dataesr/scientific-paragraphs-categorization huggingface dataset:
Hence, finding general properties to classify detected peaks is difficult. To address this problem, we used a clustering-based approach. For each ECG capture, we applied a clustering algorithm to the data: DBScan. The features used for the clustering were the peak prominence and the peak width. We chose DBScan as the clustering algorithm because of its density-based approach and because it can detect outliers. Thereby, we could discard any possible false detections by the find peaks algorithm. As the features in our dataset were on different scales (hundreds of ms for peak duration; mV for peak prominence), we scaled our data by the standard deviation. Furthermore, in order to better separate the clusters, especially for values near zero, we took the logarithm of the scaled data. The result of the clustering is shown in Fig.
A. Data format As noted above, we recorded the IQ signal just after the ADC. We reorganized it in a 4-dimensional matrix of complex numbers: [Rx antennas, frames, chirps, samples]. In order to have equally spaced points in the time dimension (carried by both the frames and chirps dimensions of our matrix), we kept only the first chirp of each frame, in order to avoid the apparent gap between chirps introduced by the inter-frame time. Thus we had a 3-dimensional matrix to process. The "samples" dimension is the fast time dimension, whereas the "frames" dimension is the slow time dimension.
19. In order to ensure that we would detect the perturbation in the ECG, we began recording the ECG 3 s before the radar. This experiment was performed on a smaller number of subjects (3 subjects, 2 males, 1 female): its purpose was to validate our synchronization hypothesis so that we could use it to synchronize the data recorded without the impulse on the ECG. 2) Synchronization: Once we detected the perturbation, because we know that it coincides with the time of the start of the radar capture, we could apply a shift to the ECG time to ensure the t 0 of both signals corresponded. The signal before the perturbation was discarded. Finally, in order to remove the perturbation from the ECG signal, we removed 1 s of data at the beginning of both signals. The resulting signals were then processed in the same way as described in the previous sections.
Observations Observations of WASP-79b used in the present analysis are part of the Panchromatic Exoplanet Treasury (PanCET) program (HST GO Proposal #14767, PI D. K. Sing and M. Lopez-Morales). We aim to complete the WASP-79 b spectrum proposed in  Sotzen et al. (2020)  and  Rathcke et al. (2021)  by adding UV measurements between 0. 2 and 0. 3 µm. We analysed two transits with the Hubble Space Telescope Imaging Spectrograph (HST STIS) instrument. The observations were conducted with the NUV-Multi-Anode Microchannel Array (NUV-MAMA) detector using the E230M echelle grating with a 0. 2 ′′ × 0. 2 ′′ aperture. E230M has a resolving power of about 30 000 and covers the wavelength range of 2280-3070 Å. The spectra have an average dispersion of 0. 049 Å per pixel, about two pixels per resolution element.
We observed one transit in each of the two visits of WASP-79b, numbered #65 and #66 of the PanCET program. Each observation consisted of five consecutive orbits covering the full transit with a significant baseline. These two observations were taken on  January 12, 2018, and March 11, 2018 , for the first and second visits, respectively. The data were acquired in time-tag mode. We extracted a sequence of 350-s-long sub-exposures from these using calstis version 3. 4. From the shorter observation of the first orbit of each visit, we obtained six sub-exposures, and from the long observation of the second to the fifth orbit, we obtained eight sub-exposures per orbit. This yields a total of 38 subexposures per visit. Visit #65's raw data display a large ramp effect compared to visit #66. This ramp is mainly correlated to the HST orbital phase.
Systematics correction Spectrophotometric light curves taken with STIS aboard the Hubble Space Telescope (HST) are highly affected by instrument-related systematics. For instance, the thermal 'breathing' effect periodically modifies the point-spread function (PSF). Systematic effects are correlated to instrumental parameters or external factors that vary with time during the observations  (Sing et al. 2019). Before the work of  Sing et al. (2019) , only the thermal breathing effect characterised by the correlation with the HST orbital phase was taken into account. However, corrections limited to only these systematic effects are not enough for data that present a high level of correlated noise, especially for the first HST orbit that presents different systematic effects. This led to removing all the data of this first orbit from the analysed data set.
