dataesr/scientific-paragraphs-categorization huggingface dataset:
C. Evaluation of the IR across three study sites Following our validation of the proposed approach based on the IR, the method was applied to soil moisture data from eight ground stations located within the three study sites (Occitania, Merguellil, Banizombou) described in section II. Five of the selected ground stations (BZ1, BZ2, Bouhajla, Barrage, Barrouta) are characterized by a landscape with either bare soil or low-density vegetation cover, and three others (INGC, Mouthoumet, Narbonne) are characterized by an agricultural landscape with important temporal dynamics in vegetation cover. We identified a 1 km 2 zone centered around each moisture measurement station and derived an averaged radar signal (in the linear domain) for each Sentinel-1 acquisition for each zone. Only pixels with radar signal values between -20 dB and -5 dB were taken into account to avoid possible extremes from surfaces other than natural surfaces (such as water coverings or buildings)  [28].
The choice of the 1 km 2 size was intended to limit the effects linked to roughness as much as possible and to enable the assumption of relatively stable average roughness. Indeed, in areas of limited size, the effect of roughness can be much greater and thus affect the proposed algorithm, as illustrated in section IV-B. For each site, we considered data from one orbit with an approximately constant incidence angle. This approach notably reduced the number of images used by the change detection application but prevented errors resulting from empirical incidence angle normalization, which could change from one pixel to another and from one season to the next. We then analyzed the Sentinel-1 data time series over a four-year period. For each station, the I SSM and IR values were converted to volumetric moisture as described in section III and by using the ground moisture time series data.
Strong agreement is observed between the ground measurements and the soil moisture estimated from the two considered indices. At the Barrouta site, RMSE and R are equal to 0. 034 and 0. 73 and to 0. 04 and 0. 74 for the IR and I SSM approaches, respectively. At the INGC site, RMSE and R are equal to 0. 06 m 3 /m 3 and 0. 6 and to 0. 056 m 3 /m 3 and 0. 61 for the IR and I SSM approaches, respectively. However, differences in the rate at which the soil moisture decreases after rainfall events were noted. This is probably due to the effective penetration depth of the S1 radar, which is theoretically smaller than the value of 5 cm used for the ground-truth measurements  [59]. In these cases, limited differences were observed between IR and. Table  II  summarizes the results obtained with the Sentinel-1 data products using and.
At stations with temporal vegetation cover dynamics, errors may be more important, particularly during the wet season, when a strong vegetation effect that is not corrected for in the proposed algorithm occurs. At stations located in West Africa, the application context in semiarid areas could generate volume scattering on the driest dates  [10]  and high vertical soil moisture profile heterogeneity  [59] , which are also a source of errors in the application of the change detection technique. The analysis of a relatively restricted database (4 years) does not allow us to observe the trends within each range of soil moisture (0-0. 1 m 3 /m 3 , 0. 1-0. 2 m 3 /m 3 , 0. 2-0. 3 m 3 /m 3 ) with precision. Table III illustrates the RMSE observed for each site. The differences between the I SSM and IR approaches are generally small.
In these simulations with constant roughness condition, the RMSE decreased from 0. 055 m 3 /m 3 to 0. 023 m 3 /m 3 when this new index was used. With introduction of roughness variation in IEM simulations, the RMSE decreased from 0. 067 m 3 /m 3 to 0. 038 m 3 /m 3 when IR was used. The proposed algorithm was validated using Sentinel-1 data recorded over three study regions (Banizombou, Merguellil, Occitania). Eight ground moisture stations (five with bare soil or low-density vegetation cover and three with agricultural landscapes showing temporal vegetation change) were used for this validation. For each station, when the IR was converted to volumetric moisture, it was found to be strongly consistent with ground measurements, with RMSEs of less than 0. 06 m 3 /m 3 for seven of the eight stations.
