dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Data are expressed as medians with ranges for continuous variables and frequencies with percentages for qualitative variables. Non-parametric Mann-Whitney test and Fisher exact tests were used as appropriate to determine the difference between the groups. Survival curves and time to relapse were expressed with Kaplan Meier curves and tested with log rank test. Multivariable adjusted analyses were not performed, due to the low amount of GCA patients with stroke. Statistical analyses were carried out using GraphPad Prism version 5. 1 (GraphPad Software, San Diego, 2007). A p value <0. 05 was considered as statistically significant.
Architecture The cellular network architecture mainly consists of three components. First, User Equipments (UEs) are carried by subscribers (we shall use both terms alternatively) and are typically smartphones or IoT devices containing a USIM card. Second, Home Networks (HNs) contain a database of their subscribers' and their corresponding USIM cards and are in charge of their authentication. However, it is often the case that UEs are in locations where their corresponding HN has no base station (i. e. , antennas that may connect UEs to the network). Therefore, the architecture also considers a third entity: the Serving Networks (SN ) to which UEs may attach to and that play the role of relays to HNs. Each UE contains a USIM having cryptographic capabilities (e. g. , symmetric encryption, MAC) which notably stores: -an unique and permanent subscriber identity, called International Mobile Subscriber Identity (IMSI), -a unique, permanent secret symmetric key that we indicate as K IMSI (used as a shared secret between a UE and its corresponding HN ), -and a 48-bits counter, called Sequence Number that we denote as SQN (used as replay protection, as explained later in this section).
Feasibility in 5G. Due to unavailability of 5G devices and networks, we only demonstrate our attack in a 4G environment. As already mentioned, we know that the 5G, phase 1 specification already suffers from our attacks. Moreover, we believe that it will be feasible to demonstrate our attack against real 5G networks soon due to the fast open source developments for 5G  [28]. For example, 2G has been launched in 1991 but the first open source software were only made available in 2010 (with OpenBTS  [29] ). In contrast, 4G has been launched in 2009  [30]  and open source 4G software was already supported the same year by OpenAirInterface  [31].
Attacker's Setups We now explain how hardware and software components can be combined into different setups to demonstrate our attack. Obtaining authentication challenges. We used the software srsUE from the srsLTE suite  [34]  configured with the target's IMSI with the USRP B210 for obtaining authentication challenges. Essentially, the USRP B210 tries to impersonate the target's USIM. When doing so, each session fails because srsUE does not know the target's secret key K (so it cannot compute the appropriate RES) but, before the failure, we obtain a new, genuine authentication challenge that is intended for the target's USIM. We were able to fetch authentication tokens using the USRP at a surprisingly high speed (see discussion later) but, if for some reason, a network recognizes the USRP as a fake smartphone, we can still use genuine phones with programmable  [35]  USIM cards (ca.
Security Analysis and Lessons Learned In this section, we discuss the AKA specification issues and their impact on 3G/4G/5G security principles. First, we describe AKA protocol design choices and discuss the trade-off considerations related to security, availability, and cost which partly are responsible for our attacks. The following analysis is based on the data and practical experiments in 3G/4G networks. Finally, we draw conclusions and summarize lessons learned that may be relevant for the in-progress standardization of 5G, phase 2. Choice of symmetric key encryption. We demonstrated (Section 5) how a low cost setup allows an attacker to fetch unused challenges (R, AUTN ) of any active 4G subscriber in the world from any network. We now explain the AKA protocol design choice of authentication method and trade-offs responsible for enabling access to R and AUTN ).
Countermeasures As already explained, the main attack vector we exploit in our attacks is the use of XOR and the lack of randomness in AUTS making the concealing of SQN by AK * inefficient. We propose three main countermeasures F1, F2, and F3 to solve this problem. We also discuss how our fixes also provide an opportunity to fix the known linkability attack based on failure messages discussed in Section 2. 3. We conclude by a formal analysis of our fixes using the state-of-the-art Tamarin automated prover. Note that, when discussing our countermeasures, we also consider practical aspects related to the existing and next-generation cellular networks. Thus, the following fix we propose is easy to deploy in the current cellular system and only requires changes in baseband and authentication server software in the HSS.
