dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis. Due to the low number of replicates, data from visual surveys were compared between sites by means of Kruskal-Wallis tests. Data obtained from visual survey in nMPAs and MPAs were compared by means of Mann-Whitney U tests. Time series of acoustic data were obtained for each site by plotting the mean values of sound pressure level (SPL RMS ) and ACI over a 24 h period with a 1-h resolution. Time series were divided in day-time (06:00-17:00, corresponding to the time period of visual surveys) and night-time (18:00-05:00). Dawn (05:00-07:00) and dusk (17:00-19:00) were included in day-time and night-time respectively. Since data were not normally distributed (Shapiro-Wilks tests, W = 0. 95-0. 99, P = 0. 03-0. 001), between sites differences of day and night values of SPL RMS and ACI were examined by means of Kruskal-Wallis tests, followed by Tukey's post-hoc tests for pairwise comparisons.
Acoustic data obtained during day-time were correlated with biological data, i. e. total number of fish species, total number of fishes, Shannon-Wiener index of diversity (H), number of vocal species, number of vocal fishes and the percentage of substrate covered with living coral, rubble, sand, pavement and macro-algae by means of Spearman's rank correlation tests. Acoustic data obtained during night-time were also correlated with biological data. Even though fish survey were only performed during day-time, fishes preferentially sing at night, and correlation with night values could provide important information too. All analyses were two-tailed, at α = 0. 05 and carried out in R 3. 1. 2 (R Core Team, 2014) using customized scripts.
Numerical Modeling of the Analyzed Walls FE software ABAQUS is adopted. The Concrete Damaged Plasticity constitution model is used to simulate material behavior of concrete. A general plastic model is used to model steel bars. According to  [26] , dilation angle of concrete is between 36° to 40°, an optimal value based on material test is 38° which is adopted herein. K c is the ratio of the distances between the hydrostatic axis and respectively the compression and tension meridian in the deviatoric section  [27]. It is taken as recommended value 2/3 which conforms to test results  [27]. Eccentricity describes the rate of approach of the plastic potential hyperbola to the asymptote. The recommended value of 0. 1 is used. The parameter f bo / f co refers to the ratio of strength in the biaxial state to that in the uniaxial state.
All recordings were conducted in near calm conditions (0. 2 m wave height and 3. 8 m s -1 wind speed, http://climat. meteo. gc. ca/). Digital recordings were transferred to a PC to enable spectral composition and source sound level determination using the MATLAB (The MathWorks, Inc. ) software with customized written codes for post-recording sounds analysis. From the recordings, a sequence lasting 30 s was selected corresponding to "vessel noise" at maximum sound intensity. Moreover, a sequence of 30 s per hour was analyzed to evaluate the ambient underwater noise without anthropic noise. Larval culture. Two years-old mature mussels collected from St Peter's Bay were conditioned to spawn in the laboratory  61  and the larvae were reared  62. Introduction and transfer permits from Fisheries and Oceans Canada were obtained and our study did not involve endangered or protected species.
Post-hoc comparisons (Tukey's pairwise multiple comparison procedure; α = 0. 05) were performed to test for any differences among individual treatments. All analyses were performed using software Systat V12. 02. Enrichment level of different the dominant FA of picoplankton cultures and larvae fed with these picoplankton have been analyzed by permutational multivariate analysis of variance (PERMANOVA) using PRIMER v6. 1. 12  70 with PERMANOVA+v1. 0. 2  71. Homogeneity was evaluated using the permutation analysis of multivariate dispersion (PERMDISP) routine and pair-wise multiple comparison tests were used to identify differences among factors.
Acknowledgements We are especially thankful  Mathieu Babin  for the isotopic analyses and  Stephen Fortune  for access to his longlines and boat charter services. We would like to thank  Carla Hicks  for her manuscript review and to anonymous reviewer for their comments which helped improving the quality of our manuscript. Financial support was provided by the  National Sciences and Engineering Research Council of Canada (NSERC- Discovery Grant  to  RT 299100 ), the  French National Research Agency  ( MERCALME ,  ANR-12-ASTR-0021-03 ), the  MNHN  for a delegation of 3 years to  F. Olivier  at the  ISMER-UQAR, and BeBEST (International laboratory in Benthic Ecology ,  UQAR-UBO-CNRS ).
