dataesr/scientific-paragraphs-categorization huggingface dataset:
A thermodynamical sweet spot, pH 50 is defined, where ε(Cl -) and ε(NO 3 -) are 0. 5, and total chloride and nitrate remain 50% in the gas-phase and 50% in the particle phase. The 1100 hourly data points are color coded with ALWC (1a, 1b) and temperature (1c, 1d) to investigate the impact of ALWC and temperature on the phase-partitioning ratio (ε). The ALWC color bar shows how positively ALWC impacts the phase-partitioning, whereas the temperature color bar shows its inverse relationship with the particle-phase loading over a location. The ALWC color-coded data points show the variability in ε due to variation in ALWC at a constant pH, indicating the significance of ALWC in modulating the particle-phase loading. From Fig. 1a , it can be seen that ε(NO 3 -) can vary between 70 and 100% at pH ≈ 4, due to variability in ALWC.
A characteristic pH (pH 50 ) has been defined where total nitrate and chloride remained 50% in the gas-phase and 50% in the particle-phase (gray-color zone, region II). The 1100 hourly data points are color coded with ALWC (1a, 2b) and temperature (1c, 2d) to investigate the impact of ALWC and temperature on the phase-partitioning ratio (ε). The ALWC color bar shows how positively ALWC impacts the phase-partitioning, whereas the temperature color bar shows its inverse relationship with the particle-phase loading over a location. than NO x reduction and would be the most effective pathway to reduce aerosol loading  4, 38, 48. But in this study, we argue that the sensitivity and effectiveness of the adopted reduction mechanisms are more crucial than the cost-effectiveness. If aerosols are not sensitive to the reductions of a specific precursor, then the cost-effectiveness would not assist in developing an effective mitigation policy.
For example, the high NMB and RMSE values in pH and ALWC show that the fixed HCl + Cl -= 25 µg m -3 cannot represent the real-world variabilities of 2. 11-112. 21 µg m -3 and 2. 31-186. 31 µg m -3 for PM 1 and PM 2. 5 , respectively. In this study, we investigate the sensitivity of ammonium chloride and ammonium nitrate loading to reductions in gaseous precursors like HCl, HNO 3 , and NH 3 as a way to reduce PM 1 and PM 2. 5 mass loading. Six "sensitivity regimes" are defined as (a) "HNO 3 sensitive", (b) "HCl and HNO 3 sensitive", (c) "HCl, NH 3 and HNO 3 sensitive", (d) "HNO 3 and NH 3 sensitive", (e) "NH 3 sensitive", and (f) "insensitive" to explore the sensitivity of gaseous precursors in the formation of fine aerosols. The application of the framework to the observational dataset shows PM 1 and PM 2.
Acknowledgements The  Indian Institute of Tropical Meteorology (IITM)  is funded by the  Ministry of Earth Sciences (MoES), Government of India. We thank the Director,  IITM , for the continuous support and encouragement. The authors are grateful to the  Ministry of Earth Sciences (MoES), Government of India , for the support throughout the  Winter Fog Experiment  (WiFEX) field campaign. The lead author's fellowship was supported by the  National Supercomputing Mission (NSM)  program grant at C-DAC, and we are grateful to the Executive Director and the  Director-General of C-DAC. The  National Center for Atmospheric Research (NCAR)  is sponsored by the  National Science Foundation (NSF).
The main identified active faults in the Ubaye Region are the High-Durance, the Serenne, and the Jausiers-Tinée faults (Fig. 1 ;  Baques et al. 2021; Mathey et al. 2020 ). The first is oriented N-S and is prolonged to the Southeast by the Serenne fault. These two faults are located east of the Frontal Penninic Thrust while the Jausiers-Tinée fault (external part) is parallel to and at about 10 km south of the Serenne fault. Using satellite geodesy, there is no measurable horizontal strain (e. g. Walpersdorf et al. 2018). For the vertical deformation,  Mathey et al. (2021b)  used INSAR data to obtain the vertical strain-rate in the Western Alps and found a range of ~-0. 7mm/yr to ~0. 5mm/yr for the Ubaye Region (see Fig. 3  in  Mathey et al. 2021b). Therefore, vertical deformation is very limited and not homogenous in the Ubaye Region.
