dataesr/scientific-paragraphs-categorization huggingface dataset:
The COFFA software has been developed to help CAM operator to make the evaluation of the desirabilities of the action rules and choose the orientation of the part by trying several possible scenarios. After the operator has assigned attributes to relevant part's surfaces, the software computes every set of desirabilities for a series of rotation angles along x and y axes. All of these are presented as a series of heatmaps from which the operator can make his choice of manufacturing orientation. An example of such result is displayed in Fig. 2. From this point, the users can explore various orientations and see the values of the associated desirabilities, to nally make their choice of the manufacturing orientation. We take a desirability map generated by COFFA as an example of "Minimize part shadow on start plate" (Fig.
5 ). In Fig. 5 , the rst level of evaluation is the subject of our paper. This rst level concerns the evaluation of the user's choice using the COFFA decision support tool compared to the choice made by a group of expert. This comparison will be based on two parameters, which are the aggregate desirability and the orientation angles around the axes x and y. The following levels of evaluation are grouped together in a second phase, which focuses on the produced part. This second phase of the evaluation is related to the evaluation of the quality of the manufactured parts according to the choice of orientation made with COFFA software. For this second evaluation, we need to take into account many other factors in addition to the part orientation, such as region to support and design of supports, in the evaluation of the quality of the part.
III. 3. Our approach To evaluate the COFFA software, we set up a global evaluation approach of the parts orientation activity in EBM/AM context. The classical evaluation of software in our case does not allow us to see in detail the quality of the result obtained using COFFA software and if the tool had helped the user to make better choices. We choose to represent the part orientation activity with the simpli ed activity model of Kuutti  [31]  from the model of Engeström  [32]  (Fig. 6 ). The vertices of the triangle of the activity model were then used as variables of the empirical study to see the in uence of these key points on the result obtained by using the COFFA software. Each pair of vertices and the link between them was evaluated with a speci c indicator. Knowing that the orientation activity is a complex activity and its study in an industrial context is a di cult work to implement, the choice of experimentation with a scenario of case study was imposed to us.
IV. 1. 2 Research point of view Our research is part of the Coffa project. This empirical study focuses on the following aims: Evaluate the usability of the COFFA tool to assist the user in his part orientation activity and the help to the choices brought by the desirability-based maps; Evaluate the quality of the results obtained with COFFA; Evaluate the concordance between good desirability and good part orientation in the case of additive manufacturing by EBM; Observe and analyze the use of the concept of action rules derived from the elicitation of expert knowledge by students (novices). Evaluate the overall part orientation activity through the use of COFFA software and action rules.
IV. 3. Scenarios The main purpose of our experimentation scenario was to put in situation actors, with different levels of expertise, to make the choice of orientation of parts (with different geometries and design criteria) according to the proposed study case, to be realized in additive manufacturing of EBM type. The user had to do this by applying the knowledge acquired through the action rules, using rstly a simple 3D viewer (without assistance) and then the COFFA software. The participants were each provided with a computer with all the necessary software as well as materials describing the work required, a reminder of the action rules, and the questionnaires which they had to answer.
IV. 4. 1 Subject: students, researchers The empirical study was conducted with twelve students and seven experts. We performed the experiment with the researchers from our laboratory team. We considered that their expertise on the problem of additive manufacturing and their knowledge of the related physical phenomena give them an expert status compared to the students. Before we nish on this point, we would like to clarify the term expert that we use in our article, namely that there are three types of experts: The experts who enabled the extraction of the EBM part orientation knowledge (CAM operators from industry); The experts who worked with the rst category of experts to elicit this knowledge and develop the COFFA decision support software (researchers from the AM team of the G-SCOP laboratory); And nally the experts who did not participate in the work mentioned above but who regularly use the EBM machine (other members of the AM research team of the G-SCOP laboratory).
