dataesr/scientific-paragraphs-categorization huggingface dataset:
A demonstrative Scenario Lingware applications typically consist of many individual components that need to be used together in order to solve real-world problems  (Ziad et al. , 2018). However, the combination of multiple components in a particular order into a processing pipeline is a tedious task which can be a barrier for domain experts and especially for novice ones. LingFramework is applied to assist the user in identifying valid pipelines associated with a Morphological Analyzer for Arabic texts. As shown in Figure  8 , the user starts by selecting the preferences "Lexical level" and "Morphological level" as a Processing Level, "Arabic" as a Language and "Linguistic processing" as an Abstraction level. Consequently, based on the smart search interaction functionality, an excerpt ontological view corresponding to the expressed need is generated.
Evaluation of the Performance The evaluation of the performance of LingFramework in identifying valid NLP pipelines consists of three steps: • Step 1: the authors prepared 63 NLP tasks, which had to be solved using LingFramework. They classify these tasks into (1) Low level processing tasks and (2) High level processing tasks. Further, they classify NLP tasks in each group according to the language. Then, they classify applications in each group according to the language (i. e. , French, English and Arabic). Table  1  shows some examples of this classification. • Step 2: the authors recruit three linguistic experts. The first one is a member of the Arabic Natural Language Processing Research Group (ANLP-RG) of MIRACL laboratory (Tunisia, Sfax). The second is a member of the CEDRIC laboratory (France, Paris).
The last expert is a member of the Formal linguistics laboratory (France, Paris). The authors ask each expert to provide, manually, all the possible pipeline(s) which may solve each lingware application according to their native language (i. e. , French, English and Arabic). • Step 3: the authors identify, using LingFramework, all the possible NLP pipeline(s) corresponding to each lingware application identified in Step1. Then, the experts provide their feedback according to each generated pipeline ("Valid or Not valid" pipeline). The experts may also provide a textual explanation. The authors use the precision and recall metrics  (Su, 1994)  to evaluate the performance of the LingFramework. The recall measures the proportion of valid pipelines which have been identified using LingFramework among identified pipelines by the domain expert.
The precision measures the proportion of valid pipelines identified using LingFramework within the total number of identified pipelines. The authors evaluate the performance of LingFramework in identifying valid pipelines associated with the low and high level proposed applications treating one language as shown in Figure  11  and Figure  12. The precision and recall metrics indicate that LingFramework is efficient in identifying valid NLP pipelines for high and low processing levels. Indeed, as shown in Figure  11  the overall means of the precision associated with the English and French languages (86. 3% and 92. 3%) are almost the same. This similarity is explained by the fact that these languages share a lexical similarity (similarity in both form and meaning). Indeed, they have the same alphabet.
They sometimes use similar grammatical structures and have several words in common. However, the overall means of the precision associated with these languages (86. 3% and 92. 3%) are better than the overall mean of the precision associated with the Arabic language (78%). This gap is explained by the fact that the Arabic language differs morphologically, syntactically and semantically from the English and French languages. For instance, syntactically, Arabic sentences are long with complex syntax and its components can be swapped without affecting the structure or meaning. These issues lead to a syntactic and semantic ambiguity. Besides, the NLP toolkits and frameworks used to propose the LingOnto are more mature for English and French Languages than Arabic language. Furthermore, this gap affects the performance of LingFramework in identifying valid pipelines for high-level Arabic applications as shown in Figure  12.
CoNCLUSIoN This paper addresses the issue of assisting the user in understanding the different aspects of the linguistic domain and easing the process of proposing lingware applications. The authors propose the LingFramework which is a smart framework for handling multilingual linguistic knowledge. It targets not only expert users, but also novice ones. It is based on the LingOnto ontology. Compared to available works, this multilingual linguistic domain ontology allows representing linguistic data, linguistic processing functionalities and linguistic processing features. Furthermore, it allows reasoning, via a SWRL based reasoning engine, about the aforementioned knowledge. Currently, three languages are supported: English, French and Arabic. LingOnto is designed to be used mainly by linguistic users, who are usually not familiar with ontologies.
