dataesr/scientific-paragraphs-categorization huggingface dataset:
Detector Control System The Detector Control System (DCS)  [50]  collects data from the various detectors, hardware devices, and data acquisition elements with programmable reading cycles. For the COMPASS experiment the actual cycle times range between 2 seconds and 30 minutes. It provides a user-friendly interface, which is used to set remotely most parameters relevant for operating the experimental setup. When predefined conditions are met, namely if monitored values go beyond predefined thresholds or settings, it displays on-line warnings and alerts in the user interface, sounds acoustic alarms in the control room, notifies predefined recipients by SMS and email and, when necessary, switches off sensitive detector channels. All values and alerts are stored in a centralised Oracle database with a frequency of typically few minutes per monitored parameter.
Queries on the database are executed regularly for storage of data, or on demand. The DCS architecture consists of three layers: the supervisory layer, the front-ends layer and the devices layer. The supervisory layer of the DCS is based on a commercial SCADA system (Supervisory Control and Data Acquisition), PVSS-II [51], adopted by CERN. On top of PVSS-II, a package of software tools called Joint COntrol Project (JCOP) Framework  [52]  is also used. Developed at CERN, this package is specific for high-energy physics applications. The front-ends layer includes the drivers necessary for the hardware devices and provides the communication protocol between the supervisory layer and the devices layer. The devices layer comprises all hardware elements and sensors. The system is flexible enough to easily incorporate new detectors and monitored parameters.
For the data taking with hadron beams in 2008/2009, a number of new detectors were included in the system: RPD, Sandwich veto, Multiplicity Counter, Beam Killers, Beam Counter, PixelGEMs, liquid hydrogen target, and the new Silicon detectors with their cryogenic devices. In addition, the monitoring of the CEDARs and the two electromagnetic calorimeters was considerably improved. New high voltage and low voltage channels and VME crates were used and integrated in the DCS for the hadron data taking. They are monitored and controlled by OPC or DIM servers  [53, 54] , with which they communicate by use of the CAN 3  and CAENet field buses  [55]. The monitoring of CEDAR parameters (pressures, temperatures, HVs and motors) is done via a DIP server maintained by CERN  [56]. The ratio of the pressure to the temperature is calculated for every spill.
If the value is found outside the appropriate range, a warning signal requesting a correction of the CEDAR gas pressure is generated. The monitoring of the two electromagnetic calorimeters required a substantial extension in the number of monitored channels, namely 1500 for ECAL1 and 3068 for ECAL2. Between spills, the calorimeter modules of ECAL1 and ECAL2 are flashed by a laser and LED light pulses, respectively (see Section 6. 2. 2 and Section 6. 3. 2). The DAQ on-line filtering software collects the responses from all modules, calculates the average amplitudes for each spill, and stores them in the conditions database. The DCS reads them, compares them to the reference values, and defines its state of alert. The voltage and the current of the powering system of ECAL1 and ECAL2, the power supply of the LED monitoring system and the status of the laser of ECAL1 were added to the list of controlled parameters.
The monitoring of the liquid hydrogen target and of the cryogenic system of the Silicon detectors is done using a dedicated Programmable Logic Controller (PLC) and a control system. Pressures, temperatures, vacuum gauge values and liquid levels are transmitted to the DCS using a MODbus  [57]  server. The data generated by the DCS are temporarily saved on a local disk whose contents are transmitted to a centralised CERN Oracle database with a cycle of a few seconds. Parameters that are relevant for the physics analysis are regularly copied to a COMPASS MySQL database or are provided to users as ROOT trees or ASCII files.
Event Reconstruction and Detector Performance The main reconstruction software is called CORAL (COmpass Reconstruction and AnaLysis); it is detailed in Ref. [1]. CORAL outputs mDST ("mini Data Summary Trees") files that contain reconstructed events and information related to the detectors. The mDST files are organized in tree-like structures based on the ROOT  [58]  package. The information stored in the mDST is analysed using a dedicated software called PHAST (PHysics Analysis Software Tool). Besides accessing the mDST data, PHAST provides an environment for physics analysis and includes tools for mDST creation, further data processing and filtering. The event reconstruction in CORAL comprises all detectors except the RPD and the CEDARs, which are included at the PHAST level, i. e. during the mDST processing stage.