Evaluating part: The next part with a geometry close to that of the students' projects was to allow the evaluation the students' learning and to have a comparable result between the different students after a few hours of learning. Mastering parts: The last two parts were dedicated to the experts to see the in uence of the complexity of the geometry on the orientation activity and to see a more in-depth exploitation of the COFFA software. We did not consider it interesting in our experiment to propose these rooms for the students given the level of knowledge they had. During the part orientation activity phases, the participants had to orientate the parts with the Coffaviewer in the rst instance, following the order of the parts as presented above (Fig. 9 ). Each time they completed an orientation, the students had to record the oriented part and ll in the associated action rules questionnaire.
IV. 5. Progress of the experiment In the second phase, they had to repeat the orientation work with the COFFA software in the same way, and in addition ll in the usability questionnaire on the rst and last use of the software. An additional session was added to debrief and discuss with the students the results they had obtained and the answers they had given in the different questionnaires. This session allowed to clarify some of the answers given and to have a direct feedback on their experience of using the software. In the last phase, all the experts who took part to the experimentation were grouped together to determine the optimal orientation for each part and each case study by comparing their points of view. These socalled reference orientations allow us to compare the results obtained by the students and the experts individually and to have the corresponding desirabilities to see if they con rm our initial hypotheses.
Finally, the usage sequence of COFFA software after COFFA-viewer has been chosen to accentuate the learning and appropriation of the concept of action rules. In the case of COFFA-viewer, the user must understand the action rules and use them by mentally visualizing their in uence on the study case. This mental work facilitates the comprehension of the action rules, and consequently the use of decision support by COFFA software becomes more e cient by being less impacted by comprehension di culties. V. Results And Discussions.
Concerning the action rule n°5 relating to the risk of deformation, there is a contrast between its use by the experts and the students. The problem of incomprehension and di culty of use raised by the students during the debrie ng re ects their low use of this action rule, which is not the case for the experts, who used it in a much more signi cant way. We also see that whatever the case, the experts almost always use action rules 3 and 6 (Priority surfaces and SSDR). This shows that they tend to prioritize certain action rules over others. On the other hand, the use of action rules with COFFA is more massive for the experts than for the students, who did not exploit the software's full potential. Finally, we can also observe that the students did a good job of appropriating the action rules for orienting the parts, based on the very similar results of using the action rules with Coffa-viewer and COFFA.
V. 3. Comparative analysis of results V. 4. Decisions made by Coffa-viewer vs. COFFA From the graphs in Fig. 14 , the following remarks can be made: The choices made with the COFFA tool are much more clustered, and symmetrical orientations, with respect to the X and Y axes, appear to be the choices of most participants (students and experts). The choice proposed by the COFFA software with the highest value of aggregation of desirability, with the same coe cients, was chosen several times by the experts individually and collectively as being a good orientation, and in some cases as the reference orientation. For the pieces where this choice was not retained, we found that by aggregating only the action rules used by the experts, we found a very close result, or even the same reference choice.
11. On the other hand, the experts underlined the di culty of evaluating certain criteria (e. g. supports that are di cult to remove) for parts with complex geometry, which, according to them, require the improvement of the decision-support tool by adding some features related to the exploitation of the result (zoom in the heatmap, exploring tool, etc. ). To answer our initial research question, we implemented indicators during the empirical study, namely: aggregated desirability, part orientation in the solution space and the use of action rules. In addition, the comparison of the use of COFFA software versus Coffa-viewer highlights the usefulness of using a decision support tool for part orientation in EBM. The use of the COFFA software allows better choices to be made in most cases and sometimes avoids making a bad choice for both novice and expert users.
Vi. Conclusion This paper proposes to assess a decision-support software called COFFA for part orientation in the context of EBM additive manufacturing. We set up an empirical approach that allowed us to observe the use of the software in different situations and to collect data related to our indicators in order to answer our research question. The confrontation of the results obtained using the COFFA software and a simple 3D viewer as well as the results obtained by the students and the experts leads to the following conclusion: COFFA software prevents the user from making the wrong choice regarding the criteria related to the evaluation concepts (let us recall that these concepts were established by the elicitation of the knowledge of trade experts); The aggregation of the desirabilities for a given orientation of the part represents a good indicator of the user's choice.
