dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSION AND FUTURE WORKS This paper proposes a visualization tool for the taxonomy in  [2]. With new attacks occuring, the evolution of the taxonomy is facilitated by the tool and data being open-source. In future work, the tool can be extended to hold a more comprehensive database of OSS supply chain incidents. So far, only a subset of affected packages is referenced. Another possible extension is to reflect the results of automated or manual assessments done for a given development project, to highlight open and covered attack vectors, similar to what the Atomic Red Team tool 7 does for the ATT&CK framework.
Data collection The different parameters are detected by a dedicated sensor, as in  Zanni et al. (2021). Each monitoring station is connected through an integrated communication module, including Bluetooth 4. 1. , Wi-Fi connection 802. 11 b/g/n @ 2. 4 GHz, cellular (4 G LTE) gateway and Bracket bundle, LoRa gateway, Ethernet gateway and Bracket bundle. The size of the monitoring station is limited to 10 Â 10 Â 3. 5 cm. The monitoring stations comply with some relevant green building certifications, such as WELL, LEED, Fitwel, LBC and RESET. The monitoring network has been implemented and data collected and shared by PlanetWatch SaS. The experiment was set up by following  Zanni et al. (2021) , with the installation of a whole RESET-compliant monitoring network over a single hospitality premise.
The covered areas were both for public use (i. e. the reception area, including both the hall and lobby; the kitchen and the restaurant; and recreational area, including the fitness center) and private use (i. e. 24 guestrooms). This represented the first case in Europe, and the second in the world, of a hotel premise being monitored based on RESET air quality standards. Each parameter was sampled every 5 min for a period of 185 days, i. e. from mid-February to the end of August 2021. This allowed us to achieve what is, to the best of our knowledge, the longest IAQ indoor monitoring on a hospitality premise and the richest data set of its kind.
Data analysis The collected data were analyzed based on the proposed framework. We calculated correlation by processing the data set with OpenAir R package, which is currently recognized as a solid tool for air quality data analysis. The tool has been developed using R-Forge  (Theußl and Zeileis, 2009 ) at King's College in London, by the organization responsible for the city's air monitoring network  (Carslaw and Ropkins, 2012). Even though the tool is typically applied to outdoor air quality data, it is adaptable to different set-ups thanks to a library of possible analyses, developed in R programming language, which are free and open source. The ventilation system we examined did not allow us to fractionate or limit its effect and thereby cover the entire building equally. Consequently, we studied the effect of ventilation with the application of Pearson's correlation among environmental and IAQ parameters.
Regarding the outdoor air pollution, we selected PM 2. 5 as a proxy for a general urban pollution. We requested data about the nearest public monitoring station from Agenzia Regionale Per l'Ambiente Piemonte (ARPA Piemonte -Stazione ARPA Lingotto, available at https://aria. ambiente. piemonte. it/#/qualita-aria/dati), i. e. the local environmental authority. As data is only made publicly available in terms of daily means, we elaborated indoor PM 2. 5 data in the same form and proceeded with the analyses. For the MLR, the overall fit of the model was tested by F-test, i. e. variance explained, and the relative contribution of each of the independent variables. The MLR model is expressed as follows: y ¼ b 0 þ b 1 x 1 þ b 2 x 2 þ b 3 x 3 þ «.
Results Below, we briefly report the results obtained from the monitoring activity and the performed tests. The complete analytical results are available in the Appendix and from the authors, upon request. We collected data for more than six months, generating over 50,000 observations for each area monitored. This allowed us to first verify the general IAQ performance on the premise, differentiated by area. Based on the mean values registered, we observed differences in each area across all the parameters, both environmental, i. e. T and RH and IAQ, i. e. CO 2 , PM 2. 5 and VOCs (see Table  A1 ). These differences were more marked for PM 2. 5 and VOCs, from 60% to 90%, while T, RH and CO 2 presented more limited differences, i. e. from 10% to 20%. Moreover, considering SD, we saw that T and CO 2 presented values correspondent to about 10% of the mean values, while RH reported about 30% and PM 2.
