dataesr/scientific-paragraphs-categorization huggingface dataset:
We consider that the sea ice drift is mainly influenced by the sea ice dynamics, while the effect of thermodynamics is negligible in such a short time scale. The key configurations in the simulations are summarized in Table  1. neXtSIM is spatially discretized using the finite element method on a triangular mesh which is adaptive in time, meaning that it is automatically regenerated when and where triangles are too distorted using an efficient remeshing algorithm locally  [13]. The nominal mesh resolution (defined as the mean of the square root of triangles surface areas) used for the experiments is about 7. 5 km. The forcing fields are interpolated onto the center of the triangular elements during the model integration. neXtSIM is recently enhanced in computational efficiency, thanks to the parallelization of the code  [27].
Air drag coefficient optimization When using a forced -stand-alone-sea ice modeling system as we do here, the momentum fluxes from the wind and ocean currents to the sea ice need to be calibrated. This can be achieved by tuning the value of the air and/or water drag coefficients denoted. and  / , respectively (see Table  2 ). Following the calibration method presented in  [13] , the air drag coefficient is optimized by comparing the simulated sea ice drift with observations from the OSI SAF dataset  [29] , but only where sea ice is in "free drift" while keeping the default value of the ocean drag coefficient unchanged. We identify the free-drift events when and where the simulated sea ice drift obtained, when the sea ice rheology is activated, differs by less than 10% of that obtained when it is deactivated.
We use an additional criterion on the sea ice speed, which must be sufficiently large, i. e. , between 7 and 19 km/day as used in  [13]. The selected drift velocity vectors are compared against the OSI SAF drift vectors and for the two components U and V separately in the horizontal and vertical directions defined in a polar stereographic projection of the Arctic. We calculate the correlation coefficient and the Root Mean Square Error (RMSE) for each component. The results obtained from a set of simulations using values of air drag coefficient ranging from 0. 003 to 0. 008 are presented in Figure  2. We find an optimal value of. about 0. 0055, which is in between previously optimized values of 0. 0051 and 0. 0076 reported by  [13]  and  [17]. We note that these latter studies were using the same atmospheric forcing dataset but the neXtSIM-EB model instead of the neXtSIM-MEB.
Comparison of the simulated sea ice trajectories against the IABP dataset The spatial averaged forecast errors over the IABP buoys are presented in Figure  4 (a). The top panel shows the mean and standard deviation of the spatially averaged errors ⟨‖()‖⟩ ) as the solid lines and error bars respectively, marked by different colors, one for each experiment. The behavior of the three ensembles relative to each other is generally in agreement across successive forecasts: the horizontal variability range of ⟨‖()‖⟩ ) in COHESION is generally the smallest, contained within that of WIND and/or JOINT that are similar to each other. This implies the errors are generally larger when surface winds are perturbed and more variable geographically. One exception occurs in the last period during 18 -27 April when COHESION gives the largest errors ⟨‖()‖⟩ ).
It could be related to the seasonal change of Arctic sea ice. The period during 13 -22 March also stands out as the difference between WIND and JOINT that ⟨‖()‖⟩ ) is larger than usual. We speculate that it is due to a wind event during 11 -18 March, when wind is strong and in statistically uniform direction around the IABP buoys' deployment area shown in Figure  4 (b), which gives the daily-averaged horizontal wind velocities from the ASR wind dataset over the IABP buoys trajectories. This point is further discussed in Section 5. The parallel and perpendicular error components, C ∥ ()D ) and ⟨ ( ()⟩ ) , are shown in the middle and bottom panels of Figure  4 (a) respectively, where only the means are given as the solid lines. As defined in Section 2, a positive/negative  ∥ () indicates that the virtual drifters move faster/slower than the real buoy along the direction from  " to ().
Acknowledgments: Satellite observations and TOPAZ4 data have been downloaded from CMEMS. ASR data have been downloaded from http://iabp. apl. washington. edu/data. htmlIABP data have been downloaded from https://climatedataguide. ucar. edu/climate-data/arctic-system-reanalysis-asrThe authors acknowledge grants of  CPU  time and disk storage from the  Norwegian Sigma2 supercomputing infrastructure  ( nn2993k  and  ns2993k ). The technical assistance of  Timothy Williams  and  Einar Ólason  with the model has been very helpful.
