dataesr/scientific-paragraphs-categorization huggingface dataset:
MATERIAL AND METHODS Material During the austral springs of 2011 and 2012, four different post-breeding female SES with similar body characteristics were captured and equipped with data loggers on Kerguelen Island (Figure  1 ). These loggers included an Acousonde 3A device glued to the back of a seal on the longitudinal axis 10 cm behind the scapula; these devices have been used in previous research studies with marine mammals (e. g. ,  Burgess et al. , 1998, and Burgess, 2000). Care is taken to minimize the differences of tag alignment and positioning between one animal and the other. Besides passive acoustic data, which are not presented here, the Acousonde continuously recorded depth (via pressure), triaxial acceleration, and triaxial magnetometer data with a sampling frequency of 5 Hz.
Our analysis uses only post-dive surface intervals. The R package Biologging tools developed by Yves le Bras of the Université de La Rochelle (available at https://github. com/SESman/rbl) are used to automatically detect the post-dive surface intervals of SES, based on pressurederived depth and acceleration data. Ten percent of the surface interval duration has been trimmed from the beginning and end of each phase to avoid any transient signals related to the SES raising their head above water or diving. Also, surface intervals shorter than 60 s were removed. All data processing and analyses were conducted using MATLAB. The record of total post-dive surface intervals was 37. 4 hours long. Figure  2  indicates the migratory routes followed by the SES during the time the Acousondes were active.
ENVIRONMENTAL DATA Environmental data were extracted from the ERA-Interim data set. Provided by ECMWF, ERA-Interim performs a reanalysis of the global atmosphere covering the data-rich period since 1979  (Dee et al. , 2011). The atmospheric model is coupled to an oceanwave model that resolves 30 wave frequencies and 24 wave directions at the nodes of its reduced 1. 0° × 1. 0° latitude/ longitude grid. Gridded data products include a large variety of three-hourly surface parameters that describe weather as well as ocean-wave and land-surface conditions, and six-hourly upper-air parameters that cover the troposphere and stratosphere. Berrisford et al. (2011)  provide a detailed description of the ERA-Interim product archive. Using a spatial resolution of 0. 25° in longitude and latitude and a threehour temporal resolution, we extracted the following variables from the ERA-Interim data set: • u10: The zonal wind component (m s -1 ) 10 m above the sea surface • v10: The meridional wind component (m s -1 ) 10 m above the sea surface • swh: The significant wave height, defined as four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum • mwp: The spectral mean wave period(s), obtained using the reciprocal integral moment of the frequency wave spectrum, which is obtained by integrating the two-dimensional wave spectrum over all directions Basic quality control of the data sets was performed to remove invalid data (e.
A spectrum is monochromatic when it exhibits one salient spectral peak over its frequency range. Also, multiple peaks in our wave spectra come from SES behavior and/or multiple ocean wave components, and we will not try within this study to reconcile the ambiguities between these two contributions. This Q factor is defined as Q = findpeaks (N p , SNR), where Q is computed using the built-in MATLAB function findpeaks, with custom parameters N p that measure the number of spectrum peaks above a certain threshold, and SNR that measures the salience of each peak.
Second, measurements with satellite imaging are not exempt from errors, and error checking procedures are not reliable either, as no ground truth data exist to validate them, especially in open ocean areas  (Berrisford et al. , 2011). Regardless, this study clearly reveals that sea state and above-surface meteorological conditions constrain some long-term trends in SES surface behavior. Studying such trends can potentially provide scientists with valuable information about the ocean environment. Linear fitting models and a Pearson correlation analysis were performed to investigate the relationship between SES behavior at the surface (i. e. , pitch, roll, yaw) and above-surface environmental conditions (i. e. , ψ W : wind direction, φ W : wind speed norm and significant wave height). Our analysis reveals that for four different SES individuals recorded in two successive years, there are strong consistent correlations between certain pairs of variables.
