dataesr/scientific-paragraphs-categorization huggingface dataset:
Generating 3D Models from the Captured Photographs To reconstruct a 3D model of our captured items and scenes, we decided to rely on Schönberger and Frahm's [20] COLMAP (see Figure  4 ), which had consistently achieved satisfying results during testing. We were thus able to recover intrinsic parameters, positions, and orientations for every camera. The software's dense reconstruction step also provided us with a plausible 3D model of the minerals and environments, which we simplified and cleaned up using Blender and Jakob et al. 's  [21]  Instant Meshes. From the 41 high-resolution photographs of the pyrite mineral, we thus obtained a final 3D mesh composed of 122 k vertices and 242 k triangles. Similarly, we simplified and cleaned up the 3D model reconstructed from the 320 photographs of the museum gallery to finally obtain an output mesh composed of about 80 k vertices and 160 k triangles.
Several different angle layouts (both cylindrical and spherical) could also be tested, and the resulting reconstructions compared. Furthermore, coverage at the zenith could be improved by adding cameras facing upwards. Other paths for improvement also include using a battery instead of the current power supply, and switching to higher-quality cameras. Additionally, future works could reproduce the study with high-dynamic-range (HDR) data, by capturing multiple exposures and rendering the data on an adapted display or using a tone-mapping operator. Indeed, using HDR data may be significant, because photographs with a limited dynamic range may cause areas with highlights to be oversaturated and may thus be the source of noticeable artifacts during rendering. In this way, it could be interesting to lead a user study investigating the extent to which viewers perceive fewer artifacts when using HDR data as input.
First Delphi Round: Electronic Survey of European Microalgae Stakeholders Interviewed Stakeholders A good participation and representation of researchers and stakeholders were crucial to obtain relevant data to make a diagnosis and propose recommendations to boost the microalgae sector. A total of 53 experts responded to the online survey. As expected, most of these experts (82%) came from the Atlantic Area (AA) countries and mainly from France, Spain, Portugal and the United Kingdom, which made it possible to identify the main challenges and opportunities of microalgae in the European Atlantic territory. As the survey was disseminated on the Interreg EnhanceMicroalgae website, the opinions of international microalgae stakeholders outside the European AA were also collected, including colleagues from China, Brazil, Belgium and Italy.
Stakeholders felt that the AA origin had benefits in positively differentiating microalgae products produced in its territory in the future. Access to clean coastal areas and seawater, product certification and quality labels, excellence in R&D and the availability of a trained workforce were among the most relevant criteria cited (Figure  4 ). Among the actions to be carried out to promote the AA and stimulate the industrialization of microalgae, the promotion of product traceability, the creation of an inter-regional database referencing and updating production/processing/research projects in the microalgae territory and a federation of all the actors of the AA within an international cluster were pointed out. The development of experimental technology transfer platforms was also highlighted as being crucial for the promotion of the sector.
To this end, we recently built and published a database of AA microalgae actors (map and Excel database), accessible free of charge at https://www. enhancemicroalgae. eu/stakeholders-database/, in order to improve access to up-to-date information and to foster cooperation between companies and public institutions throughout the sector. According to the stakeholders interviewed, the geographical origin of the AA was not identified as an asset to differentiate microalgae products from other European producers. Both the AA and Europe should continue their scientific and technological progress by maintaining the level of training, R&D and increasing the establishment of largerscale cultures such as experimental platforms. The main asset of the AA to create future opportunities was linked to the establishment of a federation of actors of this territory, through clusters and databases, and also to the implementation of traceability of microalgae products, ensuring their quality through certifications and labels.
anfaco. es) dedicated to AA actors in the field of microalgae in order to facilitate and strengthen the network of collaborations. In addition, they also created a stakeholder database for the European Atlantic Area which can be very useful to find a research or industrial partner with specific skills and equipment (https://www. enhancemicroalgae. eu/stakeholders-database/). Currently, applications of microalgae-based products cover the agriculture, aquaculture and biotechnology markets, and the experts recommended that a European harmonization of administrative procedures for these sectors could be useful for microalgae-related products.
Europe should also support the acquisition of large-scale equipment to improve training in the cultivation, harvesting and processing of microalgae. Some companies do not have the facilities and expertise to develop innovative processes such as supercritical extraction or membrane technology, for example. These companies therefore need academic support, and to this end, the creation and dissemination of accessible databases on microalgae facilities, actors and contacts in Europe and in the AA will help them overcome the technological barriers. In addition, the creation of decision support tools (DST) based on in silico simulation platforms for microalgae growth and metabolite production will also help them to plan and optimize commercial biomass production according to their needs and commercial objectives.
