dataesr/scientific-paragraphs-categorization huggingface dataset:
CLUMP MEASUREMENTS In this section, we report the clump sizes measured in the derived source-plane Hα profile of the a1 lensed image computed using both the methods of reconstruction. We do not consider a2 because of the low SNR in the observed datacube. We use the ASTRODENDRO 2 python package to compute hierarchical trees of structures called dendograms  (Rosolowsky et al. 2008; Goodman et al. 2009 ) and detect clumps in the Hα maps. As compared to other nonhierarchical methods (e. g. CLUMPFIND, Williams, de Geus & Blitz 1994), ASTRODENDRO prevents the formation of 'pathological' small clumps between two prominent clumps and allows structures with very different physical scales to be detected  (Goodman et al. 2009). The algorithm selects clumps on the basis of three input parameters: (a) minimum value of flux that acts as a threshold to define clumps; we set it to 5σ where σ is the flux dispersion at a given source pixel (b) minimum difference in flux between two close structures for them to be considered separate; we set it to 10 % following  Oklopčić et al.
(2017)  (c) minimum number of pixels in a clump, which is set to be 4 pixels corresponding to an effective radius of selected clumps to be greater than the source plane RBF size used for reconstruction. After computing the dendrograms, the ASTRODENDRO package extracts the relevant parameters such as the location, surface area, effective major, minor axis and total fluxes 2 http://www. dendrograms. org/ of the identified structures. We find seven clumps in the pixelated reconstruction that satisfy the criteria we imposed. However, not all of these identified clumps are recovered using the traditional approach. We define the effective radius of a clump as the half width at half maximum (HWHM) derived from the geometric mean of the semi-major and semiminor axis of the ellipse fitted to that clump in the source plane emission-line map.
(2) Defining the range of the coordinates (x, y, z) of the reference points of the moving platform, or, the search space. Robotics 2018, 7,48 (3) The reference point coordinate is substituted into the position inverse solution equation of the mechanism, the equivalent link length l i (i = 1, 2, 3) and the Hooke joint angles θ 1 and θ 2 in the limbs are obtained. The results are checked whether they are within the allowable range. If they are, these points are recorded. (4)  The set of points satisfying the condition is the workspace of the 3-UPU parallel mechanism when a given range of values has been searched. The design parameters are given in Table  1. According to workspace search flow chart in Figure  3 , the workspaces of the 3-UPU parallel mechanism are obtained using MATLAB. Table  1.
In the initial assembly, the coordinates of the upper platform center in the static coordinate system is (0, 0, 363). The variation curves of the parameters of the limb lengths can be obtained when the upper platform moves using MATLAB simulation, as shown in Figure  10a, b. When the lower platform moves, the moving and static coordinate systems exchange. The coordinate of the lower platform center in the static coordinate system is (0, 0, -363) mm. The variation curve of parameters of each driving motor can be obtained from Equation  (6) , as shown in Figure  10c, d. From Figure  10 , the equivalent link length of each limb l i (i = 1, 2, 3) varies smoothly when the upper platform moves, that is, the displacement of each driving motor is stable. When the upper platform is conducted as the moving platform, the static coordinate system is established in the center of the lower platform.
Simulation Program A MATLAB/SIMULINK computer program (version R2017a, MathWorks, Natick, MA, USA) is developed to solve Equation (  8 ) numerically for θ as a function of roll time. The program code is a loop structure that uses initial conditions for θ and a to solve the equation for θ at the first pass of the loop. After this first pass, the program "loops back" values that are needed in order to compute θ. As part of this process, the program checks γ at each pass to determine if actuation has been triggered, and if it has, a, b, ȧ and ḃ are newly computed; ȧ and ḃ are modeled as trapezoidal velocity profiles that, when integrated, give a and b as functions of roll time. Updated values of a, b, ȧ, ḃ, ρ 1 , and ρ 2 are then looped back, along with values for θ and θ obtained through numerical integration, which the program performs using Simulink's Runge-Kutta  [20]  solver, and θ is newly computed.
