dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulation models emulated the data flow from the GBT-detectors (wide or GBT mode) while data sent to the PCIe DMA interface was recorded in a file. The CTP messages were generated by the internal CTP emulator. The FPGA Intel Avalon bus  [15]  required for configuration was connected to all the simulated components using it. An Avalon master model was used to set the various registers during the simulation. Thus, during the simulation, the configuration sequences required by the sub-detectors were simulated as they take place in real setups. This allowed a reduction of the development time, but also the possibility to reproduce in simulation error cases that were detected by users. Consequently, error resolution was faster, particularly for rarely occurring errors. To ease the simulation, a hexadecimal address table was declared in a VHDL common package and used as reference for these configuration simulations.
Custom software extracted the address table information from the VHDL common package for use in the software running on the FLP. The common firmware is distributed via a git repository to all users. Makefiles included in the distribution allow users to easily simulate or compile the firmware. A reference user logic generating known data is included in the reference design. It can be configured to generate pre-defined and random packet sizes and data rates. This feature along with the DDG is a complementary tool for stressing in-situ the firmware and validating the readout software. In ALICE the CRU firmware deployment remains under the CRU team responsibility. Subdetectors add their user-logic code to a branch of the common firmware. The CRU team validates the code before integrating it into the master branch and finally deploying it.
Conclusion An adaptable common firmware was developed to cover the needs of the upgraded detectors of AL-ICE  [16]. It was shown that by carefully designing the firmware features such as data path reader, CTP emulator, DDG, pattern player and by making them configurable, it allowed the development and validation effort to be shared between the firmware and the associated readout software. The different readout modes were validated by different detector setups in various running conditions. At this moment 565 CRUs are produced. The board distribution is over and the installation is done. The validation of the detector specific firmwares, the ones featuring user logic, is in progress. The commissioning shall be over by the end of 2021.
It was in this context that our research group initiated the French HIV-HBV cohort, which followed coinfected patients between 2002 and 2011  [17]. We recently invited participants of the cohort to return for a study visit in 2017-2018 with the purpose of (1) performing a cross-sectional, comprehensive evaluation of viral hepatitis and liver fibrosis and (2) retrieving biological and clinical data since their previous study visit in the cohort. The primary research aim of the cohort is to assess HBV-DNA replication, serological markers of HBV (hepatitis B e antigen [HBeAg] and hepatitis B surface antigen [HBsAg]), and liver fibrosis and assess how all 3 of these outcomes relate to mortality in predominately TFV-treated, coinfected patients with HIV-HBV.
Objectives In this paper, we present a protocol of this cohort. First, we aimed to expand on the procedures of the extension of this cohort study. The individual characteristics of the overall cohort were described and compared between those who did and did not participate in the cross-sectional visit mentioned earlier. Second, to provide more accurate information on mortality, we described the procedures by which we obtained the vital status of individuals who were lost to follow-up and assessed the number of cohort participants lost to follow-up. The characteristics of individuals who continued follow-up, who were lost to follow-up, and who were deceased were then compared. Finally, we discussed the importance of these data and how they will be used in future research.
In the third phase, individuals completing follow-up in either the first or second phase were invited from November 2016 to March 2018 to participate in a cross-sectional study visit, which included a comprehensive evaluation of viral hepatitis and liver fibrosis. Approximately 4 mL of additional serum and plasma samples were collected and stored at -20 °C in a centralized location (Tumerothèque, Hôpital Saint-Antoine, Paris, France). Data were also retrospectively collected from the most recent study visit of the first or second phase until the cross-sectional visit or, in the case of death, until the most recent visit before death. The retrospective visits were selected from a 3-month period before or after the date of available HBV-DNA viral load (VL) measurement or, if missing, from a 3-month period before or after the date of the yearly visit.
All patients provided written informed consent at the beginning of each study phase. Retrospective data from deceased individuals were collected only if the patient had signed consent for the use of their clinical data during noninterventional research, consistent with the French Public Health Law. Protocols for each study phase were approved by the hospital ethics committee (first phase: Hôpital Pitié-Salpêtrière; second phase: Hôpital Saint-Antoine; third phase: Hôpital Hôtel-Dieu) in Paris, France, in accordance with the Helsinki Declaration.
All-Cause Mortality Deaths observed during follow-up in the first and second phases, along with the underlying cause of death, were reported by the treating physician. At the third phase cross-sectional visit, treating physicians were asked to ascertain the vital status (ie, alive, deceased, or unknown) of patients completing follow-up in the first or second phases. If death occurred, they were requested to provide further information on the date and underlying cause of death. To obtain vital status for patients with unknown status (ie, lost to follow-up), a trusted third party (Inserm U1018) was requested to link data from the French HIV-HBV cohort to a national identification registry (Répertoire national d'identification des personnes physiques, fichiers n° 779 and 801). For deceased individuals, the cause of death was then obtained by a separate trusted third party (CépiDC), linking data from the French HIV-HBV cohort to a national registry of death certificates (certificat médical) and death notifications (bulletin d'Etat civil de décès).
