dataesr/scientific-paragraphs-categorization huggingface dataset:
From this a corrective transfer function has been established: The transfer functions of SC 2, 3 and 4 are corrected by this formula. The corrected transfer function of SC 1 is computed by averaging the other three complex transfer functions. The new transfer functions thus obtained have been applied to STAFF data and compared again with FGM data, as will be shown later on in this paper, giving satisfactory results. It was then estimated that we had found the error sources, and that we could not go further. This comparison seems to show that the facility loops had already undergone the deformation at the time of Cluster STAFF search coil calibration, in 1999, observed some years previously. Since then, a new set of lops, as circular, planar and orthogonal as possible, has been installed at Chambon la Forêt Oservatory.
Sensor rotation and coordinate systems To transform telemetry data into significant physical units, we need to convert the data from the sensor coordinate system into one or another system, and in particular to transform from the spinning system into a fixed one, with respect to the Sun and the Earth, for instance. For the waveform data, all transformations are done on the ground, whereas for STAFF-SA data, part of the transformation is done on board. The following sections are dedicated to defining all intermediate coordinate systems required for this operation. Notice that these definitions can be used for other experiments of the same type, on any other mission. All transformation matrices are named as A_to_B, where A and B are two different coordinate systems. To convert a vector given in the A system to the same vector expressed in the B system, the following expression is used: For a general computation of this kind of matrix, see  Robert (1993 Robert ( , 2003 Robert ( , 2004)).
The Geocentric Solar Ecliptic (GSE) system The GSE system is a well-known system, with the Z axis perpendicular to the ecliptic plane, and the X axis toward the Sun. To do the transformation of the SSS to the GSE, the direction of the spin axis in the GSE system is required. Due to the gyroscopic effect of a spinning spacecraft, the spin axis is approximately constant in an inertial system, and so has a yearly variation in the GSE system, except during spacecraft manoeuvres. SR2 to GSE transformation is done using the module "tsr2gse" routine of the ROCOTLIB software (see  Robert, 1993 Robert, , 2003 Robert, , 2004)). The Cartesian GSE coordinates of the direction of the spin axis are required as the corresponding time measurement. To transform spin right ascension and the spin declination angle given in the STAFF-SC CAA data in the Geocentric Equatorial Inertial (GEI) system, routine "tgeigse" can be used.
Simplification of the cumulative matrix products The cumulative matrix product requested to transform original data given in SCS coordinates into a fixed coordinate system such as SR2 can be greatly simplified if we neglect all the small misalignment angles mentioned above. By the way, the first mass processing on the STAFF-SC data was to produce a database for the level 1 data (telemetry data) in the DSS, which is delivered to the CAA. The only difference between the DSS and the SCS sensor coordinate is a circular permutation of the components to get the Z axis close to the spin axis, since we assume that the SCS is orthogonal and equal to the OSS (see Sect. 5. 3). So, to transform data expressed in the DSS into the "fixed" SR2, we have to apply the cumulative matrix product:   x y z   SR2 = [SRS_to_SR2][BBS_to_SRS][DSS_to_BBS]   x y z   DSS.
In a general way, a long window leads to a more accurate calibration, but is time consuming, and enlarges the data gap. However, a long window could also lead to a non-perfect cleaning of the large sine signal due to the DC field, especially if it is fast varying. In any case, the best compromise must be sought in relation to the nature of the data (stationary waves or magnetopause crossing can require different calibration parameters). This is why we keep available in the Cluster Archive the level 1 waveform (uncalibrated). It is the expertise of the experimenter that will lead to better results.
Comparison of the two methods has been done by applying the two different software packages to the same data set, and concludes with a good agreement. Details of these comparisons will be done in another paper. Note that the calibration software used for CLUSTER is written in Fortran90, while the calibration software used for THEMIS is written in IDL. In this last case, the convolution operation has been done by the built-in "CONVOL" IDL function, which is very efficient. So, the good agreement of the two results is a proof of the validity of these two programs, which is very important for validating archive databases.
8 • has to be applied, as well as a BBS-to-SRS rotation matrix to have Bz parallel to the spin axis (see Sect. 5). This overall treatment of STAFF-SA data gives the complete complex spectral matrix (SM), the diagonal coefficients of the matrix being the power spectral density (PSD) for the five components, in physical units. These PSD diagonal coefficients are kept at a better time resolution than the overall SM. To obtain from the spectral matrix the polarisation and propagation parameters, one can use the PRASSADCO program that has been specifically developed for use by Cluster STAFF-SA  (Santolik, 2003) , as can be seen in the following section.
