dataesr/scientific-paragraphs-categorization huggingface dataset:
Design and Optimization Method HyperStudy is a multidisciplinary design study and optimization software for engineers and designers. This software proposes different optimization approaches (DOE, Fit, Optimization and Stochastic), thus making it a very interesting way to efficiently study the design of an electric machine when coupled to an FE model such as Flux 2D. As depicted in Figure  19 , in such coupling between the two software packages, Hy-perStudy will modify the values of the selected prototype input parameters (geometrical or physical) in specified ranges, leading to different prototypes that are then modeled and studied using Flux 2D. HyperStudy automatically retrieves the results of the selected output parameters from Flux to find the optimal values with respect to the specific objectives and constraints.
Photometry After disco v ery, daily ZTF gri photometry was obtained on the P48 telescope, leading to very high cadence light curves in the three ZTF bands o v er the evolution of SN 2021rhu. The photometric data presented here is from the ZTFFPS forced photometry pipeline  (Reusch 2020 ). We observed the field with the 30 cm Ultraviolet/Optical Telescope (UV O T;  Roming et al. 2005 )  aboard the Swift satellite  (Gehrels et al. 2004  ) between 2021-07-15 and 2021-07-25 in w 2, m 2, w 1, u , b , v bands with a ∼3-d cadence. After the SN faded, we obtained a final set of images in September 2022 to remo v e the host contribution. Data were retrieved from the NASA Swift Data Archive 1  and processed using UV O T data analysis software HEASoft version 6. 30. 1. 2  Source counts were extracted from the images using a region of 5 arcsec.
The background was estimated using a significantly larger region outside of the host galaxy. The count rates were obtained from the images using the Swift tool UV O TSOURCE. The y were conv erted to magnitudes using the UV O T photometric zero points  (Breeveld et al. 2011  ) and the latest calibration files from February 2022. To remo v e the host emission from the transient light curv es, we used templates formed from our final observations in September 2022. We measured the host contribution using the same source and background apertures and subtracted this contribution from the transient flux measurements. Unfortunately the target was saturated in the b -band images and as such this data is excluded. The observed photometric data for SN 2021rhu can be found in Table  A1. K corrections have not been applied due to the low redshift of the host galaxy.
Light-cur v e properties As evident in the BV photometry in Fig. 3  , SN 1986G is subluminous when compared to normal SNe Ia. This also appears to be the case for SN 2021rhu as seen in the comparisons between its ZTF ri photometry with the RI photometry available for SN 2011fe. We note that although the response functions of the ZTF r and ZTF i bands differ slightly from the RI filters, this only leads to a magnitude difference on the order of 0. 05 mag and as such this luminosity comparison is still significant. The SN 2021rhu photometry was fit by Dhawan et al. ( 2022 ) using the light-curve fitter SALT2  (Guy et al. 2007  ) as accessed through sncosmo  (Barbary et al. 2016 ). This fit was performed solely on the ZTF g and ZTF r data as SALT2 is not well defined at w avelengths redw ard of 7000 Å.
The date of maximum light was found to be t 0 = 59410. 60 ± 0. 04. With SALT2 parameters of x 1 = -2. 074 ± 0. 025 and c = 0. 054 ± 0. 028, SN 2021rhu satisfied the criteria of | x 1 | < 3 and | c | < 0. 3 as laid out in Figure  3. Absolute magnitude light curves with no host extinction applied for SN 2021rhu in the Swift u and v bands and the ZTF g , ZTF r , and ZTF i bands compared with the UBVRI photometry of transitional event, SN 1986G and the normal SN 2011fe. The SALT2 light curve fits for SN 2021rhu are displayed as solid lines. We note that the fitting was performed on only the ZTF gr data and as such the solid lines in the BV bands are extrapolations. The extrapolation from this SALT fit was left out in the case of the U and I bands as these regions are known to be weaknesses of SALT2.
