dataesr/scientific-paragraphs-categorization huggingface dataset:
We observe that MLD0 needs more disk space than Falling Star, because MLD0 repeats spatial attributes values on every document. For instance, at scale factor sf=10 (6*10 7 facts), Falling Star needs 6GB while MLD0 needs 320 GB space. We summarize also in Table  1  data loading time by scale factor. Data is loaded into MongoDB using native instructions. By consequence, experiments also confirm that Falling Star is also better in terms of data loading time.
CONCLUSION AND FUTURE WORK In the Spatial Big Data field, many academic and industrial communities propose new Spatial DBMSs, (such as MongoDB, Cassandra, etc. ) to handle with the volume and the variety of these very huge georeferenced datasets. In particular, document Spatial DBMSs appear well-adapted to store complex and voluminous spatial data. Despite of important spatial analysis possibilities offered by document Spatial DBMSs, no work investigates their use in the context of Spatial OLAP and Spatial Data Warehouse. Therefore, in this work, we focus on the logical modelling and query processing of document spatial data warehouse. We propose a new logical schema for Spatial DW using UML profile. We generate datasets of different size according to different scale factor values. We have tested our models under MongoDB.
Here, we test the different contributions of these two factors in the Former Soviet Union (FSU), motivated by several reasons. On the one hand, the territory of the FSU encompasses 15% of the terrestrial surface, 20% of the global soil organic carbon pool and is responsible for a considerable fraction of the present-day terrestrial CO 2 sink. On the other hand, heavy economic and demographic losses have been registered in FSU during WWII, together with likely decrease in farmland due to occupation, destruction of infrastructure and shortages of manpower. Here we present a newly compiled dataset of annual agricultural area in FSU, which better matches other socioeconomic indicators and reports a decrease in cropland of ca. 62Mha between 1940-1943. We use an updated version of the landsurface model ORCHIDEE, ORCHIDEE-MICT, which is specifically developed to better represent high-latitude processes to simulate the carbon fluxes in terrestrial ecosystems over the 20 th century.
Using our new cropland dataset, we test the different contributions of the land-use change and the decadal warming reported in the 1940s to explain the plateau. As reference, we compare our results with the gap sink estimated by the group of land-surface models in Bastos et al. (2016): 0. 7PgC. yr -. We find that the massive cropland decrease between 1940-1943, even if short-termed, could result in an additional decadal sink of 0. 04-0. 07PgC. yr -  , i. e. 6-10% of the gap sink required to explain the plateau. The ORCHIDEE-MICT simulations also indicate a very strong enhancement of the terrestrial sink by 0. 4PgC. yr -  , explaining about 60% of the gap sink from the 1.
2016;  Sitch et al. , 2015)  in order to evaluate the likely contribution of the fluctuations in cropland area in FSU from our new land use dataset during the 1940s to the global CO 2 sink. Finally, since ORCHIDEE-MICT should also improve the response of high-latitude vegetation to climate variations, we assess the relevance of the warming in the 1940s reported in Bastos et al. (Bastos et al. , 2016)  to compare the relative contribution of LUC and climate to the FSU sink during the plateau period. 2 Data 2. 1 Land Use / Land Cover.
Russian and Soviet crop area The new dataset of FSU agriculture area comprises the data collected from official national statistics from  Lyuri et al. (2010). National statistics were provided for the Former Soviet Union (FSU) during the period 1917-1961, and the Russian Empire during the pre-Soviet period starting from 1913 (dataset and full list of references in Supplementary Data). The data for Russian Empire is presented in its actual borders, i. e. not conform to FSU borders, and originally derived in obsolete Russian units of land area (dessiatina, 1 Des=1. 09 ha). At the time of the Soviet Union, the statistics provided total cropland area (Mio ha), and cropland area in each of the 15 Federal Republics (thousands ha). Even though the borders of the Russian Empire and FSU were different, both covered the areas which account for most of the cropland area (Russia, Ukraine, Kazakhstan, see Figure  A2 ) and therefore we aggregate both datasets.
