dataesr/scientific-paragraphs-categorization huggingface dataset:
Vortex Detection We search the entire pressure data set for vortex pressure drops by using a moving window of a fixed time duration. We use a linear fit to the first and last 10% of the pressure signal disregarding the central 80%. The pressure minus the linear fit form a signal detrended from the daily variation of pressure. When the central point of the detrended signal is at least -0. 3 Pa, we identify a candidate vortex event and plot the pressure for the event together with a Gaussian and Lorentzian fit to quantify the duration of the event  (Ellehoj et al. , 2010). The moving window advances by 1 s on each evaluation scanning the whole MEDA pressure data set. We perform several searches of the data with time windows of 80 and 190 s with a detection threshold of 0. 3 Pa and searches with a time window of 60, 120, 180, 300, 600, and 900 s with a detection threshold of 0.
The ground-surface temperatures, also measured by TIRS, experiences a short cooling of -1. 8 K during the vortex passage followed by a gradual recovery of temperatures. This example shows that the measurement of the internal thermal structure of vortices is complex and we leave for future work a thermodynamic characterization of a selection of the best cases. The analysis of temperatures measured by MEDA in vortices at Jezero by  Jackson (2022a) , which is based in the ATS sensor that is best correlated with the pressure data during a vortex event, is a simplified version of the true process, in which winds changing in direction as the vortex passes, suggest a more sophisticated analysis. On the other hand, the capability to measure the surface-to-air temperature difference at the time of MEDA vortices is an important piece of information that we will examine in Section 4.
MEDA data allows us to demonstrate the artificial nature of some of these events thanks to wind measurements showing the direction of the winds coming from the RTG position at the back of the rover toward the front of Perseverance. Other nighttime pressure dips equivalent in shape to those caused by daytime vortices, are found in the data with winds coming from the opposite direction of the RTG, with no clear thermal counterparts, and are equivalent to other nighttime pressure drops found in other Martian locations and suggestive of turbulence. Nighttime pressure drops can be driven by orographic mechanical turbulence at Gale crater  (Ordonez-Etxeberria et al. , 2018) , and by the interaction of a low-level jet and weak stability in Elysium  (Chatain et al. , 2021). In Jezero, nighttime turbulence appears in different atmospheric fields (winds, pressure and temperature) and has the characteristics of a bore-like disturbance  (Pla-Garcia et al.
The parameters that best reproduce the observations are defined with the statistics of the 10 best models, which are visually examined to confirm they represent a close match to the observations. Figure  S7  in Supporting Information S1 shows an example of the procedure for the strong DD observed on sol 82 (see Figure  2 ). Cases in which a visual match of MEDA data and model results is not found also result in unconstrained model parameters with large range of possible values. These cases are eliminated from the analysis presented. In practice, this means that complex vortices with pressure curves with inner structures, those with noisy wind measurements, and those with weak pressure drops cannot be fit efficiently. For the DD on sol 82 shown in Figure  2  with Δp = 5. 6 Pa and an RDS Top 7 drop of 12%, Figure  12  shows a comparison of the three best models and the MEDA data.
Results There are 131 DDs with wind data in the MEDA data presented here. We selected 22 events that we analyzed individually. These DDs were selected because they were the most intense, more dusty, and those with a strongest wind signature. This implies a strong bias toward close encounters with DDs. An event specifically selected to explore this bias is the DD on sol 264, whose pressure curve is shown in Figure  3g , and has a wide central pressure drop that could be considered as representative of a distant vortex. This event was accompanied by a weak reduction of irradiance in the RDS Top 7 with longer signals in the RDS Lat detectors. Our results for the sample of 22 vortices result in values of α = 0. 65 ± 0. 08 with 40% of the vortices having clockwise rotation and 60% having anti-clockwise rotation, which implies an equal distribution of the sense of rotation.
MEDA and Model Comparison We positioned 400 synthetic stations at regular distances covering the simulation domain and analyzed the pressure time-series at each station using the same vortex detection algorithm used for MEDA including the analysis of data from multiple time windows. The 400 stations operating over the period of the simulation examined are representative of 400 sols at noon with a total analysis time of 200 hr. This number is comparable to our analysis of MEDA data at noon for 415 sols, which accumulates 242 hr of observations for the 12:00-13:00 time range. The most intense pressure drop found with the MEDA algorithm in the LES corresponded to an event with a pressure deficit of only Δp = 2. 7 Pa with a FWHM of 25s (Figure  15c ). The true characteristics of this vortex are shown in Figure  15d.
