dataesr/scientific-paragraphs-categorization huggingface dataset:
Interoperable Software Developments In section 2. 2 the mapping between planetary metadata concepts and FITS metadata description has been addressed. In order to impulse the use of FITS in planetary sciences this section will provide some example efforts of implementation of the proposed mapping. On remote sensing side, our goal is to get FITS into habits of geospatial developers, improving GDAL and QGIS compatibility with FITS. On the other side, planetary astronomers will benefit of a FITS efficient description of their data, once the new keywords fully implemented in their traditional tools. We provide here two examples chosen within the Europlanet VESPA collaboration.
FITS and GDAL Basic conversion from and to FITS is already supported by GDAL. However, dictionaries for precisely translate geospatial metadata are not implemented yet. The conversions described in section 2. 3. 1 have been performed using python scripts available from the VESPA github repository (https://github. com/epn-vespa/ fits2vrt). Those python developments benefit from the rich and well-maintained Astropy and GDAL python APIs. They will be implemented in planetary VO services with the goal to bridge between GIS and VO services. The next step will be to directly implement metadata translation into the GDAL library. This work is already in progress, and new developments are available from the VESPA github repository (https://github. com/ epn-vespa/gdal).
FITS and QGIS QGIS is a largely used open source geospatial analysis software. It is using GDAL for layer import: QGIS is then already capable to display FITS files, making use of a Virtual GDAL header, as described above. Once implemented in GDAL, FITS will officially become a supported format by QGIS. In addition to that, a QGIS plugin has been developed (https://github. com/epn-vespa/VO_QGIS_plugin;) in order to download and display files exposed via EPN-TAP servers. dwarf planet Ceres, and the asteroid Vesta). MATISSE 2-D outputs are formatted as FITS, in equirectangular projection and in planetocentric coordinates. The planetary FITS standard described in this work has been implemented in MATISSE in order to make the output file compliant with most FITS readers, especially DS9 and JS9.
The files generated by MATISSE could be also opened with standard GIS software (e. g. , QGIS, ArcGIS) after being provided as input of the fits2vrt Python library (https://github. com/ epn-vespa/fits2vrt), whose output is a. vrt GDAL Virtual Header pointing to the FITS file with the appropriate projection. Thanks to this approach, in the next major release of MATISSE (v2. 0, planned to be online by the end of 2018) JS9 will be the standard reader for the 2-D online output of the tool, thus allowing users to perform advanced analysis in both 2-D and 3-D directly inside a web-based environment.
Acknowledgments This work benefits from support of  VESPA/Europlanet. The  Europlanet 2020 Research Infrastructure  is funded by the  European Union  under the  Horizon 2020 research and innovation program , grant agreement N. 654208. C. Marmo  would like to thank  Mark Calabretta  from  Australia Telescope National Facility  for his WCSLIB library and  David Berry  from  Joint Astronomy Center Hilo  for useful discussions about TAB projection in FITS.
This paper proposes to use Flexible Image Transport System (FITS; https://fits. gsfc. nasa. gov/) format (Pence et al. , 2010; Wells et al. , 1981)  in planetary surface investigations and describes how FITS metadata can easily be inserted in the Planetary Data System (PDS) version 4 (https://pds. nasa. gov/tools/standards-reference. shtml) metadata distribution model. FITS is one of the standard formats implemented in the Virtual Observatory (VO; http://www. ivoa. net/astronomers/using_the_vo. html), therefore making obvious its connection to the Planetary VO initiative and the planetary Table Access Protocol (EPN-TAP; Erard et al. , 2014). FITS format is open, flexible, and largely implemented in open and efficient processing tools allowing to handle large amounts of raster data. The goal of this approach is to make easier data mining and reprocessing in planetary surface investigations, promoting general software based on those standards.
