dataesr/scientific-paragraphs-categorization huggingface dataset:
Method and data The method we used is based on the collection and analysis of spatial data about warehouses in Gothenburg (including an original method to "clean" warehouses' databases), as well as on-site semi-structured interviews with transport, logistics and real estate stakeholders and representatives from both local and regional public authorities. 2 In order to measure changes in the location of logistics facilities, we investigated logistics facilities location at different time periods. Similar to many European countries, there is no dedicated database on warehouses and logistics facilities in Sweden. We used the NACE (European industry classification system) to identify establishments with warehouses in Statistics Sweden's database. NACE code 52. 10 ("warehouses") is however not fully satisfactory.
Several establishments with warehouses but whose main activity is not warehousing are not identified with this code. For instance, in 2014 only 51 warehouses were identified under the NACE code 52. 10 in Statistics Sweden's database for GMA, which obviously underestimates the actual number of logistics facilities. As an alternative, we developed a methodology to build a new database based on national establishments databases, fieldwork and satellite image reconnaissance. We describe this methodology below, which resulted in 207 warehouses identified in the GMA in 2014. To identify the location of logistics facilities in the areas of study, we used a database containing all economic establishments with a minimum of five employees in Sweden provided by Statistics Sweden. Data is available for all years, but we prioritised two years  (i.
e. 2000, and 2014 ) to make a diachronic analysis possible. In this study, only data for 2000 and 2014 is used, in order to measure the overall trend. This database contains key establishment information, including: name of company owner; type of activity in the form of a NACE code that classifies activities into 732 subclasses; number of employees; address; and type of ownership. The address allows us to geocode the establishments precisely using a GIS software. The first step was to extract all potential logistics establishments from the database, using NACE codes shown on Table  1. The use of NACE codes entails the following difficulties. This classification is based on the activity declared by the economic establishment upon creation; thus, it is very likely that an establishment is assigned a code that does not reflect exactly the economic activity (there are 732 classes and some of them overlap).
For 2000, it was not possible to fully use the same method as we were missing satellite images of that time. First, we compared the 2014 location of logistics establishments with the location of the same establishments in 2000. When the addresses and names of the companies matched, we considered that the 2014 warehouse already existed in 2000. When we were not able to find a warehouse at the same address, we considered either that it was not existent at that time (the 2014 warehouse was built after 2000), or it was relocated somewhere else. We therefore looked for the name in the rest of the region. We also looked at the same address, in case of a change in owner or user. Finally, for warehouses from year 2000 not recurring in the 2014 database, we looked at their address and checked with Google Map and Street view if there was an actual warehouse that may have been missed in the 2014 database, or classified in a different NACE class.
Our main findings include the following. 1) Methodological finding. A careful examination of the Swedish databases for business establishments led us to provide a more realistic set of data for logistics facilities in years 2000 and 2014: about 40% of so-called warehousing establishments were removed from the original list, as they were not freight-related or were misrepresented as warehouses. We did that by selecting the most appropriate NACE codes, then checking consistency by identifying names and addresses, and a visual examination of satellite images. 2) Increase in the number of warehouses. The number of warehouses in Gothenburg has increased between 2000 and 2014: +57 % in the GMA and +45 % in the VGC. This actually translates into an increase in the number of warehouses per capita as well as per square metre in both the metro area and the county.
Acknowledgements The authors would like to thank  PostNord TPL, Bockasjö ,  DB Schenker Logistics, Prologis, Business Region Göteborg, Västra Götaland Region , the  City of Falköping , the  Port of Gothenburg , for their participation in this study. This research was funded by  Volvo Research and Educational Foundations (Metrofreight centre of excellence and Gothenburg Urban Freight Platform) , the  Swedish Strategic Research Area Transport  and the  Visiting Professors Programme at the School of Business, Economics and Law of the University of Gothenburg. The article was presented, in the non-review track, in the World Conference on Transport Research in Shanghai,  10-15 July 2016.
Thus, Pawel  [4]  proposed a semianalytical model allowing accounting of the finite dimension of specimens (model 3). The new equation describing the O diffusion profile in the Ti matrix is then presented in Eq. 7. Eq. 7 where is a function of or presented in Eq. 8: Here, represents the original thickness of the specimen (in m). Figure  1  presents experimental data  [7]  compared with the results provided by these different analytical models. These experimental data were obtained by EPMA measurements on a Ti 6242S alloy oxidized 1000 h in a 80% Ar / 20% O 2 gas mixture, excluding the formation of nitrides, which are known to influence the overall oxidation kinetic. According to these results, the O profile obtained from the first model appears to be slightly over-valued compared with those obtained from the two other more complete models.
