dataesr/scientific-paragraphs-categorization huggingface dataset:
Using a TanDEM-X DEM we map the extent of this fold (Figure  4 ). We suggest that this it is a fault-propagation fold, caused by a fault dipping to the south that extends under Almaty. The city building grid system runs approximately North-South and we take profiles along the major roads. The map is roughly the extent of the Pleiades tri-stereo and outlines show the extent of the two stereo (green and orange) Digital Elevation Models (DEMs). (b) to (e) Show profiles in the city from SPOT (blue), Pleiades stereo (light green and orange) and Pleiades tri-stereo (red) with field-measured Global Navigation Satellite System (GNSS) (black with error bars in gray). The best-fit lines to the GNSS data are shown in dark green, with the estimated fault offset from GNSS measurements (see Table  1. ) Note that in panel (a) we plot the DEM, but the profiles have been taken on the digital terrain model, that is, the terrain model in which buildings have been removed from the DEM.
Exploring Earthquake Scenarios for Almaty With OpenQuake OpenQuake Engine is an open-source software tool for earthquake hazard and risk modeling  (Pagani et al. , 2014; Silva et al. , 2014). We use "scenario" mode to model specific, plausible earthquakes for Almaty based upon past and credible potential seismic events. The OpenQuake engine can be considered in two components: hazard and risk. For scenario hazard calculations, an input earthquake rupture is used to calculate a distribution of ground shaking in a given region. The earthquake is described using a finite fault plane, with a given magnitude, rake and geometry. Ground motion is estimated using ground motion prediction equations (GMPEs): empirical formulae that predict intensity measures as a function of distance to the fault. Here we calculate peak ground acceleration (PGA), the maximum acceleration that occurs at a given location, and spectral acceleration (SA), the maximum acceleration that occurs at a given location on a 1-D damped harmonic oscillator for a given period.
We sample the logarithm of the ground motion using a Gaussian distribution with truncation level of three standard deviations. We assume that the ground motion should be correlated, and use the spatial correlation of Jayaram and Baker (  2009 ) for the shallow crustal earthquakes, that is,  Abrahamson et al. (2014) ;  Chiou and Youngs (2014) ;  Campbell and Bozorgnia (2014)  GMPEs. This spatial correlation is not compatible with the Somerville (  2009 ) GMPE. From these intensity measures (PGA or SA) the OpenQuake engine risk module then calculates damage and losses. This requires an exposure model, which describes in detail the assets being exposed to the hazard; at given locations the building class, number of stories, number of buildings, cost of replacement and number of occupants for daytime and nighttime are listed.
For each specific building type, OpenQuake calculates damage using corresponding fragility curves and losses (cost and fatalities) using corresponding vulnerability curves. We use fragility and vulnerability curves formulated for Central Asian building types  (Martins & Silva, 2020). 2020 ) using screening surveys from local structural engineers, data from the population and housing censuses and high-resolution satellite images. The exposure is spaced every 1-1. 5 km in the center of Almaty (Figure  S2 ). We use only the residential exposure data, with 2015 census data (details in  Pittore et al. , 2020). This exposure model potentially over-estimates the number of high-rise buildings, which we explore further in the discussion. The main types of building in Almaty are shown in Figure  5. For each district, the majority of buildings are unreinforced masonry, followed by reinforced masonry; very few of the total building stock are wooden or steel constructions.
Utility of Calculating Building Heights In Section 4 we showed that some satellite derived DEMs can accurately capture field-measured building heights. Pleiades tri-stereo performs particularly well with 13. 6% average percentage difference between measured and point cloud-derived measurements, and 80% of buildings measured accurately within error. SPOT is a lower resolution data set than Pleiades, so a larger area can be covered with lower cost. Without the two tallest buildings (12 and 13), SPOT performs comparatively well with Pleiades stereo and tri-stereo, making SPOT the most cost effective data set for this calculation. However, given that buildings 12 and 13 were not resolved at all (when processed in both Agisoft and ERDAS) in SPOT DEMs, in order to have confidence in identifying buildings heights across an entire city and not missing high-rise buildings, likely to house many more inhabitants than smaller residential buildings, we recommend Pleiades tri-stereo as the best data set for calculating building heights.
