dataesr/scientific-paragraphs-categorization huggingface dataset:
The same workflow can be used for other diagnosis applications. However, in addition to the parameters represented on Fig. 9 , one should also optimize the computational time, and the storage capacity for embedded applications. The final objective is to obtain the most accurate and universal diagnosis tool (valid for the highest number of situations in a vehicle). interests: This work has been supported by Symbio, a Faurecia Michelin hydrogen company. This work has also been supported by the EIPHI Graduate School (contract ANR-17-EURE-0002) and the Region Bourgogne Franche-Comté.
Acknowledgments We would like to thank  Luc Leydier  ( Symbio ) for having taken the time to read through this paper and provide his comments. This work has been supported by  Symbio , a  Faurecia Michelin hydrogen company, France. This work has also been supported by the  EIPHI Graduate School  (contract  ANR-17-EURE-0002 ) and the  Region Bourgogne Franche-Comté.
Abstract. The freshwater 1-D FLake lake model was coupled to the ORCHIDEE land surface model to simulate lake energy balance at the global scale. A multi-tile approach has been chosen to allow the modeling of various types of lakes within the ORCHIDEE grid cell. Thus, three different lake tiles have been defined according to lake depth which is the most influential parameter of FLake, but other properties could be considered in the future. Several depth parameterization strategies have been compared, differing by the way to aggregate the depth of the subgrid lakes, i. e. , arithmetical, geometrical, harmonical mean and median. Five atmospheric reanalysis datasets available at 0. 5 • or 0. 25 • resolution have been used to force the model and assess model systematic errors. Simulations have been performed, evaluated and intercompared against observations of lake water surface temperatures provided by the GloboLakes database over about 1000 lakes and ice phenology derived from the Global Lake and River Ice Phenology database.
Lake area and depth The HydroLAKES database  (Messager et al. , 2016)  has been used to characterize lake fractions and depths. The dataset provides georeferenced polygons for 1. 4 million lakes of size larger than 0. 1 km 2. A mean depth is provided for each water body, estimated either from in situ measurements or from geomorphological considerations allowing assessment of lake shape from which depth is derived. Additional attributes include estimates of the shoreline length, water volume and residence time, but they were not used in this work. Moreover, all lakes are co-registered to the global river network of the HydroSHEDS database used in ORCHIDEE to describe the river system and constrain the water routing scheme. This specificity will then insure the coherence for the next developments around the modeling of the water budgets and the lake-river interactions.
Lake surface temperatures The Global Observatory of Lake Responses to Environmental Change (GloboLakes) database is used to evaluate our simulations. This dataset has been developed thanks to a project funded by the UK Natural Environment Research Council in the continuity of the ARC-Lake database  (Mac-Callum and Merchant, 2012)  funded by the European Space Agency. It provides daily observations of lake surface water temperature (LSWT) for about 1000 lakes distributed all around the world, with uncertainties and quality level. The observations cover the period 1995-2016 with a spatial resolution of 0. 05 • and combine different instruments such as the AVHRR (Advanced Very High Resolution Radiometer) on MetOpA, AATSR (Advanced Along Track Scanning Radiometer) on Envisat and ATSR-1/2 (Along Track Scanning Radiometer) on ERS-1/2 (European Remote Sensing Satellite) covering different periods.
The time series from ATSR-2 are available for the period 1995-2002, from AATSR for the period 2002-2010 and from AVHRR for the period 2007-2016. A common processing methodology as well as the fact that all the satellites have a quasi polar orbit leading to a constant local solar time allowed a time homogeneity in the measurements and a homogeneous resulting dataset  (Carrea and Merchant, 2019). Given the lack and lower quality of the data before 2002 due to the older instruments, we used the reduced period 2000-2016 for the model evaluation. For the whole period between 1995 and 2009 and for the previous version of the database ARC-Lake, the product uncertainty estimated by  Carrea and Merchant (2019)  is less than 1 • C. The comparison against local measurements shows that errors are slightly larger for daytime measurements (0.
