dataesr/scientific-paragraphs-categorization huggingface dataset:
Glitch Detection by Instrument Response Deconvolution (MPS) This detection algorithm, implemented in Python  (Rossum, 1995)  and ObsPy  (Beyreuther et al. , 2010; Krischer et al. , 2015) , performs the following processing steps on a given period of three-component seismic data (components U, V, W): (i) decimate the data to two samples per second (sps), allowing all data per seismometer to be run with the same parameters and enabling faster computations; (ii) deconvolve the Color-coded symbols correspond to glitches for the different groups that are not common to all. Those common to subgroups are plotted on top of each other and so the last plotted is shown. (b) Expanded section showing that as the threshold for declaring a glitch is lowered, either in terms of signal-to-noise or correlation with the template, the results differ and a few, small candidate glitches have been missed.
10. 1029/2020EA001317 Earth and Space Science instrument response on each component and convert to acceleration; (ii) band-pass filter the acceleration data (e. g. , 10-1,000 s), so the steps in acceleration emerge more clearly; (iv) calculate the time derivative of the filtered acceleration data so the acceleration steps become impulse-like signals; and (v) on this time-derivative, trigger glitches based on a constant threshold. To avoid triggering on subsequent samples also exceeding the threshold but belonging to the same glitch, we introduce a window length in which no further glitch can be triggered. This parameter can be thought of as glitch minimum length. We note that this parameter is smaller than the typical glitch length for VBB and SP, allowing our detection algorithm to detect polyglitches.
A glitch simultaneously occurring on multiple components is detected on each affected component but the respective start times may slightly differ. However, after modeling of the full glitch waveform (section 4) we can retrospectively establish that such glitches occur at the same time to within milliseconds. This holds true for all multicomponent glitches observed to date on either VBB or SP, also for data with the highest available sampling frequency of 100 Hz. Therefore, we declare as glitch start time the earliest time detected across the UVW components. The list of unified glitch starts contains still many false-positive triggers caused by nonglitches with a steep enough acceleration change to be triggered. This is because we choose to apply a constant threshold to the time derivative of the filtered acceleration, rather than a threshold based on the current seismic noise level that undergoes strong diurnal changes (amplitudes varying by a factor of 100 and more) dominated by meteorological influences (e.
Glitch Detection by Cross Correlation With Impulse Response Function (ISAE) The principle of this MATLAB-implemented detection algorithm is cross correlation. It performs the following processing steps on a given period of three-component raw seismic data (components U, V, W): (i) a synthetic glitch is constructed by convolving the poles and zeros of the transfer function of the VBB and SP sensors with a step in acceleration. To increase the temporal resolution to subsample range, we synthesize several glitches each with a different subsample time shift; (ii) while the frequencies above 2 Hz are filtered, the long period variations of the data are extracted using a low-pass filter with 10 -3 normalized cutoff frequency for VBB and 0. 25 × 10 -4 normalized cutoff frequency for SP. These are then subtracted from the signal (and added back at the end), before (iii) the synthetic glitch is cross-correlated with the data.
Earth and Space Science In this method, the raw UVW VEL channel data are inspected for glitches and their spikes. The instrument response to a step in acceleration was termed Green's function. The 20 sps data were decimated to 2 sps and each channel was tested for correlation with the response function as follows. An inverse filter was designed that turned glitches into narrow Gaussians with rise times equal to the glitch so that each glitch represented one peak without the overshoot. This enables detection of multiple close-spaced glitches. An STA/LTA (short time average/long time average) ratio was found using convolution of the data with two boxcar functions separated by more than a glitch window. The absolute value of band-passed data was tested for peaks above the STA/LTA threshold. For the first iteration the STA/LTA was set large to remove the largest glitches.
The Green's function was correlated with the data spanning a peak and if the correlation coefficient was above 0. 90 the detection was registered. If multiple peaks occurred close together, multiple Green's functions were fit to the data using nonlinear least squares. The data were then cleaned by removing the glitches. The process was then repeated lowering the STA/LTA threshold ¼ 7, and the new glitches removed from the data. For the last iteration the STA/LTA threshold was set to 3, that is, lowered again and the correlation threshold was also lowered to 0. 8. This removed many of the small glitches. Our glitch detection is applicable to SEIS's VBB and SP sensors in both low and high gain modes.