Busker Robot Busker Robot is a system with amodular architecture, consisting of both hardware and software parameters (Figure  2 ). The hardware is composed of the painting machine, a 6-DOF robotic arm by Universal Robots, mounted in front of the painting support and equipped with a changeable brush. The software consists of the image processing and the path planning algorithms, that have been implemented in MATLAB R. The image processing algorithms, i. e. , the non-photorealistic rendering techniques, are applied to a digital input image that is processed in order to extract the contours, the details and the backgrounds that have to be reproduced on paper. Several different algorithms have been developed for Busker Robot  [1, 2] : it is the artists responsibility to choose which technique, or combination of techniques, has to be applied case by case.
The output of the artistic rendering is a structured list of points that identify the processed image. In particular, a MATLAB structure is adopted to handle the pixel coordinates of each single line or curve that composes the final image: each stroke is represented by an array that contains the coordinates of all the required way-points. These pixel coordinates are then scaled into the corresponding coordinates in millimeters on the painting canvas with a linear transformation. A trajectory planning is applied to these paths, in order to define the velocities profiles that the robot end-effector has to follow during the execution of the painting task. The trajectories are planned by adopting trapezoidal speed profiles for each motion: after the acceleration phase, the end-effector of the robot linearly moves with constant speed and circular blends through the way-points.
The generated torque was tested by using the apparatus shown in Figure  4 ; the actuator was the double helical compliant SMA, and a motor was used to lock the shaft of the mechanism, so the force/torque sensor could measure the responding torque due to the PWM input power. The torque and temperature with respect to time are shown in Figure  22. The relationship of torque and temperature is plotted as shown in Figure  23. The hysteresis loop of deflection angle with respect to the temperature is shown in Figure  20. From these experiments, all of the data were collected to calculate other parameters such as C A and C M as mentioned in Equation (1), which were calculated as C A = 188. 825 MPa/ • C, C M = 156. 8 MPa/ • C. Residual strain ǫ L = 0. 005376 for calculating the transformation coefficient Ω = -E ǫ L.
The force sensor is mounted on the coupler by special grasping (Figure  7a ). The fingertip support has been made by low-friction plastic material, as shown in Figure  7b , to ensure that the exchanged force can be considered perpendicular to the sensor itself. The control algorithm was implemented in MATLAB-Simulink ® environment, dSpace Control Desk. The command signal of the pressure control proportional valve V ref was generated by a dSPACE ® D/A board DS2101. Figure  8a  shows an image of the entire experimental setup during the static experimental tests. In order to evaluate the dynamic performance of the haptic interface, several experimental tests were conducted. An operator, after positioning his hand on the device with a fully extended index finger, as shown in Figure  4b , is invited to freely perform flexions and extensions of the index finger (Figure  8b ), as if he were virtually manipulating an object.
Simulation of the Proposed Motion Strategy The proposed approach differs totally from other existing methods, since it deals with a cable failure by planning a motion strategy under dynamic conditions, instead of addressing the static case as in existing methods. A numerical simulation is reported here to clarify the main features of the proposed novel approach by referring to the case study of LAWEX. An algorithm has been written in Matlab-Simulink in order to prove its effectiveness. In a first test, a periodic motion from left to right has been considered. Such a motion has been defined by considering the center of the end effector starting at coordinates X i = [0. 05, -0. 35, -0. 23]m and stopping its motion at coordinates X f = [0. 35, -0. 05, -0. 23]m; the period of the full motion (roundtrip) requires about 2 s equally distributed in the forward motion (lasting one second) and the backward motion (lasting 1 s).
Simulation Results of HR Collision In the ensuing analysis, a comparison between V2SOM and a constant stiffness (CS) VSM was performed through simulation of the HR collision model under Matlab/Simulink. An identical elastic deflection value was considered for CS and V2SOM deflection at 80% of T max. This torque value defines the deflection range of the normal working mode for the V2SOM after which the shock absorbing mode is triggered. The shock absorbing mode is triggered when the torque reaches T 1 value (see Figure  15 ). In this case, the springs are compressed by the followers' displacement as a result of the cam rotation (as shown in Figures  5  and 6 ). The simulation aims to emphasize the decoupling capability of V2SOM along inertia and torque in comparison to an equivalent CS-based variable stiffness mechanism.
