dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, the modelling of HVDC system including hybrid breakers  [46]  and dynamic electro-magnetic-thermal modeling  [47]  have also been inquired. Due to the nonlinearity features of dynamic models of MT MMC-HVDC systems, many nonlinear control strategies  [48] [49] [50] [51] [52] [53]  can be proposed for accurate control of such systems. In this paper, a coordinated Lyapunov-based control strategy is designed for a MT MMC-HVDC system using a new set of dynamic equations. The proposed controller is aimed to provide stable frequency and voltage magnitude of AC grids in presence of both load and DC-link voltage fluctuations. The contributions of this manuscript can be explained as the follows, 1) As the first contribution, a separated dynamic model is proposed for MMCs in MT MMC-HVDC system.
Simulation results The MT MMC-HVDC system is simulated through MATLAB/Simulink, when the proposed control strategy is applied as depicted in Fig. 6. Both load and DC-link voltage variations exist in the MT MMC-HVDC system to validate the ability of proposed controller under dynamic operating conditions. Two AC grids 1 and 2 will be under load changes at t = 15 s and t = 10 s, respectively. The parameters of the considered MT MMC-HVDC system are given in Table  1. The DC-link voltage is shown in Fig. 7. According to this figure, the DC-link voltage is fluctuated with both steady-state and dynamic errors during the entire simulation time. In addition, as it can be seen from Fig. 6 , a coordination unit is employed for MT MMC-HVDC system to provide a coordinated operation between MMC1 and MMC2 through the reference values archived for the related proposed control strategy.
In order to more survey the effects of variations of MMC parameters and state variables, as another main contribution, two novel capability curves based on the power injection capability of MMC's upper and lower arms have been introduced as well. Using MATLAB/SIMULINK software, under variation of load and DC link voltage, it has been verified that accurate steady state and dynamic responses for MMCs active and reactive power sharing, SM's capacitors voltages, the voltage magnitude and frequency of output voltages could be obtained.
, 2014; Meunier et al. , 2010; Miklos et al. , 2020) , while starspots distort the spectral line profiles when they cross the visible stellar disk  (Saar & Donahue, 1997) , producing noise or mimicking radial velocity variations due to a planet and therefore resulting in erroneous detections (e. g. ,  Huélamo et al. , 2008; Huerta, Johns-Krull, Prato, Hartigan, & Jaffe, 2008; Queloz et al. , 2001). Modelling the starspot signature affecting RV data sets is therefore key to improve the precision of planet searches and inform observing strategies to minimize the impact of activity. To this aim, a number of tools have been developed such as SOAP  (Boisse, Bonfils, & Santos, 2012; Dumusque, 2014) , DEEMA  (Korhonen et al. , 2015)  and STARSIM  (Herrero et al. , 2016). In general, the impact of starspots on RV data sets and planet detectability has been simulated under different assumptions  (Meunier, 2021).
For instance, Desort, Lagrange, Galland, Udry, &  Mayor (2007)  studied the case of a single spot on the surface of different F-K stars and with different active latitudes, inclinations and sin ;  Lagrange, Desort, & Meunier (2010)  investigated planet detection limits for G type stars using Sun-like spot coverage;  Santos, Cunha, Avelino, & Campante (2015)  used an empirical model to reconstruct a synthetic solar sunspot cycle;  Andersen & Korhonen (2015)  explored the M type regime and various activity levels, and  Dumusque (2016)  included instrumental, stellar and planetary signals in simulated RV data sets for G-K stars to analyze different recovery techniques. These works studied the effects of different activity configurations or stellar parameters on the RV data sets. In comparison, we select a representative sample of known exoplanet hosts in the F-M spectral range, find or compute their stellar parameters, characterise their starspot properties, and simulate their spotted surface to obtain reasonable estimates of the spot-induced jitter.
