dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions on the Evaluation This evaluation section demonstrates the pertinence of the proposed IoT data quality module and the impact of this module on the data to be used in the traceability smart contract. The entire data qualification process is executed in a secured and distributed application on which users agree on every datum to be included, on its qualification process and decisions to be taken based on this datum. It is worth noting that the choice of the quality thresholds has a huge impact on the data filtering process set at the data source level. The events with a quality index below the defined quality threshold will never be sent to the shipment. This leads directly to data loss at the shipment level. For this reason, stakeholders may prefer selecting a good quality threshold ([0.
We also propose an approach for the calculation of the selected data quality dimensions. The dimensions calculation results are used in our traceability smart contract to set and control the data quality of events, data sources, shipments and shipments data sources associations. The proposed architecture ensures the stakeholders agreement on the data quality calculation and application rules, and consequently their trust in the decisions taken automatically by the traceability smart contract. We evaluated our proposed IoT data quality assessment architecture based on an online available dataset, and the results show the relevancy of this architecture. This work could be extended by evaluating the scalability of the proposition when adding more stakeholders. The approach used to calculate the quality dimensions could be combined with algorithms, such as DBSCAN [39] or an assisted version of the Kalman filter [40], to improve the quality index calculation.
Natural infrastructure such as parks, forests, street trees, green roofs, and coastal vegetation is central to sustainable urban management. Despite recent progress, it remains challenging for urban decision-makers to incorporate the benefits of natural infrastructure into urban design and planning. Here, we present an approach to support the greening of cities by quantifying and mapping the diverse benefits of natural infrastructure for now and in the future. The approach relies on open-source tools, within the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) software, that compute biophysical and socio-economic metrics relevant to a variety of decisions in data-rich or data-scarce contexts. Through three case studies in China, France, and the United States, we show how spatially explicit information about the benefits of nature enhances urban management by improving economic valuation, prioritizing land use change, and promoting inclusive planning and stakeholder dialogue.
Nature-based solutions relying on natural infrastructure can help to reduce the risk of flooding; attenuate water, noise, and air pollution; mitigate the urban heat island effect, and provide attractive green and blue spaces that promote physical and mental health  [5] [6] [7] [8]. Information about how much, where, and to whom investments in natural infrastructure yield benefits can improve urban planning and decision-making and direct limited budgets towards where they can do the most good  5, [9] [10] [11]. Here we present a set of modeling tools to help evaluate the impact of investments in natural infrastructure in cities, implemented within InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs)  12. InVEST is a software suite that models the links between nature and the well-being of people, i.
e. , "ecosystem services" or "nature's contributions to people"  13. It has been widely used to demonstrate the potential of natural infrastructure investments  [14] [15] [16]  , including a recent application with a global scope  17. The set of ecosystem services important in urban areas is somewhat different than in rural areas so the new urban tools introduced here address urban cooling to reduce the urban heat island effect, stormwater management, and access to green space in cities. Like the original InVEST software, the new InVEST urban tools are free, open-source, and modular so that users can choose which services to include in the analysis  12. The tools are designed to incorporate spatial information about natural infrastructure's benefits into resilience research and practice. Alternative commonly-used tools for urban ecosystem service assessment include i-Tree, ARIES (Artificial Intelligence for Ecosystem Services), Costing Nature, and SolVES (Social Values for Ecosystem Services) (see Supplementary Table  1  and associated references  18  ).
The InVEST tools support such assessments by allowing for the consideration of more services provided by a diverse type of natural infrastructure (Table  1 ). As we detail in this paper, the tools are also flexible and broadly applicable (or 'reusable', sensu Hamilton et al. )  19  , with dedicated software maintenance, which increases the size of their user community-a critical factor in the uptake of decision-support tools. With the tremendous advances in globally available spatially explicit data, scientific understanding, and practical applications  20  along with a rise in the demand for accessible and actionable assessments-we see an opportunity to build a more integrated, globally applicable approach for natural infrastructure assessment in cities worldwide. The next sections describe the main design elements of the InVEST urban tools that support such an approach and introduce three applications in Shenzhen, China, the Twin Cities, USA, and Paris, France.
Key design features of InVEST's urban tools A review of existing urban ecosystem services tools (Supplementary Table  1 ), types of model use (Fig. 1 ), and experience with InVEST over the course of more than a decade (Supplementary Methods) point to key features of effective decision support tools: (i) production of metrics supported by sound science that are relevant to policy decisions; (ii) flexibility in the type of decisions, scales, or actions the tools can support; and (iii) free and opensource code that can be applied (and/or modified as necessary) anywhere with easily accessible data. These features, detailed in Supplementary Table  2 , guided the development of InVEST models and help to ensure salience, credibility, legitimacy, and accessibility-key features of effective knowledge systems  23  and successful modeling projects  19.
