dataesr/scientific-paragraphs-categorization huggingface dataset:
py, that must therefore be imported prior to the declaration of any vertex, import couplings as C When a vertex is instantiated, only the non-vanishing coordinates have to be included. In the four-squark vertex considered, this therefore gives (0, 0) C. GC_1 (1, 0) C. GC_1 (2, 0) C. GC_2 (3, 0) C. GC_2 where the integer counters follow this time a standard Python numbering for the elements of an array (the first element being thus associated with the index 0). The example above illustrates the fact that the foursquark vertex exemplified involves only two instances of the Coupling class (GC_1 and GC_2), as also depicted in  (2). The declaration of a Coupling object in the file couplings. py is very similar to that of an internal parameter declaration (see section 3. 2). For the two couplings GC_1 and GC_2 necessary for the four-squark vertex considered, this gives GC_1 = Coupling( name = 'GC_1', value = '-(4*ee**2*complex(0,1))/(9.
*cw**2)', order = {'QED':2} ) GC_2 = Coupling( name = 'GC_2', value = '-(complex(0,1)*G**2)', order = {'QCD':2} ) An instance of a Coupling object is declared with three mandatory arguments, namely its name (name), the algebraic coupling definition that could depend on any of the model parameters (value), and a so-called coupling order provided in the form of a Python dictionary (order). In the case of the four-squark example considered, the coupling strengths appearing in (5) are directly provided as valid Python algebraic expressions. The last attribute of a Coupling object, order, is a Python dictionary that allows users to tag certain couplings of the model with one or more strings (i. e. tags) to which a positive integer number is associated. In the examples above, the tags QED and QCD are associated with two physical quantities, the typical strength of the electroweak and strong interactions.
The function library The last mandatory component of a UFO model is the file function_library. py. It includes user-defined functions declared as instances of the Function class. The UFO format supports functions that can be defined within a single line in Python, the so-called Python lambda functions. Python lambda functions offer the advantage of being easily translatable into other programming languages, and they are consequently the only functions that can be declared within the library defined in the file function_library. py. A Function object is defined through three mandatory attributes and two optional attributes. The mandatory attributes consist of the name of the function (name), its arguments specified as a tuple of strings (arguments), and the expression of the function itself given in terms of its arguments.
The latter must be provided as a string that represents a valid Python expression, and it is given as the value of the attribute expression. By default, all the arguments of the functions are considered to be complex numbers. This behaviour can however be superseded by providing a tuple of strings through the optional attribute argstype of the Function class, which allows users to specify the type of the different arguments of the function. The supported types are real numbers (real), complex numbers (complex), and arrays of real or complex numbers (real[n] or complex[n] for an n-dimensional array, respectively, where n is an integer). The two tuples provided through the attributes arguments and argstype must have the same size. Similarly, the type of the result of the function is a complex number by default, but this behaviour can be modified by specifying the type attribute of the instantiated function, that can take the self-explanatory values real, complex, real[n] and complex[n] (with n being an integer).
Several functions are shipped by default with any UFO model. This includes in particular a series of mathematical functions for which the Python module cmath is insufficient. First, the function_library. py file contains a set of tools that facilitate the treatment of complex quantities (the real part of a complex number re, its imaginary part im, and the complex conjugation operation complexconjugate). Second, several trigonometric and cyclometric functions are implemented (the cotangent cot, the secant sec and the cosecant csc functions, together with their arcsecant asec and arccosecant acsc counterparts). As an illustration, we provide below a function returning the real part of a complex number, as well as a function associated with the secant of a complex number. These could implemented as Θ(x -23) = 1 if x ≥ 23.
We note that this output is not allowed to be a list. G el (t) = Z nuc. 1 + t/d nuc. a 2 nuc. t 1 + a 2 nuc. t 2 , (7) The complete form factor in our example should also include an inelastic part G in (t)  [38, 39]. The latter can be defined similarly as its elastic counterpart, namely as another Function object Gin. The full implementation of both contributions as a single Function object FF is thus FF = Function( name = 'FF', arguments = ('t'), argstype = ('real'), expression = 'math. sqrt(Gel(t) + Gin(t))' ) in which we illustrate how a given Function object could call other Function objects and standard Python methods.
Outputting the values of the model parameters The file write_param_card. py includes routines that write all external model parameters, together with their numerical value, into a text file following an SLHAlike format  [34, 35]. In the output file, the parameters and their values are organised in Les Houches blocks and counters, as specified by the user with the parameter declarations implemented in the file parameters. py (cf. the lhablock and lhacode attributes of the different parameters; see section 3. 2). The output file, named param_card. dat, is generated by issuing in a shell the command python write_param_card. py In addition to the model external parameters and their value, the output file includes QNUMBERS blocks  [40]  with information on the quantum numbers of all the particles of the model, as well as all particle masses and decay widths regardless of their external/internal nature.
