dataesr/scientific-paragraphs-categorization huggingface dataset:
The data logger controls a relay that supplies power to the PoE injector. In terms of protection against solids and liquids, the enclosure of the camera complies with the Ingress Protection IP66. The camera can operate from -30 °C to 50 °C and with relative humidity between 10% and 100% (including condensation). The triggering of the acquisition is programmed as follows: (i) the camera is started by the data logger via the electrical relay (typically once an hour) (ii) the camera takes a picture at the end of a 2 minutes heating period (iii) the camera sends the picture to a FTP address via the data logger and also locally records a copy of the image on its 64 GB SD memory card (iv) the camera is switched off via the relay. Ideally it would have been possible to zoom in and even orient the camera remotely.
g. lack of brightness, sun reflections, presence of dirt or water drops on the lens); (ii) the LSPIV processing steps are relatively time consuming and require a specific expertise. These steps include a possible correction (depending on the focal length used) of image distortion, orthorectification of images (transformation of the image from fixed objects whose exact GPS location is known) in order to have the same scale at each point of the images, calculation of surface velocity and flow discharge. These steps are performed using the Fudaaa-LSPIV software, a free software available online (https://forge. irstea. fr/projects/fudaalspiv/files) that uses the Fudaa libraries released under the GPL licence; (iii) the method is not applicable during the night. The selected camera is an AXIS P1435-LE based on various criteria such as image resolution, acquisition frequency, zoom and focus, power consumption, robustness to the environment, possibility of recording on SD card and presence of an integrated IR projector for tests at night-time.
The duration of the video sequences is 10 s. It is a compromise between, on the one hand, the minimum number of image pairs required to make a robust calculation and to average turbulent velocities and, on the other hand, the amount of data to be stored. The video sequences can be recorded on a SD memory card. A 10-second film at a resolution of 2 MP and an acquisition rate of 25 fps without further compression typically generates a 30 Mega Bytes (MB) file. A 64 Giga Bytes (GB) SD memory card is therefore used in this application. For night measurements, an integrated Infra-Red (IR) projector based on highly efficient Light Emitting Diodes (LEDs) with adjustable intensity and angle of illumination is available. However, primarily tests have shown that the projector is not powerful enough to illuminate up to the water surface.
The triggering of the acquisition is programmed as follows: (i) the camera is started by the data logger via the relay for 2 minutes at regular intervals (typically every 30 minutes) when triggering conditions of turbidity and water levels are overcome, i. e. during flood (ii) the camera takes a short video (10 seconds) of the river and records the acquisition directly on its 64 GB SD memory card (iii) the camera is switched off via the relay. The video sequences stored in the LSPIV camera are retrieved regularly during field visits by connecting to the camera from a PC using the Ethernet link from the RIPLE cabinet. The LSPIV processing steps will be executed back in the laboratory in Grenoble. It is important not to change the position, angle of view and focus of the camera in order to easily reproduce the LSPIV processing chain.
Every measurement is space averaged over the elliptical area targeted. The size of the targeted area, which depends on the distance from the sensor to the reflecting water surface, increases as water level decreases. The velocities appearing in this area have a specific distribution depending on the flow conditions. The velocity distribution is determined with a digital signal processor via spectral analysis and the dominant velocity in the measurement area is calculated (internal processing developed by the Sommer company and not available to the user). Spectra can be output using the RQCommander software (Sommer company) by connecting directly a PC to the RG30 using a serial connection (RS485) with no connection to the data logger. These spectra https://doi. org/10. 5194/gi-2019-33 Preprint.
15 m to 30 m above the water surface or river bed. The accuracy is +/-5 mm and the resolution is 1 mm. This radar implements the JBUS protocol on an RS485 link. MODBUS instructions of the data logger allow communication with this radar. Every 10 minutes, the measurement is ordered by the data logger. Each logged value is the average of three measuring cycles, each lasting 4 s and separated by 5 s. During the measurement cycles, the instrument makes 16 measurements per second. In addition, the standard deviation of water level, the ambient air temperature, and four quality indicators of the distance measurement are recorded. The radar is put on standby by the data logger between each measurement to limit power consumption.
