dataesr/scientific-paragraphs-categorization huggingface dataset:
After this decoding phase, useful information is filtered and then saved into a Commaseparated values (CSV) file and MongoDB database for later use. The embedded component contains also a user interface for real-time visualization of vehicle information, such as the actual speed, driving mode, position on the map, and also predicted speed. More precisely, the collected dataset is composed of the following information: current vehicle speed, driving mode, battery SOC, GPS coordinates, weather conditions, speed limit, and road traffic. It is worth noting that the vehicle speed, SOC, and GPS are collected directly from the vehicle using the CAN Bus protocol, or from the GPS module, which is connected to the in-vehicle device. As for the weather data, the Open Weather Map Application Programming Interface (API) is used.
Each minute, the program, deployed in the embedded component, sends a Hypertext Transfer Protocol (HTTP) request to the API and retrieves weather information, which is related to the current location of the vehicle. Likewise, TomTom API is used to collect the traffic and speed limit data in each road segment. However, as the TomTom API needs very precise GPS coordinates, the speed limit is, therefore, not available in some road segments due to the low precision of the used GPS module. To overcome this problem, the speed limits on those segments are introduced manually by the driver using the given user interface, which we have mainly developed for that purpose. The in-vehicle embedded device collects data and stores it in real-time, however, since the collected data does not change during a certain amount of time, two main collection steps have been used: five seconds and ten seconds.
The choice of these two collection steps is done upon some observations of the collected data as well as some factors, which are related to the used hardware, such as refresh time and communication delays. A collection's step, which is less than five seconds, resulted in having frequent redundant and constant values of some factors, such as GPS coordinates. However, a collection step greater than ten seconds resulted in a lack of some useful information, such as speed values, specifically during acceleration and deceleration. This led to a big difference between two successive speed values, which, however, affect the prediction accuracy. Therefore, collect step between five and ten seconds is likely to be fit for our case study. The application deployed into the NVIDIA Jetson nano collects the data and saves two types of datasets, one every five seconds and another every ten seconds.
B. DATA PREPROCESSING During the data collection process, some errors may occur, mainly missing values, unwanted values, or other errors. Therefore, the collected data need to be first preprocessed to be used for models' training. Data collection is performed, as mentioned above, in the NVIDIA Jetson nano, using a CAN bus transceiver with a time-step equal to one (TM1), five (TM5), and ten (TM10) seconds. The transceiver receives many messages in one second. In our study, we were only interested in a few messages, however, filtering those messages may take more than one second. Furthermore, some invehicle sensors emit messages with an interval greater than one second. Moreover, the CAN messages are in hexadecimal format. Some values, such as vehicle driving mode, are coded in one bit, and other values are coded in two or more bits.
However, we do not have all the instructions to convert, from hexadecimal to decimal, all the values in each message. Thus, strange values are sometimes received; those values are ignored and replaced with previous ones. This leads to missing values in the dataset. To overcome this problem, two methods are used. Regarding TM5 and TM10, for any given second, if a missing or strange value is received, the value of the last/next second is used as a substitute. However, in the case of TM1, we assume that during one second the value of the sensor will not change very often, and, therefore, the missing value is replaced with the value, which was collected in the previous second. After this data preprocessing step, another process was launched for preparing the data. This process prepares data for being used in the training process.
C. MODEL TRAINING AND TESTING After cleaning and preparing the collected data, we split it into a training dataset and a testing dataset. In literature, there are many prediction methods, which could be applied, using common computing hardware, for speed forecasting. However, the model training process is mainly performed on the in-vehicle embedded system. For our real testbed, selecting a prediction method is constrained by its suitability for being deployed in an in-vehicle embedded device, such as a low computing power embedded board. Hence, the selected method should manage the trade-off between forecast accuracy and computing time, which is needed for predicting new values and for retraining the forecasting model as well. For this purpose, we have selected four methods respectively, each from a forecasting methods category  [4] : LSTM, ARIMA, CNN, and ConvLSTM.