It is also important to better incorporate equity issues in urban planning  40, 41  , as illustrated in our third case study. In this paper, we used InVEST to assess eight services, with globally or nationally available data (e. g. , national land cover maps), or in combination with local datasets (e. g. , natural habitat data in Paris, temperature data in Shenzhen). These case studies complement the few case studies where 'original' InVEST tools had been used in predominantly urban environments  54, 55. In the Paris case study, InVEST tools were particularly helpful in stakeholder engagement; its accessibility (using readily available data, and producing easy-to-interpret outputs) made it possible to quickly present preliminary results to stakeholders and iterate on goals and outcomes of the projects.
The simplifications in InVEST result from important design constraints related to input and calibration data (limited to those easily accessible from public databases) as well as technical skills and experience of users (our target users are GIS analysts with little modeling experience). Although model simplifications result from deliberate choices, they need to be considered when using the tools, especially if other, more sophisticated, or tailored models are available in a city. Our case studies illustrate such choices where InVEST tools were used in combination with other models (e. g. , the urban cooling model used in the Shenzhen case study), which is especially useful when better-or more accepted -models are routinely used (e. g. , for urban water management). Specific limitations for each model are provided in the references listed in Table  1.
Assessing the economic benefits of natural infrastructure in Shenzhen, China We used InVEST and local empirical data to assess the economic value of three services provided by natural infrastructureurban flood risk mitigation, sediment retention, and urban coolingin the urban area of Shenzhen. For each service, we compared scenarios with and without contributions from natural infrastructure. We applied the InVEST urban flood risk mitigation model for a storm with a return period of 100 years. We ran the InVEST sediment retention model to simulate the avoided soil loss in 2018. To represent the value of each of these services in monetary terms, we used avoided costs of building retention infrastructure and dredging sediment (for the stormwater and sediment retention services, respectively, as detailed in Supplementary Methods and Supplementary Fig.
Prioritizing land-use change in the Twin Cities, USA We applied the InVEST tool to assess pollinator habitat, urban cooling, and stormwater runoff retention services provided by 135 golf courses relative to alternative land uses in the Twin Cities. Previous work evaluated a broader set of services provided by golf courses using a mix of InVEST and local regression models with local land cover and land use data  70. Here, we modeled three services using InVEST and national land cover data and applied a marginal value approach  71  to compare the amount and distribution of benefits provided by each golf course. We compared the services that a park or a residential area would provide at the same location by running the models with the current land use map compared to the alternative land use (Supplementary Fig.
Regional planning integrating equity metrics in Paris, France We used InVEST and regional data on natural habitat to assess eight ecosystem services in the Ile-de-France region: agricultural potential, groundwater recharge, carbon sequestration and storage, nutrient retention, nature-based recreation, urban flood risk mitigation, urban cooling, and habitat provisioning (to protect natural heritage). Our team selected these services in consultation with the stakeholder group on the basis of their perceived importance and lack of consideration in urban planning documents to-date  72. To estimate the effect of land use change on ecosystem services, we quantified the natural and semi-natural areas (including forests and open spaces outside developed areas) in both 1982 and 2017 and ran the ecosystem services models for both land use maps.
ACKNOWLEDGEMENTS Part of this research was supported by the  Marianne and Marcus Wallenberg Foundation , the  Swedish research council FORMAS  ( 2018-002371 ), and the  LuEsther T. Mertz Charitable Trust. We acknowledge funding from  Nanyang Technological University  for the first author; the  US Golf Association  for the  Twin Cities case study , the  Chinese Academy of Sciences  for the  Shenzhen case study , and the  Ministère de la Transition Écologique et Solidaire, ADEME , and  AgroParisTech  for the Paris case study. The first author acknowledges  Nanyang Technological University  for financial contribution to this research.
DATA AVAILABILITY The data generated and analyzed during this study are described in the following data record: https://doi. org/10. 6084/m9. figshare. 13910660  74. The raster files underlying Fig. 2 , Table  2  and Supplementary Fig. 1 , and the shapefile underlying Fig. 3  in the related manuscript will be shared via institutional file storage and linked via the data record. Interested readers are encouraged to contact the authors for additional information.