Alignment procedure In order to achieve the optimal reconstruction performance, a precise knowledge of the position and orientation in space of the more than 200 tracking detector planes of the COMPASS spectrometer is mandatory. In many cases, the geometrical survey of the experimental setup does not reach a precision that is comparable to the spatial resolution of the detectors. It is used as the starting point for an alignment procedure, which uses a sample of reconstructed tracks. The whole procedure is done in three steps with different sets of data. Each step is repeated until the corrections become negligible compared to the detector resolution. The first step uses data recorded with a muon beam with the spectrometer magnets switched off. Therefore, straight trajectories can safely be assumed and all spectrometer arms including the beam telescope contribute to the reconstruction of a particle track.
In order to reach a broad illumination of all spectrometer parts, these data are recorded with a widely defocused muon beam and by using both beam (see Section 7. 1) and veto counters (see Section 7. 2) as trigger. The alignment is performed by minimising the total χ 2 of all tracks in the sample, keeping four detector planes (GM04XY, GM10XY) fixed. For these pivotal points, the positions determined by an optical survey of the experimental setup has to be used in order to keep the coordinate system fixed in space. For all other planes, corrections for a translation along the measured coordinate, a rotation around the beam axis, and the effective pitch are introduced. The effective pitch takes into account a possible inclination of the detector plane with respect to the beam axis. The position along the beam axis is normally fixed to the position determined by the geometrical survey; a fit is only attempted if the residual distribution with respect to the beam axis of a given plane indicates a possible problem.
The minimisation is done by the Millipede program  [61] , which analytically inverts a large but sparsely populated matrix. For the second step of the procedure, the detector planes downstream of the target are aligned with the spectrometer magnets switched on. The magnetic field not only shifts the positions of the mechanical support of some detectors, but also influences the internal processes of charge propagation in gaseous detectors. The effect is strongest for some of the small-area trackers in the fringe field of SM1 (MM03 and GM01), where the Lorentz-force acting on drifting and amplified charges results in an apparent translation of the detector planes of up to 400 µm. For these detectors, a correction in form of an effective shift is applied, since the distortion is uniform over the active area of the respective detector within the spatial resolution.
In order to further improve the kaon identification, a different method was developed for offline analysis. It is based on beam particles reconstructed in the beam telescope before the target and makes use of the response of each PMT individually to improve PID for particles that do not travel parallel to the CEDAR optical axis. In a first step, the response of the PMTs for kaons and pions is determined as a function of the horizontal and vertical angles between track and CEDAR optical axis, θ x and θ y. These angles are obtained from tracks measured in the beam telescope, which are traced back to the CEDAR position using the known beam optics. A clean kaon sample is obtained from data taken with the CEDAR kaon trigger plus a beam trigger by selecting decays of beam kaon into three charged pions K -→ π -π + π -outside the target region.
In order to determine the efficiency for pions and kaons, their numbers as obtained from the CEDARs are divided by the respective numbers of pions and kaons assuming the known beam decomposition (see Section 3. 1). The values for kaon efficiency and purity are given in Table  7. The kaon identification efficiency is improved by almost a factor of two for the likelihood method in comparison with the multiplicity method when applied offline, while the corresponding purities are nearly identical. With the multiplicity method pion identification is not possible as the pressure was adjusted for kaon identification for the data taken with the negative hadron beam. The likelihood method allows for pion identification as well. The values obtained for pions are similar to those obtained for kaons. In the analysis of Primakoff data, the CEDAR information is needed for an efficient kaon rejection.
Electromagnetic calorimeters Event reconstruction in ECAL1 and ECAL2 is performed by using time and signal amplitude information as directly extracted from the SADC samples. The signal amplitude for each module is converted into energy applying conversion coefficients that were derived from the electron beam calibration. The variation of the amplitudes over the data taking period is accounted for by using the information provided by the Laser and LED monitoring systems. Details about signal extraction, electron calibration, and data monitoring are given in Section 6. 2. The energy calibration of each module is further improved by using the data derived from an analysis of the π 0 → γγ decay process. The π 0 calibration is performed prior to the final data reconstruction on a fraction of the collected events.
