dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, this Research Topic represents the effort of the computational modeling and analysis of biomolecular interactions field to provide well-designed web-based tools to the community. All the tools presented here are actively developed and well documented, with the intention of enriching the scientific tools landscape, and freely accessible through the World Wide Web. Science is collaborative and web-based tools and databases are the pillars of open science. As the editors of this Research Topic, we would like to thank all researchers contributing to this collection for their engagement towards Open Science best values. Following the success of this Research Topic, we have launched a second volume with similar scope.
The main hydrological runoffs are set in the zooms (96 rivers accounted for) from different source databases (Spain, English, French and Netherlands). The atmospheric forcings are picked up in the Météo France ARPEGE High Resolution (0. 1°) analysis with hourly data for the coarser grids. For the child grids, the atmospheric forcings rely on the Météo France AROME (2. 5 km) analysis with hourly data. The discretisation over the vertical axis is performed with 40 generalized -layers with a stretching function that induces refinement above 150m depth, next to the surface. A space refinement factor of 5 is used between each grid level but the time refinement is adapted according to the maximum velocity encountered locally. Thus, the time refinement coefficient is heterogenous: it is either 3 (over areas with rather slow flows) or 5 (in very energetic areas such as in the middle of the English Channel).
Satellite data Besides, satellite data are used for sea surface temperature validation at two different horizontal scales: The first one is based on SST fields extracted from the global Advanced Very High Resolution Radiometer (AVHRR) Pathfinder V5 daily dataset. The ODYSSEA chain has been modified by  Saulquin and Gohin, (2010)  to use optimal interpolation for the reconstruction of gap-free and using the previous analysis as a first guess. The product is gridded at a 0. 02° spatial resolution and freely available at https://resources. marine. copernicus. eu (Autret and Piollé, 2018). The second one is based on the Thermal InfraRed Sensor (TIRS) from the Landsat 8 satellite. As it orbits the Earth in a sun-synchronous, near-polar orbit inclined at 98. 2 degrees, one gets a track over our area of interest (Iroise Sea in paragraph 3.
Every individual perceives spatial audio differently due in large part to the unique and complex shape of their ears and head. Therefore, high-quality headphone-based spatial audio should be uniquely tailored to each listener in an effective and efficient manner. Artificial Intelligence (AI) is a powerful tool that can be used to drive forward research in spatial audio personalisation. The SONICOM project aims to employ a data-driven approach to link the physiological characteristics of the ear to the individual acoustic filters which allow us to localise sound sources and to perceive them as being located around us. A small amount of data acquired from users could allow personalised audio experiences, and AI could facilitate this by offering a new perspective on the matter. A Bayesian approach to computational neuroscience and binaural sound reproduction will be linked to create a metric for AI-based algorithms that will predict realistic spatial audio quality.
II. OVERVIEW SONICOM involves an international team of 10 research institutions and creative tech companies from six European countries, all active in areas such as immersive acoustics, artificial intelligence, spatial hearing, auditory modelling, computational social intelligence and interactive computing. The workplan is centred around three pivotal research work packages titled Immersion, Interaction, and Beyond, respectively. Sec. III summarises the aims and objectives of the first, identifying relevant technical developments and sensory aspects. On the other hand, the second focuses on the interaction between these and higher-level socio-psychological implications as briefly outlined in Sec. IV. The integration of the core research, the proof of concepts evaluations, and the creation of the auditory data ecosystem ensures that the various outputs of the project will have an impact beyond the end of SONICOM (see Sec.
Our HRTF modelling research considers a variety of approaches. On the one hand, we focus on the creation of parametric pinna models (PPM)  [2] ,  [3]  and their application to create an AI-based framework for the numerical calculation of HRTFs. On the other hand, we focus on HRTF database matching, an approach based on the hypothesis that individuals can be paired with existing high-quality HRTF datasets (measured or modelled), as long as they share some relevant pre-defined characteristics in the perceptual features space. To this end, we will expand the procedures based on objective similarity measures  [4]  and subjective listener input  [5] ,  [6]. Said measures concern geometrical variations for parametric pinna models, perceptually signal deviations of the resulting computed HRTFs, and signal domain similarities for HRTF matching, all referenced to a project database comprising geometrical scans and associated HRTF measurements from a set of individuals.
