dataesr/scientific-paragraphs-categorization huggingface dataset:
Modeling procedure Our code ProDiMo (Protoplanetary Disk Model) is a radiation thermochemical code, which self-consistently computes the physical and chemical structure of disks. The code includes 2D radiative transfer, and gas chemistry, including freeze-out and desorption processes; it can perform hydrostatic or parametric description of an axisymmetric disk structure  (Woitke et al. 2009a ). X-ray chemistry has been implemented by  Aresu et al. (2011). The code computes the energy balance of the gas, including photoelectric heating, C photoionization, H 2 photodissociation and formation heating, collisional de-excitation of H 2 , cosmic ray heating, viscous heating, cooling via Ly-α, [OI] 6300 Å, and more than 10000 atomic/molecular emission lines. Thermalization between dust and gas and free-free processes complete the energy balance.
The radiation field comprises the central star and the ISM (InterStellar Medium) UV field. The multiwavelength radiative transfer is performed using accelerated Λ iteration, and ray tracing (generally 100 rays). The statistical equilibrium of atoms/molecules, however, is computed considering a twodirectional escape probability, assuming that the line source function varies slowly in local environments, where the optical depths grows quickly, and photon escape is mainly in the vertical direction. For each species treated in the spectroscopy, the code ProDiMo computes the statistical equilibrium based on a large set of molecular and collisional data. Optionally, a detailed line radiative transfer can be performed for a selected subsample of transitions. The code has been updated with several features: a detailed computation of the photoionization and photodissociation cross-sections from  van Dishoeck et al.
(2006) , an extension of the collisional partners for the water statistical equilibrium  (Kamp et al. 2010) , a soft-edge truncation of the outer disk (Sect. 2. 1. 3), PAH (Polycyclic Aromatic Hydrocarbons) ionization balance and heating/cooling, UV fluorescence pumping, a parametric description of settling, pumping by OH photodissocation, H 2 pumping by formation on dust grains, and chemical heating  (Woitke et al. 2011). A prescription for the settling following the formalism of  Dubrulle et al. (1995)  has been implemented and is described in Section 2. 1. 2. The chemical modeling has been performed using the UMIST 2006 database  (Woodall et al. 2007). Even though the UMIST 2012 database  (McElroy et al. 2013 ) is now available, it was not implemented when our work started. If three-body reactions are included, however, the results for water are comparable with the older network  (Kamp et al.
in prep. ). A test performed with the UMIST 2012 database and the standard model shows that the disk gaseous water distribution is unchanged (except a tiny abundance variation, which is very difficult to quantify in the outer disk), and the line fluxes of the far-IR water lines are affected by less than 25% and those of the mid-IR lines by about 1%. Our chemical network contains a complete series of gasphase reactions for 94 atomic, ionic, and molecular species, involving water and related precursors. Surface reactions for water are not included in the network, as they are not relevant for the emitting regions of the transitions that we consider  (Kamp et al. 2013 ). The chemistry is calculated assuming steady state. The chemical network and heating/cooling balance are solved iteratively. Gas heating via exothermic reactions is approximated via the chemical heating efficiency  (Woitke et al.
We demonstrate the importance of future telescopes such as JWST in the investigation of water in disks. The higher sensitivity is capable of increasing dramatically the number of disks detected in water. The higher spectral resolution will permit us to detect unblended transitions, and to study in detail the physical conditions in the inner water reservoir. Acknowledgements. The research leading to these results has received funding from the  European Union Seventh Framework Programme   FP7-2011  under grant agreement no 284405.
A challenge for roboticists consists in promoting collaborative robotics for industrial applications, i. e. allowing robots to be used close to humans, without barriers. Safety becomes the key issue. For manipulation tasks, a part of the problem is solved using new arms like the KUKA LWR, which is able to physically detect a collision from torque measurements on each joint. Nevertheless it is better to avoid collisions, especially if the obstacle is the arm or the head of an operator. This paper describes how obstacles could be detected and avoided, using a single Kinect sensor for the monitoring of the workspace and the reactive planner of the KineoWorks T M software library for the real-time selection of an avoidance trajectory. Experimental results are provided as a proof that this dynamic obstacle avoidance strategy works properly.
First, using the modules available on the ROS web site (ros, 2010), it is possible to perform an experiment with the robot stopping as soon as an obstacle is detected on the trajectory, and restarting as soon as the path is free. This paper presents a more reactive approach, integrating the ROS middleware with the reactive planner developped by Siemens PLM Software (formerly Kineo CAM), and evaluating using a dedicated setup with a KUKA arm available at LAAS-CNRS. It is also proposed to integrate the SoftMotion controller developped by LAAS-CNRS for collaborative manipulators interacting with humans, in place of the Reflexxes module available with ROS. Presented here is the principle of the planner with initial experimental results. Section II proposes a very synthetic state of the art. Section III recalls how to use the OctoMap module in order to monitor the robot path during motion execution.