, 2007). Our list contains information on effective temperature ( ef f ), -colour index, spectral type, and log ′ HK index available either in the literature or on online databases. When the index is provided instead, we convert it to the log ′ HK index with  Rutten (1984)  formula. For all F, G and some K stars, we extract data from the catalogue by  Krejčová & Budaj (2012) , who investigated the influence of an exoplanet on the magnetic activity of its host star. For later spectral types, we find known host stars via exoplanet. eu (Schneider, Dedieu, Le Sidaner, Savalle, & Zolotukhin, 2011)  and exoplanet. org (Han et al. , 2014)  and we look for magnetic activity information in the literature. Moreover, we use SIMBAD  (Wenger et al. , 2000)  to obtaincolour indexes and as an auxiliary tool when the spectral type and evolutionary stage are uncertain.
Even though we expect the extrapolations to be neither the most accurate nor mathematically flawless, our estimates try to be as reasonable as possible. Further work, aimed at an improvement in the filling factor data sets, would most likely lead to better estimates. Finally, our simulations allow the spots to be placed randomly on the stellar surface and use a lognormal spot size distribution  (Korhonen et al. 2015  and references therein) peaking at 1 • in radius. This choice is motivated by the fact that larger spots are more easily modelled via tomographic imaging, so their effects can be efficiently filtered out, even though the spot jitter increases with the spot size.
RESULTS AND DISCUSSION From the RV curves of our simulations, we compute the mean peak-to-peak RV amplitude (⟨RV pp ⟩) and its minimum (⟨RV pp ⟩ min ) and maximum (⟨RV pp ⟩ max ) values over all the spot configurations for each star. The DEEMA code produces the V-band light curves associated with the spots configurations Test to determine the optimal number of surface maps to simulate, taking Eri as example. We consider a grid of 50, 100, 150, 200, 250, and 300 random surface maps and compute the mean RV jitter peak-to-peak amplitude in each case. From top to bottom, the distributions of the RV jitter amplitude for the 50, 150, and 300 cases are shown. Using 150 maps results in a symmetric distribution of the RV jitter amplitude, with a ≤50 cm/s difference relative to the 300 case. as well, therefore we include information on the photometric variability: minimum (ΔI min ), maximum (ΔI max ) and mean amplitude (⟨ΔI⟩) of the light curve.
ACKNOWLEDGMENTS HK acknowledges a research grant from the  Danish Augustinus Fonden. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France ; the VizieR catalogue access tool, CDS, Strasbourg, France; the  NASA Exoplanet Archive , which is operated by the  California Institute of Technology , under contract with the  National Aeronautics and Space Administration  under the  Exoplanet Exploration Program ; Astropy, 12 a community-developed core  Python package for Astronomy (Astropy Collaboration ,  Price-Whelan , et al. , 2018; Astropy Collaboration,  Robitaille , et al. , 2013); Matplotlib: Visualization with Python (Hunter, 2007); SciPy (Virtanen, Gommers, et al. , 2020). Based on observations collected at the  European Southern Observatory  under  ESO  programmes  082.
Each of these models are typically bound to the dominant spatial and temporal scales of key processes to model, and as a result they struggle, or become too computational expensive, in order to account for dynamics at different timescales. Hybrid (equilibrium) models are generally more computationally efficient in comparison to process-based models and have been proven reliable on seasonal or inter-annual timescales to simulate shoreline behavior  [15] [16] [17] [18] [19]. However, they typically account for a single dominant physical process and need a large, site-specific dataset for calibration purposes  [20]. Another limitation to these models is that only coastal processes observed during the calibration timeframe are accounted for  [21]. Moreover, hybrid models do not explicitly account for all individual processes that drive shoreline change but seek an overall behavior pattern as a response to the different processes.
Model Training Sites Covering Different Wave Environments To train the SF-ST model, shoreline location and wave measurements are required. A source of shoreline data are shore-based video cameras that generally collect data during daylight on a 30 min basis  [31]. In comparison to shoreline-walking GPS surveys, these video-based shorelines provide significantly better temporal resolution, which makes video-derived shorelines particularly suitable to study the importance of different, multiple, response timescales  [2, 32]. In order to make the video data suitable for SF-ST and to cover a wide range of timescales (1 day to inter-annual timescales), the shoreline dataset at Nha Trang and all wave forcing datasets are interpolated (upsampled, corresponding to the value every 24-h), such that they have a temporal resolution of 1 day.
