dataesr/scientific-paragraphs-categorization huggingface dataset:
Scalability Some important characteristics of IoT networks are dynamic changes and enormous scale, among others  [1]. Thus, as explained by Shah et al. [2] , an important challenge in IoT network is scalability. The scalability for a computer system consists of being able to adapt its size really easily. In IoT context, smart devices must join and quit a network in a simple manner. In our case, this property can be obtained because feature learning (Section 3. 2) is independent from decision parameters learning (Section 3. 4), contrary to classic deep learning models. Two configurations for feature subspaces learning are possible: (1) learning features on the N known devices dataset  [12] ; (2) learning features on a general dataset of devices  [5]. As we already explained in  [5] , the scalability for RF fingerprinting can be decomposed into two sub-properties: Feature learning approaches generally require a lot of data to be trained.
C. Imaging and data acquisition The scanner connects to a computer with 128 GB of RAM and a very fast SSD disk. The mechanical operation of the linear actuators and imaging is handled by a single script. In this configuration the code is written in Python. The image data are captured through straightforward optical imaging with standard optical lenses and cameras. In the configuration described above, there are two MER-230-168U3C 2. 3MP RGB industrial vision cameras with USB-3. 0 interfaces, from Get-Cameras, with 50 mm f/2. 4 lenses from the same provider. The lenses are fitted with NF533-17 notch filters from Thorlabs, which filter out scattered light and reflections at wavelengths around that of the laser. These cameras have the advantage that they can be controlled directly from a script that also handles data acquisition and storage.
A full image of 1000 sequential 2D images takes 20 seconds to record. In addition, writing the image to disk and resetting the laser and camera positions bring the maximum scan rate to about one full image per minute. It should be noted that this rate can be increased by reducing the image resolution or buffering all data to RAM, and by recording to a hard-disk after the experiment. Because of the high viscosity of glycerol, see Table  I , leading to relatively slow dynamics, it was deemed that the scan rate was sufficiently high at 1 scan per minute. It should also be noted that the 3D images are very time consuming to process and analyze, so that gathering larger amounts of data would lead to extended processing time and require corresponding available storage space. The imaging geometry is determined by illuminating the center cross section of the sample and shifting and refocusing one of the cameras until a desirable cropping of the sample is achieved.
D. Image processing and segmentation The scanner images conveys an intuitive mapping of the phases to our eyes, but translating this into an automated segmentation algorithm remains challenging. As can be seen in Fig. 2  in section V such an algorithm has to deal with gradients and fluctuations in light intensity and image quality. There is no unique way to do this, but rather a whole range of possible avenues, ranging from standard image processing, to using machine learning or neural networks. For this paper, we have used a combination of image processing with the OpenCV Python library and segmentation, analysis, and visualization with the Amira Avizo software from Thermo Fisher. The raw data consists of a 3D matrix of voxels that each have separate 8-bit RGB values. There are gradients in light intensity and image quality, as well as noise and artifacts, that have to be considered and adjusted for.
Our set-up allowed for the complete 3D characterization of the invading structure. By considering the balance between the (stabilizing) viscous and (destabilizing) gravitational forces, we have derived an expression for flow-rate dependence of the critical radius, beyond which the front becomes unstable. Although the experimental dataset does not extend far enough for us to accurately measure the dependence of this critical radius on the flow rate, our experiments are in reasonable agreement with the theoretical prediction. The experimental set-up illustrated here can in principle be further developed to study flows of more fluid phases, the limitation for this being the constraint of matching the index of refraction of the additional fluid phases to the ones already present. As mentioned previously, the limitations due to the imperfect matching of the refraction index can lead to a reduction of image quality, and one must take into account the desired porescale resolution when deciding how accurate the index matching needs to be.
