dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , 10 m). Recalling that TOOTS was also the only strategy to reach 100% delivery rate. On NCCU, Bubble Rap-RDP has the second smaller delay, but its delivery rate is comparable with Bubble Rap-TDP, which has much less overhead (analysis to follow). With a restricted communication range on NCCU, the delivery delay approximately doubles for most strategies. Thus, if the devices' transmission power in a given scenario is more restricted, the contents will take more time to reach their consumers opportunistically. On GRM (Fig. 12 (g)), due to the dataset's characteristic (low nodes' CD) and fewer contact occurrences, TOOTS' delivery latency increases almost ten times. Nevertheless, the delay-tolerant content reaches in an acceptable time. Bubble Rap-CDP shows smaller delivery latency than TOOTS, but its delivery rate is shallow due to the communication and transmission bottlenecks.
Overhead evaluation In Fig. 12 (d), we evaluate the overhead of each strategy. Bubble Rap-CDP appears with the smaller overhead on NCCU and GRM datasets with a 30 m communication range. On NCCU, the reason behind this is the existence of higher centrality degree nodes, making it possible for the disseminator nodes to find the destination directly for most of the generated contents. Still, as previously discussed, there is a bottleneck, lowering Bubble Rap-CDP's delivery rate compared to other strategies. As expected, Bubble Rap-RDP had the worst-case in terms of overhead. Compared with Bubble Rap-TDP, the latter performs with 53% less overhead on NCCU and 65% less on GRM. Nevertheless, we remark that the TDP-enhanced Bubble Rap needs more time to reach a 100% delivery rate on GRM when there is a 72 h deadline.
Applying other larger real-world datasets with other contacts distributions will be interesting when evaluating this strategy. TOOTS was the fastest strategy. It reached a 100% delivery rate and had 10% and 17% less overhead than Bubble Rap-RDP, respectively, on NCCU and GRM. When we restrict the communication range (Fig. 12 (h)), the overhead of all strategies also decreases on both datasets. On the other hand, particularly on GRM, no strategy reaches 100% delivery rate in the content TTL (i. e. , 72 h). On NCCU, if time is not the primary concern, the 10 m communication range is enough for TOOTS to deliver 100% of the contents in such a scenario. In this case, the strategy can save more device resources, as fewer contacts impose fewer forwarding decisions. Thanks to TDP policy, Bubble Rap-TDP is the second-best option in terms of overhead, considering a delivery rate of approximately 90% on NCCU and 65% on GRM.
Bubble Rap-CDP has a very low overhead on both datasets. On the other hand, its delivery rate is shallow, and as previously said, it tends to drain the most ''popular'' nodes. Bubble Rap-RDP has the higher overhead on NCCU, and its delivery ratio is close to 90% (i. e. , 10% less than TOOTS). On GRM, Bubble RAP-RDP has better overhead than TOOTS (at the cost of a slightly better delivery rate and low delivery latency). From these findings, we claim that due to the dataset's characteristics and the lack of heterogeneity per period, TOOTS has more difficulty delivering the content on GRM with a 10 m range.
Not surprisingly, the strategy's presented results are more correlated with the real-world dataset used (NCCU). In this dataset, the human-aware time-division made it possible to calculate the metrics to reflect the users' routines with fine-grained granularity, possibly leading to better strategy performance. There is a need for investigating other ideas of time-division through insights obtained from other populations' studies (i. e. , characterizations). Despite the increased availability of real datasets in the last years, there is still a lack of those covering larger populations (e. g. , entire metropolitan areas). These datasets are essential for identifying possible insights for future strategies and other scenarios. Further, according to the results, a dissemination strategy plays an essential role in improving the content delivery performance in a scenario such as the one presented.
Acknowledgments This work was supported in part by  CAPES, Brazil  ( 88881. 135682/2016-01 ,  88887. 468235/2019-00 ); CNPq, Brazil  ( 465. 560/2014-8 , 310. 201/2019-5 ,  432064/2018-4 );  FAPERJ, Brazil  ( E-26/203. 046/2017 );  FAPESB, Brazil  ( TIC0004/2015 );  EMBRACE associate team Inria project ,  France ; and  ANR MITIK project, France  ( PRC AAPG2019 ). We thank the outstanding contributions and presence of  Prof. Artur Ziviani  throughout this entire research work. We deeply miss him and will be forever grateful.
