dataesr/scientific-paragraphs-categorization huggingface dataset:
The current Amazon landscape consists of heterogeneous mosaics formed by interactions between the original forest and productive activities. Recognizing and quantifying the characteristics of these landscapes is essential for understanding agricultural production chains, assessing the impact of policies, and in planning future actions. Our main objective was to construct the regionalization of agricultural production for Rondônia State (Brazilian Amazon) at the municipal level. We adopted a decision tree approach, using land use maps derived from remote sensing data (PRODES and TerraClass) combined with socioeconomic data. The decision trees allowed us to allocate municipalities to one of five agricultural production systems: (i) coexistence of livestock production and intensive agriculture; (ii) semi-intensive beef and milk production; (iii) semi-intensive beef production; (iv) intensive beef and milk production, and; (v) intensive beef production.
Dimension 1: Agricultural Production We used data from the survey of municipal agricultural production, coordinated by the Brazilian Institute of Geography and Statistics (IBGE)  [34]  to evaluate agricultural production. Data was retrieved on production of beef and milk cattle, planted area of coffee, and income of the top five annual crops of the state (rice, beans, cassava, corn and soybeans). It should be noted that several other land uses (e. g. , annual and perennial crops as well as the economic exploitation of other animals) occur in Rondônia. However, the contribution of cattle (beef and milk) and the above-listed crops represent more than 93% of total agricultural income in the state. For agricultural income, the sum of the value of agricultural production and livestock, available in the literature, was considered  [35].
The selected agropastoral activities were present in all municipalities with the exception of soybean production. Although soybean is only grown in 12 out of 52 municipalities, it has a relatively high cultivated area and, where present, makes a significant contribution to the local economy  [34, 35]. To complement the original IBGE data [34], further metrics were calculated that indicate livestock productivity in relation to pasture area (derived from the TerraClass data) and the number of animals per municipality (data from IBGE). For crops, the financial income generated by each major crop to local agricultural economy was recorded [34], as was the percentage of area occupied by coffee in relation to the total production area of each municipality.
Gross Domestic Product (GDP) We used Gross Domestic Product (GDP) as a metric to study the impact and economic contribution of different land uses on the local economy. GDP is the sum of monetary values of all goods and services produced in a given region over a given period. It consists of the gross value added by agricultural activities (Agricultural GDP), the gross value added of industry (Industrial GDP), and gross value added by services (Services GDP). Municipal GDP data for the years 2000 (T1) and 2010 (T2) were retrieved from the IBGE database  [34]. GDP was divided into Agricultural GDP and total GDP for each municipality to allow calculation of the indirect contributions of the agricultural sector (e. g. , sale of fuel, services, etc. ). These indirect contributions are not included in the agricultural GDP, but they may be indirectly related to the agricultural production chain and contribute to total GDP.
GDP data is available in the form of an absolute monetary value for the entire municipality. Within each municipality, agricultural production only takes place in deforested areas (the minor exception being timber production or non-timber forest products). Thus, it may be possible to, at least partially, infer the production efficiency of different land use systems by generating new metrics that take into consideration the deforested area of each municipality (GDP per km 2 deforested and Agricultural GDP per km 2 deforested). We also estimated the GDP per capita for each municipality and the Agricultural GDP per capita for the rural zone.
Agricultural Credit Brazil's Central Bank produces a statistical yearbook of rural credit which contains information on the number of operations and the resources available for rural credit operations for each Brazilian municipality  [36]. Annual data from this dataset were used for the years 2000 to 2010. A statistical relationship was then sought between the amount of credit disbursed and the deforested area at the municipal scale. By dividing the total amount of rural credit in each year by the deforested area in each municipality for that year, we were able to calculate the average value (in R$) of rural credit per km 2 deforested.
Logistics To study the relationship between agricultural production and transport infrastructure, data was obtained for the general road network for Rondônia from the Amazon Protection System-SIPAM  [37]. We used this data to calculate a metric of municipal road density by dividing the total perimeter of roads in a municipality by the total area of that municipality. We adopted a similar approach to assess hydrographic network: data was retrieved from SIPAM  [37]  and municipal density of the network was calculated by dividing the total perimeter of the hydrographic network by the total area of the municipality.
