dataesr/scientific-paragraphs-categorization huggingface dataset:
The FEA accumulation period is always centered on the corresponding time t of the AROME forecast, with a time step of 5 minutes. For instance, the 10 min FEA is accumulated between t-5 min and t+5 min but the 15 min FEA is accumulated between t -5 min and t + 10 min. Data from the whole training dataset are concatenated, flattened and sorted. Points where both graupel mass and FEA are null are removed and model fitting is performed on normalized data. To compare the observed and modelled FEA for different time accumulations, they are all divided by their respective accumulation period to obtain comparable products expressed in fl min -1 and hereafter called Flash Extent Rate (FER). The date of the 26 May 2018 was selected to study the influence of the different accumulation period. On this day, a series of storms formed north of Spain, west of the Pyrenees at 08 UTC.
e. selecting a sufficient amount of days to sample the data, and concatenating and sorting them. Secondly, it was shown that combining proxy variables into multivariate models does not improve FEA prediction overall, implying that adding dynamical variables is unnecessary to forecast FEA. Several proxy combinations were tested, selected to avoid redundancy in the dataset, but none of them presented better FSS than microphysical proxies alone. Thirdly, results from an existing lightning calibration from the literature  (McCaul et al. , 2009)  were compared to the hereby calibration. Using a similar methodology to adjust regression functions and to deduce the calibration parameters, the predicted FEA for the validation days exhibits amplitudes largely inferior to the observed one but with an areal coverage similar to what is obtained with the microphysical proxies alone.
Historical background -The original Contiki OS The original Contiki Operating System (OS)  [3]  was open sourced in 2006, but development had started as early as 2003. It was designed for resource-constrained wireless sensor devices with code memory in the order of 100 kB and less than 10 kB of volatile memory. Contiki was a major step in the evolution of modern IoT operating systems, with its main strengths being: • Use of the standard C programming language 1 ; application developers no longer needed to learn a bespoke language such as nesC. • Event-based kernel. In conjunction with tickless, platformspecific main loop code (implemented for some platforms) it allowed for fast reaction time to external events and energy-efficient execution. • Cooperative multi-threading based process API  [4].
The Contiki-NG project Contiki-NG introduces many new features, but it also re-useswith or without modification -many of the features of the original Contiki OS, such as the scheduler, the event-based kernel, data structure manipulation libraries and storage. Contiki-NG also uses with minor modifications multiple networking-related software components, such as the original implementations of 6LoWPAN and RPL (henceforth called ''RPL-Classic''). Contiki-NG primarily targets Arm Cortex-M platforms. The official repository includes support for hardware by Nordic Semiconductor, NXP, OpenMote, Texas Instruments, and Zolertia. All those platforms are powered by Cortex-M3 or -M4 chips. Outside the main repository exist numerous Contiki-NG forks that have added support for other hardware, for example platforms powered by ST Microelectronics chips such as those used at the FIT IoT-Lab testbed.
The Contiki-NG architecture and features Broadly speaking, the Contiki-NG source base can be conceptually broken down into two parts: (i) hardware-independent and (ii) hardware-specific. The former hosts portable, cross-platform implementations of all hardware-agnostic components of the OS, including the kernel, software timers, data structure libraries, and networking protocols. The latter provides the code required to make the OS work on specific devices. It consists of drivers for hardware components including timers, radio interfaces and other on-chip and offchip peripherals, such as Universal Asynchronous Receiver/Transmitter (UART), Serial Peripheral Interface (SPI), Inter-Integrated Circuit (I2C), LEDs, user buttons and sensing elements. In order to make the addition of support for more devices as effortless as possible and in order to increase code portability, Contiki-NG defines Hardware Abstraction Layers (HALs) for common interfaces, including General Purpose Input/Output (GPIO) and SPI.