Limitations and future research The study presents some inherent limitations, which suggest pathways for future research. First, our applied monitoring methodology excluded a check of the microbial pollution in indoor air, which would have required active spot testing. Considering the relevance of the topic within the pandemic crisis, this would represent a valuable integration to the framework proposed. Second, our seasonal tests of the environmental and IAQ parameters only encompassed a single year. While the collected data supported our conclusions, it would be useful to confirm the results further by monitoring for multiple years. Third, the overlaps between changing seasons and COVID-19-related restrictions did not allow us to properly capture the relative magnitude of their effects, except in relationship to the use of IJCHM hotel areas and guest habits.
Currently, disease activity is mainly evaluated during face-to-face appointments and no PROs are collected in between visits. Due to the declining number of rheumatologists  [3] , the recommended tight face-to-face monitoring is increasingly difficult to implement in clinical routine. Additionally, RA disease activity fluctuates, often causing disease flares in between appointments. Electronic PRO (ePRO) enable continuous remote monitoring and could improve monitoring of disease activity by capturing otherwise overlooked changes of disease activity  [4]. Shaw et al. further reported that discussion of ePRO data leads to an improved patient-provider relationship  [5]. A major challenge, not limited to RA and rheumatology is poor ePRO adherence  [4, [6] [7] [8]. Poor adherence can stem from a multitude of factors, including but not limited to lack of perceived benefit  [9] , age and high disease activity among patients  [10]  or lack of integration into clinical workflows and electronic health records among professionals  [11, 12].
We conducted pilot interviews to test and refine the interview guides. No revisions were necessary. Additional sociodemographic data were collected, including gender, age, diagnosis, education and occupation or medical practice. To reduce the risk of infection and lower participant burden, the interviews were conducted via telephone. The interviews were audio-recorded and transcribed verbatim. Qualitative analysis of the interviews was performed iteratively by F. M. and S. M. based on Kuckartz's structured qualitative content analysis  [20]  using MAX-QDA software (Verbi GmbH). Relevant text passages from the interview material were coded according to a deductive-inductive procedure. Categories were developed based on the research questions and merged into a coding tree, which was then discussed by the members of the study team.
Data Reduction Procedure This analysis uses all available ground-based observations of MOA-2019-BLG-008. The list of contributing telescopes is given in Table  1. Most data were obtained in the I band (or SDSS-i), but we note that MOA observations were performed with the MOA wide-band red filter, which is specific to that survey  (Sako et al. 2008). ROME/REA obtained observations in three different bands (SDSS-i′, SDSS-r′, and SDSS-g′). The KMTNet survey observations were carried out in the I band, with a complementary V-band observation every 10 I exposures. The photometric analysis of crowded-field observations is a challenging task. Images of the Galactic bulge contain hundreds of thousands of stars whose point-spread functions (PSFs) often overlap; therefore, aperture and PSF-fitting photometry offer very limited sensitivity to photometric deviations generated by the presence of low-mass planetary companions.
For this reason, observers of microlensing events routinely perform difference image analysis (DIA;  Tomaney & Crotts 1996; Alard & Lupton 1998; Bramich 2008; Bramich et al. 2013) , which offers superior photometric precision under such crowded conditions. Most microlensing teams have developed custom DIA pipelines to reduce their observations. OGLE, MOA, and KMT images were reduced using the photometric pipelines described in  Udalski (2003) ,  Bond et al. (2001), and Albrow et al. (2009) , respectively. The LCO observations were processed using the pyDANDIA pipeline (ROME/REA, in preparation), a customized reimplementation of the DanDIA pipeline  (Bramich 2008; Bramich et al. 2013)  in Python. The data sets presented in this paper have been carefully reprocessed to achieve greater photometric accuracy, and it is these data that we used as input when modeling the microlensing event.
, i. e. , JD 2,457,800. Lines marked with " * " indicate that this data set was not used during the modeling process, as described in the text. In cases in which the rescaling parameters were not constrained, they were fixed to k = 1. 0 and e 0. 0 min =. (  1999). To model the event, we use the pyLIMA software  (Bachelet et al. 2017) , which employs the VBBinar-yLensing code  (Bozza 2010; Bozza et al. 2018)  to estimate the binary lens model magnification, and we search for a general solution including the annual parallax, but we also explore the static case for completeness. The first step of modeling involves identifying potential multiple minima in the parameter space, and for this we employ the differential evolution algorithm  (Storn & Price 1997; Bachelet et al. 2017). During the modeling process, we rescale the data uncertainties using the same method presented in  Bachelet et al.
