dataesr/scientific-paragraphs-categorization huggingface dataset:
The idea of replacing a complicated singular control by a simple constant control is important for applications. By solving (35), we choose, moreover, the best control values and switching times for such a control structure. By Lemma 6. 2 and Corollary 6. 2, the behavior of solutions of (4) with r (•) ≡ 0 is very similar to the turnpike behavior of the singular arc. Moreover, the error committed by setting the control r (•) ≡ r in place of r (•) = r s (•) is bounded. The numerical simulations show even better results when compared to the solutions obtained using the standard direct optimization method, see Sect. 7. For the numerical solution of (35) we use Bocop software. In practice, this problem can be solved by any nonlinear programming solver. The discretization of the dynamical part of the constraint in (35) can be done in various ways just as in case of the direct methods for DOCPs (see  [3]  for direct methods in optimal control).
Acknowledgements This research work has received most support from the  ANR  Project  Maximic  ( ANR-17-CE40-0024 ), and also benefited from the support of the  ANR PhotoBiofilm Explorer  ( ANR-20-CE43-0008 ) and  Ctrl-AB  ( ANR-20-CE45-0014 ). The authors are grateful to  Laetitia Giraldi  and  Agustin Yabo  from  Inria , and also to  Térence Bayen  from  Avignon University , for fruitful exchanges about singular arcs and turnpike phenomena. Funding Open Access funding enabled and organized by Projekt DEAL.
we call t 1 the time at the end of the first bang arc and t 2 the time at the end of the second and last one, they are marked on the graphics. One observes that increasing the total duration T seems to increase the duration t 2t 1 of the singular arc without notably changing the durations t 1 and Tt 2 of the bang arcs. This tendency is confirmed in Fig. 2 1 1 0. 8 0. 8 0. 6 0. 6 0. 4 0. 4 0. 2 0. 2 0 0 0 5 0 1 1 0 5 10 15 20 25 30 Fig. 1 On the left: the optimal control r (t), t ∈ [0, 15]. On the right: the optimal control r (t), t ∈ [0, 30]. 50 The observed control structure is of type bang(1)-singular-bang(0); 0 100 150 200 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9 1 250 300 1 https://coin-or. github. io/Ipopt/. 2 https://coin-or. github. io/CppAD. 3 https://ct. gitlabpages. inria. fr/gallery/.
After permeabilization with 1 mM Triton, cells were co-stained with anti-HbB and anti-HbF mAbs and washed twice with PBS. Cells were processed on a BD LSRFortessa™ with the FACSDiva Software version 8. 0. 1 (BD Biosciences Becton Dickinson) (BD Biosciences, Becton Dickinson), and the data were analyzed with FlowJo LLC software v10. 6. 1 (Becton Dickinson). All antibodies used are listed in Supplementary Table  2. The gating strategy is detailed in Supplementary Figure  4. 1. Supplementary Figure  4a. P. vivax isolates. Study protocols were approved by the Institutional Review Board of the Institute of Biomedical Sciences, University of São Paulo, and by the National Human Research Ethics Committee of the Ministry of Health of Brazil (CAAE: 64767416. 6. 0000. 5467); all patients provided written informed consent.
RhD expression was determined for each isolate (see the flow cytometry section). Seven clinical P. vivax-infected blood samples were collected in northwestern Brazil in the context of an ongoing cohort study (ClinicalTrials. gov, NCT03689036). These P. vivax isolates (Pv) were leukocyte-depleted and cryopreserved in liquid nitrogen, as described previously  32  (Supplementary Table  1 ). HIS-HEry mouse infection with P. vivax. P. vivax isolates were thawed with a stepwise NaCl method, as previously described  33  , and washed in PBS. Before being used to infect mice, the isolates were tested for Plasmodium parasitemia by examining thick PB smears with Giemsa staining. Next, RBC pellets of the isolates were resuspended in PBS at a concentration of 2. 5. 10 6 to 5. 10 6 parasitized RBCs per ml.
biosearchtech. com/ stellarisdesigner. Importantly, the P. vivax 18S probes did not cross-react with mouse or human 18S rRNA. For the flow imaging procedure, the hybridized RBCs were stained with DAPI, as described previously. Cells were processed on a BD LSRFortessa™ with the FACSDiva Software version 8. 0. 1 (BD Biosciences Becton Dickinson), and data were analyzed with FlowJo LLC software, v10. 6. 1 (Becton Dickinson). The gating strategy is detailed in Supplementary Fig. 4. 2. For sporozoite detection, salivary glands were collected from fed Anopheles mosquitoes, dilacerated, and filtered through a 35-µm mesh. After washing in PBS, recovered sporozoite cells were stained with anti-PvCSP mAbs (1:500 dilution) and fixed for 10 min in 100 µl of a 2% paraformaldehyde solution (Thermo Scientific™) in PBS.
