dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgement High Performance Computing resources were partially provided by the EXPLOR centre hosted by the  University de Lorraine , the ROMEO centre hosted by the  Université de Reims Champagne-Ardenne , and  GENCI  (allocation number  A0020807496 ). H. G. , A. M. and G. M. are grateful to  Campus France and Tubitak  for travel grants ( PHC  Bosphore  31862YG  and  35673VB ). H. G. thanks the  French Embassy in Ankara, Turkey  for co-tutelle de thèse grant, and  TUBITAK-Science.
B. OpenVIM logical architecture OpenVIM is a lightweight VIM which follows the NFV principles with interfaces to the compute domain of the NFV Infrastructure and relying on OpenFlow controller (OFC)  [18]  for the network infrastructure. The logical architecture of OpenVIM is simple, consisting of only one service, driving the main functionality. It is complemented by a CLI tool, RESTful API and a MySQL database. The "normal" operation mode implies the deployment of a controller with one or more compute nodes and requires integration with an OFC for managing the networking between them. A "host only" mode where the installation can be done on only one host without the need of an OFC is also available. The OpenVIM service (openvimd) is the core component of the architecture and is responsible for the creation, deletion and inventory of VMs, networks and images.
C. VIMs at the edge Necessary properties of software running at the edge is to be efficient, scalable and distributable. OpenStack architecture is meant to be flexible and expandable and allows additional services to be added. The communication between modules is done through messaging which brings overhead to the systems but allows horizontal scaling. OpenVIM is created with the intention to provide the minimal functionalities of a VIM and consists of a single service spawning multiple threads. This leads to more efficient implementation but with less possibilities for extension. It lacks many of the complementary services of OpenStack -Dashboard, Identity, Telemetry etc. Both VIMs can be scaled horizontally by adding additional compute nodes although they lack inherent capabilities for automatic node discovery.
The modularity of OpenStack makes the multiplication of the controller node possible with some additional efforts, while OpenVIM does not foresee controller scaling. Both projects are implemented entirely in Python, which makes them rather independent from the underlying architecture. However, additional investigation should be done to take into account the heterogeneous resource-constrained devices common for the network edge. OpenStack is capable of running on ARMv8, despite some known issues related to the interaction with the hypervisor and the configuration of devices. OpenVIM has been officially tested only on Xeon E5based Intel processors and the behavior on other architectures is not described.
A. Extending CloudBench framework The CloudBench framework is written in Python with extensibility and flexibility in mind and has a layered architecture shown in Figure  3. The support for OpenVIM was incrementally added by implementing a new cloud adapter without major changes in the main source code. To achieve this a prior research on OpenVIM mechanisms was needed to understand how to: connect to the cloud, list images flavors and SSH keys, list and get relevant information about instances. In summary, the changes are one new configuration file and one new Python class implementation, consisting of 800 lines of code. The performed steps for adding a new cloud adapter can be described as:.
B. Test environment A comparison of the minimum requirements for the deployment of a proof-of-concept environment for the two managers is shown in Table  II. OpenStack deployment through Devstack  [21]  and OpenVIM is configured to work in "host only" mode. For a hypervisor we use KVM (Kernel-based Virtual Machine), an open-source software with kernel component included in the mainline Linux  [22]. In order to have an equal test environment the VIMs were deployed on a single host with the following configuration: Intel(R) Xeon(R) CPU E5-2623 v4 @ 2. 60GHz with 32GB memory and 4TB storage and Ubuntu 16. 04. 1 Server operating system with KVM-enabled 4. 4. 0-31-generic Linux kernel.
The chromatograms obtained from the ICP-MS software were analyzed using the freeware program Unichrom (Unichrom, available from http://www. unichrom. com, 2018) and a homemade script written for the software R 3. 5. 0 (RStudio). The retention time of the internal standard was used to calculate the Retention factor, and the intensities were normalized by the main peak intensity of the corresponding chromatogram. The size distribution modes were determined from the peak retention time after a polynomial smoothing.
, Pleasanton, CA, United States) Camera with a 4096 × 4096 CCD. Elemental composition was determined using EDX with a JEOL XEDS detector with 140 eV resolution. Measurements were also carried out with a scanning TEM detector JEOL with a one nm probe operating in High Angle Annular Dark Field mode (HAADF), for obtaining a better composition contrast. EDX and scanning TEM were combined for elemental composition mapping. The diffraction images were analyzed using the programs ImageJ and Crystalmaker, combined with the RRUFF database.
Acknowledgments: The authors thank  Laura Degenkolb ,  Traugott Scheytt , and his team at the  Technical University of Berlin  for the XRFA measurements,  Lars Düster  from the  Bundesanstalt für Gewässerkunde  for measurements of soil particle size,  Wolfgang Fey  for the support provided during ICP-OES measurements, and  Andreas Felten ,  Pinar Koca , and  Mariam Abboud  for their valuable help during the methods development.