The data analysis procedure starts by defining a cluster of neighbouring modules, in which the deposited charges are larger than a pre-defined threshold (see Section 9. 7. 3). The cluster is then split into showers employing a parametrisation for the lateral spread of the shower profile  [62]. The shower parameters for the lead glass and Shashlik modules are determined using electrons from a dedicated calibration beam (see Section 3. 2). For Primakoff data taking in which mainly high-energy photons are detected, no electron beam with the corresponding energy is available. The shower parameters are therefore derived using single photons from real data events.
The parameters were obtained for the GAMS-2000 spectrometer  [62]. Since COMPASS makes use of the same lead glass modules, the parameters are unchanged. In ECAL2, the profiles of both lead glass and radiation-hardened lead glass modules are described by two contributions, with parameters derived from the electron beam calibration. For the Shashlik modules, the profile consists of three contributions. The corresponding parameters are obtained from Primakoff data events that contain a single high-energy cluster. In both cases it has to be assumed that a cluster contains only one shower. The two-dimensional ratio defined in Eq. 9 is used to calculate the relative energy deposited by a shower at the position (u j , v j ) in each module: G j (x, y) = F (u j + ∆, v j + ∆) -F (u j + ∆, v j -∆) -F (u j -∆, v j + ∆) + F (u j -∆, v j -∆).
Clustering and shower reconstruction In a first step, signals from adjacent modules are combined to form a cluster. Starting from the first module not yet used in the cluster, each of the (vertical, horizontal, and diagonal) neighbours is checked for a measured energy above a threshold of 100 MeV for ECAL1, and of 200 MeV for ECAL2. For each new module added to the cluster, its neighbours are treated the same way. In a second step, a fit of the shower profiles to the cluster data is performed. The fit improves the spatial resolution of the calorimeter and separates overlapping showers. The fit is first done with a single shower. Further showers are added one by one with a new fit being performed after each added shower. The parameters of the first shower are initialised to those of the module with the highest energy in the cluster and its neighbours.
High-energy photons For photon energies above 80 GeV, the kinematics of the Primakoff-Compton reaction, π -+ (A, Z) → π -+ γ + (A, Z), constrain the detected photons to the central 4 × 4 ECAL2 modules. The energy deposited in these modules must be accurately determined, even for values as large as 95 % of the beam energy. With the π 0 calibration alone this cannot be achieved. For higher energies a different method is applied. During the Primakoff data-taking period, muon beam data for systematic studies are periodically collected. These data also contain Primakoff-Compton events with photon energies nearly as high as the beam energy. Moreover, since the Beam Momentum Station (BMS) is present in the beam line, the muon incident momentum is known. Since the scattered muon momentum is also measured, the energy conservation (exclusivity) in the process provides an independent prediction of the energy of the emitted photon.
Monte Carlo simulation and acceptance The simulation of the COMPASS setup is performed using a dedicated Monte-Carlo (MC) code called COMGEANT. The code can be linked to external event generators specific to the reaction mechanism that is dominant in a given channel. Final-state particles are then propagated through the setup. The digitisation of the MC data and the subsequent reconstruction are carried out with the same software that is used for reconstructing the measured events. Three different event generators are used to simulate diffractive reactions, central production, and Primakoff reactions. The partial-wave analysis method employed for diffractively and centrally produced n-body final states takes into account the acceptance of the apparatus using Monte-Carlo pseudo data, where the final-state hadrons are distributed isotropically in the n-body phase space.
Charged-particle tracks are reconstructed from the simulated hits in the tracking detectors using the same procedure as for the real data (see Section 9. 1). Detector properties such as efficiencies and resolutions are implemented in the reconstruction software using information from the experimental data. For RICH-1, the purity and efficiency of the detector are determined from the measured events and separately unfolded from the simulated data. For the electromagnetic calorimeters, shower profiles are extracted from the ECAL MC data, and a π 0 calibration is performed as for real data (see Section 9. 7). The RPD information is passed directly to the PHAST physics analysis software. An example of the good MC description achieved for the Primakoff-Compton reaction is shown in Fig. 89  for the reconstructed primary vertex as a function of the pion scattering angle.
