dataesr/scientific-paragraphs-categorization huggingface dataset:
SUPERHEAT CONTROLLER DESIGN Superheat is defined as the difference between the working fluid temperature and the saturation temperature (the boiling point) for a given pressure. In summary, the superheat variable is defined as: SH = T f luid -T sat (pressure) (1) T sat (pressure) is given by known maps which can be found in thermophysical property databases such as REFPROP  (Lemmon et al. , 2002). We are referring here to the superheat at evaporator outlet or at turbine inlet (which can be considered the same, neglecting the small pipe length between the two). A positive superheat ensures that the working fluid (cyclopentane in this case) is in vapor phase, which must be maintained for the turbine to function properly. Moreover, the smaller the superheat, the more energy will be recovered by the evaporator.
The sequence is generated following the methodology described in  (Braun et al. , 2001)  with the GALOIS 2  software. The different signals obtained are depicted in Fig. 2. The structure of the LPV system to be identified is chosen as follows: ẋ = A(ρ)x + Bu y ≜ SH(model) = Cx + y 0 (2) with, A(ρ) = ρ 1 1 ρ 2 0 , B = 0 b 0 , and C = [ 1 0 ] (3) where u is the pump speed N pump , b 0 and y 0 are constants and the varying parameters ρ 1 and ρ 2 are multivariate polynomials with a maximum degree of 3 with the structure shown below: Note also that the state vector x has no physical meaning, except for x 1 , which represents the biased superheat. ρ i = a i,0 + a i,1 P evap + a i,2 P 2 evap + a i,3 P 3 evap + a i,4 N eng + a i,5 N 2 eng + a i,6 N 3 eng + a i,7 Γ eng + a i,8 Γ 2 eng + a i,9 Γ 3 eng + a i,10 N eng Γ eng + a i,11 N eng Γ 2 eng + a i, To estimate the 42 parameters of the LPV model, we use a Levenberg-Marquardt algorithm  (Gavin, 2019)  where the derivatives with respect to the parameters are calculated directly with the state equations.
• Acquisition of deep seismic reflection and refraction data for a number of TOPO-EUROPE's natural laboratories. • Transfer of insights obtained on the coupling of deep Earth and surface processes to the domain of geothermal energy exploration. TOPO-EUROPE has been extraordinarily successful in capacity building on a pan-European scale and inspiring large scale collaborative research in Europe. Several International Training Networks (ITN) funded by the European Commission for training and networking young researchers followed. The ITNs SUBITOP and TOPOMOD, for instance, have funded more than 20 young researchers together. On a national level, programs such as the TOPO-IBERIA  (Gallart et al. , 2015; Garate et al. , 2015; Cloetingh et al. , 2011)  program funded by the national research council of Spain (CSIC) with an amount of 8 M€ to unravel the structure and evolution of the Iberian topography, with an average elevation higher than that of Switzerland.
, 2014) , (iii) atmospheric, ocean and climate change models  (Donnadieu et al. , 2006; Donnadieu et al. , 2009)  and (iv) spatiallyexplicit models of species speciation, evolution and extinction  (Gotelli et al. , 2009; Leprieur et al. , 2016; Pellissier et al. , 2017; Descombes et al. , 2018; Hagen et al. , 2021b). The resulting hybrid biogeodynamical numerical modelling tools will be used to explore systematically various subduction and plate tectonics scenarios and understand their potential effects for the evolution of the environment, landscape, climate and the diversification of life. To this aim, TOPO-EUROPE will encourage geodynamicists, geologists, (geo)biologists, ecologists, geochemists, palaeontologists, geomorphologists, and climate experts to cooperate and integrate/interpret biological data with geodynamic, landscape evolution, carbon cycle and climate (sensu lato) modelling.
Geochemical analysis of samples collected by  Nomikou et al. (2012)  on the northeast cones showed evidence of low temperature hydrothermal circulation on the summit and upper flanks in the form of stream-like manganese precipitates emanating from pits and fractures. These case studies in Italy and Greece show the importance of characterising the state of stresses and the presence of fluids in tectonically and volcanically active provinces. Understanding the state of magma and surrounding crust is vital to understanding active volcanoes and improving our risk mitigation strategies and preparedness to the many hazards they present. In the past decades, we have deployed a breadth of sophisticated geophysical and geochemical monitoring equipment which have provided us with rich multi-parametric datasets to examine the magmatic pulse of the Earth and resolve volcanic unrest.
