dataesr/scientific-paragraphs-categorization huggingface dataset:
Although having a limited time resolution when compared with buoys or other platforms, altimeters provide an excellent global coverage and by now satellite data have been available for three decades (e. g. ,  Young et al. 2011 Young et al. , 2015)). It is well known that the earliest global wave measurements by satellite altimetry can be dated back to 1985, when Geosat was launched (e. g. ,  Zieger et al. 2009). So far, with the efforts of agencies from different countries, a total of 11 satellite altimeter missions have been operational, including the most recent ones by Jason-2  (Dumont et al. 2011) , CryoSat-2 (ESRIN/MSSL 2012), Satellite with Argos and ALtiKa (SARAL;  Bronner et al. 2013) , and Hai Yang-2 (HY-2; NSOAS 2013). HY-2 (also termed HY-2A), launched on 16 August 2011, is China's first dynamic environmental satellite.
Altimeter-derived 1-Hz U 10 and H s have been extensively used in the literature. As mentioned above, because of the excellent global coverage of altimeterprovided H s measurements, they can be applied to tune and validate source term components in numerical wave models (e. g. ,  Tolman 2002; Ardhuin et al. 2010; Zieger et al. 2015)  and also to verify wave hindcast data archived in operational centers or institutes (e. g. ,  Chawla et al. 2013; Rascle and Ardhuin 2013). Unlike wind observations provided by a scatterometer, the altimeterderived U 10 is not usually assimilated into atmospheric models. Therefore, as an independent database, it can be used to evaluate or intercompare different reanalyses (e. g. ,  Caires et al. 2004; Stopa and Cheung 2014 ). Another advantage of altimeter wind and wave products is their continuity in time, which provides an extraordinary opportunity to study wind-wave climatologies and their trends (e.
2008). The latter two ice-related parameters can be further utilized to study wave climates  (Zieger et al. 2013; Babanin et al. 2014 ) and to interpret the error source in numerical wave models  (Ardhuin et al. 2011)  in marginal ice zones. To sum up, by covering long periods of time and extended spatial scales, altimeter-estimated U 10 and H s are reliable, useful, and therefore valuable. The uncorrected altimeter measurements, however, tend to bias the true values. Hence, before the data can be analyzed, they should be carefully quality controlled, calibrated, and validated. A number of efforts have been conducted for this purpose. More recently, three consistent, long-term, and multiplatform altimeter datasets have been established and continuously updated-the global altimeter significant wave height (SWH) dataset  (Queffeulou 2004 and Queffeulou and Croizé-Fillon 2016) , the Global Wave Project  (Ash 2010) , and the joint calibrated multiplatform altimeter data  (Zieger et al.
2009 )-with the first two relating to each other  (Queffeulou 2013b ). The work presented in this paper is the initial attempt to incorporate the HY-2 altimeter data into the third dataset. There have been several published works assessing HY-2 H s measurements; these have consistently demonstrated its good quality  (Yang et al. 2014; Chen et al. 2013; Wang et al. 2013; Zhang et al. 2015; Ye et al. 2015). The first three works focused on the preliminary calibration of HY-2 wave observations shortly after its launch, while the latter two considered a rather long duration of measurements (. 2 yr). A conclusion, however, can be drawn that HY-2 tends to overestimate low H s (,1 m) and underestimate wave heights throughout the remaining range of heights, especially for high seas, H s. 5 m (e. g.
a. Quality control We analyzed geophysical records from four satellite altimeters: HY-2, CryoSat-2, Jason-2, and SARAL. Data from HY-2 and CryoSat-2 are denoted as the interim geophysical data records (IGDRs), while data for the other two missions are referred to as the geophysical data records (GDRs). Table  1  summarizes the information regarding these data, including their sources, data periods, and the orbit parameters of each mission. Except for SARAL, all the missions have provided records for more than 3 yr. As far as the calibration of altimeter products is concerned, the first step is to quality control them-that is, to remove the erroneous or suspicious records normally through using a number of empirical criteria. These criteria should not be so rigorous or loose that the calibration and statistics we finally obtain are unrepresentative  (Cotton et al.
