dataesr/scientific-paragraphs-categorization huggingface dataset:
Activity-based travel demand (MOIRAI) The MOIRAI module simulates the mobility choices for each agent of the synthetic population during the day. One essential challenge of the module is to represent the mobility in the most realistic way possible, by taking into account the social constraints of each agent in space and time. Several ABTDM exist in literature. (Malayath and Verma, 2013)  propose a review of existing models and their uses. Based on this review, we have decided to use the theory of random utility to simulate the choice of individuals in MOIRAI. In this theory, a stochastic approach allows to take into account rationality in the agent decisions. That is to say, the decision is described as the choice to do what is most useful, depending on possibilities. In this process, utility is generally expressed according to 2 components, one describing the observed practices and another one describing the random component.
The probability  …f€ƒD  is derived from a specification of a daily trip number based on age and priorities. This daily number of trips varies from one country to another. This parameter can be provided from local surveys, or estimated from an aggregated survey database. We used the information provided by household travel surveys, indicating that the mobility of children and old people is lower than the average population (20 -60 years). After determining the number of activities of the agent and prioritizing them, MOIRAI defines a tour sequencing to estimate the number of activities and their order. The sequences may be a Home-Based Tour (HBT), a Multiple Home-Based Tour (MHBT), or a Non-Home-Based Tour (NHABT) tour. Figure  5  presents the different types of sequencing modelled in OLYMPUS.
Assignment The transport demand previously generated by the ABTD module (MOIRAI) produces travel matrices that only supply information on the origins and destinations of the flows. The next step is to project on our grid the paths taken by the agents, in order to further provide spatialized pollutant emissions from transport. For this purpose, we only take into account the flows related to private vehicle use. There are 3 ways to deal with traffic assignment. One is the microscopic approach, which considers the traffic at the scale of each vehicle, as proposed by models like VISSIM  (Gomes and May, 2004) , AIMSUN (J. Barcelo, J. L. Ferrer, 1989)  and PARAMICS  (Cameron et al. , 1994). A second approach is that of mesoscopic models, which are interested in the evolution of sets of vehicles, as do the CONTRAM  (Taylor, 2003)  and DYNASMART  (Mahmassani et al.
As most of these transport models are not open source, we opted for the development of our own traffic assignment model inside the OLYMPUS platform: HERMES. HERMES is a macroscopic traffic module that works with average speed values for the vehicle flows, thus ignoring the dynamics of traffic within a road. This approach is compatible with our simulation scale. It is also compatible with the most common methods of estimating traffic-related combustion emissions, which rely on emission factors per driving cycle, each cycle being characterized by an environment (city, highway, etc. ), and by a mean speed per strand. There are main 4 stages in the allocation of agents to the road network in the HERMES module (See Figure  7  First, the road network is extracted from GIS road data and transformed into a graph that records the connections between the different road sections, thus creating a set of edges and nodes (intersections) using the graph theory  (Bondy and Murty, 1982).
The speed limit is the main attributes of edges. Second, HERMES proceeds to the computation of the shortest path for each trip by solving the Dijkstra algorithm  (Dijkstra, 1959). For each trip, the module identifies the nodes of the graph that are closest to the geo-referenced O and D points. To choose the shortest path from the algorithm outputs, HERMES uses the time spent on a link as a weighting. In a third step, the integration of the regional traffic flow -including the goods and different patterns of interregional transportation -is made. This complimentary step is necessary because the MOIRAI travel demand only considers personal trips for agents living in the city. Inter-regional transportation, heavy-duty vehicle (HDV) transportation and light commercial vehicle (LCV) transportation are thus not taken into account.
Building energy demand Figure  8  presents the flowchart of HESTIA, the OLYMPUS module in charge of simulating the building energy demand. HESTIA uses the type of housing, the living area of the household and their average annual energy consumption as input parameters. The main task of this module is to spatialize the energy demand in the territory. (Swan and Ugursal, 2009)  have proposed a review of models and methodologies for simulating the energy demand of buildings. In this frame, both Top-Down and Bottom-Up approaches rely on the econometric, statistic and engineering aspects of the energy demand. They are mainly developed to achieve a better understanding of the efficiency and cost of energy policies. Due to its global approach, the Top-Down method lacks flexibility to create scenarios involving a change in methodology.