D Attacker's Setup: Using Reprogrammable USIM In addition to the setup described in Section 5. 1. 1, we have shown that one case uses genuine smartphones with reprogrammable USIM for fetching a large number of authentication tokens intended for the target's USIM. We used a tool pySIM  [52]  to program USIM cards using an external smartcard reader. While programming commercial USIM cards are not possible, Sys-moUSIM  [35]  cards can be reprogrammed to store any given IMSI. We inserted the programmed USIM in a smartphone to read all the traffic (i. e. , signaling messages) as explained next. Having access to signaling messages, we stored the received authentication challenges (R and AUTN ) sent by the networks intended for the targeted USIM. We used the SCat tool  [53]  to gain access to signaling messages We selected Android-based Huawei and Asus smartphone models due to the availability of direct access to baseband using AT commands  [54]  to store signaling messages automatically.
Acknowledgment We would like to thank the anonymous reviewers for valuable feedback. We are also thankful to  Steve Babbage  from the  Vodafone Group  for the fruitful discussions. This work was partially conducted when  Ravishankar Borgaonkar  was working at the  University of Oxford, UK  and when  Lucca Hirschi  was working at LSV,  CNRS & ENS Cachan, France  and then at  ETH Zurich, Switzerland. This work was partially supported by a  COST grant  ( COST-STSM-IC1306-33371 ) granted by the  European Cooperation in Science and Technol-ogy  ( Crypto Action ) and a mobility grant granted by the  Doctoral School of the Paris-Saclay University.
Automotive industry faces numerous evolutions regarding environment regulations and parts reliability. Through the specific case of a cylinder head, actual and forthcoming challenges for low-cycle and thermal-mechanical fatigue design in an industrial context are presented. With a description of current design approaches and highlighting limitations, this work focuses on variable loadings, constitutive models and their interaction with the environment, fatigue criteria, and structure validations, the four major steps to meet a reliable design. The need to carry out extended experimental databases for different complexity levels is emphasized to provide a better understanding of loadings and their impact on the strength of materials and structures, as well as the production of more physically-based models that are easier to identify and lead to higher levels of reliability in the thermal-mechanical design process.
It is then essential to carry out as many measurements as possible in order to estimate the different moments of the observed probability densities assigned to the different loading conditions. A detailed description of the methods used to build this experimental database is given in  [10]. The first step consists in obtaining reliable information on drivers' driving habits by using statistical studies-surveys-on a fairly large panel of representative drivers of the overall customer population. The use of the vehicle can thus be easily categorized by the type of used road (as "highway", "road", "city", and "mountain"  [15] ). These first categories can then be modulated by the vehicle load or any other parameter likely to modify the fatigue behavior of the component under study. The result is a finite number of elementary life situations.
Surveys must then be regularly repeated to estimate, for each of the users, the percentage of time or the number of kilometers that correspond to each of these life situations and thus obtain a statistical measure of the variability in the use of the vehicle. This step makes it possible to build representative usage sequences. It is then a question of quantifying the loading amplitude for each usage and then the damage they cause to the structure according to the severity of the customer's use. In order to answer to this problem, vehicles are equipped with sensors to be subsequently used either by professional drivers on calibrated paths representative of the identified life situations. A database containing the temporal monitoring of the vehicle's use is then collected. TMF damage to the cylinder head results from the temporal variation in its temperature field and the thermal gradient created during this variation both on the surface and in the core of the material.
The first one varies as the inverse of the distance between precipitates λ ppt , the second one increases as the precipitate volume fraction f ppt , the height of precipitate h ppt , and the number of Orowan's loops n  [27, 28]. A reduced and optimized database combining tensile and cyclic hardening tests  [19]  with specific observations is proposed. The model parameters are identified from initial and extreme overaged conditions by combining physical constants from literature, average microstructure parameters from TEM observations, and optimized values from experimental database. The model can then be used on a range of alloy compositions and over-aging conditions while keeping almost the entire parameter set initially identified  [26]. A few examples, revealing excellent matches between simulation and experiments, are given in Figure  9.