Discussion and Conclusions European research and development on microalgae has significant space for improvement, and important market growth is expected in the coming decades. To target these market opportunities, current scientific, technical/technological, commercial and legislative gaps and barriers need to be accurately and comprehensively identified in order to put in place actions and guidelines to overcome these limitations. In this paper, we have carried out an in-depth analysis of the perceptions and recommendations of stakeholders and experts in the European Atlantic Area, in order to stimulate the development of the food, feed, health, cosmetics, nutraceuticals, biomaterials, pharmaceuticals and biofuels sectors. Among the main scientific gaps, the lack of genomic data on microalgae reliably interpreted by experts, the need to improve knowledge on the risks and prevention of culture contamination, including inter-and intra-species interactions, and the need to validate proofs of concept for microalgal products and to improve knowledge of the biomass composition of microalgae were highlighted.
Although many sectors of activity are still in their infancy, proper adaptation of legislative barriers could unlock the research potential of the European Atlantic Area, opening the door to new cutting-edge applications and markets that are not limited by science or technology. This study was geographically limited to the European Atlantic Area that included 36 coastline administrative regions from five countries (Portugal, Spain, France, UK, Ireland) at the time of this research, as defined by the Interreg Atlantic Area funding program for the 2016-2020 period (Figure  18 )  [31]. The UK regions were included in this study because they belonged to this area and benefited from Interreg funds before the effective Brexit. S1  for details of the survey questions). The data collection revealed convergence of opinions, consensus and areas of uncertainty.
Designed with SurveyMonkey software, the survey asked stakeholders about the current limits of industrial development of microalgae in the AA, the main threats and opportunities for microalgal markets and their views on a longer-term vision for the microalgal market. The results of the survey were divided into and analyzed as 4 themes (Figure  19 ). The introductory questions aimed to describe the skills and sectors of activity of the actors interviewed as well as their nationality and type of organization. After situating the position of the Atlantic Area in the world market and highlighting its main assets, the first part focused on the current limits to the development of research, development and markets for microalgae, including the crucial obstacles in the economic, technological, scientific, legislative and social fields.
Designed with SurveyMonkey software, the survey asked stakeholders about the current limits of industrial development of microalgae in the AA, the main threats and opportunities for microalgal markets and their views on a longer-term vision for the microalgal market. The results of the survey were divided into and analyzed as 4 themes (Figure19). The introductory questions aimed to describe the skills and sectors of activity of the actors interviewed as well as their nationality and type of organization. After situating the position of the Atlantic Area in the world market and highlighting its main assets, the first part focused on the current limits to the development of research, development and markets for microalgae, including the crucial obstacles in the.
Lehner et al. , studied urban footprint with unsupervised algorithm but only focused on Europe  [21]. These products rely on spectral information but some, such as the GHSL, also integrate texture for the identification of urban footprint  [22] [23] [24] [25]. The detection of the urban footprint by texture analysis can be associated with the extraction of Haralick indices thanks to the Grey Level Co-occurrence Matrix (GLCM) or the use of other texture approaches  [26] [27] [28] [29]. More importantly, the quality of urban footprint products is usually well estimated in Europe  [21, 30] , due to the availability of validation datasets, as illustrated by Reba and Seto  [9]  showing that 75% of studies focused on high or upper-middle-income countries. In southern countries, training and validation datasets are often missing or of low quality.
This lack is not even bridged by OpenStreetMap, a common and essential source of training and validation datasets in supervised approaches, since only 11% of the contribution between 2014 and 2018 was located in Africa  [31]. That is why in this study we favor an unsupervised texture-based approach. When focussing on medium-scale description of urban areas (named meso-scale hereafter), there is no generic method that can be applied everywhere. This is partly due to the fact that there is no global definition of urban areas at the meso-scale. The meso-scale is usually referred to as neighbourhood scale. This term may have many different meanings or uses, but it can refer to similar units nested within larger sets  [32]. One good example of meso-scale approach is the nomenclature of local climate zones (LCZ) in urban areas by Steward and Oke  [33]  which was designed to be applied globally at the at the meso-scale.
Both for meso and micro-scale, this is mainly due to the limited availability of very high resolution imagery that is usually required for such scales of study. Some studies have highlighted the use of texture to characterize more finely urban areas  [39] [40] [41]. Some authors point out that even if no global dataset is available, future applications on remote sensing over urban areas have to be based on methodologies that can be applied globally on any urban areas  [7, 42, 43]. The methodological framework we propose aims at describing urban areas at three scales to characterize urban footprint (macro-scale), urban units or neighbourhoods (meso-scale) and buildings (micro-scale). We focused on an unsupervised approach to try and overcome the current limitations of remotely-sensed urban characterizations described previously, especially the lack of training and validation datasets in southern countries.
