dataesr/scientific-paragraphs-categorization huggingface dataset:
Besides, PEACEFULNESS, the software based on this framework, allows a huge variety of scenarios to be tested, with thousands of customers, some adopting flexibility contracts. To achieve this, aggregators are among the agents modeled. In terms of contribution, this paper presents: • a holistic energy management approach  [77, 91]  independent from the energy vector and a whole set of technologies (fossil and renewables, controllable or not, etc. ); n. b. : this corresponds simultaneously to an energy-service company for the customers (consumers and prosumers) and to a peer-to-peer type interaction between the upper-level agents (aggregators, system operators)  [21]. • various DSM contracts (in variable proportion), obeying heuristics but which can also handle optimization tools  [92] ; • aggregators, whose number and positioning are configurable, close to multi-agent systems  [93] , but also from stand-alone home energy management systems (HEMS)  [94].
Model and methods The methodology is deeply guided by policy contexts, in their present form but also on their possible evolution  [77, 91, 97]. Similarly, the type and distribution of aggregators and flexible contracts among the customers attempted to integrate the conclusions and remarks provided by social sciences  [98]. If the software, PEACEFULNESS, is only dedicated to simulations actually, it is planned to extend the approach in experimental applications and even in real-world energy grids management. Fig. 1  represents a multi-energy smart grid. In the current framework, agents are the legal entities present in energy grids: dwellings, private companies, institutions, etc. They can be consumers with different types of loads (residential, commercial, hospital, etc. ); they can also be producers, that are centralized or distributed; and finally, there are prosumers, producing one or several types of energy (electricity, heat, gas, etc.
Meteorological data Weather conditions can be required for RES such as PV and WT, or for heating/cooling needs. Here, wind speed, total and normal irradiation, outdoor temperature and the position of the sun are taken from PVGIS SARAH  [100] , a public European database initially focused on PV-related data and then enlarged to more general meteorological data. Here, it is important to mention that such a choice was mainly governed by transparency and openness of the data  [78, 81]  first, and second to propose embedded weather data easily accessible and controllable  [16]. As a last remark, it must be added that better database could be used without any problem; yet non-free ones cannot be made publicly available in the github repository associated with the present work. In this respect, it would also have been feasible (as planned in the near future) to rely instead on the SWERA database.
Acknowledgements The project leading to this publication has received funding from  Excellence Initiative of Université de Pau et des Pays de l'Adour -I-Site   E2S   UPPA , a  French "Investissements d'Avenir" program. Besides, the authors would like to thank the " Région Nouvelle Aquitaine " for the financial support, especially through the funding of the Ph. D. of T. Gronier. The authors are also grateful to  Pr. Haritchabalet  from  the laboratory of Economics of the University of Pau  for her help in understanding the energy market, and also for fruitful discussions and collaborations together, and to  Dr. de Fontenelle  from the  department of Laws  and to the " Pau Droit Énergie Consortium " temporarily joined by the corresponding author. Finally, the second author would like to thank  Pr.
We present the implementation of trajectory surface hopping nonadiabatic dynamics for a polarizable embedding QM/MM formulation. The time-dependent density functional theory has been used as the quantum mechanical level of theory, whereas the molecular mechanics description has involved the polarizable AMOEBA force field. This implementation has been obtained by integrating the surface-hopping program Newton-X NS with an interface between the Gaussian 16 and Tinker suites of codes to calculate QM/AMOEBA energies and forces. The implementation has been tested on a photoinduced electron-driven proton-transfer reaction involving pyrimidine and a hydrogen-bonded water surrounded by a small cluster of water molecules and within a large water droplet.
The only problem is that analytical gradients of the corrected energies are no longer possible; for this reason, in the following, only an LR formulation will be used in the TSH dynamics. SS effects will be exclusively considered as a posteriori correction of the LR trajectories, precisely as proposed in the original cLR 2 protocol. 27. 2 Nonadiabatic dynamics with QM/AMOEBA Figure  1 : Schematic representation of Newton-X-Tinker-Gaussian implementation. Newton-X acts as a driver providing at the beginning of each step the coordinates of the system to Tinker, that in turn calls Gaussian to compute energies and gradients with TD-DFT/AMOEBA PES. Then, to evaluate the transition probabilities between states, the overlap of wave functions at two subsequent time steps are computed by a program written on purpose (ci ovlp in the scheme, it is provided together with Newton-X) that uses the orbital overlap matrix computed using Gaussian.