Lake ice phenology The Global Lake and River Ice Phenology Database is used to validate the lake freezing processes. This dataset provides the start, end and duration of the freezing period for 857 lakes located in the northern latitudes. The dataset is based on in situ measurements, which are sometimes discontinuous and acquired over different periods, and reports the dates of full ice coverage. The data are available for some lakes back to the year 1850 updated to 2020  (Benson et al. , 2000). The data can be downloaded at https://doi. org/10. 7265/N5W66HP8.
Atmospheric forcings Different atmospheric reanalyses were used in this work to force the ORCHIDEE model and assess the model sensitivity to meteorological forcing uncertainties. These reanalysis differ by the model and data used to constrain the atmospheric fields, differences that may introduce some biases. Here, we used five different datasets available at three different spatial resolutions: 31 km, 0. 25 and 0. 5 • and three different temporal resolutions, hourly, 3-hourly and 6-hourly. Then, for the simulations performed at 0. 5 • resolution, we used the following forcing datasets: (a) WFDEI-CRU, a 3-hourly 0. 5 • global forcing product which is a combination of the ERA-Interim reanalysis and the Climate Research Unit (CRU)-TS 3. 1 climatological data  (Harris et al. , 2014)  presented by  Weedon et al.
(2011) ; (b) CRUJRA, a 6hourly 0. 5 • global forcing product, which is based on JRA-55 reanalysis  (Kobayashi et al. , 2015)  corrected by CRU-TS 3. 2  (Harris et al. , 2014) ; and finally (c) CRUNCEP, a 6-hourly 0. 5 • global forcing product, which is a combination of two datasets, the CRU-TS 3. 2 and the National Center for Environmental Prediction (NCEP) reanalysis product described by  Viovy (2018). ERA5  (Hersbach et al. , 2020) , an hourly global forcing product at a resolution of 31 km, and E2OFD  (Beck et al. , 2017) , a 3-hourly 0. 25 • global forcing product complete the selection. All the data have been formatted by the ORCHIDEE team to be used and interpolated at the half-hourly time step, following the methodology presented in  Wei et al. (2014)  and summarized in https://forge.
ORCHIDEE land surface model ORCHIDEE is the land surface component of the IPSL ESM. It simulates the surface energy balance and the water, carbon and nitrogen cycles for large-scale and climate applications. The model is composed of two main modules named SECHIBA and STOMATE. SECHIBA resolves the thermal and hydrological processes at a half-hourly time step and computes the key surface variables and fluxes such as the soil temperatures  (Wang et al. , 2016)  and water content  (de Rosnay, 2003)  with an 11-layer discretization, the evolution of the snow cover with an explicit 3-layer representation  (Wang et al. , 2013) , and the photosynthesis of the vegetation  (Vuichard et al. , 2019). The second module STOMATE computes with a daily time step and the fully coupled carbon and nitrogen dynamics and therefore calculates key processes such as the vegetation respiration, the soil carbon dynamics, the litter decomposition and the vegetation phenology, constrained by the nutrients availability  (Vuichard et al.
Integrating FLake into ORCHIDEE The FLake model has been implemented in the ORCHIDEE model, following the same approach proposed by Salgado and Le Moigne (2010), i. e. , a multi-tile approach where separate energy budgets are computed for the vegetation and the lake respective fractions within a grid cell. The developments were done in the SECHIBA routine driving the resolution of the water and energy surface budgets. All the tiles shared the same atmospheric forcing data and exchange fluxes with the atmosphere according to their respective surface area. The number of lake tiles within a grid cell is variable and can be defined by the user, in order to allow the simulation of different types of water bodies which could present different properties such as depth or radiative properties. Figure  1  presents a general scheme of the ORCHIDEE-FLake coupled model.
