dataesr/scientific-paragraphs-categorization huggingface dataset:
Several sensors, embedded to monitor the stress under the heavy traffic, are located in the center of I-13 and between I-12 and I-13. This is why results presented for defect I-13 in the next section will show a lack of values in the center of the data. C-scans were performed at 396 K, 500 K, 600 K, 720 K, and 800 K loading stages, using the commercial software 3D-radar Examiner. Figure  9  shows the horizontal slices of the C-scans taken at the interface (or defect depth). These slices give an overview of the global test section and its general evolution. Most of the defects are clearly visible and their geometry well drawn. Nevertheless, the thickness and the EM state of the top layer varies due to the implementation and evolution of the asphalt layer. Therefore, they can present some bias in estimating the importance of the defects.
Such a decrease of EM contrast suggests an internal moisture evaporation and possible auto-repair of the structure. Lastly, we note that at the internal radius area the RW amplitudes are stronger. It could be due to the transverse slope towards the center of the carrousel, as shown in Figure  6 , which trap the moisture at the border of the defects. C-scans were performed at 396 K, 500 K, 600 K, 720 K, and 800 K loading stages, using the commercial software 3D-radar Examiner. Figure  9  shows the horizontal slices of the C-scans taken at the interface (or defect depth). These slices give an overview of the global test section and its general evolution. Most of the defects are clearly visible and their geometry well drawn. Nevertheless, the thickness and the EM state of the top layer varies due to the implementation and evolution of the asphalt layer.
Study of Defects I-1 to I-3 Defects I-1 to I-3 correspond respectively to defects I-11 to I-13 with a narrow width of 50 cm. Measurements were only done at 1. 5 and 2. 6 GHz, with GSSI systems during the second stages of experiment. Only 2. 6 GHz data are processed and presented in this section as the 1. 5 GHz and 2. 6 GHz data are very similar. From Figures  25 26 27 28 , we note that the narrowness of the defects induces different behavior under traffic than I-11 to I-13. The amplitudes do not appear to be stable along the longitudinal traces of I1 to I3. Additionally, no V-shaped variation in trends is seen for the transversal results over the largest defects. This phenomenon could be explained by the loading transfer in the asphalt partially supported by the healthy borders, and then not damaging the surveyed interface.
Study of Defects I-1 to I-3 Defects I-1 to I-3 correspond respectively to defects I-11 to I-13 with a narrow width of 50 cm. Measurements were only done at 1. 5 and 2. 6 GHz, with GSSI systems during the second stages of experiment. Only 2. 6 GHz data are processed and presented in this section as the 1. 5 GHz and 2. 6 GHz data are very similar. From Figures 25-28, we note that the narrowness of the defects induces different behavior under traffic than I-11 to I-13. The amplitudes do not appear to be stable along the longitudinal traces of I1 to I3. Additionally, no V-shaped variation in trends is seen for the transversal results over the largest defects. This phenomenon could be explained by the loading transfer in the asphalt partially supported by the healthy borders, and then not damaging the surveyed interface.
This could induce a merge of an aerial direct wave and with the direct wave in the surveyed medium, and then an increase of amplitude as the permittivity of the layer decreases. Measurements were performed on six parallel lines of 1. 4 m long. While considering an average bulk density of the asphalt, a void content value is calculated every 2 cm. As the gamma ray is very narrow (>10 mm), an average is done so that values are representative of the mixing vs. location of the traffic (Figure  41b ). Data show that the asphalt presents higher void content (~10. 5%) above the defect than in healthy zone under traffic (~8. 7%), which can be explained by higher density of micro-cracks. In parallel, two actions were performed to estimate the permittivity of the asphalt layer 1 along the transversal direction.