Treatment Data on antiretroviral and anti-HBV treatments, including the specific agent, start and stop dates, doses, and reason for discontinuation, were obtained during all phases. Data on all concomitant treatment, which included the same information as antiviral treatment, were also obtained during the first and second phase; however, data on only a specific set of concomitant treatments (given by Anatomical Therapeutic Chemical [ATC] Classification codes in Multimedia Appendix 1) were obtained during the third phase retrospective visits and at the third phase cross-sectional visit. All treatments were classified using the ATC codes  [23]. Treatment information was retrieved from patient files and verified by the treating physician.
Clinical Events Data on clinical events, including date of presentation, date of resolution (if applicable), and necessary intervention were obtained during all phases. Data on all clinical events were collected during the first and second phase; however, data on only a specific set of clinical events (given by International Classification of Diseases [ICD]-10 codes in Multimedia Appendix 2) were collected during the third phase retrospective visits and at the third phase cross-sectional visit. All clinical events were classified using the ICD-10 codes  [24]. Information on clinical events was retrieved from patient files and verified by the treating physician.
Data Management Data were collected using paper clinical report forms, which were filled out by trained clinical research associates. Data for the third phase retrospective and third phase cross-sectional visits were manually entered in a centralized database maintained at INSERM UMRS_1136, Hôpital Saint-Antoine. To ensure data validity, we used a 10% random sample of study participants and double-entered their data. For any data field with >5% discordance between data entries, the entire data field was reverified, and discrepancies were resolved with the data manager.
Lost to Follow-up and Deceased Participants of the Cohort Study and Their Characteristics Of the 161 patients not followed up at the third phase cross-sectional visit, we were able to establish vital status for the 55 patients who were already known to have been deceased (n=35), incarcerated (n=1), not proposed or refused to participate (n=5 and 13, respectively), or did not complete the third phase study visit (n=1). The remaining 106 patients not followed at the third phase cross-sectional visit did not have known vital status because of an unknown reason for lost to follow-up (n=69), being followed at a nonparticipating center (n=36), or unknown reason for noninclusion (n=1). Of these, 28 patients were able to be linked to the CépiDC database: 21 were still known to be alive and 7 were identified as deceased from the death certificate or notification registry.
At the same time, we aim to associate these primary outcomes with all-cause mortality. In addition, by extending our previous cohort, we collected data for up to 15 years of follow-up. There are certain facets of HIV-HBV coinfection that make data sets containing longer follow-up important. HBsAg seroclearance is a slow process  [29]  and is achieved in approximately 1% per year in treated coinfected patients  [17]. TFV-containing ART regimens have shown effective antiviral potency  [4]. However, long-term toxicity issues related to treatment and/or comorbidities among specifically coinfected patients with HIV-HBV may hamper its clinical effectiveness. It is also unknown how HIV-HBV coinfection evolves in an aging population, especially with respect to severe non-AIDS and non-HBV-related clinical events.
Assessment of Participation in the Third Phase Study Visit and Loss to Follow-up Similar to other longitudinal studies, one major concern is loss to follow-up and the possible biases induced thereto. At the third phase of follow-up, occurring roughly 15 years after the start of the cohort, almost half of the patients did not continue follow-up. When compared with rates of loss to follow-up in HIV-positive (5. 4% of patients lost to follow-up from 1999 to 2009 in the French Hospital Database on HIV study  [39] ) or HIV-HCV coinfected (10% of patients lost to follow-up from 2006 to 2010 in the Agence Nationale de Recherche sur le Sida et les Hépatites (ANRS) CO13 HEPAVIH study  [40] ) cohorts from France, loss to follow-up would seem to occur at a much higher rate in our cohort. Individuals lost to follow-up could have a higher risk of more advanced disease (opportunistic infections, advanced liver fibrosis or cirrhosis, cancer, advanced cardiovascular diseases, etc), which might make them less willing to participate or continue follow-up.
No particular population characteristic at cohort inclusion was significantly different between participating and nonparticipating patients across phases, except for a higher proportion of AIDS-defining illnesses in those not participating in the third phase cross-sectional visit. There could also be other characteristics during follow-up, either measured or unmeasured (eg, nonreported comorbidities) in our cohort that were associated with loss to follow-up. This might induce differential loss to follow-up bias, the effect of which depends on the research question addressed. Therefore, the assessment of this bias is imperative in future studies from our cohort. Previous studies linking data from the French Hospital Database on HIV study and other mortality outcome registries or cohorts demonstrated that 29.
