dataesr/scientific-paragraphs-categorization huggingface dataset:
We characterize and monitor subsidence of the Bursa Plain (southern Marmara region of Turkey), which has been interpreted as resulting from tectonic motions in the region. We quantify the subsidence using Interferometric Synthetic Aperture Radar (InSAR) time-series analysis. The Stanford Method for Persistent Scatterers InSAR package (StaMPS) is employed to process series of Sentinel 1 A-B radar images acquired between 2014 and 2017 along both ascending and descending orbits. The vertical velocity field obtained after decomposition of line-of-sight velocity fields on the two tracks reveals that the Bursa plain is subsiding at rates up to 25 mm/yr. The most prominent subsidence signal in the basin forms an east-west elongated ellipse of deformation in the east, and is bounded by a Quaternary alluvial plain undergoing average vertical subsidence at ~10 mm/yr.
The present study focuses on the surface deformation associated with groundwater extraction, and its temporal evolution in the Bursa Plain to understand how surface subsidence evolves in response to the water table fluctuations and changing soil properties. We investigate this evolution by integrating SAR interferometry and geological and hydrogeological data to shed insight on the underlying processes governing subsidence. We have restricted our study to the Bursa Plain located in the east of the Bursa basin along the northern slope of the Uludag Mount (Figures  1  and 2a ). We use PS-InSAR time series with Sentinel-1 data acquired between 2014 and 2017. The observed subsidence signal is interpreted by comparison with spatio-temporal piezometric level variations in the aquifer, and analyzed in terms of local geological conditions.
InSAR Datasets We use C-band (~6 cm wavelength) Sentinel 1 A/B TOPS images to map the spatial distribution and rate of subsidence in the Bursa Basin. The data used in the present study consist of two datasets with short temporal sampling (Figure  3 ). 105 images were acquired on ascending Track 58 between 21 October 2014 and 28 December 2017, and 96 images were acquired on descending Track 138 between 8 November 2014 and 4 December 2017. The study area is fully covered by all tracks (Figure  1b ). Remote Sens. 2019, 11, x FOR PEER REVIEW 5 of 17.
InSAR Datasets We use C-band (~6 cm wavelength) Sentinel 1 A/B TOPS images to map the spatial distribution and rate of subsidence in the Bursa Basin. The data used in the present study consist of two datasets with short temporal sampling (Figure  3 ). 105 images were acquired on ascending Track 58 between 21 October 2014 and 28 December 2017, and 96 images were acquired on descending Track 138 between 8 November 2014 and 4 December 2017. The study area is fully covered by all tracks (Figure  1b ).
PS-InSAR Processing Methodology All interferograms were generated using the latest version of the open source (GNU General Public License) InSAR processing system called "Generic Mapping Tools Synthetic Aperture Radar (GMTSAR)"  [61]. To correct the topographic contribution to the radar phase, we used the Shuttle Radar Topography Mission (SRTM) 3-arcsecond digital elevation model  [62]. All interferograms were computed based on a single master network for Permanent Scatterer InSAR (PS-InSAR) analysis. The choice of the master images for both tracks (blue dots on Figure  3 ) minimizes the spatial and temporal baselines. The single master stacks of interferograms were processed using the software package, StaMPS  [63, 64]. StaMPS allows the identification of PS points, using both amplitude and phase information.
Then, 3D unwrapping of the PS phase is performed both spatially and temporally. This analysis enables retrieval of the average Line-Of-Sight (LOS) subsidence rate maps. To remove atmospheric effects, we used the freely available Toolbox for Reducing Atmospheric InSAR Noise (TRAIN) developed by  Bekaert et al. [66]. This toolbox uses ERA-Interim (ERA-I, European Center for Medium-Range Weather Forecasts) numerical weather model datasets  [67].
