dataesr/scientific-paragraphs-categorization huggingface dataset:
Fishery Stress The removal of fish populations, which graze algae, is highly likely to entail an ecological shift in a coral reefscape in favor of the dominance of macroalgae  [3, 43, 44]. We hypothesize that the number of fishery vessels anchored in ports is correlated with the fishery pressure exerted on the reef fish populations  [17]. The sizes of the fishing boats commonly found in the Okinawa and Kagoshima prefectures range between 8 and 15 m. Monitoring fishing boats therefore requires access to very-high-resolution (VHR) spatial datasets that can be achieved using the freely available Google Earth geoportal, whose VHR information is based on satellite images gridded between 0. 5 and 2. 4 m (2002-2012). A total of 441 ports were targeted and 12,798 fishing boats (8302 moored and 4496 on dry docks) were individually counted.
Coral Protected Area In addition to the self-organization of the investigated socio-ecosystems, human populations can preserve coral reef ecosystems and their inherent ecological services, including coastal protection. A comparison of approaches to coral reef management indicated that the effective conservation of managed areas significantly surpassed that of unmanaged areas  [57]. Because watershed management also has a large influence on coral reefs  [42] , terrestrial protected areas were jointly examined with marine areas. Surface areas were extracted from the Protected Planet project, which lists the characteristics of 23 areas, including vector data and dates of implementation (http://protectedplanet. net/). Polygons were first rasterized at 1 arcsec and then used to nest 3 arcsec (land) and 0.
Of the 12 resilience sectors cited by  [15]  and ranked by  [12] , we managed to spatialize (i. e. , rasterize) eight at 1 arcsec spatial resolution. The remaining four sectors depicting coral and fish populations were not included because they require additional effort in processing the spatial resolution (i. e. , <5 m)  [17] , which is only useful for localized studies at present. The SEE resilience was mapped over a regional area at a fine resolution and over one decade with a one year lag. For transferability, we aimed to find freely available data sourced from non-governmental, inter-governmental, and governmental institutions, as well as low-cost data, i. e. , commercial products provided by Google Earth. Both the fishery pressure and bathymetric complexity were derived from the Google Earth image database.
This caveat is moreover corroborated by past events, such as the 7. 5 tsunami in 1771, which caused approximately 12,000 deaths in the southern areas of these two sub-archipelagoes (Global Historical Tsunami Database). Okinawa Island (#4 in Figure  11C ) should be particularly targeted because of its relatively low SEE resilience and very high population exposure. Interestingly, the resilience values of the southwestern-most Yonaguni and northeastern-most Tanega Islands (#5 in Figure  11C ) are very close to Okinawa Island, which is located in the middle of the Nansei archipelago. From this counterintuitive observation, we advocate that the overarching guidelines of an efficient ecosystem-based management of Japanese reefscapes may be applied to any islands along the latitudinal gradient. In the context of ocean warming and the northward migration of marine populations, implementing a management plan of the northern-most shallow coral reefs in the Northern Hemisphere, i.
Acknowledgments This research was fully supported by  Grant-in-Aid for JSPS Fellows  (No. 2402800 ) of the  Japan Society of the Promotion of Science (JSPS)  and was partially supported by  Grant-in-Aid for Scientific Research  (A) (Nos. 24246086  and  25257305 ) of  JSPS. The authors deeply acknowledge both anonymous reviewers who assist in greatly improving the manuscript.
This was achieved by the use of fixed cameras connected to a computer running ICATCHER software (iCode Systems; http://www. icode. co. uk/ icatcher/). Activity was scored as the rate of movement around a 290 Â 201 Â 212 mm plastic box, once the cricket had emerged from the tube. For exploration, we counted the number of unique virtual tripwires (set to measure activity, possible scores range from 0 to 8) the cricket crossed in the first minute after it emerged from the tube. See  [52]  for further details of the laboratory assays. Before tests, crickets were held in standardized 150 ml plastic containers for at least 15 min while they were weighed. Tests ran for 30 min, after which the crickets were returned to their plastic collection containers. They were held in isolation in the same 150 ml plastic container for 30 min, and then the assay was repeated.
