Vincent Luboz
email: vincent.luboz@texisense.com

Christelle Mathieu Bailet
email: mathieu.bailet@univ-grenoble-alpes.fr

Boichon Grivot

Michel Rochette
email: michel.rochette@ansys.com

Bruno Diot
email: b.diot@ids-assistance.com

Marek Bucki
email: marek.bucki@texisense.com

Yohan Payan
email: yohan.payan@univ-grenoble-alpes.fr

Christelle Boichon Grivot
email: christelle.boichon@ansys.com

Personalized modeling for real-time pressure ulcer prevention in sitting posture

Keywords: Biomechanical modelling, Reduced order model, Pressure ulcer prevention

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

More than 50,000 people suffer from paraplegia in France (HAS, 2009). Among them, 80 % will develop a pressure ulcer (PU). PUs result from the compression of soft tissues between a bony prominence and a supporting surface, e.g. a wheelchair. PUs start near bony structures and progress towards the skin, causing significant subcutaneous damages before being clinically observable. Paraplegic persons use cushions to evenly distribute pressure below their buttocks and try to regularly change their sitting posture to prevent PUs. Unfortunately, this is not sufficient since subjects' attention can decrease over time. Measuring pressures at the skin/seat interface can help to prevent skin injuries [START_REF] Pipkin | Effect of model design, cushion construction, and interface pressure mats on interface pressure and immersion[END_REF], but these measurements cannot predict dangerous internal tissue strains responsible for deep PUs (Linder- [START_REF] Linder-Ganz | Strains and stresses in sub-dermal tissues of the buttocks are greater in paraplegics than in healthy during sitting[END_REF]. Intuitively, a person having sharper ischial tuberosities is more at risk of developing a PU than a person with blunt ischia, even with similar pressures below their buttocks [START_REF] Sopher | Exposure to internal muscle tissue loads under the ischial tuberosities during sitting is elevated at abnormally high or low body mass indices[END_REF]. Estimating internal strains is consequently the only solution to assess PU risk level. Animal studies [START_REF] Loerakker | The effects of deformation, ischaemia and reperfusion on the development of muscle damage during prolonged loading[END_REF] suggest that two strain thresholds should be monitored as there is a long term risk of ischemic PU if the internal strains are above 20 % for about 2 hours, and a short term risk of mechanical PU if the strains exceed 50 % for about 10 minutes (these values, namely 20% for 2 hours and 50% for 10mn, are orders of magnitudes that should be taken with cautious in the case of human tissues). These internal strains can only be estimated with a subject-specific biomechanical model to study mechanical interactions between soft tissues and bony prominences [START_REF] Elsner | Is obesity a risk factor for deep tissue injury in patients with spinal cord injury[END_REF]. The gluteal region has been modelled by several groups for different applications. A real-time 2D biomechanical Finite Element (FE) model using a linear elastic constitutive law for the muscles (E = 8.5 kPa, υ = 0.49) and the fat/skin tissues (E = 32 kPa, υ = 0.49) was proposed by [START_REF] Linder-Ganz | Real-Time Finite Element Monitoring of Sub-Dermal Tissue Stresses in Individuals with Spinal Cord Injury: Toward Prevention of Pressure Ulcers[END_REF] to evaluate the internal strains in the buttocks of a paraplegic person. Earlier, a 3D FE hyper-elastic model using a Neo Hookean constitutive law to simulate the skin (E = 150 kPa, υ = 0.46) and a Mooney Rivlin constitutive law to simulate the other soft tissues (A1 = 1.65 kPa, A2 = 3.35 kPa, υ = 0.49) was proposed by [START_REF] Verver | A Finite Element Model of the Human Buttocks for Prediction of Seat Pressure Distributions[END_REF]. It showed that the pressure distribution and therefore the internal strains depend on the stiffness of the chair cushion, on the constitutive material of the buttocks' soft tissues, and on the subject's posture. Using another 3D FE hyper-elastic model, [START_REF] Luboz | Biomechanical Modeling to Prevent Ischial Pressure Ulcers[END_REF] have studied the simulation sensitivity to the elasticities of the various layers of soft tissues in the buttocks, concluding that generic values of 200 kPa, 30 kPa, and 100 kPa can be used for the skin, fat and muscles, in a Neo Hookean context. The goal of this paper is to study the feasibility of coupling a 3D biomechanical model with an embedded pressure mat to form a personalized PU risk assessment device. To this aim, we introduce a reduced order modeling technique to run our FE hyper-elastic buttock model in real-time and to assess internal strains over time in different sitting postures.

Materials and Methods

Buttock FE hyper-elastic model

The modeling and simulations are performed within the ArtiSynth open source framework [START_REF] Lloyd | Artisynth: a fast interactive biomechanical modeling toolkit combining multibody and finite element simulation[END_REF] (www.artisynth.org) using a dynamic numerical solver. This study is based on the morphology of a male subject (38 years old, 100 Kg and 1.90 m). The external surfaces of the skin, muscles and bones were semi-automatically segmented from a CT scan (image size 512x512x403, resolution 0.97x0.97x1 mm 3 ), using the ITK-Snap snake tool [START_REF] Yushkevich | User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability[END_REF]. The muscles were segmented as a single entity as they were too difficult to separate. To have the least deformed contours of the modeling domain, the subject was lying on his side in the CT scan, which limited the distortion in one side of his buttocks. However, deformations induced by gravity were not compensated for. The external surfaces of our model are a combination of the least deformed side and its mirrored, contralateral part. A meshing tool provided by Texisense Company was used to generate a three layer FE mesh, corresponding to the skin, fat and muscles. The hex-dominant mesh is composed of 27,649 linear elements, fig. 1a. The skin is a 1-element 1.5mm-thick layer of elements [START_REF] Hendriks | The relative contributions of different skin layers to the mechanical behavior of human skin in vivo using suction experiments[END_REF] at the FE mesh surface. The segmented muscle surface delimits the muscle elements. Finally, the elements between the skin and muscle layers are considered as fat tissues. Fig 1b shows a mesh cross section after identifying these structures. In this model, the bones are assumed to be rigid. The nodes at the tissue/bone interface are attached to these bones with no sliding. The three soft tissues layers are modeled using a Neo Hookean constitutive material (Bonnet & Wood, 1997) to simulate a nonlinear hyper-elastic behavior. The strain energy density function W is given by: W=C10(I1-3)+(J-1) 2 /D Where I1 is the first invariant of the left Cauchy-Green deformation tensor, C10 is a material parameter (under a small strain hypothesis, it is related to the Young's modulus E≈6* C10), and J is the determinant of the deformation gradient F. D is the material incompressibility, related to the Poisson ratio υ [D=(1-2υ)/ C10)]. The mechanical properties of the different soft tissue layers are taken from [START_REF] Luboz | Biomechanical Modeling to Prevent Ischial Pressure Ulcers[END_REF]. Equivalent Young modulus values of 200 kPa (C10 = 33 kPa), 30 kPa (C10 = 5 kPa), and 100 kPa (C10 = 17 kPa) are respectively chosen for the skin, fat and muscles. A Poisson ratio of 0.49 is used for these three layers, as they are assumed to be quasi-incompressible. These mechanical properties are generic: for better accuracy, subject specific properties would need to be measured (for example, using an elastography procedure or an in vivo aspiration device (Schiavone et al., 2010)). The acquisition of pressures below the sitting subject is performed using a commercial pressure sensor: TexiMat (www.texisense.com). It is composed of a textile matrix of 32x32 sensors of 1.5cmx1.5cm each. Pressure acquisition lasted three hours, during which the subject could move freely, fig. 2: put his back on the chair rest, lift his feet, move his weight on either side of the buttocks. Shear loads on the cushion were not measured by the sensor and the subject was asked to avoid as much as possible leaning postures. To determine where to apply the pressure on the model, the ischial tuberosities are detected on the pressure frames. The ischial tuberosity footprints on the frames are two relatively close pressure peaks. The first stage of the detection pipeline consists in a Gaussian filtering to remove sensor noise (fig. 3a). In the second stage, a Laplacian filter with a 9-pixel kernel followed by a binary threshold on the normalized resulting image are applied to detect blob-like structures that could correspond to ischial pressure footprint (fig. 3b). This filter is independent of pressure magnitudes. Once the pressure image has been thresholded, several pairs of regions of interest can potentially correspond to the ischia. At the last stage, we use a geometric filter based on the relative position of each pair of centroids to find the most appropriate one (fig. 3c). The retained ischia are matched below those of the FE model. Finally, the buttock's surface FE nodes are projected orthogonally onto the pressure array. A bilinear interpolation between the four sensors surrounding each projected node is performed to compute the pressure applied at each FE node and to produce a continuous 3D pressure field. The normal at the buttock surface is taken as the pressure normal. In order to ensure simulation convergence, interface pressures are applied following a linear ramp from 0 % at 0.1 s to 100 % at 1.1 s. After 1.1 s, the pressure is maintained until the continuum reaches a steady state at about 1.3 s.

