dataesr/scientific-paragraphs-categorization huggingface dataset:
93172. The first validation exercise considered the effect of the fuselage on the C L (α) curve. Figure  19  compares the C L (α) curves from the aerodynamic module, with or without a fuselage taken into account, with the CFD results from ONERA obtained within the DPW framework. The module considers a zero-lift incidence of α 0 = -1. 55 • to improve the comparison. A quite good agreement can be observed for the estimation of the lift slope of the wing-fuselage arrangement by the module with the CFD data. Regarding the overall drag estimation, Figure  20  compares the CL(CD) curves computed by the module with the CFD results from ONERA  [23]. The drag coefficients from the CFD results were obtained by the ffd72 far-field analysis tool  [20]  that allows the elimination of the artificial spurious drag from the numerical solution and gives the drag breakdown of the physical components (lift-induced, viscous and wave).
It can be seen that the agreement is quite good (ΔCD = 1 d. c. maximum) between the module evaluation and the CFD results up to lift coefficients of around 0. 50 (using SREF = SWING) as well as for the wing-fuselage configuration and for the wing-fuselage-horizontal tail planes case. Regarding the overall drag estimation, Figure  20  compares the C L (C D ) curves computed by the module with the CFD results from ONERA  [23]. The drag coefficients from the CFD results were obtained by the ffd72 far-field analysis tool  [20]  that allows the elimination of the artificial spurious drag from the numerical solution and gives the drag breakdown of the physical components (lift-induced, viscous and wave). It can be seen that the agreement is quite good (∆C D = 1 d. c. maximum) between the module evaluation and the CFD results up to lift coefficients of around 0.
50 (using S REF = S WING ) as well as for the wing-fuselage configuration and for the wing-fuselage-horizontal tail planes case. Regarding the overall drag estimation, Figure  20  compares the CL(CD) curves computed by the module with the CFD results from ONERA  [23]. The drag coefficients from the CFD results were obtained by the ffd72 far-field analysis tool  [20]  that allows the elimination of the artificial spurious drag from the numerical solution and gives the drag breakdown of the physical components (lift-induced, viscous and wave). It can be seen that the agreement is quite good (ΔCD = 1 d. c. maximum) between the module evaluation and the CFD results up to lift coefficients of around 0. 50 (using SREF = SWING) as well as for the wing-fuselage configuration and for the wing-fuselage-horizontal tail planes case.
CRM-NLF Configuration The next validation exercise considers the CRM model with a modified wing with a natural laminar flow on the upper surface. This CRM-NLF wing has been designed for tests in the NASA NTF wind tunnel  [24] [25] [26] , and some experimental results are provided on the NASA CRM website. The data used for the CRM reference plane are modified for the inboard wing section in order to take the wing planform change (Figure  23 ) into account. Similar to the CRM reference case, the experimental data are corrected in order to take the change in the reference wing area into account. Aerospace 2022, 9, x FOR PEER REVIEW 19 of 27 on the NASA CRM website. The data used for the CRM reference plane are modified for the inboard wing section in order to take the wing planform change (Figure  23 ) into account.
It can be seen that an extended natural laminar flow is possible (around 50%) for a large portion of the wing, except for some portions of the inboard wing, which are more limited in terms of NLF capabilities. on the NASA CRM website. The data used for the CRM reference plane are modified for the inboard wing section in order to take the wing planform change (Figure  23 ) into account. Similar to the CRM reference case, the experimental data are corrected in order to take the change in the reference wing area into account. The flow conditions used for the validation are similar to the NTF ones: a Mach number of M = 0. 8565 and a Reynolds number of 15×10 6. The NLF capabilities of the aerodynamic module are considered for this validation exercise, but wing lower surfaces are considered turbulent, as during the wind tunnel tests.
NLF Regional Aircraft Configuration The CRM-NLF configuration considered laminar flow on the upper wing only. In order to evaluate the performance prediction capabilities of the aerodynamic model for a wing in which laminar flow extents on both surfaces, we considered the AG2-NLF wing design by ONERA in the framework of the Clean Sky 2 AIRGREEN2 program. The reference aircraft considered is a 90-pax turboprop configuration with wing airfoils redesigned by ONERA at cruise conditions for natural laminar flow capabilities  [27] [28] [29]. This configuration is referred to as AG2-NLF. Figure  27  presents the complete configuration, which considers some winglets, an under-carriage fairing and a wing-body junction Karman that are not modeled in the aerodynamic module. In order to take into account the effect of the Karman on the viscous drag, the first wing section is considered a thick airfoil (t/c = 40%) with a leading-edge sweep of 30 • at the symmetry plane.
design by ONERA in the framework of the Clean Sky 2 AIRGREEN2 program. The reference aircraft considered is a 90-pax turboprop configuration with wing airfoils redesigned by ONERA at cruise conditions for natural laminar flow capabilities  [27] [28] [29]. This configuration is referred to as AG2-NLF. Figure  27  presents the complete configuration, which considers some winglets, an under-carriage fairing and a wing-body junction Karman that are not modeled in the aerodynamic module. In order to take into account the effect of the Karman on the viscous drag, the first wing section is considered a thick airfoil (t/c = 40%) with a leading-edge sweep of 30° at the symmetry plane. The flow conditions are a Mach number of M = 0. 52 at an altitude of Z = 6100 m (20,000 ft). The NLF capabilities of the module are evaluated by comparison with 3D RANS results obtained by the ONERA elsA software with integrated transition prediction capabilities  [30].