The event is a vortex of 85 m in diameter crossing at a minimum distance of 20 m of the meteorological station with a Δp 0 = 3. 35 Pa. There are many other more intense vortices in the simulations that are never detected, or that are detected as weaker vortices because of the distance at which they approach one of the measurement points. Figure  S8  in Supporting Information S1 shows some of the most intense vortices in the simulation and how they are perceived by the closest meteorological station. A systematic comparison of our detections of MEDA vortices and those in the LES analysis is given in Figure  15e. For pressure drops larger than 0. 5 Pa, MEDA detects three times as many events per hour at noon as in the simulation. The MEDA data also has stronger events than those found in the equivalent analysis of the LES.
0 Pa or more. Over the 1,800 s of the LES, there are 20 vortices active with pressure drops from 1. 0 to 8. 4 Pa with a mean pressure drop intensity of 2. 0 Pa. This results in a vortex production rate in the simulation of at least 0. 2 events km -2 at noon (with Δp obs > 2. 0 Pa). Assuming that the MEDA observations and LES scale are linear, a factor difference of 3 between modeled and observed activity leads to a production rate of comparable vortices in MEDA data of 0. 6 km -2 at noon. These events are nearly always dusty in MEDA data. In addition, the integrated vortex and DD activity over a sol can be 3-4 times larger than the peak activity at noon, scaling to a DD production rate at Jezero of ∼1. 8-2. 4 DD km -2 sol -1. This estimation is in agreement with an independent estimation of the DD production rate at Jezero of 2.
This is compared with the number of vortices detected for different pressure drop thresholds. A DD production rate of 2. 5 DDs km -2 sol -1 is compatible with detected pressure drops in MEDA data of 2. 0 Pa (most of them being dusty at Jezero) being caused by more intense vortices crossing at distances of about 30 m or smaller. The very large number of vortices detected in MEDA with Δp obs > 0. 5 Pa cannot be fitted by this production rate, and requires the more frequent formation of vortices of smaller intensity. Thus, the production rate of small intensity vortices must be substantially larger than the 2. 5 DDs km -2 sol -1 that we consider can fit the intense vortices that we detect with values of about 2. 0 Pa and tends to be dusty. In addition, there are three DDs in the MEDA observations with a diameter of 87-135 m crossing at distances of about 30 m.
The first event occurred during the dust storm period and affected the WS boom 2. Figure  S9  in Supporting Information S1 shows images of the WS and MEDA measurements obtained during passage of this DD. The delay between the peak pressure drop and the largest decrease in light measured by RDS Top 7 suggest this was not a direct impact with the vortex, but a more tangential one. Wind measurements obtained just before the WS failure peaked at values of 22 ms -1 , which are on the same level as the peak winds observed in the most intense vortices detected in the MEDA data set. All these factors suggest a vortex passage at a distance comparable to the vortex radius, which would be coincident with the strongest winds in the vortex. Using Equation (  1 ) to invert the value of the central pressure drop, this would require a vortex with a central pressure drop of at least 7 Pa.
Acknowledgments The authors are very grateful to the entire Mars 2020 science operations team. The authors would also like to thank  Lori Fenton  and an anonymous reviewer for many suggestions that greatly improved the manuscript. This work was supported by Grant  PID2019-109467GB-I00  funded by  MCIN/AEI/10. 13039/501100011033/  and by  Grupos Gobierno Vasco   IT1742-22  and by the  Spanish National Research, Development and Innovation Program , through the Grants  RTI2018-099825-B-C31 ,  ESP2016-80320-C2-1-R , and  ESP2014-54256-C4-3-R. Baptiste Chide.
• A comparison of vortex activity at Jezero with a LES calculated for Ls = 45° shows similar characteristics, although MEDA measurements of vortex activity suggest three times higher activity at Jezero than the simulation. The LES allows to examine biases from a meteorological station that has a low probability of finding very close events. The comparison of MEDA data and the LES suggests a DD production rate of 1. 8-2. 4 DD km -2 sol -1. • MEDA pressure and wind data allow us to fit models of vortices that were applied over DDs causing intense pressure drops. These fits result in vortex diameters from 5 to 135 m. The DDs with the closest trajectories to Perseverance have a mean diameter of 17 m and the mean diameter of all vortices that we could fit to models was 34 m. Model fits predict that at least one of the vortices detected had a central pressure drop of 9.