Funding This work has received funding from the  European Union ,  7th Framework Programme , HEALTH-F2-2007-202224  European IPF Network , the  Fonds Européen de Développement Économique et Régional ,  Agence Nationale de la Recherche  ( 11-BSV-011-01  meso-IPF and  Investissement d'Avenir   ANR-11-LABX-0021-01-LipSTIC LabEx ),  La Ligue Régionale Grand Est Contre le Cancer, Le conseil Régional de Bourgogne, l'Institut National du Cancer (INCa) , and the  Fonds de Dotation "Recherche en Santé Respiratoire" , the  Société de Pneumologie de Langue Française  and the  Fondation du Souffle. O. B. and P. B. are lauréat 2014 de la  Fondation du Souffle et du Fonds de dotation Recherche en Santé Respiratoire. O. B. , PS. B. and G. W. are supported by  Fonds de Dotation « Recherche en Santé Respiratoire » et la Société de Pneumologie de Langue Française.
g. ,  Rautiainen et al. , 2012 Rautiainen et al. , , 2014 ; European Space Agency: ESA SMOS + Innovation Permafrost, ESA CCI Soil Moisture, ESA SMOSHiLat; National Aeronautics and Space Administration: NASA SMAP cal/val). In the Skjern River catchment in western Denmark related actions are ongoing coordinated by the Danish Center for Hydrology (HOBE). Therefore, a joined effort aimed at calibrating the used soil moisture sensors, namely, the capacitance Decagon ECH2O 5TE sensor (Decagon 5TE) 1 and the impedance Delta-T ThetaProbe ML2X (ThetaProbe) 1 , for organic substrate. At both sites, the Decagon 5TE sensors are installed at permanent network stations  (Bircher et al. , 2012a; Ikonen et al. , 2016)  providing data to the International Soil Moisture Network (ISMN,  Dorigo et al. , 2011)  -a global in situ soil moisture database to support validation and improvement of satellite observations and land surface models.
Additionally, at one of the two stations a sample from the underlying mineral A horizon was collected. At this location a field calibration experiment (Sect. 4. 2) took place. The resulting series of field data measured in organic horizons also include some Decagon 5TE-ThetaProbe-gravimetric sample couples collected around other Decagon forest stations, taken in the scope of cosmic-ray neutron detector calibration  (Andreasen et al. , 2016). In order to further increase the number of field calibration points some measurements acquired during a remote sensing cal/val campaign in 2013  (Jonard et al. , 2014)  were added to the database. This includes samples from the organic surface layer as well as the underlying sandy A horizon of a heathland soil (Gludsted Plantation, Denmark) measured by means of Decagon 5TE sensors, ThetaProbes, and gravimetric samples.
Field calibration measurements During the field calibration experiment in the vicinity of one Danish forest network station (see Sect. 2. 2), a Decagon 5TE sensor was installed in the organic horizon and logged continuously. After the first measurements of extremely dry conditions in summer 2013, the soil was saturated. During the drying period, three ThetaProbe readings and gravimetric samples were acquired and averaged for each measurement in time. In the case of these data and all additional field observations used in this study (Decagon 5TE-ThetaProbegravimetric sample couples described in Sect. 2. 2), sensor installation, measurement, and drying protocols were identical to the ones described above for the laboratory calibration.
Fitting of calibration functions and validation All field and laboratory data were gathered and sensor output was plotted against volumetric moisture content for Decagon 5TE-ThetaProbe and organic-mineral samples, respectively. In the case of continuously logged Decagon 5TE data, the two measurements closest to each ThetaProbe/sample timestamp were extracted and averaged. The resulting number of available data points per site and sensor type is indicated in Table  1. Sensor calibrations based on our measurements were carried out for the ensemble of data measured in the organic horizons of all studied sites, while for the data from underlying mineral soil layers the validity of the manufacturer calibrations was tested. Calibration curves were fitted through the data using mathematical descriptions already reported in the literature on soil moisture sensor calibration.
