dataesr/scientific-paragraphs-categorization huggingface dataset:
Flames that pass through the PbO fog are significantly slowed, as observed in  [69]. While TEL has been demonstrated to be an excellent knock inhibitor, lead unfortunately poisons catalytic converters by chemically bonding to active surface sites of the catalyst and forming lead alloys. This reduces catalyst performance by decreaing the fraction of surface area available to promote CO and UHC oxidation, while it increases the distance through which the reactant molecules must diffuse within the pore structure before undergoing reaction. Disposal of lead contaminated catalysts can be environmentally problematic and very costly. Mohamed  [273]  investigated the effects of diethylamine (DEA) (doped at ~5% mol) on stoichiometric n-pentane and n-heptane mixtures in synthetic air. Pressure-time data and total light output data (via a photomultiplier) were recorded.
The increasing understanding of fluid and gas dynamic processes, which can limit the capabilities of both devices, has greatly improved the agreement between experimental data and model predictions, where these must take into account not only the compressed conditions, but the evolution of boundary layers and other physical phenomena that can be influential during a particular test. Advances in instrumentation and understanding of various physical processes, such as properly suppressing vortex roll-up, has also led to much better consistency between RCM datasets. For example, studies of iso-octane autoignition can now be shown to agree to within ±25 % across a variety of different machines  [62] , where the origin of variations in individual measurements can be traced to the very high dependence of autoignition chemistry on gas temperature history and its spatial distribution.
Some mixtures and state conditions are more sensitive to facility effects, while other reacting systems are less so. To date there are no definitive metrics or rigorous guidelines that can be used across a wide range of conditions towards evaluating chemical kinetic models, or comparing datasets between different RCM facilities, and other experimental apparatuses. Theory on ignition phenomena have started to address these issues, as seen by recent advances in defining ignition regimes in terms of dimensionless parameters (presented in Section 4), but this remains a serious need for the entire combustion community. Significant progress has been made towards generating consensus for 'best practices' towards machine design and operation, some of which were discussed in  [39] , with additional detail presented in sampling work, combined with mechanistic information from other studies.
There is a need to extend the current database with increasing use of time-resolved diagnostics, some of which are highlighted in Section 3. Speciation via gas sample analysis along with additional absorption and LIF diagnostics will increase both the extent of mechanistic information for a range of fuels, as well as provide additional constraints on absolute and relative reaction rates, which are critical for the construction and confident use of rate rules  [372, 485] , and for converging on unique solutions to represent combustion chemistry. Challenges of adequately closing the carbon, or atom balance for tests with large molecular weight species can be mitigated by, for example, coupling the sampling and analytical units with heated glass-lined tubing  [486]. The use of time-resolved absorption diagnostics tuned to specific species is evolving quickly  [487] [488] [489] [490] [491], and while currently limited, could add further to the arsenal of diagnostics providing mechanistic information, particularly as longer and shorter wavelength sources become more accessible and reliable.
Philipps-Universität Marburg [64-67], Leary et al. at the Massachusetts Institute of Technology (MIT) [68,69], and Fish et al. at Shell Thornton [70-73]. Griffiths et al. at the University of Leeds [74-78] and Minetti et al. at Université Lille Sciences et Technologies (ULST) [79-84] expanded upon these and acquired more quantitative datasets utilizing sophisticated diagnostics over a wider range of conditions. Recent developments in LTC and DLE engines require extension of experimental databases covering an expansive range of pressures (p = 5-100+ bar), temperatures (T = 600-1100 K), and dilution levels (O 2 = 5-30 %), from very lean to rich fuel loadings (f = 0. 25-4. 0), and a range of residual gas concentrations (EGR = 0-60 %), specifically to understand the role of CO 2 , H 2 O, NO x and UHC present in dilution strategies.
higher fluorescence signals compared to acetone, and toluene fluorescence has high sensitivity to temperature for the conditions studied. The authors used a frequency quadrupled Nd:YAG laser with 10Hz repetition rate and the fluorescence data were recorded with a 10 Hz sampling rate using an intensified charge coupled device (ICCD) camera. As with earlier investigations, multiple tests were required in order to build a composite dataset of the evolution of the temperature field. Their optical arrangement yielded similar spatial resolution as Mittal and Sung [148]  of about 0. 1 mm/pixel. Careful calibration methods were used to create accurate, absolute, quantitative temperature measurements. The spectroscopic theory and data analytics applied are well described in Strozzi et al. [122]. Some results of their study are shown in Fig.