B. Practical analysis methodology The numerical procedure for the practical calculation of (  29 ) is composed of two steps, described in the following and shown in the algorithm of Table  II. The code has been written in MATLAB. Step 1. Calculation of the transfer function H(t,ω) For the calculation of ( , ) ( , ) p H t H t     , one should replace the original circuit input with a small-signal sinusoidal source vin(t), at the high-frequency frequency . In the positive spectrum, the sinusoidal input is represented as jt in Ve  , where Vin is a constant value. The function H(t,ω) is obtained as the ratio between the output-signal envelope at , expressed as ( , ) out Vt  , and Vin. That is: ( , ) ( , ) out in Vt Ht V    (30) To calculate the LTV transfer function H(t,ω) in  (30) , one sets the magnitude of the sinusoidal input source to the low level that should be used in the practical application.
We thank  M. Hermes ,  I. Peters ,  B. Saint-Michel ,  T. Gibaud  and  S. Manneville  for insightful discussions. Work funded in part by  SOFI CDT, Durham University , and  EPSRC  ( EP/L015536/1 ); the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013 ) and  Horizon 2020 Programme / ERC  grant agreements  279365  and  740269. MEC is funded by the  Royal Society.
These experiments were performed by placing 20 mL of solution onto a glass cover slip, then lowering into an NMR spectrometer and allowing the sample to dry overnight in the magnetic field. This method is discussed in more detail in Fig. S3 (ESI † ). Fig. 3b  and Fig. S12  (ESI †) show optical microscope images of the samples prepared in this manner under cross-polarised (CP) light. This approach is extremely effective and results in a high degree of alignment of the sample (Fig. S12a-c , ESI †). At a concentration of 50 mg mL À1 , liquid crystalline-like texture appears at the edge of the sample (Fig. S12d , ESI †) agreeing with data shown in Fig. 1c. SEM images of samples dried under a magnetic field also showed this alignment (Fig  S13c  and d, ESI † ) compared to samples not dried in the field (Fig.
, 2012) , little is known about the subjective importance of social odors in everyday life across different countries. In order to cover the broad spectrum of variables potentially correlated with odor awareness, it is crucial to conduct a study on a large sample, including participants from various geographic regions. Therefore, we conducted a large-scale analysis of data collected from 10,794 participants from 52 study sites all over the world. Korea, Canada, Kenya, Austria, United Kingdom, South Korea, Italy). In some countries, in addition to locations specified above, the research was conducted during individual visits in participants' homesthis was done, for example, in Kazakhstan, China, Croatia, Hungary. Further, we used chainreferral method (snowball sampling)in all participating countries the researchers invited friends and family members of the participants, their acquaintances, and their students to take part in the study.
Finally, some samples included also parents of children taking part in a different project (USA), and participants recruited through online and journal announcements (Italy and USA). All participants were naïve to the hypotheses of the study, they completed the questionnaires independently and individually. The data collection was a part of a larger project-the participants took part also in a study on interpersonal distance preferences  (Sorokowska et al. , 2017) , and in some countries, married participants completed additional questionnaires on their marital satisfaction  (Hilpert et al. , 2016; Sorokowski et al. , 2017). -----------Table  1  Here -----------.
Measures and Procedure The data were collected by co-authors and their respective research teams. All respondents took part in the study voluntarily and provided written consent prior to participation. They were not compensated for their participation. Participants completed paper-and-pencil questionnairesthe original version of the questionnaire was in English, but in all non-English speaking countries, the questions were translated to the native language by researchers fluent in English and a given language using backtranslation procedures  (Brislin, 1970). The social odor awareness questionnaire applied in this study comprised items from the Odor Awareness Scale  (Smeets et al. , 2008). The participants did not complete the full scale, instead, we A c c e p t e d M a n u s c r i p t 20 selected 6 items related to perception of odors in interpersonal situations, i.
87 [South Korea], median  =. 76), with only two countries reliability estimated as below. 60 (Bulgaria,  =. 53, Saudi Arabia,  =. 59). In addition to participants' report on gender and age, we measured their education level quantified as "1no formal education, 2primary school, 3secondary school, 4high school or technical college, 5bachelor or masters degree". The material situation was rated on a 5-point scale, from "1much better than average in my country" to "5much worse than average in my country". As individuals were nested within countries, we analyzed data using multilevel regression models (aka hierarchical linear models). We started with a baseline (empty) model to estimate the variability of the social olfactory awareness in the social context across countries. In the next model, we included individual-level variables: gender, age, the level of education, self-reported material situation and preferred interpersonal distance.
