dataesr/scientific-paragraphs-categorization huggingface dataset:
Two proposals were submitted for the EDL Science (March 2011) and selected (June 2011) to be combined into one EDL experiment: AMELIA. The main objective of the AMELIA experiment was to assess the local atmospheric science and the surface landing site by using the existing EDLS engineering sensors for more than their primary role in monitoring and evaluating the performance of the EDL technology demonstrator. The data recorded during different EDL phases would be used for an accurate trajectory and attitude reconstruction and for the retrieval of atmospheric profile to study the atmospheric structure, dynamics and static stability and to assess the landing site context. AMELIA's objectives included studying some of the major properties of the martian atmosphere, such as density, pressure, temperature and wind, from an altitude of about 120 km all the way down to the surface, and characterizing the nature of the landing site by means of touch-down measurements.
2018]. After a successful landing, Schiaparelli telemetry data would have been downloaded by the Mars relay orbiters. The engineering sensors, their measurements and the expected flight data are reported in Table  1. The telemetry data included also GNC processed data derived from these measurements; EDL measurements and GNC estimates were downsampled or decimated as required to fit the maximum data volume to be transmitted during EDL (e. g. essential telemetry data) and to be stored on-board for post-landing download.
SIMULATION AND RECONSTRUCTION METHODS An atmospheric profile of density, pressure and temperature can be retrieved along the entry and descent trajectory using similar methods to those employed in previous entry probe missions, e. g. ESA Huygens at Titan  [Fulchignoni et al. 2005 , Aboudan et al. 2008 , Colombatti et al. 2008] , NASA Mars Pathfinder  [Magalhães et al. 1999] , Mars Exploration Rovers  [Withers and Smith 2006] , Phoenix  [Desai et al. 2011  • and landing (proximity), from retrorocket activation to touch-down. The Schiaparelli EDM, protected by its frontal heat shield, was modelled as a single rigid body with 6 degrees of freedom (dof) subject to Mars' gravity and aerodynamic forces. A priori expected atmospheric conditions were retrieved from the Mars Climate Database (MCD 5.
2)  [Millour et al. 2015 ] derived from numerical simulations performed with the Laboratoire de Meteorologie Dynamique (LMD) Global Climate Model  [Forget et al. 1999 ]. Different "dust loading" atmospheric scenarios were used to compute the aerodynamic forces and torques starting from the aerodynamical database. After parachute deployment, the Schiaparelli module and the parachute system were modelled as two bodies connected by a spherical joint, resulting in a 9 to 12 dof system depending on whether the parachute was modelled as a 3 or 6 dof body. For the separation phase, a model at 18 dof was used for three rigid bodies (i. e. surface platform, front shield and aft cover). The aerodynamical interactions between the different elements of the EDM were taken into account and modelled using inputs from the aerodynamic database.
ATMOSPHERIC RECONSTRUCTION Atmospheric profiles of density, pressure and temperature, can be derived by several methods including: direct inversion from deceleration measurements, from hypersonic dynamic pressure data recorded during entry, or using more sophisticated estimation methods in which a priori knowledge of atmospheric parameters and vehicle dynamics are blended with all the available measurements. In order to reconstruct the atmospheric parameters (e. g. density, static pressure and temperature) from the measured accelerations and dynamic pressures, the physical properties of Schiaparelli in its different configurations (ref. Fig. 4 ), its aerodynamical and its aerothermal characteristics must be known.
RECONSTRUCTION FROM INERTIAL DATA Of the possible methods, historically the primary approach [e. g  Seiff, and Reese,1965, Seiff and Kirk 1976] , has been the direct numerical integration of the IMU measurements to derive velocity, position and altitude. Measured accelerations can be converted into atmospheric parameters by using the vehicle's aerodynamical database (AeDB): starting from the velocity and position of the probe, its angle of attack (AoA) and the side slip angle (SSA) can be obtained as the ratio of the normal and side to axial accelerations. Atmospheric density is directly related to the deceleration by the atmospheric drag, a pressure profile can be computed using the hydrostatic equilibrium law and then temperature by means of the ideal gas law. Furthermore, having a dynamic model of the EDM (mass, inertia and AeDB), having a sensor model of the on-board accelerometers, and assuming an atmospheric standard model, makes it possible to predict the atmospheric drag and hence update the density profile to match the measured accelerations using an Extended Kalman Filter  [Aboudan et al.
