dataesr/scientific-paragraphs-categorization huggingface dataset:
These are the reasons why, in the subsequent REVAMP and MegaM@Rt2 projects, we rather opted for extending/refining UML as 1) allowing atomic editing of components and 2) being already widely known in the industry. The main elements of a RE model in Modelio are shown in Figure  4 , with respect to the MegaM@Rt2 project as a practical example. Conforming to our RE language as described in IV-B, the overall structure of the RE model includes both the Requirements and Architecture levels. For the Requirements level, we directly benefited from Modelio Analyst features such as Requirements Containers, Tabular, Diagram view, Traceability matrices and Import from Excel. Modelio also allowed us to specialize the Requirements properties (that correspond to the columns in the tabular view) in order to implement our approach and language.
For the Architecture level, we relied on the standard implementation of UML as currently available in Modelio. We refined this UML implementation in order to limit the use of UML to the concepts considered in our RE language. Moreover, in order to facilitate the initial use of the RE language, we also built-in a specific template for tool components and made it available directly from the Modelio workbench. This way, the users of our RE language in Modelio can benefit from clear guidelines on what is expected from them when elaborating on their Requirements model. The Modelio Document Publisher was also a major feature that helped us to construct our MS Word document generator. Indeed, Modelio provides a simplified document template editor with graphical interface that has pre-build functionalities for navigating the model, filtering model elements, extracting textual notes and diagrams, as well as building sections, paragraphs, tables and matrices.
These features were particularly useful, since they allowed us to generate important parts of our documents. This is notably the case for roadmaps, where we created tables displaying mappings between case study requirements and tool requirements indicating planned delivery dates. Based on such roadmaps, the case study providers could anticipate and organize both the building of their tool chains and their validation activities. Moreover, the traceability information contained in the RE model was extremely important in order to identify the case study requirements that are not addressed by any of the tool components. This way, we were able to conduct gap analysis from our RE model in order to better plan corrective actions. In addition to the base implementation of our MBRE approach and corresponding language, our Modelio-based implementation also provides additional features which are particularly relevant in the context of RE processes within large collaborative projects.
V. EVALUATION In order to evaluate our MBRE approach and language, we collected various kinds of data associated with the three large collaborative projects considered in the present experience report. We describe quantitative and qualitative data resulting from both the projects' execution, in Section V-A, and a survey realized after the end of these projects, in Section V-B. In Section V-C, the collected data is then used to assess the relevance of our RE approach and language according to main targeted properties.
A. General Data on Projects The proposed MBRE approach and language have been designed, developed and then deployed in the context of three large collaborative projects, financially supported by the European Commission under different R&D funding programs. The first project is named DataBio, standing for Data-Driven Bioeconomy (Horizon 2020). It lasted 3 years from 2017 to 2019 and involved 48 partners from 17 countries, for a total budget of C 15M. 27 industrial pilots / use cases in the agriculture, forestry and fishery areas have been considered during the DataBio project. The second project is named REVaMP2, standing for Round-trip Engineering and Variability Management Platform and Process (EUREKA ITEA3). It lasted 3 and a half years from 2016 to 2019 and involved 27 partners from 5 countries, for a total budget of C 22M.
In terms of the Requirements models themselves, the data highlight the globally high number of handled requirements and related elements in the context of the three projects. We can observe differences between the projects but, as stated before, this can be explained by the specificity of each particular project e. g. the number of partners and the use cases to be covered. These differences are also directly reflected and visible in the number/size of the various project's documents or deliverables generated from the Requirements models in the three projects. We also want to note that the initial use of ArchiMate in the DataBio project, as previously mentioned in Section IV-C, does not have a significant influence since the number of concepts considered for modeling the architecture is exactly the same than in UML.