FOTOTEX Algorithm Based on different versions of the original algorithm FOTO coded in R or Matlab, we developed the package FOTOTEX in python and added new features. To have an easy, robust and efficient tool, we decided to deploy the algorithm with Python thanks to the Python Package Index. The algorithm has been optimized in terms of process and calculation time: FOTOTEX is able to extract urban footprints and indicators from 50-100 millions pixels images within minutes on a conventional computer. We included in FOTOTEX the possibility for the users to activate or not several parameters such as the DC component or the normalization. In the original FOTO algorithm, the application was only dedicated to homogeneous areas of forest canopy, DC component and normalization were automatically on default mode.
Influence of Spectral Bands as Input The SENTINEL 2 data allows for testing the different spectral bands to determine if one is more efficient than the other in delineating the urban macro-scale footprint. Tests showed that texture information from blue (B2), green (B3), red (B4), and red-edge (B5) bands are more discriminating to identify urban footprint than bands B6 to B8A. Using B2 to B5 as input allowed us to discriminate the urban footprint thanks to a RGB composite of the 3 first principal components (Figure  4 ).
Conclusions We developed the open FOTOTEX algorithm, available at https://pypi. org/project/ fototex/, and demonstrated the potential of an unsupervised texture-based method for the multi-scale characterization of urban areas on Bouake and Brasilia. At macro-scale, the cross-comparison of the results between FOTOTEX, GHSL and GUF revealed that FOTOTEX allows a more realistic delineation of the urban surface with a single Sentinel 2 image than common global products over urban areas such as GHSL, and GUF. At meso-scale, the use of Pleiades images allows identifying urban units based on texture, which is the main originality of this work given the lack of studies at this scale of analysis. The textural information at meso-scale appeared to be strongly linked to metrics such as building density and the proportion of built-up and vegetated areas.
At micro-scale, FOTOTEX proved its efficiency at detecting urban objects. For each scale of analysis, the parametrization of the algorithm was tested and the best configurations were determined. The method we propose at three scales can take advantage of the diversity of remote sensing data which is currently at the disposal of the community of urban experts. Global recommendations have been made, based on the different results, to guide future users of the FOTOTEX algorithm to characterize urban landscapes at the multi-scale. The present approach showed a strong potential to analyze the complexity of urban landscapes, on any kind of urban environment. This method answers the objective of studying complex objects using one single method while reducing calculation time, the number of images used, and overcoming the absence of training and validation datasets.
Figure10. Study of the relationship between texture information at the meso-scale extracted using FOTOTEX on Pleiades images on Sao Sebastiao, Brasilia (a) and the northeastern part of Bouake (c), with the density of buildings represented as heatmaps using building contours from the SEDUH database (http://www. seduh. df. gov. br/ accessed on 20 September 2020) for Brasilia (b) and Open-StreetMap for Bouake (d). On this figure, black/colored lines and numbers are urban units extracted from the combination of texture and contours from the urban footprint produced with FOTOTEX and Sentinel 2.
Measures, sample and data collection To test our proposed hypotheses, we used a cross-sectional survey design. A survey (both webbased and paper-based) was developed both in French and English to collect data from m-wallet users in Cameroon. Measurement items drawn from prior studies and adapted to the context of m-wallet were used to design the survey. More precisely, our constructs were adapted from various sources as following: perceived ease of use from  Davis et al. (1989) ;  Venkatesh and Davis (2000) , perceived usefulness from  Venkatesh and Davis (2000) ;  Davis et al. (1989) ;  Davis (1989) ;  Venkatesh et al. (2003) , intention to use from  Davis (1989) ;  Venkatesh and Davis (2000) , societal collectivism from  House et al. (2004) , Humane orientation from Hofstede (2006);  House et al.
(2004) , fun to use from  Bruner and Kumar (2005) , actual use of m-wallet from  Choudrie et al. (2017) , peer influence from  Kim et al. (2011) , monetary value from  Lee et al. (2012) , perceived status benefits from  López-Nicolás et al. (2008) , and financial inclusion from  Le et al. (2019). A 7-point Likert scale enabled us to assess all the measurement items. A survey pre-testing involving eight active m-wallet users was conducted to check for content validity. Based on their comments and suggestions, minor changes were made on the final surveys before the pilot testing. The pilot study of the two types of survey was conducted from 7 to 10 December 2019, with a total of 61 usable observations from active m-wallet wallet users. The pilot enabled us to test the robustness of our proposed model before the final data collection.