Abstract. The MetOp/Infrared Atmospheric Sounding Interferometer (IASI) instruments have provided data for operational meteorology and document atmospheric composition since 2007. IASI ozone (O 3 ) data have been used extensively to characterize the seasonal and interannual variabilities and the evolution of tropospheric O 3 at the global scale. SOftware for a Fast Retrieval of IASI Data (SOFRID) is a fast retrieval algorithm that provides IASI O 3 profiles for the whole IASI period. Until now, SOFRID O 3 retrievals (v1. 5 and v1. 6) were performed with a single a priori profile, which resulted in important biases and probably a too-low variability. For the first time, we have implemented a comprehensive dynamical a priori profile for spaceborne O 3 retrievals which takes the pixel location, time and tropopause height into account for SOFRID-O3 v3.
5 retrievals. In the present study, we validate SOFRID-O3 v1. 6 and v3. 5 with electrochemical concentration cell (ECC) ozonesonde profiles from the global World Ozone and Ultraviolet Radiation Data Centre (WOUDC) database for the 2008-2017 period. Our validation is based on a thorough statistical analysis using Taylor diagrams. Furthermore, we compare our retrievals with ozonesonde profiles both smoothed by the IASI averaging kernels and raw. This methodology is essential to evaluate the inherent usefulness of the retrievals to assess O 3 variability and trends. The use of a dynamical a priori profile largely improves the retrievals concerning two main aspects: (i) it corrects high biases for low-tropospheric O 3 regions such as the Southern Hemisphere, and (ii) it increases the retrieved O 3 variability, leading to a better agreement with ozonesonde data.
Concerning upper tropospherelower stratosphere (UTLS) and stratospheric O 3 , the improvements are less important and the biases are very similar for both versions. The SOFRID tropospheric ozone columns (TOCs) display no significant drifts (< 2. 5 %) for the Northern Hemisphere and significant negative ones (9. 5 % for v1. 6 and 4. 3 % for v3. 5) for the Southern Hemisphere. We have compared our validation results to those of the Fast Optimal Retrievals on Layers for IASI (FORLI) retrieval software from the literature for smoothed ozonesonde data only. This comparison highlights three main differences: (i) FORLI retrievals contain more theoretical information about tropospheric O 3 than SOFRID; (ii) root mean square differences (RMSDs) are smaller and correlation coefficients are higher for SOFRID than for FORLI; (iii) in the Northern Hemisphere, the 2010 jump detected in FORLI TOCs is not present in SOFRID.
Introduction Ozone (O 3 ) in the stratosphere protects life from solar UV radiation. Close to the surface, O 3 is an oxidative pollutant harmful for human health through irritation of respiratory tracts  (Brunekreef and Holgate, 2002)  and for vegetation through deposition on leaves that leads to the reduction of plant growth  (Ainsworth et al. , 2012). Tropospheric O 3 is also a powerful greenhouse gas whose increase during the 20th century has significantly contributed to global warming  (Shindell et al. , 2006). The radiative forcing of O 3 is particularly important in the tropical upper troposphere-lower stratosphere (UTLS)  (Chen et al. , 2007). It is therefore important to document the evolution of O 3 in these different layers independently. There is clear evidence from satellite databases that upper stratospheric O 3 has in-Published by Copernicus Publications on behalf of the European Geosciences Union.
creased since 1997 following the ban of chlorofluorocarbons (CFCs) by the Montreal Protocol  (Ball et al. , 2018). Nevertheless, the total column O 3 has been stable since 1998. According to  Ball et al. (2018) , this contradiction is due to the fact that lower stratospheric O 3 is declining and compensates both stratospheric and tropospheric O 3 increase. Based on Ozone Monitoring Instrument/Microwave Limb Sounder (OMI/MLS) tropospheric ozone columns (TOCs), they state that TOC is globally increasing. OMI/MLS data for the 2005-2016 period are indeed documenting global positive TOC trends with particularly large increases over Asia  (Ziemke et al. , 2019). Based on 10 years of retrievals with the Fast Optimal Retrievals on Layers for Infrared Atmospheric Sounding Interferometer (IASI) O 3 (FORLI-O3) software,  Wespes et al.