Glitch Analysis Our working hypothesis is that glitches in SEIS's time series data represent sudden steps in the sensed acceleration convolved with the instrument response of the respective seismometer, either VBB or SP. We can use that assumption to constrain the physical mechanism that led to the glitch. When interpreted as an inertial acceleration of the seismometer frame, a step in acceleration translates to a unlimited, linear change of velocity. This of course becomes quickly nonphysical and can be ruled out because it implied that SEIS would have left its landing location by now. On the other hand, accelerometers like the VBB or SP are also sensitive to changes in gravity. One way this can occur is by tilting the instrument, thus changing the projection of the local gravity vector onto the directions of the sensitive sensor axes.
There we demonstrate that our polarization analysis is correct and that some resulting angles may not be intuitive for special cases for VBB and SP. Figures 3-5 demonstrate the polarization analysis of the VBB and SP glitches for 2019. The plots incorporate the VBB data at 20 and 10 sps (channels 02. BH? and 03. BH?, respectively, "?" stands for the components U, V, and W), and the SP data at 20 and 10 sps (channels 67. SH? and 68. SH?, respectively). These are the data rates that, depending on the actual satellite down-link capacities, are continuously returned to Earth. Besides some minor data gaps in the continuous operation, there is a large period with no data return between Sols 267-288 (28 August 2019 to 19 September 2019). This is due to the solar conjunction period where Earth-Mars communications were obscured by the Sun as consequence of their relative orbital positions.
Glitch Removal Once a glitch has been detected (section 2), the raw waveforms are modeled as a linear combination of the glitch-the response of the seismometer to a step in acceleration-and the glitch spikethe response of the seismometer to a step in displacement. The two responses can be modeled from the poles and zeros of the transfer function of either the VBB or SP seismometer. Only the amplitudes and the precise timing of the source (which might be between two recorded samples) are to be inverted with such model. Due to the time-limited extent of glitches and spikes as opposed to permanent (everlasting) steps in acceleration and displacement, respectively, all methods prefer to correct the raw data rather than the data after conversion to physical units. The MPS group models a glitch waveform for each detected glitch using three parameters: an amplitude scaling factor, an offset, and a linear trend parameter.
To find the best fit, the model is iterated over the data for each (sub)sample and the best fit for the three parameters is determined using nonlinear least squares Earth and Space Science (NLSQ, via the Trust Region Reflective algorithm). The deglitched data then is obtained by subtracting the fitted glitch without the offset and linear trend from the original data. To avoid introducing tiny DC offsets in the data caused by the fact that glitches are not yet fully returned to their baseline after, for example, 30 s, the fitted glitch is not only removed for the fit windows but for time windows corresponding to 10,000 subsequent samples (independent of the data sampling period). The same procedure is done for glitch spikes once a glitch has been removed; however, the subsample search grid is finer than for glitches because it has greater impact on the goodness of fits.
10. 1029/2020EA001317 Earth and Space Science shape of the spike over one or two sample intervals, the starting model must find the location to within a fraction of a sample interval (e. g. , 0. 05 s). Glitches are easier to fit than spikes, being low frequency, and requirements on the starting model are less stringent. Spikes are much smaller in 2 sps data relative to glitch sizes. Thus 2 sps data were used to generate a glitch catalog (section 2). The starting parameters from the 2 sps fits were then used to fit glitches in the 20 sps data and residuals were calculated. The residuals were examined for the presence of a spike in the data before the glitch peak, by requiring its amplitude to be greater than 5 standard deviations of the residuals after the peak. If true, an iterative forward model was run by shifting the phase of the spike template about the corresponding peak in the residuals (in steps of sample interval/10), and finding the amplitude and phase of maximum cross correlation.
