dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Analysis All recordings were transcribed and coded using software for qualitative analysis (MaxQDA). Two researchers independently coded 4 metrics of the aggregated dataset: 1. Time-on-task: the average time the participants spent on executing a task. 2. Hints: the average number of hints that were given during the execution of a task. 3. Success rate: the proportion of participants that completed the task successfully without any hints, completed the task successfully with hints, and could not complete the task. Errors: the average amount of errors that were made by participants during the execution of a task. A distinction was made between the following: strategy errors: not knowing how to approach the task (eg, not knowing how to add exercises to the weekly schedule); interaction errors: not knowing how to execute the strategy (eg, unable to find the play button); and operating errors: being unable to operate the device (eg, swiping).
Acknowledgments The authors thank  Jesse Beekman  and  Aduen Darriba Frederiks  for developing the app. Also, gratitude is extended to  Janine Cornelissen  and  Diede Kesler  for their help with the usability study. This work is part of the research projects  MOTO-B (Motivating Technology for Older Adults' Behavior ; funded by  Netherlands Organisation for Scientific Research , the  Netherlands Organisation for Scientific Research , grant number  023. 006. 013 ) and VITAMIN (VITal Amsterdam elderly IN the city; funded by the  Amsterdam University of Applied Sciences program Urban Vitality).
Concept of the serious game and the eGreen platform The Greenplay solution was composed of 3 main connected components: -Data collection is provided by smart meters to monitor electrical consumption installed in more than 150 homes for about 1. 5 years -A customized platform eGreen ( http://www. egreen. fr/ ) to inform occupants about their own energy consumption. This platform is accessible through login and password sent to each dwelling in order to help people to reduce their consumption with customized advices and questions that provide "Greenies" (points). Diverse advices are proposed to occupants and have been designed based on scientific literature and are to be spread through the eGreen platform and the Apolis Planeta serious game using the sub-module called smart advice generator.
Split units were sporadic. In France nearly all apartments had a roomwise programmable thermostat. In Spain roomwise control was also general, but approximately 40% had manual thermostat. In the majority (90%) of the apartments electric water heating in a tank (50-150 l) was installed. Nearly all apartments (90%) had an exhaust ventilation with temporarily operated fans and balanced ventilation with heat recovery was in 2% of the homes only. Household devices and their operation schedules were also investigated. The monitoring of the energy consumption and indoor air temperature was conducted in the participating homes. The monitoring was running from January 2017 till the end of June 2018. During the project there were homes who backed out or the data collection was temporarily or permanently interrupted (users were able to disconnect the sensors).
After closure of the monitoring data collection the data was evaluated and where data quality did not fulfil certain criteria those datasets were excluded from the analysis. The main criteria were as follows: • General consumption (covering all electric use in the home): more than 85% of daily consumption data available for the period 01. 01. 2017-30. 06. 2018 Other criteria were applied as well such as too high heating consumption (more than realistic low power mode consumption)   in summer or the sum of heating and DHW consumption should be lower than general consumption. There were homes where it has turned out that some additional appliances were connected to the heating (or to the DHW) circuit, thus the heating data included the consumption of these additional units as well. It was decided case by case if such datasets were included into the analysis or not depending on the significance of the related error.
Data collection In order to monitor the consumption of the homes and get numeric results about the impact of the experiment, consumption monitoring devices were installed in the participating homes. The applied technology pack included sensors, transmitters and associated internet platform eGreen ( Figs. 7  and 8  ). The devices worked using ammeter clamp sensors, measuring current in the electric cables on the principle of the electromagnetic effect. Three sensors were installed in the electric panel of each home to monitor the general, the heating and the water-heating consumption. All homes were equipped with electric heating and electric domestic hot water heating devices. A transmitter sent data of consumption to a wireless gateway. This gateway transmitted the information in a secured way to the internet box of the household.
In addition, an indoor air temperature sensor was installed as well to measure thermal comfort of the home. Consumption data were registered on a private internet platform, reachable through the eGreen website ( www. egreen. fr ). Each home had access to view its energy consumption, both real time and historical data. Diverse functionalities were proposed by the connected energy-focused serious game (Apolis Planeta) to users to encourage sustainable behaviors, in a social and entertaining way: monitoring of consumption, decrease objectives, comparison with friends or anonymous neighbors results, advices or alerts in case of overconsumption.
Outdoor temperature data Measured outdoor temperature data were retrieved from Ogimet Weather Information Service's closest weather station corresponding to each sample home  [53]. Both daily and weekly average data were used for different evaluation purposes. Fig. 9  shows weekly data for the three pilot areas from January 2017 to June 2018. VIGO and OPAC show very similar evolution, due to the similar weather conditions (proximity of the Atlantic coast). LE COL is located in a more continental area and therefore annual amplitude is higher (slightly colder winter, slightly warmer summer). However, in all three areas moderate winter and summer conditions were registered during the monitoring period with (weekly average) temperatures above zero most of the time ( Fig. 10  ).
