dataesr/scientific-paragraphs-categorization huggingface dataset:
Reference samples and material exposed samples were prepared and tested using 20 cells in parallel. Storage, transfer and measurement operations were investigated. Careful handling, cleaning and visual inspection were applied at each step. The materials in contact with liquids were classified into two main categories: permanent materials (acrylic, glu- ing, stainless steel vessels) and temporary materials such as calibration devices. Databases of all materials with their characteristics and tests were developed. The final material certification even required the use of a 500 l glove box operated under highly pure N 2 (6. 0 grade). Dedicated preparation methods and protocols were developed with reference measurements and time evolution studies of liquids exposed to detector materials. The final certification protocol was then based on extrapolation scaling laws to full detector size and exposure time with safety margins.
From the flowmeter data for the far detector we get a GC mass of 18. 08 t (15 • C) with a relative uncertainty of 0. 7%. In the near detector the flowmeter indicated a volume of 22. 608 m 3 in very good agreement with the calculated volume estimate. This corresponds to a mass of 18. 18 t. This mass is determined at a filling level 3 cm above the start of the GC chimney. Moreover, the weight of the GC liquid transport truck was measured before and after filling. Subtracting the liquid not filled into the detector or chimney we obtain a mass (volume) of 18. 16 t (22. 581 m 3 ). Although all volume calculations, flowmeter data and weight measurement agree at the per mille level, a rather large uncertainty of 1. 7% was set on the last two values to account for a possible bias from leakage. This value is obtained from the estimated amount of GC liquid identified in the Buffer taking into account UV-Vis and GC-MS measurements as well as LED calibration data.
The acrylic PMT support structure was fixed to the Buffer walls by intermediate U-shaped stainless steel pieces attached to its bottom part. These pieces were attached to stainless steel vertical struts placed around the cylindrical Buffer walls and to stainless steel ring structures in the top and bottom Buffer lids. The position and orientation of the 390 PMTs were optimized for a homogeneous detector response. They were distributed in 10 rings on the wall with 15 PMTs in the two    15  shows the final distribution of the PMTs installed in the detector. To assure uniform and identical detectors the PMT serial numbers and anode orientations with respect to the supporting frame were randomized. The final positions of all the PMTs after their installation in the Buffer were surveyed and registered in our database.
Neutrino data acquisition The Neutrino DAQ system included two software packages: the Read Out Processor (ROP) and the EventBuilder. The ROP software ran on each single-board computer controlling a VME crate. Commands were sent from the Event-Builder to the ROPs to configure, initialize, begin and end acquiring data. During acquisition, each ROP sent data to the EventBuilder, which combined the data from each ROP to form a complete event. The EventBuilder processed the data using rapid online algorithms, determined whether to store the complete event or not, and wrote the event to disk. Additionally, information about the run and the data collected was recorded periodically via a ROOT macro, allowing the shifters to identify technical problems rapidly.
Read out processor The ROP software was written in Ada with the GNAT 4. 3. 2 compiler from Ada Core Technologies, running on a single core MVME3100 PowerPC  [48]  for the data acquisition of the Near Detector and a faster dual-core MVME7100 Pow-erPC  [49]  for the Far Detector system. The home areas of the PowerPCs were stored on an external server, such that the home areas on all the PowerPCs were identical. An Ada VME library was developed providing a low level interface to the VME driver for the PCI-VME bridge Tsi148, distributed by Emerson-Network-Power. High level interfaces were provided to manage master and slave windows on VMEBus, and allowing VME DMA transfer. In addition, interfaces to the Linux pread, pwrite, ioctl, select and pselect system calls were provided. The ROP software comprised two tasks; the first communicated with the EventBuilder and the second with the VME cards held within its crate.