The extrapolated B -band light-curve is the source of the peak M B and m 15 (B) measurements for SN 2021rhu seen in Fig. 4. (Phillips et al. 1999 ). Dhawan et al. ( 2022 )  as typical cosmological cuts. These criteria are slightly looser than the previously mentioned cuts employed in  Riess et al. ( 2021 ). SN 2021rhu w ould have in f act been excluded as a calibrator for the SH0ES measurement. It was argued that despite the low x 1 value -also seen in other peculiar, fast-decliners -the clear ZTF r shoulder and ZTF i secondary maximum are characteristic of normal and transitional SNe Ia that have been used for cosmology. While there is only limited data available for SN 1986G in the I and R bands, it has a clear secondary maximum in the infrared photometry presented in  Frogel et al. ( 1987 ).
AC K N OW L E D G E M E N T S The research conducted in this publication was funded by the  Irish Research Council  under grant number  GOIPG/2020/1387. KM, MD, and GD are funded by the  EU   H2020   ERC  grant no. 758638. SD acknowledges support from the  Marie Curie Individual Fellowship  under grant  ID 890695  and a junior research fellowship at Lucy Cav endish Colle ge. SS acknowledges support from the G. R. E. A. T research environment, funded by  Vetenskapsr ådet  , the  Swedish Research Council , project number  2016-06012. SR acknowledges support by the  Helmholtz Weizmann Research School on Multimessenger Astronomy , funded through the  Initiative and Networking Fund of the Helmholtz Association ,  DESY , the  Weizmann Institute , the  Humboldt University of Berlin , and the  University of Potsdam.
MS and ER has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 759194-USNAC ). MWC acknowledges support from the  National Science Foundation  with grant numbers  PHY-2010970  and  OAC-2117997. Based on observations obtained with the Samuel Oschin Telescope 48-inch and the 60-inch Telescope at the  Palomar Observatory  as part of the  Zwicky Transient Facility  project. ZTF is supported by the  National Science Foundation  under grant No. AST-2034437  and a collaboration including  Caltech ,  IPAC , the  Weizmann Institute for Science , the  Oskar Klein Center at Stockholm University , the  University of Maryland ,  Deutsches Elektronen-Synchrotron and Humboldt University , the  TANGO Consortium of Taiwan , the  University of Wisconsin  at Mil w auk ee,  Trinity College Dublin , La wrence  Liv ermore National Laboratories, IN2P3, France , the Downloaded from https://academic.
We show the possible spin-orbit evolution and resonances for eccentric orbits and explore the possibility of spin-orbit resonances raised by the obliquity of the planet. Then, we focus on the additional effect of a thick atmosphere on the possible resulting spin equilibrium states and explore the effect of the evolution of the stellar luminosity. Methods. We implemented the general secular evolution equations of tidal interactions in the secular code called ESPEM. In particular, we focus here on the tides raised by a star on a rocky planet and consider the effect of the presence of an atmosphere, neglecting the contribution of the stellar tide. The solid part of the tides was modeled with an anelastic rheology (Andrade model), while the atmospheric tides were modeled with an analytical formulation that was fit using a global climate model simulation.
In this article, we use the particular case of Venus to present our recent implementation of planetary tides  (Boué & Efroimsky 2019 ) in a secular code called ESPEM (French acronym for Evolution of Planetary System and Magnetism,  Benbakoura et al. 2019; Ahuir et al. 2021 ). Here we study the case of a Venus-like planet around a Sun-like star. The rotation of Venus is thought to be an equilibrium between the gravitational bodily tides and thermal atmospheric tides  (Correia & Laskar 2001 , 2003b; Correia et al. 2003). It is therefore a good laboratory for studying these mechanisms. Some unresolved issues still remain, however, such as whether the rotation of Venus is currently in equilibrium, and how it reached its current rotational state. The competition between the tides, gravitational and thermal, strongly depends on the internal state, but in the case of Venus, little is known about its internal structure.
Implementation in the ESPEM code We implemented the secular equations of  Boué & Efroimsky (2019)  in the code ESPEM  (Benbakoura et al. 2019; Ahuir et al. 2021). This is a secular code integrating the dynamical evolution of a star-planet system. The code takes the coupling between the two layers of low-mass stars into account (convective and radiative layers), as well as the effect of the stellar wind and the torque due to the tides raised by the planet on the convective envelope star and the torque due to the star-planet magnetic interactions for circular and coplanar orbits  (Ahuir et al. 2021). The code was only used to compute the angular momentum exchange between the planetary orbit and the stellar radiative core and convective envelope angular momentum  (Benbakoura et al. 2019; Ahuir et al.
