dataesr/scientific-paragraphs-categorization huggingface dataset:
Other toolboxes include the Fluvial Corridor Toolbox (https://github. com/EVS-GIS/Fluvial-Corridor-Toolbox-ArcGIS;  Roux et al. , 2015) , the NCED Stream Restoration Toolbox  (Lauer, 2006) , the River Bathymetry Toolkit  (McKean et al. , 2009)  and the RVR Meander toolbox  (Abad and García, 2006)  to measure channel features and processes (e. g. migration rates). The River Analysis and Mapping engine (RivaMap) has been developed to facilitate the computation of large-scale hydrography datasets (i. e. extracting the river centreline and width) from Landsat data in a short time period  (Isikdogan et al. , 2017). The Valley Bottom Extraction Tool (V-BET;  Gilbert et al. , 2016)  and the Valley Bottom Confinement Tool (VBCT;  O'Brien et al. , 2019) , used across networks, allow the categorization of channel confinement categories and degrees.
The shape/morphology of different channel units (i. e. concave, convex and planar surfaces) can be mapped along reaches using the Geomorphic Unit Tool (GUT)  (Wheaton et al. , 2015; Kramer et al. , 2017)  as well as the Geomorphic Change Detection (GCD) software for sediment budgeting  Wheaton et al. , 2010; see www. riverscapes. xyz). Digital grain sizing algorithms developed by  Buscombe (2013)  (pyDGS: http:// digitalgrainsize. org/) and  Detert and Weitbrecht (2012;  Basegrain: https://basement. ethz. ch/download/tools/basegrain. html) are also available online as well as an algorithm for calculating roundness index  (Cassel et al. , 2018)  (https:// github. com/EVS-GIS/2D-Roundness-Toolbox). Most of these datasets and toolboxes are free to use, globally applicable and represent a valuable resource for researchers and managers worldwide.
Online platforms and repositories Sharing data and knowledge is an indispensable component of stakeholder-integrated problem solving  (Lehmann et al. , 2017; Dick et al. , 2018). The wide range of automatic feature extraction toolboxes listed above indicates that mapping/detecting geomorphic features is possible. However, collective organization and repository tools are needed. One example is the international long-term ecological research (ILTER) network, which gathers more than 600 sites worldwide in a broad variety of terrestrial, freshwater and marine environments  (Haase et al. , 2016; Dick et al. , 2018). Networking is based on the DEIMS-SDR data system (Dynamic Ecological Information Management System -Site and Dataset Registry: https://data. lter-europe. net/deims/), which includes a repository of remotely sensed data.
Similarly, a spatial data infrastructure (SDI) has been developed within the Human-Environment Observatories network, which brings together 13 French and international observatories, including river observatories  (Chenorkian, 2012). Web GIS, metadata and other visualization tools developed in this SDI are available for scientists and stakeholders. Additionally, the Data Center of the San Francisco Estuary Institute provides a broad range of tools and web services to upload, access and visualize remotely sensed datasets and other GIS layers to support and inform natural resource management in the area  (Grosso and Azimi-Gaylon, 2018 ; https://www. sfei. org/ sfeidata. htm). In the Earth surface sciences, the Community Surface Dynamics Modeling System (CSDMS) maintains a code and metadata repository for numerical models and scientific software tools (https://csdms.
colorado. edu). In hydrology,  Lehman et al. (2014)  reviewed innovative global observation solutions that provide a suite of hydrological standard specifications to the BRIdging Services Information and Data for Europe (BRISEIDE) project to visualize, manage and process geospatial resources useful for hydrological model development. Google Earth and NASA WorldWind also offer capabilities to visualize spatiotemporal data. An example is the Global Dam Watch initiative (http://globaldamwatch. org/), which aims to maintain the world's most comprehensive and freely available global dam data, including a repository for the GlObal georeferenced Database of Dams (GOOD 2 ) obtained from Google Earth satellite imagery, and an open list of existing dam data available at regional and global scales.