We use the software OpenQuake Engine to assess the damage, fatalities and economic losses for a series of earthquake scenarios. These include three historical earthquakes known to have damaged Almaty, and four plausible earthquakes that we base on our observations of city splays and palaeoseismic evidence from the literature. These scenarios show that the districts with the greatest number of residential buildings, Medeu and Alatau, frequently have highest collapses but the particular district at most risk is highly affected by the exact location of the fault. In our scenarios, the worst case explored would be if a fault to the north of Almaty that dips south under the city were to rupture in a magnitude 6. 5 earthquake, causing a total of ∼12,300  E 5,000 building collapses, ∼4,700  E 2,700 Million US dollar cost to be incurred and ∼4,100  E 3,500 fatalities.
Data Availability Statement Pleiades images made available by CNES in the framework of the CEOS Working Group for Disasters. ©CNES (2012, 2014, 2017), and Airbus DS, all rights reserved. Commercial uses forbidden. TanDEM-X mission topography DEM data made available through the German Aerospace Centre (DLR) science service (proposal number DEM_GEOL1336-Active Tectonics of the Northern Tien Shan). ASTER GDEM is a product of METI and NASA. AW3D original data are provided by JAXA. The DEMs derived in this study are available on OpenTopography (datasets https://doi. org/10. 5069/G9H41PMP, https://doi. org/10. 5069/ G9MS3QZT and https://doi. org/10. 5069/G9RJ4GN3). The OpenQuake engine (version 3. 8. 1 used here) is an open-source, freely available code, see https://www. globalquakemodel. org/openquake to download the latest version.
MATERIALS AND METHODS: This was a retrospective study of 37 cases referred for fetal DTI at 30. 4 weeks (range, 25-34 weeks) because of an isolated short corpus callosum less than the 5th percentile by sonography at 26 weeks (range, 22-31 weeks). Tractography quality, the presence of Probst bundles, dysmorphic frontal horns, callosal length (internal cranial occipitofrontal dimension/length of the corpus callosum ratio), and callosal thickness were assessed. Cytogenetic data and neurodevelopmental follow-up were systematically reviewed. RESULTS: Thirty-three of 37 fetal DTIs distinguished the 2 groups: those with Probst bundles (Probst bundles1) in 13/33 cases (40%) and without Probst bundles (Probst bundles-) in 20/33 cases (60%). Internal cranial occipitofrontal dimension/length of the corpus callosum was significantly higher in Probst bundles1 than in Probst bundles-, with a threshold value determined at 3.
Study Design This study was a retrospective review in 2 tertiary level fetal medicine referral centers (Necker Hospital and Poissy Hospital, Paris, France) in a 4-year period from November 2016 to January 2020. We retrospectively analyzed all fMRI studies of fetuses referred for an SCC. The inclusion criteria were fetal imaging of a single pregnancy with a CC length below the 5th percentile according to the reference charts of Achiron and Achiron 2 for which the integrity of the morphology could not be guaranteed by the referring fetal imaging radiologists in our center. The exclusion criteria were complete agenesis of the CC, an SCC associated with other brain anomalies on MR imaging, or incomplete data. Data were obtained from the medical records and included maternal history and prenatal history (sex, genetic testing, Toxoplasma gondii, other agents, rubella, cytomegalovirus [ToRCH serology; Bio-Rad]).
For the PB, the fiber trajectory was not known a priori, except in its middle portion, where it clearly showed a rostrocaudal direction in the anatomic images and in the color-coded FA maps, creating the roof of the lateral ventricles. 20 To determine the potential connectivity of the callosal remnant in each patient with an SCC, we drew single irregular polygonal ROIs encompassing the entire remnant area in the axial plane, focused on the regions of the centra semiovale. The ROIs were drawn manually according to anatomic landmarks. The examination was considered to be uninterpretable and de facto excluded by the radiologist when fetal motion artifacts interfered with interpretation of fDTIs. Postnatal MR imaging, when applicable, was systematically performed by an experienced radiologist blinded to the prenatal data between 0 and 12 months, to confirm the prenatal fMRI data and to exclude any other brain anomalies.