(f ) Relating wild and laboratory behaviours We constructed separate generalized linear mixed models for each behaviour in the R package MCMCglmm  [58]. These models have each laboratory behaviour score as a fixed effect, and that individual's next measured score for that behaviour in the wild as the response variable, following Herborn et al. [13]. A significant, positive effect of the laboratory behaviour on the wild behaviour indicates the two assays correspond to the same trait. We only used one wild behaviour measure for each laboratory score, so if two wild measures were recorded after a laboratory measure, only the first one was used. Furthermore, we never used a laboratory measure on the same day as a wild measure. We also included the number of days between the laboratory and the wild scores as a fixed effect, as well as the interaction between this interval and the laboratory behaviour score.
We were interested in whether the interaction was significant (as our analyses were in a Bayesian framework, here 'significant' is used as a synonym for important) as this would show whether the ability of the laboratory measure to predict the wild measure depended on the timespan between them. For activity and exploration, we also included the number of minutes that crickets were under observation for the scored day as a fixed effect. We used a Poisson error structure, with additive errors and a log-link function for all models. We also included random effects of individual identity and, for wild shyness, burrow number. This also allows us to control for multiple measures per individual and per burrow. Adjusted repeatability (R Ad ) for behaviours in the laboratory was calculated using all the data for the laboratory assays in three separate GLMMs, with laboratory behaviour as the response, age, temperature and sex as fixed effects, and individual identity as a random effect.
The R Ad measure is (having corrected for additional variables) the proportion of total variance that is reproducible among repeated measures of a certain group, and is also referred to as the intra-class correlation coefficient, in this instance the group/class being a single individual  [59]. We calculated R Ad of the wild behaviours using all data collected with multiple environmental covariates as the fixed effects (described in the electronic supplementary material). For the number of samples, number of unique individuals and the mean, standard deviation and range of number of tests per individual for each analysis, see electronic supplementary material, table  S1. We also implemented a bivariate approach, where behaviours in each context are two responses, and we tested for significant covariance.
Acknowledgements. We would like to thank  Paul Hopwood  and  Geetanjali Mishra  for constructive comments on the manuscript,  Luke Meadows  and  Robin Brown  for assisting with data collection, and  Morgan David  for discussions relating to the design of the behavioural assays. We thank the editor,  Mark Briffa  and an anonymous reviewer for constructive comments that greatly improved the manuscript. We also thank  L. Rodrı  ´guez and  M. C. Mun  ˜oz for providing access to the study site and its facilities.
The proposal in this paper is to make accessible the hydrology analysis tools that were developed by our research team in the past years through an interoperable Spatial Data Infrastructure. To this aim we chose to develop add-ons for the geOrchestra OGC-compliant platform. Such add-ons trigger algorithms and retrieve their output in real time through OGC standard WPS. We then introduce a watershed WPS add-on and its functioning modes. In so doing we exemplify the fact that the use of OGC standards make it straightforward (and transparent to the user operating a common web browser) to remotely trigger a process on a distant server, then apply it to distant data present on a remote cartographic server, and drop the outcome onto a third-party cartographic server while visualizing it all on a browser.
Such watershed calculation is highly dependant on the quality and accuracy of topographic data: scale and resolution of, and protocol for computing, a digital terrain model (DTM) -itself highly dependant on corrective procedures. As a result a slight variation may lead to dramatic discrepancies in terms of watershed delineation and subsequent on-field resource management. Our proposal is therefore to integrate a number of functionalities to a SDI based on the Free Open Source Software (FOSS) architecture geOrchestra 1 (2009), with the aim to assist water management  (Bera et al. , 2015). In this paper we focus on watershed delineation from catchments. The Geospatial Web now offers solutions and software to exchange data, and to some extent tools, including hydrological modelling ones  (Feng et al.
