dataesr/scientific-paragraphs-categorization huggingface dataset:
Preliminary experiments with tap water and air in a pipe of 90 mm diameter were conducted at the Liquid Gas Flow Research Facility of Tom Dyakowski Process Tomography Laboratory (Lodz University of Technology, Poland). The swirl element has been placed 1 m downstream the air and water injection point. Gas pressure and liquid flow rate were varied. Exemplary results for stable gas core are shown in Fig. 8  (pressure between 0. 5 and 2. 0 bar and liquid flow rates between 10 m 3 h -1 and 25 m 3 h -1 ). As image reconstruc tion is slow, current work aims at accelerating this by fast linear reconstruction schemes and applying a direct raw data analysis. Recently, the increasing computational capabilities of com puters to process massive data at high rate has made it pos sible to use tomographic sensors to quantify physical prop erties of a process in an industrial environment in real time.
Tomographic sensors, like wire mesh sensors and ERT sen sors, are generating large data sets. Two main parallel pro cessing architectures are being used for concurrent compu tation. One of them is the field programmable logic array (FPGA) and the other is the graphic processing unit (GPU). Both differ in how an algorithm is being executed. The parallel computing platform CUDA (computer uni fied device architecture) from Nvidia is used to prototype the algorithm for processing of tomographic sensor data. CUDA gives the ability to control a massively parallel pro cessing environment and the algorithms have been tested on a Nvidia RTX 2070 mobile graphic card that has 2304 CUDA cores. Thus, the card can perform 2304 floating point operations in parallel per cycle. An FPGA is a semiconductor device that can perform logical operations enabling the user to program its func tions.
The building block of an FPGA are wires, logic gates and registers. Modern FPGA, such as Arty 7 (Xilinx Inc. ), have additional components, like memory blocks, trans ceivers, protocol controllers, clock generators and even a central processing unit (CPU), which greatly enhance data processing capability. Eventually, the system will consist of a workstation com bining GPU and FPGA capacity. The GPU will be a primary processing engine while the FPGA has a supporting role in the data acquisition. Thus, data from the wire mesh and tomography sensors will be acquired and processed in real time, so that information on flow properties can be pro vided in continuous time inter vals for control. Therefore, the existing wire mesh sensor elec tronics is extended with an FPGA. A subsequent GPU is used to process the sensor data on a high level as well as compute and execute control actions.
For the numerical simulations the IMFT in house CFD code JADIM  [28]  and its solvers for IBM, LES, Lagrangian, and VoF are used. For this study they need to be coupled. This is done via the mass and momentum conservation equations (Navier Stokes equations) in the following way: U i x i ¼ 0 (7) U i t þ U j U i x j ¼ 1 r P x i þ v 2 U i x i x j þ g i þ F IBM i þ F g i þ F LAG i ( 8 ) Here r is the fluid density, U i is the fluid velocity, P is the pressure, g i is the gravity, n is the kinematic viscosity and F IBM i ; F LAG i ; F g i are the volumetric forcing coming from the IBM, VoF and Lagrangian methods, respectively. The Navier Stokes sys tem of equations are discretized using a second order finite volume method. Time advancement is achieved through a third order Runge Kutta method for the advective and forc ing terms and the Crank Nicolson method is used for the viscous stress.
Finally, the Loop assembly schema was introduced into each vector backbone using gBlocks through Gibson assembly, generating the uLoop vector kits. uLoop vector kits were then used for Loop assembly. DNA parts were domesticated into L0 using the pL0R-lacZ vector, an entry vector generated to domesticate sequences using SapI. DNA sequences have been deposited with the GenBank database under following accession numbers: pANe-1 (MN436792), pANe-2 (MN436793), pANe-3 (MN436794), pANe-4 (MN436795), pANo-1 (MN436796), pANo-2 (MN436797), pANo-3 (MN436798), pANo-4 (MN436799), pCAe-1 (MN436800), pCAe-2 (MN436801), pCAe-3 (MN436802), pCAe-4 (MN436803), pCAo-1 (MN436804), pCAo-2 (MN436805), pCAo-3 (MN436806), pCAo-4 (MN436807), pCOe-1 (MN436808), pCOe-2 (MN436809), pCOe-3 (MN436810), pCOe-4 (MN436811), pCOo-1 (MN436812), pCOo-2 (MN436813), pCOo-3 (MN436814), pCOo-4 (MN436815), pL0R-lacZ (MN436816), pSBe-1(MN436817), pSBe-2 (MN436818), pSBe-3 (MN436819), pSBe-4 (MN436820), pSBo-1 (MN436821), pSBo-2 (MN436822), pSBo-3 (MN436823) and pSBo-4 (MN436824).
