dataesr/scientific-paragraphs-categorization huggingface dataset:
Hence, this information must be considered with extra caution. For instance, the great proportion of floods without overflow of the Bruche River is clearly an artefact directly related to the nature of the sources that could be analysed (a large number of technical reports document each abnormally high runoff for this specific river). All in all, it should be stressed that ORRION, and, hence, our paper, aims at presenting "raw" flood information avoiding subjective choices as much as possible; therefore no explicit correction of potential biases related to sources has been achieved. By contrast, the information required to conduct a rigorous geohistorical analysis of the event chronologies is provided. Specifically all sources referring to events remain fully available within the database.
It appeared, for example, that the spatial distribution of the consequences of the 1910 flood in the Rhine plain can only be understood if one considers the uneven development of river correction works at that time  (Martin et al. , 2017). However, much work remains to be done to generalize such approaches at the scale of our entire database. According to this aim, rather than disseminating in ORRION "corrected" records and "definite" conclusion", the idea is to provide as much information as possible. This should allow anyone to conduct their own analysis, so as to determine and weight the different factors that combine to explain the flood record for each river or location. In this perspective, it is planned that further information regarding past engineering activities is documented in ORRION.
January 1936, that was the time of an extremely widespread and intense flood episode over the North Atlantic  (Ballesteros-Cánovas et al. , 2019) , is also in our record but corresponds to an event for which we could not determine the severity class. All in all, of course, much more can be done in terms of comparison between OR-RION and other records. However, this quick comparison already further suggests that, even if the event chronologies that can be derived from the ORRION database are clearly affected by human factors related to sources, and vulnerability, etc. , they also include information related to the physics of flood processes and to their underlying hydro-meteorological causes.
Pros and cons of the data structure and chosen event typologies The space and time comprehensiveness of ORRION and the easy browsing of the database allows information that is highly valuable for supporting risk management policies to be easily extracted and used. For instance, its original organization by event facilitates the easy characterization of areas potentially threatened within a single meteorological situation, with a direct measure of the likelihood of different rivers flooding at the same time, a very valuable contribution to operational risk management at the local scale. As a further example of its value, the database has already been F. Giacona et al. :  Improving the understanding of flood risk mobilized for the positioning of flood marks in the framework of the French PAPIs (flood protection plans).
The participative choice: a long-lasting but rewarding effort Given the now well-known limits of top-down approaches in the field of risk management, the TRANSRISK 2 research programme focused on the implementation of a participatory approach. The ORRION database has been developed in this way, with the double objective of knowledge capitalization and sharing. The participatory dimension of the database remains for the moment somewhat weak, with only a small amount of the information currently in ORRION coming from the public. However, this is not surprising since many studies have already highlighted the low public interest in natural hazard issues and their management. These studies identified different factors influencing the active engagement of the public, such as "the direct experience of flooding", "education" and "knowledge", "the technical resources of the public", and "public perceptions of technical complexity related to hazard mitigation"  (Koehler and Koontz, 2008; Kenyon, 2007; Godschalk et al.
Indeed, things may have been different in the context of more frequent major floods or just after a disaster. For instance, during the June 2013 Pyrenees floods, researchers from the University of Toulouse (France) issued a call for experiences (lived, felt, etc. ) and testimonies, including photographs and videos, not only of the current event but also of older episodes. This call for contributions was successful as more than 5000 photographs were collected (Program June 2013 Pyrenees' Flood, 2017; http://cruespyr2013. canalblog. com). Thus, the participative part of the ORRION is currently not perfect but it represents an encouraging start. The platform is now very well identified by local risk management actors, but some work remains to be done to involve the general public more. According to this aim, communication is clearly necessary to make more people aware of the existence of ORRION, of the reality of flood risk in the Alsatian region, and of the importance for disaster reduction that any information they have is made largely available on the platform.
Consequently, the database helps to renew our knowledge of flood hazard and risk in the French Upper Rhine catchment area, providing long spatio-temporal chronicles of floods for 13 rivers, and for all Alsatian municipalities, as well as their main characteristics according to various typologies (seasonality, causes, damage types, severity). It also helps identify the major changes and their social and environmental drivers throughout the study period. However, no attempt of any kind was made to correct the different bias in the records. Instead, in ORRION, the choice was made to provide as much information as possible to allow the records to be meaningfully interpreted by any potential user. Methodological developments now exist to remove the trend due to population increase and source availability  (Macdonald and Sangster, 2017).