b. Calibration against in situ measurements Although not free of errors, the in situ measurements are always regarded as ground truth and were applied to calibrate satellite measurements and to validate wave models (e. g. ,  Bidlot et al. 2002; Li and Saulter 2014). Of all globally operating buoy networks, data from the buoys maintained by the U. S. National Data Buoy Center (NDBC) are most widely used and referenced in the literature due to their excellent quality and long duration, dating back to the early 1970s. After being quality controlled by NDBC staff, the historical NDBC data are archived at the National Oceanographic Data Center (NODC). For this paper, we obtained data for the period from 1 October 2011 to 31 November 2014, which nearly covers the durations of all available altimeter records.
c. Cross validation against other altimeters The altimeter-buoy collocated data were basically limited to an area off the U. S. coastline; therefore, altimeter measurements needed to be further validated against other simultaneously operating altimeters on a global scale. For HY-2 validations, three other missions were available: Jason-2, CryoSat-2, and SARAL. Jason-2 and CryoSat-2 operate on the same Ku-band frequency as HY-2. SARAL operates in the high-frequency Ka band (35. 75 GHz), which reduces the size of the altimetry footprint and provides better spatial and vertical resolutions. This Ka band-operated altimeter is expected to provide more accurate H s and an improved performance along coast lines  (Hithin et al. 2015 , see also our section 3). These advantages are, however, countered by the fact that Ka band is more sensitive to water vapor and cloud cover than Ku band, and therefore the transfer function needed to derive wind speed would need to be revisited (e.
The solid gray line in (c) is the two-branch linear calibration for CryoSat-2 H s measurements given by Eq. (  6 ). Program (NOPP;  Tolman et al. 2013 ) dedicated to transition the latest advances in wave science to operational wave models has made several new source term packages available (e. g. ,  Ardhuin et al. 2010; Filipot and Ardhuin 2012; Donelan et al. 2006; Babanin et al. 2010; Rogers et al. 2012; Zieger et al. 2015)  and the performance of wave models has been further enhanced. Modeled wave heights were sourced from the Integrated Ocean Waves for Geophysical and Other Application (IOWAGA) database [Institut Fançais de Recherche pour l'Exploitation de la Mer (Ifremer); http://wwz. ifremer. fr/iowaga], which is available at a resolution of 0. 58 spatially and 3-hourly temporally. The database is based on WAVEWATCH III using the semiempirical parameterizations (ST4, TEST471).
1) HY-2 Performance of the HY-2 H s measurements is illustrated in Fig. 2a. Based on 1814 collocations, HY-2 has a slightly high negative bias (20. 22 m), a very high correlation coefficient (0. 98), and a low RMSE (0. 3 m) that is within the stated accuracy (0. 5 m). These statistics agree well with the existing studies on HY-2 H s measurements as summarized in Table  2. The slope and intercept of the RMA regression are 1. 077 and 0. 063, respectively, comparable with those of  Chen et al. (2013). Seen in Fig. 2a , an underestimation tendency of HY-2 H s measurements exists throughout the whole range (1-6 m) and becomes a little more marked for higher values. This is consistent with other studies (e. g. ,  Zhang et al. 2015). The overestimation tendency for low sea states (H s , 1 m) as addressed in previous studies is, however, absent here due to a severe dearth of small values in our quality-controlled dataset.
2009). Similar to what has been seen above, differences between Jason-2, CryoSat-2, and SARAL (Figs. 4d-f ) are relatively insignificant and remain so with time. Only a bias of a few centimeters is to be expected between CryoSat-2 and Jason-2/SARAL (see also Figs. 3e  and 3f ). A noticeable jump in HY-2-measured H s values occurred in April 2013 and is consistently captured in Figs. 4a-c  (the shaded area). According to NSOAS, this jump is related to the switch of HY-2 to backup status and the subsequent update of data processing software. On the basis of monthly bias and the RMSE of HY-2 H s relative to NDBC buoys,  Ye et al. (2015)  subdivided their data period into three phases: the first phase for the period from October 2011 to September 2012, the second for the duration from October 2012 to March 2013, and the third for the period after April 2013.
