dataesr/scientific-paragraphs-categorization huggingface dataset:
Examples of the application of these methods include the following: an iterative algorithm used for sizing optimisation of a residential microgrid  [126] ; a linear programmingbased EMS for a hybrid system comprising PV and wind energy, fuel cells (FCs), and batteries  [127] ; and three suggested EMSs for a standalone HPS comprising PV and wind energy, and FCs, where the battery efficiency is assessed using an available control algorithm software. In addition, a hybrid microgrid consisting of two RESs (WT and PV generators), a battery energy storage system (BESS), and a variable load was described in  [128] , and an EMS of a smart home based on comprehensive MILP was introduced in  [129, 130]. Lastly, an MPC-based EMS to control the power flow of a standalone system based on PV, FCs, and batteries are presented, along with an EMS for a PV-battery microgrid that is also based on MPC  [131, 132].
Fortunately, some low-cost emulator schemes have been proposed, as shown for example in Figure  44. Indeed, using an Arduino and a few electronic components (sensors, opto-isolators, power switches, etc. ), it is easy to build a low-cost real-time wind turbine emulator. Additionally, it is easy to build a control scheme of the wind turbine by using Matlab/Simulink and uploading the Arduino package, as shown in Figure  45.
C. 2. 4. 1. 21), branching enzymes (BE; E. C. 2. 4. 1. 18) and isoamylase-type debranching enzymes (DBE;  EC. 3. 2. 1. 68). That biosynthetic pathway leads to the formation of highly ordered amylopectin molecules which are responsible for the physico-chemical properties of the polysaccharide such as its insolubility and its semi-crystalline nature. Starch synthases are in charge of the building of the linear glucose chains, branching enzymes catalyze the hydrolytic cleavage and transfer of an α-1,4 bond within the glucan chain and give rise to α-1,6-branch linkages. The branched polysaccharide obtained requires a trimming process catalyzed by isoamylases to achieve the fine structure of amylopectin  (Ball et al. , 1996). Branching enzymes belong to the α-amylase superfamily (GH13) in the CAZy database  (Lombard et al.
Phylogenetic tree reconstruction Protein sequences homologous to Chlamydomonas starch branching enzymes were collected by similarity searches using blastp  (Altschul et al. , 1990)  recursively against RefSeq (O'  Leary et al. , 2016)  and against a custom protein sequence database mainly composed of algal proteomes. All hits were combined, dereplicated, and sequence headers were standardized. A rough phylogenetic tree was reconstructed with the following procedure: (1) sequences were aligned using mafft:FFT-NS-2 v7. 503  (Katoh and Standley, 2013). The multiple alignment was trimmed using trimal:gappyout v1. 2rev59 and used to infer an approximate Maximum Likelihood phylogenetic tree with fasttree:LG+Ⲅ v2. 1. 11 (Price et al. , 2010). The tree and the alignment were manually inspected to exclude (1) sequences not related to Viridiplantae or Rhodophyceae, (2) sequences composing orthologous groups not related to BE, (3) partial sequences.
Selection and characterization of branching enzymes disrupted mutants of Chlamydomonas. For each of the three branching enzyme genes described above, two independent insertion lines were selected from the Chlamydomonas CLiP mutant library  (Li et al. , 2019)  (see materials and methods for accession numbers). Insertion tags in exons were favored to optimize chances of obtaining strains deficient for each gene's corresponding activity. PCR amplifications of genomic DNA purified from each strain were performed using primers specific to flanking regions of the exogenous DNA insertion site (Table  S1 ). All mutant strains proved to have a specific interruption in the expected exon (Fig. S4 ), leading to the loss of expression of the corresponding genes (Fig. 3A ). Biochemical analyses were then conducted to determine if those strains are indeed devoid of the relevant enzymatic activities.
[63] Autolab PGSTAT30 potentiostat/galvanostat (Metrohm Autolab, The Netherlands) and Nova 2. 1 software were used for controlling the RRDE, RDE, LSV, and CV experiments in a 3electrode cell. SCE was the reference electrode and a carbon rod (diameter 3 mm) was employed as a counter electrode. All the reported potentials were converted to the reversible hydrogen electrode (RHE) using the solution pH value of 0. 29  [64]  and the formula: E RHE = E SCE + 0. 241 + 0. 059 × pH. The RDE and RRDE polarisation curves for ORR were recorded in the cathodic direction with the IR compensation using the data from electrochemical impedance spectroscopy measurements. [65] ysical characterisation SEM imaging was performed using Nafion®-free catalyst ink coated Si plates with a Helios Nanolab 600 (FEI Company) high-resolution scanning electron microscope in the secondary electron mode with a Through-the-Lens detector at an accelerating voltage of 10 kV.
