dataesr/scientific-paragraphs-categorization huggingface dataset:
Accuracy and dispersion assessments In order to assess the quality of our calibration, a very accurate survey of the wall was performed using a Faro Focus3D. The resolution of the resulting geo-referenced point cloud is 2 mm. It is then down-sampled to the SR4500 resolution. The Root-Mean-Square (RMS) deviation is computed between the reference FARO plane and the depth maps. Fig. 14  shows the RMS deviation as a function of the sight distance, for both the depth maps provided by the Mesa-Imaging software, which uses the internal default calibration of the camera, and the depth maps obtained by our method. The accuracy is about 4 cm with our calibration and 6 cm for the Mesa output. To estimate the dispersion of the data points, a plane is first adjusted to the depth maps by robust PCA and the standard deviation of the depths with respect to this plane is computed.
FIRST ATTEMPT AT OUTDOOR DYNAMIC ACQUISITIONS Since our laboratory results were satisfactory, a dynamic acquisition test was performed outdoors, at slow motion. The object we survey is a white grid (with spacing 1 m±2 cm) painted on a contrasting road surface, with an almost horizontal profile (see Fig. 16 ). For practical reasons, the acquisitions were performed with the constructor software SR3DView and, in this first approach, we used the default calibration given by Mesa.
In the present work we have evaluated the gammaray flux from the Moon using the data collected by the Fermi LAT in its first 7 years of operation, from August 2008 to June 2015. Not only is the current data set much larger, but the data were processed with the newest Pass 8 reconstruction and event-level analysis  [4] , allowing the useful energy range to be extended well below 100 MeV. We have studied the time evolution of the gamma-ray flux from the Moon, finding the expected correlation with the solar activity. Gamma rays from the Moon are mainly emitted with sub-GeV energies and their flux depends on the fluxes of CRs impinging on the Moon and on their inelastic interactions with the lunar regolith. The chemical composition of the lunar surface also plays a crucial role in determining the gamma-ray yield.
The analysis presented in this paper has been performed using the newest Pass 8 data  [4] , specifically P8 SOURCE photon events starting from a minimum energy of 30 MeV. A crucial point in the Moon gamma-ray data analysis is the treatment of the background, which originates variously from the diffuse gamma-ray emission, from the gamma-ray sources that the Moon drifts past along its path in the sky, and from the tiny residual fraction of charged CRs that are misclassified as photons. As the Moon is a moving source, the use of a background template might lead to inaccurate results. Hence, for our analysis we chose to evaluate the background directly from the data, by using properly selected signal and background regions. The signal region is defined as a cone centered on the Moon position, with an energy dependent angular radius given by: θ = [θ 0 (E/E 0 ) -δ ] 2 + θ 2 min ( 5 ) where E is the photon energy, E 0 = 100 MeV, θ min = 1 • , θ 0 = 5 • and δ = 0.
[3] at energies above 150 MeV. The minor discrepancies in the range below 150 MeV can be ascribed to the solar modulation effect on CRs, which affects the energy spectrum of gamma rays emitted from the Moon (see also the discussion in sec. V). The 2-year interval analyzed in ref. [3]  corresponded to the minimum of solar activity at the beginning of Solar Cycle 24. On the other hand, the dataset used in this analysis spans a period of 7 years, covering more than half of Cycle 24. As a sanity check we applied the analysis technique illustrated in this paper to the data taken by the LAT in the first 2 years, and the results were consistent with those of ref. [3]  in the whole energy range. In Fig. 3  only statistical error bars on the fluxes are shown. The systematic uncertainties, not shown in Fig.