In this paper, we present the KOBE (K-dwarfs Orbited By habitable Exoplanets) experiment 2  , a legacy program of the Calar Alto Observatory with the CARMENES instrument to look for new habitable-zone planets around K-dwarfs. The paper is organized as follows. In Sect. 2, we describe the opportunity that this stellar type regime offers in the instrumental plane and the astrobiological context. In Sect. 3, we describe the goals and strategy of the programme based on the awarded observing time. In Sect. 4, we describe the target selection process followed to reach the final target list for the survey. Section 5 summarizes the supporting observations used during the selection process. In Sect. 6, we present a preliminary characterization of the KOBE sample of targets based on the first year of spectroscopic observations.
The HARPS GTO program (PI: X. Bonfils) has also followed-up several K-dwarfs  (Bonfils et al. 2005). However, these studies were not focused on the HZ of K-dwarfs and the sample and cadence were thus insufficient to reach the rocky regime in the habitable zone on a significant number of targets. This was mainly due to the lack of telescope time. Hence, a dedicated service program with the flexibility of a moderate number of nights per semester and distributed along a sufficiently large time span would be required to reach this regime. Based on the planet occurrence rates from the Kepler mission (e. g. , Kunimoto & Matthews 2020) and while taking into account the guided nature of this experiment, we expected to detect 15-40 new planets, with a relatively high percentage of them residing in the habitable zone and being in the super-Earth regime (3 < M p < 10 M ⊕ ), detectable with CARMENES (see Sect.
TESS photometry We used observations collected by the transiting exoplanet survey satellite (TESS,  Ricker et al. 2014)  during the first 2 yr of its operation (sectors 1 to 26), to make an assessment of the photometric variability of the initial list of 6458 potential KOBE targets (the pre-BTL, see Sect. We used the 2-min cadence lightcurves and the TESS-SPOC light curves from the full frame images provided by the TESS science team and available for download from the Mikulski Archive for Space Telescopes (MAST 9 ). In both cases, we used the PDCSAP (pre-search data conditioning simple aperture photometry) fluxes. Details of data processing steps included in the pipelines that produced these light curves can be found in  Jenkins et al. (2016)  and  Caldwell et al. (2020). We searched the MAST archive for the light curves using the Gaia ID in order to avoid false identifications and analyzed 6404 2-min light curves for 2990 objects (many objects were observed in two or more sectors) and 3351 full frame images (FFIs) light curves for 1936 objects, of which only 230 did not have 2-min light curves.
We analyzed each sector separately. Prior to the variability analysis, data points marked as low quality in the TESS data files were excluded. Since the light curves also exhibit several types of instrumental artifacts (including slow drifts and significant deviations at the beginning or end of the continuous data segments), some light curve sections were manually removed. Once we had clean light curves, we automatically searched for variability signals by measuring the excess of noise in terms of the amplitude of the light curves over 9 https://mast. stsci. edu/ the root mean square (RMS) and the RMS using different bins (e. g. , every 2 h and every 10 h). A Lomb-Scargle periodogram calculation was also performed in order to search for periodic signals. We inspected a set of light curves by eye in order to set a threshold for those values (amplitude, rms, and amplitude/rms) to automatically discard variable sources.
Spectral energy distributions To verify the absence of companion stars, we studied the spectral energy distributions (SEDs) to evaluate possible contamination by additional sources with significantly different spectral types. We obtained the distributions by means of VOSA 10 (  Bayo et al. 2008) , a spectral analyzer from the Spanish Virtual Observatory (SVO 11 ) tool. This facility provides a complete SED gathering archival photometric data in the whole electromagnetic spectrum range (e. g. , from 2MASS All-Sky Point Source Catalog  (Skrutskie et al. 2006) , Tycho-2 Catalog  (Høg et al. 2000) , and Pan-Starrs PS1 DR2  (Kaiser et al. 2002)  among others) and fits it minimizing the reduced χ 2. As our targets are K-late dwarfs we expect to have a black-body spectrum peaking around the visible and near-infrared range.