The shoreline datasets at Narrabeen and Tairua have a daily interval corresponding to the procedures outlined in  [6, 33] , respectively. Moreover, the raw shoreline position data is detrended by a second-order polynomial in order to filter out shoreline trends which have a larger temporal scale than the duration of the dataset. The importance of multiple beach response timescales will be investigated at three different study sites: Narrabeen (Australia), Nha Trang (Vietnam) and Tairua (New Zealand). These sites were chosen to cover different wave environments and for the availability of daily shoreline data. All three beaches are subjected to a small tidal range (microtidal, <2. 0 m, <1. 6 m and <2. 0 m, respectively), such that SF-ST has an optimal model performance  [3, 34, 35] , respectively.
Narrabeen-Collaroy Beach, Australia The Narrabeen-Collaroy beach (34 • S) is situated near Sydney in the southeast of Australia (Figure  1 ). The embayed beach is bound at the north and south by two headlands. Narrabeen beach is situated in the north of the embayment and Collaroy beach is situated in the south. In 2004, an ARGUS system  [31, 37]  was installed, from which shorelines can be extracted. In this paper, alongshore averaged shoreline data a bit southwards of the center of the embayment is used (2400 m to 2800 m from the northern edge of the embayment), in line with previous studies at this beach  [17, 33]. Narrabeen beach is characterized by sand with a D50 of 0. 4 mm. The wave climate is largely influenced by swells from the SSE (mean H s = 1. 6 m and mean T p = 10 s). Additionally, the wave climate consists of larger waves (H s = 3 m) that originate from storm events and can hit the coastline in any direction.
Nha-Trang Beach, Vietnam A video system  [41, 42]  was installed in 2013 at Nha Trang beach in Vietnam (12 • N) (Figure  1 ). The camera location is considered far enough for the beach not to be influenced by the edges of the bay  [3]. Cross-shore shoreline positions are estimated from this camera data on a daily basis. The shoreline location used in this paper is retrieved by alongshore averaging the video-derived shoreline data (over 75 m) from the northern part of the bay relative to the camera location. Nha Trang beach is characterized by sand with a D50 of 0. 3 mm. Interestingly, the tropical wave climate at Nha Trang is characterized by multiple wave conditions with a distinct timescale, of which the seasonal variation is the most pronounced. The offshore annual mean significant wave height (H s ) is 0.
Tairua Beach, New-Zealand Tairua Beach is situated at the east coast of New Zealand's northern island (37 • S) (Figure  1 ). This 1. 2 km long beach is situated in between two headlands, where on the southern headland, a video camera was installed in 1998. In this study, high-resolution shoreline data (alongshore averaged over the bay: 1050 m, see  [6] ) is used, which is extracted from those camera images. Tairua beach is characterized by sand with a D50 of 0. 3 mm. The beach is subjected to easterly and northeasterly swell and storm waves  [35]. The offshore wave climate has a mean H s of 1. 4 m, and can reach values of up to 6 m during storm events  [43]. In the winter (southern hemisphere) of 2000 a series of huge storms occurred, which caused erosion of the shoreline of roughly 20 m  [44].
Distinguishing Multiple Timescales To distinguish timescales, the raw shoreline position and wave forcing (H s , T p ) data is filtered using a running average filter with varying (enlarging) window sizes. The running average filter is used to allow for data gaps in the datasets. Per window, and hence timescale (i. e. , 1 until the length of the timeseries, with steps of 1 day), a residual variance of the shoreline position and wave forcing timeseries is calculated. With residual variance of the filtered signals a temporal spectrum is constructed. In other words, we can plot the temporal spectrum as the (remaining) variance of all filtered signals as a function of the timescale. Dominant timescales are defined by the largest variance. In contrast to, for example, Fourier spectra, the linear superposition of all filtered signals is not equal to the raw signal since the filter function is shape preserving but not energy conserving.
The lower the local variability of the spectrum, the wider the bins, the more filtered signals fit within a bin (Figure  2D ). Hence, the bin distribution is determined by adding up filtered signals (starting with the signal that has a timescale of 1 day), until that summation reaches the maximum variability of the bins. For the remaining filtered signals, a new bin is used. This procedure is continued up to the point where all filtered signals fall within a bin. In the wave forcing spectra (i. e. , the individual spectra for H s and T p ), an identical bin distribution is used such that corresponding timescale clusters are formed. Thereafter, the different timescale clusters in the wave forcing data (H s,i and T p,i , where i indicates the timescale cluster) are related to ones in the shoreline data on multiple scales.