Gavalas et al. (2014)  and  Gavalas et al. (2015)  designed various cluster-based heuristics and tested it on instances created based on the metropolitan area of Athens. The TDOPTW adds both time dependency in travel time and time window constraints on the moment of service to the basic OP. Abbaspour & Samadzadegan (2011)  applied multimodal shortest path finding modules and two adapted genetic algorithms to the TDOPTW. Their algorithm is evaluated over datasets derived from the metropolitan area of Tehran without reporting any performance measure. Verbeeck et al. (2017)  proposed an effective and efficient solution method by applying the swap and replace local search procedures and based on the ant colony system with a pre-processing step. This algorithm is verified by realistic instances that originate from a large road network of Benelux (Belgium, The Netherlands and Luxembourg) with available time profiles of link travel times.
VND Three local search moves are used in the VND part of the algorithm. The pseudo code for the VND is shown in Algorithm 4. 3. In this local search procedure, the general idea in each iteration is to first insert a higher number of vertices with short service time and then prolong the service time and or replace vertices if it improves the solution. In an overview, the VND starts with Insert-minS which tries to increase the total profit by adding a new vertex with its minimum service time. This move is designed to intensify the solution by spending time on adding an extra vertex rather than increasing service times at visited vertices. The VND continues with a Swap move that attempts to decrease the total travel time by exchanging two included vertices without any changes in their service times.
Results and discussion In this section, the performance of the proposed VNS is analyzed using various numerical experiments. Since no test instances are available for the TDOPTW-STP, in Section 5. 1, two different sets of instances are generated based on the TDOPTW benchmark instances. In Section 5. 2, first, the set of input parameters of the proposed algorithm are tuned, and then, the results of the VNS are evaluated over TDOPTW instances and the generated TDOPTW-STP instances with known optimal (best) solutions. In Section 5. 3, a real-world data set from the city of Shiraz (Iran) is used to show the applicability and suitability of the proposed problem and solution method. All generated instances including Sets 1 and 2, as well as real data instance of Shiraz are available online at https://www.
Real-world instance The purpose of this section is to create a real instance for the TDOPTW-STP, to perform a sensitivity analysis to show the potential use of the model in a real application, and to demonstrate the importance of variable service times, leading to higher profits than any kind of prefixed service times (min, average or max). Moreover, it shows that in case of congested (road) networks, our formulation and solution approach is required to obtain high quality solutions. Because of the various tourist attractions, the city of Shiraz with approximately 240 km 2 area and 1. 6 million population, is one of the top three tourist destinations in Iran for both domestic and international tourists. In this section, a real data set is created based on the urban road network of Shiraz.
Data generation In a real TDOPTW-STP instance, travel times between POIs are variable and rely on the time of the day. Each POI has a time window and service time dependent profit. The profit is defined as a linear function of the service time and has lower and upper bounds. Which means, that at each POI, the minimum visit time should be spent to gain the minimum profit. After that, the longer the visit time, the higher the profit collected by the tourist, so that the profit increases with a constant slope (profit per unit time) until it reaches its maximum value. The required information to make the TDOPTW-STP instance can be divided into three general categories; specifications of POIs (including location, time window, and maximum and minimum service times), network information (including time dependent travel times between origin-destination pairs during the day) and tourist considerations (including tour time budget, and maximum and minimum profits of POIs).
Tourist considerations It should be noted that, in the tourist trip planning, the aim is to provide a personalized plan for a specific tourist according to his/her known preferences. Therefore, tourist considerations including the time budget and the minimum and maximum profits for each POI depend on the personal preferences of that tourist. In this study, we consider an arbitrary tourist, and related tourist data are generated as follows. According to Eqs. (3-1) to (3-3), for each POI, the profit function is generated by estimating four values of minimum and maximum service time and profit. As explained earlier, minimum and maximum service times are properties of the POIs. However, maximum and minimum profits that can be obtained from visiting a POI depends on the personal preferences of the tourist, and in practice can be directly asked from him/her.
14). To evaluate the Y m l basis function, we use the work of Sloan  [Slo13]. BRDF coefficients To compute BRDF coefficients, we use the same principle as proposed by Kautz et al. [KSS02]. A BRDF is defined by two directions ω i and ω o , yielding a 4-dimensional function. When we evaluate F , the direction ω o is fixed; the BRDF becomes 2-dimensional and is projected on SHs. In a pre-processing step, we tabulate SHs coefficients according to discrete samples of the direction ω o = (θ o , φ o ). Tab[θ o , φ o ] = F ωo (16) where F ωo is the SHs coefficient vector of F computed for the output direction ω o. We uniformly sample θ and φ with one degree steps, and build a database of materials projected on spherical harmonics from the MERL database  [MPBM03]  and the database produced by Dupuy and Jakob  [DJ18].
