dataesr/scientific-paragraphs-categorization huggingface dataset:
The results show that it was possible, in the context of an energy audit, to estimate the compressed air leakage using NILM techniques in this dataset with less than a 1% difference when compared to the estimation made with actual measurement. Finally, savings estimations considering the elimination of the leaks were performed, varying between 10% and 100% of the leakage repair. Considering the ideal scenario of complete leaks elimination, the savings would represent around 44% in the compressed air system and 4. 75% of the current annual global consumption.
3). Section 2. 4 presents the link between energy audits, quantification of compressed air leaks, and NILM techniques. Section 3 presents a study case, quantifying the leaks in the compressed air system of a tertiary building located in the French Alps region. Firstly, the leakage is estimated directly by electric input measurements during a no compressed air consumption period. Then, the FHMM technique is applied to the data. The results show that it was possible, based on an energy audit, to estimate the compressed air leakage in this dataset. After that, energy savings reached with the repair of the leaks are estimated, to demonstrate how impactful this energy efficiency measure can be.
The second type is the Walk-through Audit. It consists of a quick tour of the facility to visually inspect the target systems. This may include the analysis of energy consumption patterns and provide comparisons to average benchmarks for similar facilities. When the inspection of the target systems shows promising savings potential, this audit can lead to a more complex audit later  [14]. The Standard Audit seeks to quantify energy consumption and losses by performing a detailed analysis of the performances of several energy systems. This analysis usually includes on-site measurements, historical data collection, and testing to determine the efficiency of the analyzed systems. Specific energy engineering calculations are applied to determine efficiencies and calculate energy and financial savings based on improvements and changes to each system.
One way to perform this data collection is by incorporating the team into the environment to be evaluated. In this manner, the team can experience the daily operation of the facility. However, besides the fact that the auditors' time to perform this task is limited, usually a few days or weeks, the ideal is that their activities have as little impact as possible on the normal operation of the systems being evaluated. Due to this, some operating modes of certain equipment may not be measured during the auditors' data collection period. The second phase consists of analyzing the collected data and determining the consumption and efficiency of the evaluated systems. To perform this task, the data collected in the preceding phase is applied to procedures specific to each system. In this stage, possible opportunities to promote energy sobriety can also be identified.
Results and Discussions This section presents a study case of the quantification of compressed air leakage in a tertiary building environment. Firstly, the leaks are quantified by applying the procedure presented in Figure  3 , using BMS data available for benchmarking. In a second moment, the FHMM NILM technique is applied to the global consumption of the building, and its output is used to also quantify the leaks. Finally, an analysis of the potential savings with the repair of the leaks is presented.
Study Case-Estimating Compressed Air Leakage in a Tertiary Building Environment In order to perform an estimation of compressed air leakage in a facility using NILM techniques, consumption data from a rotary-screw fixed-speed air compressor were extracted from a dataset of a tertiary building located in the French Alps region that is available for downloading in open source at the open science platform Mendeley Data  [33]. The data come in CSV (comma-separated values) files that contain the timestamp and the cumulative electricity consumption with 10 min sampling. The dataset also has files with data on the external temperature, which has an influence on the building's energy consumption because of the nature of the cooling loads. The computational code was developed in Jupyter Notebooks, an environment in python that combines code, text, and images.
Furthermore, as the up sampling was performed considering the actual power values associated with each state, and the most important feature to estimate the compressed air flow rate is how long the appliance remains in its load state, higher resolutions, such as 1-s time step, or even sub-second resolution, would not improve the estimations. In the case of the air compressor installed in the GreEn-ER building, the actual power associated with the load and unload states are different from the values retrieved In order to up sample the air compressor data keeping the original average power value, it is necessary to determine how much time the compressor operates in each state. For each 10 min period that the compressor presents 18 kW of average power, it has operated for 4 min in load condition (36 kW) and 6 min in unload condition (6 kW).
