dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, the CEOS report (Fig. 2. 3 and Table  2. 1 of the report) indicates where biomass fits within the set of key GEO satellite requirement areas and core GEO observational elements necessary to quantify the current state and dynamics of the terrestrial carbon cycle and its components. Central to the GEO Carbon Strategy is the combination of data and carbon cycle models, not least because models provide the only way in which the many available space-based and in situ measurements can be integrated into a single consistent structure for performing carbon flux calculations. There are many possible forms for these models but data can interact with them in essentially four ways: by providing estimates of current model state variables, estimates of model parameters, tracking of processes and testing of model predictions.
After a review of the mission objectives (Section 2), the associated measurement techniques (polarimetry, polarimetric interferometry [Pol-InSAR] and SAR tomography [TomoSAR]) are described in Section 3. Pol-InSAR and TomoSAR require the combination of multi-temporal stacks of data; this imposes very strong conditions on the BIOMASS orbit pattern, with significant consequences for the production of global biomass products (Section 4). The orbit pattern also imposes strong requirements on the ability of the AGB and height inversion techniques, discussed in Section 5, to adapt to changing environmental conditions. Section 6 deals with the use of BIOMASS data to estimate severe forest disturbance, while Section 7 describes the development of the reference datasets to be used for algorithm calibration and product validation.
 If the true AGB exceeds 50 t ha -1 then the RMS error (RMSE) of its estimate is expected to depend on biomass and be less than AGB/5. For all values of AGB < 50 t ha -1 the RMSE is stipulated to be 10 t ha -1 or better, though it is likely that changes in ground conditions, such as soil moisture, may cause the RMSE to increase beyond this value. Similarly, the RMSE of estimates of forest height should be less than 30% of the true forest height for trees higher than 10 m.  Below-ground biomass cannot be measured by BIOMASS (or any other remote sensing instrument), but can be inferred from above-ground biomass using allometric relations combined with climate data  (Cairn et al. , 1997; Mokany et al. , 2006; Thurner et al. , 2014). In particular,  Ledo et al. (2018)  used an extensive tropical, temperate and boreal forest dataset to develop a regression, with just tree size and mean water deficit as predictor variables, which explains 62% of the variance in the root-to-shoot ratio.
Forest AGB and height estimation techniques BIOMASS will exploit properties of all three SAR techniques, PolSAR, Pol-InSAR and TomoSAR, to estimate biomass, while both Pol-InSAR and TomoSAR will provide estimates of forest height. However, because BIOMASS will be the first spaceborne P-band SAR, the experimental data needed to support the development and testing of these techniques is based on limited airborne and groundbased measurements. Six major ESA airborne campaigns were carried out (BioSAR-1, -2 and -3 in the boreal zone, and three in tropical ecosystems: TropiSAR in French Guiana, AfriSAR in Gabon and Indrex-2 in Indonesia) using the E-SAR and F-SAR (DLR, Germany) and SETHI (ONERA, France) P-band SARs (see Table  2 , which includes the objectives of the campaigns and essential properties of the test-sites).
These campaigns have provided the most accurate and complete set of Pband SAR (PolSAR, Pol-InSAR and TomoSAR) and associated in situ data currently available over boreal and tropical forests. In addition, long-term continuous P-band tower-based measurements were made in French Guiana (Tropiscat), Ghana (Afriscat) and Sweden (Borealscat) to investigate diurnal and seasonal variations in backscatter and temporal coherence. Earlier P-band datasets from the NASA AirSAR system were also helpful, especially tropical forest data from Costa Rica, to extend the range of tropical biomass values  (Saatchi et al. , 2011) , and NASA was heavily involved in the AfriSAR campaign, providing lidar coverage of the AfriSAR test-sites  (Labrière et al. , 2018). No specific ESA campaigns were conducted in temperate forests, but substantial amounts of tomographic data are available for such forests from experimental campaigns carried out by DLR.