A simple method of correction of the effect of temperature on the EC measurement is applied assuming a linear relationship between temperature and EC. The CS 547 probe is resistant to water and corrosion. It is easy to clean. The output signal is analogical (4-20 mA). The range of measurement for EC is from 0. 005 to 7 mS/cm and the accuracy is +/-10% of reading for 0. 005 to 0. 44 mS/cm and +/-5% for 0. 44 to 7. 0 mS/cm. The range of measurement for temperature is from 0°C to 50°C and the accuracy is +/-0. 4 °C. Every 10 minutes, the measurement is ordered by the data logger. Every measurement is time averaged over 30 records obtained in a time interval of 30 seconds. In addition to the average EC and average temperature of water, the min and max values of EC, the standard deviation of EC and the average value of raw EC (the value with no correction for temperature effect) are recorded.
The time interval between two sampling depends mainly on the site and the season. (ii) The PASS sampler is a more flexible alternative than the ISCO in terms of the number and shape of its containers. Any container in plastic, glass or other material with a top opening can be used. This system is controlled by a Campbell CR200 data logger that controls a pump and 4 stepper motors (2 in X and 2 in Y). This system was completely developed and tested in the M-TROPICS Critical Zone Observatory in Laos  (Ribolzi et al, 2017). In this study, the same pump as the ISCO 3700 portable sampler was tested.
The Susix sensor is equipped with a wiper to remove mineral and organic deposits from the optical cells. A SuSix Converter without display (10 -30 V DC) on which certain parameters can be adjusted (unit, measuring range) is needed between the Susix sensor and the data logger. The standard RS-485 is used for the serial communication between the sensor and the converter with a proprietary protocol. An additional Display Unit for SuSix is installed and connected exceptionally for the The converter is started by the data logger via the relay every 10 minutes. Every measurement lasts for 30 seconds and records 30 values. In addition to the average turbidity, the min and max values of turbidity, the standard deviation of turbidity and raw value of turbidity in mV are recorded. Finally, the converter is switched off via the relay.
Echo sounder The echo sounder Airmar SS510 was selected to be integrated into RIPLE platform. It allows to perform a continuous measurement of the distance to the riverbed at one point of the cross section. As a result, changes in the geometry of the cross section related to erosion/deposition of sediment or bedload transport can be detected at high temporal resolution, especially during flood events when geomorphic processes may occur. As shown in Figure  2 , the echo sounder provides information on the bathymetry of the cross section that is useful for both bedload and hydrometry. The Airmar SS510 sensor, featuring embedded micro-electronics, processes depth and temperature signals inside the sensor, transmits data via two separate communication protocols. The first is a bi-directional interface compliant with the NMEA-0183 protocol and the second is a transmit-only interface with a proprietary protocol using RS-485, which is used in this study.
An adjustable attachment piece was designed in this study to fix the echo sounder from a river bank or bridge pier. The mechanical integration of the echo sounder into the river is the main problem associated with this instrument as it is potentially exposed to bedload transport and there is not always a hard point available to fix it. Every 10 minutes, the measurement is ordered by the data logger via a RS-422 serial communication. By default, the echo sounder returns the measured depth and water temperature every second. Every measurement is time averaged over 30 records. The recorded measurements are the average, min, max and standard deviation of depth as well as the average, min, max and standard deviation of water temperature. Additionally, the RS-485 interface available on the echo sounder allows to retrieve detailed information on each measurement made by the echo sounder.
Hydrophone The deployment of hydrophones in the watercourse enables a continuous monitoring of the sounds naturally generated by bedload transport in the river  (Marineau et al. , 2016; Geay et al. , 2017a). The hydrophone Colmar GP0190 interfaced with the SDA14 acoustic data recorder were selected in this study. The Colmar GP0190 is a preamplified omnidirectional hydrophone, for application up to 170 kHz (working band: 5 -170. 000 Hz). The hydrophone can work up to 1000 m depth. The body of the instrument is in stainless steel. The SDA14 acoustic data recorder was designed by the RTsys company for the acquisition of acoustic signals from passive or pre amplified hydrophones. It integrates four analog receivers, allowing recording four sound sources simultaneously. Its broadband analog inputs allow over 500 kHz (from 3 Hz to over 500 kHz) with a dynamic range greater than 100 dB guaranteeing efficient signal to noise ratio.