She received the engineering degree in computer science from the University of Tunis and the M. S. degree from Nancy II University in 2008. Elisabeth Métais is a Full Professor in Computer Science at the CNAM University (Paris, France) and a researcher in the CEDRIC Laboratory since September 2000. Her research initially concerned Data Bases Design and she has been interested since the early nineties in applying Artificial Intelligence and Natural Language Processing techniques to Database Design. In 1995 she initiated the NLDB conference in Versailles (France). Since the 2000s she has turned to medical computing, through projects such as Data Warehouses in resuscitation, or the design of Serious Games for therapeutic purposes in urology. She is currently interested in memory prosthesis for elderly persons and Alzheimer disease patients, based on the Semantic Web technologies and has recently managed a project concerning the help of ICTs and smart cities for Alzheimer patients living in the City of Paris.
This enables changes to be divided into those that take place on the borders of existing features and those that form new, disconnected features. Bringing this chapter on landscape patterns to a close, Sect. 3 presents a technique for quantifying allocation errors in simulation maps and more precisely on the minimum distance between the allocation errors in simulation maps and the nearest patch belonging to the same category on the reference map. The comparison between a raster-based and a vector-based approach brings us back to the differences in measurement inherent in the representation of entities in raster and vector mode. These techniques are applied to two datasets. Section 1 uses the Asturias Central Area database, where CORINE maps are compared to SIOSE maps and simulation outputs.
GOF values range from 0 to 1. Maximum GOF (1) is obtained when there is full overlap between two polygons or patches. If there is no overlap, GOF is 0. If overlap affects half the area of the polygons or patches, GOF will be 0. 5. When comparing pairs of maps, the GOF value may vary depending on whether the assessed map is evaluated against the reference map or the reference map is evaluated against the assessed map. Map Curves calculates the GOF values for both these operations. It then uses the highest of these two GOF values in the comparison. GOF values may be obtained either for the whole dataset or for the set of patches or polygons that make up each category on the map. Although it is technically possible to calculate a GOF for each individual polygon or patch, it is computationally very demanding and is not normally done.
Utility Exercises 1. To validate a map against reference data/map 2. To validate a simulation against a reference map 3. To validate simulated changes against a reference map of changes 4. To validate a series of maps with two or more time points Map Curves provides a simple metric for assessing the extent to which two datasets share the same spatial structure, i. e. the same number and shape of polygons or patches. Unlike many other metrics, GOF evaluates the spatial agreement between maps at a polygon or patch level. In most cases, this type of analysis is based on raster data and comparisons are made at cell level. However, polygons or patches reflect the real structure of a landscape better than cells. GOF therefore provides a better, more realistic method for validating the similarity between maps than cell-based metrics.
QGIS Exercises Available tools • Processing Toolbox R Pattern evaluation Map Curves raster R script Map Curves vector R script There is no default tool in QGIS for carrying out Map Curves analysis. It is however implemented in R. We have developed two R tools for QGIS to perform the Map Curves analysis for either raster or vector data. To learn how to configure QGIS to work with R scripts, see Chap. "About This Book" of this book. This also explains how to install the different R scripts required to do some of the exercises presented in the book. The Map Curves raster script is based on the code developed by Professor Emiel van Loon from the University of Amsterdam. 1  The script provides full Map Curves results. These consist of: (i) the GOF value of the analysis, with details of the map used as a reference; (ii) the table for the GOF between categories; and (iii) the Map Curves graph.
The R code of the Map Curves raster script also allows us to compare raster and vector maps. However, the vector option is unstable and does not always produce correct results. Its use is therefore not recommended. The Map Curves vector script, which can only be employed to compare vector maps, is based on the "Sabre" R package. 2 Unlike the previous script, it only provides information on the overall GOF between the two maps and the map used as a reference when obtaining it. The Map Curves raster script provides more information than the Map Curves vector script. It is also much faster and more efficient. We therefore recommend that this analysis be carried out with raster data. Exercise 1. To validate a map against reference data/map.
Execution If necessary, install the Processing R provider plugin, and download the MapCurves_raster. rsx R script into the R scripts folder (processing/rscripts). For more details, see Chap. "About This Book" of this book. Step 1 Open the Map Curves Raster function and fill in the required parameters. These are basically the two LUC maps to be compared: "Land Use map 1" (SIOSE) and "Land Use map 2" (CORINE) (Fig. 2 ).