BWB Configurations As the module is used for an MDAO design process of BWB configurations, it is important to check if the formulations used are valid for such configurations with high values of leading-edge sweep angle. The flow conditions are a Mach number of M = 0. 52 at an altitude of Z = 6100 m (20,000 ft). The NLF capabilities of the module are evaluated by comparison with 3D RANS results obtained by the ONERA elsA software with integrated transition prediction capabilities  [30]. The results are presented in Figure  28. The zero-lift angles of attack considered for the C L (α) curves are α 0 = -1. 7 • for turbulent conditions and α 0 = -1. 96 • for the NLF aircraft. It can be seen that the agreement on the drag evaluation is quite good for both the turbulent and NLF flow conditions at around C L_Adapt = 0.
AVECA design by ONERA in the framework of the Clean Sky 2 AIRGREEN2 program. The reference aircraft considered is a 90-pax turboprop configuration with wing airfoils redesigned by ONERA at cruise conditions for natural laminar flow capabilities  [27] [28] [29]. This configuration is referred to as AG2-NLF. Figure  27  presents the complete configuration, which considers some winglets, an under-carriage fairing and a wing-body junction Karman that are not modeled in the aerodynamic module. In order to take into account the effect of the Karman on the viscous drag, the first wing section is considered a thick airfoil (t/c = 40%) with a leading-edge sweep of 30° at the symmetry plane. The flow conditions are a Mach number of M = 0. 52 at an altitude of Z = 6100 m (20,000 ft). The NLF capabilities of the module are evaluated by comparison with 3D RANS results obtained by the ONERA elsA software with integrated transition prediction capabilities  [30].
AVECA The first BWB configuration considered is one of the optimized planforms of the AVECA project  [31, 32]. AVECA is a long-range BWB configuration designed by Airbus. Several wing planforms, considering some changes in the geometrical characteristics or different volume constraints for the passenger cabin or cargo, have been optimized by ONERA using the adjoint method. One of the optimized configurations is selected for the validation purpose considering only four wing segments or five wing sections (Figure  29 ), which is very crude. The flow conditions are a Mach number of M = 0. 850 at an altitude of Z = 11,000 m. The results of the aerodynamic module are compared to the CFD results post-processed with the ffd72 tool in order to obtain the drag breakdown between the different drag components.
Note that the engines were not considered for both CFD and the module. The first BWB configuration considered is one of the optimized planforms of the AVECA project  [31, 32]. AVECA is a long-range BWB configuration designed by Airbus. Several wing planforms, considering some changes in the geometrical characteristics or different volume constraints for the passenger cabin or cargo, have been optimized by ONERA using the adjoint method. One of the optimized configurations is selected for the validation purpose considering only four wing segments or five wing sections (Figure  29 ), which is very crude. The flow conditions are a Mach number of M = 0. 850 at an altitude of Z = 11,000 m. The results of the aerodynamic module are compared to the CFD results post-processed with the ffd72 tool in order to obtain the drag breakdown between the different drag components.
The first BWB configuration considered is one of the optimized planforms of the AVECA project  [31, 32]. AVECA is a long-range BWB configuration designed by Airbus. Several wing planforms, considering some changes in the geometrical characteristics or different volume constraints for the passenger cabin or cargo, have been optimized by ONERA using the adjoint method. One of the optimized configurations is selected for the validation purpose considering only four wing segments or five wing sections (Figure  29 ), which is very crude. The flow conditions are a Mach number of M = 0. 850 at an altitude of Z = 11,000 m. The results of the aerodynamic module are compared to the CFD results post-processed with the ffd72 tool in order to obtain the drag breakdown between the different drag components.
NACOR-SMILE Configuration The second BWB configuration considered for the validation exercise is the optimized SMILE planform from the CS2 NACOR program. This configuration is designed for a short-medium range (SMR) mission, similar to the A320 aircraft. Therefore, compared to the AVECA configuration, its span is much smaller (36 m for SMILE, 80 m for AVECA). It should be noted that the aerodynamic module is used within the OAD-MDAO definition phase, but the final optimization details are carried out using the CFD methods  [33]. Figure  32  compares a CAD rendering of the final optimized shape with the data used by the aerodynamic module. Only six wing sections are considered to model the BWB planform. Winglet and nacelles are also considered for the performance evaluations by the module.
