Vianney Bruned

Alice Cleynen

André Mas

Sylvain Wlodarczyck

Evaluation of mineralogy per geological layers by Approximate Bayesian Computation

Keywords: Mineralogical Inversion, Inverse problem, Wellbore log, Approximate Bayesian Computation, Clustering

We propose a new methodology to perform mineralogic inversion from wellbore logs based on a Bayesian linear regression model. Our method essentially relies on three steps. The first step makes use of Approximate Bayesian Computation (ABC) and selects from the Bayesian generator a set of candidates-volumes corresponding closely to the wellbore data responses. The second step gathers these candidates through a density-based clustering algorithm. A mineral scenario is assigned to each cluster through direct mineralogical inversion, and we provide a confidence estimate for each lithological hypothesis. The advantage of this approach is to explore all possible mineralogy hypotheses that match the wellbore data. This pipeline is tested on both synthetic and real datasets.

Introduction

One of the main goals of reservoir evaluation is the determination of petrophysical parameters like porosity, permeability or water saturation. In order to get an accurate estimation of these parameters, a complete characterization of the lithology or the nature of the rocks is necessary. The petrophysicist proceeds to the analysis of wellbore logs which often requires the input from an expert. Indeed, petrophysical inversion of wellbore logs yields a selection of minerals or fluids belonging to the formation usually with more unknowns (the mineralogy) than measurements (the logs). In a bulk density-neutron porosity cross-plot, an expert may identify the presence of gas, limestone or an exotic mineral. But these choices may not always be obvious from a direct lecture of the logs.

We can summarize roughly the characterization of the lithology based on conventional logs using a classical inversion approach like Elan or Multi-min (described in [START_REF] Mayer | Global, a new approach to computer-processed log interpretation[END_REF], [START_REF] Quirein | A coherent framework for developing and applying multiple formation evaluation models[END_REF], [START_REF] Cannon | Applying mineral knowledge to standard log interpretation[END_REF] or [START_REF] Peeters | A comparison of petrophysical evaluation packages: Logic, flame, elan, optima, and ultra[END_REF]) by the following steps:

• Definition of the zones of the well.

• Selection of the mineralogical components and fluids for each zone, and computation of the lithology using an inversion approach.

• Tuning the physical parameters (endpoints) of the components (when needed).

The petrophysicist will iterate the last two points until they reach a model fitting the data or having a good match with core data. Tuning the endpoints is often necessary for the shaly component. For instance, the variability of the endpoints of the kerogen is very high through the geographical areas: the gamma ray ranges between 500 API to 4000 API.

We propose a Bayesian approach to select minerals in a stratum. Forward modeling liberates us from the underdetermination of the classical inversion problem using conventional logs. Bayesian inversion methods have been mainly used to solve inverse problems in the domain of rock-physics or geophysics [START_REF] Tarantola | Inverse problem theory and methods for model parameter estimation[END_REF]). Many methods use a Bayesian framework to solve the amplitude versus offset (AVO) inversion and to obtain petrophysical attributes like porosity, water saturation or volume of clay [START_REF] Grana | Bayesian linearized rock-physics inversion[END_REF], [START_REF] Xu | Bayesian rock classification and petrophysical uncertainty characterization with fast well-log forward modeling in thin-bed reservoirs[END_REF], [START_REF] Rimstad | Hierarchical bayesian lithology/fluid prediction: A north sea case study[END_REF]).

Other petrophysical approaches to retrieve interesting features have been tested in [START_REF] Qin | Bayesian inversion of well logs for petrophysical properties estimation[END_REF], da [START_REF] Da Costa | An automatic porosity and saturation evaluation based on the inversion of multiple well logs[END_REF], [START_REF] Sanchez-Ramirez | Synthetic and field examples of the bayesian stochastic inversion of gamma-ray, density, and resistivity logs[END_REF]. These methods mainly focus on the uncertainties of the logs and the analysis of the posterior distribution of the petrophysical outputs. Except in [START_REF] Yang | Joint stochastic interpretation of conventional well logs acquired in hydrocarbon-bearing shale[END_REF], the minerals considered in the Bayesian inversion are very limited (Shale, Sand, Water, Hydrocarbron and sometimes Carbonate) and the model linking the minerals to the logs are quite simple. Moreover, these methods often have a number of unknowns equal to the number of logs available.

In this paper, we assume that the geological layers are known (we may refer to historical references [START_REF] Wolf | Faciolog -automatic electrofacies determination[END_REF], [START_REF] Moline | Identification and characterization of pressure seals through the use of wireline logs: A multivariate statistical approach[END_REF] and [START_REF] Ye | A new tool for electro-facies analysis: multi-resolution graphbased clustering[END_REF] or to the more recent [START_REF] Rebelle | Rock-typing in carbonates: A critical review of clustering methods[END_REF] for a description of some segmentation algorithms for layer detection). Hence we assume that these geological layers have a constant mineral composition, and we describe the model in Section 2.1. Unlike previous works, our model is flexible enough to allow complex relationships between the logs and the volumes, and we do not restrict ourselves to a low number of minerals. We introduce a three-step method to get the different lithological hypotheses per layers in order to solve the underdetermined mineralogical inversion problem. First, we use an Approximate Bayesian Computation (ABC) technique to get the posterior probability of the lithology (see Section 2.2.1). Then we use a density-based clustering algorithm on this posterior probability to distinguish the different lithological hypotheses, as described in Section 2.2.2. Finally, for each of these hypotheses, we propose a method to tune the endpoints based on the resolution of a global optimization problem. Our method is therefore entirely automated, and proposes different plausible lithological hypotheses as well as some confidence estimate for each of them. We illustrate its performance on both synthetic (see Section 3) and real datasets (see Section 4.)

Methodology

Model and Context

In mineralogical inversion, a crucial step is the choice of the components of the lithology. Indeed, starting from d logs and using a classical inversion imply a maximum of d + 1 minerals or fluids in the inversion model. But usually, M the number of mineral components is such that M d, so a model selection is needed. Here, we denote the elements of the lithology by V ∈ R M or volume (volumetric fraction) and the logs by L ∈ R d . V represents the volumetric fraction of the minerals and the fluids. We assume that for each depth n, we have:

L n = G (V n ) + X n , ( 1
)
where X n ∼ N (0, Σ) (N is a multivariate normal distribution and Σ ∈ M R d is the covariance matrix) and G an operator from R M to R d . Besides, the volumes V i,n are constrained by:

M i=1 V i,n = 1, V i,n ≥ 0 for all i.
Our aim is to select the minerals that may appear within the geological stratum. Computing the exact amount of the volumetric fraction of the minerals is performed in the final step. Notice that whenever the number of selected components is M with M < d + 1, a classical inversion program can be run to obtain the exact volumetric fraction. The physical parameters/endpoints of the different minerals or fluids are fixed. We consider that d is around 4-5, we often have a triple combo (gamma ray: GR, neutron porosity: φ N , bulk density: ρ b ) plus the resistivity R t and the photoelectric factor pef or the sonic ∆ t . Petrophysical models involve usually between 10 and 15 components. We order them in different classes of minerals: Shale, Sand, Carbonate. The porosity φ, containing the different fluids (water, oil or gas), is added to the model. This classification is important for the prior of the lithological model. Table 1 illustrates an example of a lithological model with the main families of minerals.

Method

We propose in this section a two-step method based on Approximate Bayesian Computation or ABC (see [START_REF] Marin | Approximate bayesian computational methods[END_REF] for a review of this Bayesian method) and density-based clustering to get the lithological hypothesis on a given stratum. Figure 1 summarizes the proposed methodology.

Figure 1: Global methodology: from the logs of a layer to the petrophysical hypothesis. The inputs, the methods used and the outputs are respectively in green, yellow and red.

Bayesian inference with ABC

About the Dirichlet distribution The Dirichlet distribution of order k, Dir(α) where α ∈ R k , allows us to generate a vector x = (x 1 , . . . , x k ) ∈ R k in the k -1 simplex, or in other words: k i=1 x i = 1 and 0 ≤ x i . Its probability density function is given by:

f (x 1 , • • • , x k ; α 1 , • • • , α k ) 1 B(α) M i=1 x α i -1 i ,
where B is the beta function. The expectation of each element of We observe a mode at

x is E[x i ] = α i k i=j α j
1 3 , 1 3 , 1 3 .
The inference model is based on a Dirichlet distribution as prior for the mineralogy (V ) and introduces a Gaussian noise for the logs (L). The Dirichlet distribution fulfills the closure constraints of the volumes. The model can be specified as :

V ∼ D (α 1 , . . . α M ) , L ∼ N (G (V ) , Σ) .
(

) 2
Once for all, we set the known operator G as linear and bounded, so G ∈ M d,M (R) the set of rectangle real-valued matrices with d rows and M columns. The elements of G are the cells M d,M and stand for the endpoints. We can rewrite this model in terms of likelihood function:

           p (V ) = 1 B(α) M i=1 V α i -1 i , p (L|V ) = exp - 1 2 (GV -L) T Σ -1 (GV -L) .
(3)

Using Bayes' rule, the likelihood function of the combined model can be written as:

p (V | L) = p (L|V ) p (V ) p (L) , p (V | L) ∝ exp - 1 2 (GV -L) T Σ -1 (GV -L) 1 B(α) M i=1 V α i -1 i . (4) Because d
M , the matrix G is not invertible so we cannot compute the maximum a posteriori (MAP) estimation of the lithology V. To compute the posterior distribution of the lithology we need a numerical method. Bayesian frameworks have been used to solve ill-posed inverse problem because they handle the non-uniqueness of the solution (see [START_REF] Stuart | Inverse problems: a bayesian perspective[END_REF]). In this study, we adopt the Approximate Bayesian Computation: it is a simple rejection method. Let θ be the parameter of interest, y the observations, a tolerance level, π the prior distribution of the model, and ρ a distance function. The original algorithm of ABC is given below:

Algorithm 1 General ABC for i = 1 to J do repeat Generate θ from the prior distribution π(.) Generate z from the likelihood f (.|θ ) until ρ (z, y) ≤ set θ i = θ end for
For our problem, we adapt Algorithm 1. At each depth n we state :

Algorithm 2 Adapted ABC for i = 1 to J do Generate V from the prior distribution Generate the corresponding L from the model if ∀1 ≤ i ≤ d, L i -L n,i < δ i then accept V end if end for
Where the δ i are defined for each log and should be calibrated and J is around 10 6 . We apply the ABC procedure for all the depth of the layer and get a posterior distribution on the layer of the lithology. Due to the non-uniqueness of the inverse problem, the posterior probability on the layer of lithology is often multi-modal. For instance, if two shales similar in terms of physical parameters appear in the lithological model, it will be hard to differentiate them.