Lake surface area and depth The HydroLAKES database  (Messager et al. , 2016)  was analyzed to parameterize lake surface area and depth in OR-CHIDEE. In the study of  Bernus et al. (2021) , three categories were defined according to depth ranges: shallow lakes with depth below 5 m, deep lakes with depth larger than 25 m and the intermediate class in between (depth range between 5 and 25 m). Figure  2  shows the lake surface fractions (in logarithmic scale), plotted at 0. 5 • resolution, for the three categories of lake depths and aggregated in a single class. The median values of the corresponding depths are shown in the same figure. The higher amount of lake surfaces in the high northern latitudes, mainly in northern Canada, Scandinavia and western Siberia, are clearly highlighted. The lake distribution shows a minimum at the latitudes between 20 • N and -30 • S, in coherence with the arid climates found at these latitudes, produced by the descending branch of the Hadley circulation.
Performed experiments ORCHIDEE-FLake was run at global scale forced by the various atmospheric datasets presented in Sect. 2. 1. 5 and for two different configurations of lake tiling: the first one when all the lakes are grouped in a single class and the second one, where lakes are grouped in three classes according to lake depth. A control simulation corresponding to the standard ORCHIDEE configuration without lakes (i. e. , considered as bare soils) and using only one atmospheric forcing (CRUJRA) was also performed. Table  1  presents the list of all the simulations performed, the atmospheric forcing used and its spatial resolution, the number of lake categories, and the way the depth was set for each class (median depth value of all the lakes within the tile, arithmetic, quadratic or harmonic averages).
LSWT The simulated LSWT values obtained in CJR3med configuration have been compared to the observations provided by GloboLakes for the 979 lakes matching ORCHIDEE simulated lake tiles. We recall that CJR3med corresponds to the simulation performed with the meteorological forcing CRU-JRA and prescribing three lake tiles differing by their depth (shallow, intermediate and deep lakes). The comparison was done with the lake tile corresponding to the depth category of the observed lake. The RMSE calculated on the 17-year period  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016)  are plotted in Fig. 3  along with latitudinal and zonal averages. Each dot corresponds to a lake of the GloboLakes dataset. Figure  3  shows that the observations are well distributed globally and sampled a large variety of climates and continents.
Ice phenology Freezing processes were evaluated against the data provided by the Global Lake and River Ice Phenology database over the lakes observed in the northern latitudes. Since the observations provide the dates of the full ice end, start and duration of a complete ice coverage, we compared with the same simulated dates and did not consider ice-free periods (if there were any) within this interval. In Table  2 , the mean, quartiles and median of the differences between simulated and observed three stages of the ice phenology are given in days for the CJR3med simulation. The results show that the ice start errors are all negative and the ice end errors are all positive. That means that the lakes are generally freezing earlier and melting later in the model compared to the observations.
Discussion and conclusion This work presents how lake energy balance is now represented in the ORCHIDEE land surface model, thanks to the FLake lake model. The methodology and the model evaluation at global scale were presented. Several simulations were performed to evaluate the model sensitivity to the meteorological forcing and to the depth parameterization of the lake tile. The evaluation of LSWT and ice phenology against the GloboLakes and Global Lake and River Ice Phenology databases showed that the lake surface temperature is quite well simulated in this new ORCHIDEE version. Compared to the observations, LSWT errors range between 2 and 4 • C, even if in some few cases, the errors can reach 8 • C. These results are comparable to what was obtained by previous works, such as that of  Dutra et al.
Atmospheric forcing uncertainties A large part of the LSWT errors is explained by the atmospheric forcing uncertainties. We have examined in more detail the lakes which present the largest RMSEs. Among the 969 lakes studied, only 15 present large RMSEs, larger than 6 • C. For those lakes, we observed strong biases in the atmospheric temperatures and downwelling longwave radiation (whatever the reanalysis product), which explain the largest part of the error. If we consider the whole dataset, our results show the large sensitivity of LSWT to the atmospheric conditions and the large uncertainties of the reanalysis products. When looking to the RMSE decomposition, biases were explaining more than 50 % of the error for more than 50 % of lakes. It was particularly the case for the tropical lakes where the LSWT seasonal amplitude is lower than for higherlatitude conditions.