Funding: The work presented in this paper was funded by the internal research project CICAV funded by ONERA from 2015 to 2019 and by the EU NACOR Project, which has received funding from the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation program under grant agreement N •. CS2-AIR-GAM-2018-2019-01. Part of the results used in this paper were carried out in the framework of AIRGREEN2 Project, which received funding from the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation program, grant agreement N •. 807089-REG GAM 2018-H2020-IBA-CS2-GAMS-2017. Institutional Review Board Statement: Not Applicable. Informed Consent Statement: Not Applicable.
ACKNOWLEDGEMENTS The authors wish to thank the laboratories and partners involved in the work related to the stand 1. Univ. Lille, Arts et Metiers Institute of Technology ,  Centrale Lille ,  Yncrea Hautsde-France ,  ULR 2697 -L2EP   -Laboratoire d'Électrotechnique et d'Électronique de Puissance ,  F-59000 Lille ,  France Institut de Recherche sur les Composants logiciels et matériels pour l'Informatique et la Communication Avancée (IRCICA ,  USR-3380 ,  CNRS/Université de Lille ). We also wish to thank  Helene Deleuze  for kindly sharing information and insight required for the completion of this article.
In this article, we propose a model to identify the spatial and structural characteristics of a specific type of social networks: the family. The model is the third  (Giordano & Cole, 2018 , 2019)-and the most fully realized-in a series we have developed in the last four years as part of our work on the topic of platial GIS, or the GIS of place. The model is both a visualization of a spatial social network and an analytical tool and it is also replicable and adaptable to projects in which the dataset Dunder study includes temporal and spatial information as well as demographic data. The case study we discuss in this article concerns the immediate post-Holocaust period in Budapest: in the summer of 1945, the Hungarian Section of the World Jewish Congress-American Joint Distribution Committee and the Jewish Agency for Palestine Statistical and Search Department (World Jewish Congress in brief) commissioned a door-to-door survey to determine where Jewish survivors were living in the city, which at the time was the largest single urban concentration of Jews in post-war Europe.
was published in the volume Counted Remnant  (World Jewish Congress, 1946) , which was disseminated globally to enable family members to find out if their relatives were still alive, and if so, where they could be found. The volume lists the names of over 70,000 individuals, including their first and last name, place of birth, mother's name, maiden name (if applicable), and address of residence. Over the course of two and a half years, we digitized and mapped in a historical GIS the address of residence of the over 70,000 individuals in Counted Remnant. The map in Figure  1  shows the spatial distribution of survivors in the city at the resolution of the over 10,000 individual addresses in the database. The starting point to georeference these addresses was the historical GIS of the Holocaust in Budapest  (Cole & Giordano, 2014) , which we supplemented with geolocating tools from Esri, manual checks in Google Maps, and internet searches on the urban history of Budapest.
F I G U R E 2 Example of one family and its related AFGs (Arrested Family Groups). In light grey are individuals for which no entry exists in the database they provide a framework for searching for key terms or themes throughout multiple textual sources, narratives, and testimonies, highlighting and visualizing the presence of spatial relationships that are not necessarily mappable in Cartesian coordinate space and in a traditional GIS setting  (Knowles, Cole, & Giordano, 2014; Miranker & Giordano, 2020; Pavlovskaya, 2018). In addition to QSR and CL, a third technique is posited to play a fundamental role in the future toolbox of the GIS of place: social networks analysis, or to be more specific, spatial social networks analysis, the focus of this article. Social networks are graph structures that consist of nodes and edges, in which nodes typically represent individual entities such as people or institutions (which in the case of social spatial networks can be spatialized), and edges represent interpersonal relationships, connections or ties  (Andris, 2019).
The model enables a simplified representation of family groups (FGs) to allow for comparative analysis. The model draws from the literature on microhistories, social networks analysis, geography and GIScience, and Holocaust Studies, and reconstructs the social history of selected neighborhoods in 1945 Budapest as anchored in place, people, and networks. The model offers a graphical solution to the challenge of representing spatial social networks that incorporate the historical dimension-they include, in other words, context-as well as recognize cultural limitations and constraints-for all social networks are rooted in culture and society. The model also explicitly incorporates and visualizes uncertainty in its formulation. While all geographical datasets are inevitably saddled with an amount of uncertainty, this is an especially thorny issue with historical geographical datasets, that is, with datasets that cannot be collected again or field checked.
The script compares the records of each individual in the database with those of every other individual and look for matches between the names of the known kin of the first individual and the names of kin of others: for example, if the name of the mother of the first individual matches the name of the mother of the second one, and if their last name at birth is the same, then a match is found and the two are siblings. The script also flags likely but incomplete or uncertain relationships, including cases in which last names are spelled slightly differently and in which individuals may be family members but are living at different addresses. Researchers can then review these cases to manually correct them if they believe that individuals are indeed related and/or that spelling mistakes have been made.
