dataesr/scientific-paragraphs-categorization huggingface dataset:
CyDet Trigger Hodoscope The CyDet trigger hodoscopes (CTH) are placed at the upstream and downstream ends of the CDC to generate the first level trigger. Figure  37  shows the location of CTH in the cross sectional view of CyDet. Their position defines the fiducial region which is for tracks entering the CDC between the CTH counters and then triggering after the first or subsequent turns. Each hodoscope consists of 48 modules, each module comprising a plastic scintillators and a Lucite Cherenkov counter, separated by a few cm, as shown in Fig. 38. The Cherenkov counters, together with the scintillation counters, identify electrons from the protons from nuclear muon capture and cosmic-ray muons. The Cherenkov and scintillation counters are tilted by specific angles to the tangent of the concentric circles so that a four-fold coincidence (two-fold in both Cherenkov and scintillator rings) can be made with a high acceptance for the signal electrons and a reduction in the fake triggers caused by γ-rays as shown in Fig.
Prototype tests A beam test using 155 MeV/c electrons was carried out to evaluate detector performance without a magnetic field. Figure  40  shows typical waveforms measured in the beam test. The scintillator modules produce more light than the Cherenkov modules, and higher light yields are recorded for the upstream modules, which have shorter light guides. For all four modules types, the readout meets the required S/N ratio. Figure  41  shows the distribution of the difference in detection time between the Cherenkov detector and the scintillator (Both were arranged closely so that the electron beam hit both counters). From fitting the distribution, the combined ∆T resolution is measured to be 0. 8 ns which meets the requirement of resolution better than 1 ns. CTH trigger rates A CyDet trigger is made by a four-fold coincidence of two adjacent CTH pairs of a scintillation and a Cherenkov counter.
StrECAL Trigger In the StrECAL mode (right figure of Fig. 82 ), the trigger is provided by the ECAL. The energy deposition from a single track can be divided among several crystals and so a summation is necessary to reconstruct the full energy. The summed energy over crystals which form a 4 × 4 square can effectively include almost all the energy deposited by electrons with energies of about 100 MeV. The basic trigger unit (cell) will therefore be a group of 2 × 2 crystals ( one ECAL crystal module), and the total energy determined by using the sum of an array of 2 × 2 trigger cells referred to as a trigger group. The effectiveness from simulation is shown in Fig. 83  with at least a 10 6 DIO rejection for around a 90 % conversion electron detection efficiency. formed by analogue-summing the preamplifier outputs and hence 16 signals going to the digitising readout and 4 signals to a dedicated pretrigger board.
Partial event building Figure  88 : Schematic of the event building front-end processes running on a front-end PC Table  10 : Experiment operating parameters assumed in this section. In most cases these are not fixed and will be optimised. In tests of 8 GeV SX, the accelerator cycle time is 2. 38 s, during which each spill lasts around 0. 5s. For the CyDet The basic trigger rate for the CTH is about 26 kHz. Even with the shielding the rate is still mostly due to low-energy particles. Using the CDC hit information, this can be reduced to a more manageable 1. 3 kHz. Signal-like triggers (from the high energy tail of DIO) should pass the high level trigger with probability close to 1, and as such represent a minimum floor for the online readout rate. Older simulations that don't incorporate the latest software developments suggest a rate of a few hundred per second.
Online Software MIDAS  [89]  will be used for the DAQ software, which will be made of several distinct parts. Front-end PCs will run detector-specific programs to read directly from the hardware and asynchronously send packed data to the back-end PC using MIDAS RPCs (Remote Procedure Calls) to store the data in the form of MIDAS banks. The back-end PCs will run the following: • An event builder to combine the MIDAS banks from several front-ends into a single MIDAS file. • An "online converter and monitor" to convert the data into ROOT format and produce histograms for live monitoring and offline analysis. • A "logger" to write the MIDAS data to disk. • A "run control" program to start, stop and monitor runs. Most of these programs will be implemented within COMET's ICEDUST software framework which has the benefit of being based on the T2K-ND280 framework, which also uses MIDAS, and as such already has libraries to perform these transformations.
