dataesr/scientific-paragraphs-categorization huggingface dataset:
In all this work, considerable advances have been made recently and a number of technical issues have been clarified  (Ginzburg, 2016) , yet a major experimental hurdle related to the evaluation of the penetrability of sub-resolution pores, which at this point remains an arbitrary parameter in the models. As discussed in detail by  Baveye et al. (2017) , this penetrability cannot be deduced simply from grayscale values in CT images, and there is no practical alternative yet available. The Lattice-Boltzmann method has indisputably become the de-facto standard in pore-scale studies of water retention and transport in soils. One of the drawbacks of the method, however, is the very long (sometimes weeks-long) computational time it typically requires on personal computers. Open Lattice-Boltzmann environments like Palabos 3  or OpenLB 4  offer options to run the code on massively parallel computers and arrays of graphics processing units (GPUs), or to decompose the flow domain into manageable subportions, and researchers are increasingly resorting to these speeding techniques in applications of the Lattice-Boltzmann method to soils.
g. ,  Liu et al. , 2016). In a recent article, for example,  Gerke et al. (2018)  introduce the free software Finite-Difference Method Stokes Solver (FDMSS) that solves Stokes' equation using a finite-difference method (FDM) directly on voxelized 3D pore geometries (i. e. , without meshing). Based on explicit convergence studies, validation on sphere packings with analytically known permeabilities, and comparison against lattice-Boltzmann and other published FDM studies, these authors conclude that FDMSS provides a computationally efficient and accurate basis for single-phase pore-scale flow simulations. By implementing an efficient parallelization and code optimization scheme, permeability inferences can now be made from 3D images of up to 10 9 voxels using modern desktop computers. Tracy et al.
5, -1, and -2 kPa). The phase distribution data were derived from synchrotron X-ray CT images at a resolution of 4. 6 µm. Water distribution was simulated by a two-phase Lattice-Boltzmann model (LBM) and a morphological model (MOSAIC). Results indicate that the lattice-Boltzmann model is able to predict remarkably well the location of air-water interfaces (Figure  4 ). When one lifts the assumption, motivated by capillary theory, that a pore can drain only if a connecting pore is already full of air, MOSAIC gives an acceptable approximation of the observed air-water interfaces (Figure  4 ). However, discretization of pores as geometrical primitives causes interfaces predicted by MOSAIC to have nonphysical bulbous shapes. Nevertheless, given the huge difference in computing time required to run these two models (minutes for MOSAIC versus tens of hours for Lattice-Boltzmann),  Pot et al.
In addition to these observations, the Hubble Space Telescope (HST) archive contained images of the SN site obtained with the WFPC2 camera in 2001. An object was identified at the SN location in all three available optical HST bands (F450W, F606W, and F814W). Our analysis of these data and inferences about the progenitor system are given in Methods [see also  16, 18]. To interpret the physical process that governed the behaviour of the supernova at the time of discovery, we plot the luminosity versus the rise rate and compare these data with available early optical observations of other supernovae and transients (Fig. 2 ; see ref. 9  for a similar graph in the near-ultraviolet range). The low luminosity and fast rise seen in SN 2016gkg place the discovery data in a clearly different location on the diagram compared with later observations and with data for other objects.
SN discovery data. SN 2016gkg was discovered on We estimated the detection limit on the stack of 40 images from the first series. Following  [32] , we set the 5-sigma detection limit at the magnitude level where point sources are detected with a 50% probability. To find this magnitude, we first calibrated the V -band zeropoint of the combined image using the DAOPHOT photometry of the comparison stars. Then, we added artificial point sources in an area around the SN location, with varying apparent magnitudes in the range of 19. 0 < V < 20. 0 mag. We did this in groups of 1000 artificial stars within each 0. 1 mag bin. We finally used the "daofind" task in the DAOPHOT package of IRAF  [33]  to recover the artificial stars and thus determined the recovery fraction at each magnitude bin. We set the detection threshold Q in "daofind" as a factor of 3-4 times the background noise level.
