dataesr/scientific-paragraphs-categorization huggingface dataset:
A series of meetings were held between SOCIB and IEO, and within SOCIB, in order to develop the initial idea, define the team and define a minimum viable product (MVP). The initial MVP was created and tested through a structured feedback session with a group of end-users in 2017 (see Figure  5 ). This test group has experts working in regional sustainable marine resources science, mainly, but not exclusively, from fisheries. The feedback from this face-to-face session was very positive and provided clear end-user consensus on requirements for the next stage, giving the team vital guidance around development needs for interface and functionality of the Tool. The user session identified 2 key needs lacking in the initial MVP, the ability to (i) save user searched data and time series selections and (ii) deliver data at a range of depths.
Both of them with common look, feel and features, see Figure  6. The Android mobile platform is not a core competency of SOCIB, so the implementation for this was outsourced to a mobile application development partner -Apploading S. L. The products enable the user to:. browse through the 353 monitored beaches through a select menu and input search;. access through a select menu and input search to past (calendar selection), present (default view) and future (48 h) metocean data, such as sea temperature, waves (height, direction, period), winds (direction, intensity), rain, pressure;. relative scales (Beaufort scale and Douglas scale) are provided, as requested, to better address end-user data norms. This enables the lifeguards to more easily foresee dangerous scenarios and thus supports hazard mitigation (decision support tool) and improved planning.
It was guided by business best practice, in taking a user centric approach, with awareness of SOCIB core competencies and unique value or intellectual property, and in harnessing the creative forces of a multidisciplinary development team, in this case a mix of scientists, data engineers and business experts. In other words, SOCIB and similar leading research infrastructures worldwide, with a multidisciplinary, integrated and targeted approach, are capable of establishing new symbiotic ecosystems that facilitate missionoriented innovation. Furthermore, regional observing systems have a role to play in bringing the benefit of the observing system to end-users, and thereby increasing connection to regional stakeholders and supporting blue growth activities associated with coastal areas. Although marine infrastructures can innovate in this area, as demonstrated here, it will not be possible for government to fund the development of all the potential products and services for all the potential users.
We believe that the commercial sector can also leverage the market potential of marine data and play a bigger role in exploiting the wealth of this resource, and that user sectors can also fund development of tools and services. To aid this process, SOCIB will try to 'seed' the market by bringing these examples of focused products, with documented users and associated development partners, to marine industry forums, exhibitions and innovation fairs, to demonstrate the value that has been created and the relatively low threshold of the additional development cost for product development. Most of the cost of development has already been invested, in taking the observations, running the forecast models, managing quality control and the open access interfaces to these valuable data and forecasts.
Conclusion SOCIB cannot yet offer definitive user numbers for the new products, however, operational use has been requested for the Oceanography Exploration Tool, and the feedback has been positive and the SocorristaIB App is in its first year of 'field' trials, again with initial positive feedback. SOCIB is a resource limited infrastructure and, although we have a clear mission to deliver value to society, we can only support a limited number of welltargeted products. Through adopting this approach to innovation, SOCIB identified key user sectors and product ideas that had a greater chance of success and capitalised on our scarce resources. Through EU initiatives such as JERICO-NEXT, ODIPS2 and/or CMEMS, SOCIB promotes its approach to innovation in the ocean products and services area, reaching a broader European audience and advocating for the benefit of open, free-of-charge data and well-targeted product development, to support sustainable blue growth.
Acknowledgements The authors would like to acknowledge the SOCIB team and project partners, the  Spanish Institute of Oceanography (IEO-COB) ,  Agencia Estatal de Meteorología (AEMET) ,  Puertos de Estado , and  Apploading S. L. , for supporting and developing the SOCIB Products and Services Strategy. Also, the  JERICO-NEXT EU - FP7 Project and EUROGOOS  for their focus on ocean data products and the  AtlantOS EU-FP7  Project and the  OECD Ocean Economy Group  for their focus on understanding the value of ocean data. Appinventiv for provision of Figure 4, Reasons why we Trust Agile for our Mobile App Development Process,  Android Developer , 2018, https:// appinventiv. com/blog/reasons-why-we-trust-agile-for-our-mobileapp-development-process.
