dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations and research needs While compiling the data for this report, we noticed a considerable scarcity in comprehensive dietary surveillance data at the population level for most countries, especially LMIC, even when national and international agencies have thoroughly collected data on sociodemographic measures and morbidity and mortality, and there are global nutrition-related surveys (albeit limited) for women and children. Dietary assessment may be challenging in countries with limited resources and impeding social dynamics  [109] , yet stronger global and national efforts should be undertaken to document their population's diet systematically so that we can identify main food sources and trends in dietary habits, and inform interventions and food policies accordingly. One limitation is that the FAO Food Balance data does not distinguish the level of processing or refinement of individual food sources, and changes in carbohydrate quality were inferred from other non-systematic reports.
Acknowledgements This work was a collaborative project with the  Global Nutrition and Epidemiologic Transition (GNET) Initiative at the Harvard T. H. Chan School of Public Health. We acknowledge our colleagues in each country, as well as all co-investigators and staff who worked on the various projects described on this article. Funding sources for each country are: Brazil:  National Counsel of Technological and Scientific Development (CNPq ;  506345/2004-1 ); China:  Chief Scientist Program  of  Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences  ( SIBS2008006 ,  KSCX1-YW-02  and  KSCX2-EW-R-10 ), the  National Basic Research Program of China  ( 973 Program ,  2011CB504002 ), and the  National Natural Science Foundation of China  ( 30930081  and  81021002 ); Costa Rica: anonymous gift donation, and  Costa Rican Institute for Research and Education on Nutrition and Health (INCIENSA); India: USA-National Institutes of Health-Fogarty Institute  Award  R03TW008726 ; Kuwait:  Dasman Diabetes Institute ; Malaysia:  Research Universiti Grant  (Individual),  Universiti Sains Malaysia  ( 1001/PPSP/812136 ); Mexico: Non-restricted investigator-initiated grant from  AstraZeneca ; Nigeria:  USA-National Institutes of Health-Fogarty International Centre, Training Research Program (TRAPING-NCD)  Grant  D43TW009106 ; Puerto Rico: anonymous private donations,  Northarvest Bean Growers Association-Dry Bean Health Research Program Incentive Award, USA-National Institutes of Health-National Institute of Dental and Craniofacial Research  Grant  K24DE016884  and  USA-National Institutes of Health-National Institute on Minority Health and Health Disparities  Grant  S21MD001830 ; Tanzania:  Private Funds ,  Harvard T.
Young's modulus, E, and tensile yield stress, s y , of HIPS were measured by uniaxial tensile tests performed on the dumbbell specimens. For ABS, E was measured by tensile tests on bars machined from the plates, whereas s y was calculated as 70% of the yield stress measured by compression tests. E and s y data are reported in Table  2. The ESIS TC4 multi-specimen approach  [1]  was also applied to the two materials. Fig. 2  shows the J R curves obtained. The same specimen geometry (sN specimen, Table  1 ) and testing conditions (crosshead rate and temperature) of the RR tests were used. Fig. 2  displays, for each material, the experimental (Da; J) data points (full circles), fitted by the power law best fitting curve (solid line); the exclusion lines (vertical lines); the blunting line with equation J ¼ 2$s y $Da (broken line); the point on the J R fitting curve corresponding to J 0.
notching of specimens, and measurement of actual a 0 of bN specimens; iv. execution of the low rate fracture tests (recording of load, P, vs displacement, u, curve), with a crosshead rate of 1 mm/min, at a temperature z 23 C; v. breaking-opening of sN specimens and measurement of actual a 0 ; vi. execution of the test for indentation correction (see  [1] ); vii. data processing according to the procedure described at Section 3; viii. forwarding of data (specimen dimensions, raw loading curves, resulting fracture parameters) and broken-open specimens to Brescia. The data were collected and compared at the laboratory of Brescia.
e. G s ðb s =WÞ ¼ A$½b s ðu pl Þ=W h pl , where A is a constant parameter and h pl is the plastic geometry factor, assumed to be 2, as analytically derived  [15]  and widely adopted in literature. Before showing how Equation 9 can help to understand the physical meaning of m S , some remarks need to be made on Equation  9. R S curves evaluated from S sb curves according to Equation 5 were compared with the trends of R a data points plotted as a function of u pl , where the values of R a were evaluated from data of actual crack length (a), measured during fracture propagation tests, according to Equation 10: R a À u pl Á ¼ W À a À u pl Á W À a 0 !2 (10) This comparison was carried out for several materials and specimen dimensions at the laboratory of Brescia; in all these cases results leading to the same conclusion were obtained.
