dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure10a,c compares experimental IDTs for stoichiometric (φ = 1. 0) fuel/air mixtures of n-C3-C6 alcohols in STs 39, 40, 48  and RCMs (this work and ref 38) at p = 10 and 30 bar with CRECK model predictions. Figure10b,d shows only the comparison with the experimental measurements obtained in this work using the RCM at NUIG. Compositions of the mixtures and operating conditions for the new data are reported in Table1. ST simulations have been carried out using an adiabatic constant volume batch reactor, accounting for the average pressure rise behind the reflected shock wave reported in the referenced experimental studies. 39, 40, 48 RCM simulations have been performed accounting for the volume histories as obtained from nonreactive experiments (Section 2). The model correctly reproduces temperature and pressure effects, as well as the different reactivity of the different fuels.
Acknowledgements The authors at  Politecnico di Milano  and  CNRS-Nancy  acknowledge the financial support of  IMPROOF  project ( H2020-IND-CE-2016-17/H2020-SPIRE-S016 ) European Union's  Horizon 2020 research and innovation program  (grant agreement no. 723706 ). The authors at NUI Galway wish to acknowledge funding from  Science Foundation Ireland (SFI)  via project numbers  15/IA/3177  and  16/SP/3829.
Contributions of each constituent of the sandwich material (front skin, core, and back skin) were calculated as a numerical average between mass loss profiles as a function of time, through each constituent thickness, using 100 integration points for each one. We included in Figure  6  experimental results measured by Legrand et al. [29]  for an appropriate comparison. The experimental data were measured on an ATLAS Cone2 cone calorimeter in the same condition using a radiative source having a heat flux of 50 kW/m 2. Both numerical and experimental curves show excellent agreement. Numerical results were obtained for a heat flux of 50 kW/m 2. Figure  6  shows the total mass loss of the E-glass/polyester/balsa sandwich sample, following the conversion of unit control volumes to real volumes shown in Figure  2 , and using Equation  (13)  to calculate the remaining mass fraction of the sandwich sample.
Contributions of each constituent of the sandwich material (front skin, core, and back skin) were calculated as a numerical average between mass loss profiles as a function of time, through each constituent thickness, using 100 integration points for each one. We included in Figure  6  experimental results measured by Legrand et al. [29]  for an appropriate comparison. The experimental data were measured on an ATLAS Cone2 cone calorimeter in the same condition using a radiative source having a heat flux of 50 kW/m 2. Both numerical and experimental curves show excellent agreement. Temperature profiles through sandwich thickness were also determined numerically (Figure  7 ). The model calculates the instantaneous temperature T(x,t) for each x-coordinate in the thickness direction of the sandwich, which of course evolves differently depending on the thermal properties of each constituent of the sandwich sample.
Simulation of nuclear systems requires complete data that represents the relevant nuclear physics. This requires many types of experimental measurements, theoretical physics, semi-empirical models and software systems, as well as experts to integrate and guide the process. This discipline is collectively known as nuclear data, and separate programmes within various European countries, the USA, Japan, Russia, and other OECD Nuclear Energy Agency (NEA) member countries have been operating for many decades. The NEA Working Party on International Nuclear Data Evaluation Co-operation (WPEC) exists to improve the quality and completeness of nuclear data by bringing together representatives of the major nuclear data evaluation projects of NEA member countries and selected Invitees. The Sub-and Expert Groups of the WPEC typically focus on specific technical topics, while the Collaborative International Evaluated Library Organisation Pilot Project (CIELO) was established to generate complete evaluations for a selection of the most important isotopes for criticality in nuclear technologies: 235,238 U, 239 Pu, 56 Fe, 16 O and 1 H.
This project stimulated numerous activities, resulting in major contributions to the Special Issue of the Nuclear Data Sheets journal and the production of a suite of new nuclear data evaluations that have been incorporated in major nuclear data libraries ENDF and JEFF. The outcomes of these evaluations include significant harmonisation of discrepancies between the independent programmes, improvement in the performance for international standard nuclear criticality and neutron transmission benchmarks, complete uncertainties for nearly all parameters and the utilisation of modern data storage technologies. This work has leveraged the considerable, parallel experimental work in collecting improved experimental measurements to support nuclear data and highlighted high-priority areas for further study.