Such an approach could be put to use for ORRION's events, so as to help interpret them in terms of increase and decrease of flood frequency and more generally in terms of changes in hazard and risk through time. Indeed, in this work, only a simple statistical analysis of the summed up abundant information was performed. This choice was made because it is arguably sufficient to highlight the main characteristics in space and time of the data and to highlight and discuss the factors that explain the spatiotemporal distribution of flood events, namely a combination of anthropogenic factors related to sources, vulnerability, and infrastructures, but also of natural factors related, for example, to climate, both at the scale of the entire database and/or for more specific rivers and locations. Assessing association solely on the basis of the Pearson coefficient may be seen as oversimplified, but it was feasible with our binary, continuous data set and sufficient to identify couples of variables, rivers, and municipalities, etc.
These were already used for the Kinzig River within the German part of the TRANSRISK project. Also, ORRION's information already allowed the historical floods of 1852 and 1860 for the Ill in Mulhouse to be better characterized and the corresponding discharge modelling that has been realized within the framework of the European Floods Directive to be validated. Finally, the study of historical water levels already revealed that the computation of the centennial flood of the Zorn River should be reconsidered  (Martin et al, 2018). Hence, we are well aware of the potential of the available information for discharge reconstructions and of the usefulness of doing this for improving operational flood risk management. Preliminary approaches will therefore be expanded and generalized, in order to, in the future, associate discharge information with ORRION events within the database.
The available information, although impressive, is certainly not exhaustive. Further browsing of archival documents, local press, and official documents initiated after disaster occurrences and integration of their output into ORRION are envisaged if the funds can be obtained. This would allow for better documentation of the Alsatian rivers that have not been studied so far because the number of flood events involving these rivers has not been regarded as representative enough of the physical reality of flood hazard and risk. In parallel, documentation of certain major events of the past such as the 1910 and 1919 floods could thus be improved, and more contextual information regarding, for example, past engineering work and ancient river profiles will be included in the database. However, the main challenge in terms of data improvement is to promote database enrichment through its participative interface.
The use of remote sensors to acquire metric information for building information modelling (BIM) of heritage buildings is now common. Problematically, the creation of models from that information is still largely a manual and non-quantifiable process. As a result, a key aspect of the scan-to-BIM process is verification of the accuracy of the model in relation to the metric information. The most common method to check a model element constructed from a point cloud seems to be the analysis of deviations between this element and the corresponding point cloud  (Anil et al. , 2013; Tang et al. , 2011). It is comprised of three main steps: the computation, the visualisation and the analysis of deviations. The verification process is particularly onerous for large-scale buildings where it is necessary to ensure that all elements of a model are consistent with metric data that may come from diverse sources  (Chow and Fai, 2017).
INTRODUCTION The use of remote sensors to acquire metric information for building information modelling (BIM) of heritage buildings is now common practice. Point cloud data is often used for the digital reconstruction of buildings. The manual generation of BIM models from point clouds consists of using point cloud data as visual support in BIM software. The scan-to-BIM process tends to be automated  (Macher et al. , 2017; Thomson and Boehm, 2015)  but remains still mainly manual. Whether or not they are automatic, the scan-to-BIM approaches lack clear methods for geometric quality assessment of BIM based on point clouds  (Bonduel et al. , 2017). The verification process is a key aspect of scan-to-BIM process since it guarantees the reliability of BIM models for further uses by other stakeholders.
Process currently in use At CIMS, BIM models are generated from point clouds by using the commercial software Revit from Autodesk. Point clouds are used as visual support to model building elements. Since the architectural geometry found in heritage buildings typically show deformation, the modelling process may involve some approximations. As a result, an acceptable tolerance has to be agreed upon by all project partners at the onset of a project. At CIMS, that tolerance is a maximum deviation of 2. 5 cm. If the deviations are higher than the allowable tolerance, the element has to be replaced or remodelled. The process for verifying a model created from point clouds involves creating multiple sectional views along elements in Revit and measuring the deviations that appear to be the greatest between the point cloud and the model element.
Overview Revit software provides an Application Programming Interface (API) namely a set of subroutine definitions, protocols and tools for building application software. Custom tools and features can be created by C# programming (Microsoft Visual Studio) and plugged directly into Revit, extending its functionality  (Autodesk, 2018). A plug-in for Revitnamed 3D Analysiswas developed at CIMS to automate the verification process. The plug-in aims to visualise the deviations between Revit wall elements and adjacent point cloud data in a 3D view. Figure  2  presents an overview of the developed plug-in. Figure  2 : Overview of the developed plug-in The user is prompted to select a wall and a point cloud in the Revit environment either in a 2D or a 3D view. Points which are within a given distance from the two faces of the wall are selected.
Selection of points and computation Once the user has selected a wall and a point cloud, points, which are within a given distance from the two faces of the wall, are selected. In order to do that the following parameters are extracted from the Revit wall element (Figure  3a ): expanded, support was extended for metric units but the existing database structures were preserved for backward compatibility. The length values extracted from the Revit database are in imperial units. Thus, the extracted parameters have to be converted into metric units. A distance d between the points and the wall has been fixed to 5 cm. This value is two times higher than the tolerance used at CIMS so that points which were missed during the modelling process can be located. Considering the modelling process, points are located either inside or outside the wall, therefore, points for which the distances from the wall are smaller or equal to 5 cm are selected in the two sides of wall faces and deviations are between -5 cm and 5 cm.