Clustering on the results of ABC

In order to retrieve the most probable lithology hypothesis on the given stratum, we perform a density-based clustering algorithm on the ABC results. The aim of the clustering is to distinguish the modes that may appear in the results of ABC. Ideally we seek a density-based clustering algorithm detecting the outlying lithologies and with a data-driven tuning of the number of clusters. In the literature, several generic density-based algorithms may be found such as density-based spatial clustering of applications with noise (DBSCAN) [START_REF] Ester | A density-based algorithm for discovering clusters in large spatial databases with noise[END_REF], ordering points to identify the clustering structure (OPTICS) [START_REF] Ankerst | Optics: ordering points to identify the clustering structure[END_REF] and Hierarchical DBSCAN or HDBSCAN Campello et al. [2013]. We choose HDBSCAN for many reasons:

• Conversely to DBSCAN, the distance value (main parameter of DBSCAN) disappears.

The main parameter is the minimum size of the cluster.

• Identification of the outliers or noise is available in DBSCAN but not in OPTICS.

• The hdbscan package in Python [START_REF] Mcinnes | hdbscan: Hierarchical density based clustering[END_REF]) is quite efficient, could be run in parallel and offers low computational time. The size of a stratum could reach sometimes hundreds of feet so we may have 100 000 points to cluster.

The two main parameters of HDBSCAN are: the number of points m pts used for the core distance (DBSCAN, OPTICS) and the minimum size to form a cluster m clSize . Usually, m clSize = m pts and having a smaller m pts implies fewer points detected as noise. Here is a description of the algorithm: The core distance of a point x p , d core (x p ), is the distance from x p to its m pts -nearest neighbor (including x p ). The mutual reachability distance between two points x p and x q in X, with regards to m pts , is defined as d mreach (x p , x q ) = max{d core (x p ), d core (x q ), d(x p , x q )}. An output of HDBSCAN is the condensed tree which gives a hierarchical visualization of the data in form of a tree. The Excess of Mass (EOM) criterion described in [START_REF] Campello | Hierarchical density estimates for data clustering, visualization, and outlier detection[END_REF] allows a flat clustering by selecting the stable leaves of the condensed tree as clusters. Figure 4 is an example of condensed tree. The height of the leaves represents the local density.

With HDBSCAN, defining the minimum size of a cluster is mandatory, we choose m clSize = m pts between 1 and 5% of the number of points. This parameter has to be calibrated. We can order the different clusters of lithology by their size and/or the error of reconstruction of a classical inversion method when the number of lithological component obeys M ≤ d + 1.

We can define an empirical probability pi for each lithological hypothesis:

pi = m i N ABC , ( 5
)
where N ABC is the number of lithology selected by the ABC part and m i is the size of the cluster i found by HDBSCAN. Note that pi < 1 because HDBSCAN considers a part of the lithologies as noise. 3 Results on synthetic data

Synthetic example and Monte Carlo sampler

We use below a synthetic generator of logs that may be roughly summarized in three steps. First, we generate an average lithology using a Dirichlet distribution, and then we add a Brownian bridge to get a synthetic lithology over the synthetic stratum. Finally, we transform the lithology into logs using equation 1 and we add noise.

In our test with ABC, we select M = 10 lithological components: 3 carbonates (Calcite, Ankerite, Dolomite), 2 sands (Quartz, N-Feldspar), 4 shales (Illite, Kaolinite, Chlorite, Smectite) and water for the porosity. Here we do not use a simple Dirichlet distribution where all the α i are equal. Indeed, the porosity is generally below 35% and we assume that a mix of minerals of the same family is not probable. For this reason, we draw first V water ∼ U [0, 0.35], then the proportion of the families (carbonate, shale and sand) (V sand , V shale , V carbonate ) ∼ (1 -V water ) D (1, 1, 1). Finally, we draw the minerals according to a Dirichlet distribution with a small alpha parameter equal to 0.1. For instance, we generate the volumes of the carbonate family: (V calcite , V dolomite , V ankerite ) ∼ V carbonate D (0.1, 0.1, 0.1). Indeed, we prefer a sparse model fitting the reality: sparsity is a well-known feature of most lithological databases. We defined a lithological sampling model. An example of prior distribution on the lithology using the sampling model is shown in Figure 5. Besides, we generate 1 million lithologies for ABC. Here are some examples of distributions that the defined sampling model generates over 1 million samples. The histogram of the total volume of shale is presented in Figure 6, the probability near one is very low because of the water distribution which is not correlated to the mineral volumes.

Results

We show here the results on the Shaly-Sand 1 (see Table 2) example with a stratum of 250 samples and with the following logs: gamma ray GR, bulk density ρ b , neutron porosity φ N . On average we have V Illite = 0.34, V Quartz = 0.49 and V W ater = 0.17. With a fixed rejection factor δ on the logs (δ GR = 12 API, δ ρ b = 0.05 G/C 3 , δ φ N = 0.03 V/V), we obtain an average of 3000 lithologies per depth. In Figure 8, the boxplot of all the volumes selected by ABC on the stratum is provided. We see that the volumes of Illite, Smectite, Quartz, and N-Feldspar have a large variance. We remark too that the volumes of carbonate are low. For more details on these distributions, we look at the histograms of the first and second components of the principal component analysis (PCA) of the results. The percentage of variance explained by the first and second components are respectively 40% and 33%. Figure 9 displays a cross-plot of the two components and the associated histograms. We notice the multi-modality of the first projection with a main mode and the bimodality of the second projection. An explanation is that the modes of the first projection accounts for the competition between Kaolinite and Smectite. The second projection shows the competition between the Quartz and the N-Feldspar. After the ABC step, we apply HDBSCAN in order to find the most plausible lithological hypothesis. We fix the minimum number of samples to form a cluster to 5% of the number of the lithology selected by ABC. In this case, HDBSCAN finds 3 clusters and classifies around 35% lithologies as noise. Figures 10 illustrates the results of HDBSCAN on the first and second component of the PCA of the volumes selected previously. We display the clustering on the cross-plot between the first and the second projections of the PCA. We distinguish clearly the clusters.

The boxplots of the three clusters are shown in Figures 11, 12 and 13. Figure 12 is the boxplot of the cluster with some Illite and Quartz corresponding to the synthetic lithology. In this cluster, the average of the three main components are: VIllite = 0.30, VQuartz = 0.37 and VXW ater = 0.17. The presence of Calcite, with an average volume VCalcite = 0.05 explains why we underestimate the real volume of Quartz and Illite. A possible reason for this fact relies on the flat clustering of HDBSCAN. It tends to advantage large clusters and as consequence, we still have some competitions between Carbonate and Quartz which may have a similar response to the logs. Cluster 0 (Figure 11) and Cluster 2 (Figure 13) are other possible lithological hypotheses where respectively Illite is replaced by another shale, Smectite, and Quartz is replaced by another sand, N-Feldspar.

After removing the 35 % of lithology considered as noise, we can order the different hypotheses by the number of points:

• Cluster 1 (p 1 = 40%). Main minerals: Quartz/Illite/Water.

• Cluster 2 (p 2 = 15%). Main minerals: N-Feldspar/Illite/Water.

• Cluster 0 (p 0 = 10%). Main minerals: Quartz/Smectite/Water.

Here the classification by the size of the hypothesis gives a good result: Cluster 1 is the lithological hypothesis (Quartz/Illite/Water) matching the real lithology. Our methodology can provide either most probable lithological hypotheses or empirical distributions for the components common to all hypotheses. For instance, we can define the empirical distribution of the water fW ater :

fW ater = m i fi,W ater m i , ( 6
)
where fi,W ater is the empirical distribution of the water in cluster i and m i is the size for cluster i. Figure 14 shows the empirical distribution for our synthetic case. The mode of this distribution is closed to the real average value of the water of the synthetic data (red vertical line on the figure). Figure 14: Empirical distribution of the water volume. We can derive from this plot an optimistic, average and pessimistic scenarii (percentile 10, median and percentile 90). The true average volume of water is 0.17 (vertical line in red).