(2018)  document a decrease in tropospheric O 3 levels in the Northern Hemisphere (NH). Another IASI tropospheric O 3 product (Karlsruhe Optimized and Precise Radiative transfer Algorithm Fit; KOPRAFIT-O3) displays a TOC decrease over continental China  (Dufour et al. , 2018). In their exhaustive work on TOC evolution,  Gaudel et al. (2018)  clearly highlight the contradiction between global increase (OMI/MLS and other UV-vis products) on the one hand and global decrease (IASI) on the other hand. They also show that the different satellite products agree on a TOC increase over Asia. Among the two global IASI TOC datasets used in  Gaudel et al. (2018) , FORLI-O3 indicates a significant global decrease and O 3 retrievals with the SOftware for a Fast Retrieval of IASI Data (SOFRID) indicate a slightly weaker and less significant one.
, 2012). Furthermore, the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) L2 atmospheric temperature products retrieved from IASI and used for FORLI (v20141022 and v20151001) and for SOFRID-O3 v1. 5 retrievals are not stable in time  (Boynard et al. , 2018). Therefore, we have reprocessed the whole IASI database using ECMWF operational analyses for temperature and humidity to produce SOFRID-O3 v1. 6. SOFRID-O3 has been shown to overestimate low tropospheric ozone over the Southern Hemisphere (SH)  (Dufour et al. , 2012; Emili et al. , 2014 Emili et al. , , 2019)). Emili et al. (2014)  have hypothesized that this overestimation was due to the use of a single a priori profile biased towards NH midlatitudes O 3. In order to verify this hypothesis and to improve our O 3 retrievals, we have developed a new version of SOFRID-O3 (v3.
5), with a dynamical a priori profile based on a global O 3 climatology  (Sofieva et al. , 2014). The aim of the present paper is to validate both of the latest SOFRID-O3 products (v1. 6 and v3. 5) for the whole IASI period  (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017)  in order to infer their ability to reproduce tropospheric O 3 levels and variability on seasonal to decadal timescales. The validation is based on O 3 profiles from ozonesondes retrieved from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) database. In Sect. 2, we describe the characteristics and differences of SOFRID-O3 v1. 6 and v3. 5 retrievals. Section 3 is dedicated to the description of the validation methodology based on comparisons between smoothed and raw ozonesonde data, and we provide our validation results in Sect.
, 2018 ) also based on morning data. The SOFRID software first described in  Barret et al. (2011)  is based on the RTTOV (Radiative Transfer for TIROS Operational Vertical Sounder) operational radiative transfer code  (Saunders et al. , 1999; Matricardi et al. , 2004)  combined with the 1D-Var software  (Pavelin et al. , 2008) , both developed within the framework of EUMETSAT Numerical Weather Prediction Satellite Applications Facility (NWP-SAF). The O 3 profiles are retrieved from the 980-1100 cm -1 spectral window encompassing the 9. 6 µm O 3 absorption band. Only cloud-free or weakly contaminated pixels are processed. Pixels with Advanced Very High Resolution Radiometer (AVHRR)-derived fractional cloud cover larger than 25 % are excluded. We also use a test based on brightness temperatures at 11 and 12 µm when AVHRR cloud cover is not available as described in  Barret et al.
(2011). The two SOFRID-O3 versions that are validated and compared in the present paper have significant differences that are described below. 2. 1 Single a priori profile: v1. 6 SOFRID-O3 v1. 6 is almost similar to v1. 5 described in  Barret et al. (2011). It is based on RTTOV v9. 3  (Saunders et al. , 1999). In RTTOV, the optical depths are expressed as a linear combination of profile-dependent predictors that are functions of temperature, absorber amount, pressure and viewing angle. In RTTOV v9. 3, the regression coefficients are derived from computations with the line-by-line radiative transfer model v11. 6 (LBLRTM;  Clough et al. , 2005)  on 43 atmospheric levels using the HITRAN2004 spectroscopic database  (Rothman and Jacquemart, 2005). The single difference is that v1. 6 uses temperature and humidity profiles from ECMWF operational analyses for the RTTOV simulations and v1.
Dynamical a priori profile: v3. 5 As v1. 6, SOFRID-O3 v3. 5 uses interpolated temperature and humidity profiles from ECMWF analyses. It is based on the more recent RTTOV (v11. 1) (Hocking et al. , 2015) , where regression coefficients are derived from LBLRTM v12. 2 computations on 101 vertical levels with the HITRAN2008 spectroscopic database  (Rothman et al. , 2009). The second and more important one is that it uses dynamical a priori profiles from TpO 3 , the O 3 profile tropopause-based climatology of  Sofieva et al. (2014). This climatology is based on ozone profiles resulting from merging ozonesonde data in the troposphere and SAGE II v6. 2 data  (Wang et al. , 2006)  in the stratosphere. The ozonesonde profiles (36 000) extracted from the Binary Database of Profiles (BDBP) come from 136 stations for the period 1980 to 2006  (Hassler et al.