Discussion This section focuses on verifying the practical applicability of our revised calibration provided in the section above. One may argue that the dataset we used to fit the calibrations Eqs. (  7 ) and (  8 ) is limited and less representative for real high seas. Here, we attempt to indirectly prove the validity of the revised calibration. Numerical wave models can provide excellent resolution in both temporal and spatial scales. Comparing altimeter data to a model can definitely increase the sample size of the collocated dataset and, consequently, the probability of representing a wide variety of sea states. However, since wave models are inclined to be FIG. 5. The revised two-branch calibration of the HY-2 H s measurements: (a) phase 1 and (b) phase 2. The x axis represents the raw HY-2 H s values, while the y axis represents the calibrated Jason-2 H s values (Fig.
8c  and 8f. As seen in these two panels, our calibration significantly improves the performance of HY-2 H s values, particularly when bias is considered. Both the statistics and the patterns are much closer to those of Jason-2 (Figs. 8a  and 8d ), except that HY-2 H s values are slightly scattered. It is worth noting that the overestimation of HY-2 values in low sea states, which is not conspicuous in collocated measurements by HY-2 and buoys or other altimeters (Figs. 2  and 3 ), now becomes noticeable. These small waves, however, were not resolved sufficiently by our quality-controlled datasets (see also section 3a) and hence no attempt was made to correct them.
Conclusions The H s values obtained by the HY-2 altimeter (IGDRs) and three other concurrently operated satellite altimeters were evaluated against NDBC buoys. A cross validation of these four altimeters was also undertaken. From our studies, the following conclusions are drawn. 1) Among the four altimeters, SARAL gives the lowest RMSE (0. 13 m) and the lowest scatter index (0. 07), followed by Jason-2, CryoSat-2, and HY-2. Despite this, HY-2 obtained H s values are still well within its stated accuracy (Fig. 2 ). 2) When compared with in situ buoy measurements, HY-2 H s values have a tendency to underestimate real sea states when H s. 1 m (Fig. 2a ) and overestimate small waves (Figs. 8c  and 8f ). However, this latter characteristic is not well resolved by our quality-controlled datasets, in which a severe dearth of small waves exists.
The absence of the ''good'' small waves in HY-2 H s estimates probably resulted from the error in the data retrieval algorithm, as FIG. 7. As in Fig. 4 , but for the difference between buoy and HY-2 (a) raw H s measurements and (b) calibrated H s measurements using Eqs. (  7 ) and (  8 ). pointed out in  Zhang et al. (2015). The underestimation of HY-2 H s values becomes more marked for a high sea state (H s. 6 m), contributing to the mismatch between the HY-2 and other altimeters (Fig. 3 ). Furthermore, influenced by the switch to backup status of HY-2 sensors and the subsequent update of data processing software, HY-2 altimeter-measured H s values moved to a better quality level in April 2013 and afterward (corresponding to cycle $ 41), and approached the accuracy of CryoSat-2 wave products.
Acknowledgments. Q. Liu acknowledges the fellowship supported by the  China Scholarship Council (CSC). Radar altimeter data were kindly provided by  NSOAS, AVISO , and  NOAA/NESDIS. The in situ buoy observations were provided by  NDBC  and  NODC. The wave model output provided by the  Ifremer IOWAGA  project is highly acknowledged. C. Guan  and  J. Sun  appreciate the support from the  National Natural Science Foundation of China  (Grants  41276010 ,  41376010  and  U1406401 ). AVB acknowledges support from the  Australian Research Council Discovery  Grant  DP130100227  and the  U. S. Office of Naval Research  Grant  N00014-13-1-0278. We would like to thank the two anonymous reviewers and the editor for their comments and suggestions that have improved this paper.
Strong Perturbation Technique. The strong perturbation technique is performed when a tabu search agent tabu search agent observes deep search stagnation. The perturbation agent uses the shared archive of elite k-colorings to create a new solution. It extracts the number of occurrence of each vertex x colored by each color class V i. Starting with an uncolored graph, each vertex x is colored with a color class V i which has the smallest occurrence number. Dedicated data structures are employed to avoid the creation of the same solution for future calls to the perturbation agent.
