ketone (MEK), and C 8 aromatics. In this study, a multisite solution was used by aggregating the BCN and MSY data into a single dataset. The main benefits of running a multisite PMF were the inclusion of a larger dataset when compared to separate single-site PMF models. A multisite PMF produces more robust results and can obtain source profiles common to both sites, thus allowing a direct comparison between the two stations. This has not been previously done for VOC measurements in the study area but has been successfully applied to the chemical speciation of particulate matter  (in 't Veld et al.
Source apportionment To determine the sources of all VOCs, we performed a multisite PMF source apportionment. The model was applied to an aggregated dataset from both the summer and winter periods of both stations. This made it possible to compare source profiles between the different seasons and stations. The source apportionment analysis identified five factors (Table  4 ), with the profiles presented in Fig. 4. For ease of interpretation, all factor names are written in italics in this paper. The optimal number of sources was selected by inspecting the Q values, residuals, G space plots, and physical meanings of the factors. To confirm the optimal factor profiles of the PMF model, the data were also bootstrapped. Each dataset was bootstrapped 100 times with a minimum correlation Rvalue of 0.
, 2016; Viana et al. , 2013). Notably, its contribution remains significant since a study conducted during winter 2011 at BCN attributed 8 % of the PM 2. 5 mass to biomass burning  (Reche et al. , 2012). At MSY, the peaks associated with anthropogenic traffic emissions were absent due to the distance from their source, with the mixing ratios of this factor being mostly even throughout the day. During summer, the diel variation of Factor 2 showed a peak at approximately 18:00. (Fig. 7 ); however, this was due to a biomass-burning event observed on the 13th of June. The correlation with BC further indicates that this peak was caused by a biomass burning event, with a correlation of r = 0. 66 for the entire dataset of Factor 2 during summer, while the traffic & industry factor (Factor 1) did not correlate with either this factor or the BC levels.
The isoprene oxidation factor was previously identified by  Yáñez-Serrano et al. (2021a)  in Montseny as a biogenic source along with monoterpenes. While monoterpenes are mostly of biogenic origin, isoprene is not necessarily biogenic. Combining the datasets of the two stations (i. e. , BCN and MSY) separated this biogenic source between the isoprene oxidation factor (Factor 4) and the monoterpene factor (Factor 5). The isoprene oxidation factor is mostly biogenic at MSY, with high levels in the summer and a clear correlation with temperature (Fig. S3 ). Meanwhile, at BCN, an additional anthropogenic source of isoprene exists, especially during winter, as shown by the diel variation and correlation with other anthropogenic contaminants (Fig. 10 ; Fig. S1 ).
Conclusions The study presents the first urban VOC source apportionment in Spain, and also shows the possibility of using a multisite VOC PMF to use as an comparison between an urban background station and rural background station to determine the most significant contributors to O 3 and SOA formation. In this study, we quantified 11 VOC mixing ratios during summer and winter at both the BCN urban background site and the MSY rural background site (Table  5 ). Applying PMF to the combined dataset revealed five factors (Table  6 ). The first factor, traffic & industries (Factor 1), originated from anthropogenic sources such as traffic and industry in and around Barcelona, mostly consisting of aged air masses at MSY. The second factor, traffic & biomass burning (Factor 2), represented a secondary source combining traffic emissions and biomass burning.
Acknowledgments This study was supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  101036245  ( RI-URBANS ), the  "Agencia Estatal de Investigación " from the  Spanish Ministry of Science and Innovation, FEDER  funds under the projects  CAIAC  ( PID2019-108990RB-I00 ), the  Generalitat de Catalunya  ( AGAUR 2021 SGR 00447 ), and the  Direcció General de Territori. AMYS  acknowledges a  Ramón y Cajal  grant ( RYC2021-032519-I ) and the  La Caixa Foundation Junior Leader retaining  fellowship. RS acknowledges a  Ramón y Cajal  grant ( RYC2020-029216-I ) funded by  MCIN/AEI/ 10. 13039/501100011033  and by " ESF Investing  in your future". IDAEA-CSIC  is a  Severo Ochoa Centre of Research Excellence (MCIN/AEI , Project  CEX2018-000794-S ). The authors thank the  MASSALYA instrumental platform (Aix Marseille Université , lce.