This will considerably facilitate BSM studies in flavour physics. Contrary to the existing public codes in this field MARTY aims at providing a unique, free, open-source, powerful and user-friendly tool for high-energy physicists studying predictive BSM models, in effective or full theories up to the one-loop level, which does not rely on any external package. With a few lines of code one can gather final expressions that may be evaluated numerically for statistical analysis. Features such as automatic generation and edition of Feynman diagrams, comprehensive manuals and documentation, clear and easy to handle user interface are amongst notable features of MARTY.
GRAFED (Feynman diagram generation and edition) When doing calculations in particle physics, it is often convenient to visualize what the code is doing, and possibly include diagrams in publications. GRAFED was developed for this purpose and is also fully independent of the other modules of MARTY. It has three major features: • An algorithm that finds an optimal way to place nodes in a 2D space to display Feynman diagrams. This algorithm is fully general (with no limit in the diagram size or number of loops) and automated. This allows one to quickly draw all diagrams for a particular process, without asking anything from the user and independently of the diagram topologies. • A Graphical User Interface (GUI) that displays the generated diagrams. When asked, MARTY will run GRAFED with all the diagrams of a particular process.
MARTY design philosophy The design of MARTY is guided by strong principles ensuring a final result corresponding to programming standards. First, the general principles unrelated to physics are: • Independence. MARTY is written from scratch and is thus fully independent of any other framework. As such, there is no limit in what can be implemented in the code. Developers have a full control on any aspects of MARTY, to modify or extend its capabilities. • User-friendliness. The code must be easy to use. The fact that it is written in C++ is a supplementary challenge in that purpose, but a modern knowledge of this language provides freedom for the user-interface. We think that this objective is fulfilled, since the normal usage of MARTY does not require any particular C++ knowledge and would be similar in many languages, including Mathematica.
• Modularity. MARTY is built as modular as possible. This means that unnecessary logical connections between different parts of the code are avoided. This is an important advantage for maintainability, since replacing or correcting a part of the code will become simpler. • Readability. It is important for a code to be easily understandable by everyone, and in particular by a user willing to further develop the code. Strict coding conventions, clear naming for files / functions / variables and clever separation of different logical units make MARTY easy to understand considering its large size. • Performance. C++ and python were the two main languages possible for MARTY as they are well-known in the high energy physics community. The choice of the language, C++, is related to performance reasons.
A C++ code will run much faster in average than python for this type of code. Concerning physics aspects, MARTY has been written with the following aims in mind: • Generality. MARTY is designed to be as general as possible in the models it can handle, algebraic simplifications it can do, and calculations automated with it. A high level of generality has already been reached, and further developments will continue to focus on this aspect. In particular there is no hard-coding because MARTY is expected to be extended even further in its future developments. • Model independent calculations. In order to have an easy-to-use code for high energy physics, the computations have to be done in a model-independent way. The same code computing a given quantity should work for all models. Studying new models would hence imply only to write the Lagrangian or Feynman rules associated to it, and then using the same scripts to calculate the same quantities in the given model.
Usage (scripting) As a C++ framework, MARTY can be used in a C++ program after writing a few lines to include it, provided it is installed on the computer 2  : #include <marty. h> using namespace mty; using namespace csl; using namespace std; The three lines of using namespace are not necessary, but will allow us to omit prefixes mty:: (MARTY ) csl:: (CSL) and std:: (C++ standard library) in front of objects and functions. The main function containing the program can now be written: int main() { // Your program return 0; }.
Dependencies MARTY has been written from scratch. Thus it has no dependency before the numerical evaluation of symbolic results. It contains in particular its own computer algebra system, CSL (C++ Symbolic computation Library), as a separate module. Therefore for the physics calculations from the Lagrangian to a final one-loop result simplified as much as possible, MARTY uses nothing but its own code and the C++ standard library. For the numerical evaluation of results, there are two dependencies. The first, Loop-Tools  [4] , provides numerical values for scalar integrals arising at the one-loop level. Momentum integrals have always the same form, like the following 3-point function integral I 3 ≡ d 4 q iπ 2 p µ 1 p ν 2 (q 2 -m 2 0 )((q + p 1 ) 2 -m 2 1 )((q + p 2 ) 2 -m 2 ) 2. (1) A way to treat this kind of integrals (including regularization) is to decompose the result in different possible Lorentz structures, each having a scalar factor in front, that is calculable with standard prescriptions  [12].
The second option is to define a high energy Lagrangian with all symmetries preserved, and give MARTY prescriptions to break it. The initial Lagrangian has much less interaction terms and is simpler to write. Based on correct prescriptions (gauge, flavour symmetry breaking, replacements, renaming, etc) MARTY will basically re-derive the final Lagrangian for the user. This solution will not necessarily be the easiest one depending on the model but is certainly a practical option. It is in particular the way chosen to build the MSSM in MARTY. In the following we present a sample code building a SU (2) L gauge with one quark in the doublet representation Q L = u L d L , (5) broken by MARTY with a single instruction. We also ask the code to rename the broken fermions Q 1 and Q 2 to u and d which corresponds to standard conventions.
