dataesr/scientific-paragraphs-categorization huggingface dataset:
E A RT H Q UA K E S L O C AT I O N S The REVOSIMA reported 30 000 events between 25 February 2019 and 10 May 2020, based on manual screening of the continuous land data. Using NLL software and our new 1-D hybrid velocity model within SeisComP3, we located more than 5000 of those events, which have already been detected from the land stations (Fig. 5 ;  Saurel et al. 2021). The mean absolute location accuracy is around 2 km vertically (Fig. 5a ) and 2. 5 km horizontally (Fig. 5b ). The formal horizontal and vertical uncertainties are both lower than 5 km with an azimuthal gap less than 175 • (Fig. 5c ) for 95 per cent of all earthquakes. The location accuracy does not show much variations with the different OBS network geometries and the number of OBSs used for location (Fig. 5d ). That is to say, the OBSs network is always sufficient to reduce the azimuthal gap to less than 175 • and to increase the number of phase arrivals used for location to more than 15 phases.
Because we processed the data in descending magnitude order, we always achieved a magnitude of completeness of 3. 0 or lower (Fig. 5e ). At present, we now have a data set of nearly 125 000 manual P and S phases between February 2019 and May 2020 (Fig. 5f ). This relocated earthquake data set, while only comprising a part of the recorded seismicity allows us to identify two distinct seismically active zones (Fig. 6a ). The Proximal cluster, with 90 per cent of the located earthquakes, is situated close to the Mayotte island of Petite-Terre. In map view, it has a circular shape (Fig. 6c ) and spans depths ranging 20-50 km (Fig. 6b ). Most of the seismicity is concentrated on the eastern side of the circle, where the highest magnitudes are located (all M4-M5+ events of the cluster). On the western side of the circle, closer to Mayotte's coasts, the events magnitudes are lower, and the seismicity is more diffuse.
At depth, 2 different patches can be identified (Fig. 6d ): one patch is dipping towards Petite-Terre, with events located between 35 and 45 km depth and a second patch is more vertical, with events located between 25 and 40 km depth. The north-south thin (2 km wide) cross-section (Fig. 6e ) also shows 2 subvertical lines of earthquakes, suggesting a cylindrical cluster with a lower earthquake density in the centre (Fig. S3 ). Based on the earthquake density, we can estimate an outer diameter of 11 km, and an inner diameter of 3 km. The distal cluster is located just a few kilometers to the east of the Proximal cluster, and contains earthquakes with depths ranging between 25 and 50 km (Fig. 6g ). It is not seismically connected to the Proximal cluster in our data set. Its seismicity is spread on a N130 • E trend extending towards the new volcano edifice (NVE).
D I S C U S S I O N A N D C O N C L U S I O N S Thanks to the collaborations among several research institutions, we, the MAYOBS/REVOSIMA seismology team, has facilitated the deployment of multiple seismometers, as well as the collection and interpretation of their data since early 2019. The numerous pickathons we have held since then have improved our understanding of the seismicity occurring offshore Mayotte. The seismicity manually-picked and relocated in this study, from February 2019 to May 2020 is in agreement with the analysis of 2018 activity  (Cesca et al. 2020; Lemoine et al. 2020a ; Table  1 ), as seismicity clusters identified in this work seems to have been active since summer 2018. The Distal cluster correlates with the first cluster of activity that represents rock fracturing and dyke opening from the centre of deflation towards the volcano, leading to the creation of the NVE  (Cesca et al.
2020; Lemoine et al. 2020a; Feuillet et al. 2 km s -1 below each OBS-dashed red) and when using P arrivals only (dotted-dashed blue). (d) Mean and standard variation of the RMS, depth and vertical error distributions for various P-wave velocity speeds and this study's V P /V S ratio of 1. 66 using a homogeneous half-space velocity model. All tests were performed using NLL software.