VI. MONTE CARLO SIMULATION OF CR INTERACTIONS WITH THE MOON We have implemented a full Monte Carlo simulation of the interactions of CRs with the surface of the Moon based on the FLUKA [7-9] simulation code. This simulation has been used to evaluate the yields of gamma rays produced in these interactions. FLUKA is a general-purpose Monte Carlo code for the simulation of hadronic and electromagnetic interactions. It is used in many applications, and is continuously checked using the available data from low-energy nuclear physics, high-energy accelerator experiments and measurements of particle fluxes in the atmosphere. Hadronic interactions are treated in FLUKA following a theory-driven approach. The general phenomenology is obtained from a microscopic description of the interactions between the fundamental constituents (quarks and nucleons), appropriate for the different energy ranges.
8% in the energy range from 0. 1 GeV/n up to 1 GeV/n, while the 3 He/ 4 He ratio increases from about 8% up to 18% in the same energy range. Since deuterons and 3 He are secondaries produced in the interactions of primary CRs with the interstellar medium, it is reasonable to think that their abundances do not increase significantly at higher energies. Therefore, assuming these values for the isotopic ratios, we expect that the error on the lunar gamma-ray spectrum calculated neglecting the isotopic composition of primary CRs will be of percent order. The contribution to the differential gamma-ray intensity of the Moon from the i-th species of CR projectiles (protons and 4 He nuclei) may be calculated as: dI γ,i (E γ |T ) dT = Y i (E γ |T )I i (T ). (10) The corresponding photon energy flux can be then evaluated as: The results from the LAT data analysis (black points) are compared with the expected fluxes obtained after folding the CR proton and helium spectra measured by AMS-02 in 2011-13 with the gamma-ray yields evaluated in sec.
C. Comparison of the Moon gamma-ray data with the predictions from direct observations of the CR proton spectrum As mentioned in sec. I, the dataset used for this analysis was taken in a period of time overlapping with the data-taking period of AMS-02  [5, 6]. This provides, for the first time, the possibility to test our Monte Carlo simulation against the direct measurements of the CR proton and helium spectra performed by AMS-02. Our dataset is also partially overlapping with the data-taking period of PAMELA. However, at present, a test of the simulation against the PAMELA data is not possible. Although the PAMELA Collaboration has measured the CR proton spectra in two different one-month time intervals at the end of 2008 and 2009  [39] , they did not provide a measurement of the helium spectra in the same intervals.
To test our simulation against the AMS-02 data we selected a data sample taken in the period from May 2011 to November 2013. However, it is worthwhile to point out here that the time intervals selected for our analysis of the gamma-ray emission from the Moon most likely do not match those used for the AMS-02 data analysis in ref. [5]. In particular, when applying the event selection described in sec. III, we disregarded those time intervals corresponding to transient events, such as solar flares, that might be included in the AMS data analysis. We then folded the CR proton and helium reference spectra with the gamma-ray yields obtained from our simulation with the MP and TUR models. When evaluating the gamma-ray flux we assumed the LAT-Moon distance equal to its average value during the data-taking period from May 2011 to November 2013.
D. Evaluation of the low-energy CR proton and 4 He spectra and of the solar modulation potential The data shown in sec. V indicate that the lunar gamma-ray spectrum is sensitive to the solar modulation effect. This is because, as discussed in sec. VI A, the main contribution to the gamma-ray spectrum of the Moon is that of CRs in the energy range up to ∼ 10 GeV/n. In the present section we will illustrate an application of our Monte Carlo simulation to the study of the solar modulation potential. We start from a model for the CR proton and 4 He LIS  [40, 41] , evaluated using a customized version of the CR propagation code DRAGON  [42, 43] , in which we included a set of cross sections for the production of secondary particles in CR interactions calculated with FLUKA. Both the proton and 4 He LIS of ref.
VII. CONCLUSIONS We measured the fluxes of gamma rays produced by the interactions of charged CRs impinging on the surface of the Moon using data collected by the Fermi LAT from August 2008 to June 2015. Thanks to the high statistics of the data sample and to the newest version of the Fermi LAT event-level analysis and instrument response function, we have been able to measure the gamma-ray fluxes in an energy range that extends from 30 MeV up to a few GeV. The time evolution of the flux shows that the gamma-ray emissivity of the Moon is correlated with the solar activity. We also developed a full Monte Carlo simulation of the interactions of CR protons and helium nuclei with the Moon using the FLUKA simulation code to evaluate gamma-ray yields. We implemented two different composition models of the lunar surface and we found that the gamma-ray emission from the Moon depends on the elemental composition of its surface.
