dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020)  as Eq. (  6 ). log a f ð Þ ¼ 0:011f 2 1þf 2 þ 4:4f 2 4100þf 2 þ 2:75 Â 10 À5 f 2 þ 0:0003 ð6Þ Where f is the carrier frequency determined in kHz and the absorption coefficient a f ð Þ is measured in dB/km. We have discussed the energy consumptions of the nodes during data collections and transmission. The following lines provides details about the energy consumption for the CH and AUV. Here, the energy consumption for CH considers the energy dissipation during the data receiving from CMs and data sending to the AUV. The Eq. (  7 ) calculates the total energy consumption of the cluster head in one round of data collection if there are N nodes in the cluster. EC total CH ¼ P N i¼1 e rec Q i rec recrate ! þ e trans Where e rec and e trans are energy consumptions for data receiving and transmission of the CH per unit of time and Q i rec presents the amount of data collected by CH from the i th CM, Q data trans: by CH is the amount of data transmitted by CH to the AUV.
rec rate and D trans: rate are the receiving and transmission rate of the CH respectively. Secondly, the energy consumption of AUV is calculated, which consists of the energy consumed by moving along CH and energy utilization during the data receiving from each CH. If the energy is consumed at / dis per unit of distance with a velocity of VL AUV!l and the energy consumption during the data collection is / rec per unit of time, then the overall energy consumption of AUV can be determined by Eq. (  8 ). EC AUV ¼ / dis X l 2 CHset VL AUV!l þ / rec X l 2 CHset Q l!AUV AUV data rec: rate ð8Þ where Q l!AUV is the amount of data to be sent to the AUV byCH l , AUV data rec: rate is the data receiving rate of the AUV and CH set is the set of CHs. Energy consumption of AUVs varies due to delays, so we must also find and calculate these delays to check this consumption.
7. These surface sinks remained static throughout the simulation, but inside the water, other nodes moved between 1 and 3 m/s. Only the horizontal movement of the nodes was considered and the vertical and diagonal movements were ignored in the simulation environment. The data packet size was fixed at 512 bits, while each source node generated data with a constant bit rate. The major simulation environment settings are represented in Table  7. Sink nodes and AUVs are the main factors that were considered to determine the performance evaluation. The measured metrics include packet delivery ratio, energy dissipation, end-to-end delays, network throughput and network lifetime, while a detailed analysis of these parameters is discussed below.
Energy consumption Energy consumption is a key parameter that is considered a prime concern in wireless sensor networks. However, it is even more crucial for underwater environments due to the unavailability of any recharging or battery replacement options. The energy dissipation results present the sum of all activities and tasks, including the sending and receiving of packets, as well as the power consumed during the sleeping or idle state. Overall, the energy consumption indicates the performance of the proposed scheme and mirrors the lifespan of the whole network as lower energy consumption ultimately leads to a greater lifespan. The results in Fig. 9 (a) highlight that MD2F was more energy efficient due to two main reasons. Firstly, data packets were not traveling from the source to the sink as packets were largely being delivered through the AUV.
End to end delay Based on the delay results, it is easy to determine whether an algorithm is suitable for a particular application and how it is better than other algorithms. Fig. 11  compares the end-to-end delay where packets received at AUV are considered delivered for both MLCEE and MD2F. Here, two factors play important role. One is clustering, and the other is partitioning the network into two layers. In the top layer, nodes are not required to wait and keep forwarding the packets toward the sink without waiting, leading to reduce delays. Furthermore, clustering plays a crucial role as only neighboring nodes participate in the packet delivery process. Therefore, the collected data can be swiftly delivered to the AUV, even when a smaller number of nodes are part of the network. On the other hand, when nodes started to decrease, E2LR started to face problems as it purely depends on the hop-by-hop mechanism during the data forwarding.
Network throughput (NT) Fig. 12  illustrates the network throughput for the MD2F and the compared schemes. Due to its hybrid nature, the proposed technique obtains the maximum number of data packets with minimum duplications. The upper part of the network (top layers) followed the hop-by-hop option, while AUV was used to collect the data from the lower part of CHs. This helped MD2F to prevent issues such as packet retransmissions and redundancy. Furthermore, the cluster size was adjusted based on a stable range of com-munication, where the cluster member nodes could easily forward their data packets through the respective CHs. Firstly, when we consider the network throughput based on number of nodes, as shown in Fig. 12 (a), both other schemes performed reasonably when average numbers of nodes were part of the network.