Introduction In the last few years, increasing interest has been devoted towards compliant and underactuated hands as a compact, reliable, and flexible grasping solution in manipulation applications  [1, 2]. However, relatively limited attention has been given to the development of simulation tools that address the specific challenges connected with underactuated grasping  [3]. Notable examples are GraspIt! [4]  and OpenGrasp  [5]. Both simulators allow a set of common objects and various types of grippers to be analyzed, but they are not well suited for grasp stability analysis, especially when underactuated architectures are considered. Other recent efforts that address the specific design of under-actuated hands include  [6] , whereas SynGrasp  [7]  is a MATLAB toolbox for grasp analysis of fully or underactuated robotic hands.
A functional scheme of the designed mechanism is shown in Figure  1 , while a more in-depth analysis of the proposed mechanism can be found in  [10, 11]. This paper extends the study, preliminary presented by the authors in  [12] , on the contact forces generated by the underactuated fingers during enveloping grasps. The overall goal is to optimize their features by maximizing the contact conditions for which a stable grasp can be achieved. As explained in the following section, these contact situations are identified by a combination of phalanx flexion/extension angles and contact points of the phalanges with the object to be grasped. In order to simplify the analysis that involves a high number of variables, a software is presented in Section 4 to support a user during the design stage. The software framework, available upon request, is developed in the Mathematica environment, which is a very powerful symbolic language and well-established in the scientific and industrial world.
Proposed Software The software toolbox provides a useful tool to simplify the grasp-state space analysis and parametric optimization. It was developed under the Wolfram Mathematica environment  [14]. The graphical user interface (GUI) is shown in Figure  7. It consists of four main areas. The first row (2-phalanx finger) and the second row (3-phalanx finger) refer to the finger geometric parameters explained in the previous sections; the third row collects the parameters relative to the target object (position, size, shape), other parameters used to define the limits of the grasp-state space, and the constraints on the normal and tangential contact forces both for two-and three-phalanx fingers; the fourth row includes the graphs that represents the grasp-state space (k 2 , θ 2 ) and the scheme of the two-phalanx finger on the left side, whereas two graphs representing the grasp-state spaces (θ 2 , θ 3 ) and (k 2 , k 3 ) and the scheme of the three-phalanx finger are shown on the right side.
Acknowledgments: The authors would like to acknowledge helpful discussions with  Russ Rhinehart  and donations of materials for the robot by  Slate Rehm ,  Darby Rehm , and  Chanten Rehm. Acknowledgments: The authors would like to thank  M. A. Grimaldi  for his help in the development of the Random Splines algorithm. Acknowledgments: This work was sponsored by the  French government research program Investissements d'avenir through the Robotex Equipment of Excellence  ( ANR-10-EQPX-44 ). Acknowledgments: This work was sponsored by the  French government research program Investissements d'avenir through the Robotex Equipment of Excellence  ( ANR-10-EQPX-44 ).
Acknowledgments: The authors would like to acknowledge  Marco Carricato ,  Department of Industrial Engineering, University of Bologna ; for his valuable feedback with respect to the analyses presented in this article. Funding: This work was partially supported by  JSPS KAKENHI  Grant Number  JP17H03162 , and  JKA  and its promotional funds from the  AUTORACE. Funding: This research was funded by the  Austrian Ministry for Transport, Innovation and Technology (BMVIT)  within the framework of the sponsorship agreement formed for 2015-2018 under the project RedRobCo. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest. Funding: This work was supported by the Project  ID 37_215 , MySMIS code 103,415 "Innovative approaches regarding the rehabilitation and assistive robotics for healthy ageing" co-financed by the  European Regional Development Fund  through the  Competitiveness Operational Program 2014-2020, Priority Axis 1, Action 1.