In practice, they are implemented as a modification of the relevant spin structure of the vertices declared in the file lorentz. py, following the replacement L 1. n j (p 1 ,. , p n ) → F j (p 1 ,. , p n ) L 1. n j (p 1 ,. , p n ). (9) This amounts to allow the value of the structure attribute of a Lorentz object to make use of functions defined in the file function_library. py (see section 3. 4), 6 See the webpage https://feynrules. irmp. ucl. ac. be/wiki/ ModelDatabaseMainPage. and of parameters defined in the file parameters. py (see section 3. 2). This obviously requires to import the list of parameters and the set of relevant functions in the preamble of the file lorentz. py. As an example, we consider the case of a form factor given by m W /E, where m W stands for the mass of the W boson (represented below by the Parameter object MW) and E is the energy scale relevant for the associated process.
Particle decays Many applications of calculations involving massive unstable particles require the evaluation of the total and partial decay widths of all particles of the model, together with the estimation of the decay channels that are kinematically allowed. This task is highly dependent on the mass spectrum of the model, and it requires a re-evaluation of the widths for each choice of external parameters. In order to provide a simple solution to this problem, the UFO format allows users to input analytical formulas for LO two-body decay rates associated with the particles of the model. These are all collected inside the file decays. py [3]. As two-body decays might sometimes be insufficient (when for instance highermultiplicity decays are the dominant decay modes or when higher-order corrections are important), it is up to the code using the UFO model to decide how (and if) they should include such extra contributions in their computations.
The analytic formula for the two-body decays of a Higgs boson into a pair of top quarks or weak bosons are hence also present (even if not kinematically allowed for a light Higgs boson). It is then up to the high-energy physics tool to filter out at run time the kinematically allowed channels (that depend on the chosen set of external parameters), and to combine them consistently into the total width and branching ratios for a given particle. When implementing the file decays. py, it is strongly recommended to include all two-body decay channels for all the particles of the model, kinematically allowed or not, in order to prevent the code that relies on this option of the UFO format from producing incorrect results for some benchmark scenarios and correct ones for others. The example of the Higgs boson is also a case where tree-level two-body decays are not sufficient for an accurate calculation of the total width of the particle.
Renormalisation group running effects In many practical applications in particle physics, the free parameters of the Lagrangian are provided at a given input scale that could be quite different from the natural scales relevant to the physics process considered. One possibility to increase the precision of the predictions can therefore be to include renormalisation group (RG) running effects, which amounts to re-evaluating couplings masses of the model at a specific scale. The UFO format has been extended  [5]  so that information on RG running could be provided within the optional file running. py. Following the general UFO philosophy, the UFO format only contains information on the running of the model's parameters, and it does not provide any method allowing to handle it numerically. It is hence up to the high-energy physics software employed to handle this, and/or to rely on any existing external tools like those introduced in  [31, 32, 47, 48].
In practice, a Running object is defined as: This declaration relies on three mandatory attributes. The first of them is the name of the object that is provided as a string (name), while the second attribute (value) refers to the analytical formula associated with the elements of the γ tensors considered. This formula has to be provided as a valid Python expression that follows the same technical limitations as those inherent to the value attribute of the classes Parameter and Coupling (see sections 3. 2 and 3. 3). Moreover, this expression should not depend on any running parameter. RGE_1 = The value of the run objects attribute contains a list in which each element is a list of external parameters (that must therefore be declared as instances of the Parameter class), with the exceptions of the standard QCD and QED couplings α s , α, g s and e that could be used despite their external/internal nature.
An additional zero subscript denotes the bare quantities compared to the renormalised fields or parameters, and a δ precedes the renormalisation constants. In the above expression, the wave function renormalisation constants have been expanded at one loop, and we consider that each fermion chirality is renormalised independently, as in the general case fermionic matter is chiral. The bare Lagrangian is then the sum of a renormalised Lagrangian, depending only on renormalised quantities, and a counterterm Lagrangian at least linear in the renormalisation constants, L 0 = L + δL. (21) The UV counterterm vertices originate from the counterterm Lagrangian δL, and they must be provided in an NLO UFO model. Their implementation is split within the files CT_vertices. py, CT_couplings. py and CT_parameters.
