dataesr/scientific-paragraphs-categorization huggingface dataset:
This paper proposes a dynamic model of a solar-based micro-cogeneration system called photovoltaic-thermal (PVT) collector to perform a design optimization of the multi-stage PVT system. The parametric study reveals the most important design parameters influencing the water-based flat-plate PVT system performance. The analysis also shows an existing trade-off between thermal and electrical efficiencies during the PVT operation. A novel exergy-based multi-objective design optimization method is demonstrated to find a trade-off design solution of the multi-stage PVT collector which compromises between the electrical and thermal exergy efficiencies under different weather conditions. The electrical and thermal exergy efficiencies are defined to be the objective functions of the Matlab gamultiobj-function, which is a multi-objective evolutionary algorithm using nondominated sorting genetic algorithm-II (NSGA-II).
Within the given framework, there is a lack of studies considering the multi-objective optimization of the number of collectors in a PVT series based on the genetic algorithm and exergy approach. Additionally, there is a research question if the optimal number of the PVT collectors varies according to the weather conditions. This paper proposes a dynamic model of a PVT collector with a crystalline silicon roll-bond collector in Matlab/Simulink. The model is extended to describe a multi-stage PVT collector. Energy and exergy analysis is conducted with the simulation model, and the most influencing design variables are identified and evaluated with a parametric study. Due to fact that the optimal design of the multi-stage PVT system cannot be defined with the simulations alone, the multi-objective optimization of the electrical and thermal exergy efficiencies with the non-dominated sorting genetic algorithm (NSGA-II) is conducted and the Pareto optimal set is derived in order to show the set of the optimal solutions taking into account the trade-off between exergy efficiencies.
They generated 3D-models of each system by SolidWorks software to conduct the study. The results indicated 19% higher daily thermal power of PVT-ST compared to a PVT system but the electrical power was equal. The exergy analysis is an effective way to investigate the true potential of an energy system. In terms of PVT application, Sarhaddi  [26]  conducted an exergy analysis of a PVT connected solar still system. The results of the study showed that two PVT collectors in series improved the most the overall exergy efficiency of the PVT-solar still system.
The electrical efficiency η pv(T) depends on the photovoltaic layer temperature T pv. Due to this, the electrical efficiency of the PV depends linearly on the temperature T pv , the temperature coefficient β pv and on the efficiency at standard conditions T ref. The efficiency is calculated according to the following relation: 𝜂 pv(T ) = 𝜂 STC × [ 1 -𝛽 pv × ( T pv -T re f )] (3) Additionally, the photovoltaic layer temperature T pv depends on the following parameters based on the geometry of the collector  [8, 10] : A pv-a = A ( 1 - D o W ) (4) h pv-t ,CD × A pv-t = H pv × L W ×k pv 2 + H adh k adh × H pv D o (5) m pv = 𝜌 pv × H pv × A × r c ( 6 ) The absorber layer (a): m a × c a × d T a dt = h a-pv,CD × A a × ( T pv -T a ) + h a-t × A a-t × (T t -T a ) + h a-i,CD × A a-i × (T i -T a ) (7) The tube bonding (t): m t × c t × d T t dt = h t -pv,CD × A pv-t × ( T pv -T t ) + h t -a × A a-t × (T a -T t ) + h t -i,CD × A t -i × (T i -T t ) + h t -f × A t -f × ( T f -T t ) (8) The insulation layer (i): m i × c i × d T i dt = h a-i,CD × A a-i × (T a -T i ) + h t -i,CD × A t -i × (T t -T i ) + h i-e,CD+CV × A × (T e -T i ) (9) The coolant fluid (f): m f × c f × d T f dt = h t -f × A t -f × ( T a -T f ) + ṁ × c f × ( T f ,in -T f ,out ) (10) Equations (  1 )-(  7 ) are implemented into Matlab/Simulink with details to assess the energy and exergy performance of the PVT collector.
