dataesr/scientific-paragraphs-categorization huggingface dataset:
Algorithm Architecture A proprietary code, written in Python 3. 8, was developed for this study. It is objectoriented and separates into three parts: case definition, simulation and optimization (Figure  1 ). In the "case definition" part, the various pieces of equipment of the energy chain and how they are connected to each other are defined, the meteorological conditions, the objective function (economic, independence, maximum consumption. ) as well as the optimization tool (genetic algorithm, particle swarm optimization, Bayesian, Levenberg-Marquardt. ) and the energy allocation strategy. The "simulation" part assembles the various components following the defined topology and runs the model accordingly to the energy allocation strategy. A time step of 1 h will be used so as to not have to take into account the transitory phases.
Optimization Settings The objective of the optimization is to size the different equipment. The scipy-optimize python library  [65]  was used to perform optimization. First, a Bayesian method is used to locate the global minimum neighborhood. Then, a simplex algorithm (Nelder-Mead method) is applied to refine the results. This case being applied to one house, restrictions on the size of equipment are implemented because of volume considerations. The chosen boundaries are presented in Table  3 : they are of the same order of magnitude as the load. PV panel's max size is determined by the house size (see Equation (  2 )). The fuel tank size is based on geometric considerations: taking a pressure of 100 bar and a temperature of 55 °C and a maximum mass of 20 kg, ending with a volume around 2.
", Gif sur Yvette (France). Electrochemical measurements were conducted using a PG-STAT 128 N Autolab or a µ-autolab (type III) electrochemical analyzer driven by the GPES software. All the electrochemical studies were carried out in a conventional three-electrode cell under an inert atmosphere (argon). The preparation and the purification of the supporting electrolyte [Bu 4 N][PF 6 ] were as described previously  [50]. The working electrode was a vitreous carbon disk of 0. 3 cm in diameter, polished with alumina prior to use. A platinum wire was used as counter electrode. The reference electrode was an Ag|Ag + electrode, however, all the potentials (text, tables, and figures) are quoted against the (Fc + /Fc) couple; ferrocene was added as an internal standard at the end of the experiments.
Crystal data for compound 1 were collected on an Oxford Diffraction X-Calibur-2 CCD diffractometer, equipped with a jet cooler device and graphite-monochromated Mo-Kα radiation (λ = 0. 71073 Å). The structure was solved and refined by standard procedures  [51]. Deposition number CCDC 2179366 contain the supplementary crystallographic data for 1. These data can also be obtained free of charge from the Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request/cif (accessed on 15 June 2022). Calculations have been performed using the TURBOMOLE 7. 4. 1 suite of programs  [52]. A triple-ζ TZVP basis (for all atoms) and the pure functional BP86 were used  [53] [54] [55]. This particular basis-set/functional combination has been widely validated in the context of Fe/Nithiolate bioinorganic and biomimetic systems, also in relation to hydrogenases  [56] [57] [58] [59].
Data Availability Statement: The data presented in this study are available in supplementary material. Acknowledgments: CNRS (Centre National de la Recherche Scientifique), the Université de Bretagne Occidentale, are acknowledged for financial support. We are grateful to F. Michaud for the crystallographic measurements of 1 and to the 'Service général des plateformes, Brest' for NMR measurements on the Brucker DRX 500 spectrometer. We thank M. Albrecht and S. Bertini for their help for the preparation of MIC ligands.
0 ml min -1 was used as a carrier gas. The acquired peaks of compounds were compared with the MS library (Agilent, ChemStation software) and chemical standards. For quantification of the presented compounds, a gas chromatography (GC) (Model 6890, Agilent) connected to a flame ionization detector (FID) and attached with the same capillary column was employed. The analysis was conducted under identical conditions as GC-MS analysis. The integrated peak areas calculation was made based on the standards calibration curves plotted with known concentration. The conversion of glycerol, products yield, and selectivity were then calculated using Eqs 1-3, respectively.
x =    . x 1. x 2. x 3. x 4     =     . X. X T. Q. Y      =     r x r x •V A•q X T /Q -Y     (9) where x 1 is the concentration of biomass in g•L -1 , x 2 is the newly produced biomass in grams, x 3 is the amount of light consumed in moles of photons and x 4 is the biomass yield on light energy in grams per moles of photons. The fourth equation is in fact algebraic but is expressed as a differential equation. The integration of the mass balance model must be done with an integrator able to solve DAEs (differential-algebraic equations) such as ode15s or ode23t in Matlab  [39]. The mathematical model for photosynthetic biomass growth has been validated on Chlamydomonas reinhardtii [23] and Scenedesmus quadricauda  [37] , strains that have been cultivated in rectangular laboratory PBRs lighted on one side.