In Figure 15, we present the synthetic lithology and the corresponding logs used to perform a Bayesian inversion per layer. In the last three tracks, the different hypotheses found by HDBSCAN (average of the minerals per hypothesis) are given.

For the other synthetic cases, corresponding to 2 minerals of different families and water, we are able to determine the right lithology. The volumes found are close to the reality. For the mix of carbonate, sand or shale, the true hypothesis is not necessary the main one (it depends on the choice of the δ and the α parameter). Changing the parameter alpha has some consequences on the results. Moreover, when 3-4 components (shaly-sand-carbonate) are detected and one of them is around 10% it seems difficult to identify the smallest one.

Case study

The dataset presented in this section comes from the Kansas Geological Survey, Oil and Gas Well Database (http://www.kgs.ku.edu/Magellan/Qualified/index.html). We present here a well from the Wellington Field: well 1-28 (API number: 15-191-22590). From this well, we get 9 logs: the gamma ray GR, the resistivity R t , the compressional slowness ∆ t , the bulk density ρ b , the neutron porosity φ N , the photoelectric factor pef , the uranium concentration U , the thorium concentration T H and the potassium concentration K. We have also a computed lithology and the mud log to check our results. These data are part of the South-central Kansas C0 2 project.

For Well 1-28, we use the segmentation algorithm PELT [START_REF] Killick | Optimal detection of changepoints with a linear computational cost[END_REF]) to identify the different beds where we assume a fixed lithology. As inputs for PELT, we took GR, ρ b , φ N and pef and a Gaussian model is used. Figure 16 gives the results of the segmentation

In Figure 17, we display the lithology provided by the Kansas Geological Survey, Oil and Gas Well Database, and also the mud log (description of the mineralogy) with the geological age. We notice that the segmentation algorithm provides layers coherent with the change of lithology given by the experts.

We apply our methodology on the different segments of PELT using d = 4 logs (GR, ρ b , φ N , pef ). We use the same lithological model as in the synthetic case with M = 10 components: Water, 3 carbonates (Ankerite, Dolomite, Calcite), 2 sands (Quartz, N-Feldspar) and 4 shales (Illite, Chlorite, Smectite and Kaolinite). The hyper-parameters of ABC are tuned the following way. We choose J = 10 6 and we set the rejection thresholds for the different logs:

           δ GR = 50 API δ ρ b = 0.05 G/C 3 δ φ N = 0.03 V/V δ pef = 0.2 b/e (7)
The parameter δ GR is quite important since the values of the Gamma Ray are not necessarily corrected and in a classical mineralogical inversion program, the weight of this log is very light. Because of the rejection method used, it is possible that we do not select in a segment several lithologies if our lithological model does not fit the data. In this case, we set a threshold of an average minimum number of lithology per depth selected by the ABC step to launch the clustering algorithm otherwise we do not give any hypothesis. We fix this quality parameter of the lithological model to 50. In Figure 18, we display the main lithological hypothesis found by our method for each zone. In parallel, we performed a classical petrophysical inversion using more logs (8 logs available) and with minerals selected accordingly to the information given by the Kansas study. In general, the first hypothesis matches quite well the main minerals present in the layers. Sometimes, it may be difficult to distinguish minerals from the same family: Quartz and N-feldspar (layers 30 and 31) or the different clays (layers 27 and 33). Another point is that we are not precise in terms of percentage: in layer 42, we should have around 70% of dolomite, in our main hypothesis the volume of Dolomite is around 50%.

Focusing on the layers 37 to 41 (Figure 19), we do not provide any lithological hypothesis for the layers 37, 39 and 40. It is due to the lack of lithology selected by ABC because of a Gamma Ray too high (around 250 API) or a photoelectric factor too low (around 1.6 b/e) which do not fit out lithological model with 10 elements. Another issue is visible on layer 41: the main hypothesis is a large volume of sand (around 80%) and a small volume of clay (around 10%) whereas the classical inversion program is more balanced (mixed of Quartz and Illite). This kind of error is due to the fact that we select lithologies only in the range of depth where there is a majority of quartz; and we do not pick any lithology in the shaly area because of a high GR. Layer 41 has large variations of its lithology. We must check that the main lithological hypothesis is well distributed on the layer.

Discussion

We propose a new methodology giving promising results on both synthetic and real examples. The method is fully automated and delivers lithological hypotheses weighted by posterior probabilities. The method needs few logs to produce results, and augmenting the number of logs reduces the uncertainties associated with the hypotheses. We could use other Bayesian inference methods such as Markov chain Monte Carlo (MCMC), but ABC may convince the reader by its simplicity of parametrization and implementation. However, during the tests, we identified several issues :

• A problem of identifiability with many minerals in small proportion. We are not able to detect the presence of many minerals of the same family when no log differentiates them.

For instance, differentiating carbonate without pef is not easy.

• Detecting specific minerals such as Kerogen, Pyrite, or Anhydrite may be tricky because adding them into a lithological model always improves the fit and consequently leads to a bias. Our method tends to select them in small proportion. This kind of problem could be solved with better prior on the lithological model.

• The final selection of minerals is not very clear when we select more minerals than available logs. For instance, if we have a hypothesis with M components in small proportion (less than 5% in average) and M > d + 1, d being the number of logs, we cannot run a classical inversion program to get the lithology depth by depth.

• We do not integrate hydrocarbons in the lithological sampling model. The problem is that when we introduce oil or gas in a lithological model using the triple combo (GR, ρ b , and φ N ), they are usually selected even if they are not present. The resistivity equation is needed to determine the difference between water and hydrocarbons, but its parametrization is difficult.

• Some clusters selected by HDBSCAN are located on a subsection of the stratum. This problem is due to the segmentation part or the definition of the stratum. Strata with changing or unstable lithologies may yield surprising results: a multiplication of lithological hypotheses covering different subsections of the stratum.

• The method may lead to the absence of results because of a lithological sampling model not adapted or noisy data that do not fit in the δ i of ABC.

• The calibration of the HDBSCAN parameter (essentially m pts ) can be tricky. The default parameter of 5% of the size of the data seems to work in our tests, but a more complex study is needed.

Conclusion

Some of the issues mentioned above in the Discussion section may be overcome. We give below, as a conclusion, some tracks for potential improvement.

In order to deal with the lack of identifiability and the presence of minerals in small proportion in our hypotheses, a solution relies in grouping the minerals by family before the clustering phase and apply the clustering on the family. It should reduce the uncertainty on the presence of some mineralogical families. The size of the stratum could play a role in the determination of the lithology if the logs on the stratum are not homogeneous. Indeed, noisy logs will produce less dense regions in the solution space (lithology) so the clustering algorithm will have difficulties to find lithological hypotheses. Thus, instead of using only a stratum of a single well, applying our method on a global stratum (the same stratum from different wells) should eliminate or differentiate more lithological hypotheses. For the determination of the water saturation S w (ratio water/porosity), a possible solution to handle the problem is to first have a rough idea of the porosity using a model without hydrocarbons; and then use the resistivity equation on the first estimation of the porosity to determine the presence of gas or oil. We can iterate the process to improve the precision of the method by adding the selected hydrocarbon in the inference model. It is an imitation of the petrophysicist's workflow. We can also improve the lithological sampling model with a more complex Bayesian hierarchy by adding prior on the α or the δ i . For the absence of lithologies selected by ABC, a variant of the method consists in selecting the k first lithologies generated by the lithological sampling model which minimizes the error between the real logs and the generated one. Choosing k = 500 gives satisfactory results on synthetic data. Even if the Bayesian inference will always deliver results, the quality of these results can be dubious: the lithologies can be selected because of only one log (for instance the bulk density). The reliability of the results is an issue. This concern could eventually be attacked by raising alarms when the data are poor or warning the users when their lithological hypotheses are not appropriate.

  α 1 = . . . = α k = 1, Dir(α) is the uniform distribution on the k -1 simplex. When α 1 = . . . = α k = 0.1, sparsity appears over the simplex, the corners and the edges of the simplex have more density mass. If the α i > 1 (α 1 = . . . = α k = 5), a mode will appear clearly on the location of the average. Figures 2 and 3 illustrate these cases in R 3 .

Figure 2 :

 2 Figure 2: Ternary Density plot of a Dir(1, 1, 1). It is a uniform distribution over the simplex of R 3 .

Figure 3 :

 3 Figure 3: Ternary Density plot of a Dir(5, 5, 5).We observe a mode at

Figure 4 :

 4 Figure 4: Example of a condensed tree on the two moons dataset (toy example of the hdbscan library in python McInnes et al. [2017]). We have the three coloured clusters found by HDBSCAN. The width of different nodes and leaves represent the number of elements. The encircled nodes and leaves are the clusters selected by the EOM criterion. The λ axis represents the density of the cluster.

  Different lithologies tested. The lines correspond to the alpha parameter. They are similar to the proportion.

Figure 5 :

 5 Figure 5: Example of prior distribution of the lithology. On the boxplots, we represent the percentile 5 and percentile 95 as the whiskers (applied to all the box plots).

  Figure 7 displays the histogram of the volume of illite generated by the sampling model. An explanation of the sampler parameter is that an important variance for the volume fits well the reality. Indeed, according to the Dirichlet distribution with α 1 = • • • = α 10 = 1, Var [V i ] = 0.008185 and if α 1 = • • • = α 10 = 0.1 then Var [V i ] = 0.045. With the defined sampling model Var [V i ] 0.02 (depending on the family of the mineral) except for the water Var [V XW ater ] = 0.01).

