dataesr/scientific-paragraphs-categorization huggingface dataset:
Data processing As a dataset, we use Ingersoll and coworkers  [22]  as these data provide the longest published data set. Ingersoll et al. [22]  identified several tens of groups based on the days on which observations were taken. Due to the temporal variability on longer time scales (mainly 4 and 11 years) and aperiodic contributions, it is challenging to fit the data. Inspired by  [22] , we have constructed a template from the groups obtained within seven days (groups 1, 2, and 3 from June 2017) due to their global coverage of the orbit and their temporal proximity. For the template, we assumed diurnal periodicity and smoothed the data using the Savitzky-Golay filter  [42]  and its SciPy implementation  [43]. Then each of the data groups was rescaled by the factor A D to the template. The rescaling factor is obtained by the minimization of misfit S between data d i (for mean anomaly t i ) and template T S 2 = min N D i=1 d i - T (t i ) A D 2 , (1) where N D is the number of measurements in the group D.
Data fitting The rescaled dataset was used to identify the free parameters in the expression for the modelled plume activity A given by Eqs. (  9 )-  (11). As no error estimates are provided for the data by  [22] , we considered the error of measurement σ i to be 10% of the maximum, i. e. σ i =0. 1 for all i for the normalized data. In the inversion routine, we are searching for four parameters A 0 , c, E eff , and ∆u 0 h. Time series for ∆u h and σ nn are taken as spatial averages from the numerical simulations  [17] , see A value of l=1 has been chosen to suppress the effect of outliers. To obtain the posterior distribution of models, we employ Bayesian inversion using the MCMC and emcee library for Python  [50]  based on the sampling methods proposed by  [51] ; 50 chains and models beyond 50 times the autocorrelation time was used.
Acknowledgements We thank  Olivier Averbuch ,  Jean-François Deconinck ,  François Guillot  and  Alain Trentesaux  for sharing their knowledge of the geology of Boulonnais, as well as the reviewers and the  CR Geoscience team , who helped make things possible and better than they were initially. We are grateful to  Monique Gentric  for her administrative management of the project. Our thanks to undergraduate intern  Cynthia Richard. This project received financial support from the  INSU Tellus Syster program. We sincerely thanks the  Earth Science Department of University of Lille  for its support.
, 2005). Despite ongoing efforts to provide line positions, intensities and shape parameters of HONO in the thermal infrared  (Armante et al. , 2021; Tchana Betnga et al. , 2023) , spectroscopically resolved parameters are not yet available for all the absorption bands. Therefore, we used the cross sections of HONO from the Pacific Northwest National Laboratory (PNNL) infrared database  (Sharpe et al. , 2004)  to ensure consistency within the different bands. In the past, the HRI was found to exhibit small biases between the different IASI instruments and to be sensitive to small changes to the instrument calibration or post-processing  (Van Damme et al. , 2021) , which occurred several times through the course of a decade-long satellite mission. It was recently shown that these biases can be largely removed by disregarding the eigenvectors of the covariance matrix S y in Eq.
This is illustrated in Fig. 3 , where certain IASI measurements showing significant HONO enhancements exhibit high HRI's for C 2 H 4 and weak values for NH 3 , or vice versa. Including both NH 3 and C 2 H 4 in the filter allows us to cover a broader range of biomass burning conditions. Another advantage is the higher sensitivity of IASI to NH 3 and C 2 H 4 in the thermal infrared and their longer lifetimes compared to HONO. As a result, the proportion of genuine HONO enhancements mistakenly discarded because of the absence of NH 3 and C 2 H 4 detection is negligeable. To set up the filter, we specifically investigated observations with HONO HRI > 4 (i. e. , four standard deviations beyond the gas background), and we set relatively high HRI values as thresholds for NH 3 and C 2 H 4. While this conservative approach may lead to the omission of some genuine HONO detections, our priority has been to produce a reliable IASI HONO dataset with minimal false detections rather than maximizing the number of HONO detections at the expense of their quality.
, 2020). This choice ensures a more comprehensive and coherent dataset across the entire IASI operational time series than with the IASI Level 2 data  (Van Damme et al. , 2021). The parameter z 0 is set based on a 1 • × 1 • monthly climatology of fire plume altitudes derived from CALIPSO data (Sect. 3. 2). The same approach was applied to estimate the dust altitude for retrieving dust optical depths from IASI observations  (Clarisse et al. , 2019). For σ, we used a value of 350 m.