EventBuilder The EventBuilder software ran on a DELL PowerEdge R610. It communicated with the Run Control server, sent commands to the ROPs, received data from the ROPs, assembled the data, performed simple analyses on the waveforms, and zipped and wrote the data and run-wise monitoring information to disk. After data were collected from each ROP, the waveforms were analyzed online in 5 parallel processes. The online analysis served two main purposes. The first was to provide reduced channel-wise data to be used by the shifters to determine whether the detector was functioning normally or not. The second was to reduce the quantity of data written for light noise  [34]  events at the far detector and muon events at the near detector. The combination of the online data reduction and parallelized zipping of the data was necessary to reduce the quantity of data written to disk, transferred offsite and eventually analyzed.
Online data reduction The FADC waveforms comprised the dominant data written to disk. In order to reduce the quantity of data written to disk, for certain types of events only the online analyzed variables were stored (i. e. , the FADC waveforms were removed). To make a significant reduction in the data volume, FADC waveforms were not written for -Light noise at the far detector -determined by the eventwise variable that measured the spread of the inner detector pulse start-times (RMS_TStart >70). This online cut removed waveforms from approximately two thirds of light noise events (175 Hz). -Muons at the near detector -determined by the IV muon bit from the trigger system. Waveforms were removed for ∼ 190 Hz of muons and stored for only about 1% of the muons to perform dedicated studies. At the end of data taking, the far detector wrote a 13 GB/hourly run for a trigger rate of 470 Hz, and the near detector wrote a 9 GB/hourly run for a trigger rate of 275 Hz.
Data handling and transfer The DC Collaboration relied on the database software MySQL for the collection, handling and distribution of various data in real time. These included detector monitoring data, metadata regarding the processing and storage of physics data files, and metadata regarding simulation data files, as well as performance data for several detector subsystems. Several instances of MySQL servers were implemented in different locations to provide a fast and fail-safe data handling system  [50]. For the two-detector phase, a system built from seven servers was used to provide the database infrastructure. At each detector site, two servers were installed. These were the master servers of the laboratories and their backup servers. The remaining three servers were located at a computing centre (centre de calcul of the French IN2P3).
Two servers duplicated the data from the laboratories and allowed access locally at the computing centre. The third one was used as a standalone server for data produced at the computing center. Looking at the long-term performance of the system, we find an overall good performance with an average daily downtime of less than 1. 5 s. After the online data reduction, binary data written to disk were sent to the IN2P3 computing center in Lyon, France. The data transfer system was developed in Python using iRODS [51] and the MySQL database. Event reconstruction, such as vertex reconstruction and energy calibration, was performed and combined two data files from the neutrino DAQ and OV DAQ. Original and reconstructed data files were stored in the High-Performance Storage Systems (HPSS) in Lyon.
In addition it created a so-called random trigger internally. It was derived from the internal clock and fired at a fixed, programmable frequency. The trigger master board recorded all trigger decisions in a 32-bit trigger word. It was possible to prescale every trigger, i. e. , to accept only every n-th positive decision, with a programmable scale-factor n. A trigger mask was applied to the trigger word afterwards, excluding triggers currently not in use. The remaining bits of the trigger word were "OR"ed into the final trigger decision. Upon a positive trigger the master board sent a trigger bit to all other subsystems, initiating the readout. It also sent the trigger word and an event number which was the number of triggers since the start of a run. The information in the trigger word was used by some of the subsystems to adjust the amount of data recorded according to the event type.
No relevant information was lost. A complete trigger for each of the two detectors consisted of 3 trigger boards handling the two redundant chains for the inner volumes and the veto, a trigger master board, a clock module (either the one at the near or the far detector was in use at any time), and a number of fanout cards for clock, trigger decision, trigger word and event number. All the hardware was implemented in the VME standard. The trigger logic was implemented in field programmable gate arrays (FPGA: Xilinx, Virtex-II, model XC2V500). Each trigger included a microcomputer with an Ethernet connection to the data acquisition for the programming and control of the trigger and its readout. The trigger efficiency was about 50% at 0. 3 MeV and reached > 99. 9% at 0. 5 MeV with a negligible uncertainty.