Conclusion In this paper, we have presented the current 2. 0 update of the UFO format for (B)SM models, that we have coined the 'Universal Feynman Output' format. This new name has been adopted to distinguish the current format from its initial version, as the UFO has evolved significantly during the last decade. Moreover, the UFO is not solely connected with FeynRules anymore, but it lies at the heart of many high-energy physics software tools. The UFO 2. 0 format includes several new features that were not part of the initial proposal, thanks to the flexible and modular structure that drove the design of the UFO ten years ago, and that allows it to be easily expandable and encompass features relevant for the interest of high-energy physics software at a given time. Initially, the UFO format has been designed to include information on a model's particles, the list and values of the parameters appearing in the model's Lagrangian and the associated interaction vertices.
Acknowledgments This work has been partly supported by grants from the  French ANR  (grants  ANR-21-CE31-0013  ' DMwith-LLPatLHC ' and  ANR-20-CE31-0015  ' PrecisOnium '), the  Deutsche Forschungsgemeinschaft  (grant  499573813  'EFTTools', grant  396021762-TRR 257  and  Germany's Excellence Strategy-EXC 2121 'Quantum Universe' -3908333 ), the  German Federal Ministry for Education and Research (BMBF  contract  05H21WWCAA ), the  ERC  (grant  101041109  ' BOSON '), the  CNRS IEA  (grant  205210  ' GlueGraph '), the  European Union 's  Horizon 2020 research and innovation program  (grant 824093  STRONG-2020 ,  EU Virtual Access 'NLOAccess '), the F. N. R. S (MAXLHC IISN convention  4. 4503. 16 ),and the  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility  managed by  Fermi Research Alliance, LLC (FRA) , and acting under Contracts No.
Features of agricultural robots for research We considered six agricultural robots reported in the literature: Strathclyde  [21]  , two Husky-based (GRAPE  [22]  and agRob  [23]  ), La-dyBird  [24]  , Robotanist  [25]  , and BoniRob  [26]. They were chosen to give a wide and diverse technological baseline (showed in Scheme 1 ) for a future comparison to CARs. As ROS (Robot Operating System) is being used for robot navigation and control on most robots, a brief introduction of this platform follows. Nowadays, general-purpose software for robot development is a reality after a decade of evolution. The Open Source Robotics Foundation (OSRF) maintains two combined open-source products called ROS and Gazebo. ROS is a robotics middleware composed of a large set of software libraries, conventions for data exchange between different processes, and data visualization tools.
However, obstacles are detected using the 3D laser sensor, even beneath the canopy. The robot integrates two ROS nodes to accurately determine its position. The first node corrects the GPS coordinates of the robot at the base frame (improving the accuracy of GPS positioning) using the orientation info from the AHRS and the relative poses of the GPS antenna, and the base coordinate frame of the robot. The second node calculates a current estimation of the robot pose (3D position and 3D orientation) and velocity relative to an Earth fixed coordinate frame  using an Unscented Kalman Filter (UKF). The UKF is used to fuse the corrected GPS data and info from the AHRS and from encoders in the wheels. 6. BoniRob: It is a big agricultural robot comparable to the combination of a small tractor and its implement.
It was developed by a collaboration between the Fachhochschule Osnabrück and BOSCH Deepfield Robotics to operate in arable land. A modular mechanical, electrical, and logical set of interfaces enable to incorporate different hardware modules according to the research applications. For example, different modules were implemented to conduct plant phenotyping, soil monitoring, weed control, and web-based communication  [26]. The robot geometry can be modified to adapt to the track width and clearance of the crop field. BoniRob has four driving and auto-steering wheels, providing an important degree of maneuverability. Its autonomous navigation capability is based on the perceived information of a 3D LiDAR, which scans just in front of the robot path. The ground surface is extracted from the LiDAR data (3D Point Cloud) by computing the Hessian plane, so the rows of the land are identified from the transversal profile of the 3D point cloud.
Acknowledgments The authors acknowledge the cooperation of companies that provided information about their robots and their experiences as innovative agricultural robotics companies. We appreciate the contribution of Eng. Matías A. Costanzó on Figures 1 and2, which will undoubtedly improve the readers' experience. Last but not less important, we acknowledge the exchanges on sustainable agricultural practices of Eng. Daniel Di Criscio , from the  Facultad de Agronomía (Universidad de Buenos Aires ).
Material flow analysis (MFA) is a powerful tool to track and estimate material flows and stocks and evaluate the efficiency of the materials used in the modern infrastructure. Several industries, such as raw materials, construction, energy, and transportation, have extensively employed the MFA method and approach. However, the use of MFA is mainly limited due to the lack of regional data, uncertainty in the available data, and lack of input data in some cases. Data limitation for MFA is addressed but with limited success, particularly for building infrastructure. This study has addressed this issue by employing Google Earth as a secondary method that can potentially extract data applicable to MFA. This data collection approach is applied to the French Railway Infrastructure by systematically selecting 95 railway tracks (20% of the total number of tracks) covering more than 9,000 km of track length (~31% of the total), and the results were verified with the SNCF (Societe Nationale des Chemins de fer Francais) statistical data based upon the developed parameters.