Boundary conditions, i. e. , and are set to imposed values. They can change with time, but in the following calculations they were taken constant. At each new time step, oxygen fluxes are calculated between slides using the concentrations calculated at the previous time step. Then, O concentrations in each slide of both oxide and metal are calculated from matter preservation principles. A first calculation was performed with the same input data used for calculation presented in Figure  3. The results of the analytical and numerical models are compared in Figure  4. According to Figure  4 , results from numerical model appears to deviate from analytical model results at around 50 h and 100 h, for a sample of 50 µm and 100 µm thick respectively. This occurs when the metal become saturated in O, evidencing a kinetic transition.
Although popular with hobbyists, the method has little value in the commercial or research environment due to high cost (purchase of components), time requirement and the need for specialized equipment (thrust stand, dynamometer). This method provides the most accurate results, but the number of combinations needed to be tested increases geometrically with each added component. This process can be significantly sped up using calculators such as Drive Calculator  [2]  and eCalc  [3] , which incorporate some of the data in their databases, but still the selection needs to be performed manually. Although not solving the problem completely, there are several methods that help with the preliminary multirotor design. Basset et al. [4]  present past and current efforts to develop UAV pre-sizing methodologies.
Propeller sizing and selection The propeller sizing and selection process starts by defining a propeller database represented as a set of available propellers P := {p i : i = 1. n p } (2) where the ith propeller p i is defined by the pair p i := (f pi , g pi ) (3) where f pi denotes the ith propeller performance, which will be defined later, and g pi denotes its physical properties expressed as a 4-tuple g pi := (d i , θ pi , m pi , s ni ) (4) where d i is the ith propeller diameter, θ pi is its pitch angle, m pi is its mass and s ni is a discrete parameter representing the propeller series name. The propeller set P is then filtered to obtain a set of propellers P p ⊆ P that satisfy a requirement 4-tuple g pr = d min , d max , m p max , S nr (5) where d min is the minimum diameter, d max is the maximum diameter, m p max is the maximum mass, and S nr is a set of preferred series names S nr := {s nk : k = 1.
The whole actuating system sizing methodology is depicted by the data flow chart shown in Figure  6. It shows the dependence of motor sizing on propeller specification and ESC and battery sizing on motor specification. The light cyan blocks correspond to the methodology stages, the dark blue blocks show requirements and constraints and the orange ellipses signify points of database access. The output data in green ellipses includes specification parameters for sizing all of the major components of the propulsion system (namely propeller, motor, ESC and battery) and the calculated propeller and motor operating points that can be used for calculating additional data, such as estimated flight time. The data corresponding to each of the outputs can be found in Table  2. In figure 6 a substantial impact of estimated total drone weight can be also seen -it is used to calculate required thrust T r that plays a key role in selecting the propeller, and consequently the   Motor specification name; KV rating; rated speed ω (2)  selected ; rated torque τ (2)  selected ; rated mech.
Example results The methodology presented has been implemented as a MATLAB script. This allows to easily process large quantities of data from propeller and motor databases and to plot component characteristics on every stage of the selection process. In this example, performance data published by APC Propellers  [12]  will be used for the propeller database. It contains static and dynamic performance obtained through analytical methods of all products currently manufactured by the company. Due to the reliance on external computer software, airfoil drag (and consequently, torque) may be under-predicted at low speeds. Additionally, wind tunnel measurements of selected propellers  [13]  show over-prediction of thrust coefficient (and consequently, thrust) of around 12% on average across all tested propellers.
What is more, cheap batteries are known for parameters varying between each unit, further justifying the need for an additional safety measure. Therefore, Equation (30) becomes For the motor database, a database bundled with Drive Calculator  [2]  software was used. It is based on measurements done and uploaded by its users, so it is impossible to accurately measure the discrepancies with real products, but they are estimated to be around 5-10% overall. However, a significant inaccuracy is introduced with the simplified motor model used to calculate characteristics based on scarce data. The model, applicable both to BLDC and DC motors, considers only two sources of losses: copper losses, calculated using winding resistance r C = I ESC × n rot × SF C. (43) P Cu = R m I 2 motor ( 44 ) and iron losses, calculated using no-load current where P Cu are copper losses, R m is the windings resistance, I motor is the current delivered to motor windings, P iron are iron losses, V is the nominal voltage and I 0 is the no-load current.
The MATLAB script has been run considering 2 operating points: hover and WOT. The goal was to reduce energy usage at hover, as the platform's main purpose is photography. For the thrust the unit of gram-force (gf), which corresponds to the force acting on 1 gram of mass in a standard gravitational field, is used due to intuitiveness in this application. Additional sizing parameters are listed in Table  5. The results are below: Results For a 4-rotor drone with estimated AUM of 1375 g: • APC 9x4. 5E propeller should be chosen for the highest specific thrust of 8. 81 gf/W per motor at hover. • Flyduino X2208 (1160 KV) motor should be selected with 0. 15 Nm torque at maximum speed of 9600 RPM. • The motor uses 39 W of electrical power at hover and 174 W of electrical power at WOT. • The drive should be controlled by a 12 A ESC per motor.