2021 ). However, a notable difference with our catalogue is the lack of earthquakes in the first 20 km below the volcano. This could be explained by a magma path towards the surface now opened and generating only very small events not detected by the land network while the path fracturing the crust in 2018 implied strong felt earthquakes  (Duputel et al. 2019 ). The Proximal cluster correlates with seismic activity that initiated during July 2018  (Cesca et al. 2020; Lemoine et al. 2020a)       2018. This correspond to the beginning of the island subsidence and eastward displacement as recorded in GNSS data, inferred to be due to the drainage of an at least 30 km deep magma chamber  (Briole 2018; Cesca et al. 2020; Lemoine et al. 2020a; Feuillet et al. 2021 ). This proximal cluster appears not connected to the Distal cluster in our data set and this seems to have been the case since the beginning of the crisis.
The 'ADofal' model smooth velocity changes and the low local V P /V S ratios can be associated with an heterogenous volcanic island context: hot material and presence of gas or fluidfilled fractured rock. This low V P /V S ratio is also consistent with the H-k stacking from receiver functions performed by  Dofal et al. (2021)  and could support their interpretation of a continental crust with underplating similar to the magmatic continental domain of the southeastern coast of Madagascar  (Rindraharisaona et al. 2017). In terms of monitoring, the regular recovery and deployment of OBSs and the subsequent and immediate data analysis have been essential to monitor the Mayotte sismo-volcanic crisis. The wide collaboration between many scientists, engineers, and students, from different institutes and universities, during the pickathons continues to bolster rapid earthquake processing and has also fostered discussions on the evolution of the seismicity with the most up-to-date data.
Tsunami modeling studies  (Lemoine et al. 2020b; Poulain et al. 2021) , up to impact mapping, were conducted with tsunamigenic sources derived from this improved knowledge of the seismicity. The resulting high-quality data set of manually picked arrivals is now used in several detailed on-going studies (lithosphere structure investigations, seismicity time and space evolution, seismic sources studies, high-resolution locations) that will better constrain the seismicity, active structures and geological setting of the area. Various non-earthquake signals, such as hydro-acoustic or seismic waves not clearly associated to earthquakes, have been discovered during routine data screening and are currently being investigated. While REVOSIMA has reported more than 30 000 events for the period covered by the catalogue after daily manual screening of the continuous land stations data, our refined 5000 earthquake locations and 1-D velocity model ascertained by using OBSs data provides a solid foundation for future studies.
DATA A N D R E S O U RC E S RA network  (Résif 1995 ) YTMZ and MILA stations data available from Résif data centre (http://seismology. resif. f r). ED network MCHI station data is available upon request at EduSismo. 1T  (Feuillet, Van der Woerd and RESIF, 2022)   ObsPy  (Beyreuther et al. 2010 ) was used to convert NonLinLoc results into QuakeML files. GMT  (Wessel et al. 2019)   NonLinLoc software was used for earthquake locations with OBS data. Hypo71 software was used for preliminary earthquake locations of the first MAYOBS1 data set. VirtualBox software was used on all analyst's computer to run SeisComP3 graphical user interface client. Lcheapo software (https://github. com/WayneCrawford/lcheapo) was used to pre-process OBS data (clock correction and conversion to miniSEED). Past felt earthquakes statistics on Mayotte from SisFrance database: http://www.
Figure2. Typical pickathon organization. After each OBS recovery, the deployment time-span is divided in three groups, that is Group 1, Group 2 and Group 3. Each group processes the same number of events, by decreasing magnitude order, using the same software setup and the same picking guidelines and recommendations. The daily catalogue of events, automatically detected and manually confirmed on land-based network data (BRGM then RéNaSS and OVPF-IPGP), is synchronized in the pickathon database. It also feeds in real-time the REVOSIMA/MAYOBS catalogue. When OBS recovery is performed by a research vessel, each group alternates a 4-hr day and night shift. At the end of the pickathon, the relocated events, with additional manual picks and polarities on OBSs data, is merged within the REVOSIMA/MAYOBS database for further use for monitoring and research.
The joint test of multiple traits can also be an efficient way to detect genetic variants missed by univariate screening  [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] , especially those with association patterns that deviate from the observed phenotypic correlation  [24] [25] [26]. Nevertheless, while simulation studies and examples from real data applications in best case scenarios have confirmed the relevance of multitrait association tests, they have seldom been applied to large-scale datasets. The application of multitrait association tests to a large heterogeneous set of traits requires overcoming several practical issues including careful preprocessing of individual GWAS summary statistics to avoid statistical artifacts, the estimation of multiple global parameters, and addressing widespread missing summary statistics.