Multi-objective optimization using gamultiobj-function In this paper, the Matlab function called gamultiobj of Global Optimization Toolbox is used to run the multi-objective optimization of the multi-stage PVT collector. The gamultiobjfunction creates a set of optimal solutions in the space of decision variables on the Pareto front. The function uses a controlled, elitist genetic algorithm, which is a variant of NSGA-II  [30]. Generally, the multi-objective optimization problem of two or more trade-off objectives is described mathematically as follows  [5] : In this formulation, the objective functions are minimized or maximized subject to certain constraints. Minimized∕Maximized f m (x) , m = 1, 2, … , M ; Subject to g j (x) ≥ 0, j = 1, 2, … , J ; h k (x) = 0, k = 1, 2, … , K ; x i (L) ≤ x ≤ x i (U ) , i = 1, 2, … , n; In this work, the overall electrical and thermal exergy efficiencies are two objectives to be optimized.
RESULTS AND DISCUSSION The simulation-based multi-objective optimization process was conducted with the PVT model in Matlab/Simulink. The main objective of the optimization was to find an optimal number of PVT collectors in series to satisfy the operational purpose of the multi-stage PVT collector. First, the decision variables were varied, and the initial population and score matrixes for the genetic algorithm were generated by the simulation model. These matrices were given for the gamultiobj-function of Global Optimiza- tion Toolbox, which started the iterative process by evaluating the fitness functions with the simulation model and making selection, crossover and mutation of the population. Based on this process, a new population or generation was created. The new generations were created as long as a stopping criterion was met.
The design of the multistage PVT collector was optimized in terms of the thermal and electrical exergy efficiencies as objective functions in order to maximize the quality of the produced energy. The optimization was performed by using gamultiobj-function of Matlab Global Optimization Toolbox. The function is based on NSGA-II algorithm. The set of optimal solutions called Pareto front was obtained with the generation number of 100 and the population size of 50. Figure  10  presents the obtained Pareto front of the optimal solutions under the medium weather conditions of 800 W/m 2 , 25 • C and 1 m/s. The decision variable values are shown for three selected solutions. The values of each solution are presented in Table  3. The values of the decision variables show the same behaviour as in the parametric study.
CONCLUSION In this paper, solar cogeneration based hybrid energy system was modelled into Matlab/Simulink environment. The parametric study was conducted and the optimal sizing problem was solved using NSGA-II algorithm. The ideal point method was used in the decision-making process to select the final optimal solution from the Pareto front for three different weather conditions. The dynamic model of the water-based multi-stage PVT system was optimized using gamultiobj-function of Matlab Global Optimization Toolbox which is based on the fast elitist multiobjective optimization algorithm NSGA-II. The electrical and thermal exergy efficiencies were used as objective functions because of their trade-off nature in the PVT operation which leads to difficulties for decision-makers to select the electrical and thermal efficiencies and the design variables.
Observations of RCW 120 We have used the upGREAT (German Receiver for Astronomy at Terahertz) instrument on the Stratospheric Observatory for Infrared Astronomy (SOFIA) to survey RCW 120 in the [CII] 1. 9-THz fine-structure line of ionized carbon. These observations are part of the SOFIA FEEDBACK legacy program dedicated to study the interaction of massive stars with their environment  (30). We have also used the Large APEX sub-Millimetre Array (LAsMA) instrument on the Atacama Pathfinder Experiment (APEX) to observe the J = 3-2 isotopologues of CO (see Materials and Methods). The strongest [CII] emission toward RCW 120 is found in a limb-brightened ring surrounding the HII region, with lower integrated intensities toward the HII region interior (see Fig. 1 ). The  [CII]  emission is morphologically similar to the 8-m emission, which is a reliable tracer of the photodissociation region (PDR), the interface between the fully ionized HII region, and the surrounding neutral and molecular gas.
Here, we emphasize that the CO data do not probe the expansion of the HII region but rather indicate the presence of two distinct clouds (see the Supplementary Materials). The observed morphology and velocity signature of the [CII] emission, however, reveal a relatively homogeneously expanding shell in RCW 120, making it unlikely that the shell structure itself was directly produced by the collision of two molecular clouds. This is very different from the study of the Infrared Dark Cloud G035. 39-00. 33, whose emission is interpreted in terms of colliding clouds  (45). More work is required to fully understand the formation mechanism of RCW 120 in the context of the rapidly expanding shell seen in  [CII]. The short lifetime of the bubble suggests that magnetic fields do not play a large role in its evolution.
As the environment then was metal-poor, the stars are expected to have been massive and therefore would have produced strong feedback effects. Our observations suggest that rapid triggered star formation around these massive stars may have played an important role, possibly contributing to the large star formation densities observed in high-redshift galaxies  (51). A broader survey of massive star formation regions in [CII] emission, as is currently undertaken in the SOFIA FEEDBACK legacy program  (30) , is required to understand the role of massive stars in the collection, compression, fragmentation, and collapse of molecular clouds.