Personal Air Vehicle Current implementation of methodology as a MATLAB script does not allow to size heavy platforms, such as PAV, due to the lack of sufficiently large propellers in the database. However, the methodology can be implemented with different databases and even modified to help with the design of components: propeller specification provides enough data for pre-sizing of an electric motor, and thrust requirements along with size and weight constraints can be used as input in propeller design. Additionally, using only scarce data, a flight time estimation can be performed to validate the design of components. This is an especially important feature, as the methodology has been designed with the ability to be used inside another algorithm to further enhance the optimisation process.
Although there are no conceptual constraints preventing the use of the methodology for sizing large passenger multirotors, considerable limitations are introduced by the databases used, which rarely provide data on large propellers in the 50-60 inch range and motors able to support them. However, it is assumed, that certain elements of the methodology, such as flight time estimation based on limited data, can be useful in the process of PAV design. Unfortunately, one of the most important disadvantages of this methodology is its low, hard to estimate, accuracy. Great care was taken to make the results as close to reality as possible, but due to assumptions made for the sake of simplicity and speed, such as the use of thrust-to-weight ratio instead of calculation of maximum required thrust, the accuracy of calculations is impossible to measure.
The rms noise varies between 10 mJy/beam and 23 mJy/beam along four declination ranges, which complicates the measurements of cosmic structures on large angular scales. As LoTSS will eventually cover all of the Northern sky and detect about 15 million radio sources, it will allow us to overcome statistical limitations due to shot noise and substantially reduce cosmic variance in cosmological analysis, two issues from which contemporary wide area radio continuum catalogues suffer. In this work we study the one-and two-point statistics for the sources in the LoTSS data release 1 (DR1). Covering an area of 424 square degress over the HETDEX spring field, DR1 contains 325 694 radio sources, detected by means of PyBDSF (Python Blob Detector and Source Finder 2 , Mohan & Rafferty 2015) with a peak flux density of at least five times the local rms noise.
The factor N d (N d -1)/2 scales the Poisson noise with the overall number of pairs and the factor G p (θ) accounts for how many independent pairs can be probed at angular separation θ. For calculating the correlations we make use of the publicly available code TreeCorr 6  in version 3. 3  (Jarvis et al. 2004 ). TreeCorr uses an algorithm that structures the sources in cells according to a logarithmic binning of cell separation. In that way the numerical problem of calculating the two-point correlations for objects in cells with N 1 and N 2 members is reduced from scaling with O(N 1 N 2 ) to O(N 1 + N 2 ), which leads to a huge speed-up compared to a naive algorithm. As it is advised to use mock catalogues that are much larger than the data catalogues, the computational time scales linearly with the number of mock sources considered.
Higher flux density threshold means smaller correlation amplitude in case of 'mask d', which is contrary to the findings of  Wilman et al. (2003)  in terms of the Boötes Deep Field and  Rana & Bagla (2019)  in terms of the TGSS-ADR1. This difference may reflect issues arising from flux scale issues which, as mentioned previously, may be affecting the results presented in this work and should be improved using the next data release of the LoTSS survey. Alternatively, 7 http://lmfit. github. io/lmfit-py/index this may reflect the changing distribution of populations at different flux density limits within our samples as we will have a larger fraction of SFGs at lower flux density limits. This could make direct interpretation of the clustering amplitude difficult as the SFG and AGN are thought to have different bias measurements  (Magliocchetti et al.
(  28 ) with the random-random pairs obtained by TreeCorr and the expectation from CAMB in order to subtract it from the expectation. We find reasonable agreement for angular separations below a few degrees, for the 2 and 4 mJy samples of 'mask z', as well as of 'mask z1'. The agreement between the theoretical expectations and the results for the 2 and 4 mJy samples is remarkable as we did not adjust any model parameter. Besides varying the flux density threshold, we can also put the data into several redshift bins, as done previously in Sect. 5. 3 and 6. 2, which allows us to test the bias model in more detail. We compare two scenarios for the 2 mJy 'mask z' sample only, as the angular two-point correlation behaves similarly for 'mask z' and 'mask z1'. For the first scenario we use of the Halofit option of CAMB, together with the bias function b(z) and include the effect of lensing.
Conclusions We have presented the first statistical analysis of the spatial distribution of radio sources from LoTSS, based on the observation of 424 square degrees of the sky. We did so in order to characterise the global properties of the survey, check the quality of the LoTSS-DR1 catalogues and test whether upcoming data releases will provide promising opportunities to probe cosmology. We achieved all three of those goals. The data quality was assessed by a suite of tests on top of those already presented in  Shimwell et al. (2019)  and  Williams et al. (2019). We measured the point-source completeness of the survey and found it to be complete to better than 99 per cent above a flux density of 1 mJy. We demonstrated that in the mean, source counts are independent from the distance from the pointing centre out to an angular separation of approximately 1.