Present day lengths at first catch were extracted from the fisheries data collection of the Joint Research Centre (https://datacollection. jrc. ec. europa. eu). New lengths at catch were computed as follows: Lc exploited species = λ × Lc current (1) where λ ∈ {1. 1,1. 2,1. 3,1. 4,1. 75}, and Lc current corresponds to the current length at first catch of the targeted species. In line with previous studies assessing changes in the exploitation schemes of Mediterranean species  (Colloca et al. 2013 , Maravelias et al. 2014 , Vasilakopoulos et al. 2014 , Tserpes et al. 2016) , 2 supplementary scenarios of size selectivity were tested with the length at first catch corresponding to either the size at maturity (Lmat) of exploited species or to the optimal length at first catch (Lopt) defined for each exploited species.
Acknowledgements. We acknowledge the  Pôle de Calcul et de Données Marines (PCDM)  for providing DATARMOR computational resources (https://pcdm. ifremer. fr/),the  CALMP computation centre  (Grant  P1331 ) for the HPC re sources, and the support of the SIROCCO team (http://sirocco. obs-mip. fr/). We also acknowledge the  CNRM  for making the physical data available. The climatic simulations used in this work were downloaded from the Med-CORDEX database (www. medcordex. eu). This work was partly funded by the  USBIO  project of the  LabEx CeMEB , an  ANR 'Investissements d'avenir' program  ( ANR-10-LABX-04-01 ), the 2017-2018  Belmont Forum and BiodivERsA joint call  for research proposals, under the  BiodivScen ERA-Net COFUND programme , and with the funding organizations  ANR  ( ANR-18-EBI4-0003-01 ),  DFG ,  MINECO-AEI ,  NSERC ,  OUC  and  TÜBITAK  and the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  N°869300  ( FutureMARES  project).
Textile supports characterization Five textile materials (see table S1_supplementary data for more details) were purchased on https://www. tissusactifs. fr and their micro-texture and physico-chemical properties were characterized with several techniques. The micro-texture of the materials was observed using a stereomicroscope (Zeiss, Discovery V12, Germany). The images were taken in a size of 2. 30 mm × 1. 72 mm with 63× magni cation (Fig. S1 ). The area of the pores on the surface of each material was quanti ed using the Fiji software  (Kostajnšek et al. 2021 ). The relative opening surface area (i. e. , the total opening area in 1 cm 2 of textile) was afterwards calculated. The surface roughness of the textile materials was quantified using a microtopographe (STIL CHR 150, France). At least ve positions on each material were randomly selected for roughness determination.
The surface roughness (Ra) was analyzed by the Mountains Map Universal software (Digital Surf Sarl 3. 0, Besancon, France). The hydrophobicity of textile materials was determined using the sessile drop test with an automatic drop tensiometer (Tracker Teclis/IT Concept, France). 5 µL of distilled water (as reference liquid) was pipetted onto the surface of the materials, and the images of water drops were analyzed using WDROP 2010 software to characterize the static water contact angle (θ) which re ects surface hydrophobicity (0° < θ ≤ 90°, hydrophilic surface; 90° < θ < 180°, hydrophobic surface; θ = 180°, ultra-hydrophobic surface, respectively)  (Van Oss et al. 1988 ). An ATR-FTIR (Fourier Transform Infrared Spectrometer with Attenuated Total Re ectance) PerkinElmer Spectrum-two spectrometer (PerkinElmer, Waltham, MA, USA) was used to analysis the chemical properties of the textile materials.
Statistical analysis All results were presented as mean values ± standard deviations (n ≥ 3). After the tests of normality (Shapiro-Wilk test) and variance homogeneity (Levene's test), one-way ANOVA analysis of variance followed by Tukey's post hoc test for multiple comparisons in cells, attachment/detachment and physicochemical properties among different textiles were carried out using IBM SPSS Statistics 25. 0 (SPSS Inc. , Chicago, IL). Signi cant differences in cells growth, activity, composition and bio lm productivity between different treatments (two supports, two inoculum densities and two inoculum pre-acclimated light intensities) were performed by Students's t-tests with pairwise comparisons testing. The statistical signi cance of the data was shown at the levels of P < 0. 05, P < 0.