Being able to consistently and repeatably evaluate and quantify the improvements brought by these technological advancements will be absolutely key not only for the development of new techniques, but also for unlocking new approaches to understanding the mechanisms of human spatial hearing. Our approach to personalisation presents a new perspective on this problem, linking Bayesian theories of active inference  [7] ,  [8]  and binaural (i. e. related to both ears) sound reproduction to create datasets of human behaviour and perceptually-valid metrics modelling such behaviour. By having the acoustic simulations validated against acoustic measurements, and human auditory models validated against actual behaviour, we will provide important tools for the development of AI-based algorithms predicting realistic spatial audio quality.
These evaluation metrics are therefore derived through complimenting the above mentioned project database with perceptual results of the same set of individuals to a variety of perceptual tests. We will concentrate on the issue of blending virtual objects in real scenes, which is one of the cornerstones of AR. In order to blend the real with the virtual worlds in an AR scenario, it is essential to develop techniques for the automatic estimation of the reverberant characteristics of the real environment. This will be achieved by characterising the acoustical environment surrounding the AR user, using this in-situ data to synthesize virtual sounds with matching acoustic properties. The data extracted can then be employed to generate realistic virtual reverberation matching the real world. After a set of pilot studies looking at the perceptual needs in terms of reverberation processing (e.
V. BEYOND Ensuring that the project's accomplishments (algorithms, AI-based models, evaluation data, etc. ) remain available to the various stakeholders including the wider research communities beyond SONICOM is of primary importance in scientific projects. In order to facilitate this and consolidate all the developed tools within a common structure, the SONICOM Ecosystem will be created, which will include open source software modules implementing the various tools, algorithms and models developed within the project. The main part of the SONICOM Ecosystem will be the SONICOM Framework consisting of the Binaural Rendering Toolbox (BRT), auditory data and models, and dedicated hardware. The rendering core of the BRT is inspired by the work already done on the development of the 3D Tune-In Toolkit  [18].
It will be implemented as an interchangeable module, allowing the use of various rendering engines for various software platforms, connected with the rendering core modules using interfaces and communication protocols. Once benchmarked and evaluated, the SONICOM Framework will become part of the SONICOM Ecosystem. The Ecosystem will further include the Auditory Model Toolbox (AMT)  [19] ,  [20]  and toolboxes dealing with HRTFs stored in the spatially oriented format for acoustics (SOFA)  [21]. SOFA, a standard of the Audio Engineering Society (AES69-2015,  [22] ), has received a recent upgrade and will further extended towards the needs of SONICOM and its Ecosystem. Further component of the Ecosystem will be Mesh2HRTF, an application to numerically calculate HRTFs. Originally developed in 2015,  [1] , it will receive a major upgrade when integrated in the Ecosystem.
Within this context, the attempt to make use of the collected data to model social-level processing within existing sensory models is a novel element, and will allow to better predict responses to complex tasks such as speech-in-noise understanding and, more in general, sonic interactions within AR/VR scenarios. Finally, it seems clear that to ensure an adequate level of standardisation and consistently advance the achievements of research in this area, a concerted and coordinated effort across disciplines, research institutions and industry players is absolutely essential, and this is precisely what we are trying to do within SONICOM.
