dataesr/scientific-paragraphs-categorization huggingface dataset:
The scatter of the data around the one-to-one line is similar for all channels (note the difference in scale for the blue channel in Figure  4 ). The range of Rrs values spanned in the data set represent a wide range of conditions. However, HydroColor has yet to be tested in highly turbid waters (turbidity >80 NTU). The median percent error of the HydroColor measurement of Rrs relative to the WISP was 18%, 16% and 26% for the red, green, and blue channels, respectively. The median bias for the same comparison was 1. 3 × 10 -4 sr -1 , -5. 2 × 10 -4 sr -1 , and -9. 5 × 10 -4 sr -1 , respectively. The error in ρ is not realized in this comparison because the same value of ρ is used for both the HydroColor and WISP calculations of Rrs. To show a meaningful comparison, the WISP spectra were averaged using the camera spectral sensitivity curves as weights Equation (3).
These results suggest that HydroColor's measurement of Ed is the largest source of error. Data collected on 21 May 2017 using multiple Apple and Android devices allowed for an interdevice comparison of reflectance and turbidity. The study sites ranged from relatively clear water in Lake Washington (~2 NTU) to moderately turbid water of the Puyallup River (~7 NTU). Figure  6  show the reflectance and turbidity measured by each device at each study site. Only small variations in reflectance and turbidity exists between the six devices tested. None of the devices showed a consistent bias that was greater than the uncertainty of the HydroColor measurement.
Validation of Turbidity Measurement The relationship between turbidity and HydroColor measured R rs in the red channel is shown in Figure  7. A function was fit to the data that provided an equation for converting HydroColor R rs (Red) to turbidity. When applying this equation (turbidity = 22. 57R rs (Red) / (0. 044 -R rs (Red))) back to the data in Figure  7 , the median percent error in retrieval of turbidity was 24%. Compared to other methods for measuring turbidity  [18] , this accuracy is more than sufficient to collect meaningful turbidity measurements in inland and coastal waters. Sensors 2018, 18, 256 11 of 15.
Validation of Turbidity Measurement The relationship between turbidity and HydroColor measured Rrs in the red channel is shown in Figure  7. A function was fit to the data that provided an equation for converting HydroColor Rrs (Red) to turbidity. When applying this equation ( = 22. 57 ( ) (0. 044 -( )) ⁄ ) back to the data in Figure  7 , the median percent error in retrieval of turbidity was 24%. Compared to other methods for measuring turbidity  [18] , this accuracy is more than sufficient to collect meaningful turbidity measurements in inland and coastal waters. To determine if the relationship between turbidity and Rrs (Red) is reasonable, an oceanic radiance model from Gordon et al. [19]  was fit to the data. To fit the model, the relationship between turbidity and SPM concentration was assumed to be 1 NTU:1 g m -3  [18, 20].
Fitting the radiance model to the turbidity and Rrs (Red) data provided a mass specific backscattering (bbp*) value of 0. 010 m 2 g -1 and a mass specific absorption (ap*) value of 0. 0086 m 2 g -1. These values are the effective bbp* and ap* values for the red channel of the camera, which spans a large portion of the visible spectrum. If a flat mass specific backscattering spectrum is assumed  [21] , the fitted bbp* provides the magnitude of the mass specific backscattering spectrum. A value of 0. 010 m 2 g -1 is within the range of values seen in the environment for the red portion of the visible spectrum  [20]. The fitted value for ap* is slightly harder to interpret since the mass specific absorption spectrum of particles tends to be an exponentially decreasing function of wavelengths.
However, using the iPod red sensitivity curve, an example of an absorption spectrum that would provide the value of 0. 0086 m 2 g -1 after a weighted average is: * = 0. 015. ( ). Both the magnitude and slope of this function are within the values seen in the environment  [22, 23]. Therefore, the relationship between turbidity and HydroColor Rrs (Red) is consistent with other published works. The error between the modeled and measured Rrs (Red) values is likely caused by the errors in ρ and variations in bbp* and ap* that are undoubtedly present in such a spatially large dataset.
Validation of Citizen Collected Data Turbidity from volunteer collected measurements was derived from HydroColor reflectance using the relationship in Figure  7. Only the HydroColor derived turbidity was collected from volunteers, the metadata was not recovered. Therefore, it was not possible to determine if the volunteer collected measurements were performed correctly. SPM of the collected water samples and the turbidity reported by HydroColor were correlated (Figure  8 ). However, the relationship is much weaker than what is seen in Figure  7. This is likely due To determine if the relationship between turbidity and R rs (Red) is reasonable, an oceanic radiance model from Gordon et al. [19]  was fit to the data. To fit the model, the relationship between turbidity and SPM concentration was assumed to be 1 NTU:1 g m -3  [18, 20].