The stress-state is then determined through a grid-search inversion in order to maximize the shear-stress acting on the slip direction for all earthquakes. The MSATSI software requires as input the dip direction (strike+90°), the dip angle, and the rake, and computes the orientation of the principal stresses and the shape-ratio  (Michael, 1984) : The shape-ratio informs on the stress regime in the region associated with the orientation of the principal stresses (e. g. Gephart and Forsyth, 1984). Equivalently, we will use the R-ratio, defined as : A R-ratio of 0 indicate a purely extensive regime, while values of 0. 5 and 1 indicate strike-slip and compressive regime, respectively. We also calculate the uncertainties associated with the orientation of each stress component and shape-ratio, following the approach of Martínez-Garzón et al.
Acknowledgements Some of the figures were made using GMT (Wessel et al. 2019), and the colour scales used in the different figures come from the  Scientific Colour Map of Crameri  ( 2018 ). The focal mechanism catalogues are taken from the supplementary materials of Thouvenot et al. (2016), Mathey et al. (2021a), and Mazzotti et al. (2021). M. Baques  would like to thank  IRSN  and  CEA  for her funding grants. We would also like to thank  Henri Leclère , which provides the focal mechanisms catalogue for the 2003-2004 swarm. We also thank the  CEA , which provides us the LDG catalogue for the 1965-2020 period in the studied area. We thank the editors, two anonymous reviewers and  F. Thouvenot  for their thoughtful comments that have helped to improve the manuscript.
Data Availability Seismic data used for the focal mechanisms of the 2014 aftershocks are available online through the RESIF portal (http://seismology. resif. fr/), with permanent stations on https://doi. org/10. 15778/RESIF. FR and temporary ones on https://doi. org/10. 15778/RESIF. XG2014 and through the INGV portal (http://terremoti. ingv. it/), with permanent stations on https://doi. org/10. 13127/SD/X0FXNH7QFY. The resulting focal mechanisms catalogues, the stressstates, the fluid-pressure values, and the codes developed during this paper are available in request. The LDG catalogue (http://www-dase. cea. fr) used in this study is provided in the supplementary materials.
Background Pegunigalsidase alfa is a PEGylated α-galactosidase A enzyme replacement therapy. BALANCE (NCT02795676) assessed non-inferiority of pegunigalsidase alfa versus agalsidase beta in adults with Fabry disease with an annualised estimated glomerular filtration rate (eGFR) slope more negative than -2 mL/min/1. 73 m 2 /year who had received agalsidase beta for ≥1 year. Methods Patients were randomly assigned 2:1 to receive 1 mg/kg pegunigalsidase alfa or agalsidase beta every 2 weeks for 2 years. The primary efficacy analysis assessed non-inferiority based on median annualised eGFR slope differences between treatment arms. Results Seventy-seven patients received either pegunigalsidase alfa (n=52) or agalsidase beta (n=25). At baseline, mean (range) age was 44 (18-60) years, 47 (61%) patients were male, median eGFR was 74.
29  Elimination plasma half-life is approximately 90-110 min for agalsidase alfa and 80-120 min for agalsidase beta. 18 32  Pegunigalsidase alfa has an elimination half-life of ~80 hours, effective Gb3 clearance from renal tissue and a favourable safety profile up to 12 months. 30 33  The phase III BALANCE study (NCT02795676) is the first randomised, double-blind, active-control, head-to-head clinical trial of ERTs in FD and is the first study to directly evaluate efficacy, safety and tolerability of pegunigalsidase alfa versus agalsidase beta in adult patients with previous agalsidase beta treatment and deteriorating renal function.
Pegunigalsidase alfa and agalsidase beta infusions were planned to be initially administered over 3 hours at the study centre. If well tolerated, infusion duration was gradually reduced to 1. 5 hours, and patients could receive home infusions. If previously used, premedication was continued but gradually decreased over 3 months at investigator's discretion based on patient tolerability. After study completion, patients were invited to participate in an open-label extension of pegunigalsidase alfa (PB-102-F60; BRILLIANCE; NCT03566017).