White light curve fitting We integrated the flux over the entire wavelength range to obtain a white light curve, with 33 measurements for each of the two observed transits. We normalised each light curve with respect to the average flux over a visit, and we modelled the transit using the batman python package (Basic Transit Model Calculation in Python;  Kreidberg 2015). We held the inclination, semi-majoraxis-to-star radius ratio, and limb-darkening coefficients fixed to the value in Tables  1  and 3. We modelled the flux measurement over time f (t) as a combination of the theoretical transit model T (t, θ), where θ is the set of the transit parameters, the total baseline flux from the host star F 0 and the systematic error correction model S(X), derived as described in Sect. 3. 1: f (t) = T (t, θ) × F 0 × S (X).
(5) We include a linear baseline time trend in S (X) to correct flux variations over time. The number of free parameters depends on the corrections and S (X) terms. In the search for the best fit, the planet-to-star radius ratio, the polynomial coefficients, and the baseline flux were left free. We used batman's convention and normalised the time with respect to the centre of the transit. We did not fit the mid-transit time in our analysis as that did not improve the fitting results. However, our code is flexible, and this parameter can be easily added as a free parameter if necessary. The best-fit parameters are determined using Levenberg-Marquardt's least-squares method (L-M, Markwardt 2009). The limb-darkening effect is modelled using a non-linear law given by the following equation: I(µ) = I 0 × [1 -c 1 (1 -µ 1 2 ) -c 2 (1 -µ) -c 3 (1 -µ 3 2 ) -c 4 (1 -µ 2 )], (6) where µ = √ 1x 2 , x is the normalised radial coordinate and atomic lines that can behave anomalously at the limb, especially in temperature inhomogeneities.
Validation using WASP-121b STIS data To validate our fitting method described above, we applied it to WASP-121 b's STIS data. WASP-121 b is a hot Jupiter orbiting an F6V star with a similar temperature to that of WASP-79, which is 6460 K  (Delrez et al. 2016). The planet has an inflated radius of 1. 753 R J  (Bourrier et al. 2020 ) and an equilibrium temperature reaching 2720 K (Mikal-  Evans et al. 2019 ). These observations are also part of the PanCET program and were studied and published in  Sing et al. (2019). Our analysis includes all HST NUV observations of WASP-121 b, except for the first exposure of each orbit. For WASP-121 b, we find a R P / R ⋆ of 0. 135 ± 0. 003 for both visits, which is consistent within 1-σ with the values found by  Sing et al. (2019) , that is 0. 1364 ± 0. 0110 and 0.
Parameters Visit #65 (a) Visit #66 v2_dom - - v3_dom - - v2_roll - - v3_roll - - si_v2_avg - - si_v2_rms 5. 429995 - si_v2_p2p - - si_v3_avg - 0. 163022 si_v3_rms - - si_v3_p2p - - ra - - dec - - roll -267. 9753 - limbang - - los_zenith 0. 0015797 - mag_v1 -0. 3187546 - mag_v2 - - mag_v3 - - Notes. In this case, all polynomials are of degree 1 (see Sect. 4. 1. 1). ( a) The correction model of the visit #65 of the PanCET program also includes a polynomial of degree 8 of the HST orbital phase correlated to the large ramp effect (see Fig. 1 ). The coefficients are the following in increasing degree order: -0. 5844569, -1. 05257, 17. 27887, -20. 33507, -37. 87583, 251. 4479, 7. 275825, -1671. 329. We also corrected the linear effect correlated to the planetary phase. The coefficients are 0. 12850 and 0.
006832 for visit #65 and visit #66, respectively. measurements in the NUV and visible wavelength range (HST STIS G430L G750L). We represent the value around 2400 Å in a 200 Å band and the large band value after 2500 Å to show the steep increase in the planet-to-star radius ratio at short wavelengths. Combining different transmission spectroscopy datasets is difficult, even more so when there is no spectral overlap. The orbital parameters and limb-darkening coefficients can differ from one study to another. The treatment of systematic effects and stellar activity can also vary, leading to variations in planetto-star radius ratio and transit depth measurements  (Tsiaras et al. 2018; Yip et al. 2020; Changeat et al. 2020; Pluriel et al. 2020; Edwards et al. 2021 ). Even while using the same system parameters, prescriptions for limb darkening and a lack of stellar activity,  Nikolov et al.