Boundary conditions

Reduced Order Model

For each pressure map, the FE buttock model requires about 20 minutes to compute the simulation and assess internal strains. To design a solution that will provide a real-time computation of our FE model we used the Reduced Order Modelling (ROM) technique developed by ANSYS (www.ansys.com). For a given subject, ROM is applied on the outputs of the FE solver (ArtiSynth) which computes the model deformations. Note that for each new subject, a new ROM must be computed as subjects' morphology and tissue mechanical properties vary greatly and thus influence internal strains. More precisely, an off-line learning step is first performed for each subject model: a set of pressure frames acquired for the subject is compressed into a small number of modes by using a proper orthogonal decomposition inspired by [START_REF] Chaturantabut | A state space error estimate for POD-DEIM nonlinear model reduction[END_REF]. This technique allows each pressure frame to be linearly described by a small set of scalar coefficients. Then the most relevant pressure frames to describe the pressure variations are selected, based on an optimal repartition of the learning points in the mode coefficients' space. The FE analyses corresponding to this training set of typical boundary conditions are carried out and the resulting strain fields are also compressed into a small number of modes. A machine-learning approach is then used to learn the relationship between the strain field modes and the pressure map modes. This approach uses response surface techniques based on Kriging interpolation and available in ANSYS 18.0 Design Xplorer. Once built, the ROM interactively gives an accurate approximation of the solver solution for a new set of interface pressures applied on the model. For this, the new pressure map is first projected by the ROM algorithm onto the basis of pressure modes. Then, from the projection coefficients, the ROM evaluates the response surface that gives the strain field coefficients. The whole strain field is finally built as a linear combination of strain modes. All these steps are performed in nearly real-time. The accuracy of this ROM process is measured on the resulting strain fields and it depends on three parameters: the number of pressure modes chosen to compress the pressure field, the targeted precision of the statistical reduction of the strain field (related to the number of modes used to represent the strain field), and the number of pressure frames in the training set.

Results

In our case, the ROM was generated based on 9,100 pressure frames selected from a 3-hour recording acquired at a framerate of 1 Hz below the sitting subject. Statistical analysis showed that 19 pressure modes were able to describe the 9,100 pressure frames with a root mean square error of 1.98 %. Then, the most significant pressure frames were selected based on their variations across those modes, to serve as a training set for the ROM. We chose three training sets: the first 100, 150, and 200 most significant frames. We then selected 50 random frames, outside these training sets, to serve as validation frames for the comparison between the strains estimated by the ROM and the strains computed by the actual FE model. A sensitivity analysis was performed to assess the three ROM parameters' influence onto the accuracy of the estimated strain field.

The strain field error on each validation pressure frame f was calculated by using the Euclidean norm of the error field, given by the node to node (i = 1 to n, with n the number of FE nodes) difference between the m = 50 exact strain fields at node i in frame f, FEstrain(f, i), and the m estimated strain fields ROMstrain(f, i). This error was then normalized with the mean Euclidean norm value of those exact strain fields. Finally we considered the mean error over the mean value, MeanEMV, and the max error over the mean value, MaxEMV, of this relative error on the 50 validation frames to assess the accuracy of the estimated strain field:

∑ ∑ , , ∑ ∑ , (1)
f 1…m ∑ , , ∑ ∑ , (2)
At first, we fixed the training set frame number to 100, and estimated the sensibility over the two other parameters, namely the number of pressure modes and the targeted precision. Fig. 4 summarizes the sensitivity analysis over MeanEMV for 5, 10, 15 and 19 pressure modes, and a targeted mean precision of the statistical reduction for the strain field of 0.2, 0.5, 1, 2, and 5 %. This figure shows that a plateau is reached if a targeted precision parameter of 0.5 % and 19 modes are used, with an actual MeanEMV of 1.09 %. Preferring 15 modes could be interesting when ported to a micro-controller as it reduces the computation time with only a slight increase of the errors compared to 19 modes. The same conclusions can be drawn from Fig. 5, summarizing the sensitivity analysis over MaxEMV. A plateau is reached if a targeted precision parameter of 2 % and 19 modes are used, with an actual MaxEMV of 2.63 %. In this figure, outliers appear on the 10-mode curve for targeted precisions below 1 %. This is due to a specific node in the buttock model, presenting a large strain, not representative of the rest of the FE mesh and leading to a strain misevaluation for a validation pressure frame. This event can be related to the uncertainties induced by considering maximal strains for PU prevention. Using "cluster analysis" could overcome this limitation (see [START_REF] Bucki | Clinical workflow for personalized foot pressure ulcer prevention[END_REF] for details) since it is based on the study of the largest volume of contiguous elements having a strain above a certain threshold, therefore smoothing possible misinterpretation due to a single node with a high strain value. We kept 19 pressure modes and a targeted precision of the strain field statistical reduction of 0.5 % for our second sensitivity analysis aiming at defining an optimized number of learning frames over the three sets defined earlier. We analyzed the MeanEMV and MaxEMV ratios given by the ROM on the 50 random validation frames, for 100, 150 and 200 learning frames. Fig. 6 shows that a plateau is reached for 150 learning frames with a MaxEMV of 1.64 %. A ratio of 0.79 % is recorded for MeanEMV for 200 frames. With no more than 200 pressure frames in the learning set up, 19 pressure modes and a targeted precision of 0.5 % for the strain compression, it is therefore possible to reach accuracies of 0.79 % and 1.64 % respectively for MeanEMV and MaxEMV. Using these ROM settings, it is thus possible to compute accurately the strain field for any acquired pressure frame in less than a second, on a standard PC, instead of 20 minutes for the FE hyper-elastic model. The resulting deformation field, fig. 7, permits a mapping of the PU formation risk in the buttock soft tissues, while the pressures evolve below the subject. Fig. 7: Two examples of deformation fields, where the subject sits with: (a) his arms on his knees, back away from the backrest, leading to an even deformation field in his soft tissues below the ischial tuberosities and sacrum (maximum strain in red is 23 %), and (b) most of his weight on his left buttock, leading to higher strains below his left ischia (maximum strain is 31 %).

Conclusions

A 3D FE non-linear subject-specific buttock model was generated to provide pressure ulcer prevention in the gluteal region. The personalized FE model is composed of three soft tissue layers: skin, fat and muscles, all modelled with Neo-Hookean constitutive materials. PU prevention can be achieved by continuously monitoring strains within soft tissues undergoing compression. Because FE estimation of internal strains on such a complex model takes about 20 minutes, a Reduced Order Model (ROM) was introduced. The ROM was built by statistically reducing 9,100 pressure frames acquired below the sitting subject, and by projecting the responses of the personalized biomechanical model on 19 pressure modes. A sensitivity analysis showed that the optimal ROM settings were 19 modes, a targeted precision of the strain field statistical reduction of 0.5 %, and 200 learning frames. Using these parameters, the optimal ROM provides strain fields in less than a second, with an accuracy of 0.79 %.

To demonstrate the feasibility of a PU prevention device, we used the TexiMat textile pressure sensor to continuously measure pressure frames under the sitting subject. These measurements, coupled with real-time strain evaluation inside his gluteal soft tissues provided by the ROM, and compared to the time and strain threshold proposed by [START_REF] Loerakker | The effects of deformation, ischaemia and reperfusion on the development of muscle damage during prolonged loading[END_REF], allowed interactive PU risk assessment. Since all on-line computations can be ported to a microcontroller embedded within a pressure mat placed on a wheelchair, the ROM could be used in a daily PU prevention set-up. The resulting device could wirelessly send warnings, through a smartphone or a watch, to the wheelchair user or the nursing staff in a clinical setting. The training phase would take less than an hour, while the patient would assume various key positions on his chair (patient on one side of his buttocks, on both sides, with his back on the chair…), to initialize the ROM with the measured pressures under his buttocks, and consequently avoiding a risk of creating a pressure ulcer for patients at risk. Nevertheless, before using this reduced model in a daily PU prevention set up, the software needs some adjustments. Besides porting the existing ROM technology onto a mobile platform, another technical hurdle is the ability to easily generate an accurate subject-specific biomechanical model from available medical data. To this aim, the method proposed by [START_REF] Bucki | Clinical workflow for personalized foot pressure ulcer prevention[END_REF]) could be of interest and would ease the generation of the patient specific reduced model needed for each new subject.

Fig. 1 :

 1 Fig. 1: (a) Finite element model of the buttocks, (b) Sagittal cross section showing the three layers of materials defining the buttock model: skin, fat and muscles. Bones are assumed to be rigid and fixed to the nearby FE nodes.

Fig. 2 :

 2 Fig. 2: Two pressure map examples where the subject sits with (a) his arms on his knees, back away from the back-rest, and (b) most of his weight on his left buttock. The highest measured pressure, in red, is (a) 1.38 N.cm -2 and (b) 1.57 N.cm -2 .

Fig 3 :

 3 Fig 3: Ischial detection with (a) Gaussian smoothing, (b) Laplacian filtering, and (c) thresholding and morphological filtering to pair the ischium (empty circles are possible matches, full circles are the detected ischium).

Fig. 4 :

 4 Fig.4: Mean Error over Mean Value (MeanEMV) for 5, 10, 15 and 19 pressure modes, and a targeted precision for the strain statistical reduction of 0.2, 0.5, 1, 2, and 5 %.

Fig. 5 :

 5 Fig. 5: Max Error over Mean Value (MaxEMV) for 5, 10, 15 and 19 modes, and a targeted precision of 0.2, 0.5, 1, 2, and 5 %.

Fig. 6 :

 6 Fig. 6: Max Error over Mean Value (MaxEMV) for 100, 150 and 200 learning pressures frames, 19 modes, and a 0.5 % targeted precision.

Acknowledgements

Competing

interests: Some authors are involved with TexiSense (http://www.texisense.com/home_en) and some with ANSYS (http://www.ansys.com/). Funding: This work was partly funded by the 2010 ANR TecSan IDS project, by the CAMI Labex (ANR-11-LABX-0004). Ethical approval: the study was approved by the French institutional review board of Grenoble (IRB 5891 (CECIC) for Rhône-Alpes-Auvergne).