Conclusions A major GPR experiment was performed on a full-scale accelerated pavement testing at the university Gustave Eiffel, on a section presenting three artificial defects of bonding (tack-free, geotextile, and sand-based). Measurements were done periodically with several GPR systems all along the life-cycle of the road structure in two stages: 300 K loadings in 2012-2013 and 500 K loadings in 2019-2020 leading to a structure considered as strongly degraded. Four GPR systems were tested, mainly during the second stage of experiment from 1. 5 to 5 GHz central frequency. More than 500 GPR profiles were acquired, most of them being processed for this paper. Data processing was focused on the amplitude analysis (amplitude ratio test) of the reflected waves at the interface of the defects and on the direct waves.
Acknowledgments: The authors would like to thank the technical staff of  univ. Gustave Eiffel Carrousel  ( S. Trichet ,  T. Gouy ,  G. Coirier ,  Y. Baudry ,  M. L. Nguyen et J. Blanc ), the one of  GeoEND laboratory  ( O. Durand ,  G. Gugole  and  T. Devie ) and  J. M. Moliard  from  S2I laboratory. The authors also thank  P. H. G. Ching  for experimental help in the frame of a collaboration with  Polytechnic university of Hong Kong. The first part of the data collection in 2012, has been a contribution of the RILEM technical committee TC241 -MCD  (" Mechanisms of cracking and debonding in asphalt and composite pavements ", www. rilem. org (accessed on 4 March 2021)) and the  COST  action TU1208 (" Civil engineering applications of ground-penetrating radar ").
Gridded summer temperature from a tree-ring width and density network The reconstruction of climatic conditions and cooling induced by the 1600 Huaynaputina eruption is based on the NVOLC v2 dataset  (Guillet et al. , 2017 (Guillet et al. , , 2020)) , which was created with the specific goal to detect and quantify volcanic cooling; it includes 13 tree-ring width and 12 maximum latewood density chronologies from across the NH (see  Stoffel et al. , 2015; Guillet et al. , 2017, for details). The characterization of regional climatic change and the illustration of spatial temperature anomalies after the 1600 Huaynaputina eruption was based on the NVOLC v2 network of treering proxies and served the development of a climate field reconstruction of extratropical NH summer (JJA) temperatures as follows: in a first step, we grouped the 25 chronologies into 11 regional clusters using a correlation coefficient (r) threshold exceeding 0.
This result also confirms that intrinsic climate variability, including climate modes such as the NAO and Arctic Oscillation, plays a major role in setting winter conditions over Europe, including Baltic Sea temperatures and sea ice  (Moreno-Chamarro et al. , 2017b ; see also  Chen and Hellström, 1999; Omstedt and Chen, 2001; Eriksson et al. , 2007; Zanchettin et al. , 2019). In particular, the duration of Baltic sea ice and port closures shows a negative correlation with the NAO index  (Jevrejeva, 2002) ; thus, the absence of a post-eruption positive NAO anomaly helps account for the longer port closure dates ca. 1600. As shown in Fig. 9 , change points in Tallinn harbor ice breakup dates were detected in 1597 and 1630. The change in mean of the Tallinn ice breakup data time series indicates colder winters and greater persistence of Baltic sea ice in the period 1597-1630 than during 1563-1596 or 1631-1664.
Our study underlines the potential of historical climatology to reconstruct highly resolved local climatic and environmental conditions relevant to studies of model-derived mechanisms. Moreover, the reconstructions and observations presented in this study have helped clarify human dimensions of climate variability during this period, including roles of short-term post-eruption anomalies as well as longer-term cooling and sea-ice expansion in societal impacts and adaptations. Code and data availability. Code and model data to reproduce  Figs. 1, 2, 3, 4, 7, 8, and 10  can be found at https://doi. org/10. 5281/zenodo. 6417226 (Moreno-Chamarro, 2022). Underlying data for gridded temperatures (Fig. 6 ), sea ice breakup dates (Fig. 9 ), and North Sea wind direction (Fig. 10 ) are available in the published literature cited in the references.
Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "Interdisciplinary studies of volcanic impacts on climate and society". It is not associated with a conference. Acknowledgements. This study is a product of the PAGES-VICS and PAGES-CRIAS working groups. We thank Astrid Ogilvie for providing information on Icelandic sea-ice observations and commenting on the paper. Financial support. This research has been supported by the Swiss National Science Foundation (grant no. P2BEP1_175214), the Swiss National Science Foundation through the SNSF Sinergia CALDERA project (grant no.