CONCLUSION In this study, we used accelerometer and magnetometer data from biologged southern elephant seals to evaluate whether ocean environmental parameters influence their behavior during postdive surfacing time. Our results showed that long-term trends of SES behavior at the surface, modeled with Euler angles characterizing its three-dimensional dynamics, are constrained by wind direction, significant wave height, and mean wave period. This study demonstrates that biologged SES can provide valuable field-based auxiliary data for monitoring the global ocean environment. The high-resolution temporal sampling acquired with biologged SES complements large-scale satellite imaging. We demonstrated that biologged SES could be used as oceanographic samplers to estimate wind direction and wave period.
ACKNOWLEDGMENTS This study is part of a national research program (no. 109 ,  H. Weimerskirch  and the observatory Mammifères Explorateurs du  Milieu Océanique ,  MEMO SOERE CTD 02 ) supported by the  French Polar Institute (Institut Paul Emile Victor, IPEV ). This work was carried out in the framework of the  ANR Blanc MYCTO-3D-MAP  and  ANR VMC 07 : IPSOS-SEAL programs and  CNES-TOSCA program  " Élephants de mer océanographes. "The authors also thank the  Total Foundation  for financial support. All animals in this study were treated in accordance with the IPEV ethical and Polar Environment Committees guidelines. We thank all the people who contributed to the fieldwork and data processing, with special acknowledgments to  M. Authier ,  A. Chaigne ,  Q. Delorme ,  N. El Skaby ,  J. C. Vaillant , and  F.
TLS Switching implementation TLS is implemented by various libraries such as GnuTLS 8  or OpenSSL 9  in C, and JSSE 10  in Java. We implemented a proof of concept of the TLS Switching and OTDP architecture in Java v1. 7 with OpenJDK's TLS engine (see source code  11  ). The Proxy and the trusted app exchange messages through TLS records over TCP. In state T, the Proxy simply forwards messages from the trusted app to the server app, and from the server app to the trusted app. In state P, the Proxy should only process application data messages, and forward the rest to the trusted app. We use two new types of clear messages, enabling the Proxy and the trusted app to exchange information when changing state: • 't' messages sent by the Proxy to the trusted app in order to go into state T. Theses messages are followed by a renegotiation performed by the trusted app.
Privacy watchdog in Smart Grid metering Smart Meters are plagued by privacy considerations  [17, 33] , because the high frequency of real-time energy consumption that they report to the energy provider results in big data that can reveal information like behaviour patterns in the home, what devices are used (coffee machine vs. tea cooker) and when, or even what brand of devices (thus life style and wealth). Solutions often involve putting an intermediary between the meter and the energy provider. The  OTDP can be used, with the smart meter as the client app behind the proxy implemented by Smart Meter manufacturers with the functionality of enforcing various privacy policies that a user can configure. Security critical functionalities can be done on a specific trusted device of the user, e.
e. , only during state T. Indeed in state P, the Proxy being able to read, modify, and write records, exchanged information would be exposed. In state T, the client device ignores the current session keys, protected by the forward and backward secrecy of the TLS handshake. Moreover, a Proxy cannot enforce state P as it cannot read nor write records in order to perform a renegotiation. In order to avoid phishing attacks, handshake server authentication is asked by, and performed by, the trusted app with TLS certificate verification (see Sec 4. 1. 1) guaranteeing the origin (or destination) of sensitive information during state T. Although the client device is unable to read exchanged data between the trusted app and the server app, the client device is able to know when sensitive information are exchanged, as well as their length, more or less the cipher block size (or padding length) used.
* The first author was partially supported by the project OffPAD with number E!8324 part of the  Eurostars program  funded by the  EUREKA. † The second author was partially supported by the project DiversIoT funded by the  Norwegian Research Council. ‡ The third author was partially supported by the project  Oslo Analytics  funded by the  IKTPLUSS program  of the  Norwegian Research Council. 1.
SFCOMPO-2. 0 is a database of experimental isotopic concentrations measured in destructive radiochemical analysis of spent nuclear fuel (SNF) samples. The database includes corresponding design description of the fuel rods and assemblies, relevant operating conditions and characteristics of the host reactors necessary for modelling and simulation. Aimed at establishing a thorough, reliable, and publicly available resource for code and data validation of safety-related applications, SFCOMPO-2. 0 is developed and maintained by the OECD Nuclear Energy Agency (NEA). The SFCOMPO-2. 0 database is a Java application which is downloadable from the NEA website.
