dataesr/scientific-paragraphs-categorization huggingface dataset:
Effect of the Damping Resistance and the Damping Capacitance In this section, we study the transient response of the damped QTF effect while varying the damping resistance and the damping capacitance. The transient response was simulated using a SPICE software (OrCAD PSPICE)  [16]  and the results were validated experimentally. Before all, we should underline the difficulty of simulating the QTF transient response due to its high-quality factor (the time constant to oscillation period ratio (τ/T = Q/π ≈ 4000) is very high). On the one hand, the discrete timestep must be small enough compared to the rate of change of the circuit, for instance much lower than the QTF oscillation period. On the other hand, the simulation time must be larger than the QTF time constant. A small timestep and a large duration means a large dataset.
Practically, for a timestep of 200 ns and a simulation time of 1 s, 5 million data points are required. For the study of the damped QTF, the decay is faster than the undamped QTF and we were able to obtain significant results with a simulation time of only 50 ms, i. e. , only about 250 k data points. The schematic of the simulated circuit is shown in Figure  3. It includes the damping circuit, a sinewave voltage source, an electro-mechanical relay, the QTF equivalent model and a transimpedance amplifier (TA). The effect of the damping circuit onto the QTF is well described by displaying the QTF energy (Li 2 (t) + Cu 2 (t)) or the output voltage of the amplifier. The latter is more relevant because it is the one practically measurable. Experimentally, the measurements are performed using a lock-in amplifier (LIA) (Zurich Instruments MFLI, Zurich, Switzerland) located after the transimpedance amplifier.
Method Five quarterly field surveys were conducted under contrasting hydrological conditions to represent a complete hydrological cycle from: May 2018 and June 2019 for spring, August 2018 for summer, December 2019 for fall, and February 2019 for winter. Data acquisition includes hourly monitoring of water level in the peatland (T4) and in the alluvial aquifer (F4) from June 2018 to June 2019. Secondly, it includes a monthly sampling for δ 18 O and δ 2 H of the river (R18) and Moltifau rainwater from May 2018 to June 2019 to constrain the local δ 18 O variation. Thirdly, the data acquisition includes a quarterly survey for in situ measurements all over the study area of physicochemical parameters (EC, HCO3 -) and sampling for major ions (NO3 -, Cl -, SO4 2-, Na + , Ca 2+ , Mg 2+ , K + ), stable isotopes of the water molecule (δ 18 O, δ 2 H), and tritium ( 3 H).
Constraints on cosmic strings using data from the third Advanced LIGO-Virgo observing run The LIGO Scientific Collaboration, the Virgo Collaboration and the KAGRA Collaboration (compiled February 1, 2021) We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 data set. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks and, for the first time, kink-kink collisions. A template-based search for short-duration transient signals does not yield a detection. We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension, Gµ, as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models.
I. INTRODUCTION The Advanced LIGO  [1]  and Advanced Virgo  [2]  detectors have opened a new channel to observe the Universe through the detection of gravitational waves. In their first three observing runs (O1, O2, and the first half of O3) the LIGO Scientific Collaboration and the Virgo Collaboration have reported the detection of 50 candidate gravitational-wave events from compact binary coalescences  [3]. These detections have yielded important information on the population properties of these compact binary sources  [4]. In the future, groundbased detectors may discover new sources of gravitational waves  [5] , some of which could probe the physics of the early Universe. Cosmic strings  [6]  belong to this category of sources. The third observing run (O3) started on  April 1, 2019, and ended on March 27, 2020 , and we use the data from the LIGO-Hanford (H1), LIGO-Livingston (L1) and Virgo (V1) interferometers to place constraints on cosmic strings.