The obtained results show the impact of cloud users and their locations (either remote or local) on the application response time. Moreover, Application Characterization Environment (ACE) whiteboard is used to simulate the overall flow of data across different tiers from start to end of the application task for V ID creation process either based on IBE or PBE  [10]. We believe that, the simulation based performance evaluation is not enough to measure the system performance. Moreover, modelling security communication is well known in this domain  [11]. Therefore, we support our previous OPNET Modeler simulation by an analytical model, which is investigated based on queuing network principles to evaluate the impacts of the proposed solutions on the virtual infrastructure. The mathematical models are built based on BCMP theory  [12]  for both IBE & PBE workflows  [13]  and the validation for different network access conditions is done using MATLAB tool  [14].
This is especially clear with cloud computing access and multi-tenant services like cloud Software-as-a-Service (SaaS) model. But, keeping many identities for each customer services is so difficult. For this, there are many directions towards treat this problem. One direction went to using identity provider who is capable of managing users identities under the form of third party providers (i. e. Identity Provider). While others like Google went to Google Apps identity services (IDaaS) for managing all Google services using one identity which is the user Gmail address for example. Moreover, Google direction attacked the first direction as it avoids users dependency on third party solutions. But, at the same time this third party solution is considered in some vital accessing domains over Internet like banking systems.
B. MIRACL VALIDATION TOOL In MIRACL (Multi-precision Integer and Rational Arithmetic C/C++ Library)  [8]  most programs are provided in both C and C++ versions which simplify any program development. It is considered the primary tool for cryptographic system implementers. MIRACL supports RSA, Diffie-Hellman Key exchange and the latest version offers full support for Elliptic Curve Cryptography over GF (p) and GF (2m). MIRACL also supports Elliptic Curve Diffie-Hellman (ECDH) key agreement protocol, Menezes-Vanstone Protocol and Elliptic Curve Digital Signature Algorithm (ECDSA), which we will utilize to implement and design our proposed protocols. To evaluate the speed of the cryptography operations in the proposed framework, the IBE and PBE are provided with ''MIRACL library'', the communications between entities are modified and implemented.
According to Table  3  and Table  4 , the processing times for all messages are around 0. 05 Sec and 0. 045 Sec for IBE and PBE respectively. The results obtained by computing machine specs; Intel Core 2 Duo CPU E8400 @ 3. 00GHz x 2, memory 4GB in Linux Ubuntu 12. 10. The processing times shown in those tables are captured during the two scenarios validation in MIRACL. For the scalability issue, we can clearly see from the execution time mentioned before that, the proposed cryptosystem is feasible in anonymous communication in cloud and distributed environments. Table  5 , evaluates the time needed to create the V ID for different number of users as a simulation scheme for large number of cloud access users. The PBE provides a short time for V ID creation because of the low number of messages used to create it.
2) SIMULATION VERSUS ANALYTICAL RESULTS The results show that the number of users and their locations (either remote or local) affect the response time. ACE whiteboard is used to simulate the overall flow of data across different tiers from start to end of the application task for V ID creation. The mathematical models are built based on BCMP theory and the validation for different network access conditions is done using MATLAB tool  [14]. Four different scenarios are implemented. In all scenarios, the application response time is measured, which is described as the time taken for all the tasks in the custom application to complete. Moreover, the overall delay is evaluated. Therefore, Cloud-based evaluation can be measured by increasing the number of users as it is logic for cloud service providers.
The complex transverse water proton magnetization subject to diffusion-encoding magnetic field gradient pulses in a heterogeneous medium can be modeled by the multiple compartment Bloch-Torrey partial differential equation. Under the assumption of negligible water exchange between compartments, the time-dependent apparent diffusion coefficient can be directly computed from the solution of a diffusion equation subject to a time-dependent Neumann boundary condition. This paper describes a publicly available MATLAB toolbox called SpinDoctor that can be used 1) to solve the Bloch-Torrey partial differential equation in order to simulate the diffusion magnetic resonance imaging signal; 2) to solve a diffusion partial differential equation to obtain directly the apparent diffusion coefficient; 3) to compare the simulated apparent diffusion coefficient with a short-time approximation formula.