Agricultural area from reference datasets We use for comparison with our new dataset the land-use harmonization from  Hurtt et al. (2011) (LUH1)  since it is the one used to force the dynamic global vegetation models in  Bastos et al. (Bastos et al. , 2016). The LUH1 provides annual values at half-degree from 1500 until 2100 of fractional data on cropland, pasture, primary vegetation, and secondary vegetation, as well as the underlying transitions between land-use states. In this dataset cropland, pasture, urban, and ice/water fractions between 1500 and 2005 are calculated based on the HYDE 3. 1 database  (Klein Goldewijk et al. , 2011 ) that provides gridded time series of historical population and land-use data for the Holocene. The HYDE 3. 1 database relies on U. N. Food and Agricultural Organisation data  (FAO, 2008)  national statistics of agricultural areas from 1961 onwards, and extrapolates these country-level estimates backwards in time using population dynamics, with cropland and pasture values per capita allowed to change only 4 Biogeosciences Discuss.
, https://doi. org/10. 5194/bg-2017-267 Manuscript under review for journal Biogeosciences Discussion started: 7 July 2017 c Author(s) 2017. CC BY 4. 0 License. slightly prior to 1961  (Klein Goldewijk et al. , 2011). The LUH dataset further includes wood harvest and shifting cultivation. This data was used to produce the land-cover maps to force the land-surface model, with the forest, grassland and crop classes being converted to the model's plant functional types following the method by  Poulter et al. (2011). In the model, grazing and pasture management are not explicitly represented and pastures are therefore treated as natural grassland.
Socio-economical statistics In order to assess how the datasets described in Section 2. 1 capture the transitions that occurred in FSU during the first decades of the 20 th century, we compare them with several sources of data on population and gross domestic product (GDP). The relationship between population and economic output with total crop area likely changed over the 20 th century due to agricultural mechanization and fertilization or to rural exodus. Nevertheless, they provide reasonable proxies to evaluate the variability of crop area reconstructed by FSU-REF and the FSU-NEW statistics. Information on total population in the FSU was collected from national statistics (Supplementary Data). Total population was partitioned as urban and rural population, however, urban and rural population are defined from the designation of permanent residence, which does not necessarily mean population working in agriculture.
We, therefore, collected information on active population and population working in agriculture. Even though less data is available (especially for the latter), both variables present similar variations during the WWII period. The Maddison Project (Bolt and van Zanden, 2014)  extends the original work of  Maddison (2001)  in order to provide percountry population and GDP data extending back (when available) to Roman times. Data are provided for the FSU from 1928 onwards, although during 1941-1945 GDP values are constant, which is possibly due to lack of data. Harrison (2000a)  focused on the Soviet economy during the war period and provides estimates of GDP from 1940 to 1945 that was used to fill in the information lacking in Maddison's data. The socioeconomic indicators described above are shown, together with the crop area estimates in FSU-REF and FSU-NEW in Figure  1.
We further collected information about total grain production in FSU from three different sources based on national statistics  (Nove, 1982; Sapir, 1989; Davies et al. , 1994)  in order to evaluate the model simulation results. These statistics were based on grain harvest reported farmers but, at least between 1920 and 1940, these numbers were then revised by state statisticians that applied "correction factors" based on sown area that likely exaggerated the production  (Davies et al. , 1994). In their original works,  Nove (1982) ,  Sapir (1989)  and  Davies et al. (1994)  have tried to obtain values closer to the original raw data using different assumptions, therefore we keep them as separate evaluation datasets of crop grain harvest in ORCHIDEE-MICT simulations.
ORCHIDEE-MICT ORCHIDEE is a global land-surface model  (Krinner et al. , 2005)   developed for high-latitude processes: ORCHIDEE-MICT  (Zhu et al. , 2015; Guimberteau et al. , 2017). The model includes an enhanced description of high latitude land surface processes such as an enhanced hydrological balance, the effect of snowpack insulation in winter and its coupling with soil temperature, and an improved description of soil carbon interactions between soil freeze, soil water holding capacity and thermic conductivity. The new soil carbon module was shown to reproduce the amount of soil carbon in the high latitudes and the seasonal exchange of CO 2 resulting from the seasonal imbalance between gross primary productivity (GPP) and total ecosystem respiration (TER). Fire occurrence is simulated using the SPITFIRE fire model as described in  Yue et al.