On the other hand, some of the input parameters considered in a Bottom-Up approach go beyond what is feasible on a regional scale. They sinclude detailed data by type of building (structural properties, equipment, usage) as well as individual parameters such as the orientation of buildings in relation to the sun. In OLYMPUS, the combustion emissions modeling is carried out in two steps by the HESTIA module. It lists combustion activities for residential, institutional and commercial heating, as well as for hot water and cooking. The process is similar to Top-Down approaches, but the implementation of Bottom-Up factors related to energy efficiency or household characteristics makes it possible to envisage the implementation of energy scenarios. The generation of the energy demand of the residential sector is done by modeling the energy demand of each household.
Emissions The calculation of pollutant emissions from both road transportation and building energy consumption is the role of the VULCAN module, which constitutes the last step of OLYMPUS. There, the quantification of pollutant emissions is based on methodologies recommended by the European environment agency (EEA) guidebook (European Environment Agency, 2013) for air pollutants and Green House Gases (GHG) emissions. They rely on the use of emissions factors, which may depend on the type of fuel, but also on the age and combustion technology of engines and stoves. The VULCAN flowchart is shown in figure  9. a. Geosci. Model Dev. Discuss. , https://doi. org/10. 5194/gmd-2018-154 Manuscript under review for journal Geosci. Model Dev. Discussion started: 30 July 2018 c Author(s) 2018. CC BY 4.
Road emissions Emissions from road transport -labeled as mobile emissions in the inventory -are calculated along linear road sections where the traffic properties at a given time are homogeneous (driving cycle, average speed). As for passenger vehicles, the flow of traffic is derived from the travel matrices of the assignment module. From a quantitative point of view, emission factors depending on traffic characteristics are applied to each road section to obtain quantities of pollutants emitted into the atmosphere per unit of time. In literature, there are three main databases that offer exhaust emission factors. These are HBEFA  (Keller et al. , 2017) , COPERT  (Ntziachristos et al. , 2009)  and MOBILE6  (US EPA, 2003). They differ to the extent that some rely on instantaneous speeds, while others consider average driving speeds, or apply to specific driving cycles such as standard highway traffic.
This methodology is used because of the excessive uncertainty on the fret fleet. The number of vehicle N is generated by HERMES using standard ratios of fleet composition. The emissions are calculated for CORINAIR pollutants (NOx, VOCs, PM) and for CO2. As mentioned above, emissions related to tire and brake wear are added to exhaust emissions, according to the two following equations (European Environment Agency, 2013): Finally, the VULCAN module considers gasoline evaporation, using an aggregated still based on (European Environment Agency, 2013) : 𝐸 hî"ƒ = 𝑁 × 𝜀 hî"ƒ × 365, ( 32 ) where  hî"ƒ is the evaporation emission factor depending on ambient temperature.
Configuration The simulations were carried out in 2009 due to an important database for this year (surveys, censuses, inventories). The simulation domain is the region Ile de France (Greater Paris). It is a monocentric urban area with a population density of 21,000 inhabitants / km² in the city center, and a decreasing density of the inner suburb and the outer suburb which are predominantly rural (Figure  10 ). The population of the territory is more than 11 million inhabitants. In terms of transport infrastructure, Paris is the city with the best public transport network. Individual mobility is 3. 87 trips per person per day on average, with 41 million trips being completed each day in the region. The majority of travel does not include trips to the city center(70%): the majority of trips in Ile-de-France are short (4.
4 km on average) and close to home. Modeling anthropogenic emissions from the combustion of individuals requires a large database whose main parameters and their sources are described in Table  1. To generate the synthetic population in GAIA, we have used aggregated data from the city census, mainly derived from INSEE. They include the distribution of the population on the territory by age and sex, the number of households by IRIS and the average distribution of households by type of households (alone, couples, family, single-parent family). Mobility calculations in MOIRAI rely on several types of data, mainly surveys or national statistical databases. First, the public transport accessibility of the domain was carried out on the basis of the density of public transport networks as provided by the regional transportation agency STIF.
Once these values determined, the determining parameter for the realization of the activity in THEMIS will be the estimated travel duration inside the Paris region. Here, this parameter was derived from the calibration of the THEMIS module through an online application based on GTFS data from all regional transport agencies. It allows the constitution of a matrix of average transport times between the different classes of urbanized areas (UTAI). In the end, the combination of the transport network data and population density allows the calculation of the accessibility of any activity area. The vehicle fleet used in HERMES dates back to 2009 and is based on the  (Carteret et al. , 2015)  survey. It includes passenger cars, LCVs and HDVs. This study is based on video observation to characterize a fleet of vehicles and fleets are compared to the global transport survey.