5) to prevent regressions. The implementation is also interoperable with the ''Copper (Cu)'' 8 and ''Copper for Chrome (Cu4Cr)'' 9 browser addons. Contiki-NG also supports version 1. 0 of the Open Mobile Alliance (OMA) Lightweight M2M (LWM2M) specification with plain text, JavaScript Object Notation (JSON) and Type-Length-Value (TLV) data formats. The implementation supports the LWM2M security mode with pre-shared keys and the server and security objects for configuring security. The LWM2M engine implementation is accompanied by an implementation of the LWM2M ''IP for Smart Objects'' (IPSO) objects, which be used as an example starting point by developers wishing to adopt LWM2M for their work. The implementation is interoperable with Eclipse Leshan 10 as well as with Eclipse Wakama 11. Interoperability with Leshan is also tested as part of our CI workflow (Section 2.
Continuous integration testing Contiki-NG is automatically tested using a Continuous Integration (CI) workflow on GitHub Actions. This workflow replaced the older test suite on the Travis-CI 16 platform with the release of version v4. 6. The test suite is triggered automatically each time a code change gets merged into one of Contiki-NG's main git branches, as well as each time a pull request is opened or updated. All pull requests must pass all CI tests before they can be considered for inclusion in the official source codebase. All source code changes must take place through a pull request, including code changes proposed by members of the Contiki-NG maintainers team. This strategy enforces scrutiny by peers, therefore increasing overall code quality and reducing the likelihood of introducing errors.
The Contiki-NG test suite comprises seventeen jobs, with each job executing multiple CI tests. The test suite covers the following code elements: • Successful compilation of code examples for multiple hardware platforms under multiple different configurations. • Validation of the correct operation of various elements of the networking subsystem through multiple Cooja simulations and native code execution scenarios. • Successful compilation of the doxygen API documentation (Section 2. 4). Users who wish to exercise their code with Contiki-NG's CI workflow can do so trivially by enabling GitHub Actions on their fork repository. Alternatively, the Contiki-NG docker image provides all tools required to execute the test suite locally on their computers.
Nightly builds In addition to the per-contribution CI described above, we perform automated nightly testbed runs on real hardware: a bespoke, 25-node testbed installed at RISE SICS. Each node consists of both a control device (Raspberry PI) and a set of sensors (at the time of writing, a Zolertia Firefly and a JN516x dongle). Every night, a cron job schedules four 2-hour experiments, each with a different configuration of the network stack. Control nodes deploy to all 25 Firefly devices a firmware that performs response-request communication between the root node and every other node in the network. Each experiment involves over 10k round-trip network packet exchanges over multiple hops on power-saving devices. A log file post-processing script extracts key metrics from each run: end-to-end round-trip delivery ratio, latency, radio duty cycle, hop counts, and frequency of network topology changes.
In the case of TSCH, radio interrupts are disabled. Using the NETSTACK_RADIO API, the platform-independent TSCH implementation polls the radio driver for received frames at the correct time during timeslot operation, thus ensuring the timing accuracy required by TSCH. Frames get processed by the implementation of the respective protocol as they are handed upwards the network stack, and the application code gets notified by way of a function callback. Under the /examples directory, Contiki-NG provides numerous example projects that can be used as first steps with Contiki-NG, or as a starting point by users who wish to develop their own application. These examples cover all elements of the network stack described in this paper, as well as all hardware abstractions. To try out the example in this section, or any of the other existing examples on the repository, we recommend using the Contiki-NG Docker container image 19 and accompanying wiki documentation.
Impact Since its open source release in 2006, the original Contiki OS has been used by numerous research projects funded by a host of organisations, for example: (i) The European Commission (EC) under Horizon Europe, Horizon 2020 (H2020), as well as by previous framework programmes, (ii) Various national research funding bodies, such as the UK's Engineering and Physical Sciences Research Council (EPSRC) or the Swedish Knowledge Foundation. The added value and benefits of Contiki-NG can be summarised as: • Simplified porting to new hardware platforms due to removal of legacy code, and due to new, platform-independent system initialisation, main loop code and HALs. • Simplified development of new features due to improved documentation and cleaner examples. • Increased code quality due to modern development practices including the git-flow workflow, continuous integration, nightly testbed runs, and a script to test in excess of 1200 project builds for all platforms.
