dataesr/scientific-paragraphs-categorization huggingface dataset:
These data can be used to study the behavior of the different components in the simulated platform. For example, we can estimate the instructions processed by the ALU and the memory access activity. These estimations will depend on the data being processed by the core or even on the activity of any custom accelerators that can perform direct-memory access. In our case, we focus on the first scenario. As the activity of the multiple underlying components will affect the power consumption of the entire system, we propose that the simulation statistics can be used to approach the electrical behavior of a simulated platform. Therefore, if there is any correlation between the metrics of the simulation and the application data, then gem5 could be employed to emulate SCAs. In this paper, we use gem5 to perform the emulation of an ARM processor with a Linux-based operating system.
Even then, some statistics files reached the size of 1TB. We developed basic Python and Matlab scripts to separate all the statistics into independent time-series and analyze the data. The main idea of using the simulation statistics to approximate the power dissipation of the system comes from  [Han22]. A simple non-representative power model can be approximated as shown in Equation 1. Evidently, the dynamic component of the model would be the most useful for power analysis. In Figure  2 , we illustrate how the statistics retrieved from a gem5 simulation can be used to approach a power estimation. pow = dyn + stat dyn = V × (2A × ipc + 3pA × dentry misses) stat = 4 × temperature (1) An important challenge in the analysis of the samples obtained from the operation of a processor is to accurately find the different parts of its program.
From this experiment we found only 30 stats with a   3a ) the behavior of gem5 is consistent. However, when the core frequency is decreased (to 10MHz in this case) the execution of the program becomes irregular (Figure  3b ). correlation coefficient under 0. 5 across sets, and only six stats with a correlation coefficient under 0. 3 across sets. This is shown in Figure  5. 2. Then, we computed the correlation matrix of the statistics for one of the keys against their own set (auto-correlation) and the correlation matrix between the statistics in both sets (cross-correlation. The first experiment would give us information about the dependence between statistics and the second would highlight which statistics held more information related to the data processing. These findings are detailed in Section 5.
So, we expect that using the OP-TEE trusted applications as power wasters would have a similar effect on a physical device. It is interesting to note that for this experiment we didn't request any frequency changes to the kernel. The frequency variation seen in Figure  5  is the result of the decisions taken by the schedutil governor of Linux. But despite this interference the pattern produced to encode a secret message is clearly distinguishable and could be easily decoded with the application of some filters and basic heuristics.
In this study, we estimate and compare malaria burden at first ANC visits with data from cross-sectional surveys and clinical cases in three settings in southern Mozambique with different transmission levels. We correlate temporal and spatial trends at both RDT-and quantitative polymerase chain reaction (qPCR)-detection levels, and characterise the effect of HIV and gravidity on the correlation. Finally, we assess the added value of antibody data obtained from a beadbased multiplex immunoassay against VAR2CSA and general malaria antigens, and a newly developed hotspot detection algorithm, as innovative tools to improve surveillance in malaria-endemic areas.
On the other hand, the results imply that clinical cases can be used to predict changes in malaria burden among pregnant women at first ANC visit. Similar spatial patterns of P. falciparum cases and hotspots were observed among pregnant women at ANC and children in the community, regardless of the women's gravidity or HIV status. Spatial clustering decreased from year 1 to year 2, reflecting trends in burden. The new software EpiFRIenDs revealed finer spatial structures of P. falciparum infections and detected several hotspots from ANC and clinical case data. ANC data detected 60% of the case hotspots. At RDT-detection levels, fewer hotspots were identified from HIVpositive and multigravidae than when including all women, probably due to lower sample size and positivity rates. Differences in the temporal distribution of hotspots were observed between ANC and clinical case data, which might result from the time lag observed, different denominator populations, inclusion of asymptomatic cases in ANC data, and variations in care-seeking behaviour.