Acknowledgements. This work is based on observations made with the  NASA/ESA Hubble Space Telescope  obtained at the  Space Telescope. Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. Support for this work was provided by  NASA  through grants under the  HST-GO-14767 program  from the  STScI. A. G. and A. L. d. E. acknowledge support from the  CNES  ( Centre national d'études spatiales, France). This work has been carried out in the frame of the  National Centre for Competence in Research PlanetS  supported by the  Swiss National Science Foundation (SNSF). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  SPICE DUNE , grant agreement no.
This paper presents an experimental investigation conducted on a new single-phase immersion/ liquid-cooling technique developed at the OVHcloud laboratory, combining a direct-to-chip water-cooling system with passive single-immersion cooling technology. The experimental setup tested the impact of three dielectric fluids (S5X, SmartCoolant and ThermaSafe R), the effect of the water circuit configuration, and the server power/profile. Results suggest that the system cooling demand depends on the fluid's viscosity. As the viscosity increased from 4. 6 to 9. 8 mPa s, the cooling performance decreased by approximately 6%. Moreover, all the IT server profiles were validated at various water inlet temperatures and flow rates. The energy performance of the proposed technique and the liquid-cooling system previously used by OVHcloud was compared at a 600 kW data centre (DC).
210 • C after calibration. The National Instruments data-acquisition system NI cDAQ-9174, a compact DAQ system, was used to record all the temperatures, pressures, and flow rates throughout each experimental test. Two NI 9214 cards were used to measure and record temperature data. NI 9214 has a typical measurement accuracy of 0. 37 • C at 100 • C when using type-K thermocouple. Two NI 9205 cards were used to measure and record the pressure and flow rate data. The data-sampling frequency was one measurement per second (1 Hz). Data acquisition was entirely automated using LabVIEW software. In addition, the solenoid valve was controlled by a Carel DN33B9MR20 (Model B) integral and derivative (PID) temperature controller. The control is set to be linear, and the operating cycle is an automatic program that can have a maximum of 5 set points to be reached in the 5 respective time intervals.
For example, the uncertainty of the thermal heat load (Q) was evaluated as ΔQ = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ [ ( ∂Q ∂ ṁΔ ṁ) 2 + ( ∂Q ∂T 1 ΔT 1 ) 2 + ( ∂Q ∂T 2 ΔT 2 ) 2 ] √ √ √ √ ( 6 ) where ṁ is the water flow rate, T 1 is the inlet temperature, T 2 is the outlet temperature, and Δ ṁ, ΔT 1 , and ΔT 2 denote their respective absolute uncertainties. Table  4  lists the uncertainties of the parameters involved in the measurements. A flexible I/O (fio) tester script was used to stress the memory components of the servers, e. g. , hard disks. CPUs and RAMs were jointly stressed using a script that allocated a quantity of RAM to each logical core of the CPUs to be used by a memtester process. Different Intelligent Platform Management Interface (IPMI) temperature measurements were also collected on the servers (e.
g CPU junction temperature …). These data can be used to extract chip to ambient thermal resistance as explained in Appendix A at the end of this paper. Different IT components such as CPUs, GPUs, RAMs, and motherboards were analysed during each test. Other tools such as an Intel® Performance Counter Monitor and AMD uProf were used to check the stability of the server performance under different applied TCs  [28]. The CPU frequency (MHz) stabilities were monitored in real time and exported at a rate of 1 value every 30 s throughout each test. In addition, the rate at which data could be read from or stored in semiconductor memory by a processor, as expressed by the memory bandwidth (bytes/second) stability, was supervised. The server power was measured continuously with a time step of 1 s using a power distribution unit (PDU).
The fan affinity laws indicate that the power consumed by a fan increases as the cube of the airflow rate ( V): Fan Power∝( V) 3 (8) The airflow rate, pressure drop, and outlet air temperature, based on the outdoor conditions, were provided by the manufacturer. The power consumption of the fan is determined from the physical parameters at the operating point of the fan: P = ∑ 3 i=0 a i vi (9) where i and a i are constants and v is the airflow rate (m 3 /h) generated by fans. The method for calculating the power consumption, given as a function of the external temperature of the dry cooler, was presented previously  [32]. The EC water consumption was calculated as a function of the water demand of the EC system using cooling pads. It was determined based on the technical data provided by the manufacturer including the airflow rate, external air temperature, and humidity conditions.