Measurements of OD600 and fluorescence (RFP ex: 585 em: 620/GFP ex: 485 em: 516) were made every 10 min for 24 h. The data were analyzed using Python routines available in our repository (https://github. com/Prosimio/Plate_reader_analy sis) to obtain relevant parameters such as fluorescent expression and growth rate (see 'Plate fluorometry data analysis' on Supplementary data for further details).
Similar results were found for pCA comparing values obtained from flow cytometry experiments (Supplementary Tables  S5  and S6 ). In addition, similar analysis on flow cytometry data showed statistically significant higher ratios for level 2 of pAN with respect to level 1 and 3 of the same kit (Supplementary Tables  S5  and S6 , Figure  4A  and B ). We calculated the coefficient of variability (CV) for plate fluorometry and flow cytometry readings for each kit as a measure of internal variation. The lowest CV was found in pSB plasmids with a 15% and 21%, followed by pCO plasmids with a 23% and 19%, pCA plasmids with 36% and 41%, and finally pAN plasmids with   111% and 41%, for fluorometry and flow cytometry, respectively (Supplementary Table  S7 ). Next, we analyzed growth rates of bacterial cells carrying L1, L2 and L3 assemblies for all vector kits (Figure  4C , Supplementary Table  S8 ).
Use of uLoop vectors across biological kingdoms To demonstrate the universality of uLoop across different kingdoms, vectors containing transformation/transfection/conjugation elements were generated to allow transfer of DNA into different target organisms. Elements used in current transformation methods for each specific target organism were included in the vectors during the assembly routine along with TUs encoding fluorescent proteins. Then, vectors were transferred to the target organisms using described protocols and their function was assessed by fluorescence microscopy. A pCAL2-1_FPrep vector for transformation into P. tricornutum was constructed by hierarchical assembly. A module containing the origin of transfer (oriT), the yeast centromere CEN-ARS-HIS and a bleomycin resistance cassette (shBle) was assembled along with two fluorescent proteins tagged with localization signals assemblies for all vector kits was assessed by measuring the ratio of mean population values for green fluorescence (ex.
FUNDING This work was funded by the  Gordon and Betty Moore Foundation  [ GBMF5007. 01  to C. L. D. and V. B. ,  GBMF4981. 01  to F. F. and P. v. D. ]. B. P. was supported by the  Programa de Apoyo a Centros con Financiamiento Basal  [grant number  AFB 170004 ]. A. K. was supported by the  National Institutes of Health  [grant number  1R01DK11077001A1 ]. F. F. , B. P. , T. M. and I. N. were supported by  Instituto Milenio  iBio and PvD by the  Instituto Milenio de Oceanografı ´a  [grant number  IC120019 ] of the  Iniciativa Cientı ´fica Milenio MINECON. I. N. was also supported by a  PUC VRI  PhD studentship and A. C. was supported by  CONICYT scholarship for doctoral degrees in Chile  [grant number  21150778 ].
Acknowledgements We want to thank  J. Elgeti ,  T. Eisenstecken ,  J. Hu ,  K. Qi ,  S. -Y. Reigh ,  S. Rode ,  G. Saggiorato , and  M. Theers  for stimulating, fruitful, and enjoyable collaborations. Support of this work within the  DFG priority program SPP on ''Microswimmers From Single Particle Motion  to  Collective Behaviour  is gratefully acknowledged. Acknowledgements This work was supported by the  German Research Foundation DFG ; SPP 1726 Microswimmers. Acknowledgements I thank  Nuno Miguel Oliveira  for help with Fig 2. Funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement  682754  to EL) is gratefully acknowledged. I thank  Alex Chamolly ,  Debasish Das ,  Hélène de Maleprade ,  Christian Esparza López ,  Michael Gomez ,  Nuno Miguel Oliveira  and  Maria Tătulea-Codrean  for feedback on an early draft of this paper.
Acknowledgements I am very thankful to current and past students and collaborators in this topic:  Mingcheng Yang ,  Daniel Lüsebrink ,  Martin Wagner ,  Zihan Tan ,  Sergi Roca , and  Alberto Medina. This work was supported by the P io i P og am Microswimmer ( SPP 1726 ) of the  German Research Foundation (DFG). We also gratefully acknowledge the generous computing time granted on the supercomputer JURECA at  Jülich Supercomputing Centre (JSC).