The pulses are of 3-5 nC, 5-10 ps, with an electron energy of 6-8 MeV, at a repetition frequency of 10 Hz. 16, 17  The transient absorbance was measured with a broadband pumpprobe detection device, whose principle was already described elsewhere. 18 In the visible range, a transient absorbance due to the fused silica cell windows was detected in the empty cell when irradiated,  19  and was subtracted from the absorbance data of the cell containing the DEC solutions. The broadband pump-probe system was operated in two configurations, one for the visible using a single crystal of CaF 2 for continuum light generation, one using a single crystal of yttrium aluminum garnet (YAG) for continuum light generation optimized on the near Infrared (NIR). Probe and reference beams were each coupled into an optical fiber, transmitted to an adapted spectrometer, and dispersed onto the specific line scan detectors.
33 × 10 4 L mol -1 cm -1. The dose per pulse in water was typically around D = 48 Gy. Spectro-kinetic data analysis. The data matrices resulted from the averaging of about 30 scans, after removal of singular points arising from outlier laser shots. 12 They were then corrected for a wavelength-dependent baseline in order to be analyzed by a multivariate curve resolution alternating least squares (MCR-ALS) approach. 22, 23 The number of absorbing species in a global matrix was assessed by Singular Value Decomposition (SVD)  24  of the global matrix, and a MCR-ALS analysis with the corresponding number of species was performed. Positivity constraints were imposed for both spectra and kinetics. The residual maps were systematically inspected to assess the absence of model defects. The corresponding MCR-ALS code was adapted from the ALS package by K.
Spectro-kinetic data analysis. As several transient species are present, the global analysis of spectrokinetic data matrices was performed, including all data available for neat DEC and 0. 05, 0. 1, 0. 5 and 1 mol L -1 LiClO 4 concentrations in DEC. The 400 -1400 nm wavelength range was selected and, to exclude signals with very low signal/noise ratios, we removed the signal around 780 nm corresponding to the fundamental of the probe laser, which is present in the supercontinuum. We imposed the absorption band of the solvated electron and the radical DEC(-H) ▪ obtained independently from previous study. 32 The SVD analysis of the global data matrix shows that in addition to the solvated electron and the DEC(-H) ▪ there are 3 distinguishable components. The MCR-ALS analysis with 5 species results in a lack-of-fit (LOF) of 5.
Acknowledgements This work was supported by a public grant from the  "Laboratoire d'Excellence Physics Atom Light Mater" (LabEx PALM)  overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  (reference:  ANR-10-LABX-0039 ). Financial support from the  national FT-ICR  network (FR 3624  CNRS ) for conducting the research is also gratefully acknowledged. We thank  Jean-Philippe Larbre  and  Pierre Jeunesse  for their help during the experiments at ELYSE, and  Jean-Marie Teuler  for assistance in the use of GIBBS code. The authors want also to thank  Vincent Steinmetz  for his help in the HRMS experiments. Jean-Pierre Baltaze  is gratefully acknowledged for the NMR experiments.
) that allows for individual control of each injector and up to five injections per cycle for each DI injector. This system also controls all of the on-engine actuators, such as rail pressure, VGT position, VSA position, and EGR valve position. In-cylinder pressure data were acquired with Kistler model 6125C pressure transducers, which were flushmounted via the machined glow plug ports on each cylinder. Each transducer was connected to a Kistler model 5010 dual-mode amplifier, and the resulting signals were fed into the National Instruments system. Calculation, monitoring, and recording of combustion metrics were performed using Drivven combustion analysis software. High-speed data synced to the crankshaft encoder (in-cylinder pressure, injector current probes, etc. ) were recorded for 300 consecutive cycles for all four cylinders, and low-frequency data (flow rates, temperatures, pressures, emissions, etc.