Introduction The technical capability to accurately predict spent fuel isotopic compositions is one of the foundations upon which all safety-related applications and licensing evaluations involving spent nuclear fuel (SNF) are based. A sound experimental database of measured isotopic concentrations, with uncertainties, design specifications and operational history data is therefore required for the proper validation of depletion methodologies (codes and data) which are applied to safety-related studies. Having a centralised and internationally reviewed database of SNF assay data for a large selection of international reactor designs is of obvious interest to the nuclear energy scientific community in general. The NEA and ORNL have collaborated in recent years on a major development effort which has resulted in the production of the SFCOMPO-2.
From SFCOMPO to SFCOMPO-2. 0 In the early 1990's, in view of building an experimental database for validation of codes used in burn-up calculations, the Japan Atomic Energy Research Institute (JAERI) initiated the compilation of existing nuclide composition data of SNF obtained from the open literature of post-irradiation experiments. In subsequent years this led to the creation of the first version of the spent nuclear fuel database named SFCOMPO, which consisted of a series of searchable HTML Web pages which contained data coming from 246 samples taken from 7 PWR reactors and 7 BWR reactors. In 2001, this version of SFCOMPO was migrated to the NEA web server, and had little development until SFCOMPO-2. 0. The a e-mail: franco. michelsendis@oecd. org original SFCOMPO was discontinued in 2016, replaced by SFCOMPO-2.
0. The SFCOMPO-2. 0 relational database presented in this paper is a downloadable JAVA application (www. oecd-nea. org/sfcompo/) that contains data from 44 different reactors, representing 8 international reactor designs (AGR, BWR, CANDU, MAGNOX, PWR, RBMK, VVER-1000, VVER-440), giving access to over 750 measured SNF samples. SFCOMPO-2. 0 aims at providing access to open experimental assay data of SNF in order to ensure their preservation and facilitate their qualification as evaluated assay data suitable for the validation of methodologies used to predict the composition of irradiated nuclear fuel. Like other existing NEA databases and visualization tools, SFCOMPO-2. 0 helps the analyst or user in identifying data that is best suited for their specific application and facilitates access to the primary sources of relevant reference information.
Assay data Assay data are sets of measured nuclide concentrations of well-characterised spent nuclear fuel samples, complemented with reactor operational data describing the irradiation of the samples, in sufficient detail for it to be potentially used as a depletion benchmark model. The review and evaluation of assay data is therefore a multidisciplinary task involving experts in radiochemistry, reactor operation, spent fuel modeling, and simulation, among others. These different expertise domains are represented within the NEA Expert Group on Assay Data of Spent Nuclear Fuel (EGADSNF)  [1] , which is responsible for the overview of SFCOMPO development and has proposed clear guidelines for the evaluation of assay data before they are considered fully suitable for code benchmarking uses (for a given application)  [2, 3].
The database stores all data in a hierarchical vertical tree structure which has 5 main levels of description. These 5 levels are, in descending order, Reactor, Assembly, Rod, Sample and Measurements. Figure  1  illustrates this hierarchy as would be seen by an user browsing the database. Each level has additional horizontal attributes which describe that level and these entries are mandatory. The temporal evolution (operational history data) of any physical parameter can be stored in the database and bound to the appropriate level. Figures  2  and 3  are views of the graphical user interface of SFCOMPO-2. 0.
ND2016 reports. A comprehensive review was extensively performed by one of the authors (Martinez-Gonzalez) to ensure standardization of all SFCOMPO entries. While the data in SFCOMPO-2. 0 have been independently reviewed and cross-checked for consistency with the primary sources, these data have not been formally evaluated. Any errors in measurements, omissions, or inconsistencies in the original reported data may therefore be reproduced in the database. It is therefore important that any user of the data for code validation consider and assess the potential data deficiencies. The evaluation of assay data will provide a more complete assessment and may result in the development of benchmark specifications in cases of high quality experiments.
