dataesr/scientific-paragraphs-categorization huggingface dataset:
The energy management system includes sensors to measure the energy production (voltage, current, power) and the rotational velocity of the water turbine. The resistive torque generated by the generator at the shaft of the propeller (P shaft ) is estimated from the measurement of the output current of the generator. To measure the wind, a CV7-LCJ ultrasonic anemometer was installed at the top of the mast. As the Hobie Cat Tiger is equipped with a rotating wing mast, a mast angle sensor was positioned at the bottom of the mast in order to be able to correct the wind angle measurement. The control and data acquisition system is based on a Raspberry Pi 3. It is integrated in a waterproof case (Pelicase) on the starboard float deck. It is connected to the various sensors by wire links. The acquisition software was developed at LHEEA in Python.
Search Strategy We perform manual and automated searches to find studies in scientific databases (i. e. , IEEExplorer, ACM, Science Direct, Google Scholar, and Scopus). Since diverse databases have restrictions in the number of keywords accepted within queries, we create programs to generate customized searches per database. Further, we use APIs to perform the query execution and article extraction processes in two of them. Although, the automation facilitates the retrieval of possible studies from databases. Yet, we use manual extraction when we do not have access to the corresponding SearchAPI. The authors simplify the scope by identifying domains of study and related keywords. From the RQs, we select the following study domains: (1) Energy Efficiency, (2) Residential sector and residential buildings, (3) Analytics techniques, (4) Energy modeling, and (5) Policy Design.
Keyword Identification and Selection In this stage, we propose an objective approach for keyword selection that relies on keywords from formal taxonomies and terms available in top journals and conferences. In contrast with the classic method, in which researchers select keywords based on expertise and domain knowledge. Our approach includes relevant keywords and excludes non-used terms in publications oriented to the research domain. First, the selection process involves the identification of keywords for each domain of study in formal taxonomies (e. g. , IEEE and ACM) and journals related to energy efficiency policy (e. g. , Energy Efficiency Journal, Energy Policy, Energy and Buildings, Applied Energy and Energy Procedia). Second, using this initial list of terms, we look for synonyms present in scientific databases, using trial queries.
Finally, we select the final keywords using the following criteria: (1) Keywords present in more than one publication are included in the final keyword list (2) Keywords with one or more synonyms are included in the list (e. g. , data driven, data-driven) and (  3 ) If synonyms share a similar expression(e. g. , model-based, modeling, models), we use the shared term as key term (e. g. , model). Table  3  shows the keywords used for query construction. We automate the query generation using a Phyton application to obtain relevant domain-related articles. Although, the program generates a set of queries that includes all possible keyword combinations. The creation process reveals that some databases require numerous searches to obtain all potential publications. A circumstance that can result in an unmanageable number of publications to review.
In future examinations, we plan to automate this screening process. After applying the inclusion/exclusion criteria, the program returns a total of 396 accepted queries. See Table  5  for the query generation summary per database. The previous procedure permits quality assurance of the results and eases the automatic extraction of articles from databases. The query generation program is available for review in the following GitHub repository: https://github. com/tsetsuna/Systematic-Review (accessed 30 August 2021).
Query Execution We automate the query execution and article retrieval for IEEE and Scopus databases using IEEE Python and ElsClient Python APIs. On the other hand, we download publications from ACM, Google Scholar, and Science Direct directly from each database portal. The query execution summary available in Table  5  reveals a total of 340 retrieved publications. From Google Scholar and Scopus databases, we retrieve most of the articles with 118 and 112, respectively. Also, we remove 148 out of 340 publications identified as duplicated per specific database and by comparing all databases (e. g. , de-duplicated). In the end, we obtain 192 unique articles after inclusion/exclusion criteria. For duplicate removal, we use the EndNote application.
Screening Phase We use the Rayyan application which is a specialized tool to perform systematic reviews and to filter publications according to a defined inclusion and exclusion criteria. The program facilitates paper labeling and parallel revisions among reviewers. More information about Rayyan can be found in: https://rayyan. qcri. org/welcome (accessed on 30 August 2021). To begin article screening, we export unique articles from EndNote to Rayyan and coordinate reviews among contributors. All authors participate in the screening process. Lastly, we apply inclusion and exclusion criteria to examine the articles.