He is currently scientific director of the THEIA Land Data Centre (https://www. theia-land. fr/en). Jerome Demarty received his PhD degree from the Paris Diderot University (France) in 2001. He has been working for 12 years as researcher for the French Institute of Research for the development (IRD, France), at the HydroSciences Montpellier laboratory. He is specialized in ecohydrology, surface modeling and remote sensing, especially on Sahelian and Mediterranean ecosystems. Sekhar Muddu received the Ph. D. degree from the Indian Institute of Science, Bangalore, India in 1993 in the area of hydrology & water resources. Since 1994, he is with Department of Civil Engineering, Indian Institute of Science and currently he holds the position of Professor. He is also member of the scientific team of the Indo-French Cell for Water Sciences at IISc.
Out of the two Venus flybys that BepiColombo uses as a gravity assist manoeuvre to finally arrive at Mercury, the first took place on 15 October 2020. After passing the bow shock, the spacecraft travelled along the induced magnetotail, crossing it mainly in the Y VSO direction. In this paper, the BepiColombo Mercury Planetary Orbiter Magnetometer (MPO-MAG) data are discussed, with support from three other plasma instruments: the Planetary Ion Camera (SERENA-PICAM) of the SERENA suite, the Mercury Electron Analyser (MEA), and the BepiColombo Radiation Monitor (BERM). Behind the bow shock crossing, the magnetic field showed a draping pattern consistent with field lines connected to the interplanetary magnetic field wrapping around the planet. This flyby showed a highly active magnetotail, with e.
For MPO the magnetometer boom could be deployed; however, it is rather close to the MTM with its ion drives and solar panels, which will create stray fields in the measurements. We use data from the BepiColombo magnetometer MPO-MAG onboard the MPO spacecraft  (Glassmeier et al. , 2010; Heyner et al. , 2021) , at a cadence of 1 s (Fig. 2 ) and a lowpass filter for periods below 5 min (Fig. 3 ) in order to get the large-scale structure of the induced magnetosphere undis-  turbed by high-frequency oscillations. We limit the discussion of the observations to the interval of 04:14 UT (crossing of the bow shock) to 12:00 UT, spanning the region of ∼ 0 ≥ X VSO ≥ -40 R V (Venus radius, R V = 6052 km). This work focuses on different regions within the induced magnetosphere that are marked with purple, green, blue, and red colours along the trajectory in Fig.
In particular, data from the Mercury Electron Analyzer (MEA) 1 in solar wind mode (3-3000 eV) are used to investigate the lowenergy electron distribution during the flyby at a cadence of 4 s. Since the MMO spacecraft is stuck behind the MOSIF Sun shield during the cruise phase, MEA1 has a limited field of view but, despite this, useful scientific observations can be obtained since low-energy electrons are almost isotropic. In order to account for the solar wind activity responsible for the IMF disturbances around the Venus 1 flyby, data from the BepiColombo Radiation Monitor (BERM) are used  (Pinto et al. , 2021). BERM is a particle detector able to provide radiation information, in a way similar to the Standard Radiation Environment Monitor (SREM) instrument aboard several ESA missions such as Rosetta  (Honig et al.
, 2019). In particular, it is able to measure high-energy charged particles (e. g. electrons from ∼ 100 keV to ∼ 10 MeV and protons from 1 to ∼ 200 MeV), and the higher-energy channel background counts can be used as a proxy for galactic cosmic rays. Moreover, we also use data from the Large Angle and Spectrometric Coronagraph (LASCO) instrument onboard the Solar and Heliospheric Observatory satellite (SOHO)  (Brueckner et al. , 1995). In particular, we use data from the c2 white light coronagraph imaging from 1. 5 to 6 solar radii. We also use the Heliospheric Imager (HI) instrument, which forms part of the Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) suite of remote sensing instruments onboard the Solar TErrestrial RElations Observatory (STEREO)-A spacecraft. The HI is a wide-angle visible-light imaging system for the detection of coronal mass ejection (CME) events in interplanetary space covering the region of the heliosphere from 4 to 88 • elongation measured from the Sun's centre  (Howard et al.
g. Spreiter et al. , 1966; Spreiter and Stahara, 1994; Schmid et al. , 2021). Overall, however, the counts decrease when BepiColombo moves further into the downstream region. There are a few significant increases in the count rate, marked by grey and blue transparent boxes in Fig. 9. The grey boxes are when the spacecraft has entered the magnetotail, and there does not seem to be a good correlation between the PICAM and MPO-MAG data as far as these increases in counts are concerned. The first one is during a rotation of the field from B x into B y , the second shows no real peculiarities in the MPO-MAG data, and the third shows a strong dip in B x. The lack of one correlation between MPO-MAG and PICAM could be due to the field of view of PICAM, which limits its visibility of the whole sky, so that some ion jets might be missed in observations.
