dataesr/scientific-paragraphs-categorization huggingface dataset:
42  ), which was attached to the CTD rosette. Particle sizes in the range 128-151 µm were determined using the wellestablished and standard regression approach  42  , therefore we were able to detect particles with ESD between 128 and 4096 µm (the upper and lower limits of the UVP system). The lowering speed of the CTD ranged between 0. 5 and 1 m s -1 and data were averaged in 5 m depth bins. Particles within each depth bin were binned into size classes of 128-256, 256-512, 512-1024, 1024-2048 and 2048-4096 µm based on the pixel size of the UVP and to account for the decrease in particle abundance with increasing size. Particles in the 2048-4096 µm size bin were incredibly rare (on average 0. 022 L -1 ) so they were excluded from further analysis. Nutrient samples were collected during the CTD rosette upcast and measured immediately on board.
test) depending on normality of the data. Linear regressions were carried out to determine the correlation of anammox rates with particle abundance (particle volume per liter) for the four size classes 128-256, 256-512, 512-1024, 1024-2048 µm. The linear model (lm) function of R was used for individual and multiple linear regressions. Figures were made using the ggplot2  93  package and Adobe Illustrator. Collection of sinking organic material. Sinking organic material was collected non-destructively using a Marine Snow Catcher (OSIL, UK) lowered at 1 m s -1. The snow catcher was closed in the oxycline or upper part of the OMZ (from oxygen concentrations of 6 µmol L -1 down to 150 m below the oxic-anoxic interface, at water depths between 50 and 230 m, dependent on the station; see Supplementary Table  2 ).
A scaled image was taken of each particle or aggregate for size determination (see below). Size determination and categorization of sinking organic material. In order to determine the volume and type of the sinking organic material collected from the snow catcher, scaled images were taken of each particle or aggregate using a SLR-Camera (Nikon D-90) fitted with an AF-S Nikkor zoom lens (f = 16-88 mm). Area was determined by manually identifying the edges of the organic material and integrating the inner pixels using a MATLAB script. The volume of each particle was estimated from the equivalent sphere diameter (ESD), assuming a spherical shape. Based on their visual properties, the sinking organic material was categorized manually, following ecotaxa categories where possible (https://ecotaxa.
obs-vlfr. fr; ref. 94  ). The main categories were fluffy detritus (loosely packed material), compact detritus (densely packed material), faecal pellets and other sinking material. In addition to the ecotaxa categories, a category for loosely packed detritus containing faecal pellets was created for this dataset. Total organic carbon (TOC) content determination of single particles and aggregates. TOC was determined using a combined elemental-analyser/isotopicratio mass spectrometer (EA-IRMS, Delta Plus XP coupled to a Flash Element Analyzer 112). Sixty-nine individual particles or aggregates were picked, transferred into sterile-filtered seawater for photography (as described above) and to remove surrounding material, frozen in a droplet of seawater and transported to the laboratory in Eppendorf tubes.
Subsequently, the carbon content of the samples and standard series was measured on the EA-IRMS. Export production. Export production was estimated based on both satellite imagery and UVP data. Briefly, for the satellite-based estimate chlorophyll a, temperature and PAR data for the region were downloaded from Giovanni, NASA (https://giovanni. gsfc. nasa. gov/giovanni/, last visit: 11. 05. 2020, ref. 95  ) for the time of the cruise. Primary production was calculated based on chlorophyll a concentrations using a temperature and photosynthetically active radiation (PAR)based transfer function  96. We derived carbon-based export production using a particle export to primary production ratio (pe-ratio) determined via two methods: (1) using the global empirical relationship described in ref. 77  (based on temperature and chlorophyll a) and (  2 ) using a pe-ratio of 0.
Acknowledgements We thank  Gabriele Klockgether  and  Philipp Hach  for mass spectrometry and elemental analysis,  Matija Lagator  for help with C content measurements,  Angelina Klett  for help with particle size determination,  Gerd Krahmann  and  Hermann Bange  for CTD and nutrient data,  Markus Dengler  and  Hermann Bange  for their support during M136 and M138 respectively, and  Simon Ramondenc  for discussion of the multiple linear regression. We thank the captain and crew of  R/V Meteor  for their support during cruises  M136  and  M138. Further, we thank  Bennett Lambert  for discussions on encounter rates. This work was supported by the  Max Planck Society  and the  DFG-funded collaborative research center 754  " Climate-biogeochemistry interactions in the tropical Ocean " (Work Packages  B4 ,  B3  and  B8 ).
