dataesr/scientific-paragraphs-categorization huggingface dataset:
The Mutation ++ library provides accurate and efficient computation of physicochemical properties associated with partially ionized gases in various degrees of thermal nonequilibrium. With v1. 0. 0, users can compute thermodynamic and transport properties, multiphase linearly-constrained equilibria, chemical production rates, energy transfer rates, and gas-surface interactions. The framework is based on an object-oriented design in C++, allowing users to plug-and-play various models, algorithms, and data as necessary. Mutation ++ is available open-source under the GNU Lesser General Public License v3. 0.
These include mixture thermodynamic and transport properties, species chemical production rates, and energy transfer rates. Each of these properties further depends on the selection of a variety of specialized algorithms and data, such as species partition functions, transport collision integrals, and reaction rate coefficients. The implementation, testing, and maintenance of the models, algorithms, and data required to simulate thermal nonequilibrium flows represent a significant cost, in terms of human resources and time necessary to develop a simulation tool. As new models, algorithms, or data become available, additional effort is required to update existing codes, especially when models are "hardcoded. " A number of commercial and academic software packages are available which provide gaseous properties, including CEA  [1] , EGlib  [2] , PEGASE  [3] , Chemkin  [4] , MUTATION  [5] , Cantera  [6] , and KAPPA  [7] , however, these libraries tend to focus on a specific application, a narrow range of collisional time-scales, or are specialized in providing only certain types of properties.
These observations have led to the desire to reduce the work necessary to implement new models and algorithms and centralize their development into a single software library which may be used by multiple CFD codes to maximize code reuse, testing, and open collaboration. This paper presents the Mutation ++ library, which has been developed to meet this objective. The library is designed with several goals in mind, including 1. provide accurate thermodynamic, transport, and chemical kinetic properties for multicomponent, partially ionized gases, 2. ensure the efficient evaluation of these properties using state-of-the-art, object-oriented algorithms and data structures in C++, 3. be easily extendable to incorporate new data or algorithms as they become available, 4. interface to any simulation tool based on the solution of conservation laws through a consistent and logical interface, 5.
use self-documenting database formats to decrease data transcription errors and increase readability, and 6. be open source to promote code and data sharing among different research communities. The latest version of Mutation ++ (v1. 0. 0) has recently been released open-source under the Lesser GNU Public License (LGPL v3) and is freely available on Github 3. In the remainder of the paper, we present an overview of the library and its impact on the research community to-date. In particular, the four main modules of the library -thermodynamics, transport, chemical kinetics, and gas-surface interaction -are presented, with a few examples to illustrate the library's use.
Software Architecture Mutation ++ is designed with a strong focus on Object-Oriented Programming (OOP) patterns in C++. The library's Application Programming Interface (API) is thoroughly documented using the Doxygen format. A continuous integration strategy has been employed. Regression and black box testing are performed through a combination of the Catch2 header-only testing framework and CTest. The primary access to the library is through a Mixture object, which is implemented as a set of submodules encapsulating clearly separated physical quantities as depicted in the simplified Unified Modeling Language (UML) diagram in Fig. 2.
Thermodynamics The thermodynamics module provides pure species and mixture thermodynamic quantities, such as enthalpy, entropy, specific heats, or Gibbs free energies. Mixture thermodynamic quantities are derived as sums of pure species properties, weighted by the composition of the mixture. Thermodynamic data for pure species can be found in several references  [9] [10] [11] [12] [13] [14] [15] [16] [17]. Differences exist between each database, such as their format, temperature range of applicability, or degree of nonequilibrium supported. Such differences often sway simulation tool designers to select a single database format to support, or hard-code thermodynamic data directly into their models. This approach makes it difficult to update data as needed, or compare with other tools using different databases.
The Mutation ++ framework provides an abstraction layer which enforces a weak coupling between the concrete thermodynamic database, used for any given set of species, and the computation of mixture thermodynamic quantities. Such a design provides the flexibility to swap out different databases as needed, with minimal effort. The NASA 7-and 9-coefficient polynomial databases  [18] [19] [20] [21]  and a custom XML format which implements a Rigid-Rotor/Harmonic-Oscillator (RRHO) model are currently implemented. The NASA format is widely used and provides thermodynamic properties for pure species in thermal equilibrium. The RRHO model is suitable for thermal nonequilibrium calculations. In addition, a new database including more than 1200 neutral and ionized species containing C, H, O, and N is shipped with the library in the NASA 9-coefficient format.