TECHNOLOGY The overall approach builds upon the OCG-compliant data architecture geOrchestra and implements extra specific code based on MNTSurf, an in-house DTM analysis piece of software. geOrchestra geOrchestra (  2009 ) is a Free Open-Source Software (FOSS) Spatial Data Architecture (SDA) which is a consistent bundle of geospatial software amongst which: GeoNetwork (catalogue), GeoServer (cartographic and mapping server), mapfishapp (a viewer implemented with OpenLayers, GeoExt, ExtJS). We instantiated this SDA as Spatial Data Infrastructure (SDI)  GéoSAS (2009).
MNTSurf The services detailed further rely on MNTSurf (Squividant, 1994) a piece of software developped at the SAS department and dedicated to Digital Terrain Models (DTMs) in connection with hydrological modelling. It offers a method for DTM correction using a vector hydrology layer on the one hand, and a mechanism to remove flow anomalies based on a graph structure (drain tree) on the other hand  (Aurousseau and Squividant, 1997). MNTSurf implements a representation of surface water flows by means of a uni-directional as well as multi-directional drain model  (Aurousseau and Squividant, 1996). MNTSurf is the core element for a number of research outputs: potential wetlands  (Montreuil et al. , 2011) , modelling of N dynamics  (Beaujouan et al. , 2001 (Beaujouan et al. , , 2002) )  and pesticide transfer  (Tortrat et al.
Execution The WS WPS is triggered using menu Hydrology / Watershed. It can manage four alternative types of inputs namely: (a) a mouse click on a location of the map, hence sending point coordinates (x,y); (b) a list of point coordinates in a GML file imported to the viewer by the user; (c) a reference to a whole OWS Web Map Service (WMS) point-layer (then the actual data flow directly between the OWS cartographic server and the WPS server); (d) an object (outlets, therefore of the point type) selection from a WMS layer cast by a third party OWS server. A variant (e) consists of passing the reference to the selection instead of the selection itself in addition to the reference to the WMS layer.
Output and result Once calculated the layer processed by the WS WPS is sent to a thirdparty cartographic OWS server and added as a WMS layer to the list of visible layers in the viewer. This layer therefore benefits from all the OGC functionalities implemented on the viewer (the Web client) including: display (WMS), download in ESRI SHAPE format thanks to Web Feature Service (WFS), spatial and attribute requests (WMS, WFS), styling (Styled Layer Descriptor, SLD), context (Web Map Context, WMC), etc. Figure  1  shows the flow sequence between the SDI modules from the execution of add-on WS WPS depending on the chosen option (pertaining to the type of input): mouse click (Fig. 1    example only the reference or description of the selection goes through). Table  1  complements Fig. 1  as a comparative list of data flow chronology for each option.
We thus prove spatial analysis can be seamlessly done on delocalised data using delocalised processes. Following the same principles we are now working on making available an increasing number of MNTSurf functions as WPS, and facilitating access and use by associating geOrchestra add-ons to each of these (in addition, every client implementing WPS, e. g. Quantum GIS through its WPS plugin, can use MNTSurf's functions irrespective of the use of geOrchestra and its add-ons). An objective is to offer the users WPS tools linking modelling and real-time on-field measurements (e. g. Macro Flux,  Vinson, 2003). A number of add-ons to geOrchestra that we have developed are already available (e. g. Vidae, 2009 Vidae, , 2013)) , and are being retuned as WPS. Vidae is dedicated to mining the data collected as ongoing time-series by a number of environmental sensors disseminated on several experimental sites.
The data collected include stream-and ground-water physical and chemical properties and composition; the sites are located in Brittany, France (two sites) and around the Mediterranean basin (one in France and one in Tunisia). Moreover, we do not restrict ourselves to the field of hydrology when it comes to developping WPS, as we have also produced a soil-database query tool as a WPS for projects Websol  (Chafchafi and Bargeot, 2013)  and Soils of Brittany  (SdB, 2013). Other ongoing projects aim at increasing the number and variety of available WPS: putting GRASS's functions as WPS (52north. org), and a similar trend exists involving ArcGIS. The perspective of having all the processes and models available will likely increase and transform practices in geomatics, and unify the different communities (which are largely based on software).