Visualisation For each point of a CloudPoint structure, one can access the colour of the point by using point. Color. Unfortunately, this field is in a read-only mode and cannot be used to apply a colour map to the points depending on deviation values. To display the computed distances thanks to a colour map, the Analysis Visualization Framework (AVF) was used. AVF is a part of the Revit API, which enables to draw colour graphics in a Revit project with different display styles (colored surface, markers, diagram, and vectors). Graphics are not residents of the database and thus don't increase the size of the Revit model. They are only stored until the document is closed. AVF is normally used for visualising data like lighting, energy, structural analysis, airflow, and temperature. The visualisation of point cloud deviations from a model is an unusual use for the AVF.
Benefits of the plug-in The benefit compared to the previous method described above is that the deviations are represented in a 3D view for the whole element making it easier to see potential modelling errors and deviations. The plug-in is easy to use, the users only have to select the wall element and adjacent point cloud. The computation and the display of the result takes a few seconds. The analysis of the result is facilitated with information about deviation repartition. The plug-in is currently being used for the verification of a large-scale heritage BIM project. Since the plug-in automates part of the verification process, it increases efficiency by eliminating the need to manually check the deviations between the point cloud data and wall elements through multiple section locations.
Image processing & segmentation A combination of the open source image-processing program ImageJ  [60] , and of either custom-written MATLAB scripts or available MATLAB functions were used to segment and analyze the image sequences captured during the experiments. Two types of analyses were performed. Firstly, the foams images taken in the 1mm Hele-Shaw cell were treated to measure the bubble size distribution of the foams, for the various foam qualities. The images were first thresholded and transformed into black and white pictures were the bubbles appeared white on a black background (see figure  2 (a)), then the equivalent diameter of all connected white regions in the images was measured as from their area. We thus obtained the  = (/) 1/2   probability density functions for the bubbles' apparent diameters.
We used package PIVLab  [61]  in MATLAB, with a single interrogation size of 128× 18 on the first pass, and 64 × 64 pixels on the second pass. The raw images were first applied a high pass filter of size 15 pixels (see figure  3(a-b )). The horizontal dimension of 2048 pixels corresponded to a length of 260 mm, hence one pixel corresponded to 127 µm in real space, about one order of magnitude smaller than the typical bubble size, but the PIV procedure provided a horizontal resolution of the velocity map of 16 mm. Note that the bubbles whose velocities were measured in this manner belonged to the top layer of bubbles, in contact with the upper wall of the fracture. For some subparts of the image, the filtered image did not contain any significant "dots" to be traced, so no velocity was measured in these regions (see figure  3(bd )).
The average shear rate was also computed for the series of experiments at constant foam quality 85% and various flow rates ranging between 10 and 60 ml/min, but the acquisition frequency of 1 Hz allowed obtaining meaningful velocity fields only for the data recorded at 10 and 20 ml/min (at 11. 5 and 22. 6 m/s, respectively), with an increase when increasing the flow rate, as expected. In contrast, the dependence of the apparent viscosity could be obtained from Eq. (  1 )) for all flow rates. Figure  6 (b) shows apparent viscosity decreases with flow rate in both fracture and Hele-Shaw cell as expected due to shear thinning behavior of foam. It is shown in Figure  6 (c) and seems consistent with a power law behavior, of exponent -0. 41, to be compared to the exponent -0. 27 for the Hele-Shaw cell data of Osei-Bonsu et al.
The initial conditions are CS = CS,0 for the solid phase and CL = 0 for the liquid phase. CS,0 is the initial concentration of lipid in larvae particles (g/m 3 ) and CL is the concentration of lipid in solvent (g/m 3 ). The boundary conditions for Eq. 1 are: = 0 (2) - ( , ) = ! ( ) (3) where A is the surface area of larvae particles (m 2 ), V is the volume of solvent used for extraction (m 3 ), r is the radius of larvae particles (m). The "pdepe" function in Matlab, R2010a (The MathWorks, Inc. , MA, USA) was used to solve the aforementioned parabolic partial differential equation  (Tao et al. , 2019). To be exact, the original partial differential equation (Eq. 1) was first discretized spatially. After that, the resulting ordinary differential equations were integrated in time and solved by the pdepe solver.