ICEDUST Framework For the calibration, reconstruction and analysis of data from COMET Phase-I, it is essential to have an offline software framework that treats this data in the same way as for simulated data. Since the development and testing of a new framework is a significant undertaking, it was decided that the COMET framework be based upon an existing framework that has been well tested in a real data-taking scenario. A number of existing frameworks were compared against the requirements of COMET. And the framework used for the near detector for the T2K experiment  [90]  at J-PARC, known as ND280, would be the best choice upon which to develop the ICEDUST, which had already seen several years of data-taking and debugging and use in published physics analyses. It also includes a novel interface with the MIDAS online data acquisition system.
The structure and the data flow of ICEDUST are illustrated in Fig. 89. Notably, data from the experiment and the simulation are processed in the same way, following the same steps through the chain. The important components of ICEDUST are as follows: • Data formating: The oaEvent package provides the event data format both for simulated and real data, as well as the geometry information. The experimental data of MIDAS data format is unpacked to oaEvent format. • Simulation: While Geant4 plays the central role in simulation, any other package can be cooperated in generating and simulating events. The simulated data of each proton are collected into a proton-bunch event. The detector responses are applied afterwards. • Reconstruction: A global reconstruction includes the signal track finding and fitting, and subsequent analysis.
This generates simpler event data with the format provided by oaAnalysis package. Details are described in the following sections. Significant improvements have been made to the software in recent years, and the physics content of the software is essentially independent between the experiments, given the differences between COMET and T2K ND280. Many of these improvements are now being fed back into the ND280 software. To ensure that software developed for ICEDUST is done in a consistent way across the collaboration, naming and coding conventions have been defined and followed.
Data Formats The key strength of the approach used is the ability to treat experimental data on such an equal footing as the simulated data. This is achieved in two ways: • An unpacking mechanism which converts the raw MIDAS data into offline root files. • A wrapping package which can provide a semi-transparent method to process raw data. The core event structure of ICEDUST is provided by oaEvent package. Data in the the oaEvent format is built by a hierarchical structure of objects from the TNamed class of ROOT, that provides a stronger memory management policy and avoids the need for global pointers to access the output data hierarchy. Collections of data are stored in a similar manner with the map class of Standard Template Library (STL) of C++. The description of the geometry is stored alongside the data, either in the form of a hash-tag pointing to a particular archived geometry which is automatically retrieved as needed, or else as a persisted ROOT object.
Simulation The simulation of COMET has been sub-divided into smaller tasks. The standard simulation chain for production Monte-Carlo data is: 1. Simulate production target. This is done separately so we can use packages that include different hadron production models. 2. Particle tracking in Geant4 (SimG4). Geant4 has a highly optimised tracking algorithm as well as many well tested experiment-based physics models. This package tracks particles from the production target to the various detectors and produces the simulated energy deposits. 3. Detector Response Simulation. Energy deposits produced by SimG4 are converted into realistic detector outputs such as ECAL crystal waveforms or CyDet wire hits. Various detector effects such as finite resolution, cross-talk and random noise can be added here.
4. Rare-process selection. Occasionally we may wish to focus a study on one of the rarer signal or background processes. Since this would normally require the simulation of a lot of unimportant processes, a package is being developed to merge hits from rarer processes together to artificially increase their statistics. Up to truth information, steps 1 and 2 can be done by any of the various external Monte Carlo packages that have been incorporated into ICEDUST. These include Geant4  [55] , MARS15  [56] , PHITS  [91]  and FLUKA  [92, 93]. Figure  90  shows the implementation of this simulation procedure in ICEDUST.