Ten future avenues for RS of Anthropocene rivers are: 1 Exploring existing data more deeply, such as national (maps and aerial photos) or satellite (Landsat archives) resources to assess channel behaviour and trajectories. This gap is particularly important in regions of the world where river corridor studies are rare, or where human activities such as damming are an issue (e. g. where channel sensitivity or bedload transport is not monitored). Additionally, recent advances in the digitization of old archives and maps, alongside increasing computational power and the availability of novel geomatic toolboxes, are opening new opportunities to generate vast databases of digital historical information, ready for big-data analysis. More work may be done on derivation of DEMs from stereo-photo pairs.
Recent (10-20 years) dynamics could be detected by stereoscopic acquisitions from aircraft or satellite highresolution images. Some satellites now acquire images at sub-meter resolution in stereoscopic mode (e. g. Pleiades and WorldView) and it would be worth testing their accuracy to explore their utility for Earth surface process monitoring. Finally, we might also question whether, after almost a decade of methodological development, more efforts could be made to use the existing data and place more collective effort on geomorphic understanding, theory and practice, rather than always seeking technological development. RS also still fails to capture key information on rapid phenomena such as the changes and bedload transport that occur in river channels during floods (high-frequency monitoring).
A new era is also emerging in this domain with Big Earth Data. It seems we are just at the beginning of this new period. Fluvial geomorphologists do not really use Big Data yet. There are very few deep learning papers in the river literature because the data are not available. This is especially true with VHR airborne data, where there are no papers on multiple catchments. River scientists still lack a shared global infrastructure to compile and organize data collectively. This is a new avenue for fluvial geomorphologists, and satellite archives are one of the key resources suitable for a Big Data approach. 7 Developing long-term integrative science observatories within which RS data are shared, managed and archived. Compiling data on river basins is critical to validate modelling studies and to develop simulations and scenarios.
Existing archives can be used to characterize large-scale historical trajectories and then advance our capacity to predict future change. Participatory approaches and citizen science are also a key future avenue to obtain information on channel geometry, status and attributes (e. g. grain size), for quality control or validation and for knowledge transfer. 9 Using RS to re-explore theories. Many concepts that were developed in the 20th century using small datasets can now be quantified and tested systematically using RS over much larger scales and at greater temporal resolutions than ever before. RS generates new opportunities to disentangle and quantify the role of natural and anthropogenic drivers in shaping river systems, rank them in terms of impact, identify the mosaics of riverscape conditions, better understand the timescales of adjustment and lag times, generate conclusions and assess their range of applicability.
Casado et al. (2015)  demonstrated that a low-complexity, shallow, artificial neural network (i. e. a multilayer perceptron) was capable of identifying geomorphic features in a short river reach with an accuracy of 81%. Recently, Buscombe and Ritchie (2018)  use a large dataset to demonstrate that a convolutional neural network (CNN) could be adapted to fluvial imagery in order to classify images and report mean F1 scores ranging from 88% to 98%. Carbonneau et al. (2019)  developed a novel approach dubbed 'CNN-supervised classification', which uses a pre-trained CNN to replace the user input in traditional supervised classification. They report mean F1 scores ranging from 90% to 98%. The result of 90% reported in Carbonneau et al. (2019)  is for rivers that were never seen by the classifier during the training phase.
Acknowledgements-We thank colleagues and students, including 40 PhD students who have worked with us during these 25 years of exciting research on emerging RS techniques applied to riverine sciences. This work was performed within the framework of the ZABR, the  EUR H 2 O'Lyon  ( ANR-17-EURE-0018 ) of  Université de Lyon (UdL)  and the  Observatoire Hommes-Milieux Vallée du Rhône (OHM VR)  of the  Labex DRIIHM  ( ANR-11-LABX-0010 ); the latter two are part of the  French program 'Investissements d'Avenir'  operated by the  French National Research Agency (ANR). Research on Fluvial RS has been highly supported by river practitioners, such as the  Agence de l'Eau Rhône-Méditerrannée & Corse , the  French Biodiversity Agency (AFB) ,  some Regions (ARA, PACA, Occitanie ,  Grand Est , etc. ), the  Compagnie Nationale du Rhône (CNR)  and  EDF  (main electric French company).