During the calibration process, we produce an additional data product from the emission-free background source (OFF-source) measurements by subtracting subsequent OFF positions from each other. These "OFF-OFF" spectra are calibrated the same way as the "ON-OFF" spectra containing the astronomical information. The created set of spectra contains the dynamics of the telescope system and the atmosphere but no emission from the astronomical source. Subsequently, we identify features that account for most of the variance from the mean over the OFF-OFF spectra using principal component analysis (PCA). We do this individually for each of the 14 heterodyne pixels of the upGREAT low-frequency array (LFA) receiver and for each SOFIA flight where RCW 120 was observed. The PCA produces a set of components that describe the variance in the spectral structure over the course of the observations from which the baseline artifacts originate.
In order to identify the clumps, we apply the clump finding algorithm GAUSSCLUMPS (76) to the 13 CO(3-2) data cube. The algorithm decomposes the map into a number of clumps by assuming a Gaussian emission distribution in space and velocity for each clump  (77, 78). The clump properties were then calculated according to the conversion formulas described above. We extracted ~100 clumps above the spatial and kinematic resolution limits with masses between 2 and 530 M☉, and radii up to 1. 1 pc. Some of the most massive clumps correspond to the clumps detected in continuum (17), although we do not expect a one-to-one correlation since we included kinematic information in our clump finding algorithm. The average clump density is typically a few 10 4 cm -3. Most of the clumps are pressure confined or transient which can be seen in Fig.
Moreover, Perseverance carries a second microphone as part of the Entry, Descent, and Landing Camera (EDLCAM  15  ), which has a frequency response from 20 Hz to 20 kHz at a sampling rate of 48 kHz. The EDL microphone is mounted on the port side of the rover, 1 m above the ground. It was activated on Sol 2. Figure  1  provides an overview of sounds acquired by SuperCam's microphone (see Methods). Sol 38a is the quietest recording in our dataset. Later on that same day (Sol 38b), the power spectral density (PSD) increases above the quiet state at frequencies below 100 Hz. On Sol 117, we associate this increase of power to an increase in the turbulent activity, which extends up to 300 Hz; this is the situation we observe most often. The recording of Sol 148 is the most active one shown, with the same shape starting towards higher frequencies but with a slope break near 200 Hz; turbulence is detected up to 600 Hz.
As a result, at 0. 6 kPa, the relaxation frequency is about 240 Hz on Mars. The recording of pulsed waves generated in LIBS mode provides a unique opportunity to measure directly and repetitively the local speed of sound for acoustic waves above 2 kHz, that is, for f > f R (see Methods). From the daytime measurements, sound speeds between 246 m s -1 and 257 m s -1 are obtained (Fig. 3a ), with maximum values between 11:00 to 14:00 Local True Solar Time (LTST) and minimum values around 18:00. The 1σ-dispersion of the sound speed during the approximately 20 min of a target analysis with LIBS is at its maximum at noon (1. 5%) and is reduced to 0. 5% at 18:00, which highlights the vanishing of the atmospheric turbulence at dusk. These measurements are compared with temperature-derived speeds of sound obtained from: (1) the MEDA temperature datasets at the surface, at the heights of 0.
85 m and 1. 45 m, and (2) the temperature at the surface and at a height of 2 m given by the Mars Climate Database (MCD 32 ) (see Methods), using γ ∞ = 1. 4 (because f > f R ). The agreement between the MEDA and MCD predictions is excellent. SuperCam sound speeds are comparable with temperature-derived values at the height of the MEDA's 0. 85-m temperature sensor or higher. This is consistent with the fact that the speed is integrated between a height of 2. 1 m and the surface, possibly biased towards the surface when the temperature gradient is larger. Ingenuity's blade passage frequency (BPF)  33  is close to a harmonic source centred around 84 Hz, and -in that case -for f < f R (see Methods). This signal recorded by SuperCam's microphone is modulated by the variations of the distance range between the microphone and the helicopter.