After a week of exploratory data taking period in 2004, an important part of the COMPASS hadron programme was conducted in 2008 and 2009. Over the years, several new components were added to the setup, according to the requirements of the hadron physics programme and also to improve the overall performance of the apparatus. Immediately upstream of the COMPASS setup, two CEDAR detectors were installed into the M2 beam line. Based on the Cherenkov effect, the CEDARs identify the hadron beam particle, separating kaons, pions and protons. A new target system, consisting of either a solid-state target holder or a liquid hydrogen target was built. A Recoil Proton Detector, surrounding the target, provides access to exclusive measurements. An accurate vertex resolution was achieved by adding nitrogen-cooled Silicon microstrip detectors upstream and downstream of the target.
Acknowledgements We gratefully acknowledge the  CERN laboratory  and the  CERN BE , EN, IT, TE and PH departments for providing constant and efficient support during the upgrade phase of our experimental setup and during data taking. We express our gratitude to the numerous engineers and technicians from our home institutions, who have contributed to the construction and later to the maintenance of our detectors and equipment. We are also grateful to  A. Altingün  for help in the preparation of the numerous figures. We acknowledge support from  MEYS  Grants  ME492  and  LA242  ( Czech Republic ),  CEA (France) ,  Bundesministerium für Bildung und Forschung, DFG cluster of excellence "Origin and Structure of the Universe"  and  DFG   Research Training Group Programme 1102 "Physics at Hadron Accelerators " (Germany), CERN-RFBR Grants  08-02-91009  and  12-02-91500 ,  Israel Science Foundation , founded by the  Israel Academy of Sciences and Humanities (Israel) ,  INFN  and  MIUR (Italy) ,  MEXT  and  JSPS  Grants Nos.
i Supported by the  German Bundesministerium für Bildung und Forschung  j Supported by  Czech Republic MEYS  Grants  ME492  and LA242 k Supported by  SAIL (CSR), Govt. of India l Supported by  CERN-RFBR  Grants  08-02-91009  and  12-02-91500  m Supported by the  Portuguese FCT -Fundação para a Ciência e Tecnologia, COMPETE  and QREN, Grants  CERN/FP/109323/2009 ,  CERN/FP/116376/2010  and  CERN/FP/123600/2011  n Supported by the  MEXT  and the  JSPS  under the Grants No. 18002006 , No. 20540299  and No. 18540281 ;  Daiko Foundation and Yamada Foundation  o Supported by the  DFG cluster of excellence 'Origin and Structure of the Universe'  (www. universecluster. de)p Supported by  EU   FP7  ( HadronPhysics3 , Grant Agreement number  283286 ) q Supported by the  Israel Science Foundation , founded by the  Israel Academy of Sciences and Humanities  r Supported by the  Polish NCN  Grant  DEC-2011/01/M/ST2/02350  * Deceased.
The soil hydraulic properties are calculated by analysing the cumulative water-infiltration curve. Several methods to estimate the soil hydraulic properties have been proposed. : (i) methods based on the  Wooding (1968)  equation, which uses steady-state data  (Smettem and Clothier, 1989; Ankeny et al. , 1991; Reynolds and Elrick, 1991) , (ii) methods based on the transient state data (e. g. Vandervaere et al. , 2000)  and (ii) methods which combine both transient and steady states, like BEST methods  (Lassabatere et al. , 2006; Yilmaz et al. , 2010). Compared to the standard steady-state water flow method, the transient water flow procedure, that requires shorter experiments, involves smaller sampled soil volumes and more homogeneous initial water distribution  (Angulo-Jaramillo et al. , 2000) , which leads to better estimates and better representativeness of the local hydraulic properties.
At first, the water infiltration was computed for the case of zero water pressure head at surface and dry initial state for 12 different synthetic soils, using HYDRUS-2D. This numerical data were analyzed with the NSH method and the estimated hydraulic values were compared to the original ones. The effect of noise on data (e. g. due to air bubbling in the Mariotte reservoir of the infiltrometer) and the consequences on the quality of NSH estimates were also numerically assessed. In a second step, the NSH method was evaluated under field conditions and tested on 266 experimental infiltration curves. The results were compared to the DL procedure, which can be considered as a reference method for the analysis of the transient state of infiltration. The influence of the infiltration curves noise and the effect of the contact sand layer on the K and S estimates was evaluated and discussed for all methods.