(2019) , which introduces the parameters k and e min for each data set: k e , 3 2 2 min 2 ( ) s s ¢ = + where σ and s¢ are the original and rescaled uncertainties (in magnitude units), respectively. The coefficients for each data set are given in Table  1. Finally, we explore the posterior distribution of the parameters of each minimum that we identify using the emcee software (Foreman-Mackey et al. 2013).
026, 0. 262 0. 032  et al. 2018) , one can estimate the reddening E(gi) = 3. 74 ± 0. 1 mag, E(ri) = 1. 41 ± 0. 1 mag, and extinction A i = 2. 3 ± 0. 1 mag. The best model returns a source magnitude of (g, r, i) s = (24. 2 ± 0. 8, 19. 47 ± 0. 05, 17. 31 ± 0. 03) mag. Because the event occurred at the beginning of the season, the event was poorly covered by the ROME data in the g band and the source brightness is not well constrained. However, we can use the (ri) color and i magnitude to estimate θ *. As described in Appendix A, we used the same catalog as  Boyajian et al. (2014) ( )    (Ruiz-Dern * ( ) ( )( ) ( ) ( ) ( ) ( ) q = -  - +  - - +  This relation returns θ * = 9. 8 ± 1. 2 μas, while the second relation using the g band returns θ * = 22. 9 ± 8. 4 μas. The latter value is inaccurate owing to large uncertainties in the source brightness in the g band.
Analysis of the Blend As can be seen in the different CMDs, there is a significant blend flux in the data sets, which likely belongs to the population of foreground stars of the Galactic disk. The measurements from MOA/OGLE are (V, I) b = (18. 5 ± 0. 1, 16. 88 ± 0. 01) mag and are consistent with a late F dwarf located at ∼2. 5 kpc  (Bessell & Brett 1988; Pecaut & Mamajek 2013 ; assuming half extinction). Measurements from the ROME survey indicate that the blend brightnesses are (g, r, i) b = (19. 43 ± 0. 01, 17. 91 ± 0. 01, 16. 97 ± 0. 02) mag, consistent with a G dwarf at ∼2. 0 kpc  (Finlator et al. 2000; Schlafly et al. 2010 ). These results are in agreement with the lens properties derived in the next section. The source and blend have similar brightness in Cousins I band, but the former is much redder.
Assuming that the blend is the lens, π E ∼ 0. 2, a source distance D S ∼ 8 kpc, and that the light detected by Gaia is solely due to the blend, we can estimate θ E ∼ (0. 39 -0. 125)/0. 2 ∼ 1. 3 mas and M L ∼ 0. 8 M e. So the astrometric solution is also compatible with a G dwarf at ∼2. 5 kpc, with the notable exception of the relative proper motion. Indeed, we can 39 https://gaia. ari. uni-heidelberg. de/index. html 40 https://gea. esac. esa. int/archive/documentation/GDR2/Gaia_archive/ chap_datamodel/sec_dm_main_tables/ssec_dm_ruwe. html 41 https://gea. esac. esa. int/archive/documentation/GDR2/ 42 https://github. com/agabrown/gaiaedr3-flux-excess-correction estimate the geocentric relative proper motion to be μ geo = θ E /t E ∼ 1. 3/80 ∼ 6 mas yr -1. Because this event peaked in early March, the heliocentric correction v ⊕ π rel /au ∼ (0.
(2014) , while the kinematics of the population are described in  Bienaymé et al. (2015). We select the  Marshall et al. (2006)  3D map to estimate the extinction for the simulation. Finally, we note that the Besançon model has been used in several studies for microlensing predictions. Based on the original work of  Kerins et al. (2009) ,  Awiphan et al. (2016)  and  Specht et al. (2020)  developed the MaBμls -2 software that computes theoretical maps of the distribution of optical depth, event rate, and timescales of microlensing events, which are in good agreement with observations. In particular, MaBμls -2 predictions of event rate and optical depth are in excellent agreement with the 8 yr of observations from the OGLE survey  (Mróz et al. 2019 ). The Besançon model has also been used by  Penny et al.