Acknowledgements. The authors deeply thank  A. Balogh , first PI of the FGM experiment, and the following ones,  E. A. Lucek  and  C. Carr , and the whole FGM team, who contributed to delivering to the CAA an invaluable set of data available to the scientific community. All engineers who have been working on the STAFF data since the beginning of the mission, too numerous to be mentioned, are fully thanked. The authors are indebted to  Christophe Coillot  for the establishment of the corrective transfer function formula,  Bertrand de la Porte  for the test on the transfer function, and  Kateryna Musatenko  for processing the in-flight calibration data. Many thanks to the  CAA itself  for its action, and for the organisation of a long series of cross-calibration meetings from which a large part of this work is issued.
AGRIBALYSE is a French research program dedicated to producing Life cycle inventories (LCI) of agricultural products, based on a strong partnership of 14 research and technical institutes. It provides a homogenous and consensual LCI database to support environmental labelling policies and to help the agricultural sector to improve its practices. The public LCI database of more than 100 products and its detailed methodology report were published in 2014. The database mainly contains LCIs for average French products, the functional unit is the product mass (kg) and the perimeter cradle-to-farm-gate. The datasets go along with a detailed methodology report and a project report, both available at: www. ademe. fr/agribalyse). Several key objectives for a follow-up project have already been identified: better estimate of uncertainties, accounting for soil carbon dynamics, impacts on biodiversity and impacts of water consumption.
Introduction AGRIBALYSE is a French research program  (2010) (2011) (2012) (2013)  dedicated to producing public Life Cycle Inventories (LCI) of agricultural products. A database of more than 100 products and its detailed methodological report have been published in 2014  (Koch and Salou, 2014). AGRIBALYSE started in 2010, in response to the "Grenelle de l'Environnement" roundtables which decided on the use of Life cycle assessment (LCA) to support environmental labelling. The LCA approach was also seen as a tool to support farmers and their organisations to better identify the environmental impacts of their products and to support ecodesign strategies to improve their production systems. At the same time, a lack of LCA results and especially homogenous ones was identified concerning French agricultural productions  (Ecointesys, 2008).
Thus, AGRIBALYSE was launched by the French Environment and Energy Management Agency (ADEME). A strong partnership of 14 research and technical institutes (INRA, Agroscope, CIRAD, ACTA and 10 agricultural technical institutes) worked together to build a homogenous and consensual LCI database for French, as well as a few imported agricultural products. This article describes the main methodological choices and the general content of the database. Other articles and conference proceedings describe more specific choices and results for each product sector (animal products/crops/fruits)  (Basset-Mens, et al. , 2014; Gac, et al. , Salou, et al. , Tailleur, et al. , 2014; Willmann, et al. , 2014). The potential use and limits of the database will be discussed in the last paragraphs.
General modelling framework and tools In AGRIBALYSE, data describing agricultural practices have been provided by the technical institutes in an Excel file (called data collection tool), developed for the occasion. The data collection tool was then connected to models to calculate direct (on farm) emissions and resource use via another Excel file (called calculation chain). Indirect fluxes were then added in SimaPro, to obtain LCI and LCIA data. The characterization methods used were chosen based on the recommendation by the ILCD Handbook, the Envifood program and the ADEME-AFNOR platform  (AFNOR, 2011; JRC and IES, 2011; ENVIFOOD, 2012; GT1, 2012).
System description, data sources and representativeness Initially, AGRIBALYSE aimed to provide a representative LCI for every major French agricultural product. The description of production systems by the technical institutes was based on (in order of preference): statistical sources, typical cases (documented in as much detail as possible), expert opinion or individual case/estimate as the last option. The quality of the description of the production systems was a key step for the quality of the database. For many products it was not realistic to identify a single production system to represent the French average. Therefore, several production systems for the same product (variations) were often described. E. g. for potatoes, the following LCIs were calculated: industrial use, fresh market firm flesh, fresh market other varieties, starch potato and a national average.
The national average is a weighted average based on the share of each system in the national production. For production systems that count a large number of variations, the weighted average may not be representative of the national production (e. g. the five dairy systems LCIs represent 21. 1% of the national production of milk). For some products an average product was not meaningful (e. g. grapes) because each product is specific of a production system or an area of production (Beaujolais, Maconnais, etc. ) and no national average could be provided. It is very important to be aware of the representativeness of each LCI when using the database.
Then, for the animal classes where allocation was required (ex: dairy cow in production phase), allocation was based on the metabolic energy required to produce each coproduct (calf, milk). The impacts of the animal classes producing a single product were fully allocated to this product. For data imported from pre-existing databases (e. g. ecoinvent v2, INRA Rennes internal database), such as for animal feed components, the allocation choice made in the pre-existing database (mostly economic allocation) was not modified.