Eligibility and Inclusion Phases The quality validation of articles aims to guarantee the inclusion of studies that contain information that allows answering the RQs. We design a straightforward checklist to identify publications that are compliant with the requirements of the study. The complete QA checklist is available in Table  6. The quality criteria CK1 aims to identify the articles' orientation concerning bottom-up energy models and energy efficiency policy. If the publication does not consider those subjects, the answer to the question is negative. Secondly, CK2 and CK4 are used to identify specific features of bottom-up energy models. In this regard, if definitions of variables/methods are not present, the answer to the criteria is negative. Lastly, Ck3 and Ck5 are used to assess how accurate the data analysis is.
However, their limited geographical coverage does not allow their applicability for regional or international policy design. In contrast, single-sector bottom-up energy models with project coverage are short time horizon models that rely on granular time split with minute resolution. Two studies use the Pecan Street dataset to analyze household baseload consumption  [54]  and model appliances benchmark  [47]. Likewise, Mohseni et al. [43]  use smart meter samples to create an energy model for day-ahead planning. Given the short time horizon and data time split, we deduce that these models deal with imperfect information barriers among consumers and energy suppliers  [6] , which allows us to correlate them with information and feedback policy instruments. Also, we identify a limited set of multi-sector models in the literature.
g. , scenarios, CO 2 emissions, and cost) is documented by Pradhan et al. [49]  in 2017; however, the model is useful exclusively for the cooking end-use. These statistics reveal concerns if we compare these models with a model-based decision support system. Since this last should include the following components to guarantee decision support  [58] : (1) a user interface (e. g. , scenarios) and organizing module (2) a database (3) a model base (e. g. , variables and metrics) and (  4 ) and algorithmic base or library (e. g. , analytical methods portfolio). Therefore, based on this definition, we can affirm that only 50% of the analyzed studies create decision/policy support models.
Hybrid Interventions Wang et al. [52]  evaluate building energy efficiency standards in China. The study reveals that the building design performance differs from the actual energy-saving operation. The authors recommend implementing outcome-based compliance with current standards and creating energy consumption databases to support policy design. Finally, the model of Krarti et al. [56]  allows evaluation of retrofit programs. The study reveals that the implementation of large scale retrofit programs can generate economic, environmental, and social advantages. Finally, evidence reveals that residential building models (i. e. , hybrid and engineering) do not support information and feedback instruments. On the other hand, for the residential sector hybrid models have not documented support for regulatory instruments.
Findings in RQ2 The research allows identifying all kinds of techniques used in the design of bottom-up energy models. The results reveal that the residential sector relies on data-driven energy models, while residential buildings depend on engineering models. However, the studies show a disconnection between the analytics techniques, the model base, and the user interface (e. g. , scenarios). Likewise, we also realize that these models do not provide a portfolio of analytical methods which constrain their capability to support the design of policies. On the other hand, the study reveals a trend in adopting hybrid methodologies in both consumption sectors. We can explain this by considering the current availability of information from diverse sources (e. g. , smart meters, surveys, databases, etc.
Along this line, data concerning structure-activity relationships are usually missing, with not many studies yet dedicated to this matter  [11, 42, 91, 491]. Similar to the CLSI guidelines defined for antibiotics, uniform research methodologies would be useful to assay aPDT systems under well-defined standards and guidelines, considering notably (i) illumination settings, (ii) positive controls  [492] , (iii) microorganisms and cell lines relevant for a given application, and (iv) assessment of antimicrobial efficacy; this would guarantee better-conducted preclinical and clinical trials of aPDT systems used as mono or combinatory therapy. Furthermore, a public database compiling the efficacy/side effects of various systems would also be useful, facilitating meta-analyses for delineating (quantitative) structure/activity relationships and computational simulations  [493].