The embedded digital signal processor allows high speed acquisition, filtering, storage and pre-processing of the acoustic data. Its power consumption is between 600 mW to 2 W in active mode (i. e. during measurements) and less than 1 mW in sleep mode. The system is designed to operate in standalone mode or towed mode. The standalone mode is used in this study. Configuration of the SDA14 acoustic data recorder is possible by connecting a PC via Ethernet and using a web interface. The SDA14 is controlled by the data logger via an RS-232 link and a 0-5 V output. The triggering of the acquisition is programmed as follows: (i) the data logger sends a 5V pulse to wake up the SDA14; (ii) the SDA14 starts and automatically launches an acquisition (manual mode): a record of 30 seconds at 156 Khz and with a 24 bits resolution is performed.
For information, the parameters of duration, frequency and resolution of the acquisition can be modified in the data logger Concerning step (iii) on data storage, the SDA14 is equipped with a 128 GB SD memory card and a 2 Tera Bytes (TB) hard disk drive (HDD) (ext4 format). In order to limit the number of on/off times of the HDD (which would severely limit its lifetime), acquisitions are first recorded on an SD memory card and once it is full, the HDD is turned on to empty the SD card. This is called the "hybrid" mode. There is an RS-232 command returning the available memory in the SD card and in the HDD. The data logger will send an alert when the hard disk is almost full. The SDA14, the SD memory card and the HDD are located in the main electronics box of the platform. The transfer time from the 2 TB HDD to a PC would be far too long to be done in the field.
Remote data transmission The 2G/3G modem Erco&Gener GenPro 325e was selected in this study as it allows the data logger to upload data and images to the FTP server of the laboratory in Grenoble and also to send alert SMS (text) messages. 2G antennas are no longer maintained by access providers in France, so we opted for a modem that can use 3G to remotely transmit data. A SIM (Subscriber Identity Module) card linked to an M2M (Machine to Machine) subscription is inserted into the modem to ensure its operation. The solution of the private IP address was selected in this study. By making this choice, we accept the dependence on the Loggernet software of Campbell Scientific, which makes it easy to manage the data logger and set up automatic collections of data. Having a private IP address avoids the possibility of being hacked by a "robot" circulating on the net, which could cause a very significant increase in expenses related to the GPRS subscription.
In France, Internet service providers only provide dynamic IP addresses. The Loggernet software was configured to establish a connection with a station whose address is dynamic by accepting the possibility of losing temporarily the connection because of a change in IP address. The IP address is changed on average every 24 hours and also when the modem starts up. It is unlikely that the change of IP address occurs within a few hours after the modem is started. As long as the connection is established, the data logger sends its data tables and control images to the FTP server of the laboratory using FTPClient instructions. The advantages of the selected solution are (i) the possibility to use a classic GPRS modem (low energy consumption and low cost), (ii) the management of data flow in case of transmission errors, (iii) the possibility to have a 2-way communication link.
Housing RIPLE platform is organized in several parts: (i) the control block, (ii) submerged instruments and (iii) non-intrusive instruments. (i) The control system (CR6, CDM-A108, 4 SDM-SIO1A), the power supply (battery, solar regulator, 4 relays, 2 PoE injectors), the instrument electronics boxes (SuSix turbidimeter interface, power supply of the "capteur marseillais", A547 interface of the conductivity probe, SDA14 card of the hydrophone) and the remote transmission module (modem, antenna) are grouped in an electrical box (dimensions H = 1. 40 m, L = 0. 80 m, D = 0. 46 m) located on the the river bank at a height sufficient to avoid being flooded (Figure  3 ). Everything is grouped into a single element to facilitate the installation and relocation of RIPLE to other sites. In addition, the ISCO automatic sampler is placed next to the electrical box and the solar panel is fixed on a mast or against the structure of a bridge.