Leakage Estimation from NILM Techniques The leakage estimation presented in the previous section was done thanks to the availability of power consumption data in a no compressed air consumption period. However, that is not the typical case in the context of an energy audit. Usually, only the historical data of the global consumption is available, and the auditors have only a few days or weeks of data from the air compressors that were measured by themselves. In addition, it is unusual that the on-site measurement period contains a no compressed air consumption one. Therefore, the use of NILM techniques to retrieve the air compressor's power from global consumption would allow the quantification of compressed air leaks in such circumstances. To address this task, the FHMM NILM algorithm was applied with the help of the NILMTK  [38, 39] , an environment in Python developed to enable the comparison between several state-of-art approaches.
A week, during a no compressed air consumption period, was selected for testing the model. Some major consumers were selected in order to create a sub dataset regarding these loads; the global consumption and the air compressor. This sub dataset is used in the NILM task. Five major consumers were selected, and they are named as follows: • Crous-The Crous load belongs to the University Restaurant, where some typical loads are connected, such as dishwashers, dryers, furnaces, heaters, etc. • ASI-The ASI load represents loads connected to the datacenter, such as computers, uninterruptible power supply, etc. • TD-GF-The TD-GF represents chillers that are responsible for the air conditioning during the summer; • AHU-The load called AHU represents a set of 16 Air Handling Units present in the building; • Ghost-This load is inserted as noise, and it corresponds to the difference between the main load and the sum of the other loads.
As the sampling interval of the dataset was originally 10-min, all these loads were up sampled to 1 min sampling intervals using forward fill, to match the sampling interval of the air compressor. Figures 8-10 illustrate the load curves of the loads described above and the global consumption, during the period used for the training and the period used to estimate the leaks. Table  3  presents the average power of these loads during both periods. The air compressor load curves were already shown in Section 3. 1. 1. Using the NILMTK framework, the FHMM algorithm was then applied. The algorithm uses the training data to model the loads into finite states and associates each state with an electric power value. Table  4  presents the number of states that the FHMM uses to model each load and the power associated with them.
These results are presented in Table  6. The results presented in Table  6  suggest that it is possible to estimate the compressed air leakage using NILM techniques in the context of energy audits, considering It can be seen in the previous figure that the FHMM algorithm was able to retrieve most of the air compressor consumption in this example. In order to quantify the estimation of the compressed air flow rate in this period, the association between flow rate and power shown in Section 2. 2 was applied. These results are exposed in Table  5. Then, the average compressed air flow was compared to the flow estimated to the actual compressed air power. These results are presented in Table  6. The results presented in Table  6  suggest that it is possible to estimate the compressed air leakage using NILM techniques in the context of energy audits, considering the dataset investigated.
Firstly, using raw data from the dataset, with 10 min sampling interval, the flow rate was poorly estimated, making the leakage estimation seemingly incorrect, suggesting that the air compressor changes its state at a higher rate than the 10 min sampling interval. With the up sampling to a 1 min sampling interval, and the retrieval of the actual powers associated with the load and unload states of the air compressor, the flow rate estimation seemed more accurate. The percentage of leaks was then estimated at around 62%. Finally, by the application of the FHMM as NILM technique, the power load curve of the same air compressor was retrieved from the building's global consumption. The results obtained show that it was possible to satisfactorily estimate the number of leaks, with an error of less than 1%, when considering the estimations made from the measurements in the studied dataset.
Funding: The  Carnot Énergies du Futur Institute , under the project  ORCEE , the  French Centre National de la Recherche Scientifique (CNRS) , the  Grenoble-INP , the  Université Grenoble Alpes (UGA) , the  Federal University of Santa Catarina (UFSC)  and the  Fundação Stemmer para Pesquisa, Desenvolvimento e Inovação (FEESC)  funded this research. This work has also been partially supported by the  ANR (Agence Nationale de la Recherche ) project eco-SESA (https://ecosesa. univ-grenoble-alpes. fr/, accessed on 2  February 2022 )  ANR-15-IDEX-02  and by the joint laboratory  LIA-Maxwell.
Data Availability Statement: The data presented in this study are openly available in Mendeley Data at DOI: 10. 17632/h8mmnthn5w. 1, reference number  [20]. A resumed dataset can also be found at: https://gricad-gitlab. univ-grenoble-alpes. fr/martgust/power-consumption-data-quality  [21]  (accessed on 2 February 2022).