The XPS studies were carried out with Nafion®-free catalyst ink coated Si plates by the SCIENTA SES-100 spectrometer using 200 eV pass energy and the residual pressure in the analysis chamber was below 10 À 9 Torr during the data collection. For excitation, the nonmonochromatic Al K α X-rays (1468. 6 eV) were used and the electron take-off angle was 90°. The XPS data were processed according to the method published previously. [49] e XRD analysis was performed with the catalyst materials in powder form using Bruker D8 Advance diffractometer with Nifiltered Cu K α radiation, with 525 s per step of 0. 013°from 5 to 85°( 2θ). Micro-Raman spectroscopic technique was used to characterise the Nafion®-free catalyst ink coated Si plates. The Raman spectra were recorded using an Ar ion laser (514. 5 nm), inVia Renishaw spectrometer, and Olympus confocal microscope (50X objective).
2) Differential Scanning Calorimetry Differential scanning calorimetry (DSC) experiments were performed using a Micro DSC III differential scanning calorimeter (Setaram, France). The aluminium capsules containing the samples were filled with 500 µL of 0. 1 % and 0. 2 % (w/v) solutions of DPPC and DP-DGTS respectively. The lipid suspensions were extruded with a thermostated extrusion kit from Avanti Polar Lipids (Alabaster, AL, USA) by passing the suspensions 11 times through 0. 1 µm pore diameter polycarbonate membranes. The two samples were heated and cooled between -4° and 55°C, with a heating rate of 0. 5°C/min. Only the last three of four cycles were identical and used in the data analysis, the first one being always different from the following cycles due to the annealing of the vesicles after the extrusion step.
As a consequence, our KA-based treatment, which is only valid wherever the intensity is weak compared to the incident beam, does not correctly capture the specular maximum of the first Bragg sheet, where this condition is typically violated. In the past, we therefore ignored the first Bragg sheet and relied on the second one  [31, 65]. In line with our more recent work  [67] , we combine here information from the first two Bragg sheets (see Extended Data Figure  4 ): while the Caillé parameter 𝜂 = 𝜋𝑘 𝑇 2𝑑 𝜅𝐵 𝑑 ⁄ (6) is obtained from the specular/diffuse scattering intensity ratio in the second Bragg sheet, the de Gennes parameter Λ = 𝜅 (𝐵𝑑) ⁄ (7) is obtained from the decay of the off-specular intensity in the first Bragg sheet along q||, excluding the specular intensity which violates the KA.
Small angle neutron scattering (SANS) For the SANS experiments, aqueous solutions of DPPC and DGTS were prepared in pure D2O at 10% (w/v) and 3% (w/v), respectively, and then vortexed before transfer into 1 mm path length quartz cells (Hellma, Germany) for the data acquisition. For DPPC, this is well above the swelling limit commonly observed at 40% water for phosphatidylcholines. SANS data were collected at the D16 cold neutron diffractometer of the Institut Laue-Langevin (ILL, Grenoble, France). D16 uses a monochromator made of nine highly oriented pyrolytic graphite (HOPG) crystals whose orientation can be set to focus the beam vertically in a continuous manner from unfocussed to detector or sample focusing. For the SANS experiments, the D16 instrument was set to pinhole geometry by collimating the beam both horizontally and vertically to produce a symmetrical angular resolution and beam size in the two directions.
In detail, the nitrogen group and the lipid tails are identical and the additional methylene groups are taken from the OPLS force field which results in total charge neutrality on the betaine headgroup. All headgroup bonded parameters stem from OPLS. An initial structure of a DP-DGTS molecule was created using the geometry optimization of the Avogadro simulation package  [82]. All the simulation input files are available at https://doi. org/10. 18419/darus-2360 and corresponding molecular topologies are displayed in Extended Data Figure  7.
5) Computer simulations All atomistic Molecular Dynamic (MD) simulations are performed using versions 2020 and 2021 of the GROMACS simulation package  [83]. All simulations are performed with an integration time step of 1 fs in the canonical constant pressure ensemble as described above. Temperature was maintained at T = 300 K for the gel phase and T = 330 K for the fluid phase using the canonical velocity rescaling thermostat  [84]  with a characteristic time of 0. 5 ps. The time constant for the pressure coupling is set to 1 ps. Lennard-Jones interactions are truncated and shifted to zero at 𝑟 = 1. 4 nm. Water molecules are kept rigid using the SETTLE algorithm  [85]. Analysis of the simulations is performed using our freely available MAICoS package (https://www. maicos-analysis. org/) and chemical potentials are evaluated using the Mulistate Bennet Acceptance Ratio method (MBAR)  [86]  and the toolchain of the Alchemistry project (https://alchemistry.