Software implementation Few smartwatches provide a software Development Kit and a programming language that enables embedded computing on the smartwatch. This is nevertheless required to dynamically customize the interaction with users. This allows to design customized interactions that fit between minimal interactions available on other commercial wearables and full symbiosis that are investigated in research. We implemented the WE-nner framework using the GARMIN Connect IQ environment and its MonkeyC object oriented programming language. GARMIN is one of the few smartwatch manufacturer that provides a Software Development Kit that enables to develop a program on a PC and to upload and embed the software on the watch itself. GARMIN offers a dedicated programming environment called Connect IQ 1  which enables to program and upload several kinds of applications on a smartwatch (face, widget, data field and apps).
Different types of applications have different degrees of access to the sensors and actuators of the watch and allow for different types of interactive capabilities. GARMIN wearables support four types of interactions components:  WatchFaces provide personalized "passive" displays of the main screen. Users are able to choose and download the watch face they prefer and add any information they like.  Widgets provide at-a-glance information to the user that meets the individual customization. They are usually small practical tools like a compass or a weather report and are limited in terms of interaction capabilities.  DataFields are fields displaying data which is computed or available at runtime (e. g. speed, time). The expert user can select the fields that she wants to be displayed and their order of presentation.
DataFiels do not support any interaction.  Apps are the most interactive components that can be uploaded on the watch. They can contain menus, data, textual and graphical messages that can be selected and combined at runtime. An app is explicitly started from the main menu of the watch. Interaction with advanced sport smartwatches such as a GARMIN watch can be quite tricky for the ordinary user. Furthermore, even apps available on the GARMIN store do not provide flexible and personalized multimodal interactions. We implemented our WE-nner framework as a Connect IQ App in order to benefit from the maximum access to sensors and actuators. Fig. 1  illustrates how the WE-nner software uploaded on the GARMIN Fenix 5 smartwatch provides interaction that is personalized at runtime before an activity starts.
Software architecture The smartwatch provides sensors, screen and loudspeaker that enables a user to track her daily physical activities. The activity information that includes time, duration, speed, distance, calories burned and heart rate is mostly saved in. FIT binary files. The GARMIN connect web site enables a user to login and visualize the data related to his/her recorded activities and health status. The GARMIN Connect Mobile Application is required to synchronize the data between the watch and the web site. We have developed an Android application called WEnnerMobile. It enables a user to view his activity history, visualize his heart rate records by curve chart, and test his/her pressure level by a answering a questionnaire. It will also provide a virtual coach to give the user personal advice on exercises.
The user can choose a FIT or CSV fitness file stored in the phone memory. When a FIT file is chosen, it is be decoded into a CSV file, and the data can be processed and displayed in tables and charts. Finally a website is used (WEnnerWeb) to store data collected with the watch and the mobile phone. The administrator of this web site can define the sport types, user's initial preferences of the sport and the target training time. Each time when an activity is finished, the WEnnerWatch app can collect Ecological Momentary Assessment and ask the user to self report on her mood (e. g. "Positive", "Negative" or "Neutral"). Such information can be exploited to tailor and adapt follow-up sessions.
This requires considering contextual information (e. g. if the user is on the move, a vibration can be used to inform the user that she should stop and look at an important message on the watch). Frameworks for multimodal output generation will be considered  [19]. We will also consider the design of consistent interactions between the smartwatch, the mobile phone and a web site in order to support the relation between the user and her personalized coach which is in fact dispatched over several devices (possibly including other sensors and wearables). We are also considering on how an animated and expressive agent displayed on the smartphone (and simple representations of it on the watch) can motivate the user based on affective reasoning and data  [18]. These dimensions of personality and multimodal interaction will be considered as input for the modelling and implementation of the coach using the WEnner technological framework introduced in this paper.
The material distribution inside tree crowns is difficult to quantify even though it is an important variable in forest management and ecology. The vertical distribution of a relative density index (i. e. , vertical profile) of the total, woody, and leafy material at the crown scale were estimated from terrestrial laser scanner (TLS) data on two species, sugar maple (Acer saccharum Marsh. ) and balsam fir (Abies Balsamea Mill. ). An algorithm based on a geometrical approach readily available in the Computree open source platform was used. Beta distributions were then fitted to the vertical profiles and compared to each other. Total and leafy profiles had similar shapes, while woody profiles were different. Thus, the total vertical distribution could be a good proxy for the leaf distribution in the crown.