2016) , which gives the internal dissipation and the evolution of the stellar quantities (e. g. , the mass, radius of the radiative core, and convective envelope, and luminosity). The evolution of the stellar luminosity is used in Sect. 4. 2. The user also needs to specify the initial conditions of the osculating elements of the planet as well as the rotation rate. The code also needs a data file describing the frequency dependence of the real and imaginary parts of the tidal Love number of the planet. This latter file should also provide the mass, radius, and the radius of gyration of the planet (which represents the internal density distribution). The Love numbers provided in these data files were computed with the method described in Sect. 2. 4. As explained in Sect. 2. 1, several frequencies are excited depending on the eccentricity and inclination.
Luminosity variation Dynamical studies of the thermal tides  (Correia & Laskar 2001 , 2003a; Leconte et al. 2015)  have considered a constant luminosity. As the thermal forcing depends on the heat flux of the host star, we also investigated the effect of an evolving luminosity on the rotation evolution of a Venus-like planet. The luminosity evolution of the Sun-like star in ESPEM comes from simulations with the stellar evolution code STAREVOL  (Amard et al. 2016). Figure  14  shows the luminosity variation of the Sun-like star we considered. The thermal Love number was computed with the luminosity dependence with the formulation of  Auclair-Desrotour et al. (2017b)  as ℑ(k atm 2 (σ)) = - 4 32 κτςϵL ⋆ a R A T 0 M ⋆ R σ σ 2 + ω 2 0 , (13) with L ⋆ and M ⋆ the stellar mass and luminosity respectively, R the radius of the planet, a the semi-major axis, τ a weight parameter that gives the efficiency of the coupling between the atmosphere and the surface (0 < τ < 1), ς a shape factor depending on the spatial distribution of tidal heat sources, κ the power per mass unit radiated by the atmosphere (where the atmosphere is assumed to behave like a graybody, i.
Conclusion We presented the recent implementation of the effect of the tides raised by the star on a telluric Venus-like planet in the code ESPEM. We added the secular evolution of the osculating elements of the planetary orbit (a, e, i, ω, and Ω), that is the semi-major axis, the eccentricity, the inclination, the longitude of ascending node, the argument of periastron, and the planetary spin. We followed the secular equations published by  Boué & Efroimsky (2019) , which describe the evolution of the osculating elements of the orbit of the planet under tidal perturbations following the Kaula formalism  (Kaula 1964). Our implementation includes gravitational and thermal tides, which allowed us to study the tidal effect of an arbitrary atmosphere on an arbitrary planet, provided that the tidal Love numbers k 2 associated with its atmosphere and internal structure are known.
We cannot reproduce this with only two bodies. Further developments of the ESPEM code will include these effects. In Sect. 4. 1, we assumed that the spin state of Venus was in equilibrium state to fit an internal model to the thermal tides. Our results in Sect. 4. 2 showed, however, that this may not be the case, and the spin of Venus may still be evolving because of the variation in the solar luminosity. Thus, we investigated the effect of the evolving luminosity on the thermal tides. The evolution of the stellar luminosity leads to a continuous change in the balance between the gravitational and the thermal tides. The rotation of the planet will then continually increase as the luminosity increases. The luminosity evolving faster than the spin, the rotation of the planet will chase the equilibrium state without reaching it.
Acknowledgements. This work has been carried out within the framework of the NCCR PlanetS supported by the  Swiss National Science Foundation  under grants  51NF40_182901  and  51NF40_205606. The authors acknowledge the financial support of the  SNSF  (grant number:  200021_197176 ). All the members from CEA acknowledge support from  GOLF  and  PLATO CNES  grants of the  Astrophysics Division at CEA. The computations were performed at  University of Geneva  on the Baobab and Yggdrasil clusters. This research has made use of  NASA 's Astrophysics Data System. The authors thank  Drs. Jérémy Leconte ,  Pierre Auclair-Desrotour  and  Gwenaël Boué  for interesting discussions about the thermal tides.