Extended Data Figure  2  shows a mosaic of the rising SN as seen in the series of combined images. We performed point-spread-function (PSF) photometry of the SN using the DAOPHOT package. Photometric zero points were computed for each individual and combined image, using the five comparison field stars. The results are listed in Extended Data Table  2  and shown in Figure  1  of the main text. The quick initial rise is evident both from the individual-image and combined image photometry. A linear fit to the resulting light curves yielded slopes of 37. 3 ± 5. 3 mag d -1 using the photometry from individual exposures, 43. 4 ± 6. 1 mag d -1 using eleven data points, and 47. 2 ± 7. 8 mag d -1 using three data points. The reduced χ 2 is about unity in all cases, which indicates that no significant departures from a linear rise in magnitudes was seen.
We performed aperture photometry of the SN and comparison stars, and converted the results to the standard system. The Clear-band data were transformed to V magnitudes. Extended Data Table  3  provides those magnitudes. Follow-up B , V , R , and I multiband images of SN 2016gkg were obtained with both the Katzman Automatic Imaging Telescope [KAIT; 34] and the 1 m Nickel telescope at Lick Observatory. All images were reduced using a custom pipeline  [35]. Aperture photometry was then obtained from a customised Interactive Data Language (IDL) tool using the IDL Astronomy User's Library Note that owing to the small field of view for both KAIT and the Nickel telescope, we were only able to use one appropriate nearby star from the APASS catalogue as the reference star for calibration. Its magnitudes were first transformed into the Landolt system using the empirical prescription presented by Robert Lupton  [36] , and then transformed to the KAIT and Nickel natural systems.
Apparent magnitudes were all measured in the KAIT4/Nickel2 natural system  [35, 37]. The final results were transformed to the standard system using local calibrators and colour terms for KAIT4 as given in Table  4  of  [35] , and updated Nickel colour terms as given by  [37]. Extended Data Table  3  lists the resulting standard-system magnitudes from the KAIT and Nickel telescopes. Extended Data Figure  3  shows the resulting light curves of SN 2016gkg compared with those of the well-observed Type IIb SN 1993J and SN 2011dh. In the figure we also added the early-time V -band (or transformed) photometry from ATLAS, ASAS, LCOGT, and Swift. Spectra. Spectroscopic observations of SN 2016gkg were performed using the Kast spectrograph on the Lick Observatory 3 m Shane telescope on 2016 September 24, November 3, December 4, and December 23, the Low Resolution Imaging Spectrometer [LRIS; 38] on the 10 m Keck-I telescope on 2016 September 28 and 2017 January 2, and the Deep Imaging Multi-Object Spectrograph [DEIMOS; 39] on the 10 m Keck-II telescope on 2016 October 25 with both the 600 and 1200 line mm -1 gratings.
Data were obtained at the parallactic angle  [40]  to ensure accurate relative spectrophotometry; moreover, LRIS is equipped with an atmospheric dispersion corrector. Standard data reduction (including bias subtraction, flat-fielding, and spectral extraction) was performed within IRAF. The spectra were flux calibrated via observations of spectrophotometric standard stars at similar airmasses to the SN observations. The spectra are shown in Extended Data Figure  3. Also displayed for comparison are spectra of the Type IIb SNe 1993J  [41, 42]  and 2011dh  [43] ; these spectra were all obtained from WISeREP  [28]. We find, generally, that the SN 2016gkg spectra bear a stronger resemblance to those of SN 2011dh than to those of SN 1993J. We produced synthetic spectra using the SYNOW code [see 44, and references therein] with the aim of estimating expansion velocities as a function of phase.
SYNOW provides approximate continuum and line-strength levels, based on simple assumptions. It can, however, provide a robust estimate of the velocity at the photosphere from the overall fit to the observed spectrum including lines of a number of ionic species. Each SYNOW spectrum was computed consistently with the rest of the epochs, by keeping a smooth variation of the photospheric temperature and velocity with epoch. The derived photospheric velocities, shown in panel b of Extended Data Figure  4 , were used to compare with the hydrodynamical models. Hydrodynamical model. To analyse the SN photometry and photospheric velocity evolution, we used a one-dimensional Lagrangian hydrodynamics code that assumes flux-limited radiation diffusion for optical photons, and a one-group approximation for the nonlocal deposition of rays produced by radioactive decay  [45].