The partial differential equations are solved by P 1 finite elements combined with built-in MATLAB routines for solving ordinary differential equations. The finite element mesh generation is performed using an external package called Tetgen. SpinDoctor provides built-in options of including 1) spherical cells with a nucleus; 2) cylindrical cells with a myelin layer; 3) an extra-cellular space enclosed either a) in a box or b) in a tight wrapping around the cells; 4) deformation of canonical cells by bending and twisting; 5) permeable membranes; Built-in diffusion-encoding pulse sequences include the Pulsed Gradient Spin Echo and the Oscillating Gradient Spin Echo. We describe in detail how to use the SpinDoctor toolbox. We validate SpinDoctor simulations using reference signals computed by the Matrix Formalism method.
Some software packages using this approach include 1. Camino Diffusion MRI Toolkit developed at UCL (http://cmic. cs. ucl. ac. uk/camino/); 2. DIFSIM developed at UC San Diego (http://csci. ucsd. edu/projects/simulation. html); 3. Diffusion Microscopist Simulator  [25]  developed at Neurospin, CEA. The second group relies on solving the Bloch-Torrey PDE in a geometrical configuration. In  [30] [31] [32]  a simplifying assumption called the narrow pulse approximation was used, where the pulse duration was assumed to be much smaller than the delay between pulses. This assumption allows the solution of the diffusion equation instead of the more complicated Bloch-Torrey PDE. More generally, numerical methods to solve the Bloch-Torrey PDE. with arbitrary temporal profiles have been proposed in  [33] [34] [35] [36].
The Crank-Nicolson method was used in  [36]  to also allow for second order convergence in time. The efficiency of diffusion magnetic resonance imaging simulations is also improved by either a high-performance FEM computing framework  [39, 40]  for large-scale simulations on supercomputers or a discretization on manifolds for thin-layer and thin-tube media  [41]. In this paper, we present a MATLAB Toolbox called SpinDoctor that is a simulation pipeline going from the definition of a geometrical configuration through the numerical solution of the Bloch-Torrey PDE to the fitting of the apparent diffusion coefficient from the simulated signal. It also includes two other modules for calculating the apparent diffusion coefficient. The first module is a homogenized apparent diffusion coefficient mathematical model, which was obtained recently using homogenization techniques on the Bloch-Torrey PDE.
In the homogenized model, the apparent diffusion coefficient of a geometrical configuration can be computed after solving a diffusion equation subject to a time-dependent Neumann boundary condition, under the assumption of negligible water exchange between compartments. The second module computes the short time approximation formula for the apparent diffusion coefficient. The short time approximation implemented in Spin-Doctor includes a recent generalization of this formula to account for finite pulse duration in the pulsed gradient spin echo. Both of these two apparent diffusion coefficient calculations are sensitive to the diffusion-encoding gradient direction, unlike many previous works where the anisotropy is neglected in analytical model development. In summary, SpinDoctor 1. solves the Bloch-Torrey PDE in three dimensions to obtain the diffusion magnetic resonance imaging signal; 2.
placement of non-overlapping cylindrical cells (with an optional myelin layer) of different radii close to each other in a canonical configuration where they are parallel to the z-axis; 3. inclusion of an extra-cellular space that is enclosed either Built-in diffusion-encoding pulse sequences include 1. the Pulsed Gradient Spin Echo ; 2. the Oscillating Gradient Spin Echo (cos-and sin-type gradients). SpinDoctor uses the following methods: 1. it generates a good quality surface triangulation of the user specified geometrical configuration by calling built-in MATLAB computational geometry functions; 2. it creates a good quality tetrehedra finite elements mesh from the above surface triangulation by calling Tetgen  [42] , an external package (executable files are included in the Toolbox package); 3.
Finite element mesh generation SpinDoctor calls Tetgen  [42] , an external package (executable files are included in the toolbox package), to create a tetrehedra finite elements mesh from the surface triangulation generated by Algorithm 2: Surface triangulation of spherical cells and ECS. Suppose we have n cell spherical cells with nucleus. Denote a sphere with center c and radius R by S(c, R), we use the built-in functions (convex hull, delaunnay triangulation) in MATLAB to get its surface triangulation, T (c, R). Call the radii of the nucleus r 1 , • • • , r ncell and the radii of the cells R 1 , • • • , R ncell. Then the boundaries between the cytoplasm and the nucleus are {Γ i = T (c i , r i )}, i = 1, • • • , ncell; and between the cytoplasm and the ECS {Σ i = T (c i , R i )}, i = 1, • • • , ncell; For the box ECS, we find the coordinate limits of the set i S(c i , R i ) ∈ [x 0 , x f ] × [y 0 , y f ] × [z 0 , z f ] and add a gap k = ECS gap × max{x f -x 0 , y f -y 0 , z f -z 0 } to make a box B = [x 0 -k, x f + k] × [y 0 -k, y f + k] × [z 0 -k, z f + k].