The details of this database have been published in  [22]. The user can specify the concrete thermodynamic model when creating a mixture (Fig. 3a ). A closely related task to the calculation of thermodynamic properties is the solution of chemical equilibrium compositions. The efficient and robust computation of multiphase, constrained equilibrium compositions is an important topic in several fields, including combustion, aerospace and (bio)chemical engineering, metallurgy, paper processes, and the design of thermal protection systems for atmospheric entry vehicles (e. g. ,  [23] [24] [25] [26] [27] [28] ). Several challenges associated with computing chemical equilibria make conventional methods hard to converge under some conditions  [1, [29] [30] [31] [32]. A new multiphase equilibrium solver, based on the single-phase Gibbs function continuation method  [33, 34] , has been developed specifically for Mutation ++.
enforces consistency of standard ratios. Just-in-time loading and efficient evaluation of this data is handled by a CollisionDB object, as shown in Fig. 2. The solution of the linear transport systems represents a significant CPU time for some CFD applications. Several algorithms have been proposed in the literature for reducing this cost  [42, [49] [50] [51] [52]. Mutation ++ provides plug-and-play transport algorithms through the use of selfregistering algorithm classes. For example, the abstract class ThermalConductivityAlgorithm, shown in Fig. 2 , provides the necessary interface that all thermal conductivity algorithms must include, namely functions for computing the thermal conductivity and thermal diffusion ratios. Specific algorithms are then implemented by creating a concrete class which implements the interface.
Gas-Surface Interactions The Gas-Surface Interaction (GSI) module provides surface boundary conditions for Eq. 1. They are obtained by applying the conservation of mass, momentum, and energy in a thin controlvolume on a surface at steady-state in the form (F g -F b ) • n = S s , (3) where F g and F b are gas and bulk phase fluxes, n is the surface normal, and S s is the source term associated with surface processes. Mutation ++ provides several built-in terms that can be mixed to create a model through a custom XML format, as shown in Fig. 3d. Once a model is specified, balance equations are created dynamically through an object-oriented approach forming a Surface object (Fig. 2 ). The resulting nonlinear equations are functions of the surface thermodynamic state (stored as Û = ρi ρe ρẽ m T in SurfaceState), thermochemical properties of the interface, and the two connecting phases (e.
Thermodynamic and Transport Properties Fig. 5  presents a selection of thermodynamic and transport properties computed by Mutation ++ for an 11-species, isobaric air mixture in thermochemical equilibrium. The equilibrium mole fractions and thermodynamic properties are provided using both the NASA-9 and RRHO thermodynamic databases. Where possible, comparisons with equilibrium air curve-fits of D'Angola et al. [55]  and thermal conductivity data from Murphy  [56]  and Azinovsky et al. [57]  are also shown. Both "frozen" and "equilibrium" curves are shown for the specific heat at constant pressure and specific heat ratio. The frozen curves neglect the dependence of the species composition on the temperature through the equilibrium reactions, while equilibrium curves do not. This distinction is important, as it shows that these properties can vary substantially, depending on the thermochemical model employed by the user.
When coupled with the minimization of Gibbs energy at a known surface condition, Eq. 4 can be solved to obtain species composition and char mass blowing rate B c. Fig. 6  shows such a calculation performed using Mutation ++ with the custom thermodynamic database discussed in Sec. 2. 3. 1. The B c results are compared with those obtained with the thermodynamic database from the NASA Chemical Equilibrium with Applications (CEA) code. More discussion on the differences in these databases can be found in  [22].
Conclusions The Mutation ++ library provides an OOP framework for computing thermodynamic, transport, and kinetic properties of non to fully ionized gas mixtures at all points on thermochemical nonequilibrium spectrum. The library leverages the simple dependence of thermochemical properties on the local thermodynamic state of the gas to implement a weak coupling between the computation of those properties and the simulation tools which need them, through a clean and consistent API. The code is freely available on GitHub with an LGPL v3. 0 license, following a continuous integration development strategy with periodic versioning to alleviate backward compatibility concerns for its users. This paper marks version v1. 0. 0 for the library. Future versions will aim to provide additional features and greater flexibility for the end-user.