Fitting the radiance model to the turbidity and R rs (Red) data provided a mass specific backscattering (b bp *) value of 0. 010 m 2 g -1 and a mass specific absorption (a p *) value of 0. 0086 m 2 g -1. These values are the effective b bp * and a p * values for the red channel of the camera, which spans a large portion of the visible spectrum. If a flat mass specific backscattering spectrum is assumed  [21] , the fitted b bp * provides the magnitude of the mass specific backscattering spectrum. A value of 0. 010 m 2 g -1 is within the range of values seen in the environment for the red portion of the visible spectrum  [20]. The fitted value for a p * is slightly harder to interpret since the mass specific absorption spectrum of particles tends to be an exponentially decreasing function of wavelengths.
However, using the iPod red sensitivity curve, an example of an absorption spectrum that would provide the value of 0. 0086 m 2 g -1 after a weighted average is: a * p = 0. 015e -0. 01(λ-550). Both the magnitude and slope of this function are within the values seen in the environment  [22, 23]. Therefore, the relationship between turbidity and HydroColor R rs (Red) is consistent with other published works. The error between the modeled and measured R rs (Red) values is likely caused by the errors in ρ and variations in b bp * and a p * that are undoubtedly present in such a spatially large dataset.
Validation of Citizen Collected Data Turbidity from volunteer collected measurements was derived from HydroColor reflectance using the relationship in Figure  7. Only the HydroColor derived turbidity was collected from volunteers, the metadata was not recovered. Therefore, it was not possible to determine if the volunteer collected measurements were performed correctly. SPM of the collected water samples and the turbidity reported by HydroColor were correlated (Figure  8 ). However, the relationship is much weaker than what is seen in Figure  7. This is likely due to errors in the measurement that went undetected, or differences in the absorption or scattering properties of the sediment at different location in the bay. The relationship of SPM and turbidity is also highly dependent on particle size and composition  [18].
Acknowledgments: We would like to thank  Lisamarie Windham-Myers ,  Sarah Ferner , and the  San Francisco Bay National Estuarine Research Reserve  volunteers for organizing the HydroColor citizen science collection day on 24 July 2014. Thanks to  P. Hill  and  T. Milligan  for assistance in the measurements in Columbia River. Thanks to  L. Mayer  for suggesting the use of the 18% gray card and to  NASA Ocean Biology and Biogeochemistry  for supporting the development of the HydroColor application for Android. Significant improvements have been made to this paper thanks to the comments from three anonymous reviewers.
2017; 2018). For example, the occurrence of Incertae sedis is typical of type D0018. 3, whereas the large bryozoans have a classic occurrence in type D0018. 4. Therefore, with a precise description of all the component elements making up the flint of the various genetic types of the Upper Turonian of Touraine, we can identify the different varieties in their secondary positions, and particularly in the multiple secondary deposits in which they are found, highlighting the gitological variability of the type. To achieve this outcome, we developed a database using Microsoft Access containing more than 120 fields distributed across two forms  (Fernandes et al. in preparation). The first form records information on the petrographic characteristics of SRO, that is, those characters derived from its genesis: e.
g. , rock structure, petro-fabric, abundance, sorting, roundness, sphericity, size and nature of each class of allochem, nature of the dominant orthochem; these provide the genetic type definition. The second form records the alterological data resulting from the pre-depositional alterations whose main characters are the colour inherited or acquired, ratio of the orthochems and of the allochems, description of the neocortex and of the sub-cortical band, color and intensity of the pre-depositional patina and the intensity of the pre-depositional gloss, the combination of which define the gitological type. This database, developed for the geological sample, is applied to the archaeological material through information recorded on two supplementary forms, the 'taphonomy' form containing information on the changes related to the post-depositional life of the SRO and a second sheet containing information about the use of the object which we call the 'traceology' form.
Integrated data in a GIS The goal of this approach is to gather and integrate data from the various disciplines concerned with the study of SRO into one database that includes the discriminating indicators for each type of material. The database that tabulates our research is linked to a geographical information system (GIS) that can be interrogated at ArcGIS Online. Not only does the GIS display a layer showing the points that correspond to sampling locations, it also has a layer of polygons corresponding to the extent of the relevant geological formation defined by the geological maps. The sampling point link provides the basic information for the attribute table of the various formations and allows their extent to be verified  (Fernandes et al. 2013; 2016). With rare exceptions, to have real value, a type definition requires the consideration of all the observations made on material from the same formation and not just information from one location taken alone.
