dataesr/scientific-paragraphs-categorization huggingface dataset:
Site 2 captures the northern rim slope and floor of an ancient ~45 km diameter crater in Arabia Terra. The steeply sloping hillslopes have many slope streaks, believed to represent avalanches of dust  [21] [22] [23]. Many new slope streaks have been observed and they have also been observed to fade  [24, 25]. Their exact trigger is still an open question  [26] [27] [28]. The flatter areas host numerous transverse aeolian ridges. Site 3 comprises a ~55 km diameter crater in Noachis Terra which hosts a dunefield (USGS dune database 0175-546) with active dune gullies  [29] [30] [31]. The CaSSIS image is taken at a time of year when the seasonal frosts are retreating, creating distinctive albedo patterns on the surface [32-34]. These defrosting spots represent areas where dark dust has been deposited on top of the bright seasonal ices (mainly carbon dioxide ice) by CO 2 gas escaping from underneath the ice  [35].
Assessment Methods For validation and quality assessment, we follow the standard image quality metrics, which include PSNR, Mean Structural Similarity Index Metric (MSSIM), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), and Perception-based Image Quality Evaluator (PIQE), using HiRISE and downsampled HiRISE (at 1 m/pixel) as reference/validation dataset. CaSSIS images and SRR results are co-registered with HiRISE using our in-house multi-resolution image co-registration pipeline  [96]  in order to calculate PSNR and MSSIM, and also to compare against the BRISQUE and PIQE scores. These metrics are available via Matlab's "Image Quality Metrics" bundle (https://uk. mathworks. com/help/images/ image-quality. html (accessed on 1 May 2021)) and can be summarised as follows: (1) PSNR: PSNR is derived from the MSE and indicates the ratio of the maximum pixel intensity to the power of the distortion.
Training and Testing Our training dataset included ~1. 8 million pairs of HiRISE (i. e. , HR; at 0. 25 m/pixel) and down-sampled HiRISE (i. e. , LR; at 1 m/pixel) cropped samples. The training HR samples were extracted from 466 unique HiRISE images, containing non-overlapping unique features of Mars (see Figure  4 ), including dunes (Figure  4a ), craters (Figure  4b ), hills (Figure  4c ), layering (Figure  4d ), slopes (Figure  4e ), cones (Figure  4f ), scallops (Figure  4g ), gullies (Figure  4h ), falls (Figure  4i ), deposits (Figure  4j ), rocks (Figure  4k ), chaos (Figure  4l ), and other unique features (a list of these features with associated HiRISE image IDs is provided in Supplementary Materials). Note that all experiments (training and testing), in this work, are performed with a scaling factor of 4x between LR and HR (or SR) images.
Training and Testing Our training dataset included ~1. 8 million pairs of HiRISE (i. e. , HR; at 0. 25m/pixel) and down-sampled HiRISE (i. e. , LR; at 1m/pixel) cropped samples. The training HR samples were extracted from 466 unique HiRISE images, containing non-overlapping unique features of Mars (see Figure  4 ), including dunes (Figure  4a ), craters (Figure  4b ), hills (Figure  4c ), layering (Figure  4d ), slopes (Figure  4e ), cones (Figure  4f ), scallops (Figure  4g ), gullies (Figure  4h ), falls (Figure  4i ), deposits (Figure  4j ), rocks (Figure  4k ), chaos (Figure  4l ), and other unique features (a list of these features with associated HiRISE image IDs is provided in supplementary material). Note that all experiments (training and testing), in this work, are performed with a scaling factor of 4x between LR and HR (or SR) images.
In Experiment-3, the batch size and spatial patch sizes are the same as Experiment-2. We re-use the pre-trained MARSGAN model from Experiment-2 for initialisation for the generator and continue training with the MARSGAN loss in Equation (  12 ), with λ = 5 × 10 -3 , γ = 0. 5, and a higher η = 0. 5 in order to encourage solutions with minimised synthetic artefacts. The initial learning rate is 10 -4 , and halved at 50k, 100k, 300k, and 500k iterations. Standard Adam optimisation  [100]  is used with β 1 = 0. 9 and β 2 = 0. 999. Training and testing are achieved on the latest NVIDIA RTX 3090 GPU. Our testing dataset is a collection of CaSSIS colour images for the Perseverance rover's landing site, and as well as several selected science-oriented scenes introduced in Section 1. 1. Note that our HiRISE training dataset is in greyscale.