<collisions> <!--Default collision integral data --> <defaults> <!--Charged interactions --> <charged> <Q11 type="Debye-Huckel"/> <Q12 type="Debye-Huckel"/> <!--Other integrals. --> </charged> <!--Other interactions types. --> </defaults> <!--Explicitly defined collision pairs --> <pair s1="e-" s2="N"> <Q11 type="table" units="K,A-A" multpi="yes"> 2000 4000 6000 8000 10000 15000 20000, 9. 04 4. 06 2. 93 2. 53 2. 34 2. 13 1. 98 </Q11> <!--Other integrals. --> </pair> <!--Other collision pairs. --> </collisions> (b) Collision database.
Acknowledgements The development of Mutation ++ v1. 0. 0 was partially supported under the  European Research Council   Starting Grant  # 259354  and Proof of Concept Grant # 713726. The authors would like to thank  Drs. Nagi N. Mansour ,  Jean Lachaud , and  Alessandro Turchi  for invaluable discussions and guidance during development of the library. Other contributions, including development and testing of the library, are listed on the project's GitHub site.
Multicomponent Thermodynamic And Transport properties for IONized gases in C++ Computational Fluid Dynamics API M ++ utati n Model Abstraction Physical Laws Modules Discretization local state-vector Thermo. Transport Coordinate System physicochemical Kinetics GSI Boundary Conditions properties Utilities Databases Algorithms Figure 1: Overview of the Mutation ++ library and its cou- pling to CFD. 3 https://github. com/mutationpp/Mutationpp.
To complete our assessment, we focus on three global FFCO 2 emissions inventories: the Fossil Fuel Data Assimilation System (FFDAS), the Open-source Data Inventory for Anthropogenic CO 2 (ODIAC), and the Emission Database for Global Atmospheric Research (EDGAR). We examine the differences in the spatial distribution and magnitudes of these inventories at the urban scale for cities in the Middle East, focusing on Riyadh, Saudi Arabia, but also analyzing Saudi Arabian cities Mecca, Tabuk, and Jeddah, as well as the city of Baghdad, Iraq. We next quantify the relationship between observations of XCO 2 from the OCO-2 satellite and simulations of XCO 2 using the column version of the Stochastic Time-Inverted Lagrangian Transport (X-STILT) model coupled with Global Data Assimilation System (GDAS) reanalysis products and the three inventories.
, 2014; Hutchins et al. , 2017; Rayner et al. , 2010). Emissions due to cement production and gas flaring are excluded from this product  (Asefi-Najafabady et al. , 2014). In this study, we use the 2014b (beta) data set, an early version of the FFDAS v2. 0 data set. This data set is at both hourly and yearly temporal resolutions and provides emissions data for the year 2014. For 2014, total global emissions in this set come out to 8. 9 PgC. ODIAC is a monthly global product that distributes FFCO 2 emissions with a spatial resolution of approximately 1 × 1 km (0. 008333 × 0. 008333°)  (Oda et al. , 2018). In the ODIAC2017 version, the inventory begins with national emissions estimates separated by fuel type (liquid, gas, solid, cement production, gas flaring, and international aviation and marine bunkers) as opposed to emissions sectors, from the Carbon Dioxide Information Analysis Center (CDIAC) at the Oak Ridge National Laboratory (ORNL).
These emissions data are then re-categorized into the following categories: point source, nonpoint source, cement production, gas flare, and international aviation and marine bunkers. For the more recent years that are not included in the CDIAC estimates  (2015) (2016)  global fuel statistical data  (BP, 2017; Oda et al. , 2018). These emissions statistics are then spatially distributed with multiple spatial proxies such as the Carbon Monitoring for Action (CARMA) database for point sources, nighttime light data collected by Defense Meteorological Satellite Program (DMSP) satellites for nonpoint sources, and ship and aircraft fleet tracks. The emissions are also distributed temporally using  Note that all inventories are shown at their native resolutions, with ODIAC having land emissions at a resolution of 1 × 1 km and international aviation and marine bunker emissions at 1 × 1°.