Consequently, a dedicated validation methodology for the IASI HONO product will need to be carefully designed in the future, contingent upon the availability of exploitable third-party datasets. So far, HONO has been identified with spaceborne TIR observations only in exceptional pyroconvective smoke plumes from the 2009 and 2019/2020 Australian bushfires  (Clarisse et al. , 2011; De Longueville et al. , 2021; Dufour et al. , 2022). In this study, we have demonstrated that HONO can be identified and retrieved from IASI spectra in concentrated fire plumes worldwide. This is an important step toward a better understanding of reactive nitrogen in the atmosphere and its oxidative capacity. Through our work, we also contribute to laying the foundation for the methodology of routinely monitoring pyrogenic HONO from future TIR satellite instruments.
On the other hand, the geostationary MTG-IRS will provide continuous spatial coverage of the Earth's surface at a higher spatial resolution (up to 4 × 4 km 2 ), with a revisit time of up to 30 minutes above Europe. This will offer a unique opportunity to closely track HONO and study its chemistry along its transport in a fire plume. Data availability. The IASI pyrogenic HONO product is publicly accessible at https://doi. org/10. 5281/zenodo. 10141825.
Acknowledgements. IASI is a joint mission of Eumetsat and the  Center National d'Etudes Spatiales (CNES, France). The IASI Level-1C data are distributed in near real-time by Eumetsat through the EumetCast distribution system. The authors acknowledge the AERIS data infrastructure (https://www. aeris-data. fr/) for providing access to the IASI Level-1C data and Level-2 temperature data. This paper contains modified Copernicus data (2018/2023) processed by BIRA-IASB. The authors thank  Apolline Tricard  for the preliminary analysis of the IASI HONO data in the framework of her master's thesis.
Ionospheric response To investigate the ionospheric effects observed in the region of interest, we exploited data acquired by different observing platforms, both ground-based (GNSS receivers, ionosondes and Digisonde stations) (Figure  2 ) and space-based (DORIS/ Jason3). During the earthquake, eight GNSS (GPS and GLONASS) satellites were visible by ground-based GNSS receivers covering the region of interest (Figure  2 ). To search for the ionospheric TEC response to the 2020 Samos earthquake and the following tsunami, we applied two methods: a 6-28 min window as band pass running mean filter  (Astafyeva et al. , 2009)  and the VARION (Variometric Approach for Real-Time Ionosphere Observation), algorithm  (Savastano et al. , 2017). VARION is an open source, entirely Python-based software (https://github.
Availability of data and materials The full sequence of the Ne gradients given in figure  6  is available at: http://chapman. upc. es/. 4lucilla/Samos_event_animation/ All dataset used by the authors are available and accessible on demand writing to the corresponding author: lucilla. alfonsi@ingv. it Electronic Space Weather upper atmosphere database (eSWua) -HF data, version 1. 0 (1. 0). Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi. org/10. 13127/ESWUA/HF GNSS stations providers: • Uranus network (http://uranus. gr) operated by the Tree Company corporation National Observatory of Athens (NOA) network, http://geodesy. gein. noa. gr:8000/nginfo/ (Ganas et al. , 2008. ;Chousianitis et al. , 2021). HxGN SmartNet operated by the Metrica SA (https://www. metrica. gr) • Turkish National Permanent GNSS Network-Active (TNPGN-Active/CORS-TR) https://www.
After applying these criteria, we are left with ≳ 50% of the waveforms in the CNN dataset. We are interested in the following parameters: amplitude of the primary peak, electron prespike presence and amplitude, and secondary peaks' presence and amplitudes, and the primary peak's rise and decay times; where the former two peaks (electron  and primary peaks) are assumed to be common in all three channels, the latter (secondary) is analyzed channel-wise. For a comprehensive description how these are extracted, the reader is referred to the Appendix D.