(  4 ), the infiltration flux can be easily defined. In addition, the use algebraic combinations and the derivation with respect to the square root of time, lead to the following expression: 3 1 2 1 2 D q C C t   (8) 3 1 2 D I C C t t   (9) 3 1 2 2 D dI C C t d t   (10) The estimation of constants C 1 and C 2 is a prerequisite for the estimation of S 0 and K 0. To do so, different procedures can be employed: (i) fitting Eq. (4) to experimental cumulative infiltration; (ii) fitting Eq. (  8 ) to the experimental infiltration rate; (iii) performing the linear regression of the dataset (t 0. 5 ,I/t 0. 5 ), or finally, (iv) performing the linear regression of the dataset (t 0. 5 ,dI/dt 0. 5 ). These methods were referred to as CI for cumulative infiltration, IF for Infiltration Flux, CL for Cumulative Linearization and DL for Derivative Linearization  (Vandervaere et al.
, 2000) , is less affected than K by the infiltration time. The NSH method appears interesting and promising since it uses the analytical expression developed by  Haverkamp et al. (1994)  that is valid for all times. Thus, opposite to other methods, this technique does not require the attainment of steady state, which can be quite time consuming for some soils. Yet, these calculations show that time duration of the dataset to be analyzed with the NSH method needs to be long enough. However, we consider that a constant water infiltration volume can be better criteria to obtain fixed accuracy for all soils. For a 10 cm diameter disc infiltrometer, we have found that this volume is about 50 mm.
96) (Fig. 11b ). For the remaining measurements, a poorer K DL vs K NSH correlation was found, where DL tended to overestimate K. Two reasons could explain these results: (i) the DL method results are highly inaccurate for noisy infiltration curves (R 2 DL < 0. 6. ) (Fig. 10b ); and (ii) the application of DL method is restricted to relatively short time (i. e. 150s). As above demonstrated, the selection of longer datasets with NSH method, that remain valid for all times, allows to increase the quality and accuracy of estimates and to reduce uncertainty. The analysis of time duration of the experiments on the estimates of K and S using the NSH method (Fig. 7 ) reveals that the infiltration time used in the field experiments (ranged between 480 and 900 s) was in most cases insufficient to estimate K and S with a 10% or 90% accuracy.
The results show that a constant water infiltration volume is a better criteria to obtain fixed accuracy for all soils. For a 10 cm diameter disc infiltrometer, we have found that this volume is about 50 mm. The method (NSH) was subsequently compared to the differential model (DL) on 266 infiltration measurements taken under different soil conditions. Compared to the DL procedure, NSH allowed more robust estimates of K and S, independently on the infiltration curve noise and the presence of contact sand layer between the soil surface and the base disc. The proposed method has the advantage to work with raw cumulative data, instead of differentiated data and to be valid for all times, which allow to consider large datasets. The results demonstrated that the NSH method means a substantial advance to estimate the soil hydraulic properties from transient water infiltration flows.
2005). Two modeling studies for TW Hya and DG Tau performed with ProDiMo show different gas temperatures in these regions ( 30 K for TW Hya;  Kamp et al. 2013; 50-600 K for DG Tau; Podio et al. 2013 ). Next to these observational studies, disk modeling has been carried out using various degrees of complexity and selfconsistency in the numerical codes. Meijerink et al. (2008)  modeled far-IR water transitions with an X-ray thermochemical code using a typical T Tauri disk model. They show that the far-IR lines probe the physical conditions in the outer disk (10-100 AU) and the level of turbulence. These lines are typically in non-LTE because of their large critical densities (n cr > 10 8-12 cm -3  Meijerink et al. 2009). Using a simplified water abundance prescription,  Meijerink et al. (2009)  find that dust depletion (due to settling, dust growth, lower dust-to-gas ratio) is required to produce the observed line-to-continuum ratio in the far-IR line emitting regions.