Acknowledgements. Financial support for conducting this research by the following institutions is gratefully acknowledged:  European Space Agency (Support to Science Element -Changing Earth Science Network and Expert Support Laboratory) ;  Centre National d'Etudes Spatiales  ( Terre, Océans, Surfaces Continentales, Atmosphère Programme, TOSCA ), France;  Villum Foundation (Hydrological Observatory, HOBE), Denmark; University of Copenhagen (Department of Geosciences and Resource Management), Denmark ;  Finnish Meteorological Institute ;  German Federal Ministry of Education and Research (Terrestrial Environmental Observatories, TERENO) ; Transnational Access to Research Infrastructures activity in the 7th  Framework Programme  of the EC under the  ExpeER  project. Edited by:  C. Ménard.
I. INTRODUCTION Binary Large Objects (or Blobs) are an increasingly popular model for addressing the storage needs of data-intensive applications. Their low-level, fine-grained binary access methods provide the user with a complete control over the data layout. This enables a level of application-specific optimizations, which other structured storage systems such as key-value stores or relational databases simply cannot provide. Such optimisations are leveraged in a variety of contexts: Microsoft Azure  [1] , for instance, uses blobs for storing virtual hard disks images, while in RADOS  [2]  they stand as a base layer for higher-level storage systems such as Distributed File Systems or Object Stores. The unstructured nature of blobs also makes them ideal candidates to store large numbers of small related objects efficiently by grouping them in a single storage container, thereby avoiding the cost of storing metadata for each individual object.
State-of-the-art blob storage systems are typically meeting these requirements at the expense of weaker consistency guarantees. Those systems do not provide the multiobject transactional semantics that have been offered for decades by relational database systems  [6] , and that are proposed by a new generation of key-value stores  [7] ,  [8]. Strengthening those consistency guarantees while preserving the original performance of those systems is very difficult. Although high-performance algorithms have been proposed for efficient transaction processing on distributed file systems, key-value stores or databases, they are not easily adaptable to blobs. Indeed, such algorithms usually operate on relatively small objects: at the object level for databases or key-value stores, or at the metadata level on specialized nodes for distributed file systems.
This yields more than 1. 1 million measurements pushed to MonALISA, each with an update frequency of one second. In order to be presented to the end-user, the raw data is aggregated to produce about 35,000 system-overview metrics, and grouped under different time granularity levels. Managing monitoring data: what could be improved. The current implementation of ALICE is based on a Post-greSQL database  [14]. Aggregation is performed by a background worker task at regular intervals. With the constant increase in volume of the collected metrics, this storage architecture becomes inefficient. Time-series databases such as OpenTSDB  [15]  or KairosDB  [16]  were considered to replace the current architecture. However, storing each event individually, along with the related metadata such as tags, leads to a significant overhead.
This system comes in three flavors: append blobs, block blobs and page blobs. Append blobs are optimized for append operations, block blobs are optimized for large uploads, and page blobs are optimized for random reads and writes  [11]. For our experiments, we used RADOS 0. 94. 4 (Hammer), BlobSeer 1. 2. 1, and the version of Azure Storage Blobs available at the time these experiments were run. Dataset and workload. In order to run these experiments, we used a dump of real data obtained from the MonALISA system  [9]. This data set is composed of ∼ 4. 5 million individual measurement events, each one being associated to a specific monitored site. We used multiple clients to replay these events, each holding a different portion of the data. Clients are configured to loop over the data set to generate more events when the size of the data is not sufficient.
B. Read performance We evaluated the read performance of a 32-node Týr cluster and compared it with the results obtained with RADOS and BlobSeer on a similar setup. As a baseline, we measured the same workload on the Azure Storage platform. We preloaded in each of these systems the whole MonALISA dataset, for a total of around 100 Gigabytes of uncompressed data. We then performed random reads of 800 Byte size each from both the raw data and the aggregates, following a powerlaw distribution to increase read concurrency. This read size corresponds to a typical 100-minute average of MonALISA aggregated data. To prevent memory overflows, servers throttle the number of concurrent requests in the system. We have configured Týr to have a maximum of 1,000 total concurrent requests in process. We plotted the results in Figure  6.