Acknowledgements I thank the past and present collaborators who have participated at different levels in the two commented projects. J. Ignés-Mullol  as general experimental supervisor,  P. Guillamat ,  B. Martinez  and  J. Hardoüin  in the active nematic subject, and  J. M. Pagès ,  A. Straube  and  P. Tierno  in the phoretic colloids study. Financial support from  MINECO (AEI/FEDER)  under Projects  FIS 2013-41144P  and  FIS2016-78507-C2-1-P  is acknowledged.
Acknowledgements The work was supported by the  National Science Foundation  under Grant  NSF PHY1707900 Figure 2. (A) Trajectories of bacteria Bacillus subtilis obtained by consecutive snapshot accumulation in Newtonian liquid. The bacteria display the well-known circles close to a wall. (B) Trajectories of bacteria over 8. 5s near mucus/water interface. The bacteria penetrate mucus in the direction of the red arrow. Scale bar 100 μm. Courtesy of  N Figueroa Morales, Aranson's lab. I thank  T. Bäuerle ,  A. Fischer  and  T. Speck  for stimulating discussions. This work was supported by the microswimmers SPP 1726 of the  German Research Foundation (DFG)  and the  ERC Adv. Grant ASCIR (693683).
Acknowledgements The author thanks  A. Zöttl ,  J. Blaschke ,  M. Hennes ,  J. -T. Kuhr ,  F. Rühle ,  K. Schaar , and  K. Wolff  for collaborating on this inspiring topic and acknowledges funding from the  DFG  within the research training group  GRK 1558  and the priority program SPP  1726 , project number  STA 352/11.
Acknowledgements I thank all collaborators who contributed to the work on collective motion and escape in starling flocks that inspired this paper. I am grateful for financial support by the 6th European framework under the  STREP-project S a -Flag ( 12682) i he NEST-programme of Tackli g C le i i Scie ce, C g i ie Pil jec f NWO  ( 051. 07. 006 ), the  Nederlandse Organisatie voor Wetenschappelijk Onderzoek  (NWO; file  823. 01. 017 ), the  Netherlands Organisation for Scientific Research (NWO) , the  Open Technology Programme (OTP) , project number  14723 , two visitor grants from  NWO  ( 040. 11. 468  and  040. 11. 573/ 1798 ), a startup grant of the  University of Groningen  and a grant from the  Gratama foundation.
Acknowledgements We thank the past and present members of the  Biology-inspired Physics at MesoScales  group as well as our collaborators from the  Physical Approach of Biological Problems group. The BiPMS group is a member of  IPGG  and a member of the CelTisPhyBio Labex that we acknowledge for funding. VY acknowledges partial funding from the  EU   PRESTIGE program. TS is funded by the  IC3i Phd program  which has received funding from the  European Union  s  Hori on research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  666003.
In this paper, we report on progress with SWAMI to date. The UM has been extended from its original upper boundary of 85 km to run stably and accurately with a 135 km lid. Developments to the UM radiation scheme to enable accurate performance in the mesosphere and lower thermosphere are described. These include addition of non-local thermodynamic equilibrium effects and extension to include the far ultraviolet and extreme ultraviolet. DTM has been re-developed using a more accurate neutral density observation database than has been used in the past. In addition, we describe an algorithm to develop a.
, 2004] ) combined with a new solar activity proxy, the 30 cm solar radio flux (F30)  (Dudok de Wit et al. , 2014) , which is shown to be better suited than the more commonly used radio flux at 10. 7 cm (the F10. 7 index) for upper atmosphere specification. Here, we report improvements to the DTM mainly in the 170-450 km altitude region, by assimilating a more extensive dataset of observations than has been used in the past. The aim is for the storm time modeling performance to be significantly improved thanks to the high cadence Hp indices. This additional data assimilation and the new and higher cadence geomagnetic indices will further strengthen DTM's position as the leading model in this area. A representation of the impacts of space weather in a whole atmosphere model also requires nowcasts and forecasts of solar and geomagnetic indices.