Geometry definition SimG4 is the package tasked with running Geant4 tracking and producing geometry files that all other packages use through the ROOT format described above. This task is made non-trivial by the necessity for a highly detailed description of the geometry in order to check all possible sources of backgrounds combined with COMET's staged approach meaning the experiment is likely to change quite drastically throughout its lifetime. In practice, a user writes the geometry in C++ code, then defines all the parameters in a Geant4 macro which is processed by a "messenger" and "controller", as shown in Fig. 91. These parameters can be defined using complex expressions involving other parameters, which provides a flexible way for building the geometry.
Reconstruction The aim of the reconstruction software is to take a collection of hits stored in a ROOT file, either from simulation or experimental data, and produce a collection of reconstructed objects such as tracks and clusters. This requires track or cluster finding and fitting code specific to each detector system. GENFIT  [95]  is integrated into ICEDUST, which provides: • integration with SimG4; • the ability to run on experimental data; • integration with analysis codes; • a simple interface to validate geometries and magnetic fields. One important requirement of the ICEDUST framework is to provide a full audit trail that allows an exact recreation of an analysis plot. This requires persisting the precise process used by the reconstruction to create the reconstruction objects. This will obviously be different depending on which code is used, e.
N RMC = N proton × R µ-stop/p × B RMC93 ×P γ-e × A geo × A mom × A time × ε tracking , (25) No experimental data of the photon spectrum from RMC on aluminium near the endpoint is available and so theoretical predictions must be used to extrapolate to the endpoint. Following Hwang  [97] , the spectrum based on Hwang-Primakoff theory is given by R(x) = C(1 -2x + 2x 2 )x(1 -x) 2 , (26) where x = k/k max , k is the photon energy and C is a constant. For the overall normalisation, C, we make use of the measured rates of RMC on aluminium from Reference  [98]. The result of this extrapolation is shown in Fig. 98. From this, the probability per muon capture of producing a photon with energy exceeding 93 MeV is estimated to be B RMC93 = 2. 97 × 10 -7. One aim of COMET Phase-I will be to measure the RMC photon spectrum on aluminium.
In order to study cosmic ray-induced backgrounds, two kinds of simulation studies are being considered. One is a general approach in which cosmic rays are generated widely around the COMET experimental hall. It is useful to examine the overall performance of the CRV and characteristics of cosmic ray-induced backgrounds. The second is a focused approach, in which some specific combinations of location and direction are chosen and cosmic rays are generated in these areas. In particular the second studies will be made for the locations where the cosmic ray veto is weak or does not provide complete coverage. Geant4 simulations are used to estimate cosmic ray-induced backgrounds. The data set of cosmic rays is based on a CERN input file  12  which contains about 23 million µ ± events. A full air shower simulation code based on CORSIKA was used.
Plan for the Beam Measurement In case of the beam momentum and profile measurement, the beam power reduction by a factor of 1000 is necessary, as the tracking reconstruction is required by StrECAL. This beam power reduction corresponds to 2 × 10 9 protons on target per second, and consequently 10 kHz hit rate in StrECAL. With this hit rate, the measurement less than an hour will be enough for the beam measurement. When including the positive particle measurements, a few days of data taking will be sufficient for the momentum and profile measurements. For the beam timing measurement, track reconstruction is not required and hence the Detector Solenoid and the Straw detector can be turned off during this measurement. PID will rely on the ECAL signal shape discriminator only. When the Detector Solenoid is off, the hit distribution in the ECAL is almost uniformly distributed, and the momentum spectrum above 40 MeV/c is essentially unaffected.
Background Assessment Programs The background processes of µ-e conversion in aluminium have never been measured in most cases. The background estimations described in Section 10 are based on simulation. The measurement of background sources is one of the most important goals for COMET Phase-I, for understanding the Phase-I data and for preparing Phase-II experiment. Examples of such measurements are summarized in Table  16. Muon decay in orbit The electron momentum spectrum of Muon decay in orbit (DIO) in the high-momentum region near the endpoint have not been measured. In the COMET Phase-I experiment, CyDet will be used to measure the DIO electron spectrum precisely with a momentum resolution of around 200 keV/c. Cosmic-ray-induced background Cosmic rays are potentially a significant source of backgrounds.