We obtained between 2000 and 50 000 frames depending on the target magnitudes and selected the 10% best frames by using the Strehl ratio  (Strehl 1902). We then proceeded to the alignment and stacking of the selected frames. Based on this final image, we computed the sensitivity curve by using our own developed astrasens package 12  with the procedure described in  Lillo-Box et al. (2012 , 2014). We find no evidence of additional sources within this field for any of the observed targets and within the computed sensitivity limits. Overall, we can discard contaminant sources down to 0. 2 arcsec with contrast magnitudes in the SDSSz band of brighter than ∆m SDSSz < 5 mag. An example of this contrast curve and high-spatial resolution image is shown in Fig. 8.
Vetting spectroscopy with CARMENES We obtained one spectrum per target for all the BTL and bBTL samples to check that the rotational velocity of the stars in the sample are small enough as to obtain precise RV measurements. In total, all 100 stars were observed and all of them presented FWHM < 9 km s -1. The observations were carried out in December 2020, prior to the start of the observations of the KOBE experiment. The data will be released together with the whole KOBE dataset at the end of the survey in 2024-2025.
Spectroscopic characterization of the KOBE sample We used the first 10 months of KOBE data to create high signalto-noise spectra of the KOBE targets in order to perform a detailed spectroscopic characterization of their properties. We adapted the method described in  Marfil et al. (2021)  for the analysis of the CARMENES M-dwarf sample to derive the stellar atmospheric parameters of the KOBE sample (i. e. , effective temperature, T eff , surface gravity, log g, and metallicity, [Fe/H]). We used the STEPARSYN code  (Tabernero et al. 2022 ) as a Bayesian implementation of spectral synthesis along with a synthetic grid based on BT-Settl models  (Allard et al. 2012). The original grid in  Marfil et al. (2021)  was expanded towards the hot regime (up to 5000 K) to effectively cover the parameter space encompassed by K-type dwarfs.
We carried out a detailed four-step target selection process (see Sect. 4) to ensure the sample selection is optimal for planetary searches. This selection included data from different A102, page 10 of 12 techniques, including TESS photometry, high-spatial resolution imaging, inspection of the Gaia catalog, analysis of the spectral energy distributions, vetting spectroscopy, and inspection of the target properties including magnitude, sky coordinates and visibility. Our final sample has a roughly Gaussian distribution in effective temperature around 4100 K (with a slight overdensity around 4300K) and a distribution of metallicities similar to that of the solar neighborhood (see  Udry et al. 2000; Santos et al. 2004 ) ranging from -0. 55 to +0. 25 dex, with a median around [Fe/H] = -0. 07 dex.
Acknowledgements. We thank the anonymous referee for providing insight comments on the first versions of the manuscript that allowed to improve its final quality. We thank  J. Iglesias-Páramo  for coordinating the observations of the KOBE Legacy from the Observatory side. This publication makes use of The Data & Analysis Center for Exoplanets (DACE), which is a facility based at the  University of Geneva (CH)  dedicated to extrasolar planets data visualisation, exchange and analysis. DACE is a platform of the  Swiss National Centre of Competence in Research (NCCR) PlanetS , federating the Swiss expertise in Exoplanet research. The DACE platform is available at https://dace. unige. ch. In particular, we thank  N. Buchschacher  for his continuous help. J. L. -B. , O. B. -R. and A. C. -G. acknowledge financial support received from  "la Caixa" Fundation  ( ID 100010434 ) and from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Slodowska-Curie  grant agreement no.
847648 , with fellowship code  LCF/BQ/PI20/11760023. This research has also been partly funded by the  Spanish State Research Agency (AEI)  Projects No. PID2019-107061GB-C61  and no. MDM-2017-0737  Unidad de Excelencia "María de Maeztu" -Centro de Astrobiología (INTA-CSIC ). A. M. S. acknowledges support from the  Fundação para a Ciência e a Tecnologia (FCT)  through the Fellowship 2020. 05387. BD. and  POCH/FSE (EC). This work was supported by  FCT -Fundação para a Ciência e a Tecnologia  through national funds and by  FEDER  through  COMPETE2020 -Programa Operacional  Competitividade e Internacionalização by these grants:  UID/FIS/04434/2019 ;  UIDB/04434/2020 ;  UIDP/04434/2020; PTDC/FIS-AST/32113/2017 & POCI-01-0145FEDER-032113; PTDC/FIS-AST/28953/2017 & POCI-01-0145-FEDER-028953; PTDC/FIS-AST/28987/2017 & POCI-01-0145-FEDER-028987.
