dataesr/scientific-paragraphs-categorization huggingface dataset:
2. Reuse data: This database stores data relating to recovery for a new assembly. Specifically, for each of the components, the following data are recorded: a) Coefficient of employment (α); b) Reusability index (I r ) [%]; c) Unit cost of treatment [€/pc]; d) Unit cost of purchase/production [€/pc]. 3. Recycling data: This database contains data relating to recovery for recycling. Specifically, for each of the materials, the following items were recorded: a) Recyclability index (I rec ) [%]; b) Cost of recycling per kg [€/kg]; c) Selling price per kg [€/kg]; d) Amount of CO 2 emitted to produce one kg of material from primary sources Y GWP [kg CO2-eq /kg]; e) Amount of CO 2 saved by producing one kg of material from recovery sources δ GWP [kg CO2-eq /kg]; 4. Component compositions: This database defines, for each of the components, the quantity in kg present for each material in the bill of materials.
The virtualization layer (also called hypervisor ) manages the mapping between those physical components and virtual equivalents. As such, the NFVs -hosted by virtual machines running inside the NFV infrastructure-never have direct access to the physical resources. Instead, the VNFs access the virtual resources. The NFV management and orchestration module runs the combined infrastructure, including: the lifecycle of the instantiated VNFs, resource allocation for VNFs, or overall management in view of particular, given network services. Deep Attestation (DA). Virtualization enables efficient, versatile remote network configuration and administration; however, the fact that multiple virtual processes share resources can introduce hazards to security. One way to ensure that a component runs correctly is by using attestation.
It is a group signature without the feature that a signature can be opened, i. e. , the anonymity is not revocable. Such primitive are well described using cryptography as a variant of signature scheme. However, provable cryptography has also been used successfully to formalize security protocols as authenticated key exchange  [7, 11]. This is precisely our goal to model the different security components independently and to compose them to prove the security of a new security mechanism. Indeed, the attestation server must authenticate the whole platform, i. e. , the hypervisor and the NFV running on top. This problem has been addressed by others in the context of secure boot or for instance in  [6] , where the authors propose an attestation mechanism for swarms of device softwares in IoT and embedded environment.
The adversary A will play a game against a challenger G. Initially, the challenger sets the system up by running aBSetup to output ppar which is given to A. The unique party is generated, such that its corrupt bit is set to 1 (P. γ = 0). Since A now has ppar, it can now run the aBAttest and aBVerif algorithms. In addition, it has access to the OBAttest oracle: OBAttest() → (quote ∪ ℵ). This oracle calls the aBAttest() algorithm for the (corrupted) party P and returns the output to the adversary A. The challenger stores the result in a database DB. The adversary wins if, and only if, there exists a quote in DB (possibly with quote = ℵ) such that aBVerif(ppar, quote) = 1. Note that by construction our basic attestation primitive is secure, since once the compromise bit is set, the output is ℵ, which always yields aBVerif(ppar, ℵ) = 0.
Implementation We provide a proof of concept implementation of our authorized linked attestation scheme. The implementation consists of three parts, a client for the hypervisor, a client for the Virtual Machines, and an attestation server written in Python 3. We do not consider the underlying NFV or cloud infrastructure, since our scheme abstracts those environments and can be used in any kind deep-attestation scenario. Therefore, any computer equipped with a TPM 2. 0 (which can also be emulated) and which has virtualization capacities suffices for the purposes of our implementation. We provide our code as well as a detailed tutorial on how to install and configure both the infrastructure  [3]. Fig. 8 : Architecture for tests. The infrastructure. We summarize our testing architecture in Figure  8  (note that some of our tests use more than 2 VMs -up to 55).