These are the SBO peak, the post-shock cooling peak (CP), and the nickel-powered peak (see also Figure  3  in the main text). (2) All models indicate that the discovery data can be interpreted as the SBO, regardless of the adopted progenitor radius. This is still valid for the values proposed by other authors  [18, 51]. (3) The progenitor radius has a much more noticeable effect on the decline rate than on the rise rate during the CP. (4) The larger the progenitor radius, the more luminous and later the CP becomes. To further test our SBO interpretation of the early-time data, we extensively explored parameters other than the radius that could increase the slope of the CP to a similar rate as that of the SBO peak (i. e. , ~40 mag d -1 ). We found that the explosion energy has the strongest effect on increasing this slope (see panel d of Extended Data Figure  4 ).
Smartt). We also obtained images of the SN itself on 2016 October 10 with HST, with theWide-Field Camera 3 (WFC3) UVIS channel in band F555W, as part of the Target of Opportunity (ToO) program GO-14115 (PI S. Van Dyk). The observations consisted of 24 dithered frames, each of 10 s duration; the short exposure time mitigated against possible saturation by a potentially bright SN (we knew that the SN brightness was increasing at the time, but not to what level). The frames were combined into a final mosaic of 240 s total exposure using AstroDrizzle  [60]  in DrizzlePac  [61]  within PyRAF. Adopting 13 stars in common between the WFPC2 image mosaic at F606W and the WFC3/UVIS mosaic at F555W, we astrometrically registered the two datasets with a root-mean-square (RMS) uncertainty of 0. 42 WFPC2/WF pixel (0".
We note that, owing to the short DEIMOS slit length, for strong emission lines (in this case, H) not much spatial area exists on the spectral image for accurately estimating the overall night-sky value, likely introducing systematic uncertainty in the strong-line flux. Nonetheless, we measured the Balmer decrement from the observed spectrum and estimate that A V = 3. 5 mag for the nebula. The line-of-sight Galactic foreground contribution to the extinction is comparatively low, A V = 0. 053 mag  [69]. A high extinction is plausible, given the conspicuous presence of a counterpart of the H II region in archival Spitzer Space Telescope data. The nebula corresponds to a luminous source at both 3. 6 and 4. 5 μm, and one of the brightest sources at 24 μm, in the outer disk of the galaxy. We corrected the spectrum for this extinction, as well as for an assumed recession velocity of 1481 km s -1 (from the NASA/IPAC Extragalactic Database, NED), and show the corrected spectrum in Extended Data Figure  6.
Research at Lick Observatory  is partially supported by a generous gift from  Google. KAIT  and its ongoing operation were made possible by donations from  Sun Microsystems, Inc. , the  Hewlett-Packard Company, AutoScope Corporation, Lick Observatory , the  NSF , the  University of California , the  Sylvia & Jim Katzman Foundation , and the  TABASGO Foundation. Some of the data presented herein were obtained at the  W. M. Keck Observatory , which is operated as a scientific partnership among the  California Institute of Technology , the  University of California , and  NASA ; the observatory was made possible by the generous financial support of the  W. M. Keck Foundation. O. G. B. is a member of the  Carrera del Investigador Científico de la Comisión de Investigaciones Científicas de la Provincia de Buenos Aires (CIC), Argentina.
Data Gathering and Energy Transfer Dilemma Bringing the network closer (e. g. , for data gathering or to deliver low-latency services) to end users is increasingly becoming a key function of wireless networks and also a hot topic. Here, we aim to use UAV to collect data from ground IoT devices. It is worth recalling that data gathering can be broadly classified into three categories: (1) The first scheme is "static data collection" in which the sink node is static, and the ordinary nodes upload data through multi-hop routing networks. The problem here is the high energy consumption by relay nodes that perform the routing between ordinary nodes and the sink, which results in a short network lifetime; (2) However, in the second method, the excessive energy consumption can be solved by using a mobile sink.