1. 4,through the financing contract  20/01. 09. 2016 , between the  Technical University of Cluj-Napoca  and  ANCSI as Intermediary Organism  in the name and for the Ministry of European Funds. Funding acquisition, M. A. and S. Z. ; Investigation, Y. A. , M. A. L. , M. A. and S. Z. ; Methodology, Y. A. and M. A. L. ; Project administration, M. A. ; Software, Y. A. and M. A. L. ; Supervision, M. A. L. and S. Z. ; Validation, Y. A. , M. A. L. , M. A. and S. Z. ; Writing-original draft, Y. A. and M. A. L. ; Writing-review & editing, Y. A. and M. A. L. Funding: This research was funded by the  French National Research Agency , convention  ANR-14-CE27-0016 , under the  ANR  project  SISCob  " Safety Intelligent Sensor for Cobots ". This research was also supported by the  French region "Nouvelle-Aquitaine" (program  HABISAN  2015-2020 ) with the financial participation of the  European Union (FEDER/ERDF ,  European Regional Development Fund).
We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the  bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for 10, 699 test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code BAGPIPES, our ML-based method outperforms template fitting on all of our predefined performance metrics.
To solve this dilemma, we have developed GALPRO, a highly intuitive and efficient Python package for rapid, on-the-fly generation of n-dimensional PDFs. GALPRO is documented and available for fellow researchers to use in their analyses at https://galpro. readthedocs. io/. An interesting application of GALPRO could be to generate joint redshift-luminosity PDFs for measurements of the Hubble constant from gravitational wave events that lack an electromagnetic counterpart  (Schutz 1986; Palmese et al. 2019; Soares-Santos & Palmese et al. , 2019). The use of full redshift PDFs rather than point estimates is very important for standard siren measurements  (Palmese et al. 2020b) , and the inclusion of joint redshift-luminosity PDFs allows one to correctly define the selection function of the galaxy sample at the same time.
DES Deep Fields As part of the DES Year 3 (Y3) cosmology analysis, observations from the SN survey were combined with community data, additional DES exposures (particularly in -band) and coincident near-infrared data to form the DES Deep Fields (DF) catalogue  (Hartley & Choi et al. , 2020). The principal aims of the DF project are to improve calibration of redshift distributions in the main survey and to act as a prior on the population of full multicolour images for Balrog (discussed in the next section), to better understand the systematics and selection function of the wide-field (WF) survey. These goals rely on the fact that the DF represents a statistically complete, yet effectively noiseless, population of the galaxies that are found in the WF survey. Other motives include conducting galaxy evolution studies, science with the faintest possible sources and the properties of the host galaxies of transient events.
The Y3 DF catalogue consists of data from three SN fields plus the COSMOS field, with a total coverage of 5. 88 deg 2 and photometry of over 1. 7 million objects (after masking for image defects) in DECam  and VIRCam  bands. We combine the deep (∼ 1. 25 mag fainter than the WF data) and precise  photometry in this catalogue with the accurate redshifts and stellar masses from the COSMOS2015 catalogue to produce a 'baseline' DF data set. Specifically, we utilise the bulge+disc model-fit magnitudes computed using the Multi-Object Fitting (Drlica-Wagner et al. 2018) algorithm. Our goal is to produce valid posterior PDFs of galaxies in the main DES survey and to achieve this we require a suitable data set with which to train a RF model. The photometric errors in the DF data set would not reflect those in the WF and so would lead to biased results if used directly as training data.
Balrog Balrog is a Python package designed for the purpose of measuring the selection function of imaging surveys  (Suchyta et al. 2016; Everett et al. 2020). The process by which it achieves this is as follows. A realistic ensemble of fake stars and galaxies are generated using GALSIM  (Rowe et al. 2015) , including survey characteristics appropriate to their intended sky location, e. g. seeing FWHM. The fake objects are then embedded into real survey images, thus inheriting many of their properties. Finally, the objects are detected and measured using SExtractor  (Bertin & Arnouts 1996)  in the same way as the original survey images. The output catalogue comprises a Monte Carlo sampling of the selection function and measurement biases and naturally accounts for systematic effects arising from the photometric pipeline, detector defects, seeing and other sources of observational systematic errors.
