dataesr/scientific-paragraphs-categorization huggingface dataset:
The integrated land ecosystem-atmosphere processes study (iLEAPS) is an international research project focussing on the fundamental processes that link land-atmosphere exchange, climate, the water cycle, and tropospheric chemistry. The project, iLEAPS, was established 2004 within the International Geosphere-Biosphere Programme (IGBP). During its first decade, iLEAPS has proven to be a vital project, well equipped to build a community to address the challenges involved in understanding the complex Earth system: multidisciplinary, integrative approaches for both observations and modeling. The iLEAPS community has made major advances in process understanding, land-surface modeling, and observation techniques and networks. The modes of iLEAPS operation include elucidating specific iLEAPS scientific questions through networks of process studies, field campaigns, modeling, long-term integrated field studies, international interdisciplinary mega-campaigns, synthesis studies, databases, as well as conferences on specific scientific questions and synthesis meetings.
Major observation campaigns and long-term monitoring An innovative, comprehensive, multi-scale, multidisciplinary approach to land-atmosphere processes is a key element of iLEAPS observations. The success of ambitious multidisciplinary international field campaigns and long-term monitoring in Amazonia  (Keller et al. , 2009; Artaxo, 2012)  has led to calls for similar projects to be set up to monitor other tropical forests, including those in Africa and south-east Asia  (Hewitt et al. , 2010). Perhaps the most fundamental innovation was that the project encouraged interdisciplinary collaboration among physicists, chemists, meteorologists and biologists. Unusually for the time, economists and social scientists were also introduced into the research program in order to fully understand all the factors influencing such a complex system  (Artaxo, 2012).
New datasets Recent advances in up scaling and data integration across multiple data streams have enabled iLEAPS scientists to produce gridded datasets of regionally and globally explicit flux products. The sites that comprise the flux and spectral measurement networks FLUXNET and SPECNET have become an important source for "bottom-up" inputs to models that upscale flux quantities from canopies to landscapes and from landscapes to   , 1-2,  and 2-4 km). Adopted from  Li et al. 2011. the globe  (Baldocchi et al. , 2001). Data from tower measurements also serve as a validation tool for top-down modeling based on satellite and aircraft optical measurements  (Barkley et al. , 2009). These capabilities have brought climate and ecosystem scientists within reach of quantifying carbon, water and energy fluxes 'Everywhere, and All of the Time'.
They have also enabled insights into climate-ecosystem interactions and trends over a range of spatial and temporal scales; and particularly at the largest scales. Global datasets such as these have been used to reveal important connections among plant physiology and atmospheric composition. For example,  Mahecha et al. (2010)  found marked differences in the long-term fate of carbon taken up through photosynthesis, as well as in the availability of this carbon for respiration, across a number of sites in different biomes. The sensitivity of respiration to changes in temperature, however, fell within a narrow range, and proved to be independent of the mean annual temperature and biome. The global mean temperature sensitivity was also significantly lower than previous estimates. The authors attribute the difference to the exclusion of confounding processes in their analysis, such as the seasonal variability of biological activity.
Land-use transitions determine whether forest within a grid box is primary (old-growth) or secondary (recovering from previous human land-use activities). Carbon budgets and physical feedbacks between the land and the atmosphere are very different in these two forest types; therefore, land-use transitions should be included in all DGVMs contributing to the IPCC 5th assessment report. The conference drafted a wish list of datasets for representing land use in climate models, including details on crop management, irrigation, and fire suppression. Finally, the programme outlined important processes hitherto omitted in climate modeling, including the role of microorganisms in atmospheric chemistry such as aerosol formation and photochemical processes and the role of volatile organic compounds emitted by vegetation.