R. K. and H. H. were furthermore supported by the " CUSCO-Coastal Upwelling System in a Changing Ocean " project ( WP5 ) funded by the  Federal Ministry of Education and Research (Germany). R. K. also acknowledges support via a  "Make Our Planet Great Again" grant  of the  French National Research Agency  within the " Programme d'Investissements d'Avenir "; reference " ANR-19-MPGA-0012 ". S. A. and J. M. acknowledge funding from the  Max Planck Society (MPG)  for the " Multiscale Approach on the Role of Marine Aggregates" (MARMA)  project. H. K. M. and M. H. I. received funding from the  DFG under Germany's Excellence Strategy  (no. EXC-2077-390741603 ). M. H. I. was supported by the  Alfred Wegener Institute Helmholtz Center for Polar and Marine Research  and the  HGF Young Investigator Group SeaPump "Seasonal  and regional food web interactions with the biological pump":  VH-NG-1000.
Data availability Nutrient data from M136 are deposited on Pangaea (https://doi. org/10. 1594/ PANGAEA. 904404) in supplement to ref. 85. Nutrient data from M138 are deposited on Pangaea (https://doi. org/10. 1594/PANGAEA. 914948). UVP data are available upon request to R. Kiko and will be made available in a data collection to be found at https:// doi. org/10. 1594/PANGAEA. 924375. The UVP data are also freely available via the particle module of ecotaxa at https://ecotaxa. obs-vlfr. fr/part/ (particle projects 25, 95, 96, 97 and 98; please note that usage of ecotaxa requires user registration). The satellite imagery data used in this study was downloaded through the Giovanni online data system, developed and maintained by the NASA GES DISC. All other data are provided within the text or the supplementary material.
Feed Station Body weight and feed intake were individually and continuously recorded throughout the experiment thanks to an electronic feed station (https://www. feed-a-gene. eu/media/bird-eautomate-de-consommation-alimentaire-pour-volailles). The feeder has a circular shape and consists of 8 independent accesses to feed, without corridors, so that the chickens can express their natural feeding behavior. Each access includes one feed tube, one feed trough, one antenna placed on the top of the feed trough to detect the animal's RFID chip, one scale for feed weight, and one scale to record animal weight placed under the tray on which the animal climbs to eat. The feed troughs and the trays can be changed according to the size of the animals. Raw data obtained from the station are 1) feed weight by access every second, 2) identity of animal, time and access number every time an antenna detects a chip, and 3) mean animal weight during each visit.
When less than 10 seconds separated two successive meals M1 and M2 of respective durations D1 and D2, we did not obtain enough stable feed weight values to calculate a reliable mean feed weight between M1 and M2. A total feed intake (FI M1M2 ) was calculated as the difference between mean feed weight before the start of M1 and after the end of M2. The feed intake of each meal (FI M1 , FI M2 ) was then calculated according to the respective duration of each meal as: = 1 1 + 2 × = 2 1 + 2 × In order to check the reliability of feed intake measured by the station, each time the feed tubes were refilled, the added quantity of feed was weighed and compared with the data obtained from the feed station after refilling. The daily feed intake (DFI) was calculated as the sum of the feed intake of all meals eaten during a 24-hour period.
Body weight and daily gain calculation Before calculating individual body weight (BW) on the different days, abnormal data were removed (weights below 25 g and above three times the mean BW of the previous day). Data outside the interval deviating from the mean by more than three standard deviations were then J o u r n a l P r e -p r o o f removed. Body weight (BW) was then calculated as the mean of all available weight data during a day for each animal. In order to check the reliability of animal weight data from the station, animals were weighed manually, weekly for standard chickens and every two weeks for Label Rouge chickens.
Cumulative feed conversion ratio The daily cumulative feed conversion ratio for animal i on day j (DCFCR ij ) was calculated as the ratio of cumulated feed intake between the first day of data collection and day j to the weight gain over the same period: '*+ = ∑ - -" - # " -# J o u r n a l P r e -p r o o f with DFI ik being the daily feed intake of animal i for day k and BW ij the body weight of animal i on day j.