A. SET-UP AND PERFORMANCE METRICS It is well known that the accuracy and performance of each forecasting model depend mainly on its parameters (hyperparameters, lags, coefficients). Therefore, before models' training, it is mandatory to fine tune them using suitable parameters. Unfortunately, this process seems to be time consuming, as it requires testing different combinations in order to find the best parameters. We used the Keras python library for LSTM, CNN and ConvLSTM and the statemodels python library for ARIMA. The models are presented as follows: • ARIMA: the values of the parameters p, d and q are: 1,0,1 respectively. • LSTM: A model with three layers is used. The first layer is an LSTM layer with 16 neurons, and the second and third layers are both dense layers. The model is trained using 500 epochs.
• CNN: the model is composed of five layers. The first one is a convolution layer with 64 filters and a kernel size of 2. The second layer is a max pooling layer with a pool size of 2. Then the output of the second layer is flattened and passed to a dense layer. • ConvLSTM: The model used for ConvLSTM is an encoder/decoder model. The encoder is the ConvLSTM with 64 filters and a kernel size of (1,3) to extract the Spatio-temporal data from the dataset. Then the output of the encoder is fed to the LSTM layer, which plays the role of a decoder. The model is trained using 200 epochs. It is worth noting that different epoch sizes were tested and the best ones are selected to avoid underfitting or overfitting phenomena. The activation method used is relu and adam as optimizer. Regarding the performance evaluation of the abovementioned algorithms, three different processing units have been used; a personal computer (PC), a Raspberry PI 3 B+ and an NVIDIA Jetson Nano development board.
B. EXPERIMENTAL RESULTS In order to study the effectiveness of the multi-variate LSTM forecasting algorithm against ARIMA, CNN, and ConvL-STM, several experiments have been conducted following two evaluation methods. In the first one, the whole trajectory is used to train the model (WT), while in the second, the trajectory is divided into segments (ST) and each segment's dataset is used to train a related model. The first method (WT) was tested by training the models upon the whole dataset of a trajectory. One of the driving cycles is chosen for the test, the algorithm uses the pretrained model to forecast the future speed values. The results of the forecast are then stored and error metrics are processed to compute the models' accuracy. It is worth mentioning that this process is repeated in each time step (i.
3. The training time is processed for each algorithm using the same conditions (e. g. , epochs, layers, steps, forecast horizon) in PC, NVIDIA Jetson nano and also a Raspberry Pi 3 B+. It is clearly seen that, as expected, the PC takes less time, followed by the NVIDIA Jetson nano, and last, the Raspberry PI 3 B+. Also, the time to train a model, using a collect step of five seconds, is higher compared to a time step of ten seconds. This result is due to the fact that the dataset collected with a time-step of five seconds is twice bigger than the dataset collected with a time-step of ten seconds. On the other hand, it is worth mentioning that, in the first test, the Raspberry Pi 3 B+ outperforms the NVIDIA Jetson nano regarding the training time. This was due to the power supply issues. In fact, the NVIDIA Jetson nano requires a power supply with five Volts and a maximum current between three and four Amps.
2) ST: Training upon segments of trajectory As an attempt to improve the accuracy of speed forecasting, another method is used for models' training. In fact, instead of using the whole trajectory dataset to train one model for this specific trajectory, the trajectory is split into segments, and a model is trained for each segment. Therefore, for each trajectory, there are one or many forecasting models depending on the type of trajectory. Fig. 15  presents the diagram flows of the used algorithms. Fig. 15a  depicts the forecasting process. It starts with reading data streams from the in- This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and  then the corresponding model for the given segment and starts using it to forecast the vehicle speed.
