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# 21
Derrien, Allan • Richter, Nicole • Meschede, Martin • Walter, Thomas
Abstract: The eleven islands of the South Sandwich volcanic arc are amongst the least explored places on Earth. The mostly glacier covered volcanoes are home to the largest penguin colonies worldwide, and nine of them have reported (historic) eruptive activity. Any attempt of systematically mapping volcanic activity, or landscape- and glacier dynamics at the South Sandwich Islands is challenging due to their remoteness and inaccessibility. The data presented here were acquired in the framework of the volcano-related project “SSIVOLC” during cruise PS119 on board the German icebreaker research vessel RV Polarstern that headed to the South Sandwich Islands on 15 April 2019 from Punta Arenas and retuned on 31 May 2019 to the Falkland Islands. A major aim of SSIVOLC was to collect photogrammetric data of the glacier-covered Mount Michael Volcano on Saunders Island, which is highly active and holds an active lava lake within its summit crater, which seems to be persistent since the 1990s (Grey et al. 2019). Here, we are providing full access to optical DSLR camera footage and to a selection of still images acquired by unmanned aerial vehicles (UAVs) that we were able to collect on May 17 and May 22, 2019. Because of the remoteness, inaccessibility, and difficult climatic conditions, footage like this is extremely rare, but of great value to various scientific communities, including volcanologists, biologists, and glaciologists. The data were acquired using handheld DSLR cameras and two different UAV models. The former were taken by scientists aboard RV Polarstern using consumer cameras of type Panasonic DMC-G6, Canon EOS 7D Mark II, or SONY DSC-RX10M3 that carried the DMC-G6 (integrated), EF28-300mm (f/3.5-5.6L IS USM), and DSC-RX10M3 (integrated) lenses, respectively (cf. Table 1). The UAV images were acquired in 2-second time-lapse mode using the DJI Mavic 2 Pro, and the DJI Phantom 4 Pro quadcopters. The performance of the UAVs under very cold (-15°C to 0°C) and windy (8 to 25 knots) conditions, and during low light or dark hours exceeded our expectations. Our UAVs were operated under special permission that was designed by the Govenor under Article 6 of the Air Navigation (Overseas Territories) Order 2013, and issued by the Government of South Georgia and the South Sandwich Islands (GSGSSI) and the Air Safety Support International Ltd. This special permission allowed for the operation of the small unmanned aircraft Beyond Visual Line of Sight (BVLOS) and up to an altitude of 5,000 ft, in the United Kingdom Overseas Territory of South Georgia and the South Sandwich Islands. We were launching the UAVs from the RV Polarstern (located just offshore the island), and reached a maximum UAV altitude of 1,370 m above sea level, which allowed for the collection of the unprecedented UAV based photo archive of Saunders Island. The associated data descriptinon summarizes the basic parameters of the UAV flights, the weather conditions, and the major issues that we were facing while operating the drones under the given circumstances. We are summarizing important metadata of our footage in Table 1, and the footprints and viewing geometries are given in Figure 1. The data are provided in .JPG format. Each drone acquisition carries the GPS coordinates (GCS Lat/Long WGS84) of the UAV position in their properties. Panorama pictures (named PA-xx-xx-xx) are not provided in full resolution (for storage reasons), but can be shared in full resolution upon request (please contact the corresponding author, N. Richter). We also discuss some details and give interpretations for selected acquisitions below, referring to an additional labelled version (provided in .PDF format). Please note that the scales on labelled pictures are rough estimates only as in fact scales vary significantly throughout the depth of each picture.
# 22
Petricca, Patrizio • Trippetta, Fabio • Billi, Andrea • Collettini, Cristiano • Cuffaro, Marco • (et. al.)