The testing and validation datasets for the selected science targets is presented in Table  1. The proposed quantitative assessment is only applied to the results of Jezero Crater (see Section 3. 1). For the science-oriented scenes, only visual qualitative comparisons are given In addition, CaSSIS uses multiple combinations of long-to-short wavelengths to synthesise colour in R-G-B colour space  [101] , e. g. , NIR-PAN-BLU, and empirically speaking, CaSSIS images generally have a better SNR on their longer wavelength channels, i. e. , NIR band (centred at 936. 7 nm), RED band (centred at 836. 2 nm) and PAN band (centred at 675 nm) and have a lower SNR on their shorter wavelength channels, i. e. , BLU band (centred at 499. 9 nm). Figure  6  shows that the BLU band is obviously noisier compared to the NIR and PAN band.
Therefore, running SRR on the R-G-B colour channels separately may provide an opportunity to produce a better SRR result via different treatment on the three channels, e. g. , applying denoising on the BLU channel. However, the issue of the resulting different colour appearance needs to be tackled in a future study. Finally, for validation purposes, we only pick the CaSSIS testing images that has one or more corresponding HiRISE observations. If multiple HiRISE images are available for comparison, the one that was captured with the closest date and/or Solar Longitude (Ls) to the CaSSIS scene is used. Note that none of these validation HiRISE images are used for training (see supplementary material for a list of the training HiRISE image IDs). The testing and validation datasets for the selected science targets is presented in Table  1.
The proposed quantitative assessment is only applied to the results of Jezero Crater (see Section 3. 1). For the science-oriented scenes, only visual qualitative comparisons are given Finally, for validation purposes, we only pick the CaSSIS testing images that has one or more corresponding HiRISE observations. If multiple HiRISE images are available for comparison, the one that was captured with the closest date and/or Solar Longitude (L s ) to the CaSSIS scene is used. Note that none of these validation HiRISE images are used for training (see Supplementary Materials for a list of the training HiRISE image IDs). The testing and validation datasets for the selected science targets is presented in Table  1. The proposed quantitative assessment is only applied to the results of Jezero Crater (see Section 3.
The first experiment (second column of Figure  8 ) refers to the CaSSIS SRR processing with the ESRGAN network that was trained with HiRISE images. The second experiment (third column of Figure  8 ) refers to the CaSSIS SRR processing with the proposed MARSGAN network that was trained with the same HiRISE training dataset (but optimised with the ESRGAN' loss function and with a smaller patch size for faster convergence; hereafter referred to as MARSGAN-m1). The third experiment (fourth column of Figure  8 ) refers to the CaSSIS SRR processing with our proposed MARSGAN network with our rebalanced loss function (hereafter referred to as MARSGAN-m2). For more details of the three experiments, please refer to Section 2. 4. 2 ). Figure  8  shows zoom-in views of the 8 selected areas for detailed comparison.
Extendability with other datasets This paper focuses on SRR processing of the TGO CaSSIS images. However, it should be pointed out that the proposed MARSGAN model can also be applied to other extrahigh resolution, e. g. , 0. 25m HiRISE, or medium-to-high resolution, e. g. , 6m CTX and 18m Compact Reconnaissance Imaging Spectrometer (CRISM), Mars imaging datasets.
Extendability with Other DatasetsThis paper focuses on SRR processing of the TGO CaSSIS images. However, it should be pointed out that the proposed MARSGAN model can also be applied to other extrahigh resolution, e. g. , 0. 25 m HiRISE, or medium-to-high resolution, e. g. , 6 m CTX and 18m Compact Reconnaissance Imaging Spectrometer (CRISM), Mars imaging datasets.
shows examples of the MARSGAN SRR result, in comparison to the original CTX image (rectJ21_052811_1983_XN_18N282W_v7pt1_6m_Eqc_latTs0_lon0; https: //planetarymaps. usgs. gov/mosaic/mars2020_trn/CTX/ (accessed on 1 May 2021)) over Jezero crater. Figure22shows an example of the MARSGAN SRR result, in comparison to the original CRISM image (using bands 233, 78, and 13 of frt0000d3a4_07_if164l_trr3_raw downloaded from PlanetServer at http://planetserver. eu/ (accessed on 1 May 2021)), over Capri Chaos, Valles Marineris. In the future, with cross-instrument training (using different datasets with different resolutions to form the LR/HR training dataset), further improvement of the MARSGAN model can be expected.