In total, we had 154 individual contact persons: 55 for DataBio, 43 for REVAMP and 56 for MegaM@Rt2. We received 15 complete answers, including 1 person not involved in RE activities. We explain this level of participation by a couple of factors: (1) Few of these contact persons were active contributors to the RE process in these projects, most of them were "readers" or contributed very few elements; (2) It has been at least 1 year since the projects terminated and at least 2 year since the end of the corresponding RE work. While this amount of data cannot be considered statistically representative, we believe the received feedback still provides interesting and relevant complementary insights. Overall, 76. 95% of respondents would agree or strongly agree that our model-based RE approach implemented in Modelio was useful.
Notably, the mostly appreciated features were related to the support for collaborative work, integration with a model repository, etc. The combination of well-known diagrams coming from UML and SysML with tabular views was also perceived as a good way to facilitate the use of our solution, especially for partners already having experiences in modeling or related activities. • Learning curve -Some participants in our projects had a somehow limited previous experience in modeling, both conceptually and technically. This resulted in difficulties for them to catch up with some of the concepts proposed by our RE language. In this same vein, it also appears that the guidelines initially provided, both on the approach/language and on the Modelio tooling itself, were not sufficiently detailed in order to allow for a first usage.
B. Threats to Validity In this paper, we evaluated the proposed MBRE approach and language according to different kinds of data we were able to collect both during and after the end of the three European collaborative projects mentioned earlier. The main threat to validity probably concerned the amount of data we have been able to gather. Our MBRE approach and language have been deployed in "only" three different projects from which we have extracted mostly quantitative data. However, the fact that these were large projects that run over 5 years in total already provides a certain level of confidence about the quality and relevance of the collected data. Moreover, we complemented this quantitative data with extra data (quantitative but also qualitative this time) we obtained as a result of a survey largely distributed among the three projects participants.
Indeed, this dipole marks the eastern edge of the Oman upwelling system which contains cold water. Apart from the study by Böhm et al. , the Ras al Hadd dipole has not been studied in detail. Taking advantage of a new, high-resolution altimetric dataset, analyzed via vortex identification and tracking software (AMEDA: Angular Momentum Eddy Detection and tracking Algorithm), of ARGO profiling float data in the region (from the ARGO program), of a regional primitive equation model (HYCOM), the structure and the evolution of the Ras al Hadd dipole are examined here. This paper is organized as follows: First, we present satellite and profiling float data, the AMEDA algorithm, and the HYCOM model simulations (Section 2). Then we analyze the Ras al Hadd dipole by studying its life cycle over a year (April 2014 to April 2015), and its horizontal and vertical structures using the altimetric and the float data, and the model results (Section 3), evaluating its inter-annual variability over a 15 year period.
Materials and Methods Time series of sea surface height data were obtained via a combination of up-to-date measurements by altimetric satellites (Topex/Poseidon, ERS-2, GFO, Jason-1, Envisat, Jason-2, Cryosat-2, Altika et HY2A). Using all available missions, an ADT (Absolute Dynamic Topography) product for the Arabian Sea was processed by CLS-Argos on a 1 8.
• Mercator grid, with time intervals of 24 h, for the 2000-2015 period. Those products were processed by SSALTO/DUACS and distributed by AVISO+, https://www. aviso. altimetry. fr (accessed on 1 December 2020)) with support from CNES, Centre National d'Etudes Spatiales, Toulouse, France The geostrophic velocity fields were then derived from absolute dynamic topography. The MDT used to calculate the ADT from SLA is described on the AVISO/CNES website https://www. aviso. altimetry. fr/en/data/products/auxiliaryproducts/mdt/mdt-description. html (accessed on 1 December 2020) and results from an average of sea surface height above the geoid over the 1993-2012 period. It takes into account the last geoid model GOCO05S (based on the complete GOCE mission and on 10. 5 years of the GRACE mission) and 25 years of altimetric and in situ data (hydrological profiles, drifting buoys).