Abstract: This data publication includes a grid composed by contiguous 25 x 25 km square elements covering the Italian area and each parametrized by 1) the maximum length of faults included within the cell, 2) the maximum magnitude from instrumental seismic data, 3) the maximum magnitude from historical seismic data, 4) the maximum magnitude calculated from fault length using empirical scaling laws. This collection represents the basis to a work (Trippetta et al., 2019) aiming to test a fast method comparing the geologic (faults) and the seismologic (historical-instrumental seismicity) information available for a specific region. To do so, (1) a comprehensive catalogue of all known faults and (2) a comprehensive catalogue of earthquakes were compiled by merging the most complete available databases; (3) the related possible maximum magnitudes were derived from fault dimensions, upon the assumption of seismic reactivability of any fault; (4) the calculated magnitudes were compared with earthquake magnitudes recorded in historical and instrumental time series. Faults: to build the dataset of faults for Italy, the following databases were merged: (1) the entire faults collection after the Italian geological maps at the 1:100,000 scale (available online at www.isprambiente.it); (2) the faults compilation from the structural model of Italy at the 1:500,000 scale (Bigi et al., 1989); (3) faults provided in the ITHACA-Italian catalogue of capable faults (Michetti et al., 2000); and (4) the inventory of active faults of the GNDT (Gruppo Nazionale per la Difesa dai Terremoti, Galadini et al., 2000). To improve and implement the database, published complementary studies were selected for some specific areas considered to not be exhaustively covered by the aforementioned collection of faults, including Sardinia, SW Alps, Tuscany, the Adriatic front, Puglia, and the Calabrian Arc. For these areas, faults were selected on the grounds of scientific contributions that documented recent fault activity based on seismic, field, and paleoseismological data. In particular, for the southern Sardinia, the fault pattern proposed by Casula et al. (2001) was used. For the SW Alps, the works of Augliera et al. (1994), Courboulex et al. (1998), Larroque et al. (2001), Christophe et al. (2012), Sue et al. (2007), Capponi et al. (2009), Turino et al. (2009) and Sanchez et al. (2010) were followed. For the Tuscany area, Brogi et al. (2003), Brogi et al. (2005), Brogi (2006), Brogi (2008), Brogi (2011), and Brogi and Fabbrini (2009) were consulted. For the buried northern Apennines and Adriatic front, the fault datasets provided by Scrocca (2006), Cuffaro et al. (2010), and Fantoni and Franciosi (2010) were used. For the Puglia region, data from Patacca and Scandone (2004) and Del Gaudio et al. (2007) were used, while for the Calabrian Arc data were obtained from Polonia et al. (2016). Seismicity: to obtain a complete earthquake catalogue for the Italian territory, the following catalogues of instrumental and historical seismicity were integrated: (1) the CSI1.1 database (http://csi.rm.ingv.it; Castello et al., 2006) for the period 1981–2002, (2) the ISIDe database (http://iside.rm.ingv.it/iside/; IsideWorkingGroup, 2016) for the period 2003–2017 (Figure 3) and the CPTI15 (https://emidius.mi.ingv.it/CPTI15-DBMI15/; Rovida et al., 2016) for the period 1000-1981. The CSI 1.1 database (Castello et al., 2006) is a relocated catalogue of Italian earthquakes during the period 1997–2002. This collection derives from the work of Chiarabba et al. (2005). Most seismic events are lower than 4.0 in magnitude and are mostly located in the upper 12 km of the crust. A few earthquakes exceed magnitude 5.0, and the largest event is Mw 6.0. Due to their poorly constrained location, events with Mw < 2.0 were removed. The ISIDe database (IsideWorkingGroup, 2016) provides the parameters of earthquakes obtained by integrating data from real time and Italian Seismic Bulletin earthquakes. The time-span of this compilation begins in 1985. To avoid an overlap with the CSI database, only the time interval 2003–2017 was considered. Mw = 2.0 is the lower limit used for earthquake magnitude. The CPTI15 database integrates the italian macroseismic database version 2015 (DBMI15, Locati et al., 2016) and instrumental data from 26 different catalogues, databases and regional studies starting from the 1000 up to the 2014. To avoid overlapping of data with the utilized instrumental datasets, from the CPTI2015 we took data for the period 1000-1981 in the range of Mw 4-7. Method: starting from the entire faults dataset, the length of each structure was calculated (Lf, in km). Then, the Italian territory was divided into a grid with square cells of 25 x 25 km. The length of the longest fault crossing each cell characterizes the parameter “fault length” (Lf) of the considered cell. In the second step, these lengths were used as the input parameter to empirically derive the magnitude. The equations provided by Leonard (2010), were applied for earthquake magnitude-fault length relationships to infer the Potential Expected Maximum Magnitude as M = a + b ∗ log (Lf), with a=4.24 and b=1.67. The obtained magnitudes were assigned to each single cell. Furthermore, the maximum magnitude recorded/reported in instrumental/historical catalogs is associated to each containing cell. The resulting datasets are presented in txt format and included in the following files: - Grid_Coordinates.txt (contains ID and coordinates of grid's elements)- Grid_Structure.txt (contains geometry and specifications of the used grid)- Table_results (five columns table containing 1=element ID, 2= element max fault length (Lf_max in km), 3=element max Mw from instrumental record (MwInstr_max), 4=element max Mw from historical record (MwHist_max), 5=element max Mw derived by empirical relationship (PEMM).- The full list of references is included in the file Petricca_2018-003_References.txt
# 23
Böhnert, Tim • Luebert, Federico • Ritter, Benedikt • Merklinger, Felix F. • Stoll, Alexandra • (et. al.)