In this work, only the results obtained for the two materials used in the RR exercise (ABS and HIPS) are shown. These results were obtained from the elaboration of the data of the fracture tests carried out to evaluate the J R curves reported in Fig. 2  (for ABS, also the data at Da levels higher than those used for the J R curve construction are taken into account). Several nominally identical sN specimens were tested according to the testing conditions of the RR exercise. The tests were stopped at different levels of displacement, in order to produce, at the final points, different levels of crack extension, Da, which were then optically measured on cryo-fractured surfaces. These different crack growths were treated as consecutive frames of a unique fracture propagation process. From each sN specimen, a single (u pl ; R a ) point, corresponding to the final point, was obtained (R a and u pl were evaluated according to Equations 10 and 2, respectively).
ACKNOWLEDGMENTS This research was supported by the  CNRS , the  French Agency of National Research , the  French Aquitaine region , the  Cluster of Excellence LAPHIA. Confocal imaging was performed at the  Photonic Imaging platform of Bordeaux Imaging Center  ( UMS 420 CNRS ). AFM images of Figure 6 were recorded by  Fabien Morote  at the  NIS platform of LOMA.
Mesh contraction Contraction of the meshes at 14 days post-implantation was determined by image analysis of photographs taken at the moment of the euthanasia. The photographs were scaled and the surface area of each mesh was measured using image analysis software (Image J, NIH; http:// imagej. nih. gov/ij/). The area recorded for each implant was compared to the initial area of the meshes (5 x 2 cm). The results were expressed as the mean percentage of mesh contraction suffered by each study group.
Supposing that infection occurs, a short-time study would give us important data regarding not only the distribution of the bacterial contamination along the implant but also the possible alterations of the tissue repair process. We considered thus that 14 days postoperatively was an adequate study time to evaluate both events. Regardless of the presoaking solution, both PP implants exhibited better tissue integration than DM+. This observation was recorded after 14 post-operative days and therefore does not show the complete process of tissue repair which requires longer study times. However, this short-time behavior of the different implants is consistent with previous observations stating that the reticular materials allow the infiltration of the granulation tissue through the mesh pores, while the laminar ones usually favor implant encapsulation  [35].
In so doing, we obtained an exhaustive inventory of locations of PPM HTOF. The inventory data are available in  Rousselet et al. (2015). The French institute IGN (Institut National de l'information Geographique et Forestiere http://www. ign. fr/) provides a national forest inventory (referred to as IFN for ''Institut National Forestier'' in French) of all the stands with an area [2. 25 ha, i. e. approximately corresponding to a resolution of 150 9 150 m pixels (http://inventaire-forestier. ign. fr/carto/carto/afficher Carto/28) (Fig. 2 ). This institute also develops and maintains a database called BD Topo Ò IGN http:// professionnels. ign. fr/bdtopo, which is a vector database documenting both geographical and administrative entities at a 1:25,000 scale over the whole French territory.
We used the log of 1? the distance to the nearest object corresponding to any sort of building as the covariate. The covariate values were computed from the data of the BDO TOPO 2. 1 using the SIG software Quantum GIS (Quantum GIS Development Team 2014) (Fig. 2c ). Our rationale behind the use of a Poisson process was to mimic citizen behavior with regard to ornamental tree plantations. People plant ornamental species in the close vicinity of their house, for example in their yards or in front of their property  (Pauleit et al. 2005). Public land managers plant on lands distributed all over the built-up areas, especially along public thoroughfares serving the buildings  (Konijnendijk et al. 2005). We account for this behavior by using a nonstationary model where the nature of the covariate directly conveys the effect of building closeness.