Methods This section starts with a reminder on acoustics adapted to Mars. It provides details of the different datasets: acoustic data from the microphones of SuperCam and EDL; artificial sounds from the Mars Oxygen In-Situ Resource Utilization Experiment (MOXIE) and Ingenuity, in addition to the recording of LIBS shock waves and rover noises; wind speed, temperature and pressure data from the MEDA and temperatures extracted from the MCD. Processing methods are presented: computation of PSDs; analyses of LIBS shock wave time series; extraction of the Doppler effect from Ingenuity. Finally, supporting explanations on the attenuation with distance are given.
Acoustic dataset The SuperCam microphone dataset used in this study extends from Sol 1 to Sol 216, when the first solar conjunction of the mission occurred. At this date, a total of 4 h and 40 min of Martian sounds have been recorded, including atmospheric turbulence (46% of the total duration), the accompanying pressure waves of LIBS sparks (12%) and mechanical noises (for example, MOXIE  43  , Ingenuity  33  , mast rotation of Perseverance, Mastcam-Z mechanisms, 42%). In the same period, the EDL  15  microphone has recorded a total of 56 min of Martian sounds, mainly during rover operations (for example, rover drive, arm motion). Extended Data Table  1  lists all acoustic files used or mentioned in this study, except for those shown in Figs. 3a, 4a , which are too numerous to cite individually (see below).
SuperCam's microphone records air pressure fluctuations from 20 Hz to 12. 5 kHz at a 25-kHz sampling frequency, and up to 50 kHz when the 100-kHz sampling mode is used. The analogue signal from the microphone, ranging from 0 to 5 V, is digitized (12-bit depth) using one of four electronic gains to boost the sensitivity from 0. 6 to 21 V Pa -1 and the resolution from 2 to 0. 06 mPa. Gain 0 is used to record the accompanying sounds from LIBS sparks on calibration targets, gains 1, 2 and 3 to record the accompanying sounds from LIBS sparks at various distances and gain 3 is used for atmospheric recordings. Recordings of the atmosphere and mechanical noises are generally 167 s long. The EDL microphone can record 10 mn and longer time series, with a fixed gain amplifier followed by a 24-bit/44-kHz digitizer (the key characteristics are summarized in Extended Data Table  2 ).
Dataset of artificial sounds Both EDL and SuperCam microphones are also used to record sounds produced by the rover. They help to inform operators about equipment health (for example, rover driving, MOXIE) and provide sources of sound that are well localized in space and time (for example, during Ingenuity flights or LIBS sparks). All recordings also pick up some perturbations, such as intense single-frequency emissions at 195 Hz, 198. 75 Hz and the following harmonics at 780 Hz and 795 Hz that result from the rover's internal heat pump used for thermal management. Sounds of the rover's instruments or pumps propagate through both structural vibrations (microphonics) and acoustic propagation in the atmosphere. The EDL microphone  15  was used to record the rover drive on Sol 16 (Extended Data Fig.
Distinct transitions in Extended Data Fig. 1c , recorded during a night-time run (Sol 81), correspond to commanded changes in motor speed from 50 to 58. 3 Hz. The loudest harmonics are near 500 Hz, at which several more frequencies are also excited. This range corresponds to resonant frequencies of the MOXIE instrument, as observed during dynamics testing. Even recorded during one of the quietest times of the day, the amplitude of the signal only reaches 1. 5 mPa. Recording LIBS sparks was the main rationale to develop SuperCam's microphone to infer physical properties of rock targets, such as their hardness  13. Typical LIBS sequences consist of 30 laser shots at the same position per observation, fired at 3 Hz (Extended Data Fig. 1d ). LIBS operations are monitored by the microphone at a 100-kHz sampling rate for 60 ms around each laser pulse.
Wind speed, temperature and pressure data The wind, temperature and pressure data are recorded from the MEDA  21  instrument. The wind data are acquired up to 2 Hz, pressure at 1 Hz and temperature at 1 or 2 Hz. Wind speed and direction are independently acquired from two individual booms separated by 120° (termed boom 1 and boom 2), for which one is preferred for a given wind direction (see accuracies and resolutions in Extended Data Table  2 ). The MCD  32  provides climate predictions derived from 3D simulations of Mars atmosphere performed with the Mars global climate model developed at the Laboratoire de Météorologie Dynamique (http:// www-mars. lmd. jussieu. fr). The Laboratoire de Météorologie Dynamique Mars global climate model is described in ref. 32  but -since then -it has adopted more sophisticated and realistic modelling for the CO 2 , dust and water cycles, photochemistry, radiative transfer and so on.