Abstract: The dataset contains material of the historical biogeographic study of the plant genus Cristaria (Malvacae) in the Atacama Desert. We provide here (1) the time calibrated phylogeny of the family Malvaceae in .tre data format (Cristaria_cp_4Foss_BeastTree.tre). Further (2) we provide two nexus files used for all phylogenetic analysis of the paper. The first nexus file (Cristaria_cp_Master.nex) is the concatenated sequence alignment with character set annotations for the three chloroplast marker (trnK/matK, rpl16, ndhF) as well as for hotspot regions and sequence inversions which were excluded and reverse complemented for analysis. The second nexus file (Cristaria_cp_Master_HSEX_INVREV.nex) is identical to the first one except from the fact that all hotspot regions were excluded and the inversions reverse complemented. This dataset was used for all phylogenetic studies as it is presented here. Finally (3), we provide a combined tree file (Cristaria_cp_RAxML-tree_BS_PP.xtg) including the phylogenetic tree of the Maximum Likelihood analysis (RAxML) with additional posterior probability values from the Bayesian Inference analysis (MrBayes). The tree file can be loaded with the program TreeGraph2 (Ströve
# 24
Kaplan, Nils Hinrich • Sohrt, Ernestine • Blume, Theresa • Weiler, Markus
Abstract: Version history17. July 2019: release of Version 2.0. This version includes the catchment boundaris provided as subfolder of geodata.zip Data descriptionWe used different sensing techniques including time-lapse imagery, electric conductivity and stage measurements to generate a combined dataset of presence and absence of streamflow within a large number of nested sub-catchments in the Attert Catchment, Luxembourg. The first sites of observation were established in 2013 and successively extended to a total number of 182 in 2016 as part of the project “Catchments As Organized Systems” (CAOS, Zehe et al., 2014). Setup for time-lapse imagery measurements was inspired by Gilmore et al. (2013) while the setup for EC-sensor was proposed by Chapin et al. (2014). Temporal resolution ranged from 5 to 15 minutes intervals. Each single dataset was carefully processed and quality controlled before the time interval was homogenized to 30 minutes. The dataset provides valuable information of the dynamics of a meso-scale stream network in space and time. The Attert basin is located in the border region of Luxembourg and Belgium and covers an area of 247 km². The elevation of the catchment ranges from 245 m a.s.l. in Useldange to 549 m a.s.l. in the Ar-dennes. Climate conditions across the catchment are rather similar in terms of temperature and pre-cipitation. Hydrological regimes are mainly driven by seasonal fluctuations in evapotranspiration caus-ing flow to cease in intermittent reaches during dry periods. The catchment covers three predominant geologies: Slate, Marls and Sandstone. The dataset features data from catchments covering all geologi-cal characteristics from single geology to mixed geology. It can be used to test and evaluate hydrologic models, but also for the assessment of the intermittent stream ecosystem in the Attert basin.