Also, ELS does not show a drop in flux at the B x = 0 nT crossing, as is observed in the MEA1 data in the red box. Naturally, these differences may well be caused by the difference in location of the spacecraft with VEX near X ≈ -1. 5 R V and BepiColombo near X ≈ -15 R V as well as the different spacecraft speeds and instrument cadences. Dimmock et al. (2018)  studied the interaction of an ICME with the Venusian-induced magnetosphere, both with numerical simulations and VEX observations. They showed that the magnetic environment around Venus can be strongly altered, albeit mainly on the dayside, by bringing the bow shock more planet-ward, increasing the field strength at the magnetic barrier up to a value of ∼ 270 nT and magnetizing the ionosphere. The field line draping pattern was similar to "regular" conditions, although the simulation did not match the nightside draping pattern very well.
Mariner 10, Galileo, and Pioneer Venus As Mariner 10 was on its way to Mercury, it passed through Venus's magnetotail (wake) on 5 February 1974 in a similar orbit in the X-R plane as BepiColombo but at positive Z (see  Lepping and Behannon, 1978). The wake magnetic field data were studied starting at a distance of ∼ 100 R V from the planet. No evidence was found for a bow shock crossing entering the far tail. No typical magnetotail structure (as compared to Earth) was observed, but the authors found that, when the magnetic field direction and the spacecraft velocity vector aligned, the direction was not predominantly along X VSO , as one would expect for a magnetotail along the orbit of Mariner 10. The data were categorized into three bins: quiet, disturbed, and mixed. The longitudinal (cylindrical) component of the field was observed to rotate clockwise, when the spacecraft crossed from a quiet to a disturbed region.
Lepping and Behannon (1978)  interpreted this as Mariner 10 entering into the planetary magnetotail, however with a significant Y component for most crossings. The IMF, after Mariner 10 crossed the bow shock, exiting the induced magnetosphere, was F = 20 nT (in the paper denoted as 20γ ), φ ≈ 360 • , and θ ≈ 0 •  (Ness et al. , 1974). This means a mostly radial magnetic field. These multiple crossings do not seem to happen for BepiColombo. During the Solar Orbiter flyby such crossings were observed  (Volwerk et al. , 2021). The Galileo spacecraft used a Venus gravitational assist on its way to Jupiter. During this Venus flyby, the orbit skimmed the bow shock. Kivelson et al. (1991)  used the magnetometer data to investigate the cross section of the bow shock. They found that it seemed to be smaller when its direction was aligned with the IMF when compared to when its direction was perpendicular to the IMF.
Nevertheless, thanks to pre-and post-flyby observations made by the MPO-MAG, BERM, and MEA1 instruments, together with observations made by STEREO-A, SOHO, and a solar wind numerical simulation, the space weather context of the encounter is reconstructed. Figure  11  shows some STEREO and SOHO observations of a potential CME that may have hit Venus during the Bepi-Colombo flyby. The Space Weather Database Of Notifications, Knowledge, Information (DONKI) catalogue reports a single CME event for the period 7-13 October, which corresponds to the time needed for a CME to reach Venus by the time of the BepiColombo flyby. The DONKI catalogue indicates that this event was observed by SOHO-LASCO c2, c3 and STEREO-A SECCHI instruments with a starting time on 10 October 2020 04:24 UT and a speed of 270.