Conclusions Long-term and more comprehensive cohorts of coinfected individuals with HIV-HBV are pivotal in understanding how liver-related and nonliver-related morbidity and mortality can develop in this patient population. We were able to extend our cohort data on HBV replication, HBV serology, and liver fibrosis to up to 15 years; however, given the fact that approximately half of the patients were unable to participate in the third phase cross-sectional study, either due to death or lost to follow-up, the biases associated with this high rate of loss to follow-up need to be considered in future studies from this cohort. Nevertheless, these data will be helpful in enhancing our knowledge of the clinical trajectory during coinfection. More specifically, the clinical implications of persistent viremia, lack of HBeAg or HBsAg seroclearance, and progressive development of liver fibrosis will be evaluated along with their association with all-cause mortality.
Acknowledgments The authors are grateful to the patients and clinical teams for their commitment to the French HIV-HBV cohort study. The authors acknowledge  L Roguet  for managing the logistics of the French HIV-HBV cohort;  L Melila ,  R Kassime , and  R Loisel  for their help with data entry;  G Pannetier  and  Prof F Carrat  for their help in data management; and  Prof P Cales  for kindly providing Fibrometre scores. Finally, the authors would like to thank  Prof J-F Flejou  and  E Roux  of the  Tumorothèque HUEP at Hôpital Saint-Antoine  for storing samples. This work was supported by a grant from the  ANRS. Gilead Sciences, Inc , provided an unrestricted grant for the French HIV-HBV cohort in the first phase and was not involved in any part of the data collection, analysis, and writing of the paper.
Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High Resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric-effects-corrected products is also provided. This database, which is the first of its kind made available open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation variable retrieval using either physically based or empirical approaches such as machine and deep learning algorithms.
, 2015; Ouaadi et al. , 2020d)  for crop stress detection. In situ measurements of vegetation and soil characteristics are always needed to improve our understanding of the radar response, to develop and calibrate radiative transfer models, and to propose generic retrieval methods for the inversion of soil or vegetation variables. Nevertheless, in situ data dedicated to these objectives are really specific in the sense that, for instance, soil roughness is only of interest for understanding the physical principle of observations in the microwave domain. Likewise, aboveground biomass is often measured by agronomists for crop modeling for instance, but the partition between dry and wet matter, a key variable for radar acquisition, is hardly ever performed. Indeed, the latter relies on heavy destructive measurements consisting in cutting all the vegetation elements within square samples in the field and a double weighing before and after drying the samples in an oven.
In this paper, a recent, multiyear, complete database composed of processed Sentinel-1 SAR data (the backscattering coefficient and the interferometric coherence); the Sentinel-2 NDVI; and measured variables on the soil, on the vegetation and on agricultural practices is made available. The in situ data include automatic measurements as well as observations carried out during measurement campaigns once or twice every 15 d throughout the growing sea-son. This database covers three wheat seasons (2016/17 to 2018/19) of three different irrigated fields  (Ouaadi et al. , 2020b). It is a unique and valuable data set that can be used for vegetation and soil moisture monitoring applications including from radar observations. In addition, the multiyear database can be useful for multiyear time series analysis.
Study area The database described in this paper is collected in the Haouz plain in the Tensift watershed, central Morocco (Fig. 1 ). This plain is one of the most important plains in Morocco located at 550 m above sea level and covers about 6000 km 2 of which 2000 km 2 is irrigated. The climate in the region is Mediterranean semi-arid, with annual average precipitation of about 250 mm. The distribution of precipitation highlights a wet season with around 85 % of annual precipitation between October and April and a dry season from May to September. The maximum average of temperature occurs during summer in July-August (about 35 • C), and the minimum occurs in January (about 5 • C)  (Abourida et al. , 2008). The average air humidity is about 50 %, and the reference evapotranspiration ET0 is around 1600 mm yr -1  (Jarlan et al.
Experimental sites The database concerns three irrigated fields (F1, F2 and F3) located within a private farm in the province of Chichaoua located 65 km west of Marrakech city (Fig. 1 ). F1 and F2 are monitored during two successive growing seasons (2016/17 and 2017/18), while F3 is monitored during the season 2018/19. The fields are sown using an automatic seed drill. They are irrigated using the drip technique. For all the fields, the wheat is cropped once a year during winter-spring (see Table  1  for sowing and harvest dates). After harvest, the fields are generally used for cattle grazing until mid-July when the plowing work starts. Table  1  summarizes some general information about the fields. Please note that during the 2017/18 season, wheat in F2 was affected by specific growing conditions: (i) the development of adventices belonging to the wild thistle family characterized by a horizontal structure, (ii) the seeding density being higher than in F1, and (iii) the seeding being a mixture of barley and wheat within F2.