We put 2 triangles on each face of B to make a surface triangulation Ψ with 12 triangles. For the tight-wrap ECS, we increase the cell radius by a gap size and take the union W = i S(c i , R i + ECS gap × R mean ), where R mean = Rmin+Rmax 2. We use the alphaShape function in MATLAB to find a surface triangulation Ψ that contains W. Algorithms 2 and 3. The FE mesh is generated on the canonical configuration. The numbering of the compartments and boundaries used by SpinDoctor are given in Tables  4  and 5. The labels are related to the values of the intrinsic diffusion coefficient, the initial spin density, and the permeability requested by the user. Then the FE mesh nodes are deformed analytically by a coordinate transformation, described in Algorithm 4.
The top and bottom boundaries is just the interior of Q. However, the surface triangulation cannot be done on Q directly. We must cut out D(c i , r i ), the disk which touches the axon, and A i = D(c i , R i ) -D(c i , r i ) , the annulus which touches the myelin. Then we triangulate Qi D ii A i using the MATLAB built-in function that triangulates a polygon with holes to get the boundary that touches the ECS. The surface triangulation for A i and D(c i , r i ) are straightforward.
SpinDoctor calls MATLAB built-in ODE routine ode23t to solve the semi-discretized system of equations. 2. rtol controls the accuracy of the ODE solve. It is the relative residual tolerance at all points of the FE mesh at each time step of the ODE solve; Algorithm 6: HADC model. Eq. (  12 ) can be discretized similarly as described for the BTPDE and has the matrix form M ∂ζ ∂t = -S ζ + Q ζ ( 15 ) where ζ is the approximation of w and ζi = σ i F (t) u g • n(x i ). We note that the matrices here are assembled and solved separately for each compartment. SpinDoctor calls MATLAB built-in ODE routine ode23t to solve the semi-discretized equation. 3. atol controls the accuracy of the ODE solve. It is the absolute residual tolerance at all points of the FE mesh at each time step of the ODE solve; We varied the finite element mesh size and the ODE solve accuracy of SpinDoctor and ran 6 simulations with the following simulation parameters: SpinD Simul 5-1: rtol = 10 -3 , atol = 10 -6 , Htetgen = -1; The geometry simulated is the following: • 3LayerCylinder is a 3-layer cylindrical geometry of height 1µm and the layer radii, R 1 = 2.
Computational time and comparison with Monte-Carlo simulation In this section, we compare SpinDoctor with Monte-Carlo simulation using the publicly available software package Camino Diffusion MRI Toolkit  [26] , downloaded from http://cmic. cs. ucl. ac. uk/camino. All the simulations were performed on a server computer with 12 processors (Intel (R) Xeon (R) E5-2667 @2. 90 GHz), 192 GB of RAM, running CentOS 7. SpinDoctor was run using MATLAB R2019a on the same computer. We give SpinDoctor computational times for three relatively complicated geometries. We also give Camino computational times for the first two geometries. We did not use Camino for the third geometry due to the excessive time required by Camino. The number of the degrees of freedom in the SpinDoctor simulations is the finite element mesh size (the number of nodes and the number of elements).
We used a tight-wrap ECS: this choice means we do not need to have a complicated algorithm to avoid large empty spaces as would be the case when the ECS is box-shaped. 2. DendriteBranch. See Figure  11. This is a dendrite branch whose original morphological reconstruction SWC file published in NeuroMorpho. Org  [55]. By wrapping the geometry described in the SWC file in a new watertight surface and using the external FE meshing package GMSH  [56] , we created a FE mesh for this dendrite branch. The FE mesh was in imported and used in SpinDoctor. We note this is an externally generated FE mesh and this illustrate the capacity of SpinDoctor to simulate the dMRI on general geometries provided by the user. 3. ECS200axons. See Figure  12. This models the extra-cellular space outside of 200 axons.