The use of satellite instruments to detect and quantify methane emissions from fossil fuel production activities is highly beneficial to support climate change mitigation. Different hyperspectral and multispectral satellite sensors have recently shown potential to detect and quantify point-source emissions from space. The Sentinel-2 (S2) mission, despite its limited spectral design, supports the detection of large emissions with global coverage and high revisit frequency thanks to coarse spectral coverage of methane absorption lines in the shortwave infrared. Validation of S2 methane retrieval algorithms is instrumental in accelerating the development of a systematic and global monitoring system for methane point sources. Here we develop a benchmarking framework for such validation. We first develop a methodology to generate simulated S2 datasets including methane point-source plumes.
These benchmark datasets have been created for scenes in three oil and gas basins (Hassi Messaoud, Algeria; Korpeje, Turkmenistan; Permian Basin, USA) under different scene heterogeneity conditions and for simulated methane plumes with different spatial distributions. We use the simulated methane plumes to validate the retrieval for different flux rate levels and define a minimum detection threshold for each case study. The results suggest that for homogeneous surfaces, the detection limit of the proposed S2 methane retrieval ranges from 1000 kg h -1 to 2000 kg h -1 , whereas for areas with large surface heterogeneity, the retrieval can only detect plumes in excess of 5000 kg h -1. The different sources of uncertainty in the flux rate estimates have also been examined. Dominant quantification errors are either wind-related or plume mask-related, depending on the surface type.
By doing so, the satellite radiance observations were not altered while the plume enhancement was integrated into the scene. We adopt this latter methodology here, and refine it in section 3 to account for a compensation term that corrects for the convolution of the methane transmittance signal after its band integration. This error is present for every type of instrument at different levels, but for multispectral missions with a large bandwidth such as S2, a larger impact is expected. Our simulated datasets validate the retrieval algorithm proposed in section 4 for a set of sites and methane plumes presented in section 2. These plumes represent the temporal evolution of a point source plume with changing spatial distribution. The selected sites are representative of well-known O&G basins with different scene heterogeneity.
The validation process presented in subsection 6. 2 generates several datasets at different flux levels. This methodology validates the methane flux rate at different levels and, at the same time, defines the detection limit of the instrument for a set of sites and methane plumes. The results highlight several uncertainty contributions associated with the methane flux that we disentangle into different sources in subsection 6. 3. The bands in the SWIR region, B11 and B12, measure the TOA radiance at an approximate 20 m spatial resolution. Although they were not designed to detect and quantify methane point-source emissions, these two bands are sensitive to methane absorption and have shown capability to monitor large emissions at a global scale  (Varon et al. , 2021; Ehret et al. , 2022). Figure  1  illustrates the spectral response of bands B11 and B12 for both satellite units together with the methane transmittance for a surface concentration of 1904 ppm and air-mass factor (AMF) of 2 Both of these bands cover a small spectral region of methane absorption.
Thus, this is the method proposed when a large number of simulations are required and implemented for the datasets in  Gorroño et al. (2022). Once the transmittance of the methane plumes has been calculated, it is convolved with the S2 spectral response to obtain a methane plume transmittance map for each spectral band. This can be directly multiplied with the original S2 TOA radiance maps. This approach has the largest advantage since it reproduces the exact same observation conditions as the original obser-   2021 ) included a compensation term in the calculated total atmospheric transmittance capable of partially correcting for the bias introduced by this a-posteriori convolution of the methane transmittance for PRISMA and WV-3 simulations respectively. In those two cases, a simplified approach was selected by considering the total transmittance of the atmosphere T atm (λ) rather than reconstructing the full TOA radiance.
Here, we model this correction as: ϵ convolution = B12 L TOA (λ)T plume (λ)dλ B12 L TOA (λ)dλ B12 T plume (λ)dλ (3) with the L TOA (λ) modelled as: L TOA (λ) = T atm (λ)ρ surface (λ)E g (λ) (4) T atm refers to the total transmittance through the atmosphere, ρ surface represents the surface reflectance, and E g is the downwelling irradiance at the ground level. The path radiance and spherical albedo are not considered in this case since their impact in the SWIR region can be considered negligible in the correction. Combining Eqs. 2 and 3, the term B12 T plume (λ)dλ is cancelled and we can also reconsider the equation as a simulated plume transmittance convolved with a simulated hyperspectral TOA radiance. This mathematical framework has been implemented in a software capable of producing S2 datasets with simulated methane plumes  (Gorroño et al.