Then, 3D unwrapping of the PS phase is performed both spatially and temporally. This analysis enables retrieval of the average Line-Of-Sight (LOS) subsidence rate maps. To remove atmospheric effects, we used the freely available Toolbox for Reducing Atmospheric InSAR Noise (TRAIN) developed by Bekaert et al. [66]. This toolbox uses ERA-Interim (ERA-I, European Center for Medium-Range Weather Forecasts) numerical weather model datasets  [67]. The results obtained from the InSAR time series were compared with the groundwater level pressure head variations obtained from piezometric measurements. The water level data were collected in two wells (#58581 and #62821), whose locations are shown in Figure  2b , and used to demonstrate the correlation in time between land subsidence and the level of water in the aquifer.
Self-Consistency Checking between the Ascending and Descending InSAR observations In order to show the self-consistency between InSAR measurements from two different viewing geometries, we first compared InSAR mean velocity measurements calculated from the two different Sentinel 1A/B datasets for the subsidence area in the Bursa plain and its surrounding region. Figure  6  shows the correlation between both datasets within the region of interest. The inter-comparison of.
Self-Consistency Checking between the Ascending and Descending InSAR Observations In order to show the self-consistency between InSAR measurements from two different viewing geometries, we first compared InSAR mean velocity measurements calculated from the two different Sentinel 1A/B datasets for the subsidence area in the Bursa plain and its surrounding region. Figure  6  shows the correlation between both datasets within the region of interest. The inter-comparison of the two line-of-sight displacement rate maps in this area was performed using a two-step procedure. In the first step, the mean velocity fields were resampled to a grid of 100 × 100 m to minimize the error of geo-localization (spatial mismatch between measurements). In the second step, we selected all pixels in the localized deforming areas that are common to both tracks.
Then, we compared the distribution of the subsidence rates for both tracks. We found a root-mean-square value of 1. 53 mm/yr and the coefficient of determination (R 2 ) was close to 0. 85. These values indicate that the two subsidence patterns (mean velocity) obtained from the different datasets are correlated with each other dominated by vertical motion. This agreement demonstrates that the deformation obtained from both datasets exhibits similar linear motions between 2014 and 2017, lending confidence to the subsidence patterns and amplitude determined by these datasets. Comparison of the deformation rates between ascending and descending InSAR measurements of the same or different sensors is a common practice for quantitatively checking the consistency of measurements. The observed differences could stem from insufficient acquisitions, georeferencing uncertainty, seasonal biases, long temporal gaps, different temporal coverage, and, most importantly, different looking geometries.
In order to show the impact of the looking geometry on the observed differences of the land subsidence induced by a single point source, we simulated a simple subsidence depression (Figure  7a ). When this synthetic subsidence signal is observed with an incidence angle of 37. 5 • , considered as a mean incidence angle for Sentinel 1, the observed LOS deformation pattern will differ depending on the looking geometry of the sensor, either with a heading angle of -13. 5 • on ascending orbits, or -166. 5 • for descending orbits (Figure  7b, c ). The maximum subsidence value moves away from the center along the azimuth look directions between the ascending and descending tracks. This idealized subsidence pattern gives a large dispersion and low correlation between the ascending and descending tracks as observed for our datasets.
Remote Sens. 2019, 11, x FOR PEER REVIEW 9 of 17 the two line-of-sight displacement rate maps in this area was performed using a two-step procedure. In the first step, the mean velocity fields were resampled to a grid of 100 × 100 m to minimize the error of geo-localization (spatial mismatch between measurements). In the second step, we selected all pixels in the localized deforming areas that are common to both tracks. Then, we compared the distribution of the subsidence rates for both tracks. We found a root-mean-square value of 1. 53 mm/yr and the coefficient of determination (R 2 ) was close to 0. 85. These values indicate that the two subsidence patterns (mean velocity) obtained from the different datasets are correlated with each other dominated by vertical motion. This agreement demonstrates that the deformation obtained from both datasets exhibits similar linear motions between 2014 and 2017, lending confidence to the subsidence patterns and amplitude determined by these datasets.
An extremely high subsidence rate for the period of 2007-2010 was probably due to either droughts or more intensive pumping of the aquifer. We have detected the very localized subsidence signal in the eastern tip of the basin overlapping with the agricultural area results from water pumping activities, but we do not have sufficient data to characterize this local signal. composed of predominantly soft clay and loose silt and sand  [50]. Such highly compressible lithological units are more vulnerable to land subsidence due to groundwater extraction. We estimated a maximum vertical subsidence rate of 25 mm/yr for the 2014-2017 period in the Bursa plain. The spatial distribution of subsidence is characterized by an east-west oriented, elongated, elliptical shape of deformation along the axis of the Bursa plain, in areas where agricultural activities are well developed (Figure  5c ).