During acquisition, each ROP sent data to the OV EventBuilder, which combined the data from each ROP to form a complete event based on timestamp. The OV EventBuilder processed the data using rapid online algorithms, determined if an event met coincidence requirements and was above a certain threshold; if so, the complete event was written to disk. Calibration data were also saved and used offline to calibrate the MAPMT. Additionally, information about the run and event builder quality monitoring were written periodically and at the beginning and end of each run, and stored in a MySQL database table. Both Event Builder and ROPs stored and retrieved calibration data from the MySQL database. A series of perl scripts monitored the data quality and hardware time responses automatically, including HV values for each of the MAPMT, determining the operation of the OV DAQ system.
For each event, the measured data format consists of trigger information and a waveform for each PMT representing the digitized response recorded by the FADC. This is done by summing each PE waveform of each PMT to form the PMT's signal, which is converted to a digital waveform using 2 ns time bins. In this way, the simulation models nonlinearity effects as observed in data. After calibration, data and simulation agree to better than 1%.
Each LED pulser box had 8 channels, each with 3 LEDs of 385, 425 and 475 nm wavelength and a PIN photodiode to monitor the light intensity. Flash rate, light intensity, pulse width and choice of wavelength were controlled remotely by the control box via an RS232 interface to a computer in the DC online system. The IDLI system provided a rectangular timing signal, which was divided into two. One part was fed into the trigger system to create an external trigger, and the second part was recorded by FADC. Calibration data with the IDLI system were taken regularly by the automated calibration run sequence to measure the PMT and readout electronics gains, time offsets and stability in time. Gain calibration corrected for nonlinearity arising from readout electronics and charge reconstruction (see Sect.
7. 2) by using data taken with various light levels from under 1 PE up to more than 10 PE. The time offset of each PMT was measured from the time offset of the signal relative to the external trigger provided by the IDLI system, accounting for the distance between the light injection point and the illuminated PMT. The stability of the time offset was shown to be within the accuracy of the IDLI system measurement, which was estimated to be 0. 5 ns by a study using the MC simulation. For calibration of the IV PMTs, a pulsed light source (LEDs with wavelengths of 365 nm and 470 nm) was used to measure the gains and time offsets of the PMTs on a regular basis. The light entered the IV through an LED-fiber system (IVLI). Fiber ends were attached to the PMT cases a few cm from the PET windows.
Acknowledgements We thank the company  EDF ("Electricity of France") , the  European fund FEDER , the  Région Grand Est  (formerly known as the  Région Champagne-Ardenne) , the  Département des Ardennes  and the  Communauté de Communes Ardenne Rives de Meuse. We acknowledge the support of the  CEA ,  CNRS/ IN2P3 , the computer centre  CC-IN2P3  and  LabEx UnivEarthS in France ; the  Max Planck Gesellschaft , the  Deutsche Forschungsgemeinschaft DFG , the  Transregional Collaborative Research Centre TR27 , the excellence cluster " Origin and Structure of the Universe " and the  Maier-Leibnitz-Laboratorium Garching in Germany ; the  Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT)  and the  Japan Society for the Promotion of Science (JSPS) in Japan ; the  Ministerio de Economía, Industria y Competitividad (SEIDI-MINECO)  under grants  FPA2016-77347-C2-1-P  and MdM-2015-0509 in Spain; the  Department of Energy  and the  National Science Foundation  in the United States; the  Russian Academy of Science , the  Kurchatov Institute  and the  Russian Foundation for Basic Research (RFBR) in Russia  and the  Brazilian Ministry of Science, Technology and Innovation (MCTI) , the  Financiadora de Estudos e Projetos (FINEP) , the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) , the  São Paulo Research Foundation (FAPESP)  and the  Brazilian Network for High Energy Physics (RENAFAE) in Brazil.
This is a special case of domain adaptation where simulated data is to train and ground truth data (HR) cannot be accessed in the target domain. Currently, our work focuses on finding the best simulation parameters, but different semisupervised techniques could also be used (such as Cycada  (Hoffman et al. , 2018) ) to leverage simulation data and real data during training. It should be noted that we are in the case where LR/HR pairs are available also known as aligned super resolution in contrast to the case where only 2 datasets are available: one LR and one HR with non-corresponding images in the two sets. Generative techniques are then well indicated for this problem as they do not require to have a tuple of LR and HR images  (Lugmayr et al. , 2019). Furthermore, for classical photography, state-of-the-art approaches include a loss term based on a discriminator network  (Wang et al.