Arezki Bounaceur

Elisabeth Rodier
email: rodier@enstimac.fr

Jacques Fages

STUDY OF THE MATURATION OF A KETOPROFEN/ -CYCLODEXTRIN MIXTURE, ASSISTED BY SUPERCRITICAL FLUID

Supercritical fluids have found many applications due to their versatile physicochemical properties. They can be used as media for the formation of inclusion complexes, for helping encapsulation of an active substance into cyclodextrin (CDs) cavities. In addition, several active pharmaceutical substances such as ketoprofen, are not very polar, thus not very water-soluble, and in order to increase their rate of dissolution, such drugs can be included in a molecular cage, such as CD with the aid of supercriticalfluid processing.

Several inclusion processes in supercritical media have been developed and the maturation process, in which supercritical carbon dioxide (SC-CO 2 ) is generally used, is among the most recent. It consists of using supercritical CO 2 to bring the active substance, the cyclodextrin and a quantity of water in contact one with another over certain duration, in order to obtain an inclusion complex. We have also studied the influence of several operating parameters on the inclusion rate e.g pressure, temperature, duration of maturation, density of the SC-CO 2 , agitation and factors related to the physical mixture, the amount of water, the molar ratio -cyclodextrin/ketoprofen and the mass ratio SC-CO 2 /mixture, as well as the effect of the method of preparation of the physical mixture on inclusion.

We found that pressure, temperature, duration of maturation, agitation and density of SC-CO 2 all had a positive effect on inclusion of ketoprofen (KP) in -cyclodextrin ( -CD). The stoichiometry of inclusion of KP in -CD is 1:2. The method of preparation of the powder mixture and mass ratio SC-CO 2 /mixture has an effect on inclusion.

INTRODUCTION

Several processes using supercritical fluids for the formation of the inclusion complexes have been already developed. They are based on the solvent and transfer properties of supercritical fluids. The principal processes used in supercritical medium are: the RESS Process (Rapid Expansion of Supercritical Solution), the SAS Process (Supercritical Antisolvent) and the PGSS Process (Particle from Gas-Saturated Suspension/Solution) [1]. The supercritical maturation process, in which supercritical carbon dioxide (SC-CO 2 ) is generally used, is among the most recent. It consists of holding in contact the active substance, the CD and some water, in supercritical medium over certain duration, in order to obtain an inclusion complex. This process was first developed in 1999, by the team of Pr. Delattre from Liège University (Belgium). Previous work using this process involves the inclusion of a nonsteroidal anti-inflammatory drug (NSAID), Piroxicam [2].

Our work tries to clarify the phenomena involved in the inclusion process using the supercritical maturation process. We also studied the influence of the process parameters and those related to the physical mixture composition and method of preparation.

I -MATERIALS AND METHODS

1-1-The experimental set-up

The tests of maturation are carried out in a multifunction pilot plant (Separex, France). Figure 1 presents the configuration of the plant for maturation. CO 2 is initially stored in liquid form at 50 bars and 0°C in reservoir (2). Liquid CO 2 is then pumped by the membrane pump (3), to be pressurized up to 350 bars. The cooling of the pump head makes it possible to maintain solvent in the liquid state and avoid cavitation. Compressed CO 2 then passes through a heat exchanger (4) to become supercritical. The mass flow of CO 2 is fixed at 20 kg/h. The autoclave [START_REF] Fromming | Cyclodextrins in Pharmacy[END_REF] with a capacity of 2 litres is electrically heated and contains the mixture to be maturated. Digital and analog indicators of CO 2 flow, pressure and temperature, on the control panel give direct reading of these process parameters.

We carried out some maturation tests using a Labsize pilot, also built by Separex, to determine the effects of agitation and of the mass ratio SC-CO 2 /mixture on the inclusion process. The Labsize pilot is similar to the main pilot but the maturation autoclave has a capacity of only 0.5 litre and is fitted with a mechanical stirrer.

1-2-Operating mode

To perform complexation in the supercritical medium, we first prepared a physical mixture of the components. This involves manually mixing with a spatula, a certain quantity of KP, -CD and water. The physical mixture is then placed in a basket closed by two sintered steel porous plates, which is then placed in the maturation autoclave [START_REF] Fromming | Cyclodextrins in Pharmacy[END_REF]. CO 2 flows in, until the desired pressure and the temperature is reached. The autoclave is then isolated for the duration of maturation. At the end of the experiment, the apparatus is depressurized by sending SC-CO 2 to the vent.

1-3-Materials

Four different substances, CO 2 , KP, -CD and water were used in the experiments.

• CO 2 CO 2 (purity 99.995 %), is supplied by Air Liquids S.A.

• Ketoprofen

The active compound (KP) was supplied by Boehringer-Ingelheim. KP belongs to the class of NSAID (non-steroidal anti-inflammatory drug), derived from aryl-carboxylic acid. It has an activity in the treatment of inflammatory rhumatoid diseases and for the relief of acute pain. It is effective against period pains, pain after surgery, and fever. Its chemical formula is C 16 H 14 O 3 (2-(m-Benzoylphenyl) propionic acid) having a molecular weight of 254.28, a melting point of 95 °C. The solubility of KP in SC-CO2 was determined. It was found that, at 200 bars and 65 °C corresponding to a SC-CO 2 density of 0.661 g/l, the solubility of KP is 0.15 g/ml (y KP = 4,1 10 -5 ). According to the literature, at 220 bars and 58.35 °C, the molar fraction of KP in SC-CO 2 is y KP = 15 10 -4 and at 100 bars and 39.3 °C y KP = 4 10 -6 [3]. In addition, KP is not very water-soluble, its solubility at 25 °C is found to be 0.13 g/l [4] • -cyclodextrin -CD Cavamax W7 Pharma was provided by Wacker. -CD is insoluble in SC-CO 2 . Comparative to other natural CDs, -CD is the least water-soluble. At 20 °C and atmospheric pressure, the water solubility of -CD is 16.4 g/l; it reaches 18.5 g/l at 25 °C and 196.6 g/l at 80 °C [START_REF] Fromming | Cyclodextrins in Pharmacy[END_REF].

• Water

Distilled water was used. The evolution of the solubility of water at 50 °C in CO 2 for various pressures can be found in [START_REF] Stahl | Dense gases for extraction and refining[END_REF] 1-4-Analytical methods

• Differential Scanning calorimetry (DSC)

The DSC allows the determination of the percentage of included active compound. A Perkin-Elmer DSC-7, was used to measure the heat absorbed by the sample when nonincluded active compound melts by increasing the temperature from 20 to 120 °C at 2 °C/min, under nitrogen flow. The sample sizes were 4.5-5 mg. The comparison of the thermogram corresponding to the fusion of non-included or free KP with that of pure KP enables us to calculate the percentage of inclusion. To determine the percentage of inclusion of KP in CD, we have used the equation (1):

%AC complexed = 1 ( Hf1 / Hf 2) (M 1 / M 2) (1)
% AC complexed: is the mass percentage of the complexed active compound (KP). H f1 is the enthalpy of fusion of non-included KP after complexation (J/g). H f2 is the enthalpy of fusion of pure KP (J/g). M 1 is initial mass of KP contained in the physical mixture (g) before maturation. M 2 is the mass of physical mixture (g) before maturation. This thermal characterization method for inclusion compounds is now a standard method as confirmed in the review by Giordano and al. [START_REF] Giordano | [END_REF].

II-EXPERIMENTS: PROCESS PARAMETERS AND RESULTS

2-1-Process parameters

The parameters varied are : Process parameters: pressure, temperature, duration of maturation, density of SC-CO 2 and agitation; Composition mixture parameters: amount of water, molar ratio KP/ -CD, mass ratio SC-CO 2 /mixture ; Method of preparation of the physical mixture: water added to the KP/ -CD mixture, water added to -CD before the addition of KP.

2-2-Main results

For the same complex, obtained at 150 bars and 65 °C, we made three DSC of three different samples. The standard deviation (defined here as the ratio of the mean deviation from the mean value to the mean value) on the measured heat of fusion is 0.7%. The inclusion of KP into CDs can be considered to be homogeneous in the entire complexed mixture. Three experiments were also performed for the same mixture composition, at 150 bars, 65°C, 2 hours of maturation and a molar ratio KP/ -CD 1:2. The standard deviation on the measured heat of fusion of non included KP is 0.9%. The process therefore has a satisfactory reproducibility.

2-2-1-Effects of the operating variables

It is found that temperature has a positive effect on inclusion. This positive effect can be explained by the fact that an increase in the temperature accelerates the kinetics of formation of the complex. The temperature has also a negative effect on the solubility of the KP in the SC-CO 2 at a fixed pressure. Hence, if SC-CO 2 is a transfer medium for water and KP; it is less efficient when increasing the temperature. However, the positive effect of the temperature on inclusion kinetics may prevail. Increasing the contact time between SC-CO 2 and the physical mixture is also favorable. Indeed, for one and a half hours maturation, the inclusion is 47.62 %, reaches 65.19 % after 2 hours and 75.16 % after 5 hours of maturation. In other words, time of contact has a very important effect on inclusion up to 2 hours of maturation and becomes less significant for longer durations. The increase in the time of contact in SC-CO 2 allows increasing the transfer in SC-CO 2 of dissolved KP and water and the diffusion of KP and water through the mixture. This would suggest that the process is mass-transfer limited. A higher density of SC-CO 2 has a positive effect on inclusion (see Figure 2). By increasing the density of SC-CO 2 the quantities of water and solubilized KP in SC-CO 2 will be more important. Consequently, the transfer of KP and water towards the CDs, which can be done via CO 2 medium, will be more important (there will be more opportunity for contact between -CD, water and KP). Figure 2, given for a fixed temperature, indicates also a positive effect of pressure on inclusion. We carried out two tests on the Labsize pilot. The first made without agitation and the percentage of inclusion is 60.22 %, the second made using the mechanical agitator gives percentage of inclusion of 72.55 %. Thus there is an increase in inclusion of 12.33% when the mixture is stirred at an agitation speed of 350 rpm. Agitation makes it possible to increase the transfer of KP and water in CO 2 , as well as the transfer of KP in the mixture, thus a more homogeneous mixture.