Analysis of Variance Analyses were done separately for each genotype, since the experiments had been conducted independently. The effect of the diet was first estimated separately for each day by applying the PROC ANOVA procedure of SAS 9. 4 (2013) with diet as the single fixed effect to data calculated for each day: body weight (BW), average daily gain (ADG), feed intake (DFI), feed conversion ratio (DFCR), and cumulative feed conversion ratio (DCFCR). In a second step, three rearing phases were defined according to the feeding period: starter (S), grower (G), and finisher (F) phases when the animals were fed with the starter, grower, and finisher diets, respectively. The birds' response to the diet depending on the feeding period was then analyzed with the following ANOVA model: J o u r n a l P r e -p r o o f y ijk = D i + P j + DP ij + e ijk with y ijk being the trait for animal k with diet i and period j, D i the fixed effect of diet i, P j the fixed effect of phase j (i.
Correlations with Daily Data Correlations between the daily data (BW, ADG, DFI, DFCR) and the data measured at slaughter (final BW and final cumulative feed conversion ratio (CFCR f ), BMY, PMY, PmY, AFY, TY, L, a*, b* and pHu) were calculated by using the Rcorr function of the package Hmisc of the R software (R Core team, 2017).
Rhizobial HAP Phytase Gene Primers Design The design of the rhizobial HAP phytase primers was performed online at the National Center of Biotechnology Information (NCBI, http://blast. ncbi. nlm. nih. gov/Blast. cgi). The highest hit by BLASTp to rhizobiaceae group of a known amino acid sequence of the Escherichia coli HAP phytase (GenBank accession number: AAN28334. 1) was obtained with a conserved hypothetical HAP-type phytase of Azorhizobium caulinodans (GenBank accession number: 5692057 AZC_4319). The gene sequence of this hypothetical HAP-type phytase was obtained from A. caulinodans full genome (GenBank accession number: AP009384. 1). Thereafter, the following pair of primers were designed with the primer-BLAST tool from this HAP gene-sequence: forward primer (HAPfor), 5 ′ CAGTTCACGCCAAAGATGCC3 ′ ; reverse primer (HAPrev), 5 ′ CGCGTATGGTCCATCCTGAA3 ′.
Numerous studies (e. g. , Refs. [13, 14] ) have handled the limitations of such design guides and considered them conservative material. Recent studies have been conducted (e. g. , Refs. [13, [15] [16] [17] ) to demonstrate the feasibility of using FRP bars in RC elements subjected to axial compression forces. Such studies change the trend of some design guides to consider the contribution of FRP bars in concrete members subjected to compression forces. For instance, the recent version of the Canadian highway bridge design code  [18]  allows the use of FRP bars in FRP-RC columns subjected to eccentric force while considering their contribution until a compressive strain of 0. 002 is reached. Based on the recent trend towards using FRP bars under compression actions, it is important to provide the necessary safety factor(s) required for design purposes.
Herein, the rules of ACI-318  [19]  code format were considered to derive the ultimate axial eccentric capacity P u of short concrete columns reinforced by means of steel bars. Nevertheless, the steel bars contribution is replaced by the FRP bars contribution. The ultimate capacity can be taken as P u = φ(F c + F FRP ), where F c and F FRP are the internal forces carried by concrete and FRP bars, respectively. Evaluation of these forces depends mainly on the position of the neutral axis, strain profile across the RC section and concrete crushing strain ε cu = 0. 003 (see Fig. 1 ). φ is the strength reduction factor proposed to be calibrated in the present study. Currently, the ACI 440-1R-15 guide  [9]  proposes a strength reduction factor φ that addresses only FRP RC beams, as given in Eq.
The results revealed that both the loading path and load correlation can affect the reliability of the columns. In another study by Diniz and Frangopol  [22] , the effect of long-term creep of concrete on the structural reliability of eccentrically loaded nonsway slender RC columns was investigated, where many design factors, such as the reinforcement ratio, load eccentricity and concrete strength (i. e. , normal and high strength concrete), were studied. The major conclusion drawn by the authors is that ACI-318  [19]  code rules for the case of long steel-RC columns using high strength concrete are too conservative compared to the normal strength concrete case. The longitudinal Fig. 1. Discretization of concrete section.