The hierarchical modelling strategy shows good consistency with the Reynolds decomposition from the mathematical foundation. The systematic data treatment process shows its great potential for multiscale and multi-frequency modelling. Inspired by the hierarchical Markov model  (Fine et al. 1998) , we apply a scaledependent hierarchical clustering to the classic network modelling under the meanfield consideration. The time-scales of the different flow components provide a good indicator for figuring out the typical structures in multiscale flows, and enable the hierarchical model to address the complex dynamics of multiscale problems. The resulting cluster-based hierarchical network model (HiCNM) can systematically identify complex dynamics involved in the case of multiple attractors. Both the global trends and the local structure during the transition can be well preserved by a fewer number of clusters in the hierarchical structure, which leads to a better understanding of the physical mechanisms involved in the flow dynamics.
We consider the two-dimensional incompressible flow configuration of Bansal & Yarusevych (2017), defined as the unforced "fluidic pinball" in  Deng et al. (2020). With increasing Reynolds number, the wake undergoes a first instability leading to a periodic vortex shedding, then a static symmetry breaking, and finally a transition to a quasiperiodic regime before transiting to a chaotic regime. HiCNMs are built for these flow regimes, which have multiple invariant sets and exhibit different transient dynamics. We provide a principle sketch of our HiCNM framework in figure  1. The manuscript is organised as follows: § 2 describes the numerical plant of the fluidic pinball and the flow features at different Reynolds number. § 3 discusses the different perspectives on the cluster-based hierarchical network modelling strategy.
In this work, we derive the HiCNM by replacing the Markov model by the network model. The hierarchical structure is identical, and the only change is the way to describe the transient dynamics between clusters. A typical structure between the parent and child layers is shown in figure  7. We start with cluster C k1,. , k l-1 in the parent layer L l-1 , where the leading subscripts k 1 ,. , k l-2 refers to the cluster number in each upper layer. For convenience, when the context will be unambiguous, the cluster will be only referenced by its number in the current layer, e. g. C k l-1. As indicated with the sequence of cluster numbers in its complete name, this cluster comes from the sub-division of the cluster C k1,. , k l-2 in the parent layer L l-2. We suppose that M snapshots u m , m = 1,.
Acknowledgements Nan Deng  appreciates the support of the  China Scholarships Council  (No. 201808070123 ) during his Ph. D. thesis in the  ENSTA Paris of Institut Polytechnique de Paris , and numerical supports from the laboratories LISN ( CNRS-UPR 3251 ) and IMSIA ( UMR EDF-ENSTA-CNRS-CEA 9219 ). This work is supported by a public grant overseen by the  French National Research Agency (ANR)  under grant ' FlowCon ' ( ANR-17-ASTR-0022 ), the  National Natural Science Foundation of China (NSFC)  under grant  12172109 , and the  Polish Ministry of Science and Higher Education (MNiSW)  under the Grant No. :  0612/SBAD/3567. We appreciate valuable discussions with  Guy Cornejo-Maceda ,  Hao Li , and  Franois Lusseyran  and the  HIT  fluidic pinball team:  Bingxi Huang ,  Wenpeng Li ,  Yiqing Li ,  Kexin Zhao ,  Qixin Lin ,  Ruixuan Shen ,  Xin Wang  and  Shangyan Xie.
All tests were simulated via the UUV simulator. We applied these two controllers to the RexROV 2, a six degrees of freedom cube-shaped remotely operated underwater Vehicle (ROV) converted in an AUV. We propose several interesting contributions as a result of these tests, such as making the SAC control and guiding the AUV simultaneously, outperforming the PID controller in terms of energy saving, and reducing the amount of information needed by the SAC algorithm inputs. Moreover, our implementation of this controller allows facilitating the transfer towards real-world robots. The code corresponding to this work is available on GitHub.
The authors detailed the need for better evaluation metrics, as well as better simulations for robotic vision. They reviewed the perception, planning, and control in robotics, and concluded by saying that neural networks generally do not perform well when the state of the robot falls outside the training dataset. The work also defined the concepts of programming and data as spectra, allowing to automatically derive deep learning algorithms from reasonable amounts of data and suitable priors. Reference  [15]  presented a large comparative study of reinforcement learning applied to control theory. A controller based on neural networks and reinforcement learning was tested on several systems, i. e. , an AUV, a plane, the magnetic levitation of a steel ball, and the heating coil (belonging to the set of heating, ventilation, and air conditioning (HVAC) problems).
