dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The synchrotron facilities  ESRF (Grenoble, France)  and  Elettra (Trieste, Italy)  are gratefully acknowledged for beam time. This work was supported by the  University of Bologna  [grant  FARB2012  to MP, SF, MZ and FF]. SF thanks the  Consorzio Interuniversitario di Ricerca in Chimica dei Metalli nei Sistemi Biologici (CIRCMSB). Dr. Antonino Principato  is kindly acknowledged for help in the purifications of CrTK and EcE4PDH.
Crc and Hfq Mutants Rearrange the Perturbed Metabolome in Different Ways We then investigated the underlying changes in metabolism linked with the different swarming phenotypes. We grew the strains at 37 C in a fully defined liquid media with glycerol as the sole carbon source and ammonium nitrate as the nitrogen source. We extracted the intracellular metabolites we Left: The progression of swarming colony morphologies in the six replicate populations shown at the end of each 24 h of growth (1 through 3 and 4 through 6 were conducted in two separate dates). The first four days of the experiment were similar among the six populations and we termed this the "parallel phase. " The last three days were different and were termed this the "divergent phase" (see also supplementary fig. We built a clustergram using hierarchical clustering of the metabolomics data obtained from six replicates for each strain (fig.
The fraction of the actor strain is determined by: (number of actor cells/total cells observed). The number of cells is determined by CFU count. The benefit to the DsRedExpress labeled DrhlA recipient is calculated by: (DrhlA total cell number when mixed with actor/DrhlA total cell number when mixed with GFP labeled DrhlA). Data shown was always from three biological replicates with three technical replicates each for every actorrecipient pair for a total sample size of 9.
The metabolites were identified by accurate mass matching (mass tolerance < 0. 005 Da) and where available retention time matching against chemical standards. Metabolite ion counts were extracted using Profinder 8. 0 and Qualitative Analysis 6. 0 (Agilent). Targeted analysis was performed to identify metabolite levels in each strain. Putative metabolites were identified based on accurate mass (m/z). We include the peak areas in the supplementary file metabolomics. xlsx, Supplementary Material online. In order to correct for variation in sample concentration, we calculated the scaling factor by fitting a generalized linear model using the fitglm. m function in Matlab using log as the link function with the following formula in Wilkinson notation: Log(peakArea) $ mutant_id * experiment_id þ metabolite_ id.
Acknowledgments This work was supported by the Office of the Director,  National Institutes of Health of the National Institutes of Health  under Award Number  DP2OD008440  to J. B. X, Award Number  T32 AI007621  to K. E. B. , a  National Science Foundation  graduate research fellowship  GRFP DGE-1257284 2012  to H. T. M. and  National Science Foundation  award  MCB-1517002/NSF 13-520  to J. B. X. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The sequencing data were deposited in the  Sequence Read Archive (SRA)  of the NCBI with accession SRP103014.
Modified Denavit-Hartenberg (MDH) is usually used to model robots consisting of revolute or prismatic joints including the manipulator used in this research work. The MDH parameters defined for this robot and are used thereafter with the support of the software SYMORO+  (Khalil and Creusot, 1997)  to determine the relationship: T = f (q) i ∈ [1. 6] (5) Where x 6 , y 6 and z 6 of the equation (  4 ) represent the coordinates of the point O 6 in the fixed frame R 0. This point was noted by: 0 P 6 =   x 6 y 6 z 6   Then the transformation matrix of the end-effector in R 0 is given as : 0 T 6 = 0 R 6 0 P 6 0 1 The FSW tool is in direction of the 6th axis of the manipulator with the same orientation of the frame R 6 , The position of O t is 6 P t =   0 0 L t   Where L t is the length of the tool, see Fig.