Acknowledgements We thank  KEK  and  J-PARC, Japan  for their support of infrastructure and the operation of COMET. This work is supported in part by:  Japan Society for the Promotion of Science (JSPS) KAKENHI  Grant Nos. 25000004  and  18H05231 ;  JSPS KAKENHI  Grant No. JP17H06135 ;  Belarusian Republican Foundation for Fundamental Research  Grant  F18R-006 ;  National Natural Science Foundation of China (NSFC)  under Contracts No. 11335009  and  11475208 ;  Research program  of the  Institute of High Energy Physics (IHEP)  under Contract No. Y3545111U2 ; the  State Key Laboratory of Particle Detection and Electronics of IHEP, China , under Contract No. H929420BTD ; Supercomputer funding in  Sun Yat-Sen University ,  China ;  National Institute of Nuclear Physics and Particle Physics  ( IN2P3 ), France;  Shota Rustaveli National Science Foundation of Georgia (SRNSFG) , grant No.
As a consequence the first solvation shell of lithium cations includes both water and anions. It was previously reported that as the concentration of the mixture increases the coordination environment is expected to change from solvent-separated ion pairs to a majority of contact ion pairs and even ionic aggregates. 12, 18, 50 In the present work, we employed the trajectory analysis program TRAVIS  51  to analyze the coordination of the molecules in these electrolytes. Its methodology is based on Voronoi tessellation:  52, 53  The subsets were defined so as to match the three types of molecules (anions, water and lithium) in the mixtures, and two subsets were considered to be neighbours if they share at least a common face. This approach provided us with valuable information on the neighborhood of the reference subset, and the results are included in Figure  4a.
Simulation details MD simulations of WiS electrolytes were carried out using the LAMMPS package  55  (typical input files for all the systems are provided as Supplementary Data). All cubic simulation boxes contained 473 water molecules and 128 salt ion pairs that were randomly distributed using Packmol. 56 The SPC/E water model was used in these simulations,  57  whereas the parameterization of the anions was made in the framework of the CL&P 58 force field for ionic liquids, for which we employed the set of parameters reported in Ref. 33. Lithium cations were modelled as a single site whose Lennard-Jones (LJ) parameters are = 6. 25 kcal/mol and σ = 1. 25992 Å. It must be noted that the charges of both ions were uniformly scaled by a factor of 0. 8 so as to accelerate the dynamics of the mixtures, which was shown necessary to have good agreement with the experimental results in LiTFSI WiS.
42 In order to reach proper density, each system was firstly equilibrated at 298. 15 K and 1 bar for 4 ns in the NpT ensemble by using Nosé-Hoover thermostat and barostat  [59] [60] [61]  with relaxation times of 10 and 500 femtoseconds, respectively. The results are included in Table  1 ; an error of 1 % w. r. t. experimental data is obtained for the LiTFSI-based liquid 42 (no data is available for the other systems). Then we performed a second equilibration of 60 ns within an NVT ensemble, followed by a production run of around 90 ns (with a time step dt = 1 fs) that was used to obtain structural and dynamic information about the systems.
Acknowledgments This work was supported by the  French National Research Agency (Labex STORE-EX , Grant No. ANR-10-LABX-0076 , and  ANR BALWISE , Grant No. ANR-19-CE05-0014 ). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 771294 ). We acknowledge support from  EoCoE , a project funded by the  European Union  Contract No. H2020-EINFRA-2015-1-676629 , from the  DSM-Energie programme of CEA  and from the  Eurotalent programme. This work was granted access to the HPC resources of CINES under the allocation A0080910463 made by  GENCI.