We addressed these issues in recent studies, developing the RAISS  [27]  approach for imputation, and JASS preprocessing and multitrait analysis pipeline software  [28]. Nevertheless, the relative performances of existing multitrait tests in real data have not been fully addressed. In brief, two types of methods have been developed, weighted sum of univariate statistics, assuming a specific multivariate genetic effect distribution  [12, 14, 15] , and an omnibus approach, allowing for one degree-of-freedom per statistic  [11, 29] , with some approaches using a combination of both  [30]. An extensive and fair comparison of these methods is challenging as most face some of the aforementioned practical issues, no readily available implementation  [28] , and power in real data highly depends on the true (and unknown) multitrait genetic effect distribution.
Biological meaning of genetic clusters We conducted series of in silico functional analyses with the objective of mapping clusters to candidate biological functions. For each phenotype set, we evaluated two types of enrichment: tissue-specific chromatin mark enrichment per cluster (S13 Table ) and a pathway enrichment framework (S14 and S15 Tables) which integrates multiple databases such as Gene Ontology (GO) and KEGG. Here, we focused on the Immunity and Metabolism sets as a case study. Given a phenotype set, while using the same set of traits for all clusters, we observed large differences in pathways, tissues and cell type enrichments between clusters. For the Immunity set, clusters 1 and 4 predominantly capture genetic effects of bone-mineral density; clusters 2, 3 and 5 affect inflammatory bowel disorder (IBD); and clusters 6, 7 and 8 capture variants with pleiotropic effects on rheumatoid arthritis and IBD (S27 Fig).
In addition to these well-known drugs, we provide a systematic listing of potential drug targets by cluster (S20 Table ) based on the druggable genome database  [64]. Altogether, these results suggest that drug development might be more effective by accounting for the gene context, i. e. , by selecting candidate genes not from their individual features but based on the disease association trend of genes displaying similar multitrait association profiles. Under this working hypothesis, the proposed inference of genetic functional groups can provide a means to identify those genes and therefore to select potential candidates.
Simulation studies on test statistical power To assess the statistical power of the Omnibus test and the SumZ tests, we designed the following simulation scenarios. A 10 trait z-score vector was generated for each of the 5000 causal SNPs as the sum of a genetic effect and a residual effect: Z = Z g + Z res. In the following, random covariance matrices were generated using the randcorr R package  [77]. Z res was sampled from a normal multivariate distribution with covariance matrix (S r ) terms equal to s z ¼ rn s = ffi ffi ffi ffi ffi ffi ffi ffi ffi n 1 n 2 p where n 1 is the sample size of the first study, n 2 is the sample size of the second study and ρ is the phenotypic correlation among the n s overlapping samples. As demonstrated in  [9] , ρ = ρ g +ρ e where ρ g is the genetic correlation and ρ e the environmental residual correlation.
The observations can be viewed as arising from the following hierarchical model: I i � Multinomialð1; pÞ Z i ðI ij ¼ 1Þ ¼ Nðm j ; S j Þ One major difficulty in applying the GMM was to deal with incomplete data. Indeed, even after imputation of some missing statistics, our datasets still contained some missing values. To solve the clustering, we implemented the statistical framework described by Ghahramani et al  [83]  which we recently implemented in a R package MGMM  [40]. This method relies on EM optimization techniques enabling the inference of unobserved variables from observed variables and an assumed Gaussian mixture model. In classical GMM, the only variable inferred is the posterior probability cluster membership. In the MGMM algorithm, missing Zscores are also inferred taking into account the observed Z-scores and the inferred probability to belong to a cluster.
Functional enrichment of metabolism clusters We used FUMA  [86]  SNP2GENE function to associate SNPs with genes based on two criteria, the physical position (in 30kb radius of a protein coding gene) and eQTLs (all significant cis-eQTL from GTEx up to a distance of 1Mb). Note that we restrained the eQTLs to the one that were found in relevant tissue for the Immunity and Metabolism set: immune cells for Immunity and adipose, intestine, liver and brain tissues for Metabolism (see S1 Data for complete parameters). After chaining genes to clusters based on SNPs, we performed a functional enrichment for pathways defined in KEGG  [87]  and GO  [88]  databases and derived report pvalues using FUMA GENE2FUNC function. Here, cluster's gene were compared against a background of protein coding genes. Finally, we used the R package pathview  [89]  to project genes onto KEGG pathways maps.