While by definition 𝜇 is constant over the simulation volume in thermal equilibrium, its contributions 𝜇 and 𝜇 are not due to the inhomogeneous water distribution perpendicular and depend on the coordinate perpendicular to the membrane surface. We thus choose to measure the chemical potential at the center of the water slab between the bilayers and fix the test particle in the plane parallel to the membrane surface using a harmonic potential acting on its center of mass. To overcome potential sampling issues due to the slow dynamics of lipids in the gel phase five independent systems are constructed as described above at 𝑛 = 30 waters per lipid and then step-wise dehydrated by a 100 ns equilibration run. The five equilibrated systems at each 𝑛 are then sampled at each 𝜆 state for 100 ns, corresponding to 5 × 38 × 100 ns = 19 μs sampling time per data point shown in Figure  5  and Extended Data Figure  5.
6) Phylogeny The Microchloropsis gaditana locus Naga_100016g36 (annotated as Protein of unknown function DUF3419) was identified as putative betaine lipid synthase by homology with Nannochloropsis oceanica BTA1  [40]. The Naga_100016g36 CDS was translated and the amino acid sequence used as a query for a series of BLAST searches in all the publicly available databases. Both tBLASTn and PSI-BLAST  [87, 88]  were performed together with an human-curated process in order to obtain the widest dataset possible and the most robust one. The same was done with all the sequences used by  [40]. The final dataset contained amino acid sequences from at least 14 families of eukaryotes, bacteria and archaea. Because in most eukaryotes the two domains of the BTA protein (the 3-amino-3-carboxypropyltransferase and the S-adenosyl-L-methionine-dependent methyltransferase (SAM) domains) are arranged in two different configurations, two separate alignments were produced.
The amino acid sequences of the two domains were compiled in two separate fasta files and aligned using MUSCLE  [89]  then curated using BMGE (Block Mapping and Gathering with Entropy) software  [90]  to select the phylogenetic informative regions. For each alignment the substitution model that best fit the data was selected by running the 'Find Best DNA/Protein Model' utility implemented in MEGA X (Molecular Evolutionary Genetics Analyses) software  [91]. The Le-Gascuel substitution model for Maximum Likelihood (ML) phylogenetic inference method was chosen as the best fitting for both datasets  [92]. Non-uniformity of evolutionary rates among sites was modelled using a discrete Gamma distribution (+G). For the SAM domain alignment, a certain fraction of sites were considered to be evolutionarily invariable (+I).
For each analysis, a total of 1,600,000 generations was implemented, with successive samples separated by 100 generations after an initial "burn in" period of 25 % of the number of samples. The Bayesian posterior probabilities (BPP) were estimated by two independent runs of four Metropolis Coupled chains (MCMCMC). For each dataset, the model selection was done during the analysis by estimating the posterior probabilities of the different models together with their parameters.
This work was supported by  ANR-DFG ANR-18-CE92-0015  and  SCHN 1396/2. SB was supported by a joint funding  Glyco@Alps / ILL PhD Program , by the  French National Research Agency  in the framework of the " Investissements d'avenir" program   Glyco@Alps  ( ANR-15-IDEX-02 ), " Origin Of Life"  ( ANR-17-EURE-0003 ), " Oceanomics"  ( ANR-11-BTBR-0008 ) and the  Labex GRAL , financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche)   CBH-EUR-GS  ( ANR-17-EURE-0003 ). AS acknowledges funding from the  DFG under Germany 's  Excellence Strategy  - EXC 2075 -390740016. Computing time was provided by the state of  Baden-Württemberg  through bwHPC.
The quantitative results were obtained by data treatment using Analysis Program. Elemental analyses of the Schottky junctions were performed using ICP-AES. The limit of detection of the instrument was 0. 1-0. 2 mg L -1 for the chosen metals. Quantitative analysis of the noble metal component of the M/TiO 2 composites allowed the calculation of deposition yield which is defined as the ratio between the actual deposited mass of the specific metal (deduced from ICP-AES analysis) and the mass of metal introduced during the deposition step (Table  1 ). X-ray scattering (XRS) measurements were carried out on a Mo rotating anode generator (Rigaku Corp. , Japan) equipped with a multilayer mirror (Osmic) delivering a monochromatic beam with incident wavelength λ Mo,Kα = 0. 711 Å. Two-dimensional XRS patterns were collected on a MAR345 image plate detector (marXperts GmbH, Germany, pixel size = 150 µm) placed at a distance of 150 mm from the samples.