In the end, all the proposed deglitching methods are nevertheless based on the same idea of assuming a step in acceleration and displacement to model a glitch and spike, respectively, by using the instrument impulse response of either the VBB or SP seismometer. Removal differences across the methods are mostly due to thresholds below which a glitch is removed or not, and by how these methods attempt to fit glitches that do not fully correspond to our acceleration step model. No general rule on the thresholds can be provided as they depend on the data processing target. As an example, all methods provide similar deglitching for the large glitches occurring during the cooling periods and during the night. More freedom is available for fitting longer source duration glitches during the day although some may represent the real response of SEIS to small pressure drops (section 6.
We see in the Earth and Space Science seismometer data the first move up and the resulting tilt up to the NE. The arm resonates after it loses contact with the mole, and we see that as the 4. 2 Hz ringing in the seismometer data. The seismometer does not have a significant response to the radial outward move. Then on the third move, it appears that the IDA actually touched the mole while stopping and then rebounded and resonated while hovering in midair just above the mole. Finally, the IDA moves down to load the mole and we see a tilt down to the NE at the seismometer. We also observe several glitches (accleration steps) that happen at the same time as the IDA motions (red circles). We interestingly observe that the BHE component shows steps of the same sign for both the arm loading and unloading.
Glitch Mitigation Given the abundance of glitches and their influence on the data analysis, the question arises how glitches could be mitigated for future installations. For thermally related glitches, the most obvious action is to decrease the thermal amplitudes the seismic sensors are exposed to. While for the SEIS instrument great care was taken to achieve just that (Wind-Thermal Shield, Remote Warm Enclosure Box, Evacuated Container, heaters, Thermal-Compensation Device; see  Lognonné et al. , 2019) , the daily temperature cycle still exceeds those of fine terrestrial stations by 4 orders of magnitude, that is, around 15 K compared to a few mK. Given the harsh environments typically found on extraterrestrial, planetary bodies, it may not be easy to achieve 10. 1029/2020EA001317 Earth and Space Science higher thermal stability; however, it should be considered by engineers.
We depicted the two most common cases of glitch production and a sketch of SEIS and its major components in Figure  11. While terrestrial data influenced by glitches and glitch spikes may simply be discarded due to their difficult handling, this represents no valid option for the seismic data returned from Mars. Especially glitches influence virtually all single station methods available to seismologists and therefore can bias many analyses. We therefore devoted much of our efforts to develop code for the glitch and spike removal (section 4, Figures  6 7 8 ). Our algorithms have proven successful in many cases for both seismometers VBB and SP. Of course, there remain glitches and spikes especially of smaller amplitudes that we cannot sufficiently well fit and therefore confidently remove. To correct for such glitches, nevertheless, we have slightly deviated from our step model in acceleration, that is, we introduced fits for nonzero rise times (MPS), for a combination of multiple source functions (UCLA), and for three consecutive acceleration steps of varying amplitudes (IPGP).
Earth and Space Science As no glitch removal algorithm can warrant a perfect clean-up of all glitches and their spikes, we prefer to not provide a deglitched time series of all available data. Instead, we have assembled our algorithms for glitch detection and removal into one Python/ObsPy toolbox. Some convenient functions for data retrieval and handling are also implemented. The package further holds MATLAB scripts to perform glitch detection and removal tasks as presented. Its link is at this site (https://pss-gitlab. math. univ-paris-diderot. fr/data-processing-wg/seisglitch). Documentation is available. Together with this code we also provide deglitched data for a selection of seismic events.
Methods Ethical and Risk Assessment Approval was obtained from the University of Edinburgh's Moray House School of Education, and use of the surgeon's data was approved by The London Bridge Hospital Clinical Governance Committee, and information governance was maintained throughout the study. Following informed consent, a retrospective analysis of patients aged 18 to 50 years who had undergone MAT by a single surgeon between 2008 and 2014 was undertaken. The indications for MAT were symptomatic patients with complete or subtotal loss of a meniscus causing pain and reduced function, with evidence of post-meniscectomy degenerative damage where artificial joint replacement surgery was not yet indicated. Each patient had previously been assessed for suitability for MAT by history, clinical examination, imaging and arthroscopic findings, with the principles of treatment fully explained.