112). If we observe the two maps, most of the agreement and disagreement is due to the fact that they follow different Minimum Mapping Unit (MMU) and Minimum Mapping Width (MMW) criteria. Thus, if a patch is larger than the MMU and MMW of both maps, it will be similarly mapped in both cases. However, if a patch is drawn in SIOSE, but is too small for the MMU and MMW of CORINE, this will lead to disagreement between the two maps. This explains the results for Category 6 (road and rail networks). Whereas many patches representing road and rail networks are mapped in SIOSE, most of them are not Step 1. Map Curves Raster R script mapped in CORINE because they are less than 100 m wide and therefore do not comply with its MMW criterion (Fig. 3 ). As a result, the agreement for this category in terms of overlapping patches is very low.
Execution If necessary, install the Processing R provider plugin and download the MapCurves_raster. rsx R script into the R scripts folder (processing/rscripts). For more details, see Chap. "About This Book". Step 1 Open the Map Curves Raster function and fill in the required parameters: "Land Use map 1" (CORINE simulation) and "Land Use map 2" (CORINE reference map) (Fig. 6 ).
Execution If necessary, install the Processing R provider plugin and download the MapCurves_raster. rsx R script into the R scripts folder (processing/rscripts). For more details, see Chap. "About This Book". Step 1 Open the Map Curves Raster function and fill in the required parameters: "Land Use map 1" (Simulated CORINE changes) and "Land Use map 2" (CORINE changes) (Fig. 7 ).
Execution If necessary, install the Processing R provider plugin and download the MapCurves_raster. rsx R script into the R scripts folder (processing/rscripts). For more details, see Chap. "About This Book". Step 1 Open the Map Curves Raster function and fill in the required parameters: "Land Use map 1"  (CORINE 2005)  and "Land Use map 2" (CORINE 2011) (Fig. 8 ).
AR CLOUD PLATFORM The AR Cloud platform has been developed using the SolAR framework  1. Indeed, SolAR is an open-source framework under Apache V2 license allowing the creation of spatial computing pipelines dedicated to AR. Based on a C++ interface, it provides an API for more than 50 low and high level components widely used in the field of spatial computing. It also provides numerous component implementations from open-source libraries integrated in modules (e. g. OpenCV, g2o, FBoW, Ceres, OpenGV or PCL). The SolAR framework allows the development of spatial computing pipelines by assembling abstract components. Before starting the spatial computing pipelines, the instantiation and configuration of the components are performed at runtime based on a description file defining the implementation choices and configuration parameters.
The Soviet Mars 2, 3, and 5 spacecraft first measured the ion escape from Mars and also identified characteristic plasma boundaries such as bow shock (e. g. ,  Vaisberg and Smirnov 1986, and references therein). Later Phobos 2 spacecraft also measured a significant loss of ionospheric ions through the magnetotail (e. g. ,  Lundin et al. 1989; Lundin et al. 1990; Lundin and Dubinin 1992). These spacecrafts estimated ion escape rates to be of the order of 1024-1025 s -1 , which correspond to evacuation of the total oxygen content in the present-day atmosphere of Mars in less than 108-109 years. Recently the Ion Mass Analyser (IMA) instrument, one of the Analyser of Space Plasma and Energetic Atoms (ASPERA-3) package aboard ESA's Mars Express  (Barabash et al. 2006) , has successfully measured heavy ions such as O + , O 2 + , and CO 2 + escaping through the induced magnetotail (e.
g. ,  Carlsson et al. 2006; Lundin et al. 2009 ). IMA has measured ion distribution functions since 2004 to date, and the > 16 years of observations have provided many opportunities to study the ion escape for characteristic solar events and seasonal changes (e. g. ,  Futaana et al. 2008; Dubinin et al. 2009; Edberg et al. 2010; Hara et al. 2011; Yamauchi et al. 2015). The long-term dataset of IMA has also provided comprehensive views in the heavy ion escape rates from Mars, varying within 1024 s -1 order of magnitude under a variety of external conditions in terms of the solar wind and solar EUV flux (e. g. ,  Lundin et al. 2008; Nilsson et al. 2010 Nilsson et al. , 2011;; Ramstad et al. 2015 Ramstad et al. , 2017 Ramstad et al. , 2018)). Recent observations of the Suprathermal and Thermal Ion Composition (STATIC) instrument  (McFadden et al.