Study design and patient population PARADIGMS was a randomised, double-blind, doubledummy, active-controlled, parallel-group, multicentre study of up to 2 years, followed by a 5-year open-label extension period. 18 Patients (N=215) were randomised (1:1) to receive either oral fingolimod (N=107; 0. 5 mg once daily for >40 kg body weight and 0. 25 mg once daily for ≤40 kg body weight) or intramuscular IFN β-1a 30 µg once weekly (N=108). The study included patients aged 10-<18 years at randomisation with a diagnosis of MS; an Expanded Disability Status Scale (EDSS) score of 0-5. 5 and ≥1 MS relapse in the past year; ≥2 MS relapses in the past 2 years; or evidence of ≥1 gadolinium-enhancing lesion on T1-weighted MRI within 6 months before randomisation (including the screening MRI). Further details of the study design and population have been reported in the primary publication (PARADIGMS ClinicalTrials.
Acknowledgements The authors acknowledge the patients, investigators and staff at participating sites for supporting the conduct of the study. They thank  Arshjyoti Singh  and  Anuja Shah  ( Novartis Healthcare, Hyderabad, India ) for providing medical writing support, which encompassed a literature search, writing of the manuscript, formatting, referencing, preparation of tables and figures per the journal guidelines and incorporating the authors' revisions and finalising the draft for submission, all under the direction of the authors. The authors thank  Gillipsie Minhas  ( Novartis Healthcare, Hyderabad, India ) for providing technical assistance for submission of the manuscript and  Rohit Bhandari  ( Novartis Healthcare, Hyderabad, India ) for helping during the manuscript revisions. Contributors LK contributed to the conception of the study, execution of the study, interpretation of the study data, outlining the manuscript content and critical revision of the manuscript for content.
Data analysis We measured baseline and stress-induced (30 min after capture) hormone concentrations during the chick-rearing period in both 2016 and 2017 (Table  1 ). Baseline and stress-induced prolactin concentrations were obtained from 44 birds, while baseline and stress-induced corticosterone concentrations were measured in 41 birds. Hormone concentrations after 24 h of captivity were measured (only in 2017) in an attempt to understand the consequence of prolonged stress on hormone concentrations. We used linear models to assess the importance of various factors on hormone concentrations. In first analysis (ordinary GLM), we compared baseline hormone concentrations, as well as these concentrations after acute stress (30 min after capture) and after prolonged stress (24 h after capture).
The predefined factors in the global model included duration of stress (three concentrations: baseline, 30 min and 24 h), sex (male or female), body condition (estimated as residuals of body mass regressed on total head length) and interactions of these effects. We limit this analysis to only the days of overlap between groups, therefore this analysis included only nests from 2017 and only adults which were trapped between the 5th and 7th days of chick life. In the model, we excluded the number of chicks, age of brood, date of laying and colony to avoid overfitting the dataset and because the variation in these factors was small (there were 2 or 3 chicks; all the eggs were laid during 3 weeks; chick age was 5-7 days; birds which were held in captivity were trapped in a different colony from birds in other groups).
Furthermore, initial data exploration indicated no significant relationship between these factors and hormone concentrations. In two separate analyses, we investigated whether the baseline and stress-induced (30 min after capture) level of hormones were correlated with chick age and other predefined factors. The factors in the global model (ordinary GLM) included the sex of the parent bird; year factor (two levels); brood age (referred to the day of trapping since hatching) measured in days (range 0-7; the hatching day of the oldest chick, coded as day 0); body condition; and sex-year, sex-brood age and sex-body condition interactions. For the reasons given above, we excluded brood size and laying date from the analysis. In another separate analysis, we investigated whether predefined factors influenced stress-induced hormone concentrations (24 h after capture).
The factors in the global model (ordinary GLM) included body condition, sex and the sex-body condition interaction. We excluded the number of chicks, age of brood, laying date and colony to avoid overfitting the dataset and because the variation in these factors was small (there were 2 or 3 chicks; chick age was 4-9 days; all the eggs were laid during 3 weeks; there were only two colonies). Moreover, initial data investigation showed no significant relationship between these factors and hormone concentrations. Since the sample size was small (Table  1 ), we applied nonparametric bootstrapping  (Davison and Hinkley 1997)  to our data to estimate variances and compare mean stress-induced hormone concentrations between males, females that did and did not desert after the removal experiment (24 h in captivity).