Models for the calculation of the direct emissions AGRIBALYSE identified the most appropriate calculation methods for all direct emissions and resource consumptions, while pre-existing datasets (mainly ecoinvent v2) were used for calculating indirect emissions (up-stream ex: inputs production, material, etc. ). Finding appropriate models to estimate direct emissions for each pollutant substance was one of the main challenges. According to the AGRIBALYSE set of rules, such models should not require input data that are too difficult to collect, should have been published, should have been validated for France (or tropical conditions for the tropical crops) and should be recognized internationally. The priority was given to inter-product consistency, with the idea of having specific models for each emission mechanism used across the entire database rather than having specific models for each product or type of product.
Results The outcomes of AGRIBALYSE are a public LCI database, a methodological report  (Koch and Salou, 2014 ) and a final report describing the project organization and main lessons learnt  (Colomb et al. , 2014). The database is available in several formats with various levels of detail depending on the user's requirements: • Factsheets: a simplified version in PDF format presenting most common and most robust impact indicators. The factsheets are freely available and do not require an LCA software. • The AGRIBALYSE_vIMPACTS database (system processes, and Ecospold_V1 formats): containing aggregated LCI data sets considered by the AGRIBALYSE consortium to be sufficiently reliable to be used for a product environmental labelling approach (robustness, representativeness). An LCA software is required to analyse these data.
• The AGRIBALYSE_vcomplete database (unit processes, Ecospold_V1 format). These data are intended primarily for ecodesign projects and provide the results in a transpar-ent format. An LCA software is required to analyse these data. Ecospold formats are suitable for LCA specialists as the data can be used in current LCA software. The reports and summary factsheets are available on line (www. ademe. fr/agribalyse), while the full database is available on demand to ADEME. Finally, a short document "Advices for proper use of AGRIBALYSE results" (AGRIBALYSE, 2014) highlights the critical points when using and interpreting the data. The publication of data and the efforts made to ensure transparency, with several formats to respond to different needs has been an important time investment, but should contribute to a large and appropriate use of the datasets.
Improvement needs and perspectives AGRIBALYSE proposes a comprehensive database and a detailed and coherent methodological framework. For further work several methodological improvements have been identified. The main issues to be resolved are the modelling of changes in soil and biomass carbon stocks (due to farmer practices and land use change), of the impact on biodiversity, and the impact of water consumption. The methods and models for calculating emissions (nitrate, phosphorus, trace metals, etc. ) may also be modified to take better account of the specific characteristics of crops, agricultural and animal production practices and soil-climate characteristics. For France, IPCC tiers 2 models for GHG emissions should be ready soon and could significantly modify the results. It will be therefore important to update the database and use the best models available.
D104, page 4 of 6 V. Colomb et al. : OCL 2015, 22  (1) D104 The LCI database could also be enlarged, by including new products (ex: fishery products, other vegetables, more imported products, etc. ) and new product variations (regional products, Protected Geographical Indication products, quality label products, etc. ) could be included to strengthen the representativeness of the database. For a proper comparison of products, it would be necessary to better quantify the uncertainty of the LCI data. The quality of the data was assessed using the ecoinvent pedigree matrix and ILCD criteria. Data for tropical products and for less studied production systems (with less data available, like organic systems) hold the largest uncertainty. The future AGRIBALYSE database will probably allow the inclusion of LCI data proposed by third parties.
Contribution to environmental labelling and eco-conception The LCI data needed for the implementation of environmental labelling will depend on the labelling scheme. The companies will need reliable background data (such as AGRIBALYSE) and tools to implement ecolabelling schemes. By the end of 2013, a French parliamentary report on environmental footprinting highlighted the interest of consumers for environmental labelling of food products; it also identified several specific methodological issues for the food sector  (Errante and Saddier, 2013). It advocates that, in France, environmental labelling should become a voluntary but regulated process in the future. The volunteer companies should apply a common methodology and communication framework, which would be defined by public institutes and would limit the risk of green-washing or incomplete analysis.
No clear time frame for this development was given, but the will to implement environmental labelling was reaffirmed. The European Union is also currently carrying out an experimentation on the Product environmental footprint (PEF) to determine the appropriateness and feasibility of implementing environmental labelling at the European scale. AGRIBALYSE data could be used for this experiment. Thus the AGRIBALYSE LCI database is a key element for the environmental labelling of food products. It is however not sufficient as it does not include processing, marketing and end of life stages. The processing steps for food products are currently analysed in ACYVIA, another French LCI database program, that should provide results by the end of 2016. Both data from AGRIBALYSE and ACYVIA will feed ADEME "IMPACTS" LCI database, which should be the official multisectorial database supporting the French ecolabelling schemes.
AGRIBALYSE helps to guide the farming sector towards more ecoefficient agricultural systems. By providing a comprehensive method for future LCA studies of agricultural products, it will allow more comparable results and clearer conclusions. By November 2014, the database had been downloaded by 97 institutions (200 individual licences provided), research and technical institutes, agro-food companies, consultancies working on ecodesign. The data have also been integrated in some LCA computer tools. The database is used for instance in French Ecoalim research project which tries to identify animal feeding strategies with lower environmental impact. Data have also been used for policy studies, for instance in a study for the Swiss government comparing the impacts of Swiss and imported products, in order to define an appropriate agricultural and environmental strategy.