Time-lapse Imagery Dörr Snapshot Mini 5.0 consumer wildlife cameras were used for time-lapse imagery. Time lapse mon-itoring was realized with the internal software with a temporal resolution of 15 minutes. Cameras were mounted at trees or structures close to the channel. For improved image analysis a gauging plate was installed in the channel. This method was closely related to a time-lapse camera gauging system published by Gilmore et al. (2013). EC-sensorsOnset HOBO Pendant waterproof temperature and light data logger (Model UA-002-64, Onset Com-puter Corp, Bourne, MA, USA) with modified light sensor to measure electric conductivity were used to monitor electric conductivity (EC) as proposed by Chapin et al. (2014). EC values were classified into no-flow situations for EC-values below 25microSi/cm and flow situation for EC-values above 25microSi/cm. Conventional GaugesConventional Gauges are divided into two sub-datasets. Data from ID values CG1 to CG11 were de-rived from water level data measured by METER/Decagon CTD pressure transducers in stilling wells. Data from ID values CG 12 to CG 18 were derived from discharge values measured by the Luxembourg Institute of Science and Technology (LIST). GeodataGeodata comprises of information on proportional shares of geological units in the catchment, the average slope in the catchment and the catchment area upstream of each site. Geological information is derived from a geological map (1:25.000) provided by the Administration des ponts et chaussées Service géologique de l'Etat, Luxembourg (2012). The the original map was created from 1947-1949. GIS analyses were performed using QGIS and SAGA on a 15 m resolution digital elevation model (DEM), which is based on a combined 5m resolution LIDAR scan of Luxembourg (Modèle Numérique de Terrain de Luxembourg, Le Gouvernement du Grand-Duché de Luxembourg, Administration du cadastre et de la topographie, 5m LIDAR, https://data.public.lu/en/datasets/bd-l-mnt5/) and 10m resolution LIDAR scan of Belgium (Relief de la Wallonie - Modèle Numérique de Surface, Service public de Wallonie, Département de la Géomatique. 10m LIDAR, http://geoportail.wallonie.be/catalogue/6029e738-f828-438b-b10a-85e67f77af92.html). The generat-ed 15m DEM has been pre-processed by burning in the digitalized stream network ( min. border cell method, epsilon = 3) and filling sinks (Wang Lui algorithm, minimum slope = 0.1°). The catchment area was calculated by using the pre-processed DEM with 15m resolution and the catchment area recursive tool from the SAGA toolbox using the D-8 method. The same DEM was used to calculate the average slope of each catchment. The “slope, aspect, curvature” tool from the SAGA toolbox was used to calcu-late the slope [radians] with the 9 parameter 2nd order polynom method (Zevenbergen & Thorne 1987) which uses a 3x3 pixel window of the DEM to calculate the slope. Catchment boundaries for each site are included as shape files. These shapefiles were calculated with the Watershed tool from the ArcGIS Hydrology toolbox using a flow direction raster as input which was derived from the Flow Direction tool (ArcGIS Hydrology toolbox) from the DEM described above. Raster output was trans-formed to shape files without simplification of the geometry (subfolder: boundaries).
# 25
Köhler, Andreas • Weidle, Christian • Nuth, Christopher
Abstract: Glacial contribution to eustatic sea level rise is currently dominated by loss of the smaller glaciers and ice caps, about 40% of which are tidewater glaciers that lose mass through calving ice bergs. The most recent predictions of glacier contribution to sea level rise over the next century are strongly dependent upon models that are able to project individual glacier mass changes globally and through time. A relatively new promising technique for monitoring glacier calving is through the use of passive seismology. CalvingSEIS aims to produce high temporal resolution, continuous calving records for the glaciers in Kongsfjord, Svalbard, and in particular for the Kronebreen glacier laboratory through innovative, multi-disciplinary monitoring techniques combining fields of seismology and bioacoustics to detect and locate individual calving events autonomously and further to develop methods for the quantification of calving ice volumes directly from the seismic and acoustic signals.
# 26
Gaucher1, Emmanuel • Maurer, Vincent • Grunberg, Marc
Abstract: This report describes the passive seismic data acquired by the TOPASE network deployed over Rittershoffen geothermal field (Alsace, France). The monitoring period extends from March 2013 to November 2014, which includes the stimulation of the first well of the doublet, the drilling of the second well and well tests. These data were acquired using 31 Earth Data Loggers PR6-24 and MARK-SERCEL L-4C-3D 1 Hz seismometers of the Geophysical Instrument Pool Potsdam (GIPP), which were provided to the KIT-AGW-Geothermal research division.