In this paper, we report on searches for gravitational waves produced by cusps, kinks and kink-kink collisions, using O3 LIGO-Virgo data. In addition to distinct individual bursts, the incoherent superposition of weaker gravitationa-wave bursts from cosmic strings produced over the history of the Universe would create a stochastic gravitational-wave background  [22, 24]. The gravitational-wave emission from cosmic string loops is introduced in Sec. II. We consider two simulationbased models  [25, 26]  (labeled A and B) for the distribution of cosmic string loops. In addition, we develop a third model (labeled C) which interpolates between the other two models. From these, we derive gravitationalwave burst rates and the dimensionless energy density spectra in Sec. II. Individual gravitational-wave bursts are searched in O3 data with a dedicated analysis presented in Sec.
2  with a ±1σ shaded band. The candidate events, obtained with no time shift, are all compatible with the noise distribution within ±2σ. The cusp, kink and kink-kink collision waveforms are very similar, resulting in the loudest events being the same for the three searches. The ten loudest events were carefully scrutinized. They all originate from a well-known category of transient noise affecting all detectors, that are broadband and very short-duration noise events of unknown instrumental origin  [45, 46]. From the non-detection result, we measure the LIGO-Virgo sensitivity to cosmic string signals by performing the burst search analysis over O3 data with injections of simulated cusp, kink and kink-collision waveforms. The amplitudes of injected signals comfortably cover the range where none to almost all the signals are detected.
Note that with many cusps on the string, the decay constant Γ d is enhanced and the lifetime of the loop is hence greatly reduced. Consequently, a high number of cusps on the loops gives qualitatively the same result as increasing the number of kinks: for model A, the constraints are weakened, whereas for models B and C the bounds are insensitive to N c ; this has been confirmed by our numerical study. One can also compare these results with limits obtained from pulsar timing array measurements, and indirect limits from Big Bang nucleosynthesis and cosmic microwave background data  [56]. Repeating the analysis done in  [28]  with N k up to 200, we find that for model A, the strongest limit comes from pulsar timing measurements, excluding string tensions Gµ 10 -10. For model B and C-1 the strongest limits are derived from the LIGO-Virgo stochastic search.
VI. CONCLUSIONS Using data from the third observing run of Advanced LIGO and Virgo, we have performed a burst and a stochastic gravitational wave background search to constrain the tension of Nambu-Goto strings, as a function of the number of kinks per oscillation, for four loop distributions. We have tested models A and B already considered in the O1 and O2 analyses  [49]. The current constraints on Gµ are stronger by two and one orders of magnitude for models A and B, respectively, when fixing N k = 1. In addition, we have used two variants of a   right). Note that the stochastic result combines the data of O1, O2 and O3 while the burst search only includes O3 data. We also report limits from other experiments: pulsar timing arrays (PTA), cosmic microwave background (CMB) and Big Bang nucleosynthesis  [56].
(B2) Here, S n (f ) is the single-sided noise power spectral density of the detector. It is estimated locally over a few minutes of detector data. The O3 LIGO-Virgo dataset is analyzed in five consecutive chunks of data to account for the detectors' noise evolution over the entire run. The chunk boundaries are defined by sudden changes of sensitivity of one detector and by commissioning interventions, including the 1month commissioning break in October 2019. The signalto-noise ratio time-series ρ i,j,det (t) is computed for each detector, accumulating a total of 245 days, 252 days and 250 days for the LIGO-Livingston, the LIGO-Hanford and the Virgo detector respectively. The signal-to-noise ratio time-series are required to be above 3. 75 and are clustered among templates. We obtain a set of events, or triggers, the parameters of which are given by the template with the highest signal-to-noise ratio peak value.
The authors gratefully acknowledge the support of the NSF, STFC, INFN and CNRS for provision of computational resources. This work was supported by MEXT, JSPS Leading-edge Research Infrastructure Program, JSPS Grant-in-Aid for Specially Promoted Research 26000005, JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2905: JP17H06358, JP17H06361 and JP17H06364, JSPS Coreto-Core Program A. Advanced Research Networks, JSPS Grant-in-Aid for Scientific Research (S) 17H06133, the joint research program of the Institute for Cosmic Ray Research, University of Tokyo, National Research Foundation (NRF) and Computing Infrastructure Project of KISTI-GSDC in Korea, Academia Sinica (AS), AS Grid Center (ASGC) and the Ministry of Science and Technology (MoST) in Taiwan under grants including AS-CDA-105-M06, Advanced Technology Center (ATC) of NAOJ, and Mechanical Engineering Center of KEK.