Statistical Analysis Statistical analyses were conducted using ad hoc routines implemented in R 4. 0. 0 software (http://www. r-project. org). Data description was performed using median and interquartile range (IQR) for quantitative data (ICOFD/LCC ratio and thickness of the CC). Median values were compared between patients with the presence of PB (PB1) and no PB (PB-), using the nonparametric Wilcoxon rank-sum test. The pROC package (https://rdrr. io/cran/ pROC/man/pROC-package. html) for receiver operating characteristic curve analysis and calculation of sensitivities and specificities assessed the diagnostic performance. A bootstrap method was used to calculate the 95% CI of the area under the receiver operating characteristic curves, sensitivities, and specificities. All tests were 2-sided, with a set at 5%.
This advanced technique, therefore, provides an objective criterion for an optimal description of the SCC. 2) Genetic anomalies are more frequent in an ISCC with PB1 (23% versus 10%). This finding correlates well with previously published data suggesting that callosal dysplasias are more often associated with genetic syndromes. 24, 25 However, our results are also in line with the recent literature, which points out that the presence of PB is not necessarily associated with a poor prognosis. 26 Accordingly, neurologic follow-up was normal for most children with an ISCC and PB-, suggesting, in our preliminary study, that ISCCs with no PB are more likely to be variants of normal development of the corpus callosum. Regarding the case of trisomy 21 in PB-, the diagnosis was suspected as of the first trimester due to an increased nuchal translucency, for which the couple chose not to pursue any confirmatory prenatal cytogenetic investigations.
At least on the basis of the data in our study, we believe this case confirms once again that prenatal genetic exploration is essential for the appropriate classification of ISCC. 3) In the absence of the PB, a thick corpus callosum is usually related to lipomas (4/5 in PB-versus 0/3 in PB1). An antenatal diagnosis of lipoma is often challenging and underdiagnosed because the lipomatous components are difficult to demonstrate on fMRIs and antenatal ultrasound,  [27] [28] [29]  whereas this entity is well-recognized in the postnatal period on MRI, showing typical hyperintensity on T1-weighted MR images (Online Supplemental Data). Indeed, Chougar et al  29  and Atallah et al  27  concluded in their fMRI series that the type and size of a lipoma influence prenatal T1 signal intensity, and the variability of the T1 intensity may also be related to fat maturation within the lipoma and perhaps reflects changes related to gestational age.
He highlights that this technology led to a 38% reduction in real-world rear-end crashes. Moreover, they affirm there was no significant difference between urban and rural crash benefits. Moreover, Low Speed AEB technology needs widespread fitment for maximum benefits. Although these driver assistance systems are considered, the ideal solution for reducing the number of accidents, the first developments of AVs, representing the pinnacle of driver assistance systems, has faced many cases of accidents, some of them fatal. The works of  Morando et al. (2018)  using traffic simulation (Vissim software) showed that the number of potential safety conflicts will be reduced whatever the test scenario is. However, despite millions of kilometers traveled in automated mode, Waymo, Tesla, Delphi, Audi, and others have already faced serious accidents such as the fatal accident of Uber in 2017 which put its AV program on hold or even the Tesla accident in 2016.
Is CAV Really Reducing Collisions and Improve Safety? In 2015, according  (Schoettle and Sivak, 2015) , AVs had statistically five times more accidents than vehicles driven by human beings. It is worth mentioning that this study focused on prototype vehicles. Automated features are still under development, and the crash rate may therefore be higher. Also, according to  Hsu (2017) , an AV should drive more than 250 million miles without incident to be considered as safe as a standard vehicle. At the time of its fatal accident in 2016, the Tesla had only traveled 130 million miles. CAVs are still under development, therefor the access to real crash database involving SAE level 3 or more CAVs are limited. Trials, worldwide, depict the current state of the art in terms of automated driving systems.