# 27
Pilz, Marco • Woith, Heiko • Festa, Gaetano
Abstract: This data set contains continuous recordings of seismic noise, which have been made on the surface of a shallow volcanic crater in the Phlegrean Fields volcanic complex near Naples, Italy, where a significant level of volcanic-hydrothermal activity is presently concentrated (MED-SUV = Mediterranean Supersite Volcanoes). As part of the Phlegrean Fields, the Solfatara crater is a 0.4 × 0.5 km sub-rectangular structure whose geometry is mainly due to the control exerted by N40–50W and N50E trending normal fault systems, along which geothermal fluids can ascend. These systems crosscut the study area and have been active several times in the past.
# 28
Matzka, Jürgen • Stolle, Claudia • Kervalishvili, Guram • Rauberg, Jan • Yamazaki, Yosuke
Abstract: Purpose and design of the Hp indices, test dataset The geomagnetic Hp indices are developed as part of the SWAMI project (http://swami-h2020.eu) funded by the European Union’s H2020 research and innovation program. They are designed to resemble the geomagnetic Kp index, but have a higher temporal resolution of 90, 60 and 30 minutes. Whereas the Kp index is a measure of energy input from the solar wind during a 3-hour interval, the Hp indices aim at being a similar measure for the energy input, but over shorter intervals. The geomagnetic Hp indices can be provided back to 1995. Their derivation procedure is similar, but not identical, to the Kp index. Hp values range from 0 to 9 (like Kp), and have mean occurrence rates that are comparable to those of the Kp index. However, users have to appreciate that the Hp indices are not identical to the Kp index of the corresponding time interval. Therefore, it is to be expected that they represent the energy input from the solar wind slightly differently than when using the Kp index. Disclaimer to users of the Hp indices test data set Please carefully test and validate all your model output and services for which you use the Hp indices (including the ap90, ap60, ap30) as input parameter. This is especially true when these models and services were originally derived or parameterized with the Kp index. Which files to use? We provide a number of test data files with different time resolutions. By default, we recommend to use the 1-hourly Kp-like Hp60 index (e.g. data file Hp60_2003.dat) or ap-like ap60 index (e.g. ap60_2003.dat). Hp test dataset description The Hp test dataset consists of 24 files. It is accompanied by the presentation given on the index at the IUGG General Assembly 2019 in Montreal (Stolle et al., 2019). For each year 2003, 2004, 2005 and 2017, there exist annual files for 90, 60 and 30 minutes time resolution) in 2 different formats (Hp and ap). In the format 'Hp' the Hp values are given as 0, 0.7, 1, 1.3, 1.7, 2, 2.3, ... 8.7, 9. In the format 'ap', the Hp values are mapped onto ap values in the same fashion as Kp values are mapped to ap values. The index is provided with an hourly resolution (Hp60 and ap60), and also with a 30-minute (Hp30 and ap30) and 90-minute version (Hp90 and ap90). The years 2003 (Halloween storm in October and November), 2005 (frequent geomagnetic storms) and 2017 (geomagnetic storm in September) were chosen for the occurrence of strong geomagnetic activity. The files are ASCII and have 7 header lines. The data is blank separated and fixed length. The 7th header line indicates the start time (in UTC) of the index interval. For Hp90 there are 16 intervals per day, for Hp60 there are 24 intervals per day, for Hp30 there are 48 intervals per day. Every line with data contains the index values for one day and starts with the date (year-month-day) in the format YYYY-MM-DD. The index values for each interval are written below the start time of the 7th header line. Missing data is indicated by -1. For more information on the Kp and ap index, please refer to https://www.gfz-potsdam.de/en/kp-index/ and to Siebert and Meyer (1996). For more information on the Hp indices test dataset, please refer also to the presentation (Stolle et al., 2019) which can be downloaded from the FTP server.