Acknowledgments. -The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory  and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Netherlands Organization for Scientific Research , for the construction and operation of the Virgo detector and the creation and sup-port of the EGO consortium.
This paper provides an overview of the Dragon 4 project dealing with operational monitoring of sea ice and sea surface salinity (SSS) and new product developments for altimetry data. To improve sea ice thickness retrieval, a new method was developed to match the Cryosat-2 radar waveform. Additionally, an automated sea ice drift detection scheme was developed and tested on Sentinel-1 data, and the sea ice drifty capability of Gaofen-4 geostationary optical data was evaluated. A second topic included implementation and validation of a prototype of a Fully-Focussed SAR processor adapted for Sentinel-3 and Sentinel-6 altimeters and evaluation of its performance with Sentinel-3 data over the Yellow Sea; the assessment of sea surface height (SSH), significant wave height (SWH), and wind speed measurements using different altimeters and CFOSAT SWIM; and the fusion of SSH measurements in mapping sea level anomaly (SLA) data to detect mesoscale eddies.
Introduction The ocean, which covers 71% of the earth's surface, is an important component of the global climate system. Ocean satellite monitoring (including frozen regions, i. e. , sea ice) is of great significance for observing large-scale spatial and temporal changes of various ocean parameters over long periods of time and for linking them with global climate change. Furthermore, remote sensing is an important means for continuous monitoring of single ocean regions and the generation of products that provide information about, e. g. , regional wind, wave, or sea ice conditions. The European EnviSat, SMOS, CryoSat-2, and Sentinel series satellites, as well as the Chinese Haiyang series and CFOSAT satellite, provide a large amount of remote sensing data for ocean and sea ice monitoring.
Besides the jointly published paper  [1] , Chinese team members also focussed on the retrieval of sea ice thickness by Sentinel-3. In the study by Shen et al. [32] , the comparison between ice freeboard products from altimeters on board Sentinel-3A and CryoSat-2 is constructed from February 2017 to January 2018, excluding summer months. The comparisons of echo waveform shapes and along-track radar freeboard estimates suggest that the freeboard difference between these two sensors is caused by the signal range bin number and the chosen retrackers for different surface types (leads and sea ice floes). In general, the Sentinel-3A data set shows lower freeboard estimates than the CryoSat-2 data set; this phenomenon is found in both first-year ice and multi-year ice regions. Mean freeboard estimates for the entire Arctic differ generally by not more than 0.
Dierking et al. [36]  extended the investigations on the statistical errors that occur when retrieving drift vectors from SAR image pairs and their effect on the calculation of different deformation parameters (divergence/convergence, shear, vorticity, and total deformation). Zhang et al. [37]  studied an automatic Sentinel-1 SAR sea ice drift detection method based on multi-scale observation. The proposed method detected the ice cracks or lead structures from down-sampled sequential SAR images firstly. These extracted structures could help to detect the overall drift of the sea ice region. Then, a modified feature tracking method was used to extract sea ice drift vectors from the original SAR imagery by considering the constraints of the overall drift of the sea ice region. Wang et al. in  [38]  evaluated the sea ice drifty capability of Gaofen-4 geostationary optical remote sensing data and analysed the statistical errors of sea ice drift extracted from Gaofen-4 sequential images.