The accelerometer was fixed at corner (1, 1) and the hammer was moved at each point of the grid. The tap tests were performed in the range [0-5000 Hz] with a frequency resolution of 0. 15 Hz (32768 lines). 10 measures per point and Hanning windowing were used. The force and acceleration signals were then treated by dBFA Suite software to obtain the Frequency Response Functions (H1-estimator) and extract the modal properties with Star7 modal analysis software. The natural frequencies, mode shapes and modal dampings are given in Fig. 1 , 2 and 3 for laminates 1, 2, 3 respectively. To help numerical model construction, ply properties obtained in the framework of the project SUCCESS  [44]  by calibration between the experimental natural frequencies and the numerical ones are given in Table  3.
In order to perform 3D digital image correlation analysis, a speckle was painted on the back face of the sample plate as shown in Fig. 6  (left). The support plate was also speckled in order to get a fixed reference. The speckle was composed of random dark spot adapted in order to improve the resolution of DIC analysis with respect to the image size. The order of magnitude was 3-5 pixels per dark spot. 704x512 pixels 2 was the maximum image size available on the cameras at the 20,000 img/sec sampling frequency considered, with shutter speed equal to 1/34000 sec. The scene was lighted by six light spots. The DIC analysis was performed with VIC3D software with a subset size of 23x23 pixels 2 , see Fig. 6  (right). In particular, it allowed to check that, contrary to  [41] , the frame remained fixed during the impact event.
59 ± 0. 34 mmol kg -1 a -1 ), A T (0. 50 ± 0. 21 mmol kg -1 a -1 ) and pCO 2 (3. 3 ± 0. 7 μatm a -1 ) and a negative trend in pH (-0. 003 ± 0. 001 unit a -1 ) have been assessed over the 18-year period. This study presents ten years of carbonate chemistry data collected between January 2010 and December 2019 in the NW Mediterranean Sea, at the ANTARES and DYFAMED sites, with the aim of highlighting the main drivers of the temporal variability of the carbonate system at each site. In this paper, these two timeseries sites, located ca. 150 km apart but in distinct hydrological conditions with respect to NC dynamics, are used to identify the main drivers of carbon variability. With a focus on the carbonate data within the mixed layer, this work: (1) describes the dataset and the specificity of each site, (2) evaluates the drivers of the seasonal carbonate chemistry variability and, (3) estimates and identifies the main drivers of carbonate chemistry changes at longer timescales.
Measurements Measurements are carried out monthly from a research vessel at the DYFAMED and ANTARES sites in the NW Mediterranean Sea (Figure  1 ). A CTD rosette is deployed with 12 Niskin bottles in order  (1)  to acquire data with sensors (Conductivity Temperature and Depth -CTD and associated parameters) along vertical profiles, and (2) to collect discrete seawater samples for chemical analysis. A detailed description of the sampling methodology and the CTD sensors can be found in  Coppola et al. (2018)  for the DYFAMED site. The same procedure is applied at ANTARES. Samples for C T and A T were collected into acid-washed 500 cm 3 borosilicate glass bottles, poisoned with 200 mm 3 of a 36 g. dm -3 HgCl 2 , as recommended by  Dickson et al. (2007)  and stored in the dark at 4°C. Analyses were performed after a few months of storage (less than 6 months).
Derived data 2. 3. 1 Estimation of the mixed layer depth The Mixed Layer Depth (MLD) was computed using a potential density threshold of 0. 03 kg. m -3 with a reference depth of 10 dbar  [D'Ortenzio et al. , 2005]. Vertical profiles of temperature and salinity from the CTD were used to estimate the density. When no value was available at 10 dbar, the shallowest value available above 20 dbar was used. For the MLD determination, only CTD downcast performed when samples were taken have been used in this study.