Acknowledgements. The work was partially done during SK's stay with OBSPM, LESIA and SK thanks them for their hospitality. Authors sincerely appreciate the support of  Solar Orbiter/RPW Investigation team. This work made use of publicly available data provided by A. Kvammen at Zenodo, doi. org/10. 5281/zenodo. 7404457. AT and SK were supported by the  Tromsø Research Foundation  under the grant  19-SG-AT. IM was supported by the  Research Council of Norway  under the grants  262941  and  275503. JV was supported by the  Czech Science Foundation  under the grant  22-10775S. 36  https://doi. org/10. 5194/egusphere -2023-2067  Preprint. Discussion started: 12 September 2023 c Author(s) 2023. CC BY 4. 0  License.
Image reconstruction The reconstruction is essentially based on the algoRIM treatment  46, 47  developed for the analysis of 2D-RIM images, and which is accessible at https://github. com/teamRIM/tutoRIM. In brief, the N images taken under different speckled illumination (N ∼ 100 -200 in most cases) are first processed with a Wiener filter, which can be written in the Fourier domain: g = h * ⊥ | h⊥ | 2 + η ( 10 ) where h⊥ is the Fourier transform of the extended depth PSF, a * stands for the complex conjugate of a, and η is a regularization parameter set at 10 -4. This filter enhances high frequencies inside the OTF and removes noise beyond the OTF cutoff. We then estimate empirically the standard deviation of these pre-filtered speckled images. Finally, an iterative, moment-matching algorithm is used to achieve a super-resolved reconstruction of the object.
Acknowledgments We thank  Sophie Brasselet ,  Sandro Heuke  and  Hervé Rigneault , for support and fruitful discussions on the project. This work was funded by the following agencies:  Agence Nationale de la Recherche  ( ANR-18-CE13-028 ,  ANR-20-CE45-0024 ,  ANR-22-CE13-0039 ,  ANR-22-CE42-0010 ,  ANR-22-CE42-0026 );  Institut Carnot  star ( 3D-RIM ). This project is funded by the  ≪ France   2030 ≫  investment plan managed by the  French National Research Agency  ( ANR-16-CONV-0001 ,  ANR-21-ESRE-0002 ), and from  Excellence Initiative of Aix-Marseille University  -A* MIDEX.
Acknowledgments We would like to acknowledge  Anna Schmid  ( Precision Sensing GmbH, Germany ) for her assistance during the OxyBase sensor installation and its functioning. We are grateful to  Gregor Christandl  ( Elyte Diagnostics GmbH, Austria ) for his help during the Arduino programming. Financial Support This development was financed by the  SEPSOL  project, held by the  Continental and Coastal Ecosphere Structuring Initiative (EC2CO) , a program coordinated by the  INSU  and supplemented by two other  CNRS  institutes: the INEE in particular and the INC. https://doi. org/10. 5194/egusphere-2023-2482 Preprint. Discussion started: 14 November 2023 c Author(s) 2023. CC  BY 4. 0  License.
ACKNOWLEDGMENTS KMS and BES were funded by the  NASA   Astrobiology program  grant  Oceans Across Space and Time  (Grant No. 80NSSC18K1301 ). MRN was supported by  NASA  grant No. 80NSSC20K0570  issued through the  Solar System Workings program. MLB acknowledges the financial support from the  French Agence Nationale de Recherche , project  ANR COLOSSE  (grant No. ANR-2020-CE49-0010 ).
Some of the data quality checks are performed on the single telescope data, while others are performed after the stereo trigger analysis. Finally, the data quality results of all sag-dq pipelines are aggregated and then stored in a database. These results are also used to check if the data quality level required to generate a candidate science alert are satisfied. The DL3 resulting from the sag-reco pipeline is the input for the High-Level Reconstruction pipeline (sag-sci) that executes scientific analyses on the DL3 with several science tools. First, the DL3 data are collected in a database. Then, the Pipeline Manager, a component of sag-sci, executes the configured analyses in parallel to obtain the DL4 results (counts maps and analysis) stored in a database. During these analyses, the sag-sci can detect candidate science alerts and send them to the Transients Handler system to perform further investigations.
The operator and the support astronomer can visualise the results of both the sag-dq and sag-sci pipelines using the operator HMI, an ACADA system that shows the results stored in the databases. Slurm manages the workload of different pipelines, also allows the scaling on a cluster of machines. Prototyping versions of the SAG, called Real-Time Analysis (RTA), was developed in the past  [14] [15] [16] 21]. Lesson learned and experience has been included in the current design.