Control software The data logger controlling program is written in CRBasic programming language, the proprietary format of Campbell Scientific. As shown in Figure  5 , there is a main program that reads a configuration file, initializes the instruments and controls two families of subprograms: those which are active every 10 minutes or every hour. The subprograms that are active every 10 minutes control all instruments except the control camera. When the water level and turbidity conditions are exceeded and the time since the last sample exceeds a certain interval set by the user, the first automatic sampler is launched. A SCAF measurement is also started, preferably using a second automatic sampler dedicated exclusively to SCAF measurements. Similarly, when a water level condition is exceeded and the time since the last video shot exceeds a certain interval set by the user, the LSPIV camera records a film sequence.
The other subprograms that are called at an hourly frequency are only active during day time. This concerns the operation of the modem, image capture by the control camera, image and data transmission, SMS message sending in case of alerts (low battery voltage, full ISCO sampler, etc. ). RIPLE platform is interfaced with the rest of the world via the FTP server, which allows exchanges between the interface (see section 5) and the platform as shown in Figure  6. The procedure for the operation of data storage and transfer can be illustrated by Figure  7. Concerning specifically the control camera, we use the data logger as an FTP server, on which the camera places an image every hour. The data logger then transmits this image to the FTP server of the laboratory in Grenoble. Since the Loggernet software is not able to automatically collect files other than data tables, the data logger must therefore perform PUSH on the FTP of the laboratory server to retrieve the control images (the data logger can be both server and FTP client).
User interface A solution with a remote web server was chosen, i. e. it is the server in the lab that generates a web page from the collected data. The RIPLE user interface is developed in R using the Shiny package (JavaScript elements for web interfaces) and dygraphs (graphics). An executable file for this application has been generated to display the results on a dedicated web page to avoid having to install RStudio and to have access to the interface from any terminal equipped with an internet connection.
Supervision The "Supervisions menu" allows to remotely control the proper functioning of the platform. The data displayed in this menu are read from the Riple_SAV. txt file that RIPLE uploads to the FTP (Figure  6 ). These are technical variables concerning the control unit (reference identifiers, OS version, internal battery voltage, PakBus address), the name of the current program of the control unit, the status of the power supply and temperature in the electrical cabinet of RIPLE platform, the data collected (number of measurement, date and time of measurement, watchdog errors, skipped scans, error with the SDA14 card, status of the SD memory card and of the HDD of the hydrophone). For example, it is possible to check the status of the power supply of the platform by looking at the battery voltage time series (Figure  11 ).
). Automatic sampling makes it possible to collect water during flood periods or during low water conditions. The collected samples are also useful for performing analyses (chemical, microbiological, DNA, etc. ) on the particulate phase. In the end, we want to show that RIPLE platform is a unifying tool that contributes to multidisciplinary studies on understanding the functioning of the Critical Zone. This is the way this tool has been designed and will continue to evolve. is an autonomous and low-power instrument platform, which transmits real time data to a remote server and can be controlled remotely enabling to fully exploit its potential. The visualization software interface that has been developed allows an easy follow-up of all measured variables and a beginning of data quality control.
https://doi. org/10. 5194/gi-2019-33 Preprint. Discussion started: 23 October 2019 c Author(s) 2019. CC BY 4. 0 License. All the procedure for the data collection and data transfer to the remote FTP server is presented in Figure 7. The modem queries the Internet service provider to obtain a private IP address. The data logger is then connected to the 2G/3G network. The data logger contacts the Loggernet server via port 6786. The Loggernet software running on the Loggernet server recognizes the data logger with its Pakbus number (225 for RIPLE), the connection between the data logger and the Loggernet server is then established. It is then possible for the manager of the platform to communicate remotely with the data logger. The Loggernet software can manage different stations, the distinction between different data loggers is made by a unique internal Pakbus address assigned to each station and not by IP address or domain name.