Our hypervisor is a laptop running Ubuntu 20. 04. 3 (kernel version 5. 11. 0-40) with an Intel i7-10875H CPU, 32GB RAM and a STMicroelectronics TPM. We used KVM to turn this laptop into a hypervisor. For high attestation performance, we used full virtual TPM implementation, using QEMU  [1]  with libtpms 0. 7  [8]  and swtpm 0. 5  [20]. All virtual machines are QEMU virtual machines (version 4. 2. 1) with 1 core and 512 RAM running Fedora 35 Cloud. The VM as well as the virtual TPM instances are managed using Vagrant and Vagrant-Libvirt plugin. The hypervisor, server, and VMs communicate through a private network created with Vagrant. Thus, connection time is not considered in our tests. To communicate with the TPM we used tpm2-tss, tpm2-abrmd and tpm2tools from the tpm2-software  [2]. Note that the tpm2-tss project implements the TPM software stack (TSS), which is an API specified by the Trusted Computing Group to interact with a TPM.
The tpm2-abrmd implements the access broker and resources to manage concurrent access to the TPM and manage memory of the TPM by swapping in and out of the memory as needed (hardware TPM have limited memory). The attestation server is also a virtual machine, with the same characteristics as those above. This allows us to test our implementation on a single machine. We establish a secure connection between the client and the server by using Python's SSL library. Tests. We perform three types of experiments. The first is a comparison of hypervisor attestation time and VM attestation time. Although both those processes have some (very small) amount of noise, our values faithfully show the difference between attesting a component through the physical TPM -hypervisor attestation -and attesting it by using a virtual TPM -VM attestation.
This study was partially supported by the  French ANR , grants  16-CE39-0012  (SafeTLS) and  18-CE39-0019  ( MobiS5 ). There are also the  French government research program  " Investissements d´Avenir " through the  IDEX-ISITE initiative   16-IDEX-0001  ( CAP 20-25 ), the  IMobS3 Laboratory of Excellence  ( ANR-10-LABX-16-01 ), the  French ANR  project  DECRYPT  ( ANR-18-CE39-0007 ) and  SEVERITAS  ( ANR-20-CE39-0009 ). The research leading to these results partly received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 871808  ( 5GPPP  project  INSPIRE-5Gplus ). The article reflects only the authors' views. The Commission is not responsible for any use that may be made of the information it contains.
This paper aims at sharing 3D geological models that were constructed at different scales in two Mexican geothermal areas as part of the European-Mexican GEMex project. The project was devoted to investigate superhot resources in Los Humeros and enhanced geothermal systems in Acoculco, both areas located in eastern Mexico. To build confidence in the resultant datasets and to potentially inform the development of models in similar contexts, the methodology is also described. The models integrate the main geological and geothermal features of the study areas and served as a framework for subsequent calculations and simulations. Preliminary models were based on data available at the beginning of the project, and were updated several times as new geological, geochemical, and geophysical field-data were obtained.
Data and information. A wide range of data and information were used to set up the Los Humeros geomodels. Digital Elevation Model (DEM), geological map and sections, wells, analogue model, geochemistry information, and geophysical models were all used to constrain the successive versions of the geomodels. Geological information from only sixteen wells was available for the preliminary study  28. A total of 56 wells were used for the updated local and integrated geomodels. The new lithological and structural information provided by the 40 additional wells resulted in a more detailed local model that changed our perception of the upper geometry of the basement rocks and the relevance of some of the main faults. Table  1  specifies the data used for the preliminary and updated geomodels, as well as the additional data for the integrated geomodel.
Integrated geomodel Analogue modelling  47, 48  Guide to interpret the shapes of the structures Qualitative information Regional fault system network  49  Main structures in the basement Qualitative information Geochemistry and hydrology  50  Guide for the regional faults interpretation Qualitative information 3D resistivity model  51, 52  Main fault systems -Used to integrate geophysical datasets in the cross-plotting Imported as. csv Seismic structures  13  Las Papas fault interpretation at depth Qualitative information 3D gravity model  54  Regional faults location -Basement interpretation -Constrain the Antigua fault: range 0. 2 to 0. 713875 (max) -Used to integrate geophysical datasets in the cross-plotting Imported as. csv Integrated geophysical model  55  Imaging and validation of geological structures Qualitative information Results from cross-plotting data integration  33  Used as imported surfaces to check and refine the geomodel surfaces Imported as.