Figure 6 :

 6 Figure 6: Histogram of the shale volume generated by the sampling model.

Figure 7 :

 7 Figure 7: Histogram of the Illite volume generated by the sampling model. The frequency around 0 is near 600 000 samples.

Figure 8 :

 8 Figure 8: Boxplot of the volumes selected on the stratum by ABC.

Figure 9 :

 9 Figure 9: Density cross-plot of the first and second component of the PCA.

Figure 10 :

 10 Figure 10: Cluster found by HDBSCAN (PCA projection).

Figure 11 :

 11 Figure 11: Boxplot of the volumes of cluster 0 (HDBSCAN).

Figure 12 :

 12 Figure 12: Boxplot of the volumes of cluster 1 (HDBSCAN), the main cluster.

Figure 13 :

 13 Figure 13: Boxplot of the volumes of cluster 2 (HDBSCAN).

Figure 15 :

 15 Figure 15: Synthetic lithology with its logs and results of our methodology. Track 1: GR. Track 2: ρ b and φ N . Track 3: Corresponding lithology. Track 4: Lithological hypothesis of cluster 0. Track 5: Lithological hypothesis of cluster 1. Track 6: Lithological hypothesis of cluster 2.

Figure 16 :

 16 Figure 16: Well 1-28 with the zonation provided by PELT. Track 1: Zonation from PELT. Track 2: GR. Track 3: ρ b and φ N . Track 4: pef .

Figure 17 :

 17 Figure 17: Well 1-28. Well analysis provided by the CO 2 project. From the left to the right, we have the depth, the lithology computed, the mud log (brief description of the mineralogy) and the geological age. The horizontal red lines correspond to the change points found by PELT and the numbers are corresponding to the segmentation of Figure 16. The change points are linked to an important change in the lithology.

Figure 18 :

 18 Figure 18: Well 1-28. Results of our methodology. Track 1: zonation obtained by PELT. Track 2: GR. Track 3: ρ b and φ N . Track 4: pef . Track 5: Results of our methodology (displaying the mean lithology of the first hypothesis). Track 6: Lithology obtained by a classical solver using more logs. We notice that the first hypothesis matches quite well the results of the classical inversion even if we are not precise in terms of percentage.

Figure 19 :

 19 Figure 19: Well 1-28. Results of our methodology focusing on few layers. Track 1: zonation obtained by PELT. Track 2: GR. Track 3: ρ b and φ N . Track 4: pef . Track 5: Results of our methodology (displaying the mean lithology of the first hypothesis). Track 6: Lithology obtained by a classical solver using more logs. In layers 37, 39 and 40, we do not provide any hypothesis due to the few lithologies selected by ABC step.

Table 1 :

 1 Example of lithological model

	Family

  Algorithm 3 HDBSCANCompute ∀x p the core distance d core,mpts (x p ) Build a minimum spanning tree (MST) using the mutual reachability distance d mreach,mpts (x p , x q ) Derive the cluster hierarchy from the MST. Transform the MST into a dendrogram Condense the dendrogram using m clSize


Solenn Percelay
email: solenn.percelay@unicaen.fr

Valentine Bouët

Thomas Freret

Michel Boulouard New

Valentine Bouet

Michel Boulouard

New mouse model of schizophrenia: "3 hits" model, genetic and environmental factors

come

Introduction

Discussion and conclusion

• Preliminary data revealed behavioral deficits: decreased working memory performance, increased anxiety-like behavior, decreased spontaneous activity, altered sociability (not shown). • Now, we are going further in the characterization of cognitive alterations by studying : spatial and non-spatial recognition memory, sensory-motor gating (both altered in schizophrenia) through the assessment of place and object recognition prepulse inhibition of acoustic startle reflex (Imetronic, Pessac, France). • Next, we will assess the effects of pharmacological treatments to obtain a pharmacological validation of the model. We will study the effects of lurasidone (atypical neuroleptic, recently marketed in USA), galantamine (acetylcholinesterase inhibitor and agonist of nicotinic central receptors, currently used for early forms of Alzheimer's disease).

Schizophrenia is a frequent psychiatric disease (1% of population), with an unknown multifactorial etiology. Even if pharmacological treatments exist, they don't cover all symptoms and 30% of patients are resistant (Millan et al., 2016). Numerous animal models have been developed, but unfortunately, they are often based on a sole factor. To design a model taking into account the multifactorial nature of this pathology, we chose to use 3 factors, "3 hits" (Ellenbroek et al., 2003), one genetic and two environmental. Preliminary data revealed that 3 hits combination induced behavior alterations that mimic cognitive and sociability deficits observed in patients. Further behavioral characterization is ongoing with a particular focus on mood (anxiety), memory (spontaneous alternation), and sensory-motor gating deficits (pre-pulse inhibition).


Solenn

Solenn Percelay
email: solenn.percelay@unicaen.fr

Valentine Bouet

Thomas Freret

Michel Boulouard

Combining adverse

Introduction

Our first results showed that each factor, taken separately (1-hit), induced behavioral impairments: MAP6 partial deletion impairs working memory; maternal separation impairs social recognition, and MK801 induces deficits in all tests. The 3-hit group showed an increase of locomotor activity (positive-like symptoms), alteration of working memory and of social recognition performances. These behavioral results are encouraging for the development of a new schizophrenia model. Indeed, construct validity is relevant, taken into account the multifactorial nature of this pathology. Face validity is interesting, notably given alteration of cognitive functions. Ongoing immunochemistry study will give possible mechanisms involved. Ellenbroek et al. (2003). Behavioural pharmacology. Lewis, D. A. et al. (2012). Trends in neurosciences. Millan et al. (2016). Nature reviews Drug discovery. Shimizu et al. (2006). Schizophrenia research.

Working memory Spontaneous alternation

Activity and anxiety

Open Field Schizophrenia is a disabling psychiatric disease found in approximately 1% of the population, with an unknown etiology. Currently patients are treated with antipsychotic drugs, which are only symptomatic and not curative. Unfortunately, these treatments do not cover the entirety of symptoms and are inefficient in 30 % of patients (Millan et al., 2016). Moreover, antipsychotics treatments can even worsen cognitive symptoms. In this context, we are developing a mouse model of schizophrenia based on three factors (3-hit), which take into account the multifactorial nature of this pathology (Ellenbroek et al., 2003). A behavioral exploration was performed, then, density of parvalbumin-positive neurons was measured via immunohistochemistry (Lewis et al., 2012).


Mehdi Zaghouani

Oscar Gayraud

Vincent Jactel

Sébastien Prevost

Ambre Dezaire

Michèle Sabbah

Alexandre E Escargueil

Thanh-Lan Lai

Christophe Le Clainche

Nathalie Rocques

Sébastien Prévost

Stéphane Romero

Alexis Gautreau

Florent Blanchard

Gilles Frison

Bastien Nay

Multifaceted Study on a Cytochalasin Scaffold: Lessons on Reactivity, Multidentate Catalysis, and Anticancer Properties

Keywords: Natural products, Antitumor agents, Organocatalysis, Medium-ring compounds, DFT calculations

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

Introduction

Cytochalasin natural products have provided some of the most useful chemical tools for cellular biology. [START_REF]A thiourea-accelerated IMDA reaction toward key cytochalasin 6[END_REF] Cytochalasins B, and mostly cytochalasin D (1, Figure 1), are indeed powerful actin polymerization inhibitors, targeting the barbed ends of actin filaments. 2 Other interesting biological properties were reported, like the inhibition of glucose transporters, [START_REF] Kapoor | Proc. Natl. Acad. Sci[END_REF] while related compounds proved to be cytotoxic like periconiasins, [START_REF] Zhang | [END_REF] which make this family of natural products relevant to anticancer research. However, only little biology-oriented synthetic achievements have been described on cytochalasins, [5][6][7] probably due to the reputedly high toxicity of cytochalasins B and D. Periconiasins A-C (2-4) were recently isolated from a Periconia plant-associated fungus (Figure 1). [START_REF] Zhang | [END_REF] Their originality not only relied on their remarkable cytotoxicity on human colorectal adenocarcinoma cell lines (HCT-8) at sub-micromolar concentrations -making them potential anticancer leads-but also on their peculiar structure showing for the first time a smaller nine-membered cycle, instead of the usual macrocyclic ring. 8 Owing to their biological properties and simpler functionalization pattern than usually found in cytochalasins (see Figure 1), periconiasins may thus be interesting medicinal synthetic targets for anticancer purposes. The synthetic challenge of the natural products has recently been addressed by Liu and Tang who reported the first total syntheses of 2-4 and related compounds using biomimetic transformations. 9 They employed an intramolecular Diels-Alder (IMDA) reaction to get the isoindolone core. Interestingly, an IMDA reaction resulting in a cyclononanecyclohexene (9/6) bicyclic system had only been once previously described by Houk, in 1986, 10 which directly finds echoes with the synthesis of periconiasins. However, this IMDA transformation stands as a difficult and low yielding one. Herein, we describe a biology-oriented synthetic work involving a thiourea accelerated IMDA reaction of triene 5 (Scheme 1) into tricyclic cycloadduct 6, before transformation of this product into periconiasin-inspired scaffolds. DFT calculations were performed to understand the origin of the thiourea effect on the cycloaddition rate, strongly supporting a multidentate interaction involving cooperative hydrogen bonding with the substrate. In addition, this study provides rich lessons on the reactivity of cycloadducts like 6 which furnished a wealth of structural diversity. Biological investigations led to the identification of two cytotoxic compounds (20 and 21), demonstrating the high biological potential of a small chemical library centered on a biologically relevant scaffold.