Comparison with VPL, VSL and path-traced reference To fairly compare HVL, VPL and VSL, we use only microfacet based BRDF (Fig. 11  and 12 ) and for clarity, we compile timings in Table  2. Indeed, if VPL/VSL computation time might be greatly impacted by the use of measured, SH encoded BRDF, our HVL method  is agnostic to the used BRDF model and performs similarly on parametric or measured BRDF. In our comparison, we use the GGX microfacet distribution and evaluate the Fresnel reflectance using the exact term. We used material from MERL database that were fitted on GGX on the work of Ribardière et al. [RBMS17]. The three methods are also compared against path-traced references, obtained using pbrt-v3  [PJH16] , with three metrics : RMSE, PSNR and SSIM. Fig. 11a , 11b and 11d are computed using the same budget of virtual lights.
B Illuminance of a circular symmetric luminance Ramamoorthi and Hanrahan use a 4 × 4 matrix to compute illuminance  [RH01]. It can be replace by a 2 × 2 matrix when using a circular symmetric luminance function as it can be only projected on ZH when orienting the projection according to the direction of luminance emission. M = c 3 L 0 2 c 2 L 0 1 c 2 L 0 1 c 4 L 0 0 -c 5 L 0 2 c 2 = 0. 511664 c 3 = 0. 743125 c 4 = 0. 886227 c 5 = 0. 247708 Initially, they evaluate illuminance only with the normal surface n. illuminance is evaluated accordingly to the direction of luminance emission ω l , thus if we note z = ω l • n, illuminance is computed with E(o) = o t M o with o t = z 1 Hence, we can write too E(z) = c 3 L 0 2 z 2 + 2c 2 L 0 1 z + c 4 L 0 0 -c 5 L 0 2 C Materials used from MERL/RGL database MERL  [MPBM03]  (1) alum-bronze, (2) alumina-oxide, (3) aluminium, (4) beige-fabric, (5) blue-acrylic, (6) blue-fabric, (7) blue-rubber, (8) goldmetallic-paint, (9) gold-paint, (10) green-plastic, (11) red-fabric2, (12) red-phenolic, (13) red-specular-plastic, ( We implemented a simpler version of VSGL from the code available online of a paper on fast VSGL  [Tok15b] , because there is no code available for the full VSGL method  [Tok15a].
In the subsequent sections the selected algorithms will be described with deeper emphasis. In this work, four metrics are used for the comparison of 2D Slam algorithms, they were created and processed in MATLAB, and are explained in the following paragraphs. The map accuracy was measured using k-nearest neighbor method  [29] , by measuring the euclidean distance from each of the ground truth points to the nearest map point generated by the SLAM algorithm under test. A mathematical representation of the metric can be found in Equation  (1) , where N is the amount of points to sample, x 2ix 1i and y 2iy 1i represent the x-coordinate and y-coordinate difference between the ground truth point and the nearest map point generated by the algorithm, respectively. The measurement units used for this metric are centimeters.
Pose tracking accuracy was developed by a set of iterative loops calculating the euclidean distance between the ground-truth pose and the estimated pose  [30]. It can also be represented by equation (  1 ), but with a modified interpretation of the variables. For this metric N is the number of poses to sample, x 2ix 1i and y 2iy 1i represent the x-coordinate and y-coordinate difference between the ground truth pose and the estimated pose generated by the algorithm, respectively. The measurement units used for this metric are meters. d E = 1 N N ∑ i=1 (x 2i -x 1i ) 2 + (y 2i -y 1i ) 2 (1) Finally, CPU and memory usage were recorded using Python psutil library  [31]. These both metrics are mathematically represented by averaging the whole measurements taken during the test run, and their units are percentage of for CPU usage, where a number beyond 100% means it is using more than a single core, and MB for memory usage.
