dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank the institutes who maintain the  IMAGE Magnetometer Array :  Tromsø Geophysical Observatory of UiT the Arctic University of Norway (Norway) ,  Finnish Meteorological Institute (Finland) ,  Institute of Geophysics Polish Academy of Sciences (Poland) ,  GFZ German Research Centre for Geosciences (Germany) ,  Geological Survey of Sweden (Sweden) ,  Swedish Institute of Space Physics (Sweden) ,  Sodankylä Geophysical Observatory  of the  University of Oulu (Finland) ,  DTU Technical University of Denmark  (Den-.
Code availability. Unsupervised image classification method by Vincent Teissier: https://github. com/Tadlai/auroral-classification ; Master's thesis on unsupervised image classification by Vincent Teissier: http://kho. unis. no/doc/Vincent_master_thesis-final. pdf. The software to calculate the equivalent currents is available as supplementary material of  Vanhamäki and Juusola (2020). Data availability. Labelled images and their morphological classes are available at http://doi. org/10. 11582/2023. 00132. IMAGE magnetometer data are available at https://space. fmi. fi/image/www/index. php, Sony keograms are available at http://kho. unis. no/Keograms/keograms. php.
Therefore, the scope of G-VAP activities is to highlight the strengths, differences and limitations of water vapour climate data records through consistent evaluation and intercomparison studies. The stability of long-term datasets is a key focus of the assessment. Through these activities, G-VAP supports the selection process of suitable water vapour data records by GDAP and the general climate analysis community (further details are available from www. gewex-vap. org). Phase 1 of G-VAP concluded in 2017 with the publication of a World Climate Research Programme (WCRP) report  (Schröder et al. , 2017)  and an archive of total column water vapour (TCWV), specific humidity and temperature profiles used within the analysis  (Schröder et al. , 2018). In 2018, the assessment entered its second phase, focusing on the following objectives: -The characterisation of water vapour data records, -Informing users of issues within water vapour data products, -Climate and process-oriented analysis, -Continued link to key scientific questions and add more focus on process evaluation studies (PROES), -Enhance regional analysis.
ERA5 The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) atmospheric general circulation model and 4D-Var assimilation system are based on the IFS Cycle 41r2 and IFS Cycle 41r2 4D-Var versions of the ECMWF integrated forecast system (IFS). It is conducted at a resolution of about 31 km in the horizontal and 137 levels in the vertical from the surface to 0. 01 hPa, and the analysis is available at a 1-hour temporal resolution. ERA5 is the successor of the ERA-Interim reanalysis. Among others, ERA5 exhibits significant improvements in resolution, including a 10-member ensemble of data assimilation and various additional parameters, and incorporates an improved 4D-Var and variational bias correction scheme through the utilisation of (newly reprocessed) datasets and recent instruments.
ERA5 exploits a vast amount of datasets, including in situ measurements from land stations as well as measurements from ships and drifting buoys, radiosondes, pilot balloons, aircraft, and wind profilers. The largest amount of data comes from satellite observations, focusing on polar-orbiting and geostationary satellites and improvements in all-sky assimilation. ERA5 provides complete atmospheric products globally from 1950 onwards at a mixed hourly/3-hourly output frequency and is continued with updates in near real-time. ERA5 and its quality are described in  Hersbach et al. (2020). Monthly means of TCWV with a spatial resolution of 2 • x2 • were downloaded from https://cds. climate. copernicus. eu/#!/home in November 2020.
NOAA 20CR V2C and V3 The NOAA-CIRES-DOE (National Oceanic and Atmospheric Administration, Cooperative Institute for Research In Environmental Sciences) Twentieth Century Reanalysis (20CR) project provides a comprehensive dataset of reconstructed global weather spanning over 150 years at sub-daily resolution. The analysis is generated by assimilating only surface pressure observations into the NCEP Global Forecast System (GFS) Model with prescribed sea surface temperatures and sea ice concentrations. Currently, two versions of 20CR are available: i) version 2c (V2c), which runs from 1851 to 2014 on 6 hourly time steps on a 2 • x2 • resolution grid, and ii) version 3 (V3) that has an extended coverage spanning from 1836 to 2015, and a high temporal and spatial grid of 3 hours and 1 • x1 • respectively  (Slivinski et al.