Analysis of heating consumption data Analysis of weekly heating consumption data was carried out for those users where more than 80% of weekly consumption data was available for weeks 5-22 in 2017 and for the same period in 2018. There were 47 homes fulfilling this criterion. The heating energy consumption of apartments was in the range of 0-72 kWh/m 2 year. The low figures can be explained by the moderate climate (average outdoor temperature of the heating seasons were in the range of 6. 84-10. 52 °C).
Other electric consumption Analysis of general (total) consumption data was carried out for those users where more than 85% of daily consumption data was available for the period 01. 01. 2017-30. 06. 2018 (LE COL: 19 homes, OPAC: 11 homes, VIGO: 19 homes). Altogether 49 homes were analyzed. Periodic (annual) consumptions were calculated from the average of daily consumptions of days with valid data. The general consumption of apartments was in the range of 59-158 kWh/m 2 year with one outstanding value of 268 kWh/m 2 year. Other consumption means consumption of all electric devices used in the home excluding heating and DHW production. It was determined as the difference of measured general consumption and heating + DHW consumption.
Statistical analysis Study data were collected and managed using Research Electronic Data Capture (REDCap) electronic data capture tools hosted at Clermont-Ferrand University Hospital  [10]. REDCap is a secure, web-based application designed to support data capture for research studies, providing (1) an intuitive interface for validated data entry; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for importing data from external sources. Statistical analysis was performed using Stata software (version 13, StataCorp LP, College Station, TX, United States). The tests were two-sided, with a type I error set at α = 0. 05. Continuous data were presented as mean ± standard-deviation or median (interquartile range) according to statistical distribution (assumption of normality assessed using the Shapiro-Wilk test).
Categorical parameters were presented as frequencies and associated percentages. To assess the factors associated with CFREM at W52, univariable analyses were realized using usual statistical tests: for continuous outcomes Student t-test or Mann-Whitney test when assumptions of t-test were not met (normality, homoscedasticity assessed by the Fisher-Snedecor test) and for categorical data chi-squared or Fisher's exact tests. Regarding Fcal, ROC curve analyses were performed to determine the best thresholds to predict CFREM. The optimal threshold was determined according to clinical relevance and usual indexes reported in medical literature (Youden, Liu and efficiency). According to univariable results, a multivariable analysis (logistic regression) was carried out considering an adjustment on relevant clinical parameters.
2%) reminding that clinical remission is a necessary but not sufficient therapeutic goal in patients with CD. We also investigated the measurement of CRP value at W12 in patients receiving anti-TNF agents as predictor of mid-term CFREM (W52). While CRP normalization after 12 wk demonstrated moderate performances (PPV = 63. 2%) to predict CFREM at W52, the absence of CRP normalization at W12 was highly predictive of treatment failure (NPV = 95. 2%). Our data seems slightly different from two studies dedicated to CRP normalization as predictor of remission. Reinisch et al  [12]  reported in a post-hoc analysis of the landmark ACCENT 1 trial that normalisation of CRP at week 14 led to a higher probability of maintained response or remission during one-year of infliximab maintenance therapy (P < 0.
Elevation Model Production and Filtering We produced a digital terrain model (DTM) for the study site from two HiRISE stereo images; PSP_002932_1445 and PSP_003209_1445. The DTM was produced with the software packages ISIS3 and SocetSet following the workflow described by  Kirk et al. (2008). The relative vertical precision of the DTM is 0. 11 m, calculated as maximum resolution/5/tan (convergence angle) (cf. Kirk et al. , 2008). To minimize the influence of small errors and uncertainties in the DTM, we applied a 3 × 3 low-pass filter (cf. Kolb et al. , 2010)  and thereafter filled sinks via standard GIS procedures.
Science requirements and user needs As the mass signals related to the science objectives cover a range of spatial and temporal scales, in accordance with  Pail et al. (2015b). and IGSWG (2016) a threshold scenario is de ned, which allows us to make a signi cant advance with respect to our current knowledge, and a target scenario, which means a breakthrough in the way we use gravity data in Earth mass transport monitoring. Specically, the science requirements for MOBILE are: (1) To recover temporal gravity and mass variations in the Earth system with a 150 km spatial resolution or better, with a global coverage; (2) To recover small amplitude mass variations with an approximately 5 times (threshold) to 50 times (target) increased sensitivity as compared to current knowledge; (3) To resolve mass variations at daily to decadal time scales.