Acknowledgements We greatly appreciate the cooperation of the fish farmers concerned; we are especially grateful to  Jerzy Wojciech Adamek  ( Experimental Fish Farm  in Zator). We thank  Antonia Łobodzińska ,  Stanisław Broński ,  Przemysław Kurek  and  Nataly Atamas  for their assistance in the field, and also  Peter Senn  for his linguistic guidance. We thank  C. Parenteau ,  C. Trouvé , and  M. Pallud  for their expertise in radio-immunoassays. The fieldwork was carried out with the consent of the Local Ethical Committee (permit No. 11/2016 ) and the  Regional Directorate for Environmental Protection  (permit no. OP-I. 6401. 211. 2015. PKw).
Participants Fifteen youth players participated (16. 27 ± 0. 86 years-old; 2. 12 ± 0. 71 years from peak height velocity; 64. 14 ± 10. 98 kg; 172 ± 9 cm). All procedures were approved by the local Human Research Ethics Committee (protocol #2650204; CAAE85994318. 3. 0000. 5398) and Brazilian Clinical Trials Registry ReBEC (http://www. ensaiosclinicos. gov. br/; included in the network of WHO primary registries) under number RBR-8prx2m. In #Experiment 1 which tested exercise-induced changes in kicking parameters, a subsample of 13 players was evaluated. This was considered to be the required sample size estimated owing to the expected declines in kicking performance following general intermittent high-intensity exercise mode (effect size = 0. 92; power = 85% and α = 0. 05) [data from a systematic review by Palucci  Vieira et al.
Experimental design In #Experiment 1, a pre-post paired test design was used, in order to analyse the impact of the running exercise protocol on measures of kicking performance. Players were firstly asked to perform a standardised 15-minute warm-up (dynamic stretching, jogging and submaximal kicks). Thereafter, they performed a running protocol involving high-intensity intermittent exercise (see below). Immediately prior to and at end of this exercise stimuli, players undertook a kicking protocol that allowed monitoring of lower limb movement mechanics and performance. In #Experiment 2, a repeated measures pre-post testing design was adopted, randomised and counterbalanced between data collection days, where subjects acted as their own controls. After completing the same warm-up and running protocol as in Experiment 1, each participant was assigned to one of the 2 experimental conditions [control or 5 minutes cooling (COOL)] on two separate days, 24-26 hours apart.
Kick testing collection and data processing The kick testing protocol adopted was based on that employed in a previous study (Palucci  Vieira et al. , 2022b). In brief, the participants were asked to perform instep kicks, 18 m from the midpoint goal line, using FIFA-approved stationary balls (PENALTY® brand, 5-sized, 70 cm diameter, 430 g weight, and air pressure kept at 0. 7 atm). Kicks were performed at maximal velocity and aimed at the centre of a 1 x 1 m target fixated in the contralateral goalpost upper corner. The approach run was constrained to 3. 5 m and 45 degrees, with a 40 s passive rest interval between repeated attempts within the same block. Differences to the original protocol included: trying to increase standardization as much as possible between the two proposed intervention conditions as well across the time-moments, no goalkeeper was used, and three kick attempts were allowed per block (time-moment).
49 cm and 1. 29 m/s; Palucci  Vieira et al. (2022c) ). A calibration frame was defined using 49 reference points with absolute 3-dimensional coordinates known (4. 11 x 4. 05 x 1. 30 m). Following tracking and appropriate correction of the radial distortion  (Rossi et al. , 2015) , screen coordinates of both cameras were inputted in a specific Python 3. 8. 3 algorithm (Python Software Foundation, Delaware-USA) to run 3-dimensional Direct Linear Transformation (DLT) reconstruction. Timeseries positional data was then extrapolated following impact (20%) and smoothed (dual filter 4 thorder Butterworth/rloess) in MATLAB software (R2019a MathWorks Inc. , Natick-USA). Residual analysis helped define filtering parameters (cut-off frequency=25 Hz and span=0. 1, respectively). After treatment, extrapolation was then removed.