Dendrite branch SpinDoctor was run with the following 2 sets of simulation parameters: SpinD Simul 6. 2-1: rtol = 10 -3 , atol = 10 -6 ; SpinD Simul 6. 2-2: rtol = 10 -2 , atol = 10 -4 ; The finite elements mesh was generated by an external package and imported into SpinDoctor. The finite elements mesh size is (n nodes = 24651, n elem = 91689). We do not refine the FE mesh, rather, we vary the ODE solve tolerances in the SpinDoctor simulations. Camino was run with the following 3 sets of simulation parameters: Camino Simul 6. 2-1: N = 1000, T = 200;.
Non-standard diffusion-encoding sequences Given the interest in nonstandard diffusion sequences beyond PGSE and OGSE, such as double diffusion encoding (see  [58] [59] [60] [61] ) and multidimensional diffusion encoding (see  [62] ), it is natural that SpinDoctor should easily support arbitrary diffusion-encoding sequences. Besides the PGSE and the sine and cosine OGSE sequences that are provided in the SpinDoctor package, new sequences can be straightforwardly implemented by changing three files in the SpinDoctor package • SRC/DMRI/seqprofile. m defines f (t) • SRC/DMRI/seqintprofile. m defines the integral F (t) = t 0 f (s)ds • SRC/DMRI/seqbvaluenoq. m defines the associated b-value. In the example below, we simulate the double-PGSE (Eq. 19) sequence: f (t) =                1, 0 ≤ t ≤ δ, -1, ∆ < t ≤ ∆ + δ, 1, τ ≤ t ≤ δ + τ, -1, ∆ + τ < t ≤ ∆ + δ + τ, 0, otherwise.
T 2 relaxation When T 2 -relaxation is considered, the Bloch-Torrey PDE (Eq. 1) takes the following form ∂ ∂t M in i (x, t) = -Iγf (t)g • x M in i (x, t) - M in T in 2 + ∇ • (σ in ∇M in i (x, t)), x ∈ Ω in i , (20) ∂ ∂t M out i (x, t) = -Iγf (t)g • x M out i (x, t) - M out T out 2 + ∇ • (σ out ∇M out i (x, t)), x ∈ Ω out i , (21) ∂ ∂t M e (x, t) = -Iγf (t)g • x M e (x, t) - M e T e 2 + ∇ • (σ e ∇M e (x, t)), x ∈ Ω e , (22) We plan to incorporate T 2 relaxation effects in the next official release of SpinDoctor. In the meantime, this additional functionality can be found in a development branch of SpinDoctor available on GitHub. The source code in this development branch allows the ability to add relaxation, with different relaxivities in the different compartments  [63, 64]. T 2 relaxation is incorporated using the format T 2 = [T in 2 , T out 2 , T e 2 ] where T in 2 , T out 2 , T e 2 are the T 2 values for the three compartments, respectively.
Discussion Built upon MATLAB, SpinDoctor is a software package that seeks to reduce the work required to perform numerical simulations for dMRI for prototyping purposes. There have been software packages for dMRI simulation that implements the random walkers approach. A detailed comparison of the Monte-Carlo/random walkers approach with the FEM approach is beyond the scope of this paper. SpinDoctor offers an alternative, solving the same physics problem using PDEs. After surveying other works on dMRI simulations, we saw a need to have a simulation toolbox that provides a way to easily define geometrical configurations. In SpinDoctor we have tried to offer useful configurations, without being overly general. Allowing too much generality in the geometrical configurations would have made code robustness very difficult to achieve due to the difficulties related to problems in computational geometry (high quality surface triangulation, robust FE mesh generation).
The geometrical configuration routines provided by SpinDoctor are a helpful front end, to enable dMRI researchers to get started quickly to perform numerical simulations. Those users who already have a high quality surface triangulation can use the other parts of SpinDoctor without passing through this front end. The bulk of SpinDoctor is the numerical solutions of two PDEs. When one is only interested in the ADC, then computing the HADC model is the good option. When one is interested in higher order behavior in the dMRI signal, then the BTPDE model is a good option for accessing high b-value behavior. Because time stepping methods for semi-discretized linear systems arising from finite element discretization is a well-studied subject in the mathematical literature, the ODE solvers implemented in MATLAB already optimize for such linear systems.