0 [3] , JEFF-3. 3 [4]  and TENDL-2017  [5]  libraries. A follow-up activity, focusing on multiple isotopes, including the suite of plutonium, has been started at the IAEA as the International Nuclear Data Evaluation Network (INDEN) that operates as a more loosely connected joint project in the spirit of CIELO. A new Subgroup on reproducibility has been established to work hand-in-glove with INDEN and all member country projects by integrating the wealth of NEA-coordinated validation expertise, NEA tools such as NDaST and the new collaborative NEA GitLab platform.
Experimental data One of the guiding principles of the CIELO project was the direct engagement and utilisation of cutting-edge experimental techniques and data. There is no substitute for quality differential data and this provides the basis of an improved, general-purpose evaluation. Results in comparisons against integral data, including criticality benchmarks, provide many of the performance criteria that are used to judge evaluations. Impressive progress in integral assimilation methods  [6]  and the development of automated sensitivity-based tools such as the NEA NDaST  [7]  provide focus points for evaluators, while differential experiments provide the primary evidence for nuclear data evaluation. The list of measurements 1 performed during, and used in, the CIELO project are listed in Table  1.
An example is shown in Figure  1 , where the availability of new measurements (then preliminary results) directed the new 239 Pu capture evaluation. Although the Subgroup was completed, with the delivery of final files and reports, many of these experimental campaigns have continued without pause, acquiring and analysing new data 2 on the CIELO isotopes and others. In addition to these experiments, the CIELO project established a consensus on the adoption of the IAEA Neutron Data Standards  [10] , which represents a detailed, parallel evaluation. The Standards working group not only considered many experimental sources, but re-evaluated uncertainties in experiments. This resulted in a doubling of standard fission cross section uncertainties due to previously unrecognised systematic uncertainties.
Fission in 235 U and 239 Pu The re-evaluation of the IAEA Neutron Standards included a revision of the fission cross section uncertainties due to previously unrecognised uncertainties. These have a significant impact on the propagation of nuclear data uncertainties into critical benchmarks, providing a more credible estimate of the true uncertainties based on differential data  [11]. New measurements  [13]  supported a softening of the prompt fission neutron spectra for 235 U for incident energies below 2 MeV. This also directly affects the ratio of PFNS, making 239 Pu relatively 'hotter' than 235 U, as shown in Figure  2. This is best seen in the mixed metal fast systems, which had an average improvement by over 100 pcm in integral testing by S. van der Marck of the NRG  [2, 14].
Evaluation of 56 Fe The evaluation of iron includes a resonance evaluation up to the first inelastic level at 847 keV, which is followed by resonating cross sections up to around 2 MeV. New semi-differential data from RPI, as with 238 U was used to perform this component of the evaluation. As in previous evaluations, the addition of a background cross section is required above 400 keV and, in the CIELO-1 evaluation, a background was added to extend the characteristic thermal 1/v shape up to 10 keV. These are partly driven to address issues found with integral benchmark studies and are responsible for some of the improved performance of both ENDF/B-VIII. 0 and JEFF-3. 3 in integral benchmarks  [15]. Inelastic scattering above 4 MeV remained a complex issue during CIELO, with normalisation issues being raised in some of the most recent data from JRC-Geel.
Final CIELO file suites It must be noted that experimental uncertainties provide evaluators with a space of possible vales that are all be equally valid. Each nuclear data projects must support, develop and use their own models and tools to create final evaluations that meet the needs of their stakeholders. The CIELO project achieved considerable harmonisation in the adoption of Neutron Standards and resolution of several outstanding discrepancies. However, the goal of the CIELO project was not to generate one monolithic evaluation. As the project completed, two different suites of files were found to have excellent performance in validation exercises (as discussed in Section 4). To accommodate the two, self-consistent evaluated file sets that emerged from the CIELO activities, two versions were issued.