More data with higher resolution does not necessarily mean more knowledge. A key challenge and a goal for fu-ture river science will be to translate information into knowledge and to critically consider the data quality, meta-data and resolution accuracy. 3 ning strategy to target actions. 4 Implementing large-scale models and upscaling catchment characterization to continental or global scales. We are at the beginning of large/network scale modelling. In the fu-ture, river scientists should invest efforts to generate consis-tent hydrological, morphological and biotic datasets at global scales, working with local, national and interna-tional environmental agencies/institutions to characterize river status and develop model frameworks capable of tackling the network scale at which most fluvial processes operate.
Originally, it was applied to substitution-permutation networks and Feistel networks. Later, bit-based division property was proposed by Todo and Morii  [41]  and applied to the Simon block cipher with 32-bit blocks. Due to the high computation complexity of the search algorithm, it is infeasible to apply the technique to ciphers with larger block sizes. However, Xiang et al. [43]  discovered that the bit-based division property propagation can be efficiently encoded as an mixed-integer linear programming instance (MILP), and, surprisingly, can be solved on practice using modern optimization software (Gurobi Optimizer  [22] ) for practically all known block ciphers. Sun et al. [38]  described a way to encode the modular addition operation using MILP inequalities, extending the framework to ARX-based primitives.
Software Implementations Alzette was designed to provide good security bounds, but also efficient implementation. The rotation amounts have been carefully chosen to be a multiple of eight bits or one bit from it. On 8 or 16 bit architectures these rotations can be efficiently implemented using move, swap, and 1-bit rotate instructions. On ARM processors, operations of the form z ← x <op> (y ≪ ) can be executed with a single instruction in a single clock cycle, irrespective of. Alzette itself operates over two 32-bit words of data, with an extra 32-bit constant value. This allows the full computation to happen in-register in AVR, MSP and ARM architectures, whereby the latter is able to hold at least 4 Alzette instances entirely in registers. This in turn reduces load-store overheads and contributes to the performance of a primitive calling Alzette.
The consistency of operations allows one to either focus on small code size (by implementing the parallel Alzette instances in a substitution layer in a loop), or on architectures with more registers, execute two or more instances to exploit instruction pipelining. This consistency of operations also allows some degree of parallelism, namely by using Single Instruction Multiple Data (SIMD) instructions. SIMD is a type of computational model that executes the same operation on multiple operands. Due to the layout of Alzette, an SIMD implementation can be created by packing x 0. x n b , y 0. y n b , and c 0. c n b each in a vector register. That allows 128-bit SIMD architectures such as NEON to execute four Alzette instances in parallel, or even eight instances when using x86 AVX2 instructions.
The situation is a bit different for 8-bit AVR. The arithmetic/logical operations of Alzette amount to 78 instructions altogether, each of which executes in a single cycle, i. e. 78 clock cycles in total. Each of the used instructions has a length of 2 bytes, yielding a code size of 156 bytes. However, in contrast to ARM, we can not take it for granted that the whole state of a cipher fits into the register file of an AVR micro-controller, which means the load and store operations should be considered when evaluating the execution time. Loading a byte from RAM takes 2 cycles, while loading a byte from flash (e. g. for the round constants) requires 3 cycles. Storing a byte in RAM takes also 2 cycles. Consequently, when taking all loads/stores into account (including the loading of a round constant from flash), the execution time increases from 78 to 122 cycles and the code size from 156 to 196 bytes.
On the other hand, a possible cancellation merely multiplies the number of possible trails by 2. An algorithm enumerating all related-tweak truncated trails such that the probability of all differential trails that conform to them is below a given threshold, is therefore easy to write and is efficient. In fact, our straight-forward Python implementation returned all the results needed for this paper in a matter of seconds at worst. Large S-boxes such as Alzette are therefore very convenient building blocks to construct tweakable block ciphers with strong security arguments. Below, we present three Alzette-based (tweakable) block cipher structures for which we provide upper bounds on the probability of the best differential trail in both the single-key and the related-tweak setting. Of course, we also investigate other attacks.