Then a block average of the medians is taken in bins of 0. 1°by latitude. Most of the overpasses are observed in Nadir Mode, though seven are in Glint Mode. For Riyadh, Mecca, Tabuk, Baghdad, and Jeddah, we use 11, 6, 3, 3, and 3 overpasses, respectively. The OCO-2 data are not filtered by warn level (see supporting information Text S1 for details). Table  S1  details the 26 overpasses used in this study, and Figure  S1  maps the overpasses in relation to the cities.
Since FFDAS has an hourly temporal resolution for just the year 2014, we aggregate the data up to monthly emissions by day of week and hour, such that we have 24 grids per day of week in each month to maintain both the diurnal and weekly cycles internal to the 2014 data set. (Note that these internal diurnal and weekly cycles are derived through application of the TIMES scaling factors. ) For ODIAC, we use years 2014-2016 to correspond with the appropriate years' simulations and use the 2016 grids for the 2017 simulations. As these are monthly grids, we use both the diurnal and weekly TIMES scaling factors along with the ODIAC data sets, which we keep in their native spatial resolutions of 1 × 1 km and also aggregate up to 0. 1 × 0. 1°. For EDGAR, the most recent year available is 2012; we use this annual file along with both the diurnal and weekly TIMES scaling factors.
Each of these adjusted emissions inventories are then convolved with footprints to find the fossil fuel contribution to CO 2. sim,n , or the enhancements. We also simulate the natural contribution to CO 2. sim,n by using biospheric flux data from CarbonTracker Near-Real Time (CT-NRT) v2016 and v2017. These biospheric fluxes are convolved with footprints in a similar fashion to the inventory FFCO 2 fluxes. Fluxes from oceanic and fire-related sources are deemed negligible in comparison to the anthropogenic and biospheric fluxes for this domain and not included. The boundary conditions to add to the natural and fossil fuel enhancements are determined using the trajectory endpoint technique. CO 2 concentrations sourced from CT-NRT 3-D mole fractions of CO 2 that correspond with the endpoints of the STILT trajectories serve as the boundary conditions.
Any biases between the OCO-2 observations and the CarbonTracker-derived background are then corrected for using observations with little local influence. In this way, we account for large-scale variation in XCO 2 , while also correcting biases using the observations. 2. 2. 5. Quantifying the Relationship Between the Modeled and Observed Enhancements We evaluate the relationship between the simulated and observed enhancements for each overpass of interest by generating cumulative enhancement curves, which is a modified area-under-the-curve technique. In this technique, the data points that are enhanced for each day or in aggregate are placed in ascending order, and their cumulative sums are plotted against each other, in a similar fashion as to the cumulative emissions curves described in section 2.
This suggests that for FFDAS, large point sources carry more weight than they do for the other two inventories, whether that is due to the method of distribution of emissions by proxy, or due to large point source data containing much higher values for FFDAS than those used by the other inventories. In Figure  3 , we show cumulative emissions curves for each inventory and city. These curves reaffirm that the total magnitudes of emissions for FFDAS and ODIAC tend to be more similar than they are for EDGAR. These figures also give us a further understanding of the distribution of the emissions for each inventory representation of a city. For each city (other than Jeddah), the FFDAS and ODIAC curves are more rounded out than the EDGAR curves, suggesting that more of the total emissions in the domain of interest are due to fewer and higher-emission grid cells.
10. 1029/2019JD031922 Journal of Geophysical Research: Atmospheres FFDAS and ODIAC (and its aggregated version) both also appear to underestimate emissions in comparison with the OCO-2 data, though their emission magnitudes are within the large confidence intervals of three of our optimized estimates of individual cities. These two inventories share the use of nighttime lights as a proxy for emissions distribution, and this work is somewhat suggestive this may be a useful proxy for global representation of urban domains, but further work is required to further parse out their similarities, differences, and reasons for their underestimation of the studied urban emissions. Our work also has the potential to identify larger errors in inventory representations of cities. For example, the ODIAC representation of Jeddah has an error in it due to a mismatch between two nightlight data sources (one for distributed emissions and the other for gas flare emissions, see  Oda et al.