(2011)  where mean backscatter differences between the boreal and tropical data were ascribed to calibration errors and removed by shifting the data. The careful calibration of the datasets shown in Fig. 2  indicates that the difference is real and that different physical and biological factors (such as forest structure) are at play in the different forest types. The regression lines indicate that in natural units the HV backscatter is approximately related to biomass, W, by a power law relationship, i. e. ( ) 2 where c and p are parameters. Analysis in  Schlund et al. (2018)  indicates such relationships are found for the full set of available P-band SAR datasets that are supported by adequate in situ data except where there is strong topography. Although the model coefficients (and their coefficients of determination) vary across datasets, they are not significantly different when similar AGB ranges are considered.
Its starting point is a simplified scattering model that describes the backscattering coefficient in each of the HH, HV and VV channels as an incoherent sum of volume, surface and double-bounce scattering  (Truong-Loï et al. , 2015). The model involves 6 real parameters per polarization, which are estimated using a combination of a scattering model and reference data. Biomass, soil roughness and soil moisture are then treated as variables to be estimated from the data. Initial analysis found that this model was too complex and the associated parameter estimation was too unstable for this to be a viable approach for BIOMASS. However, a crucial technical development was to demonstrate that both tomographic and Pol-InSAR data can be used to cancel out the terms involving the ground (surface scatter and double bounce) and isolate the volume scattering term  (Mariotti d'Alessandro et al.
g. Scharf, 1991). A current unknown in this approach is to what extent changes in the covariance matrix of undisturbed forest caused by environmental effects, such as changing soil moisture due to rainfall events, will ESA are addressing challenge (i) and (ii) by working with existing networks to develop suitable extensive in situ reference data before launch through the Forest Observation System (http://forestobservation-system. net/). A further encouraging development is the ESA-NASA initiative to collaborate in developing the in situ data requirements for GEDI, BIOMASS and NISAR. Cooperation along these lines is already in evidence from joint contributions to the AfriSAR campaign by ESA and NASA. As regards (iii), for the temperate zone, southern hemisphere sites, e. g. in Tasmania, would be suitable, while Siberia is the most desirable region for the boreal zone.
5 latitude. Hence without the data from all three missions, wall-to-wall estimation of global forest biomass will not be possible. There will, however, still be lack of temporal and/or spatial coverage in regions where BIOMASS cannot operate because of SOTR exclusions and where AGB exceeds the 100 t/ha threshold for NISAR. For lower values of AGB (less than about 50 t/ha) P-band measurements will be much more affected by soil conditions than L-band, and NISAR should provide more accurate AGB estimates. The high temporal frequency of NISAR observations will also allow the effects of soil moisture changes and vegetation phenology to be mitigated. Currently the theoretical basis of the algorithms proposed for NISAR and BIOMASS are the same  (Truong-Loi et al. , 2015) , which offers the possibility of a combined L-and P-band algorithm that optimises the capabilities of each.
and fluxes associated with the terrestrial biosphere, including carbon fluxes associated with Land Use Change, forest degradation and forest regrowth. Of major mutual advantage in meeting this objective will be the information provided by other space missions flying within the next five years, for which pride of place goes to GEDI and NISAR, but supplemented by optical and other radar missions. Of great importance is that the structures for making use of these new data in carbon cycle and climate models are being developed and implemented. The physical and technical capabilities embedded in the BIOMASS mission in order to measure biomass can be turned to many other uses. At present, known applications include sub-surface imaging in arid regions, estimating glacier and icesheet velocities, and production of a true DTM without biases caused by forest cover.
a. Dispersion in the data. For the boreal data in Fig. 2, major factors causing dispersion in the backscatter values are slope and soil moisture variations. The Krycklan campaign over boreal forest in Sweden (Table indicates such relationships are found for the full set of available P-band SAR datasets that are supported by adequate in situ data except where there is strong topography. Although the model coefficients (and their coefficients of determination) vary across datasets, they are not significantly different when similar AGB ranges are considered. Despite this strong regularity in the relation between HV backscatter and biomass, exploiting it to estimate biomass faces a number of problems:.