In order to keep track of the F I G U R E 11 Number of residents, Dobozi utca original data, the modified data are stored in additional columns in the database. The script is then rerun with the updated information, returning two separate files: (1) the original list of individuals with new information, including a family ID common to all members of the same family group, as well as the ID of the mother, father, and spouse of each individual if they are also in the database; and (2) a list of all relationships between any two individuals, with a code to identify the type of relationship (1 for siblings, 2 for father-child, etc. ). We will return to this point with an example in the next section. The second script takes the output of the first and applies a set of rules to create the visualization presented in Figure  4 , in which individuals are nodes and relationships are edges.
The rules are essentially those presented in the legend: different colors for the nodes based on gender and certainty of the data, position of the nodes based on the place and date of birth, and relationships placed between the node with symbology depending on the type and certainty of the relationships. Additional rules are implemented to avoid as much as possible the overlap of both nodes and links. For each family group, links and nodes are placed on the same background square grid. The scripts then align all the family groups graphs for one building and output one summary image for the building. Although, by default, this second script treats all the family groups of one single building, it can easily be modified to sequentially work on several buildings, for instance, all buildings in one street.
Here, we seek to represent both the certainty in the data (the woman in FG2 was married) and the uncertainty in the data (the date and place of F I G U R E 1 3 Proportion of individuals in family groups, Dobozi utca birth of the husband in FG2 were unknown and indeed whether he was still alive or dead was unknown in 1945). Thus, uncertainty becomes part of the visualization and is explicitly represented in the model when it is present in the data itself. "Ghost husbands" are only one type of uncertainty we dealt with in the analysis. To dig a little bit more on this issue, we return to the first script. At that stage, the script flagged likely but incomplete or uncertain relationships, including misspelling of last names and cases in which individuals may be family members but are living at different addresses.
The survey is a snapshot of a moment and a place in time (Budapest in 1945, the immediate aftermath of the Holocaust), collected door-to-door among a population understandably hesitant to share too many details about their Jewish family, and put together and published quite quickly immediately after the survey was completed. It contains, therefore and inevitably, many spelling mistakes and transcription errors. We corrected many of these mistakes during the course of the preparation of the database of 70,000 and the creation of the HGIS. The script allowed us to catch additional inconsistencies and mistakes, which we then reviewed to manually correct when we believe that individuals are indeed related and/or that spelling mistakes have been made. This manual intervention is a necessary and unavoidable step in working with historical geographical datasets, at least in our experience.
Overall, Dobozi utca has both more small families and more large families than Nyár utca, while the size of family groups in Nyár utca is less varied. In terms of types of family relationships, the script identified an average of 1. 35 relationships per individual in Nyár utca and 1. 65 in Dobozi utca, a reflection of the larger family size in F I G U R E 1 5 Family size, Dobozi utca Dobozi utca than Nyár utca. If we consider only individuals belonging to family groups, the average number of relationships per individual for Nyár Utca is 2. 13 compared to 2. 74 for Dobozi Utca. The script also identified the types of relationships between family members: sibling, mother, father, spouse, child, and others, with others corresponding to extended family relationships, including uncles and aunts, grandparents, in-laws, etc.
As concerns, the proportion of single individuals to family groups, a pattern of concentration is more evident in Dobozi utca, where 11 of 26 addresses (42. 3%) only have one resident, while in Nyár utca the distribution is more uniform, with only five of 22 (22. 7%) addresses with one resident. Overall, for the two streets, the scripts produced a total of 385 boxes: 213 for Dobozi utca and 172 for Nyár utca, each corresponding to a family group or an individual. We do not have the space to include them all in the article, but they are available online. When implemented at the level of the entire city, we can predict that the script will produce anywhere between forty and fifty thousand such boxes, a frankly unmanageable number of visualizations. For this reason, we see the boxes as a visual tool to complement GIS mapping and analysis, as shown in the maps in this article, and to more traditional statistical charts, diagrams, pies, etc.
In short, there is a need to allow the metadata to intrude into the representation of the data. Herein lie some of the major challenges we are facing in extending our analysis to the entire dataset, as the list contains ambiguities that are difficult to address and reconcile. Two are worth mentioning here: (a) the dataset includes 7565 last names, ranging from the most common  (Weisz, with 1913  individuals) to 3016 cases with only one individual, and while the Hungarian language simplifies the work of reconstructing family groups, much uncertainty still exists; and (b) it is sometimes very difficult to map with certainty the place of birth of the survivors, due to the tormented history of this part of Europe, as the same village could alternatively have been part of Poland, the Austro-Hungarian Empire, Hungary, or the Soviet Union, changing name along the way.