Optimization Results For a good quality of welding an optimization algorithm in MATLAB is developed that minimizes ER and EP. This has been done by optimization the following function: E = (λEP + ER) 2 (32) Where, λ is the Lagrange multiplier. The position and the orientation errors are in terms of the parameters of the parallel structure which are defined with the matrix: V =      r p r M x M y M z M      Then, as previously mentioned r p is the dimention of the platform, r M is the dimention of the base, x M , y M and z M are the coordinates of the base center O M (see Fig. 2 ). In addition, these errors depend on the forces created by the three actuators of the parallel structure: F p =   F 1 F 2 F 3   To minimize E, we developed an optimization algorithm in MATLAB. This algorithm calculated the optimal parameters which correspond to the minimum deviation.
The Workspace of the Structure The idea to add a parallel structure allows improving the rigidity of the robot. However, it limits the workspace and the index of the manipulability  (De Backer, 2014). The workspace of the hybrid structure was not calculated in this work. However, it has been simulated using the software Catia. This simulation shows what happens by using a real system and how this system works in the face of real disturbances. Furthermore, From this simulation it has been observed that the tool can go up to y 0 = ±1. 2m (because of the structure symmetry on the plane (x 0 , z 0 )). Further, different configurations have been tested in the plane y 0 = 1. 2m using Catia as shown in Fig. 10. It has been revealed that there is no collision between the serial and the parallel structure in each configuration.
CONCLUSIONS The elasticity of industrial robots and limited force capability are barriers for achieve a successful robotic FSW which limits its use for high quality welding. Usually, this elacticity causes errors to follow the desired trajectory. There are two types of error; position error (EP) and orientation error (ER). These two errors are calculated in Cartesian space using the joint stiffness model, the kinematic and dynamic models of the manipulator. In this work a parallel structure was added to polyarticulated robot to minimize them. Optimization has been carried out using an algorithm developed in Matlab. The algorithm calculates the parameters of the parallel structural V and the three forces generated by this structure F p to minimize the position and the orientation error during welding.
For ours example we obtained a small orientation error of 0. 1 • and a very small position error of approximately zero. Using the software Catia we estimated approximately the workspace of the serial and parallel robots by a parallelepiped. The volume of the parallelepiped equals 5. 76m 3. With the proposed solution, the accuracy of positioning the tool and the FSW welding performance can be greatly improved. This assist device allows to improve the stiffness of an industrial robot. This approach allows us to gain in terms of stiffness, however, it limits the workspace. Moreover the main advantage of a serial robot is its workspace. But, despite this limitation of the application flexibility, this solution can ensure a process with good accuracy for simple paths and also for complex paths as the angle between the axis of the tool and the axis z 0 stays in the workspace of the hybrid structure.
The extracted sub-layouts were analysed by two independent analysis packages: Eventdisplay from VERITAS, MAGIC Reconstruction Software (MARS). A cross-check for a small subset of configurations was also provided by the Image Pixelwise fit for Atmospheric Cherenkov Telescopes (ImPACT) from H. E. S. S. (details can be found in  [5, 13, 14, 15] ). As in the site selection case, the optimisation is performed with respect to the differential sensitivity, calculated as the average between the north and south pointing directions, of a point-like source located in the centre of the field of view. For simplicity, a single parameter is associated to each calculated sensitivity, the so-called performance per unit time (PPUT)  [8] , defined as: PPUT = N ∏ i=1 F sens,ref (i) F sens (i) 1/N (2. 1) where F sens,ref (i) and F sens (i) are the reference and achieved sensitivity in the i-th bin in energy.
We also showcase how authentication delegation may be a risk to user personal data with the example of the WebRTC identity specification in Section 2. 2. In particular, we show that users may be drastically limited in their choice of an IdP, and that they may not be correctly informed that the IdP may access call information. We then present our investigation in Section 3. In that section, we propose three hypothesis that could explain the absence of IdP discovery on the web. To verify these hypothesis, we conduct a data collection experiment using a browser extension. We visit SSO services used on the top-500 worldwide websites. The browser extension then collects OAuth 2/OIDC URL requests in the navigation history. These requests allow us to discover a large number of websites using OAuth 2/OIDC and the type of information requested to supported IdPs.