Powder samples were filled into borosilicate capillary tubes (WJM-Glas/Müller GmbH, DE) that were mounted on a goniometer head. Typical exposure time was 900 s. Scattered intensity I as a function of the scattering vector modulus Q (Q = 4π/λ sin(θ) where 2θ is the scattering angle) is obtained by angular integration over the 2D scattering patterns using homemade software. The Scherrer equation was used for the determination of the average crystallite size of the nanoparticles onto the support: L = Kλ/βcosθ with λ the incident wavelength and β the Full Width at Half the Maximum of the X-ray scattering peak (in radians) at a Bragg angle q. K is defined as the shape factor and is taken to be 0. 9 for spherical-like particles. Fourier-transform infrared spectroscopy. FTIR spectra were recorded on dry sample powders using a Nicolet iS50 (Thermo Scientific) spectrometer equipped with a KBr beam splitter and a DTGS/KBr detector.
All data was recorded at 150 W and a spot size of 700 × 300 µm. Survey scans were recorded at a pass energy of 160 eV, and high-resolution scans recorded at a pass energy of 20 eV. Electronic charge neutralization was achieved using a low energy electron flood gun. Filament current = 0. 27 A, charge balance = 3. 3 V, filament bias = 3. 8 V. All sample data was recorded at a pressure below 10-8 Torr and a room temperature of 294 K. Data was analyzed using CasaXPS (v2. 3. 19PR1. 0). The energy shift due to electrostatic charging was subtracted using the carbon adventitious signal, i. e. , the C 1 s peak located at 284. 8 eV. Peaks were fit with a Shirley background prior to component analysis with the appropriate experimental sensitivity factors of the normalized photo ionization with modified Wagner factors  [56].
Acknowledgements EP and PJC acknowledge the financial support of the  French ANR agency (ANR)  under grant agreement  ANR-18-CE09-0001  ( C3PO ) and the  CNRS  through the  International Emerging Actions program  (no. 08216 ). The results used the  Imagerie-Gif core facility  supported by l' Agence Nationale de la Recherche  ( ANR-11-EQPX-0029 /Morphoscope ,  ANR-10-INBS-04 / FranceBioImaging ;  ANR-11-IDEX-0003-02 / Saclay Plant Sciences). The X-ray photoelectron (XPS) data collection was performed at the  EPSRC National Facility for XPS ("HarwellXPS" ), operated by  Cardiff University  and  UCL , under Contract No. PR16195. The authors acknowledge financial support from the  CNRS-CEA "METSA" French network  ( FR CNRS 3507 ) on the platform STEM-LPS. This project has received funding from the  European Union 's  Horizon Europe research and innovation programme  under the  Marie Skłodowska-Curie grant  agreement  N • 101068996.
This work has received support from the  French State  through the  National Agency for Research  under the program of future investment EQUIPEX TEMPOS-CHRO-MATEM with the reference  ANR-10-EQPX-50. The authors thank  Stéphane Cabaret  ( LPS/CNRS, Orsay ) for the design and realization of the photoreactor,  Anne Boos  ( IPHC/ECPM, Strasbourg ) for the ICP-AES analyses and  Fabrice Gros  ( CNRS/U. Clermont Ferrand ) for fruitful reactor details discussions.
Operation after Open-Circuit Failures without Suitable Control Strategy To point out the effects of an OCF on the system, simulations have been performed using Matlab-Simulink Software (version R2022a). The simscape electrical library has been employed to implement both the TLIBC and also the electrolyzer model based on an equivalent electrical circuit considering static and dynamic operations  [26]. The technical specifications of the TLIBC used for the simulations are given in Table  1. The switching frequency of the converter has been set at 10 kHz. It should be noted that in this analysis, the output current is regulated by a modified sliding mode-based controller designed in  [49] ; this solution allows the fault effect to be minimized. Moreover, this control law is designed for two main objectives: first, to control the hydrogen flow rate, and second, to ensure the balance of both input capacitor voltages.
Experimental Test Bench To assess the performance of the fault diagnosis method in identifying the OCF and fault-tolerant control strategies, an experimental platform has been built at the Renewable Energy Research Centre (RERC), King Mongkut's University of Technology North Bangkok; a picture of the test rig is given in Figure  11. The platform encompasses the designed TLIBC (1) with the technical specifications provided in Section 2, a DC power supply (2), a dSPACE Microlabbox (3), a computer with the dSPACE Control Desk Software (4), a C2000 Microcontroller (5), a PEM electrolyzer  (6) , and an 8-channel oscilloscope  (7). For the load of the system, a PEM electrolyzer emulator described and designed in  [50]  has been employed in this work. This emulator enables the recreation of the static and dynamic behavior of a real PEM electrolyzer through the use of an equivalent electrical circuit as shown in Figure  12.