Examining the seasonal range of the archive records (Figure  5 , 4 th column ) reveals that areas with high standard deviations, in general, have a range of ≥50 kg/m 2 , while regions with high CV see seasonal ranges of 10-20 kg/m 2. For very dry regions, 10-20 kg/m 2 can be 3-10 times the season mean TCWV, whilst for tropical regions, high standard deviations are associated with high monthly mean TCWV values. Therefore, this highlights a significant disagreement between the archive records for dry atmospheres, especially at high latitudes. The variability of TCWV observed within the archive is investigated further by comparing each record to the ensemble mean for the 'common period'. For each dataset, the monthly difference to the ensemble mean has been calculated before being averaged over the time period.
Next, these differences were summed to calculate each dataset's global value. Finally, all results were sorted from driest to wettest and shown in Figure  6. It should be noted that here, the ranking of datasets is not a statement of climate performance (i. e. bias to characterise reference/truth) but rather highlights characteristics related to differences in the observational/assimilation systems. From these global mean differences, the initial inferences we can make are: -11 datasets are drier than the ensemble mean, while the remaining 17 are wetter, with the EMiR record exhibiting the smallest global ∆TCWV. Evolution has a ∆TCWV of 0. 74% while MPIC OMI is much larger at 9. 2%. -Overall, reanalyses are wetter than the global ensemble mean TCWV except for JRA55 and ERA 20C. We also analysed ∆TCWV as global distributions relative to the ensemble mean over the whole common period to expand upon these singular global values.
Shown in Figure  7 , global maps of ∆TCWV reveal that the Intertropical Convergence Zone (ITCZ), Polar, storm track, and low stratiform cloud (as defined in  Klein and Hartmann (1993) ) regions are all key regions in the observed differences. Relating to the singular global values, these results also show: -IR satellite records show general dryness relative to the ensemble mean, except over regions with low stratiform clouds where wetter differences are observed. -MODIS NIR TCWV, like the IR products, are drier over oceans. However, overland regions, especially South America and North Africa, are wetter than the ensemble mean. The Tropical Warm Pool is a key region, where ∆TCWV values can go as low as -14%. -The visible records show very different patterns, especially in the Tropics. While the GOME Evolution product shows a general dry difference to the ensemble mean TCWV in the Tropics to mid-latitudes, the MPIC OMI dataset is much wetter.
These differences can be attributed to i) spectral range (GOME Evolution uses the strong absorption band in the red part of the spectrum while MPIC OMI uses the blue spectral range with much weaker absorption bands, ii) overpass times between GOME/SCIA and OMI are different (differing cloud amounts), and iii) MPIC OMI is less sensitive to surface albedo changes across different surfaces, while for GOME Evolution the surface albedo differs strongly between ocean and land. -ERA 20C and JRA55 reanalyses are drier between ±30 • (especially land surfaces) compared to other reanalysis datasets. One exception to this is NCEP-DOE 2 reanalysis which has a drier ITCZ region over ocean surfaces.
12±0. 17 to 0. 94±0. 92 kg/m 2 /decade (ERA-Interim, NCEP CFS-R/CFSv2), and 0. 11±0. 15 to 0. 53±0. 32 kg/m 2 /decade (ERA5, NCEP CFSR/CFSv2) for ocean and land respectively. In addition to these trends, Figure  8  also gives the regression of each TCWV record against SST and T2m surface temperatures. For ice-free ocean regression coefficients, we also include the expected theoretical range described in  Wentz and Schabel (2000). As with the first phase of G-VAP  (Schröder et al. , 2019) , we see large differences between the records within the archive. The first noticeable result is that the HIRS records exhibit more extreme behaviours relative to the other datasets. For NNHIRS, regression against ERA5 T2m gives the values -13. 46±1. 19 & -1. 96±0. 43 %/K over ocean and land, respectively.
Regression against ESA CCI SST over ocean shows a similar result to ERA5 with a value of -13. 17±1. 17 %/K. The UWHIRS product displays the largest regression coefficients with values of 92. 81±1. 02 & 88. 28±1. 04 %/K over ice-free ocean surfaces (ERA5 T2m, ESA CCI SST respectively) and 24. 94±0. 23 %/K over land (ERA5 T2m). For the other products, the main results we observe are: -For regression coefficients over ice-free oceans we found ranges of 6. 77±0. 24 to 27. 02±0. 51 %/K for ERA5 T2m, and 6. 17±0. 24 to 24. 17±0. 45 %/K against ESA CCI SST. -Over land surfaces, the range of regression coefficients are between 3. 00±0. 17 to 7. 77±0. 16 %/K against ERA5. -Accounting for the uncertainty on the regression coefficients against ERA5 T2m and ESA CCI SST, we find that ERA-Interim, MERRA2, NCEP-DOE 2, and ERA5 fall within the theoretical range for both temperature datasets.