Finally, the gender parity and the employment rate correspond to the census population. And these two parameters are perfectly equal because they were taken as constraints. Based on the low percentage distribution error of the working population, we consider that the model generates an acceptable synthetic population for transport modeling. The other attributes of the agents also correspond with the forcing data, including the gender distribution, the unemployment rate, the distribution of the type of household and the average household size. Because OLYMPUS relies on Bayesian statistics to generate a synthetic population, it is necessary -to get results close to reality -to have a large database which offers specific information on the distribution of the characteristics of the agents of the population as a starting point.
OLYMPUS emissions, although slightly lower than AIRPARIF emissions, present, for each pollutant, total values that are very satisfactory and speciation by activity (or vehicle) that reproduces the variability of the emissions calculated by AIRPARIF. The only sector that falls outside this rule is residential combustion with a factor 2 of underestimation of the value of the AIRPARIF inventory for PM10, but it is recalled that could not rely on local data and equipment technology is a major determinant of particulate emissions related to heating. For nitrogen oxides (NOx) and the particulate matter (PM2. 5 and PM10), the differences with the regional inventory are approximately 20% for road transport. Moreover, AIRPARIF has its own residential heating modeling hypotheses including emission factories that do not rely on local data as well as the fleet of chimneys, stoves, etc.
Thus, when we compare the EMEP and AIRPARIF estimates, their differences are of the same magnitude as the one we have with AIRPARIF. Our results are therefore in a range of values consistent with the estimates of the reference inventories. Thus, comparison with other inventories shows us that most of the differences between our calculations and the reference inventories are of an order of magnitude consistent with the use of different working hypotheses, on parameters insufficiently constrained by surveys or censuses. Comparing results with existing databases validates the ability of the OLYMPUS platform to produce combustion and road traffic emissions from a population's activity. After evaluation, and considering the main improvements to come such as freight transport and congestion, our modeling approach appears relevant for carrying out impact analyzes of transport and energy policies in a given territory.
It may also be interesting to set up feedback loops between the modules of OLYMPUS in order to simulate their interactions and to develop a multi-agent model. For instance, we could couple the discrete choice module with the displacement assignment module in order to make a feedback to integrate a realistic network congestion into the modal choice. One of our main perspective will also be to implement the model on other cities, so that we can test the transposability of our results, and have a broader vision of decision support in urban planning. Finally, because it relies on the evaluation of pollutant emissions from the activity of its synthetic population, in connection with the nature and the functioning of the simulated territory, OLYMPUS can produce diagnoses of durability for differentiated situations: widespread cities, compact cities, cities oriented along transport corridors, etc.
Ontologies are considered a backbone for supporting advanced situation management in various smart domains, particularly smart health. It plays a vital role in understanding user context in order to determine patients' safety, situation identification accuracy, and provide personalized comfort. The smart health domain contains a huge number of different types of context profiles related to interactive devices, linked health objects, and smart-home. The key role of context profiles is to deduce urgent situations that are needed to run adaptation components on a specific smarthealth Fog. Existing platforms and middlewares lack support to efficiently analyze a large number of heterogeneous specific profiles and continuous context changing in near real time. In this paper, we focus on data and dissemination of information from services related to the field of e-health.
Smart connected objects are deployed in smart-homes and healthy environments for real-time context data acquisition, which may be used for accurate decision and innovative services. Hence, they provide a complete environment automation system but may lead to a negative impact on interactive applications in the growth of smart objects. Such strong and multiple connections between heterogeneous physical things will raise a potential problem of processing more complex set of contextual data in a set of emergency situations. Therefore, we need to rethink about efficient context data management for accurate and timely decision making and fast information dissemination strategies across distributed smart environments. In this domain, building context-aware systems require using multiple heterogeneous specific patients' profiles related to interactive devices, health due to its heterogeneous contexts such as context monitoring environment and its social activities.
RELATED WORKS Challenges in context-aware are monitoring, aggregating and analyzing of the context information in a semantic manner and selection of situation context-aware services for accessing/broadcasting multimedia documents using middleware-based platforms. Our work consists of efficiently managing patients' situations through context-aware users' profile modeling, situationaware identification strategy and providing all distributed adaptation services that facilitate users to share multimedia contents using fog-based Kalimucho middleware  (Da et al. , 2014)  in dynamically changing environments. Several cloud-based platforms and middleware were proposed for managing pervasive healthcare data for the identification of situations for large context users' profiles using various context-aware users' profiles modeling.
Identification of situations for large context user's profiles Situation identification is classified into two categories: knowledge-based and non-knowledge-based techniques. Knowledge-based situation identification relies on a conceptualization of the context model encoded in resource definition framework (RDF) interpretable machine format. Non-knowledgebased situation identification uses Event-Condition-Action rules and other learning techniques that allow automatic learning from the history of events and detecting daily life situations from the data context. These techniques lack simultaneous real-time semantic-based situation identification of multiple users. We are interested in smart fog-based and ontology-based real-time ondemand urgent situation identification due to mobility of the user and usage contexts.