Results and Discussion

The synthesis of 5 started with the protection of ethyl 3-oxo-6heptenoate (7) 11 as a 1,3-dioxolane, which was followed by the Wacker-Tsuji oxidation of the terminal olefin in presence of catalytic amounts of PdCl2 and CuCl2 in wet DMF, providing ketone 8 in 84% overall yield (Scheme 2). Interestingly, the association of microwave heating (110 °C, 20 min) and pbenzoquinone (p-BQ) as the oxidant 12 gave better and reproducible results compared to conventional heating, avoiding the formation of palladium black. Then, Barbier-type allylation of ketone 8 in the presence of CoCl2, zinc and trifluoroacetic acid (TFA) furnished tertiary alcohol 9, 13 which was submitted to highyielding cross-metathesis with commercial vinylboronic acid pinacol ester, in presence of Grubbs second generation catalyst. 14 Microwave activation allowed decreasing catalyst loading from 10 to 1.2 mol%, compared to thermal heating, furnishing the olefin with perfect E selectivity. The pinacol boronate product was then engaged in a boron-iodine exchange, to install E-iodoalkene 10 (E/Z ratio = 4:1), through a sequence of boronate hydrolysis and halogenation in the presence of I2. 15 After extensive optimization of the reaction conditions, [START_REF]9) was observed in all cases[END_REF] the Suzuki-Miyaura reaction of 10 with freshly prepared boronic acid 11 [START_REF] Patil | [END_REF] was successfully performed in the presence of PdCl2(dppf)•CH2Cl2, followed by TBS-protection of the tertiary alcohol, giving 12 in 68% yield over two steps. Finally, ester saponification and carboxyl activation as an imidazole amide, 18 achieved the preparation of intermediate 13, prior to dienophile installation. The reaction of 13 with the lithium salt of pyrrolidinone 14 at -78°C 19 gave compound 15 in 65% yield as a mixture of stereoisomers (Scheme 3). After selenation of the resulting ketoamide, in agreement with previous reports on cytochalasin synthesis, 20 dienophile 5 was released upon selenide oxidation (H2O2, m-CPBA in chloroform), as a mixture of two diastereoisomers. This IMDA substrate proved to be unstable upon concentration but could be stored at low temperature as a diluted chloroform solution. The IMDA reaction of 5 was readily undertaken after the oxidation step. An extensive array of conditions including the use of Lewis acids and organocatalysts was tested as the reaction proved to be long and difficult. Under thermal conditions at 140°C (sealed tubes), four days were needed to convert 5 into a separable mixture of two endo diastereoisomers, depending on the configuration at position 6 (Scheme 4), providing compounds 16 and 17 (11% and 15% yields, respectively, over two steps from 15). The structure of 16 was confirmed by X-ray crystallography (see also the following discussion on stereochemistry). 21 The use of Brønsted or Lewis acids was attempted to accelerate the reaction, including catalytic amounts of TFA, Et2AlCl, MgBr2•OEt2, ZnCl2, Cu(OTf)2, AgSbF6, Yb(OTf)3, LiClO4, LiBF4, or CeCl3, yet without success, leading to degradation or no reaction in chloroform. Thiourea organocatalysts were also explored, reasoning that their multidentate character would perfectly match our 1,3,5-tricarbonylated substrate. Although dimethylthiourea and diphenylthiourea failed to improve the Diels-Alder reaction, a catalytic amount of Schreiner's thiourea 18 (10 mol%) [START_REF] Wittkopp | [END_REF] allowed increasing yields to 26% and 23%, respectively, and dramatically shortened reaction times to 24 hours instead of 4 days. The multidentate mode of this substrate-catalyst interaction (Scheme 4, bottom), increasing catalysis efficiency and involving cooperative hydrogen bonding, may be responsible for this remarkable effect, as evidenced by DFT modelisation (see below). Although significantly increased, the IMDA yield remained moderate and could not be improved, probably due to the high reactivity of the dienophile, leading to substrate degradation. It is worth noting that the first IMDA reaction leading to a fused 9/6 bicyclic system reported by Houk and co-workers in 1986, 10 performed at 155°C for 96 hrs in the absence of catalyst, provided low yields and stereoselectivity (14-19% for a cis/trans ratio of 46:54). It was improved by Me2AlCl catalysis at room temperature, with a yield of 23-37% and a cis/trans ratio of 90:10. During the first total synthesis of periconiasins A-E, Liu, Tang and co-workers obtained the endo and exo cycloadducts in 38% and 12% maximum yields, respectively, still showing the difficulty of this reaction. In contrast, we only observed the endo-isomers 16 and 17, in 49% global yields.

Calculation (see the SI for details) revealed close interaction of the catalyst with a model substrate, stabilizing the starting material by 14.2 kcal•mol -1 . The stabilization of the transition state is larger, which reduces the barrier of the Diels-Alder reaction by 4.9 kcal•mol -1 , and could explain the acceleration of the reaction (Figure 2). This is explained by a substantial lowering of the π*cc orbital of the polarized dienophile, by 0.91 eV, calculated in the presence of the catalyst. On the structural point of view, the thiourea adopted a twisted conformation when bound to the substrate, with the hydrogen bond network H

2 •••O 2 •••H 3 •••O 3 .
Distances (1.8-2.2 Å) were typical of those involving thioureas and carbonyl compounds 23 and are the shortest for for TS2, compared to A•B•18 and C•18 (see Figure S1), in accordance with the observed effectiveness of the thiourea catalyst. The aromatic H 1 was also observed in short distances (ca. 2.5 Å) with benzoyl and lactam carbonyls (O 1 and O 2 ), signing secondary hydrophobic interactions. On this model, the CF3 groups also seem to have privileged interactions with the substrates, especially with the diene and the acetyl residue of the dienophile. Although this model is much simpler than our Diels-Alder substrate 5, the CF3 groups may also play an important role in substrate-catalyst interaction to stabilize the transition state. Such multidentate binding of thioureas had previously been demonstrated by Schreiner and Wittkopp on dicarbonylated N-acyloxazolidinones, using a combination of IR, NMR and calculation experiments, 24a and was used in intermolecular Diels-Alder reactions by the same authors. 24b Our cytochalasin precursor, with three 1,3,5successive carbonyl groups, shows larger binding energy with the catalyst and thus offers extended insights on this kind of cooperative multidentate catalysis. Unexpectedly, X-ray crystallographic analyses of cycloadduct 16 (Scheme 4 and supporting information) showed this compound had crystallized as an equimolar mixture of two enantiomers, [START_REF]Crystallographic data of compound 16 are available free of charge at the Cambridge Crystallographic Data Centre under number[END_REF] casting doubt on the stereochemical outcome of the reaction. An explanation of this surprising result came from the analysis of 1 H and 13 C, 1D and 2D, NMR spectra of the unstable Diels-Alder substrate 5, showing the presence of a contaminant, which was partially assigned as enol isomer 19 (Scheme 5). This epimerization reaction, previously observed with similar unsaturated lactams 25 (and occasionally put to use in successful total syntheses 26 ), resulted in the loss of the chiral center originated from L-leucine, in enol 19, and explains the racemic character of cycloadducts 16 and 17 (Scheme 5, grey box). To check the enantiomeric ratios of cycloadducts, although we were not able to derivatize debenzoylated cycloadducts by chiral reagents, both compounds 16 and 17 were separately analyzed by chiral HPLC (Cosmosil chiral 3B column, Figure S3). Under both reaction conditions a and b (Scheme 4), the two cycloadducts were found racemic, demonstrating that although dramatically accelerating the reaction, Schreiner's catalyst 18 did not prevent this epimerization. [START_REF]Occasionally during this work, the IMDA reaction provided scalemic mixtures of pure compounds 16 and 17[END_REF] The structural diversity was finally uncovered from cycloadducts 16 and 17 upon deprotection attempts, revealing the peculiar reactivity landscape of these compounds (Table 1). This preliminary work was undertaken in a total synthesis context towards periconiasin A-C when Tang and Liu first reported their achievements, 9 and thus brings additional reactivity insights. Disappointingly, none of the following deprotections could be applied to the synthesis of periconiasins. Classical basic hydrolytic conditions (NaOH, MeOH; entries 1 and 2) allowed selective deprotection of the lactam ring, quantitatively yielding compounds 20 and 21, respectively. Under weak acidic conditions (AcOH, H2O; entry 3) the acetal group was removed without affecting the silyl ether and the benzoyl amide of 17, yielding 22 in 95% yield. The lactam group was also selectively deprotected in the presence of KF by heating in methanol (entry 4) or TBAF in THF at room temperature (entry 5), giving 21 in moderate to good yields. Increasing the temperature of the last condition to 75 °C (entry 6) led to the concomitant cleavage of the silyl ether and resulted in bridged acetal 23 in 65% yield. HF•pyridine was unreactive towards 16 (entry 7). However, aqueous HF in acetonitrile afforded retro-Claisen cleavage products 25a from 16 (entry 8) or 24 and 25b from 17 (entry 9), all in excellent yields. The selectivity of this cleavage was dependent on the stereochemistry of the intermediary tertiary alcohol obtained from the silyl ether. We hypothesized that cleavage products 24 and 25b (or relatively 25a) would arise from transient bridged acetals 27 and 28, formed after the double deprotection of the remote tertiary alcohol and the ketone in 17 (Scheme 6). The two bridged acetals may be prompt to retro-Claisen C-C bond cleavages, either leading to 24 or to 25b thanks to adequate bond alignment. AcOH/H2O (4:1), 130 °C, [a] 10 min 22 (95%) 4 17 KF, MeOH, 150 °C, [a] [a] 15 min 26 (17%) 11 17 aq. HCl (1.5N), acetone, 140 °C, [a] 15 min degradation [a] Microwave heating. [b] Sealed tube.