The lightweight read protocol of both Týr and RADOS allows them to exhaust the CPU resources quickly and to outperform both BlobSeer and Azure Storage peak throughput by 44%. On the other hand, BlobSeer requires multiple hops to fetch the data in the distributed metadata tree. This incurs an additional networking cost that limits the total performance of the cluster. Under higher concurrency, we observe a slow drop in throughput for all the compared systems except for Azure Storage due to the involved CPU in the cluster getting overloaded. Once again, linear scalability properties of Azure could be explained by the higher number of nodes in the cluster, although this can't be verified because of the lack of visibility over Azure internals. Týr and RADOS show a similar performance pattern. Measurements show RADOS outperforming Týr by a margin of approximately 7%.
C. Reader/writer isolation We performed simultaneously reads and writes in a 32node cluster, using the same setup and methodology as with the two previous experiments. To that end, we preloaded half of the MonALISA dataset in the cluster and measured read performance while concurrently writing the remaining half of the data. We ran the experiments using 200 concurrent clients. With this configuration, all three systems proved to perform above 85% of their peak performance for both reads and writes, thus giving comparable results and a fair comparison between the systems. Among these clients, we varied the ratio of readers to writers in order to measure the performance impact of different usage scenarios. For each of these experiments, we were interested in the average throughput per reader.
D. Performance stability Týr data structures have been carefully designed so that successive writes to the cluster impact access performance as little as possible. We validated this behavior over a long period of time using a 32-node cluster, and 200 concurrent clients. We used a 65% read / 35% write workload, the most common workload encountered in MonALISA. We depict in Figure  8  the aggregated read / write throughput over an extended period of time. The results confirm that Týr performance is stable over time. On the other hand, BlobSeer shows a clear performance degradation over time, which we attribute to its less efficient metadata management scheme: for each blob, metadata is organized as a tree that is mapped to a distributed hash table hosted by a set of metadata nodes. Accessing the metadata associated with a given blob chunk requires the traversal of this tree; as the height of this tree increases, the number of requests necessary to locate the relevant chunk metadata also increases.
Throughout our experiments, we have measured the relative impact of this choice. Overall, the results show that ZooKeeper accounts for less than 5% of the total request latency for write storage operations. Although faster, more optimized distributed locks such as Redis  [32]  may be available, this is unlikely to have had a significant impact on the results. We considered using a distributed transactional middleware to replace locks with a faster alternative. Although general-purpose transactional middleware such as  [33]  exist in the literature, the few open-source systems we could find are primarily targeted at SQL databases.
VIII. RELATED WORK Blob Storage. As the data volumes handled by modern large-scale applications is growing, it becomes increasingly important to minimize as much as possible the metadata overhead incurred by traditional storage systems such as relational databases or POSIX file systems. RADOS  [2] , upon which Ceph  [17]  is based, is a highly scalable distributed storage system from which Týr took great inspiration and with which it shares a similar data model. RADOS blobs are mutable, with fine-grained data access. However, unlike Týr, RADOS does not focus on providing transactional semantics, or in-place atomic updates. BlobSeer  [12]  introduces several optimizations. A versioning-based concurrency control enables writes in blobs at arbitrary offsets under high concurrency. The metadata is decentralized and disseminated in the cluster using a distributed tree.
Similarly to Týr, its design allows for high-performance single-hop reads. However, Ceph does not support built-in support for transactions. CalvinFS  [34]  uses hash-partitioned key-value metadata across geo-distributed datacenters to handle small files, with operations on file metadata transformed into distributed transactions. However, in contrast to Týr, this system only allows operations on single files. Multifile operations require transactions. The underlying metadata database can handle such operations at high throughput, but the latency of such operations tends to be higher than in traditional distributed file systems. The Warp Transactional Filesystem (WTF)  [35]  is a transactional file system based on the HyperDex key-value store for metadata storage, and uses Warp as its transactional protocol.