The Amazon Deforestation Monitoring Project (PRODES) The Amazon Deforestation Monitoring Project (PRODES)  [31]  has been run by the Brazilian National Institute of Space Research (INPE) since 1988. The project produces annual maps of deforestation in the Brazilian Legal Amazon, and is able to identify deforested areas larger than 6. 25 ha. We used these data in their original format of annual deforestation polygons. We also evaluated the full total area deforested, disregarding the date of occurrence.
TerraClass Land use and land cover data are essential for understanding landscape configuration and for revealing the organizational strategies and patterns of agricultural production  [47, 48]. TerraClass data  [33]  with a spatial resolution of 30 m allows for both regional level and municipal level analysis of land use and land cover dynamics. TerraClass 2010 is available and contain the following classes: deforestation up to 2010; crop area (annual); unobserved area; urban area; mining; mosaic occupations; other areas; pasture with exposed soil; clean pasture; "dirty" pasture; regeneration with pasture; reforestation; and secondary vegetation. To study the spatial configuration of these data, the following landscape metrics were calculated: area/perimeter ratio, as an indicator of polygon shape; average area; density of polygons; and density of classes as indicators of landscape fragmentation.
Dimension 4: Social Characteristics Socioeconomic development indices can be used as estimators of quality of life, and are relevant in the context of regional socioeconomic development analysis  [49] , although they should be applied with caution given their many limitations  [50]. We used a Municipal Human Development Index (MHDI) and density of people living in poverty (data from the Atlas of Human Development in Brazil  [51] ) to assess whether the specialization in certain agricultural production systems was associated with social differences between groups of municipalities. We also retrieved data on the total number of inhabitants and the number of inhabitants in the rural zone for each municipality (data from a population estimates conducted by the IBGE [34]), and the areas of settlement projects within each municipality.
Although the MHDI is insufficient to fully capture the level of municipal human development, it does provide a synthetic view of some of the key development issues such as health, education and income  [50]. A similar procedure was used for GDP and Agricultural Credit (see Sections 2. 2. 2. 1. and 2. 2. 2. 2. ) which were used to create an index to represent the population from the deforested area within each municipality. We also collected data of the boundaries of settlement projects for agrarian reform, aimed to account for the percentage on the municipality occupied by such projects. Polygons of the settlements were downloaded from the website of the National Institute of Colonization and Agrarian Reform (INCRA), a Brazilian federal agency responsible for agrarian reform and land consolidation  [52].
In this exploratory analysis, we perceived that the percentage of clean pasture, dirty pasture and annual agriculture were an important factor for the separation of the municipalities in groups. Using an expert approach, we empirically arbitrate the classification of municipalities based on the proportional area of this land use/land cover classes. The analysis indicated the existence of three main groups: (1) The first group was associated with annual crop activity; (2) The second group with the predominance of "clean pastures"-characteristic of intensive livestock farming; and (3) The third group was associated with "dirty pastures"-characteristic of semi-intensive livestock farming. The identification of metrics and their value in classifying these three main groups was optimized empirically using specialized literature, empirical knowledge and exploratory analysis of the database, applied in the construction of the decision tree, which was designed from a set of rules defined by the expertise of the authors and other researchers.
Its construction was guided by the "rule-based" approach  [53]. In Rondônia, as throughout the Brazilian Amazon, there is a clear predominance of cattle pasture for beef production  [54] [55] [56]. There are also areas characterized by a predominance of small farms, where dairy farming has significant local importance  [57] [58] [59]. Unfortunately, it is not possible to separate pastures for beef production from those used for milk production using only TerraClass data. However, IBGE publishes annual figures for the size of cattle herds and the number of cows milked for each municipality. These two datasets can be combined to create a new metric that quantifies the relationship between the number of cows milked and the size of the local herd (metric NVacReb_T2 in Appendix A). This metric allowed a new level of the decision tree to be added that was able to distinguish municipalities where dairy farming coexists with beef farming from those where dairy farming is rare or non-existent.