Clinical assessment and questionnaire A request to complete an online self-administered questionnaire was sent via email in December 2016 using QuestionPro software. The date of follow-up was the date the patient completed the online questionnaire. Validated patient-reported outcome measures (PROMs) for knee pathologies (IKDC, KOOS, Tegner and Lysholm) were incorporated into the questionnaire  [25]. A Visual Analogue Score (VAS) pain score (0 to 10) and SF-12 Short-Form Health Survey were also used. The SF-12 was also split in the 'mental' and 'physical' subscales, which were analysed separately. Where questions from the IKDC and KOOS asked about specific joint-loading activities that the patients had specifically been advised not to perform, a 'Not Applicable' option was added. Questions on sport were included in the questionnaire, despite the surgeon having specifically advised all patients against ever returning to impact or twisting type activities.
Materials and Methods This retrospective analysis was performed using data from a prospective database of patients who underwent PTA for HCC at our institution. This study was approved by our institutional review board (NCT03428321 [www. clini caltr ials. gov]), and written informed consent for the procedure and the prospective anonymized data collection was obtained from all patients. Inclusion criteria were HCC diagnosed by histopathology or by imaging criteria, no extrahepatic metastasis or macrovascular invasion, tumor size ≤30 mm, World Health Organization performance status 0 or 1, prothrombin time ratio >50%, and platelet count higher than 50 G/L. Exclusion criteria were follow-up <3 months, Child-Pugh class ≥B8, HCC nodule adjacent to the hepatic hilum, history of biliary-digestive anastomosis or endoscopic sphincterotomy, combined treatment with embolization or chemoembolization, and no baseline MRI.
patient anD tumoR Data The following patient data were collected: age, sex, American Society of Anesthesiologists physical status score (assessed by the anesthesiologist), body mass index (BMI), diabetes mellitus, liver disease (cirrhosis was defined as typical hepatic dysmorphia on imaging, or by noninvasive evaluation of fibrosis, or by histological analysis of liver biopsy; patients were considered noncirrhotic based on liver biopsy or noninvasive evaluation of fibrosis; the others were considered undetermined), steatosis (defined as signal intensity loss on opposed-phase gradient-echo sequences at baseline MRI), liver fat content,  (19)  and Child-Pugh, Model for End-Stage Liver Disease (MELD), albumin-bilirubin (ALBI), and AFP scores. Steatotic HCC was defined by two radiologists (5 and 15 years of experience in liver imaging, respectively) when signal intensity loss was noted on opposed-phase compared with in-phase gradientecho images for at least one HCC nodule.
692 The median (and 95% confidence interval [CI]) follow-up was calculated using the reverse Kaplan-Meier method. Survival curves were estimated using the Kaplan-Meier method and compared with the log-rank test. Univariate and multivariate Cox proportional-hazards models of all potential baseline predictors were built to compute the hazard ratios (HRs) with their 95% CI. A robust variance estimator was used systematically. Log linearity was checked using fractional polynomials. All analyses were performed with the Stata software version 16. 1 (Stata Corporation, College Station, TX). A P value < 0. 05 was considered significant.
8% in steatotic vs. 4. 4% in nonsteatotic HCC). On the other hand, despite a comparable follow-up, no death from HCC was reported in the group with steatotic HCC, whereas it concerned 14. 2% of patients with nonsteatotic HCC. Both TTR and RFS were also longer in patients with steatotic HCC (median of 32. 4 vs. 18. 6 months and 30. 4 vs. 16. 4 months, respectively). Early (<2 years) tumor relapse is primarily related to metastatic spread. (7) The lower rate of early recurrence (32. 7% vs. 49. 2%; P = 0. 041) and the longer time to IDR (16. 4 vs. 9 months; P = 0. 006) in patients with steatotic HCC strongly pleads for slower metastatic escape in this group. Limited data are available on steatotic HCC. (12, 23) In these studies, fatty change was detected by pathology analysis in approximately 20% of patients with HCC treated by surgical resection.