These authors propose to complete the current set of EUROP indicators (based on carcass weight and sex, conformation and fat cover) with five other ones, namely: hindquarter weight, meat color, retail cut yield, rib eye area and marbling score. In 2018 and 2019, a survey was conducted in various French slaughterhouses to determine, from the point of view of operators, which are the most important carcass quality indicators to consider (unpublished data). This work was carried out with 13 organizations marketing meat after slaughter, representative of the diversity of operators: four producer organizations or cooperatives, four slaughterhouses, three butchers and two breeders engaged in direct sales. In this study, we processed and analyzed data using an interface of R (R Core Team 2018), named IRaMuTeQ.
Based on R software and python language, IRaMuTeQ extracted information from texts using descriptive statistics  [11]. This survey did not intend to be exhaustive but rather to provide elements to validate the indicators proposed by [10] within the 1st French agricultural region: "Nouvelle-Aquitaine". The objectives of the survey questionnaire were to: • determine what are the expectations of the operators in the sector (slaughterers, butchers, direct sales farmers, cooperatives, etc. ) regarding carcasses, according to their customers and market requirements • determine what constitutes an optimal quality carcass for different breeds and categories of animals according to the various stakeholders • establish minimum quality thresholds to be reached for each of the specifications or each of the customer types • highlight the criteria for assessing carcass quality Outputs of this study were first the expectations of the various stakeholders in the meat sector in the region of "Nouvelle-Aquitaine" in terms of carcass quality for suckler cows.
Regression Models We recently developed a new computational methodology that simultaneously selects the best regression model and the most interesting covariates  [17]  to predict carcass and/or meat quality. With such a method, we might predict one parameter using many variables. For instance, it could be possible to predict beef tenderness by various breeding factors and/or by animal performance. In the modvarsel R-package, different models were tested (the linear regression, the PCR and the Slice Inverse regression, but also the random forest) but other ones (Support Vector Machine-[SVM], Ridge, Partial Least Squares-[PLSR]) could easily be practically implemented by the user. For each model, a number of selected variables has been reported. More precisely, this R package was used to select the proteins that could be considered predictive of meat tenderness among a pool of 21 candidate proteins assayed in semitendinosus muscle from 71 young bulls of the European ProSafeBeef project (Figure  2 ).
This approach allows: (1) clarifying the interactions among different parameters of interest (for instance: animal performances, nutritional value, meat quality traits), and (2) assessing how to simultaneously control different parameters of interest that are not always positively correlated. To test this method, data from 71 young bulls of the European ProSafeBeef project were used  [14]. For each animal, 97 variables were collected and organized in three sets of data, characterizing animal efficiency and performance, nutritional value and sensory quality  [20]. A clustering of variables was conducted using the R-package ClustOfVar. This is a dimension reduction method that can be a helpful tool to select variables. Indeed, each synthetic variable from ClustOfVar is a linear combination of a subset of original variables (whereas the principal components in principal component analysis are a linear combination of all the original variables).
This model was constructed from a large database of consumer tests using a standard protocol. A dozen parameters having a statistically significant effect on eating quality, such as traits characterizing animals, pre-slaughter and slaughter conditions, meat, and post-mortem events are considered in the model as well as the interactions between them. In practice, the slaughterhouse is the backbone of the system. A specific grader, who is accredited after training and who is periodically trained, grades the carcasses for marbling, fat and meat color, ossification, which is an indicator of age and more precisely of physiological maturity. Temperature and pH are also recorded. The MSA model then predicts the overall quality score of MQ4 on a scale of 0 to 100, as well the quality class for each piece of meat associated with a cooking method and a specific ageing time.
All factors related to the animal, to its carcass and its meat are recorded and included in the model for prediction. So far, marbling, ageing time and the carcass hanging method were not taken into account in France. However, a French private company (Beauvallet/CV Plainemaison) has launched a new premium beef breed called OR ROUGE based on these traits. Research on the application of the substantial data gained using MSA protocols in Europe and elsewhere will continue within the activities of the International Meat Research 3G Foundation. This Foundation was launched in 2017 under the auspices of the United Nations Economic Commission for Europe (UNECE)  [52, 53]. The Meat3G foundation is likely to develop a new MSA-like model to predict eating quality of beef across countries and based on data gathered in different countries.