The lower bound, I 0(min) , is critical because the biomass decreases under this value, while the upper bound is not, and can remain constant (e. g. , 2000 µmol•m -2 •s -1 ). Even though I 0(max) can inhibit the microalgae growth, it is attenuated inside the culture. The lower and the upper bounds are required by most of the unidimensional optimization methods  [41] , e. g. , fminbnd function in Matlab. The general form of the optimal control problem which searches the control u(•) that maximizes the objective function J is: J(x, u) = t f t 0 L(x(t), u(t), t)dt (12) where the term L(x, u, t) is the intermediate cost function. The optimal control aims to find the state and control trajectories x and u so that J(x, u) is maximized: x * , u * = arg max x,u J(x, u) such that. x(t) = f (x(t), u(t)) for all t (13) Finding the optimal incident light intensity for which the consumption of light energy, for any amount of newly formed biomass, is minimal, requires maximizing the biomass yield on light energy, thus the optimal control input has the following form: I 0(opt) = arg max I 0 (t), t 0 ≤t≤t f Y(t) (14) The optimization and control horizons are usually different, while the optimization can be performed on the entire batch time t 0 t f , the control horizon is usually lower, resulting that the u(t) is the vector of input variables.
Acknowledgments: M. T. is thankful for the support given by the following project:  80PRIME2021   PROOHMICS  (financed by  CNRS, France). J. L. G. is thankful for the support given by the following projects:  PID2020-112709RB-C21  (financed by the  Spanish Ministry of Science)  and the  Horizon Europe-the Framework Programme for Research and Innovation  ( 2021-2027 ) under the agreement of grant no. 101060991  REALM. G. A. I. and J. L. G. would like to express their gratitude to  Polytech Nantes  for the visiting professor grants at  GEPEA Laboratory.
Literature search methodology For this purpose, Web of Science database was searched (updated on March 16, 2021) by using the following keywords in manuscript titles: (("Fenton" or "chemical oxidation" or "H 2 O 2 ") and ("soil" or "soils")). This search technique is often used in bibliometric analyses where the use of quotation marks ("") ensures finding the exact phrases by deactivating the synonym feature of Web of Science  (Usman and Ho, 2020; Vanzetto and Thomé, 2019). Term "or" was used to find different expressions which were connected by using "and" to ensure that these words appeared in the title of each article. This search yielded 309 articles and each article was read to identify if it focuses on the remediation of historically contaminated soils by using Fenton-based treatments.
Of these, ATCC 30,412, SAG 211-14, and UTEX 32 are the most cited in the literature. As well as the strains listed in Online Resource 1, there are several others that have been cited but cannot be tracked down to any culture collection database, putatively due to researchers maintaining private cultures or their loss from public collections. Within this review paper, the species will be referred to according to the strain number, if available, to make comparisons between different isolates, or C. zofingiensis. C. zofingiensis can grow phototrophically, mixotrophically, and heterotrophically and its high-density growth, scalability, and tolerance to a wide range of environmental conditions make it a desirable species for biotechnology  [9] [10] [11] [12] [13]. The different growth modes can be used to tailor the biochemical composition of the cells most commonly to increase the biomass production and concentrations of secondary carotenoids and lipids, turning the cells from green to red.