I N T RO D U C T I O N Understanding the response of underground structures to earthquake excitations is an important task for the assessment of the seismic hazard. In deep sedimentary valleys the response can be complicated and also depend on the earthquake backazimuth. It can be studied either by analysing real earthquake recordings or by simulations. In this article, we will investigate the seismological wavefield produced by regional earthquakes in the Santa Clara Basin in California both by analysing real earthquake recordings and by simulating two of the analysed earthquakes. The data of this study have been recorded between 1999 and 2003 by the San Jose Dense Seismic Array, a temporary acceleration sensor array in California. The array was located in the city of San Jose, near the eastern edge of the Santa Clara Valley, where deep sedimentary layers build the C The Authors 2016.
A R R AY S E T U P, B A S I N M O D E L A N D DATA B A S E The San Jose Dense Seismic Array was deployed by the United States Geological Survey (USGS) in early 1999 and recorded until May 2004. The array consisted of 36 Kinemetrics K2 accelerometers in total, which were operated in triggered mode, that is, did not record continuously, but only if the acceleration exceeded a predefined threshold. The original array layout is shown in Figs 1(a) and (b). The USGS has a detailed seismic velocity model of the whole San Francisco Bay Area (USGS 2014), which is based on the works of  Brocher (2005)  and  Phelps et al. (2008). The shear wave velocity profile based on this model along the black line of Fig. 1(b ) is shown in Fig. 1(c ). Along this profile, the maximum depth of the sedimentary filling of the basin reaches down to over 5 km.
We selected 22 earthquakes for our study for which we have a good signal-to-noise ratio and more than 12 recording seismic sensors for the array analysis. The properties of these earthquakes are given in Table  1. Fig. 3  shows the spatial distribution of these earthquakes with respect to the array. Example accelerograms are shown in Fig. 4. The first example is for event 5, the Hector Mine earthquake, which is the strongest event in the database and for which the recording is the longest. The frequency spectrum of this earthquake shows energy between 0. 04 and 3 Hz. The second example is from event 19, one close event out of two distinct clusters of events 13-16 and 18-21. Each of these earthquake clusters is composed of four events with extremely similar parameters, that is, the same location and depth, but different   Wentworth (1997).
The energy of these 'wrong' components is, by construction of the method, always smaller than the energy of the components corresponding to the identified wave. The columns for the retrograde and prograde Rayleigh waves are analogue to the Love wave columns. However, it should be mentioned that the MUSIQUE code does not directly identify a wave as a Rayleigh wave if the sum of the radial and vertical energies is larger than the transverse energy. It is also necessary that the polarization angle is within certain limits (between 45 • and 135 • for retrograde and between 225 • and 315 • for prograde Rayleigh waves). For all other polarization angles, the wave is left unclassified. The table shows that the energy of the classified components represents between 39. 1 and 60. 1 per cent of the total energy for the different events, with an average of 47 per cent.
Dispersion curves As the different earthquake signals carry wave energy in different frequency ranges, they can also give us insight on different parts of the Love and Rayleigh wave dispersion curves. The dispersion curves are retrieved in the following way: For each frequency, a velocity histogram with 50 different velocity values is calculated, where only waves identified as the respective wave type are considered and their respective energies are summed at each histogram point. The histograms for the different frequencies are then normalized individually and plotted one next to the other. In the resulting plot, the dispersion curves can be picked in a reasonable way. We will not show the dispersion curves for all events, but limit this study to showing some examples in Fig. 12. The Love wave dispersion curve for event 4 exhibits some clear data points which are in good agreement with the theoretical dispersion curve of the Bay Area Model at least for frequencies above 0.
1. 5 (an integrated processing chain commercialized by AgiSoft ® , which is more and more widely used) and MicMac ®  (an open-source photogrammetric toolset developed by Institut Géographique National (IGN ® ), the French National Institute of Geographic and Forestry Information). In this perspective, a high resolution photogrammetric survey was carried out by a multi-rotor UAV. Because the nature of the terrain of the study area affects the quality of the reconstruction, we selected a challenging environment (rugged terrain, range of roughness size) for the purpose of this study, in order to put to the test the performance of the UAV and both SfM software solutions. Furthermore, the topography of the study area prevented an optimal distribution of the Ground Control Points (GCPs) and involved poor GPS reception implying constraints on the flight plan and errors on some GPS measurements, in particular on the positioning of the GCPs.