The simple reason underlying this data surge is that we know WHERE volcanic activity takes place. Despite this convenience, we lack an understanding of and WHEN and HOW volcanic activity will occur. We need to improve our ability to forecast when an eruption begins and ends and how the eruption will proceed. In fact, one of our most important lacking's is our inability to detect and image magma in the crust using current datasets and existing modelling tools; this inability has led, for instance, to the serendipitous drilling of a magma body at 2. 1 km below the Krafla volcano, Iceland, during IDDP-1 (see 4. 3. Energy), despite being one of the most thoroughly monitored volcanoes worldwide, where data seemingly suggested magma at ~5 km depth. The aforementioned shortcomings (where, when, how) point to two important needs for the volcanological community; we need to 1) ground-truth our observations, and 2) develop robust, quantitative, predictive tools that comprehensively integrate magma generation, transport and eruptions in the Earth system.
Acknowledgements The  ILP ,  Academia Europaea ,  Young Academy of Europe ,  European Science Foundation ,  Horizon Europe , the  Balzan Foundation , the  Alexander von Humboldt Foundation , and numerous national research councils are thanked for continuous support and major grants. Magdala Tesauro ,  Alexander Koptev , and  Pietro Sternai  thank the  ILP  for bestowing them with the  Flinn-Hart award  to young scientists. This work is part of a project that received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant agreement No. 864045 ). This work has also been supported by  Fondazione Cariplo and Fondazione CDP  (Grant n  • 2022-1546_001 ) and by the  Italian Ministry of Education, MUR (Project Dipartimenti di Eccellenza , TECLA,  Department of Earth and Environmental Sciences, University of Milano-Bicocca ).
Chemical analysis Mineral chemistry analysis was performed on petrographic thin sections with a Scanning Electron Microscope associated to Energy dispersive X-ray spectroscopy (SEM-EDS) JEOL JSM IT 300 LV equipped with an Oxford INCA Energy 200 ED. Microanalysis operating conditions were 15 kV and 5 nA, 10 5 CPS and 30 s counting time; relative wt. % errors are <1% for major elements and <5% for minor components. Standards comprise pure elements, simple oxides or simple silicate compositions. Before the analysis, samples were coated with a carbon layer. Statistical data treatment was performed with OriginPro® software. The full set chemical analysis of NC antigorite samples is reported in  (Petriglieri et al. , 2021).
Mineral identification Mineral identification was confirmed by micro-Raman spectroscopy. Raman spectra were obtained with a Horiba JobinYvon HR800 Raman spectrometer equipped with an Olympus BX41 confocal microscope, a 600-grooves/mm holographic grating monochromator and a high-gain Peltier-cooled CCD. A Nd solid state laser at 532-nm has been used as excitation and density filters were used to avoid sample heating. Spectra were obtained on 50 (elongated) particles with a 100x objectives. Under these working conditions, the minimum lateral resolution is around μm. The spectrometer calibration was carried out using the 520. 7 cm - Raman peak of silicon before each experimental session. Spectra were collected for 1 min (6 acquisitions for 10s) in the low and high wavenumber spectral range. Data treatment was performed with Lab-Spec® software.
These compositional differences are highlighted in the ternary diagrams (Fig. 1  and Fig. S1 ) and are a consequence of the different degree of supergene alteration that weathered the Atg minerals to different extent, namely f-Atg1 < f-Atg2 ≈ f-Atg3. Contamination with chrysotile was investigated by micro-Raman spectroscopy. Raman analysis revealed no (f-Atg1) or negligible (1 Ctl fiber out of 50 fibers of f-Atg2 and f-Atg3 analyzed) contamination (data not shown) and Atg samples could be reasonably considered as "pure phases". Mineralogical purity is an element of particular importance in toxicological studies on serpentine minerals. In fact, due to the typical closely intergrowth of Atg and Ctl minerals in metamorphic context  (Groppo and Compagnoni, 2007; Tarling et al. , 2018) , the preparation of a pure specimen is often quite challenging.