Finally, heating 16 in an aqueous hydrochloric acid-acetone solution (entry 10) led to complete deprotection and elimination of the OTBS group, affording compound 26 (bis-iso-periconiasin C) in 17% yield, while 17 led to degradation (entry 11). Compound 26 differs from periconiasin C (4) by the position of the two olefins.

NOESY NMR experiments demonstrated the Z-geometry of the cyclononene olefin.

Overall, these reaction conditions provided a wealth of natural-product-like chemical diversity which is reminiscent of that found in natural cytochalasins. Indeed, highly functionalized cytochalasins are frequently found with additional modifications like bridged cycles across the macrocycle or opening of the macrocycle resulting in pendent side-chains on the isoindolone part. 1b We thus turned our attention to the biological potential of these compounds. The screening of compounds on human breast adenocarcinoma cells (MCF-7, MDA-MB-231) revealed important cytotoxicity for some of them. Compounds 20, 21 and 25a were strongly cytotoxic at the concentrations (IC50) of 0.21, 1.74, 2.25 µM on MDA-MB-231 cells, and at 1.2, 0.94 and >2.5 µM on MCF-7 cells, respectively. Videomicroscopy showed that 20 and to a lesser extent 21, both at a concentration of 5 µM, have significant effects on the morphology of MDA-MB-231 (Figure 3) and MCF-10A (Figure S4) cells, showing reduced motility and rounding cells which may be related to actin modifications. However, actin polymerization essays of 20-26 did not show any inhibiting properties of the compounds, compared to the reference compound, except an atypical decrease of the initial rate of actin polymerization in the presence of lactone 25b at micromolar concentrations. This last result clearly steers the activity of 20, 21 and 25a towards another, yet non-elucidated, mechanism of action, and dispels an actin-based effect for these compounds.

Conclusions

The synthesis of diverse periconiasin-inspired compounds was achieved, employing an IMDA reaction efficiently accelerated by Schreiner's thiourea 18, an effect that we explained by the multidentate character of both the catalyst and the dienophile, also demonstrated by DFT calculation experiments. We hope to generalize the use of this catalyst to the synthesis of other cytochalasins currently targeted in our laboratory. In addition, although no periconiasin natural product could be reached, this work gives a straightforward access to periconiasin-like derivatives, all reminiscent of cytochalasin structural diversity. Upon biological screening, a few compounds showed important cytotoxicity on breast cancer cell lines, especially 20 with an IC50 of 0.21 µM on MDA-MB-231 cells, opening new avenues to further biological and medicinal studies on these compounds.

Experimental Section

Full experimental details are available in the supporting information.

Figure 1 .

 1 Figure 1. Structures of cytochalasin D (1) and periconiasins A-C (2-4) as inspiring scaffolds for biological studies.

Scheme 2 .

 2 Scheme 2. Synthesis of the dienyl compound(13).Conditions: a) (HOCH2)2, (±)-CSA, CH(OEt)3, DCM, 20°C (93%); b) PdCl2 (10 mol%), CuCl2•2H2O (20 mol%), p-BQ, DMF/H2O (5:1), MW (110°C, 20 min) (84%); c) allyl acetate, CoCl2, Zn, TFA, CH3CN, 20°C (82%); d) vinylboronic acid pinacol ester, Grubbs 2 nd generation catalyst (1.2 mol%), DCM, MW (120°C, 20 min) (quant.); e) (1) Cs2CO3, THF/H2O, 20°C; (2) I2, THF (92% over two steps, E/Z = 4:1); f) 11, PdCl2(dppf)•CH2Cl2 (5 mol%), KOH, THF/H2O, 20°C (78%, E/Z = 4:1); g) TBSOTf, 2,3,5-collidine, THF, 0°C (88%); h) NaOH, EtOH, 20°C (quant.); i) CDI, Et3N, DCM, 20°C (91%).

Scheme 3 .

 3 Scheme 3. Synthesis of the IMDA substrate 5. Conditions: a) 14, LiHMDS, THF, -78°C, then 13 (65%); b) LiHMDS then PhSeBr, THF, -78°C (91%); c) (1) H2O2, m-CPBA, CHCl3, -50°C→0°C; (2) sat. aq. Na2S2O3 (100% conv., based on NMR, 5 was kept diluted in chloroform).

Scheme 4 .

 4 Scheme 4. IMDA cycloaddition step toward the periconiasin carboheterocyclic core. Conditions: a) 140 °C, CHCl3, 4 days; b) 18 (10 mol%), 140 °C, CHCl3, 1 day. Yields are calculated on two steps from lactam 15. Bottom figures: Proposed binding of Schreiner's catalyst 18 to 5 (left) and X-ray crystallographic structure of 16 (right: ellipsoids are drawn at the 50% probability level and H atoms are shown as spheres of arbitrary radius).

Figure 2 .

 2 Figure 2. Energy profile of the Diels-Alder reaction model between dienophile A and diene B with and without thiourea 18. It shows the multidentate interaction and cooperative hydrogen bondings between 18 and A within transition state TS2. Hydrogen bond lengths (Å) are indicated in red numbers. The enthalpies (Gibbs free energies in parentheses) relative to the starting materials are given in kcal•mol -1 .

Scheme 5 .

 5 Scheme 5. Mechanism explaining the epimerization of compound 5, involving enol 19. Grey box: stereochemical consequence of epimerization at position 5' on the Diels-Alder products, leading to racemic cycloadducts 16 and 17.

Scheme 6 .

 6 Scheme 6. Possible mechanisms explaining the retro-Claisen C-C bond cleavage of 17 leading to 24 and 25b.

Figure 3 .

 3 Figure 3. Effect of cytochalasin D (1, at 2 µM), compounds 20 (5 µM) and 21 (5 µM) on MDA-MB-231 cells, compared to control experiment (CTL). Scale bars at 50 µm.

Table 1 .

 1 Deprotection attempts of cycloadducts 16 and 17 and products thereof.

	Entry	Starting	Condition	Product
		material		(yield)
	1	16	NaOH, MeOH, 0 °C, 3 h	20 (quant.)
	2	17	NaOH, MeOH, 0 °C, 3 h	21 (quant.)
	3	17

Acknowledgements

The French National Research Agency (ANR) is acknowledged for funding this work and a PhD fellowship to MZ (SYNBIORG grant number ANR-12-BS07-0028-01). We thank CNRS for funding supports and ENS Lyon for a PhD grant to OG.

Supporting information for this article is given via a link at the end of the document.


ON THE ROLE OF SATELLITE COMMUNICATIONS FOR EMERGENCY SITUATIONS WITH A FOCUS ON EUROPE

Laurent

Objectives and target audience

In this paper, we describe how satellite communications may contribute to disaster management. During emergency situations, satellite communications present many advantages in supporting or replacing a terrestrial infrastructure (if any) subject to destruction or saturation. However, devising the right deployment model deserves close scrutiny in order to fit the responders' needs and sustain financial viability. After reading the paper, we expect that the answers to the following questions will be made clear: what are the roles of telecommunications in emergency situations? How can satellite technologies help? How to mitigate the cost of deploying and using satellite facilities? Who are the actors currently involved in emergency telecommunications and more specifically satellite-assisted emergency telecommunications? Addressing emergency telecommunication systems without taking into account actual emergency field practice is likely to miss the objective. Therefore, Section 2 starts by defining what is understood by emergency situations and what are the key guidelines for deploying emergency telecommunication. In Section 3 we detail the roles of emergency telecommunication. The challenges and stakes impacting the deployment of these telecommunication infrastructures are described in Section 4. In Section 5, the role of satellite communications is extensively discussed, pinpointing the strengths and weaknesses of this technology. The institutions and projects currently active are described in Section 6 with a focus on Europe where there is a rising will to set up a European co-ordinated civil protection effort. Section 7 addresses the issue of financial feasibility and identifies several paths to it. Section 8 lists what are the future directions of R&D on satellite communications for emergency. Finally, Section 9 concludes by providing a list of what we shall name flawed popular wisdom. This paper has been contributed both by experts from satellite communications and from emergency operations, therefore ensuring a representative coverage of the subject. We expect it to be a useful primer on the topic for a wide range of readers. Users of emergency telecommunications will learn about the future technical developments, giving them the opportunity to provide valuable feedback. Industry representatives will find here material in order to better understand the challenges driving the market of emergency telecommunications. Researchers will derive from our conclusions some guidelines for future activities. Finally, this contribution is also targeted at political bodies: regional, national and European. Indeed, we strongly believe that they are the key progress-enablers in this field.