This partially con rms our hypothesis that a good desirability corresponds to a good choice of orientation of the part; COFFA software allows experts to verify the orientations of parts in terms of desirability and thus to con rm their choices and even justify them. As an underlying result, the overall approach undertaken for the evaluation of the COFFA software can be generalized for application to other software in other contexts, with the following steps: As a perspective to this work, we propose to see the implications of the orientation choices on the rest of the additive manufacturing process, going up to the parts made with the EBM machine. As already indicated, there is another level of evaluation that needs to be explored in more depth by trying to pay attention to other elements that can in uence the nal result of the manufacturing process (choice of areas to be supported, design of supports, post-processing.
the Ensemble Kalman Filter (EnKF), a sequential data assimilation tool that combines model forecasts from Salammbô and in-situ data to determine an optimal estimation of the actual conditions  (Evensen, 2003). It is expected that the implementation of Salammbô-EnKF in the fully operational SafeSpace pipeline, will operate with observations from the NOAA GOES-16 electron monitor  (Onsager et al. , 1996)  as well as the EMU instrument onboard Galileo GSAT 0207 90 and 0215 satellites  (Sandberg et al. , 2019). The input data required by Salammbô-EnKF (in the SafeSpace project context) are: time-dependent diffusion rates describing the radial diffusion process, using the solar wind-driven neural network-based EMERALD model  (Aminalragia-Giamini et al. , 2022). time-dependent pitch angle and energy diffusion rates, induced by resonant interaction with waves and computed by the FARWEST code  (Dahmen et al.
Using an extensive historical distribution is necessary to ensure that the computed statistical distribution is representative of the full-range of the electron radiation belts' dynamics. Indeed, it is notorious that the solar cycle 24 (2008 -2019) was a weak cycle during which few intense events were observed. Studies dedicated to the analysis of extreme events in the electron radiation belts (such as  Meredith et al. , 2015; Bernoux and Maget, 2020)  show that no extreme events where witnessed during solar cycle 24, whereas multiple ones where observed during solar cycle 23  (1996 -2008). Therefore, using a short dataset made of measures from the solar cycle 24 alone would probably bias the statistical distribution, and more importantly its tail. The "red" threshold would hence be too low, and fluxes above such threshold could be observed more often than simply 2% of the time, which would trigger too many alerts.
Example forecast on St Parick's 2015 storm To assess the reliability of the SafeSpace safety service segment, we decided to test it over the March 2015 period when the CME-driven St-Patrick storm occurred (see figure  4 ). Precisely, the dynamics of the electron radiation belts during this intense and representative event were replicated with the SafeSpace pipeline. Salammbô-EnKF assimilated measurements from the Magnetosphere Electron Detector (MAGED) embarked on the GOES-15 satellite  (Hanser, 2011)  and the CXD instrument 160 embarked on the GPS-NS54 satellite  (Carver et al. , 2018) , shown in figure  4. differential flux measurements for electrons at 3 MeV, 1. 6 MeV, 300 keV, and the Kp index. In order to measure the nowcast and the forecast capabilities of the SafeSpace pipeline, we conducted two different simulations of the March 2015 period : -An ensemble simulation of the whole month with the assimilation of GOES and GPS data during the whole simulation period.
Conclusions In this article, we introduced new space industry tailored internal charging dedicated indices, derived from the SafeSpace physical chain of codes that models the outer electron radiation belt dynamics. We presented the different components of the pipeline sun-to-earth, with its heliospheric propagation, inner magnetosphere and safety warning system segments. We focused on the latter part of by presenting the dedicated safety indices defined on three standard orbits (LEO,MEO,GEO) and constructed on the daily averaged electron fluxes. We also specified the risk levels for moderate and active alarms in the warning system and showed the data sets used to build the statistical distribution for the indices on each orbit. We discussed the importance of a long and representative historical distribution for a reliable and trust-worthy warning system.