In order to better understand the mechanisms driving the vertical arrangement of material in forest ecosystems, studies integrating both conifer and broadleaved species in mixed stands are needed. Moreover, most studies on vertical distributions face important limiting factors: the methods used to obtain the distributions are both labor intensive and destructive. Light Detection and Ranging (LiDAR) technology has been increasingly used to quantify the amount of plant material (leaf and wood separated or not) and its 3D distribution at the stand and tree scale  [24] [25] [26]. There are three types of returns a LiDAR sensor can record (i. e. , single, or multiple returns and full waveform, the two first can be derived from the third), and the algorithms must be developed accordingly. However, several limitations of LiDAR data affect their ability to detect the exact distribution of canopy components, namely signal occlusion, the presence of noise returns, and in some cases the variable point density in the point cloud.
Among the different challenges in LiDAR data processing, two seem to be particularly important to quantify the vertical density profiles. The first one is to separate wood returns from leaf returns, for which different methods have been explored. A simple and intuitive method is to scan leaf-on and leaf-off trees to obtain the leaf returns by subtracting the two-point clouds  [26, 27]. This approach, which needs bi-temporal scans, is obviously inapplicable to evergreen species and introduces uncertainties due to the time laps between the measurements. Another approach is to use an intensity return threshold to separate the wood related returns from the leaf returns. This approach worked on LiDAR pulse wavelength at 1535 nm or higher  [28, 29] , which is not compatible with all Terrestrial Laser Scanners (TLS).
Moreover, the absence of calibration of the intensity values and variable objects orientation could also make intensity return thresholding unreliable. Dual wavelength devices also seem to offer promising solutions, but they are not widely available  [30, 31]. Oshio et al. [32]  manually separated woody and leafy returns in airborne laser scanner point clouds. The manual method is, however, too time consuming and subjective to be applicable over large areas or complex datasets. Approaches based on geometric modelling, which solely rely on the xyz coordinates of the point cloud, appear to be the most generalizable. These methods are not as limited by sensor specifications (wavelength or multispectral) or acquisition characteristics. As a result, they can be applied to data acquired using different instruments and experimental protocols.
Different approaches have been explored and tested using geometrical feature extraction and machine learning  [33] [34] [35] [36] [37]. Tao et al. [38]  have shown that circle and line fitting on 2D projections of the 3D point cloud allowed a better wood/leaf separation than approaches based on intensity returns. An important limitation of LiDAR data is occlusion, which occurs when part of the scene is hidden by objects between the scanner and the area of interest in the point cloud. This limitation depends on vegetation density, scanner resolution, and in the case of TLS, it depends on the number of scans used from different positions in a plot  [39] [40] [41]. With TLS data, occlusions are particularly apparent in the upper part of the canopy because the scans are taken from the ground. By contrast, occlusions for Airborne Laser Scanners (ALS) data are more apparent in the lower part of the canopy.
The scanner rotation angles ranged from 0 • to 360 • in the horizontal direction and from 60 • to 90 • in the vertical direction. The FARO Focus 3D TM is a single return instrument. Scan alignment was required to create one global point cloud from the four scans. Alignment was facilitated by using six spherical targets placed around the TT, with a minimum of three targets visible in each scan. No filters were activated on the FARO instrument during the scans, but two filters were applied during data preprocessing using the software FARO Scene 5. 0 (FARO Technologies Inc. , Lake Mary, FL, USA): (i) the dark scan points filter to remove points with a very low reflectance (less than 300) which correspond to false returns or sky points and (ii) the stray filter, which validates that the return is due to an object by comparing the distance between the object and the scanner with the distance of the surrounding points (grid size of 3 × 3 × 3 points).
The return is retained as a scan point if 50% of the differences in distance with the surrounding points is less than 0. 02 m. Files were prepared for wood/leaf separation and the correction of signal occlusion by extracting several files format. An ASCII file containing the four aligned and filtered point clouds was exported from FARO ® Scene to be used by the wood/leaf discrimination algorithm. The TT of each site was previously manually isolated using the Computree software (http://rdinnovation. onf. fr/, consulted on 14 March 2017). Tree isolation was easy since the TT neighbors had been felled before the scans were taken and the TTs were thus left clearly isolated in the middle of a gap (Figure  1a ). Filtered point clouds were necessary for this step since raw point cloud contained too much noise points which could bias the geometrical approach.
A raw point cloud file ". xyb" without filters was extracted for each scanning position to be used by the occlusion correction algorithm (see section Voxel representation of material distribution). The ". xyb" file was chosen as it is faster to extract from FARO ® Scene and faster to process with the software used, Computree (http://rdinnovation. onf. fr/, consulted on 14 March 2017). Target trees (TT) were scanned with a TLS, the FARO ® Focus 3D (FARO Technologies Inc. , Lake Mary, FL, USA), during the summer of 2013 at four opposite viewpoints after the neighbor trees were felled. The TTs were scanned with a TLS with a resolution angle of 0. 036° in both the horizontal and vertical directions. The scanner rotation angles ranged from 0° to 360° in the horizontal direction and from 60° to 90° in the vertical direction.