In order to test the validity of this approach, the retrieval method has been tested on the datasets generated in section 3  (Gorroño et al. , 2022). The test has set the temporal normalization against the same observation without plume emission. This cancels the noise of the sensor, and an ideal scenario can be tested. Figure  5  shows the test of such an ideal scenario with plume 2 (see Figure  3b ) and Q of 5000 kg h -1. In the absence of noise, only the linear regression between retrieved and reference methane enhancement pixels is shown. The figure includes a simulation with no correction (i. e. ϵ convolution = 1), another with the correction of the T atm (λ), and a last one with the L TOA (λ) correction as explained above. This plot shows that the methodology proposed in Figure  4  results in simulated datasets with a large agreement.
Although this masking process alters the original enhancement map, it is helpful to reduce the significant noise levels present in the S2 methane retrievals, and the resulting mask is applied to the original concentration map. A median filter (or other high frequency filtering process) is expected to have a low impact on the plume signal since this is distributed in the atmosphere as a low-frequency signal. Nonetheless, this process could remove the signal for very narrow plumes (just one or two pixels' width). The result of the process is a masked image that flags the potential pixels with a plume emission. The actual plume detection is achieved with an automated cluster detection algorithm in the scikit-learn Python package  (Pedregosa et al. , 2011) , which searches for clusters of at least 40 masked pixels.
Retrieval results The simulations for the three sites described in subsection 3 have been used to test the methane retrieval methodology described in Section 4. or supervised criteria as defined in section 4) against the validation reference obtained for a simulated dataset. This includes the methane plume 2 (see Figure  3b ) with a flux rate Q =5000 kg h -1 for Korpeje and Hassi Messaoud sites. A value of Q =20 000 kg h -1 has been used for the map of ∆XCH 4 values in the Permian basin due to its higher surface heterogeneity. For the Korpeje site, the methane plume with a flux rate Q =10 000 kg h -1 is clearly visible in the enhancement map in Figure  7a. The major structure visible in the image are the dunes that follow a North-west to South-East direction. Similarly, Figure  7b  shows a visible plume in Hassi Messaoud over a similar dune structure but with slighly lower intensity.
Results of the retrieval for the simulated datasets that include the map of ∆XCH4 values (7a, 7b, and 7c), the mask obtained under a conservative masking criteria (7d, 7e and 7f) and a supervised masking criteria (7g, 7h, and 7i). The selected simulations include the methane plume 2 (see Figure  3b ) with a flux rate Q =5000 kg h -1 for Korpeje and Hassi Messaoud and Q =20 000 kg h   a) Scatter Q=10 t h -1 (Korpeje) (b) Scatter Q=10 t h -1 (Hassi Messaoud) (c) Scatter Q=10 t h -1 (Permian) Figure  8. Scatter plots of the retrieval results against the validation reference at a pixel level for Korpeje (8a), Hassi Messaoud (8b) and Permian basin (8c) for a simulation that includes the methane plume 2 (see Figure  3b ) scaled to a flux rate Q =10 000 kg h -1 Similarly to the ideal case shown in Figure  5 , the scatter plot of the retrieved ∆XCH 4 values against the reference enhancement map presents a good agreement for Figures  8a, 8b , and 8c.
Flux rate validation As previously explained in Section 3, the methane plumes can be scaled at different flux rates and cover a wide range of possible emission levels. For both Korpeje and Hassi Messaoud scenes, the  Gorroño et al. (2022)  datasets have been scaled to different flux rates from 0 kg h -1 to 20 000 kg h -1. A step of 100 kg h -1 is set from 600 kg h -1 to 3000 kg h -1 so that the expected range of detection limits in the area can be accurately defined  (Irakulis-Loitxate et al. , 2022b). Larger steps of 500 kg h -1 and 1000 kg h -1 define the rest of the covered range. For the Permian scene, the covered range goes from 0 kg h -1 to 50 000 kg h -1. Due to the larger potential range of detection limits, a step of 200 kg h -1 is set from 5000 kg h -1 to 15 000 kg h -1. The step above 15 000 kg h -1 is relaxed to 1000 kg h -1.