Acknowledgments: The research leading to these results is receiving funding from the  UKSA Aurora programme  ( 2018-2021 ) under grant  ST/S001891/1 , as well as partial funding from the  STFC MSSL  Consolidated Grant  ST/K000977/1. S. J. C. is grateful to the  French Space Agency CNES  for supporting her CaSSIS and HiRISE related work. CaSSIS is a project of the  University of Bern  and funded through the  Swiss Space Office via ESA 's  PRODEX programme. The instrument hardware development was also supported by the  Italian Space Agency (ASI) (ASI-INAF  agreement no. 2020-17-HH. 0 ), INAF/Astronomical Observatory of Padova , and the  Space Research Center (CBK) in Warsaw. Support from SGF (Budapest) , the  University of Arizona (Lunar and Planetary Lab. ) and  NASA  are also gratefully acknowledged.
The research leading to these results is receiving funding from the  UKSA Aurora programme  ( 2018-2021 ) under grant no. ST/S001891/1  as well as partial funding from the  STFC MSSL  Consolidated Grant  ST/K000977/1. S. J. C. is grateful to the  French Space Agency CNES  for supporting her CaSSIS and HiRISE related work. Institutional Review Board Statement: Not applicable.
4. 3. 3 Extendability with Other DatasetsThis paper focuses on SRR processing of the TGO CaSSIS images. However, it should be pointed out that the proposed MARSGAN model can also be applied to other extrahigh resolution, e. g. , 0. 25 m HiRISE, or medium-to-high resolution, e. g. , 6 m CTX and 18m Compact Reconnaissance Imaging Spectrometer (CRISM), Mars imaging datasets.
Point simulations allow more in depth analysis than a complete regional simulation, in order to inform future regional simulations using this sequential cropping method. The locations of the selected points are shown on a map of the surface altitude for South Asia in Fig. 2    JULES is run using a 3-hourly timestep using driving data from ERA-interim  (Dee et al. , 2011; Simmons et al. , 2007)  downscaled to 25 km using the HadRM3 regional climate model (RCM-Jones et al. (  2004 )). This RCM simulation is one of an ensemble of simulations produced for the EU-HighNoon FP7 project for the whole of the Indian subcontinent (25 N, 79 E-32 N, 88 E) for the period 1991-2007. The HighNoon simulations are described in detail in previous publications such as  Kumar et al. (2013)  and  Mathison et al.
(2013 Mathison et al. ( , 2015)). HadRM3 provides more regional detail to the global data with lateral atmospheric boundary conditions updated 3-hourly and interpolated to a 150 s timestep. These simulations include a detailed representation of the land surface in the form of version 2. 2 of the Met Office Surface Exchange Scheme (MOSESv2. 2; Essery et al. (2001) ). JULES has been developed from the MOSESv2. 2 land surface scheme and therefore the treatment of different surface types is consistent between the RCM and JULES  (Essery et al. , 2001; Mathison et al. , 2015). Sowing dates are prescribed using climatologies calculated from the observed dataset,  Bodh et al. (2015) , from the government of India, Ministry of Agriculture and Farmers welfare. Thermal times are calculated using these climatological sowing and harvest dates from  Bodh et al.
India observations Crop yield observations from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT, 2015)) provides seasonal yields for each crop for comparison with the point simulations. We also show average crop yield observations from  Ray et al. (2012a)  for three, 5-year periods between  1993 and 2007 (1993-1997, 1997-2003, 2003-2007)   period of the point simulations which are from 1991-2007. We show both of these datasets to highlight that there is a range in the estimates of yield for this region. 6 Results.
In order to validate this model, its simulation results are compared with the ones obtained from Esmeralda, the SNCF professional software. A first configuration is led without the inverter and gives certified Esmeralda results and validates the proposed model despite some gaps in powers and voltages due to differences in input data and models. A second comparison with inverter is presented to highlight the main difference between the proposed model and Esmeralda. In addition, laboratory experimental activities are put forward to investigate the proposed model by using power-hardware-in-the-loop simulations. Finally, a simulation test under MATLAB software with fifty train's traffic is presented to estimate the energy saving thanks to the installed inverter. For this latter case study, the system sent back to the national AC grid around 6.