Soil moisture SSM is automatically measured every 30 min using time domain reflectometry (TDR) sensors (Campbell Scientific CS616) using two sensors buried at a depth of 5 cm: one under the drippers and another one between them. The average is computed in order to obtain a representative SSM value of the field. In addition, similar sensors are buried for RZSM measuring at 25 and 35 cm of depth in F1 and F3, while one sensor is buried at 30 cm in F2 because of the lack of an additional sensor. Figure  2a  illustrates an example of TDR sensors at different depths. TDR sensors are calibrated using the gravimetric technique. The calibration is performed during the 2016/17 season using samples taken from the first 5 cm from both fields F1 and F2, and then the calibrated equation is applied to F1, F2 and F3 data as the soil characteristics are similar and the same sensors are used.
For that purpose, an aluminum core of 392. 5 cm 3 is used to collect samples at the TDR installation depths. Three samples are collected per day and per field during 5 d chosen with different soil moisture conditions in order to cover a wide range of values (0. 08 to 0. 33 m 3 m -3 ). A linear regression is established between the volumetric water content and the square root of the TDR time response (named τ , in seconds) as follows: SSM = a TDR × √ τ + b TDR. (1) The calibrated values using data of both fields are a TDR = 0. 275 m 3 m -3 s -0. 5 and b TDR = -1. 154 m 3 m -3. Figure  3  https://doi. org/10. 5194/essd-13-3707-2021 Earth Syst. Sci. Data, 13, 3707-3731, 2021  illustrates the calibration results with all the samples displayed. The statistical metrics are the correlation coefficient R = 0.
Canopy height, green leaf area index and cover fraction The canopy height (H ), green leaf area index (GLAI) and cover fraction (F C ) are measured every week during the growing season. Values from 11 different places are averaged and considered a representative measure of the field. H is simply measured using a measuring tape, while the GLAI and F C are computed by processing hemispherical photos (Fig. 6b ) using MATLAB software following the method described in  Duchemin et al. (2006)  and  Khabba et al. (2009). The eight photos per date and per field are taken using a Canon 6EOS 600D camera with a SIGMA 4. 5 mm F2. 8 EX DC circular fisheye HSM (Fig. 6a ). Photos are taken in optimal lighting conditions to avoid shadow effects and overexposure phenomena which make classification more difficult.
F2 and F3 are irrigated according to FAO recommendations, while F1 is stressed voluntarily. The stress involved in F1 is during the first season (2016/17) only. By contrast, the 2017/18 season was wet, so there is no clear stress observed  on the field. The irrigation dates and amounts in F1 and F2 during both seasons are made available throughout this database, while the irrigation in F3 is not available. The weather data including precipitation, air temperature, relative humidity, solar radiation, and wind speed and direction are collected by an automatic weather station installed over an alfalfa field near the studied fields (Fig. 7 ). The weather station provides continuously meteorological data every 30 min. The Campbell sensor CS215 is used to measure the air temperature and the relative humidity (Fig.
7 ). The global solar radiation and the wind direction and speed are measured using the Campbell SKP215 and Campbell WindSonic4, respectively. The precipitation is measured using the rain gauge (Campbell SBS500) shown in Fig. 7. Level-1 products are systematically processed and available within 24 h, free of charge from the Copernicus Open Access Hub website (https://scihub. copernicus. eu, last access: 19 July 2021). The website provides data for two types of products: GRDH (Ground Range Detected High Resolution) and SLC (Single Look Complex). In this database, 561 GRDH and SLC products are processed (Table  3 ). Among them, 124 images are acquired over F3 during the 2018/19 growing season and 437 over F1 and F2 from 1 October 2016 to 31 July 2018, along the ascending no. 118 (221 images) and descending no.
SAR images are affected by the speckle noise, which is mainly due to the relative phase of individual scatters within a resolution cell. Many filters have been developed to remove the speckle noise although the best filter is the spatial average. The presented database is generated using a simple average per field of 120, 121 and 1100 pixels for F1, F2 and F3, respectively, with a mean standard deviation of around 1. 55 dB. In order to visualize data dynamics, backscattering coefficients are converted into decibels.
Sentinel-2 NDVI Sentinel-2 optical satellites S2A and S2B were launched by ESA in June 2015 and March 2017, respectively. They are placed in opposition on the same orbit at an altitude of 800 km. Sentinel-2 provides data every 5 d with a width of 290 km and a resolution of 10 to 60 m according to spectral bands (13 bands) ranging from visible to the medium infrared. The National Centre for Space Studies (CNES) provides Level-2A products atmospherically corrected free of charge via PEPS (https://peps. cnes. fr/, last access: 19 July 2021) or the Theia website (https://theia. cnes. fr/, last access: 19 July 2021). Data are corrected for atmospheric effects by the Center for the Study of the Biosphere from Space (CES-BIO) using the MAJA chain  (Hagolle et al. , 2015). The atmospheric corrections are performed in three steps: 1.