AGRIBALYSE II ADEME has decided to launch a follow up program for AGRIBALYSE. The general goals, providing data for environmental labelling and supporting ecodesign are still considered to be very relevant and will be conserved. The main goals for this program will be to (a) help users to produce LCI data following the AGRIBALYSE methodology, (b) update and maintain the data, (c) augment the database with LCIs of new key products, (d) promote and include methodological improvements from the LCA field. Thus, AGRIBALYSE II should be a platform coordinating several thematic projects on dataset production and methodology improvements that would feed the database. The platform should be strongly connected to other European and international database projects.
Conclusion The AGRIBALYSE partnership produced a consensual and homogenous LCI database for French agricultural products. By its ambition and innovativeness it also consisted in a major learning process for the partners involved. Nevertheless it must be stressed that the amount of work and skills required for such program has been considerable. The database is now used by many French and international organisations: research and technical institutes, agrofood companies, consultancies, etc. These data are publicly available and can be used in many projects dealing with sustainable production system, eco-labelling, diet studies, agricultural policy strategies, etc. For the future, several methodological issues remain to be implemented in the AGRIBALYSE methodology (dynamics of soil carbon stocks, water consumption, impacts on biodiversity) and the characterisation of uncertainty should be improved.
The polyproline helix type II (PPII) is a regular protein secondary structure with remarkable features. Many studies have highlighted different crucial biological roles supported by this local conformation, e. g. in the interactions between biological macromolecules. Although PPII is less frequently present than regular secondary structures such as canonical alpha helices and beta strands, it corresponds to 3-10% of residues. Up to now, PPII is not assigned by most popular assignment tools, and therefore, remains insufficiently studied. PolyprOnline database is, therefore, dedicated to PPII structure assignment and analysis to facilitate the study of PPII structure and functional roles. This database is freely accessible from www. dsimb. inserm. fr/dsimb_tools/polyproline.
Introduction Fifty percent of local protein conformations are constituted of the two regular secondary structure, i. e. a helices and b sheets, while the remaining protein structure is essentially constituted of turns that can overlap the two previous local conformation, and coil  (1). Regular secondary structures are fundamental descriptor for the analysis and the understanding of the structure and function of proteins at a molecular level. As such, they are automatically used to visualize the protein 3D structures with popular software like PyMOL  (2) , VMD  (3)  or Chimera  (4). Thus, the secondary structures assignment is an essential step for studying protein architecture, folding and for the prediction of 3D protein structure. Besides a helices and b sheets, a number of other regular secondary structures are often ignored, despite their importance in biological processes  (5).
Among other regular secondary structures the polyproline II helix (PPII) is of significant interest. PPII conformation was primarily identified in the 1950s in collagen helix by  Pauling and Corey (6) , and in structures containing many repeating proline amino acids  (7). It was not until the end of the 1990s that this conformation has been demonstrated to occur frequently in globular protein  (8) , with a very high conservation ratio of 80-100% in proteins families sharing 20% sequence identity or more, a ratio close to the conservation found for a helices and b strands  (9). Depending on the tools used for the assignment of secondary structures, their frequency varies in the range of 3-10% of all conformations with a common core of more than 1. 6% assignment shared by all tools  (10). Other studies have shown similar frequency, Adzhubei and co-workers in a recent review  (11)  estimated about 2% of residues in Protein Databank to be in PPII-helices of length 3 and more residues.
Thus, it was suggested that they might have an important functional role, particularly in protein-protein or protein-nucleic acid interactions and recognition  (12, 13). Regrettably, PPIIs are still insufficiently studied. In fact, PPII assignment is not done with the most common method of secondary structure assignment such as Dictionary of Protein Secondary Structure (DSSP; 14) and STRIDE  (15) , and therefore, newly solved protein structures are not assigned with PPII in Protein DataBank  (16). Here we introduce a new assignment method and a dedicated webserver for PPII.
Aim and overview of database The PolyprOnline database (http://www. dsimb. inserm. fr/ dsimb_tools/polyproline) contains secondary structure assignments on a large subset of the Protein Databank. It also allows to dynamically handle any new user submitted structures. Unlike other databases established for protein secondary structure analysis, PolyprOnline particularly focalize on PPII, an assignment that is rarely documented in experimentally solved structures as well as in services and tools dedicated to the analysis of protein structures. For instance, 2struc (http://2struc. cryst. bbk. ac. uk/about/) assigns protein in three secondary states using six different algorithms  (17) , but none of them address PPII assignment. More general tools such as PDBsum  (18)  give assignment by one method, PROMOTIF  (19)  in this case, with no details about PPII.