Acknowledgements The authors would like to thank  Vladimir Tikhonchuk  for fruitful discussions. The authors acknowledge support from the project  High Field Initiative (HiFI)  ( CZ. 02. 1. 01/0. 0/0. 0/15_003/0000449 ) and  ELI Tools for Advanced Simulation (ELITAS)  ( CZ. 02. 1. 01/0. 0/0. 0/16_013/0001793 ),both from  European Regional Development Fund , the  Czech Science Foundation  project  18-20962S  and  Czech Technical University  grant  SGS16/247/OHK4/3T/14. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  633053  ( EUROfusion  project  CfP-AWP17-IFE-CEA-01 ).
Experiment Setup and Data Collection We have designed two identical E-noses for nitrogen dioxide (NO 2 ) monitoring. Each one was made up of three gas sensors, an electrochemical sensor (NO2-B43F from Alphasense, Essex, UK), and two MOX sensors (MICS-2714 from SGX Sensortech, Corcelles-Cormondreche, Switzerland and GGS7530 from UST Umweltsensortechnik, Geschwenda, Germany). We used a homemade diffuser system to control and generate different concentrations of NO 2 in pure air (Figure  1 ). Two mass flow controllers were used to monitor the flow of the pure air and nitrogen dioxide cylinders. The total flowrate was set to 400 mL/min and we could generate different concentrations by varying the percentage of nitrogen dioxide flow over the total flow. The nitrogen dioxide cylinder had a dilution of 10 ppm (parts per million).
Therefore, to generate 250 ppb, we had to dilute 10 mL/min of nitrogen dioxide in 390 mL/min of pure air. Nitrogen dioxide and pure air were first introduced in a gas-mixing chamber to ensure the homogeneity of the gas before crossing to the sensors cell which contained two identical electronic noses. We collected 508 measurements. Each measurement took 300 s to reach the steady state response of all sensors. The 508 measurements were distributed between different concentrations of NO 2 ranging from 25 ppb to 250 ppb, with a step of 25 ppb in order to simulate the real pollution rates found near highways. The collected data were organized in two matrices: X1 D N for unit 1 and X2 D N for unit 2. Their corresponding known concentration was grouped in Y N : X1 D N =    x1 1 1. x1 D 1. x1 1 N.
Experiment Setup and Data Collection We have designed two identical E-noses for nitrogen dioxide (NO2) monitoring. Each one was made up of three gas sensors, an electrochemical sensor (NO2-B43F from Alphasense, Essex, UK), and two MOX sensors (MICS-2714 from SGX Sensortech, Corcelles-Cormondreche, Switzerland and GGS7530 from UST Umweltsensortechnik, Geschwenda, Germany). We used a homemade diffuser system to control and generate different concentrations of NO2 in pure air (Figure  1 ). Two mass flow controllers were used to monitor the flow of the pure air and nitrogen dioxide cylinders. The total flowrate was set to 400 mL/min and we could generate different concentrations by varying the percentage of nitrogen dioxide flow over the total flow. The nitrogen dioxide cylinder had a dilution of 10 ppm (parts per million).
Therefore, to generate 250 ppb, we had to dilute 10 mL/min of nitrogen dioxide in 390 mL/min of pure air. Nitrogen dioxide and pure air were first introduced in a gasmixing chamber to ensure the homogeneity of the gas before crossing to the sensors cell which contained two identical electronic noses. We collected 508 measurements. Each measurement took 300 s to reach the steady state response of all sensors. The 508 measurements were distributed between different concentrations of NO2 ranging from 25 ppb to 250 ppb, with a step of 25 ppb in order to simulate the real pollution rates found near highways. The collected data were organized in two matrices: 1 for unit 1 and 2 for unit 2. Their corresponding known concentration was grouped in : 1 = 1 … 1 ⋮ ⋱ ⋮ 1 … 1 2 = 2 … 2 ⋮ ⋱ ⋮ 2 … 2 = ⋮ where = 508 represents number of samples and = 900 is the dimension of samples composed from 300 data points for each of the three sensors.