Further details on the matching between the two engines will be provided in the forthcoming optical study. The first set of experiments, described in Table  5 , consisted of sweeps of the DI SOI timing for various fuel combinations. For each test, the sweep started at either 2180°CA after top dead center (aTDC) or the stability limit (ability to maintain combustion phasing within 61°CA across all cylinders for the duration of data acquisition period) and ended when the peak pressure rise rate exceeded 15 bar/°CA or combustion efficiency fell below 80%. The first three tests all used neat PRFs, with PRF0 as the DI fuel and PRF100 as the PFI fuel, yielding a PRF difference between fuels (DPRF) of 100. These tests were performed at premix ratios of 70%, 80%, and 90%, resulting in a global PRF70, PRF80, and PRF90, respectively.
Possible explanations for this change will be discussed in the following sections. As a means of quantifying the control authority of SOI over CA50, robust linear least squares fitting of CA50 versus SOI was performed within the RCCI regime for each of the SOI sweeps, using data from all four cylinders. For each case, the average CA50 from each cylinder was fitted to a first-order polynomial with weights v = 1=s 2 to reduce the impact of cylinders with higher variance. The fitting was performed using MATLAB's robust least squares algorithm with bisquare weights, which reduce the impact of points farther from the fitted line. The range of SOI used for each fit and the resulting slope and R 2 values are provided in Table  7. The ULSD case was binned into two  regions, both of which included the point at 250°CA aTDC.
2015; Pokhrel et al. 2016) , which has been suggested as being due to feedback from such objects. In this paper, we present far-infrared (70 µm-500 µm) observations towards the Monoceros R2 GMC. The observations were performed using the Herschel PACS and SPIRE instruments. We complement these observations using SCUBA-2 data (in the submillimetre regime), Spitzer and WISE data (in the mid infrared range) and IRAM 30m Telescope observations (in the millimeter domain). The observations and data reduction are presented in Section 2. In Section 3, we give a qualitative description of the overall region. Section 4 presents dust temperature and column density maps of Mon R2. Section 5 presents the IRAM 30m kinematic data, and discusses its significance for the region. Section 6 details the methods used to detect starless and protostellar cores, fit their fluxes to SEDs and classify them.
The area of overlap between the two instruments' scans was about 0. 85 • × 0. 85 • (or 12 pc × 12 pc), centred on the J2000 coordinates 06 h 07 m 30 s -06 • 15 00. A Herschel composite map (70 µm, 160 µm, 250 µm) is shown in Figure  1. The data were reduced using the Herschel Interactive Processing Environment  (HIPE, version 11. 1. 0; Ott 2010). SPIRE data were reduced using the SPIA (SPIRE Interactive Analysis; Schulz 2011) pipeline. This corrects for relative bolometer gains and then maps the data with the HIPE Destriper, which adjusts the bolometer timelines iteratively until they converge. The SPIRE data used the HIPE point source calibration  (Bendo et al. 2013; Griffin et al. 2013). This produces two sets of maps, three calibrated for "point source" emission (in Jy/beam), and three for "extended emission" (in MJy/sr), which have also been given the zero-point emission offset using Planck data.
Other observations In addition, mid-infrared data were used. These included 24 µm observations with the Multiband Imaging Photometer for Spitzer (MIPS;  Rieke et al. 2004; Werner et al. 2004 ), which were taken from the Spitzer Heritage Archive. Data were also taken from the AllWISE Source Catalog, a catalogue of objects discovered as part of the Wide-field Infrared Survey Explorer (WISE;  Wright et al. 2010)  and NEOWISE  (Mainzer et al. 2011)  missions. In both cases, the data were used only to calculate source bolometric luminosities (defined here as the integral of flux densities from 2 µm to 1 mm;  Tigé et al. 2017 ). In addition, 3. 6 µm data from Spitzer 's Infrared Array Camera (IRAC;  Fazio et al. 2004 ) were used, solely for source visualisation, as seen in Appendix D. Both Spitzer and WISE are partially saturated at the central hub and cannot be used to determine the luminosities at that location.