Acknowledgments The  Fermi LAT Collaboration  acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis. These include the  National Aeronautics and Space Administration  and the  Department of Energy in the United States , the  Commissariat à l'Energie Atomique  and the  Centre National de la Recherche Scientifique / Institut National de Physique Nucléaire et de Physique des Particules in France , the  Agenzia Spaziale Italiana  and the  Istituto Nazionale di Fisica Nucleare in Italy , the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) ,  High Energy Accelerator Research Organization (KEK)  and  Japan Aerospace Exploration Agency (JAXA) in Japan , and the  K.
Acknowledgements. This study was funded by the  Agence Nationale pour la Recherche (ANR)  through the  SUMER, Blanc SIMI   6-2012. The new simulations presented in this paper have been done using the high-performance computing resources of  CINES (Centre Informatique National de l'Enseignement Supérieur, France ) under allocations 2015-016066 made by  GENCI  ( Grand Equipement National de Calcul Intensif ). We greatly thank the anonymous referee and  S. Cornford  for the judicious and numerous remarks about this work. Edited by:  A. Vieli.
The SOLENE-Microclimat Model The SOLENE-microclimat project was first developed for the purposes of an urban insolation assessment. Sub-modules have since been added, now making it possible to incorporate: -LW radiation exchanges using the radiosity method; -conduction heat transfer and thermal storage in walls and soil; -airflow and convective exchanges through the coupling with a computational fluid dynamics (CFD) code. The near-surface air velocity and temperature calculated by the CFD code allow evaluating the convective heat transfer of the surfaces used as the entries for the SOLENE software. On the other hand, the surface temperatures calculated with SOLENE software are used as entries for the conservation of energy equation of the CFD code, evapotranspiration from natural surfaces, like plants and water ponds or humidification systems and, lastly, the energy balance (i.
e. , energy demand or indoor temperature) for a building within the simulated zone. Towards this end, a multi-zone building nodal network model is used. The temperature nodes correspond to each building floor, and the boundary conditions are the canopy air temperature and humidity calculated by the CFD code. In addition, the heat fluxes exchanged by radiation are computed with the thermo-radiative model  [26]. It should be noted that the energy balance of each node includes ventilation and infiltration loads, both latent and sensible. Only the SOLENE-microclimat thermo-radiative validation as a whole has been carried out, and its coupling with the Saturne CFD code is presented in  [21]. Due to the difficulty in getting complete and accurate experimental datasets (airflows, canopy and surface temperatures, turbulent heat fluxes, etc.
), it is very difficult to validate the SOLENE-microclimat model as a whole. Nevertheless, some validations have been carried out concerning sub-models of the software. For instance, walls, roofs and soil surface temperatures were compared successfully to measurements  [22]. It is the same for green walls' behavior  [23]. In the future, it is planned to increase these validation efforts. The SOLENE-microclimat thermo-radiative model and its validation have been presented in  [22]. The coupling with the Saturne CFD code is also explained. The modules added to study the direct and indirect impacts of adaptation and mitigation solutions are detailed in  [23, 25]. These latest developments introduced into SOLENE-microclimat models for lawns, green roofs and walls are now available for use. In all of these models, the vegetation layer acts on the following energy transfers: -Evapotranspiration, i.
Acknowledgments: This research work was carried out within the scope of the  VegDUD  Project, funded by the  French Research Agency (ANR)  under Contract No. ANR-09-VILL-0007. The authors are grateful to the  Pays de la Loire Regional Council and ADEME (French Environment and Energy Management Agency)  for their financial support of this study, as well as to the  Laboratory Energy, Environment & architecture (LEEA) at Haute Ecole du Paysage, d'Ingénierie et d'Architecture de Genève (HEPIA) facility  for providing us with experimental data.