2008 ]. This method can be further extended to blend any other available measurements from both on-board sensors (e. g. the radar Doppler altimeter, FADS) and remote sensing data (e. g. imaging and radio science). During the last part of the descent (terminal descent) a direct measurement of the elevation and velocity of the EDM above Mars' surface was provided by the radar Doppler altimeter. These data, together with the descent images of DECA, were intended to contribute to the assessment of the EDM trajectory and attitude in its terminal part of the parachuted descent.
RECONSTRUCTION FROM PRESSURE DATA Schiaparelli was equipped with aerothermal sensors embedded into the thermal protection system (TPS) of the frontshield and aft cover  [Ballet al. this issue]. These sensors together with CoMARS+  [Gülhan et al. this issue]  were aimed at monitoring the entry vehicle's surface pressures and heat shield temperature in order to investigate Mars' entry environment and the vehicle's response to it. The readings of the four heatshield pressure sensors, one placed at the stagnation point and the others at three radial locations, provide the pressure distribution across the shield and could be used for an independent reconstruction of the atmospheric parameters. Atmospheric free stream conditions and density may be determined with the help of aerothermal dynamical database (ATDB) using all pressure sensors at once, or at a single location  [Karlgaard et al.
1999]  have been implemented into a Mars Climate Database (MCD wwwmars. lmd. jussieu. fr) that can provide the expected mean environmental parameters at a specific place, season, time of the day, and altitude (from the surface to the thermosphere). The amount of dust in Mars' atmosphere is one of the main sources of variability for the weather conditions, with different processes acting at different scales, e. g. from global dust fronts, to regional dust storms, to local gusts and dust devils, dust laden convective vortices that cross the surface of Mars. Within the AMELIA team, considerable effort has been put into atmospheric modelling [e. g. Forget et al. 1999, Spiga and Forget 2009 ] and data assimilation [e. g. Lewis et al. 1999 , 2007 , Montabone et al. 2005 ] in order to improve.
CONCLUSIONS The ESA -Roscomos ExoMars Project provided a rare opportunity (once per mission) for in situ measurements within the atmosphere of Mars and at the landing site. These measurements allow for sounding the atmospheric structure along the trajectory of the descent module and the assessment of the landing site. We have been able to make reconstructions of the profile down to 4 km, but at reduced resolution compared to what would have been achievable had the spacecraft landed successfully and returned all its data. The vertical resolution achieved was still significantly higher than is possible by remotely-sensed measurements, even those made with a limb-sounding instrument such as MCS, and cover a much larger altitude range than is possible with radio occultation retrievals. Unfortunately, data taken during the plasma blackout (e.
ACKNOWLEDGEMENTS AMELIA is an experiment for scientific investigations of Mars' atmosphere and surface by means of the Schiaparelli measurements during its entry, descent and landing on Mars. The International AMELIA team built under the joint coordination of Principal Investigator:  Francesca Ferri  (Italy) and 3 Co-Principal Investigators (CoPIs):  François Forget (France) ,  Stephen R. Lewis  ( United Kingdom ),  Özgür Karatekin (Belgium). The team includes scientists from  Italy, France, UK, Belgium, Finland, Germany, Australia  and  USA. The support of the national funding agencies of Italy (ASI, grant no. 2017-03-17 and I/018/12/3 ),  Belgium (BELSPO and PRODEX) ,  UK  ( UKSA , grant no. ST/M00306X/1 ) and  France (CNES)  is gratefully acknowledged.