Study area and population The study was conducted between November 2016 and November 2019 in Manhiça and Magude districts in Maputo Province, southern Mozambique. Malaria transmission is low in Manhiça district  29  , with some moderate-to-high transmission areas, such as Ilha Josina  30. Magude district is a low-transmission area resulting from elimination interventions since 2015  25. Data was obtained from 8,745 pregnant women (Supplementary Fig. 4 ), residing in the study area, who attended their first ANC visit at Manhiça District Hospital, Ilha Josina Health Centre, or Magude Health Centre  31. Weekly numbers of RDT-positive clinical malaria cases among children <5 years old attending the three health facilities (n = 15,467) were obtained from the District Health Information System 2 (DHIS2).
In Manhiça district, 37,131 RDT and microscopy results from children <5 years attending health facilities were available from OPMSS. Data from 3,933 children aged 2-10 years was collected in age-stratified cross-sectional surveys conducted every May from 2015 to 2019 in the catchment areas corresponding to the three ANC health facilities of this study. Gender was not considered to define this population, providing an even representation with 50. 3% of female and 49. 2% of men (with 0. 5% of unavailable information). Geolocalisation of the residence of pregnant women and children was obtained from a local health and demographic surveillance system using their permanent or family identification number  24, 32  , from their household identification number or by registering the geo-localisation of the households (Supplementary Methods).
Recruitment and data collection Pregnant women were only included in the study if they gave consent to participate in the study and if they met the inclusion criteria: attending a first routine ANC visit or a visit for delivery, and being from the study area with a permanent identification number issued by the demographic surveillance system or residing in the area. All the pregnant women at first ANC visit who agreed to participate were requested to donate finger prick blood onto filter paper for detection of parasite DNA and antibodies by qPCR and quantitative suspension array based on Luminex technology, respectively, together with routine tests. A standardised electronic questionnaire using REDCap was filled in with information including the visit date, age, gravidity, area of residence and recent movements.
Data analysis Study years 1 to 3 were defined from November to October in 2016-2017, 2017-2018 and 2018-2019, respectively. Infections with densities above 100 parasites/µL were defined as RDT-detectable  34. Primigravidity was defined as a first pregnancy, and multigravidity as having one or more previous pregnancies. The threshold of seropositivity against P. falciparum antigens was defined as the geometric mean plus 2 standard deviations of the first component from twocomponent normal mixture distributions of mean fluorescent intensity values (R package mixtools). The relationship between PfPR RDT or PfPR qPCR detection limits in pregnant women and children was analysed using linear regressions, PCC, and χ 2 statistics (Supplementary Methods). Similarly, anti-P. falciparum seroprevalence in pregnant women was compared with PfPR RDT and PfPR qPCR in children.
The 2PCF measurements were done using 10 bins in distance in a range from 0 to 60 km. The choosing of these bins was based on the spatial range of pairwise distances and finding the balance between spatial granularity and the statistical power (sample size) of the measurements. The agreement between different 2PCFs was quantified with χ 2 statistics. A novel malaria hotspot detector, EpiFRIenDs, was developed to detect areas with higher levels of P. falciparum infections (hotspots) and seropositivity against P. falciparum antigens (seroclusters) than statistically expected in a stable period of time 2. EpiFRIenDs was designed to detect structures of arbitrary shapes and sizes that account for the background population distribution, a difference with the most commonly used scan statistics based on the SaTScan software  [26] [27] [28]  that detects structures of pre-defined shapes.
EpiFRIenDs detects hotspots and seroclusters by linking positive cases when they are closer than a given pre-defined distance and indirectly linking them to all the positive cases that are close to their connections. The negative cases are then included in the hotspots from their close positive cases. The EpiFRIenDs software is publicly available in Python  35  and R  36  and it is described in detail in the section EpiFRIenDs of Supplementary Methods. The hotspot detection using EpiFRIenDs and SaTScan (scan statistics) was first compared in simulated data with a prevalence of 20% reproducing three different scenarios: first, a random spatial distribution of positive and negative cases; second, four circular clusters of positive cases on top of a background random distribution of negative cases; and third, a sinusoidal distribution of positive cases on top of a background random distribution of negative cases.