The authors divided UAV tasks into several categories: path planning (using real-time images, with or without ground maps), navigation (for discrete or continuous action spaces), and control (for attitude control, longitudinal and lateral control, image processing, or UAV swarm control). Reference  [18]  presented a review on a more specific subject: the use of UAVs for obstacle detection and collision avoidance in the Internet of Things (IoT) field. They provided many useful resources, such as a list of available datasets or different hardware and communication architectures. The review mainly listed convolutional neural network (CNN) works applied in a number of industrial applications. The UAVs controlled in these industrial environments need to be aware of the possible collisions with workers, mobile vehicles, robots, or even heavy-duty tools.
• MATLAB: We chose to analyze post-simulation results using MATLAB  [81] , a wellknown computing tool. Besides MATLAB, all tools were used inside ROS components. This is why the middleware ROS encapsulated all of the other tools in Figure  4 , except MATLAB. ROS can interact with the UUV simulator during the execution of a simulation and can use a variety of features in real-time, e. g. , receiving the state variables of the AUVs, changing the direction and the magnitude of the ocean currents, sending commands to the thrusters, simulating sensor failures, etc. This whole simulation setup can be reusable for a variety of tasks involving marine robotics and/or machine learning.
1. 2). It computes an input vector u based on the tracking error e(t) = r(t)ŷ, with r being the reference (corresponding here to the waypoint) and ŷ being a measurement of the output variables of the AUV. This vector is defined as follows: u(t) = K p e(t) + K i t 0 e(τ)dτ + K d de(t) dt (12) with e(t) = [x e , y e , z e , φ e , θ e , ψ e ] T where K p , K i , and K d are real-value matrices called the gains, (x e , y e , z e ) are the errors between the waypoint position and the AUV position (expressed in meters, in Cartesian coordinates, and in the absolute reference frame of Gazebo), and (φ e , θ e , ψ e ) are the Euler angle errors corresponding to the nummber of angles that the AUV lacks to point towards the waypoint (expressed in radians, in the local reference frame of the AUV). The PID controller provided by the RexROV 2 package was already tuned and its gains were found using the sequential model-based optimization for general algorithm configuration (SMAC)  [86]  ( https://github.
As said earlier, each thruster can affect a combination of several degrees of freedom. As shown in Figure  8 , the thruster manager defines a thruster allocation matrix (TAM) to transform the PID input vector u into a thruster command vector c, as follows: c(t) = TAM. u(t) (15) The RexROV 2 package gives the following 6 × 6 TAM (here, the coefficients are rounded to three digits for more clarity):         0. 0 0.         (16) Each line of the TAM corresponds to a different thruster of the RexROV 2. After this computation, each component of the command vector c is sent to its respective thruster, following the same indexing as shown in Figure  5. The thruster manager also allows changing specific parameters, such as the maximum thrust, which can be applied to the AUV, the update rate, or the type of thruster.
Definition of the Performance Metrics When the testing phase was completed, the records of the test episodes of both controllers were analyzed using MATLAB. We defined the metrics to measure different aspects of the performance of these controllers, such as the speed of convergence of the learning algorithms, the efficiency in the tracking of the waypoints, and even the energy consumption of the AUV. The only metric used during the training phase of the SAC algorithm was called the Number of successes for every 100 episodes. It counted the number of successful episodes obtained (each one hundred training episodes). It counted the episodes in which the AUV reached the waypoint. It allowed measuring how fast the SAC algorithm was able to reach an acceptable behavior during its learning of the neural network parameters.