Conclusions The effect of inoculum density/physiology and support nature on bio lm growth/productivity, activity, and composition was evaluated. Results show a decrease in bio lm growth when using high inoculum density, probably associated to a reduction in light/nutrients availability. Support micro-texture affects cells distribution, consequently impacting bio lm formation and activity. A higher rETR max and carbohydrate/protein ratio were exhibited by cells grown on a polyamide-based fabric (Terrazzo), suggesting a higher light availability to cells than those on Cotton. Our study also suggests that inoculum physiology, poorly considered in literature, affects bio lm productivity. Higher productivities were reported for bio lms inoculated with cells photo-acclimated to high light. Therefore, our data con rm that in the inoculation step, the support selection and inoculum density/physiology must be carefully considered in order to optimize bio lm-based systems productivity.
In the introduction of this review, the authors cite comparisons between the measurement of the size of "culturable" bacteria (Colony Forming Unit-CFU method) and that made by microscope observations with or without in situ hybridization with reference to the "great plate count anomaly" mentioned by  Staley and Konopka (1985). The data presented in the Amann's review highlights that the percentages of culturable bacteria in some terrestrial ecosystems (soil and freshwater) varied between 0. 1 and 1%, while in seawater they are below 0. 1%  (Amann et al. , 1995). This article is quoted extensively (7604 in WoS database, March 2023) for this result which, even if it only concerned a few examples of ecosystems, led the many authors citing this article to retain this key value of 1% in the introduction to their own article, justifying the use of cultureindependent methods.
PCR products were sequenced using standard Sanger sequencing technique (GENEWIZ, Beckman). The complete or partial 16S rRNA sequences of the bacteria were blasted within the National Center for Biotechnology Information server. 1 The 16S rRNA gene sequences of 439 strains were compared with the most similar neighboring phylogenetic sequence(s) from GenBank databases with RefSeq accession numbers ON629815 to ON630253. 2 We completed the identification prediction using a second databases, EzBioCloud. 3 The length of the 16S rRNA gene sequences we obtained was variable, due to the difficulty of sequencing some PCR products despite the use of four primers: fd1, S10 (5 -CCGTCAATTCATTTGAGTTT-3 ), P3 (5 -GCCTACGGGAGGCAGCAG-3 ), and S17. Therefore, bacterial strains were identified using the whole 16S rRNA gene or its V1-V4 region and assigned to species when the percentage of identity was higher than 98.
5 µl of template DNA. Cycling conditions included an initial denaturation at 98 • C for 30 s, followed by 30 cycles of denaturation at 98 • C for 10 s, annealing at 70 • C for 30 s and extension at 72 • C for 30 s and an additional extension step at 72 The analysis was performed using FROGS, a Galaxy-supported pipeline designed to consider large sets of amplicon sequences and produce abundance tables of Operational Taxonomic Units (OTUs) and their taxonomic affiliation  (Escudié et al. , 2018). The clustering was achieved using Swarm  (Mahé et al. , 2014). Metabarcoding SRA data are available with the BioProject accession number PRJNA837115. 5.
Bioinformatics and statistical analyses Statistical tests and graphical representation were performed using R software version 3. 5. 3. Data processing and graphing were done using tidyverse packages  (Wickham et al. , 2019) , correlation between variables were performed using cor function. For each pairwise test, p-values were adjusted by BH correction. Each univariate analysis was performed using the Kruskal-Wallis test. If a parameter had a significant influence on a variable, a pairwise post-hoc Dunn test was used to determine group significance differences using PMCMRplus package. 6 Homogeneity groups were calculated automatically using the multcompLetters function with a p-value threshold = 0. 05. Alpha-diversity was calculated by taxonomic richness (i. e. , the number of taxa, expressed as the number of observed OTUs) and Chao1 index.