Sporozoites were then stained with DAPI, as described above. Sporozoites were analyzed with an Image stream ISX MkII flow cytometer (Amnis Corp, EMD Millipore), with channel 01 for BrightField, channel 02 for FITC-conjugated anti-PvCSP mAb detection, and channel 07 for DAPI detection. Data were analyzed with Luminex corp Software IDEAS version 6. 2. All antibodies used are listed in Supplementary Table  2. Secondary transfers of BM cells from infected HIS-HEry mice into naive HIS-HEry chimeras. BM human CD235a + cells collected from infected HIS-HEry chimeras were first enriched with an AutoMACS pro. Then, they were sorted with a MoFlo Astrios Cell Sorter (Beckman Coulter). Next, cells were either directly injected intravenously into clodronate liposome-treated, secondary naive HIS-HEry mice, or they were frozen in 50% IMDM 50% freezing solution (SVF 20% DMSO).
Blood feeding was confirmed by visualizing blood engorged female mosquitoes. Unfed mosquitoes were removed, and the fed mosquitoes were maintained at the CEPIA facility under standard breeding conditions. At day 14 post-feeding, salivary glands from pooled mosquitos were dissected, and sporozoites were identified on an Image stream ISX MkII flow cytometer (Amnis Corp, EMD Millipore) and quantified with FlowFISH analysis (version 10. 6. 1). Statistics. The mean ± SD values are indicated in figures and/or legends. The twotailed paired Student's t-test was performed to compare two groups (Fig. 2b ). The P-values deemed significant are indicated in the figures. Statistical analyses were performed with Prism 9. 0a software (GraphPad Software).
Acknowledgements We are grateful to  Maria JoséMenezes  for her expert help in parasite sample collection and processing. The anti-PvCSP MRA-184 mAbs from a 2F2 hybridoma were obtained through  BEI Resources, NIAID, NIH. We thank  Sara El Hoss , from the  Université de Paris ,  UMR_S1134 ,  BIGR , the  Institut National de la Santé et de la Recherche Médicale (INSERM) , and the  Institut National de Transfusion Sanguine (INTS) , F-75015 Paris, France, for Band3/CD49d staining. We thank  Prof. Yves Colin  for the anti-human RhD mAbs clone  F5 INSERM U76 , INTS; and INSERM  U409 ,  Faculté de Médecine Xavier Bichat, Paris, France. We acknowledge the  Center for Translational Science (CRT) Cytometry and Biomarkers Unit of Technology and Service (CB UTechS ). , and CEPIA, at  Institut Pasteur , for support in conducting this study.
Anopheles stephensi mosquitoes were obtained from  CEPIA at Institut Pasteur and Radboud University Medical Center, Nijmegen. We thank  Coralie Guerin  and  Anna Chipont  from the  Cytometry Core from Institut Curie  for providing access to ImageStream. C. L. B. is part of the  Pasteur-Pasteur-Paris University International doctoral program (PPU)  under the  Marie Sklodowska-Curie grant. This work was supported by the  French Parasitology Consortium ParaFrap  ( grantANR-11-LABX0024 ), the  Fondation Pasteur Suisse both  awarded to A. S. , a  Pasteur Cantarini-Roux  postdoc fellowship awarded to F. N and a  ANR  grant reference  ANR-20-CE15-0031  awarded to L. M. S. Field work was supported by the  Fundacaõ de Amparo aP esquisa do Estado de Saõ Paulo (FAPESP) ,  Brazil  ( 2016/18740-9 ), awarded to M.
Field notes and site photos provide direct benefit to MHVSR calculation and interpretation. Documentation of metadata becomes cumbersome as the quantity of microtremor measurements increases into hundreds and thousands of sites. Large quantity microtremor campaigns performed for seismic microzonation mapping, site characterization of a seismic network's stations, or post-earthquake reconnaissance purposes (e. g. ,  Puglia et al. 2011; Albarello 2011; Molnar et al. 2020; Ladak et al. 2021 ) may include multiple equipment, practitioners, and/or span multiple years. In such cases, the importance of minimal standardized yet robust metadata of the microtremor measurements and their processing and interpretation increases. Recommendations on suitable equipment and deployment and acquisition selections provided in the next two sections are based on outcomes of the SESAME project and updated when applicable.