The semantic context model is extracted and computed based on ontology similarity measures. Gyrard et al. (2016)  proposed an ontologybased approach to describe formally user's context metadata for improving the assistance of users in their daily life activities and prediction of some urgent situations. The proposed tool aims at collecting context data, inferring and reasoning over these data for the situation identification and decision making. The tool is based on inference rules provided by domain experts to generate appropriate services. More recently,  Chabridon, Bouzeghoub, Ahmed-Nacer, Marie, and Desprats (2017)  proposed an efficient mechanism to identify and compute the quality of situation using an ontology-based approach, and quality criteria aggregated using the fuzzy Choquet operator.
In this work, large context data of context-aware systems are processed in the cloud and not in local server machines. If any problem occurs such as mobility of the user, availability of context information, instability of the communication network, and the context data cannot be transmitted to the cloud and that leads to the interruption of the system. Our approach has to react rapidly on new situations among those previously observed and grouped in a cluster by analyzing current locations, profiles and physical environments of users. Nevertheless, this work does not mitigate semantic heterogeneity into a generic model that enables more advanced automated deriving adaptation from a large number of heterogeneous users' profiles and the mobility of the users and the instability of the communication network cannot be performed automatically.
Aguilar et al. (2005)  proposed a context awareness middleware in cloud computing called CARMiCLOC, which is a web-service-based middleware that can behave as a software as a service (SaaS). Due to the amount of data involved to define the context, this work offers an excellent context management using a cloud computing but it is not able to analyze a large number of simultaneous users to identify services with generic similar profiles. Every work that presented above treats focuses on a specific situation for a particular user profile and explores individually each service description and computes matching distance in order to select the relevant services. In many cases, this issue could exhaust the system. Many profiles cloud be specified, accordingly, many comparisons must be computed and updated.
The Service Adaptation component makes partial or full adaptations according to current constraints: players, codecs, resources, user profile, and hardware (step 8 in Figure  7 ). As mobility of users and low battery constraints which can break the execution of mobile services in health pervasive systems, we are looking for an intelligent manner to ensure the service continuity on mobile devices by deploying the different heterogonous services. Therefore, the Service Deployer will deploy services that are selected by the Service Adaptation on the users' mobile device. It generates configuration files automatically by JAVA code generator after scanning the environment and sends it to the Kalimucho platform (step 9 in Figure  7 ). It is responsible for executing services, assuring the continuity of these services and saving the new service configuration in case of low device resource situations on mobile devices.
Prototype Implementation Our ontology model is implemented in Protégé tool  (Horridge, Tsarkov, & Redmond, 2006). This visual modeling tool supports creating and managing ontology models for smart domains and their applications. Protégé is supported by a strong community of academic, government and corporate users to design formal, common and shared semantics with OWL (Web Ontology Language) language. Protégé plug-in architecture can be adapted to build both simple and complex ontology-based applications. Developers can integrate the output of Protégé with rule systems to construct a wide range of intelligent systems. We used the Protégé tool to explicitly define different classes of GUSP-Onto, their properties, and instances. The prototype is developed with a two-layered architecture: fog-based and cloud-based.
The monitored context information is collected in a smart gateway. This gateway collects context data monitored by sensors. In the first scenario, as presented in our architecture, the main role of the fog-based SSAC component is to supervise several patients in order to identify urgent situations. The fog-based Context Collector collects and preprocesses raw context data from the smart devices and stores them in the profile repository. Then, it transforms the raw context data into semantic using the fog-based Context Transformer component which is then saved in our ontology. The fog-based Profile Clustering component groups equivalent patients' profiles into a generic context-aware profile model related to their similar context information and their location (as shown in Table  1 ). Situations related to patients are also analyzed automatically on the Cloud.
1, 1]). We collected results consisting of patients' profiles from a local data center, the number of situations identified and their precisions. Our fog-based SSAC component groups similar patients' constraints in order to optimize the situation identification time. Figure  9  reveals that in the majority of the cases, the response time of identification situation of patients was higher than that found in  Aazam and Huh, (2015). We noticed also that our algorithm, compared to  Aazam and Huh, (2015) , minimized the number of tested profiles into 34. 23%. This explained that our work groups some individual patients' profiles, which share similar context properties, and accelerates the search process of relevant services at run-time better than the work of  Aazam and Huh (2015).