Model evaluation With so many new applications and new processes being introduced to the models, there is now a strong imperative to evaluate the models. The availability of new observational datasets, for instance for soil moisture or land surface fluxes  (Seneviratne et al. , 2010; Jung et al. , 2010) , offer exciting opportunities for better representing basic processes and their interactions with the climate. These evaluations focus on fluxes at diurnal to annual timescales, and there is a need to include biogeochemical processes and ecosystem states in a systematic evaluation of models across multiple spatial and temporal scales  (Randerson et al. , 2009; Luo et al. 2012). Models of the terrestrial carbon cycle must be additionally tested for long timescale (decadal to centennial) demographic processes (such as mortality), biogeochemical processes (such as litter decomposition and soil organic matter formation), and whole-plant physiological processes (such as carbon allocation).
, 2011b). The objectives of EUCAARI were (I) reduction of the current uncertainty of the impact of aerosol particles on climate by 50% and quantification of the relationship between anthropogenic aerosol particles and regional air quality, and (II) quantification of the side effects of European air quality directives on global and regional climate. EUCAARI also contributed to technological developments in the aerosol measurements, enhancing future experiments and air-quality monitoring networks. New ground-based, aircraft and satellite measurements were integrated with existing data to produce a globally consistent dataset with the highest possible accuracy (Fig. 9 ). A European measurement campaign was designed along north-south and eastwest transects around simultaneous multi-station observations, Lagrangian aircraft measurements and carefully selected "supersites".
A hierarchy of models was developed based on the results of the laboratory and theoretical investigations. The models will be used to interpret the measurements and will be integrated into regional air quality and global climate models. The result was measurable improvements in the project's climate and air quality models. The outcomes (scenarios, recommendations, models, harmonized datasets and new knowledge) were disseminated to authorities, policy makers, the research community, industry, instrument designers, and the EU-ESA Global Monitoring for Environment and Security (GMES) Program. The iLEAPS newsletter with a hard-copy circulation of ca. 3000 and available online to the whole community and beyond highlights important aspects of iLEAPS work to large audiences.
ads is an R package that performs multi-scale spatial point pattern analyses through methods derived from Ripley's K-function. These methods apply to univariate, multivariate or marked point data mapped in a rectangular, circular or irregular-shaped sampling window. Specific tests of statistical significance based on Monte Carlo simulations are associated to these methods. The main features of ads is to call fast C subroutines for computing Ripley's unbiased local correction of edge effects for various sampling window configurations and for performing Monte Carlo simulations. It thus allows one to analyze large datasets and to compute robust confidence envelopes. This paper is an introduction to ads version 1. 5, focusing on its complementarity with the other R packages for spatial point pattern analysis, and on recent original developments towards the introduction of multivariate functions for analyzing spatial pattern of species diversity.
Introduction ads is an R package for spatial analysis of mapped data based on second-order multi-scale analyses of spatial point patterns derived from  Ripley's (1977)  K-function (the K-function family). Initially implemented for forest ecologists who often need to analyze large naturally heterogeneous tree maps, it is based on C subroutines that include geometrical functions to perform unbiased isotropic correction of edge effects for sampling windows of any shape (i. e. , rectangular, circular, polygonal or with holes), as well as procedures to fast computation of Though spatial only allows a rectangular sampling window and splancs doesn't allow a polygon with holes, the four packages return, in all comparable cases, exactly the same numerical results for K(r) (see replication file). Similar differences in performance were obtained for the other K-functions listed in Table  1.
robust confidence envelopes of basic spatial null hypotheses from a large number of Monte Carlo simulations. It therefore combines efficiency of historical packages for point pattern analysis that interface foreign languages, i. e. , spatial  (Venables and Ripley 2002)  or splancs  (Rowlingson and Diggle 1993) , with the flexibility of the more recent spatstat  (Baddeley and Turner 2005) , especially for data manipulation, point pattern modeling and simulation. As an illustration, Figure  1  shows that in terms of run time 1 , ads largely outperforms the other R packages, except splancs for a single polygonal sampling window, probably because of two different implementations of the isotropic edge effects correction (see  Agterberg 1994; Goreaud and Pélissier 1999). In ads, a point pattern is defined as a set of point coordinates in a finite sampling window of rectangular, circular or polygonal type, optionally with hole(s).
variable (e. g. , tree stem diameter), so defining various types of point patterns (i. e. , univariate, multivariate or marked). The computational functions automatically recognize these objects and their types, and call accordingly the appropriate C subroutines. Version 1. 5 of ads computes the most classical members of the K-function family, as in particular does spatstat, but also more specific functions for the spatial analysis of species diversity from multivariate point patterns, which, from our knowledge, do not exist in any other R package (Table  1 ).