It is defined as the sum of the evaporated water and bleed-off quantities required per hour to operate a dry cooler equipped with an EC system. In addition, the WUE can be extracted as the total water required per kilowatt-hour (kWh)  [32]. Temperature and relative humidity data for the past 40 years were collected from the weather underground community site [36] and analysed (one record every 30 min). Based on these data, we selected 2018 as the warmest year in this 40-year range. Fig. 9 (a) shows the hourly temperature frequency for the climatic data from Roubaix during 2018. The average temperature ranged from -7 to 41 • C, with the bulk of the distribution ranging from 5 to 20 • C. The annual energy consumption of the cooling system, E cooling (kWh), is the sum of the power consumption at each outlet temperature multiplied by the corresponding number of hours during the year:  (10)  Fig.
The data of 9 cohorts (n=2343) from France (N=2), Hong Kong (N=1), the Republic of Korea (N=2), the Netherlands (N=3), and Singapore (N=1), were pooled. For supplementary analyses on cognitive screening instruments, 3 additional cohorts ([n=607] from the Netherlands [n=1], and the United Kingdom [N=2]) were identified with a cognitive screening test (Mini-Mental State Examination [MMSE] or MoCA), but without multidomain neuropsychological assessment. In all patients, language ability had to be sufficiently preserved to undergo cognitive testing. Inclusion and exclusion criteria of participating cohorts are presented in Table  S1. Data processing and analysis were performed at the University Medical Center Utrecht. The article adheres to the observational cohort guideline.
Participant Characteristics Several sociodemographic and clinical characteristics were collected (Table  1 ). All participating cohorts provided level of education, however using different methods, for example, years or categories. Harmonization of educational level data was done as described previously,  13  by recoding the original education data into a 4-category variable according to the approach in the Stroke and Cognition (STROKOG) consortium. 14  Severity of stroke was measured by the National Institutes of Health Stroke Scale. 15  Several cohorts used the Informant Questionnaire on Cognitive Decline in the Elderly to assess prestroke cognitive decline. 16  For the current study, infarct type was divided into small subcortical infarcts versus other infarct type, which are mutually exclusive.
MoCA Montreal Cognitive Assessment NTB neuropsychological test battery PSCI poststroke cognitive impairment STROKOG Stroke and Cognition consortium available tests available per domain). PSCI was defined as cognitive impairment in ≥1 cognitive domains, in accordance with the VASCOG criteria for Vascular Cognitive Disorders. 20  The number of available tests and domains could differ between cohorts. To be able to rule out PSCI at patient level, data on a minimum of 3 cognitive domains needed to be available. The percentage of patients assessed per domain ranged between 34% and 98% (Table  2 ). With regard to cognitive screening tools in the 9 cohorts with a multidomain neuropsychological assessment, MMSE was utilized in 4 cohorts (n=1814, missing score n=1), MoCA in 3 other cohorts (n=278, missing score n=22), and no cognitive screening test was available in the other 2 cohorts (n=251).
Statistics Women and men were compared using independent samples t tests for parametric data, Mann-Whitney U test for nonparametric data, and χ 2 tests for proportions. We used general linear models to obtain age-adjusted women-to-men differences and 95% CI for characteristics of cognitive impairment. Logistic regression analyses with cohort as covariate were performed to obtain odds ratios (ORs) for PSCI and impairments in the separate cognitive domains; women denoted the reference group. To identify PSCI, norm-referenced data was used to express cognitive performance in percentiles. This normative data was already corrected for age, level of education and sex, therefore we did not add these covariates to the regression analyses. To investigate the potential influence of age, education, and infarct type in further detail, the analyses were repeated stratified by age (<65 versus ≥65 years), education (<high school versus ≥high school), and infarct type (small subcortical versus other infarct type).
In addition, we evaluated a putative interaction with the 3 aforementioned stratification variables with sex by adding an interaction-term (eg, sex×age<65 years) to separate models. Sensitivity analyses on the risk of PSCI per cohort were performed (Figure  S1 ). All of the above mentioned analyses were done in SPSS 26. For the meta-analysis of the sensitivity and specificity of cognitive screening tools, only cohorts with both NTB and MoCA (n=3) or NTB and MMSE (n=4) were used. PSCI was diagnosed on the above-described criteria of NTB. The standard cutoffs were used for the MMSE  21  (<25) and for MoCA  22   15 ) 325 (  23 ) Technical/college diploma 226 (  10 ) 63 (  7 ) 163 (  11 ) University or higher 357 (  15 ) 64 (  7 ) 293 (  21 )  Test Accuracy was used for the analysis. 23  Heterogeneity is to be expected in meta-analyses of diagnostic test accuracy.