Furthermore, it is low-cost and allows avoiding the use of a real PEM electrolyzer whose performance could be degraded during experiments as pointed out in  [19, 51]. The operation of the PEM emulator is based on its dynamic model as explained in the following. 𝜏 = 𝑅 𝐶 (6) 𝜏 = 𝑅 𝐶 (7) In summary, the "essence of electrolysis" in our case is fully represented by our emulator since we demonstrated that it behaves like a real electrolyzer; as a consequence, we can adopt this circuit to test the dynamic performance of our converter. It is different from a simple electrolyzer model considering only the static behavior of the electrolyzer using a simple resistance  [27]. A detailed description of the emulator can be found in  [50]. The modified sliding mode-based controller (shown in Figure  3 ) designed in  [49]  to control the output current and balance the two input capacitors (C1, C2) of the TLIBC, has been implemented in the dSPACE Microlabbox (dSPACE, Paderborn, Germany) (through the use of Matlab/Simulink software (version 2019b).
The proposed fault diagnosis (as presented in Figure  5 𝜏 = 𝑅 𝐶 (6) In summary, the "essence of electrolysis" in our case is fully represented by our emulator since we demonstrated that it behaves like a real electrolyzer; as a consequence, we can adopt this circuit to test the dynamic performance of our converter. It is different from a simple electrolyzer model considering only the static behavior of the electrolyzer using a simple resistance  [27]. A detailed description of the emulator can be found in  [50]. The modified sliding mode-based controller (shown in Figure  3 ) designed in  [49]  to control the output current and balance the two input capacitors (C1, C2) of the TLIBC, has been implemented in the dSPACE Microlabbox (dSPACE, Paderborn, Germany) (through the use of Matlab/Simulink software (version 2019b).
It is different from a simple electrolyzer model considering only the static behavior of the electrolyzer using a simple resistance  [27]. A detailed description of the emulator can be found in  [50]. The modified sliding mode-based controller (shown in Figure  3 ) designed in  [49]  to control the output current and balance the two input capacitors (C 1 , C 2 ) of the TLIBC, has been implemented in the dSPACE Microlabbox (dSPACE, Paderborn, Germany) (through the use of Matlab/Simulink software (version 2019b). The proposed fault diagnosis (as presented in Figure  5 ) has been realized via an analog circuit including operational amplifiers. In comparison, fault-tolerant control strategies have been embedded in the C2000 Microcontroller. The dSPACE Control Desk Software (version 7. 0) allows for the monitoring, supervising, and recording of the conducted experiments.
Obtained Experimental Results After performing simulations through Matlab/Simulink, experimental tests have been carried out to corroborate the obtained simulation results. Moreover, the experimental results enable conclusions on the performance of the fault diagnosis coupled with faulttolerant control strategies. Like in the simulations, an OCF in the power switch Q 3 has been generated at 4 ms and the output current of the TLIBC is regulated at 5 A. The obtained experimental results are given in Figures  13 14 15. In Figure  13  (relying on the fault diagnosis scheme given in Figure  5 ), the output "relay", the output "AND GATE", the output "XOR GATE", and the Q output of the SR Flip Flop are depicted. Before the occurrence of the failure, the outputs "AND GATE" and "XOR GATE" indicate that the TLIBC operates in healthy mode as highlighted in Figure  5.
) has been realized via an analog circuit including operational amplifiers. In comparison, fault-tolerant control strategies have been embedded in the C2000 Microcontroller. The dSPACE Control Desk Software (version 7. 0) allows for the monitoring, supervising, and recording of the conducted experiments. The required current and voltage sensors for control and fault diagnosis purposes are inserted into the designed TLIBC. The acquired current and voltage signals are sent to the inputs of the dSPACE Microlabbox.
Acknowledgments: The authors would like to sincerely thank  Surin Tohtubtiang , distinguished Member of the KMUTNB council committee, in supporting  Burin Yodwong 's Ph. D. thesis. Furthermore, the authors would like to address their heartfelt acknowledgments to the  Thai French innovation institute of KMUTNB , the  GREEN laboratory of the University of Lorraine , and the  Italian National Research Council  for their constant support in research cooperation with Thailand, France, and  Italy.