DisplayAbbreviations(). Using the toy model presented in section 5. 1, the calculation of an amplitude in MARTY is very simple. One has to give the order (mty::Order::TreeLevel here), the model, and the field insertions. Let us consider the process uū → d d at tree-level, the following instruction is needed to run the computation: auto res = model. computeAmplitude( Order::TreeLevel, {Incoming("u"), Incoming(AntiPart("u")), Outgoing("d"), Outgoing(AntiPart("d"))} ); The code is in C++, therefore variable types must be declared. However since C++11, the compiler does not need the user to specify the type returned by a function anymore. By typing auto the compiler is told to find the exact type itself. Then, res. expressions contains the different terms of the amplitude and res. diagrams the Feynman diagrams.
The results are at first symbolic mathematical expressions, but numerical C++ libraries are built automatically by MARTY allowing us to explore in full generality the parameter space of the model for some user-defined quantities. A spectrum generator specific to the user's model is also created automatically by MARTY when needed. Most of popular BSM models can be built in MARTY. The MSSM, extended gauge models, and vector-like quarks are examples of possible BSM implementations. MARTY can already be very useful for BSM phenomenology in this current version. The particular advantage of MARTY is to be written as a unique code, not depending on any framework. Within MARTY, every aspect of model building and high-energy physics calculation are under control, in the same program and in the same language.
CO 2 fluxes Our reference fluxes combine several information sources. Fossil fuel emissions are from the gridded fossil emission dataset GCP-GridFED (version 2019. 1)  (Jones et al. , 2021). Biomass burning fluxes vary inter-annually and are described by the GFED 4. 1s database (https://www. globalfiredata. org/ data. html, last access: 7 February 2022). Monthly air-sea CO 2 exchange is prescribed from the Copernicus Marine Environment Service database  (Denvil-Sommer et al. , 2019). The GPP and respiration fluxes have been simulated at a resolution of 0. 5 • in both longitude and latitude by the OR-CHIDEE land surface model  (Krinner et al. , 2005). OR-CHIDEE explicitly parameterizes the main processes influencing the water, carbon and energy balances at the interface between land surfaces and atmosphere.
3) Resource Orchestration Support: The ROS part of a slice is a component that supports resource orchestration. It cooperates with SM in order to proactively allocate resources to a slice (using slice usage predictions) before the resource congestion occurs and enables QoE-driven resource allocation. By analysing the slice traffic, ROS can also request the relocation of a specific virtual function or the deployment of a new one. It may play the role of a dedicated VNFM of MANO and OSS/BSS that interacts with NFVO for slice update. After the sub-network slice deployment, ROS has the knowledge about the slice topology, the location of virtual functions and the allocation of resources to the sub-network slice. The initial information is obtained by ROS from the orchestrator. ROS plays an important role during the subnetwork initialization phase -it obtains the sub-network slice configuration details from the orchestrator and sends the data to SM, which configures all virtual functions of the subnetwork slice.
• The slice authentication function (SAF) that is used for authentication of users and for mutual authentication of stitched sub-network slices. The slice users database (SUD) stores the information about the users that are attached to a slice. SEF and SAF take part in the process of slice selection. In case of horizontal stitching of several sub-network slices, only one of them implements the functionality. • The mobility management function (MMF) that is used for handling mobility of users attached to the end-to-end slice as well as the "mobility" of slice entities, i. e. , their relocation, in order to improve slice KPIs. The latter is realized via the ROS functionality. MMF also reflects the decisions made by the multi-domain orchestrator (MDO) regarding optimal slice placement by preparing the slice and its users for relocation.
e. , SM. For the purpose of KPI monitoring, the TP has a database that stores KPIs of each slice. KPIs are defined in the slice template and are calculated by the management components of a slice (SM). The TP may provide visualization of slice KPIs. A proposal for such slice-agnostic KPIs can be found in  [46]. The portal also keeps all the accounting data concerning slice tenants. For that purpose, the requested and obtained slice KPIs are compared for SLA validation. On the one hand, the TP interacts with tenants, and on the other hand, it interacts with the MDO and indirectly with the SSC. • MDO. The MDO performs multiple roles. This is the entity that is contacted for the deployment of all slices; it also provides the TP with information about KPIs of all already-deployed slices. It includes the database of all templates that can be used for slice deployment within the framework (even those which are used by other orchestrators of the framework).