2-2-2-Effects of the mixture composition

It should be noted that the addition of water is necessary for inclusion to occur (see table 2). With a physical mixture prepared without any additional water (thus just with water adsorbed on initial -CD), we obtain 0 % of complexation and for 25 % of added water; we have more than 95 % of complexation. The water added to the physical mixture solubilizes the surface of -CDs and cracks them, thus increasing their contact surface. It also destabilizes the water initially present in the cavities of CDs. This internal water becomes unstable with a higher energy state. The thermodynamic changes occuring during maturation will tend to restabilize the system. This could be achieved when the destabilized water leaves the CD cavities to allow access by the KP molecules. The included KP molecules in the cavities of CDs form hydrophobic links with the glucosidic groups of CDs. This exchange and hence this destabilization phenomenon is a key-contribution to the inclusion process. We also studied the stoichiometry of inclusion of KP in -CD. For the molar ratio KP/CD 1:1, the percentage of inclusion is 30.81%, and goes to 73.96 % for a ratio 1:2 and then varies no more using a ratio 1:2.5. Thus the stoichiometry of inclusion of KP into -CD is 1:2, i.e. a KP molecule is included in two molecules of CDs. To study the influence of the mass ratio CO 2 /mixture, we did maturation tests with both pilots. The volume of the autoclave of the first pilot is 2 liters. In the second pilot the volume of the autoclave is 0.5 liter. The percentage of inclusion seems to increase when the mass ratio SC-CO 2 /mixture decreases. Hence, for the same density of SC-CO 2 and similar experimental conditions, at the end of the experiment, the total quantity of KP, which remains solubilized in SC-CO2, is more important in the larger autoclave than in the smaller one. Thus the quantity included in CDs is more important in the smaller autoclave than in the larger one. Therefore, for small quantities of the physical mixture, it is more interesting to use not too great a quantity of SC-CO 2 , so as to minimize the diluting effect of SC-CO 2 .

2-2-3-Method of preparation of the physical mixture

In order to find out if there is an incidence of the method of preparation of the mixture on inclusion, we prepared the mixture by two different methods. In the first case, we mixed CD with KP and then we added water and mixed all together. In the second case, we mixed CD with water and then we added KP and mixed a second time. Then we studied the effects of pressure, temperature and duration of maturation. When we add water after KP, the pressure and the temperature have a positive effect on inclusion. By adding water before KP, the results of the complexation show that, at 65 °C, the pressure does not have any effect on inclusion. However, at 75 °C, the increase in the pressure generates an increase in inclusion, as for the first method. The addition of water before KP makes this water structured in a more stable way on the CDs surface and in the cavities of CDs. It is more difficult to destabilize to allow substitution by KP. Moreover KP was badly distributed onto CD surfaces. For a higher temperature, here 75 °C, water in the cavities becomes less stable, and it is thus more likely to leave the cavities and to be replaced by KP. The pressure has then a positive effect on the solubility of KP. This additional limitation for the inclusion process has to be compensated by increasing temperature. The higher temperature will then favour the destabilization of the internal water and then allow the pressure on the inclusion process to be more effective.

III -CONCLUSION

This work confirms that the batch maturing process using supercritical CO 2 is an effective and straightforward process of encapsulation by CDs. However, the phenomena at play are not so simple and need to be further investigated, for instance the role of the added water has not yet been totally elucidated. To implement the work done on this complexation process, we tried to put in light the effect of some parameters that have not yet been widely studied such as the mass ratio SC-CO 2 /mixture and the method of preparation of the physical mixture. It may be concluded that the initial physical mixture must be carefully prepared by only introducing water at the end of the mixing, and that conditions of temperature, ambient humidity and stirring have to be controlled. The use of great volumes of CO 2 causes the dilution of KP in SC-CO 2 , which decreases the quantity included in CDs. Therefore, a high powder density in the autoclave favours inclusion.

Figure 1 :

 1 Figure 1 : Experimental set up

Figure 2 :

 2 Figure 2 : The effect of the density of CO 2 on inclusion


Mîra Anna

Ann-Katherine Carton

Serge Muller

Yohan Payan

BREAST BIOMECANICAL MODELING FOR COMPRESSION OPTIMIZATION IN DIGITAL BREAST TOMOSYNTHESIS

Introduction

Today, projection mammography is the key imaging modality for breast cancer screening and plays an important role in diagnostics. During the exam, the women breast is compressed between two plates until a nearly uniform breast thickness is obtained. Breast compression improves image quality and reduces the absorbed dose of ionizing photons. But breast compression can also be the source of discomfort and sometimes pain for the patient during and after the exam. The discomfort perceived during the exam could deter women from getting the exam. Therefore, an alternative technique with reduced breast compression is of potential interest.

The aim of this work is to develop a biomechanical Finite Element (FE) breast model allowing to investigate alternative breast compression strategies. Ultimately, their impact on mammography image quality and radiation dose could be investigated using a simulation framework generating numerical breast-like phantoms, FE mammographic breast deformation and image acquisitions. The 3D strain/stress cartography derived from the FE solution could be used as a first measure of pain and discomfort.

Previously developed biomechanical breast models have used a simplified breast anatomy by modeling adipose and fibroglandular tissues only [START_REF]Release 16.2, Help System[END_REF]. However, breast reconstruction surgery has proven the importance of suspensory ligaments and breast fasciae on breast mechanics [START_REF] Hipwell | A review of biomechanically informed breast image registration[END_REF]. We are considering using a more complex breast anatomy by including breast ligaments as well as the skin and muscles.

A physical correct modeling of the breast deformation requires the knowledge of the stress-free breast configuration; i.e. the absence of gravity load. Here, the stress-free shape was computed using the prediction-correction iterative scheme [START_REF] Carter | Application of Biomechanical Modelling to Image-Guided Breast Surgery[END_REF], the unloading procedure used the breast configuration in prone and supine position to find a unique displacement vector field induced by gravitational forces. Finally, the estimated breast deformations under gravity load were compared with the experimental ones obtained from the MR images.

Materials and Methods

Data acquisition and preprocessing.

The biomechanical breast model is built using MR images of two women between 50 and 55 years old and with respectively small and large breasts. Both women participated on a voluntary basis and signed an informed consent form approved by the ethics committee. The MRI was performed at 3T (Achieva 3.0TX, Philips, NL) at IRMaGe MRI facility (Grenoble,France). Both women were imaged in three configurations: prone, supine and supine tilted positions (Figure 1). The positions were chosen to assess the largest possible breast deformation without any contact area between the patient and the MRI scanner bore. Before image acquisition, ten fiducial markers were fixed on the skin surface; four on the chest wall and six on the breast skin. The markers were used to aid pre-processing registration of the breast volume and to validate the modeled breast deformation.

Image pre-processing starts with an image registration step. The rigid transformation between two breast configurations was computed using the marker's coordinates and the chest wall position in the three images. We assumed that only the four chest-wall markers impact the rigid transformation between breast configurations. Next, the MRI data were classified according to three tissue types; muscle, breast and skin (Figure 2). The segmented data set was then used to reconstruct the 3dimensional breast geometry. The image preprocessing steps were performed using ITK libraries VERSION_4.3.2 and ITK-Snap software VERSION_3.4.0 [START_REF] Yushkevich | User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability[END_REF].

Patient Specific Finite Element Model (PSFEM)

From the segmented data, a tetrahedral finite element mesh was generated using ANSYS meshing tool [START_REF]Release 16.2, Help System, ANSYS Meshing[END_REF]. Two components were created: one representing the breast soft tissues and its surroundings, and the second representing the pectoral muscle (figure 2). The contact surface between the two components was defined as a "no-separation" contact (the two components are bonded) with an infinite friction coefficient [START_REF]Release 16.2, Help System[END_REF]. As the skin is a thin layer organ (thickness of 2mm) we used triangular shell elements to represent it. Finally, the new finite element mesh was subjected to a hyper-elastic quasi-static simulation. Soft tissues were modelled as quasi-incompressible (Poisson Ration = 0.45) neo-Hookean materials. The optimal elastic parameters were determined by minimizing the error between the simulated and the experimental data, using a manual dichotomy on the elastic modulus. The search intervals were defined for each tissue type based on the data found in the previous studies [START_REF] Hipwell | A review of biomechanically informed breast image registration[END_REF][START_REF] Krouskop | Elastic moduli of breast and prostate tissues under compression[END_REF][START_REF] Rajagopal | Creating individual-specific biomechanical models of the breast for medical image analysis[END_REF]. The elastic parameter of the pectoral muscle was set to 30kPa and the elastic parameters of breast tissues and skin were varied from 0 to 30kPa and from 5 to 80 kPa respectively.