Our reactive planner has been firstly integrated with the Reflexxes framework. Even if the Reflexxes framework is very efficient, it does not guarantee an error bound between the initial path and the executed trajectory. The current version of our system takes profit of the SoftMotion framework as the robot controller  (Broquere et al. , 2008) (Broquère and Sidobre, 2010). The SoftMotion controller has been independantly developped in order to generate motions for collaborative manipulators, i. e. soft motions more tolerable by an operator; this module was previously validated and adapted for a KUKA LWR-IV arm, as it was presented in  (Zhao et al. , 2014). Our reactive planner produces a path to be executed by the robot as a polygonal line defined by a sequence of via-points. Using smoothing techniques, SoftMotion computes firstly a trajectory stopping at via-points and then smooth the trajectory near each vertex.
OBSTACLE DETECTION The Kineo T M Collision Detector (KCD) provides fast and reliable collision detection, based on minimal distance computations. This module is synchronized with the OctoMap module  (Hornung et al. , 2013). The OctoMap is updated at 30Hz with the point clouds acquired by an Xtion PRO LIVE RGB-D camera. In the OctoMap updating process there is a compromise between updating speed and noise: the faster the update, the easier noise appears. Has we are doing reactive planning, we want the OctoMap to be updated as quickly as possible. In order to avoid the introduction of noise in the OctoMap, we must use robust noise filters. In fact, before a point cloud is added to the OctoMap, it undergoes three filtering steps. The first step removes the points corresponding to NaN depth values, typically points at infinity or on specular surfaces, and points out of reach of the robot's arm, with which there is no possible collision.
SIEMENS REACTIVE PATH PLANNING ROS PACKAGE As part of the ICARO project, we have developed a reactive path planning package over the ROS middleware. This kws ros interface package (hereafter kws ros), is built on top of the KineoWorks T M software component. KineoWorks addresses all aspects of motion processes including collision-free automated path planning. It includes the most common motion types, such as joint motion, and features an advanced algorithm for detecting collisions along a trajectory, which is both fast and exact, regardless of kinematical complexity. The reactive path planning involves two packages: the kws ros package dedicated to trajectory planning, interacts with the Kineo T M Collision Detector (hereafter, KCD).
General Structure The kws ros package contains two nodes, one in charge of all collision-free path planning tasks and the other performing reactive control  (Yoshida and Kanehiro, 2011)  over the executing trajectory. Both nodes embed a representation of the scene into KCD. This one has to be defined offline in a dedicated CAD modeller, the Kite module, by providing CAD models of static obstacles and parts of the robot, together with its kinematics model. The representation is loaded at node launch and it is then enriched, for reactive purpose, by the dynamic point cloud data such as those obtained through laser scanning or optical vision sensors. KCD enables collision detection between point clouds and the rest of the environment. Each one of these nodes is implemented using a robust state machine fully driven by the ROS actionlib protocol allowing multi process communication.
A first robot motion control architecture presented on Figure  3  has been integrated and validated. It uses the standard OMPL module for path planning, and the Reflexxes framework (T. Kröger, 2011)  for motion generation. The experiment presented in the next section, have been performed using the architecture presented on Figure  4 , where the kws ros interface for the planning function and the Kineo T M Collision Detector (KCD), replace the standard ROS modules. The planned trajectory has to be pre-processed for feeding the Reflexxes module in order to generate motions. In the last version, we have replaced Reflexxes by the SoftMotion module, which generates better motions with respect to the operator acceptability; see Figure  5.
CONCLUSIONS This paper presented a reactive planification algorithm, that has been integrated in a ROS architecture devoted to the execution of collaborative manipulation tasks between a KUKA LWR arm and an operator. Such a planner, associated with a module dealing with obstacle detection, guarantees that a robot arm could detect and avoid a human before any collision. The detection module is built upon the OctoMap algorithm already integrated under ROS. It has been necessary to adapt OctoMap and to apply specific tunings so that (1) detected obstacles disappear from the map as soon as the corresponding actual objects are no longer on the trajectory the robot is executing, and (2) the robot is not itself detected as an obstacle. As soon as an obstacle detection occurs, the planner module tries to find another path, continuous with the initial ones in order to reach the goal.