With the improved SSH data of Sentinel-3, Haiyang-2 and other altimeters, sea level anomaly (SLA) data along the track are fused to the regular spatial-temporal data with the resolution of 25 km/daily can be obtained by the objective analysis methods  [42]. As for Objective (O2. 2), wind speed (WS) and SWH of Sentinel-3A/B were validated by using buoys data and satellite Meteorological Operational Satellite Programme (MetOp-A/B) Advanced Scatterometer (ASCAT) data. Regarding Haiyang-2B, the quality of SSH, SWH, and sea surface wind speeds (SSWS) of Haiyang-2B radar altimeter was assessed by comparing with the Jason-2 and Jason-3 data. Finally, the CFOSAT SWIM L2 products accuracy was validated by comparing with NDBC buoys and Jason-3 data. Based on regular grid SLA data and the mean dynamic topography model, the geostrophic current is calculated by the geostrophic balance, thus achieving the objective (O2.
In addition, Sentinel-3 operates in close-burst mode and the transmitted pulse phase between bursts is not preserved, so a model to correct for inter-burst changes is required. This aspect is not to be considered for the Sentinel-6 case since it operates in an interleaved mode, and the transmitted phase is expected not to change during the whole synthetic antenna aperture. The validation of the implemented algorithms is developed by estimating the alongtrack and across-track resolutions over point target scenarios and comparing them with expected theoretical values as computed in  [10]. For the Sentinel-3 case, real data over transponder is used, while for Sentinel-6, data from an in-house simulator is to be used for this assessment. Finally, improvement of SSH and SWH retrievals is assessed by processing a set of Sentinel-3 tracks over the Yellow Sea and comparing them with operational solutions, as ones provided from DD processing techniques.
Indeed, with the extensive used mean dynamic topography model named MDT_CNES_CLS18  [49] , the geostrophic current data in 2018 are calculated according to the geostrophic equilibrium relation and absolute dynamic topography, which is the sum of SLA and mean dynamic topography. Then, characteristics of the Kuroshio main axis are extracted by identifying and tracking the maximums of ocean current. Results show that the Kuroshio can be divided into three sections, the East China Sea section, the southern section of Japan, and the eastern section of Japan. The main axis of the Kuroshio of the East China Sea section is stable, and the Kuroshio bends with monthly characteristics only in the northeast of Taiwan. The large meander of the Kuroshio in the southern Japan section can be detected clearly, and the monthly variations are obvious.
The estimation performance of the FF-SAR processing is compared with DD processing from the ESA L2 products and ESA L1B DD products with an in-house retracker. The data set used in this study consists of four tracks over the Yellow Sea recorded on 2, 9, 17 and 21 April 2020. Figure  4  shows in blue solid line the normalized power of ESA L1B DD return waveform and FF-SAR waveform multilooked to 300 m, computed originally with an along-track resolution of 0. 8 m, and a range zero-padded to 250 samples. The two echoes are returned from the closest surface location oversea. The fitted DD model is shown in red dashed line, and the text box contains the fitted parameter SWH and the Pearson correlation coefficient ρ as a measure of goodness of fit. In the case of FF-SAR waveform fitting, 180 contributing beams have been used to build the model stack.
Conclusions This study verified the accuracy of the SSH and SWH data of the new satellite data (such as Sentinel-3 and Haiyang-2 series altimeters and CFOSAT SWIM), and it is shown that the data of these satellites have high accuracy and good stability. Using multi-source satellite altimeter data, including Sentinel-3, Haiyang-2, and other altimeters, SLA is mapping, and the geostrophic current in 2018 was calculated to extract and analyse the main axis of the Kuroshio. The monthly variation characteristics of the main axis of the Kuroshio were obtained. In addition, spatial distribution and temporal variation characteristics of mesoscale eddies of the western Pacific Ocean and their movement characteristics are extracted and analysed. The results show the mesoscale eddies are greatly related to the Kuroshio in the western Pacific Ocean.