Diversity of culturable bacteria A total of 507 bacterial strains were isolated from the 3 sites from which 439 were identified at the genus or species level (87%), including 251 strains isolated from Timoudi (103/87/61, respectively in TM1/TM2/TM3; Supplementary Table  5A ), 103 strains isolated from Béni Abbès (40/29/34, respectively in BA1/BA2/BA3; Supplementary Table  5B ), and 85 strains isolated from Taghit (39/24/22, respectively in TG1/TG2/TG3; Supplementary Table  5C ). The identification prediction was performed using both NCBI NR and EzBioCloud databases. Of the 439 bacterial strains, 290 strains were identified to species level (66%) (Supplementary Table  6 ), and the remainder to genus level (19%) or potential new species with identity percentage of partial 16S rRNA sequence lower than 98.
Data collection The Normalised Difference Vegetation Index (NDVI) is a standard way to determine vegetation cover and productivity. High NDVI values (approximately >0. 7-0. 8) indicate healthy vegetation, dense and productive canopies, while low NDVI values indicate land with little or no vegetation or stressed canopies. Satellite-derived NDVI data for the period 2018-2020 were retrieved for the two study areas by processing the Sentinel-2 imagery. The images (10-m spatial resolution, level2A) were atmospherically and topographically corrected with the Sen2Cor processor (https://step. esa. int/main/snap-supported-plugins/sen2cor). The images were filtered on a per-pixel basis with the scene classification (SCL) map, retaining only top quality, and cloud-and shadowfree pixels. The downloading and processing of the data were performed on Google Earth Engine (https://earthengine.
google. com) with a dedicated Python (https://www. python. org) script  (Hufkens, 2017). Seasonal NDVI trajectories were used to retrieve growing season start and end based on a fixed threshold method (20% seasonal amplitude) similar to what proposed by  Shen et al. , (2015a). These dates were then compared to those retrieved form the snow cover seasonal pattern (table  1 ) and found to be in good agreement. Aboveground biomass (AGB) and leaf area index (LAI) were measured in both areas following standardised protocols  (Filippa et al. , 2015). An empirical model was fitted between AGB/LAI observations and the corresponding S2-derived NDVI, and the resulting equations (Supplementary material, section 1) were then used to convert S2-NDVI data in AGB and LAI data for the three productivity macro-types of each study area.
For future periods, Representative Concentration Pathways 4. 5 and 8. 5 (RCP4. 5, RCP4. 5) were selected, with ambient CO2 concentration at 450 (RCP4. 5) and 470 ppm (RCP8. 5) for near future and 540 and 670 ppm for mid future. The delta-change approach was applied as a downscaling procedure, where the observed daily weather data available for each given site were modified using as forcing factors the outcomes obtained from the RCM simulations. These were calculated as the mean absolute monthly differences between the RCM baseline  and the future RCM periods selected for simulations  for minimum and maximum air temperatures and the percentage variation in monthly cumulated rainfall, wind speed and solar radiation. These differences were then added, month by month, to the observed daily meteorological data from PNE and PNGP to derive future weather data that were used to feed model simulations for future periods.
This choice was made in order to better assess changes in model estimates as a result of changing assumptions and to identify the most significant ones that reflect reality. In addition, this approach helps to identify the range of variability over which model outputs may vary. In the selection phase, two models were identified in which processes are represented at different levels of detail. While PaSim is a complex (grassland-specific) model, simulating C-N and water cycles in detail (with a module dedicated to livestock impact and livestock-atmosphere feedbacks), DayCent is a more empirical (generic) model, with relatively simple relationships between driving variables and fluxes. The two models differ in the representation of soil properties, vegetation type, agricultural practices and environmental forcing, as well as in the initialisation of C pools.
However, there may also be biases in the simulation of SWC, with grassland models that may not be accurate enough to estimate these dynamics. This is often associated with an unrealistically low amplitude of the annual cycle (fluctuation damping, after  Wu et al. , 2002)  of the soil water content curve compared to field measurements  (Sándor et al. , 2017). It is known that the quality of soil water content simulations can seriously affect model outputs. In case of poor estimation of soil water content, model calibration may result in biased parameter values. Several factors, such as permanent wilting point, root distribution and maximum transpiration rate, are in fact related to the rate of water infiltration into the soil during precipitation events and snowmelt periods  (Philip, 1993) , which would require detailed datasets for an accurate description.