Stress-free geometry estimation

In this work, the stress-free geometry was computed using a prediction-correction iterative scheme. The algorithm first proposed by Carter et al. [START_REF] Carter | Application of Biomechanical Modelling to Image-Guided Breast Surgery[END_REF] was improved and adapted to our problem. First, we assumed zero internal stress for the breast supine configuration generated from MR images (Figure 3.a Geometry of Measured supine configuration). Then, the gravity load was applied in the reverse direction to give a first estimation of initial stress-free state (Figure 3.b Geometry of Estimated stress-free configuration). Next, the gravity load was applied to the stress-free geometry to estimate the breast geometry in prone configuration (Figure 3. c). At this step, we defined a group of nodes (active nodes) which were used to compute the difference between the nodes position in estimated and measured prone configuration. The nodes were selected at the external surface of the breast. These active node difference was then applied to the stress-free geometry. The process was repeated until convergence was achieved.

Results

The breast stress-free geometry was estimated for each pair of elastic parameters defined from the search intervals. After the stress-free geometry estimation, the prone and supine breast positions were computed to evaluate the model. The best fitting of real data in both positions is obtained using an elastic parameter equal to 0.3kPa for breast tissue and equal to 10kPa for skin (see Figure 4). The manual adjustment of elastic parameters conducted to breast tissues far softer than most values found in the literature.

Figure 1 :

 1 Figure 1: Breast configurations under gravity loads.

Figure 2 :

 2 Figure 2: a) MRI data for supine position, b) segmented data, c) corresponding finite element mesh

Figure 3 :

 3 Figure 3: Prediction-correction iterative scheme for stress-free geometry estimation. GM = measured breast geometry; GE= estimated breast geometry. ∆ = difference between estimated and measured geometries. λ = regularization factor.

Figure 4 :

 4 Figure 4: Comparison of simulated configurations with the measured ones.

Acknowledgments

This research project is financially supported by ANRT, CIFRE convention n°2014/1357. We are thanking the IRMaGe MRI facility (Grenoble,France) for their participation in image data acquisition.

Discussion

The results derived from the manual adjustment of tissues elasticity revealed that our breast model is highly sensitive on the value of elastic parameters. Therefore, a good estimation of the breast mechanics requires a precise estimation of tissues elasticity for each individual volunteer. We should note however that our results are only consistent with models developed and validated with data using MR images. Most of published papers on breast tissue elasticity propose very high values for fat, glandular and skin elastic parameters [START_REF] Rajagopal | Creating individual-specific biomechanical models of the breast for medical image analysis[END_REF]. The large difference may come from the inter-women variability of breast elastic properties but also by a wide range of mathematical models and experimental methods.

Knowing that we need a proper estimation of the elastic properties for each volunteer, we will develop an automatic optimization procedure replacing the manual one described here. An automatic optimization should allow a more accurate estimation of elastic parameters. One of the major difficulties of an automatic optimizations is the accumulation of finite elements simulations on the same mesh. As a hyper-elastic model with large deformations is used, the finite element mesh can be distorted after each simulation. Therefore, the optimization process can be stopped due to the poor-quality elements without achieving the convergence.

As a future work, we intend to consider the breast heterogeneity in our model. More specifically, it is necessary to differentiate the glandular tissues and adipose tissues, as well as to introduce the Cooper's ligaments and facias. Our biomechanical model will be then validated using the third breast configuration (supine tilted figure1) and will be used to simulate artificial tomographic images.


D Aaron

Aaron Roberts
email: aaron.roberts@nasa.gov.

James Thieman

Vincent Génot

Todd King

Michel Gangloff

Chris Perry

Chiu Wiegand

Darren De Zeeuw

Shing F Fung

Baptiste Cecconi

D Aaron Roberts

Sébastien Hess

Spase Consortium

The SPASE Data Model: A Metadata Standard for Registering, Finding, Accessing, and Using Heliophysics Data Obtained from Observations and Modeling

HAL is

Abstract

The Space Physics Archive Search and Extract Consortium has developed and implemented the "SPASE Data Model" that provides a common language for registering a wide range of Heliophysics (HP) data and other products. The Data Model enables discovery and access tools such that any researcher can obtain data easily, thereby facilitating research, including on space weather. The Data Model includes descriptions of Simulation Models and Numerical Output, pioneered by the Integrated Medium for Planetary Exploration (IMPEx) group in Europe, and subsequently adopted by the Community Coordinated Modeling Center (CCMC). The SPASE group intends to register all relevant Heliophysics data resources, including space-, ground-, and model-based. Substantial progress has been made, especially for space-based observational data and associated observatories, instruments, and display data. Legacy product registrations and access go back more than 50 years. Real-time data will be included. The NASA portion of the SPASE group has funding that assures continuity in the upkeep of the Data Model and aids with adding new products. Tools are being developed for making and editing data descriptions. Digital Object Identifiers (DOIs) for Data Products can now be included in the descriptions. The data access that SPASE facilitates is becoming more uniform and work is progressing on Web Service access via a standard Application Programming Interface. The SPASE Data Model is stable; changes over the past nine years were additions of terms and capabilities that are backward compatible. This paper provides a summary of the history, structure, use, and future of the SPASE Data Model.

Motivation and Overview

Over three decades ago, what NASA now calls the Heliophysics (HP) community already knew what was needed to have orderly general access to the data produced by its many missions (National Research Council, Committee on Data Management and Computation, 1982-the "CODMAC Report"; also, [START_REF] Bogart | Report of the Sun-Earth Connection Study Team of the Space Science Data Systems Technical Working Group[END_REF]. In current terms, the data system would need to be standards-based, with scientist input at all levels, allowing users to easily find, access, and use data from all Heliophysics systems and models. The system would have the ability to bring old data to life and to preserve raw and processed data from old and new missions. The standards for formats and access would facilitate producing general browsing and analysis tools. It took many years, the advancement of computer technology, and the gradual buy-in by the HP community to make substantial progress on achieving these goals, but we now have a NASA Heliophysics Scientific Data Management Policy (NASA HPDD, 2016) that provides a basis for progress. One major step has been the establishment of a standard for metadata to describe HP data and its sources-the SPASE Data Model-that provides a route to discovery and access for the various resources in Heliophysics including data from observatories and models, catalogues (including event lists), and overview "display" data in the form of prepared graphs. Our work is consistent with the widely adopted "FAIR" principles (Findable, Accessible, Interoperable, and Re-useable: see https://www.force11.org/group/fairgroup/fairprinciples and: https://www.nature.com/articles/sdata201618).

The general structure of the Heliophysics Data Environment, while grounded in the ideas of the "CODMAC" report, is now based on the idea of a "Virtual Observatory" (VO) which extends the earlier ideas given what is possible with new technology. The astronomical community realized nearly two decades ago that taking advantage of existing observations stored throughout the community drastically changed the amount of data available to a researcher.

Initially termed a "Digital Sky," this became the National Virtual Observatory (see https://heawww.cfa.harvard.edu/USVOA/) in the US, and the International Virtual Observatory Alliance (IVOA; http://ivoa.net) worldwide. As the earlier efforts had foreseen, to be successful, standards were required primarily for: (1) data formats, (2) metadata descriptions, and (3) access methods (Application Programming Interfaces, or APIs). A primary goal of the IVOA is to make all observations, in whatever wavelength, appear the same as all others in terms of discovery, access, and, to the extent possible, use. Uniformity of the data format, metadata, and APIs makes it possible for client software (in IDL, MatLab, Python, or other languages or tools) to load data arrays using just dataset and variable IDs as if from a local disk. Given the infrastructure facilitated by standards, Web portals (also a possible API use, but with direct user interaction) allow one easy route to data, and these can provide everything from quick-looks that enhance research efficiency to the ability to make publication-ready plots and images.

Heliophysics is not yet quite as unified as Astrophysics, but what started as a wide variety of idiosyncratic formats has become FITS for solar images, CDF for space physics data, and NetCDF for some major datasets closer to the Earth in the Ionosphere-Mesosphere-Thermosphere realm. (ASCII is decreasingly used as an archival format.) Access has been improved with the Virtual Solar Observatory API providing nearly universal solar access, and a few APIs (particularly "CDAS" for the CDAWeb archive; https://cdaweb.gsfc.nasa.gov/WebServices/) providing access to much space physics and some ground-based data. The "Heliophysics API" ("HAPI"; https://github.com/hapi-server/) has now been defined and is being implemented with the intention of providing a single route to all HP time series data with easily adopted methods for servers and clients. Most central to this paper, the metadata for HP has been defined for many years now using the SPASE Data Model (hereafter simply "SPASE"). Two decades of effort has gone into defining and refining SPASE, with input from all HP disciplines and, from the start, with international collaboration. One significant area of effort is relating the European use of IVOA standards to SPASE and HAPI. Ultimately the users should not see any of the details; they will simply request and use data via a common route. For example, AMDA (a space plasmas analysis tool, http://amda.cdpp.eu) and 3DView (a spacecraft trajectory, in-situ data, and model visualization tool, http://3dview.cdpp.eu) have been using metadata compliant with SPASE for several years to build their hierarchy (or tree) of data for observations as well as simulations (see for instance [START_REF] Erard | VESPA: A community-driven Virtual Observatory in Planetary Science[END_REF].

The next section provides historical background on SPASE, but those only interested in the content of the data model can skip to subsequent sections that deal with the structure and uses of SPASE for both observations and models. The NASA context of this work is given in NASA HPDD (2016).

