dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and Discussion As a reference, Figure  4  shows a measurement with glued electrodes. Data were taken via the serial monitor while the Arduino was attached to a laptop via a docking station, i. e. , connected with the grid, in this case working at 50 Hz. Textiles 2022, 1, FOR PEER  REVIEW 5  showing the depolarization of the atria (the upper heart chambers), and the broader T-waves after the QRS complexes depicting the repolarization of the ventricles. De-noising the signal using this software filter worked for the complete ECG, as visible in Figure  4a , as well as in all other ECGs that were collected in this study. However, the next tests were performed with the Arduino attached to the pure laptop without a connection to the power grid. The residual 50 Hz noise was very well-filtered by the AD8232 in the case of glued electrodes (not shown here) and for textile electrodes, as depicted in Figure  5.
Conclusions Several textile ECG electrodes were evaluated with respect to their skin contact compared with commercial glued gel electrodes. Measurements were performed with an inexpensive ECG module based on the AD8232 chip. The inevitable 50 Hz noise, visible in all measurements in which the laptop for data acquisition was connected to the power grid, was used to evaluate the skin-electrode contact. This simple method is suggested as a possibility to rate the skin contact of textile electrodes in future developments. Regarding the choice of textile ECG electrodes, none of the tested ones worked well In the case of the sewn electrode 4. 3, the skin was slightly wetted before the measurement by putting a few drops of tap water using the fingers at the respective position. Figure  7a  shows that the 50 Hz noise started increasing after approx.
Conclusions Several textile ECG electrodes were evaluated with respect to their skin contact compared with commercial glued gel electrodes. Measurements were performed with an inexpensive ECG module based on the AD8232 chip. The inevitable 50 Hz noise, visible in all measurements in which the laptop for data acquisition was connected to the power grid, was used to evaluate the skin-electrode contact. This simple method is suggested as a possibility to rate the skin contact of textile electrodes in future developments. Regarding the choice of textile ECG electrodes, none of the tested ones worked well on dry skin. Electrode 4. 2, containing a Powersil coating under which the proband started sweating slightly, gave the best results. This indicates that, if the electrodes are not meant for sports but for daily use, a coating that supports sweating is indispensable for textile ECG electrodes.
In the steam condensation process, a considerable amount of thermal energy is released, which also causes burns on the bodies of wearers  [34, 37]. Through inference testing (hypothesis and a 95% confidence interval) of the dataset shown in Table  3 , it has been found that a significant difference exists between the TTTE of air-impermeable and airpermeable fabrics (p-value < 0. 05), and this difference always remains negative. This demonstrates that the TTTE through an air-impermeable fabric is much lower than an airpermeable fabric, because air-impermeable fabrics do not allow a steam transfer, lowering the TTTE  [42, 43]. In the case of multilayered, impermeable fabric systems (shown in Table  3 ), a fabric system including a moisture barrier in its outer layer has less TTTE (or high SPP) than a fabric system including a moisture barrier in its inner (middle) layer.
In the past decade, research has focused on emerging, novel and exciting sources of natural dyes: bacteria  [3] [4] [5]. Bacterial, or, as they are sometimes called, microbial dyes are byproducts of bacteria that represent a relatively novel and scarcely investigated source of natural dyes, with tremendous potential for dyeing various textile materials in very intense, durable and esthetically beautiful colors, which are nontoxic and safe for human skin, thus producing environmentally sustainable textile products. Research into the possibility of using bacterial pigments as natural sources of dyes for the textile industry has grown. For this study, the Scopus and ScienceDirect databases were used with several research keywords, including "bacterial pigments dyes microbial textile fibers fabrics dyeing.
5D interlock section, while the error in 2D fabric section was lower with values such as 0. 44% in tension moduli. As for the longitudinal Young's modulus, the maximum average error is shown with 10% but with an average percentage of error of transversal strains of 4%. This may be due to a lack of precision in the mechanical properties of the components, i. e. , the fibers, which are used as input data. The percentage is related to the lack of an accurate and precise database structure of the used material, but it shows a great ability to use the algorithm. The suggested hypotheses are the simplest that can be applied for determining the engineering data with all required information, and specifications from the user are presented in a "user friendly" form that can make studying and searching a new level of excitement and interactivity.