Support Vector Machine Regression Support vector machine regression is a machine learning method widely used for building calibration models  [22]. Its structure guarantees a good generalization and accuracy with a limited number of learning samples  [23]. Support vector machine establishes a linear regression function between dataset x and the target values y as follows: f (x i , w) = w. x i + b ( 9 ) where w is the regression coefficients vector and b is the bias term. In order to calculate the regression coefficients, SVM regression attempts to minimize the loss function as defined by: |y i -f (x i , w)| ε = 0 |y i -f (x i , w)| -ε if |y i -f (x i , w)| ≤ ε otherwise ( 10 ) where ε is the maximum difference between the predicted value and the target value that can be neglected. In order to simultaneously minimize the empirical risk and model complexity, support vector machine regression was formulated as an optimization problem with constraints: Minimizing : 1 2 ||w || 2 + C n ∑ i=1 (ξ i + ξ * i ) (11) Subject to      y i -f (x i , w) -b ≤ ε + ξ * i f (x i , w) + b -y i ≤ ε + ξ i ξ * i , ξ i ≥ 0 ( 12 ) where C is a constant regularization parameter which determines the tradeoff between the flatness of f (x) and the empirical risk.
• Gaussian kernel with δ = 30 • The constraint C = 2000 • Epsilon-insensitive band ε = 0. 0001 Figure  2  summarizes our procedure for calibration transfer. We used kernel SPXY to select the desired number of standardization samples from the dataset of the master unit (E-nose 1). The remaining data were used to build a model calibration using SVM regression. Support vector machine regression hyperparameters were optimized using generalized pattern search (GPS). Next, from the slave unit (E-nose 2), we selected the analogue samples of the selected samples from the master unit. SVM regression was used again to create another model used to standardize data from the slave unit. Finally, we could predict concentration using the model calibration built on the master unit with data from the slave unit.
Conclusions Air pollution monitoring systems based on low cost sensors is a promising instrument that can complement the actual air pollution monitoring network. In this work, we built two identical electronic noses to study the performance of the calibration transfer. First, we showed that both electrochemical and metal oxide sensors can detect a low range of concentrations that corresponded to the real concentrations near highways. We saw that two identical electronic noses under the same conditions provided different signals in terms of the baseline as well as the sensitivity. The goal of our study was to develop a new approach to make a calibration transfer with a minimum number of samples used for matching between units. To perform the calibration transfer, two essential considerations should be taken into account: the selection of a subset of samples that should be as representative as possible for the entire dataset, and finding a mathematical method that needs as few as possible samples to make the relation between data units.
However, the performance of the detection was closely dependent on the terrain database used (type of surfaces, etc. ) As a consequence and in order to focus the evaluation on the haptic assistance by itself, it has been assumed for the piloted simulations that obstacles positions were precisely known in advance. A few simple obstacle benchmarks were tested, corresponding to emergency avoidance procedure or navigation in a congested area. A speci¦c scenario has been developed and tested, corresponding to a very simple urban environment. Two di¨erent logics have been used in order to generate force feedbacks on the cyclic active sidestick. These force feedback laws were implemented on PycsHel prototype and evaluated in piloted simulations on the di¨erent obstacle ¦eld benchmarks.
Description of the Map and Obstacles A speci¦c map has been designed by placing various 3D obstacles on a plain, §at ground. Those obstacles are derived from a benchmark proposed by the U. S. Army to evaluate the performance of guidance and navigation algorithms of UAVs in the vicinity of obstacles  [3]. Since the dimensions and maneuverability of the helicopter considered here are rather di¨erent from those of the UAVs, the benchmark was devised for, the obstacles have been resized at scale (Fig. 2 ). Wall obstacle has been implemented in the terrain database but no haptic function has been developed yet for this speci¦c task. The dashed lines represent the prescribed §ight paths for the obstacle avoidance. For the case of the cube, wall baªes and cube baªe obstacles, both §ight paths and obstacle avoidance trajectories are considered in the horizontal plane only: the obstacles are supposed to be of in¦nite height and, thus, the avoidance cannot be done by §ying above them.