EXPERIMENTS We used a dataset composed of simulation data based on the simulation process described in the paragraph above. The images used for the simulation are images of cities for most part but also some other landscapes (seas, etc. ). We generated 7. 6k training patches (before the data augmentation process) and 2. 4k validation patches of size 128x128. We evaluated different training strategies: different parameters for the SRResnet architecture and the different loss functions described above (one parameter at each time). We trained the different SRResnets using the previously mentioned losses with a Lamb optimizer with a batch size of 16, an initial learning rate of 5. 10 56 and a schedule strategy applying a 0. 5 scale factor to the learning rate when the loss stagnates for more than 10 epochs.
We ran the training for 200 epochs and kept the best validation checkpoint for each training. All trainings were run on an instance running on Google Cloud Platform with a single Nvidia-V100 GPU. We kept two evaluation datasets aside from the training procedure: • a dataset consisting of simulation data from other aerial images with simulation parameters similar but different from the ones used in training, • a dataset consisting of hand selected PNeo images targeted for super resolution models evaluation. On the evaluation dataset, where groundtruth is available, we compared the prediction and the ground truth using the multiscale SSIM index, the PSNR, and the LPIPS index. The LPIPS index integrates a perceptual component and is used by the superresolution community in complement to standard image processing metrics.
For the dataset without groundtruth (real imagery) we used no-reference indices such as Total Variation and BRISQUE, since we do not have the ground truth available. In practice, especially for super resolution, numerical metrics show strong weaknesses when compared to visual inspection e. g. , a model can be visually pleasing while having poor metric values or the inverse when some artifacts are not reflected in the metric. As such, a strong emphasis is put on visual inspection to evaluate models and the last model selection step consists of validation by trained satellite image datasets. The evaluation is done by experts on the held out PNeo dataset where dedicated locations have been selected to compare super resolution models such as airports and ground patterns to evaluate sharpness and denoising properties of a given network.
Results We report the results obtained by our method on simulated imagery (Figure  6 ). Images show very good results on both denoising and zoom operation, with fine details being restored without major artifacts. We also notice that some very fine details (which could only be seen at the native HR GSD) are not restored as they don't exist in the input image and thus are not "hallucinated" by the model. We obtain similar results when applying our model to real images (Figure  7 ). This highlights the generalization power of a model trained on synthetic imagery. We find that model outputs appear very under different acquisition conditions and landscapes, even those which were not present in the training dataset. We also report with-reference metrics on the simulated dataset (Table  1 ) as well as no-reference metrics on Pleiades Neo imagery (Table  2 ).
Application to multispectral bands While this article mainly focuses on the application of approach to the Panchromatic band of Pleiades Neo, we adapted our method for the super-resolution of the PNeo multispectral channels, notably the blue, green, red, and near-infrared channels. The objective of such an approach is to be able to produce a better pansharpening by super resolving the MS channels before the fusion process, as MS channels at acquired at ¼ of the GSD of the panchromatic channels by the PNEo imaging system. We kept the same zoom factor (2) and applied a similar method with our dataset simulated at a GSD of 60/120cm, while keeping the 4 multispectral bands. We obtained similar results to the panchromatic approach with a similar network (Figure  10 ). This approach leads to the spectral components matching the panchromatic band more closely, thus making the colours appear sharper and without "smearing" effects.
CONCLUSION In this paper we propose a realistic simulation process to generate a training dataset of paired LR/HR satellite images from very high-resolution aerial images. We show that this simulation process enables us to train a residual-based CNN architecture to jointly restore and zoom (single-image super resolution) its input image, and that it generalizes well to real satellite imagery. Thus, our process enables the development of image restoration models from purely synthetic training datasets. We highlight that image quality metrics on the simulated dataset may not reflect the final perceived quality and usability of the model on real imagery, a point to keep in mind when selecting the best model to deploy. It is likely that the final choice between several models will be done with a blind image quality assessment by qualified image quality engineers.