Epi-FRIenDs was then applied to identify hotspots and serological clusters from ANC, OPMSS and antibody data (Supplementary Methods). Hotspots were compared between ANC and clinical data from the Manhiça District Hospital and the Ilha Josina Health Centre, for which geolocation information was available. Since EpiFRIenDs is a densitybased clustering algorithm, clinical data was randomly sub-sampled from the 37,131 visits recorded in Manhiça district to obtain the same sample size of positive cases than for ANC data. In order to obtain a statistical significance of the differences between the hotspots detected from ANC and from clinical cases, EpiFRIenDs was run on five hundred different random sub-samples of the clinical data. Linking distance (1 km) and temporal windows (one month or one year) were defined based on sample density.
Statistical significance was set at p < 0. 05. χ 2 values were interpreted as good consistency from thresholds corresponding to p < 0. 05 in a null-hypothesis statistical test (Supplementary Methods). Error bars and 95% confidence intervals were obtained from bootstrap resampling with replacement. The analysis was conducted using Python 3. 8. 12, Jupyter Lab 3. 1. 14, R 4. 2. 1 and EpiFRIenDs 1. 0.
Acknowledgements We are grateful to the women, children and their families who participated in the study, the clinical teams at the health centres, the field and lab teams at CISM ( Henrique Cossa ,  Judice Miguel ,  Manuel Muamede , and  Helga Guambe ) and  ISGlobal  ( Laura Puyol ,  Diana Barrios ,  Alfons Jiménez ,  Marta Vidal ,  Rebecca Santano ,  Martina Fort Suñé ,  Marta Menendez García ,  Gemma Porras ,  Haily Chen ,  Elena Buetas , and  Ianthe de Jong ). We would like to express our gratitude to the communities of Magude and Manhiça districts and the district authorities for their support of the project. We also thank  Patrick Walker  and  Joseph Hicks  for the fruitful discussion on this project, and  Gillian Stresman ,  Maria Tusell ,  Arantxa Roca-Feltrer , and  Dídac Macià  for useful discussions on the design of EpiFRIenDs.
We acknowledge support from the grant  CEX2018-000806-S  funded by  MCIN/AEI/10. 13039/ 501100011033 , and support from the  Generalitat de Catalunya  through the  CERCA Program. This research is part of the  ISGlobal's Program  on the  Molecular Mechanisms of Malaria  which is partially supported by the  Fundación Ramón Areces. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data availability The raw epidemiological, clinical and GPS data are protected and are not available due to data privacy laws. The datasets generated and/or analysed during the current study are available under restricted access for data privacy laws involving personal data. Access can be obtained for research purposes by requesting to the corresponding author by email, specifying contact details, affiliation and purpose of the request. All requests for processed data used in this study are reviewed in a three-month timeframe by Manhiça Health Research Center to verify if the request is subject to any intellectual property or confidentiality obligations. Patient-related data not included in the paper might be subject to patient confidentiality. Any data that can be shared will be released via a Data Transfer Agreement.
The objective of this technical report was to provide practitioners with feedback on such an achievement based on industrial-level data. The development of the city model begins with the registration of all the BIMs from different firms in a common Geographic Information System: this gives the opportunity to confront the operational requirement of a construction phase and the actual current practice of architecture firms. A first prototype was developed using the archviz tool TwinMotion. In order to increase the realism of the model, we describe the creation of a pipeline in Unreal Engine with the automated tasks of material and mesh replacement and the lighting and landscape configuration. The main contribution of this work is to give relevant experience on building such a large-scale model, with the Python script when possible, as well as the necessary manual steps.