MHVSR interpretation The SESAME (2004) MHVSR guidelines provide three criteria to identify reliable MHVSR curves (see Appendix 3). If these criteria are not met, adjustment of processing parameters may improve the reliability of the MHVSR curve, as per the first two criteria. Albarello et al. (2011)  developed a reliability classification scheme for consistent MHVSR processing that is more conservative than the SESAME guidelines to reduce variability in the extensive processed MHVSR dataset amongst multiple practitioners following the 2009 L'Aquila, Italy, earthquake. Six criteria for trustworthy and interpretable MHVSRs (class A) are based on the Fourier spectra (peaks relate to reduced vertical component amplitude) and sufficient duration leading to robust statistics and ability to evaluate MHVSR consistency with time and azimuth.
Grilla is a proprietary software to analyze Tro-mino® recordings (http:// moho. world/ en/)  (Castellaro and Mulargia 2009a, b)  and accepts several other non-proprietary input formats. It has the capability to perform spectral analyses, as well as time-dependent and direction-dependent MHVSR analyses. Geopsy  (Wathelet et al. 2020 ) is an open-source software developed from the SESAME project. It accepts a variety of different input file formats. Both single measurement and batch processing of MHVSRs are supported. Geopsy automatically provides a value of f 0 from the mean MHVSR curve (deterministically), as well as from the individual windows (statistically) that can be adjusted by the user such as to delete spurious individual MHVSR curves. Geopsy provides tools to compute the MHVSR as a function of azimuth (rotate) and complete time frequency analysis, which can aid in wave composition analyses  (Fäh et al.
2009 ). Geopsy has modules to forward model the ellipticity curve and the SH-transfer function for layered earth models. ModelHVSR  (Herak 2008 ) consists of several MATLAB-based modules and algorithms. It does not have the capability to deal with raw time series data, so the input data must already be to frequency domain. AVG_HVSR calculates the average MHVSR from any number of observed MHVSRs. The input to this module is the spectral data, and an option is available to apply spectral smoothing or alternatively to use Landweber filtering on the unsmoothed spectra. ModelHVSR can forward model the theoretical S wave transfer function of a layered viscoelastic model for vertically incident S waves (horizontal response) and the theoretical P wave amplification spectrum for vertically incident P waves and calculates the MHVSR as the ratio between these two quantities.
OpenHVSR  (Bignardi et al. 2018 ) is a MATLABbased program capable of generating and displaying MHVSR curves from raw field data. One of its strengths is the ability to handle large volumes of data and spatially correlate different forms of informative data content. All data are loaded into the same environment, which facilitates batch processing of MHVSRs, allowing easy implementation of sensitivity analyses to test the choice of a specific set of processing parameters on the output MHVSRs. An interesting feature of this software is the implementation of directional analysis of spectral ratios to check whether the data contain non-isotropic components. Spatial map products can be produced of resonance frequency values, amplitude at resonance peaks, and preferential direction of f 0. Open-HVSR utilizes the same forward modelling strategy as ModelHVSR; however, it also provides the ability to simulate the surface wave contribution using the modelling routine of  Lunedei and Albarello (2010).
HVSRPy is an open-source Python package for HVSR processing that was developed recently by  Vantassel (2020)  to implement new statistical MHVSR processing techniques proposed by  Cox et al. (2020)  and  Cheng et al. (2020; 2021). Several features of HVSRPy make it unique from other available software. These novel functionalities include a frequency-domain window-rejection algorithm (FWA), which allows for the automatic rejection of contaminated time windows in the frequency domain, the ability to incorporate azimuthal variability into the statistical representation of f 0 , and the use of lognormal statistics, which allow for consistent representation of uncertainty, regardless of whether f 0HV or its reciprocal T 0 is the desired parameter of interest. Additionally, HVSRPy allows for calculating an unbiased, statistical representation of f 0 or T 0 from numerous, spatially distributed MHVSR measurements using Voronoi tessellations for spatial declustering.