178897 nm radiation). The diffractograms were recorded in the 3-80 • 2θ range, with a 0. 03 • 2θ step size and a collecting time of 3 s per point, by using sample holders equipped with glass domes, which were filled under a glovebox to maintain the samples under a N 2 atmosphere (for reduced samples only). Phase identification was performed by using the software Diffrac Eva and the Crystallography Open Database (COD). 57 Fe Mössbauer spectroscopy. Transmission Mössbauer spectroscopy was performed between 298 and 11 K, with a constant-acceleration Mössbauer spectrometer, a 512 multichannel analyzer (Halder Electronic Gmbh, Seehausen, Germany), and a 50 mCi source of 57 Co in Rh, maintained at room temperature (RT). Data were obtained from appropriate amounts (10 mg of Fe per cm 2 ) of solid samples to obtain optimal experimental conditions.
Analyses were carried out between ambient and 11 K on the ARS cryostat (Advanced Research Systems, Macungie, PA, USA) equipped with a vibration-isolation stand developed in the LCPME laboratory, while the recordings at 4 K were taken on the spectrometer coupled to the Janis Cryostat. Mössbauer spectra were collected in transmission mode. The 50 mCi source of 57 Co in the Rh matrix was maintained at room temperature and was mounted at the end of a Mössbauer velocity transducer. The spectrometers were calibrated with a 25 µm foil of α-Fe at RT. Spectra were computer-fitted (Recoil Software, Ottawa University, Ottawa, ON, Canada) with the Voigt-based fitting model for paramagnetic sites that correspond to doublet, which have distributions of quadrupole splittings (QSDs), and for magnetic sites that correspond to sextet, which have a distribution of magnetic hyperfine fields (HFDs), as implemented in the Recoil software.
Pb-Bearing Ferrihydrite Characterization The concentrations of Fe and Pb within the synthesized Fh_Pb series were measured by using AAS. The concentrations that were obtained were approximately equal to the Pb/(Fe+Pb) molar ratios that were used during the syntheses (i. e. , 2 and 5%). The X-ray diffraction patterns obtained for the coprecipitates displayed two broad peaks at 2θ around 41 and 75 • , and d-spacings of ca. 0. 258 and 0. 149 nm, respectively (data not shown). Such peaks are representative of pristine and metal-substituted 2-line ferrihydrite (for example, Sb-containing ferrihydrite)  [18, 37]. No other ferric phase was obtained, according to the XRD analysis that we performed.
06 ± 0. 01 and 0. 04 ± 0. 01 (Fe(II)tot/Fetot) day -1 , respectively, for the Fh and Fh_Pb(0. 02) systems. The reduction extent reached around 17 and 18%, respectively, in the pristine Fh and Fh_Pb(0. 02) systems. On the contrary, no reduction was determined in the assays that were run with Fh_Pb(0. 05), as Fe(II) quantification was below the detection limit, if any occurred in the system (Figure  1B ). The concentrations of Fe(II) ions that were measured in solution during the RP1 and RP2 phases were inferior to the ferrozine quantification limits in all of the systems. No reduction was observed in the blank assays (data not shown). Both the total and bioavailable concentrations of Pb were measured at the end of the RPs, and the oxidation periods were inferior to the measurement methods' quantification limits (0.
In order to incorporate the large volume of the atmospheric and oceanic data  [39]  to properly examine the spatial variability of methanol production, a python package called pyseafuel was developed; a capability not available through other chemical process simulation software, as they are plant centric (COCO, https://www. cocosimulator. org/index. html; last accessed 10 November 2022, DWSIM [40], nor Aspen HYSYS, https://www. aspentech. com/en/ products/engineering/aspen-hysys; last accessed 10 November 2022). Our criteria for optimal locations is comprised of three main constraints: • Available power, • Environmental risk, • Available methanol production. The power constraint will be analyzed in terms of available wind and solar energy in the region. Other energy extraction methods exist, but are not as mature as wind or solar  [37].
Environmental risk is assessed through the maximum wave heights found in different parts of the region. The available methanol production is analyzed in terms of the volumetric flow rate per Watt and per required area of power generation. Then, the methanol production will be compared to the energy consumption of two island communities to determine its potential use for these communities. The organization of the paper is as follows: Section 2 outlines the modelling of the methanol island through the pyseafuel python package and covers the atmospheric and ocean model data used to drive said modelling. Section 3. 1 presents the power availability of the region for the two power resources. Section 3. 2 presents the maximum wave heights calculated for the region. Section 3. 3 covers the simulated methanol production and its results.