When only taking into account ERA5 T2m, the JRA55 product also falls within this range. Examination of the spatial distributions of TCWV trends is used to investigate the inter-variability of these global trends further. Figure  9a  presents the median absolute deviation (MAD) of 'long' period trends between the respective archive datasets. From looking at all records in this way, we can see three key features: -Sea-ice boundaries show up as noise at high latitudes. -Tropical oceans exhibit high levels of variability. -Regions of high variability are observed over South America and central Africa. The first feature is easily accounted for by applying the sea-ice mask to the satellite records impacted by sea-ice. From Figure  9b , we can observe that the mask successfully removes the previous noise.
Correlation to climate indices For the final analysis, TCWV anomalies for each long-term record were compared to the seven climate indices detailed in Table  B1. An example of NAO, NINO34 and PDO correlation maps is shown in Figure  12 , with statically significant correlations indicated with hatching. Figures for each data record with all seven climate indices can be found in the supplementary material. Next, these correlations were inter-compared between the datasets for each climate index. Notably, we identified regions where at least 50% or 100% of the records agreed on either positive or negative correlations. The absolute values differ over land and ocean surfaces as the MW satellite records provide only data over ice-free global oceans. The results from this test are shown in Figure  13 , with negatively correlated regions shown on the left-hand side in orange, while regions of positive correlation are presented on the right-hand side in purple.
The key results from these comparisons show: -Generally, positive correlations between all datasets occur in expected regions related to the specific index. -We also observed positive correlations between all water vapour records off the coast of Antarctica related to ENSO3. 4 and the southern ocean and tropical warm pool regions with AMM. -Ambiguity in the agreement among data records is also found over Africa for PDO, the Pacific Ocean for NAO, and over the African and Antarctic continents for PMM. -Consistent negative correlations with PDO within the archive are observed over North West America and Eastern Europe. -Over Greenland and North America, all water vapour records agree on a negative correlation to NAO and AO. -However, we observe an ambiguity in negative correlations above 80 • N for PMM, the broader Pacific regions for NINO3.
Acknowledgements The authors would like to thank  Leigh N. Fletcher  and  Glenn S. Orton  for sending us the zonal mean temperature data on Neptune and Uranus, and  Bernard Schmitt  for sending us the optical constants of H 2 S ice. G. Milcareck ,  S. Guerlet  and  A. Spiga  acknowledge funding from  Agence Nationale de la Recherche (ANR)  project  SOUND ,  ANR-20-CE49-00009-01.
Acknowledgment Computations were performed at the  High-Performance Computing Center of the University of Strasbourg. A part of computing resources was funded by the  Equipex Equip@Meso  project ( Programme Investissements d'Avenir)  and the  CPER Alsacalcul/Big Data. The authors thank  Dr. Claudia Bonfio ,  Dr. Quentin Dherbassy ,  Dr. Claude Legault ,  Dr. Giulio Ragazzon , and  Maciej Piejko  for helpful discussion and proofreading of the manuscript, and  Wahnyalo Kazone  for help with LC/MS measurements.
Within the context and structure of these rover-level interactions and flows, the MOMA team additionally is establishing a set of scenario-and data-based evaluation metrics and decision-making procedures to aid in efficient and robust science operations in line with overall MOMA analytical objectives. One line of science scenario preparation is understanding what aspects of sample characteristics and preliminary mineralogy, as determined by other rover instruments, will be used to set initial MOMA parameters for LDMS or GCMS runs. Such settings are empirically based on laboratory data as well as rover mission data up to that point. Any follow-up analyses by MOMA on the same sample, such as selection of a particular type of chemical derivatization and GC column, would similarly depend on advance understanding based on analog and flight data.