In practice, this identifies benchmarks with significant discrepancies and evaluation projects must identify ways to correct these while maintaining the agreement found on the majority of wellperforming cases. χ 2 = 1 n n i=1 (C i /E i -µ) 2 σ i 2 , (1) There are well over 4000 cases in ICSBEP and thousands were simulated during the CIELO project. Drawing meaningful conclusions from such data is often challenging, due to the sheer size of the information as well as the unquantified correlations between experiments and few benchmarks with suspiciously low uncertainties. Carefully selected suites of benchmarks, such as the Mosteller suite, are used to cover essential physics while minimising computational burden. The results for this suite are shown in Figure  3. The small subset of problematic cases for ENDF/B-VII.
Future As shown in Figure  4 , CIELO directly precipitated the most recent major nuclear data libraries. The areas that CIELO made progress on are continuing on virtually all fronts. More measurements are ongoing or planned to apply the facilities and techniques mentioned above to reaction channels in other isotopes, such as the continuing Chi-Nu experiments or RPI semi-differential measurements. These will continue to be shared through the IAEA International Nuclear Data Evaluation Network (INDEN) and WPEC as the evaluation projects maintain the co-  operation that enabled CIELO to successfully deliver on its goals. Even before the CIELO files were released through the ENDF/B-VIII. 0 and JEFF-3. 3 libraries, the community of users and evaluators were already probing the data, highlighting differences and looking for areas where we can further harmonise our data  [17].
6 10 -17. m². s -1 ) in very good agreement with previous data  [25]. At 550°C, longest oxidations are required to consolidate the first data obtained as the diffusion zone was too thin to perform hardness profile. In the temperature range tested, no nitrogen enrichment has been evidenced contrary to what is reported for higher temperature  [5]. For samples tested at 450°C, cross sections have been prepared using FIB/SEM. The oxide thickness was then measure for an average value of 170 nm after 6816 h of oxidation. The thickness for the sample oxidized 3168 h was around 130 nm but more difficult to measure due to surface roughness. For these two samples, the EDS resolution in the SEM was not sufficient to detect an oxygen profile below the oxide scale. However, a TEM cross-sectional preparation using FIB has been carried out for the M1 sample (471 h at 500°C).
2) VR SYSTEM An L-shaped Virtual Reality system called the Holospace (Barco) was used at the Groupe PSA Research Center. There were three projection screens: front, ground and lateral (right) covering a total of 4 × 2. 5 × 2. 5 meters. These three surfaces were illuminated by 3DLP projectors (Barco NW-12, 1920×1200 pixels). Front projection was used for the ground screen, via a set of mirrors, and back projection for the two other surfaces. Stereo-views were achieved through NVIDIA active 3D Vision glasses working at 120 Hz. Head motion was tracked using an ART system including 6 IR-cameras and the stereograms were adjusted accordingly. A wireless interaction device (a Flystick) was used to record participants' responses. The virtual environment was displayed using software Deltagen (RTT Dassault System) and the experimental scenario was built via a custom C++ program.
6) DATA ANALYSIS To obtain an estimate of depth perception from a single measurement block, data from 80 trials were fitted with a cumulative Gaussian function with the parameters bias, α, and variance, β. To account for potential guesses and lapses  [39] , errors independent of stimulus features, we introduced the free parameters γ and λ in the model: φ(x; α, β, γ , λ) = γ + (1 -γ -λ)F(x; α, β) (2) The parameters of this function were estimated using a maximum log likelihood criterion. PSEs were computed by taking the inverse of the cumulative Gaussian function at 50% ''more than 90 degrees''. JNDs (index of uncertainty in the task) were computed by taking the difference between the point at the 0. 75 probability level and the point at the 0. 25 probability level and dividing this value by two.