The validation results show that the RMS difference of Aquarius SSS corrected for this effect with respect to in situ bulk salinities is 0. 7, while it is 0. 98 without correction for this effect. A new attempt for high wind speed retrieval based on the Haiyang-2B microwave radiometer has been performed. The random forest passive (RF-passive) model was trained by using the feature parameters. The verification result of the RF-passive is shown in Figure  11. The maximum wind speed retrieved by this model is above 50 m/s. The RMSE, mean The rain-induced roughness effect was studied using the sea surface emissivity of H polarisation derived from the Aquarius L-band measurements after eliminating the emissivity of the flat sea surface using bulk in situ SSS (Figure  10 ). The data in rain conditions show a higher noise level than the data in non-rain conditions.
The validation results show that the RMS difference of Aquarius SSS corrected for this effect with respect to in situ bulk salinities is 0. 7, while it is 0. 98 without correction for this effect. A new attempt for high wind speed retrieval based on the Haiyang-2B microwave radiometer has been performed. The random forest passive (RF-passive) model was trained by using the feature parameters. The verification result of the RF-passive is shown in Figure  11. The maximum wind speed retrieved by this model is above 50 m/s. The RMSE, mean We developed an empirical model that uses the L-band scatterometer data to correct the rain-induced roughness effect in the TB because the scatterometer observations are only sensitive to sea surface roughness but not sensitive to the SSS variation. Based on this model, a SSS retrieval algorithm for rain conditions is developed and validated by the Argo buoy data.
Overall Discussion The different activities addressed within this project have responded to the necessity of optimising the scientific yield from the data provided by the current family of Sino-European Ocean-monitoring satellites. Special efforts have been put into improving essentially the precision, accuracy, and spatial resolution of the main ocean-related physical parameters. Taking the wind speed 1 m/s as bin, the wind speed error of the RF-passive regression model under different wind speeds was analysed. When the wind speed is below 40 m/s, the wind speed bias is between -2 m/s and 2 m/s, and the std-dev is between 1 m/s and 3 m/s. At higher wind speeds, the absolute values of the two values are increasing, but both are within 10% of the wind speed. The results are close to the 5-year SMOS-storm database with the root mean square error on the order of 5 m/s  [15].
Abstract. The water and energy transfers at the interface between the Earth's surface and the atmosphere should be correctly simulated in numerical weather and climate models. This implies the need for a realistic and accurate representation of land cover (LC), including appropriate parameters for each vegetation type. In some cases, the lack of information and crude representation of the surface lead to errors in the simulation of soil and atmospheric variables. This work investigates the ability of the Weather Research and Forecasting (WRF) model to simulate surface heat fluxes in a heterogeneous area of southern France using several possibilities for the surface representation. In the control experiments, we used the default LC database in WRF, which differed significantly from the actual LC.
In addition, sub-grid variability was not taken into account since the model uses, by default, only the surface information from the dominant LC category in each pixel (dominant approach). To improve this surface simplification, we designed three new interconnected numerical experiments with three widely used land surface models (LSMs) in WRF. The first one consisted of using a more realistic and higher-resolution LC dataset over the area of analysis. The second experiment aimed at investigating the effect of using a mosaic approach; 30 m subgrid surface information was used to calculate the final grid fluxes based on weighted averages from values obtained for each LC category. Finally, in the third experiment, we increased the model stomatal conductance for conifer forests due to the large flux errors associated with this vegetation type in some LSMs.
The simulations were evaluated with gridded area-averaged fluxes calculated from five tower measurements obtained during the Boundary-Layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign. The results from the experiments differed depending on the LSM and displayed a high dependency of the simulated fluxes on the specific LC definition within the grid cell, an effect that was enhanced with the dominant approach. The simulation of the fluxes improved using the more realistic LC dataset except for the LSMs that included extreme surface parameters for coniferous forest. The mosaic approach produced fluxes more similar to reality and served to particularly improve the latent heat flux simulation of each grid cell. Therefore, our findings stress the need to include an accurate surface representation in the model, including soil and vegetation sub-grid information with updated surface parameters for some vegetation types, as well as seasonal and man-made changes.