Acknowledgements The authors are the individuals among the current group of Contiki-NG maintainers who were available to contribute to the preparation of this manuscript. While the authors have written a very significant proportion of the source code, we do not claim ownership of the entire codebase. Over the years, source code has been developed and contributed by in excess of 250 individuals, including hobbyists, students, researchers and industry professionals. The GitHub ''contributors'' page 21 provides a list of contributors within the last 10 years, but the full code history dates back to 2006. With that in mind, first and foremost we gratefully acknowledge individuals who have contributed their work to  Contiki-NG  and the original Contiki OS since its first open source release in 2006.
To supply the models predicting the JOG and ROG layer thickness, the TAF-ID (Thermodynamics of Advanced Fuels-International Database  [3] ) thermodynamic database is being developed on the (U-Pu-O)-(I-Cs-Te-Mo)-(Fe-Cr-Ni) system using the CALPHAD method  [2, 3]. In the CALPHAD method  [4] , the Gibbs energies of all the phases (solids, liquid, gas) in the binary and ternary sub-systems are described as a function of temperature, composition, and pressure (only for the gas phase). By extrapolation from the sub-systems, the thermodynamic equilibrium in a multi-component system can then be calculated by minimizing the total Gibbs energy of the system. This powerful method has been extensively used since the '80s to calculate phase equilibria and thermodynamic properties of multi-component systems.
In these models, the thermodynamic parameters are assessed by fitting the available phase diagram and thermodynamic data. Nevertheless, the propagation of these fitting data uncertainties onto the thermodynamic parameters featured in the Gibbs energy functions in the CALPHAD models are rarely estimated. A few works have been published on this topic, see  [5] [6] [7]  for the most recent analyses. Moreover the propagation of these uncertainties on the calculated phase diagram and thermodynamic data are almost never determined. However it is crucial to estimate these uncertainties, in particular when these models are used in multi-physics codes like for instance the Fuel Performance Codes (ALCYONE and GERMINAL) which is used to simulate the thermomechanical behavior of a fuel pin  [1, 2]. The influence of these uncertainties on the physical properties and the behavior of the fuel (margin to the melting, thermal conductivity,.
) has to be studied. In the present work, the thermodynamic modeling of the U-Te binary sub-system is first performed using the PARROT module in the Thermo-Calc software  [8]. In a second step, a Bayesian approach is applied to estimate the uncertainties on the thermodynamic parameters and their propagation on the calculated phase diagram and thermodynamic data. The state of the art on the crystal structure data of the U x Te y compounds, the phase diagram, and thermodynamic data is first presented in Section 2. The models to describe the Gibbs energy of the phases are described in Section 3. From these selected data and Gibbs energy models, a least-square method is applied to assess the Gibbs energy functions of the phases. In Section 4, the results of the fitting procedure are presented and the uncertainties on the assessed thermodynamic parameters are estimated using a Bayesian approach.
Phase Diagram Data The phase diagram data coming from the literature are summarized in Table  2  and reviewed in this section. Slovyanskikh et al. [13]  investigated the UTe-Te region using differential thermal analysis (DTA) and X-ray diffraction. From the DTA measurements, a eutectic reaction between UTe 5 and Te and the peritectic decompositions of UTe 5 , UTe 3 , U 3 Te 7 , UTe 2 , and U 3 Te 5 were highlighted. A polymorphic transition for UTe 2 was also found to occur at 1125 °C. The compounds U 2 Te 3 , UTe 2 , U 3 Te 5 , U 3 Te 7 , UTe 3 , and UTe 5 were identified using X-ray diffraction. A small region of homogeneity was assumed for UTe 2 and U 3 Te 5. Finally, liquidus temperatures were carefully measured from UTe 2 to Te. The U rich region from 0 to 65 at. % Te was later investigated using the same methods by  Ellert et al.
Thermodynamic Data All the data are reported in Table  3. No data exist on the liquid phase and very few measurements were performed on the solid compounds. Westrum  [21]  estimated the standard entropies of UTe, U 3 Te 4 , U 2 Te 3 , UTe 2 , U 2 Te 5 , and UTe 3 compounds. There are not any heat capacity measurements. Only estimations are provided by Czechowicz  [19]  and Wolf  [20]. The estimation values from Czechowicz  [19]  are significantly higher than those from Wolf  [20]  at high temperatures. Due to the lack of experimental data, the C p (T) data of Wolf  [20]  based on the Kopp-Neumann rule are considered in the present work for all compounds. All the enthalpy of formation data are reported in Figure  1. The enthalpy of formation for UTe and U 3 Te 4 was measured by direct-reaction calorimetry using a Calvettype high-temperature liquid metal (tin) solution calorimeter.