Scope, definitions and key principles

This paper deliberately focuses on emergency response. To put this term in a time context, emergency response starts from the very first second after the disaster and may last from a couple of hours to several days depending on the event. The emergency phase bears obvious relations with earlier disaster mitigation, preparedness and alerting phases [START_REF]Satellite Emergency Communications; Overview of present satellite emergency communications resources[END_REF] where systems similar to the one discussed here can also be used (e.g. during the preparedness phase, telecommunication equipments can be prepositioned and stocked in order to enable rapid deployment when necessary). There are also strong links with the post-disaster relief phase because telecommunication systems used during emergency situations could -technically speaking -replace the destroyed infrastructures.

Assuming a focus on emergency response, emergency telecommunications are here defined as the telecommunication facilities used during the emergency response in order to support emergency operations and mitigate the humanitarian and economical impacts of the disaster.

This last decade has witnessed the occurrence of several disasters listed in Table 1.

Table 1: examples of recent disaster events

Regardless of their origin (man made, industrial or natural), these catastrophic events may be classified as "catastrophe with limited impact" and "catastrophe beyond response means". Falling in one category or the other depends on whether regional response forces are able to cope with the situation or not. This is typically determined from a combination including but not restricted to the nature of the event, the number of victims and size of the operation field. The hurricane Katrina and the Indian Ocean tsunami are examples of a catastrophe beyond response means.

Three key principles should be considered in designing a solution for emergency telecommunication systems: 1. regardless of the scope and category, a common and modular emergency telecommunication architecture must be used; 2. devising an emergency telecommunication system with solely catastrophes beyond means in mind is a mistake because such situations are so exceptional that they do not yield representative design guidelines; 3. as shown in Table 1, disasters know no borders, so cross-border operations must be considered as a usual situation stressing the need for common practices and interoperable equipments.

This contribution covers two types of emergency telecommunication infrastructures: (a) a global system to be deployed over Europe in the framework of a prepared and coordinated European effort and (b) a small scale emergency telecommunication systems ready for deployment on specific call for assistance in Europe or abroad.

The first category yields both political and technical challenges and is deeply rooted in the strategy of a European civil protection. However, advocated in Section 7, global systems provide a unique opportunity to design a cost-wise, integrated and effective approach to emergency communications.

The second category includes small scale systems like EMERGESAT, TRACKS, MESA and WISECOM. These are good examples of systems providing "generic" telecommunication support. The ETSI SatEC initial report refers to them as Easily DEployable Communication Cells (EDECC) [START_REF]Satellite Emergency Communications; Overview of present satellite emergency communications resources[END_REF]. One possible application for such systems is the consular flying squads identified in Michel Barnier's report [Barnier]. These systems can also be considered as the building blocks of a larger scale system.

The two categories have their own financial implications. Indeed, for the first category, there is an important economical risk of spending large amounts of money in provisioning a system with large overcapacity. The second category is by definition less prone to scale savings. A trade-off between specific solutions with no potential of economies of scope and sharing of infrastructure with other applications has to be made.

Roles of an emergency telecommunication architecture

The organisation of an emergency response encompasses diverse aspects such as: what is done at what time, which institution is taking the lead on a given mission and what is the chain of command. Reports addressing the aftermaths of recent emergency responses frequently pinpoint the role of telecommunications as a corner stone for the organisation of an effective response.

It is of chief importance to map the telecommunication architecture onto the response organisation and not the other way round. There are -at least -two good reasons:

• Emergency response practices rely heavily on prior training of the responders so to impair the negative effects of stressing and often chaotic operation conditions. Modifying the operating modes therefore compromise prior training and possibly call for heavily reconsidering it. There is a risk to fall into a permanent state of motion where technology could drive field practices at the expense of effectiveness and coordination. • The way of organising emergency response is usually governed by national or regional laws, modification of these laws is a complex and lengthy process.

It is important to emphasize that presently the telecommunication market is often technology driven. Because of the reasons mentioned before, an opposite stance must be adopted: the telecommunications solutions must be primarily driven by the user needs (still paying attention not to rule out innovation by excess of virtue).

Two interleaved perspectives exist when considering the position of emergency telecommunication systems:

• The system perspective where the focus is put on the organisational entities taking part to the telecommunication network and how telecommunication help these entities to co-operate effectively. • The service perspective where the focus is put on the application and type of data supported by the network.

These two perspectives are further detailed below.

The system perspective

The organisation of the chain of command during emergency response differs from country to country. However, the global organisations are comparable: the operation is made of multiple missions co-ordinated at a multi stage level. For various reasons ranging from equipment availability to journey time, the organisation is never deployed at once but rather incrementally. So are the telecommunication infrastructures which must be operative whatever the deployment stage and operation scale. For example, at the very beginning, scouting teams are dispatched on the field. Such teams assess the situation, directly co-ordinating with the authorities at the back.

When following a chain of command (from the bottom of Figure 1 to the top), one can see that several concurrent paths are present. This complexity stems from the difference between operational authority and employer authority. For example, the field emergency control centres have to report to the field task for co-ordination (operational authority) but also their respective (back) control centres (employer authority). This is a driver for using interoperable, standard based equipment. Also, the principle of chain of command must be enforced by the telecommunication network, preventing as much as possible communications not conforming to the implemented chain.

The framework in Figure 1 supports missions of several types such as first aid and medical care, psychological assistance, emergency lodging/sheltering, water sanitisation, food/medicine supply. Operation co-ordination can also be considered as a specific mission supporting the complete operation.

Each mission displays specific needs in term of communication capabilities. These needs are expressed according to:

• The time of occurrence and typical duration of the mission.

• The number and location of the users: field, near field and back.

• The mobility requirements (fixed, transportable, mobile).

• The services deployed. As described earlier, it is important to devise a proper mapping between the emergency telecommunication architecture and the organisation. In the following paragraphs, we list the various challenges awaiting emergency telecommunication designers. The section ends with an overview of the stakes of emergency response.

The service perspective

Challenges

Experienced operation managers agree on the fact that every operation is specific. Weather conditions, the nature of the event, place and time of occurrence put unexpected constraints on the organisation of the response. For example, during the AZF chemical plant explosion, a significant number of car drivers abandoned their vehicles on the spot, making it difficult for the responders to reach the rescue scenes or their brigades. This certainly complicated the management of human resources and called for extra co-ordination among the participating institutions. This high potential of randomness leads to two conclusions: (1) while planning and training are important, flexibility is definitely the keyword and (2) flexibility can only be achieved through ubiquitous communication capabilities among the different operation actors.

Similarly, it has often been witnessed that initial scene reports are flawed, if not missing. Having at hand accurate information is the key to effective management in order to dispatch initial (and often scarce) forces at the right place and anticipate the need for support. From the previous discussion it appears that the emergency telecommunication must be rapidly deployable, rapidly meaning here in the first ten minutes after the event. These are not the only constraints.

The needs and characteristics of the emergency response evolve with time in terms of personnel and equipment engaged. Telecommunication needs are also likely to evolve as time goes by, resulting in an increased network size and bandwidth demand. Additional services are also likely to be deployed. Incremental deployment and scalability are therefore mandatory properties of the proposed architecture. Since the onfield telecommunication infrastructure will be deployed incrementally or be available with limited capacity, the applications and underlying network protocols must either be able to cope with temporary shortage of connectivity or to seamlessly switch to another technology offering global coverage, enhanced resources, etc (e.g. WIMAX to satellite vertical handover).

Emergency response management is often cross-institutional (fire brigades, police departments, emergency medical care, first aid organisation, …). Beside the different field practices, it also means disparate equipment (at best different brands) and technical skills for the set up and use of telecommunication equipments. It is therefore important to promote standard based telecommunication infrastructures in order to guarantee interoperability and possibly pooling of resources.

Last but not least, the need for security (in its computer and network understanding) must not be underestimated. Confidentiality provides privacy against the public media and more generally the casual eavesdropper. Authentication contributes to controlling resources usage in an environment where such resources may be scarce.

To summarise, the challenges awaiting emergency telecommunication infrastructures are: rapid and scalable deployment, versatility in functionalities, interoperability and security.

Stakes

While telecommunications indubitably improve co-ordination among teams and institutions, the stakes go further than this operational standpoint and the possibility to mitigate casualties.

Studies [OECD] have demonstrated the correlation between effective disaster management and the economic impact on the country or region. This impact is two-fold: the direct consequence on the socio-economic activity and the impact on insurance contracts (rates increase, coverage decreases).

The earthquake that took place in Turkey in 1999 resulted in a loss of about 2 points of the GNP of Turkey during 5 years. Conversely, it is widely recognised that the efficient management of the Kobe disaster (Japan,1995) made it possible to significantly restrain the negative impact on the city economy (Kobe was the largest commercial harbour in Japan port before the earthquake). During the September 11 th terrorist attacks, several Manhattan-located financial institutions had to stop their activity because of telecommunication links being cut off [OECD].

For natural disasters, experts agree that the cost of these disasters is likely to increase because of environmental degradation (e.g. landslides happen more often due to deforestation), climate change, population growth (especially in the cities) and globalization [START_REF]World disasters report 2007 -focus on Discrimination[END_REF]. It is also agreed that disasters affect more hardly developing countries, for their fragile economy is more exposed [Toya].