In the present work, we focus on the coupling of TSH with LR-TD-DFT performed with polarizable embedding in the IPD formulation. To implement such a methodology, we developed an interface between the TSH software Newton-X  30  and a modified version of Tinker 31 that can, together with a modified version of the Gaussian 16 suite of codes,  32  compute energies and geometrical gradients of the TD-DFT/AMOEBA Hamiltonian. In our implementation (Figure  1 ), Newton-X NS (new series) handles the time propagation and the surface hopping algorithm. At each step of the dynamics, it calls the Tinker/Gaussian interface to compute the required electronic-structure quantities: (a) the potential energies, (b) the potential energy gradients, (c) the single-excitation coefficients (corresponding to the elements of the X + Y matrix), and (d) the molecular to atomic orbitals transformation matrix.
All the implementations described up to now are self-contained in a Fortran module and a Perl script used to interface Newton-X NS and Tinker/Gaussian, providing the data needed to the core code in the correct format (and units). Since the TSH core code was not ready to deal with QM/MM calculations, some minor modifications have also been performed. In particular, manipulation of the system's kinetic energy is needed to handle frustrated hoppings, velocity rescaling after hopping, and decoherence corrections. 53 When a QM/MM potential is used, it is more sound to exclude atoms from the environment and to only use the kinetic energy of the QM part.
Computational Details Due to the importance of accurately describing CT states, we used the long-range corrected functional CAM-B3LYP,  60  which should provide a balanced representation of the different electronic states involved in the process. The calculations were done with the 6-31+G(d) basis set. All TSH simulations were performed applying the same protocol. We started from the optimized structure of the cluster; then, we computed the Hessian with respect to the nuclear coordinates. Geometry optimizations for QM/AMOEBA and QM/TIP3P were performed with the program minimize. x from the Tinker package using a threshold of 0. 1 kcal mol -1 Å -1 on the gradients' root-mean-square. Since analytical second derivatives are not implemented in our software for QM/AMOEBA Hamiltonians, we used numerical differences of analytical gradients with a modified version of vibrate.
x program from the Tinker package. Ground-state equilibrium geometries and Hessian were used to sample 512 structures from a harmonic-oscillator Wigner distribution  61  of the clusters, using Newton-X. 30, 62 On each structure, we computed the lowest five excitation energies at the same level of theory. Finally, we selected initial conditions from each state according to a stochastic algorithm based on the state's oscillator strength within a 0. 5 eV energy width around the equilibrium excitation energy of S 4. Preparation of initial conditions for the pyrimidine-water droplet was performed differently: we took 128 configurations extracted from the Wigner distribution of Pm(H 2 O) 4 and, using PACK-MOL, 63 we added 1148 water molecules inside a sphere of 20-Å radius around the original cluster.
As mentioned in Methods, only QM kinetic energies were considered for decoherence, frustrated hoppings, and velocity rescaling. Due to limitations of TD-DFT to describe the crossing region between the ground and the first excited state,  46  only hoppings between excited states were evaluated. Whenever the S 1 /S 0 energy gap dropped below 0. 2 eV, the trajectory was terminated and the corresponding time was taken as the time for the internal conversion to the ground state. Trajectories were considered valid only if they were longer than 5 fs. They were terminated if they underwent a total energy drift superior to 0. 5 eV. The trajectories were analyzed using a Python code based on NumPy,  64, 65  SciPy,  66  and MD-Analysis 67 libraries. Data visualization was performed using Matplotlib,  68  while molecular visualization was performed using PyMOL.
Results We ran three different sets of TSH simulations to establish a robust dataset to test the implementation of TD-DFT/AMOEBA. The first reference set focused on Pm(H 2 O) 4 with pyrimidine and one water molecule in the QM (TD-DFT) and three water molecules in the MM (AMOEBA) regions (Figure  2 ). The second set adopted the same QM and MM regions but with regular electrostatic embedding, using the TIP3P force field. The third set striped the spectator water molecules and simulated only Pm(H 2 O) 1 with TD-DFT. As a first analysis, we compare the EDPT probabilities in the three trajectory sets. The results are shown in Figure  3 , where we report the Pm(N)-H(water) distance computed along TSH trajectories of QM/AMOEBA, QM/TIP3P, and Pm(H 2 O) 1. As it can be seen, for all three sets of trajectories, the H transfer occurs in the first 100 fs of the excited state radiationless decay.