Updated gridded LUC data Based on FSU-NEW data for total crop area in FSU (subsection 2. 1. 1, Figure  1 ), we produced new spatially explicit maps used to force ORCHIDEE-MICT by combining patterns from FSU-REF and statistics from FSU over administrative units. First we fill the two missing years (1918 and 1919) using a linear adjustment. Since FSU-NEW data always estimate lower cropland area than FSU-REF, in each pixel the crop area was reduced proportionally to the corresponding contribution to the overall FSU crop area (i. e. more crop area is reduced in pixels with high cropland area in FSU-REF) at each time-step to match the FSU statistics in each administrative unit. The country-level cropland area data from FSU-NEW was then compared with the crop area represented in FSU-REF and our updated maps (Figure  A3 ).
Total vegetation carbon in the FSU region between 1993 and 1999 is very close to the reported values of  Liu et al. (2015)  for S Ref and S GRA , and overestimated by 13% in S FOR. Generally, the ORCHIDEE-MICT simulations forced by new LUC maps with lower cropland area estimate lower LUC emissions and, in some cases even a LUC-related sink, as during period between 1913 and the early 1920s (coincident with decreasing cropland area during the WWI and the Russian Civil War, but also with the transition between the two datasets) and also during the early 1940s (contemporary with the peak of land abandonment during WWII). The two simulations with FSU-NEW cropland area (S FOR and GRA ) estimate very high C accumulation in soils starting in 1920 and peaking in the first years of the 1940s (Figure  A5 ), likely due to the forest area expansion between 1910 and 1920 (also in S Ref ) combined with the effect of crop area decrease between 1913-1920 due to the correction in FSU-NEW dataset.
Emissions from LUC in the 1940s using FSU-REF maps are 0. 09PgC. yr -  , very close to the average E LUC reported by the TRENDY models (Figure  4 ). Our two scenarios using lower crop area extent and accounting for abandonment during the 1920s and 1940s lead to significantly lower E LUC , 0. 05 -1 and 0. 02 -1 for FOR and GRA scenarios (i. e. 55% and 22% of E LUC-Ref ) respectively. Such strong differences are not observed in the previous decade (less than 7%) and, therefore, result mainly from the sharp decrease in cropland area observed in FSU-NEW data between 1940 and 1942 which is not captured in FSU-REF as it captures mainly decadal variations. Even though the TRENDY models also simulate a small increase of NBP coinciding with the warming in latitudes below 65 o N, this increase is only about 30% of the one estimated by ORCHIDEE-MICT.
Discussion In the early decades of the 20 th century, the FSU regions were mainly composed by agrarian societies, with a large majority of the population living in rural areas (above 80%, Supplementary Data). At the time, agricultural and industrial production contributed in comparable amount to the national income (13. 1 and 18. 3 million roubles in 1927, respectively  (Nove, 1982) ). with the mechanization of agriculture in later decades, the relationship between agricultural area, crop production and GDP is expected to weaken. Indeed, we find a good agreement in the evolution of cropland area in our new dataset, with active population and GDP values until ca. 1955 (Figure  1 ), which suggests that our data may better reflect crop area variations during this period. This implies that using total population to extrapolate country-level crop area (as used in Klein Goldewijk et al.
Relative variations in population working The method used here to update LUC maps does not account explicitly regional dynamics, such as the displacement of farmland from the front and occupied regions during WWII to the eastern countries  (Linz, 1984). We deliberately did not account for this displacement, as it would imply changing also natural vegetation fractions in other regions of the FSU (e. g. require forest/grassland removal), and increase the possible inconsistencies between the datasets. Even though such differences may be relevant at local scale, they are unlikely to significantly change our results for the aggregated FSU. Our data still provides a better match to country-level estimates of crop area (Figure  A3 ) than previous reconstructions. ORCHIDEE-MICT simulations show the characteristic slow increasing trend of terrestrial uptake over the 20 th century, consistent with the CO 2 fertilisation and warming effect in vegetation productivity (Figures  2  and A4a ).