This token can then be consumed by the RP to access user data on the RS. In practice with web sign-in, IdP, RS, and AS are often the same actor. For the sake of simplicity, we do not distinguish them in this paper. Most RPs often require user profile information to complete their local profile. Thus, authentication and authorization delegation are often mixed together in a single process. OAuth 2 is an authorization delegation protocol. In the OAuth 2 protocol, the RP asks for specific authorization by requesting scopes. Once redirected to the IdP page, the user is presented with the requested scopes and given the choice to grant or refuse authorization. Figure  1c  shows the GitHub consent page on which the user is presented with a choice to share his email with GitLab RP. Depending on the implementation, authorization may be individually given by the user, however it is more often an all or nothing choice.
However it does not appear that the IdP is able to know on which CSP the call happens, as the Peer-Connection interfaces between calling sites and the IdP Proxy. Except if the user authentication procedure is used. To reuse the classification proposed by Vapen et al. and presented in Section 2. 3, this ability to track user call history could at least be classified as R+, or R++ regarding the verifying user, as it would fall under the Friend's data class of privacy risk. However, user may not have authorized or even be aware of such tracking capacity. Let us follow the example of user signing in with an OAuth 2 service onto a CSP. In order to access the service, the user would give consent to authorization requested by the CSP. However, in the WebRTC speci-fication, the Identity Assertion is not requested by the CSP but by the IdP Proxy, who plays the role of RP.
As the IdP Proxy is from the same domain as the IdP, the Identity Assertion generation could be done outside of any authorization flow. For instance, a simple authentication check between the User Agent and the IdP would be enough. Even if the exchange between the IdP Proxy and the IdP where covered by an authorization flow, this authorization would have been given by the user to the global IdP Proxy RP, rather than for a particular CSP. Any CSP could trigger a WebRTC IdP authentication without asking or notifying the user, as it is the CSP's client-side JavaScript which sets the WebRTC identity parameters. The verifying user would get no control over the IdP Proxy as it is an automatic process controlled by the UA. He would only be informed after the UA notifies the success of the Identity Assertion verification by the IdP.
OAuth 2/OIDC scope usage collection To evaluate the impact of the first hypothesis, we implement a browser extension to parse the browser navigation history and look for OAuth Note that these URLs do not contain private information regarding the user as they are accessed before the user is identified or authenticated. To collect data, we manually visit each of the top 500 from the Alexa ranking 1 and try to use each one of the SSO solutions offered by these websites. The visited URLs are recorded and parsed by our extension. Finally, we use data on collected RPs and IdPs to also evaluate the impact of hypothesis H2 and H3. As our data-collection search is focused on OAuth 2, we do not collect requests for IdPs using other proto-1 http://www. alexa. com/topsites cols, in particular Twitter and Amazon.
However we scarcely encounter RP offering only these IdPs, as a result the large majority of visited RP is captured by our extension. In total, we observe 103 unique RPs and 23 OAuth 2 provider's domain names. The two biggest observed IdPs are undoubtedly Facebook and Google, respectively serving 63 and 52 RPs. The third most observed IdP is Twitter with 30 request URLs, but it uses OAuth 1 and as such is not included in our data collection. While our claim that users are offered a limited choice of IdPs is confirmed by our study, we globally observe three different patterns based on the RPs geographical origin. Firstly, Chinese websites only offer to login through Chinese SSO solution such as QQ. com. Technically, they also often offer phone authentication through a QR code. Occidental websites, i.
API and data format limitation The RPs not only require user authentication but also authorization to access protected resources. These resources may vary in nature, and may not share a standardized data format when of the same type. One reason for RP not to allow signing in with any IdP could be that they require specific resources, which are not available on any IdP. We classify RP in three categories: authentication, profile, and specialized, in function of the scope observed in OAuth 2 authorization requests. Authentication classed RPs only require an assertion that the user got authenticated by the IdP. As such, any IdP could be able to serve for such RP, given a standardized assertion (e. g. a signed JSON Web Token  [11] ). RPs classified as profile require basic user profile information, most often to complete their own database and provide a personalized user experience.