Given the widespread usage of the TIMES factors for capturing diurnal cycles, it is possible that there is a systematic bias across many model representations of diurnality of emissions. The TIMES factors are potentially unable to fully correct for the lack of diurnality in the emissions inventories, particularly at the urban scale, a scale for which these factors were not designed. While this potential bias cannot be tested using OCO-2 data alone, as is done in this study, observations from multiple times of day from other satellites such as the recently launched OCO-3 could allow for assessments of the diurnal cycle represented by the TIMES factors. Moreover, there is great potential for carbon-observing satellites in geostationary orbit to assess the diurnal cycle of CO 2 emissions, as they would be able to make multiple observations of the same locations throughout the day.
Acknowledgments Funding for this study was provided by the  National Aeronautics and Space Administration (NASA)  (Grants  NNX15AI42G ,  NNX14AM76G ,  NNX15AI40G ,  80NSSC19K0092 , and  80NSSC18K1307 ) and the  National Science Foundation Graduate Research Fellowship  (Grant  DGE 1256260 ). The authors thank  Dr. Genevieve Plant  for assistance in error calculations, and the  OCO-2 science team  for general feedback. The OCO-2 data are hosted at the OCO-2 data archive maintained at the  NASA Goddard Earth Science Data and Information Services Center  and can be found online (https://disc. gsfc. nasa. gov/datasets?project=OCO). The most recent version of FFDAS (v2. 2) data can be found online (http://ffdas. rc. nau. edu/Data. html). The ODIAC emission data product is archived at the  Center for Global Environmental Research, National Institute for Environmental Studies (NIES ), Japan (http://db.
cger. nies. go. jp/dataset/ODIAC/DL_odiac2017. html). EDGAR is provided and archived by the  European Commission ,  Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL)  (http://edgar. jrc. ec. europa. eu/overview. php?v=432_ GHG&SECURE=123). Derived data and model products can be found at the  University of Michigan Deep Blue archive  (https://doi. org/10. 7302/rbjy-7e98).
In this study, we exploit a publicly available human mobility dataset, the Google COVID-19 Mobility Reports (GCMR)  (Google LLC, 2021). The GCMR reports changes in mobility of Google Maps users across different categories (e. g. supermarkets and pharmacies, workplaces, residential areas) with respect to the start of 2020. This dataset has been leveraged to monitor national mobility in the United Kingdom following the implementation of NPIs  (Drake et al. , 2020) , and it has been employed (alongside social contacts data) to estimate the effect of tiered restrictions in England and the lockdown in Wales implemented in October 2020  (Davies et al. , 2020). We model the association between the reduction in human mobility and the excess all-cause mortality during the first wave of the COVID-19 pandemic.
Data We employ two data sources in our study: the UK Office for National Statistics (ONS) for mortality and population data, and the Google COVID-19 Community Mobility Reports (GCMR) for mobility data. The ONS reports the weekly number of deaths registered in England and Wales, broken down by age group, sex and region of usual residence (Office for National Statistics, 2021). In the ONS classification, weekly data do not refer to calendar weeks, but to rolling 7-day periods, from Saturday to Friday, and we keep this notation throughout the article. For our purposes, we retrieve the weekly number of deaths by region for the years 2015-2020. We consider a total of ten regions based on the NUTS-1 (Nomenclature of Territorial Units for Statistics) subdivision, namely Wales plus nine regions in England (North East, North West, Yorkshire and The Humber, East Midlands, West Midlands, East, London, South East and South West).
We focus our analysis on the first wave of the COVID-19 pandemic, hence we use data until week  2020 -33 (i. e. until August 14, 2020)). We obtain data on total population in each region for the years 2015-2019 from the ONS (Office for National Statistics, 2020a). The GCMR reports daily mobility data in six categories of location: residential, workplaces, supermarket and pharmacy (grocery), transit, retail, and parks (Google LLC, 2021). Data are provided as percentage variations in number of visits or time spent in each category, relative to a pre-COVID-19 baseline period, defined from January 3 to February 6, 2020. The data account for weekly seasonality of movement by estimating a set of seven baseline weekdays using the median value for each particular weekday during the 5-week baseline period.