Overall, the simulations using the maps derived from FSU-NEW cropland data provide a better agreement with observed carbon fluxes and stocks in the late 20 th century, especially S GRA. Total soil carbon stocks are overestimated in the recent period by 18-20% (Table  2 ) relative to the dataset presented in  Guimberteau et al. (2017)  which are based on observation-based soil organic carbon maps from NCSCD  (Hugelius et al. , 2013)  and HWSD  (FAO et al. , 2012). However, the simulated soil carbon in crop areas in the late 20 th century is consistent with the data from  Smith et al. (2007) , especially for the two simulations with new crop area (S FOR and S GRA ). Simulated grain production in FSU is in line with estimates of Soviet grain production until 1945, and captures partially the increase from the mid 1950s until 1980 reported in different works (  (Nove, 1982; Sapir, 1989; Davies et al.
which results in reduced E LUC. However, in the first period, the differences in E LUC estimates may also partly arise from the discontinuity in the cropland area (Figure  A2 ). In the 1940s, even though E LUC simulated by ORCHIDEE-MICT remains within the (large) inter-model range of the TRENDY models, the two land-cover datasets FSU-AGR and LUH result in substantial differences in the estimated E LUC. These differences do not result from FSU-NEW indicating systematically lower crop area, since they are not present in the previous decade. Rather, the lower E LUC are mainly due to the sharp decrease in cropland area during 1940-1943 and the resulting increase in natural vegetation growth. Considering average reference values of CO 2 uptake by different forest types  (Luyssaert et al. , 2007)  and assuming that the decrease in cropland extent between 1940 and 1942 was replaced by young growing trees proportionally for each PFT, the estimated net CO 2 uptake resulting from forest expansion over abandoned cropland would be of about 0.
09PgC. yr -. This value is consistent with the differences in net CO 2 uptake relative to the reference simulation in the FOR scenario of 0. 07PgC. yr -. The difference for the grassland scenario is higher, ca. 0. 12 PgC. yr -1 , since grasslands are more productive than forests in the model (Table  A1 ) and grasses allocate more carbon to belowground biomass. As there was an increase in cropland area from 1944 onwards, the resulting differences in E LUC over the decade are attenuated. However, the simulations using FSU-NEW data still lead to values of emissions from LUC 45% to 78% lower than the one using FSU-REF during the 1940s. Over the full decade, the decrease in cropland land during the peak of WWII contributes to an increased sink of 0. 04-0. 07PgC. yr -  , i. e. 6-10% of the additional sink required to explain the discrepancies between observations and recontructions of atmospheric CO 2 during the plateau  (Bastos et al.
7PgC. yr -  estimated by DGVMs). Here, we attempt to evaluate the hypothesis proposed in  Bastos et al. (2016) , that the economic and social disruption imposed by WWII could have resulted in lower emissions from land-use change during the 1940s, partly explaining the stabilization in atmospheric CO 2 during this decade (plateau). Their rationale was that massive and abrupt cropland abandonment could have been followed by carbon stock recovery in many the FSU regions affected by war, and therefore result in an increased terrestrial sink. Such short-term fluctuations are not currently represented in the datasets used to estimate emissions from LUC, as they are developed with the main purpose of estimating E LUC over centuries  (Hurtt et al. , 2011). In order to evaluate the hypothesis, we compiled a new dataset of cropland area in the Former Soviet Union, based on annual economic reports between 1913 until 1961.
It should be noted that while the agricultural output compiled in Soviet national statistics has been questioned, the area occupied by agriculture is likely reliable  (Nove, 1982; Davies et al. , 1994). Our dataset shows good agreement with other social and economical statistics, indicating that the changes in cropland area reported in our dataset are realistic. We find that the massive reduction of cropland area between 1940 and 1943 (62 Mha) leads to differences in E LUC of 0. 07-0. 12PgC. yr -  relative to the reference simulation during these few years and affect considerably the decadal average NBP. Even if the reduction in E LUC due to land-abandonement in this decade corresponds only to a small fraction of the sink gap during the plateau period (6-10%), it is likely that decreases in agricultural areas during WWII might have also occurred in other regions and could additionally contribute to the sink gap.