Online High-Level analysis: science monitoring and candidate science alert generationsag-sci The High-Level online analysis (sag-sci) is designed to perform scientific analysis in real-time during the observations and detect candidate science alerts. This pipeline shall execute analysis in parallels for different sub-array observing at the same time and is developed using the RTApipe framework described in  [12]. It shall detect candidate science alerts with a latency of 5 and an integral sensitivity within a factor two of that required for the off-line analysis. The input data for this component is the DL3 produced by sag-reco, saved into a database and automatically triggers the sag-sci pipeline analysis. Candidate science alert is cross-checked with the result of the sag-dq before issuing them to the Transients Handler system.
For the examined experimental conditions an ice accretion threshold existed beyond which there was continuous accretion or complete shedding. The experimental findings of this study not only consolidated previous studies in similar conditions, but also help identify new findings for heated surfaces at negative wet bulb temperatures and unheated surfaces with mixed-phase conditions at positive wet bulb temperatures. They form a large collection of new data on ice accretion and shedding and provide a comprehensive experimental database quantitatively analyzed for validating future physics based and numerical models.
, 2018; Reitter et al. , 2021; Karpen et al. , 2021). The collected experimental data supported the development and validation of empirical and physical models  (Trontin et al. , 2016; Tippmann et al. , 2013; Roisman and Tropea, 2015; Kintea et al. , 2016a; Roisman, 2022; Senoner et al. , 2022 ). Numerous experimental and numerical studies of the ice accretion and shedding process components of aircraft engines or on NACA airfoils, hemispheres, and wedges were performed on non-heated substrates. Ice accretion and shedding processes were analyzed with respect to the ice shapes, ice masses, surface temperatures, water-ice layer compositions, and accretion and shedding cycles. Mazzawy developed the first ice accretion model taking into account mixed-phase conditions, i. e. , supercooled droplets mixed with ice crystals  (Mazzawy, 2007).
Objectives The current models of ice crystal accretion and shedding aim at relating the influences of T wb , ambient pressure, airspeed, particle size distributions, melting ratio, and angles of attack to the accretion growth rate and to the shedding mean frequency but they do not take into account the influence of a heat flux from the wall. Systematic experimental studies on the influence of an externally applied heat flux to ice crystal ice accretion and shedding were only recently investigated by  Karpen et al. and Malik et al. (Karpen et al. , 2022; Malik et al. , 2022)  on a limited dataset. A constant heat flux from the substrate to the ice layer adds more complexity to the heat and mass balance and therefore has a great influence on accretion and shedding phenomena. Contrary to unheated surfaces, heated substrates can lead to significant icing in fully glaciated ice crystal conditions even at negative T wb conditions.
Ice accretion due to the sticking of ice crystals to a surface is affected by the presence (or the absence) of a liquid water film on the outermost ice layer surface which is able to retain the ice crystals by capillary force. In mixed-phase conditions, ice accretion at low negative T wb is limited due to the complete icing of the liquid water, after which no capillary force causes additional ice crystals to stick by capillary imbibition. An external heat flux can keep the outermost surface of the ice layer wet, thus maintaining the "sticky" water film in place for a long time. In this work we performed new experimental studies focused on characterizing the ice accretion and shedding processes. In particular, the objectives of this study were to. • build on previous research by generating a comprehensive database of purely glaciated ice crystal cloud ice accretion and shedding test cases and including the influences of parameters investigated in the prior studies, such as the airspeed, IWC, and T wb (from below to slightly above freezing) • examine the additional effects of a regulated substrate heat flux on accretion and shedding, • quantify all the parameters which govern accretion and shedding and compare the parameter influences between T wb above and below freezing, which represent majorly different icing behaviors, • further our understanding of the icing physics, and to support the future development and validation of a more complete ice crystal accretion and shedding model.
Fig. 1  shows a schematic drawing of the test setup. Two machine vision video cameras were installed to record ice crystal impact, accretion and shedding on the leading edge of the NACA-0012 airfoil from a top view and from a side view. To obtain well contrasted videos, LED lights (Vision Devices VD7000) and milky glasses for light diffusion were used. For the optical characterisation of the icing cloud particle size distribution a high speed camera was used to record shadowgraphy images of the ice particles in the air flow prior to their impact on the NACA profile. A python script was developed for analysing the high speed videos. An average MVD of 110 μm with a standard deviation of 20 μm, a D10 of 50 μm with a standard deviation of 10 μm and a D90 of 225 μm with a standard deviation of 25 μm was assessed.