The basic parameters of the overall source dataset (temperature, mass, bolometric luminosity, and reference size) are given in Table  3. We note that not all of the bound cores are likely to be true prestellar cores; although such objects could be detected separately at this distance (the 160 µm resolution is ∼0. 05 pc at Mon R2, while prestellar cores have sizes of 0. 1-0. 2 pc;  Roy et al. 2014) , the larger bound cores are potentially clusters of several prestellar cores. These are likely to be on the path to star formation. As can be seen from Table  3 , those sources in the central hub (defined here by the N-PDF excess from  Schneider et al. 2015 , which is equivalent to column densities above 3. 5 × 10 22 cm -2 ) are both hotter and more luminous than those outside. There appears to be a difference in the mass ranges, too, with the more massive sources found in the hub.
Acknowledgements. We are grateful for the comments of an anonymous referee, which have helped us to improve this paper. The Herschel spacecraft was designed, built, tested, and launched under a contract to ESA managed by the  Herschel/Planck  Project team by an industrial consortium under the overall responsibility of the prime contractor  Thales Alenia Space (Cannes) , and including  Astrium (Friedrichshafen)  responsible for the payload module and for system testing at spacecraft level,  Thales Alenia Space (Turin) responsible  for the service module, and  Astrium (Toulouse)  responsible for the telescope, with in excess of a hundred subcontractors. SPIRE has been developed by a consortium of institutes led by  Cardiff Univ. (UK)  and including  Univ. Lethbridge (Canada) ;  NAOC (China) ;  CEA ,  LAM (France) ;  IFSI ,  Univ.
Additional funds for the construction of SCUBA-2 were provided by the  Canada Foundation for Innovation. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This publication makes use of data products from the  Wide-field Infrared Survey Explorer , which is a joint project of the  University of California, Los Angeles , and the  Jet Propulsion Laboratory/California Institute of Technology , and  NEOWISE , which is a project of the  Jet Propulsion Laboratory/California Institute of Technology. WISE and NEOWISE are funded by the  National Aeronautics and Space Administration. N. S. acknowledges support by the  ANR-11-BS56-010  project "STARFICH". Part of this work was supported by the  French National Agency for Research (ANR)  project " PROBeS" , number  ANR-08-BLAN-0241.
The analog output can seem like an obvious choice as it can immediately be integrated into existing systems. However, it is important to note that: (i) in transmitting the analog output, additional noise and signal attenuation may be introduced to the analytical signal adding increased uncertainty to the resulting pH, and (ii) the analog output does not include the temperature measurement associated with each Durafet, while the digital output does. The digital output is therefore preferable for use in laboratory settings. Four UDA-Durafet units were routed through an Ethernet switch, in order to transmit eight pH and temperature data streams to one computer for data logging (Figure  1 ). A customized National Instruments LabVIEW TM (2013) program was developed in order to control signal averaging and sampling frequency (Supplementary Material).
Following methods for Durafets in oceanographic sensor packages, signal averaging was set to 10 measurements per data point, resulting in a maximum sampling frequency of 1 min. For experiments lasting longer than a day, a sampling frequency of 5 min was used. As resolution of the UDA digital output (<0. 0001 units pH) is much greater than that of our calibration methods (0. 001), data logging was set to a resolution of 0. 001. UDAs will display a temperature-compensated factorycalibrated value for pH (NBS scale) and temperature for each Durafet. We note that it is necessary to distinguish between the built-in sensor temperature compensation for the Durafet and the optional solution temperature compensation available on the UDA user interface. Solution temperature compensation is an added feature of the UDA that is not appropriate for use in seawater, as it applies only to specific industrial process and/or medical applications.
5 L) with flow-through, pH-and temperature-controlled seawater (pH T 8. 0-8. 1 at 14 • C). The seawater was homogenized with a motorized paddle (6 rpm) to reduce spatio-temporal mismatch sampling errors, which could arise in static water. The pH of discrete seawater samples was determined spectrophotometrically (Ocean Optics, USB2000+ with CUV-UV-10 cuvette holder) via the standard protocol  (Dickson et al. , 2007). Five replicate seawater calibration samples were drawn sequentially from the calibration vessel into 10-cm path length cuvettes and immediately analyzed for pH T at 22 • C using purified m-cresol purple (R. H. Byrne, University of South Florida). Temperature corrections were calculated in R (version 3. 2. 3) using the seacarb package  (Gattuso et al. , 2016)  and average values of recent total alkalinity and salinity measurements.