First, this is a retrospective, monocentric study, although data came from a prospectively maintained database with a strict imaging follow-up policy. Second, the median follow-up was relatively short (28. 3 months). Finally, like in most HCC studies, the very low percentage of patients with liver biopsy did not allow exploring histological/molecular/ genetic features of steatotic HCC. In this context, the SH-HCC subtype is associated with overexpression of C-reactive protein, and lack of satellite nodules and vascular invasion. (9, 40) These tumors belong to the G4 subclass with more favorable prognosis. (9) Genetically, SH-HCCs very rarely harbor activated Wnt/betacatenin pathway, and they lack CTNNB1, TP53, and TERT promoter mutations. (9, 40) It is certainly worth exploring molecular and genetic pathways in the wider subgroup of steatotic HCCs.
InSAR Data Processing We used synthetic aperture radar interferometry (InSAR) to capture the deformation produced by the Durrës earthquake (Figure  2 ). In the Mediterranean, InSAR is systematically used to map the ground deformation produced by large earthquakes after removing the signal from the topography (e. g. ,  [23] [24] [25] [26] [27] [28] [29] [30] [31] ) and minimising the tropospheric noise. 3 ). DUR2 -5 ± 4 -3 ± 4 10 ± 8 -13 ± 2 -23 ± 2 13 ± 4 TIR2 -4 ± 4 -4 ± 4 7 ± 8 -5 ± 2 -6 ± 2 0 ± 4 We processed Sentinel-1 Interferometric Wide (IW) acquisitions from ascending and descending orbits, tracks 175 and 153, respectively. We used the open-source SNAP v7. 0 ESA software  [32] , implemented in an offline version of Automated Interferogram Processing Station (AIPS, http: //aips. space.
GNSS Data Processing We analysed the GNSS data of the stations DUR2 (Durrës, 19. 4510 • E, 41. 3156 • N) and TIR2, (Tirana, 19. 8095 • E, 41. 3357 • N) both belonging to the Albanian Positioning Service (AlbPOS) geodetic network (http://www. geo/edu. al/gnss/albpos; see Figure  1b  for locations). The processing was made with the Precise Point Positioning (PPP) strategy  [35]  by means of the GIPSY/OASIS II software (ver. 6. 4) developed by the Jet Propulsion Laboratory (JPL; http://gipsy-oasis. jpl. nasa. gov; [36] ). We used the JPL final orbits (flinnR) and clocks, absolute antenna calibration, random walk troposphere estimation, and the FES2004 ocean loading model. We calculated the static offsets for both September 21, 2019 and November 26, 2019 events (Table  3 ; Figure  3 , vertical lines indicate timing of earthquakes).
Geodetic Determination of Earthquake Parameters Our modelling shows that the November 26, 2019 EMSC epicentre must be shifted by 15 km to match the geodetic data (see Figures  1b  and 5  for locations; the same distance holds for epicentres determined by CMT and INGV; see Figure  1b ). On the other hand, the USGS epicentre is located about 7 km NW of the geodetic centroid and the GFZ epicentre about 3 km to the SW, respectively. We note that such shifts between the geodetic solution and the EMSC solution (among others) has been observed for several large earthquakes that occurred along the eastern Ionian coast in the recent years, i. e. , the Cephalonia doublet 2014  [26] , Lefkada 2015  [30]  and Zakynthos 2018  [38]. This systematic shift might be primarily due to the velocity model used for the Ionian Sea and Adriatic area by some seismological agencies.
Acknowledgments: We thank two anonymous reviewers for constructive reviews and  Efthimios Lekkas ,  Mark Handy ,  Christian Beck ,  Efthimios Sokos , and  Nikos Theodoulidis  for discussions. We thank  ESA ,  Geohazards Lab and Terradue  for providing access to  Geohazards Exploitation Platform (GEP)  for InSAR cloud processing. GNSS data were provided by  ALBPOS  and IGEWE https://www. geo. edu. al/gnss/The ALOS global digital surface model-30 m used in Figure 1 is available from https://www. eorc. jaxa. jp/ALOS/en/aw3d30/index. htmWe thank the  QGIS Development Team-Open Source Geospatial Foundation  Project http://qgis. osgeo. org.
Species distribution models (SDM) have been increasingly developed in recent years but their validity is questioned. Their assessment can be improved by the use of independent data but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability. We used opportunistic presence-only data along with presence-absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models' performance by (1) cross-validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent data sets derived from citizen science monitoring.