Precision based on replicate analyses was better than ±5 µmol L -1. For samples with a very low pH (<4. 5), we bubbled the water with atmospheric air in order to degas the CO 2. Consequently, the initial pH increased above 5, and the TA titration was then performed  (Abril et al. , 2015). We calculated DIC from pCO 2 , TA, and temperature measurements using the carbonic acid dissociation constants of  Millero (1979)  and the CO 2 solubility from  Weiss (1974) , using the CO 2 SYS software  (Lewis et al. , 1998). The δ 13 C-DIC samples were collected using 120 mL glass serum bottles sealed with a rubber stopper and treated with 0. 3 mL of HgCl 2 at 20 g L -1 to avoid any microbial respiration during storage. Vials were carefully sealed such that no air remained in contact with samples and were stored in the dark to prevent photo-oxidation.
Statistical analyses K-means clustering analysis  (MacQueen, 1967)  was used to classify waters either as forest-dominated or as cropland-affected (Tab. 1). Indeed, K-means clustering analysis allows partitioning a dataset into k groups (i. e. , clusters) pre-specified by the analyst  (MacQueen, 1967). Contrary to forest waters at our study site, crop waters exhibit disproportionately higher NO 3 -concentration as a result of N fertilizer use on maize cropland  (Canton et al. , 2012; De Wit et al. , 2005; Jambert et al. , 1997 Jambert et al. , , 1994)). Consequently, in the K-means clustering analysis we used NO 3 -concentration data as a proxy to establish a statistical distinction between forest and crop waters (Tab. 1). K-means clustering analysis was performed one time with the groundwater dataset (but excluding the riparian groundwater) and a second time with the firstorder streams dataset.
We excluded data from riparian groundwater because we have considered riparian groundwater as a cluster itself (Tab. 1). Principal component analysis (PCA) was used to condense multivariate information on correlated biogeochemical parameters to a set of uncorrelated variables called principal components (further referred to as dimensions). PCA was performed one time with a dataset consisting of each measured parameter in groundwater (but excluding the riparian groundwater) and a second time with the corresponding firstorder streams dataset. PCA was performed separately for groundwater and streams because particulate parameters were not present in groundwater. If PCA were not performed separately for groundwater and streams, all data from groundwater would have been removed from the analysis (indeed, if one parameter is missing for a given sampling station, the sampling station is entirely deleted from the PCA).
Linear regressions were performed to model the relationships between two variables by fitting a linear equation to observed data. K-means clustering analysis (package Stats) and PCA analysis (package FactoMineR for analysis and package factoextra for visualization;  Kassambra and Mundt, 2017; Lê et al. , 2008)  were performed with R software version 3. 1. 4 (R Core Team, 2018). Mann-Whitney tests and linear regressions were performed with Graph Pad Prism version 7 software.
Hydrology In previous work based on the same dataset, but excluding cropland sampling stations, we identified two major hydrological seasons  (Deirmendjian and Abril, 2018). One defined a high flow period as two relatively short flood events that occurred in Jan. 2014 -Mar. 2014 and in Feb. 2015 -Mar. 2015, whereas  we defined the base flow period as two longer periods of low flow occurring in  Apr. 2014 -Jan. 2015 and Apr. 2015 -Jul. 2015. During high flow, the average and the maximum river flows were 50 m 3 s -1 and 119 m 3 s -1 , respectively. During base flow, the average and the minimum river flow were 10 m 3 s -1 and 5 m 3 s - cropland (Fig. 2 ). The water table in the riparian area exhibited intermediate depth between the forest and cropland sites (Fig. 2 ). As surface runoff was negligible in the studied sandy and flat catchment, most of the stream water likely originated from groundwater discharge.
Land use influence on water composition of shallow groundwater PCA on the groundwater dataset revealed that groundwater biogeochemical variability was strongly dependent on land use (maize cropland vs. pine forest) and hydrological seasons (base flow vs. high flow) (Figs. 3a, b ). The first three PCA dimensions covered 44%, 17. 5% and 10. 5% of the total variance within the dataset, respectively (Figs. 3a, b ). PCA dimension 1 clearly separated forest groundwater from crop groundwater based on two groups of variables negatively correlated with one another (Fig. 3a ). One group of variables was characterized crop groundwater and was composed of EC, NO 3 -, δ 13 C-DIC, and O 2 , whereas the second group of variables was characterized forest groundwater and was composed of DIC, pCO 2 , CH 4 , Fe 2+ , and NH 4 + (Fig.