It therefore seems mandatory, in view of these initial results, to add crack propagation tests on SEN-type specimens  [30, [47] [48] [49] [50]  to have access to reliable laws and to carry out test campaigns systematically proposing repetitions of one or more tests to obtain first estimates, even partial, of the effective fatigue lifetime scattering of the couple material/process to be evaluated. In the case of materials sensitive to thermal aging, the choice of relevant tests to be carried out is all the more crucial, as is the need to have a large database. The development of strategies to maximize the information obtained on fatigue resistance by minimizing the number of tests to be performed remains an important challenge for the coming years. The use of micro-propagation laws also opens the way to a new formalism for criteria and introduces a notion that until now has often been set aside from design protocols for TMF: non-linear accumulation of damage.
More precisely, one must compute the probability of the presence of at least one pore of a given size in the critical volume V c (as explained in  [49] ) and estimate the associated lifetime using Equation (2). This method, applied on AlSi7Cu3. 5Mg0. 1 single cylinder specimens, provides encouraging results as highlighted on Figure  17. A good estimation of the lifetime mean value is obtained and a first approximation of the standard deviation could also be underlined. Finally, it is crucial to underline that the use of elasto-viscoplastic constitutive models combining specific equations such as those presented above, induces a major difficulty for finite element analyses. Such complex models are usually not integrated in industrial solvers as Abaqus which however offer to code a user model.
In fact, the in-depth analysis of loading conditions and the development of models that are as close to physical reality as possible for both behavior and damage are nowadays indispensable ways of ensuring reliable fatigue design. We therefore proposed an original solution to model the behavior of aluminum alloys using a metallurgical model linking microhardness to the thermal aging condition. By introducing a dislocation density as an internal variable in the constitutive model and using an adapted experimental database, we propose a reliable constitutive model that successfully reproduces the alloy behavior for many aging conditions without increasing the number of parameters to be identified and by introducing physical constants from the literature, the evolution of which with temperature is well known.
X-ray diffraction and elemental CHNS analysis To confirm the presence or absence of siderite in samples with high and low MinC values, we performed X-ray diffraction (XRD) analyses on freeze-dried and powdered bulk sediment samples, with detection limits fluctuating between 0. 2 and 0. 5 wt% of MinC (Table  S1  -supplementary material -Fig. A1 ). Whole sediment compositions were determined using a Thermo Scientific ARL X-TRA diffractometer at the Institute of Earth Sciences (University of Lausanne, Switzerland), following the methods described by  Adatte et al. (1996). Reported intensities of siderite were observed at 2h = 32°. Additionally, total carbon (TC) and TOC, were measured on 6 samples, covering the range of TOC and MinC values inferred by Rock-Eval analyses (Table  S1 , Figs. A2  and A3 ), with a Carlo Erba EA 1108 Elemental Analyser at the Institute of Geological Sciences, University of Bern, to evaluate the Rock-Eval dataset before and after EMA.
End-Member Analysis Thermal boundaries used in the equations for the determination of Rock-Eval parameters  (Lafargue et al. , 1998)  were empirically defined considering the cracking temperature ranges of organic and inorganic carbon phases. The accuracy of the empirically determined thermal boundaries was tested in this study using End-Member Analysis (EMA). In particular, EMA was applied to better constrain the contribution of siderite C to quantify the MinC. We used AnalySize, a Matlab-based Graphic User Interface typically used for processing and unmixing of grain-size distribution data  (Paterson and Heslop, 2015). We followed a Single-Specimen Unmixing (SSU) method, which unmixes individual grain-size distributions (GSD) into unimodal parametric distributions using a least squares approach.
To allow our dataset to sum to one, thermograms were normalized to 100%, i. e. intensities at each temperature increment (°C) of each thermogram were divided by the integral of the entire signal (200-650 °C). The parametric EMA was applied to CO and CO 2 thermograms using a lognormal distribution fit. Thermograms measured at the oxidation step (S4CO, S4CO 2 , S5; Table  1 ) were not considered for EMA since all siderite should have been degraded during the pyrolysis stage  (Espitalié et al. , 1985). This is clearly visible in thermograms (Fig. 3 ) with siderite intensities negligible after 550 °C. Each end-member's intensity was integrated and included in the equations of Rock-Eval parameters  (Behar et al. , 2001). Results of EMA and in particular the resulting relative amounts of siderite in our samples, were compared to the parameters obtained via the Rock-Eval classic thermal boundaries.