We developed the following R and python packages to perform our study: JASS_Preprocessing: https://gitlab. pasteur. fr/statistical-genetics/jass_preprocessingJASS: https://gitlab. pasteur. fr/statistical-genetics/jassRAISS: https://gitlab. pasteur. fr/statistical-genetics/raissMGMM: https://cran. r-project. org/web/packages/MGMM/index. htmlFigs 3-5 have been generated using the following R packages: https://cran. r-project. org/web/packages/UpSetR/index. htmlhttps://cran. r-project. org/web/packages/radarchart/README. htmlhttps://cran. r-project. org/web/packages/ggalluvial/vignettes/ggalluvial. htmlhttps://cran. r-project. org/web/packages/gplots/index. htmlhttps://cran. r-project. org/web/packages/igraph/index. htmlAcknowledgments This research has been conducted using the UK Biobank Resource under Application Number  42260.
In any case, the match between Comsol and Green's function is perfect. The Comsol and Matlab programs are provided in the supplementary material (Temperature_layer. mph, Fig4. m). Method 2, requiring the use of the heat transfer module, was found also effective with metal layers. Meshing infinite domains has to be done with caution, but it enables one to use much smaller computation domains. The code Temperature_nanohole. mph provided in the supplementary material makes use of infinite domains. In Fig. 5 , we also take the opportunity to show experimental measurements performed on this exact system. The correspondence looks good, supporting the effectiveness of the numerical approaches we propose here. The only free parameter was for Fig. 5(c ) the Cr layer thickness, which was not known precisely, and had to be set to 6.
Figures  10(a )-10(c) plots results of Comsol simulations of the temperature and heat power density associated with a 100-nm nanohole in a 100-nm thick gold film, illuminated with a focused laser at λ 0 = 650 nm and a power of 1 mW. This wavelength corresponds to the LPR resonance. The temperature map is similar to the one of a plain layer (see Fig. 4 ). However, the heat power density looks localized at the boundary of the nanohole. These simulations were obtained using the Temperature_nanohole. mph Comsol program provided in the supplementary material.
E. Effect of the wavelength We now consider a gold layer deposited on glass with a chromium adhesion layer. As discussed in Sec. II G, light absorption can be markedly enhanced by the presence of an adhesion layer, leading the community to usually illuminate the sample from the water side for trapping applications, for instance. 64 In the following, both illumination directions will be considered from the water and the glass sides [Fig. 11(a) ]. In this section, we consider only a hole diameter of 200 nm. The COMSOL program provided in the supplementary material can be used to compute any other configuration. Figures  11(b ) and 11(c) compare the effect of the wavelength. At 450 nm, gold behaves as a simple absorber, like chromium [Figs. 3(h) and 3(i)]. No surface plasmon can be excited. In any case, the absorption cross section is slightly negative when using an excitation wavelength below 500 nm, in agreement with Fig.
11(f ). At the same wavelength of 700 nm, while a hole in a gold layer (case 1) has a larger absorption cross section than with a Cr adhesion layer, it, however, yields a weaker temperature increase. An example of temperature and heat power density distribution is given in Figs. 10(d)-10(f). To end this section, we wish to notice that all the descriptions and the theory described so far could be applied to nanostructures on top of metal film, not only for nanoholes. Nanostructures would also have LPR that could couple to SPP, for which absorption cross section could also be defined by Eq. (48). Addressing such a system is out of the scope of this Tutorial. However, it could straightforwardly be modeled by adapting the Comsol program Temperature_nanohole. mph provided in the supplementary material.
V. CONCLUSION This Tutorial intends to provide a comprehensive description of the photothermal effects that can occur when illuminating a metal layer, without and with the presence of nanohole. The aim is to tackle all the aspects of the problem, from the physics, to the numerical approaches. We investigate and discuss the influence of parameters such as the layer composition, thickness, the presence of an adhesion layer, the illumination wavelength, incidence angle, polarization, and the nanohole size. We also define the absorption cross section of a nanohole and discuss its meaning in the case of a 50-nm thick gold layer, with or without chromium adhesion layer. We also provide in the supplementary material all the numerical codes used in this Tutorial, namely, (i) the Matlab code for Fresnel coefficients calculations, (ii) the Mathematica code for the calculation of Green's function for a three-layer system, (iii) the Matlab code for Green's function calculation of the temperature profile in a metal layer, (iv) the Comsol program for the calculation of the temperature profile in a metal layer under focused illumination, and (v) the Comsol program for the calculation of the temperature profile in a metal layer pierced with a nanohole.