This paper makes several contributions. While our troubleshooting model (Sec. 3), algorithm (Sec. 4) and software implementation (Sec. 5) are interesting per se, we believe our major contribution is the rigour of the evaluation methodology (Sec. 6), which overcomes state of the art limits (Sec. 2). Indeed, on one hand, previous practical troubleshooting efforts  [4, 6, 16, 17, 19]  are valuable in terms of domain knowledge and engineering, but lack theoretical foundations and rigorous verification. On the other hand, prior analytical efforts are cast on solid theoretic ground  [9, 21] , but their validation is either simplistic (e. g. simulations) or lacks ground truth (e. g. PlanetLab). In this work, we take the best of both worlds, as we (i) propose a practical methodology for network troubleshooting with an open source implementation; (ii) provide a model of the expected fault detection probability that we contrast with experimental results; (iii) use an experimental approach where we emulate controlled network conditions with Mininet  [13] ; (iv) perform a calibration of the emulation setup, an often neglected albeit mandatory task; (v) in spirit with Mininet and the TMA community, we further make all our source code available for the scientific community at  [1, 2].
On the other hand, the ground truth is either completely missing in real operational networks (such as PlanetLab  [21] ) or partially missing in testbeds  [15, 18] , where network events outside of the control of researchers can happen. Our setup employs controlled emulation through Mininet  [13]  which is (relatively) fast to implement, uses real code (including kernel stack and our software), and allows testing on fairly large scale topologies. This setup allows full control on the number, duration, and location of network problems. Additionally, by running the full network stack, Mininet keeps the real world noise in the underlying measurements, thus providing a more challenging validation environment with respect to simulation. As a side effect of this choice, the NetProbes software that we release as open source  [2]  has also undergone a significant amount of experimental validation.
Calibration of the emulation environment Before running a full-fledged measurement campaign, it is mandatory to perform a rigorous calibration phase, yet this phase is often neglected  [22]. In this work, we follow an experimental approach using emulation in Mininet, to control the duration and the location of the faults. However, it is unclear how well state-of-the-art delay and bandwidth measurement techniques perform in Mininet. In order to disambiguate inconsistencies due to Mininet from measurement errors intrinsic to measurements techniques, we perform calibration experiments for a set of delay (expectedly easy) and bandwidth (notoriously difficult) measurement tools and assess their accuracy in Mininet. In this section, we first briefly describe Mininet and NetProbes, the diagnosis software we develop for this work (Sec.
Software Tools Mininet  [13]  Mininet is an open source emulator which creates a virtual network of end-hosts, links, and OpenFlow virtual switches in a single Linux kernel and supports experiments with almost arbitrary network topologies. Mininet hosts execute code in real-time, exchange real network traffic, and behave similarly to deployed hardware. All the software developed for a virtual Mininet network can run in hardware networks and be shared with others to reproduce the experiments. Mininet provides the functional and timing realism of testbeds in addition to the flexibility and full control of simulators. Experimenters configure packet forwarding at the switches with OpenFlow and link network characteristics (e. g. , delay and bandwidth) with the Linux Traffic Control (tc). Reproducing experiments from tier-1 conference papers 2  indicates that results from Mininet and from testbeds are in agreement.
NetProbes  [2]  We design NetProbes, a distributed software written in Python 3. x that runs on end-hosts and executes a set of user-defined active measurement tests. Net-Probes agents deployed at end-user devices and gateways form an overlay. They perform a set of periodic measurements to monitor the paths in the overlay and collect a baseline network performance. When the user experiences network performance issues, the NetProbes agent running at the user device launches a troubleshooting task to assess the severity of the performance issue and the location of the faulty link. It is worth pointing out that the set of measurement tasks that can be performed by NetProbes agents (e. g. , HTTP or DNS requests, multicast UDP tests, etc. ) is far larger than what we consider within the scope of this paper, and that the software is available at  [2].