Translation to practical applications also implies strong collaborations with the different sectors of health care and pharmaceutical companies. In these conditions, aPDT and its many therapeutic combinations could become a frontline routine treatment to fight against microorganisms possibly responsible for the next healthcare crises  [61]. Funding: This work was supported by grants from ANR/BMBF (TARGET-THERAPY; PIs: Tony Le Gall and Holger Schönherr; ANR grant number: ANR-20-AMRB-0009, RPV21103NNA; BMBF Förderkennzeichen: 16GW0342). We also thank the "Association de Transfusion Sanguine et de Biogénétique Gaétan Saleün" (France) and the "Conseil Régional de Bretagne" (France) for their financial support. Raphaëlle Youf is recipient of a PhD fellowship from the French "Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation" (Paris, France).
D. N. D. Phase 3 NCT02848482, MB N. A. NCT03750162, ICG Phase 2 NCT02043340, Periodontic Aggregatibacter Actinomycetemcomitans, Porphyromonas gingivalis, Prevotella intermedia, Tannerella forsythia and Treponema denticola Methyl aminolevulinate Phase 2 NCT00933543, MB N. A. NCT03262077, MB Phase 2 NCT03074136, Phenothiazine hydrochloride Phase 4 NCT03498404, TB Phase 4 NCT03412331, Distal subungual onychomycosis Fungi infecting nails 5-ALA Phase 2 NCT02355899, Endodontic E. faecalis and C. albicans MB Phase 2 NCT02824601, HPV infection Human Papillomavirus (HPV) 5-ALA Phase 2 NCT02631863, Leg ulcers Streptococci, anaerobes, coliform, S. aureus, P. aeruginosa, yeast, and diphtheroids PPA904 Phase 2 NCT00825760, Studies collected from ClinicalTrials. gov (https://clinicaltrials. gov/ct2/results?cond=photodynamic+therapy, Accessed Date: 1 March 2021).
To assess the amount of electrocatalyst covalently bound on CC, a comparative ICP analysis was performed on the pristine CC starting material and on Mn/CC (see supporting section 4). A surface coverage Γ of 1. 67×10 -9 mol cmECSA -2 was obtained, in reasonable agreement with the previously reported value (1. 4×10 -9 mol cmECSA -2 ), determined via the indirect method of charge integration of the CV data collected from CC bonded nitroaniline. 40, 41   (mM) 1 50 175 At such a low amine concentration (1 mM), at least three orders of magnitude lower than what is commonly used for CO2 capture (MEA 5 M, 30% w/w), most of the captured CO2 is absorbed by physical dissolution (see Table  1 ). In fact, at room temperature and under a partial pressure p(CO2) = 1 atm, this gas has a solubility of 0. 007 in MeOH,  44  which translates into a concentration around 175 mM.
A recent report by the European Environment Agency  [19]  acknowledges these challenges and addresses the adaptation needs for the European energy system. It further highlights the need for member states to "consider the impacts of climate change in the development of national climate and energy plans and long-term strategies under the Energy Union". Several initiatives are being developed that use climate data to assess impacts for the power sector, from commercial products (e. g. POWEL software) to research (e. g. EUPORIAS  [20] ; ECEM  [21]  and CLIM4E-NERGY  [22] , with the worthy more recent additions of the H2020 projects S2S4 E  [23] ), SECLI-FIRM  [24]  and PLAN4RES  [25]. However, these are concentrating on individual RES impacts and not on combined effects on all of the mentioned impacts on the power system.
Methods This section outlines the methods used for the analysis regarding considered climate data and its input into the eTIMES-EU energy system model, followed by a description of the developed eTIMES-EU, and an overview of the modelled scenarios. The overall approach to consider climate variability within eTIMES-EU was developed within the C2P project, as mentioned. and is depicted in Fig. 1. A set of long-term climate projections from EURO-CORDEX for three climate models and two representative concentration pathways (RCP) scenarios (4. 5 and 8. 5) was used as a starting point. RCP4. 5 translates an intermediate climate stabilisation pathways in which radiative forcing is stabilised at approximately 4. 5 W m -2 by 2100, whereas RCP8. 5 translates a pathway for which radiative forcing reaches greater than 8.