Carbonate system derived parameters Seawater carbonate system parameters were derived from A T and C T values with the software program CO2SYS-MATLAB  [van Heuven et al. , 2011]  using temperature, salinity, pressure, silicate and phosphate concentrations. As recommended for the Mediterranean Sea by A ́lvarez et al. (  2014 ), the carbonic acid dissociation constants K 1 and K 2 from  Mehrbach et al. (1973)  as refitted by  Dickson and Millero (1987)  and the dissociation constant for HSO 4 -from  Dickson (1990)  were used. Uppström (1974)  was used to calculate the ratio of total boron to salinity and  Dickson and Riley (1979)  to calculate the hydrogen fluoride constant K F. To remove the impact of salinity variations (evaporation/precipitation) on A T and C T values, salinity-normalised A T (NA T ) and C T (NC T ) were calculated by dividing by in situ salinity and multiplying by a constant salinity set at 38 following previous studies conducted in the Mediterranean Sea  [Bensoussan and Gattuso, 2007; Rivaro et al.
78 μmol. kg -1. a -1. At the DYFAMED site, previous studies reported decreasing trends for the pH in subsurface waters (-0. 0022 pH unit. a   2020 ) reported positive surface trends for both C T and pCO 2 equal to 0. 59 ± 0. 34 μmol. kg -1. a -1 and 3. 3 ± 0. 7 μatm. a -1 , respectively, and a pH annual decrease of -0. 0030 ± 0. 0010 pH unit (between 0 and 20 meters). Using the raw datasets at ANTARES and DYFAMED, an increase in C T within the mixed layer between 2010 and 2019 of +3. 8 ± 1. 4 μmol. kg -1. a -1 at ANTARES and +2. 4 ± 1. 1 μmol. kg -1. a -1 at DYFAMED was estimated (Table  3 ). Trends were not significant for pCO 2 IS and pH IS at DYFAMED. Moreover, no significant trends were observed for either A T or NA T at either site. Due to the strong seasonal cycle affecting the C T content (but also the pCO 2 and pH), the trends reported could be affected by the data distribution along the year.
To address this issue, datasets have been "deseasonnalised". This analytical procedure allows the comparison of trends with different increments and lengths  [Wanninkhof et al. , 2019]. The long-term temporal trends have been calculated using the residual between the harmonic fits and measured values. At both sites, the deseasonnalised datasets were significantly different (t-test according to the significance of the test (p-value) and the degree of freedom (df) obtained) in comparison with the raw datasets for C T , pCO 2 IS and pH IS (Figure  6 ). Based on the deseasonnalised datasets, significant increases in C T (+3. 2 ± 0. 9 μmol. kg -1. a -1 -ANTARES; +1. 6 ± 0. 8 μmol. kg -1. a -1 -DYFAMED), seawater pCO 2 IS (+5. 5 ± 1. 8 μatm. a -1 -ANTARES; +2. 5 ± 1. 0 μatm. a -1 -DYFAMED) and decreases in pH IS (-0.
Through a unique and new 6-step sequence of procedures, a new best practice for assessing and reporting ocean acidification trends as well as variability in ocean carbonate has been proposed. By applying these trend analysis techniques to the ANTARES and DYFAMED datasets, significant decreases in pH IS of -0. 0060 ± 0. 0012 pH unit. a -1 (n=42, p=1. 9×10 -5 ) and -0. 0018 ± 0. 0009 pH unit. a -1 (n=58, p=4. 6×10 -  2 ), respectively, have been obtained. Trends assessed following this procedure are in agreement with the ones derived using the deseasonalised datasets, confirming a higher seawater pH decrease at ANTARES than at DYFAMED. Due to rough weather conditions, the ANTARES sampling site has experienced more cruise cancellations than DYFAMED. To test the impact of this reduced number of cruises at ANTARES on the calculated temporal trends, the DYFAMED dataset was filtered to follow the same sampling grid than the ANTARES one (Table  3 ).
, 2006]. It is also worth noting that the datasets, by merging measurements acquired during convective events years and low-resolution sampling grids, induce several limitations. Nevertheless, previous estimates based on in situ data measured at the DYFAMED site reported the same seasonal pattern for air-sea CO 2 fluxes [e. g. ,  Beǵovic and Copin-Monteǵut, 2002]. At the studied sites, while the pCO 2 IS temporal trends reported are higher than the increase in the pCO 2 ATM  [Dlugokencky et al. , 2021] , no clear temporal trend was observed for DpCO 2 at either site. This suggests that, over this 10-year period, mechanisms of control seem to counterbalance the increasing atmospheric pCO 2 trend. Nonetheless, it can be noticed that, in the future, higher seawater pCO 2 IS values can be expected in response to the warming tendency of the Mediterranean Sea waters [e.