The values are equal to: -182. 4 ± 11. 3 kJ/mol for UTe and -228. 0 ± 1. 3 kJ/mol for UTe 1. 33. The enthalpy of formation for UTe 2 was also estimated to be -318 kJ/mol by the authors assuming a linear relation to the Te/U ratio. Czechowicz  [22]  reported uncertainties in the work of Baskin et al. [23]  due to the uncertain percentage of each phase in the samples for calorimetric measurements. Later Mills  [24]  reviewed the data and assigned them larger uncertainties: -182. 4 ± 20. 9 kJ/mol for UTe and -684. 1 ± 83. 7 kJ/mol for U 3 Te 4. These solely experimental data of Baskin et al. [23]  for UTe, U 3 Te 4 are selected in this work. There are no other enthalpy of formation measurements. However in 1985, Czechowicz  [19]  published estimated thermodynamic data (∆H 0 f ,298. 15K , S 0 298.
15K , C p (T), ∆H f usion ) on UTe, U 3 Te 4 , U 2 Te 3 , U 3 Te 5 , UTe 2 , U 3 Te 7 , UTe 3 , and UTe 5. The authors only mentioned that the thermodynamic data were obtained from the literature or estimated without any further explanation. Recently Wolf  [20]  derived a Calphad model on U-Te. The authors also reported thermodynamic data (∆H 0 f ,298. 15K , S 0 298. 15K , C p (T)) for the compounds. For all the compounds except UTe and U 3 Te 4 , the data of Czechowicz  [19]  were selected. The data of Wolf  [20]  were discarded due to the disagreement with the data of Baskin et al. [23]  for U 3 Te 4. More recently, density functional theory (DFT) calculations were performed on U-Te compounds by Qian et al. [25]. Additionally, other DFT data are available in the OQMD database  [26]. Very large discrepancies are found between both DFT studies.
Gibbs Energy Models All the Gibbs energy functions are referred to the enthalpy of the pure elements in their stable state at 298. 15 K and 1 atm (designated as H SER i ). For the pure elements U and Te in their different states, the Gibbs energy functions are taken from the SGTE pure database  [29]. For all the stoichiometric compounds, the Gibbs energy function is expressed as follows G φ m -∑ i b φ i H SER i = a 0 + a 1 T + a 2 Tln(T) + a 3 T 2 + a 4 T -1 (1) where b φ i is the stoichiometric factor of element i in the compound φ and a n are unknown parameters to be estimated. For the U x Te y compounds, the a n coefficients (with n = 2, 3, 4) are fixed using the C p (T) functions provided by Wolf  [20]  assuming a Kopp-Neumann law. For the liquid phase, the ionic two-sublattice model  [30]  is chosen to keep the consistency with the model for this phase in the TAF-ID database  [3].
Optimization Procedure As previously mentioned, the C p (T) functions provided by Wolf  [20]  were selected which allowed the a 2 , a 3 , and a 4 coefficients to be fixed for all the compounds. As there are no experimental data on the melting enthalpy of UTe, the value from the Solgasmix database published by  Czechowicz (1985)   [19]  was taken. A first set of parameters (a 0 , a 1 ) for UTe was assessed as well as the entropy of melting of the compound using the following data: ∆H 0 f ,298. 15K , S 0 298. 15K , ∆H f usion , and T melting. The other compounds were progressively introduced and interaction parameters in the liquid were assessed to have a starting set of variables. All the compounds were forced to be stable at 298. 15 K by setting their driving forces to be equal to 0. The optimization was then performed with PARROT to reproduce both the thermodynamic and phase diagram data.