Concentration of Annual Crops and Coffee Each production system uses different strategies that can be translated into greater or lesser specialization in production. These specializations lead to differences in the degree of concentration in the production of annual and/or perennial crops. Knowing the degree of this concentration thus strengthens the understanding of production systems. We sought to identify the predominant annual crop by comparing the production value (in 2010; R$) of five main annual crops (rice, beans, cassava, corn and soybeans). These crops were chosen because they are the most commonly used annual crops in the state  [35]. Using data on annual income (in 2010; R$) for each crop from IBGE's municipal agricultural research database [34], we calculated the percentage share of the production value for each of the five crops, identifying the culture with the largest financial contribution in each municipality.
This can be explained by the large aggregation of PRODES data before 2000. Deforestation data were aggregated into a single class containing all (deforested) polygons detected between 1988 and 1997. This aggregation added many polygons and misrepresented the results of the landscape analysis. When only the polygons of the period from 2000 to 2010 were analyzed, the average area of deforested polygons (AMPRD_T1aT2) was lower in production systems with significant milk production. This result is in accordance with the claim that milk production occurs mainly in small properties that produce small clearings. In contrast, municipalities with the CA production system contain larger polygons, and systems linked to meat production show intermediate values. The value of the ratio area/perimeter of deforested polygons between 2000 and 2010 (APPRD_T1T2) was different in all production systems, following the same trend as for average size of polygons: average value was highest for municipalities with the CA system, followed by systems linked to the production of meat and, finally, systems related to the production of milk.
The average size of TerraClass 2010 polygons (AMPTC_T2) showed no statistical differences between production systems. The mean area/perimeter of TerraClass polygons 2000 to 2010 (APTC_T1T2) showed statistical differences between all systems, with higher values for the CA system, intermediate values for beef-associated systems, and the lowest values for milk-associated systems. These trends follow those for the PRODES data and are consistent with expectations: the polygons in municipalities with the CA system are more regular, presumably due to the high level of mechanization in this production system. Municipalities with milk-associated production systems had lower values, indicating more irregularly shaped polygons associated with a greater reliance on manual labor.
All production systems linked to livestock had stocking rates similar to or better than the national averages. Our landscape analysis indicated that the relationship area/perimeter of PRODES and TerraClass data varied significantly, with higher values in regions with predominantly mechanized agriculture and lower values for regions characterized by beef farming and, especially, milk production. This pattern was also reflected in a gradation in the shape of area polygons, with simpler, more regular forms associated with the CA system and more complex, irregular forms associated with the IBM and SIBM systems. The results in the dimension territorial configuration were promising, even at the scale of sociopolitical units of municipalities. From these results, the possibility of a landscape analysis with a more detailed level, performed by using cell arrays, is inferred.
Acknowledgments: The authors wish to thank the  Coordination for the Improvement of Higher Level Personnel (Capes) , for providing scholarships process  BEX 6083/13-3 , and the  Institute of Research  for Development through the unit ESPACE_DEV by host of the Ph. D. project of the first author. To  Renne Poccard-Chapuis  for the constructive comments and suggestions on the manuscript.
Then the calibrated set of parameters of each hydrological cluster was extrapolated to the ungauged subbasins within the same cluster. This approach was necessary to represent the spatial variability of hydrological properties in the wide region of the Danube basin, considering the limited availability of measured data. According to the authors, the results of the model simulation of water flow for the whole basin are fine consistent both in time   (Britz, 2004) ; CSGE: Center for Sustainability and Global Environment  (Monfreda et al. , 2008) ; CLC: Corine Land Cover; GLC: Global land Cover (Bartholome and Belward, 2005); CCM2: Catchment Characterization Modeling Version 2  (Vogt et al. , 2007) ; GLWD: Global Lakes and Wetland Database  (Lehner and Doll, 2004) ;GranD: Global reservoir and Dams database  (Lehner et al.