In Figure  7 ,  Favarò et al. (2017)  found that CAVs were over-represented in the front position during rear-end collisions. The detailed database from the California Department of Motor Vehicles (CaDMV) shows the collision often results from human driver misunderstanding of the CAV behavior. Events such as "emergency braking when nothing happens in the environment" or "stopping at an intersection when the roads are clear" are mentioned in the collision report. Such collisions occur mostly at low speed with no injuries reported. At the time of this study, the CaDMV (Californian Department of Motor Vehicles) published 26 reports. Moreover, several methods and approaches exist to estimate the impacts of AD and their limitations. In fact, three types of methods are used: the first approach employs historical crash data to investigate the potential impact of CAVs, the second uses integrated multi-level custom-built simulation framework to simulate CAVs, which generally incorporates traffic simulator, and the last one uses traffic micro-simulation software and its extensions.
It is also mandatory for all experimenters to report disengagements in less concise reports. Dixit et al. (2016)  and  Favarò et al. (2018)  (Figure  7 ) evaluate from a component of the dataset (n = 2,616, on nearly 6,000 now) events resulting in a disengagement. System failure (52%) and Human factors (30%) refer mostly to the understanding of the situation and the interaction between users. They are the most predominant reasons (reckless driver, cyclist, pedestrian, or take over from the expert driver because of safety and discomfort). The disengagement mostly occurs in an urban environment (street 49%) in good weather conditions (noted as clear 82%). Of course, this result does not formally demonstrate that the Tesla vehicle is more dangerous in automated mode than in normal mode, but it demonstrates that one should not have blind faith in these systems.
Is Embedded Sensor Technologies Recently, new methods and applications have been proposed for two complementary objectives. The first objective is about accurate and certain objects and situation detection and identification. The methods which are now developed are mainly based on the use of IA and Deep Learning methods to be more efficient in the environment segmentation and semantic area recognition. Unfortunately, even if, in the majority of cases, the results are good enough, some specific configurations (intentional or unintentional disturbances) provide failures. Moreover, these methods are based on a training stage. This implies that applications learn only the features included in the training dataset, and it needs to use a very large and representative database. The second objective addresses the fault, drawbacks, and failures detection and identification.
For the available period, measurements from the Jason-3, Sentinel-3, and SARAL/AltiKa missions are processed and analyzed, and their energy spectral and seasonal distributions are characterized in the small mesoscale domain. In anticipation of the upcoming SWOT (Surface Water and Ocean Topography) mission data, the SASSA (Satellite Altimeter Shortscale Signals Analysis, https://doi. org/10. 12770/1126742b-a5da-4fe2-b687-e64d585e138c, Quilfen and Piolle, 2021) data set of denoised SLA measurements for three reference altimeter missions has already been shown to yield valuable opportunities to evaluate global small mesoscale kinetic energy distributions.
The main sources of noise are induced by instrumental white noise, Published by Copernicus Publications. Y. Quilfen et al. :  Towards improved analysis of short mesoscale sea level signals errors related to processing, including the retracking algorithm and corrections, and errors related to the intrinsic variability of radar echoes in the altimeter footprint that causes the notorious spectral hump in 20 and 1 Hz data  (Sandwell and Smith, 2005; Dibarboure et al. , 2014). Furthermore, because retrieved parameters are obtained from the same waveform retracking algorithm, they have highly correlated errors, i. e. , the standard MLE4 processing produces four estimated parameters with correlated errors (SSH; significant wave height, SWH; sigma-0; and off-nadir angle). These errors directly limit the accuracy of the SSH measurement, requiring advanced denoising techniques  (Quartly et al.
An EMD-based technique was successfully applied to altimetry data to more precisely analyze along-track altimeter SWH measurements to map wave-current interactions  (Quilfen et al. , 2018; Quilfen and Chapron, 2019)  known to predominate at scales smaller than 100 km. In particular, the method is suitable for processing non-stationary and non-linear signals, and thus for accurate and consistent recovery of strong gradients and extreme values. Building on local noise analysis, the denoising of small mesoscale signals is performed on an adaptive basis to the local SNR. A detailed description of the EMD denoising approach applied to satellite altimetry data is given in  Quilfen and Chapron (2021). In this paper, the method is extended to more thoroughly evaluate an experimental data set of denoised sea level anomaly (SLA) measurements, from three reference altimeters, the Jason-3, Sentinel-3, and SARAL/AltiKa, in order to capture short mesoscale information.