The FARO Focus 3D TM is a single return instrument. Scan alignment was required to create one global point cloud from the four scans. Alignment was facilitated by using six spherical targets placed around the TT, with a minimum of three targets visible in each scan. No filters were activated on the FARO instrument during the scans, but two filters were applied during data preprocessing using the software FARO Scene 5. 0 (FARO Technologies Inc. , Lake Mary, FL, USA): (i) the dark scan points filter to remove points with a very low reflectance (less than 300) which correspond to false returns or sky points and (ii) the stray filter, which validates that the return is due to an object by comparing the distance between the object and the scanner with the distance of the surrounding points (grid size of 3 × 3 × 3 points).
Wood and Leaf Separation A cylinder fitting algorithm was used to determine which returns were tied to a wood structure and which ones were from a leaf. This algorithm is available in the Computree software and was initially designed to detect stems. In the present study we kept the default parameters proposed in the software. The approach also needs some treatments in R (cylinder filtering in step 4 step 5)  [49]. The algorithm processes each point cloud to identify the linear and cylinder-like features by applying the following five steps: (1) Clustering of the point cloud into 2 cm high horizontal slices in order to identify small trunk arcs and branches. Two points are considered part of the same cluster if they are separated by no more than 3 cm. (2) Vertically merging point clusters to form linear elements following these two conditions: (i) the vertical distance between clusters has to be less than 10 cm and (ii) the bounding box of two clusters must intersect each other in the XY plan.
This step was originally designed to detect tree stems, but a continuous branch which is not vertical may also meet the two conditions. (3) Merging parallel linear elements to form the trunk and branches. The XY distance threshold to merge linear elements must be large enough to merge non-vertical branches. It was set to 50 cm after preliminary visual tests on the datasets. (4) Adjusting cylinders on linear elements by using variable angles (from vertical to horizontal). This adapts the cylinder fitting procedure on tree stem segments and branches that are not vertical. Several cylinders can be adjusted for each linear element. The points associated to a cylinder are projected on a plan, perpendicular to the axis of the cylinder, and a circle is fitted on the 2D point cloud. An absolute and relative error of the fitted circle is associated to each cylinder.
Moreover, a linearity error is also associated to each cylinder. This last parameter is the mean square error of the 3D linear regression on the barycenter of three consecutive cylinders (i. e. , belonging to the same linear element). Cylinders considered as a wood linear element in the point cloud has a maximum absolute and relative circle error of 4 cm and 30%, respectively, and a linearity error of 0. 001 m. More details on each step are given in the Computree software documentation (http:// rdinnovation. onf. fr/, consulted on 14 March 2017). (5) Exporting the point cloud with an associated cylinder ID. The points associated with a cylinder (points of step 4) were considered as returns from wood (R W ) and the other points were considered to be returns from leaves (R L ). Finally, the point cloud was divided into 10 cm edged voxels to calculate the proportion of R W and R L in each voxel (Figure  1 ).
The Advantages and Limitations of the Method Wood/leaf separation using geometrical shape (i. e. , cylinder fitting) presents an advantage over other methods as it can be applied to a point cloud with xyz coordinates information without requiring information on return intensity or color. Geometrical-based methods are more generalizable (i. e. , they can be applied to any sensor) and have already proven to be efficient on adult trees  [33, 38]. Moreover, in the present study, the cylinder fitting algorithms used are already available and operational to detect stems in Computree, an open software (http://rdinnovation. onf. fr/, consulted on 14 March 2017). The wood/leaf separation approach is a good starting point using a user-friendly geometrical approach on TLS data. It can however be improved and has to be validated.
In order to completely validate the approach used here, it would be interesting to demonstrate the reliability of the approach by running the algorithms on simulated tree  [27, 65]. Finally, we used TLS data to quantify the vertical distribution of the relative density of the material but we did not transform the density to an area unit (e. g. , leaf area). If tree growth and photosynthesis have to be simulated using leaf surface area, a thorough validation of the algorithm is needed to establish if any bias remains. A transformation from the RDIT or RDIL to the leaf area is possible at the voxel scale using methods such as those developed by Hosoi and Osama  [26] , Van der Zande et al. [66] , or Béland et al. [28]. It is also possible to use the total leaf area index at the tree or stand scale obtained by other methods (e.
Wood leaf separation and occlusion are two important challenges in using TLS data in forest ecology. This approach is thus a starting point for a generalizable method to separate wood and leaves using LiDAR technologies and already operational tools (Computree). For one species, total and leaf material vertical distributions were similar, suggesting that the leaf vertical distribution can be inferred from the total distribution of the canopy material that is easier to quantify at large scale using airborne LiDAR data.