Within each band, no linear trend is present as the filter function only captures timescales which are smaller than the full length of the dataset. Hence, b should be zero when the full timeframe is considered. However, during the calibration phase only part of the entire dataset is used such that a (small) trend can be present. Subsequently, this trend will be assigned to b (or partly, depending on the wave characteristics). As this linear trend will be extrapolated during the validation phase, it will result in a wrong shoreline prediction as no trend is present over the full period.
Model Calibration and Validation The modelling of cross-shore shoreline change on multiple timescales is divided in two phases: model calibration and model validation. During model calibration the site-specific model-free parameters (c i and ϕ i ) are determined using the wave forcing (H s,i and T p,i ) and shoreline data (x i ), where the subscript i indicates the multiple predictions from the direct forcing, upscaling and downscaling approaches. At the time of model validation, wave forcing data is used as model input only and together with the calibrated parameters shoreline predictions are generated. Table  1  presents the calibration and validation timeframes for all three datasets. Furthermore, note the difference in the percentages of data gaps in the shoreline position data (the wave forcing data is continuous for all three study sites).
Results In this section, calibration (Section 4. 1) and validation (Section 4. 2) results are presented, when SF-ST and the multi-timescale model (SF-MT) are applied to all three datasets. An overview of model results assessed by four different model skill indicators (correlation, NMSE, BSS and AIC), is presented in Section 4. 2 as well. Table  2  provides information regarding the three datasets at the model training sites used in this study: Narrabeen, Nha Trang and Tairua. The number of timescale bins is presented, which represents the number of timescale clusters used in the multi-timescale implementation. Or stated otherwise, in how many bins the temporal spectra are divided. The most striking difference between the datasets is the number of dominant timescales in the shoreline position and wave forcing data (i.
e. , local maximums in the temporal spectra, Section 3. 1 and Figure  1 ). At Narrabeen, only one dominant timescale is found in the shoreline position signal that is related to the seasonal variation (i. e. , 322 days, Table  2 ). Besides the seasonal variation, the storm/swell timescale is dominant in the wave forcing data (i. e. , 2-14 days). At Nha Trang, two dominant timescales are present in the wave forcing: the monsoon and seasonal timescale, which implies that typhoons are not dominant. In the shoreline position signal, only the seasonal variation is dominant. At Tairua, a monthly and seasonal to inter-annual timescale is dominant in the shoreline position signal, while storms/swells dominate the wave climate. The dominant timescales in the wave conditions observed during the research period correspond well with the literature mentioned in Section 2 and are thus considered representative for each of the study sites.
Calibration Figure  5  presents the results (calibration and validation) when SF-ST (black) and SF-MT (red) are applied to the datasets at Narrabeen (Figure  5A ), Nha Trang (Figure  5B ) and Tairua (Figure  5C ). The transition between the calibration and validation time periods is indicated with a dashed black line. Figure  6  presents the contribution of each model improvement step over time (left, in similar order) and the corresponding relative contribution of each model improvement step (right) for the calibration period only. The blue, orange and purple lines correspond to the direct forcing, upscaling and downscaling approaches, respectively. In Figure  7  the standard deviation per timescale cluster is shown (left panels) for the shoreline position data (green), the SF-ST (black) and SF-MT (red) model result (calibration + validation) for all datasets (in similar order as Figures  5  and 6 ).
For the Narrabeen dataset (Figure  5A ) SF-ST (black) is able to capture the smaller timescale storm/swell response (order of days), but the larger timescale shoreline variations are captured to a lesser extent (ϕ = 3 days and c = 1. 96 × 10 -7 (m/s)/(W/m) 0. 5 ). The SF-MT model (red) also captures the storm timescale to a certain extent, but yields a considerable increase in skill (BSS of 0. 61 or an 'excellent' rating) by better capturing shoreline change on larger timescales (larger than the storm timescale). This occurs, for example, in 2009, where first the large erosion period halfway through 2009 is captured well by the SF-MT model and poorly by SF-ST, while the same occurs for the accretive period during the second half of 2009. Regarding the energetic storm sequence in June 2007 (see Section 2), neither model is capable of capturing the rapid shoreline recession.