Section 2 provides a description of the data sets used and Sect. 3 describes the denoising methodology main principles. In Sect. 4, which presents the results, examples of denoised SLA signals are given, and the energy spectral and seasonal distributions of denoised measurements are characterized in the small mesoscale domain for these three altimeters. Section 5 presents key features for comparison with other distributed data sets that make our approach more attractive. A discussion follows, analyzing the main results, and a summary is given. Appendices A and B provide details on the denoising scheme and power spectral density (PSD) calculation, respectively.
Data The Copernicus Marine Environment Service (CMEMS) is responsible for the dissemination of various satellite altimeter products, among which the level 3 alongtrack SSHs distributed in delayed mode (product identifier: SEALEVEL_GLO_PHY_L3_REP_OBSERVATIONS_008 _062) are the state-of-the-art product that takes into account the various improvements proposed in the framework of the SSALTO/DUACS activities  (Taburet et al. , 2021). The input data quality control verifies that the system uses the best altimeter data. From these products, which include data from all altimetry missions, we use the "unfiltered SLA" variable to derive our analysis of SLA measurements. The present study aims to provide research products, the SASSA (Satellite Altimeter Short-scale Signals Analysis) data set, and innovative solutions for better exploitation of the mesoscale mapping capabilities of altimeters.
At this ground sampling, the average noise affecting the range measurements is different for each altimeter, with SARAL and Sentinel-3 showing significantly lower level of noise than Jason-3  (Taburet et al. , 2021). The data set available at CMEMS for the current analysis covers the period until June 2020 with a beginning in March 2013, June 2016, and May 2016 for AltiKa, Sentinel-3, and Jason-3, respectively. Although only the quality-controlled CMEMS data are used as input in our analysis, ancillary data are useful in supporting the analysis of SLA data. Indeed, since some of the larger non-Gaussian SLA errors, correlated with high sea state conditions and rain or slick events, are expected to remain after the EMD analysis, SWH and radar cross-section (sigma-0) are also provided in the denoised SLA products to allow for further data analysis and editing.
Methods The proposed denoising technique essentially builds on the EMD technique  (Huang et al. , 1998; Wu and Huang, 2004; Huang and Wu, 2008)  and its filter bank characteristics when applied to Gaussian noise  (Flandrin et al. , 2004). The technique was first adapted to process satellite altimeter SWH measurements  (Quilfen et al. , 2018; Quilfen and Chapron, 2019; Dodet et al. , 2020) , and the algorithm is described in detail in  Quilfen and Chapron (2021). For the processing of the SLA data analyzed in this study, only limited modifications were made, and the algorithm is only briefly described below. Three main elements characterize the properties of the denoising algorithm: (1) the EMD algorithm that adaptively splits the SLA signal on an orthogonal basis without having to conform to a particular mathematical framework; (2) the denoising algorithm that relies on high-frequency local noise recovery and analysis; (3) an ensemble-average approach to estimate a robust denoised SLA signal and its associated uncertainty.
These higher PSD values are even more important in the Gulf Stream area due to larger variety of sea states encountered and sampled by the altimeter passages. A is an important factor to adjust because it is directly related to the improvement obtained in the SNR. Kopsinis and McLaughin (2009)  perform the optimization of the A factor by simulating a variety of input signals and SNR values. Following these results, first approximate values were tested for the altimeter data set, with a careful analysis of the obtained denoised SLA measurements. However, the adjustment of A for the different altimeters was refined. Indeed, the values of A showing the best SNR improvement on average may depend on the average SNR of the input signal, which is different for each altimeter. Section 4. 2 details the practical rule for determining A that uses an approach to make the denoised SLA PSD consistent with the mean PSD of the unfiltered data minus the PSD of the white Gaussian noise (WGN) estimated in the range of 15-30 km, and consistent for different altimeters.