Results Analysis Exploratory factor analysis (EFA) with principal component analysis was first conducted to purify and confirm the scale dimensionality, using a varimax rotation. The factor loading of each item for EFA in both the US and Bangladesh dataset were from 0. 501 to 0. 836. Then, confirmatory factor analysis (CFA) was conducted to test the measurement model. The result of the measurement model exhibited an acceptable model fit (χ 2 (df = 129) = 212. 295, p = 0. 000, χ 2 /df = 1. 646; RMSEA = 0. 088; CFI = 0. 915; TLI = 0. 9177)  [61]  in terms of US data. The measurement model for the Bangladesh data exhibited an acceptable model fit (χ 2 (df = 129) = 297. 777, p = 0. 000, χ 2 /df = 2. 308; root mean square error of approximation (RMSEA) = 0. 162; comparative fit index (CFI) = 0.
664; Tucker-Lewis index (TLI) = 0. 602)  [61]. For both the US and Bangladesh data, the convergent validity and discriminant validity were assessed to represent the construct validity. All CFA loadings were higher than 0. 5, which provided evidence for convergent validity; and the average variance extracted (AVE) for each construct was greater than 0. 5  [62] , suggesting that each construct is well represented by its own indicators. Finally, the structural equation model (SEM) was then conducted to evaluate the proposed hypotheses 470 Textiles 2021, 1 based on the literature review  [61]. The statistical results revealed an acceptable model fit (χ 2 (df = 131) = 365. 908, p < 0. 000, χ 2 /df = 2. 79; RMSEA = 0. 189; CFI = 0. 532; TLI = 0. 454) for the Bangladesh dataset and an acceptable model fit (χ 2 (df = 131) = 285.
Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Funding: This research was funded by  Shanghai Natural Science Foundation  ( 20ZR1400500 ) and the  Fundamental Research Funds for the Central Universities  ( 2232021G-01 ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Funding: This work was supported by the start-up at  North Carolina State University. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Funding: This work was supported by the  Ministry of Education, Science and Technological Development of the Republic of Serbia  (contract no.
451-03-68/2022-14/200135 ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Funding: The authors would like to thank  Technology Alliance of Upper Franconia (TAO)  and the  Bavarian Polymer Institute (BPI)  for financial support. Funding: This work was supported by start-up funding at  Sichuan University. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Funding: This research received no external funding.
Acknowledgments : This study was supported by the project "Modular platform for autonomous chassis of specified electric vehicles for freight and equipment transportation", Reg. No. CZ. 02. 1. 01/ 0. 0/0. 0/16_025/0007293 and the project of Ministry of Education, Youth, and Sports of the Czech Republic and the European Union-European Structural and Investment Funds in the Frames of Operational Programme Research, Development and Education-project Hybrid Materials for Hierarchical Structures (HyHi, Reg. No. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000843). Data Availability Statement: Not applicable.
While CalCOS typically produces "ready to go" final spectra, it was necessary to re-reduce a large portion of our data sets owing to the extended nature of ∼60% of the CLASSY targets. HST/COS is a spectrograph optimized for UV spectroscopy of point sources. This is evident in several aspects of the COS pipeline, most notably in the TWOZONE extraction technique, which utilizes a point-source profile for the extraction of the data on the FUV detector (815-2050 Å). The NUV detector (1700-3200 Å) instead uses a fixed extraction box, which we describe in detail below. 37 The full details on the extraction methodologies employed by CalCOS can be found in the COS data handbook (DHB). 38 Due to the extended nature of ∼60% of our targets, where nonnegligible amounts of flux can extend beyond the extraction profiles used in CalCOS, it was necessary to test different extraction techniques with the aim of increasing the S/N of the CLASSY spectra of extended sources.