If it fails, the robot performs as it would do by now with the OMPL module under ROS: it waits until the obstacle disappears from the map. As a future work, the OctoMap module has to be modified in order to minimize the latency when an obstacle appears or disappears in the map. Moreover the link between the kws ros reactive planner and the SoftMotion controller has to be refined in order to make more dynamic the obstacle avoidance.
The lack of replicability and reproducibility of scientific studies based on computational methods has lead to serious mistakes in published scientific findings, some of which have been discovered and publicized recently. Many strategies are currently pursued to improve the situation. This article reports the first conclusions from the ActivePapers project, whose goal is the development and application of a computational platform that allows the publication of computational research in a form that enables installation-free deployment, encourages reuse, and permits the full integration of datasets and software into the scientific record. The main finding is that these goals can be achieved with existing technology, but that there is no straightforward way to adapt legacy software to such a framework.
Introduction In the course of a few decades, computers have become essential tools in scientific research and have profoundly changed the way scientists work with data and with theoretical models. Until now, these changes have had little impact on the scientific record, which still consists mainly of narratives published in articles that are limited in size and type of contents, and linked to each other through citations. Some particularly data-intensive fields of research also have their own digital repositories. An early example is the Protein Data Bank  1  , which publishes and archives structures of biological macromolecules. Some scientific institutions also propose digital repositories for their members, e. g. as part of the Dataverse community 2. However, for most domains of research and most scientists, no suitable repositories have existed until very recently, and most datasets are still neither published nor archived.
Only the recent advent of digital repositories accepting any type of scientific data  [3] [4] [5]  and peer-to-peer networks  6  for scientific data has finally made the publication of scientific data accessible to any scientist willing to do so, all the more since these repositories generally do not charge any fees for publication. The increasing number of mistakes found in published scientific findings based on non-trivial computations 7,8 has made evident the necessity of making computational science more transparent by publishing software and datasets along with any descriptions of the results obtained from them. The fundamental problem introduced by computing is an enormous increase in the complexity of both theoretical models and computational methods, and a similar increase in the size of experimental datasets.
These models, methods, and datasets can no longer be handled without a computer, and must therefore be published in computer-readable formats. At the same time, this material must be presented in form that facilitates human understanding as much as possible. The tacit convention in science is that published results are assumed correct unless there is clear evidence suggesting a mistake. As C. A. R. Hoare famously said  9  , "There are two ways of constructing a software design: One way is to make it so simple that there are obviously no deficiencies, and the other way is to make it so complicated that there are no obvious deficiencies. " This observation applies equally to computational science, where the vast majority of published results have no obvious deficiencies, but are obtained using software that is much too complicated for anyone, authors and readers alike, to be certain about its correctness.
While publishing software and other electronic datasets is now technically possible, and initiatives have been started to create incentives for scientists to invest the additional effort required for making such material available 10 , much more work remains to be done to ensure that published software and datasets can actually be understood, verified, and reused by other scientists. This is particularly important because computational methods are becoming an essential aspect of all scientific research, including experimental and theoretical work in which computations are not the main focus of activity. It is therefore more appropriate to discuss these issues in the context of "computer-aided research" rather than the more narrow specialty called computational science. Most current efforts in this direction (see e.
g. Ref. 11-13) start from the status quo of computation in science and propose smallstep improvements in order to facilitate adoption by the scientific community. The work presented in this article takes the opposite approach of starting from the requirements of the scientific record and exploring how software and electronic datasets need to be prepared in order to become useful parts of this record. Both approaches are complementary: while ease of adoption is important for rapid improvement, it is also important to have a clear idea of the goal that should ultimately be reached, in order to avoid getting stuck in technological dead-ends. This article presents the lessons learned from developing a new computational framework, called ActivePapers 14 , and from using it for several research projects in the field of biomolecular simulation.
The first section discusses the conditions that electronic datasets, including software, must fulfill in order to be fully integrated into the scientific record. Next, the concept of an ActivePaper as a collection of electronic datasets with references and provenance information is introduced. A brief overview of tools developed by the Reproducible Research and Open Science communities explores similarities to and differences from the definition of an ActivePaper. Two concrete realizations of the ActivePapers idea are presented, and experience with their use in several research projects is reported. The main contribution made by this work are the following observations and insights:.
g. F1000Research: a DOI refers permanently to a specific version of an article, but multiple versions of this article remain accessible permanently in order to document the evolution of the publication. The permanence of the scientific record applies only to the preservation of the original expression of each information item, but not to its semantics. A published article can become unintelligible because of changes in terminology and in the scientists' education. A modern physicist would not recognize the theory of classical mechanics in Newton's "Philosophiae Naturalis Principia Mathematica" 16 without prior training in Latin and in the history of science. Scientific work that has been recognized as important is reformulated and summarized in the course of time in the form of review articles, monographs, and textbooks for education.