Data analysis Package ads basically performs second-order neighborhood analyses derived from  Ripley's (1977)  K-function for univariate, multivariate or marked point patterns (the K-function family). These functions use "spp" objects as input argument and return distance-dependent summary statistics stored as objects of the class "fads". Ancillary functions also compute local values of first-and second-order densities stored as objects of the class "vads". There exist print and plot methods for both these classes of objects.
In case of a non-rectangular sampling window, the torus connects the top and bottom edges of the rectangular frame enclosing the sampling window so that some of the type 1 points can be shifted outside the limits of the actual sampling window and will be thus ignored in computation. This however is not expected to bias K 12 (r) estimations, since type 1 points are the focal points for which we estimate the number of type 2 neighbors. Problems may however arise in extreme cases when the number of type 1 points ignored is too large (e. g. , when the enclosing rectangle is too large with respect to the actual polygonal window), so that the remainder doesn't allow a correct estimation of the mean number of type 2 neighbors (Figure  4 ). As an alternative, the Figure  4 : Bivariate functions estimated for "beech" and "oak" species from dataset BPoirier (left) using k12fun with a 99% local confidence envelope obtained from 1000 Monte Carlo simulations of population independence hypothesis (PI-tor; top panel; run time 1 = 0.
610 sec) or random labeling hypothesis (RL; bottom panel; run time= 0. 993 sec). Note that in this example (2 different species populations) RL is probably not an ecologically sound null hypothesis (dataset Allogny provides a more appropriate example). It however illustrates the two different theoretical expectations (red lines) and confidence envelopes (green lines) under PI and RL null hypotheses. BP4 is an "spp" object as defined in the replication file. mimetic point process  (Goreaud, Loussier, Ngo Bieng, and Allain 2004)  introduced in Section 3. 6 below can be used to replicate the pattern of type 1 points, keeping here also the pattern of type 2 points unchanged (PI-mim). Here also it is recommended for efficiency to avoid defining a too large enclosing rectangle because the points are first generated within the rectangle and then retained only if they do not fall within a triangle.
e. , k ij = 1 in g pq (r) and K pq (r) whatever the relative locations of point i of species p and point j of species q with respect to the sampling window boundary; see Section 3. 2). Both functions quantify the phylogenetic/functional spatial structure of a community conditionally to the multi-species spatial pattern (see  Shen et al. 2013) , with in both cases, a theoretical expected value of 1 at all r under the species equivalence hypothesis. Figure  5  gives an example of analysis of the pattern of species diversity for a tropical forest plot of 250 × 250 m with 4128 trees of 332 different species (dataset Paracou15; Gourlet-Fleury, Guehl, and Laroussinie 2004).
Conclusion and perspectives Package ads has been developed in the late 90's for the requirements of forest ecologists, initially as a standalone C application within a special section of ADE-4 software  (Thioulouse, Chessel, Dolédec, and Olivier 1997 ) -now package ade4 for R  (Dray and Dufour 2007). The core C code used for the implementation of the geometrical functions of edge effects correction was written by  Goreaud (2000)  in the framework of his PhD thesis, based on triangle geometry  (Goreaud and Pélissier 1999). The original C code was ported to R in 2007. At this time the paper by  Agterberg (1994) , that presents the geometrical principles behind the method of edge effects correction used in splancs, and which proved faster for polygonal sampling windows than the one used in ads (see Figure  1 ), was unknown by us.