The model also identifies merging (connection) and splitting of vortices, via the connection or separation of contours. This algorithm contains a minimal number of tunable parameters. It has been successfully tested on AVISO altimetry datasets, on ROMS model output, and on velocity fields issued from laboratory experiments. Here the AMEDA algorithm was applied to the high resolution altimetric maps. Finally, ARGO profiling float data were extracted from the Coriolis database with only validated data with a high quality index that corresponded to the period of study, used. The ARGO profilers are deep reaching oceanic floats, equipped with temperature, conductivity (salinity) and possibly oxygen concentration sensors. These floats first dive to a given depth (the parking depth, usually between 500 and 1000 m), then they drift at this depth under the influence of the oceanic currents, for a prescribed duration (their cycle period, usually 5 or 10 days).
Once every cycle, the ARGO floats move up to the sea surface, thus performing a vertical profile of temperature, salinity, and sometimes oxygen concentration, of the ocean. At the surface, the floats are positioned via GPS and they transmit their data back to the scientific institutes, via a satellite link. Then, they dive back to their parking depth for a new cycle. Therefore the vertical profile data are positioned but the exact trajectory of the ARGO floats at depth is unknown. The ARGO data used here (temperature and salinity only) were collected by floats with cycling periods of 5 or 10 days, and a parking depth of 1000 m. The Ras al Hadd AMEDA outputs for the studied period with their respective Argo profiles given in https://github. com/aayouche/RASALHADD (accessed on 1 December 2020).
The AMEDA algorithm applied to the altimetric data (the satellite measurements of the sea surface elevation) allows the tracking in time of the eddies detected. This tracking can thus be reversed in time to determine the origin of the two eddies (the cyclone and the anticyclone) which form the Ras al Hadd dipole. These two eddies are detected in the altimetric data as early as April 2014. At that time, the two eddies lie far from each other, in the Arabian Sea. The cyclone is then at the mouth of the Gulf of Oman, near the Iranian/Pakistani coast. The anticyclone is then located east of Ras al Hadd. Altimetric maps show that the two eddies slowly migrate during the 2014 summer monsoon (see Figure  2 ). The cyclone (CE) drifts southeastward (early to mid-June), then it remains at the same location and elongates until mid-August.
Variations of the Vertical Structure of the Dipole The vertical structure of the Ras al Hadd dipole is now evaluated using ARGO float co-localization in the two eddies. The co-localization is performed during the summer monsoon for 6 years (from 2010 to 2015). In the inner core of each eddy, the colocalized profiles give the structure of potential density and a few background profiles are obtained outside of the eddy (at 150 km from the eddy outermost closed contour). At each time step (when ARGO data are available), the inner core density anomaly is computed (ρ eddyρ background ). The background state is calculated out of the outermost eddy closed contour (using an average over 5 points). Then a time average is calculated (over the summer monsoon) and over all the detected profiles. The number of ARGO floats co-located with the dipole varies from year to year.
Conclusions The objective of this paper was the description of the seasonal life cycle and of the inter-annual variability of the two eddies composing the Ras al Hadd oceanic dipole. Using altimetric data and eddy identification and tracking software (AMEDA), we were able to determine the characteristics of the two eddies composing it, for a year (2014-2015). We determined their origin and drift from their initial position to their final junction near Ras al Hadd. We showed the importance of eddy-eddy interactions during the lifetime of the dipole. The evolution of the dipole was also driven by the local wind stress curl, by the upwelling circulation, and by the incoming Rossby waves. On a longer period (about 15 years), we noted that the anticyclone was larger than the cyclone of the Ras al Hadd dipole.
Signal-to-noise ratio (SNR) Because SOFI is free of localization bias when its core assumptions are satisfied  [5] , the quality of a SOFI image can be readily assessed by quantifying the noise on the image. Here we achieved this using the signal-to-noise ratio (SNR)  [25, 26] , which was calculated in the following way: for each set of parameters we simulated 20 different datasets, leading to 20 different SOFI images. The per-pixel SNRs were then calculated as the ratios of the average intensity and standard deviation seen for that particular pixel in the replicate images. The overall SNR was then calculated by averaging the per-pixel SNRs.