Furthermore, numerical differentiation leads to a potential oscillation of the numerical procedures in the vicinity of the most likely design point  [40]. Thus, full analytical forms of the derivatives of the function H(u) are essential requirements. Therefore, the limit state H(u) was approximated using the quadratic response surface RS, which can be expressed as: H(u) = θ o + ∑ n i=1 θ i u i + ∑ n i=1 θ ii u 2 i = Uθ T ( 9 ) where θ is the unknown coefficient vector, θ = [θ o θ 1 , θ 2 … θ n , θ 11 , θ 22 , …θ nn ] T , n is the number of variables and U is vector of basis polynomial functions U = [1, u 1 , u 2. u n , u 2 1 , u 2 2 , u 2 i … u 2 n ] T. To obtain the coefficient vector θ, a dataset consisting of m = 2n+1 u-vectors is constructed around a reference point u* and their corresponding U m-vectors.
The constructed dataset consists of two samples on each axis of the random variables (u i = u * i ± r, where r is an arbitrary scalar  [41] ). Accordingly, the vector of ultimate loads is obtained using the structural model described in the previous section. Defining a matrix V as [U 1 , U 2 … U m ] T ,. the vector θ can be calculated on the basis of minimizing the error between exact and approximated values in the constructed dataset. The conventional least squares method (LS) can be used to minimize such errors  [42, 43] , and the moving least squares MLS method can be used to provide a better approximation of the response function H(U). MLS is a weighted LS method that involves weight functions W(U) that depend on the approximation position. The MLS can be expressed as: MLS = ∑ m j=1 ( H ( j u 1 , j u 2 , j u i … j u n ) -θ o - ∑ n i=1 θ i j u i - ∑ n i=1 θ ii j u 2 ii ) 2 = (H(U) -Uθ) T W(U)(H(U) -Uθ) (10) Hence, the estimate of θ can be expressed as where W(U) is a diagonal matrix and can be defined as θ = [ V T V ] -1 W(U) [ V T H(V) ] (11 W(U) = ⎡ ⎢ ⎢ ⎣ w 1 0 0 w j … 0 … 0 ⋮ ⋮ 0 0 ⋱ ⋮ … w m ⎤ ⎥ ⎥ ⎦ (12) where w j is the product of two weight functions and can be defined as follows  [42] : w j = exp ( - H ( U j ) -H(U * ) H ( U j ) ).
O. Ali et al. The calibration procedure aims to determine the value of the strength reduction factor φ that minimizes the function: f (φ, β T ) = ∑ N i=1 ω i (β i -β T ) 2 (16) over the N classes of structures considered  [25, 46]  and where β T is the target reliability index considered in codes. β i , w i are reliability index and weight/frequency of a certain class of structure (i) of the considered class of structures. The corresponding frequency ω i for each ith class of structure means that ∑ N i=1 ω i = unity. Herein, all design situations were assumed to have an equal frequency of 1/N, where N is the number of situations in the combination set. All mathematical calculations were carried out using MATLAB software  [47]. To perform the calibration algorithm, the following introductory data should be defined: N-class of structures (depending on the expected ranges of design variables), failure modes, design rules (Eq.
(  15 )), goal of the code, the target reliability index β T (i. e. , according to Szerszen and Nowak  [24] , ACI-318 code β T proposes a target reliability index β T of a value equal to 4. 0 for the design of RC columns), and set of size M of strength reduction factors (i. e. , φ = 0. 5:0. 025:0. 9). Consequently, the calibration procedure was performed according to the following main steps  [25] :  Log-normal MPa See Table  2  max ω i = unity. Herein, all design situations were assumed to have an equal frequency of 1/N, where N is the number of situations in the combination set. 5. Deduce from this set the value of P u using the structural model described by Eq. (  4 ) and Eq. (  5 ). Then, calculate the applied working loads P LL or P DL from Eq. (  15 ). 6. Apply FORM-RS algorithm to Eq.
However, φ = 0. 6 could be recommended to maintain more than 95% of situations that match the requirements of the ACI code (β≥β T ) for the design of RC columns. Referring to the Top-Bottom configuration, FRP rupture and concrete crushing failure modes could possibly occur. Fig. 7a  and b  shows that concrete crushing provides higher reliability indices if the same strength reduction factor is considered because tension failure relies on the variation in both concrete and FRP materials, while compression failure relies only on the variation in concrete material. This context matches the ACI-440 recommendation, as it considers the concrete crushing failure mode for FRP-RC members under flexural loading, which is marginally more desirable than FRP rupture. From this point of view, compression failure can be recommended for FRP-RC columns because it provides a more desirable and safer design than tension failure.