Validation During model validation, wave data in combination with the calibrated free parameters found in Section 4. 1 are used to predict shoreline evolution. However, measured shoreline data is still available and is used to compare with the shoreline predictions. For the validation phase, shoreline predictions are shown in Figure  5  as well. The validation results at Narrabeen reveal that the result of SF-MT outperforms that of SF-ST, but differences are less pronounced as for the calibration phase (BSS of 0. 26 or a 'good' rating, compared to 0. 61 or an 'excellent' rating for the calibration phase). From the timeseries in Figure  5A , it becomes clear that especially after 2013, the prediction of SF-MT is closer to the data than the prediction of SF-ST. The NMS error between the data and model prediction is 0.
The NMS error for SF-ST and the SF-MT model is 0. 63 and 0. 26, which corresponds to a 'fair' and 'excellent' rating, respectively. The similar characteristics for the calibration and validation phase are also emphasized by the BSS, which is 0. 61 for the validation phase while it was 0. 62 for the calibration phase (i. e. , an 'excellent' rating). The ∆AIC score is smaller than 1, which indicates that the SF-ST model is preferred due to the model's simplicity compared to the relative goodness of fit. A comparison of the validation results for both models at Tairua (Figure  5C ) shows that some improvement is made by implementing multiple timescales in SF-ST, especially during the last 2 to 3 years of the dataset (BSS of 0. 18 or a 'fair' model improvement). Overall, the SF-MT model predicts shoreline change better than SF-ST, which results in a decrease in the NMS error of 18% and an increase in the correlation coefficient of 8% (Table  3 ).
Hence, it is possible to create a model for multiple timescales, based on SF-ST, that represents a sum of individual components (i. e. , timescale clusters) from several equilibrium shoreline models with different timescales. In  [50] , a different approach to model multiple timescales (from storm events to decadal time-scales) was developed (based on the Complete Ensemble Empirical Mode Decomposition method), and applied to the Narrabeen and Tairua dataset. Their approach first identifies and subsequently links the primary timescales in the large-scale sea level pressure and/or waves with identical timescales in the shoreline position (i. e. , a direct forcing approach). Intriguingly, they found similar results as presented in this paper. For Narrabeen they found timescales of 181, 363 and 709 days to be important shoreline position timescales (besides the long-term trend).
Moreover, the distribution of shoreline variability over the different modelling approaches and timescale clusters in SF-MT (last column in Figure  7 ), showed that the upscaling approach is responsible for capturing shoreline change on the largest timescales in the dataset, which is logical, as this approach is modelling the persistent effect of short high-intensity wave forcing events on larger shoreline response timescales. Conversely, the modelled shorelines generated with the downscaling approach correspond better to shoreline data on smaller timescales, which is logical as well, as this approach is modelling the effect of the larger timescales in shoreline variation on the efficiency with which smaller timescale wave forcing events induce cross-shore sediment transport. The direct forcing approach does not account for particular shoreline response timescales as it seems to be dataset dependent.
• Due to the linear interpolation of the shoreline location (for Nha Trang) and wave forcing parameters (i. e. , H s and T p ) for all study sites to a single value uniformly spaced every 24 h apart, information is lost, which could induce errors when predicting shoreline change (e. g. , storm and tidal aliasing effects). This data handling has a larger effect on the storm timescales compared to seasonal and inter-annual timescales (which were generally captured well by the model). Figure  7  shows that sub-weekly timescales are captured to a lesser extent compared to larger timescales (small standard deviation and correlation). Moreover, at these smaller timescales detailed morphodynamics can become critical for beach response, but they are not incorporated in SF-MT, and besides that, the equilibrium concept is at its limit for those small timescales.
• A second-order polynomial was used to detrend the raw shoreline position timeseries to remove timescales which are larger than the length of the dataset. However, it is possible that there is a relation between this second-order polynomial trend and how the beach responds to incoming wave forcing. For example, a strong erosive trend throughout the timeframe of the dataset would mean that the impact of monsoons become less as time progresses. This phenomenon is not implemented in SF-MT.