, 1999). It was shown that the retrieved O 3 columns were largely improved when the tropopause was taken into account in the choice of the a priori profile. In a first attempt to take the tropopause into account for satellite retrievals,  Sellitto et al. (2013)  have implemented two a priori profiles in the KOPRAFIT-O3 retrieval algorithm to basically discriminate the tropics (tropopause higher than 14 km) from other latitudes. Dufour et al. (2015)  have slightly improved the approach with a set of three a priori profiles for high latitudes (tropopause lower than 10 km), midlatitudes (tropopause between 10 and 14 km) and the tropics (tropopause higher than 14 km). Eremenko et al. (2019)  have tested a set of profiles for retrievals on a synthetic database. In SOFRID-O3 v3. 5, we compute the tropopause using the WMO lapse-rate definition from the ECMWF interpolated temperature profiles.
(2018) , who did not compute a tropopause-based TOC for their validation (see Sect. 5). The DFS corresponding to these different layers is displayed in Fig. 1  for v1. 6 and v3. 5 averaged over the validation dataset. The total DFS ranges from 2. 4 to 3. 3 for v3. 5 and is about 0. 2 lower for v1. 6. The DFS values for the troposphere (WMO lapse rate), UTLS and stratosphere are almost identical for both versions. The tropospheric DFS is the lowest (0. 3-0. 5) at high latitudes where surface temperature, thermal contrast and tropopause height are the lowest and the highest in the tropics (about 1. 5) where surface temperature and tropopause height are the highest. At midlatitudes, the tropospheric DFS is about 0. 6. Therefore, except in the tropics, SOFRID retrievals provide less than one independent piece of information in the troposphere.
Ozonesonde data Ozonesonde data come from the WOUDC database (https: //www. woudc. org/, last access: 29 september 2020). For consistency purposes, we have chosen to use data from electrochemical concentration cell (ECC) sondes only. For the 10 years (the IASI period; 2008-2017), valid comparisons were effective for about 12 000 ozonesonde profiles among the 16 000 downloaded. A map with the number of sondes used for the validation at each station over the 2008-2017 period is displayed in Fig. 3. Most (∼ 7000) of the validation sondes were launched in the NH midlatitudes, with 15 stations providing more than one profile per month on average (more than 120 profiles for 10 years) mostly in western Europe and North America. For all other 30 • latitude bands, the number of validation profiles ranges from 800 to 1200, with only three to four stations providing more than 120 profiles.
Taylor diagram In order to validate remote sensing with reference in situ observations, we need to determine how well they are able to reproduce the same behavior. There are four statistical indicators that have to be computed: (i) the absolute difference or bias which documents the accuracy, (ii) the root mean square of the differences (RMSDs) which tell whether the bias is significant or not, (iii) the coefficient of correlation (R) which documents the consistency and phase of the variabilities of both datasets and (iv) the ratio of the standard deviations of both datasets which documents the goodness of the amplitude of the retrieval variability. In the case of IASI O 3 , the first three indicators are frequently computed  (Boynard et al. , 2016 (Boynard et al. , , 2018;; Barret et al. , 2011) , but the last one is rarely compared  (Dufour et al.
, 2012) , which makes most validation exercises incomplete. https://doi. org/10. 5194/amt-13-5237-2020 Atmos. Meas. Tech. , 13, 5237-5257, 2020 Based on the relationship between correlation coefficients, RMSDs and variances of the reference (validating) and test (validated) datasets,  Taylor (2001)  has developed the Taylor diagram initially for climate model evaluation. It displays all of these parameters (except the biases) in a more convenient and synthetic way than tables with numbers. Each experiment or observation to be validated correspond to a point placed within a quarter circle. The reference is located in the middle of the x axis (see Figs. 4, 5 ). The correlation coefficient between the reference and test dataset is given by the azimuthal position of the point. The RMSD is proportional to the distance between the test and the reference point.