# 29
Uhlig, David • von Blanckenburg, Friedhelm
Abstract: The data herein were used to assess the importance of geogenic-derived nutrients on long-term forest ecosystem nutrition in two mountainous temperate forest ecosystems in southern Germany (Conventwald/Black Forest and Mitterfels/Bavarian Forest). Presented are element concentrations of various forest ecosystem compartments along with the soil pH, chemical depletion fractions (CDF), mass transfer coefficients (τ_(X_i)^X), radiogenic Sr isotope ratio (87Sr/86Sr) of soil and saprolite as well as in situ 10Be concentrations of bedload sediment. Element concentrations measured by X-ray fluorescence (XRF) are provided for drilling core samples (depth: 20 m, site Conventwald (CON), and 30 m, site Mitterfels (MIT)) including unweathered parent bedrock (paragneiss) and regolith comprising soil, saprolite and weathered bedrock but also for bedload sediment. Element concentrations were also measured by ICP-OES to determine the element composition of the soil´s and saprolite´s water-soluble, easily exchangeable, carbonate and organic-bound fraction. In addition, ICP-OES derived element concentrations are reported for plant tissues such as needles, leaves, and stem wood comprising heartwood (dead part of wood) and sapwood (living part of wood) of the two tree species European beech (Fagus sylvatica) and Norway spruce (Picea abies). Along with the chemical composition of soil and saprolite calculated weathering indices such as the chemical depletion fraction (CDF) and the mass transfer coefficient (τ_(X_i)^X) are reported for regolith and bedrock. Further, the dataset contains phosphorus (P) concentrations measured by ICP-OES and UV spectrometry from various P fractions obtained by sequential extractions following the Hedley fractionation method. Additionally, the pH of soil and saprolite measured by a pH meter as well as the radiogenic Sr isotope ratio, namely 87Sr/86Sr measured by MC-ICP-MS for bulk bedrock and regolith are reported in the dataset. Finally, to estimate the landscapes lowering rate (total denudation) in situ 10Be concentrations were measured by accelerator mass spectrometry (AMS) on bedload sediment at the outlet of the catchment. The data presented here stem from sampling campaigns described in Uhlig et al. (2019) to which they are supplementary material to. Samples were mainly processed in the Helmholtz Laboratory for the Geochemistry of the Earth Surface (HELGES) and the GFZ section of Inorganic and Isotope Geochemistry (XRF analyses), the University of Bonn (P Hedley fractionation), and the University of Cologne - Centre for Accelerator Mass Spectrometry (AMS) (10Be measurements). This dataset represents the supplementary material to Uhlig et al. (2019). Tables (including data quality control) supplementary to the article are provided in pdf and xls formats. In addition, data measured in the course of the study is given in machine readable ASCII files. All samples are indexed with an International Geo Sample Number (IGSN). Sample metadata can be viewed by adding the IGSN to the “http://igsn.org/” URL (e.g. igsn.org/GFDUH00LT).
# 30
Loibl, David
Abstract: All data in this archive were processed using the Open Source SAR Investigation System (OSARIS; https://github.com/cryotools/osaris) v. 0.7.2. Processing was conducted on the Cirrus Cluster at the Climate Geography department, Humboldt-Universität zu Berlin. Input data were ESA Sentinel-1 IW SLC files.
With the advent of the two Sentinel-1 (S1) satellites, Synthetic Aperture Radar (SAR) data with high temporal and spatial resolution are freely available. This provides a promising framework to facilitate detailed investigations of surface instabilities and movements on large scales with high temporal resolution, but also poses substantial processing challenges because of storage and computation requirements. Here we present OSARIS, the ‘Open Source SAR Investigation System’, as a framework to process large stacks of S1 data on High-Performance Computing (HPC) clusters. Based on GMTSAR, shell scripts, and the workload manager Slurm, OSARIS provides an open and modular framework combining parallelization of high-performance C programs, flexibility of processing schemes, convenient configuration, and generation of geocoded stacks of analysis-ready base data, including amplitude, phase, coherence, and unwrapped interferograms. Time series analyses can be conducted by applying automated modules to the data stacks. Here, a demonstration dataset is presented that was generated using OSARIS in a case study from the northwestern Tien Shan, Central Asia. After merging of slices, a total of 80 scene pairs were processed from 174 total S1 input scenes. This archive contains full time series in original resolution of ~31 m for selected OSARIS interferometric processing results, i.e. amplitude, coherence, connected components, interferometric phase, line-of-sight displacement, sums of forward plus reverse pair unwrapped interferograms, and 'Unstable Coherence Metric'. In addition, results from the coherence-based 'Stable Ground Point Identification' module and coherence statistics for time series of selected subregions and landforms discussed in the associated publication are included. Wall clock processing time for the case study (area ~9,000 km²) was ~12h:04m on a machine with 400 cores and two TB RAM. In total, ~12d:10h:44m were saved through parallelization. OSARIS thus facilitates efficient S1-based region-wide investigations of surface movement events over multiple years.
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