Following a method tested in the Nouvelle-Aquitaine region, the formations are digitized with GIS software (such as QGIS or ArcGIS), using a WMS flow (Infoterre from the BRGM, including the geological maps scanned at 1:50000). Formations are displayed at a scale of 1:10000 and digitized with the same protocol for the regions already studied (Tallec 2015;  Tuffery et al. 2017; in press ). Since the attribute table of the SRO formations is the same for everyone using it, all project team members can use the same method for describing and comparing the attribute data. The shape files created are centralized by a nominated researcher and downloaded to the ArcGIS server where they can be searched by any member of the lithotheques review projects (PCR). The strength of this GIS lies in the combination of the different layers of referenced data: 1) the SRO formations as described above, 2) integrated data about the deposits through the forms described above and, 3) ID cards for each gitological type, produced from the integrated data that includes the concept of the evolutionary chain of SRO (above).
Conclusions The purpose of this paper was to present a review of the "Réseau de lithothèques en région Centre-Val de Loire" project and to describe the methodological foundations of our multi-faceted approach. Briefly, the identification of collected locations must include the evolutionary chain of SRO concept, a source inventory that is as complete as possible, a description of discriminating characteristics according to the types of superficial formations and an online GIS grouping of all these data incorporated into a single database. We have attempted to demonstrate the importance of inter-relationships between, and homogenization of, protocols and methodologies in data acquisition and sharing. The degree of collaboration achieved by the PCR in sharing knowledge is made simple today thanks to computer tools that allow work to proceed simultaneously and at different rhythms in scattered locations.
Acknowledgements This work has been funded by the  French Ministry of Culture and Communication  with the support of the  Conservateur regional de l'Archéologie, S. Révillion. In addition to all the organizers of the 11th international symposium on knappable materials, "From toolstone to stone tools", held in  Buenos Aires (Argentine)  from  07  to 12 November 2017, we would like to thank the members of the  PCRs "Réseau de lithothèque en région Centre-Val de Loire" "Réseau de lithothèque en région Nouvelle-Aquitaine"  and " Réseau de lithothèque en région Auvergne-Rhône Alpes ", as well as the board members who participated in the elaboration of the  GDR "Silex"  for discussions and advice that enriched our reflections and  V. Rots  and all the member of the  Traceolab of Liège  for the access to microscopy equipment.
I. INTRODUCTION The number of IoT devices in the world is expected to increase to 30 billion by the end of 2025 with the rapid adoption of IoT technology in several domains including health, transportation, and manufacturing 1. Several standalone IoT applications have been developed for these domains which rely on sensor data collected from dedicated 'things'. The number of connected things is expected to reach 125 billion by the end of 2030, and they will generate a huge amount of sensor data 2. This sensor data is multi-modal in nature comprising of various formats including video streams, images, and strings, and is often available via application platforms such as ThingsBoard 3 or SmartSantander 4. The sensor data from multiple platforms, when combined, is used to actuate devices within applications and reused to extract humaninterpretable knowledge.
These issues can be addressed at different levels of the Semantic Web Stack for IoT  [4]  -Modeling, Data Processing, and Services and Applications. Ontologies defined at the modeling level of the stack define concepts associated to things' characteristics and capabilities. These ontologies enable sensor search, sensor discovery, and data integration functionalities. The ontologies defined for the next level -Data Processing -propose concepts to enable data abstraction and access and support description logics to reason, infer, and interpret the data. Ontologies defined for the last level -Services and Applications -enable service search, service discovery, and service composition. Ontologies may also define concepts catering to multiple levels of the stack. Ontologies are now commonly used to solve the issues associated with semantic interoperability in IoT 1 Author version domain because of the following benefits  [5] :  (1)  exchanging data among systems, (2) providing interoperability among systems, (3) designing knowledge, (4) sharing knowledge, and (5) simplifying operations.
The existing ontologies defined for IoT are either too generic (causing loss of information because of coarse granularity), or limited to only a specific domain (verbose and hard to use)  [6]. As technology progresses, additional things/information will be incorporated within IoT applications which will require more concepts to be introduced within ontologies (existing or new). This results in a need to introduce more concepts to make the ontologies more explicit with respect to applications. Thus, ontologies need to be redesigned and formalized over time. Existing works have proposed the use of unified ontologies to tackle issues of interoperability and automation associated with heterogeneity of sensor data  [7] -  [9]. Such unified ontologies will still need to be redesigned and formalized with time.
Also, multiple possible unifications developed by domain experts pose several challenges such as dealing with constantly redefining ontologies and a lack of standards  [10]. Figure  1  illustrates an example scenario where the use of several possible unifications of ontologies causes issues. Consider, for example, two IoT testbeds deployed at different (or same) geographic locations and using different unified ontologies for building applications for smart-health and smart-building domains respectively. Accessing data from both the testbeds at a remote location (e. g. , a remote server using data from both the platforms for developing another application) leads to the following challenges. First, accessing data from a particular sensor based on context (such as location or time) requires discovering the sensor.