Perspectives The results obtained in the present study can be considered as an initial guess of the strength reduction factor and the analysis can be extended to include any other expected ranges of the design parameters. In addition, the calibration algorithm developed in the present study can be extended over other code formats such as Euro or Canadian codes. Furthermore, the study can also be used to include other loading configurations (e. g. biaxial loading) and/or slender FRP-bars RC columns. Moreover, other secondary effects can be included in the analysis such as confinement effect, bulking of FRP bars, etc.
Data collection and analysis The image of the bead displayed diffraction rings that were used to estimate its 3D position as previously described  (53). From position fluctuations of the bead, both the mean elongation of the molecule and the force applied to it could be deduced  (54). The z-axis fluctuations were acquired at 16 Hz. A typical experimental acquisition spanned almost 24 h, corresponding to ∼2000 open/close cycles. Bead position at each force (2, 7 or 20 pN) were automatically recorded and analyzed using a software developed in house (Supplementary Figure  S3-S5 ). The experimental mean folding time (T f ) of each G4 sequence was inferred by dividing the whole time spent in the unfolded state (the cumulative time of oligonucleotide blockage before a structure is observed), summed over all beads and over the entire acquisition, by the number of times a G4 structure was formed (T f = oligo blocking time spent in unfolded state/total number of folded G4).
ACKNOWLEDGEMENTS The authors thank  Patrick Mailliet  for providing the 360B ligand and  Kevin D. Raney  for providing us with the BG4 antibody. The authors thank  Bertrand Ducos  and  Jessica Valle Orero  for discussions,  Jean-Francois Riou ,  Jean-Louis Mergny ,  Nicolas Desprat  and the anonymous reviewers for fruitful comments on the manuscript.
FUNDING National Research Agency [MuSeq ,  ANR-15-CE12-0015 ,  G4-crash -19-CE11-0021-01 ]; CNRS; INSERM;  Museum National d'Histoire Naturelle  to the  Genome Structure and Instability  unit; P. L. T. T. was supported by a  Marie Sklodowska Curie individual fellowship  ( 2019-2021 ); work in the group of V. C. is part of ' Institut Pierre-Gilles de Gennes' ['Investissements d'Avenir' program   ANR-10-IDEX-0001-02   PSL ,  ANR-10-LABX-31 ];  Qlife Institute of Convergence (PSL Univesité ). Funding for open access charge:  ANR.
With the use of the convolutional neural network (CNN) architecture for audio features and learning and classification parts for commands and language identification, the speech module handles intermixed requests in English and Korean languages, giving corresponding feedback. Evaluation with test data showed high accuracy of prediction, having on average 95. 7% and 99. 6% on the F1-Score metric for command and language prediction, respectively. The proposed speech interaction module in the aircraft maintenance metaverse further improved education and training, giving intuitive and efficient control over the operation, enhancing interaction with virtual objects in mixed reality.
What is more, built-in speech recognition is limited to a specific language. In scenarios where users communicate with intermixed commands, such as Korean aircraft maintenance engineers who use documentation in English and combine languages, existing voice modules do not work. This provides a trigger for alternative solutions such as deep learning  [12]. Deep learning shows enormous potential in speech command recognition tasks  [12]. We analyzed the literature on speech and sound recognition for mobile devices since, to the extent of our knowledge, there are no similar related works that worked on custom voice interaction models for smart glasses. The most related works, such as  [13] [14] [15] [16] [17] [18] [19] [20] , dealt with single-word simple speech commands, based on the Dataset for Limited-Vocabulary Speech Recognition  [21].
The common property they share is the architecture, which is based on convolutional neural networks (CNN) for features extraction and fully connected layers for classification  [22]. The authors of  [13, 14, 16, 20, 23]  applied 1D convolutions with raw audio, whereas  [13, 15, 17, 18, 24, 25]  used 2D convolutions with special audio features such as mel frequency cepstral coefficients (MFCCs)  [26]. According to  [13] , both approaches are effective, but MFCCs are a great approximation to the human voice that help to discard background noise  [26]. Summarizing the limitations of the referenced works, they do not deal with complex commands, datasets they experimented with are monolingual, and their architectures are not suitable for command and language detection at the same time. Therefore, following their best approaches, we built our own speech command recognition deep learning model.