5 W m -2 by 2100  [27]. The climate data was translated to timeseries of maximum capacity factors for RES electricity from hydro, wind and solar PV power, as well as impacts of temperature on energy demand using different approaches, from machine learning to specially developed simulation tools. This results in daily values disaggregated at NUT2 administrative regions level across Europe for PV and wind capacity factors. The hourly variation of electricity demand for space heating and cooling to temperature, as well as daily hydropower capacity factors, are obtained at national level only. Each of these are calculated at least for the 2020-2065 period (more details in the next section) and are used as inputs into the eTIMES-EU model. At this moment, it was necessary to consider only national values for wind and PV due to computational limitations of eTIMES-EU.
Translating climate variability into energy system model inputs In terms of climate model data, sixteen combinations of global and regional climate models (respectively GCM and RCM) were explored using simulations made available by the World Climate Research Programme's CORDEX initiative (www. euro-cordex. net). Further information on EURO-CORDEX can be found in. e. g. Refs. [28, 29]. The spatial scale of the simulations available is 0. 11 • (around 12. 5 km) and 0. 44 • (around 50 km). Nevertheless, the latter was disregarded considering the recognised added value of the higher-resolution dataset (EUR-11) regarding the local-scale climate features of the studied areas. Out of the whole GCM-RCM combinations, some were not available for all the selected variables and scenarios. For selecting the RCMs to be studied, the full set of RCMs was analysed over the Europe domain, in such a way that models with a lower degree of satisfaction simulating the climate of our study regions could be excluded  [30].
Although averaging across different climate models is quite common, this is difficult to interpret and might lead to misleading and physically meaningless results. Averaging models may cause effects of smoothing the spatially heterogeneous patterns of climate variability across Europe, as well as their temporal variability. One of the key aspects considered in this dataset preparation was the model time horizons (near future and mid-century), as well as spatial dimensions. A forecast can be assessed regarding the future projections (e. g. hotter or colder than average season in the future) and looking at how that relates to the conditions in the past. The choice of spatial resolution may depend on the issue and variables being addressed. For example, too little resolution can fail to capture the small-scale variability of orographic precipitation, whereas too much resolution can cause the model to become computationally impracticable.
The power production of each turbine installed in Europa has been calculated based on information provided by thewinpower. net database and model wind speed. The wind curve has been generated using the approach described in Ref. [36]. Finally, particular attention has been paid in choosing a model setup allowing a fast calculation. This has been achieved by using a LUT approach. More information on this approach can be found in Ref. [37]. It should be mentioned that because some countries currently have very low wind installed capacities, they will possibly be more sensitive to results. The adopted method is more robust for countries or areas with bigger wind capacities. These wind and solar PV capacity factors are then aggregated per NUT2 regions of Europe (circa 263) and, for the case of wind offshore, 96 maritime regions (obtained by intersecting the International Hydrographic Organization sea limits and Exclusive Economic Zones areas) as in Fig.
4 in MATLAB® R2018b. In Ref. [40] , we compare the performance of these five algorithms in terms of correlation coefficient, adjusted coefficient of determination, mean absolute and mean square percentage errors. This comparison indicated that the models based on Random Forests usually exhibit the best performance. Thus, the results in this paper are obtained feeding the RF prediction model with the climate projections adjusted with respect to the reanalysis data from DWD. The projections cover the period from 2020 to 2060, and for this paper is particularly relevant the years of 2030 and 2050 (which have policy relevance). The predicted hydropower CF for these years were obtained as the calendar mean of the 20 years' time series projections centred in 2030 and 2050, respectively. The obtained results for 2050 are depicted in Fig.