The code size, RAM consumption, and execution time of Crax-S-10 on an 8-bit AVR and a 32-bit ARM Cortex-M3 micro-controller are summarized in Table  11 , along with those of five other lightweight block ciphers, namely Speck-64/128  [7] , Simon-64/128  [7] , Rectangle  [44] , Sparx  [19] , and Hight  [23]. We took the results for the five ciphers from the best implementations contained in the FELICS project  [18] , which is in the case of Speck the implementation "03" for ARM Cortex-M3 and the implementation "06" for the AVR architecture. 8 ince all these ciphers have a block size of 64 bits and a key size of 128 bits, they serve as good references for comparison. The ARM implementation of Crax we benchmarked is exactly the optimized C code included in Appendix D. 1. Encrypting a single 64-bit block on a Cortex-M3 takes 239 cycles (including function-call overhead), and the decryption has exactly the same execution time.
Conclusion Alzette is a component of a new kind, a wide S-box operating on 64 bits that can nevertheless be argued to provide strong security against many attacks. Because of its reliance on ARX operations with carefully chosen rotations, a constanttime implementation is both easy to write and very efficient on a wide class of processors and micro-controllers. The NIST LWC submission Sparkle  [8]  provides the first application of the Alzette S-box, but we showed that Alzette can also be used to design softwareefficient (tweakable) block ciphers on a variety of block lengths. A modified long-trail argument allows us to estimate the number of rounds needed to provide security with regard to (related-tweak) differential and linear attacks. We provided two concrete instances of this approach: the 64-bit block cipher Crax and the 256-bit tweakable block cipher Trax.
D Implementation Details In this section, we provide basic C implementations of Crax-S-10 and Trax-L-17. Both use the following macros for the computation of Alzette and its inverse. The decryption performs the same operations as the encryption, but in reverse order, and uses the inverse of Alzette. A reference C implementation of the decryption is given below. The optimized C implementation of the encryption function differs from its reference counterpart in three main aspects to improve the execution time. First, the optimized C code advises the compiler to keep the state words x, y and the words of the key in registers. Second, the optimized code partially unrolls the loop to eliminate the if-then-else clause of the reference implementation and reduce the loop overhead. Third, it duplicates the five state words to avoid the modulo-5 reduction in the calculation of the index through which the round constant is loaded.
Acknowledgements. Part of the work of  Christof Beierle  was funded by  Deutsche Forschungsgemeinschaft (DFG) , project number  411879806 , and part of the work of  Christof Beierle  was performed while he was at the  University of Luxembourg  and funded by the  SnT CryptoLux RG  budget. Luan Cardoso dos Santos is supported by the  Luxembourg National Research Fund  through grant  PRIDE15/10621687 /SPsquared. Part of the work of  Aleksei Udovenko  was performed while he was at the  University of Luxembourg  and funded by the  Fonds National de la Recherche Luxembourg  (project reference 9037104). Part of the work by  Vesselin Velichkov  was performed while he was at the  University of Luxembourg. The work of  Qingju Wang  is funded by the  University of Luxembourg Internal Research Project (IRP) FDISC.
The organization of the individual components and their inter-connections are achieved through a Finite State Machine (FSM). The proposed FSM fuses the data received from the robot sensors and the vision module, to distinguish expected interactions from disturbances and faults, and accordingly, to self-tune control gains and trajectories in real-time. Note that, the presented FSM is connected to the main experiment we conducted to validate the proposed method. Nevertheless, the algorithm can be applied to many other applications by modifying its states, as it is described in a section dedicated to the algorithm scalability. We prepared an agricultural experimental setup to demonstrate the potential of the presented methodology in one of the most promising but somewhat disregarded fields in robotics.