Land use influence on water composition of first-order streams Figures  3c-d  present PCA based on first-order streams data set. The first three PCA dimensions covered 28. 6%, 18. 5% and 13. 8% of the total variance within the dataset, respectively (Figs. 3c, d ). Interestingly, the PCA based on the first-order streams dataset did not clearly separate crop streams from forest streams as it did for groundwater dataset (Figs. 3a-d ). This implied lower spatial variability in streams in relation to land use in than in groundwater (Tabs. 2-4; Fig. 4 ). Nevertheless, a land use gradient was observed on PCA dimension 2 (Figs. 3c, d ). PCA dimension 2 was best defined by a group of variables composed of EC, CH 4 , NO 3 -, NH 4 + , DOC, TSM, and POC, which collectively characterized crop streams (Figs.
Biogeochemistry dynamics in the groundwater-stream continuum PCA with groundwater and streams datasets indicated mathematically that streams were fed with two   6, p<0. 001, n=192)  between CO 2 and O 2 for all sampled groundwater and streams (Fig. 6a ). On the one side, stream samples were mostly characterized by high O 2 (mean was 290 µmol L -1 ) and low CO 2 (mean was 4,480 ppmv), excepting some forest streams during summer that were characterized by low O 2 (down to 110 µmol L -1 ) and high CO 2 (up to 27,200 ppmv) (Tab. 2; Figs. 6a, S4f, h). On the other side, anoxic conditions associated with high CO 2 (mean was 50,630 ppmv) were characteristic of forest groundwater, whereas crop groundwater exhibited O 2 (mean was 220 µmol L -1 ) and CO 2 (mean was 30,650 ppmv) intermediate between streams and forest groundwater (Tab.
On the contrary, where the water table is far from the soil surface, strong oxygen depletion in the vicinity of the water table does not occur since the higher residence time of infiltrating water results in almost complete degradation of soil-generated DOC in the unsaturated zone  (Malard and Hervant, 1999; Starr and Gillham, 1993). A regional study in forest soils of Switzerland  (Hagerdon et al. , 2000)  and a study compiling a global database of soil carbon (Camino-Serrano et al. , 2014) both found that soil-generated DOC was preferentially mobilized under reducing conditions in soils because of dissolution of Fe oxides. Deeper water tables in croplands do not reach topsoil that exhibits high labile organic C content. Thus, reducing conditions in topsoil and the leaching of soil-generated DOC are prevented as is the consumption of the groundwater O 2 stock, as occurs in forests during high flow stages (Tab.
Conclusion The present study demonstrates that C and IN concentrations in shallow groundwater and in first-order streams are strongly sensitive to land use. In sandy lowland catchments, simultaneous measurements of biogeochemical parameters in groundwater and streams are crucial for identifying and quantifying biogeochemical processes involved at the groundwater-stream interface. We also show that a statistical clustering analysis based on NO 3 -dataset enables partitioning of groundwater and streams into crop- The groundwater-stream interface is a biogeochemical hotspot and hot moment for C emissions and N removal processes  (McClain et al. , 2003). Future studies focusing on the groundwater-stream interface in relation to land use are needed to better understand C and N dynamics in aquatic systems in order to correctly close C and N budgets at regional and global scales.
Implementation and Simulation Results The OMNet++ network simulator environment has been used to implement and evaluate our routing table's update scheme. This network simulator provides an extensive graphical user interface (GUI), intelligent support, short computation times and an effective implementation of large-scale networks  (Bilalb and Othmana, 2012). Moreover, the flexible NED language offered by OMNet++ allows full network topology customization even when the simulation is running, which meets the needs of our study. In the simulations, we consider the different networks in Table  1. These networks satisfy our hypotheses and have been randomly generated to better assess the ability of our update strategies to apply to any network that satisfies our hypotheses. The simulations have been performed on a computer with the following configuration: Core i5 2.