Such events may not be included in FSU-REF data due to the use of different sources of information as is the case, for example, of China  (He et al. , 2013) ). Furthermore, here we show that extreme but relatively short LUC events (such as the land-abandonment reported here) may have an impact on the decadal resulting CO 2 fluxes which are not accounted for by time-series that represent decadal variations, highlighting the relevance of the temporal resolution in LUC datasets. The Northern Hemisphere warming during the 1940s discussed here and in  Bastos et al. (2016)  is a consistent feature in observation-based and proxy data  (Mann et al. , 1999; Compo et al. , 2013). Our simulations indicate a very strong enhancement of net CO 2 uptake by 0. 4PgC. yr -  , vegetation growth and accumulation of carbon in soils in the mid-to high-latitudes coinciding with warming, which is supported by tree-ring data  (D'Arrigo et al.
Most of these values are available only for the 1990s and cover different regions of FSU or certain vegetation types. The simulations of ORCHIDEE-MICT show good 8 Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-267 Manuscript under review for journal Biogeosciences Discussion started: 7 July 2017 c Author(s) 2017. CC BY 4. 0 License. agreement with observation-based data of carbon fluxes and pools for the late 20 th century (Table.
Significant differences between S Ref and S FOR , S GRA occur only in two periods: the one from 1913 to ca. 1925 and in the 1940s. Both correspond to sharp abandonment of cropland in FSU-AGR data 11 Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-267 Manuscript under review for journal Biogeosciences Discussion started: 7 July 2017 c Author(s) 2017. CC BY 4. 0 License.
Acknowledgements. The work is supported by the Commissariat à l'énergie atomique et aux énergies alternatives (CEA). The authors would like to thank the TRENDY modelling teams and  S. Sitch  and  P. Friedlingstein  for providing and maintaining the TRENDY outputs. We thank the valuable help of  Dan Zhu ,  Fabienne Maignan ,  Shushi Peng & Albert Jornet  during the development of this study.
Support of multiple RDs Having multiple RDs might be preferable not only for redundancy reasons but also to support and provide different services. Indeed, a provider might be willing to register with an RD that supports a specific contentformat/protocol. Similarly, a client might prefer to query an RD supporting its required parameters. PRD provides support of the discovery of multiple RDs in a network. To do so, each RD manages a separate Trickle. This requires nodes to keep separate Trickle timers per RD in a parallel Trickle approach. Such an approach might increase the burden on sensor/actuator nodes. However, taking into account the simplicity of Trickle code along with the small number of expected RDs in a network, this solution may present a fair trade-off. In section 6. 1, an optimized version for supporting multiple RDs will be introduced.
PRD in FRD for code and memory optimizations Because of the similarities between PRD and FRD forwarding algorithms, it might be preferable to consider a way of integrating them in a simple code that responds to both requirements. Although FRD's Trickle-based proactive advertisement does not need strict consistency in its generic form, it can provide strict consistency for a few crucial resources if needed. For instance, a flag in a resource entry of an _ can indicate that this is an important resource, and hence nodes keep gossiping about it (as if it is one of their own resources) to ensure that it reaches all network entities. With this propriety of FRD's Trickle algorithm, incorporating PRD functions into FRD translates into adding another CoRE link format attribute, resource criticality (), for specifying the criticality of a resource.
As commonly stated, these kinds of issues should be discovered early in the development process. The SysML-Sec methodology was introduced to handle the design of such complex systems, in terms of safety, performance, and security  (Apvrille and Roudier, 2015). SysML-Sec extends UML for the design of embedded systems. One of its important early development phases is the hardware / software partitioning phase, which distributes functions to be real-ized by the system among candidate hardware architectures. This phase decides on the "best" architecture according to several criteria such as cost of the platform and its performance. To better address embedded system security, the choice of architecture should also be based on the ability of the architecture to support security features for secure communication.