Method The main purpose of the presented framework is to enable robots to acquire an original set of skills to explore various environments, identify their characteristics and accordingly adapt to them, build the knowledge, and use it for future interactions. This methodology is based on the concept of self-adaptability, even after building the knowledge on task or environments. In this way, the robot has the ability to adapt by starting from a reasonable initial condition, even if the environmental conditions were subject to changes. The required theoretical and technological components to build such a framework are integrated into five main modules, that are illustrated in Fig. 2:  (1) a Cartesian impedance controller whose parameters can be tuned online, (2) a multiaxes self-tuning impedance unit to tune the aforementioned parameters when an interaction with the environment is predicted, (3) a trajectory planner to calculate the spatial points to be reached by the controller, (4) a visual perception module that locates the materials' positions in the robot workspace, and (5) a Finite State Machine (FSM) that, based on the data provided by (4), triggers unit (2) and (3), being also responsible of detecting system faults.
Visual perception module Visual perception plays a key role in the Finite State Machine, providing information about the materials which are going to be manipulated by the robotic arm. The module splits in two sub-systems, using as input RGB-D data from a range sensor (ASUS Xtion PRO) placed in fixed position with respect to the arm and facing the materials. In the first sub-system, different types of materials are detected in the scene, using RGB data, and their three-dimensional (3D) surface convex hull polygon is calculated, using depth data (materials localization sub-module). In the second one, a peak point per each material is localized in the base robot frame, using the depth data (peaks localization sub-module). Materials Localization To localize different materials in 3D, we use color-based region growing segmentation.
Finite state machine To increase the autonomy of the system, we designed a Finite State Machine that is responsible of managing the transitions between the different phases of the framework (see Fig. 2 ). This unit gets as input the data sent by the Visual perception module, and by processing them, defines the target poses that are sent to the Trajectory planner. Moreover, this module is in charge of determining if an interaction with the environment is to happen, in order to activate the Self-tuning impedance unit. The FSM is formed by four states. The "Workspace definition" state is responsible of acquiring the knowledge regarding the environment where the robot will operate in the next steps. To do so, it gets as input the vertices of the polygons that shape the materials within the robot workspace.
Experimental setup The framework software architecture is developed with the robotics middleware Robot Operating System (ROS) using C++ as client library. The modules illustrated in Sect. 2 are implemented as ROS nodes, and they communicate through the ROS topics depicted in Fig. 2  by means of the publisher/subscriber design pattern. The experimental setup (Fig. 5 ) includes two Franka Emika Panda robotic arms. The main robot carries out all the steps of the described method, and the other one is used as a support to the first one, providing the pot where the materials are poured after the scooping task. The presented architecture relies upon a tailored version of franka_ros metapackage, the ROS integration for Franka Emika research robots. This package integrates libfranka, Franka Emika's open source C++ interface, into ROS and ROS Control.
Deployment units -types and concepts In order to introduce the peculiarities of the separation problem in a Microgravity environment, this section shall provide an overview of the different requirements imposed onto the deployment of the MASCOT package to the Ryugu surface. Later we will investigate the suitability of common deployment mechanisms regarding these requirements and justify the chosen design to be described in a following chapter. There are two key requirements, which satisfy mission success: (i) The lander shall survice the launch and cruise phase attached to the carrier spacecraft and (ii) The lander shall land safely on the surface of the asteroid. In combination with the given environment during launch, cruise and at the asteroid these yield a list of further broken down requirements as displayed in Table  1.
System architecture The MASCOT space system (Figure  5 ) as installed on the HY2 carrier consisted of two main elements: (i) the Landing Module (LM) and (ii) the Mechanical Electrical Support System (MESS). These two elements were the compounds of the main system elements, which contributed to the deployment functionality. The physical breakdown (in terms of system blocks) and functional allocation is displayed in Figure  4. In order to understand the peculiarities of the design of these elements, in addition to the functions as mentioned earlier, it is important to mention that the MASCOT system was bound to very strict volume and mass constraints given by JAXA responsible for the design of the carrier HY2. In order to meet these, structural interfaces were minimized. The fixation of the LM to the MESS (Figure  6 ) and its associated deployment functionality was based on four cup-cone shaped stand-off elements shown in Figure  7  (right), which were held in place by a single load-bearing bolt to be released at separation.