For instance, the glacial isostatic adjustment (GIA) signal over Antarctica leaks by about 200 km into the oceans, with a 1 cm EWH/yr magnitude  (Pail et al. 2015a , Pail et al. 2015b ). The target scenario would allow for the separation of this signal from the ice mass variations. The high accuracy would also enable us to resolve the small ocean circulation signals, such as inter-annual variations of the Atlantic Meridional Overturning Circulation (AMOC). Just the extension of the time series beyond GRACE-FO will allow us to understand how anthropogenic forcings interact with climate changes in the changing water cycle, and the higher spatial resolution resulting from MO-BILE will improve the water cycle versus solid Earth signal separation. By providing long time series of mass variations, MOBILE, as a precursor of a sustained mass transport observing system from space, will thus also enhance the value of data from preceding missions.
HORIZONTAL CABLE TRAYS FIRE MODELING A semi-empirical model of horizontal cable trays fire in a well-confined enclosure has been developed and implemented in the SYLVIA software. This model is derived from the FLASH-CAT model  [9] , developed for horizontal cable trays fires in open atmosphere. The model differs from the FLASH-CAT one, on the flame spread velocity along cables, on the upward fire propagation in the cable tray stack, and on the expression of the fuel mass loss rate, for which a correction is applied to take into account the effect of the oxygen depletion in the fire room on the heat release rate by the fire.
In order to have an accurate simulation of the confined configuration, a secondary fire-source model is then used in the SYLVIA software for the fire spread within the stack. This model consists in placing a thermally thick target, whose thickness corresponds to the thickness of the cable sheath, at the supposed ignition zone of each cable tray. Thus, the ignition time of cables is related to the fuel properties, to the ambient conditions and to the incident heat fluxes (convective and radiative). The ignition of the cable tray is then operated on a temperature criterion given in 5. 1 and taken from the data of CHISTIFIRE experiments  [26]. Thermal physical properties of cable samples tested in CFS tests are given in Table  2.
Chemical reactions occurring during the burning of cables are complex. These depend on the nature of the fuel as well as the level of oxygenation of the fire source. In the SYLVIA software, a simplified combustion reaction (one step, irreversible) expressed in terms of mass is used as: 1 kg of fuel +  O2 O 2   CO2 CO 2 +  CO CO +  H2O H 2 O +  CH4 CH 4 +  C C (14) It is assumed that the fuel is homogeneous and that chemical reactions occur simultaneously with the same kinetics. This equation does not take into account the release of hydrogen chloride from PVC cables, in the absence of available experimental data. Averaged values of the reaction coefficients over the fire duration are used, although these could change, depending on the level of oxygenation of the fire source. They are obtained experimentally from the mass of released species and the pyrolysed mass of fuel (mass ratio, see Table  3  for the CFS cable fire tests performed in confined conditions).
SIMULATIONS OF THE CFS 1 TO 4 TESTS USING THE TWO-ZONE MODEL SYLVIA The model of horizontal cable trays fire was implemented in the two-zone model SYLVIA. The SYLVIA software  [5] [4]  is developed at the Institut de Radioprotection et de Sûreté Nucléaire (IRSN) to simulate a full ventilation network, fire scenarios in a highly confined and ventilated facilities, and airborne contamination transfer inside nuclear installations. This software is based on a zone approach which consists in calculating mass and energy balances in each of two zones, separated by an interface, which constitutes the enclosure (fire room or not): the lower zone simulating the fresh gas and the upper zone, simulating the combustion products and the gas entrained by the plume. In a zone approach, a plume feeds the upper zone of the fire room, whose volume increases, which has the effect of lowering the interface if the gas flow in the exhaust duct or at the level of openings of the fire room is not sufficient to remove all gases supplied by the plume.
Moreover, this approach requires a specific calibration for each user and it seems that the model is dependent to temporal trials, as well as to motion activities  [24, 25]. Lastly, the accuracy measured by the regression coefficient (R 2 ) is low, with significant value variations even in the same subject at the same activity level  [26, 27] , but a more-sophisticated model may lead to a loss in generalization and make the model subject-specific. Different mathematical models have been proposed to model the Time Delay and BP  [28] [29] [30]. Some of them will be presented, followed by the principles of measurements of the proposed method. The architecture of the device is then analyzed with the algorithm for the estimation of the BP. The software has been embedded in the CareUp R smartwatch: the ease-of-use and portability of this device make it a perfect solution to check the BP anywhere the user needs.
In  [41]  the authors compared four different models, exploiting the PAT delay, for the BP estimation on the Boston's Beth Israel Deaconess Medical Center (BIDMC) database. In particular, they used a logarithmic model, a linear model and two polynomial models. The average error and standard difference between the PAT-based method and the reference value was for DBP measurements 0 ± 2. 13, while for the SBP, it was 1. 3 ± 7. 02. The larger disagreements between reference and measured SBP and DBP values for some of the data points was due to inclusion of data of hypertensive patients. Another study using PAT and polynomial models has been presented in  [45]. It is a subject-specific study with parameters that also depend on the height of the subject. The results showed an average error and standard deviation of 0.