1000 s of kilometres, about 30 vs. 360 min). As such, it will be a first of its kind. The more accurate reference from the Earth's surface will also enable global space weather science in the 80-140 km altitude range. Regarding operational implementation, MCM will be made available to the broad community, including via ESA's Virtual Space Weather modelling Centre (VSWMC). VSWMC uses a cutting-edge framework aimed at providing an integrated environment for installing and operating models and applications. This includes support for installing model components on a central server as well as on distributed platforms, and definition of data streams, data exchange mechanisms, and definition and implementation of coupling mechanisms between models. There is also process control to allow synchronous or asynchronous execution of model runs, making use of the defined coupling mechanisms, and the use of a standard approach for implementation of interoperability based on the High Level Architecture (HLA) IEEE 1516 standard.
g. provision of software) to transition MCM into operations. A set of acceptance criteria, e. g. regarding robustness of model code, near-real-time data processing and forecast delivery, are being defined to ensure that the model system can be considered ready for operational use. The outline of the paper is as follows. An overview of the aims of SWAMI appears in Section 2, while progress in developing the DTM and the upward extended version of the UM appears in Section 3. Work on the higher cadence version of the geomagnetic index, and on nowcasts and forecasts of this index, is in Section 4. Section 5 includes plans to develop an operational version of MOWA and conclusions appear in Section 6.
In order to transition the models to an operational state, interfaces, coupling mechanisms and data streams between the various components have to be designed, documented and implemented. The ESA VSWMC project is developing a framework based on HLA that provides mechanisms and tools for these purposes. The VSWMC architecture will be analysed in preparation of an implementation of MCM, DTM and the geomagnetic indices, and the associated coupling between these components. In addition, smaller scale prototype services will be set up outside the VSWMC framework in order to test implementations. The prototype environment will also allow rapid deployment of components for testing and validation purposes. 3 Thermospheric modelling.
DTM Semi-empirical thermosphere models are mainly used in satellite orbit determination and prediction to compute aerodynamic drag. They predict temperature, total density and most often composition too as a function of location (altitude, D. R. Jackson et al. : J. Space Weather Space Clim. 2020, 10, 18  latitude, longitude, local solar time), solar and geomagnetic activities, and season. The first Drag Temperature Model, developed in the seventies (DTM78;  Barlier et al. , 1978) , has been upgraded several times  (Berger et al,. 1998; Bruinsma et al. , 2003 Bruinsma et al. , , 2012 Bruinsma et al. , , 2014)). DTM models are constructed by fitting to a density database, with the objective of reproducing the mean climatology of the thermosphere. The quality of the database, i. e. its consistency, accuracy and its distribution in time and space, is essential to the achievable model accuracy.
DTM development and database For SWAMI, two models are actually under development: an operational and a research grade model. The density database, of which the main datasets are shown in Figure  2 , underlying the model development is also not the same. However, for both models they notably contain the full CHAMP  (Reigber et al. , 1996) , GRACE  (Tapley et al. , 2004)  and GOCE  (Bruinsma et al. , 2014)  high-resolution accelerometer-inferred density datasets. The research grade model is based on density data after 2000, because the high cadence geomagnetic activity indices Hp cannot be computed for the entire density database back to the sixties. Results presented here on both models are preliminary. The final models will be computed as late as possible in the project, in 2020, in order to benefit from new density data (e.
g. , GRACE Follow-On densities, additional year of Stella and Cryosat2). The new density database is very different from the one used in the construction of DTM2013. We provide in the following a summary of the changes. It should be noted that even if data from the same satellites are used, there can be differences in the accuracy and consistency. The GOCE  (Bruinsma et al. , 2014)  and Swarm-A (  Van den IJssel et al. , 2019)  data used were computed by TU Delft (TUD) for ESA, and the CHAMP data was computed with the same aerodynamic model by TUD too. The TUD satellite aerodynamic modelling is much more complex, and as a result more accurate and realistic, than the models used in the US Air Force operational High Accuracy Satellite Drag Model (HASDM;  Storz et al. , 2005)  or at the Centre National D'Etudes Spatiales (CNES).