Statistical analysis Statistical tests were performed in IBM Statistical Package for the Social Sciences v. 25 (IBM Corp. ©, Armonk-USA) with an alpha level set at p ≤ 0. 05 for determining significance unless otherwise stated. Data normality was firstly assessed using the Shapiro-Wilk's test. If data was flagged as non-normal, then kurtosis, skewness and frequency plots were checked. If logtransformation was not efficient, non-parametric versions of tests were used. In #Experiment 1, Student's t test for dependent samples was used to compare measures pre-and post-RHIR. Effect size for paired comparisons was obtained using Cohen's d where d > 0. 20 (small), > 0. 50 (medium), and > 0. 80 (large). In #Experiment 2, Student's t test or Wilcoxon signed-rank test was employed to obtain estimates of reliability of the responses to the running protocol between testing days/conditions.
Participants were identified through two types of non-probability sampling methods: (i) purposive sampling and (ii) snowball sampling. Each global women leader had the option of answering the questions via semi-structured interviews (N = 8) or by providing a written response (N = 26). Global women leaders were asked to identify processes and systems that can aid the development of women ECRs in becoming leaders of marine research. The analysis of raw data were completed following an inductive approach, based on grounded theory analysis. Grounded theory is an iterative approach to the analysis of qualitative data that aims to generate theory out of research data by achieving a close fit between the two  (Bryman, 2012). Data analysis was undertaken by the lead author (R. J. S) to maintain independence of interpretation.
Though, three practices were undertaken to ensure the validity of the emerging themes and subthemes: (i) the themes were continually verified against the raw data from which they were derived  (Marshall et al. , 2011; Norström et al. , 2020) , (ii) initial coding of transcripts (n = 5) was undertaken by three authors (R. J. S. , C. C. , and M. M. ) and the findings were discussed with and refined by the co-ordinating authors, and (iii) the findings were shared with participants to give them the opportunity to clarify, corroborate, or approve the findings  (Bryman, 2012). Broad themes were populated from the perspectives of women leaders and integrated with findings from a narrative review of literature to identify specific actions for supporting women ECRs. Narrative reviews (or "literature reviews") are the most traditional form of evidence synthesis.
, 2021; Shah et al. , 2021). r Use gender-neutral language and stimuli to attract funding applications from women ECRs  (Born and Taris, 2010; Gaucher et al. , 2011; Lee and Ellemers, 2015). r Establish DEI committees and have trained DEI representatives for each grant panel. r Ensure there is a minimum number of women representatives on grant panels. r Require grant reviewers to undergo training in peer review and biases. r Provide clear guidance for inclusive grant review processes. r Adopt blinded peer review for grant applications  (Dewidar et al. , 2022). r Make funding data available online  (Llorens et al. , 2021). (4) Offer mentoring opportunities to women ECRs. r Establish an online mentoring platform for women ECRs in marine science  (Van Stavel et al. , 2021). r Implement well-structured and long term mentoring programmes.
Funding R. J. S is supported by the  Centre for the Public Awareness of Science (CPAS)  and the  ANU Futures scheme. NS  is funded by the  Natural Environment Research Council (NERC) QUADRAT  ( Queen's University Belfast & Aberdeen Doctoral Research and Training )-QUADRAT Training Grant Reference: NE/S007377/1. A. B. thanks  CNPq productivity scholarships # CNPq  (#  301592/2017-9  and  CNPq #  304170/2021-6 ). AV is funded by the  European Research Council-ERC  (grant agreement No  804599-MARIPOLDATA-ERC-2018-STG ). H. C. G was funded by the  UKRI GCRF  under grant agreement  NE/P021107/1  and project reference no. IF052-2017  in  Universiti Malaya. JP is funded by the  Slovenian Research Agency  (Project code:  J5-2562 ). MB is supported through the  Nippon Foundation Ocean Nexus Centre  and a  Canada First Research Excellence Fund  grant through the  Ocean Frontier Institute.