, 2013) and population density maps (Batista e  Silva et al. , 2013). In addition, a tourism density map was computed and a proportionally higher water use assigned per  (Vandecasteele et al. , 2013)  tourist. Industrial water withdrawals were mapped by disaggregation of the country-level statistics to the industrial land use classes  (Vandecasteele et al. , 2013). In a similar way, country-level energy withdrawals were disaggregated using the locations of thermal power stations (EPRTR dataset), which were assumed to account for the majority of water use in the sector  (Vandecasteele et al. , 2013). Livestock water uses were provided by  Mubareka et al. (2013) , where withdrawals are mapped considering the livestock density and water requirements per livestock type. Finally, since we used the SWAT model results without irrigation, we considered the water use for irrigation computed by the EPIC model by  Bouraoui and Aloe (2012).
Data Main   (Falkenmark, 1989; UN-WBCSD, 2006). The FLK indicator (m3 water per capita) was calculated for each subbasin considering the total renewable water (TRW, m 3 ), environmental flow requirement (EFR, m 3 ) for riverine ecosystems, and population per subbasin (Eq. (  1 )): FLK TRW EFR /Population 1 = [( )-( )] ( ) where TRW is the sum of groundwater recharge and runoff of the subbasins. The values of FLK were classified using the thresholds of water stress and water scarcity proposed by  Falkenmark (1989)  to map potential water stress and environmental water scarcity in the Danube's subbasins (Table  4 ). The second indicator of water scarcity that was used in this study is the Water Exploitation Index Plus (WEI þ ) (EEA, 2010). It has been applied in studies on water scarcity at the European scale, particularly in the Blueprint for Europe's Waters  (De Roo et al.
6a, c, d,  and e ). A significant amount of water used for the agricultural sector is situated in the Hungarian and Romanian lowlands (Fig. 6b ). Sectorial water uses were aggregated per Danube countries. The water use of Ukraine and Montenegro is less than 1% of the total amount, mostly due to their small area and the lack of withdrawal data in this region in the Danube. The highest industrial water use per unit area is located in Austria (49%), the highest agricultural water use in Romania (45%) and the highest energy use in Hungary (29%) (Fig. 6a, b, d, e ). Romania also has the highest livestock water use (27%) (Fig. 6c ). The public water use is ranked in descending order by Romania with 24%, Hungary with 11%, Serbia, Austria, and Bulgaria with 10% (Fig. 6a ). Green water consumption, representing the water consumed by agro-ecosystems and natural plants, has the great proportion of Danube freshwater cycle with more than 50% of total (Fig.
The highest costs per unit area charged for agricultural water use take place in Austria, Serbia and Czech Republic with total expenses varying between 500 and 7000 thousand Euros. Although Romania is the largest consumer of agricultural water in the Danube river basin, Romanians do not bear heavy cost due to very low prices charged to the agricultural sector (Fig. 8b ). The highest expenses of industrial water use are found in Austria and Germany in accordance with their significant industrial water use and the relative higher cost of water (Fig. 8c ). 3 According to the Global reservoir and Dams database (GRanD) there are 187 artificial reservoirs and dams in the Danube region of which 40 are used for water supply of urban areas, 24 for irrigation and 92 for producing hydroelectricity  (Lehner et al.
0 Table 2 The geo-datasets (Bouraoui and Aloe, 2009) used in the SWAT modelling. Data Main Source Time Resolution-scale SWAT model inputs DEM SRTM 2009 100 Â 100 m Soil HWSD 2008 1 Â 1 km Landuse CAPRI (2012) 2004 1 Â 1 km CSGE 2008 CLC 2000 GLC 2005 River network CCM2 2007 Subbasins HydroEurope 2009 Average geodatabase size E180 km 2 Reservoirs & GLWD, CCM2, GranD 2004 lakes Climate data MARS 1980-2009, 25 Â 25 km daily time series EFAS-METEO 5 Â 5 km SRTM: Shuttle Radar Topography Mission; HWSD: Harmonized World Soil Database (Nachtergaele et al. , 2012); CAPRI: Common Agricultural Policy Regionalized Impact Modeling System.