Ground IoT Device to UAV Link We consider a set of m islands (remote areas), and each includes N IoT devices to be covered by the flying base station. The UAV retrieves information from islands in a sequential manner. It collects data from an island before moving to another island. The communication link between the IoT devices and the UAV depends on the UAV altitude. For instance, it can use a medium range standard such as IEEE 802. 11 (WiFi), which allows a line-of-sight range up to several hundred meters and high data rates. However, if the UAV operates at a higher altitude spanning several kilometers, wide area networks (e. g. , LoRaWAN, LTE, LTE-A, IEEE 802. 16 (WiMAX)) would be preferred in this case as they can reach much higher data rates. In our model, we consider that the data to be collected by the UAV can be either best effort or time-sensitive traffic.
Indeed, after the coordination between the UAV station and ground IoT devices during the joining stage where communication parameters are negotiated (see Figure  2 ), the UAV can estimate the required time for both data collection and batteries recharging. Then, • the required time to collect data from an IoT devices of Island N is calculated by: T N data = ∑ i∈N DATA i ρ i • 1 {B(i)>B} , (11) • the required time for energy transfer (batteries' charging) is given by: T N nrg = B ∑ k=B 0 t k nrg • E[N k ], (12) where -B(i) = B 0 , B 1 , • • • , B N is the current battery level of IoT device i; -t k nrg is the allocated time to recharge a depleted ground devices of battery level k. This time allocation will be calculated for each IoT device and will be explained in the next subsection; -B is a threshold beyond which no battery recharging is needed; -B is the battery level's critical threshold.
Simulations and Numerical Investigations In this section, we implement the proposed model with MATLAB software to evaluate the performance and the added value of using UAV-based flying stations with energy transfer capabilities and energy harvesting modules. To this end, we study an example of a network composed of 1000 IoT devices distributed following a PPP in a geographical area. We consider a single UAV station serving ground IoT devices. Table  1  summarizes the main values of the parameters used for numerical examples and simulations. We analyze the behaviors of the energy harvested by the UAV station and on the energy transferred to IoT devices as functions of both data gathering time (i. e. , during which UAV station harvests energy) and devices recharging time. As mentioned earlier, the UAV coverage time is split into two parts weighted by α; energy transfer time T nrg = α • T cover , and data gathering time T data = (1α) • T cover.
In this study, we focus on tomato crops because it is one of the most consumed and appreciated vegetables in the world, due to its high content in carotene, a natural antioxidant. In 2017, the world production was approximately 177 million tons  [1] , which emphasizes the relevance of optimizing tomato crops by applying precision agriculture. One of the key technological aspects in this setting is the current possibility of monitoring climatic variables with sensor networks, which is receiving increasing attention. In the first companion paper  [12] , we described the analysis of the WSN topologies and their configuration, in terms of the design and implementation of hardware and software for the nodes with different communication technologies. For this purposes, three WSNs were designed and implemented: ZigBee technology with star topology, ZigBee with mesh topology (referred to here as DigiMesh); and WiFi technology (access point topology).
Chapman et al. (2018) [48] Air relative humidity, PH, and air temperature. Design of Bayesian networks to predict the performance functions of three commercial oil palm farms. The acquired data were conditioned and used for the creation of the data packages or frames, and then, these frames were transferred from the Waspmote card to the communication module, where they were wirelessly transmitted to the Coordinator Node. This last one transmitted the environmental data of each sensor to the personal computer containing the Human-Machine Interface (HMI) designed in LabVIEW TM , and the mobile device allowed the user to visualize the data for each variable and for each WSN node. Sensors were selected according to the environmental variables that most affect the tomato growth. They were low consumption and compatible with processing cards.
The data acquisition used the PRO 2. 0 agriculture card (air temperature, solar radiation, luminosity, air relative humidity, wind speed, wind direction, and UV radiation) and the gas card PRO 2. 0 (CO 2 ). These cards have very low power consumption. The Waspmote card with low energy consumption processed the data. The internal memory of the Waspmote stored the data after being processed, and data packets were transferred to the communication module by UART serial controller (for DigiMesh and ZigBee) or by HTTP protocol (for WiFi). The reception and transmission of the Coordinator Node packets used the communication modules XBEE ZB S2 PRO and XBEE ZB S1 PRO (for ZigBee and DigiMesh, respectively), with low energy consumption. The transmission speeds (9600, 19200 and 57600 bauds) were configured according to the operation rates of the Waspmote modules.