Some formalization propositions are designed with ad hoc declarative languages based on a set of geometric constraints  [17]  or by declaration of effective conditions and consequences  [5]  and are limited by their expressiveness notably in terms of usable geographic features. Existing standards are focused on representation of regulation geographic features. [18]  is a French National standard that defines all necessary cartographic information such as regulation zoning, local Right to Build restriction (i. e. , historical building to preserve, limitations due to energy transport infrastructures). In the context of urban planning national infrastructure, all local administrations must provide information about their territory for 2016 in order to produce a national reference database with respect to this standard.
Modelling Scope In order to produce an operational model, the scope of our model is limited by a set of filters: • Considered features: our model is mainly focused on rules concerning buildings. Thus, we do not include in this study rules not dedicated to building, such as rules about vegetation or about the number of parking spaces. Inside an article, some rules which do not concern buildings are also not considered; • Frequency of uses: as each PLU is local, it is difficult to have an exhaustive study of all existing documents. Thus, the concepts of our model are based on the study of a set of PLU and on its synthesis that highlights the most frequent legal elements. Such simplification strengthens the interest of integrating an extension mechanism; • Availability of data: in order to design applications based on our model, we integrate in the model concepts related to existing features in common databases (i.
e. , CityGML Level of Detail 1 or 2 for buildings). Thus, some elements concerned by regulations, such as balconies or windows, missing in most databases, are not considered in this study. Nevertheless, information that can be assessed with geometric operators or heuristics are kept in the final model; • Objectivity: PLU rules can be classified into objective rules (defined by metric or numerical expressions) and qualitative rules (that requires a judgement from local authority). As we want to formalize rules interpretable by computers, we consider qualitative rules out of the scope of our formalization. Thus rules such as "(. )imposed for harmony reasons(. )" that require human judgement are not considered in our study.
Implementation and Rules Checking A rule checker based on our proposition has been implemented. It takes geographic data formatted according to the proposed model and OCL files as the inputs. This implementation is based on two Open-Source projects (Figure  13 ) and is released itself as SimPLU3D Open-Source project in two modules: SimPLU3D-rules for the model part  [35]  and SimPLU3D-ocl for the OCL rules management  [36] : • GeOxygene: a GIS platform providing a 3D module  [37]. It provides a framework to implement the general model and geometric operators for data integration and for OCL evaluation purposes; • OCL Dresden: a library  [38]  dedicated to OCL constraints management. This includes parsing, checking of OCL constraints and methods to check their respect. Constraints assessment is made through a meta-model that enables the representation of a geographic model and its instances.
Thus, the library provides an implementation of this meta-model that allows the representation of Java object concepts (such as classes and attributes). A mechanism in OCL Dresden library allows for instantiating the Java model from the set of Java classes that represent our model. Then, this model can be directly populated by adding Java instances provided by GeOxygene. During the interpretation phase, the OCL checker can therefore access these instances (and their properties) instantiated in the Java model in order to check the constraints. For more rule information about rule formalization, a translation of each rule family of the Strasbourg UB Zone. PLU is provided on SimPLU3D-ocl repository  [39]. In order to instantiate the model, we propose a data integration process adapted to French datasets.
They include: • Parcels, from BD Parcellaire R database  [40] ; • Zoning plan and graphical prescription, manually captured according to format  [18] ; • Roads, from BD Topo R database  [41] ; • Buildings, either in LOD1 from BD Topo R database or LOD2 from Bati3D R  [42]. According to the type of data, all the information of the model is not instantiated. For example, with LOD1, attributes relative to roof structure are missing. A test zone is provided in SimPLU3D-rule with all data in Shapefile R format  [43]  and can be loaded with the LoadShp class  [44]. More detail about the integration process can be found in  [9]. Once the model is instantiated, it is possible to check OCL constraints on the features. If we consider the constraint described in Section 4. 3, the constraints evaluation is processed as follows according to information provided during data integration process: 1.