For this reason, the signals were acquired by the two pulse oximeters for 30 s, filtered in real time and stored in the internal flash memory (512 KB). Only the correlation function was then allocated in the RAM to compute the Time Delay. Figure  3  shows the hardware architecture for the whole process. We can describe the process as divided in two parts: the acquisition and the processing. During the acquisitions, the drivers of the back and front oximeters give the values of the measure by I2C communication to the CPU. The CPU stores those values, first in two temporary buffers in the RAM: one for the back and one for the front sensor. The capacity of these two buffers is limited by the RAM size and cannot contain the whole data acquisition of 30 s. For this reason, when the buffers are full, they are empty in two bigger buffers in the flash memory.
Estimation of the BP The signal processing scheme is reported in Figure  4. After the filtering phase, two steps followed: one for identifying the Time Delay and another one to estimate the HR. The best technique to estimate the Time Delay revealed to be the cross-correlation between the two input signals. Nevertheless, we explored some other alternatives, such as those proposed by Laguna et al. in  [49] , to obtain the PPG Time Delay. Specifically, we also used the time difference between the points of maximum positive dependency and the time difference between the feet of the PPG waveforms. A comparison, on our Training Database, shows heavier computational costs in BP estimation by using these approaches. At this point, due to the lighter model criteria for portable technology, we assumed that there was no importance in the returns observed in the diastolic period and we imported only the aortic artery exit impulse observed in the systole period.
Database The validation was performed on two databases: the Learning Database (LDB) and the Test Database (TDB). The LDB consisted of 22 acquisitions on five healthy male persons (mean age 34 ± 4 years) at Farasha Labs. Each person was informed about the study and gave their oral consent before the experiment. The measures were performed with a sphygmomanometer as reference and the CareUp R , Magnien R and Thuasne R devices were used as comparisons. Magnien R and Thuasne R are commercial devices, both fully automatic, worn at the level of the wrist and employ an oscillometric algorithm for BP estimation. For this database, all the acquisitions were performed in sitting position, at rest, with the arm at the level of the heart for the sphygmomanometer and the wrist at the level of the heart for the other devices.
The patients identity was anonymised by a numeric code and annotated in the consent forms. A copy of the consent was provided to the patients and the original was attached to the patient's clinical files. The large participation to this study can be explained by the fact that the participants could benefit from more BP recordings, thus a better health monitoring. In this study, the dignity, rights, safety and well-being of all participants were taken into primary consideration. In Figure  6 , we report the boxplots of the TimeDelay found for each patient, in both the databases. The computation of the TimeDelay follows the method depicted in Figure  5.
Results on the LDB and TDB Concerning the LDB, it can be said that the results are comparable to those of the devices already available in the market: Magnien R and Thuasne R. The numerical values of mean error and standard deviation error are described in Table  1. Table  1. Mean error and standard deviation error for the twenty-two measures performed on five healthy male subjects in the Farasha Labs, with three different devices: CareUp R , Thuasne R and Magnien R. For the results on the Test Database, Figure  7  and Table  2  report the comparison on 126 measures, between the sphygmomanometer and CareUp R. Figure  8  and Table  3 , instead, report the results for the comparison, on 78 measures, among the reference, CareUp R and Magnien R. Finally, Figure  9  and Table  4  report the comparison among the sphygmomanometer, CareUp R and Thuasne R , on 76 measures.
The need of calibration represents a counter-factor and remains another weakness of the proposed approach. We can imagine that, at first, the calibration will be done at the medical office when the physician advises the patient to monitor their own BP. In the model, it is assumed that the HR does not change over time, considering a mean cardiac frequency for the model; however, in patients with hypertension, this parameter presents a high variability, which would require a constant calibration of the device. In a further step, we can collect a very large database of BP and Time Delay measurements in order to identify the most general models for the BP estimation. In this way, we can associate a specific model to each user, without the need for a calibration procedure. So, we suggest a collection of Time Delays and arterial pressures be included, so that the model has the capacity to interpret the changes in HR.
This allows us to systematically study the impact of larynx height on vowel production, to find the optimal height for the production of widely-used vowels, and the compensatory strategies that can mitigate the impact of extreme larynx positions. While still far from perfect, we think that our model represents an important advance, allowing more refined answers to questions surrounding the impact of larynx height on vowel production, and providing a platform for further improvement and application to other aspects of inter-group and inter-individual variation in speech, both pathological and normal  (Dediu, Janssen, & Moisik, 2017). Given that the work reported here is in many ways novel, one of our main aims was to start from as "generic" and "theory-free" assumptions as possible and to write our code as easily replaceable and upgradeable modules.