In particular, it shows the IMF measured by the MPO-MAG in panel (a), the proxy for galactic cosmic ray flux measured by BERM in panel (b), and the solar wind energetic electron spectra from MEA1 in panel (c). The reason for the large data gaps in panel (c) is that MEA1 only operated for a few hours around the closest approach. This figure shows that the solar wind was indeed clearly disturbed. The overall magnitude of the solar wind is ∼ 15 nT for most of the period, similarly to the induced magnetic field values observed at Venus (yellowish box). The CME arrived at BepiColombo on 13 October at 04:20 UT (vertical purple dashed line), where a moderate rise in the IMF magnitude from 10 to 15 nT was observed simulhttps://doi. org/10. 5194/angeo-39-811-2021 Ann. Geophys. , 39, 811-831, 2021 taneously with a rotation in the three components of the field, mainly seen in B x.
Nevertheless, other effects more difficult to discern may also be playing a role, such as shadowing from one's own spacecraft due to attitude changes. Interestingly, Rosetta also saw a similar reduction in the GCR flux of 8 % in the vicinity of the comet 67P/Churyumov-Gerasimenko which could not be attributed to any known mechanisms  (Honig et al. , 2019). Right after the flyby, there was still significant solar wind variability and, according to the simulation in Fig. 12 , these perturbations are most probably caused by the trailing edge of the CME. This means that during the flyby to Venus, the system was most probably immersed in the CME. The MEA1 observations also corroborate this finding, showing a large perturbation in the solar wind electrons on 15 October at 22:00 UT at the same time that a large variability is observed in the MPO-MAG data with B x and B z IMF rotations.
Acknowledgements. Based on observations obtained with  Bepi-Colombo , a joint  ESA-JAXA  science mission with instruments and contributions was directly funded by  ESA  member states and JAXA. MEA data analysis was performed with the CL software developed by  Emmanuel Penou  at IRAP and the AMDA science analysis system provided by the  Centre de Données de la Physique des Plasmas (CDPP)  supported by  CNRS ,  CNES ,  Observatoire de Paris , and  Université Paul Sabatier, Toulouse. Financial support. Beatriz Sánchez-Cano  was supported by  UK-STFC  grants  ST/S000429/1  and  ST/V000209/1. Cyril Simon Wedlund is supported by the  Austrian Science Fund (FWF)  under project  P32035-N36. Daniel Heyner  was supported by the  German Ministerium für Wirtschaft und Energie  and the  German Zentrum für Luft-und Raumfahrt  under contract  50 QW 1501.
Nicolas André  and  Sae Aizawa  were supported by  CNES  for the  Bepi-Colombo mission. Daikou Shiota  and  David Fischer 's work is financially supported by the  Austrian Research Promotion Agency (FFG)   ASAP MERMAG-4  under contract  865967. Sebastián Ro -jas Mata was funded by the  Swedish National Space Agency  under contracts  79/19  and  145/19. Review statement. This paper was edited by  Elias Roussos  and reviewed by two anonymous referees.
Data availability. The BepiColombo MPO-MAG, PICAM, MEA, and BERM data as well as the Venus Express MAG and ASPERA-4 data are available through ESA's Planetary Science Archive (PSA, https://archives. esac. esa. int/psa/#!TableView/ VenusExpress=mission, PSA, 2021). The STEREO data are available through the HELCATS catalogue (https://www. helcats-fp7. eu/ catalogues/event_page. html, HELCATS, 2021). The SOHO data are available through NASA's SOHO website (https://soho. nascom. nasa. gov/data/Theater/, SOHO, 2021).
Indeed, the use of biological samples and personal data in research has been extended in two ways: first, collecting biological resources has become easier thanks to the link between healthcare and research; second, their use has been expanded thanks to the new role of specific infrastructure as biobanks and databases. In the light of these considerations and of ethical perspectives of transparency, respect for individuals and scientific expectations, we aimed to assess the characteristics of various informed consent forms used by CARPEM researchers at the moment they collect data and biological samples for their research purposes by reviewing the current IC process of CARPEM: what kind of information current IC forms provide to research participants and how could they be improved? Our review focused on the written content of IC forms and allow us to question the use of IT technology to improve information and the consent process.
Data collection about biological samples and data treatment Our analysis aimed to identify the written information content provided about data treatments and the scientific use of biological samples. After distinguishing the types of research (non-interventional or interventional), we focused on pieces of information about the collection and the use of biological samples and data for scientific purposes which were recorded in the documents. After a comprehensive overview, documents were read carefully to identify "codes", "descriptive or conceptual labels" that qualify significant excerpts about biological samples utilization and data treatment. The codes were completed with legal provisions and helped to elaborate an analysis framework; a spreadsheet used as a matrix to summarized relevant data.