Batch-style processing is simple and easy to implement using HVSRPy. For those who wish to Vol. : (0123456789) quickly process a single MHVSR record without the need to deal with Python code, a web-based application called HVSRweb has also been developed (  Vantassel et al. 2018). HVSRweb currently has all of the functionality of HVSRPy except for the ability to calculate statistics from spatially distributed MHVSR measurements. HVSRweb can be accessed at https:// hvsrw eb. desig nsafe-ci. org/. The HVSR IRIS station toolbox  (Bahavar et al. 2020 ) corresponds to the Incorporated Research Institutions for Seismology (IRIS) Data Management Center's (DMC's) openly available HVSR station toolbox. The platform includes a variety of ways to compute the spectral ratio for ambient seismic noise by providing different averaging routines.
Acknowledgements The  Consortium of Organizations for Strong Motion Observation Systems  (COSMOS) consisting of the  US Geological Survey (USGS) , the  Geological Survey of Canada , and a group of North American power utility companies ( Southern California Edison  and  Pacific Gas and Electric)  identified the need for blind trials and provided funding and encouragement to facilitate this project. Other than for the  USGS , any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the authors' organizations or their governments. Our manuscript was improved from reviews by  Guest Editor Stefano Parolai  and  Dario Albarello  and an anonymous reviewer and internal USGS reviews by  Oliver Boyd  and  Jamison Steidl.
This paper reviews the most recent research advances in the area of sensible and latent heat storage through the porous media as potential technology while providing useful information for researchers and engineers in the energy storage domain. To this end, the state and challenges of PCMs incorporation methods are drawn up, and an updated database of various research is provided while discussing the conclusions concerning the sensible and latent heat storage in porous media, their scopes of application and impact on energy consumption. In the light of this non-exhaustive review, it turns out that the adoption of porous matrices improves the thermal performance of systems, mitigates energy consumption and drops CO 2 emissions while ensuring thermal comfort within buildings. In addition, at the representative elementary volume (REV) and pore scales, the lattice Boltzmann method (LBM) is examined as an alternative method to the commonly used, traditional numerical methods.
These authors stated that the active systems design requires proper maintenance to achieve suitable incorporation, taking into account various conditions and requirements that may be climatic, aesthetic and/or operational. Each type of TES has its advantages and disadvantages, the operating principle of which depends on the targeted application, such as capacity or power. A comparison of some TES methods is reported in Table 2  [21]. For some time now, various articles on energy storage have been published, pointing out research trends and drawing up the state of the art  [9, [22] [23] [24] [25]. In this, Cabeza et al. [26]  issued a bibliometric analysis using VOSviewer software to develop research related to TES categories and identify research gaps. For TES topics, the approach performed using the co-occurrence of the keywords found is illustrated in Figure  3.
[144]  implemented an LBM algorithm with a non-uniform mesh and found an excellent agreement with previous experimental and numerical results. Due to the general physical description of the Boltzmann transport equation, a whole series of Lattice Boltzmann methods have been proposed to simulate various processes. On this basis, software tools such as Power FLOW and Open LB have been developed to simulate practical engineering problems. Initially, the LBM was designed to deal with isothermal flows. To broaden the applications, the LBM has also been extended to simulations of thermal flows in porous media, whether or not involving PCMs, etc. There are three typical collision operators in the LBM context: the single relaxation time (SRT) collision operator, the two relaxation time (TRT) collision operator, and the multiple relaxation time (MRT) collision operator.
Rai et al. , 2021) , consistent with our approach here that λ -1 (θ, ϕ) is to represent a large-scale process that arises from the collective feedback of smaller-scale dynamical processes. We refer the reader to Appendix A for the technical details of the relevant data diagnoses for the inverse method. We leverage the Firedrake software  (Rathgeber et al. , 2017) , an automatic code generation framework with high level specification in Python that utilizes the finite element formalism (e. g. ,  Durran, 2010, Chapter 6). The procedure is that, given a finite element mesh, we specify the function space on which we seek our solution, taken to be continuous Galerkin with first-order Lagrange polynomials as the basis here. We then implement our constraining model in the weak form (e. g. ,  Evans, 1998, Chapter 1) , which in this case involves multiplying by a test function with sufficient differentiability such that integration by parts may be performed, and implementing any natural or imposed boundaries on the problem accordingly.
Since Firedrake employs the finite element framework, we need a finite element mesh. For this work a choice was made to solve the problem on a two-dimensional spherical mesh embedded into the standard three-dimensional Euclidean space ℝ 3 , where the relevant periodicities and land boundaries are built into the mesh itself (as opposed to constructing a longitude-latitude grid with North Pole folding, which leads to a point singularity). An unstructured mesh with triangular elements with characteristic length-scale 100 km was created using the Qmesh package  (Avdis et al. , 2018) , with the land shape generated from the NEMO ORCA1 configuration (a global ocean configuration with a nominal horizontal resolution of 1°). We refer the reader to Appendix A for technical details regarding moving data between the mesh and grids.