Thus, we divided the entire dataset into two groups using the median (0. 31 wt%) of MinC: high MinC (siderite-rich samples) and low MinC (siderite-poor samples). This was done under the assumption that in Lake Towuti sediments MinC calculated from uncorrected Rock-Eval thermograms indicates the presence of siderite as suggested by the correlation of MinC with the XRD intensity peaks at 32. 1°. The CO 2 and CO thermograms of the high MinC group show high intensities between 350 and 550 °C, whereas intensities in this temperature range are low in the low MinC sample set (Fig. 3 ). These observations are also consistent with  Pillot et al. (2014)  and  Sebag et al. (2018)  who demonstrated strong correlation between siderite content and intensity at these temperature ranges in Rock-Eval thermograms.
Conversely, using weak acids can prevent the complete dissolution of carbonates, especially siderite, which is more resistant to acid dissolution than other carbonates  (Larson et al. , 2010; Brodie et al. , 2011; Schlacher and Connolly, 2014). This could explain why C org measurements of acidified samples are systematically lower than their equivalent TOC, obtained by pyrolysis. Although EMA could improve our Rock-Eval dataset, methodological inconsistencies are still obvious and require further investigation.
Espitalié et al. (1985)  empirically defined OI as reliable if TOC was >2 wt% and unreliable if <0. 5 wt% because low TOC, causing high OI, complicates the conventional use of OI as an indicator of OM provenance. Half of our dataset presents EMA-TOC values >1. 5 wt% and can, up to a certain point, be associated to highly oxidized carbon chains. However, OI RE6 values should be interpreted with caution and OM characterization in similar settings requires complementary analyses such as o 13 C and/or biomarkers. Unlike OI RE6 , EMA-HI values are within the usual range of OM characterization and were unaffected by carbonate matrix-effects. The entire dataset presents a relatively narrow range of EMA-HI (14-289 mg HC/g EMA-TOC), with low values consistent with allochthonous or highly oxidized OM and high values generally attributed to OM sourced from aquatic organisms  (Tissot and Welte, 1984; Meyers and Lallier-Vergès, 1999).
Depositional environments and early diagenesis in Lake Towuti I and R indices can indicate the preservation and degradation state of organic matter and, unlike OI RE6 values, they are derived from S2 thermograms and thus unbiased by siderite and low TOC-matrix effects. In the I-R diagram (Fig. 7B ), the whole dataset indicates a strong correlation between these two indices. Following  Sebag et al. (2016) , this correlation indicates that the thermal stability of sedimentary OM is controlled by degradation processes. Indeed, an OM mixture from different sources would generate poorly related I-R indices  (Sebag et al. , 2016). Towuti water temperatures ranging between 28 and 30 °C  (Costa et al. , 2015)  likely enhance degradation, which is further facilitated by methanogenesis, the most important OM mineralization pathway (444 ± 172 mmol m À2 yr À1 of organic carbon) after burial in Towuti sediments  (Friese et al.
Dataset Ordoñez, Luis;  Vogel, Hendrik; Adatte, Thierry; Sebag, David; Ariztegui, Daniel; Russell, James; Kallmeyer, Jens; Vuillemin, Aurèle; Bijaksana, Satria; Crowe, Sean; Friese, André (2019) , ''Empowering conventional Rock-Eval pyrolysis for organic matter characterization of the siderite-rich sediments of Lake Towuti (Indonesia) using End-Member Analysis. ", Mendeley Data, v1 https://doi. org/10. 17632/nrzksn8jzc. 1.
Acknowledgements The  Towuti Drilling Project  was partially supported by grants from the  International Continental Drilling Program , the  US National Science Foundation , the  German Research Foundation , the  Swiss National Science Foundation  ( 20FI21_153054/1  and  200021_153053/1 ),  Brown University, Genome British Columbia , and the  Ministry of Research, Technology, and Higher Education (RISTEK). PT Vale Indonesia , the  US Continental Drilling Coordination Office , the  GeoForschungs-Zentrum Potsdam and DOSECC Exploration Services  are acknowledged for logistical assistance to the project. This research was carried out with permission from the  Ministry of Research and Technology (RISTEK) , the  Ministry of Trade of the Republic of Indonesia , and the  Natural Resources Conservation Center (BKSDA)  and  Government of Luwu Timur of Sulawesi.