Result analysis Our observations, summarized in Note that this also leads to different privacy risk level for the user. On the other hand, 34 % of observed RP are rated as MIN-Specialized. The large majority of specialized RPs use resources from Online Social Network (OSN) such as friends list, user likes, and extended profile informations. While data from different social networks can be similar from a conceptual point-of-view, the format and API used to access these data depend on the functionalities and concepts offered by social network. In these cases, the user fully depends on implementation choices by RPs. Although a standardized protocol, OAuth 2 lets the IdPs define their scope and data format. As a result, IdPs may provide similar results in different scopes and data format. For instance, we observed six different scopes for email access and seven different scopes for basic profile information.
This gives an indication on the lower bound of the implementation work that RP must complete to support these IdPs. OIDC solves this problem by standardizing basic profile claims (e. g. profile, email, address,. ) as well as the scopes, endpoint, and data format to retrieve these informations. From our observation, only four IdPs out of twenty where implementing OIDC, as reported on Table 2. Notably Google is one of the OIDC provider and serves 50 % of observed RPs while Facebook does not implement it and serves 63 % of observed RPs. Other IdPs serve less than 6 % of RPs each. However, out of the 52 RPs using Google's SSO, only 22 request standard OIDC scopes, and from these 22 only 10 arequest OIDC ID Token. Surprisingly, we also observe that out of the 34 RPs using the Google API scopes, 19 were using deprecated scopes.
Prototype implementation To evaluate the practicability in terms of use and development of our solutions, we implement a prototype for the Web authentication proxy architecture  6. This architecture relies on three components interacting together: (a) a web-browser modification providing a JavaScript API to website and a user interface for identity selection, (b) a website -client and server siderequesting an authentication from the user and able to understand and verify the provided identity assertion, and (c) an IdP Server able to provide a suitable identity assertion through a WebRTC compatible IdP Proxy. Figures  7  and 7  show sequence diagrams representing the interactions between the different actors of our implementation. In Figure  7 , after the user requested to login, the JavaScript (JS) client code calls the UA connect function which returns a promise 7  for an identity assertion.
Browser modification We develop a browser extension to implement the browser modification. This solution was chosen for its simplicity in comparison to browser source modifications. A browser extension does not expose function to the global window scope. To simulate access to a standard Web API, we offer a JS file exposing our API functions to website client code. This script can then communicate with the extension code through the postMessage API. The API offers two functions: connect(request) and register(iss, sub, type, name). The register function cre-ates an entry for an identity in the browser. It is used by IdP to let users store identity cards in the browser. iss and type are used to retrieve the IdP Proxy following the procedure in WebRTC identity. sub is an identifier for the user on the IdP, while name is the display name of the user on the identity card.
Website implementation Besides calling the API to get an identity assertion, a compatible website must also be able to understand and verify the assertion. In our implementation, the JS code on the client side sends the assertion to its backend server for verification and login. This is done by a GET to a login URL with the assertion transmitted as a URL query parameter. The assertion authenticity is then verified by the server. To do so, developer can use several libraries for JWS support. For our prototype we extended a JWS strategy for Passport 8  -a popular NodeJS authentication library-with support for JKU verification. Once the assertion has been verified, the server can extract relevant information from it and lookup for existing users in its database. If no user exists, the server creates a new entry on the fly.