Data availability. The SafeSpace safety service website (http://www. safespace-service. eu/) gathers data of the indices computed by the SafeSpace pipeline for the current day (nowcast), the day before and four days ahead in time (forecast) in addition to plots of the indices in colored bar charts, according to their activity state and the location of the indices in their associated cumulative distribution function. The NOAA GOES-15/MAGED omnidirectional electron fluxes data are publicly available at https://satdat. ngdc. noaa. gov/sem/goes/data/full/. The GPS-NS54/CXD electron fluxes are publicly available at https://www. ngdc. noaa. gov/stp/space-weather/satellite-data/satellite-systems/ gps/data/ns54.
While most of POC is remineralized in the euphotic and the mesopelagic zones (200 -1000 meters), a small but significant fraction of POC reaches the deep ocean below 1000 m to be sequestered for hundreds or thousands of years  (Lampitt et al. , 2008; Burd et al. , 2016). Despite its critical importance, the annual estimate of global carbon export remains poorly constrained, ranging from 5 to over 12 Pg C per year  (Turner,  The paper is organized as follows. Section 2 introduces the methodology use to design the databases for training and performance evaluation. Section 3 presents the neural network structure. In section 4, we evaluate the accuracy of the predictions, compare different configurations and analyse the relationship between input conditions and prediction performance. In Section 5, we discuss potential improvements and future work.
Numerical simulation around PAP station We use the realistic North Atlantic Subpolar Gyre simulation designed and validated by Le  Corre et al. (2020). This simulation is run using CROCO (Coastal and Regional Ocean COmmunity model) based on ROMS  (Shchepetkin and McWilliams, 2005) , which solves the hydrostatic primitive equations for the momentum and state variables. The domain has 2000 × 1600 grid points and a horizontal grid resolution of 2 km, resolving the mesoscale and part of the submesoscale. The model has 80 vertical sigma levels, with a vertical spacing of ∼ 5 m at the surface and up to 100 m in the intermediate layer. After a two-year spin-up period, two simulations called  POLGYR1 and POLGYR2 are run. POLGYR1 runs between Jan, 1th 2002 and Dec, 4th 2009 (eight years)  and is used to create a training and an evaluation database, while POLGYR2 runs between Jul, 24th 2003 and Jan 12nd 2009 (about 6 years and half) and is used for the test database.
Lagrangian backtracking of catchment areas A series of Lagrangian experiments are performed offline with Pyticles  (Gula and Collin, 2021) , using 3d velocities from the POLGYR1/2 simulations. Pyticles is a parallel Fortran/Python Lagrangian tool for offline 3D advection with the ability to include particle behaviour. Transport is performed along the native Arakawa C-grid and terrain-following vertical coordinates of the ocean model. The 3D velocity fields are linearly interpolated at particles positions, which are advected using a Runge-Kutta 4 numerical scheme. In addition to passive advection, a negative vertical velocity is applied to simulate the sinking of dense particles. The numerical schemes have been shown to be robust to trajectory reversibility  (Wang et al. , 2022)  using 12-hourly input fields and a time step of 120 seconds.
These data include temperature, vorticity, horizontal velocities (u and v), and sea surface height (SSH). All the variables are extracted on the same horizontal domain as Y i and downscaled to a resolution of 8 km. To capture the temporal variability, we consider a 10-day time sampling over a 30-day time window (4 time steps). To evaluate the importance of the surface in comparison to the sub-surface data (Section 4), we distinguish two datasets, D 4layers and D surf : -D 4levels : this dataset contains the variables at 4 vertical levels, i. e. surface, 200 m, 500 m, 1000 m. Each input tensor is a 68×100×100-dimensional tensor, where n input = 68 represents the number of input fields extracted for each experiment. -D surf : This dataset contains only sea surface conditions. This results in 20 × 100 × 100-dimensional input tensors.