Although neuropsychological test batteries differed between cohorts, our PSCI definition did allow us to cover a broad range of cognitive profiles and deficits. Secondly, we were able to use norm-referenced data to define impairment. Performance on cognitive tests is known to differ by geographic region and can be influenced by age, educational level, and sometimes sex. Applying normative data in research cohorts improves diagnostic accuracy in both women and men. 31  Third, we included multicenter data that enabled a much larger sample size than individual studies to date. However, this inherently resulted in heterogeneity. Some potential limitations must also be noted. First, information on prestroke cognitive status was not available for a substantial number of cohorts, therefore we could not take this into account in our analyses.
Dr Exalto is supported by Alzheimer Nederland WE. 03-2019-15 and Netherlands CardioVascular Research Initiative: the Dutch Heart Foundation (CVON 2018-28 & 2012-06). The Meta-VCI Map consortium is supported by Vici Grant 918. 16. 616 from The Netherlands Organisation for Health Research and Development (ZonMw) to Dr Biessels. Harmonization analyses were supported by a Rudolf Magnus Young Talent Fellowship from the University Medical Center Utrecht Brain Center to Dr Biesbroek. The CASPER cohort was supported by Maastricht University, Health Foundation Limburg, and Stichting Adriana van Rinsum-Ponsen. The CROMIS-2 cohort was funded by the UK Stroke Association and the British Heart Foundation (grant number TSA BHF 2009/01). The CU-STRIDE cohort was supported by the Health and Health Services Research Fund of the Food and Health Bureau of the Government of Hong Kong (grant number 0708041), the Lui Che Woo Institute of Innovative Medicine, and Therese Pei Fong Chow Research Center for Prevention of Dementia.
The GRECogVASC cohort was funded by Amiens University Hospital and by a grant from the French Ministry of Health (grant number DGOS R1/2013/144). The MSS-2 cohort is funded by the Wellcome Trust (grant number WT088134/Z/09/A to Dr Wardlaw) and the Row Fogo Charitable Trust. The PROCRAS cohort was funded via ZonMW as part of the TopZorg project in 2015 (grant number 842003011). The CODECS cohort (ongoing) is supported by a grant from Stichting Coolsingel (grant number 514). The Bundang VCI and Hallym VCI cohort groups do not wish to report any relevant funding sources. At the time of contribution, Dr Hamilton was funded by the College of Medicine and Veterinary Medicine at the University of Edinburgh and was supported by the Wellcome Trust through the Translational Neuroscience PhD program at the University of Edinburgh.
In this way the 2D assumptions of the analytical models are kept and only cross section geometric complexity is added, allowing for a comprehensive study of its influence on fiber behavior. An adaptation of existing analytical models to account for elliptical cross sections is also proposed and validated, offering a geometrically richer analytical model. Employing the data generated by the FEA, an apparent transverse elastic modulus, 𝐸 𝑇 𝑎𝑝𝑝 , is identified using the new analytical model. The influence of fiber geometry on this apparent modulus evaluated and discussed. Ideal geometric representations of the fiber's most common geometric features are studied first, to independently assess their effect on fiber behavior and apparent modulus. Specifically, the influences of lumen size, ellipticity and flat fiber sections are evaluated.
Microscopy extracted fiber geometries In order to study realistic plant fiber cross sections, that are inherently much more complex than their ideal geometry counterparts, fiber geometries are extracted from a microscopy image, using a similar method to those presented in  [36, 41, 42]. An observation of a hemp stem section is used for this purpose, shown in Figure  1. a. After binarization of the image using Otsu's thresholding method  [43]  the contours of the image are detected using the bwboundaries function of MATLAB (MathWorks, Natick, MA) as seen in Figure  1. b. With the contours in the image being detected, the contour of a cell wall can be isolated. The largest contour contained within the cell wall (the lumen's boundary) can also be isolated if visible. The contour detection algorithm results in fine irregularities in the extracted geometries (see Figure  1.