Conclusions We reported the implementation of trajectory surface hopping nonadiabatic dynamics performed with a QM method (TD-DFT) coupled with an atomistic polarizable embedding (AMOEBA). This implementation has been done by interfacing a development version, soon to be released, of the surface-hopping program Newton-X NS with the polarizable QM/AMOEBA approach from an interface between the Gaussian 16 and Tinker suites of codes. Our NX/TINKER/G16 interface will be released under the GPL3 license, freely available to anyone with access to Gaussian 16 and the Tinker version as modified in this work. We tested our approach on photoexcited pyrimidine-water clusters, a challenging system because of the relevant role played by CT states. Our analysis led us to conclude that the performance of AMOEBA and TIP3P water models in this kind of problem are very similar if AMOEBA.
is treated in a linear-response framework. Instead, significant differences in the charge-transfer states' energies are obtained when QM/AMOEBA is treated within a state-specific framework. These findings push for a reliable and efficient implementation of analytical gradients of statespecific formulations of polarizable QM/MM approaches. Acknowledgement M. Bo. and B. M. acknowledge funding by the  European Research Council  under grant  ERC-AdG-786714  (LIFETimeS). B. D. and  M. Ba. acknowledge funding by the  European Research Council  under grant  ERC-AdG-832237  (SubNano).
Aims. We conducted a spectral and temporal analysis of X-ray data from the Be X-ray binary pulsar SXP 15. 6 located in the Small Magellanic Cloud based on NuSTAR, NICER, and Swift observations during the 2021 outburst. Methods. We present the broadband X-ray spectra of the system based on simultaneous NuSTAR and NICER observations for the first time. Moreover, we used monitoring data to study the spectral and temporal properties of the system during the outburst. Results. Comparison of the evolution of the 2021 outburst with archival data reveals a consistent pattern of variability, with multiple peaks occurring at time intervals similar to the orbital period of the system (∼36 d). Our spectral analysis indicates that most of the energy is released at high energies above 10 keV, while we found no cyclotron absorption line in the spectrum.
2021 outburst The 2021 outburst of SXP 15. 6 was reported by  Coe et al. (2021)  as a result of monitoring from the Swift SMC Survey (S-CUBED;  Kennea et al. 2018). S-CUBED is a high-cadence (one to two weeks) shallow X-ray survey of the SMC that consists of ∼140 tiled pointings covering the optical extent of the SMC. The survey has enabled the early detection of some of the brightest outbursts in the MCs (e. g. , SMC X-3;  Kennea et al. 2016). Following the announcement of the outburst, NICER monitoring observations were performed, while NuSTAR observed the system near its peak flux with a ToO observation. In the following paragraphs we provide information for the NICER and NuSTAR data that were collected during the outburst, as well as Chandra archival data that were used for comparative studies.
NuSTAR The Nuclear Spectroscopic Telescope Array (NuSTAR) mission carries the first focusing high-energy X-ray telescope in orbit operating in the band from 3 to 79 keV  (Harrison et al. 2013). NuSTAR observed the system with a 42 ks DDT observation (obsid: 90701339002) on 2019 November 26. NuSTAR data were analysed with version 1. 8. 0 of the NuSTAR data analysis software (DAS) and instrumental calibration files from CalDB v20220301. The data were calibrated using the standard settings on the NUPIPELINE script, reducing internal high-energy background, and screening for passages through the South Atlantic Anomaly. We used the NUPRODUCTS script to extract phase-averaged spectra for source and background regions (60 radius) for each of the two focal plane modules (FPMA/B). Finally, we performed barycentric corrections to the event times of arrival using the satellite's orbital ephemeris files.