Delay and bandwidth calibration Setup We build a Mininet virtual network with the topology depicted in Fig. 1  on a server with four cores and 24 GB of RAM. We run the selected tools on probes 1 and 2. In our delay experiments, we impose five different delay values (0 ms, 20 ms, 100 ms, 200 ms, 1000 ms) on ℓ 3 located at depth d = 3 in the tree. At each delay level, probes 1 and 2 perform 50 measurements of round trip delays to probes 7 and 6 respectively (250 measurements in total for each pair of probes). We use Mininet processes through the Python API to issue ping and traceroute to measure RTTs (we test traceroute with UDP, UDP Lite, TCP, and ICMP). Similarly, in the bandwidth experiments, we vary the link capacity of ℓ 3 (100 Mbps, 10 Mbps, 1 Mbps) under three different traffic shapers, namely the hierarchical token bucket (HTB), the token bucket filter (TBF), and the hierarchical fair service curve (HFSC) and we make 20 measurements of the available bandwidth between probes 1 and 7 and probes 2 and 6 (120 in total for each value of the link capacity).
Experimental results We now evaluate the quality of our clustering and classification for various probe budgets (namely 10, 20 and 50 probes) for faults (e. g. , doubling delay or halving bandwidth) at controlled depths of the tree. All the scripts to reproduce the experiments are available at  [1]. We first compare experimental results in a calibrated Mininet environment (including real-world noise), with those expected by a probabilistic model (neglecting noise) (Sec. 6. 1). We next perform a sensitivity analysis by varying topological properties, probe selection policies S p , and link selection policies S ℓ (Sec. 6. 2).
Discussion CUDA technology provides a well-established software platform for developers to design parallelized workflow with C-style code, with direct access to the virtual instruction set and GPU memories. In this paper, a strategy termed Fixed Sampling Number Projection (FSNP) is devised to ensure the operation synchronization along projection lines in parallelizing ray- driven projection based on CUDA framework. The conventional cubic FOV is replaced by a rotational symmetric FOV to save computation cost in geometry parameter calculation. Backprojection parallelization is relatively simple because the computation involved in each kernel for each back-projected voxel is independent to the computation in other threads. But as to projection operator, which calculates projection values via line integration of sample intensities, the involved accumulation operation often results in racing condition that limits the computation speed.
NAND flash memory integration in the traditional I/O software stack of Unix-like operating systems (OS) was achieved without modifying most of the OS layers. In fact, one can dissociate two categories of flash memory devices: (1) those which intricacies are abstracted to the OS (e. g. SSDs, USB sticks), and (2) raw flash memory chips driven by a specific Flash File System (FFS) such as JFFS2 and UBIFS. In the latter case, the operating system I/O software stack low level layers (i. e. file system and driver) were upgraded while the higher levels (virtual file system and related buffers) were not. In order to optimize the system behavior in such a case, one must understand the interactions between the different I/O software management layers, and the performance impact of each layer for a given I/O workload.
g. SSDs, USB sticks), in this case traditional interfaces and most are used of the OS I/O layers stay unchanged. (2) Raw flash memory chips managed by specific Flash File Systems (FFS) such as JFFS  [16] , UBIFS  [14]  and YAFFS  [8]. In this embedded system specific case, low level layers (file systems and drivers) are flash-aware, but still the above layers (such as the VFS) remain unchanged. Many studies have revealed some very peculiar I/O performance behaviors for flash specific file systems  [11, 5]. Those behaviors are due for some part to the FFS intricacies, but also to the interactions between the different I/O software stack layers. In order to optimize such FFS behavior, one should understand the implications of the integration of flash-specific layers into the I/O path. As detailed in the next sections, the main layers crossed by an I/O request in a Linux OS are: the Virtual File System (VFS) and its caches, the FFS, and the NAND flash memory driver named the Memory Technology Device (MTD) (see Figure  1 ).
VFSMon traces kernel events and related parameters, such as VFS levels data read and write requests, page cache hits / misses, and Linux read-ahead calls. (2) Flashmon tool that reveals how the I/O request flow is translated to flash memory operations at the FFS output. This tool traces I/O operations at the driver level (close to the hardware). (  3 ) FuncMon is a generic tool that allows to give quantitative information about the execution times of kernel functions on the I/O path. This is of great help when trying to model the performance behavior of the system. In order to illustrate the use of the developed tools, we give a case study on modeling of I/O read operation performance on the JFFS2 file-system. Indeed, the tools can help in different phases of the modeling process: exploration, modeling and validation.