There is consequently an avenue of improvement of ads by integrating the two approaches on the basis of splancs fortran code ported to R by  Bivand and Gebhardt (2000). For similar historical reasons, the sampling window ("swin") and spatial point pattern ("spp") objects used in ads are similar but not exactly the same to the ones used in spatstat. Though we developed special functions, owin2swin and ppp2spp, for compatibility with spatstat objects, much remain to be done to allow users to shift more easily from one package to the other. On the one hand ads proved to be always much faster than spatstat, a useful property for one to deal with large datasets and robust confidence envelopes (i. e. , based on a large number of Monte Carlo simulations), while on the other hand, spatstat is more flexible for data manipulation and offers a larger panel of different spatial functions, not only limited to the K-function family.
Figure3: Univariate functions estimated for BPoirier dataset with kfun function computed from 1 to 25m by 1-m distance steps and a 99% local confidence envelope obtained from 1000 Monte Carlo simulations of CSR hypothesis (run time 1 = 1. 686 sec). BP2 is an "spp" object as defined in the replication file and corresponding to the central pattern in Figure2. L(r) shows a significant negative deviation within a neighborhood distance of 1 to 7m, indicating regularity of the pattern.
Figure5: Multivariate functions of species diversity estimated for dataset Paracou15 (left) using ksfun (top panel) and kdfun (bottom panel) with a 99% local confidence envelope obtained from 500 Monte Carlo simulations of random placement hypothesis (RP; top panel; run time 1 = 400. 735 sec) or species equivalence hypothesis (SE; bottom panel; run time= 151. 145 sec). P15 is an "spp" object as defined in replication file, with a between-species distance matrix corresponding to a cophenetic distance based on APG III phylogeny (Chase and Reveal 2009). This example shows a significant negative deviation of Simpson's diversity from the RP hypothesis from about 5 m (more conspecific neighbors than expected for the entire plot) and of Rao's diversity from the SE hypothesis (more closely related neighbors than expected) in the range 5-20m.
Figure6: Spatial pattern of tree height from dataset finpines (left) along with the univariate L-function with confidence envelope of CSR (top panel) and the marked K m -function with confidence envelope of independent marking hypothesis (IM) obtained from 500 Monte Carlo simulations (bottom panel; run time 1 = 0. 367 sec). This example shows that while saplings are significantly clustered in space, their height is spatially uncorrelated.
Acknowledgments Package ads was ported to R with the help of  Cyril Picard  and  Karine Pousseu-Tankeu , thanks to financial support from  IRD-Spirales  project  2005. Version 1. 3 was developed with the help of  Denis Redondo  and  Joris Moyano. We warmly thank  Edzer Pedesma  acting as Editor of the special issue of  JSS on Spatial Statistics , as well as two anonymous reviewers whose comments greatly helped to improve the manuscript.
FPFigure 6 : 6 Figure6: Spatial pattern of tree height from dataset finpines (left) along with the univariate L-function with confidence envelope of CSR (top panel) and the marked K m -function with confidence envelope of independent marking hypothesis (IM) obtained from 500 Monte Carlo simulations (bottom panel; run time 1 = 0. 367 sec). This example shows that while saplings are significantly clustered in space, their height is spatially uncorrelated.
Sensors from the different subsystems play the role of knowledge sources, either in raw form, for the least verbose, or through some preprocessing that extracts a summarized dataset to be exchanged with other agents. Such a data structure based approach has proven quite useful in order to exchange data flexibly between processes without hardcoding specific data exchange patterns at code level, and without knowing in advance the interested recipients. It is quite important to retain a modular definition of subsystems, and even functions. Whiteboards are an extension of blackboards, supporting less structured data. Some of these even add, among other functionalities, messaging capabilities through the introduction of a communication middleware. Such mechanisms would be especially interesting for a UAV comprising distributed processors, like for instance multiple boards communication through buses.