Another more precise kind of data are architectural BIM models  [11] , which are high-level-of-detail models and are already available for future construction or real estate development in progress. Few city models based on professional BIM are described in the literature. For instance, in  [12] , the authors linked an existing BIM to generic simulation models of construction activities implemented as script game components within a game engine, but only one BIM was used that modeled a sample house. A meta-analysis of the work available in the field of BIM's application can be found in  [13]. However, as underlined in  [14] , there is limited literature about virtual reality technologies and BIM in AEC: the most-common uses of VR are safety training  [15] , project schedule control, collaborative projects  [16] , design issues, and construction site layout.
However, to our knowledge, no publication has explained the necessary steps required for the building of a 3D city model based on professional BIM files. Let us cite some examples of realizations with GIS data. Aerometrex created a 3D model of the entire city of Adelaide, using a combination of photogrammetry data captured via helicopters and the real-time archviz tool TwinMotion  [17]. The firm used the real-time architectural visualization tool TwinMotion to create a 3D model of Adelaide and the area surrounding it-around 1000 sq km-using photogrammetry data captured via fixed-wing aircraft and helicopters. AccuCities  [18]  designed a visualization of London's architectural evolution and the growth of the city over 1 km 2 tiles and its 3D model. The digital city model was captured from three separate aerial surveys conducted in 2016, 2019, and 2022.
The 3D mapping operation created the 3D model using manual stereophotogrammetry. A simple 3D city model was first created by extruding building footprint polygons, then updated at a finer level of detail. They used the archviz tool Unreal Engine as a graphical engine. The CityGML database 3DCityDB  [19]  is a repository of free 3D models useful for urban context display. These are low-level-of-detail models useful to integrate with your high-LOD architectural BIM. Interested users can see, for instance, a 3D city model of Berlin portaldeveloped by the Technische Universität München  [20]. The interface exploits publicly available open data in CityGML format, which contain around 550,000 LoD2 building objects (basic roof shape and orientation) within the whole city area (890 km 2 ). In  [21] , the authors described how to derive CityGML building models from BIM data.
The conversion of BIM data to GIS data has several challenges due to the differences in the reference system, level of granularity, and geometric representation methods, as well as semantic mismatches between BIM and GIS data models. Our approach is the other way around, since we only integrated GIS data for the urban context of LaVallée into our city model made of a collection of BIM data. BIM models are high-fidelity three-dimensional construction plans; they are seldomly used in large city models because of their relatively large size: typically, a simple four-story building's BIM can be 300 Mo large or much bigger. Therefore, practitioners usually use lower LOD representations, which are more than enough for urban planning purposes, but are not refined enough to study architectural ambiance from a human centric point of view, which requires a high level of finesse.
Methodology To examine BIM as a means of project integration in smart city development, a case study of a project utilizing Revit, BIM 360, TwinMotion, and Unreal Engine for the immersive visit of a future neighborhood currently under construction was performed. This project involved existing GIS buildings models in a suburban city of Greater Paris, France. A total of 30 individual Revit building models were used for the project. For this study, the digital twin development process was evaluated and documented. The scope of the smart city is the city of Châtenay-Malabry in the south of Paris's suburbs. Figure  4  illustrates the workflow of the design: it goes from AEC software for handling architectural data to a game engine for photorealism and features' augmentation. It starts with the collection of the 3D models by the BIM manager from all the AEC partners of the project.
Each company is responsible for the CAD drawings of the buildings, road networks, terrain, public places, parking spots, urban furniture including lighting and benches, park and recreation furniture, fountains, and vegetation. The data are centralized through the BIM 360 cloud platform. All BIMs are registered to a common coordinate system with Revit and exported to a TwinMotion project. With this archviz software, it is possible to augment the project with the urban context of the real estate project, as a form of GIS model of the surrounding buildings and the network infrastructure of the city. A first prototype was built, which is very useful for rapid checking and first-hand visualization including the possibility of using VR head-mounted device. Yet, this prototype has been deemed too colorful and not realistic enough for a perfect immersion experience.