Note that as one increases the flux density threshold, the deviation from a Poisson distribution becomes smaller. However, for S > 10 mJy and the available 424 square degrees of survey area the counts per cell become so small that measuring a deviation from a Poisson distribution is difficult. For the value-added source catalogue we showed that the simplest compound Poisson distribution in which each cluster contains a random number of objects that are again Poisson distributed fits the data very well. A possible explanation for that finding is that this is due to multiple component radio sources, but here the reader should be aware that the statistical test is not able to distinguish real multi-component sources, e. g. lobes of radio galaxies, from a SFG with a radio artefact in its vicinity or a group of SFGs.
As the deviation from the Poisson distribution is strongest below flux densities of 1 mJy, it is unlikely that all of the clustering is due to real radio sources, at least some of that clustering might still be due to artefacts, as presumably the reliability of the LoTSS-DR1 value-added source catalogue reduces when the noise level is approached. This hypothesis is also supported by the reduction of n c when only sources with photometric redshift information are used in the data analysis. Presumably, fluctuations of the flux density scale between individual pointings also give rise to deviations from a Poissonian source distribution. A significant increase of the number of pointings in DR2 will allow us to study this issue in detail. The clearly detected deviation from the Poisson distribution suggests an additional contribution to the observed large variance of counts of sub-mJy radio sources at higher frequencies, additional to cosmic variance and sample variance  (Heywood et al.
2013). We further studied the differential source counts of LoTSS-DR1 and compared them to other data at low radio frequencies. They are in good agreement above 1 mJy and follow the expectations from the SKADS and T-RECS simulations. The photometric redshift estimates for about half of all radio sources obtained from crossmatching with optical and infra-red observations allow us to get a first impression of the redshift distribution of the LoTSS-DR1 sample. It will be important to figure out how representative they are also for the other half of the sample. An important step forward in that respect will be the WEAVE-LOFAR survey, which will obtain about a million spectroscopic redshifts. We also estimated the angular two-point correlation of the LoTSS-DR1 value-added sources in Sect. 6. Different flux density thresholds and masking strategies lead to slightly varying results.
However, obtaining the most accurate results possible with TreeCorr is still roughly 12 times faster than using our own brute force algorithm that we used for the purpose to check the performance of TreeCorr. Nonetheless, it is relevant to test other settings for TreeCorr's accuracy since, using lower flux density thresholds results in a higher number of sources and larger mock catalogues as mentioned in Sect. 5 and 6. Hence, the computational time increases significantly. Note also that a brute force estimate of the variance of ŵ scales as N 3 r , which poses substantial computational challenges for small flux density thresholds in upcoming data releases (we expect to lower our flux density threshold below 1 mJy for a cosmology analyis of LoTSS-DR2). Fig. C. 2 shows the absolute error of TreeCorr results, with respect to the brute force algorithm and using values for the bin_slop of 1, 0.
Acknowledgements. This paper is based on data obtained with the  International LOFAR Telescope (ILT)  under project codes  LC2_038  and  LC3_008. LOFAR (van Haarlem et al. 2013) is the  Low Frequency Array designed  and constructed by  ASTRON. It has observing, data processing, and data storage facilities in several countries, which are owned by various parties (each with their own funding sources), and which are collectively operated by the ILT foundation under a joint scientific policy. The ILT resources have benefited from the following recent major funding sources:  CNRS-INSU, Observatoire de Paris and Université d'Orléans, France ;  BMBF ,  MIWF-NRW, MPG, Germany ;  Science Foundation Ireland (SFI) ,  Department of Business, Enterprise and Innovation (DBEI) , Ireland; NWO, The Netherlands; The  Science and Technology Facilities Council, UK; Ministry of Science and Higher Education, Poland ; The  Istituto Nazionale di Astrofisica (INAF), Italy.
It is possible to integrate constraints during the calculation of EFMs using the DD algorithm, however the constraints must be monotonic with regard to the set inclusion of the supports; i. e. , if a given flux distribution of support S verifies the constraints, so it is for any flux distribution with support included in S  [25, 26]. Thermodynamic constraints, which are monotonic on the support, have been integrated into the tEFMA tool  [27, 28]  which uses the DD algorithm to obtain the EFMs compatible with the negative Gibbs free energy constraint. This is achieved by interfacing efmtool  [23]  with the Linear Program tool CPLEX. By using Farkas duality, we have proposed a method (thermoEFM) to compute EFMs consistent with the equilibrium constants. This method was used to orient the direction of flux in reversible EFMs and it only requires knowledge of the concentrations of external metabolites and the equilibrium constants for each reaction  [26].