Volcanoes morphology and distribution Regional faults traces interpretation (volcanoes alignment) Imported as. tif Table  1. List and sources of datasets used for the construction of the Los Humeros geomodels. The table indicates the purpose the dataset was used for and the way it was integrated in the geomodel. In the last column, "Qualitative information" means that the information was not directly imported but was rather used for checking and evaluating the geomodel. Geological descriptions. Two approaches were applied to describe the underground lithology in Los Humeros, and correlate it with the outcropping lithological units. In the first approach, the subsurface rocks were described as four groups to be considered at the regional and integration scales in the related geomodels. In the second one, the same subsurface rocks were divided in nine units to be considered at the local scale in the related geomodel.
Data and information. As with the Los Humeros, to perform the Acoculco 3D geomodels, the various datasets were imported into the GeoModeller package. Apart from the DEM of the study area, the initial geological datasets were: (a) the geological map of Acoculco  22  , constituted by a vector file with the main faults; (b) two interpreted geological cross-sections and (c) the litho-stratigraphic logs of the two deep boreholes drilled in the area  23  ; (d) structural data from fieldwork carried out during GEMex. Afterwards, the interpretation derived Fig. 12  Fifty six wells are described according to the four groups 2018 version of the geological description presented in Fig. 5. DEM is displayed as a grid including the fault network traces (pink colour, see Fig. 13 ). The coordinate system is WGS84/UTM zone 14 N.
View from SE. Fig. 13  The fault network constructed in the Los Humeros integrated geomodel, along with the wells. Surfaces are visualized as semi-transparent to facilitate the interpretation of the figure. Purple: regional faults; brown: caldera structures; red: local faults. The coordinate system is WGS84/UTM zone 14 N. from the geophysical dataset was embedded to constrain and refine the geomodel. Table  2  specifies the data used for the preliminary and updated geomodels, the additional data for the integrated geomodel, including the corresponding reference, as well as a description of the way those data were incorporated and used for the modelling. Fig. 14  A clipped view of the Los Humeros integrated geomodel including wells, fault network, and the four geological groups: basement (green), pre-caldera rocks (blue), rocks from the caldera (purple), post-caldera rocks (brown).
Imported as. tif Geological cross-sections  32  To define the geological structures at depth imported as. tif Faults system network  24  To define fractured zones Imported as. shp file Boreholes Thermo-Litho-stratigraphy  23  Constrain Results from cross-plotting data integration  33  Used as imported surfaces to check and refine the geomodel surfaces Imported as. csv Table  2. List and sources of datasets used for the construction of the Acoculco geomodels. The table indicates the purpose the dataset was used for and the way it was integrated in the geomodel. In the last column, "Qualitative information" means that the information was not directly imported but used for checking and evaluating the geomodel. Geological description. The geological description adopted for the 3D Acoculco geomodel is the result of a synthesis driven by (a) the geothermal target (EGS); (b) the direct information available from the two drilled boreholes; (c) the geological map  22  implemented by the results of the fieldwork survey, carried out in the GEMex project.
Integration process. The geomodel of Acoculco was carried out using an interdisciplinary approach thanks to the contribution of the specialists who participated by providing not only datasets but also contributing in the periodic discussions to evaluate and improve the geomodel. The available versions of the geomodel of Acoculco are the result of several steps, which are schematically presented in Fig. 16. Preliminary regional scale. The preliminary model was carried out in the first year of the GEMex project and included mainly the existing data collected in the first months of the project  28. Since only two boreholes (Fig. 17 ) drilled by CFE gave direct information on the underground, two interpreted geological cross-sections were prepared to constrain the model under the surface  28. Apart the litho-stratigraphy of the two deep boreholes  23  , an European-Mexican joint group provided a first version of the faults network after initial field work.
The datasets considered for the integrated model are listed in Table  2. An important contribution was provided by cross-plot and cluster analysis  33  that allowed a quantitative integration of the different geophysical datasets. Cross-plot are used to interpret geophysical datasets and can suggest various correlation between variables with a certain interval of confidence  33. The 3D integrated geological model at local scale 1 is shown in Fig. 23. It includes: i) the update of the location of the base of the volcanites with the resistivity outcomes; ii) the update of the skarn volumes of rocks with the defined high resistivity -high density volumes of rock obtained by the cluster analysis; iii) the introduction of a new young magmatic intrusion gathered from the regional thermal model and iv) the confirmation of the fault added in the Alcaparrosa manifestations area.