R. A. S. and E. B. gratefully acknowledge support from  NASA  grant  80NSSC19K0291. Y. T. and J. W. acknowledge the support of  DFG priority program SPP 1992 "Exploring the Diversity of Extrasolar Planets " ( WA 1047/11-1 ). K. H. acknowledges support from  STFC  grant  ST/R000824/1. J. C. Y. acknowledges support from  NSF  grant No. AST-2108414. Work by C. H. was supported by the grants of the  National Research Foundation of Korea  ( 2019R1A2C2085965  and  2020R1A4A2002885 ). This research has made use of  NASA 's Astrophysics Data System and the  NASA Exoplanet Archive. The work was partly based on data products from observations made with  ESO Telescopes at the La Silla Paranal Observatory  under program ID 177. D-3023, as part of the  VST Photometric Halpha Survey  of the  Southern Galactic Plane and Bulge  (VPHAS+; www.
vphas. eu). This work also made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC; https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. CITEUC is funded by  National Funds through FCT -Foundation for Science and Technology  (project:  UID/Multi/ 00611/2013 ) and  FEDER-European Regional Development Fund  through  COMPETE   2020Operational Programme Competitiveness and Internationalization  (project:  POCI-01-0145-FEDER-006922 ). D. M. B. acknowledges the support of the  NYU Abu Dhabi Research Enhancement Fund  under grant  RE124.
This research uses data obtained through the  Telescope Access Program (TAP) , which has been funded by the  National Astronomical Observatories of China , the  Chinese Academy of Sciences , and the  Special Fund for Astronomy  from the  Ministry of Finance. This work was partly supported by the  National Science Foundation of China  (grant Nos. 11333003 ,  11390372 , and  11761131004  to S. M. ). This research has made use of the KMTNet system operated by the  Korea Astronomy and Space Science Institute (KASI) , and the data were obtained at three host sites of CTIO in Chile, SAAO in South Africa, and  SSO in Australia. The  MOA  project is supported by  JSPS KAKENHI  grant Nos. JSPS24253004 ,  JSPS26247023 ,  JSPS23340064 ,  JSPS15H00781 ,  JP16H06287 , and  JP17H02871. Software: Astropy (Astropy Collaboration et al.
8 s between trucks in the platoon configuration, and (3) four speeds (40 km/h, 60 km/ h, 70 km/h and 80 km/h). The viscoelastic response of the pavement structure (strain field) under the test conditions applied on-site was computed using software Viscoroute 2. 0 and was compared with the results measured with the strain gauges. Finally, the effect of individual and platoon truck configurations was compared.
DOI: 10. 1201/9781003222897-36  In this context, since 2018, the European Union has been developing the research project called ENSEMBLE, which main objective is to pave the way for the adoption of multi-brand truck platooning in Europe, to improve fuel economy, traffic safety, and throughput  (Ladino et al. , 2021; Mascalchi et al. , 2020). This paper presents part of the research efforts done by ENSEMBLE to evaluate the effect of platooning trucks on pavement structures. For this purpose, a test section of a full-scale pavement was instrumented and subjected to the passage of trucks in individual and platoon configurations. The experimental results were simulated with a viscoelastic model, using the software Viscoroute 2. 0  (Chabot et al. , 2010).
Material properties and geometry As shown in Table  1 , the pavement structure used in the model is composed of seven layers, con sidering a foundation layer and three asphalt layers, with three thin interlayers at the interfaces. Table  1  shows each layer thickness, Poisson ratio, modulus, and material behavior, as well as the corresponding pavement temperature used for each test campaign and load configuration. The modeling was done using the software Viscoroute 2. 0  (Chabot et al. , 2010). It is a semi-analytical software, developed to model pavement response under moving wheel loads, considering elastic or visco-elastic pavement materials. The viscoelastic behavior of the asphalt layers was simulated using the Huet-Sayegh model, which equation and parameters are shown in Table  2. (Duong et al.