Conclusions and Openings The sensitivity analysis of the state vector presented in this work allowed analyzing which sensors are mandatory to allow the SAC algorithm to control the RexROV 2. After having successfully challenged the PID controller inside marine robotics simulations, our next objective will be to embed our SAC-based controller in a real-world AUV. We had this next step in mind since the beginning of this work and we implemented choices allowing to facilitate the transfer towards real-world robots. First, we coded our controllers using ROS middleware (mentioned in Section 4. 1. 1), which is known for providing great flexibility and allowing to easily transfer its software components (called nodes) towards embedded systems. Its component-based architecture also allows easily changing the type of robot, sensors, actuators, as well as the algorithms used in the GNC system.
e. , substituted technology) is assumed the most competitive and modern technology; (vii) the value to weight ratio determines the geographical market (local market for low ratio, continental for medium and global for high). However, these basic assumptions cannot always be suitable for transition issues. Whereas transition requires important changes at various spatio-social or socio-technical levels  (Geels, 2011) , C-LCA is only suitable for small changes at a market level for one studied product. C-LCA forecasts a business-asusual situation, with overall increasing market volumes in the long term, whereas transition can expect that some market volumes would decrease, and possibly new macro-economic models based on degrowth  (Jackson, 2016). Concerning geographical aspects, hybrid or IO LCAs are not either adapted because IO databases are produced at national scales, in a top-down approach with insufficient details concerning the very studied solutions.
, 2017). The involved actors should be able, with the help of the LCA modeler, to describe all possible choices in terms of organization, and resulting processes and activities concerning the transition solutions they want to implement. Similarly to other LCAs, classical data collection can then be conducted using specific data and specific process models when available, completed with literature based information and generic LCA databases. The existing rules applied to inventory data collection in P-LCA concerning representativeness, robustness, completeness, etc can be applied to Tr-LCA and will thus not be detailed here. Although not required, it is very interesting to integrate flow models inside processes of the transition solutions using the systemic nature of LCA  (Bertalanffy, 1968 ) which allows different levels of organization to be interwoven  (Onat et al.
, 2020). For Tr-LCA, this can also be used if the time perspective is sufficiently long. The possible evolutions of the background processes can be extracted from existing global scenarios. These choices are classified as discrete contextual variables because they are beyond the decision-making perimeter of all considered actors. The question of the applicability of transition LCA is also important. Although the databases and process models are the same as other LCAs, the coupling with MFA, the possibility of having several functional units in the same system, but especially the different steps of interpretation, require today the use of either several different numerical tools or dedicated programming. The studies or elements of studies already carried out and quoted in this article, were for the most part conducted with Matlab R or in Python language by developing modules coupled with the Brightway software.
This type of modeling can be reached by coupling LCA with MFA at the suitable geographical scale. Tr-LCA also requires specific interpretation steps dedicated to the identification of preferable scenarios and action levers hold by each involved actor. This interpretation requires integrating actors' identification and their decision models as mechanisms inside the system model. It also requires additional methods to generate all actions possibilities and to conduct suitable sensitivity analysis. This method has presently been applied by parts but not as a whole and not as a single numerical tool. These parts are currently being developed in a single computing Python language, in order to become compatible with Brightway 2. The conceptual frame is also currently applied on a case study concerning recovery of nickel slags in New Caledonia (Carboval research project) that will be the topic for future publications.
Introduction In December 2019, a new virus (SARS-CoV-2), causing a respiratory disease -later named COVID-19 , was discovered. At the time of the outbreak, many healthcare systems around the world were not well prepared for the pandemic that later emerged. While the virus was initially detected in China, measures to prevent its spread to other regions of the world were often hesitant and taken too late. Whereas compartmental spatio-temporal models of disease spread in epidemiology have been known in principle for a long time  (1) , many countries initially lacked robust and systematically collected surveillance data to which these models could be fitted. In general, it has been difficult to translate insights from modeling into actionable decision support for the government. Based on these considerations, the French-German collaborative project AIOLOS (Artificial Intelligence Tools for Outbreak Detection and Response) has recently started countries are not systematically recorded in a fully automated and digitalized manner.