To avoid that the disparity between the magnitudes of the different factors could result in bias in the model, all factors were scaled by converting them to coded units such that the values ranged from -1 to +1 over their domain  [22]. Equation  8 shows the relationship between the coded units and the original units values. x i = (z i ∆x) + x 0 i (8) Here x i is the level of the factor expressed in real units, z i is the same level expressed in coded unit, x 0 1 is the factor level 0 expressed in real units and ∆x is half the difference between the highest level and the lowest level expressed in real units. A set of Matlab routines to produce a D-Optimal design and to reproduce the analysis presented in this work are available at the GitHub repository: https://github. com/dcevoli/doe4sofi.
Results and discussion The influence of the chosen parameters can be seen directly in experiments using DNA-PAINT  [14]. In DNA-PAINT the t on is defined as the average binding time between the two oligonucleotide strands, while t off is the average time passing between two successful bindings. While the t on depends uniquely on the affinity between two strands, the t off can be easily manipulated by the operator by modifying the concentration of dye in solution (and therefore of free strands available for the binding). Hence, DNA-PAINT could constitute a good candidate to test for the effect of modifying t off on the SNR in experimental conditions. An experimental DNA-PAINT dataset is presented in the Supplement 1 for the sake of illustration. What can be seen when such experiments are performed is that changing the t off produces a direct effect on the SNR, which seems to increase when the t off decreases.
However, increasing the concentration of the dye in solution not only influences the t off , but also increases the amount of stray light. Also, since a quantitative relationship between the t off and dye concentration is not know, it would be difficult to estimate actual values of t off from the experimental data alone. Therefore, relying on experimental DNA-PAINT data for a more rigorous optimization would be extremely difficult: a complete experimental model based on simulations is the most straightforward way to assess the influence of each parameter without confounding effects. For the simulated datasets, we applied our methodology to both second and third-order SOFI analysis, which resulted in two different polynomial expressions describing the relation between the measurement parameters and the SNR of the resulting SOFI images, within the investigated range for each parameter.
Acknowledgments. The authors thank  Peter Goos  ( KU Leuven ) for fruitful discussions. We thank  Ralf Jungmann ,  Thomas Schlichthärle , and  Alexander Auer  ( Max Planck Institute for Biochemistry ) for providing the DNA-PAINT sample. This work was supported by the  FLAG-ERA  grant  SENSEI  via  FWO  grant  G0G2819N.
Mathematical Model in WRF-ARW Code The calculation code WRF-ARW (WRF -Weather Research and Forecasting, ARW -Advanced Research WRF) is actively used to predict the capacity of the wind farm. The WRF-ARW model is a model of a regional weather forecast, for the construction of which it needs boundary and initial conditions  [1, 2]. Data sources in this case can be global models, observational data, or forecast reanalysis data. As a parent model for WRF-ARW, data from calculations based on the Global Forecast System (GFS) model is usually used. Figure  3  shows an algorithm for the interaction of different calculation models for different levels of calculation of physical quantities. The WRF-ARW model allows one to obtain a large range of physical quantities that describe weather conditions. As a rule, the main parameters are the distribution of wind velocity, temperature, and pressure.
Numerical Domain, Grid, and Results of Calculation The layout of the numerical domain for the area of the town Tiksi with the image of nested domains is shown in Fig. 4. The calculations in the WRF-ARW package were performed for the following calendar dates: 29. 09. 2019, 30. 09. 2019, 01. 10. 2019. To compare the results, the Tiksi weather station was selected: 71 • 34 48 N 128 • 54 E, altitude 7 m above sea level. Data on the temperature at the level of 2 m above the surface, the pressure, and wind velocity at the surface (at the level of the weather station) were displayed at the point closest to the location of the weather station in the d05 region. The graph of the velocity comparison is shown in Fig. 6. The model configuration and simulation results are given in  [4]. In general, the results of the model correspond to real data, in any case, they repeat the profile of changes in indicators.