Study organisms A total of 147 individuals, including 51 Females, 51 males and 45 juveniles representing six species occupying different habitat types were sampled (see Additional file 1: Table  S1 ). We distinguish male agamas from females visually, based on the bulging of the hemipenes in males  [22]  Lastly, Acanthocercus atricollis (N = 39) were caught in the suburban area of Mtunzini (28° 57′ S, 31° 44′ E) and Zululand Nurseries, Eshowe (28° 52′ S, 31° 28′ E), Kwa-Zulu-Natal, in February 2017. The GPS coordinates of each lizard were recorded upon capture. Lizards were marked with a non-toxic marker (to avoid recapture), placed in cloth bags and then transferred back to the field station where they were stomach flushed. Morphological and bite force measurements were also taken. Once all the data were collected, we released the animals at the exact location where they were found.
Acknowledgements This work would not be possible without Krystal Tolley (SANBI) in the field and facilitating the many field trips, with funding from the  National Research Foundation (NRF) of South Africa (South African Biosystematics Initiative  and the  Key International Science Capacity Initiative). We are indebted to members of the  MeaseyLab ,  Gareth Coleman ,  Amy Panikowski ,  John Wilkinson ,  François Meyer ,  Jason Savage ,  Frederik Igelström  and  Ellen Birgitte. For access to various sites, local knowledge of species, data collection, and generous hospitality. For sharing their expertise on potential collection sites, we would also like to thank  Andre Coetzer ,  Gary Nicolau ,  Luke Verbugt  and  Marius Burger. Finally , we are very grateful to all the staffs and volunteers in  Welgevonden Game Reserve , especially  Greg Canning ,  Phillipa Myram ,  Shannon Beale ,  Camilla Bjerrum ,  Maria Larsen  and  Amanda Martinez Avila.
Although a rapid antigen test can provide results in a few minutes, they are less sensitive than PCR, often leading to false-negative results. Information about the analytical performance of such devices based on LFA technology can be found in the FIND database, as highlighted at the end of this section. Rapid tests for measuring the immune response of patients are also mostly based on LFAs. These tests, often referred to as serological tests, permit a determination of the presence and type (mostly IgA, IgG, or IgM) of SARS-CoV-2-specific antibodies. 32 The sensing mechanism is similar to those of antigen-based LFA; however, the targets of the test are not the viral particles but the IgG and IgM antibodies, and the sample used can either be a drop of blood (for IgG and IgM, with two test lines) or saliva (for IgA and IgG).
34 Generally, there are two main parameters used to define the performance of a diagnostic test: sensitivity and specificity. They refer to the proportion of true positives or true negatives when being compared to a standard technique (here PCR), respectively. 35, 36 Thus, a final interpretation of a diagnostic test is not just given by its operating features but also for the prevalence or pretest probability of disease. 31 ecently, the Foundation for Innovative New Diagnostics (FIND) set up a database of the commercially available COVID19 diagnostic devices, which is updated on a regular basis. Of the current 544 diagnostic devices available (data updated May 2nd, 2021), 158 are based on RNA detection and 276 on antigen detection, and 101 are serological. FIND also conducts the independent evaluation of these commercially available technologies, comparing them analytically by their effective specificity, sensitivity, and response time and giving many additional information such as the type of sample used, the connectivity, etc.
First, these should be certified by independent entities and available to the user (e. g. , on a public downloadable database, such as the FIND one). Second, their meaning should be made clear for everybody and enter in the common culture. In all these approaches to the future pandemics a transversal factor, which makes the difference, is interdisciplinary research teams and tight collaborations between all the actors involved in the development process. The collaboration of these actors as a unique unit and not as a chain of independent black boxes would allow a drastic optimization of the time and effort needed for the development of a nanodiagnostic device. Finally, the last innovation we envision is the establishment of international biobanks containing verified patient samples. Having access to those samples could be either related to specific grants or by an employment of strict criteria, such as proven high-enough TRLs.