As a result, their inferred densities are more accurate. The data were not re-scaled to HASDM, as was done for DTM2013. An example of the data scaling as applied to GOCE in DTM2013, i. e. by scaling to HASDM densities, is displayed in Figure  3. The ESA/TUD daily mean densities are 19% smaller on average over the mission than the HASDM densities, and a scaling factor of 1. 23 was applied. CHAMP daily-mean densities are also presented in Figure  3 , and the presently used CHAMP TUD densities are also about 20% smaller than the CNES CHAMP densities used in DTM2013. The main consequence of using the more accurate datasets is that DTM2019 predicts smaller densities, by about 20%, when compared with DTM2013. The GRACE data were processed by CNES, and were subsequently scaled to the CHAMP and Swarm-A densities in a procedure that compares densities after normalization to the mean altitude of both missions only when the orbital planes are within 15°(i.
e. 1 h local solar time) of each other at the equator. The Two Line Element (TLE) global daily mean densities  (Emmert, 2009)  are the longest datasets available, spanning four solar cycles, and they were used to estimate solar activity and seasonal variations only. Stella daily-mean densities, computed by CNES, did not require scaling because the aerodynamic model for a sphere is simple. All other datasets used in the construction of DTM2013, and notably the mass spectrometer data, were used with very low weights in the present modelling. Daily-mean densities inferred from satellite drag data of eight satellites in elliptical orbits, with perigee heights in the 200-500 km range, were kindly provided by Bruce Bowman. Latitude coverage is from pole to pole taking all objects into account, and most satellites cover more than a solar cycle.
The densities are computed from the estimated energy dissipation rates (EDR), i. e. orbit decay, which are derived from the orbits fitted directly to radar and optical tracking data. This method thus benefits from the true accuracy of the classified radar data. The uncertainty of the daily EDR densities is estimated to be 2%-4%. For a complete description of the method and the data we refer to  Bowman et al. (2004). A new data type has been used in the assessment of the models only, the O/N 2 ratio at 135 km from 2002 to 2007 measured by the TIMED/ GUVI instrument (http://guvitimed. jhuapl. edu/). The solar and geomagnetic activity indices that drive the model determine if it is the operational or research version. The indices used in the research model, F30 (Dudok de Wit et al 2014) and Hp60 (see Sect.
Model assessments A short selection of comparisons is presented of the ISO models NRLMSISE-00  (Picone et al. , 2002) , JB2008  (Bowman et al. , 2008)  and DTM2013  (Bruinsma, 2015) , and the two new DTM2019 versions. Table  1  lists the statistics of the observed (O) to model (C) density ratios computed for the complete main datasets in the DTM2019 database. The effect of the simplified aerodynamic coefficient modelling on the densities used in all other models is reflected in the mean O/C, which is systematically smaller than unity for all datasets except Stella (a sphere). The statistics over entire datasets cannot reveal improvement in geomagnetic perturbation modelling because storm events are rare. The solar activity modelling, i. e. the choice of proxy, is on the other hand reflected in these numbers.
This index will be open-ended, i. e. extreme events can be assigned values of Hp = 9+, 10À, 10o, 10+,. , depending on the amplitude of the event. Kp is determined from the standardised local K values for each observatory. While Kp is a measure of global energy input from the solar wind and magnetosphere into the ionosphere and thermosphere, the local K values from individual observatories are also used for regional investigations and therefore we also develop high cadence K value equivalents, i. e. H90, H60, and H30 values. Both the definitive Kp index and the nowcast Kp index are available from GFZ. The definitive index is calculated at GFZ semi-monthly from the K values provided by the Kp observatories. The nowcast K and Kp indices are calculated at GFZ from near real-time observatory data provided.
In summary, these examples indicate that the high cadence Hp indices track the Kp index well. A more detailed description of the derivation and the properties of the Hp indices will be given in a dedicated publication emerging from the SWAMI project. Currently, a test dataset for the years 2003-2005 and 2017 and its description is available for download  (Matzka et al. , 2019). These years were chosen to include the Halloween storm in 2003 and a large number of moderate geomagnetic storms (in 2005) as well as the recent geomagnetic storm in September 2017 (e. g. ,  Curto et al. , 2018). It should be noted that the published test dataset of Hp is a version of the index that is not yet open-ended, and consequently the maximum value of Hp is limited to 9o. In 2020, the high cadence, open-ended Hp index will be made available by GFZ in near real-time and as an archive back to 1995.
While this model would also not result in an "accurate" prediction, it will statistically reflect the general state of the geomagnetic activity as it will be based on recently estimated Kp from the measurements. An even more elaborate approach would be to use the measurements from the previous solar rotation ("recurrence Kp"). Such predictions would not only account for the general level of activity during a particular solar cycle, but may provide a more or less accurate forecast of the recurrent geomagnetic activity that is driven by the fast solar wind streams originating from the coronal holes. An additional improvement of the forecast can be made by utilizing the solar wind measurements ("SW model"). Combining all these datasets one can utilize all of the observations and data including history of Kp and solar wind conditions ("full model").