We investigate the privacy properties of these search engines when they: (i) present search ads to users, (ii) when a user clicks on an ad, and (iii) when the user lands on the advertiser's page. We implement an automated measurement methodology to measure if and how users can be re-identified (hence, their privacy is compromised) when clicking on search ads on each search engine (see  Section 3). We build an open-source implementation of this methodology in the form of a Puppeteer-based pipeline that simulates search queries and ad clicks. We apply this crawling methodology to the five search engines, providing a full dataset with visited websites, cookies created, locally stored values, and web requests to search engines' servers and/or other third parties when clicking ads. We use filter rules from several major open-source lists to detect web requests to online trackers, and we propose a methodology to differentiate user identifiers from non-tracking values in query parameters and cookies values.
We then present in Section 4 a systematic analysis of our dataset to investigate privacy harms before clicking an ad, during clicking an ad, and after clicking an ad and reaching the advertiser's website. We find that users' privacy is not harmed until users click on an ad. Privacy-focused search engines do not appear to attempt to reidentify users across visits or queries and do not include resources from, or make network requests to known trackers. However, we find that users' privacy is compromised by all studied search engines in various ways once users click on an ad. Disappointingly, we find that all search engines record additional information about the user and/or the users' clicks after the user has clicked on an ad. Private search engines capture data related to the clicked ad, including the ad provider, destination URL, and the ad's position within the search results page, along with the user's browsing data, such as the search query, device type, and browser language.
On the destination page, we check whether the search engine requires advertisers to abide by privacy-respecting practices by measuring whether advertisers include trackers or other known privacy-harming resources. We found that 93% of ads destination pages (across all five search engines) included tracker and privacyharming resources. Finally, we check whether search engines or redirectors aid advertisers in profiling visitors by measuring the data they receive in the form of user-describing query params. We find that advertisers receive user identifiers as query parameters in 68%, 92%, and 53% of cases for DuckDuckGo, StartPage, and Qwant, respectively. This practice, known as UID smuggling, enables redirectors to aggregate more user behavior data if they have scripts on the ads' destination websites and they store the user-identifying parameters they receive.
It also does not store or share that information other than for accounting purposes. " This implies that Microsoft can, though currently chooses not to, link the ad-click data to an existing Microsoft user profile. The privacy policy is signed by both DuckDuckGo and Microsoft. Qwant is a standalone EU-based search engine that allows users to access online resources without being tracked nor profiled  [32]. Qwant relies on Microsoft's advertising system to deliver ads in their search results pages. Although Qwant reports transmitting some information concerning search queries to Microsoft to enable the latter to present pertinent advertisements, it remains unclear which specific information is shared. In addition, to detect fraud, Qwant uses a specialized service offered by Microsoft, which has access to the user's IP address and the browser "User-Agent".
Qwant assures that this service does not have access to the search query, which is sent to another service that does not know the IP address of the user  [32]. Unlike DuckDuckGo, which also uses Microsoft advertising, we did not find any mention to ad-click behavior information on Qwant's privacy policy. They do not mention whether Microsoft stores this data and for what purposes they use it. StartPage is a meta-search engine that allows users to obtain nonpersonalized search results from Google's search index while protecting their privacy. StartPage relies on Google AdSense to show ads to users on their results page. According to StartPage's privacy policy, the search engine serves strictly non-personalized ads since it does not share any identifiable information with Google. Therefore, ads displayed on the search results page are solely based on the user's search query  [38].
MEASUREMENT METHODOLOGY We develop a measurement methodology to capture network flows when clicking on an ad from a search engine results page. Using multiple crawlers, we simulate a large number of search engine queries in order to collect a sample of information flows per search engine. For each request, we collect the cookies created, the locally stored values, and the web request sent by the browser. In addition, we rely on several open-source datasets to detect web requests to online trackers. We consider five main search engines: Google 1  , Bing 2  , DuckDuckGo 3  , StartPage 4  , and Qwant 5. We use Google and Bing as baselines to compare with the other three, which claim to have higher privacy standards and protective measures in place.