, 2009; Seinfeld et al. , 2012). However, seldom research ever conducted to investigate the volatility of fresh smoke particles  (Grieshop et al. , 2009; Pratt et al. , 2009) , and it is fundamental to study the photochemical oxidation and atmospheric aging of biomass burning aerosol. Herein, V/H-TDMA-APM system was coupled to an aerosol chamber to provide size-and fuel type-resolved hygroscopicity, effective density, and volatility of fresh smoke particles. The relationship among hygroscopicity, density, and chemical compositions of smoke particles were elaborated using Ko€hler theory analysis. Combining V-TDMA-APM data and OC-EC fractions of smoke particles, density of OM and OM/OC factors were estimated.
2e1. 4, and 1. 3 was commonly applied  (Chow et al. , 1993; Li et al. , 2007). The factor can reflect saturation degree and aging level of organic materials. The low value of OM1/OC1 may indicate more unsaturated hydrocarbon in OM1. a Chemical Index Database (http://www. chemicalbook. com). b  Tritscher et al. , 2011; Freney et al. , 2009; Park et al. , 2009; Seinfeld et al. , 2012; Tiitta et al. , 2010 ; E-AIM model III (http://www. aim. env. uea. ac. uk/aim/aim. php). c  Park et al. , 2009; Freney et al. , 2009; Petters et al. , 2007; Tang et al. , 1993. On other hand, restriction from thermo-denuder design may underestimate the value of OM/OC. Study ever compared the evaporation efficiency of organic aerosol regard to RT and T effect  (Riipinen et al. , 2010; Tritscher et al. , 2011). If the evaporation is kinetically limited, relative short RT can hardly allow the carbonaceous particle to get evaporation equilibrium at defined temperature, i.
Acknowledgement This work is supported by  National Natural Science Foundation of China  (No. 21190053 ,  21177025 ,  21177025 ),  Cyrus Tang Foundation  (No. CTF-FD2014001 ),  Ministry of Science and Technology of China  (No. 2014BAC22B01 ), and  Strategic Priority Research Program  of the  Chinese Academy of Sciences  (Grant No. XDB05010200 ), and the  IRSES-EU Marie Curie Actions project  " AMIS " ( PIRSES-GA-2011-295132 ).
The work was conducted on the matrix of information contained in the point cloud asci format. The first result is the extrapolation of new geometric descriptors. First, we create the digital maps with the calculated quantities. Subsequently, we have moved to semi-quantitative analyses that transform new data into useful information. We have written the algorithms for accurate selection, for the segmentation of point cloud, for automatic calculation of the real surface and the volume. Furthermore, we have created the graph of spatial distribution of the descriptors. This work shows that if we work during the data processing we can transform the point cloud into an enriched database: the use, the management and the data mining is easy, fast and effective for everyone involved in the restoration process.
CREATION OF 3D MODEL OF THE CHURCH OF CAROMB The case study is the Eglise collégiale Saint-Maurice also called Notre Dame des Grâces at Caromb, in the south of France. The church was the subject study of the master thesis in "Réhabilitation et Sauvegarde du Patrimoine Architectural". The previous studies were offering exceptional database and analysis procedures for the validation of experience in this work. Cartography available had been produced by the visual and photographic surveys. The starting point was: From these values, it is possible to work on the existing descriptors, such as normal, and the extraction of new geometric descriptors can provide additional information, again by opensource software. A preliminary step of decimation with MeshLab has made to ease the computational effort.
Then, also in this case, the kernel factor imposed at the beginning of the calculation results to be decisive for the calculation. By varying the kernel, the information vary: the higher the value, the greater the portion of comparison and consequently the geometrical considerations concerning a local scale, but large; the small kernel values seem to show more details, useful for smaller-scale analysis. Given the size of the object, the kernel equal to 200 (20cm) shows very interesting results: in addition to highlighting the depressed areas, the lines of stormwater runoff are defined very precisely. (Figure  14 ) For a first attempt to qualitative analysis, tests were made on the color change of the display scale. Choosing the scale more representative of each descriptor grey, the maps were overlaid with image editing software, Photoshop, with the overlay method brighten up.