Ref  [17]  illustrates the performance of a DC electric rail transit system model, which includes trains, substations, rail systems and braking regenerative system. In this context, this paper presents a detailed dynamic model for complete railway electrical network including an AC/DC bidirectional substation based on a power converter in order to increase the accuracy of the results from the simulation. The using of the modern solutions based on power electronics offers attractive performance. The valuation of the developed model is carried out using Esmeralda software and experimental implementation using power-hardware-in-the-loop simulations. Esmeralda is a calculation software developed by SNCF for its own needs. It simulates rail dynamics by taking into consideration the characteristics of trains as well as the electrical network structure.
A precise model of the power railway system is needed in order to investigate the optimization of power flows in the power system. This paper presents the modeling of the railway power network involving the reversible "Masséna" substation and its two neighboring substations "Les Ardoines" and "Quai de la Gare". The paper is organized as follows: A description of the studied electrical railway network is presented in Section 2. The proposed model with the inverter added in "Masséna" substation is detailed in Section 3. Simulation results of the proposed model are presented in Section 4 which is divided in three subsections; the first one shows a validation and comparison of the results obtained by the proposed model with the ones obtained by Esmeralda software. The second subsection provides results of real-time simulation of the proposed model using Power-Hardware-In-the-Loop concept with a real laboratory power scaled inverter.
Implementation and simulation results The model of the railway network and the SNCF rolling stock presented in Section 3 is implemented in MATLAB/using functional blocks of Simscape for the train, substations, rails and catenary models. The Masséna inverter is modeled using Sim power system toolbox. The traffic input data of the simulation gives information about circulated trains such as, speed, position, acceleration for each step time. These input data are pretreated using MATLAB script in order to sort trains data by track and portion. A traditional train speed profile is used for all cases presented in this section. It starts with an acceleration traction phase. The train pulls at a certain acceleration until it reaches the speed limit (120 km/h). Once the speed limit is reached, it goes into maintain constant traction phase (cruising phase), which allows it to maintain this speed for a certain period.
The next step is the coasting phase, which fixes at zero the traction force. Finally, the braking phase, such as the train brakes with a generally constant deceleration until its complete stop. The simulation results of the proposed model are presented in the next three subsections. In the first subsection, two cases are considered; the first case aims to validate the studied model results, without Masséna inverter, with the ones obtained by the actual professional software Esmeralda. This software is used by SNCF in the conception phase of the railway system in order to choose the best location and power size of new substations and other infrastructure hardware. It assesses the technical and economical performances of the railway power network and evaluates the load demand for the national transmission grid.
Comparison with the professional railway software esmeralda Since Esmeralda it is a professional software and plays an important role and its results have been compared and validated with experiments tests, it can therefore serve as a basis for validating the model presented in this study. This validation will be performed on simulations with the recovery inverter. The same traffic scenario is adopted for both study cases in which the simulation results from Esmeralda and MATLAB are compared. In this scenario, the line portion taken in consideration is located between P k = 0 km and P k = 7 km. The traffic consists of two trains running in opposite direction on separate tracks. Fig. 10  presents the speed of each train for the proposed model and Esmeralda software. The first train starts its trajectory at t = 0 s from "Quai de la Gare" to "Les Ardoines" and the second train starts its trajectory at = 150 s from "Les Ardoines" to "Quai de la Gare".
As depicted in Fig. 10 , the slope can be considered as zero on this line, which knows very little difference in level. The power consumption of the railway station "Bibliothèque Francois Mitterrand" is neglected. The trains are following the same driving sequence but with a different kinematic. At the end of the acceleration phase, trains speeds in Esmeralda simulation are 5 s in advance in comparison with the ones obtained by MATLAB. This difference has a very limited effect on the train position (Fig. 11 ) and is due to the computational steps. Indeed, the Esmeralda's time step is derived from a fixed spatial data and is  consequently variable, whereas the step time size used for MATLAB simulations is fixed. As the goal is to perform a validation of the model presented in this paper, voltages and powers of trains and substations without inverter are compared.