Materials and Methods To numerically simulate the methanol island in the Mediterranean, data from the Earth system modelling from the years of 1993 to 2013 are utilized. The variables utilized and their flow paths to determine available power and available methanol production are provided in Figure  2. To determine the solar and wind power available to the methanol island, 10 m wind speeds and shortwave downward radiation from an atmospheric model are used in conjunction with two python packages, windpowerlib and pvlib. The environmental risk is considered through the modelled maximum wave heights, which are calculated from the 10 m wind speed. The sea surface temperature and salinity from an oceanic model are provided to a purpose built python package for this study, pyseafuel, to model methanol production.
The latter buffer zone is used to represent the exchanges between the two bodies of water at the Strait of Gibraltar, and its sea surface height (SSH) fields are restored towards the ORAS4 global ocean reanalysis  [49]. The 3-D temperature and salinity fields of the buffer zone are restored towards the MEDRYS reanalysis  [46]. The Black Sea, runoff of 33 major rivers, and coastal runoff are represented by climatological data from  [50]. The initial conditions for each one year run were pulled from the MEDRYS reanalysis  [46]. The sea surface temperature and salinity from this model data are used to simulate the power required for the methanol production process. The effective irradiance and solar panel power are calculated using the pvlib python library  [51]  (source code available at https://github.
com/pvlib/pvlib-python; last accessed 11 November 2022). The global horizontal irradiance (GHI) is provided with the shortwave downwelling radiation from the WRF/ORCHIDEE model data. The direct normal irradiance (DNI) is determined using the DISC model  [52]  with the NREL implementation. The diffuse horizontal irradiance (DHI) is then calculated from GHI and DNI (https://pvpmc. sandia. gov/modeling-steps/1-weather-design-inputs/irradianceand-insolation-2/global-horizontal-irradiance/; last accessed 13 June 2022): DH I = GH I -DN I cos θ z , (1) where θ z is the zenith angle of the sun. The effective irradiance is then determined with the SAPM model from Sandia National Laboratory  [53]  and assuming an isotropic atmosphere. The SAPM model is also used to calculate the power generation of an arbitrary commercial solar panel, a 2009 SunPower 128-Cell Module, the data of which is provided in pvlib  [51].
The estimated power production from a Siemens Gamesa SWT-3. 6-120 turbine is calculated with the windpowerlib python package  [56]  (source code available at https: //github. com/wind-python/windpowerlib; last accessed 11 November 2022), which provides the power curve for the turbine (see Figure  4 ). A noteworthy feature of the turbine power curve is the power cutoff if too high of wind speeds are encountered. This cutoff is common to many commercially available turbines to prevent damage from stronger winds. This also limits the productivity of the areas with the highest wind speeds. This turbine has a rotor diameter of 120 m, which is used to calculate the power generation in terms of power per swept area of the turbine, allowing it to be compared to the solar panel power generation.
Methanol Production-pyseafuel A python package, pyseafuel (source code available at https://gitlab. in2p3. fr/energy4 climate/public/pyseafuel; last accessed 11 November 2022), was specifically made by the authors to numerically simulate the methanol island for this study. This Section 2. 3 describes the structure and theory used in the package. Four subprocesses make up methanol island, as seen in Figure  5 , and are simulated by pyseafuel: degassing, desalinating, electrolyzing, and reacting. Seawater is fed into both a degasser and desalinator in separate flows. The degasser extracts the CO 2 from the seawater and passes it along to the reactor. The degassed seawater is expelled at this point. Meanwhile, the seawater fed into the desalinator is purified (expelling the brine back to the ocean) and passed along to an electrolyzer.
Acknowledgments: This work was conducted in the framework of the  Energy4Climate Interdisciplinary Center (E4C) of Institut Polytechnique de Paris and Ecole des Ponts ParisTech. It has received support from the  3rd Programme d'Investissements d'Avenir  [ ANR-18-EUR-0006-02 ]. The authors would like to thank  Jean-Claude Dutay  ( LSCE-IPSL ) for his comments.