7 ). We caution against making any conclusive interpretations regarding these similarities or differences, as the quality of the underlying datasets used to produce the crater DTMs here are not as good as those for Bennu and Ryugu. One of the craters identified presented evidence of a central mound (crater crt10 in Methods Table  4 ). The material comprising the mound appears to be an accumulation of bright and dark boulders. The other craters show no obvious evidence for either a bench or mound. The crater cumulative size frequency distributions (CSFD) on both Didymos and Dimorphos (Fig. 7 ) indicate that Didymos has a higher density of craters and thus has an older surface. We provide a range of plausible surface ages for both asteroids, using cratering strengths from Y =10 10 5 Pa based on experimental data that have been used to construct crater-scaling relationships  [30]  and supplemented with recent insight into the material strength of Dimorphos based on analysis of the outcome of the DART impact  [31] [32] [33].
Observations of the DART crater, supplemented by the landing of the CubeSats on Dimorphos and accompanying measurements of their bouncing properties, will reveal the surface strength properties of the asteroid. Hera, therefore, will ultimately check with a comprehensive, global datasaet, many of the hypotheses and interpretations made here about the surface properties, geology, and history of the Didymos binary system derived from DART's small, restricted dataset. The result will not only provide a better understanding of the origin and evolution of the Didymos system, but also present an opportunity to understand how well a flyby/fly-in dataset can constrain the true nature of an asteroid.
Acknowledgements This work was supported by the  DART  mission,  NASA  Contract  80MSFC20D0004. This work was supported by the  Italian Space Agency (ASI)  within the  LICIACube  project ( ASI-INAF  agreement n. 2019-31-HH. 0 ) and  HERA  project ( ASI-INAF  agreement n. 2022-8-HH. 0 ). N. M. and C. R. acknowledge funding support from the  European Commission 's  Horizon 2020 research and innovation programme  under grant agreement No  870377  ( NEO -MAPP  project) and support from the  Centre National d'Etudes Spatiales (CNES) , focussed on the Hera space mission. SDR acknowledges support from the  Swiss National Science Foundation  (project number  200021 207359 ). R. N. acknowledges support from  NASA/FINESST  ( NNH20ZDA001N ).
LUVMI-X scientific objectives and technical specifications were then translated into a list of criteria and computed in our GIS using reclassifications, buffers, and intersections. Using our GIS, reclassified data were overlaid with different weights to define and rank areas meeting the compulsory criteria. A global analysis was led to select the landing sites, followed by a local analysis (based on higher resolution data) for the establishment of traverses. The global GIS analysis allowed us to identify six regions of interest (ROI), which were compared with previous SP ROI from the literature  (Lemelin, 2014; Flahaut, 2020). The identified ROI were further ranked based on areas and statistics on Sun and Earth visibilities, Diviner average surface temperatures, and H/water ice signatures (LPNS, LEND, M3).
Acknowledgements: F. L. R is thankful for the support of the CAPES scholarship. The following authors acknowledge the respective CNPq grants: F. B-R 309578/2017-5; R. V-M  304544/2017-5, 401903/2016-8; J. I. B. C. 308150/2016-3; M. A 427700/2018-3 ,  310683/2017-3, 473002/2013-2. This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance Code 001  and the  National Institute of Science and Technology of the e-Universe project (INCT do e-Universo ,  CNPq  grant  465376/2014-2 ). G. B-R acknowledges  CAPES-FAPERJ/PAPDRJ  grant  E26/203. 173/2016 , M. A FAPERJ  grant  E-26/111. 488/2013and A. R. G-Jr  FAPESP  grant  2018/11239-8. B. E. M thanks the  CAPES/Cofecub -394/2016-05  grant. P. S-S. acknowledges financial support by the  Spanish  grant  AYA-RTI2018-098657-J-I00  " LEO-SBNAF " ( MCIU/AEI/FEDER, UE).
We would like to acknowledge financial support from the  State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa " award for the  Instituto de Astrofıśica de Andalucıá  ( SEV-2017-0709 ) and the financial support by the  Spanish  grant  AYA-2017-84637-R. Part  of the results were based on observations taken at the 1. 6 m telescope on  Pico dos Dias Observatory of the National Laboratory of Astrophysics (LNA/Brazil ). Part of this work was carried out within the  "Lucky Star " umbrella that agglomerates the efforts of the Paris, Granada and Rio teams. It is funded by the  European Research Council  under the European Community's  H2020  ( 2014-2020/ERC Grant  Agreement No. 669416 ). This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www.