Generalities For all these experiments, since the trials and algorithms were simulated, the only equipment needed was a computer running Ubuntu 18 with ROS Melodic, the computer was a server with an Intel Xeon Silver 4114 2. 2 GHz. To simulate the environment, a software called GAZEBO 11. 0. 0 release was used to simulate the test environment, while a robot named TurtleBot 3 Burger was the one selected to be simulated in this work, because of its 2D LiDAR sensor and its differential driving mode, but other configurations can be used, such as a mecanum omnidirectional robot  [35].
Data Processing Needs Next, MATLAB 2020B was used to convert the data provided through rosbags in a manner that can be easily analyzed and synthesized, the scripts used are Ground Truth Generator, which takes the environment created through GAZEBO and builds a high resolution 2D version of it  [40]. This well-known Ground truth plot is then compared to the SLAM algorithm result by using a script that takes advantage of knn-search method provided by MATLAB  [41] , its output is the descriptive statistics of the whole comparison. There are two other important scripts, in the first it is compared the real pose towards the estimated pose of the robot, and returns some meaningful descriptive statistics about the comparison  [42] , and a script that analyzes the CPU and memory usage by the algorithm  [43].
00-5. 00 2. 00 2. 5. 4. KARTO-SLAM KARTO-SLAM is an optimized SLAM algorithm, it was developed by SRI International's Karto Robotics with a ROS extension, as an open source code. Its working base lies in the decomposition of Cholesky matrices to minimize the error, giving an optimized robot pose and trajectory  [22]. KARTO-SLAM builds the map by using nodes that save the location points of the robot trajectory and the dataset of sensor measurements. Graph borders are represented by transformations or trajectories between two consecutive poses in the space. when a new node is added, the map will be reprocessed and updated according to the border restriction in the space. These restrictions will be linearized as an scatter graph  [51, 52]. A loop closure condition can be shown if the robot revisits the same point twice or more times in the same run.
Pose Accuracy In regards of pose accuracy, it was quite hard to plot all the samples from all the algorithms together because of their range differences. To solve this, a timeseries plot was used showing all of them in separate plots, as it can be seen in Figure  30. Comparing visually the samples by their ranges, the main result was that RTAB-Map performed better than KARTO-SLAM, which also performed better than Gmapping, followed closely by Cartographer and by far HECTOR-SLAM performing worse than all of them. However, with the dataset obtained, there was only evidence to demonstrate at 90% confidence level that RTAB-MAP population mean was lower than KARTO-SLAM's population mean, which also had a lower population mean than Gmapping, Cartographer, and HECTOR-SLAM. In addition, there was no evidence to demonstrate any difference on the population mean and standard deviation between Gmapping and Cartographer, only to demonstrate that both were superior to HECTOR-SLAM by their standard deviation, which means that in terms of pose accuracy HECTOR-SLAM would give more variant results through different scenarios than these two.
Map Accuracy For map accuracy, the data presented in the Figure  31  shows a box plot for all the algorithms together, with a trend line centered on their means. With these results, it was possible to confirm at 90% confidence level that RTAB-MAP outperformed all the other algorithms, followed closely by KARTO-SLAM, then by Cartographer, next by Gmapping, and finally by HECTOR-SLAM, which was impossible to demonstrate its difference towards Gmapping by its mean, but not by its standard deviation. The data used for this section can be referenced in the Table  12.
CPU Usage With respect to CPU usage, Figure  32  shows a boxplot representation of all the algorithms with all their sample means. This figure shows that HECTOR-SLAM outmatch the other algorithms, followed closely by RTAB-Map, then by KARTO-SLAM, next by far from Gmapping, and finally by Cartographer. This finds were verified for their means by four hypothesis tests, all of them were demonstrated at 90% confidence level. The data used for this section can be referenced in the Table  13.