Bio-accessible iron Bio-accessible ions are easily accessible surface ions that can participate in biological reactions or be released in a biological environment when minerals enter in contact with endogenous chelators. The amount of ions mobilized by iron chelators was measured both at acidic and neutral pH as a proxy of iron bio-accessibility in lysosomal fluids and in extracellular environment. The amount of iron extracted at pH 4. 5 by ferrozine in presence of ascorbic acid is reported in Fig. 6  as a function of the incubation time. Nf-Atg, f-Atg2, and f-Atg3 contained an appreciable amount of bioaccessible iron, albeit to a smaller extent than chrysotile. No iron was extracted by ferrozine alone (data not shown). After 1 week of incubation, the amount of iron removed was Ctl > f-Atg2 ≃ nf-Atg > f-Atg3 ≫ f-Atg1.
A recent study comparing asbestiform (AR = 17) to non-asbestiform tremolite and crocidolite (AR = 5) showed that both amphiboles induce LDH leakage in AM, but the asbestiform samples are the most active in   macrophages (MH-S, full bars) by antigorite (120 μg/ ml), chrysotile (30 μg/ml), and glass fibers (MMVF, 120 μg/ml). Amount of (A) NO and (B) TNF-α generated after 24 h of incubation. The synthesis of nitric oxide was evaluated by an indirect colorimetric method based on measurement of the amount of nitrite, a stable derivative of NO. The levels of TNF-α were evaluated by ELISA assay. Measurements were performed in duplicate and data are presented as an average ± SEM (n = 3). Asterisks indicate means significantly different from the control (non-incubated cells):*p < 0. 05. dysregulating a number of genes responsible for acute inflammation  (Khaliullin et al.
Acknowledgements This study is a part of a multidisciplinary project " Amiante et Bonne Pratiques (ABP ) » supported by the  Center National de Recherche Technologique  ( CNRT-Nouméa, Nouvelle Calédonie ). JRP post-doc position is funded by the  University of Torino  ( CHI. 2019. 08/XXI "Development of innovative tools for the risk assessment of elongated mineral particles (EMP) in natural environment") and by the  INAIL -BRIC   2019  project (grant number  ID 57. 1 ).
Optical lightcurves Our optical lightcurve dataset for PN9 spans fourteen years, from March 2006 to November 2020. Each lightcurve is summarised in Table  1 , with indications of how each lightcurve was used in this work. Some low-quality lightcurves were not used for modelling or spin-state analysis. The subset of lightcurves used in modelling was used for both the lightcurve-only (Sect. 3. 1) and combined lightcurve and radar (Sect. 3. 2) models. As shown in Figure  1 , the asteroid was observed over a range of viewing geometries during the fourteen years of observation. This is important for shape modelling, as the entire surface of the asteroid cannot be seen with a single viewing geometry. Viewing the asteroid's rotation from different aspect angles, and under different shadowing conditions, can greatly improve constraints on its shape and rotational state.
Consideration was also taken to ensure a sufficient temporal resolution was achieved, i. e. each exposure was not a significant fraction of the asteroid's rotation period. All images were processed using standard CCD reduction procedures with bias subtraction and flat field correction, along with dark field correction where necessary. Our optical dataset includes lightcurves from ten different observatories. It should be noted that the choice of optical filter has a negligible impact on the shape and amplitude of relative lightcurves, hence we have included observations using a variety of broadband and clear filters. Information on the observations that were conducted at each observatory are given below. Observatory -2006 Observatory - , 2011  The 0. 7 m telescope at Chuhuiv Observatory (Kharkiv, Ukraine) was used to observe PN9 in March 2006 and March 2011.
Palmer Divide Station -2016 This analysis includes six published lightcurves from the Palmer Divide Station (California, USA). PN9 was observed with three 0. 35 m Meade LX200GPS telescopes equipped with commercial CCDs using the Johnson V filter. These lightcurves were obtained through the Asteroid Lightcurve Data Exchange Format (ALCDEF) database  (Warner et al. 2011)  and are discussed in  Warner (2016). Note that observations taken with different telescopes during the same night are treated as separate lightcurves.
Combined radar & lightcurve model Further modelling of PN9 was conducted using a combination of lightcurve and radar data using the shape software package  (Hudson 1993; Magri et al. 2007). As discussed in Section 3. 1, it is efficient to begin with well-constrained input parameters describing the asteroid's shape and spin-state. In this case, those estimates are taken from the convex inversion analysis. shows the model from the negative end of the Z, Y and X axes. The Z-axis is aligned with the rotational pole, which is the shortest axis of inertia. The X-axis is arbitrarily set such that it is viewed from the positive end for the plane-of-sky at 𝑇 0. Axis lengths are in arbitrary units, as lightcurve inversion does not produce scaled models. An input model was constructed for shape comprising a triaxial ellipsoid with principal axis rotation.