Conclusions and Perspectives The population of Bursa is increasing due to the city's growing economy. This growing population is causing rapid urbanization, heavy land use, and land cover change. Such a rapid land development is forcing the land use to change from agricultural to more urban activities on the city's outskirt and hinterland. We have detected ground subsidence in the northern part of Bursa at rates ranging from 5 to ~25 mm/yr in an area of 200 km 2 from the time series analysis of Sentinel 1-A/B images collected between 2014 and 2017. The use of both ascending and descending tracks, showing consistent results, is critical here to better quantification of the subsidence rate and further intercomparison with other SAR sensors' data will continue to improve the subsidence monitoring.
Funding: This research received funding from  TUBITAK  project  113Y102  and  AFAD  project  UDAP-G-16-02  to Z. C. , the  Norwegian Research Council , project  HADES , grant  250661  to F. R. This is part of the Ph. D. dissertation of  Gokhan Aslan  who is supported by the  French Embassy in Turkey (Bourse Etudes scholarship program   889075G ). Université Grenoble Alpes LabeX OSUG@2020  project of C. L. and  CMIRA scholarship program  provided by the  Rhone-Alpes Region. The APC was funded by  CNES/INSU  to C. L.
The ATLAS Distributed Computing system uses the Frontier system to access the Conditions, Trigger, and Geometry database data stored in the Oracle Offline Database at CERN by means of the HTTP protocol. All AT-LAS computing sites use Squid web proxies to cache the data, greatly reducing the load on the Frontier servers and the databases. One feature of the Frontier client is that in the event of failure, it retries with different services. While this allows transient errors and scheduled maintenance to happen transparently, it does open the system up to cascading failures if the load is high enough. Throughout LHC Run 2 there has been an ever increasing demand on the Frontier service. There have been multiple incidents where parts of the service failed due to high load. A significant improvement in the monitoring of the Frontier service was required.
Introduction The ATLAS  [1]  Distributed Computing (ADC) runs O(100k) grid jobs on more than a hundred grid sites. Each job accesses Conditions, Trigger, and Geometry database data. Some jobs use only a minimal amount of data while some some jobs access a significant amount of those data. If a job cannot access the data, it fails. During Run 2 of the LHC, the number of database accesses increased. There are several reasons for that: new more complex workloads, increasing number of computing resources, etc. There have been multiple incidents where load generated by ATLAS grid workloads caused parts of the service to fail. If the Frontier system at one site fails, conditions data are searched at another site which can overload and break its Frontier system too. As a result, effort has been put into significantly improving detection and monitoring of problematic tasks.
Frontier servers  [2] ,  [3] ,  [4]  have both a Squid process for caching and a Tomcat process for converting between http and Oracle protocols. A new real-time monitor in Kibana has been developed to analyse the Tomcat logs. All Squids are monitored by the Multi Router Traffic Grapher (MRTG) tool, i. e. the MRTG monitors both the Squids on Frontier servers and the Squids at sites. The AWStats tool is also used to monitor the Squids on Frontier servers and backup proxies. Several other monitoring pages based on AWStats and MRTG data are available for shifters and experts.
Condition database requests monitoring and alarms Detailed information concerning each single query to the Conditions database performed by any ATLAS job is recorded in the Frontier server Tomcat log files. This is the basis for a new monitoring system making use of the ELK-stack (Elasticsearch/Logstash/Kibana) [5] services deployed at CERN and the University of Chicago  [6]. The workflow of the information follows the schema shown in Figure  1 :  • The log lines from Frontier servers are read and handled by a Filebeat (Beats) service running in each server. It ensures that every single line is transferred sequentially to the computer where it will be processed. Filebeat allows some flexibility in the way the lines are grouped and even whether uninteresting lines may be skipped. Currently we are monitoring Frontier servers located at the CERN and TRIUMF laboratories and a computing centre at Lyon.