However, it should be noted that due to the slitless design of COS, all light within the COS aperture will contribute to the two-dimensional spectra, not just the light indicated by the 36 https://github. com/spacetelescope/calcos/releases  37 The TWOZONE extraction is implemented on the FUV detector only because it was designed to reliably extract spectra close to regions suffering from gain sag (which primarily result from bright Lyα airglow emission). This extraction method is only implemented in the CalCOS pipeline for data at LP3-LP5. The NUV detector does not suffer from gain sag and does not require optimized extraction. 38 https://hst-docs. stsci. edu/cosdhb; Soderblom (2021). Figure  1. CLASSY acquisition images with compact-source light profiles. For each galaxy, the NUV target acquisition image from the G130M data set is shown (left panels), overlaid with the COS 2 5 aperture (gold circle) and labeled according to its MIRRORA or MIRRORB target acquisition configuration.
To this end, light beyond the 2 5 diameter can reach the COS detector. The majority of COS target acquisitions are taken with the MIRRORA configuration (this applies to 87 of our data sets). However, if a target is particularly bright, an attenuated light configuration is utilized, called MIRRORB, which serves to protect the detector. MIRRORB was employed for 10 galaxies in CLASSY (each one labeled accordingly in Figures  1 2 3 ) that are especially bright in the UV. Unfortunately, MIRRORB has the drawback of producing a double-peaked image that impedes our interpretation of the spatial distribution of light. This can be seen most clearly in some of our compact targets acquired with MIRRORB (e. g. , J0036-3333 and J0808+3948; Figure  1 ), where the fainter secondary image (marked with a gray box) can be seen in the direction of the spatial trace.
FUV Extraction As discussed above, the observations of extended sources by COS can require a customized extraction. To illustrate the difference between the cross-dispersion profiles of a compact and an extended source, in Figure  4  we show G130M/1291/FUVA profiles of J0127-0619 (GFWHM ∼ 0 1) and J0934+5514 (GFWHM ∼ 1 5). Due to the width of the cross-dispersion profile of J0934+5514, compared to that of a compact source (e. g. , J0127-0619), an extended extraction box may be necessary to collect the full extent of its flux. As part of this customized extraction, as suggested by the COS DHB, the extended-source CLASSY data sets were reprocessed using a BOXCAR extraction technique. This involves a box of fixed height (∼27-45 pixels depending on the CENWAVE), centered on the spectral profile defined within the extraction tab reference file (XTRACTAB), thus covering the entire cross-dispersion profile, including any lowlevel emission that may exist in the wings of the profilesimultaneously increasing the signal and potentially the noise.
By comparison, the TWOZONE extraction method involves two extraction regions-an inner zone corresponding to the core of the profile, and an outer zone that covers the entire region used for the spectral extraction (i. e. , outer+inner zone). The boundaries for these zones refer to the fraction of enclosed energy within the cross-dispersion profile of a point source (80% and 99% for the inner and outer zones, respectively) and are a function of wavelength. The separation of the profile into an outer and inner zone enables the COS pipeline to only reject wavelength bins if bad pixels occur within the core region, rather than by those that lie in the outer wings of a profile. By comparison, in a BOXCAR extraction, any bad pixels that lie within the box will result in rejected wavelength bins, which can lead to a loss of S/N or gaps in the data if the BOXCAR extraction region extends into regions of gain sag on the detector (where the local sensitivity of the detector is decreased).
While this test could have also been performed with He II λ1640 and Lyα, we note that EW measurement uncertainties on these lines will be impacted by the spectral continuum fit and subtraction to the extracted spectra, thus preventing a true comparison of the two extraction techniques. Similarly, a too-narrow extraction aperture may miss the full extent of the continuum flux in extended sources, consequently leading to flux calibration issues. Indeed, several extendedsource data sets required minor scaling factors to align the continuum flux between gratings, which we describe fully in Section 3. 3. Here we note that while a BOXCAR extraction may have helped minimize minor flux calibration offsets between gratings, since the vast majority of FUV science utilizes continuum-normalized spectra, we felt that contiguous and optimized S/N data were of more benefit to the user.