Thus, the survey was not performed in optimal conditions and therefore the results do not reflect the best achievable quality that these survey methods can yield. For the comparison, the computed DSMs are exported in the widely used format of a raster grid of elevation. As the orthophoto and DSM produced by both methods need to be compared to a reference dataset, reference points were measured with centimetric precision and a 3D point cloud was acquired by a Terrestrial Laser Scanner (TLS). Several tests were performed to assess and compare the sensitivity of both processes to various factors, such as number of images, image size and number of GCPs.
Data Collection Data were collected during a UAV flight conducted on 26 May 2015. The survey was performed using DRELIO, an UAV based on a multi-rotor platform DS6, assembled by DroneSys (Figure  2a ). This electric hexacopter UAV has a diameter of 0. 8 m and is equipped with a collapsible frame allowing the UAV to be folded back for easy transportation. The DS6 weighs less than 4 kg and can handle a payload of 1. 6 kg. The flight autonomy of DRELIO is about 20 min. It is equipped for nadir photography with a reflex camera Nikon D700 with a focal length of 35 mm, taking one 12 Mpix photo per second (stored in JPEG fine format). The flight control is run by the DJI ® software iOSD. Although DRELIO is able to perform a semi-autonomous flight, take-off and landing, ground station software is used to control the UAV during the flight.
These targets are circular disks of 23 cm in diameter, colored in either red or blue. They are georeferenced with post-processed Differential GPS (DGPS), using a Topcon HiPer II GNSS receiver. The base receiver is placed at a location that has not been previously surveyed. For the duration of the survey (~3 h), this base receiver collects position measurements and saves this data to its internal memory. At the same time, the base station determines differential corrections and transmits them to the RTK (Real Time Kinematic) rover receiver. The RTK rover receiver collects position measurements and accepts corrections from the base station to compute its RTK-corrected position. Each target (GCP, reference points-REF. , TLS target) is recorded by the rover as the average position over 10 s (10 epochs) measurements/occupations.
The accuracy of the base position, computed at the post-processing step, is 3. 1 cm horizontally and 4. 5 cm vertically. The precision of the RTK rover relatively to the base position can be up to 1. 0 cm horizontally and 1. 5 cm vertically  [17]. Here, the distance from the base station, not exceeding 100 m, does not really affect the RTK accuracy (impact < 0. 1 mm). Averaging the rover position over 10 s also contributes to minimizing the measurement errors. Nevertheless, measurements uncertainties are difficult to estimate. In addition, errors on targets positions may be due to multipath effects (particularly in this mountainous environment). Moreover, as all the targets (GCP, REF. and TLS targets) are measured during the same session, relative to the same base, the accuracy of base position does not impact the comparisons of the different datasets.
This is why in the paper we use the precision rather than the accuracy to compare different methods. The 12 red targets are used as Ground Control Points (GCPs) in the SfM photogrammetry processing chains (PhotoScan ® and MicMac ® ) to compute georeferenced orthophotos and the DSM. The 12 blue targets have been distributed so as to be used as "ground truth" reference points to assess the quality of the results, independently from the photogrammetric software tools. The three blue targets (REF. 10, REF. 11, REF. 12) situated in the Western part of the area appear to be affected by significant GPS positioning errors, probably due to multi-path reflections on the nearby steep face. Therefore, these targets will not be taken into account in the following calculations, leaving nine targets for "ground truth" reference points.
As the study area is very rugged, placing GCPs and reference targets and measuring their position is a very time-consuming step of the survey. Some parts of the area are impracticable because of obstacles. As a consequence, targets were not set up all over the study area. Moreover, in such environments, it may be difficult for people who set up the targets to have a synoptic view of their distribution. Considering the imperfect distribution of the GCPs, the study area can be subdivided into areas within the control region and areas outside. Such a configuration is not uncommon when using UAVs, as they constitute a solution for surveying areas that are not easily accessible. To complement these datasets, a high resolution Terrestrial Laser Scanner (TLS) survey was simultaneously carried out using a Riegl © VZ-400 TLS.