References and comments in SFCOMPO-2. 0 SFCOMPO-2. 0 allows for a unique reference to be assigned to each sample and equally to clearly reference all data to their primary source. Comments at every level of the database may also be entered. Primary sources such as laboratory reports and evaluator reports can be accessed through an in-built PDF viewer if the reference reports (which can be provided separately through the SFCOMPO website) are locally installed.
Conclusions The Expert Group on Assay Data of Spent Nuclear Fuel provides an international framework for compilation and technical review of available assay data for benchmarking purposes. Since 2012, the NEA Data Bank undertook a significant development effort that led to the development of the new database of Spent Nuclear Fuel nuclide composition SFCOMPO-2. 0. In strong collaboration with Oak Ridge National Laboratory  [4, 5] , the effort to compile new reactor datasets expanded the representation of reactors in the original SFCOMPO database to an international selection of designs and a modern, functional database with many functionalities. More information can be obtained at www. oecd-nea. org/sfcompo.
SFCOMPO-2. 0 organizes and gives acess to these different varieties of information in an user-friendly manner. The database stores all data in a hierarchical vertical tree structure which has 5 main levels of description. These 5 levels are, in descending order, Reactor, Assembly, Rod, Sample and Measurements. Figure1illustrates this hierarchy as would be seen by an user browsing the database. Each level has additional horizontal attributes which describe that level and these entries are mandatory. The temporal evolution (operational history data) of any physical parameter can be stored in the database and bound to the appropriate level. Figures2 and 3are views of the graphical user interface of SFCOMPO-2. 0.
The purpose of this work is not to rank models, but rather to understand the extent to which the different modeling approaches can replicate reality in certain conditions, and to explore the dynamics of PDCs themselves. In this work, these models are used to recreate the inundation areas of the dense-basal undercurrent of all 13 mapped, land-confined, Soufrière Hills Volcano dome-collapse PDCs emplaced from 1996 to 2010 to test the relative effectiveness of different computational models. Best-fit model results and their input parameters are compared with results using observation-and deposit-derived input parameters. Additional comparison is made between best-fit model results and those using empirically-derived input parameters from the FlowDat global database, which represent "forward" modeling simulations as would be completed for hazard assessment purposes.
Ogburn and Calder Relative Effectiveness of PDC Models unchannelized. The simple rheological models of TITAN2D and VolcFlow are not able to recreate all features of these more complex flows. LAHARZ is fast to run and can give a rough approximation of inundation, but may not be appropriate for all PDCs and the designation of starting locations is difficult. The ∆H/L cone model is also very quick to run and gives reasonable approximations of runout distance, but does not inherently model flow channelization or directionality and thus unrealistically covers all interfluves. Empirically-based models like LAHARZ and ∆H/L cones can be quick, first-approximations of flow runout, provided a database of similar flows, e. g. , FlowDat, is available to properly calculate coefficients or ∆H/L. For hazard assessment purposes, geophysical models like TITAN2D and VolcFlow can be useful for producing both scenario-based or probabilistic hazard maps, but must be run many times with varying input parameters.
LAHARZ and ∆H/L cones can be used to produce simple modeling-based hazard maps when run with a variety of input volumes, but do not explicitly consider the probability of occurrence of different volumes. For forward modeling purposes, the ability to derive potential input parameters from global or local databases is crucial, though important input parameters for VolcFlow cannot be empirically estimated. Not only does this work provide a useful comparison of the operational aspects and behavior of various models for hazard assessment, but it also enriches conceptual understanding of the dynamics of the PDCs themselves.
The work presented herein is an example of benchmarking against natural cases, wherein the input parameters are estimated from any existing observations and field data and then adjusted in order to fit the inundation footprint of the actual PDC  (Charbonnier, 2013). Importantly, this work also compares these best-fit, "tuned" model outputs and the input parameters used to achieve them with results and input parameters based on field measurements and empiricallyderived relationships (e. g. , friction-volume relationship) from the global FlowDat database of mass flows  (Ogburn, 2012 (Ogburn, , 2014;; Ogburn et al. , 2016). This is an important advance because it avoids only evaluating highly "tuned" model results, and instead strives to also compare models' ability to forecast inundation using field-or statistically-derived input parameters.