In this case, neither the ∆XCH 4 retrieval map nor the underlying background features are expected to constitute a major source of error. Thus, these systematic errors are expected to arise from the U eff associated with the IME to Q conversion. Here, the entire WRF-LES has been explored and a specific U eff has been generated based on the dataset considered for the simulations (see subsection 3). Each plume has been scaled to 10 000 kg h -1 , and the relationship between the U eff and U 10 has been established with a Huber regressor  (Huber and Ronchetti, 2011). Figure  11  reports the specific U eff regression curve and compares it to the U eff calibration reported in  Varon et al. (2021). The results indicate that the uncertainty on the U eff calibration depends not only on that of U 10 but also on the fitting knowledge.
This fitting knowledge can be further separated into a systematic component and a random one. The former depends on the WRF-LES simulation and its specific parameterisation: emission altitude, surface roughness, turbulence. These simulation datasets and the retrieval methodology will generate different relationships between the U eff and U 10 relationship. This error is relatively small for this specific example (0-9% error for the 5 plumes considered) with higher disagreement the higher U 10 is for a linear fitting. However, it is a critical source of uncertainty for the assessment of emissions over O&G areas since these errors will largely remain correlated under similar atmospheric conditions and, consequently, largely remain in the uncertainty budget for the studied area. The latter effect is the consequence of differences in the U eff value between snapshots, and it results in an uncorrelated error between methane plume snapshots.
That results in a larger spread of disagreements depending on the plume shape and masked area. The study was also capable of separating the different uncertainty sources associated to the flux rate estimates. The effective wind speed (U eff ) calibration has been tuned with the specific LES simulation and, in addition to the uncertainty associated to the 10-m wind speed (U 10 ), it describes both a systematic and random fitting error. The former effect largely depends on the WRF-LES simulation dataset and fitting method. The disagreement between fitting curves here was relatively small for this specific example (0-9% error for the 5 plumes considered) with higher disagreement the higher U 10 is. The latter effect represents the departures from the U eff calibration between snapshots, for which we found a root mean squared error of 0.
5 ppb down to 139. 1 ppb when the temporal normalization selects the two closest S2 overpasses. Temporal normalisation requires a complex understanding of different effects that involve the consideration of different orbit viewing overpasses, satellite spectral, and geometric differences as well as the site temporal variation itself. The development of these simulations is expected to be helpful in testing novel approaches for upcoming temporal normalisation methodologies such as in  Ehret et al. (2022). Furthermore, the generation of realistic and accurate simulated datasets might be helpful to train upcoming machine learning methodologies such as the one presented in  Jongaramrungruang et al. (2022). The use of complementary methodologies and the continuous validation of the methane emissions is key for the development of robust estimates that contribute to the development of a global and systematic monitoring of methane emission from the O&G sector.
Acknowledgements. J. G. is funded by the  ESA Living Planet fellowship  ( ESA  contract no. 4000130980/20/I-NS ). This project has been funded by the  ESA   AO/1-10468/20/I-FvO-FUTURE EO-1   EO SCIENCE FOR SOCIETY PERMANENTLY OPEN CALL FOR PROPOS-ALS. Project web: https://hiresch4. upv. es/.
Did the pandemic conditions lead to a "tipping point" in public expectations about the need to increase the comfort of living in Brazilian cities? This inquiry is especially necessary in places with low socioeconomic indices, whose unequal level of access to general urban facilities became more evident during the COVID-19 pandemic. This article will employ data from the mapping of urban green spaces, as well as the calculating of geostatistical correlations with socioeconomic data, prepared by  Silva (2018)  in Brazil. In addition, we will present data from interviews with local managers made available by the National Confederation of Municipalities (CNM, Brazilian acronym) that confirm the increase in demand for green spaces in Brazil during the pandemic and highlight the potential of these spaces as one of the elements for a fair and sustainable transition for a better world (CNM 2021).
According to 48. 9% of the municipalities, adaptations took place in these areas for the return of activities under appropriate health precautions. Even when the severity of the pandemic was declining and the vaccinated population was increasing, municipal managers still stated that there was an increase in the use of urban green spaces by the population, even requiring the adaptation of these spaces to meet health standards for safe use. We therefore believe that this increase in demand for urban green spaces is not temporary, and that the COVID-19 pandemic has changed the habits of the population. Interestingly, among the Brazilian municipalities that were included in both Silva's territorial sample (2018) and the CNM survey, the trends were similar. A total of 49 municipalities are present in both databases, representing all five sampled Brazilian geopolitical regions.