Modelling and validation environment We experimented in the past with validation issues on dependable communicating embedded automotive architectures  (Schweppe, 2011)  and autonomous drones. This work has relied on our modelling system, which is based on a UML software/hardware partitioning environment named DIPLODOCUS  (Apvrille, 2006 ) / TTool (see http://ttool. telecom-paristech. fr). DIPLODOCUS is based on the UML language and includes the "Y" scheme  (Balarin, 2003). The approach consists in a three-step methodology: (i) model the functions of the system, (ii) capture the candidate hardware architectures, which is defined in terms of processors, buses and memories, and finally (iii) allocate functions and their communications to the resources of the hardware architecture and study the impact of this allocation with respect to the properties assessed.
We proposed an approach relying on the definition of the hardware and software components and their partitioning. In this paper, we illustrated this approach with the example of an autonomous drone system that we implemented. We presented early modeling and verification results based on real-time constraints and a simple function partitioning of the drone system modeled using our modeling environment TTool. We are currently refining our architecture and validation approach to develop autonomous drones in the scope of postdisaster humanitarian relief operations  (Tanzi, 2014b). The use of advanced sensors that will be necessary in such situations will require a significant effort to define the coordination patterns among the different subsystems constituting the UAV platform. We will investigate ways to express these in a simple manner.
XEOM 1 data from cuprite and nantokite test samples The XEOL from different compounds generally has a different colour balance or may even show up in a narrow band. Chemical information can therefore be obtained by imaging through colour filters in addition to using x-ray spectroscopy  [25]. Figure  2 (a, b ) shows images of a copper grid coated with nantokite resting on a copper coupon with an oxidised surface. The oxide is predominantly cuprite (>90%) with some tenorite as an impurity. The images are taken from the stack at 9 keV, just past the k-edge. The energy range in the stack is sufficient to distinguish between XANES of copper, cuprite, nantokite, and copper hydroxychlorides (in the event that the nantokite had hydrolysed). Figure  2.
A bronze chain mail link from the Mary Rose Many different types of information may be extracted from an image stack. Figure  3  shows data obtained from a bronze chain mail link recovered from the wreck of King Henry VIII's flagship the Mary Rose (Item MR81A1436). The link is in the form of a loop ∅9. 1 mm made from bronze wire ∅1. 2 mm. It has been subjected to conservation treatment to remove chlorides absorbed during 437 years of immersion in the sea. The data are abstracted from image stacks across the Cu-K absorption edge whose energy range was chosen to be sufficient for the purpose of mapping the energy shift of the edge. Figure  3 (a) is a photograph of the link showing its mounting and the analysed area. Figure  3 (b) shows a XEOL image (ironbow colour scale) of a region of the link surface 2 × 0.
(ii) An optics housing containing the lenses, optical filters, and a remote controlled focussing mechanism. The last stage of this housing is lined with 0. 5 mm thick lead foil to prevent x-rays penetrating the plastic wall and reaching the CCD camera. (iii) A CCD camera with a Peltier cooled broadband sensor. The first camera employed was an FLI Microline ML1109 (Finger Lakes Instrumentation Inc. USA) fitted with a Hamamatsu S10140-1009 sensor (Hamamatsu Photonics K. K. , Japan). The sensor is 24. 6 × 6 mm 2 in size with 1024 × 506 active pixels which with the magnification of the XEOM 1 optics (12 ×) gave an imaged area of 2 × 1 mm 2 at the sample. This device was used to gather the data in Figures  3  and 4. Problems with the sensor led to the replacement of the camera with an Andor Ikon-L 936 (Andor Technology Ltd.
The filters used in this work were red and blue Techspec® fluorescence line filters (Edmund Optics Ltd. , UK) with pass bands of 612-644 nm (# 84-103) and 457-487 nm (#67-027) respectively (measured transmission ≥95% across the pass band, <0. 1% elsewhere  [16] ). Instrument control, acquisition and data processing XEOM 1 is controlled remotely using custom software esaXAS  [16]  running on a laptop PC which drives a pair of Agilent U2300A USB multifunction cards which provide control functions for the optics and sample handling. The software also controls the cameras using the relevant software functions. The software allows the user to set up a large variety of acquisition scenarios, automatically acquires and stores the image stacks and synchronizes image acquisition with the stepping of the monochromator on the synchrotron beam line.