Data records The geomodels described in the previous section are recorded in the Zenodo repository under the Creative Commons Attribution 4. 0 International (CC BY 4. 0) license. They are listed in Tables  3, 4 , respectively for Los Humeros and Acoculco. The geomodels come with a metadata sheet in pdf format which summarizes the technical parameters related to the model such as location, description and contact. Each geomodel is available in the form of the following files and formats: • GeoModeller project format • PDF3D format • TSurf format • VTK format.
The robustness and efficiency of this co-kriging method has been proven by models constructed in various geological contexts for several applications  [35] [36] [37] [38] [39] [40] [41] [42] [43] [44]. In particular, the same potential field interpolation method embedded in the same software (GeoModeller) was used to successfully develop a geological model of a complex Alpine area  42. The authors discussed from a technical perspective the interpolation applied to the data of their zone. Even if this discussion is illustrated by their own data, most of the outcomes are relevant for any study using the potential field interpolation method. Thus, rather than repeat here the main conclusions of the authors to sustain the reliability and robustness of the interpolation method used by the GeoModeller, we kindly refer interested readers to that work.
Acknowledgements The authors wish to thank the  Comisión Federal de Electricidad  ( CFE, Mexico ) for their assistance and support. This paper presents results of the  GEMex  Project, which was funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 727550 , and by the  Mexican Energy Sustainability Fund CONACYT-SENER , Project  2015-04-268074. More information can be found on the GEMex website: http://www. gemex-h2020. eu. The collaborative and interdisciplinary work to produce the geomodels we are sharing benefited from many European and Mexican scientists. Authors want to acknowledge the contribution of all of the experts of the  H2020   GEMex  project who participated to some degree in the discussion and revision of the several stages of the Los Humeros and Acoculco geomodels.
Code availability The geomodels presented in this paper were constructed using the GeoModeller commercial package, which was developed by BRGM, the French Geological Survey, with the support of Intrepid Geophysics. This software is based on original methodologies for geological and geophysical modelling  25, 26, 45. More information at: https:// www. geomodeller. com. The following versions of GeoModeller were used to achieve the geomodels presented in this study. Los Humeros: GeoModeller Version: 4. 0. 7 Build Date: May 22 2019 Build Number: 27eee3dc31ba Acoculco: GeoModeller Version: 4. 0. 8 Build Date: Aug 04 2020 Build Number: d783d7694b8.
The assessment of human exposure to overall pesticide use is indeed limited by (i) the availability or lack of field experimental data that are useful to derive some parameters such as the characteristics of the exposed population, the agro-pedo-climatic conditions, and the pesticide properties  (Lammoglia et al. , 2017c) ; (ii) the lack of expert knowledge that is poorly documented in the literature; and (iii) the complexity of assessing the human health risk because of the wide range of exposure routes (inhalation, dermal, and ingestion)  (Damalas and Koutroubas, 2016). Some modelling frameworks such as the Pesticide Handlers Exposure Database, the UK Predictive Operator Exposure Model (UK POEM), the German Operator Exposure Model (German model), have been developed over the past decade to improve understanding and assess the exposure of human health to plant protection products (PPPs)  (Butler Ellis et al.
These include the European Predictive Operator Exposure Model (EUROPOEM), the Bystander and Resident Exposure Assessment Model (BREAM), and the BROWSE model; the latter is a probabilistic model of pesticide exposure  (Butler Ellis et al. , 2010 , 2017a , 2017b). EUROPOEM and BREAM were developed to assess both resident and bystander exposure to pesticides used in agriculture  (Van Hemmen, 2001; Anon, 2011). However, their parameterisation was based on general, incomplete regulatory databases in which real cropping practices and exposure routes are poorly incorporated  (Butler Ellis et al. , 2017a). The BROWSE model is one of the most mechanistic models developed according to the probability distributions of exposure to pesticides  (Butler Ellis et al. , 2017b). Compared with the previous models, BROWSE aims to improve and describe more realistic scenarios for analysing a wide range of current cropping practices.