The model sensitivity to the atmospheric conditions is also very strong during the intermediate seasons for the lakes which are subject to freezing. Because of the non-linearity of these processes, a small bias in the air temperature can completely change the temperature evolution of the lake and greatly influence the seasonal pattern and the ice phenology. The large sensitivity of the ice start and ice end times to the atmospheric forcing shown in our simulations is a good demonstration of this effect. Therefore, it appears very necessary to use various atmospheric forcing products in the model evaluation, since they all contain biases, which on top of that, present regional features. These biases are the result of the different atmospheric models and datasets used in the generation process, which consists of merging model simulations and multiscale observations through data assimilation techniques.
It should be noted that the land surface models used in this process do not account for lakes except for the ERA5 forcing and are constrained by static land cover maps which are not updated regularly. Biases are also linked to the spatial and time resolution of the datasets. It has been shown that LSWT errors were smaller when the two high-resolution forcings are used (E2OFD and ERA5). Increasing the spatial resolution improved LSWT RMSEs by 0. 5 • C. Grid resolution is indeed an important parameter for the modeling of land surfaces which have very different functioning than the rest of the environment. Moreover, the atmospheric conditions above a lake surface are different than in the surroundings, complicating greatly the model evaluation at global scale. The use of various reanalysis products is, therefore, the only way to separate forcing and model errors.
Scaling issues may explain also part of the errors: FLake is a 1-D model and therefore does not consider the spatial variability of the within lake thermal processes and properties, nor the temporal variability of the parameters. In particular, the lake depth is a constant parameter in FLake, whereas it is clear that in reality, it varies spatially and temporally, and so do LSWT and ice phenology. Lakes, especially those subject to anthropogenic management as reservoirs, see their surface and water level changing throughout the year. The averaged depth and surface area referenced in the databases do not report these changes. For example, Lake Chad in Hydro-LAKES has a surface area doubled compared to the current value, and the average depth is probably erroneous as well. As well as this, if we consider the ice observations, the time of ice start refers to a complete ice coverage, whereas it can take some days before the lake is completely frozen.
This assumption may also impact the model performances. Recent databases can be used to solve this issue, based on satellite measurements. Albedo products could be used to spatialize the albedo parameters. The extinction coefficients are also known to be key  (Heiskanen et al. , 2015). For example,  Zolfaghari et al. (2017)  demonstrated the improvements brought by constraining the extinction coefficient of Lake Superior using FLake, based on remote sensing observations. Missing processes could also explain part of the errors such as the impact of water salinity, which could present seasonal and interannual variations linked to human activities and water level changes. In our evaluation, among the 15 lakes presenting the largest errors, three of them are saline lakes (Issyk-Kul, Urmia and Natron) with high salinity levels, and which can present salt crust regularly  (Zavialov et al.
Future work Our results have shown the potential of ORCHIDEE-FLake coupled model to represent the energy balance of lakes, the surface temperature and fluxes at the atmosphere interface. The model performances are satisfactory, and the various sources of errors are well identified. The flexibility of our developments allows a variable number of lake tiles within a model grid cell to be prescribed and opens new perspectives for improvement. First, we intend to study the spatial and temporal variability of the lake radiative parameters and to implement them in the model. Data from high spatial resolution optical sensors such as Sentinel-2 with a 10 m resolution are very promising, and some products have recently came out such as that shown in  Wang et al. (2020)  for the lakes of the Tibetan plateau, or  Zolfaghari et al.
Acknowledgements. This work was supported by the  French National Space Agency (Center National d'Etudes Spatiales)  through the  TOSCA-SPAWET program  in preparation to the  SWOT space mission. CEA (Commissariat à l'Energie Atomique  et aux énergies alternatives) is also acknowledged for funding part of  Anthony Bernus PhD  grant. The authors would like to thank  Karine Pétrus  for her preliminary works with the FLake model in our team and  Fabienne Maignan  for her valuable advice on coding issues. Financial support. This research has been supported by the  Centre National d'Etudes Spatiales  ( TOSCA 2017  grant). Review statement. This paper was edited by  Charles Onyutha  and reviewed by five anonymous referees.