93. The other ∆H/L vs. volume regression used is derived from the global FlowDat database of over 120 BAFs with ∆H/L and volume data that remained on land (Figure  2 ): log (∆H/L) = -0. 14 log (V) -0. 97 or ∆H/L = 0. 11 V -0. 14 (2) with R 2 = 0. 61 (reflecting the scatter in mobility of PDCs at different volcanoes). These equations for ∆H/L vs. volume are used for ∆H/L cones, but are also used to calculate empirically-derived apparent basal friction angles (for TITAN2D).
Starting from either a user-selected upstream cell (usually the head of a valley), or the intersection of the stream and the proximal hazard zone boundary, LAHARZ fills in topography along the drainage, using the calculated cross-sectional area for a userspecified volume (Equation  3 ). The resultant planimetric area filled is added to a running tally, and LAHARZ uses the flow direction grid to move to the next downstream cell along the stream until the appropriate planimetric area is reached (from Equation  3 ). Widiwijayanti et al. (2008)  collected planimetric area and cross-sectional area measurements for the dense-basal avalanche component of BAFs. They recalibrated coefficients for BAFs and for a subset of data from SHV, Montserrat, calling the modified code "PFZ. " For start locations, they chose cells at what they deemed to be prominent breaks in slope of the valleys.
In this way, they were able to recreate BAF deposit inundation areas at SHV  (1996 ( -1999 PDCs) PDCs)  and Merapi (2006 PDCs) with modest success. Lee et al. (2015)  applied the same method to Merapi 2010 PDCs and achieved a 56% agreement between model results and actual inundation. In this work, analysis of variance (ANOVA) was performed following  Iverson et al. (1998)  on parameters from the FlowDat mass flows database  (Ogburn, 2012 (Ogburn, , 2014) )  to test and calibrate the relationship between A plan and A xs and V 2/3 (Equation  3 ) for PDCs. In addition to the FlowDat coefficients for all global BAFs, coefficients were also calculated for several subsets for comparison (Table  2 ). These subsets include BAFs from SHV only, BAFs < 2 × 10 6 m 3 in volume, and BAFs > 2 × 10 6 m 3 in volume.
Implications for Hazard Modeling The results of this modeling are important for hazard assessment in a number of ways. They illustrate the importance of appropriate selection of both models and model inputs, and demonstrate the utility of deriving model input parameters from global databases, such as FlowDat  (Ogburn, 2012 (Ogburn, , 2014;; Ogburn et al. , 2016). This work has focused on retrospective modeling of known PDCs, where the best-fit model runs are "tuned, " that is, input parameters are adjusted until the results replicate real flows. The need to fit parameters is a weakness of many geophysical models and is especially problematic for forward modeling. For this reason, the models were also tested using non-calibrated, deposit-derived or globally-derived input parameters. To this end, Figure  8  compares the deposit-derived and best-fit input parameters for TITAN2D and VolcFlow, and provides the equations for calibrating deposit-derived input parameters.
The well-characterized relationship of ∆H/L with PDC volume makes it easy to estimate empirically-derived basal friction angles for given simulation volumes for TITAN2D forward modeling purposes (Figure  8A ). Empirically-derived ∆H/L values (converted to basal friction) can be calculated from either the entire global FlowDat dataset (Figure  8A , blue; Equation  2 ) or from the SHV-only subset (Figure  8A , red; Equation  1 ). Deposit-derived and empirically-derived input parameters work especially well for smaller-volume flows, but for larger-volume flows, best-fit TITAN2D simulations required a lowering of the basal friction angle (Figure  8A ). This is because TITAN2D generally tends to overestimate the spread of flows, often inundating ("splashing" into) uninvolved valleys. If too much of the simulated volume enters other valleys, the overall runout is reduced.