1 to control the rates of successful interactions. As depicted in Fig. 9 , a successful synchronization between post and get operations leads to a successful DeX interaction, which is represented in the automaton by the trans_succ location. The experiments were conducted over a Linux machine with Intel Core i7, 16 Gb RAM. The rates of successful interactions are shown in Fig. 13  for various values of lifetime, time_on and δ get periods. As expected, increasing time_on periods for individual lifetime values improves the success rate. However, notice that the success rate is severely bounded by lifetime periods. For instance, when the lifetime period is very low (0 sec), the success rate, even at higher time_on intervals, remains bound at around 70% for δ get with mean 40 sec. Such behavior represents time/space coupled CS interactions, where each message is received immediately by the client (we assume that the transmission delay of the underlying network delay is negligible).
At the intermediate entity we set various lifetime periods, using the JMS API. All the interactions are performed using an Intel Xeon W3550e 3. 08 GHz × 4 (7. 8GB RAM) under a Linux Mint OS. Note that For getting reliable results, the mean values of δ post and δ get intervals are expected to be close to the expected mean values. To do so, we create sufficient number of post operations and get connections/disconnections by running each experiment for at least 2 hours. In Table  4 , we compare the results of simulated and measured success rates for time_on = 20 sec, δ post = Poisson(10) sec, lifetime = 0, 10, 40 sec and various distributions for δ get. The absolute deviation between the two is no more than 10%. This deviation may be attributed to implementation factors such as buffering at each entity (sender, receiver, intermediate entity) which may affect the success rates.
Unlike previous methods which are dedicated only to oxygen transfer, this method can be used for any other gas and over a wide range of pressures. It has recently been applied to study the CO2 barrier properties of sparkling wine stoppers at a higher-pressure difference (0. 6 MPa). However, this method is often used at 0 % relative humidity on dry samples and may derivate from real conditions. In this article, we present the versatility of the manometric method used here to study the transfer of oxygen through two different types of wine stoppers: natural cork and agglomerated cork. First, permeation experiments were performed on the thinnest possible wafers in order to quickly determine the effective diffusion coefficients of oxygen through these samples. Then, a specific statistical extrapolation method has been developed and validated with data obtained on full-length samples.
This model can be applied to determine the extrapolated diffusion coefficient in stoppers having different lengths, from 3 mm which is the minimum cork thickness required for oxygen transfer measurement up to 54 mm, the maximum length of stopper used for still wine. As an example, considering a 48 mm length cork stopper, 16 diffusion coefficient values are randomly taken from the statistical distribution of 3 mm cork wafers to calculate the mean diffusion coefficient through a 48 mm cork stopper. In order to obtain a fair statistical distribution of oxygen diffusion coefficients through the whole cork stopper, this procedure is repeated 10 4 times. For this purpose, a specific code has been developed on the Matlab software. It is worthy to note that equation 7 is a simplification that applies to a homogeneous material composed of successive layers of the same material (equivalent to a single phase at the macroscopic scale considered).
First, the oxygen saturation vapor pressure / was determined using Antoine's equation (Eq. 10) in order to calculate / ". log G -. = H -I J (Equation  10 ) With Antoine's coefficients H, L and (H=3. 9523, L=340. 024 and =-4. 144 in the case of oxygen  38  ), is the pressure (bar) and is the temperature (K). These coefficients were determined for a temperature range between 54 and 154 K. Nevertheless, they are used here to estimate / the saturation vapor pressure of oxygen by extrapolating to 298 K. The concentration / " , calculated by the relation / " = 1. , is equal to 25 186 mol. m -3. Once / " was known, the adjustable parameters of the FHH model were determined, using a code developed on the Matlab software. The parameters are shown in Table  1  and the corresponding modeling in Figure  4.