On the contrary, research as a context of the collection refers to the fact that samples are obtained during an additional procedure outside the scope of healthcare. Finally, documents with information concerning data collection were analysed to determine what kind of data was collected (medical data, lifestyle data, genetic data, or data from biological samples), how privacy was protected, what personal rights were mentioned, and whether any reuse was planned. This content analysis was performed to better define the limit of the use of biological samples and personal data for scientific purposes, thanks to informed consent. The personal rights related to biological samples and those related to personal data were considered separately because the French law distinguishes them: the GDPR regulates the rights related to personal data while the Code de la Santé Publique regulates the rights related to biological samples  [14].
Information related to personal data processing All the studies planned to use personal data and all information documents mentioned it (Table  5 ). Personal data were mainly designed as "healthcare data extracted from medical records" (in 21 out of 25 studies; "data associated with the biological samples" (7/25); "genetic data" (5/25), or "lifestyle data" (8/25)), without any explanation. All documents referred to confidentiality and guaranteed the protection of privacy and when a method was described (21 documents, 19 studies), it consisted of codification. The CARPEM platform used the following method: a shallow de-identification algorithm has been applied to patient data since its creation. All directly identifying data, as defined by the HIPAA Safe Harbour Recommendation, are removed and a number between -365 and 365 is randomly chosen to shift all dates.
Opt-out consent Total  of researchers with the patients' preferences  [5, 34]. Indeed, to make the inclusion phase more efficient and attractive, and to make the research less time consuming after the inclusion phase, biological samples are collected increasingly during healthcare. In our study, 12 out of 25 studies were non-interventional studies, which means that patients sustained no specific invasive intervention for research purposes: biological samples were thus collected during healthcare, for healthcare purposes, and a portion of them have been qualified as research resources thanks to patient's consent and IRB approval. By consenting, a patient permits researchers to use his (her) biological samples and data for research purposes in addition to healthcare purposes, without any additional constraint.
Moreover, it is difficult and even impossible to guarantee that data are completely anonymous, especially in cases of potentially identifying data, such as genetic ones  [52]. The GDPR only considers data to be anonymous if the data subject cannot be identified by any means "reasonably likely to be used (…) either by the controller or by any other person" (Recital 23)  [14]. Thus, even if a user of a database is neither able nor willing to re-identify a data subject, such a data set may still fall under the GDPR guidelines if it could be re-identified with reasonable effort. As a consequence, mechanisms for the assessment of the impact on privacy, pseudonymization and anonymization of data, fine-grained restriction of access, and requiring the use of formal agreements (Data Sharing Agreements, Data Re-use Agreements, Data Transfer Agreements) have grown in importance.
Our qualitative study highlights part of the complexity of the underpinned issues: current information documents may be too complex to comply with the literacy level of a large part of the population, even when they are dedicated only to collect a specific consent and whereas their content is not thought to inform patients about the multiple but often still undefined further uses of their samples and data. Besides, biological samples and data may be continually collected and stored for healthcare as well as for current and future unspecified studies. The increasingly frequent recharacterization of biological samples from healthcare to scientific purposes may encourage healthcare providers and researchers to adopt accountable management of biological samples for scientific purposes. It may also promote the organization of fair hospital biobanking for patient care while allowing the secondary use of the biological material for research purposes.
Dependant on the nature of the collected data, the characteristic parameters can be the occurrence frequencies (failures, recoveries, etc. ), the time intervals (time before failure, time before recovery, etc. ), or both. Thanks to these parameters, the WSS availability and reliability can be assessed. Besides, some statistical predictive models can be developed to assess the future trends of the characteristic parameters. The available failure and recovery data are generally impacted by technical, procedural, and managerial performance effectiveness  [6]. Including all these dimensions is out of the scope of our paper, as the available data do not allow the integration of such dimensions in the assessment. So, the assessment will only consider the technical dimension. The paper presents a case study of a given regional WSS in Poland.