The Balrog process requires a prior population of galaxies from which to draw objects. The DES Y3 Balrog catalogue  (Everett et al. 2020)  was produced by injecting model fits of galaxies drawn randomly from the Y3 DF catalogue into DES Y3 single-epoch images and then measuring their properties. This catalogue contains true and measured  photometry of nearly 4 million objects, and it provides us with ready-made emulated galaxies that reflect our target WF data set, the DES Y3 GOLD  (Sevilla-Noarbe et al. 2020). By combining the Y3 Balrog catalogue with COSMOS2015, we obtain a data set that closely matches and is representative of the WF data, capturing many of the details of the objects' noise properties, but with the addition of accurate redshifts and stellar masses. From the catalogue, we use composite model magnitudes in this work.
1999) , defined as 𝜇 = 𝜇 0 -𝑎 sinh -1 𝑓 2𝑏 , (2) where  0 =  0 -2. 5 log ,  = 2. 5 log ,  is the flux,  is an arbitrary softening parameter, and  0 is the magnitude zero point. The authors state that the optimal value of  = √ , where  is the standard deviation of the flux. We set the value of  to be the median of the standard deviations. Additionally, we transform flux errors into luptitude errors using 𝜎 𝜇 = 𝑎𝜎 2𝑏. (3) Luptitudes behave like magnitudes for bright photometry and like fluxes for faint photometry, with the turning point in the behaviour determined by the softening parameter. By converting to luptitudes, we avoid introducing an additional selection effect by not discarding galaxies with negative fluxes. To produce the WF data set, we start anew and match 'WF' galaxies in the Y3 Balrog catalogue to their counterparts in the Y3 DF using the ID column.
Next, we cross-match the galaxies in the intermediate catalogue to the COSMOS2015 catalogue. There are multiple scattered WF copies of each DF galaxy in the Balrog catalogue to efficiently sample the DES selection function, and to preserve this we keep all of the copies. This is an important aspect of our set-up, as it captures the selection function through the galaxy detection probability as a function of true photometry and light profile, and the asymmetric scatter between photometry and galaxy properties (redshift and stellar mass) that it induces. We remove any galaxies with erroneous flux measurements by selecting all galaxies with MEAS_CM_FLAG = 0  (Everett et al. 2020). Finally, we repeat all the aforementioned cuts and steps used in constructing the DF data set, the only difference being that on this occasion, we apply the -band cut to the magnitudes of WF galaxies.
Thus, we have 'augmented' a completely realistic target data set which effectively replicates the systematics in the WF survey without compromising on the accuracy of redshifts and stellar masses. After all the pre-processing steps, there are 53, 491 galaxies in the DF data set and 393, 276 galaxies in the WF data set. Each data set contains the following information:  luptitudes and luptitude errors, photo-zs and stellar masses. Additionally, we compute all the relevant lupticolours; and the associated errors using the standard error propagation formula: 𝜎 𝑐 = √︃ 𝜎 2 𝜇 1 + 𝜎 2 𝜇 2 , (4) where   1 and   2 are the errors on the luptitudes, and   is the error on the computed lupticolour. Fig. 1  shows the marginal and the joint distribution of redshifts and stellar masses of galaxies in the DF data set, and the distributions of  luptitudes.
The average redshift and stellar mass is approximately 0. 7 and 5 × 10 9  , respectively. For the sake of brevity, we do not show a similar figure for the WF data set as the distributions are broadly similar. We perform an 80 : 20 split on the DF and WF data sets to create their training and testing data sets, respectively. As there are multiple copies of each galaxy in the WF data set, we ensure that there is no admixture of unique galaxies in its training and testing data sets. In other words, unique galaxies that exist in the training data set do not appear in the testing data set, and vice versa. As a consequence, there are 314, 196 and 79, 080 galaxies in the WF training and testing data sets, respectively. Lastly, we randomly sample 10, 699 galaxies without replacement from the WF testing data set to construct its final version.