VI. CONCLUSIONS AND PERSPECTIVES In this study, a prototype of the HELECAR platform for data collection and real-time speed forecasting has been deployed. Several experiments have been conducted to test its efficiency in developing related in-vehicle services by mainly focusing on speed prediction. The platform was first used for data collection, since no Moroccan historical data, related to EV driving cycles, are available. A driving dataset was established on Rabat-Salé-Kénitra urban roads by our Twizy EV. The LSTM algorithm was developed and evaluated against From the obtained results, the LSTM algorithm proved its capability to forecast the vehicle's speed in comparison to the statistical-based (ARIMA) and neural network algorithms (CNN and ConvLSTM). However, despite the effectiveness of the proposed methods (WT and ST) for training LSTM's model, enhancing its accuracy is highly required.
This work is performed under the collaborative framework OpenLab " PSA@Morocco -Sustainable mobility for Africa ". It is mainly supported by  HOLSYS  project funded by  IRESEN  under the program Green  INNO-PROJECTS 2020-2022 ) and partially supported by  MIGRID  project (grant  5-398 ,  2017-2021 ), which was funded by  USAID  under the  PEER program. energy sector. deployment and development of infrastructures for the production of renewable electrical energy.
A value in the range of 0. 2-0. 4 was estimated for the in-plane Poisson's ratio of the rolls GDL fibers' part. In conclusion, this study has enabled the creation of a database of the orthotropic mechanical properties of the most commercially available GDL references allowing the selection of the best GDL for optimized PEMFC performances. These data will also be used to refine the mechanical modelling of PEMFC, by introducing the experimental orthotropic mechanical properties of the different types of GDL.
Data collection: interviews and documents We performed one semi-directive interview per selected case study to collect the discourses of the key actors concerning the development of IS-I. The guideline for each stage of the initiative was the question (Q). Specific questions were not immediately asked in the interviews to avoid introducing a bias in the interlocutor's discourse and also to keep the conversation moving naturally. The first questions were general, such as "Can you explain how the initiative emerged and developed?" To obtain all the available information, we encouraged the interviewees to give more details on any dimensions of (Q) they did not mention spontaneously. The interview guide is given in Appendix B. To improve the quality of the analysis, we searched for additional documents for each case study, including documents mentioned by the interviewees, activity reports, applications for subsidized projects, articles published in the specialized and general press (see Appendix A).
Within that work, data of some of the previously mentioned studies were compared to the horizontal flow pattern map for CO 2 of Yun and Kim  [32]  and the universally applicable maps of Thome-El Hajal  [36]  and Steiner  [37]. None of the maps was able to predict the flow pattern transitions of the CO 2 data properly. Cheng et al. [38, 39]  published an updated flow pattern map, based on the work of Wojtan et al. [11] , for horizontal two-phase flow of CO 2 that is intrinsically related to the corresponding heat transfer mechanisms. Later, this work has been updated by Cheng et al. [40]  and a map with an extended range of validity has been presented. Mastrullo et al. [41]  carried out flow visualizations during flow boiling of CO 2 in a horizontal smooth tube of 6 mm inner diameter and identified flow regime transitions from slug-to-intermittent, intermittent-to-annular and at the dryout inception.
Nature of the issue, objectives and scope of the present work Literature offers a large number of flow pattern maps for any kind of pipe orientation for liquid-gas mixtures, water and synthetic refrigerants, while several flow regime observations are available for horizontal CO 2 two-phase flow, mainly for microchannel applications. However, there are no flow pattern observations available for vertical two-phase flow of CO 2 and as a consequence, there is no validated flow pattern map available for vertical orientations. To fill in this void, a comprehensive test program was carried out to (1) develop a database of flow pattern observations for CO 2 two-phase flow in vertical direction and (2) to provide a flow pattern map as a tool for further research on vertical CO 2 two-phase flow. An experimental setup has been built at CERN that allows flow pattern visualization in both vertical upward and downward direction.