We consider all query parameters, localStorage, and cookie values. We call them tokens. There are 6 971 unique tokens in our dataset. We perform the following filtering, which is similar to the one performed by Randall et al. [33] : (i) Each iteration is executed in a new browser instance; hence, user identifiers should not be shared across browser instances. We discard tokens that are the same across all or a subset of browser instances. (ii) For each browser instance and search query, we analyze the tokens resulting from the URLs of all ads that appear on the results page (which are usually in the form of googleadservices. com/. /aclk?. cid=CAESbeD2ZWCwqFv3e-2k_. ). We discard tokens with different values for the different ad URLs as they likely correspond to ad identifiers. (iii) To detect session identifiers, we store the profile of each iteration in a separate directory and execute an extra iteration per browser instance one day later to see which values of cookies/parameters change.
They found that more than 97% of the websites they have crawled have first-party cookies set by third-party javascript and that on 57% of them, there is at least one cookie containing a unique user identifier diffused to multiple third parties. Roesner et al. [35]  measured how user tracking occurs in the wild. They found that multiple parties track most commercial pages and estimate that several trackers can each capture more than 20% of a user's browsing behavior. In response to browsers implementing partitioned cookies storage to protect users from cross-site tracking through browser cookies, several trackers are adopting other tracking strategies such as bounce tracking or UID smuggling. Koop et al. [26]  analyzed a dataset of redirection chains in the wild and found that 11% of websites redirect to the same 100 top redirectors.
Moreover, they demonstrate that these top redirectors could identify users on the most visited websites. Randall et al. [33]  measured the frequency of UID smuggling in the wild and found that it is performed on more than 8% of all navigations in their dataset. We use a similar method to identify user identifiers among all cookie values and query parameters by implementing automatic filtering followed by a manual inspection. All these studies were conducted in the wild, and to the best of our knowledge, no study focuses on navigational tracking techniques performed on search engines.
ACKNOWLEDGMENTS This research was supported in part by the  French National Research Agency (ANR)  through the  ANR-17-CE23-0014 ,  ANR-21-CE23-0031-02 , and  MIAI @ Grenoble Alpes   ANR-19-P3IA-0003  grants and by the  EU  through the  101041223 ,  101021377 , and  952215  grants.
coli  (24)  were also used as models (see materials and methods for more details). The GMI1000 metabolic network was also reconstructed de novo with the Meroom algorithm to avoid any bias in the analysis. The GMI1000 network therefore refers to the new reconstructed metabolic network whereas the model network from Peyraud et al. (  16 ) is referred to as ModelGMI1000. All the metabolic networks are available as File S1 in a table format and on a git repository in SBML formats (https://github. com/cbaroukh/rssc-metabolic-networks). The merged metabolism of all strains, i. e. the pan-metabolome, is composed of 2573 reactions and 2562 metabolites. The common metabolism of all strains, i. e. the core-metabolome, represents a big part of the pan-metabolome, since 2111 (82%) reactions and 2251 (88%) metabolites are present in all strains.
Acknowledgements The study was funded by the  Centre National de la Recherche Scientifique ,  PEPs Modélisation de Processus Infectieux  ( 2017-2019 ). Work in our laboratory is also supported by the  French Laboratory of Excellence  project " TULIP " (grant number  ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ). The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. We thank  Toulouse Plant Microbe Phenotyping (TPMP) platform (Castanet-Tolosan, France)  for the access to the Biolog phenotypic facilities. We also thank  Patrick Barberis  for his expert skills in the construction of the different mutant strains. We thank  Ludovic Legrand  for assembling the genomes of strains sequenced in the study.
Procedures The survey was developed online using SurveyMonkey. The survey opened with sociodemographic data (i. e. , age, gender, and educational level) and by asking participants if they hold Bitcoin or not. Afterward, participants answered a question about their short vs. long-term holding horizon (i. e. , "are you holding Bitcoin for short or long term?" reply = yes or no). Participants also answered the following question (i. e. , "do you trade Bitcoin daily?") using a five points-scale (0 = never, 1 = rarely, 2 = sometimes, 3 = frequently, 4 = always). Afterward, participants answered to questionnaires: one of them was focused on answering about money, and the other about Bitcoin. The order of the two questionnaires were counterbalanced across participants. Temporal discounting questionnaires were inspired by a task proposed by  Boyle et al.