For this reason, the slow decline in intelligibility of the original publications is in general not a problem. With the widespread adoption of computer-aided research methods, the size and complexity of scientific data has exploded, making a publication in the form of tables and figures both impracticable and useless. Digital repositories have therefore become an important second pillar in several domains of research, next to the traditional journals. These repositories store electronic datasets submitted by researchers and attach metadata such as author names, date of submission, file formats, or citations of related journal articles. These datasets are made available to the scientific community, usually free of charge, for use in other studies, including the application of data mining techniques in meta-analysis studies.
Permanent references to electronic datasets take the same form as for electronically published journal articles, i. e. a DOI, and the repositories have the same moral obligation as journal publishers to keep the datasets accessible as long as possible. Concerning the permanence of the contents, each repository applies its own policy. Zenodo 5 , for example, does not allow any modification of published datasets, but allows a dataset to be marked as obsolete with a reference to its successor. The Protein Data Bank (PDB)  1  updates its electronic records to correct mistakes in the representation of the data and to adapt the data to changes in the PDB's data model. However, changes to the scientific contents are not permitted and require the submission of a new entry, which like in the case of Zenodo can be labeled as making a prior entry obsolete.
The most important difference between traditional articles and electronic datasets from the point of view of the scientific record is the semantic context required for the interpretation of the stored information. For articles, the semantic context consists of a human language and the jargon and conventions of a specific scientific discipline, which change very slowly, on a timescale of centuries. For electronic datasets, the semantic context is ultimately defined by the computers and programs that read and write the data, which can change, intentionally or by accident, in a few seconds. The longevity of electronic datasets therefore relies on a careful documentation of the data models and data formats being used, on careful testing of software to ensure that this documentation is respected, and on proper curation of submissions by the repository managers to ensure adherence to these formats.
This is one of the goals of the ActivePapers project. The challenge of software preservation Among the electronic datasets used in computer-aided research, software poses the most difficult challenges for preservation because of its semantic complexity. What exactly a piece of scientific software does is defined by (1) its source code, (2) the programming language it is written in, and (3) all its dependencies: libraries, other programs called as subprocesses, etc. Since many of today's popular programming languages are not defined with precise semantics (see e. g. Ref. 18 for the languages C and C++), (1) must in practice be replaced by the specific compiler or interpreter being used. Moreover, compilers, interpreters, and dependencies are in constant evolution. Preserving a piece of scientific software with its exact semantics would thus require preserving its complete computational environment down to the hardware.
It can thus be assumed that most DSSP users are not aware of the fact that they are using a modified method. If the original method had been published in executable form and preserved until today, such discrepancies could have been avoided. Software preservation has only recently become a topic of research in its own right. One focus is the definition of the features of software that can or should be preserved 21. Various technologies for preserving software can then be evaluated with reference to this feature list. Other projects (e. g. 22) start by trying to preserve the software behind a given scientific study in order to explore the issues that need to be addressed. The ActivePapers project pursues the inverse approach: rather than working on the preservation of today's scientific software, it explores how tomorrow's scientific software could be written in a way that facilitates preservation.
Replication, reproduction, and reuse One reason for maintaining the scientific record is to support the fundamental error-correction mechanism of the science, which consists of allowing anyone to verify prior scientific studies by trying to reproduce them. Reproducibility is indeed one of the cornerstones of the scientific methods. It is what distinguishes a scientific study from an anecdotal report. The reproducibility of computational approaches in science has received significant attention recently as a consequence of several highly visible computational studied that turned out to be mistaken 7,8 , causing a credibility crisis in computational science. The most basic requirement for building confidence in computational results is total transparency: the publication of all datasets and software used during a scientific study.
Replication refers to an attempt to repeat a computational protocol under conditions that are as similar as possible to the original setting. It is essentially a quality-control measure: if scientist B can replicate scientist A's computation, this shows that A has provided a sufficiently precise, complete, and correct record of the original work. Replicability is far from trivial because a precise and complete description of a computational study is a dataset that is both large and complex. Moreover, it is often difficult to obtain in today's computing environments. Reproduction refers to an attempt to repeat a computational study following the general principles of the original work but using different tools. Reproducibility plays the same role as in other branches of science: it establishes which aspects of a computational protocol are important for reaching specific conclusions, and identifies unintended dependencies of results on technical details of the procedure.