As shown in Figure  5 , a coarse period scan with radar data indicates a clear global minimum close to 2. 5 h. 23187 ) 2000 PN9 with radar data. shape was used to optimise an ellipsoid model for six different rotational poles in the celestial sphere to fit both continuous-wave and delay-Doppler data. The lowest 𝜒 2 across the six models was recorded for the period being used, with a higher temporal resolution close to 2. 5 h, 5 h and 7. 5 h. This is a coarse scan intended to demonstrate a global minimum close to 2. 5 h independently of lightcurve data. As a visual inspection of radar data shows a shape that is consistent with the convex hull model, it can be said that the lightcurve and radar datasets independently favour the same shape and period for PN9. Having confirmed this, lightcurve data were then progressively introduced in subsequent fitting runs to produce a combined radar   and 35 (Table  1 ).
The synthetic lightcurves were generated using the combined radar and lightcurve model for (23187) 2000 PN9, with a combination of the Lambertian and Lommel-Seelinger scattering models. For plots of all 35 lightcurves, see Figure  A8. and lightcurve model. The full subset of lightcurves, marked in Table  1 , were not all included until the final iterations of modelling. Each additional lightcurve causes a significant increase in computation time whilst yielding diminishing returns. It is therefore most efficient to gradually introduce the lightcurve dataset as the model improves. During the modelling process, various penalties were applied with shape to discourage certain features. The first penalty prevents excessive deviation of the centre of mass from the origin of the body-centric coordinate system.
For asteroids where global shape dominates both optical and radar The Z-axis is aligned with the rotational pole, which is the shortest axis of inertia. The X-axis is arbitrarily set such that it is viewed from the positive end for the plane-of-sky at 𝑇 0. Axis lengths are given in kilometres. It should be noted that the model for the antipode solution has a very similar shape, such that any discussion of this model's features will also apply to the antipode model. Notes. The maximum extents are measured along the three axes of a body-centric coordinate system. "DEEVE" stands for dynamically equivalent equal-volume ellipsoid. 𝐷 𝑒𝑞 is the diameter of a sphere that has a equal volume to the model. features (e. g. Zegmott et al. (2021) ), combining the data will better constrain the model. For asteroids like PN9, where surface properties are dominant, it can be difficult to reconcile the radar and optical data.
19 ± 0. 06 for S and Q type NEAs reported in  Virkki et al. (2022). The SC/OC ratio is often taken as an analogue for structural complexity near the surface. The SC component is determined by surface roughness at scales comparable to the sampled wavelength. For mirrorlike backscattering, the SC component would be zero. A surface that is very rough on scales comparable to the transmitted signal's wavelength will have a stronger SC component  (Ostro et al. 2002). For the Arecibo CW observations of PN9, this scale is 13 cm. Results from the OSIRIS-REx mission, however, have cast doubt upon this interpretation of SC/OC ratios. Circular polarisation ratios suggest that Bennu is relatively smooth above the cm-scale  (Nolan et al. 2013). However, the spacecraft data show that Bennu's surface is much rougher than this with larger scale boulders than expected  (Dellagiustina et al.
The straight dashed line represents a constant period model for reference. There are, however, a small number of clear and repeated turning points within our dataset that can be used for a phase offset analysis. Figure  11  shows the measured phase offsets for each epoch, where temporally clustered measurements are averaged. A total of 24 lightcurves were included in the YORP fitting process, which are indicated in 1. The excluded lightcurves produced unacceptably large phase uncertainties. The best-fit YORP strength for PN9 is 0. 2 ± 1. 6 × 10 -8 rad/day 2 , which is comparable in magnitude to the smallest confirmed YORP detections, and in line with expectations for a ∼2 km asteroid. Although this measurement is poorly constrained, and it is not possible to rule out constant-period rotation, it does place an upper limit on YORP acceleration.
ACKNOWLEDGEMENTS We thank all staff at the various observatories that contributed towards this work. LD, SCL, AR, BR, SLJ, TZ, CS, AF and SFG all acknowledge support from the  UK Science and Technology Facilities Council. YNK was partially funded by  ALLEA  through  Funding Line 1  of the  European Fund for Displaced Scientists (EFDS). IB thanks the  PAUSE program  for scientists in danger for its support. Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). This work was based in part on observations collected at the  European Organisation for Astronomical Research  in the Southern Hemisphere under  ESO programmes   185. C-1033 (D, C) and  106.