, 2013). The Mescan precipitation analysis system has been primarily designed to be run in a hindcast mode, for climatological purposes, but it can also be used for daily meteorological or hydrological operational activities. It has been developed to ingest 24-h accumulated precipitation data available in the long-term historical data sets from SYNOP and climate stations, which can be found in some European databases (e. g. ECA&D 2  , ECMWF). At this development stage, it is not envisaged to use radar data in the system because these are neither long-term data nor covering whole Europe. Though the EUMETNET OPERA programme has expressed the endeavour to gather radar data across the western half of Europe, hard work has to be done in order to create reliable radar products that can be successfully used in data assimilation.
Experimental framework To validate the Mescan precipitation analysis system, a set of experiments has been conducted in a hindcast mode over a domain covering France. The experiments are summarised in Table  1. One of the main arguments for performing the validation over the French territory has been the availability of the 24-h accumulated precipitation data from a high density gauge network. This network consists of 4300 stations (synoptic, automatic and climatological) from which about 1600 with reports sent on a daily basis and 2700 climatological stations with daily data available at the end of each month. Data from all these stations are quality controlled and then archived in the Me´te´o-France Climatological database. However, the quality control does not provide an error-free data set.
The precipitation data were taken from the Me´te´o-France Climatological database and considered reliable and gross error-free. Thus, in the analysis system, the quality control of the observations was not applied. A second argument was the presence of two highresolution precipitation gridded data sets over France produced with SAFRAN and ANTILOPE operational analysis systems allowing inter-comparisons to assess the quality of Mescan analyses. ANTILOPE system  (Laurantin, 2008)  was developed at Me´te´o-France to produce rainfall products over France at a grid spacing of 1 km, by combining radar and gauge data at a high temporal scale. The set of experiments (henceforth named G1) includes analyses generated at 5. 5 km grid spacing because this is the horizontal mesh size at which the EURO4M re-analyses were to be performed at the European scale (Fig.
Observations A challenge for producing long-term precipitation reanalyses at the European scale is the availability of observations from a relatively dense gauge network. For example, it has been found that the observation density from the operational archive of ECMWF or Me´te´o-France received on the GTS is low. Particularly, the mountainous areas are poorly sampled and large regions such as southern and eastern Europe have sparse data (as illustrated in Fig. 1 ). After investigations, it has been decided to use observations from ECMWF and ECA&D (version 6) databases. Prior to creating the input observation data set for the Mescan system, a data pre-processing work has been carried out, primarily to select only 24-h precipitation reports that match the climatological day. First the data have been merged and then the duplicated reports discarded.
Finally, additional observations from the Swedish and French national networks were added. A particular attention was given to the ECA&D database which has been designed for climatological purposes (rather than for data assimilation). This archive contains data from a number of n stations (divided into the same number of individual files) across Europe, not all of them received on the GTS, each station having its own data available as a time series of daily datum from the year X to the year Y (X BY). In order to become compliant with the analysis system, datum from a particular day from each of the n stations had to be gathered into a daily product. Only the information from stations reporting amounts from first day morning 06UTC'Dt until next day morning 06UTC' Dt (Dt B 3 h) have been used, although the database includes stations with reports on different time intervals.
Acknowledgements The research leading to these results has received funding from the European Union, Seventh Framework Programme  (FP7/2007 (FP7/ -2013) )  under grant agreement no 242093. We gratefully acknowledge Jean-Franc¸ois Mahfouf for fruitful discussions, for carefully reading the early version of the manuscript and for his relevant suggestions. Also, we are grateful to Franc¸oise Taillefer for useful discussions. We thank Per Dahlgren (SMHI) for providing HIRLAM analyses at 22 km grid, Per Unde´n and Ulf Andrae for assistance and constant support. We thank Albert Klein Tank (KNMI) and ECA&D team for providing readily access to the European precipitation observation data set. We also acknowledge Eric Martin and Yves Durand for fruitful discussions regarding SAFRAN system. The authors acknowledge the constructive and helpful comments of the two anonymous reviewers which helped to improve the quality of the revised version of the article.