Design of an Application for Building Permits Survey As a first application of the model and of the rule checker, we are aiming to develop a web application in order to assist building permit surveyors. This application proposes several functionalities (Figure  15 ): • Data visualization and edition: in order to consult regulation data, we propose an interface of 3D visualization based on ITowns API  [45]. This viewer enables the consultation of data in the database according to the model and also the editing of information. Information editing is very important for such applications as concept definitions (for example: the definition of lateral borders) can vary according to concerned municipality and also because the project may modify the other geographic objects (creation of new roads, parcels fusion).
Thus, this feature allows the user to define feature attributes according to local specificities. • Capture/insertion of building project: in order to check regulations, the user needs to insert geometric data about the building project. As this information is generally given in an architect paper plan, it will require the production of simple tools in order to ease its capture. Some propositions have been made to generate 3D buildings from footprints with smart extrusions  [46] , and the surveyor just has to capture the building footprint in the plan and choose the right parameters to produce the roof. In the case where a digital model is attached to the permit request, the model has to be integrated into the dataset. One difficulty is that a generic integration procedure is impossible to realize as architects use different tools, formats and produce semantically different models.
If such data is expected, the municipality has to provide specifications to architects as it is already demanded in Monaco (Ministerial Ordinance No 2012-595 of Monaco), for example. • Capture/selection of rules: Either the surveyor chooses some existing rules or regulation in the database and applies it to parcels or s/he captures some new rules. As all surveyors are not experts in computer programming, it may be important to design an intuitive interface that produces OCL rules. Letting the surveyor choose applied rules is essential as building permits are legal and sensitive documents, and the users must perfectly know which rules the system is assessing and have a control on the whole checking process. • Rules checking and report visualization: This is the last step of the process; the selected rules are checked on the project parcel.
1. The data collecting mechanism: it represents the first step mentioned previously. In this phase, we collect a large set of data which are as follows: running applications, date, time, elapsed time of each application, and background process. 2. This phase is composed of: • The off-line application classification in terms of Wi-Fi and CPU needs. In the current version of the UNOC, we focus on two components: the Wi-Fi and CPU. These two units are among the most power consuming components in mobile system but the classification can be extended to screen brightness, microphone, GPS, etc. • The execution average time for each application is calculated, and this phase is also off-line but the data base can be updated in a weekly basis. • In-line application prediction mechanism. 3. All these phases are combined and used by the optimizer actuator which manages Wi-Fi connection and CPU frequency in order to optimize the energy consumption.
Sensors Collection Module (SCM) This module is responsible for collecting data from the embedded sensors in order to identify the most appropriate device's stand. In our mobile handset, there are several sensors such as accelerometer, ambient light sensor (ALS), simple orientation sensor, inclinometer, compass, gyrometer, and geolocation. In order to determine which collected sensors are the most relevant, some preliminary experiments have been achieved. First, we collect the sensor values in several device's position (normal standing, inclined, jostled, etc. ). We compare sensors' readings for various device positions in order to pick the most relevant. The sensor values which have a large gap in different positions are ignored. The available sensors are: • Ambient luminosity: ambient light sensor (ALS).
• Orientation: inclinometer, compass. • Motion: gyroscope, accelerometer. • Location: GPS • Ambient noise: microphone We select the following sensors: 1. For ambient luminosity and ambient noise: we use ALS and microphone because these are the only sensors that provide us these information. 2. For the orientation, we have chosen inclinometer because the obtained data from this sensor are more informative and compass data are changing due to magnetic strength. 3. For motion, both of accelerometer and gyroscope have three-dimensional metrics on axes x, y, and z. In the following example, we compare standard deviations: • Accelerometer (x, y, z) = (0. 57, 0. 37, 0. 52) The SCM is calibrated by sensory data collected while the device is in standing position. After calibration, sensors data is collected in real time.