Notes Acknowledgments. The rVSV-ZEBOV vaccine was provided by Merck and WHO. The authors thank the reception, nursing, and medical staff for voluntary assistance at Gartnavel General Hospital and Dr Emma Aarons, Public Health England Porton Down, for helpful advice. Disclaimer. The sponsor of the study had no role in study design, data collection, analysis or writing of the report. Financial support. This work was supported by the US Food and Drug Administration (grant number HHSF223201510104C), the German Research Foundation (grant number 197785619/SFB 1021), the Medical Research Council (grant number MC_UU_12014/1), and the Wellcome.
2D numerical simulations of tidal bores were obtained using the OpenFOAM CFD software to solve the Navier-Stokes equations by means of the Finite Volume Method by applying a LES turbulence model. The trajectories of non-cohesive sediment particles beneath tidal bores were estimated using a tracker method. Using the fourth order Runge-Kutta scheme, the tracker method solves the Maxey and Riley equations that requires the knowledge of the velocity field at time t. From 2D numerical simulations of tidal bores, we proposed a classification of tidal bores with respect to the Froude number F r (or r the ratio of water depths). For a Froude number 1 < F r < 1. 43 (1 < r < 1. 57), the tidal bore is undular. For a Froude number 1. 43 < F r < 1. 57 (1. 57 < r < 1. 75), the tidal bore is partially breaking, that is similar to the transitional tidal bore defined by Furgerot (2014).
An analytical study of conjugate depth ratio 10 r = h 2 /h 1 , wavelength L w and wave amplitude a w has been presented in these three studies. In the laboratories, the tidal bore phenomenon has been studied by  Favre (1935) ,  Treske (1994) ,  Hornung et al. (1995) ,  Koch and Chanson (2008)  and  Rousseaux et al. (2016)   [8, 9, 10, 11, 12]. In these studies, two types of tidal bores, undular and breaking, have been generated. The analysis of free surface, velocity and pressure of tidal bores has been performed. And numerically, the tidal bore phenomenon has been studied by  Khezri (2013) ,  Simon (2013)  and  Berchet et al. (2018)   [13, 14, 15]. They have generated numerical simulations of tidal bores using the CFD software Thétis developed in the I2M laboratory of Bordeaux University, France.
This Thétis software has been developed by applying the LES turbulence model to solve the Navier-Stokes equations  [13, 14, 15]. The flow pattern of velocity beneath a tidal bore classified into 4 types (A, B, C and D) has been performed in the study by  Berchet et al. (2018). Figure  1 : (a) Bono on Kampar river, Indonesia (photograph by Lawrence in  [16] ) and (b) Kampar river map in Sumatera, Indonesia where the tidal bore bono appears (https://maps. google. com/). There is a similitude between a hydraulic jump and a tidal bore. These two phenomena can be characterized by the Froude number F r, a dimensionless number representing the ratio between the flow inertia to the external field as gravity (see in detail the part 2. 1. 4). A hydraulic jump is a transition from a supercritical flow with a Froude number F r < 1 to a subcritical flow with a Froude number F r > 1.
They used the CFD software Thétis to generate the 2D numerical simulations of tidal bores by applying the LES turbulence model. They have also estimated the wave amplitude a w and the wavelength L w from the form of free surface. From their numerical results, the wavelength L w decreases when the Froude number F r increases. The wave amplitude a w increases when the Froude number F r increases in the regime of undular tidal bore. Otherwise, the wave amplitude a w decreases when the Froude number F r increases in the regime of breaking tidal bore. In our study, the numerical study of two tidal bores types, undular and breaking, was done using OpenFOAM (Open Source Field Operation And Manipulation) CFD software by applying a LES turbulence model to complete the studies by  Khezri (2013)  and  Simon (2013).