A Brief history of SPASE

Earlier presentations of SPASE and related tools are given by [START_REF] Harvey | Virtual Observatories for space and solar physics research[END_REF], [START_REF] King | SPASE 2.0: A Standard Data Model for Space Physics[END_REF][START_REF] Thieman | SPASE and the Heliophysics Virtual Observatories[END_REF]; these give some idea of the international origins and development of SPASE. The SPASE effort has its roots in a call for action at a data handling session of the International Solar Terrestrial Probes (ISTP) workshop held at the Rutherford Appleton Labs (RAL) in 1998, when on September 26 a resolution was passed calling on the "larger data centers" to "do something" to make data more accessible. This was to be a continuation of the work mentioned above that started with CODMAC and continued with other meetings. RAL, CDPP (Centre de Données de la Physique des Plasmas, http://www.cdpp.eu), NSSDC (National Space Science Data Center), and SWRI (Southwest Research Institute) took up the challenge. Early in 2001 a breadboard interoperability test bed was implemented between NSSDC and CDPP, and later that year, NSSDC, SWRI, RAL and CDPP submitted a proposal to NASA for "A Space Physics Archive Search Engine." Although this was not funded, a volunteer effort continued and attracted broader participation. It was recognized that a data model was needed to establish an "interlingua" to share resources across the entire space physics domain. The goals of this effort were defined in late 2002 and the new moniker of Space Physics Archive Search and Extract (SPASE) was adopted.

In 2003 the effort was organized as an international consortium with an open invitation for anyone in the community to participate. U.S. participants in SPASE were funded by NASA in July 2005 by the Living With a Star (LWS) program, which helped to accelerate the effort. Finally, in November 2005, the SPASE group released version 1.0 of its "ontology" (Data Model). Further product descriptions led to the need for updated versions. In 2006, NASA established thematic "VxOs" (Virtual Observatories with "x" being "Magnetosphere," as in VMO, "ITM," being "Ionosphere, Thermosphere, Mesosphere" as in VITMO, etc.) that helped to bring focused efforts to SPASE from each of the subdisciplines. After a period of use in NASA's VxOs, the model was streamlined and enhanced to support a wider range of resources, and the first and only (so far) non-backward compatible version (2.0) was released in April 2009. Since then, the major change was to add the Simulation Extension Product Types (e.g., "SimulationRun"; see below) in May 2014. The modifications since then have all been in the "base" SPASE model, so the Simulation Extensions remain at Version 1.0.0 (see Sec. 6).

The use of SPASE to provide descriptions and tools was slow at first, but now a large fraction of HP data has been described and registered in these terms, and all current NASA missions are described by SPASE, as required by the HP Data Policy (NASA HPDD 2016). Starting in 2017, NASA has provided long-term funding, as part of the Heliophysics Data Environment (HPDE) infrastructure, to efforts that will maintain and augment both resource descriptions and the Data Model itself (see http://spase-group.org and https://hpde.gsfc.nasa.gov). Throughout this process, links have been maintained to non-NASA agencies and groups, in the US and abroad.

The rest of this paper describes the SPASE Data Model, as an introduction to make it more easily useable. Inevitably, SPASE has grown increasingly complex, but its foundation has always been to provide basic information about HP resources in ways that are simple for users to understand. The detailed specification of the current SPASE Data Model at the time of this writing is given by The SPASE Consortium (2018). This includes a complete hierarchy of the model, many example descriptions, and a dictionary of terms. The Simulation Extensions are given by The SPASE Consortium (2014).

SPASE Resources

Each SPASE document-a basic entry in a SPASE Registry-is a description of a "Resource," the latter being the generic term for any entry in the system. Each Resource is assigned a unique Resource ID that is used as a reference, both externally and by other resources. Figure 1 shows, in the left column, the Resource Types colored to show the main categories. Each Resource Type consists of a set of attributes that characterize the resource. Each Type also has its own SPASE descriptor format, typically encoded in XML. The format for each Product Type is given in an "XML Schema" that can be used to check a description for its conformity to SPASE requirements (see the online tool for validating SPASE syntax at http://spasegroup.org/tools/validate/). Resource Types can be divided into three categories, color-coded in the Figure.

Data Resources (blue) describe one or more data products. A "data product" is a set of data that is uniformly processed and formatted, from one or more instruments, typically spanning the full duration of the observations of the relevant instruments. A data product may consist of a collection of files of successive time spans but may be high-level entities such as event catalogs. Data products can be images (Display Data), sample or observation values (Numerical Data), or event lists (Catalog). Included in the Data Resource category are the resources that describe individual files (Granules) which are part of data product sets, and assessments of a resource (Annotations). The complete list of observational Data Resources is: Numerical Data, Display Data, Catalog, Granule, and Annotation. Numerical Data Resources are typical numerical variables or quantitative images as a function of time, and Granules are the individual files or other basic division that contain these. Display Data Resources are jpeg, png, or other graphical or image representations of the Numerical Data, often used for browsing but in some instances suitable for publication. Catalogs are usually event lists that give, say, times when solar Coronal Mass Ejections occurred or times when high-resolution ("burst") data are available from one or more observatories.

Origination Resources (green) describe the generators or sources of data. People (Person Resources) are the authors/creators of all the Products, and Instruments on or in Observatories are the sources of Data. A Data Resource refers to one or more Origination Resources.

Infrastructure Resources (red) describe system components that are part of the access, transfer, and use of data. Repositories are the containers of Data Products; Services act on data or metadata; and Registries are containers of SPASE descriptions that keep track of all Products. Documents are also placed in this class as typically being supportive of the Data and other Resources. They are less formally organized than other SPASE Resources since the content of relevant papers, etc. can take many forms. Note that SPASE does not intend to register documents that are otherwise accounted for, such as journal articles, but rather things such as the NASA Data Policy or formal SPASE Data Model descriptions.

Simulation Resources are analogous to the Resources above that are based on observations. The output from simulations (Numerical Output and the related Display Output) is essentially the same as observational data, and thus these Resource Types are in blue in Fig. 1. However, instead of being generated by an Instrument of an Observatory the outputs are the result of Simulation Runs of a Simulation Model. Thus, the latter two Resource Types are included as Origination Resources in Figure 1. See below for further discussion of simulations.

Core attributes and their hierarchy

With the exception of Granule and Person, which are very simple in structure, every resource has a common set of core attributes. The core attributes provide textual descriptions of the resource and the capability to reference external sources of information (Information URL). It also describes the context of the resource in the larger data environment. This context consists of associations with other resources (Association) and with previous versions (Prior ID). These attributes are grouped in a Resource Header (see Figure 1) and consists of: Resource Name, Alternate Name, Digital Object Identifier (DOI; see below Sec. 7.7), Publication Information to go with the DOI, Funding (the source of support), Release Date, Expiration Date, Description, Acknowledgement, Contact, Information URL, Association, and Prior ID.

Data Resource descriptions also include overviews that tell the user other essential general information, such as what Repository, Instrument, and Observatory the Resource comes from, how to access it, what its measurement type is, the time span and cadence of the observations/output, what spatial and spectral region it applies to, and who provided it. Thus, the basic information in SPASE provides users with the ability to understand the relevance of a given Resource to his or her research, in addition to the most direct routes (via Access URLs) to obtaining data.

Figure 1 also shows some of the details of a Numerical Data Resource, including four levels of the hierarchical description. Note that People, Instruments, Repository, and (implicitly) Observatory are included by reference, the latter by the reference shown in the Instrument description hierarchy. The spase-group.org website provides various ways of seeing the full set of SPASE terms and the hierarchy. One method uses diagrams, as in Figure 2, to provide the full content of the hierarchy at each level (http://spase-group.org/data/model/spase-2_3_0/ ). The full hierarchy is also in the references for the main descriptions (The SPASE Consortium, 2014Consortium, , 2018)).

Parameter (data variable) descriptions

Data Products, as well as Numerical Output from simulations, contain variables of varying degrees of complexity, from simple scalars to vectors, spectrograms, images, and multidimensional quantities such as a distribution of particles by species, polar angle, azimuthal angle, and energy. A complete description of a Data Product will include specifications of all such quantities (termed "Parameters"-see Figure 1-despite other possible meanings of the word) in the product, although the Parameter descriptions are optional so that products can be described more easily to provide at least registration and search capabilities. Parameters can also include Support quantities, such as data quality flags. One unusual aspect of the description is the "Parameter Type" which is not a separate entity but just a placeholder for one of the entities on the list that is attached to it. Also of note are the purple entities that provide "keys." The use of these is in direct access APIs-see Sec. 7.4.

A current task of the SPASE group is filling in the missing Parameters in many earlier descriptions. As with the general description of the Product, the Data Model provides options for very detailed Parameter descriptions, with some elements being required if the Parameter is to be included. The elements include information concerning the data cadence, units, and coordinate system, plus the Parameter Type such as a Field, Particle, or Wave measurement. The detailed structure of the data files can be captured to allow users to be able to figure out how to use the data, no matter how complex. The richness of the SPASE Parameter schema also permits straightforward data rendering specifications for generating graphs and other figures.

Simulation Extension

From early on, the SPASE group realized that a complete view of HP research had to include simulations, the models they were based on, and a route to using the often-voluminous numerical output from them. Ultimately, the success of our models is that their results are consistent with observational data; this is the basic science requirement of having theories predict observations that in turn test theories. In May of 2014, it was decided to add the entries for Simulations as a SPASE "extension." The Resource Types added in this way (see Fig. 1) function in the same way as those in the "Base Model" (consisting of all the original Resources), but they are treated as a separate population so that Simulation Resources can evolve independently. Terms from the Base Model, when used in an Extension (e.g., "Resource Header," "Parameter," "Access Information") inherit all the properties of the Base Model term. Two Base Model terms, namely Granule and Particle, were overwritten (redefined) in the Extension, but all others remained the same.