As such, we decided to remain with the TWOZONE extraction for all the FUV extended-source data sets for which it is available (i. e. , those that were taken at LP3 and LP4). From our findings, we recommend that COS users targeting extended sources (GFWHM > 0 6) can remain with the default TWOZONE extraction method without impacting the S/N of their extracted spectra. However, this can have consequences on the flux calibration of the spectra, which we discuss further in Section 3. 3.
NUV Extraction For COS NUV data (i. e. , data taken with the G185M and G225M gratings), CalCOS employs a BOXCAR extraction by default (57 pixels in height). However, again due to the extended nature of our sources, in several cases the spectral profiles were not sufficiently covered by the size of the extraction boxes. As such, it was necessary to reprocess our NUV data sets with ad hoc extraction profiles, centered on the source. To do this, we inspected the collapsed light profile on each of the NUV detector stripes (A, B, C) and adjusted the default height of the extraction boxes in the XTRACTAB reference file. Extraction boxes 95 pixels in height were chosen such that they sufficiently covered the width of the profile, without extending into the light profile of the adjacent stripe. We show an example plot used for this assessment in Figure  6 , where the collapsed counts in the dispersion direction (i.
The highest level of contamination would be between stripes A and B, which are closest in location on the detector. The level of contaminating flux was estimated to be <1% for all cases, with the exception of J0934+5514, which reached a contamination level of 3%. This is typically well within the level of uncertainties on the emission-line fluxes measured within this wavelength regime and as such poses only a negligible effect on the scientific results. It should also be noted that it was necessary to relocate the background extraction boxes for all of our NUV data sets. During our re-extraction process for the NUV data sets, the default back-to-back positioning of background extraction regions and the source extraction regions (as seen in Figure  6 (a)) was found to be problematic for the extended CLASSY sources in that the resultant background levels were too high owing to the contribution of counts from the extendedsource profiles.
From our findings we recommend that users of NUV observations of extended-source profiles examine their NUV flat-fielded science images (FLT images), assess the width of the NUV extraction boxes with respect to the collapsed light profile, and adjust it to incorporate the entire cross-dispersion profile. Moreover, to ensure that the most accurate NUV background subtraction is applied, we advise users to use updated background extraction regions, as detailed above. We discuss the effects of our optimized NUV extractions with respect to scientific application of the data in Section 4. 2.
Interestingly, despite their somewhat lower S/N, only ∼10% of the G185M/G225M data sets lie in this regime. Throughout the CLASSY coaddition process we extract and propagate the ERROR extension of the x1dsum data files (which pertains to the Gehrels upper confidence limit) and apply propagation techniques that assume a symmetric error distribution throughout. Within the N < 20 regime, the statistical error is expected to be accurate to within 5%-6% (Gehrels 1986), which we deem sufficiently accurate for the majority of the scientific application of the CLASSY data. The decision to assume symmetric errors for all data sets (which is appropriate for 65% of all CLASSY raw data sets) is based on the need for a consistent treatment of errors throughout all data sets and the crucial requirement of propagating symmetric errors during resampling.
Deriving a technique that correctly propagates asymmetric errors is beyond the scope of this study and currently beyond the capabilities of the CalCOS pipeline owing to its mathematical complexity. While we could adopt a method that approximates a symmetrical distribution from the separate upper-and lower-bound errors reported by CalCOS, the errors would themselves remain approximate in nature. As such, users whose scientific application of our data may be affected by our error propagation in these low-count regimes are advised to revisit the original data sets and perform customized error propagation on those specific data sets. To aid users on this front, we provide a data flag in the final coadded spectrum (flag = 2) for wavelength regions where the original x1dsum data sets were below 20 counts per native pixel, where the statistical error is expected to be overestimated and correct to within 5%-6%.