The French group acknowledges financial support from the  French Programme National de Planétologie (PNP, INSU). This work has made use of the following software and databases: astropy (Astropy Collaboration 2018), matplotlib (Hunter 2007), numpy (Harris et al. 2020), scipy (Virtanen et al. 2020),  TOP-CAT (Taylor 2005 ), and the SIMBAD database (operated at  CDS, Strasbourg, France ).
Indeed, hypocentral depths are crucial to assess the current deformation of the planet, and to infer whether Mars remains internally active or if it is instead passively deformed by global contraction  (Knapmeyer et al. , 2006; Plesa et al. , 2018). In line with the aforementioned pioneering works, we present a new independent inversion study that further improves our knowledge on both marsquake locations and on the structure of crust, the mantle, and the core of Mars. To this end, we measured the arrival times of body waves (direct, multiples, depth phases, and core-reflected S-waves) for 17 marsquakes, and their back azimuths from the InSight seismic record. Relying on the corresponding set of differential arrival times, our inversions use the largest seismic arrival time data set published so far and considered for Mars, with 108 phase picks.
To date, the largest database is made of 76 picks, considering 14 marsquakes  (Durán et al. , 2022). The previous comprehensive works inverting for Mars' seismic structure considered fixed source depths  (Khan et al. , 2021; Stähler et al. , 2021). As carried out in  Durán et al. (2022) , our current approach also represents one of the first attempt to invert simultaneously for the complete location of.
For this reason, we opted for the EH45 composition for the geodynamically constrained inversions. With the knowledge of composition and thermal state, bulk mantle properties (density, thermal expansion, specific heat) are deduced. Each thermo-chemical history (corresponding to a given set of the aforementioned model parameters) is evolved for 4. 5 Gyr. The resulting thermo-chemical structure is then used to compute the seismic velocity structure at the present-day. In the mantle, our mineralogical model relies on the Perple_X Gibbs free energy minimization software  (Connolly, 2005)  with the thermodynamic database of  Stixrude and Lithgow-Bertelloni (2011). For the core, we considered a simple generic equation of state for a compressible medium following  Nimmo and Faul (2013)  that does not make assumptions about the composition of the core, in which the density is adjusted to match the mass constraint of Mars, M = 6.
Conclusion The uneven and sparse seismic data that results from InSight's single-station/multiple events setup requires inversion approaches specifically designed to best exploit this configuration. In this framework, we used the method developed in  Drilleau et al. (2020 Drilleau et al. ( , 2021) )  to constrain the quake locations and the 1-D average seismic velocity profiles of Mars, considering a new and augmented data set of direct body waves phases (P and S), surface reflected phases (PP, PPP, SS, and SSS), depth phases (pP, sS, and sP), and core-reflected S-waves (ScS). To the best of our knowledge, this is currently the largest seismic data set so far considered for the inversion of the Martian structure. Two distinct approaches were considered. One approach relying on a classic Bézier curves parameterization of the 1-D seismic velocity profiles.
The three most distant marsquakes are located southeast of InSight, in close vicinity to the Martian dichotomy, and northwest of InSight in Utopia Planitia. Given the large uncertainties on their back azimuths, additional work remains needed to understand the origin of these marsquakes, in particular their focal mechanisms. Yet, our results represent a first step toward a better understanding of the location of the seismicity on Mars. Overall, the arrival time data set we extracted out of the seismic record, combined with geodynamic and other associated considerations yields relatively tight constraints on the present-day structure of Mars, on the rheology of its mantle (except for its pressure-dependence that remains too exposed to tradeoffs between temperature and viscosity), and on its thermo-chemical history.
Data Availability Statement Derived data files used in this study  Drilleau (2022)  are available using the https://doi. org/10. 5281/ zenodo. 6334517. The numerical codes used to compute the results in this study are described in detail in Drilleau et al. (2021) (McMC inversion); Samuel et al. (2019) (Mars parameterized convection); and Crotwell et al. (1999) (ray paths and travel times).