WSN and M2M are supplementary technologies that form the basis of IoT domain. In WSN, a network of spatially distributed autonomous sensing nodes with wireless communication capabilities is deployed to "monitor physical or environmental conditions, and to cooperatively pass their data through the network to a main location"  [20]. These nodes are limited to performing sensing only. M2M refers to technologies that allow wireless/wired systems to communicate with other machines with similar capabilities. M2M technologies are limited to machines and currently require no human involvement. Building upon M2M, CPS enables strong coordination between computational (the 'Cyber' element) and physical elements of a bigger WSN system which can be used to develop interactive applications. IoT entwines these three technologies (WSN, M2M, and CPS) together by deploying nodes that are capable of performing sensing and computation; and also have actuation capabilities which makes it a smarter WSN network that relies on M2M and is capable of producing CPS.
An extension of IoT to standard web technologies and protocols results in WoT. WoT uses standard Web technologies for the The goal of IoT is to provide anytime, anywhere and any-device services. Thus, cloud-based architectures have been proposed in the literature  [21]. These cloud-based architectures rely on cloud computing technologies which provide criterion for service provisions with reduced costs and improved availability and fault tolerance. The cloud technologies are supplementary to IoT technology and support easy storage, management, processing, and analysis of largescale sensor data. The large-scale sensor data collected by IoT systems usually comprise the following characteristics as highlighted by Ma et al. [22]  -heterogeneity, inaccuracy, and data collection in real-time. Traditional data processing techniques, therefore, may not work on this data which is why IoT relies on big data technologies for data processing and analysis.
OWL-Time ontology has been used as a base ontology by several other works mentioned earlier like SSN  [42]  and SmartOntoSensor  [43]. Timeline ontology  16  is another time-based ontology that emphasizes on the use of timelines (combinations of multiple time instants and intervals) to denote temporal objects such as signals or videos. It extends the first version of OWL-Time Ontology by providing various concepts for timelines such as 'ContinuousTimeLine', 'DiscreteTimeLine', and 'Orig-inMap'. Hong et al. [65]  proposed a temporal ontology which supports reasoning over temporal queries asked in natural language. Their ontology extends the first version of OWL-Time and introduces some additional properties for easy reasoning on natural language queries and generate responses in natural language.
Access-control and security mechanisms are commonly used in areas related to databases and information security. Ontologies have been used as a popular tool for representing access-control models across domains owing to the flexibility and extensibility offered by ontologies. The concepts and properties defined in these ontologies help in identifying the rightful users of the right parts of information. In this section, we study the existing access-control ontologies proposed in these domains. Access control models essentially try to describe the relationships between different users and the information they have access to. The users of the information are known as subjects or actors who require access to resources which refer to different pieces of information being shared. These resources might vary in terms of their granularity based on the subject in consideration.
To the best of our knowledge, this is by-far the closest system proposed for solving the 20 http://www. onem2m. org/technical/onem2m-ontologies heterogeneity and interoperability issue for integrating multiple IoT systems. Still, there is a long way ahead to define standards for the IoT domain. Several frameworks catering to IoT systems have also been proposed in the literature to overcome interoperability issues using semantic techniques. For example, the service-oriented IoT middleware proposed by Wang et al. [87]  utilises semantic aspects of information fusion but fails to include semantic models for other aspects including deployment, data collection, and data interpretation to resolve interoperability issues with other systems. Another conceptual IoT framework proposed by Sezer et al.
This last approach is developed in this study, which focuses on testing the ability of these coupled fire-atmosphere models to represent micro-scale atmospheric and fire front effects on a real and large observed fire case. Numerical fire/atmosphere coupling has already undergone numerous studies, starting from the static fire simulations of  [10]  to the pioneering works of  [7, 11]  where a simplified model of  [12]  fire spread was coupled to the atmospheric model of  [13]. More recently, the original Clark/Coen model evolved to the CAWFE model  [14] , while efforts at extending the WRF weather forecasting model  [15]  have resulted in the WRF/fire module  [8] , widely used even at the scale of large fires (several square kilometers  [16] [17] [18] [19] ). A similar system is used here, based on the atmospheric Meso-NH model  [20, 21]  interactively coupled with the ForeFire fire spread model  [22].
Specifically, the fire model is different from the  [23]  model used in other approaches taking into account the effects of wind and slope through coefficients experimentally fitted to wind values "as if the fire was not there". Moreover, although large wildfires remain an objective, specific numerical methods were developed so that all phenomena could be simulated at a resolution close to their characteristic scale. The target case study is decomposed into four nested domains ranging from 2. 4-km to 50-m resolution, allowing one to explore the local to the regional impact of a large wildfire in an interactive fire-atmosphere coupling. Fire front is integrated at the scale of thermal transfer (sub-meter), fuel consumption at the resolution of the vegetation database (5 m), local atmosphere and winds at the resolution of the convective structures above the fire front (50 m) and smoke tracer transport at the meso-scale (2 km).