1 ) and allows to control the flow parameters and the saturation pressure. The enthalpy at location 2014 can be controlled by means of a 3 kW pre-heater located prior to the inlet of the transparent section. In addition, a 3 kW post-heater allows to determine the enthalpy at the outlet of the setup by fully evaporating the remaining two-phase flow to the single-vapour phase. The pressures and fluid temperatures are monitored at several locations of the test setup with absolute pressure transmitters and RTDs (PT100). The sensor tips of the temperature probes are inserted into the pipe and in direct contact with the fluid, such that the flow is perturbed minimally. The installed instrumentation has been calibrated and the uncertainties are summarized in Table  1. The data acquisition of the instrumentation, as well as the controls of the setup are realized with a PLC based DAQ system and are embedded in CERN's UNICOS environment  [47].
A digital high-speed camera PHOTRON FASTCAM Mini AX is installed on a mechanical support frame, that can be switched to face any of the transparent sections. High-speed frames and videos were recorded at two different camera   settings that are listed in Table  2. The refraction due to the thickwalled glass tubes are taken into account according to the approach by Fu et al. [49]. For calibrating the refraction effect, a calibrated checkerboard pattern was inserted into the glass tubes and images were recorded with the high-speed camera prior to data collection.
Test conditions and database The test conditions are specified primarily by the requirements of the new ATLAS ITk and CMS Inner Detector and by both the limitations of the test facility and the refrigeration plant. • The range of saturation temperatures is limited by the chiller capacity of the refrigeration plant and by the maximum pressure the glass tubes are able to withstand. Thus, the investigated ranges of saturation pressures and temperatures are given as follows: 16 bar ≤ 𝑝 sat ≤ 40 bar; -26. 5 • C ≤ 𝑇 sat ≤ +5. 3 • C • The upper limit of the mass velocity is determined by the maximum pump capacity, while the lower one is given by the flow rate, where subcooled conditions are still possible at the transparent sections. Due to the rather long transfer lines from the plant to the vertical test sections of the test rig and the resulting heat pickup from ambient, the range of mass velocity also depends on the saturation-and ambient temperatures.
This results in a range in the order of 100 kg m -2 s -1 ≤ 𝐺 ≤ 450 kg m -2 s -1 • The range of vapour quality possible is influenced by the saturation temperature, the mass flow rate and the maximum power of the pre-heater, and therefore varies due to the conditions of the experiment from 0 ≤ 𝑥 ≤ 1 Flow pattern observations in vertical upward and downward direction have been carried out one after another, starting with the vertical upward direction configuration, and then swapping the camera to the transparent section of the vertical downflow and repeating some of the tests. A total of 431 data points have been collected for vertical upflow and 123 data points for the vertical downflow configuration. Fig. 3  gives an overview of the histograms of the test data, that further describe the flow pattern observations used for the flow pattern analysis.
At a later point, improved optical measurement and signal processing techniques were implemented for the characterization of flow patterns. Revellin et al. [50]  made use of laser-and photodiodes for the characterization of flow patterns in microtubes, whereas Wu and Duan  [51]  investigated means to classify flow regimes and void fraction measurements by using collimated infrared laser light. In the present study the recorded high-speed frames and videos are analysed with computer vision and machine learning techniques, making use of Convolutional Neural Networks (CNN). The CNN, based on the ResNet architecture  [52] , has been trained on a balanced dataset of 39261 input frames that can be clearly assigned to any of the typical two-phase flow classes specified in the next chapter. As a result, a Frame-and Flow-Regime-Classifier has been built, that allows to analyse high-speed videos and outputs the percentage of the video's constituent flow regime classes.
Flow pattern observations compared to existing maps and transition lines A fundamental question in further analysing the flow pattern observations of CO 2 is whether the flow regime transitions can be properly described by generalized flow pattern maps available in literature. Fig. 7  compares all data in upward direction to the work by Barnea  [18] , that is one of the most widely used flow pattern charts and which has been drawn based on results of air-water mixtures. The transition lines from bubbly to slug (line B) and from churn to annular (J) qualitatively capture the trend of the transitions, however, they significantly offset along the superficial vapour velocity   on the abscissa. The transition line from slug to churn flow (H) does not represent the transition of the CO 2 data at all.