Aims. We present the Rotation code Using Barotropy conservation over Isopotential Surfaces (RUBIS), a fully Python-based centrifugal deformation program that is available publicly. The code has been designed to calculate the centrifugal deformation of stars and planets resulting from a given cylindrical rotation profile, starting from a spherically symmetric non-rotating model. Methods. The underlying assumption in RUBIS is that the relation between density and pressure is preserved during the deformation process. This leads to many procedural simplifications. For instance, RUBIS only needs to solve Poisson's equation in either spheroidal or spherical coordinates, depending on whether the 1D model has discontinuities. Results. We present the benefits of using RUBIS to deform polytropic models and more complex barotropic structures, thus providing insights into baroclinic models to a certain extent.
Hence, solving the hydrostatic equilibrium amounts to finding the lines of constant total potential and redistributing the matter so that the density is constant along these lines. A drawback of the SCF method is that the energy equation is only solved along horizontal averages rather than locally. To overcome this difficulty, the Evolution STEllaire en Rotation code (ESTER) was developed  (Espinosa Lara & Rieutord 2013; Rieutord et al. 2016). As a result of solving the energy equation locally, the rotation profile (which is calculated along with the stellar structure) is no longer conservative, and the stellar structure is baroclinic, that is, isodensity and isobars are now free to differ. Currently, neither code is capable of carrying out stellar evolution, and the codes instead calculate static models.
Manchon (2021)  developed a method that is more elaborate because it calculates the centrifugal deformation for CESTAM models, but then feeds the information from the 2D structure back into the 1D formalism using the approach described in  Mathis & Zahn (2004). Such a procedure could then be applied at each time step when the evolution of a star is calculated, and a greater degree of realism can be achieved in this way. In this article, we wish to develop a method analogous to that of  Roxburgh (2006)  but that is simpler. In particular, we wish to avoid having to reuse the equation of state to recalculate the Γ 1 profile. The resulting program, the Rotation code Using Barotropy conservation over Isopotential Surfaces (RUBIS), achieves this by preserving the relation between density and pressure rather than density and radius when going from the 1D to the 2D structure, as we describe below.
Hence, the equation of state is automatically satisfied throughout the model because thermodynamic quantities are simply carried over from the 1D case. With this approach, deforming a 1D polytropic structure, for instance, leads to a 2D polytropic structure, unlike what would happen with the approach in  Roxburgh (2006). Finally, we wish to make this approach applicable to stars and planets, which may include density discontinuities. Models with discontinuities require applying a different strategy when solving Poisson's equation, as we explain below. As was the case with the approach developed in  Roxburgh (2006) , RUBIS does not take the energy conservation equation into account. Hence, the models it deforms will only be suitable for adiabatic pulsation calculations. For full non-adiabatic calculations, models such as those from the ESTER code should be used instead, in which the hydrostatic structure and the energy equation are solved in a full 2D context.
Using spherical coordinates Although the first approach may sound more complicated because of the additional interpolation step, it is in fact more efficient. When the matter distribution is expressed using spherical coordinates, Poisson's equation becomes separable with respect to the spherical harmonic basis. This allows it to be solved efficiently. The interpolation of the density profile onto the spherical coordinate system is carried out using cubic splines along each latitude with the SciPy sub-package scipy. interpolate 3. Afterwards, the density profile is decomposed using the spherical harmonic basis as follows: ρ(r, θ, ϕ) = ∞ =0 m=- ρ m (r)Y m (θ, ϕ), (7) where ρ m (r) = 4π ρ(r, θ, ϕ) Y m (θ, ϕ) * sin θdθdϕ, (8) and where represents the harmonic degree, m the azimuthal order, Y m the corresponding spherical harmonic, and (•) * the complex conjugate.
A high zoom level is necessary to reveal some thickness in this point distribution, resulting from angular differences. It thus highlights the relevance of assuming the existence of such a relation even to approximate more realistic cases, where energy transfer is taken into account. Moreover, this relation does not seem to be just any relation: the (ρ, P) pairs almost perfectly overlap with the ρ(P) relation of the non-rotating model. This observation is central and constitutes a major motivation for developing a code such as RUBIS. A closer inspection of the two structures reveals some differences, as we discuss below. A drawback of Fig. 8  is that it does not reveal the regions of the star in which potential structural differences may arise. In order to compare the structures, they need to be interpolated to the same pressure values 8 , the problem being that the methods used by ESTER and RUBIS do not use the same convention when defining the pseudo-radial variable, ζ.