CONCLUSIONS This paper presents some of the results obtained from a full-scale test performed on a pavement test section subjected to individual and platoon truck loadings. Three nonautonomous semi-trailer trucks were driven by different drivers to configured platoon truck configurations with 0. 8 s time gaps and testing speeds of 40 km/h, 60 km/h, 70 km/h and 80 km/h, during two test campaigns, carried out in winter and summer conditions. The visco elastic responses of the pavement structure (strain fields) were modelled using the software Viscoroute 2. 0, for different speed and temperature conditions, and compared with the experi mental measurements. The study has led to the following conclusions: (1) the model with viscoelastic inter faces was able to reproduce correctly the experimental strain values; this confirms the capacity of Viscoroute to predict pavement response under complex multiple load conditions, and the possibility to use it as a predictive tool to evaluate the effect of other platoon configurations; (2) both transversal and longitudinal strains were considerably higher for the higher temperatures of summer, due to the thermoviscoelastic behavior of the asphalt layers; (3) the highest level of strains were obtained for the trailer tridem axle and the transversal direction, in the summer campaign; (4) transverse strains also showed important strain accumulation after the passage of the vehicles, especially in the platoon configuration; (5) only for the case of the summer campaign, platoon truck configur ations showed higher maximum transversal/longitudinal values than the individual truck configurations.
(  59 ), together with the  Wolf et al. (2010)  relation for the line-of-sight velocity dispersion. Bílek et al. (2019b)  and  Haghi et al. (2019b)  did not quantitatively assess the EFE on the velocity dispersion for DF44, since the data required as little of it as possible. We propose examining the question of the EFE in this galaxy more quantitatively and expanding the study to a larger sample of UDGs. In this article, we compare observed stellar velocity dispersion measurements of 11 Coma cluster UDGs with MOND modified gravity predictions with and without EFE. We also propose a new analytic formula in quasi-linear MOND  (QUMOND, Milgrom 2010) , assuming a spherical self-gravitating system embedded in a constant external field, which is tested against numerical computations and compared with existing formulae.
Bílek et al. (2019b)  interpreted the agreement between the velocity dispersion profile with the MOND prediction in isolation as an indication that DF44 is either far from the cluster centre (the projected distance is 1. 8 Mpc), undergoing disruption, or embedded in a dark baryonic halo. It was also noted that some degree of tangential anisotropy allowed a better fit of the outer data points, which is also true in the CDM context.
Acknowledgements. We thank the anonymous referee for their uplifting report. The authors would like to thank  M. Milgrom ,  H. S. Zhao ,  E. Asencio ,  I. Banik ,  G. Mamon ,  R. Errani ,  F. Jiang ,  D. Zaritsky ,  F. Lelli  and  F. Combes  for stimulat-ing discussions and comments. B. F. is grateful for enlightening discussions with  C. Skordis  and  A. Durakovic. J. F. , B. F. , P. -A. O. and R. I. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 834148 , GreatDigInTheSky). B. F. and R. I. also acknowledge funding from the  Agence Nationale de la Recherche  ( ANR  project  ANR-18-CE31-0006  and  ANR-19-CE31-0017 ). M. B. acknowledges support from the  Polish National Science Centre  under the grant  2017/26/D/ST9/00449  and is thankful for the financial support by  Cercle Gutenberg.
Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1. 2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1. 2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2. 0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets.
Background & Summary Large neuroimaging datasets are increasingly being used to identify novel brain-behavior relationships in stroke rehabilitation research 1,2. Lesion location and lesion overlap with extant brain structures and networks of interest are consistently reported as key predictors of stroke outcomes 3-6. However, in order to examine these measures in large datasets, accurate automated methods for detecting and delineating stroke lesions are needed. Two critical barriers limiting accurate automated segmentation in rehabilitation research are the variability in post-stroke neuroanatomy across patients and the limited amount of diverse data with which to train and test segmentation algorithms. In acute stroke, large clinical neuroimaging datasets have led to improvements in segmentation algorithms for clinical MRI protocols (e.