(2021). The field of view (FOV) of each imager is > 1 rad and the instantaneous FOV (iFOV) is 0. 4 milli-radian. Since the orbit of MMX is similar to the Phobos orbit (see "Orbit and observation window" section), which is almost a circular orbit around Mars, OROCHI will observe Mars from almost same altitude (~ 6000 km), and thus it will provide images with a stable spatial resolution. Besides, OROCHI can monitor a specific region for more than 3 h with 2. 4 km/pixel resolution and an interval of 15 min. This will give critical information for understanding how local/regional dust storms develop. The dataset from OROCHI is similar to those from a geostationary weather satellite whose spatial and temporal resolution are the order of 1 km and 10 min, respectively. Also, thanks to its wide FOV, OROCHI can observe a wide range of local time from morning to evening in one observation.
Orbit is closely linked to the spatial coverage of a single observation and the temporal resolution. MMX's trajectory, which is quite different from other missions, means that it will produce a completely different type of dataset in terms of temporal resolution and observational coverage. In order to complete our understanding of atmospheric mutual coupling system, the monitoring capability throughout the atmosphere from near-surface to the space is of importance. Figure  7  shows target altitudes of several missions including MMX and atmospheric phenomena at those altitudes. MMX covers the lowest and highest altitudes, while the other polar-orbiting satellites observe the altitudes in between, including the near-surface atmosphere. MAVEN can observe the widest range of observational altitudes from the surface to space.
However, the datasets from the two missions will be complementary, and therefore the EMM observations may help to inform the MMX observations. Both missions will investigate transport processes for water and dust in the lower atmosphere. The EMIRS can observe spatial distributions of the major tracers and temperature in the lower atmosphere although its spatial resolution on Mars is much lower than the MIRS onboard MMX. MIRS can monitor temporal variations of the much finer structures of the tracers than the EMIRS's spatial resolution while it will not be able to measure temperature accurately near the Mars surface. Both missions will also investigate escape of the Martian atmosphere to space. EMM can observe neutral molecules escaping from Mars, while MSA onboard MMX can detect escaping ions.
2009 ) on the dayside and nightside of Mars and the database that has been accumulated since the MOI reveals their seasonal and annual changes. Mars Color Imager (MARCI,  Bell et al. 2009 ) is a push-bloom camera with a wide longitudinal FOV and can scan Mars in the latitudinal direction following the satellite's orbital motion. Accordingly, MARCI can cover the whole of Mars surface in one sol. However, if we focus on a particular region, the time resolution of observations of the region is worse than half a sol except the polar regions. MARCI that uses visible wavelengths filters can take only one image of the regions every sol. MCS can observed the nightside of Mars, but the longitudinal resolution of the observations is low due to its narrow FOV and limb-viewing operation. Therefore, the instruments onboard MRO cannot monitor rapid variations of fine structures in the spatial distributions of the tracers with shorter time-scales than one sol and smaller horizontal scales than the synoptic scale.
Synergy for each science theme The study on the paleo-environment of Mars using the Martian samples that MMX will bring back to Earth is expected to have synergy with MSR first and foremost. The NASA-ESA-led Mars Sample Return (MSR) and MMX missions are both planned for the 2020s. MSR will return samples back to the Earth in 2031 from the Perseverance rover landing site Jezero crater  (Beaty et al. 2019) , whereas MMX will return the Phobos regolith in 2029  (Kawakatsu et al. 2017; Kawakatsu 2018 ). MSR will provide a comprehensive dataset regarding the single location with the geologic context. At the same time, MMX will return Martian surface materials ejected from multiple unknown locations by numerous impact processes  (Hyodo et al. 2019 ). Together MSR and MMX are the first-ever missions designed to collect material samples from the Mars system for analysis.