Note that a main goal of the CLASSY templates is to provide useful spectra to the community for a wide variety of science objectives that have different data requirements. Therefore, the steps of CLASSY coaddition process were ordered to allow for templates with different spectral resolutions to be produced. 1. Joining grating segments and stripes: The first step of the coaddition process unifies the spectral segments/stripes for each grating data set while accounting for the wavelength-dependent dispersion. To do so, the python pysynphot package (STScI Development The final coadded CLASSY spectral templates are HLSP multiextension fits files that include different extensions for observed-frame and rest-frame wavelength spectra, observed and Galactic reddening-corrected spectra, and original and binned data.
g. , Heckman 2002; Shapley et al. 2003; Steidel et al. 2010), it is imperative that velocity comparisons are only made between the same type of feature (i. e. , nebular to nebular). The lines used for the grating-tograting wavelength calibration check are listed in Table  1. Relatively strong, nonresonant, nebular emission lines are in many of the CLASSY data sets. These lines enabled robust comparisons between the G160M and G185M/G225M data sets. For 12 galaxies, no suitable emission lines were present for a comparison between the G160M and G185M/G225M gratings, and therefore the wavelength calibration of the G185M/G225M gratings remains unchecked. However, the only features of importance in the G185M/G225M gratings are nebular emission lines, and so the lack of lines/wavelength calibration has no impact on the scientific analysis of these objects.
For comparison between the G130M and G160M data sets, no pairs of strong, nonresonant emission lines are present. Instead, isolated, unsaturated ISM absorption lines of similar ionization states were utilized, as listed in Table  1. A comparison between the photospheric line profiles was also made between the G130M and G160M gratings when possible. Upon inspection, differences of <15 km s -1 were found between the gratings (see, e. g. , Figures  12(b)-(d) ). Since this is within the wavelength accuracy expected from each grating, it was not necessary to adjust the wavelength scale of the data before proceeding with the coaddition procedure. However, for one particular extended target, J1016+3753, an offset of ∼0. 5 Å (∼107 km s -1 ) was observed in the Si IV line profiles between the G130M and G160M data (Figure  12(a) ).
In this case, the two exposures were taken with very different position angles (ΔPA ∼ 100°), and as such, this wavelength offset represents a real offset in the velocity of the gas observed at the different PAs and is not due to wavelength calibration issues. Since these are physical differences, we do not adjust the coadded data for wavelength offsets such as this. As can be seen in the final coadded spectrum of J1016+3753, despite the velocity offset, the combined features in this overlap regime are not significantly broadened owing to the weighting effects of our coaddition process. However, velocity offsets may be seen when measuring lines of the same origin (i. e. , ISM, nebular, photospheric) in the final CLASSY coadded data of J1016 +3753 if they lie within different gratings. As a general recommendation for COS data, the wavelength calibration offsets between gratings should be inspected using the methodology described above (if possible with respect to line detection).
Experimental results In this section, we discuss the experimental setting of the simulated annealing algorithm and the results obtained by applying it to the instances proposed for the ITC2021 competition. We also assess the performances of Model M by running it on CPLEX within a time limit. For this latter experiments, we consider the instances of the competition and some others of smaller size obtained by performing reductions on the competition instances. Our code was developed in C++ and compiled with GNU g++ version 9. 3. 0 on Ubuntu 20. 04. 2 LTS. The tuning phase was partially performed on a cluster of virtual machines provided by the CINECA consortium. All the other experiments presented in this section were run on a machine equipped with AMD Ryzen Threadripper PRO 3975WX processor with 32 cores, hyper-threaded to 64 virtual cores, with base clock frequency of 3.
Acknowledgements The authors thank the  CINECA consortium  for the access to the Cloud Computing resources under the  Italian Super Computing Resource Allocation Project Grant   IA4ITC_C. The authors are also grateful to the ITC2021 organizers  David Van Bulck ,  Dries Goossens ,  Jeroen Beliën , and  Morteza Davari , and to the anonymous reviewers for their helpful and detailed comments.