2005). Model fitting was done using a maximum pseudolikelihood fitting algorithm implemented in the R statistical software (R Core Team 2014) within the package spatstat (Baddeley  and Turner 2005 and Turner , 2006)). Once the model linking HTOF to the covariate was fitted, it was used to perform stochastic simulations of the point process at hand within the sampling window  (Møller and Waagepetersen 2004; Baddeley and Turner 2005). A total of 1000 simulations were used to examine the model performance with regards to a set of selected landscape metrics intended to characterize landscape features. The simulated point patterns were rasterized with a resolution equal to that of the IFN database i. e. pixels of 2. 25 ha (150 9 150 m, Fig. 2 ) using the R package raster  (Hijmans 2014). The resulting binary maps were used to compute the PLAND, PD and ED metrics (described below).
Landscape configuration from the PPM viewpoint We investigated the structure of landscapes derived from (i) the IFN raster database (ii) the HTOF database, and (iii) the IFN ? HTOF database by means of 3 key landscape metrics: percentage of landscape, patch density and edge density  (Turner et al. 2001). The percentage of landscape (PLAND) was used to quantify the proportional abundance of the patches that corresponded to a suitable habitat for the PPM, i. e. corresponding to pixels where at least one individual host tree was present. Patch density (PD) described landscape structure by quantifying the degree of fragmentation of PPM host distribution. In addition, we used the edge density metric (ED) to quantify the shape and complexity of the habitat patches  (Turner et al. 2001). This index appeared of interest given that the PPM is known to preferentially colonize forest stand edges  (Samalens and Rossi 2011).
Simulations over the Beauce ecoregion Figure  4a  shows the binary maps of the HTOF distribution over the Beauce region available from the IFN (see also Fig. 1 ). Figure  4b  gives an example of a simulated HTOF distribution over the same region. Table  2  gives the landscape metric values computed from the IFN data, the simulated HTOF distributions and the sum of these data sets. The coverage of trees suitable for the PPM given in traditional forest inventory (IFN) appeared to be markedly lower than the value estimated from the simulated distributions. Similarly, both the patch and the edge density were higher according to the model simulations.
This leads to a strong spatial relationship between trees and buildings. As a consequence, 70 % of the inventoried tree formations were found in 4. 5 % of the territory defined as urbanized areas in the BD CARTO database (BD Topo Ò IGN 2013). If HTOF are locally aggregated in the vicinity of buildings, the villages are evenly distributed in the study plot (Fig. 2 ) and this leads to a regular distribution tree patches throughout the landscape with an average inter-tree distance well below the flight capacity of the PPM. As a consequence, the landscape functional connectivity clearly appears high enough to allow the expansion of the moth across that open-field agricultural landscape. Such an even host tree distribution was previously unsuspected because our knowledge of coniferous species distribution in the region relied solely on the available forest inventory databases.
Landscape characteristics for PPM host tree species Percentage of landscape (%) Patch density (number of patches per 100 ha) Edge density (m per ha) Observed HTOF refers to observed host species in the field. Simulated HTOF refers to 1000 host tree distributions simulated in the inventory plot. IFN denotes the inventory data available from the IFN database. IFN ? HTOF represents the sum of the traditional inventory data plus a component standing for the HTOF contribution to landscape structure. Standard errors are given in parentheses.
Large-scale estimation of HTOF distribution in an agricultural landscapeThe HTOF distribution was simulated (1000 simulations) on the scale of an ecoregion referred to as ''Beauce'' corresponding to open agricultural fields. IFN denotes the inventory data available from the IFN database. HTOF represents the estimates for 1000 simulated distributions. Each simulated distribution led to the computation of the landscape metrics and the resulting 1000 measures were averaged. IFN ? HTOF represents the sum of the traditional inventory data plus a component standing for the HTOF contribution to landscape structure. Standard errors are given in parentheses.