• Relevant information is extracted by Logstash instances running in a single computer at CERN. They use a filter configuration file that handles the information of a single query which appears scattered among several log lines. Queries are identified by a unique ID within a Frontier Tomcat subsystem called servlet. The extracted information includes a timestamp of the query, the number of queries being treated concurrently, machines involved, DN of job owner, SQL query details, times for different processing steps, status of the query (if it failed, how it failed), etc. Additional code within the filter file consults a SQLite database filled with information related with the job and the task it belongs to like the task identification number and the processing type. Those details are also included in tuples of variables that that are built in the configuration file and finally sent for storage as indices to an ElasticSearch database.
• The ElasticSearch DB is a highly scalable platform which allows very quick searches on large amounts of data. Our Frontier servers deal with several millions of queries daily and all their information must be treated almost in real time to allow a performant monitoring system. This infrastructure is located at the University of Chicago and also holds information on the behaviour of many other important ATLAS computing services. • A Kibana server sharing the ElasticSearch resources at the University of Chicago allows the visualization of the data in diverse ways, like histograms, tables and pie charts. It has a web server frontend to facilitate the interaction with the underlying ElasticSearch DB data. The definition of those visualization objects can be stored and also grouped into Dashboards.
Therefore, the most relevant objects can be shown together in a single web page or even linked in several pages for the usage of people monitoring the Frontier server activity. Figure  2  shows a Kibana web page with two relevant blocks: the number of concurrent queries (there is a hard-coded limit on each server above which queries are rejected), and statistics about queries not cached in the Squid that have to be served by the Oracle database. Figure  3  presents distributions of high execution times grouped according to most relevant task identification numbers (top) and Frontier servers (bottom). These are just a couple of examples of pages containing visualization objects that appear in the official ATLAS Frontier Dashboard. The Kibana Dashboard is complemented with an Alarms and Alerts (A&A) system to inform experts of foreseeable degradations of the performance of the Frontier servers.
AWStats AWStats  [8]  is a log analyser that generates advanced web, streaming, ftp or mail server statistics. Data from Squids running on each ATLAS Frontier service or backup proxy are collected and displayed by this tool. The information which AWStats monitoring (Figure  4 ) provides are: summary of number of unique visitors, number of visits, pages, hits, and bandwidth; plots of those values in various time frames; Top 10 pages, hosts and their countries/domains; visit duration; request size and time; HTTP Status codes; TCP messages of Squid Cache hits.
Maxthreads Maxthreads monitoring (Figure  5 ) checks the maximum number of threads used by frontier servlets in Tomcat. The number of threads corresponds to the number of queries that are queued waiting for a relatively small number of database connections. It also monitors client response time and DB query time. In case the number of threads exceeds a predefined threshold, an alarm email is sent to experts. The maxthreads monitoring will probably be decommissioned as the monitoring system described in Section 2 covers its functionality.
Conclusions Monitoring of the ATLAS Frontier system is constantly evolving. There were monitoring tools available for a long time (AWStats, Maxthreads, Kibana availability info and SSB) which did not allow deeper understanding of the system. Several new tools were developed to improve the situation. Monitoring of access of Conditions, Trigger, and Geometry database data is in place. An alert system informs experts in case of overload. Now, the incidents are quickly spotted and dealt with before they can destabilize the system for an extended period of time. Since this system became functional, there were no large scale job failures caused by overload of the Frontier system. Using informations from this system will allow deeper analyses of how ATLAS jobs are using conditions data. Such analyses will help to evolve the whole system for Run 3 and beyond.
( 4 ) One could also write an equation for list-mode data with continuous TOF measurements, where the system matrix elements and background contributions are expressed as continuous functions of the TOF measurement v n, u t j = 1 NLOR i=1 A ij (v)dv Ncoinc n=1 A inj (v n ) 1 k A ink (v n )λ t k + rin (v n ) + sin (v n ). ( 5 ) This equation is used in the CASToR version 2. The equation for list-mode data with quantized TOF measurements can also be derived directly from this list-mode equation with continuous TOF measurements (5) as u t j = 1 NLOR i=1 A q ij (v)dv Ncoinc n=1 A q inj (v bn ) 1 k A q ink (v bn )λ t k + rq in (v bn ) + sq in (v bn ) (6) without going through the histogram equation (3) (the superscript q stands for 'quantized').