2 Go of storage space, but it has good compatibility with the VR headset. With Steam, the demo runs fully in all parts of the district with the HTC Vive pro glasses, with no lags using a high-end PC with an Intel(R) Core(TM) i9-9980HK octocore CPU 2. 40GHz, 64 Go RAM, with an NVIDIA Quadro RTX 4000 graphical card. With the same PC and the demo converted under TwinMotion 2022, the project was subjected to a high reduction to 800 Mo: the demo runs in 2D and loads much faster, but the VR HTC Vive headset demo becomes stuck on the still portion of the field of view. Until now, we have not tested it in TwinMotion 2023 and cannot say if this bug has been fixed. Table  1  gives an overview of the principle characteristics of this first prototype of the immersive visit. TwinMotion is a powerful architectural visualization software, and it is recommended if one wants to build a fast prototype of a professional 3D city model from BIM files, as it is compatible with Revit.
• Import the urban context: The urban context of a project is a square zone selected from a world map, from which the software extracts a rough 3D model of the surrounding city, which corresponds to a group of white parallelepipeds placed above a gray plane. A context is useful to fill up spaces in an architectural project and then give a better impression of realism. Figure  11  illustrates the urban context extracted for this project. • Manual tree replacement: For each species of tree, we have to find an equivalent in TwinMotion, either by species or by shape. Nevertheless, in order not to weigh down the file of the virtual tour any further, we may not replace all the trees with their estimated equivalents, performed following Table  A1  of Appendix A. The equivalences by shape was performed manually, by checking the settings of each TwinMotion tree's form (age, height, and season) and then personalizing them accordingly; see Figure  A1.
Import of the 3D models in the game engine With Unreal Engine, importing an FBX or OBJ file is simple and fast. Nevertheless, the projects we are working on use 3D models from architecture-oriented software, such as Revit or Naviswork, which requires additional various steps to achieve a decent result. In this part, we discuss how to import the various file extensions that we can import into Unreal, the methods that can be used to do this, and then, the small possible operations to fix some import problems. Figure  12  illustrates the main source files that can be imported for architectural data handling: Therefore, specific plugins have to be enabled, as represented in Figure  12. The Python editor script plugin also has to be selected for automation purposes. Some limits of imports in general are as follows: • When importing several 3D models, one may want them to be in a different location than the one in their source file.
• The Unreal Engine plugins seek to create a hierarchy that resembles the source file as much as possible. Knowing that, it is sometimes necessary to be careful with the model in some software, in order not to obtain an over-weighted file. For instance, when importing a 3D model from Sketchup, the model of a tree may be imported in Unreal by creating an actor for each mesh of a leaf (which is resource consuming and can cause some visual problems in the unreal project).
Creation of an Automated Pipeline in Unreal Engine TwinMotion is not a 3D modeling software, nor a software that enables scripting. To solve some of those issues, we intend to develop tools from Unreal Engine that would enable more options. By using scripting, for instance, we can automatize the replacement of a group of buildings or program micro-mobility movements. The choice of using Unreal Engine was made by knowing that TwinMotion and Unreal were both developed by EpicGame, so we expected an easier setup of the various tools in Unreal.
How to Use Unreal Python Scripting Setting up a virtual tour of LaVallée requires knowing some features of Unreal that may help make the rendering more efficient, the plugins to download, making a specific headset usable, and the possible ways to experience the visit in immersive VR (one can check the VR performance features in the documentation with the link: https://docs. unrealengine. com/4. 26/en-US/SharingAndReleasing/XRDevelopment/VR, accessed on 22 February 2023). After setting up the project appropriately, one can then use Unreal Python scripting to make the process of importing and massively modifying specific elements in the model faster. This section presents a short introduction on how one can use scripting to automatize most of the steps that are usually performed manually by practitioners.