Data availability. The SOFRID-O3 data are freely available on the IASI-SOFRID website (http://thredds. sedoo. fr/iasi-sofrid-o3-co/, last access: 29 September 2020; SEDOO, 2014). IASI L1c data have been downloaded from the Ether French atmospheric database (http://ether. ipsl. jussieu. fr, last access: 29 September 2020; Ether, ). The research with IASI was conducted with some financial support from the Centre national d'études spatiales (CNES) (TOSCA-IASI project). The ozonesonde data used in this study were provided by the WOUDC (https://doi. org/10. 14287/10000008, WOUDC database, 2020).
e. , at the very least before the attack is completed. We propose a machine learning based side-channel attack (SCA) detection tool, called WHISPER that satisfies the above mentioned design constraints. WHISPER uses multiple machine learning models in an Ensemble fashion to detect SCAs at runtime using behavioral data of concurrent processes, that are collected through hardware performance counters (HPCs). Through extensive experiments with different variants of state-of-the-art attacks, we demonstrate that the proposed tool is capable of detecting a large set of known attacks that target both computational and storage parts in computing systems. We present experimental evaluation of WHISPER against Flush+Reload, Flush+Flush, Prime+Probe, Spectre and Meltdown attacks. The results are provided under variable system load conditions and stringent evaluation metrics comprising detection accuracy, speed, system-wide performance overhead and distribution of error (i.
For instance, logical physical isolation techniques include Cache Coloring  [23] , CloudRadar  [24] , STEALTHMEM  [10] , NewCache  [25]  and Hardware Partitioning  [26] ; noise-based techniques include fuzzy times  [27] , bystander workloads  [28] ; and scheduler-based techniques include obfuscation  [28]  and minimum timeslice  [29]. Our Study of these mitigation solutions reveals that most techniques focus on only one specific vulnerability in the cache hierarchy. In practice, a system can be exposed to multiple attacks simultaneously or in any temporal order. Moreover, with relatively smarter attacks, these mitigation approaches can be bypassed. Moreover, researchers have discussed the fact that these attacks have not been completely patched to-date  [7] ,  [30]. Researchers have also debated that cryptographic code designers are far away from incorporating the appropriate counters to avoid cache leakages  [31].
For the selected CSCA use-cases, we used two different versions of OpenSSL on which the attacks are demonstrated in the stateof-the-art. Table 1 details these use-cases. Later, in Section VI, we demonstrate that the WHISPER tool is also capable of detecting attacks that are independent of the cryptosystem, such as Spectre and Meltdown attacks. To serve the community at large, we provide the source code and experimental data related to all these CSCAs at our Github repository  [43] , which can be freely accessed, used, distributed and reproduced. Flush+Reload  [3]  is a trace-driven attack that relies on the presence of page sharing. State-of-the-art attacks have been performed using the Flush+Reload technique using RSA and AES cryptosystems  [44] ,  [6]. This technique has three major steps; first the attacker flushes the shared cache line using CLFLUSH instruction.
D. IMPLEMENTATION OF DETECTION MODULE Before presenting the experimental evaluation, in this section, we briefly present the implementation details of the WHISPER tool. Algorithm 1 presents an abstract level pseu-docode for WHISPER's detection module. As illustrated, the module takes as input the sampling granularity for hardware events (SamplingGranularity), which can be either user-defined or adjusted at runtime. By default, the sampling granularity is set to fine grain, i. e. , 10 AES encryptions. The sampling in WHISPER is user-defined and can be changed based on the type and the level of threat at runtime. Moreover, different attacks require observation of a different number of encryptions in order to be successful. For instance, there are certain attacks that require observation of only a single encryption to complete (e.
g. , Flush+Reload on RSA-1024 bits  [3] ) while other attacks require observtion multiple encryptions to complete (e. g. , Flush+Flush on AES  [4] ). Therefore, in the case of Flush+Reload on RSA, WHISPER can set the sampling granularity at the rate of every 10 bits, whereas 10 encryptions for Flush+Flush on AES require observation of roughly 350 -400 encryptions to complete the attack. Sampling granularity can always be tuned by the user/system to effectively control the performance overhead depending on the type and level of threat at runtime. Another input is the total number of iterations for which we tested the module (MaxIterations). The number of iterations varies for each attack as discussed in Section V. Lines 1 -3 show that a victim's (encryption) process is initialized, the detection module is activated once and the hardware events are set around the victim's process considering it as the ROI (Region of Interest).