Memory Usage For the last metric, in view of memory usage the data representation used was a set of boxplots with a trendline pointing to their means, as seen in Figure  33. From these results it was demonstrated with 90% of confidence that HECTOR-SLAM is the algorithm that best manages memory resources, followed closely by KARTO-SLAM, then by far by Cartographer, next by RTAB-Map and finally by Gmapping. It was not possible to demonstrate any population difference between Cartographer and Gmapping, neither between RTAB-MAP and Gmapping, however there was enough evidence to demonstrate that Cartographer was better performing than RTAB-Map by their means, and that population standard deviation of Gmapping would be greater than population standard deviation of RTAB-Map, which is the reason Gmapping is considered the worse of the algorithms for this metric.
, 2019; Childs et al. , 2007). In the various polymorphs of gallic acid, the most common packing motif is the acidÀacid homosynthon. The carboxyl C atom displays the characteristics of -holes with electropositive regions above and below the molecular plane. The double -hole characteristics of the acid dimer in a solvate of gallic acid with dioxane were recently addressed  (Prohens et al. , 2019). In the same study, a detailed Cambridge Structural Database (CSD) analysis on aromatic carboxylic acid dimers was performed and it was demonstrated that the centrosymmetric hydrogenbonded carboxylic dimer is well suited to form -hole interactions  (Prohens et al. , 2019). The analysis also showed that in 232 out of 497 investigated structures the carboxylic acid dimers participate in two symmetrically-related -hole interactions, above and below the molecular plane.
Single crystal data collection and independent atom model refinement A single crystal of size 0. 13 mm Â 0. 15 mm Â 0. 25 mm was chosen using a polarizing microscope and mounted on a goniometer head using perfluorinated silicon oil. X-ray diffraction data were collected on a Bruker Venture D8 fourcircle diffractometer with a Complementary Metal-Oxide-Semiconductor (CMOS) area detector Photon 100 and a microfocus source using Mo K radiation ( = 0. 71073 A ˚) at 100 K in shutterless mode. The crystal to detector distance was fixed at 40 mm, and the scan width (Á!) was 0. 5 per frame during the data collection. The data collection strategy was chosen in such a way as to yield a dataset up to d = 0. 5 A resolution, having both high-and low-angle frames. Unit-cell refinement, data integration and reduction, with face indexing for accurate numerical absorption correction, were carried out using the APEX3 software  (Bruker, 2015).
ELMAM2 electron density model A cheap and efficient alternative way of obtaining a multipole model is transferring multipoles from predefined databases. ELMAM2 is a database of experimentally derived multipolar atom types  (Zarychta et al. , 2007; Domagała et al. , 2012). The automatic charge density transfer (multipole parameters and , 0 ) from the ELMAM2 database was performed using the MoPro program. The position and displacement parameters of the atoms were kept fixed to the experimental multipolar model. The total charge of the asymmetric unit after the transfer was corrected for charge neutrality by uniformly shifting the monopole populations as implemented in the MoPro program.
Maximum entropy method Maximum entropy method (MEM) calculations were performed with the L-BFGS algorithm and a linear combination of generalized F constraints with relative weights using the Dysnomia package  (Momma & Izumi, 2014; Momma et al. , 2013) , considering the same grid over the unit cell as was used for the computation of the corresponding prior densities. The weighting scheme in the form of d x , where d is an interplanar spacing of hkl reflections, with x being 2 or 4, was tested as it is recommended for X-ray diffraction data in previous studies (De  Vries et al. , 1994; Momma et al. , 2013). Two sets of MEM densities were obtained with these two weighting schemes for each prior density. uses an adjustment factor E, according to 0 h j À Á ¼ h j À Á =E 1=2 , without any conversion of h j À Á in the reflection data of various refinement models.
Topological properties of the electron density A topological analysis according to the Quantum Theory of Atoms in Molecules (QTAIM)  (Bader, 1990)  of each of the MEM densities, each dynamic model density and the XWR density were performed in MoProViewer inside the MoPro-Suite package. For the static multipolar density, the VMoPro module was used inside MoProSuite. The density and Laplacian values of the bond critical points of covalent bonds and intermolecular interactions were obtained from 3D grids. The atomic Bader charges and volumes were also integrated.