Code and data availability. The ORCHIDEE version developed for this project is available at https://doi. org/10. 5281/ zenodo. 6383273 (Bernus and Ottlé, 2022). The observed data and meteorological forcing used in this study are openly accessible online. The HydroLAKES database can be downloaded at https://hydrosheds. org/page/hydrolakes (Messager  et al. , 2016). The Global Observatory of Lake Responses to Environmental Change (GloboLakes) can be found at https://doi. org/10. 5285/76a29c5b55204b66a40308fc2ba9cdb3 (Carrea and Merchant, 2019)  and the Global Lake and River Ice Phenology Database at https://doi. org/10. 7265/N5W66HP8 (Benson et al. , 2000).
This paper provides a detailed tutorial to develop a sandbox to emulate coexistence scenarios for road and railway services in terms of sharing telecommunication infrastructure using software-defined network (SDN) capabilities. This paper provides detailed instructions for the creation of network topology using Mininet-WiFi that can mimic real-life coexistence scenarios between railways and roads. The network elements are programmed and controlled by the ONOS SDN controller. The developed SDN application can differentiate the data traffic from railways and roads. Data traffic differentiation is carried out using a VLAN tagging mechanism. Further, it also provides comprehensive information about the different tools that are used to generate the data traffic that can emulate messaging, video streaming, and critical data transmission of railway and road domains.
The initial steps of this research are to find out the tools and technology that can be used to develop and emulate the different scenarios for railway and road coexistence. In addition, the primary objective of this paper is to provide a detailed tutorial to develop an SDN-based application using an ONOS SDN controller that should have the potential to manage the network handover and differentiate the data traffic from railways and roads. To differentiate the data traffic, SDN-based slicing is used. Over a common infrastructure, network slicing creates multiple end-to-end virtual networks. These virtual networks are logically isolated from each other and can be customized to serve different types of services with different requirements. These are the considered objectives of Work Package 6 of the 5GRAIL project  [7].
Information about the selected tools is given in Section 4. The setup description to carry out the test is presented in Section 5. Section 6 demonstrates network topology creation using Mininet-WiFi. The selected tools for data traffic generation are presented in Section 7. Section 8 elaborates on the developed ONOS SDN slicing application to differentiate the data traffic based on VLAN tagging. Validation of the developed SDN application and demonstration of selected tools are given in Section 9. SUMO integration for train and car mobility traffic generation and visualization purposes is presented in Section 10. The conclusion of this practical work is given in Section 11.
Related Work In this section, some previously executed research and findings are given. In  [9] , the authors Mininet-WiFi to compare flow-based monitoring mechanisms for the Internet of Vehicles (IoV) environment. Performance comparison of open-source SDN controllers is presented in  [10]  to investigate the end-to-end delay for SDN-based vehicular networks. The authors in  [11]  developed a network prototype to define a car as a node with Mininet-WiFi to emulate the vehicular networks. The authors in  [8]  demonstrated the emulation of software-defined wireless networks using Mininet-WiFi. An emulation framework is presented using Mininet-WiFi in  [12]  for connected autonomous vehicles. A detailed survey for connected vehicles is presented in  [13]. In the paper  [14] , the authors investigated the handover mechanism for communication at the intersection of the coverage range of the cells for vehicular networks.
They introduced multiple-access edge computing (MEC) with a roadside unit (RSU) based on SDN to improve the handover process and curtail the handover time. In paper  [15] , the authors used a simulation tool known as Mininet-IoT with a Ryu-SDN controller to create a vehicular ad hoc network (VANET). They also introduced a new interface based on the global positioning system (GPS) for cars to establish communication between cars when they are not in the coverage range of access points. This proposed system has better throughput and lower delay compared to WiFi-VANET. In  [16] , the authors emulated the 5G-NR for the vehicular network using Mininet-WiFi-Containernet and SUMO tools. From the research mentioned above, it can be observed that narrow research or emulation work has been carried out in the field related to vehicular networks based on software-defined networks.