NICER The NICER X-ray Timing Instrument (XTI;  Gendreau et al. 2012 Gendreau et al. , 2016) )  is a non-imaging, soft X-ray telescope on board the International Space Station. The XTI consists of an array of 56 co-aligned concentrator optics (52 currently active) with a field of view of ∼30 arcmin 2  in the sky. Each unit is associated with a silicon drift detector  (Prigozhin et al. 2012) , operating in the 0. 2-12 keV band, yielding a ∼100 ns time resolution and spectral resolution of ∼85 eV at 1 keV. For the current study, we analysed NICER data obtained between MJD 59535 and MJD 59605. Data were reduced using HEASOFT version 6. 29, NICER DAS version 2020-04-23_V007a, and the calibration database (CALDB) version v20210707. For the analysis, we selected good-time intervals with the nimaketime script using standard options.
After inspecting the resulting light curves in different bands, we identified increased flaring activity due to background contamination. To mitigate the effects of the background, we altered some of the standard filtering parameters. We opted for ISS not in the South Atlantic Anomaly region, a source elevation >15 • above the Earth limb (>30 • above the bright Earth), and a magnetic cut-off rigidity (COR_SAX) > 2. 0 GeV c - 1. Unfortunately, a large fraction of the obtained data -especially during the lower flux states -was not useful due to enhanced background activity during the monitoring period and was filtered out. Finally, for the timing analy-sis, we performed barycentric corrections to the event times of arrival using the barycorr tool and the JPL DE405 planetary ephemeris. Because NICER is not an imaging instrument, the X-ray background is calculated indirectly.
These residuals are often referred to as a 'soft-excess' with a physical origin that is attributed to one mechanism or a combination of mechanisms such as emission from the accretion disk, emission from the NS surface, hot plasma around the magnetosphere, or partial absorption from material around the NS  (Hickox et al. 2004). In this study we investigate the broadband spectrum for signatures of these mechanisms. NuSTAR data were obtained quasi-simultaneously (i. e. , less than a day apart) with two NICER visits (obsid: 4202430107 and 4202430108). An absorbed cut-off powerlaw model sufficiently describes the NuSTAR spectrum. However, fitting the combined NICER and NuSTAR data, we see significant structure in the residuals caused by the soft excess (see Fig. 1 ). To eliminate the residual structure, we need to add either a partially covering absorber or a soft thermal component to the model (see the lower panels in Fig.
Long-term light curves and outburst evolution Monitoring data in the soft X-ray band with NICER enables us to study the evolution of the 2021 outburst and to compare it with Chandra data from 2016. NICER monitoring data are of sufficient statistical quality to allow us to extract enough counts and perform spectral modelling. The typical Swift/XRT exposure within one day is about 1000-2000 s, but the effective area of the detector is significantly smaller than that of NICER. Thus we used XRT data to only estimate average count rates for each XRT data set. The 20 individual NICER spectra were fitted with an empirical absorbed power-law model in the 0. 5-8 keV band. We also attempted to fit the spectra with a power law with a cutoff, but the cut-off always converged to very high values (i. e.
We converted the XRT count rates into 0. 3-10. 0 keV fluxes using the average spectral parameters inferred from the NICER spectral fits. A conversion factor of 3. 16 × 10 37 erg cm -2 s -1 /(c/s) was used for all XRT data, while errors were estimated based on the count rate uncertainties. The broadband unabsorbed L X was estimated from the broadband spectra. Most of the energy is emitted above 10 keV because the ratio of the broadband (0. 3-80 keV) to narrow band (0. 3-10. 0 keV) luminosity was ∼3. 5. The 2021 X-ray light curve is shown in Fig. 3. In the same figure we overplot the 2016 XRT monitoring data, time-shifted so that the main peaks of each outburst match. Monitoring data can also be used to investigate whether the system has entered the super-critical regime, in which  (a)  → 0 → 0 → 0 → 0 <3 10 20 cm -2 TBpcf N H - - 53 +7 -6 47 +12 -10.
Temporal properties -pulse profiles To search for a periodic signal, we used the epochfolding Z-search method  (Buccheri et al. 1983 ) implemented through HENdrics command-line scripts and Stingray  (Huppenkothen et al. 2019). For NuSTAR data, we searched for a periodic signal in the 3-40. 0 keV range (35-960 PI channel). Our final estimate of the spin period and its uncertainties was based on the time of arrival (ToA) method (e. g. ,  Tsygankov et al. 2020). We first used HENdrics to derive a most probable period, then we estimated ToAs of individual pulses for 16 intervals, and we finally used PINT 3  (Luo et al. 2021 ). From the above we derived a period of 15. 6395 ± 0. 0004 s for the 2021 NuSTAR data. The reported period for the 2016 data was 15. 6398 ± 0. 0009 s and is consistent within the uncertainties with the newly derived period.