4, 17, 31, 32  No gnificant genotoxicity was observed for both materials at any concentration tested, as evident by real-time PCR analysis, DNA fragmentation assay, and H2AX DNA damage marker staining (Figure  S5 a-c ). A broad immune profiling at the single-cell level was performed focusing specifically on B cells among the other 13 well-detected immune populations. An extended cytokine panel including IL-4, IL-6, and GrB (key B cell functional mediators) was applied by single-cell mass cytometry. 33, 34    visualization tool for high-dimensional single-cell data based on the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm (viSNE) was used to reduce the dimensionality of the data set. 35 CD45 + events were analyzed by viSNE to cluster all the single-cell events into 13 populations according to 8 protein expression readouts used in the analysis (CD3, CD4, CD8, CD14, CD16, CD20, CD19, and CD123).
22 The viSNE analysis accurately identified helper and cytotoxic T cells (Th cells and CT cells, respectively), classical (C) and non-classical (NC) monocytes, myeloid dendritic cells (mDCs), plasmocytoid dendritic cells (pDCs), natural killer cells (NKs), and B cells (Figure  1a ). To deconvolute the different immune population composition after treatment with the materials we applied the sunburst software feature present on Cytobank (https://www. beckman. com/flowcytometry/software/cytobank-premium/data-visualization). We found that both GO and GONH2 induced a reduction of the total monocyte content (Figure  S6a ). We confirmed our previous findings  22  on the ability of amino functionalized GO to induce monocyte activation mostly toward a M1 polarization response, as demonstrated by the increased and coherent production of classical M1 cytokines such as TNFα, IL6, and MIP1 (Figure  S6b ).
1 (see Table  S1 ). The Volcano plots display the magnitude of the perturbations induced by the materials (Figure  3a ). Representative genes modulated by each material are labeled in both plots. Overall, following the treatment with GONH2 and GO, 496 and 619 transcripts were altered in GONH2 and GO-treated samples, respectively. Among them, 197 transcripts were exclusively up-regulated by GONH2 and 183 by GO. In contrast, 148 and 285 transcripts were uniquely down-regulated in GONH2 and GO, respectively (Figure  3b ). Seventy and 81 transcripts were respectively up-regulated and down-regulated by both materials (Figure  3b ). To provide a functional interpretation of the transcriptional changes, we performed functional analyses using canonical pathways from Ingenuity Pathway Analysis (IPA) (see Table  S2  and Figure  S9  and S10 ).
In addition, both materials induced upregulation of chemoattractant cytokines such as CCL chemokines CCL3, CCL4L1/L2, and CCL22, which are important for innate and adaptive cells cross talk. A chemokine network constructed using genes differentially modulated by either GO or GONH2 is represented in Figure  6 ; genes significantly modulated by either GO (Figure  6a ) or GONH2 (Figure  6b ) are highlighted. The robustness of our functional analysis was confirmed by RMDB, a method implemented here to comprehensively summarize and display differentially enriched pathways using 32 open-source databases (ConsunusPathDB) (see Methods for details). 47 Such analysis confirmed and expanded the results retrieved using IPA (Figure  5  and 6 ): B cell receptor activation and IFN signaling activation was detected in both GO and GONH2 comparisons, while CD40/CD40L, TLR and TNRF2 activation only in GO.
OTESPA silicon cantilevers (Bruker, UK) with a resonance frequency of 300 kHz and a nominal force constant of 42 Nm -1 were used. AFM samples were prepared by drop casting onto a freshly cleaved mica [previously coated with 20 μL of 0. 01 % of poly-L-lysine (Sigma-Aldrich, UK) for the non-functionalized GO sample], using 20 μL of the corresponding GO and GONH2 dispersions (100 μg/mL). Unbound materials were removed by washing with 2 mL of MilliQ water, and then dried overnight at 37 °C. The acquired AFM height images were processed using the Nanoscope Analysis software (version 1. 4, Bruker, UK) to elucidate the thickness distribution of the GO samples. X-ray photoelectron spectroscopy (XPS) was performed on a Thermo Scientific KAlpha X-ray photoelectron spectrometer with a basic chamber pressure of 10 -8 -10 -9 bar and an Al anode as the X-ray source (1486 eV).