Acknowledgments The authors thank the captains and the crews of the  N/O Tethys II (French oceanography Fleet, FOF)  and the scientific and technical staff involved in sampling the ANTARES and DYFAMED time-series monthly. We acknowledge the  MOOSE program  and the  GROOM-RI  project for contributing and supporting this paper. The  ANTARES  and  DYFAMED  time-series are funded by the  MOOSE program  ( Mediterranean Ocean Observing System for the Environment ) coordinated by  CNRS-INSU  and the  Research Infrastructure ILICO (CNRS-IFREMER). For seawater sample analyses, we also thank the  SNAPO-CO2 at LOCEAN, Paris , and in particular  J. Fin  and  N. Metzl. We thank the reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.
- 1 - 1 Merlivat et al. (2018) ; -0. 0030 pH unit. a -1 -Marcellin Yao et al. (2016)) and increasing trends for C T (1. 40 ± 0. 15 μmol. kg -1. a -1 -Merlivat et al. (2018)). By combining different datasets acquired between 1998 and 2016 from coastal and open ocean sites in the Ligurian Sea, Coppola et al. (.
We review recent inelastic neutron scattering experiments aimed at investigating still open issues in the microscopic dynamics of liquids. It is shown that the interpretation of experimental results is put on solid ground by the application of modern methods of analysis and lineshape modelling which ensure the fulfillment of fundamental physical properties that the spectra must obey. This last condition becomes crucial to avoid overinterpretations of the genuine information conveyed by scattering data, especially when studying weak signals in the dynamic structure factor. Moreover, we highlight the different roles that neutron data presently play compared to molecular dynamics simulations depending on the nature of the sample, including the case of quantum liquids. In particular, we show how neutron measurements remain an indispensable benchmark in assessing the present capabilities of classical and quantum simulation methods.
However, the true realm of neutron scattering is liquid H2, together with its heavier counterpart D2. Across the years, both these fluids attracted much interest when investigating general aspects of the singleparticle (self) dynamics of an incoherent scatterer like H2  [6] [7] [8] [9] , or quantum effects on either the self  [10, 11]  or the collective properties  [12] [13] [14]  of these systems. In fact, the low molecular mass of H2 and D2 and the low temperatures at which they are in the liquid phase lead to evident manifestations of quantum behaviour, as * Corresponding author: guarini@fi. infn. it further detailed in Sect. 3. Moreover, H2 and D2 are the most used moderating materials for the production of low-energy (about 1 meV) neutrons. In this respect, the design of new generation hydrogen-based cold neutron sources requires the availability of reliable databases of the scattering law taking explicitly the quantum nature of these fluids into account.
Nonetheless, the very good agreement between neutron data and simulations (see Fig. 2 ) justified an EET analysis of the simulated S(Q,ω) in the wide range 4 nm -1 < Q < 70 nm -1 probed by AIMD. In this case, a GH modelling was found to be insufficient, while spectra were perfectly described by a VE lineshape. An example of the fit quality is given in Fig. 3. The VE fits to the simulations thus permitted to better resolve the central peak, but again only a single excitation could be detected. In summary, signs of shear waves turned out to be absent in both the experimental and simulated S(Q,ω) of liquid gold. A study similar to that on Au was later carried out on liquid Ag, again by using the BRISP spectrometer and performing parallel AIMD calculations. As far as the measurements are concerned, a GH modelling was the only justified within the accuracy of the data (see Fig.
Most importantly, any grouping of cancers, each of which have their individual specificities, has a likelihood of obscuring relevant biological and clinical differences. Hence, researchers are encouraged to consider separately each cancer type arising in PLWH. Cancers with sufficient evidence for HIV causality (after exclusion of confounding and bias) were judged by an expert consensus convened by IARC in 2009. 3  Based on the data accumulated in the last 13 years, this consensus judgment on causality should be updated.
Meteorological measurements Meteorological parameters such as pressure, humidity and temperature were recorded at 1 Hz resolution with the basic meteorological sensor suite of Polar 6 fully described in previous works  (Herber et al. , 2012; Schulz et al. , 2019; Ehrlich et al. , 2019). T and RH data were merged with aircraft position and air pressure into a 1 Hz basic meteorological dataset (https://doi. org/10. 1594/PANGAEA. 902849;  Hartmann et al. , 2019). The potential temperature (TP) was calculated from measured ambient temperature (T) and ambient pressure (P) as: TP = T (P0/P) 0. 286.