Total alkalinity (2,559 ± 10 µmol kg -1 , N = 92) and salinity (38. 3 ± 0. 2, N = 92) in our experimental system do not exhibit enough variation to influence the temperature correction. Immediately following in situ pH calculation (<30 min), the pH value was entered manually using the UDA interface for each electrode. Due to rounding errors and time delays between processing the calibration sample and value entry on the UDA, manual calibration leads to small inter-Durafet differences (∼0. 01 units pH T , found by comparing pH observations of across multiple Durafets in the same solution). To correct for this error, a postcalibration reference pH sample was collected and analyzed while the LabVIEW program was logging data (Step 3, Table  2 ). This sample serves two important roles. First, it allows users to check the accuracy of the manual calibration, in order to trust the pH displayed on the UDA during an experiment (or document the offset).
Performance of Multiple Durafets Performance of eight Durafet electrodes was assessed over a range of pH and compared with reference pH samples analyzed via the spectrophotometric pH method. Eight calibrated Durafets were suspended in a calibration vessel, as described in the previous section, set in a sea table controlled at 14 • C. Data logging occurred at 1 min frequency and addition of pure CO 2 gas was used to decrease pH T to 7. 25. Every 30 min, seawater pH was incrementally increased via the addition of high pH seawater (pH T 8. 0, bubbled with CO 2 -free air) or 0. 1 M NaOH for pH T > 7. 95. Three replicate reference samples were taken at every pH step. The reference samples taken at pH T 8. 25 were used for post-processing of the Durafet time series. This postcalibration correction allowed for comparison among Durafets at a resolution greater than that of the UDA front panel.
Estimated Uncertainty In order to evaluate the performance of UDA-Durafets, we estimated the uncertainty (i. e. , accuracy) associated with the spectrophotometric pH measurements. For our laboratory, the uncertainty of spectrophotometric pH measurements is 0. 008 units pH T , which stems from the accuracy of the purified m-cresol purple based on use in certified synthetic seawater Tris buffer (±0. 006 units pH T ;  Kapsenberg et al. , 2017)  and precision of replicate measurements pulled sequentially from the calibration vessel (±0. 005 units pH T ). Precision of replicate measurements (±0. 005 units pH T ) may also reflect the functional resolution of the spectrophotometer. As such, all Durafet pH data are reported to a precision of 0. 001, despite the fact that pH precision of Durafets in seawater extend beyond this  (Martz et al.
DISCUSSION This study outlines a straightforward method for high quality data acquisition using the UDA-Durafet configuration for laboratory-based ocean acidification research. Our methods greatly improve time course documentation of pH and temperature and alleviate person-hours devoted to documenting such conditions. To facilitate implementation of this technology in marine research laboratories, we included instructions for setting up UDA-Durafet connections via Ethernet and we have made our LabVIEW program available online (Supplementary Material). This technology enables execution of complex experimental design based on fluctuating pH treatments, multi-drivers, and other long-term manipulations in order to pursue research priorities in global ocean change biology. Prior to investing in this technology, potential users should evaluate the trade-offs associated with Durafet technology compared to alternatives like discrete analyses and glasselectrode pH characterization.
Recommendations We recommend a step-by-step protocol for use of Durafets in the UDA configuration in laboratory-based seawater systems (see Table  2 ). This protocol includes, in chronological order, steps for calibration, sensor performance assessment, and data reporting. In short, following sensor conditioning to seawater (Step 1), a manual calibration on all Durafets is performed using the UDA front panel while all Durafets are in the calibration vessel (Step 2). Data logging should be active and a post-calibration  As reference samples are used to calibrate Durafets (Step 2 and 3) and assess performance (Step 5 and 6), the quality of UDA-Durafet data depends entirely on the quality of the reference samples. It is essential that these samples are collected from seawater at stable temperature and pH.