To solve this issue, additional constraint could be added to the key's origin verification. For instance, using a standard well-known  [18]  path for the JKU URL also matching the ISS domain would prevent attacker from specifying any key. Secondly, assertion manipulated by the javascript client code, and returned by the API's promise response, would be added to the page's global. The assertion could thus be read, and used, by a malicious cross-origin script embedded on the same page. This issue is similar to what can happen on an OAuth 2 implicit flow, where the client directly receives an access token. In OAuth 2 we would use a code flow to instead let the client exchange a code and authentication with the token endpoint to get the token. But as in our solution the RP/client cannot be authenticated by the IdP, the code flow could not be used.
Usability From the end-user perspective the overhead is quite limited. Compared to current authentication process, the main addition is a one time configuration of the webbrowser. This configuration can be done in a single action with the register function of the API. Identity selection on login request is also similar to current SSO solutions. Preferences storage by the browser may help reduce it further. However, the user-experience does not constitute our field of expertise and we did not conducted user studies. Regarding developer, we also believe the additional work to be limited. Table  3  compares the amount of new code lines to the total for each modules of our prototype implementation. In proportion, the biggest task is to develop the browser modification and IdP Proxy, which are new concepts.
The battery delivered 77 and 59 mAh g -1 during charge and discharge, respectively. The battery was assembled in the cell described in figure 5 without modification, i. e. with a 100 μm thick Be window. It was charged and successively discharged at a C/30 rate, i. e. with the insertion of a mole of Li per mole of active material in 30 h. The data were recorded alternately at the Fe K-edge (7112 eV) and the Co K-edge (7709 eV) in the transmission mode. Each spectrum lasted approximately 45 min. We recorded 73 spectra, 42 during the charge and 28 in the subsequent discharge. In this way we were able to accurately monitor the local charge and structure modification upon the potassium release and the lithium insertion process. Figure  7  shows the voltage profile of the charge and discharge curves of the iron hexacyanocobaltate cathode, as obtained during the operando XAS experiment.
In order to understand the structural changes of the electrode material during the operation of the battery we performed a full EXAFS analysis using the GNXAS package  [45, 46]. In this method the EXAFS signal is decomposed into a sum of several contributions, namely the n-body terms. The two-body terms are associated with pairs of atoms, and probe their distances and variances. The three-body terms are associated with triplets of atoms and probe angles and bond-bond and bond-angle correlations. The four-body terms are associated with chains of four atoms, and probe distances, angles in-between, bond-bond and bond-angle correlations. Given the presence of both Fe and Co in the electrode, a multiple edge approach was used. The multiple edge fitting allows us to obtain a more constrained structural description by doubling the number of experimental observations while using the same structural parameters  [47].
During the charge the peak relative to elemental sulfur reappears, although the system is shown to not be completely reversible. In order to retrieve the quantity of the different components during the discharge and the charge of the battery, LCF was performed using the Athena program  [58]. For the LCF we used reference spectra of the electrolyte, elemental sulfur, mid-long chain lithium polysulfides and Li 2 S in a similar environment and at a similar sulfur concentration. Figure  14  shows the relative quantities of the S species during the discharge/charge, together with the electrochemical curve. A drop of S is observed in the high voltage plateau concomitantly with the appearance of polysulfides. Nonetheless, the quantity of S never goes to zero and in the low voltage plateau the quantity of S remains constant.
IV. ATMOSPHERIC NEUTRINO BACKGROUNDS Atmospheric neutrinos are the main background for searches such as proton decay and n-n oscillations. Energetic electrons, muons, or taus can be created by chargedcurrent interactions, and if the neutrinos have enough energy, pions and other particles may also be created by resonance. These pions and other particles form the background to the search of n-n oscillations. The SNO detector response to these backgrounds is simulated in a three-step process. For the atmospheric neutrino flux the Bartol three-dimensional flux prediction  [23]  is used and neutrino interactions are modeled by the NUANCE simulation package  [24]. The output of NUANCE is then simulated in SNOMAN  [25] , which evaluates the SNO detector response to these events. Through-going events, defined as neutrinoinduced muons created outside the detector volume that subsequently traverse the detector, are used to measure the atmospheric neutrino flux  [16].