Doppler effect (Ingenuity recording) The fourth (Sol 69), fifth (Sol 76), sixth (Sol 91) and eighth (Sol 120) flights of Ingenuity were recorded by SuperCam at a 25-kHz sampling rate. We use data from the fourth flight, as this flight came closer to Perseverance than any other flight SuperCam could record. During this flight, Ingenuity climbed to an altitude of 5 m, accelerated to 3. 5 m s -1 , travelled 130 m at constant height, decelerated, turned around and returned to its base by the same route. Taking off at a distance of 76 m from Perseverance, it came as close as 69 m and moved as far as 123 m from Perseverance (Extended Data Fig. 3b, bottom ). On the PSD obtained during the whole recording, the BPF (two times the rotation rate for a two-bladed rotor) at 84 Hz and its first harmonic at 168 Hz are clearly visible above the background, which itself is higher than that of Sol 38a, a very quiet recording on Mars (Extended Data Fig.
Acknowledgements Many people helped with this project in addition to the co-authors, including hardware and operation teams, and we are most grateful for their support. This project was supported in the USA by  NASA 's  Mars Exploration Program  and in France is conducted under the authority of CNES. Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). The work of  A. Munguira  is supported by grant  PID2019-109467GB-I00  funded by  MCIN/AEI/10. 13039/501100011033.
Acknowledgments G. F. thanks  B. Basso  and  V. Kazakov  for numerous fruitful discussions, and  D. Serban  and  M. Staudacher  for their many useful comments on an early version of the manuscript. The work of S. D. is supported by the  Russian Science Foundation  project No  19-11-00131. Research at the  Perimeter Institute  is supported in part by the  Government of Canada  through  NSERC  and by the  Province of Ontario through MRI. This work was additionally supported by a grant from the  Simons Foundation  (Simons Collaboration on the Nonperturbative Bootstrap).
The results of the analysis are also interpreted in the context of beyond-the-SM (BSM) scenarios incorporating pair-produced scalar third-generation down-type leptoquarks LQ   3  [34] [35] [36] [37] [38] [39] [40] [41]  decaying to bottom quarks and neutrinos or top quarks and -leptons (Figure  1(c) ). These models are all characterised by events consisting of jets containing -hadrons (referred to as -jets), missing transverse momentum ( miss T ), and no charged leptons. Previous searches by ATLAS  [42] [43] [44] [45]  and CMS  [46, 47]  using comparable or smaller datasets have targeted similar final states. This analysis extends the regions of parameter space probed by the LHC through the use of a larger dataset than in previous ATLAS searches, new boosted decision tree (BDT) discriminants, and also new selections maximising the efficiency for reconstructing -jets with low transverse momentum generated by, for instance, SUSY models with small mass-splitting between b1 and χ0 1.
Section 2 presents a brief overview of the ATLAS detector, Section 3 describes the data and simulation samples used in the analysis and Section 4 presents the methods used to reconstruct events. An overview of the analysis strategy, including background estimation, is presented in Section 5. The systematic uncertainties considered in the analysis are described in Section 6. Section 7 presents the results and interpretation thereof. The conclusions of the analysis are presented in Section 8. Figure  1 : Diagrams illustrating the processes targeted by this analysis: (a) bottom squark pair production, (b) production of DM particles (indicated with ) through the decay of a scalar or pseudoscalar mediator coupling to bottom quarks, and (c) pair production of scalar third-generation down-type leptoquarks decaying to bottom quarks and neutrinos or top quarks and -leptons.
SRC targets signals with Δ( b1 , χ0 1 ) < 50 GeV, and exploits the information from the TC-LVT algorithm about the presence of vertices associated with low- T -hadrons produced by the bottom squark decays. When deriving mass exclusion limits on bottom squarks or leptoquarks, SRA and SRB are statistically combined, and the analysis yielding the better of the expected CL S values  [118]  from the combined SRA/SRB and SRC is used for each signal point. Finally, SRD is optimised to target the dark matter models with scalar or pseudoscalar mediators by making use of a BDT. For all signal regions, the SM background estimation is performed with a likelihood fit  [119]  where the normalisation factors of the MC datasets corresponding to the SM processes expected to contribute the most to the event yields in the SRs ( + jets for all signal regions, + jets and  t for SRC) are left free to float.