For each run, all measured (forces and displacements of the sidestick) and simulated (aircraft position, velocity, etc. ) variables were recorded continuously, at a sampling time -t = 10 ms and were put together available as. csv ¦les for later processing. As the rotor disk is not visible in the visual environment and due to the lack of visual markers in the terrain database, it was very di©cult to estimate the distance of the helicopter from the obstacle(s). This remark is true for all test cases performed in this study and, more generally, for all tests performed in simulators. Although the helicopter §ight control system is equipped with high level piloting laws (ACAH ¡ Attitude Command, Attitude Hold, RCAH ¡ Rate Command, Attitude Hold), all tests were performed by only using the SAS (Stability Augmentation System).
CONCLUDING REMARKS A speci¦c terrain database has been developed, integrating obstacle ¦eld navigation benchmarks. As a result of previous works, the tactile cueing used on the stick was a force bias. But two logics have been developed to generate it. These two logics were implemented and evaluated on four di¨erent tasks on the PycsHel prototyping and simulation environment at ONERA: the ¦rst one is based on force gradients surrounding obstacles, whereas the other one is partially based on τ -theory. A total of 179 runs have been performed over two months in order to evaluate the bene¦ts of using haptic feedback for obstacle avoidance. All simulation §ights were done by one §ight dynamics engineer. Further trials will involve actual helicopter pilots in order to get their feedback and expertise, in the framework of the ONERA/DLR cooperation.
1 Sherpa Engineering, R&D Department, La Garenne Colombes, France. [a. kasmi, d. denis] @sherpa-eng. com FirstName. Lastname@uce. fr Discrete Model (DV-RWDM)  [9]. These methods depend highly on the number of traces and only an accuracy of 60% was achieved  [8]. On the other hand, other researches work on using the collaborative OSM mapping project for intelligent vehicles. The OSM geodata includes rich information about road. In addition to the spatial information, the geodata contains detailed information such as the name, the limitation of speed and the number of lanes  [10]. As it is a collaborative project by volunteers, the accuracy of the geospatial data is undefined. In litterature, the use of the OpeenStreetMap geodata is limited to navigation tasks such a localisation  [11]. In order to determine the location of the vehicle on a OSM 'link' a Map-matching algorithm is performed.
The map matching algorithm proposed in this paper is a multicriteria algorithm that takes into account several factors: geometrical, topological and probabilistic. The three methods for computing the multicriteria depend on the availability of sensors on the vehicle. For the number of lanes estimation, in contrast to  [9] , our method does not require the collection of several GPS traces. In addition, our algorithm was performed on a set of 6596 GPS points collected in an urban area for a total distance of 50km. An accuracy rate of 82. 95% on lanes number estimation was reached. The remainder of the paper is organized as follows: Section II introduces the data model of the OSM geodata. Section III describes the Map matching algorithm used. Section IV presents the real-world experimental results.
As discussed in section II-B every Way has a unique identification number, knowing this identification number allows us to extract the number of lanes (Tag 'lanes') from the OSM geodata. However since the OSM is a collaborative project by volunteers, the Tag 'lanes' is not available in all Ways. With the objective of improving the results obtained on lanes number a heuristic has been made. The number of lanes is stored for a distance of 200m when no new information about the lanes number is available. The GPS data were collected in the region of Paris for a total of 6596 GPS points. The GPS receiver provides a decametric accuracy measurement of the longitudinal and lateral coordinates, in addition to the speed of the vehicle and the DOP. The GPS data are received with a frequency of 1Hz. In order to evaluate the performance of the proposed algorithm, We get the ground truth from the video of a camera installed in front of the vehicle.
The results obtained are shown on Figure  8. We divided the incorrect lanes number estimations in two groups. The first one "Zero estimations" denotes the absence of information about the number of lanes from the OSM geodata. In this case the algorithm provides zero for lanes number estimation. The second one "Wrong estimations" indicates the false estimations that are different from "zero estimation" group. Depending on the criterion used for the map matching, the results for the accuracy rate are shown in the Table  II  As discussed before the zero estimations are due to the absence of information about the lanes number in the OSM geodata. In order to prove it we run the same algorithm with the same data sets on a different period (April 2018). During the time interval between the two runs, the OSM map has been updated and therefore the map should provide more information about the lanes number.