Furthermore, the data fit reveals that the set of non-dimensional numbers used to describe the slug-to-churn transition is also an excellent candidate for the churn-to-annular transition providing the lowest RMSE in both up-and downward direction. It can be noticed that each flow regime transition is well described by the same set of non-dimensional numbers for both vertical upflow and downflow respectively. Bubbly-to-slug transition in vertical upward direction:    The transition lines and the entire database of flow pattern observations recorded within this study are illustrated in Figs. 9 and 10 for the entire temperature range of the experimental domain (-25 • C ≤  sat ≤ +5 • C). Fig. 11  shows detailed views to the bubbly-to-slug transition. In general, it seems more appropriate to identify transition zones instead of transition lines.
Conclusion Two-phase flow patterns of vertical up-and downflow of CO 2 are visualized in a dedicated test facility and flow patterns are recorded with a high-speed camera. A database of 431 flow pattern observations in upward and 123 in downward direction is set up and the records are analysed with machine learning techniques, that automatically identify the flow pattern transitions. It is observed that none of the existing flow pattern maps for vertical directions in the literature is able to describe the flow pattern transitions observed within the present study properly. In general it is questionable whether flow pattern maps elaborated with the data of mixtures are able to properly predict the flow pattern transitions of saturated single-component fluids. For that reason, the results of the present study are condensed into novel flow pattern maps for vertical up-and downflow of CO 2 two-phase flow.
ACKNOWLEDGEMENTS The  Global Innovation Teams  project (Agreement number:  2021-1-IE02-Ka220-HED-000023212 ) is co-funded by the  Erasmus+ Programme of the European Union. The  European Commission 's support for the production of this publication does not constitute an endorsement of the contents, which reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.
Acknowledgments. Bernard Lotz  is gratefully acknowledged for fruitful discussions and careful reading of the manuscript. C. Blanck  and  M. Schmutz  are acknowledged for technical support in TEM. P. Allgayer is acknowledged for technical support with the rubbing machine and  N. Zimmerma  nn is acknowledged for technical support in pre-patterned device preparation. Financial support from  ANR  grant Anisotherm  ANR-17-CE05-0012  ( ANISOTHERM ) is acknowledged. P. Durand is grateful for financial support from  Région Grand'Est. This work was financia lly supported by the  European Commission  through  Marie Sklodowska-Curie  project  HORATES  ( GA-955837 ).
Email: marco. di-renzo@universite-parissaclay. fr. This work was supported in part by the European Commission through the H2020 PAINLESS project under grant agreement number 812991, the H2020 SURFER project under grant agreement number 101030536, the H2020 ARIADNE project under grant agreement number 871464, and the H2020 RISE-6G project under grant agreement number 101017011. transmission coefficients are obtained by the penalty concaveconvex method. Interested readers can consult  [9] ,  [10] ,  [11] ,  [12]  for further information on IOSs. In contrast to the available works, we focus our attention on optimizing the IOS in the MIMO broadcast channel. Also, for the first time, we explicitly take into account the dependence between the reflection and transmission coefficients, based on a recently implemented IOS prototype  [13].
Low contrast experiment. Figure  5a  shows the contrast measurement for the low (high) contrast experiment with solid (dashed) line. The high contrast data is the same as in  19. For reference, we also plot the first ionization threshold of carbon assuming the field ionization by laser  24. The carbon ionization potential is obtained from the NIST Atomic Spectra Database  25  , and is plotted against charge state in Fig. 5b  by converting the threshold intensities under assumption of the field ionization  24. We do not consider here the graphene structure but just carbon atom. Nevertheless, the graphene will be destroy even one electron is kicked out by laser. In the low contrast experiment, there is a big hump of the contrast level ∼ 10 -6 before ∼ 20 ps of the peak intensity, and the before ∼ 40 ps the foot of the hump exceeds the first ionization threshold represented with the dotted hori- zontal line in Fig.