Acknowledgments We are grateful to  Francis Goussard  and  Christelle Robinet  ( INRA Orleans ) for valuable discussions regarding PPM inventory procedure. This research was partly funded by the  Region Centre  (project  ADRIEN ) and the  INRA meta-program SMaCH-Sustainable Management of Crop Health  (project  SESAME ). We are indebted to the editor and anonymous reviewers for their insightful comments and invaluable suggestions. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
JEM-EUSO measures the fluorescence light produced by the extended air showers induced by UHE cosmic rays. Part of the signal is due to the scattered Cherenkov light and to the diffusively reflected Cherenkov light originating where the shower reaches the ground or the top of an optically thick cloud the volume of the HTV transfer vehicle, a contractible/extendable structure has been adopted. After the HTV docks in the ISS Docking Port, the Space Station Remote Manipulator System (SSRMS) takes out JEM-EUSO and passes it to the JEM Remote Manipulator System (JEMRMS). JEM-EUSO shall be attached to the Exposed Facility Unit #2 of the JEM External Facility and then expanded to the operational configuration using the deploying mechanism. The principal components of the Ground Segment are the ISS ground station, the JEM Mission Control Room (MCR) in Tsukuba and the JEM-EUSO Science Data Centre (SDC).
EUSO-TA will also perform studies of the transverse profile of the shower with spatial resolution better than that of the TA fluorescence detector (TA-FD). A description of the main features and goals of EUSO-TA can be found in  [28]  (Fig. 8 ). EUSO-TA is currently taking data in a series of rather successful measurement campaigns. At the time of writing, EUSO-TA has already properly detected artificial EAS tracks simulated with the portable laser system from the Colorado Mines school and has detected its first cosmic ray events.
Acknowledgments The work on JEM-EUSO and its pathfinders has been supported by the  Basic Science Interdisciplinary Research Projects of RIKEN  and  JSPS KAKENHI  Grant ( 22340063 ,  23340081 , and  24244042 ), by the  Italian Ministry of Foreign Affairs, General Direction for the Cultural Promotion and Cooperation , by the  Deutsches Zentrum für Luft-und Raumfahrt , by the  Helmholtz Alliance for Astroparticle Physics HAP  funded by the  Initiative and Networking Fund of the Helmholtz Association (Germany) , and by  Slovak Academy of Sciences  MVTS JEM-EUSO as well as  VEGA  Grant agency Project  2/0076/13. The Spanish Consortium involved in the JEM-EUSO Space Mission is funded by  MICINN  under Projects  AYA200906037-E/ESP ,  AYA-ESP 2010 19082 ,  AYA201129489-C0301 ,  AYA201239115-C03 01 ,  CSD200900064  (Consolider MULTIDARK) and by  Comunidad de Madrid (CAM)  under Project  S2009/ ESP-1496.
The  EUSO Balloon  has been supported by the  French CNES  and  IN2P3. The US is supported by the  NASA  grants  NNX13AH55G ,  NNX13AH53G. Russia is supported by the  Russian Foundation for Basic Research  Grant  No 13-02-12175 -ofi-m. Moreover, studies for JEM-EUSO have been partly funded by the  European Space Agency (ESA)  through the  "EUSO" Topical Team Fund.
The demand for multimedia services in mobile communication is increasing day by day due to the proliferation of end devices. To overcome the future needs of data communication on mobile devices, the 3 rd Generation Partnership Project (3GPP) has introduced a new technology which is known as Long Term Evolution (LTE) UMTS. In this paper, we study video streaming behavior on the LTE network. For this purpose, we use the Vienna LTE System Level Simulator which implements network features that correspond to our goal. We consider the parameters that may affect the users experience on a network and define ten scenarios for our simulations. We finally search for methods to fit the video streaming data traffic on the LTE network and exhibit its long range dependence.
INTRODUCTION Over the last few years, cellular networks have attested a rapid growth of multimedia applications, particularly video streaming applications. This growth is due to the proliferation of the end devices and the multimedia content providers like Netflix, Youtube and the video sharing platforms we can find on the social networks. The current networks, i. e. , 3G and previous, suffer from this traffic load due to the bandwidth limitation which is the main constraint of the network operators. To overcome the future needs of data communication on mobiles devices, the 3 rd Generation Partnership Project (3GPP) has introduced a technology known as Long Term Evolution (LTE) of Universal Mobile Telecommunications System (UMTS). The LTE performance requirements are a high data rate and spectrum efficiency for both downlink and uplink transmission, a low latency, a high cell capacity.