The final data collection was conducted from 11 to 30 December 2019. For the online data collection, an invitation letter explaining the objectives of the study, as well as the link to each survey, was sent to all active m-wallet users via social networks (e. g. , Facebook, WhatsApp) in order to encourage them to participate in the study. An incentive worth € 1. 52 (or CFA 1000) was offered to those who completed the survey as qualified participants. Qualification assessment (to ensure participants are active m-wallet users) was conducted through putting a filter at the beginning of the questionnaire to eliminate non-active m-wallet users or respondents without an m-wallet account. At the end of the survey, the incentive was directly transferred into each participant's m-wallet account according to the relevant information provided.
This actually allowed us also to check for duplication cases and remove the corresponding data from the dataset. A total of 741 active m-wallet users eventually agreed to participate in the study (46 for the online survey in English, 580 for the online survey in French, 100 for the paper-based survey in French and 15 for the paper-based survey in English). After a careful analysis of the responses, 621 surveys (39 for the online survey in English, 484 for the online survey in French, 88 for the paper-based survey in French and 10 for the paper-based survey in English) were identified as being properly filled out and appropriate for further analysis, resulting in a response rate of 83. 81%. According to gender, 65. 5% of the qualified respondents were males, and 34. 5% were females (Table  4 ).
The ALBOMAURICE software runs a mosquito population dynamics model to predict the temporal and spatial abundance of Aedes albopictus, the dengue disease vector in Mauritius. For each vector surveillance zone, it solves a system of ordinary differential equations describing different stages of the mosquito life cycle. ALBOMAURICE uses daily rainfall and temperature input data to produce abundance maps used operationally by health services for targeting areas where to apply vector surveillance and control measures. Model simulations were validated against entomological data acquired weekly during a year at nine locations. Different control options can also be simulated and their effects compared.
However, to be used by public health authorities and vector control services, there is a need for the development of operational tools, with easy-to-use interfaces, from such models. Recently, 'ALBORUN', a population dynamics model of Ae. albopictus, was developed for Reunion Island  [19] , located approximately 175 km away from Mauritius Island. Based on daily rainfall and temperatures, the model accurately predicts the spatio-temporal abundance of the species at local scale. The software ALBOMAURICE  [22]  presented in this article was developed by adapting 'ALBORUN' to the local conditions and needs of the Mauritian Ministry of Health and Wellness. AL-BOMAURICE was also validated by comparing predicted larval densities with entomological data from mosquito traps set up in the vicinity of nine national weather stations.
Software architecture ALBOMAURICE is composed of two main software components (Fig. 2 ): (i) the model of Ae. albopictus population dynamics, which is written using the Ocelet modelling language  [25]  and (ii) the user interface which is written using the Java programming language. The compiled and executable software is entirely in Java though because Ocelet is a domain specific language that is translated into java sources automatically. A series of input parameters is declared (in Ocelet) by the population dynamics model. The generated code after these declarations allow any Java program to instantiate the model, send parameters values, run a simulation and obtain some feedback from the running process. From the Ocelet Modelling Platform, the model is in fact exported as a java archive (jar) file to become a standalone java class that can be controlled by a user interface.
The user interface component is developed using the JavaFx library (https://openjfx. io). Most of the graphics layout of the interface is described in XML files (fxml format). And a series of Java classes contain the control code of the interface widgets. The user interface main class creates an instance of the jar-packed model. It collects settings from the user, turn these settings into the series of input parameters stored in a key-value table, and that table is sent to the model to run a simulation.
Software functionalities Using the ALBOMAURICE interface, the user can (i) define different settings regarding the simulation, inputs and outputs, and vector control options; (ii) run a simulation. Simulation settings. On ALBOMAURICE main configuration menu (Fig. 3a ), the user can define the start and end simulation dates, and if new weather data are used. The options ''Save state file'' and ''Use previous state file'' allow saving intermediate state values of a simulation and re-use them to run ulterior simulations faster. Input settings. In the Input settings menu are defined the work directory, the weather data file, the parcels files and the weather stations file (Fig. 3b ). Output settings. The Output settings menu allows the user to choose the output directory and the output formats for the results of the simulation (Fig.
Illustrative examples Three examples are briefly presented to illustrate some uses of ALBOMAURICE: (i) mapping Ae. albopictus densities in Mauritius; (ii) comparison of predicted Ae. albopictus abundances and field entomological data and (iii) assessing the impact of vector control actions on the temporal dynamics of Ae. albopictus. For these three examples we used weather data provided by the Mauritius Meteorological Services (MMS): the daily temperature (minimum and maximum) and rainfall records from January 2017 to February 2020 at 30 weather stations (Fig. 1 ).