Analytical Models for Calculation of Wake Parameters Analytical models in comparison with models based on the solution of partial differential equations are simpler and require less computing resources of the computer. The open package FLORIS (FLOw Redirection and Induction in Steady-state) contains various analytical models for calculating the vortex traces of wind turbines in the wind farm,  [18]. The FLORIS package was developed in the Python programming language at NREL (USA), TU Delft. One of the first analytical models for calculating the parameters of the vortex wake was developed by N. Jensen in Denmark in 1983  [19]. In this paper, it was proposed to use the "top-hat shape" model for the cylinder to calculate the velocity deficit in the track (see Fig. 8 ) and written in the form: ΔU U ∞ = 1 - √ 1 -c T / 1 + 2k wake x d 0 2 , ( 19 ) where ΔU U ∞ is a normalized (dimensionless) velocity deficit.
An analytical model with a self-similar Gauss profile was obtained in  [20] : ΔU U ∞ = 1 -1 - c T 8(k * x/d 0 + ε) 2 (22) × exp - 1 2(k * x/d 0 + ε) 2 z -z h d 0 2 + y d 0 2. The analytical models (  19 )-(  22 ), which were proposed by different authors to predict the deficit of the streamwise velocity in the of wind turbine, were implemented in open source software code FLORIS. FLORIS code is written on Python and is available on github. com [18]. In this paper, the calculation was carried out for a model wind farm with 3 wind turbines according to the Jimenez model, taking into account the influence of changes in the yaw angle for the nacelle  [7]. An example of the calculation for a wind farm in the FLORIS package is shown in Fig. 10. The size of the numerical domain was selected with 500 m in the OY direction and 3200 m in the OX direction.
Conclusion This approach, using meteorological data, wind farm data, open-source software WRF-ARW and FLORIS, allows us to formulate the problem of choosing the optimal parameters for the operation of wind turbines in the wind farm. In the future, it is planned to apply the chosen approach to solve the problem of choosing the optimal parameters for the operation of 3 Japanese wind turbines in the wind farm in the Arctic town Tiksi, taking into account the wind rose and complex terrain.
This difficulty hampers the identification of the optimal moment to test a therapeutic intervention and a fine-grained description of how the drug modifies the different aspects of disease progression. In this paper, we propose a system to position any patient at any time-point in a map of disease progression, called AD Course Map. AD Course Map summarizes the range of likely trajectories followed by patients developing AD. The positioning includes the identification a precise disease stage and a patient specific trajectory showing how the patient's data will change in the coming years. The positioning of several subjects opens up the possibility to explore the variability of disease manifestation via a small number of interpretable parameters. We construct AD Course Map using a statistical learning algorithm and a longitudinal multimodal observational data set.
This construction for two biomarker data extends therefore to any kind of data on a Riemannian manifold. pattern of changes across brain regions or for the shape of the structure respectively. In this setting intermarker spacing takes the more general name of space-shift. We use here a longitudinal data sets of patients who were diagnosed with Alzheimer's disease at some point of the study to reconstruct the natural history of the disease. We construct an AD Course Map showing the reduction in glucose consumption across brain regions, the thinning of the cerebral cortex, the deformation of the hippocampus due to atrophy, and the progressive onset of cognitive deficit from the preclinical to the clinical phase of the disease. We analyse the distribution of the parameters that adjust the reference scenario of disease progression to each subject's data.
Results AD Course Map: a multimodal atlas of disease progression. We estimated AD Course Map using multimodal longitudinal data from subjects covering the pre-clinical to the clinical stage of the disease (see Methods). Figure  2  shows at four representative Alzheimer ages (AA) the normative scenario of cognitive decline (first row), spatiotemporal propagation of cortical thinning across cortical regions (second row), progressive deformation of the hippocampus in both hemispheres due to atrophy (third row), and spatiotemporal progression hypometabolism across brain regions (fourth row). AD Course Map may be visualised at a fine temporal resolution in the form of an interactive visualisation at the website: www. digit al-brain. org. We found that the AA at the time of diagnosis is of 78 (±5.