The main impetus for the Simulation Extensions was the work of the Integrated Medium for Planetary Exploration (IMPEx; http://impex-fp7.oeaw.ac.at/) project, a European Union (EU) Seventh Framework Programme sponsored project, which was subsequently endorsed by the SPASE consortium. The extension to SPASE developed by the IMPEx team was incorporated into SPASE in June of 2015. The extension for simulations was designed to be as generic as possible, but the IMPEx team focused on just a few models. New models were subsequently described in the frame of Transplanet (a simulation platform for ionosphere models http://transplanet.irap.omp.eu), using the extension with only a few additions or modifications. A French Agence Nationale de la Recherche project called TEMPETE (Temporal Evolution of Magnetized Planetary Environments during exTreme Events, P.I.: R. Modolo, LATMOS: Laboratoire Atmosphères, Milieux, Observations Spatiales; see http://www.agence-nationalerecherche.fr/Project-ANR-17-CE31-0016), aiming at simulating planetary exospheres and magnetospheres. This project started recently and will also use the Simulation Extension to describe these models.

In another modeling context, the Community Coordinated Modeling Center (CCMC) at NASA Goddard is a community resource of hundreds of models and many thousands of model runs made available to the public. The CCMC is now working to make SPASE the meta-data model for its holdings, including for the Integrated Space Weather Analysis System (iSWA; https://ccmc.gsfc.nasa.gov/iswa/) that provides near-real time datasets, as well as for the more complex model output. Each model and model version will be fully described by a separate Simulation Model resource, including authors, references, and key publications to best describe the model. The Simulation Run resource is the most complex in that it must capture all inputs to a model and how it was run with enough detail to be able to reproduce a run. Many models have a simple set of inputs, but several sophisticated models have hundreds of options and run in multiple "sessions" that change options during the run. Future integration of the Simulation Run metadata with the CCMC's Run-on-Request system will enable far more customization of options. The Numerical Output resource is very similar to its observational data counterpart (Numerical Data), although a wide variety of output with differing dimensions, cadences, and scales can make the description complicated.

The CCMC is implementing the above ideas by developing a large database called the CCMC Metadata Archive (CMA; see Fig. 3) which will act as their central repository of all the metadata to describe their data and model holdings; the current metadata reflects the variety of original model designs. When completed, this database will interact with the official SPASE registry either through APIs or through exported XML files to cooperate in the SPASE network of services. Internally to the CCMC, the CMA will become an integral part of their services, such as iSWA (a space weather real-time data viewer), Kameleon (a set of tools based on a uniform format for simulation output), runs-on-request, and metrics and validation challenges, This integration of standard metadata will allow not only more finely tuned discovery searches for data in stored runs due to, e.g., the uniformity of time formats and variable types (for example, which models output plottable O+ density, or what models can do polar outflow?), but also better visualization and analysis tools. It will enable reliable links to data APIs like HAPI to facilitate data-model comparisons. Similarly, CCMC is planning to use the SPASE metadata in the new CAMEL validation tool to help with overlaying data/model results with correct units.

SPASE in practice: Generation and Use

Contacting and contributing to SPASE

The NASA-based SPASE efforts now have an infrastructure that maintains and updates the Data Model along with needed documents and tools with input from all of the SPASE Consortium. The inclusion of new projects and products into the SPASE Registry is not yet automated and will always involve some checking by a person to make sure everything is in order.

SPASE editor

The generation of SPASE descriptions is, in principle, relatively simple at the level of the required entries (ID, Header, Measurement Type, Temporal Description, and a few others). Even that level can be enough work to be an impediment to implementation. Various experts have provided most of the descriptions to date, using prior cases as starting points or tools such as "ADAPT" that cull metadata from Common Data Format (CDF) data files for the product and other sources. As an example, here is what the beginning of an XML file describing a Numerical Data Resource looks like: In the interest of making the process simple, requiring no knowledge of XML and the "schema" used to insure they are correctly formatted (see https://www.w3schools.com/xml/schema_example.asp for the use of schema), we are constructing an online editor. Figure 4 shows a snapshot of the editor that will take care of the structure of SPASE and just ask the user for the terms and descriptions that make the product useful. When a term is part of an explicit list, the user is shown a list from which to choose. This editor will be announced to the HP community when a beta-test version is available. As mentioned above, there is already a tool available for checking the validity of independently generated SPASE descriptions (http://spase-group.org/tools/validate/).

Finding, Plotting, and Accessing HP Products

The uniformity of the SPASE metadata makes it easy to create tools to find particular data products. The Heliophysics Data Portal (https://heliophysicsdata.gsfc.nasa.gov) is such a search tool (Figure 5). It is a public face for the inventory, serving as a "card catalog" that also can deliver the "books" (data). The HDP reads the SPASE inventory into a database that allows searching using many SPASE terms or time-and text-based searches to find HP data products. In Fig. 4, the left column has boxes for text and time searches ("restrictions on the product list"), and a list of categories that can be used for further restrictions. At any point, the full list of resources consistent with the list of restrictions may be viewed: a "View Current List" link will appear at the top of the right column if more than 20 products in the list. The list can be sorted by Observatory, Cadence, Measurement Type, etc. Once the user finds a suitable product, the right columns provide descriptions based on SPASE and Access Links to the data and documentation. "Get Data" buttons provide links to plots and data from CDAWeb and other sources.

Figure 5. An instance of a search using the Heliophysics Data Portal that serves as a "public face" of the SPASE inventory. Shown here is a partial listing for a search for interplanetary plasma data in 1976.

Application Programmer Interfaces for remote machine access to data

As part of SPASE descriptions, "Product Key" and "Parameter Keys" (indicated in purple in Fig. 1) provide a unique product identifier within the serving repository and the variable identifiers required to request subsets of data using direct calls to data servers rather than through a Web portal. This allows a user to, for example, directly get data from an IDL or Python session using simple commands that construct calls to Web Services. A uniform specification of the behavior of data servers, with corresponding client (user) software to communicate with the servers, makes it possible for any data supplier to provide data with uniform calling methods. Since it is the response of the server to particular requests that is specified, any appropriate language can be used (Java, Python, etc.) to implement the "Application Programmer Interface" (API). Thus, the syntax for a simple Heliophysics API (HAPI; see https://github.com/hapi-server) call for data is: http://hapi-server.org/hapi/data?id=path/to/ACE_MAG &time.min=2016-01-01T00:00:00.000Z &time.max=2016-02-01T13:10:30.000Z&include=header

where "ACE_MAG" is the Product Key. In this case, all variables would be delivered. Using the Parameter Keys "Bx" and "By" would yield a subset of the data containing only Bx and By:

http://hapi-server.org/hapi/data?id=MY_MAG_DATA &parameters=Bx,By&time.min=1999-06-03Z&time.max=2000-07-22Z

The following URL (without the line breaks) will deliver data when entered into a browser or when generated by an application:

https://cdaweb.gsfc.nasa.gov/hapi/data?id=AC_H1_MFI &time.min=2006-01-01 &time.max=2006-01-03&parameters=Magnitude,BGSEc

Client software just needs to generate and submit URLs of the above syntax to any HAPI server to get the requested information in response. Usually the data would be directly loaded into the user application using software that "understands" the (uniform) HAPI data format.

There are a number of APIs for different systems including, importantly, that for the Virtual Solar Observatory (https://vso.nascom.nasa.gov/API/, https://sdac.virtualsolar.org/), which is now used extensively for solar data retrieval, and "CDAS", which is used for the retrieval of data from NASA's Space Physics Data Facility (SPDF) "CDAWeb" archive (https://cdaweb.gsfc.nasa.gov/WebServices/, https://cdaweb.gsfc.nasa.gov). While there are efforts on-going to obtain wide acceptance of HAPI as a simple, universal web service, it is not expected that the older APIs will lose their utility.

Present and future space weather applications

All the applications of SPASE discussed above are directly applicable to space weather research. One key to such research is the ability to use a multiplicity of datasets for a given project, and this is what SPASE is designed for. Combined with other standards, SPASE facilitates an integrated approach in which any required data can be quickly found and accessed. Current efforts will expand these capabilities by, for example, making it easier to run and validate models by keeping track of all parts of a data/simulation chain (see above on CCMC).

Near future plans include incorporating real-time data into the SPASE framework using connections to iSWA and to the Space Weather Prediction Center (https://www.swpc.noaa.gov). We plan to use the HAPI protocol to standardize these connections, and this will facilitate bringing real-time data into common graphics and analysis packages.