[m/s2] TPS sensors Pressure sensors FAD: Flush Airdata Sensing Kulite XTL-DC-123C-190M-250-mBAR-A (4) pressure [bar] (pressure field) 10 Hz 1 Hz Thermal plugs 7 on FS, composed of 3 J-type TC; 3 on BC, composed of 2 J-type TC temperature [K] (thermal loads) Thermocouples (TC) TC direct 12-J-150-118-0. 5-21-3P2LA-3. 5M-B40JX 10 Hz 0,1-1 Hz Thermistors PT200 MEAS PF0002413 (PF0002283 with a 2. 5m cable) 10 Hz -stations on Earth (Direct to Earth link): the Giant Meterwave Radio Telescope 0,1-1 Hz DECA Descent Camera 60° field of view camera black & white images 15 frames at 1. 5s (GMRT) in India. The Doppler shift and rate of the radio signal could be accurately measured during the EDL and represent a unique "signature" of FS: Front Shield, BC: Back Cover, TC: ThermoCouple, RT: Real Time; E: Entry, D: Descent, L: Landing the entry trajectory.
After training a supervised deep learning algorithm to predict attachments on the STAC corpus 1  , we then constructed a weakly supervised learning system in which we used 10% of the corpus as a development set. Experts on discourse structure wrote a set of attachment rules, Labeling Functions (LFs), with reference to this development set. Although the whole of the STAC corpus is annotated, we treated the remainder of the corpus as unseen/unannotated data in order to simulate the conditions in which the snorkel framework is meant to be used, i. e. where there is a large amount of unlabeled data but where it is only feasible to hand label a relatively small portion of it. Accordingly, we applied the completed LFs to our "unseen" training set, 80% of the corpus, and used the final 10% as our test set.
It also targets a restricted version of an SDRT graph. It trains a simple MaxEnt algorithm to produce probability distributions over pairs of 2 Segmented Discourse Representation Theory elementary discourse units, a "local model", with a positive F1 attachment score of 63. 5; global decoding constraints produce a slight improvement in attachment scores. (Afantenos et al. , 2015)  uses a similar strategy on an early version of the STAC corpus. (Perret et al. , 2016)  targets a more elaborate approximation of SDRT graphs on a later version of the STAC corpus and reports a local model F1 attachment of. 483. It then uses Integer Linear Programming (ILP) to encode global decoding constraints to improve the F1 attachment score (0. 689). (Ratner et al. , 2016)  introduced the data programming paradigm, along with a framework, Snorkel  (Ratner et al.
, 2017) , which uses a weak supervision method  (Zhou, 2017) , to apply labels to large data sets by way of heuristic labeling functions that can access distant, disparate knowledge sources. These labels are then used to train classic data-hungry machine learning (ML) algorithms. The crucial step in the data programming process uses a generative model to unify the noisy labels by generating a probability distribution for all labels for each data point. This set of probabilities replaces the ground-truth labels in a standard discriminative model outfitted with a noise-aware loss function and trained on a sufficiently large data set. 3 The STAC Annotated Corpus.
Data Preparation To concentrate on the attachment task, we implemented the following simplifying measures on the corpus used: 1. Roughly 56% of the dialogues in the corpus contain only non-linguistic DUs. The discourse structure of these dialogues are more regular and thus less challenging; so we ignore these dialogues for our prediction task. 2. 98% of the discourse relations in our development corpus span 10 DUs or less. To reduce class imbalance, we restricted the relations we consider to a distance of ≤ 10. 3. Following  (Muller et al. , 2012; Perret et al. , 2016)  we "flatten" CDUs by connecting all relations incoming or outgoing from a CDU to the "head" of the CDU, or its first DU. The STAC corpus as we use it in our learning experiments thus includes 1,130 dialogues, 13,734 linguistic DUs, 18,767 non-linguistic DUs and 22,098 semantic relations.
, 2016; Afantenos et al. , 2015; Muller et al. , 2012). Furthermore, our LFs take into account the particular behavior of each relation type, information that expert annotators consider when deciding whether two DUs are attached. Thus the LFs were divided among the 9 relation types as well as the combination of DU endpoints for each type, e. g. linguistic/non-linguistic. We also fix the order in which each LF "sees" the candidates such that it considers adjacent DUs before distant DUs. This allows LFs to exploit information about previously predicted attachments and dialogue history in new predictions. Our complete rule set, along with descriptions of each of the relation types, is available here: https://tizirinagh. github. io/acl2019/. In Table  1  we list the rules and their performances on the portion of the development set to which they apply.