ACKNOWLEDGMENTS We acknowledge funding from the European Union Hori-zon2020 Programme under Grant No. 881603 (Graphene Flagship Core 3). We acknowledge Consejo Superior de Investigaciones Científicas (CSIC) for the project "COVID19-122" granted in the call "Nuevas ayudas extraordinarias a proyectos de investigación en el marco de las medidas urgentes extraordinarias para hacer frente al impacto económico y social del COVID-19 (Ayudas CSIC-COVID-19)". We acknowledge the MICROB-PREDICT Project for partially supporting the work. The MICROB-PREDICT project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant No. 825694. This reflects only the author's view, and the European Commission is not responsible for any use that may be made of the information it contains.
We present a pioneering estimate of the global yearly greenhouse gas emissions of a large-scale Astrophysics experiment over several decades: the Giant Array for Neutrino Detection (GRAND). The project aims at detecting ultra-high energy neutrinos with a 200,000 radio antenna array over 200,000 km 2 as of the 2030s. With a fully transparent methodology based on open source data, we calculate the emissions related to three unavoidable sources: travel, digital technologies and hardware equipment. We find that these emission sources have a different impact depending on the stages of the experiment. Digital technologies and travel prevail for the small-scale prototyping phase (GRANDProto300), whereas hardware equipment (material production and transportation) and data transfer/storage largely outweigh the other emission sources in the large-scale phase (GRAND200k).
According to the Intergovernmental Panel on Climate Change, in order to limit warming to 1. 5°C, global net anthropogenic CO 2 emissions have to decline by about 45% from 2010 levels by 2030, reaching net zero emissions by 2050  [8]. Interestingly, these timescales correspond to the GRAND roadmap, as the full-size experiment is expected to be deployed by the 2030s. By that time, it is possible that unforeseen technological progress will have happened in various domains that could be implemented in the experiment, such as travel, solar panels, the carbon footprint of electronic devices and data centers. Also by the 2030s, it is highly likely that taking into account the carbon emissions will be mandatory in all scientific projects. Today, the GRAND project is starting its prototyping phase, hence it is an ideal time to adapt the experiment to climate requirements.
Travel emission factors The data used for the emission factors is divided into two categories : air travel and other travel modes. For the other travel modes (car, public transportation, etc. ), we use the ADEME carbon database  [13]. This database has several advantages: it covers a wide range of emission factors and it is easily accessible. However, this is a database centered on French specificities. For example, for public transportation such as the subway, the fuel mix for electricity generation is specific to France which, unlike other countries, has a low carbon intensity due to the use of nuclear power (see Appendix A). Still, the difference between the carbon intensity of electricity in commuting in different countries does not affect the final result for two main reasons: commuting represents only a small fraction of overall travel emissions, which are dominated by air travel; the main type of travel used for commuting by GRAND members is car (gasoline and diesel) which represent 45% of the kilometers traveled.
Concerning aviation, its effects on climate change are more than just emissions of CO 2 from burning fuels. It is the result of three types of processes: direct emissions of radiatively active substances, emissions of chemical products that produce or destroy radiatively active substances, and emissions of substances that trigger the generation of aerosol particles or lead to changes in natural clouds  [14]. Those effects are not always taken into account in carbon databases that include emission factors for planes, as they are not yet very well understood. Choosing one single database can lead to highly different results, up to a factor of 5  [4]. To compute our travel carbon footprint, four databases were chosen from different organizations: the database from ADEME  [13] , the database from the French Ministry of Ecology  [15] , data from KLM airline [16] and a computation methodology from MyClimate.