The tree topology of the ZigBee network has the disadvantage that, if one of the nodes loses communication with the Coordinator Node, then it will be permanently out of service. The mesh topology of the DigiMesh network was implemented using redundancy, where each sensor node can communicate directly with the Coordinator Node or through another sensor node. The WiFi network receives and transmits the packets through the RN-XV wireless communication module, which has low power consumption. The ZigBee and DigiMesh networks transmit the data packets to the PC using the RS-232 interface, and the WiFi network uses the TCP/IP protocol. The packages received in the personal computer were routed to LabVIEW TM. The database was read and stored in LabVIEW TM through toolkits for creating, opening, and closing the communication channel, as well as for calling and storing data.
The monitoring of the variables was made with the graphic interface developed in LabView according to the design stages, such as data separation, error detection, alarm generation, or variable monitoring. The interface was similar for the three networks and the user was able to know the real-time and precise values of each variable of the tomato greenhouses. The transmission of the data hosted in the MySQL acquisition station was controlled by the interaction with the web server. The Apache web server connected to the MySQL database to access the smartphone mobile application by means of a webpage. This application was developed with the Eclipse Integrated Development Environment (IDE) and it used the Android development environment software development kit (SDK). The user had access to the interfaces for the data generated in the greenhouse by three WSNs, from any geographical location with the availability of the mobile device.
Moreover, fluctuations were considered as possible in each ultradian frequency component. The significant components of the model were automatically selected, according to the described procedure of their inclusion in decreasing amplitude order until the bootstrap test identified them as non-significant. Ad-hoc software was developed in Matlab TM , and the analysis tool included two parameters that need to be set by the user to improve the accuracy of the models. The first parameter to scrutinize is the regulation factor (Reg) that compensates for the presence of ill-conditioning of the inverted matrix used in the LS adjustment model. This Reg parameter was considered appropriate when the signal provided by the rhythmometric analysis accurately followed the measured samples, making sure that it was not an overfitted model also learning the noise, and if the general trend of the estimation compared to the original signal was not lost.
To more efficiently adjust the model of this variable, we suggest that the samples are acquired during major time intervals, considering that its behavior is similar to CO 2 , and the data trend differs greatly from one day to another. Table  2. Relevant parameters for model adjustment. For each network, node and sensor, the filtering order (Ord), the regularization parameter (Reg), and the number of significant infradian and ultradian components ( f i _sig and f u _sig) are indicated. Error range, average relative frequency and mode of the residual histograms are indicated to summarize the residual analysis. The matching limits for the data and the model are also provided, as well as the qualitative description of the relevant features of the residual histograms. The UV radiation was measured by specific nodes in each network.
It is relevant to note that the high frequency of near-zero errors on the histogram was not strictly related to a successful adjustment, because the residuals were very low for the signal sections with the largest samples. On this basis, we deduced that, for this variable, the model was more precise in Node 3 of the ZigBee network. Among all the studied environmental variables, the air temperature was one of the least volatile, and its fluctuations between samples were slow and with reduced amplitude, so we chose the same filter applied to the air relative humidity. The data variability trend was very similar in all nodes, and for all test days. The amplitude increased from about 08:00, and decreased from 17:00 approximately, and the air temperatures were the lowest in the dawn around 02:00-05:00.
The modeled signal was perceptible from Reg = 5, however the results were quite poor as the analysis included only f o. The model quality was greatly improved for Reg values above 11, except in specific signal segments where the mismatch was more prominent because of the daily amplitude variability of the original signal; this drawback was not solved, even with the increment of Reg. Based on these results, we concluded that model optimization for this variable requires more time stored data. The wind direction sensor was mounted on Node 1 of the ZigBee network. The acquired samples corresponded to positions in a circle, represented by integers between 1 and 16. Hence, an additional programming routine was developed for the data visualization and processing. Each position was transformed to cardinal points, and all of them were represented in a three-dimensional graph.