The present implementation utilizes the Firedrake software, leveraging the inbuilt solvers as well as the automatic code generation capabilities to solve and explore the associated optimization problem and its sensitivities to parameter choices. The inferred λ -1 (ϕ, θ) has the smallest values within the Southern Ocean, Western Boundary Currents, and is western boundary intensified, regions where baroclinic turbulence is particularly dominant, and coinciding with where we expect the greatest energy flux out of the geostrophic mesoscales from dynamical considerations (e. g. ,  Melet et al. , 2015; Nikurashin & Ferrari, 2011; Rai et al. , 2021; Rocha et al. , 2018; Zhai et al. , 2010). We caveat that while the inferred spatial distribution of λ -1 (ϕ, θ) may be consistent with expectations, the resulting magnitudes should be viewed as a physically plausible lower bound.
(A2) For the stratification parameters M 2 /N and M 4 /N 2 as ratios, first we compute N 2 and N as a three-dimensional field using the CDFTOOLS package from 5-day averaged temperature and salinity data (using cdfbn2; see Data Availability). A new program (cdfsn2) was created to mirror the NEMO computation of stratification gradient parameters, with code largely taken from the NEMO subroutine and dependencies. The new cdfsn2 routine computes the isopycnal slopes M 2 /N 2 (wslp in NEMO), appropriately modified by slope limiters, partial step corrections, and tapering as the lateral, mixed layer and surface boundaries are approached, and passed through a Shapiro filter in the horizontal. The intermediary and N 2 are then multiplied by the slope variables to obtain M 4 /N 2 and M 2 /N as three-dimensional fields, which are then depth-integrated and time-averaged.
The cdfsn2 routine is able to reproduce the NEMO outputs directly up to very minor discrepancies (code tested on a simple re-entrant channel model and outputting the wslp variable and its modifications directly). The depth-averaged mean flow ũ𝑧 computed in the usual way from the available data. The long Rossby phase |c| is computed as (e. g. ,  Gill, 1982, Equation 12. 3. 13 ) |𝑐| = 𝑐 2 1 cos 𝜃 2Ω𝑅 𝑎 sin 2 𝜃 , 𝑐 1 = 1 𝜋 ∫ 0 ∫ ∫ 𝐻 |𝑁|d𝑧, ( A3 ) where θ is the latitude, Ω is the Earth's angular frequency, R a is the Earth's radius, and c 1 is an approximation of the first baroclinic phase speed (e. g. ,  Nurser & Bacon, 2014). This phase speed is computed in this case from the time-averaged |N| dz field directly. One thing to note is that, because of the NEMO tri-polar grid, the (u, v) components of the velocity only correspond exactly to the zonal and meridional velocities when south of around 20°N  (Madec & Imbard, 1996).
Rai al. , 2021). Sample calculations without the filtering step led to no noticeable changes in the results presented. To move the diagnosed data from the tri-polar NEMO grid onto the mesh, a simple change of co-ordinates from (ϕ, θ) to (x, y, z) and linear interpolation (through the Python scipy package) was performed, with extrapolations where necessary (e. g. , near the geographical locations associated with the edges of the tri-polar grid). Note that while interpolating scalars is straight-forward, interpolating vectors require preserving both the direction and magnitude, which can be achieved via a multiplication by a rotation matrix. To move data off the computational mesh onto the a regular longitude-latitude grid for further analyses or the NEMO ORCA1 tri-polar grid utilizing λ -1 (ϕ, θ) in prognostic calculations is more complicated.
A technical complication arises here that the spherical mesh is an immersed manifold (a sub-manifold of the 2-sphere S 2 embedded in ℝ 3 ), which has zero measure in the embedding space (S 2 has no "volume"). The standard procedure of probing the function values on the immersed manifold will fail as it is mathematically ill-defined: the probability of a given co-ordinate point being on the manifold is related to the measure, and an arbitrary co-ordinate point we wish to query the function value at will almost surely not be on the zero-measure manifold. One way round this technical issue is to probe finite element output using the vtk package directly, locating the cell closest to the co-ordinate of the point being queried, project that point onto the cell, which is now regarded as a sub-manifold of ℝ 2 (the measure of interest is now "area"), and the function can then be queried accordingly.