org [17]. Those four databases have diverse sources: one non-governmental organization, one airline company, and two state organizations. In order to have more accurate results, the emission factors are split in categories depending on the number of kilometers travelled by increments of 1,000 km up to over 11,000 km. The data used is already divided in such categories for the French Ministry of Ecology and ADEME. MyClimate. org divides the emission factors between short-haul flight and long-haul flights. The KLM data is a list of all the flights they offer, their distance in kilometers and the greenhouse gas emission associated to the flight. Figure  3  presents the range of flight emission factors spanned by the various datasets. The greenhouse gas emissions as a function of the distance flown in one continuous leg are presented for the 4 databases described above (ADEME 2  , MyClimate.
org, French Ministry of Ecology, KLM). The uncertainty of these emission factors is estimated at around 60 % in the ADEME database and mainly stems from the uncertainty pertaining to the emissions of contrails. The other databases do not provide such estimates. The black solid line corresponds to the average value calculated from the 4 databases, which is used in our emission estimates for one-legged trips. We have not provided uncertainty estimates for this average value as only the ADEME database provides some. It is worth mentioning that the average emission factors are respectively between 5 and 10 % greater when considering respectively two or three legs instead of one-legged long haul trip.
Digital technologies Digital technologies are often proposed as a solution for climate change mitigation: automation and connectiv-ity (e. g. , smart grids, connected mobility. ) are presented as a way to reduce energy consumption. However, as reported for example by the Shift Project  [18] , digital technologies have a non-negligible environmental impact. Carbon footprint assessments often neglect the digital contribution, and the impact of the digital equipment is largely underestimated by its users. Digital technologies are used in every aspect of the GRAND project: e. g. , for simulations, data analysis, data storage and transfer, and communication. It is hence necessary to evaluate the digital impact on greenhouse gas emissions, in order to compare it to the other emission sources. For such a study, defining the scope is essential.
Numerical simulations and data analysis Numerical simulations are essential at all stages of the project, and particularly for the preparatory phases. Over the last four years, about 2 million CPU hours were used on various superclusters (principally at CCIN2P3 in Lyon, ForHLR at the Karlsruhe Institute of Technology, and UFRJ in Rio de Janeiro). Using the emission factors from Ref. [23] , we computed the emissions associated to simulations from 2015 to 2020, as presented in Fig. 8. The emission factors take into account all the stages related to performing simulations in a data center: server manufacturing, electricity consumption, and emissions generated by the staff running the data center. In our calculations, we used the electricity-specific emission factor of the countries hosting the superclusters where the simulations were run (France, Germany and Brazil, see Appendix A).
The scope chosen has a great impact on the results of the greenhouse gas assessment. In order to see the impact of every stage, we present the emissions related to electricity consumption only, and to the larger scope including staff interventions, server manufacturing and the like. The storage of the results of the simulations also have a cost in terms of GHG emissions. The percentage of the energy use in data center for data storage is around 10% of the emissions of simulations  [24]. We thus estimated the energy consumption related to the storage of the results of the simulations at around 10% of the one related to simulations. Once experimental data has been collected (see next sections), it will be analyzed. For GRANDProto300, each year over 5 years, between 200k CPU hours and 400k hours will be needed to analyze the astroparticle data.
This amounts to 0. 9 tCO2e to 1. 7 tCO2e per year for GRANDProto300, using the same emission factors as for simulations. Data analysis is comparatively one order of magnitude less emitting than the simulations run during the preparatory phases. Here we did not take into account the analysis required to work on the Radio Astronomy science case, and focussed on the primary science case of GRAND, which is the detection of ultra-high energy astroparticles.
Data transfer In this study, we focus on the data transfer related to the first prototype of GRAND, GRANDProto300, which is being currently developed. The projections for the next phases are discussed in Section 7. 2. On the GRANDProto300 experimental site, data will be collected by the radio antennas deployed. The data collected will be sent to the nearest city via Ubiquiti Air-Fiber  [25]. The raw data will amount roughly to 10 TB per day. Assuming that the data acquisition will take place over 300 days a year (due to weather conditions, maintenance, etc. ) during 5 years, about 15 × 10 3 TB of data will be collected during the whole experiment. We calculate the GHG emissions that would be produced if this data were to be transferred via the Internet to a data center in Beijing and then transferred across the world to a few data centers harbored by GRAND institutes.