For consistency, we implemented the ToA procedure to estimate a period for the 2016 Chandra data. Results of the ToA method are presented in Table  2. This yielded a period of 15. 6396 ± 0. 0014 s. All tests indicated that the period of the NS has remained unchanged within uncertainties for more than five years. The strength of the periodic modulation is typically quantified through the root-mean-squared (rms) pulsed fraction (PF). This is given by PF rms = N j=1 (R j -R) 2 /N 1/2 R , (1) 3 https://github. com/nanograv/pint/ where N is the number of phase bins, R j is the backgroundsubtracted count rate in the jth phase bin, and R is the average count rate in all bins (e. g. ,  Wilson-Hodge et al. 2018). We used this definition to estimate the PF rms in different NuSTAR energy bands. We found no significant change in the PF within different energy bands up to 20 keV.
Spin evolution To investigate the spin evolution during the outburst, we implemented the same method on the NICER data between 0. 5 and 8. 0 keV. We first computed the most probable period by epoch folding, and then we refined the period and its uncertainty based on the ToA of individual pulses. Given the shape of the pulse profile, the template used for ToAs can drastically vary from one observation to the next. Thus, we used one universal template for all observations to characterise the off-phase of the pulse. The method was successful when two conditions occurred. Firstly, we need more than three NICER snapshots to be performed within one day, and secondly, the total number of counts must be high enough to obtain meaningful pulse profiles. For all other snapshot phases, it was challenging to connect the ToAs and even impossible at times due to multiple peaks A194, page 5 of 12  in the periodogram with similar intensity.
To model our data set, the parameter space must be properly modelled in order to identify degeneracies between the model parameters. Thus, to fit the model to the data, we implemented a nested sampling algorithm for a Bayesian parameter estimation and estimated posterior distributions for the parameters of the standard accretion A194, page 6 of 12 torque models and binary orbital parameters. In terms of statistical treatment, similar methods have been used to model radial velocity curves from binary systems  (Fulton et al. 2018). To derive the posterior probability distributions and the Bayesian evidence, we used the nested sampling MC algorithm MLFriends  (Skilling et al. 2004; Buchner 2019 ) that employs the ultranest 4  package  (Buchner 2021 ). An outline of this method with applications to accreting pulsars will be presented by  Karaferias et al.
(2022). Because of the gaps in the NICER monitoring and the high background, good-timing data exist only for the first 20 days of the monitoring. In an effort to improve our data set, we also searched for pulsations in Swift/XRT data. However, because of the low statistics and small number of XRT snapshots within a day, typical period uncertainties were about 0. 01-0. 05 s (see Fig. 7 ). They therefore offer little information for our study. To constrain our model parameters, we fixed the orbital period to 36. 411 days and limited the magnetic field strength (i. e. , log (B[G]) ∈ [10, 13]). With these assumptions and for typical parameters of the NS (12 km and 1. 4 M ), we estimated the posterior distribution for our other model parameters. These parameters are listed in Table  3 , while in Fig.
Finally, we did not measure any secular evolution of the spin period of the pulsar between 2016 Chandra and 2021 NuSTAR observations, which is consistent with the findings of  Coe et al. (2022). Nevertheless, there is evident modulation in the NICER monitoring data that is consistent with orbital motion of the binary. Modelling of the orbital motion and intrinsic spin-up during the outburst enabled us to constrain the magnetic field strength and the orbital parameters of the system. All the derived quantitative and qualitative results consistently provide indirect constraints on the NS magnetic field strength. Because a cyclotron line is lacking, the most reliable measurement comes from the intrinsic spin up, based on which we find a value of 5 × 10 11 G with an uncertainty of about a factor of ∼3.
Acknowledgements. The authors would like to thank the anonymous referee for the constructive report that helped to greatly improve the manuscript. GV would like to thank  P. S. Ray  for his comments, suggestions and advice on using PINT. This work was supported by  NASA  through the  NICER mission  and the  Astrophysics Explorers Program. Facilities:  NICER, NuSTAR. We acknowledge the use of public data from the Swift data archive.