W) has an automated sun tracker with a sun sensor. More specifically, the sun tracker is equipped with a shaded ISO 9060 class A spectrally flat pyranometer for measuring DHI and a pyrheliometer for measuring DNI. DNI and DHI data were implemented by some ASI systems during the forecasting algorithm calculations. In addition, GHI measurements directly derived from an unshaded ISO 9060 class A spectrally flat pyranometer are used for the algorithms' evaluation and for the persistence-based algorithm's calculations. Five nowcasting methodologies were implemented using these four camera setups, varying the used camera type, cloud segmentation and forecast algorithm. Moreover, a physics-based smart persistence model was also used in RE analysis. The following sub-sections give a brief overview of each ASI and persistence-based algorithm.
Data Availability Statement: Not applicable. Acknowledgments: This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE (project code: T1EDK-00681). We acknowledge support of this work by the project "PANhellenic infrastructure for Atmospheric Composition and climatE change" (MIS 5021516) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund). DLR's contribution to the study benchmarking experiment was funded by the German Federal Ministry of for Economic Affairss and Technology Climate Action within the WOBAS A project (Grant Agreement no.
RNA Extraction and High Throughput Transcriptome Analysis The total RNA was extracted from samples with the Spectrum TM Plant Total RNA Kit (Sigma-Aldrich, Missouri, USA). The purified total RNA extracts were processed with an RNAstable R LD kit (Sigma-Aldrich). Paired-end next-generation sequencing has been performed by the Annoroad company (Corporate HQ, Beijing, China) with an Illumina NextSeq 550AR Sequencer. The sequencing results and quality are reported in Supplementary Dataset 1. The raw data were processed with the Trimmomatic tool  (version 0. 36, Bolger et al. , 2014)  to remove adapters. A total of 45. 9 million high-quality clean reads (96. 15% clean reads) were aligned with the Hisat alignment software (version 2. 1. 0, Kim et al. , 2019)  on the high-quality reference transcriptome (NCBI assembly accession number: GCF_000150955.
2). Expression abundances were quantified using  Htseq-count (version 0. 11. 2, (Anders et al. , 2015)  and differentially expressed genes (FDR ≤ 0. 001; |Log2(FC)| ≥ 1) identified using the R package DESeq  (Bioconductor version 3. 11, Love et al. , 2014). Functional annotations of the transcriptome were carried out using Gene Ontology (GO) terms and enriched with the bioinformatics tool Blast2Go and a manual crossreference between published data and online public databases (https://protists. ensembl. org/; https://mycocosm. jgi. doe. gov/; https://www. ncbi. nlm. nih. gov/). Heatmaps were generated with an ad hoc Python script based on the seaborn repository (gplots version 0. 11. 0, https://seaborn. pydata. org/index. html). The scripts can be found in the following repository: https://github.
Statistics ANOVA was performed using the online freeware (https:// acetabulum. dk/anova. html). Principal component analyses (PCA) were performed with a compiled python script exploiting pandas (version 1. 2. 1), numpy  (version 1. 19. 5), matplotlib (version 3. 3. 3) , and scikit-learn (version 0. 24. 1) packages. Propagation of uncertainties in data processing was carried out following the mathematical rules  (Arras, 1998). Linear and nonlinear fitting analyses were performed with the free version of CurveExpert software available for download (Hyams, D. G. , CurveExpert software, http://www. curveexpert. net, 2010).
Transcriptomic and Metabolomic Analyses On the mindset of investigating the effects and mechanisms underlying the NRep-NS transition, transcriptomic analyses were performed on 11 time points. To observe the changes induced by the reduction of nitrogen supply (RNS), the changes in gene transcript availability were expressed compared with the control NRep. Overall, 2,560 genes were differentially expressed in at least one DNRS (Supplementary Dataset 1). The PCA based on transcriptomic data (Figure  3A ) exhibits a pattern very similar to the PCA based on the biochemical and physiological data (Figure  1A ). However, they differ by the positioning of 15 DNRS that is hereby detached from the NS phase group, suggesting that important changes in gene expression occurred at this time point. For the sake of simplicity in the figure representation, five phases will be considered: (i) NRep, (ii-iii) 5 and 6 DRNS, (iv) NS, and (v) 15 DRNS.