On the contrary, the data calculated with the model of Wolf  [20]  lead to a systematic underestimation of the partial pressure data. In conclusion, the CALPHAD model reproduces most of the selected data. However, the enthalpy of the formation of UTe is underestimated. This could be due to the inconsistency between the enthalpy of formation of UTe and the partial pressure data as already pointed out by Czechowitz  [22]. Comparison with the experimental data from Baskin et al. [23] , the models from Czechowitz  [19]  and Wolf  [20] , and the DFT calculations from Qian  [25]  and the OQMD database  [26]. Comparison with the estimated data from Westrum  [21]  and the model of Wolf  [20]. To improve the reliability of the model, new measurements of the following data would be useful: Te activity, mixing enthalpy of formation in the liquid, enthalpy of formation, and heat capacity data for the compounds, phase diagram data in the U-UTe 2 region.
For the standard entropy data at 298 K, only estimations are available. Due to the absence of experimental data, the Kopp-Neumann law was used for the heat capacity function for the compounds. The only other experimental data available are vapor pressure measurements in two phase regions: UTe-U 3 Te 4 , UTe 2 -U 3 Te 5 , and U 2 Te 5 -UTe 3. Input thermodynamic data for the model were carefully selected. The phase diagram from pure U to UTe 2 is quite uncertain. In fact, only a few liquidus data were measured. On the contrary, the phase diagram is very well established from UTe 2 to pure Te. The CALPHAD model reproduces very well the experimental vapor pressure data and enthalpy of formation for U 3 Te 4. However, the calculated enthalpy of formation for UTe is underestimated by 13% compared to the measured value.
Steroids play key roles in various biological processes and are characterized by many isomeric variants which makes their unambiguous identification challenging. Ion mobility-mass spectrometry (IM-MS) has been proposed as a suitable platform for this application, particularly using collision cross section (CCS) databases obtained from different commercial IM-MS instruments. CCS is foreseen as an ideal additional identification parameter for steroids as long-term repeatability and interlaboratory reproducibility of this measurand are excellent and matrix effects are negligible. While excellent results were demonstrated for individual IM-MS technologies, a systematic comparison of CCS derived from all major commercial IM-MS technologies has not been performed. To address this gap, a comprehensive interlaboratory comparison of 142 CCS values derived from drift tube (DTIM-MS), traveling wave (TWIM-MS) and trapped ion mobility (TIM-MS) platforms using a set of 87 steroids was undertaken.
Besides delivering three instrument-specific CCS databases, systematic comparisons revealed excellent interlaboratory performance for 95% of the ions with CCS biases within ±1% for TIM-MS and within ±2% for TWIM-MS with respect to DTIM-MS values. However, a small fraction of ions (<1. 5 %) showed larger biases of up to 7% indicating that differences in the ion conformation sampled on different instrument types need to be further investigated. Systematic differences between CCS derived from different IM-MS analyzers and implications on the applicability for non-targeted analysis are critically discussed. To the best of our knowledge this is the most comprehensive interlaboratory study comparing CCS from three different IM-MS technologies for analysis of steroids and small molecules in general.
With a focus on database-driven identity confirmation of small molecules, several experimental CCS datasets and databases for small molecules have been published in recent years. Besides extensive multiclass databases such as the CCS Compendium 30 , several datasets for certain compound classes are publicly available and include metabolites  31  , lipids  32  and steroids  5, 9, 10, 14. In addition to experimental CCS databases, the number of computationally (in silico) predicted CCS libraries, either based on structural predictions based on density functional theory calculations  [33] [34] [35]  or machine learning is also increasing  8, 36, 37. However, a comprehensive evaluation of the comparability of CCS derived from different classes of IM-MS instruments is still lacking and assessment of possible differences is mandatory before such databases can be applied across different classes of IM-MS instruments  19, 38.
Nevertheless, uncertainties on these reference CCS values have to be considered as these are propagated into secondary calibration strategies for determination of CCS across all three major instrument types  23, 46. For these reasons, the true merit of applying CCS in steroid analysis demands for a comprehensive study of all major commercial instruments including their prescribed external calibration procedures. To address these open questions, we undertook a largescale comparison of three major commercial IM-QTOF instrument classes for application to the analysis of steroids. For this purpose, new DT CCSN2 and TIM CCSN2 reference datasets were established to complement existing single laboratory and interlaboratory reference TW CCSN2 values  9, 14. To the best of our knowledge, this is the only comprehensive study comparing experimental CCS values for steroids using all three commercially available technologies relevant to this application (i.