Linking with other developments

The Solar System science community (thus including the Solar, Earth and Planetary Magnetosphere science topics) is building interoperable frameworks for finding, accessing and reusing data. A major initiative is the Europlanet-2020/VESPA project (Virtual European Solar and Planetary Access) [START_REF] Erard | VESPA: A community-driven Virtual Observatory in Planetary Science[END_REF]. VESPA is providing an infrastructure to search for data products based on science content and coverage query parameters. This is enabled through a data model called EPNcore (Europlanet core metadata) [START_REF] Erard | VESPA: A community-driven Virtual Observatory in Planetary Science[END_REF], coupled with the Table Access Protocol [START_REF] Dowler | Table Access Protocol, Version 1.1[END_REF] developed and maintained by the IVOA. Many HP related data collections are available through the VESPA query interfaces (either the main VESPA query portal, http://vespa.obspm.fr, or from interfaces embedded in tools, such as in AMDA (Automated Multi-Dataset Analysis; http://amda.irap.omp.eu from the CDPP)). The team developing the generic plotting and analysis "Autoplot" application (http://autoplot.org) is working with the VESPA team to be able to connect with the VESPA infrastructure (e.g., with the Simple Application Messaging Protocol (SAMP) [START_REF] Taylor | Simple Application Messaging Protocol, Version 1.3[END_REF]). The MASER (Measuring, Analyzing and Simulating Radio Emissions) team is aiming at sharing low frequency radio data using existing infrastructures [START_REF] Cecconi | MASER: A Toolbox for Low Frequency Radio Astronomy[END_REF]. The mapping between the relevant EPNcore and SPASE dictionary elements has already been done by the AMDA team for their internal database. Current developments in the VESPA team include working on using VESPA as a searchable registry for HP webservices, including IMPEx, HAPI and "Das2" [START_REF] Piker | Das2 server ICD[END_REF] interfaces. The webservices would then be discoverable with data collections from HP and neighboring fields such as planetary aurorae, e.g., with the APIS (Auroral Planetary Imaging and Spectroscopy) [START_REF] Lamy | The Auroral Planetary Imaging and Spectroscopy (APIS) service[END_REF], which implements a VESPA search interface. The publication of the SPASE resource tree in VESPA would enhance the visibility of the data products and collections. The bridging with the astronomy community standards and tools is also being done by the Solar System Interest Group of IVOA, whose goal is to adapt IVOA standards to the needs of Solar System science. In one other development involving SPASE, the ESA Space Situational Awareness (SSA) pre-operational space weather services development is currently assessing improved standardization of metadata and terminologies. The working group involved with this is strongly considering the use of SPASE due to its robust heritage, widespread use and ongoing active support.

A more detailed view of the operation of AMDA and its use of SPASE is shown in Figure 6. In AMDA, data are presented in a window called a "Workspace Explorer" as a hierarchy of missions, instruments, datasets and parameters. Missions may be space missions as well as ground based observatories or simulations. These concepts are mapped to SPASE resources: Observatory, Instrument, and NumericalData. The SPASE XML files are stored in a Registry and used to display information at each level in the Workspace Explorer, after transformation using a style sheet. The main component in AMDA is a SPASE parameter. SPASE Parameters are used to access the actual data in the Plot Manager. SPASE metadata are also displayed as titles for axes on the plot. A user selects a physical quantity in the Workspace Explorer that provides SPASE-based information on the corresponding mission, instrument, and dataset. The selected parameter is dragged and dropped to the Plot Manager in which it can be plotted over a selected time interval. The same Registry that is used in AMDA is also used for VESPA. The EPN-TAP protocol allows access to granules, which are typically files. Each file is described in a row of the relational database, and the SPASE XML Registry is used to populate the rows, after a translation from SPASE to EPNCore. Some metadata not defined in EPNCore are added as optional for EPN-TAP. For example, 'observed_region' (SPASE) is translated to 'spase_region' (EPNCore). Data from AMDA may be searched from VESPA using the common Registry. Users searching for data of interest through e.g. the VESPA portal (see above) may choose several search criteria, most of them compatible with SPASE (instrument, timespan...) and get a list of files (one file per row) with a link to access the actual data. The same operations are possible with other VESPA clients like 3DView. The connection of the VESPA service to AMDA is depicted in Figure 7. 7.7 Digital Object Identifiers for Data SPASE Product IDs provide unique identifiers of HP data products. This is precisely what is required for the citation of data products in what is becoming the standard approach to the recognition of data in most journals. Thus, the SPASE group has begun to issue Digital Object Identifiers for datasets, starting with NASA mission products. The mission teams determine the appropriate "Author," "Publisher," and "Publication Date," and the SPASE group uses its UCLA connection to DataCite for DOI minting. This information is then added to the SPASE record for a given product and becomes a link to a doi.org landing page, which is, in turn, based on the SPASE description. The DOI becomes the permanent link to the dataset, allowing references to datasets in the same way as to published papers. Non-NASA data product DOIs will be arranged with the organizations and people associated with each dataset, but the SPASE group, among many others (see https://doi.org) can be used to obtain DOIs. The SPASE registry provides data products at the right level for issuing DOIs, and the requirement of having DOIs for all data will ensure precise product distinctions in cases where the current descriptions are not adequate. Note that there is flexibility in the definition of a Data Product for the assignment of DOIs, and in particular a single DOI can be used for a dataset that is current, with records being added as new measurements are made. The data providers are responsible for determining how much of a "version" change merits a new DOI, although changes such as the adding of parameters would lead to a new Product, and thus a new DOI.

Conclusions

The SPASE Data Model has grown over the last decade from early ideas stating that a unified approach to data discovery, access, and use is how we will make progress on current HP problems to a rich metadata system that facilitates many tasks, from data citation with DOIs to data access with APIs. The model is stable but growing with the changes in the demands put on it. NASA has affirmed that the effort should be supported as an infrastructure project, and this assures longevity. There are still challenges to more fully integrating SPASE descriptions into, for example, a HAPI framework in which they could provide an automated source of required metadata. While the full implementation of SPASE metadata into end-to-end simulations of, e.g., space weather events will be a challenge for some time, the many applications outlined above will make it progressively more useful. Community-wide adoption of the SPASE Data Model as a metadata standard will enable the development of interoperable data tools and a more effective data environment.

Figure 1 .

 1 Figure 1. Overview of SPASE, showing the Resource Types in the left column and the (partial) hierarchy of the description of Numerical Data and Instrument Resources to the right of that.The full structure of SPASE can be found in The SPASEConsortium (2014Consortium ( , 2018)).

Figure 2 .

 2 Figure 2. A diagrammatic way of showing the full set of entries in a SPASE Resource Header. The SPASE site contains a complete set of such descriptors.

Figure 3 .

 3 Figure 3. An overview of the role of SPASE in the CCMC. The CMA is a database view of the SPASE metadata associated with CCMC models and runs. It can export SPASE XML to allow other services to interact with the simulations, thus expediting data-model comparisons.

Figure 4 .

 4 Figure 4. A sample page from an online SPASE resource editor. The user only needs to know what the product is, not the details of XML. (Use the online version of the figure to magnify the view and thus the text. This editor is to be online soon, at http://spase-group.org.)

Figure 6 .

 6 Figure 6. The use of SPASE in AMDA. The uniform metadata aids product discovery and understanding, parameter (variable) references and retrieval, and variable naming on plots. (Use the online version of the figure to magnify the view, and thus the text.)

Figure 7 .

 7 Figure 7. Metadata connection between AMDA and VESPA. Note that a SPASE-compliant Registry is key to the linkage.

  Those needing help with creating or editing SPASE documents should contact us (see https://hpde.gsfc.nasa.gov/spase_metadata.html). To see examples of SPASE descriptions, use the Heliophysics Data Portal (see Sec. 7.3 below).

  ReleaseDate> <Description>Data consist of 15-sec averages of magnetic field magnitude and GSE Cartesian components, from the MGF magnetometer on Geotail. Data are for only the solar wind phases of the Geotail orbit.

	<Contact>
	<PersonID>spase://SMWG/Person/Tsugunobu.Nagai</PersonID>
	<Role>PrincipalInvestigator</Role>
	</Contact>
	<Contact>
	<PersonID>spase://SMWG/Person/Natalia.E.Papitashvili</PersonID>
	<Role>DataProducer</Role>
	</Contact>
	<InformationURL>
	<Name>Readme file at SPDF</Name>
	<URL>ftp://spdf.gsfc.nasa.gov/pub/data/
	geotail/mgf/mag_sw_15s_ascii/00readme</URL>
	<Description>Details on creation of this data set</Description>
	</InformationURL>
	<PriorID>spase://VMO/NumericalData/Geotail/MGF/PT15S</PriorID>
	<PriorID>spase://VSPO/NumericalData/Geotail/MGF/PT15S</PriorID>
	</ResourceHeader>
	…
	<?xml version="1.0" encoding="ISO-8859-1"?>
	<Spase xmlns="http://www.spase-group.org/data/schema"
	xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
	xsi:schemaLocation="http://www.spase-group.org/data/schema http://www.spase-
	group.org/data/schema/spase-2_3_0.xsd">
	<Version>2.3.0</Version>
	<NumericalData>
	<ResourceID>spase://JAXA/NumericalData/Geotail/MGF/PT15S</ResourceID>
	<ResourceHeader>
	<ResourceName>Geotail 15-sec magnetic field data, solar wind
	only</ResourceName>
	<ReleaseDate>2018-06-28T22:02:01Z</The Geotail position vector in GSE coordinates is included.</Description>
	<Acknowledgement>Dr. T. Nagai</Acknowledgement>

Acknowledgments

Listed US Authors (except CW) acknowledge the support of NASA's Heliophysics Data and Model Consortium. CW is supported by the CCMC. The Centre de Données de la Physique des Plasmas (CDPP) is supported by CNRS, CNES, the Observatoire de Paris and Université Paul Sabatier, Toulouse; the CDPP provides support for VG, MG, and BC. The IMPEx functionalities and SH, VG, and MG have been supported by the EU FP7 project "IMPEx" under grant agreement No 262863. The "Europlanet H2020 Research Infrastructure" project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 654208; VG, MG, and BC received support from this source. MG receives support from the ASOV (Action Spécifique Observatoire Virtuel) whose activities are funded by CNRS/INSU. BC received support from the IPDA (International Planetary Data Alliance) whose activities are funded by CNES. We also thank the Editors and referees for many helpful comments.