Acknowledgement The present work was produced under the co-finance of the  EC LIFE programme for the Environment and Climate Action  ( 2014-2020 ) in the framework of the  Project LIFE PASTORALP  ' Pastures vulnerability and adaptation strategies to climate change impacts in the Alps ' ( LIFE16/CCA/IT/000060 ). It falls within the thematic area of the  French government IDEX-ISITE initiative  (reference:  16-IDEX-0001 ; project  CAP 20-25 ). The authors wish to acknowledge the commitment of  Dehia Hadjsaadi  ( Université Clermont Auvergne, INRAE, VetAgro Sup, UREP ,.
2014; Grémillet et al. 2016). Previous studies have demonstrated the impact of unprofitable foraging conditions on Cape gannet fitness, aligning with a regional population decline  (Cohen et al. 2014; Grémillet et al. 2016). Foraging efficiency plays a key role in influencing the survival rates of gannets, and assessing how this behaviour varies across individuals of varying sex and age is necessary to estimate the vulnerability of Cape gannet populations to a changing environment. We use GPS and accelerometer data to determine age-related and sexspecific resource utilisation based on energy-related costs. We predict foraging performances to follow a bell-shaped curve (e. g. Monaghan et al. 2020; Fay et al. 2022; Saraux and Chiaradia 2022)  with performance increasing to a certain age whereafter it decreases.
Study area Fieldwork was conducted on Malgas Island (33. 0528° S, 17. 9254° E, Saldanha Bay, South Africa. The Cape gannet population on Malgas Island has decreased from 52,000 breeding pairs in 1996/1997 to 22,000 breeding pairs in 2018/2019  (Sherley et al. 2019) , and continues to decline (BirdLife International 2018). At this site, gannet chicks have been fitted with metal rings since 1990  (Distiller et al. 2012) , and resighting data are still being collected and collated by the South African Bird Ringing Unit (SAF-RING). This allowed us to study the at sea movements and energetics of known age, breeding adult Cape gannets during October and November in 2017, 2019, and 2020. In total, we gathered data from 12 birds between 4 and 10 years old, 14 between 11 and 17, and 13 between 18 and 23.
This ensured that surging acceleration was measured along the longitudinal body axis of the birds, and heaving acceleration was measured dorsoventrally at 50 Hz (Fig. 2 ). The combined weight of the GPS device, accelerometer, and heat-shrink tubing was less than 2% of the average body mass of adult gannets  (Grémillet et al. 2004 ). We monitored nests every second hour during daylight to retrieve the loggers soon after the birds returned to the colony. The Tesa® tape strips were removed completely. We used the software X Manager to configure the loggers and retrieve the data from the accelerometers, and @trip PC for the GPS devices. From the GPS data, we extracted information on the following foraging trip characteristics: trip duration (time between departure from and return to the colony, in hours), maximum distance from the nest (farthest point from the colony before returning, in km), and total distance travelled (distance covered between departure and return, in km; see  Grémillet et al.
Energy expenditure At-sea energy expenditure was estimated using acceleration data. We used Igor Pro 9. 0 (WaveMetrics, OR, USA) to process data and classify behaviors  (Grunst et al. 2023). In brief, acceleration in all dimensions was smoothed using a rolling algorithm (2 s sliding window) to extract static acceleration. Static acceleration was derived from the body angle with respect to gravity, and the values ranged from + 1 to -1 g for a non-moving accelerometer. We calculated 138 body pitch using the formula: atan(SX/(sqrt(SY^2 + SZ^2)) × (180/pi)), where SX, SY, and SZ are smoothed acceleration. Minimum specific acceleration (MSA) was calculated using the formula: abs(sqrt(X^2+Y^2+Z^2) -1), where X, Y and Z are acceleration, as an indication of the bird's activities  (Simon et al. 2012).
Statistical analysis of foraging trip characteristics To test for effects of age-related changes on the foraging trip characteristics (foraging trip duration, maximum distance from the nest, and total distance travelled) of tracked birds, we included age as well as age 2 (representing a potential non-linear relationship, which was calculated by squaring the age of each bird;  Lescroël et al. 2019; Debeffe et al. 2017)  as predictor variables in Gaussian Generalized Linear Models fitted using the MASS Package  (Venables and Ripley 2002)  in R version 3. 5. 3 (R Core Team 2017). To normalize data spread we applied log10 transformation to maximum distance from the nest and total distance travelled, and log transformation to energy expenditure. Sex (and an interaction with age) was also included as a predictor variable to test for differences in aging trajectories between males and females.