The De value was adjusted iteratively to fit the experimental data, thus minimizing the root mean square error (RMSE) between experimental and predicted the content of lipid in larvae particles: "#$%(g/m ) ) = * + ∑ -. /,0 (1) -. /, (1)2 + 3 (4) where CS,e and CS,p is the experimental and predicted values of lipid content in larvae particles (g/m 3 ), respectively. n is the number of experimental points. Once the optimized De value was obtained, three statistical indicators, including R 2 (coefficient of determination), RMSE and absolute average deviation (AAD) were calculated using the data about lipid extraction yield to test the predictive accuracy of the diffusion: Following the numerical simulation results, the distributions of lipid content within larvae powders at different stages of extraction were visualized by programming in Matlab.
Theoretical predictions For an effective understanding of theoretical data that are generated by tools such as Hansen, COSMO-RS and ACD labs it is essential to establish a local solute solvent database which is specific to the sample chosen for analysis (BSF) and chemical constituents of interest present in them. In this case solvents of different polarity (n-hexane, ethanol, isopropanol, methyl acetate, ethyl acetate, ethyl lactate, dimethyl carbonate, 2-methyl tetrahydrofuran, cyclopentyl methyl ether, α-pinene, d-limonene, p-cymene) and solutes generally present in the lipid fraction of BSF were chosen after careful literature review. Preliminary parameters was set up with solutes belonging to various classes of the lipid fraction such as free fatty acids (FFA-Lauric Acid), monoglycerides (MAG-Glyceryl 1laurate), diglycerides (DAG-Glyceryl 1,2-dipalmitate), triglycerides (TAG-Lauric triglyceride), Vitamin E (VE1-α-tocopherol; VE2-γ-tocotrienol), sterols (ST1-β-sitosterol; ST2-Cholesterol) , and pigments (CA1-β-carotene).
43%, myristic acid (C14) with 9. 36% in decreasing order. These five acids combined made up almost 90% of the fatty acid profile of BSF oils in both cases. The saturated fatty acids were the largest class of fatty acids accounting for 69. 13%, followed by polyunsaturated fatty acids responsible for 15. 44 % and closely succeeded by monounsaturated fatty acids taking up 14. 34%. The data were consistent with previously published results  (Liland et al. , 2017)  with a similar trend of fatty acids being reported. Across all previous research articles that probed the fatty acid profile of BSF, lauric acid was found to be the principal fatty acid with almost 35-40% present in BSF fed average diets, without any extreme feed formulations  (Caligiani et al. , 2018; Ushakova et al. , 2016). The neutral and polar lipid classes of BSF oils were identified and quantified using HPTLC.
Ideally, since there was no visible change in the protein bands observed by electrophoretic separation it can be assumed that no deleterious effects were imparted during the defatting step at least in the soluble protein fraction. Further protein extraction, fractionation, purification might help throw some light on the elusive protein profile of BSF. The techno-functionality properties of flours and proteins from mealworm and BSFL was compared by Bubler S. et al. , 2016 who also found two soluble proteins from BSFL of which the most abundant was identified to have a molecular weight of 80. 5 kDa and the other 14. 3 kDa. Only one protein was designated in the reviewed section of the UniProt database, it was cecropin-like peptide 1 with accession number: L7VIN3 and molecular mass of 4. 84 kDa, supposedly an antimicrobial peptide active against gram-negative bacteria.
C. D. Jones et al. :  ZECMIP contribution to C4MIP ulation from comprehensive general circulation Earth system models (ESMs) and Earth system models of intermediate complexity (EMICs) -a branch from the 1 % CO 2 run with CO 2 emissions set to zero at the point of 1000 PgC of total CO 2 emissions in the simulation -with the possibility for additional simulations, if resources allow. ZECMIP is part of CMIP6, under joint sponsorship by C4MIP and CDR-MIP, with associated experiment names to enable data submissions to the Earth System Grid Federation. All data will be published and made freely available.
B3 esm-bell-2000PgC An emissions-driven simulation (fully interactive CO 2 ), initiated from esm-piControl using CO 2 emissions, amounting to 2000 PgC, following a bell-shaped curve for 100 years followed by zero emissions for at least 100 years. 2 ZECMIP analysis will draw on carbon cycle feedbacks and process understanding from C4MIP (Coupled Climate Carbon Cycle Model Intercomparison Project;  Jones et al. , 2016)  and aims to complement analysis on reversibility and CO 2 removal under CDRMIP (Carbon Dioxide Removal Model Intercomparison Project;  Keller et al. , 2018). Both C4MIP and CDRMIP encourage participation in the ZECMIP top-priority simulation. For simplicity, the data request is a replica of that for the CMIP6 emission-driven historical simulation (esm-hist). No new variables have been added.
For EMICs the request is to output the same model variables as from the 1 % run, which forms the basis of ZECMIP, with the one addition of also providing atmospheric CO 2 concentration. Data can be published via the Earth System Grid Federation (ESGF) (for ESMs contributing to CMIP6). An equivalent data repository will be avail-able for EMICs and likely based at the University of Victoria -details will be communicated during summer 2019 via C4MIP and CDRMIP websites.