We parameterized and evaluated ORCHIDEE-C lateral using observation data in Europe. The model explains 94 %, 75 %, and 83 % of the spatial variations of observed riverine water discharges, bankfull water flows, and riverine sediment discharges in Europe, respectively. The simulated long-term average total organic carbon concentrations and DOC concentrations in river flows are comparable to the observations in major European rivers, although our model generally overestimates the seasonal variation of riverine organic carbon concentrations. Application of ORCHIDEE-C lateral for Europe reveals that the lateral carbon transfer affects land carbon dynamics in multiple ways, and omission of this process in LSMs may lead to an overestimation of 4. 5 % in the simulated annual net terrestrial carbon uptake over Europe.
, 2003)  and returns back to SOC. Adsorption and desorption of DOC follow an equilibrium distribution coefficient calculated from soil clay and pH. Free DOC can be transported with the water flux simulated by the soil hydrological module of ORCHIDEE. However, DOC adsorbed to soil minerals can neither be decomposed nor transported  (Camino-Serrano et al. , 2018). All the described processes occur within each soil layer. At each time step, "the flux of DOC leaving the soil is calculated by multiplying DOC concentrations in soil solution with the runoff (surface layer) and drainage (bottom layer) flux simulated by the hydrological module"  (Camino-Serrano et al. , 2018, p. 939). More detailed information about the simulation of soil hydrological and biogeochemical processes in ORCHIDEE-SOM can be found in  Guenet et al.
The coefficients a and b in Eq. (  1 ) and c and d in Eq. (2) need to be calibrated (see Sect. 2. 3 and Table 2). In Eq. (1), the term LS i is the combined dimensionless slope length and steepness factor calculated based on the DA i and the average slope steepness (extracted from a DEM) of headwater basin i  (Moore and Wilson, 1992). C ref (0-1, dimensionless) in Eq. (  1 ) represents the cover management factor, which depends on vegetation cover and storage of plant a GFPLAIN250m only covers the regions south of 60 • N. We produced a map of floodplain distribution in regions north of the 60 • N using the same method for producing GFPLAIN250m  (Nardi et al. , 2019)  based on the ASTER GDEM v3 database (Abrams et al. , 2020). b The DEM data from HydroSHEDS and GDEM v3 are used to extract the topographic properties (e.
g. , location, area, and average slope) of headwater basins in regions south and north of 60 For the use of these reference sediment delivery estimates in ORCHIDEE-C lateral , the values were first calculated for each headwater basin derived from high-resolution geodata (Fig. 1e ), then aggregated to 0. 5 • grid cells (Fig. 1f ) -the scale used in our simulations and required to maintain computational efficiency (also limited by the availability of climate and land cover forcing data). This aggregated dataset is then used to force the simulation of the actual daily sediment delivery (S j , g d -1 grid -1 ) in ORCHIDEE-C lateral , simply based on the estimated reference sediment delivery rates of Eq. (  1 ) and on the ratios between actual runoff and land cover conditions as well as the assumed reference conditions used to create that forcing file (Eq.
4, Fig. 1g ): S ref = n i=1 S i_ref × 10 6 , (3) S j = S ref R j R 30_j R ref R 30_ref b C j C ref , (4) where R j (mm d -1 ) is the total surface runoff on day j simulated by the hydrological module or ORCHIDEE-MUSLE at 0. 5 • spatial resolution every 30 min. R 30_j (mm 30 min -1 ) is the maximum value of the 48 half-hour runoffs in each day. C j (0-1, unitless) is the daily actual cover management factor calculated based on the fraction of surface vegetation cover, the amount of litter stock, and the biomass of living roots in each PFT within each 0. 5 Daily POC delivery to the river headstream in each 0. 5 • grid cell is finally simulated based on the sediment delivery rate and the average SOC concentration of surface soil layers (0-20 cm). We assumed that litter cannot be eroded and transported to the river network; however, it can affect the soil erosion rate through the cover management factor of the MUSLE model (denoted by C j , Eq.