Overview of eTIMES-EU model The maximum possible CF and impacts on demand are input into the bottom-up optimization TIMES energy system model  [44]. A new TIMES model was developed for the whole of EU covering only the power sector (eTIMES-EU) which, as for any TIMES family models, has intertemporal optimization and minimizes the total discounted cost. eTIMES-EU has currently 29 regions, representing all countries in continental European Union (thus, it excludes Cyprus and Malta), plus Norway, Switzerland and Iceland (Table  2 ). The model runs in 1-or 5-year time-steps from 2016 to 2060. Each year is disaggregated in 64 time slices, outlining the 4 seasons (DJF, MAM, JJA, SON), 2 typical days (weekdays and weekends) and 8-time sequential day periods (P1 to P8 of 3 h each). eTIMES-EU is supported by a detailed database, with the following main exogenous inputs: (1) electricity demand from the 2016 Energy Reference Scenario  [45] ) and summarised in Table  3;  (2) characteristics of the existing and future electricity generation technologies, such as efficiency.
Electricity generation technologies Electricity generation data from Ref. [38]  and Eurostat  [46]  was used to derive country-specific power balances, which determine the characterization of power generation technology profiles in the base year. Beyond the base year, possible new electricity generation technologies are compiled in an extensive database with detailed technical and economic features based on [47] summarised in Annex 3. CO 2 storage capacity and transport is possible as illustrated by different projects  [48]. The model uses country-specific hydro, wind and solar annual availability profiles (introduced as maximum possible CF) for replicating the year of 2016 as in ENTSO-E Transparency Portal  [38]  for the modelled time-slices. Concerning electricity grids, eTIMES-EU considers both import/export processes regarding the existing infrastructures (capacity and flows) and possible new investments based on the TYNDP2016  [49].
Acknowledgements Project  CLIM2POWER  is part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate. and funded by  FORMAS (SE). DLR (DE). BMWFW (AT). FCT (PT). EPA (IE). ANR (FR)  with co-funding by the  European Union  (Grant  690462 ). CENSE is financed by Fundação para a  Ciência e Tecnologia. I. P. Portugal  ( UID/AMB/04085/2019 ). Appendix E. Impact of climate projections on amounts of electricity exported in 2050 compared with  NEUTR  scenario.
Optimal power of solar, hydro, EL and FC systems and Hydrogen tank capacity have been determined through GAMS software. The return on invested capital for the proposed system, if RES is utilized, might be 4 years. Olateju et al. [32]  have provided the integrated wind-hydrogen 'FUNNEL-COST-H2-WIND model' for optimal EL/battery sizing for energy storage to minimize hydrogen production cost. Electrolysers decrease hydrogen cost as hydrogen flow rate increases for the same EL while optimal battery capacity has been the same for a specific flow rate range. According to Ref. [32] , published in 2016, the best production cost of hydrogen has been $9. 00/kg H 2. Certain energy storage systems such as pumped hydro, thermal and gas storage systems could employ underground cavities and/or tanks which are settled above ground.
40 Knowledge of dominant known crystal faces of synthetic goethite nanoparticles also facilitates the interpretation of plausible silicate and PA species, notably disposed along rows of regularly spaced reaction centers along the dominant crystallographic faces (Figure  1 ). In this study, pH-and concentration-dependent silicate and PA loadings were investigated for 1day and 30-day equilibration periods. This strategy allowed us to account for competitive effects resulting from monomeric silicate species formed at short equilibration times, compared to those exerted by polymeric silicate species, which appear over longer reaction times. Thermodynamic modeling of batch adsorption data and chemometric analyses and interpretations of vibrational spectra identified dominant molecular species of silicate and PA formed under wet conditions typical of watersaturated soils.
Multivariate Curve Resolution Analysis of FTIR Spectra. Spectral components representative of distinct molecular species of sorbed PA were extracted from the 1300-1900 cm -1 region of spectra of goethite samples equilibrated over a range of PA surface loadings at pH 4 and 6. This chemometric analysis, built on Multivariate Curve Resolution Alternating Least Square (MCR-ALS),  53  was implemented with a new code written for this study using MATLAB 2016b (The Math-works, Inc. ). As in MCR-ALS,  53  we applied the Beer-Lambert law (Amn = εmkCkn) to extract k spectral components (ε ≥ 0) and their correlated concentrations (Ckn ≥ 0) from a matrix of experimental absorbance (Amn) data collected over m wavenumbers and n samples. In essence, the program iteratively rotates the first k orthogonal singular vectors (cf.