, 2000; Jarrett et al. , 2009; Nair and Hinton, 2010 ) activation and optimized with Adam  (Kingma and Ba, 2014). Given that our data is extremely unbalanced in favor of the "unattached" class ("attached" candidates make up roughly 13% of the development set), we also implement a class-balancing method inspired by  (King and Zeng, 2001)   In order to use this method, we had to binarize the marginals before moving to the discriminative step using a threshold of p >. 5 (the threshold that gave us the best F1 score on the development corpus). Though this marks a departure from the standard Snorkel approach, we found that our discriminative model results were higher when the marginals were binarized and when the class rebalancing was used, albeit much lower than expected overall. We first evaluated our LFs individually on the development corpus, which permitted us to measure their coverage and accuracy on a subset of the data 3.
Sampling plan Arizona State University students (equal numbers of women and men, age 18 and over) will be randomly selected from a database managed by the Elinor Ostrom Multi-Method Lab at Arizona State University and recruited by email. We will obtain informed consent from all subjects before the experiment's onset (ethical approval has been obtained from the Arizona State University Institutional Review Board, code: STUDY00007691). Participants will receive $5 for participation and an additional amount ranging from $0 to $10 depending on their performance (see Score calculation).
For H2 (Model 3), we did not record participants' time between clicking one tile and being allowed to click a subsequent tile (i. e. time to accurately solve one arithmetic problem). As such, we instead conducted a power analysis for Model 3 using a slightly different outcome variable: time to produce any answer (accurate or inaccurate) for one arithmetic problem. We then set a ROPE based on 99% statistical power, to compensate for the uncertainty introduced by basing the power analysis on a statistical model with a different outcome variable. For each power analysis, we followed the following steps (code available at https://osf. io/udm8g/): 1. Analyse the pilot data with Bayesian statistical models using map2stan in the rethinking package in R (see Analyses and Predictions)  [37, 38]. 2.
Pilot study We conducted a pre-registered (https://osf. io/udm8g/) pilot study (see electronic supplementary material for detailed results and full exclusion criteria). This study was designed to test the feasibility of the proposed design, not to test hypotheses. In conducting the pilot study, we underspecified exclusion criteria and deviated from the pre-specified pilot analysis plan. We consider all results from the pilot study to be exploratory. The pilot study involved 48 participants. We excluded data from one participant that did not complete the study. This resulted in a final sample of 47 participants (23 female, 24 male); 16 and 31 participants were assigned to the Competition and No-Competition treatments and 23 and 24 participants were assigned to the Effort and No-Effort conditions, respectively.
Results We conducted the experiment according to the in-principle accepted Stage 1 protocol (https://osf. io/24v9k/). Every four sessions of data collection, we checked whether the HPDIs for all parameters fell entirely within or outside the pre-specified ROPEs for each hypothesis. This never occurred. As such, we collected data until we reached the pre-specified maximum sample size of 260 useable participants. In total, we collected data from 269 participants. After applying the pre-specified exclusion criteria, our final sample size was 260 participants (six participants did not complete the study, two participants experienced technical difficulties and one additional participant was excluded because every data point for their time-to-make-aguess was larger than 5 s. d. from the mean). The final sample was composed of 130 females and 130 males (No-Effort condition: 65 females, 65 males; Effort condition: 65 females, 65 males).
Within individual participants, we excluded observations for which there was no data for at least one measured variable (i. e. when the game ended before participants made their guess). We also excluded observations for time-to-make-a-guess and time-to-solve-arithmetic-problems that were larger than 5 royalsocietypublishing. org/journal/rsos R. Soc. open sci. 6: 180934 s. d. from their respective means. Below, we present analyses for quality checks and confirmatory predictions, using this final sample of 260 participants. All confirmatory analyses and quality checks use the statistical models specified earlier in this report and approved in Stage 1 (see electronic supplementary materials for Markov chain convergence diagnostics).
Discussion We developed a laboratory experiment to test how competition for priority affects information sampling and accuracy in a game that mirrors aspects of scientific investigation. Following our prespecified experimental protocol, we collected data from 260 students at Arizona State University. Quality checks indicated that the experimental manipulations were successful. Confirmatory analyses indicated that competition caused participants to make guesses with less information, thereby reducing their accuracy. Confirmatory analyses provided no evidence that competition increased participant effort (i. e. arithmetic-problem solving speed) and provided no evidence for a Competition Â Effort interaction. Our experiment intended to capture several features of scientific investigation: individuals gather data to acquire information about the world, face tradeoffs between publishing quickly and increasing accuracy by acquiring more data and compete with others for priority of publication.