Whole body scans of two patients with high body mass index (BMI) were selected from the GE Signa PET/ MR scanner (GE Healthcare, Milwaukee, WI, USA). The scanner TOF characteristics were: 13. 02 ps (1. 95 mm) quantization TOF bin, 169. 26 ps (25. 37 mm) cumulative TOF bin, and 420 ps (62. 96 mm) nominal TOF resolution (FWHM). The data were formatted into a TOF histogram and a list-mode with quantized TOF measurements. The reconstruction parameters were similar to parameters in clinical routine: OSEM reconstruction with two iterations and 27 subsets, voxel size 2. 34 × 2. 34 × 2. 78 mm 3 , sieve reconstruction with kernel equal to the scanner resolution PSF  (Snyder et al 1987, Stute and Comtat 2013). It should be noted that there are differences in computation of voxel sensitivities for different data formats.
The main reason for using approximations is reducing the implementation complexity and the computation time. These depend strongly on the software framework and on computer architecture. In CASToR implementation, the approximations do accelerate computations up to a factor of 4, but a detailed analysis of computation time was not an aim of this paper. For the scanner and setup used in this study, the order of magnitude of differences between implementations (less than a couple of percents) is not larger than the order of magnitude of typical bias in standard clinical OSEM reconstructions  (Schöder et al 2004). The differences are in accordance with the presented theoretical and practical implementation details.
Acknowledgments This work is supported by the  Lidex-PIM  project funded by the  IDEX Paris-Saclay  ( ANR-11-IDEX-0003-02 ), by the  French national research agency  ( ANR-16-CE17-0011-001 ), and by the ' MMIPROB ' project funded by  ITMO Cancer (France). Thanks to  Antoine Pierucci  for drawing the diagrams in figure 2 and thanks to  Maxime Toussaint  for the encouragement.
NAN supports communication between distribution substations and field electrical devices for power distribution systems. It collects the service and metering information from multiple HANs and transmits it to the data collectors which connect NANs to a WAN. WAN is the third layer of an SG and it serves as a backbone for communication between network gateways or aggregation points. It facilitates the communication among power transmission systems, bulk generation systems, renewable energy sources and control centers  [2]. Additionally, video cameras have been used in SG management to build video surveillance system for assets safety, fire alarm and safe operations. Zhang and Huo  [73]  developed a video surveillance system to assist safety operations in smart substation. They developed integrated SCADA system with video cameras embedded in supervisory graph for improving the efficiency.
NAN supports communication between distribution substations and field electrical devices for power distribution systems. It collects the service and metering information from multiple HANs and transmits it to the data collectors which connect NANs to a WAN  [2]. The communication technologies for NANs need to cover a radius of a thousand meters. The communication channels between smart meters and data aggregation points therefore must be interference free  [95]. A gateway in the NAN collects consumers' energy consumption data from smart meters in HANs and transmits the collected data to the utility companies through either private or public WANs. Basically, the topology of a NAN is comprised of two types of gateways, NAN gateways and HAN gateways. A NAN gateway connects various HAN gateways and serves as an access point to provide a single hop connection to HAN gateways in a hybrid access manner.
The HAN gateways transmit their energy consumption data to NAN gateways through either wired (e. g. , PLC, DSL) or wireless (e. g. , cellular, mobile broadband wireless access or digital microwave technology) communication technologies. 3) Wide Area Networks (WANs): The WAN is the third layer of an SG and it serves as a backbone for communication between network gateways, NANs, distributed grid devices, utility control centers and substations. It facilitates the communication among power transmission systems, bulk generation systems, renewable energy sources and control centers  [2]. It is comprised of two interconnected networks, core networks and backhaul networks. The core network provides communication to utility control centers with low latency and high data rate through fiber optics or cellular communications.