ACKNOWLEDGMENT The authors would like to thank  Marjorie Sweetko  for english language proofreading and editing of the manuscript. The authors also thank  Pascaline Neveu  for her help in the use of the frontofocometer and  Charles-Antoine Salasc  for his assistance to use the FARO arm. This study was part of the  OpenLab agreement ''Automotive Motion Lab'' between Groupe PSA and Aix-Marseille University  and  CNRS.
Physicochemical Characterization of Biodiesel The characterization of the biodiesel produced consisted of determining its intrinsic properties as fuels and then comparing them to the physicochemical characteristics of neem crude oil as well as those of petrodiesel fixed by international standards. These physicochemical characteristics, in particular the density, the viscosity, the calorific value, and the acid value, have been determined according to the same method and under the same conditions as those of the physicochemical characterization of the crude neem oil. The statistical analysis of all the data in this study were recorded in an Excel spreadsheet using Microsoft Office software version 2013 and the results are expressed as average ± standard deviation. The variation in the data collected and the statistical significance of the treatment effect were analyzed by analysis of variance.
In  [12] , a remote laboratory for control education is implemented using common communication and programming languages: HTML 5, JavaScript for client side, JSON for data transfer, and PHP/MysSQL for the server side. Everything is open source designed, even the hardware part with Arduino boards. The project SEITI RMlab is programmed with the same logic (HTML5, JSON, etc. ) running an electromechanical practical work  [13]. Another open source project is presented in  [14]  where a solar tracking system using Raspberry Pi and open source hardware cards are used to obtain a low cost remote system. The same solutions are used in a remote smart grid demonstrator  [15]. In the other way, solution spreading to collaborators is common. Some projects have already validated their solution like the UNED network  [7]  or the PEMCWebLab  [16]  and more recently Lab@Home  [17]  to only cite some examples.
On the other hand, our project takes inspiration from the SCADA system and particularly in open source SCADA. Proprietary software is expensive and there is a problem of interoperability with infrastructures or devices. The open source SCADA platform, Thinger. IO, designed for Internet of Things (IoT), is implemented in  [15, 18] ; however, this solution is not entirely free, even if the cost is acceptable. In fact, low or medium size industries face the same issues than engineering education centers: for example, ref. [19]  works on Object Linking and Embedding (OLE) for process control (OPC) with a Python language. The work of  [20]  demonstrates an important effort to work on sensors/actuators with open source software and hardware, and they use the free tool Easy Java and JavaScript Simulations (EJS) to carry out their experiment.
To introduce the intersection of remote laboratory and SCADA system literature, some smart grid applications have been considered because these projects intermix SCADA architecture and energy monitoring on the education laboratory. These applications have an important need for control and supervision of power generation and use. The Smart Electric Power System (SEPS) Laboratory  [21]  is an example; however, it is programmed with LabVIEW, namely a proprietary solution. In the same manner, a remote laboratory based on SCADA architecture has been proposed by  [22]  using Programmable Logic Controller (PLC) to manage the laboratory and a Visual Studio with an ASP. NET solution for the web interface. Otherwise, ref. [23]  have demonstrated a low cost SCADA system with open source hardware (Arduino) and common programming language (HTML5, JavaScript, and PHP) on a microgrid test bench for academic and research applications.
Similarly to open source SCADA, interoperability issue has been studied in remote laboratories to carry out standardization on different communication protocols, programming software, and data exchange  [24]. In relation to the state of the art, it appears that there is not a real open source system (entirely open) and all developments are specifically dedicated for one system or experimental laboratory. Most of the time, proprietary software is used (e. g. , Matlab or LabVIEW). Some authors have already addressed low cost and open source solution, but, to the best of our knowledge, our work is one of the first projects to work on fully open source and modular solution to offer a wide interoperability. In this logic, software (server and controls) and hardware solution have been designed to ensure interoperability with hardware on the server side and with software on both the server side and client side.