C-0794 (A). It was also based in part on service observations made with the  Isaac Newton Telescope  operated on the island of La Palma by the  Isaac Newton Group of Telescopes  in the  Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofísica de Canarias  under programme  I/2020B/05. Observations at the Danish 1. 54m telescope at the  ESO La Silla observatory  were performed as part of the MiN-STEp project, and supported by the  Danish Natural Science Research Council (FNU). This research has been funded in part by the  Aerospace Committee of the Ministry of Digital Development, Innovations and Aerospace Industry of the Republic of Kazakhstan  (Grant No. BR 11265408 ). This work uses data obtained from the Asteroid Lightcurve Data Exchange Format (ALCDEF) database, which is supported by funding from  NASA  grant  80NSSC18K0851.
DATA AVAILABILITY The PDS lightcurves of 2000 PN9 first published in  Warner (2016)  are available on the Asteroid Lightcurve Data Exchange Format (AL-CDEF) database  (Warner et al. 2011 ) at https://alcdef. org/. Lightcurves that have not been previously published will be made available at https://vizier. u-strasbg. fr. The shape models presented in this work will be submitted to the Database of Asteroid Models from Inversion Techniques (DAMIT) at https://.
OFigure 10. 10 Figure 10. A comparison between delay-Doppler observations and the combined radar and lightcurve model for (23187) 2000 PN9, showing the first and last frame of each included dataset. Each three-panel image comprises the observational data (first panel), a synthetic echo (second panel) and a plane-of-sky projection of the model (third panel). On the first two panels, delay increases towards the bottom of the vertical axis and Doppler frequency increases along the horizontal axis. The plane-of-sky projections (third panel) are displayed with the celestial north at the top and east to the left, in an equatorial coordinate system. The rotation axis, which is closely aligned with the z-axis in the body-centric coordinate system, is marked with a purple arrow. The axes of minimum and intermediate inertia are indicated by red and green rods respectively.
Our method relies on a semi-automated robotic platform (CellCelector ™ ), which combines an epifluorescence microscope setup associated with a dedicated software allowing automated single cell identification and clonal follow-up, and a robotic arm to collect identified clones. We first showed that this semi-automated method is reliable to isolate single cell-derived clones without mixing cells during cell culture using dedicated Cellcelector nanowell culture plates. Then, we demonstrated how this method could be applied for a CRISPR/Cas9 gene editing experiment in hPSCs in order to model a genetic disorder: the Alström Syndrome (AS). This novel method is robust, easier and faster than standard dilution cloning and may be up scaled to generate various gene edition experiments at the same time.
SpCas9 purified protein is a gift from Jean-Paul Concordet (MNHN-CNRS UMR 7196/INSERM U1154). After enzymatic dissociation with Stempro Accutase (Life technologies), 150,000 hiPSCs in 7. 5 µL of resuspension buffer (Neon Transfection Kit, Invitrogen) were mixed with 7. 5 µL of RNP complexes. The mix was then electroporated at 1200 V/20 ms/2 pulses using 10 µL Neon Tips. For limiting dilution cloning experiments, the cell suspension was plated in several 100-mm culture dishes at different concentrations (1000/2000/5000/10,000 cells/dish). For semi-automated cell picking, cells were seeded in 6-well plates or directly seeded following electroporation in a vitronectin-coated nanowell plate at a density of 3000 cells per well. For the base editing strategy, different pathogenic nonsense mutations were identified within ALMS1 using the ClinVar database (NCBI).
Sequence analysis Genomic DNA was extracted with QuickExtract DNA extraction solution (Lucigen) according to the manufacturer's instructions. PCR amplification corresponding to the edited DNA sequence (p. Glu192fs: forward primer: TAT ACA CTG GCA GCG GCG; reverse primer: TCT GTA GAG ACC TAC TCA GAGGG; p. Gln3613Ter: forward primer: CCC GTA CAC CTA CCA AGT GA; reverse primer: CAT CAT CGA GCT GGG AGG TC) was subjected to Sanger sequencing (Genewiz, Germany). To evaluate edition rates, decomposition of quantitative sequence trace data was based on TIDE  [4]. Sequence analysis was performed using Snapgene software (Insightful Science).