Thus, the estimation of stocks can also be a challenge for products with a long lifetime, like buildings and infrastructure. A bottom-up approach can help but this approach requires a lot of labor and highly depends on statistical data also. In addition, very few case studies are available for cities and countries therefore, there is a need to reconstruct MFA data for various spatial ranges, including a regional scale  (Courtonne et al. , 2015; Bailang et al. , 2018). It is because the regional data that are somehow available are typically unstructured, cross-disciplinary, different in formats and qualities, and come from heterogeneous sources. On the other hand, MFA data are sometimes not based on empirically well-founded data sets but on the individual, isolated values, which are not always provided in inconsistent formats  (Schwab et al.
(2016)  extended this approach to the city level by applying a similar model to Vienna, Austria, using statistical and GIS data from various municipal authorities to estimate material stock in building infrastructures. Their research pointed out the lack of detailed localization of the material stocks while considering only statistical data. The use of GIS takes care of this deficiency. Along similar lines, Gao (2021) applied a dynamic model of material flow and stock to estimate material quantity from the residential infrastructure of Shanghai from 1950-2100  (Xiaofeng et al. , 2020). Tanikawa et al. (2015)  further proposed a bottom-up model to estimate the in-use construction material stock in urban infrastructure for 47 prefectures of Japan using GIS. The scope of this study includes buildings, roads, railways, underground water networks, airports, seaports, and dams.
Purpose Most of the studies mentioned above deal with building infrastructure systems' flow and stock estimation. There is hardly any study that considers the technosphere as a whole or specifically deals with the transportation infrastructure. Another common aspect is that all of them, except a few, are an attempt to develop the data set, which could fill the gaps in already existing data. This process is very cumbersome, and many assumptions had to be made to simplify. Regarding the utility of the GIS data, according to  Tanikawa et al. (2015) , it helps improve MFA methodology, but, like MFA, its availability is the primary concern. It is beneficial for developed countries like Japan, where it is randomly available. Still, for other nations where there is little to no GIS data, the problem of data deficiency persists.
g. , height of the structure or floor height. This limitation prevents such methods from volume estimation  (Fritz et al. , 2016). GIS data at the national level can be obtained directly from national statistical bureaus. However, at the regional and urban scale, there is limited data accessibility because data are generally dispersed among several organizations and firms and not found in one place. This deficiency makes it very tiring to produce a consistent set of data. The other issue is data availability because many required data are unailable  (Zhu 2014). Current practices of bottom-up and top-down approaches involved in MFA incorporating GIS are underestimating material inflows and outflows because of insufficient data availability (Yupeng  Liu et al. , 2022). As far as building infrastructure is concerned, there is a significant amount of GIS data and maps, but GIS data is available for very few economies for transportation infrastructure.
This problem is even more severe in the case of lesser developed societies that lack geographical data collection. GIS and NTL have solved a few issues and improved the data quality, but those methods still cannot compensate for the lack of regional data. They can also not fetch reliable data for rural parts of the infrastructure. The data collection problem is even more severe in lesser developed economies. Moreover, data collection for transportation infrastructure (road networks in general and railways in specific) is still a huge problem. Therefore, it is fundamental to combine more comprehensive and readily accessible data collection methods that may improve the application of MFA in developing countries and at a regional scale, which may help assist in the extraction of missing data.
In light of this discussion, we propose using Google Earth to address limitations and uncertainties in data collection. It is feasible to obtain in-use material stock data in developed as well as developing countries at city, regional, national, or global levels. It can also capture all types of measurements, including length, width, height, and area, enabling us to calculate volumes. This 3D view makes it possible to see the current situation of the target area. It can also allow us to estimate quantities of all types of infrastructures, including buildings, roadways, and railways.
System description This phase describes the general methodology and data sources adopted for the proposed method's development. French railway infrastructure is the selected system for the research work. Two types of data have been used as 'input' data; (a) Google Earth and, (b) SNCF maps, inventories, UIC datasheets, and available literature. The sample selection 'process' includes a selection of 'n' number of tracks for which the data will be collected later on. The criteria for sample selection, explained in Section 3. 4, are based upon the type of the track, its geographical location in France, and the respective lengths of the tracks.
Data collection This phase presents the structural entities for which the data will be collected. These entities include the length of the tracks along with their multiplicity, the number of tunnels and their respective lengths, the number of bridges, and the number of level crossings. The selected sample from the previous phase is then subjected to data collection in terms of the above entities using Google Earth. In parallel, material intensities were also taken from the available literature to be used for material estimation later in the research work.