6) could explain in some cases the prediction bias (Table  1 index n°2, 15, 16 ). This trend is confirmed by the statistical analysis over the whole test dataset shown in Figure  6. With a mass center above 200 km, the chance of having a valid prediction with U net surf is less than 30%. Similarly, we observe a trend of progressive degradation of the score for PDFs with an entropy higher than 6. 6 (Figure  6b ). Several physical factors could be responsible for the final PDF shape. To characterise the local dynamics, we choose to compute the Mean Kinetic Energy (MKE) and the Eddy Kinetic Energy (EKE) around the ST such as: 1 2 (u 2 + v 2 ) KE = 1 2 (u 2 + v 2 ) M KE + 1 2 (u ′2 + v ′2 ) EKE (1) where u and v are the two components of the horizontal ocean velocity, u and v are the velocities averaged over a 30-day window, corresponding to the characteristic time of our Lagrangian experiments.
, we consider independent training, validation and test datasets as follows. With the Lagrangian experiments presented above, we create a training and a validation dataset using the first simulation setup POLGYR1 (2002 to 2008) and obtain a total of 10260 samples. We divide the samples into a training database (8604 samples from January 2002 to September 2008) and an evaluation database (1224 samples from year 2009). Note that data from September 2008 to January 2009 are not used. The two datasets are separated by a 100-day period to ensure statistical independence. For the test dataset, 6800 samples are created with POLGYR2. They are independant of the training and evaluation experiments. https://doi. org/10. 5194/egusphere-2023-2777 Preprint. Discussion started: 5 December 2023 c Author(s) 2023.
Prediction of functions and pockets We predicted small-molecule-binding sites for representative dark cluster members by adapting a previously described approach  9. We used AutoSite to predict pockets  48  , and selected pockets with an AutoSite empirical composite score of >60 and mean pocket residue pLDDT of >90 for additional analyses. To assign putative function and predict catalytic residues, we used DeepFRI  49  to predict enriched GO/EC terms and residue-level saliency weights across available GO/EC categories (BP, CC, EC, MF). Pocket and functional predictions were then visually examined using a web app (Data Availability).
0] [11]  One trial done in collaboration between Ionis Pharmaceuticals and Roche approached non-allele selective silencing for HD individuals  12  (ClinicalTrials. gov identifiers: NCT02519036 and NCT03342053). Phase III studies were ended early because participants receiving active drug progressed more rapidly than placebo-treated participants  13  (ClinicalTrials. gov identifier: NCT03761849). In addition, two allele-selective ASOs were tested in individuals with HD (PRECISION-HD1 and PRECISION-HD2; ClinicalTrials. gov identifiers: NCT03225833 and NCT03225846) with results released recently. Unfortunately, neither ASO showed target engagement in cerebrospinal fluid. The PRECISION-HD2 core trial participants who received WVE-120102 (targeting rs362331) had a median reduction of 9. 9% in mHTT in cerebrospinal fluid (p ¼ 0.
HD subjects HD subjects from the French cohort were recruited at the PitieSalpetriere Hospital in Paris, France. All subjects gave written informed consent, and blood samples were collected in accor-dance with local French regulations (Paris Necker ethics committee approval [RBM 03-48] to A. D. ). Genomic DNA samples from the CHDI cohort were generously provided by the participants in the Enroll-HD study and made available by the CHDI Foundation. Detailed information of the HD subjects (including race, sex, region, and CAG repeat sizes) is shown in Tables  S11  and S12. Enroll-HD is a global clinical research platform intended to accelerate progress toward therapeutics for HD; certain samples and core datasets are collected annually on all research participants as part of this multi-center longitudinal observational study.