First, the trains' power is addressed. In Fig. 12 , the speed difference during acceleration found in Fig. 10  induces a 5 s delay in the train power. The braking sequences indicate also a braking lag. The electrical braking in Esmeralda is not instantaneous. It takes 3 s before being effective. This lag models the first pneumatic braking reaction, which is not taken into account in the model on MATLAB. The delays and time lags reverberate automatically on the other power and voltage results (Figs. 13 14 15 16 ). The speed difference creates also a discrepancy on trains' power (see Fig. 12  and Table  2 ). This discrepancy influences substations results and trains' voltage in a less obvious manner that will be explained in the next paragraphs. The voltage of train running on track 1 is investigated.
Because Esmeralda train consumes more energy at the end of acceleration, its voltage remains lower than MATLAB (Table  2  and Fig. 13 ). However, at 52 s, the train on track 1 in both Esmeralda and MATLAB is exactly located at "Quai de la Gare" substation (Fig. 11 ). Their voltages are then equal to the substation voltage (Fig. 13 ). Regarding trains on track 2, the biggest voltage difference is 42 V at 222 s (Table  2  and Fig. 14 ). The power discrepancy effect.
In Esmeralda, trains produced extra braking power which is completely recovered by the inverter (excepted for catenary losses) (Fig. 17 ). The recovered power reaches a 2 MW peak and the energy is estimated to 17 kWh. Because of the strategy implemented in Esmeralda, the inverter voltage is set and kept steady above the no load voltage during braking sequences (Fig. 18 ). The braking train voltage (Fig. 19 ) is calculated thanks to the inverter voltage. The braking power delivered is a consequence of the train's voltage obtained. With the implemented model in MATLAB environment, the trains, also, produce braking energy which is pathed towards the inverter energy (Fig. 20 ). The extra braking energy is about 8. 5 kWh with a 1 MW peak power. The recovered energy for the proposed model is less important with the simulation on Esmeralda because with the latter, the inverter was modeled as a storage system able to receive energy and the inverter voltage is set and kept steady above the no-load voltage during braking sequences.
21 ). For this study, the railway section presented in Fig. 1  is simulated in real-time by using a dSPACE 1103 platform with a sample time of 400 µs. The 1 MW reversible converter is replaced by a laboratory Semikron converter based on SKM50GB123D IGBT module. The nominal power of the used converter is considered to be 1 kW. The control is based on the same droop characteristic used for the control of the onsite 1 MW inverter with considered gains of voltage and current. This converter is virtually connected to the simulated system by using a programmable DC voltage source TDK Lambda (GEN-600-5. 5-3P400). The reference of the DC source is obtained from the DC voltage measurement of the simulated "Masséna" substation after being multiplied by a voltage adaptation gain 1/G v. The DC current measured in the real converter is sent back to the simulated system and multiplied by a current gain G i then, it is injected in the modeled "Masséna" substation by using a controlled current source (Figs.
Comparison between offline simulation and real-time simulation For sake of simplicity, one train is running on the considered section of the railway system with a basic speed profile presented in Fig. 23. According to this profile, the train starts its trajectory in "Quai de la Gare" substation and stops at "Les Ardoines" substation. The train power measurement shows a good fit between MATLAB simulation and RTS simulation in traction phase. However, the recovered power during braking shows a difference of 143 kW (Fig. 24 ). This difference   is originated from the DC voltage difference found on the train voltage and "Masséna" substation voltage measurements (Fig. 25 , Fig. 26 ). This could be explained by the considered simplification for the railway system model used for the real-time simulation in terms of the length of the considered section and the substations.
Conclusion This paper proposes a modeling of a DC power railway network including a power station, which was made reversible by adding a controllable inverter. The modeling is based on AC and DC equivalent electrical sources for trains, reversible and non-reversible substations. The model without inverter is compared to the model, which is used by the professional SNCF software Esmeralda. This software, designed for sizing new substations, gives certified and reliable results. The comparison aforementioned allows to validate the model without inverter despite some gaps in voltage, power and current results. A second comparison with the inverter was also carried on. This comparison results in large differences in the power recovered with the inverter which, was twice smaller with the modeling presented in this paper.