Depending on the available evidence of exposure determinants of a given scenario, available experimental data and existing models, the most useful type of model was selected. The orchard scenarios for example are modelled empirically while the arable scenarios are evaluated using process-based simulation models  (Kennedy and Butler, 2017). To calibrate and validate the BROWSE model, its outputs were compared with estimates generated by other models (e. g. EUROPOEM). Each scenario is represented in the BROWSE modelling software with default input parameters which can be changed as required. Before every model run, some inputs may be fixed while others are assigned probability distributions. The Browse model includes some predefined constants and distributions taken from EFSA guidance, US Environmental Protection Agency (EPA) or other sources.
Study sites Three different farm(er)s located in the Setif region in north-eastern Algeria were studied for three successive growing seasons  (2019, 2020, and 2021). The first farm (farm 1) was located in Mezloug (36 • 06 ′ N, 5 • 20 ′ E) in the centre of Setif, whereas the other two farms (farms 2 and 3) were located in Beni fouda (36 • 15 ′ N, 5 • 30 ′ E) in the north of Setif. Meteorological data for each farm were obtained from the National Office of Meteorology in Setif (https://www. infoclimat. fr/observations -meteo/archives/1er/janvier/2019/setif/60445. html) (Table  1 ). The climate is typically Mediterranean: a warm temperate climate and dry summer. In terms of the repartition of both mean precipitation and minimal temperatures, there is a significant difference between Mezloug and Beni fouda.
Glyphosate was only applied in notill cropping systems. This could be explained by the key role of glyphosate in controlling weed infestation, especially in conservation tillage systems (Table  2 ). Some of the BROWSE default input variables originate from European guidance and regulatory databases. To avoid relying too much on those probabilistic default values, the pesticide handling practices and handling equipment were recorded for each farmer, as well as the physical characteristics of the residents or bystanders (adults and children) living within a distance of 2-20 m from the farm plots (Table  S1 ). Table  S1  summarises the input parameters related to the scenario, operator personal protective equipment, mixing and loading methods, and resident characteristics for the three farms studied.
The farmers were asked which types of safety equipment they used when mixing, loading, and spraying pesticides. The most common safety equipment used included rubber gloves and full-face masks (Type P1), long sleeves, and trousers, but no hood or visor. The information regarding the protective equipment used by farmers was used in the Browse model to select the appropriate protective factors (Table  S1 ). Instead of using the Browse default probabilistic distribution of body weight, the actual body weight of the farmers, as well as the average body weight of the children living near the farms, was obtained from the farmer surveys (Table  S1 ). The required data relating to the physicochemical, toxicological, and environmental properties of the pesticides sprayed were collected from four online databases: Pesticide Properties Database (http://sitem.
herts. ac. uk/aeru/), Sage pesticides (https://www. sagepesticides. qc. ca/), PubChem (https://pubchem. ncbi. nlm. nih), and Bayer Crop Science (https://www. cropscience. bayer. us/products/insecticides) (Table  S2 ). For input data not obtained from the farmer surveys, the values were set at their initial default values as defined in the BROWSE software interface  (Kennedy et al. , 2017).
Data analysis Based on the surveys conducted with each of the farmers in Mezloug and Beni fouda, the names of the pesticides used, including insecticides, herbicides, and fungicides, the number of applications, and the dose applied to the fields were used to calculate the Treatment Frequency Index (TFI). This corresponds to the number of registered doses of pesticides applied to one hectare. The TFI was calculated using Eq. (  1 ). The TFI is equal to 0 when no chemical pesticides are applied. High TFI values highlight both high frequencies of application and the application of excessive doses compared with the official approved rates. TFI = (Applied Dose/Recommended dose) × (Treated area size/Field size)(1) The interaction effect of barley and pea in a mixture was calculated using a competition index called Land Equivalent Ratio (LER).