For teachers, Laborem is implemented both for entire laboratory courses, or to prepare practical work before face-to-face practical work. It represents a new educational tool to mix practice with theory, as well as to offer a multi support enhanced learning process. Another important point is to provide simplified access to the laboratory, independently of opening hours, and, in particular, each student can work at his own pace. Only one test bench is necessary to perform a wide range of practical works within one course; this represents a low-cost solution and the opportunity to share with other educational centers. The initial prototype of Laborem used a technically advanced solution running on the Windows Server operating system. The proprietary LabVIEW software from National Instruments was used to program and to create web remote access, which allows for control benchmark equipment and to exploit data results.
Student Table  1  compares the original Laborem version with the current version, highlighting the differences and evolution. To summarize our solution, firstly, the cost of the solution is reduced by utilizing open source software, a low cost server (Raspberry Pi) and developing our acquisition card. Secondly, the interface compatibility is improved employing standard web protocols while good hardware compatibility is kept thanks to open source and widely used libraries. With this solution, new perspectives were opened such as integration with a Learning Management System (LMS) and creation of an e-lab network to share practical works between universities. The Laborem architecture is centered on the server, here a Raspberry Pi which hosts the web Human Machine Interface (HMI) accessible by a student.
Programming Language The core software is PyScada, a free and open source SCADA application written in Python with a web HMI  [29]. The high-level free and open source Python framework used is Django, which allows for creating database-driven web applications. Algorithms, settings, and data models are written in Python. The Django framework core can be seen as a Model-View-Controller (MVC) architecture. As described in Figure  3 , this Python based MVC architecture uses an Object-Relational Mapper (ORM) to manage the data model and interact with the database, an URL dispatcher to receive the HTTP requests, and a web template system to generate dynamic HTML5 pages. Alongside HTML5, the application uses JavaScript and CSS (Cascading Style Sheets) to enable interactive web pages. The SCADA system with HTML5 HMI allows students to interact with various instruments and view the data on any recent web browser, independently of the operating system of the final user.
HMI Django application In PyScada, all the processes are working asynchronously and communicate through the database. It means that, on one side, the HMI queries the values to display and writes the user actions to the database. On the other side, scripts that send and request data to the equipment also query the database to know the user action. Alongside these, scripts feed the database with instrument answers relative to the original request. These processes are managed by a master process named the scheduler. The Raspberry Pi 3  [30] , which is a single board computer (SBC), replaces the previous personal computer (PC) that was used as server. It is an all-in-one, low-cost hardware solution, shared by a large community, so it is an interesting and widely chosen solution, for example  [8, 12, 19, 31]  use a Raspberry, whereas  [7]  uses a BeagleBone Black board.
Communication Protocols In order to have interoperability with many instruments, the system has to be compatible with several communication protocols (VISA, Modbus, etc. ) and several hardware interfaces (USB, Serial, Ethernet, etc. ). PyScada offers a wide range of protocols for SCADA systems based on the wide Python open source community and its useful applications such as PyUSB, PySerial, PyVISA, and PyModbus. All the protocols implemented and used are graphically illustrated in Figure  4. The developed solution is a multi-protocol and multi-instrument system. It works like a bank of instruments where one can easily choose the instruments connected to the Raspberry Pi independently of the hardware interface used. For instance, one can switch between two oscilloscopes, HP 54603B and Tektronix MDO3014, using GPIB and USB connection, respectively, where each instrument has a dedicated handler file.
Instruments Plug selection The Printed Circuit Board (PCB) was firstly designed with Multisim and Ultiboard (National Instruments proprietary software), but new ongoing projects are made with KiCad (open source software). All of the PCB designs are open source. Connectors and plugs are designed in PCI Express logic. This is a well-known electronic standard, it simplifies PCB design, and reduces cost because it only uses one connector (female). In addition, this solution is widely used and has a good mechanical resistance. This choice ensures hardware interoperability with a simple connector that fits all applications and robustness for a long life utilization. The motherboard is designed to limit human needs. The teacher needs to set up the configuration of the practical workbench at the beginning of a new course and to select the 16 different plugs that can be used by students.