Acknowledgements. We are grateful to  Daniel Hruschka ,  Robert Boyd ,  Thomas Morgan ,  Willem Frankenhuis  and  Anne Scheel  for extensive feedback on experimental design and previous drafts of this paper. Thanks to  Thomas Morgan  for his assistance with Bayesian statistical analyses, to  Peder M. Isager  for comments on a previous draft and to  Daniel Lakens  and the  Red 2 laboratory  for productive discussion of approaches to determining ROPE boundaries. We are also grateful to  Christopher Chartier ,  Adam Sparks  and an anonymous reviewer for critical feedback on previous versions of this manuscript.
g. ,  Coheur et al. , 2009; Clarisse et al. , 2011; Clerbaux et al. , 2015). The interest in exploiting highly spectrally resolved IASI data to study climate variability has been previously highlighted  (Clerbaux et al. , 2003; Brindley et al. , 2015; Smith et al. , 2015). However, relatively little has been done to generate systematic records for climate variables with IASI, although the spectral signature of climate variability and Tskin anomalies have been studied for similar instruments (e. g. AIRS,  Brindley et al. , 2016; Susskind et al. , 2019). The instrument is relatively new (radiances are provided since July 2007) and the climate community is still not fully aware of its potential. It is also computationally demanding to systematically process the large amount of data generated by the instrument.
However, since IASI is planned for flying at least 18 years, with the 3 instruments built at the same time and flying in constellation, continuity and stability are insured, and the potential of constructing a long-term climate data record is becoming evident. In addition, it is worth noting that the long-term continuation of the program is also guaranteed, as the new generation of Infrared Atmospheric Sounding Interferometers (IASI-NG)  (Clerbaux and Crevoisier, 2013; Crevoisier et al. , 2014) , will be launched on three successive Metop -Second Generation satellites within the 2022-2040 timeframe. IASI data are disseminated by EUMETSAT (EUropean organization for the exploitation of METeorological SATellites)  (Klaes et al. , 2007). It processes a Tskin product from the series of the Metop satellites for day-to-day meteorological applications.
This Tskin product is derived from IASI upwelling radiances but also relies on other microwave instruments on board of Metop, particularly for cloudy scenes. This dataset is not homogeneous in time, neither for the Level 1C (L1C), radiances, nor for Level 2 (L2) operational products (e. g. temperature, humidity, cloud cover, etc. ). Changes occurred with evolving versions of the processing algorithm  (EUMETSAT, 2017a; EUMETSAT, 2017b) , with the algorithm mostly stable after 2016. The Metop-A L1C record has been reprocessed back in time at EUMETSAT for the period 2007-2017, and is used in this work, and will be publically available in summer 2019. L1C data after 2017 are not reprocessed because they are assumed to be up to date. The Level 2 series has not yet been reprocessed back in time, which complicates the construction of a homogeneous Tskin data record from IASI.
More generally, high volumes of data resulting from IASI present many challenges in data transmission, storage, and assimilation. One of the simplest methods for reducing the data volume is channel selection. The goal of this study is to present a fast and reliable method developed to retrieve Tskin from radiances using a limited set of radiances from the newly reprocessed IASI L1C data record in the thermal infrared in order to have a consistent and homogeneous product covering the whole IASI sounding period. The challenge is therefore to find the optimal set of channels from which skin temperature can be retrieved. In the following section 2, we present an approach based on entropy reduction  (Rodgers, 1996; Collard, 2007)  from which we deduce a set of 100 channels most sensitive to skin temperature from the IASI 8461 channels.
The dataset is then used to retrieve skin temperature from IASI's cloud-free https://doi. org/10. 5194/amt-2019-185 Preprint. Discussion started: 5 June 2019 c Author(s) 2019. CC BY 4. 0 License. radiances using an artificial neural network (ANN). In section 3 we validate the product and we conclude this paper with a discussion in section 4 of the current challenges in validation and comparison of different Tskin products.