There is the core network and the Radio Access Network which consists of a set of eNodeBs (Bases Stations) and Users Equipements (UEs) which communicate through the air interface. Our work is focused on the RAN layer. There is the interoperability between the LTE network and W-CDMA, GSM systems and non-3GPP systems. Then multimode UEs will support handover to and from these other systems and legacy technologies such as HSPA+ and Enhanced EDGE will continue to operate within the new data infrastructure. As said before, the LTE introduces a novel physical layer which employs some advanced technologies that are new to cellular applications. These include Orthogonal Frequency Division Multiplexing (OFDM) and Multiple Input Multiple Output (MIMO) data transmission. In addition, the LTE physical layer uses Orthogonal Frequency Division Multiple Access (OFDMA) on the downlink (DL) and Single Carrier-Frequency Division Multiple Access (SC-FDMA) on the uplink (UL)  [11].
Each OFDM symbol is therefore a linear combination of the instantaneous signals on each of the subcarriers in the channel. Also OFDM symbols are generally much longer than symbols on single carrier systems of equivalent data rate. Then OFDM symbol is preceded by a cyclic prefix (CP), which is used to effectively eliminate ISI. The sub-carriers are very tightly spaced to make efficient use of available bandwidth, yet there is no interference among adjacent sub-carriers (Inter Carrier Interference or ICI). OFDMA is employed as the multiplexing scheme in the LTE downlink, it makes much more efficient use of network resources. Two radio frames structures are defined for the LTE: the Frequency Division Duplexing (FDD) and the Time Division Duplexing (TDD) frames of 10ms duration. Market preferences dictate that the majority of deployed systems will be FDD, so we consider LTE FDD systems only.
At the programming level, the structure of the simulator is organized in modules or packages. The ChannelModel defines the downlink and uplink channels (the uplink is modeled in Vienna only for feedback reporting, no data is sending by the user), the NetworkElements module contains the eNodeBs, the UEs and also the clock which is needed for scheduling events until the end of the simulation. The scheduler module contains different types of schedulers used by the eNodeB to allocate resources to the users every TTI. The simulator contains ten  (10)  schedulers which can be classified in three (3) types: the Weighted Fair Queueing, the maxSNR and the Proportional Fair. The simulator contains also a WalkingModel module which defines the UEs mobility. There is also a TrafficModel for the application layer.
p θ (Xn). The number of the estimated parameters can be one, two or even three depending on the distribution. The Matlab mle function takes as input parameters the sample data, the hypothesized distribution and a parameter α. The value of α fixes the confidence interval for the estimated parameters. We set α to 0. 01 which corresponds to 99% confidence level. The estimated parameters are used to plot the PDF and the CDF of the fitting distributions over the histograms and the empirical CDF of the users inter-arrival times. To check how good is the fitting, we use the Kalmogorov-Smirnov (K-S) Test  [18]. The K-S goodness-of-fit test is employed to determine the best fit among several distributions  [19]. The null hypothesis h0 implies that data samples follow a given distribution and the alternative hypothesis h1 states the opposite.
The goal of the test is to check whether to accept or reject the hypothesis h0 and to quantify the decision. Then the approach is to examine whether the empirical distribution (empirical CDF) of a set of observations is consistent with a random sample from an assumed theoretical distribution. The estimated parameters from the mle function are used with the kstest2 which returns 0 if the fit is good enough or 1 otherwise. We conclude that Rician and Logistic distributions fit the data better than the others. The autocorrelation function measures the dependence between two inter-arrival times. It tests whether the data exhibit long-range dependence or not. We use the Matlab autocorr function to plot the autocorrelation function of the inter-arrival times.
CONCLUSIONS In this paper, we consider the video streaming applications over the Long Term Evolution (LTE) networks. Our purpose is to analyze the video traffic data through the dynamics of the LTE networks. We use the Vienna LTE System Level Simulator for our study. Simulations were running with different values for the parameters: the pathloss, the users speed, the network layout, the transmission modes. The video server was integrated to the simulator. Other types of traffic were also present. We process the simulation traces: For this purpose we consider the inter-arrival times of the streaming users because they are very important in the multimedia applications. They give an idea about the performances of the network. The best representation of the data is their distributions, i. e. , the probability distribution function and the cumulative distribution function.