Acknowledgments This work was supported by the  EQUIPEX   CRITEX  project (grant no. ANR-11-EQPX-0011 ,  PIs J. Gaillardet  and  L. Longuevergne ). Yoann Michielin  benefited from an engineer contract from the  CRITEX  project. The work of Thomas Geay was funded by a research program between  Electricité de France  (EDF) and  Université de Grenoble (GIPSA-Lab / IGE). The development of this platform was carried out with the support of the technical service of the IGE. The deployment of RIPLE platform in the field was also supported by  Labex OSUG@2020  ( ANR10 LABX56 ) and the  Institut National des Sciences de l'Univers (INSU/CNRS). Most of the instruments presented in this study are the property of the CNRS. They are part of the national park of instruments for the study of the Critical Zone set up as part of the  CRITEX  project (https://www.
MATERIALS AND METHODS: Forty patients with MS were prospectively included and scanned (3T) to acquire synthetic MR imaging and conventional FLAIR images. Synthetic FLAIR images were created with the SyMRI software. Acquired data were divided into 30 training and 10 test datasets. A conditional generative adversarial network was trained to generate improved FLAIR images from raw synthetic MR imaging data using conventional FLAIR images as targets. The peak signal-to-noise ratio, normalized root mean square error, and the Dice index of MS lesion maps were calculated for synthetic and deep learning FLAIR images against conventional FLAIR images, respectively. Lesion conspicuity and the existence of artifacts were visually assessed.
0 seconds; FOV, 240 ϫ 240 mm; matrix, 320 ϫ 320; echo-train length, 10; bandwidth, 31. 25 kHz; section thickness/gap, 4. 0/1. 0 mm; slices, 30; and acquisition time, 7 minutes 12 seconds. The SyMRI software (Version 8. 0; SyntheticMR, Linko ¨ping, Sweden) was used to retrieve the R1, R2, and protondensity maps on the basis of the acquired data and to create synthetic FLAIR (with postprocessing TR, 15,000 ms; TE, 100 ms; TI, 3000 ms) and T1-weighted (with postprocessing TR, 500 ms; TE, 10 ms) images. Intracranial tissue masks were also created with the SyMRI software. 20 Postprocessing time on the SyMRI software was around 30 seconds in total using a workstation (HP Z230 Tower Workstation; Hewlett-Packard Japan, Tokyo, Japan) comprising Windows 7 (64-bit version; 16 GB memory) and a central processing unit (Xeon Processor E3-1281 v3; Intel, Santa Clara, California).
3 and the T2 signal intensity was Ͼ0. 7. We estimated the apparent inversion recovery time by subtracting the signal intensity ratio of the fifth-to-seventh raw data images from 1. Similarly, the apparent T2 signal intensity was estimated by subtracting the signal intensity ratio of the eighth-to-seventh raw data images from 1. Although this estimation process was rough, it was sufficient to achieve the first step because miscalibration and suppression of signals in other areas similar to those of CSF, if any, are supposed to be corrected in the GAN training step 2. During the training step 1, fifteen patches (128 ϫ 128 pixels) were randomly selected from the synthetic MR imaging raw data (512 ϫ 512 pixels) as a batch, and the model was trained batch-wise for 100,000 iterations with updating of the generator by the Adam  22  rule with ␣ ϭ 0.
Evaluation of the Model Quantitative Evaluation. To quantitatively compare the synthetic and DL-FLAIR image quality, we used the peak signalto-noise ratio (PSNR), normalized root mean square error (NRMSE), and lesion maps derived from these images. Conventional FLAIR images were registered to synthetic FLAIR images using the FMRIB Software Library (FSL; http://www. fmrib. ox. ac. uk/fsl). 28 First, mean square error maps were calculated for synthetic and DL-FLAIR images against registered conventional FLAIR images, and the PSNR was defined as: PSNR ϭ 20 log 10  25,500 ͱMSE , where 25,500 is the maximum range of the FLAIR signal intensity. Next the squared root of the mean square errors was calculated and scaled by dividing by the mean signal intensity of synthetic and DL-FLAIR images, respectively, after applying the intracranial masks, to produce NRMSE maps.