TU Braunschweig Icing Wind Tunnel A comprehensive dataset on accretion and shedding was generated by carrying out experiments in TU Braunschweig IWT. Fig. 2  provides pictorial illustration of the IWT facility highlighted in dark blue color. The tunnel is closed loop with airflow velocities reaching up to 40 ms -1 in a test section with cross-sectional area of 500 × 500 mm -2. Static temperature inside the tunnel can be maintained in the range of -20 ∘ C to 30 ∘ C by means of a dedicated cooling system with components highlighted in the top right corner of Fig. 2. The icing wind tunnel operates at the ambient pressures present in the laboratory, which is positioned at an altitude of 75 m with ambient pressure in the proximity of 990 mbar. Ice crystals were generated inside a cooling chamber where the generation, dosing, sieving and transport of the ice crystals to the wind tunnel test section take place.
From the wall side of the test section, camera (5) points at the stagnation line. Opposite to camera (5) another LED light source (4) was placed. With the side camera (  5 ), the contour of the ice accretion wrapping the airfoil was recorded via shadowgraphy. A high qualitative overall image of the accretion process was acquired with camera (6) positioned upstream and pointing from an oblique view to the leading edge. Variable electrical power sources were connected to the heaters. A temperature control unit monitored the temperatures of all 21 RTDs, whose signals were acquired and digitally converted via a Lab View (National Instruments, NI-9205, USA) program with temporal resolution of 10 Hz.
Data analysis The video analysis of the top and side views was done similarly at ACRT and TUBS. Before the test campaigns, the video images were calibrated with objects of known size, from which the pixel scale was determined. The frames are filtered with a binary threshold filter to yield black and white images of the recorded scene with the airfoil and ice being depicted in black pixels, and the background illumination in white pixels. With the still frames of a clean NACA airfoil a mask is generated, which is deducted from the later video frames with ice accretion on the NACA airfoil leading edge. Fig. 7  illustrates the postprocessing method for a test case with side and top view frames. More details on the TUBS in-house code used for obtaining quantitative ice accretion data can be found in Malik et al.
12 led to the start of erosion. At ACRT with MVDs of around 100 μm and Ma = 0. 175 no accretion could be observed. The combination of the lower speeds and larger ice particles in both test facilities seem to arrive at similar findings for the limits of possible accretion. In fact when superposing the data of the referenced studies and this study, the critical Mach numbers vs. the MVD above which no accretion is possible seems to follow a distinct trend (Fig. 12 ). Experiments with the 430 mm chord length NACA-0012 airfoil from TUBS in the ACRT test facility (dark blue points) showed potential for larger ice accretions towards higher airspeeds in comparison to the airfoil with shorter chord length. We assume that ice accretion also depends on airfoil thickness, especially in a test section of limited height as in the iCORE where we must consider wall effects.
However, separating and quantifying individual influences is then rendered more difficult. While with a NACA0012 model some of these important factors are lost, the quantification of the ice accretion and erosion caused by primary and measurable parameters is enabled. Similar methodologies were pursued by other studies  (Currie and Fuleki, 2016). In a holistic view, all the known phenomena inside the compressor stages should be individually investigated with quantifiable in-and out-put parameters and later linked. The NACA0012 ice accretion and shedding experiments of this study are just one of these many individual phenomena tests. The obtained data could be used to feed fundamental icing physics models. In combination with results of other studies models could interand extrapolate the currently untested ranges.
Main observations and conclusions The analysis of the collected data from sensors and videos led to the definition of accretion and shedding parameters in order to characterize observations and to rationalise results. Ice crystal accretion and shedding phenomena on non-heated surfaces were investigated by prior studies and for a limited range of test parameters. This study agreed well with the previous findings and extended the parameter range. The investigations on heated surfaces constituted the most relevant novelty of this work. A total of three test campaigns were carried out at Airbus CRT and TU Braunschweig with two NACA-0012 airfoils of different sizes, wherein the influence of governing parameters, namely airspeed, wet bulb temperature, icing water content, and heat power density on accretion and shedding process was investigated.