Motivation: Single-molecule localization microscopy allows studying the dynamics of biomolecules in cells and resolving the biophysical properties of the molecules and their environment underlying cellular function. With the continuously growing amount of data produced by individual experiments, the computational cost of quantifying these properties is increasingly becoming the bottleneck of single-molecule analysis. Mining these data requires an integrated and efficient analysis toolbox. Results: We introduce TRamWAy, a modular Python library that features: (i) a conservative tracking procedure for localization data, (ii) a range of sampling techniques for meshing the spatio-temporal support of the data, (iii) computationally efficient solvers for inverse models, with the option of plugging in user-defined functions and (iv) a collection of visualisation tools and a simple web-based interface.
Continuous increase in both the number of trajectories and particle densities has recently paved the way to global cellular mapping  (Beheiry et al. 2016 , Briane et al. 2019 , Hoze et al. 2012 , Salomon et al. 2020). The massive increase in data throughput induces new computational challenges however. Common experiments can yield millions of recorded points and hundreds of thousands of trajectories  (Giannone et al. 2012) , and models can have hundreds of thousands of parameters  (Laurent et al. 2019). This imposes significant constraints on algorithmic design and computing infrastructures. In this article, we introduce TRamWAy, The Automatic Random Walk Analyzer, a Python library for Bayesian spatio-temporal analysis of dynamic localization microscopy data. TRamWAy implements the full processing chain from raw SMLM data to biophysical landscapes ready to be visually or programmatically explored.
II. SOFTWARE IMPLEMENTATION AND METHODS In this section, we introduce a selection of key methods provided in TRamWAy. The full list of features, tutorials and notebooks can be found at https://tramway-tour. readthedocs. io (i) Particle tracking: TRamWAy can take as input both trajectories or time-stamped localizations. For treating localization data, a non-tracking graph-based algorithm is featured to extract an optimal pairing between molecules in successive frames. Trajectories can be reconstructed by linking consecutive elementary displacements. The procedure, (iv) Post-processing: Due to the large variety of possible post-analyses, TRamWAy focuses on making the outcome of these analyses robust to various sources of noise and bias. TRamWAy stores all data, inferences and analyses in HDF5-based files to be manipulated in Python with the RWA-python library, or in text files.
Inferred parameter landscapes can be rendered in many ways, in Python or using a minimalist web interface. The graphical output options include movies for displaying dynamic landscapes. Furthermore TRamWAy features a Bayesian evidence test for force/drift detection in heterogeneous diffusion landscapes  (Serov et al. 2020). TRamWAy matches the current demand for automated pipelines and high flexibility to programmatically handle multiple and large datasets. Furthermore, observed molecules are often scattered into distinct spatial domains, which may be studied independently and in parallel as regions of interest (ROIs). While exploratory data analysis in single ROIs can be done with the InferenceMAP software (Beheiry et al. 2016) in 2D and in 3D with Genuage  (Blanc et al. 2020) , TRamWAy features sets of building blocks to automate parallel processing of large-scale data with large amounts of ROIs (Fig.
1(b) ). The workflow is specified in a declarative way, i. e. in arbitrary order. TRamWAy can parallelize it on the local host and onto a remote host such as a high-performance computing (HPC) cluster. On remote hosts, TRamWAy automatically dispatches user scripts after minimal parsing for execution as job arrays in a container, which is also automatically fetched if missing. Input data are not transferred; only outputs are retrieved once the jobs are complete. User-defined functions can be added to the inference.
The taxonomic assignment of metabarcoding data strongly depends on the taxonomic coverage of the reference database. Therefore, it is fundamental to access and pool data from the two major sources of COI sequences , the BOLD and the NCBI nucleotide database s, and enrich them with custom COI data, when available. The COInr database is a freely available , easy-to-access database of COI reference sequences extracted f rom the BOLD and NCBI nucleotide databases. It is a comprehensive database: not limited to a taxon, a gene region, or a taxonomic resolutio n; therefore, it is a good starting point for creating custo m database s. Sequences are dereplicated between databases and within taxa. Each taxon has a unique taxonomic Identifier (taxID), fundam ental to avoid ambiguous associations of homonyms and synonyms in the so urce database.
TaxIDs form a coherent hierarchical system f ully compatible with the NCBI taxIDs allowing to create their full or ranked linages. The mkCOInr tool is a series of Perl scripts necessary to download sequences from BOLD and NCBI, build the COInr database and customize it according to the users' needs. It is possible to select or eliminate sequences for a list of taxa, select a specific gene region, select for minimum taxonomic resolutio n, add new custom sequences, and format the database for BLAST, QIIME, RDP classifier.