The validation and test sets are recorded separately, where we use a robotic arm 4 to apply an external force at a known location on the finger. The robotic arm has a 6-axis forcetorque sensor to measure the force applied at the end-effector. The applied force is perpendicular to the contact surface, limiting the force to a single axis. Therefore, the measured force is uniaxial along the normal axis of the contact surface. We also use the validation set to identify the parameters for FEM (see Sec. V-C). Actuation pressure for validation set and test set are 1. 5 kPa and 1. 2 kPa, respectively. The external forces have range between 0 N to 5 N and are applied at different locations along the finger. For each dataset, the data collection starts when the soft finger is fully pressurized, the robot arm approaches the soft finger and touches the finger's inner surface.
B. Resistances to Shape Regression The mapping from resistances to shape is modeled as described in Sec. III-A. We will now focus on specific details on the neural network training. Firstly, the data we collected was filtered and downsampled to 30 Hz to synchronize with the vision measurements. In this dataset, unactuated neutral states dominate the data collection process, and deformed states are under-represented. The training data is, therefore, resampled to improve the skewness of the dataset. Nominal shape vectors are then computed from the measured marker positions using the FEM model. We used orientation change as the shape vector for the flexible strip and position changes for the soft finger. This difference comes from the different constraint settings in the two cases. The soft finger is constrained to have a constant length to ensure it bends under pressure actuation, while the flexible strip is not.
ACKNOWLEDGEMENTS This work has been funded by  Credit Suisse ,  Festo SE & Co. KG ,  Agencia Nacional de Investigación y Desarrollo (ANID) in Chile  with the " Fondecyt the Iniciación " grant number  11230505 , and  SNSF  Grant number  200021 215489. Barnabas Gavin Cangan  was partially funded through a  Swiss Government Excellence Fellowship  by the  Swiss Federal Commission for Scholarships. We are grateful for all the support received.
5 nm 2 ), i. e. a MJ made of ≈ 20-25 molecules, see Methods and Supplementary Section 3, where "-" indicates a chemical bond and "//" a mechanical contact. We acquired hundreds of DC current-voltage (I-V) curves by contacting the SAM with the C-AFM tip at different places for statistical analysis of the electron transport (ET) properties of the MJs with the laser source turned on or off (Fig. 1c ). Two laser sources (continuous wave) were used, a quantum cascade laser (QCL) at 30 THz and a gas laser at 2. 5 THz, with irradiating power up to 43 mW on the MJs. Electron Transport Properties and Terahertz Switch. All three molecules showed a marked NDC behavior at RT, which is suppressed under THz irradiations. Figure  2a  shows the I-V dataset (116 I-V traces) for the TS Au-AH1//PtIr tip MJs without THz wave irradiation.
We observed a negative differential conductance (NDC), i. e. , a decrease in the current when increasing the applied voltage, for all the I-V traces shown in the dataset. The I-V curves are rather symmetric and the voltage positions of the peaks of current at positive and negative voltages (inset Fig. 2a ), extracted from the statistical distribution given in Fig. 2c , are VP+ = 0. 42 ± 0. 05 V and VP-= -0. 48 ± 0. 05 V. The peak-to-valley current ratios (RPV=IP/IV) range between 1. 5 and 18 with a mean value RPV = 3. 1 ± 1. 3 (Fig. 2d ). When the 30 THz laser is turned on, the I-V dataset measured under irradiation no longer exhibited the NDC effect (Fig. 2b ). The switching is fast and reversible. The inset in Fig. 2b  presents the time evolution of the current measured at the positive peak voltage while turning on and off the 30 THz laser (5 cycles).
Energetics of the Molecular Junctions. The I-V datasets with the THz laser off are well explained by the two-site model developed for this type of π-σ-π molecule. 20  In a simple picture, the peak of the current occurs when the applied voltage aligns the energy of the two π-arms giving rise to a resonant ET inside the MJ. Increasing the applied voltage, the NDC effect results from the breaking of this resonant condition, pulling the energy of the two π sites apart. The analytical model developed in Refs. 20, 21  was used and it well reproduced the NDC behavior of our MJs (Fig. 4  and Fig. S9 ). The model (inset in Fig. 4a ) is parametrized by the energy levels ε0 of the π moieties (at zero bias, with respect to the Fermi energy of the electrodes), the electronic coupling energy Γ with the electrodes and τ the coupling between the two π sites (see Methods).