40 GHz CPU, 4. 00 GB of RAM and 12 MB of cache. The objective of our simulations is to compare QoS of the network in the absence of persistent link failures and in the presence of such failures to show the efficiency of our routing tables update strategy for the QoS improvement in the presence of failures. To this end, we performed simulations on high data rate networks (1. 2 Mbits/sec to 512 Mbits/sec) of various scales and focused on packet routing delays and the data loss rate. We also compare our results to several existing studies. For both link and node failures, the data were obtained by performing several tests on the networks in the following order: • In the absence of failures • In the presence of a transient link failure • In the scenario where the above link or node failure was deemed persistent The analysis of packets' routing delays in various nodes and various network instability scenarios allowed us to appreciate the QoS variability.
Conclusion In this paper, our aim was to propose a routing and rerouting tables' update mechanism to overcome nodes' reconfiguration challenges in case of persistent single link, multilink and node failures in a virtual network. Hence, we proposed an update strategy that efficiently identified the nodes to be updated and defined an update scheme for those nodes. This scheme, originally built on a vector of lists of triplets inspired by OSPF's LSU packet structure has been adapted to treating the cases of persistent multilink and node failures. The simulations we performed show that our routing tables update method helps reduce packet routing delays and the data loss rate, which are two important metrics in computer networks. Consequently, this study provides solutions to the routing tables' update challenge in the SDN architectures, to offer a good QoS at all times to the endusers and avoid losing customers to infrastructure providers (InP).
Background Bibliometrics are mathematical methods used in various scienti c domains to measure scienti c output based on the number of publications, the prestige of the journal, and the number of citations of the research after its publication. To the best of our knowledge, only a few bibliometric studies have been performed in the Caribbean or the French West Indies. The aim of this study was to quantify the collaborations and scienti c output between the two university teaching hospitals of Martinique and Guadeloupe, at the regional, national and international level. Method A bibliometrics analysis was performed from the international databases Web of Science and PubMed, for the period from 1989 to 2018 inclusive (30 years). Three types of bibliometric indicators were used, namely quantitative indicators, performance indicators, and organisation-speci c indicators.
To estimate the structural indicators, the full set of data for each article identi ed was extracted from PubMed (15). A liations of the rst and last authors were identi ed from PubMed. Each a liation thus identi ed was classed by country. Martinique, Guadeloupe as well as French Guiana, Reunion island, Mayotte and French Polynesia were all identi ed with codes distinct from that of metropolitan France, to make it possible to distinguish them and compare with mainland France. Between 2014 and 2018, we recorded the a liations of all authors (from the rst to the last author) who collaborated on an article with the University Hospital of either Martinique of Guadeloupe. For authors with several a liations, the hospital a liation was retained. Results are presented in table format for quantitative and qualitative indicators, and in graphical format for organisation-speci c indicators.
Apparatus and event selection During the 2004 run, the NA60 experiment collected data with a system of nine sub-targets of different nuclear species -Be, Cu, In, W, Pb and U -simultaneously exposed to an incident 400 GeV proton beam. The individual target thickness was chosen to have approximately a similar statistical sample from each target. The total interaction length was 7. 5%. The beam intensity during the run was 4-5 × 10 8 protons per second. A general description of the NA60 apparatus can be found for example in  [9] , while in  [4]  some additional, specific details can be found, relevant to the setup used during the proton run of interest for the present analysis. The produced dimuons are identified and measured by the muon spectrometer, composed of a set of Multi-Wire Proportional Chamber (MWPC) tracking stations, trigger scintillator hodoscopes, a toroidal magnet and a hadron absorber.
The HELIOS measurement in p-Be collisions agrees with the NA60 data, while the p-Be measurement of CERES-TAPS looks compatible with the p-p point from NA27. At large A, the NA60 data cannot be directly compared to the CERES-TAPS point in p-Au, because of the different range of p T covered by the two measurements in evaluating the nuclear dependence of the η cross section. The CERES-TAPS data, taken alone, are not conclusive about the nuclear dependence of the σ η /σ ω ratio, because of the only two nuclear targets available and the rather large statistical uncertainties.