Finally, the impact of a disaster can unexpectedly take over the political scene. The 2007 forest fires in Greece and the associated perception on how the government managed the disaster had an impact on the elections. For disasters implying nuclear hazards such as in Chernobyl (Ukraine,1986) the political impacts on an international level are even more obvious.

Satellite systems for emergency telecommunications

In the following paragraphs, we provide a brief description of today's satellite technologies and services. We also describe what -in our opinion -is the best positioning of satellite communications.

Current satellite technologies

Existing satellite technologies can be categorised according to their purposes: observation, location and telecommunication.

Observation satellites provide images of the Earth. Several technologies are used from infrared to Synthetic Aperture Radar (SAR) images. These images are used for example to track forest fires or map the operation area, especially if the landscape has dramatically changed as a consequence of the disaster. Examples of observation satellites services are EUMETSAT, Spot, NOAA/POES and the Disaster Monitoring Constellation (DMC). Observation satellites depend on a fixed, ground infrastructure in order to collect and post-process data. Data is then made available to end users by means of regular terrestrial means. In case of disaster, such observation data should also be distributed to the co-ordination task forces and emergency centres by means of the emergency telecommunications.

Location satellites provide Earth terminals with accurate position information (longitude, latitude and altitude). Present location satellites are GPS and GLONASS. Galileo is the forthcoming European location satellite system.

Telecommunication satellites provide data broadcast and data communication services. Broadcast services include television and high-definition television and radio. Data communication services are further subdivided into narrowband (data rates below 1 Mb/s) and broadband (1 Mb/s and above). Besides the data rate, these systems present different capabilities in terms mobility (fixed, transportable or mobile users). Examples of narrowband satellite operators are: Globalstar (mobile), Inmarsat (fixed, mobile and transportable), Iridium (mobile) and Thuraya (mobile). Examples of broadband satellite operators are: Eutelsat, Hispasat, Intelsat and SES Astra.

However conventional communication links and resources might be saturated during disaster phases and it might be not be possible to complete a call even if that call is the concern of safety of life. In that way safety services shall have priority over any other services in order to be able to be processed whatever the crisis situation is.

In order to instate some priority level the ITU Radio Regulations encompass some safety-related provisions giving priority to (or exclusive usage of) distress and safety systems in a few MSS (Mobile Satellite Service) sub-bands.

When selecting the right system, all these considerations must be integrated to the users' requirements in order to achieve a proper balance between service availability, performance and pricing. This latter aspect is covered in Section 7 since it deserves special attention.

Role of satellite communications in emergency telecommunications

Satellite systems display several properties crucial in the framework of emergency telecommunications. The satellites are -by definition -more resilient to disasters and damages. Their weakest point, as far as Earth disaster are concerned, being the gateways and network control centres (implementing redundancy discards this risk). Geostationary satellites provide a coverage which is sufficient to serve the operation field. For large disasters, only satellites are actually able to cover the whole scene and provide broadband services. The use of spot beams supports efficient management of the satellite capacity. There is currently an accepted trade-off between data rate and mobility which is rooted in the specifics of antenna technology and signal propagation in a mobile channel. For fixed and transportable systems, data rates of hundreds of kb/s are possible, serving a variety of services. Mobile systems display on the other hand reduced data rate (several tens of kb/s) with the advantage of improved versatility in deployment. Still, current techniques and advances in digital communications give indications of the performance leverage expected for mobile technology, blurring the frontier with transportable terminals.

Finally, the regulation of the satellite related bands is already handled at an international level as opposed to analogue PMR for example. This is certainly a strong point in favour of quick deployment and it also helps to devise a pan-European system.

There are also drawbacks. Some of them are tightly bound to the physics of satellite transmissions, others relate to the business models of the various satellite industry actors. First of all, satellite transmissions often require line of sight. Indoor operations must then rely on gap filling. The data rates achievable through a satellite system is tightly linked to the size and directivity of the antenna. The higher the data rate, the larger the antenna and the more accuracy required for pointing. Finally, satellite capacity and equipment may be perceived as costly especially when the competition is harsh with other terrestrial telecommunication technologies. Satellite communications have an important role to play in emergency telecommunications provided a correct coupling is found between terrestrial and satellite technologies. This vision is supported by the belief that the following areas are especially tailored for deployment of satellite-based solutions: • Providing terminals with mobile and long haul capabilities for the first minutes/hours of the catastrophe. • Trunking and backhauling mb/s of data over large (> 100 km) distances using satellite terminals deployed "on the spot".

• Providing temporary links for "repairing" disconnected networks (either through predeployed satellite terminals or not) in order to convey critical traffic. • Broadcasting of (possibly real time) data to large areas for example providing responders and co-ordination centres with up-to-date and detailed GIS (Geographical Data Systems) feeds. • Providing location data.

• Providing observation data.

Institutional bodies and initiatives in Europe

Identification and discussion about technical gaps, future user requirements and policies is certainly a first step to effective disaster management support. The Integral Satellite Initiative (ISI) has a dedicated working group on the use of satellite communications for security issues. The aerospace and defence industries association of Europe (ASD Europe) has also set up a technical group in order to identify coming challenges on security, including the use of satellite communications. The Public Safety Communication Europe Forum (PSCEf) has similar activities with the unique advantage to group three communities: industry, research and users. These technical oriented intitiatives have a political equivalent at the European Parliament called the cross party intra-group on civil protection.

From the standardization standpoint, different organizations and institutions are involved in emergency communications via satellite. In Europe, CEN, CENELEC and ETSI have standardizing activities (EMTEL Special Committee, SES/SatEC working group) or are promoting work on the identification of standardization gaps for emergency communications via satellite (REF space mandate). Project MESA is an international partnership between ETSI and TIA.

From the operational perspective, various organizations are involved in the co-ordination of disaster management at an international basis: the EC Monitoring and Information Centre, the UN Disaster Assesment and Coordination System, the NATO Euro-Atlantic Disaster Response Co-ordination Centre and their associated bodies: the European Community Humanitarian and office, the UN office for the co-ordination of humanitarian affairs and the NATO senior civil emergency planning committee. As such, these are primarily users of satellite communications.

In order to facilitate the use and deployment of communication services for disaster management several initiatives have been taken in the past: the Tampere Convention and the international charter for major disasters. The former eases the use and deployment of telecommunication equipment in contries requesting help. The latter sets up an effective framework for sharing satellite imagery upon disaster.

Finally, projects are/were funded by the European Commission and the ESA whose aims are to study satellite-enabled communication solutions to ease disaster and risk management. For instance the TANGO-FP6 project aims at developping a software platform which should optimise, both technically and costwise, the allocation of satellite ressources to GMES service providers including for crisis management and humanitarian aid purposes. Other examples of running or recently finalised projects are Wisecom, CHORIST, OASIS, STICK and SASS-2IP.

The following Section discusses another issue which is central to the massive use of satellite technology for emergency telecommunication: how to make it viable from a financial standpoint.

Financial viability

Satellite communications certainly play an important role in the provision of emergency telecommunication facilities. However, there is an associated challenge to tackle: how to guarantee the financial viability? In the following paragraphs, we list the difficulties related to achieving a suitable business model. Then, some directions are drafted in order to relief satellite-based emergency telecommunications from the economical pressure and secure the return on investment.

The first difficulty is related to the cost of satellite terminals. Because emergency situations can be seldom predicted and because rapidity of deployment is a key issue, there is a need to pre-deploy and possibly stock terminals in key areas. In addition to the investment required, it also calls for pro-active maintenance. Even unused, these terminals will suffer from depreciation. This unfortunate effect is further increased considering that often, the organisations involved in emergency response are not coordinating themselves for their investment policies.

The second difficulty is related to the price of satellite capacity and the complexity -for an operator -to rapidly re-distribute available capacity. Indeed, when a disaster occurs, the need for capacity is likely to increase drastically. The operator must be able to release capacity, while ensuring that running contractual agreements are not breached. A similar problem to terminal stocking exists with satellite capacity: it is economically unwise to book satellite capacity in the perspective of a future event which -by definition -yields an unknown return on investment.

The third difficulty is related to the way emergency responses are financed and organised. Non-governmental organisations (NGOs) are involved as first responders and are the major actors on the mid term. NGOs suffer from the decrease in charity donations. Additionally, most fundraising campaigns are organised after the disaster (pro-active fundraising campaign are less successful), making it difficult to massively invest in advanced equipments.

In order to tackle these difficulties, we advocate that emergency telecommunication terminals should (a) incorporate both terrestrial and satellite technologies and (b) be used for daily operations and emergency situations. On the one hand, this improves the conditions for achieving a favourable return on investment. On the other hand, it also guarantees that emergency responders will be acquainted with their communication facilities, not discovering its specificities on the spot when subject to various stressing conditions. This solution may also contribute to improve daily operations since satellite communications could be used where terrestrial coverage is poor or absent.

Regarding satellite capacity, there are three possible approaches. We refer to them as pre-emption, coexistence and dedicated schemes.

The pre-emption scheme relies on dual-purpose satellite payloads, where capacity is partly dedicated to -say commercial use -partly to civil protection purposes. Upon emergency situation, resources are pre-empted from the commercial share according to a pre-established agreement and billing plan. In this context, the term pre-emptive deals with the ability of the emergency telecommunication system to borrow resources from the others active systems not being used for the emergency purpose. This goal may be achieved, for example, allowing the emergency systems to reserve channels during the crisis period. The pre-emption model relies on the presence of operational networks and resources availability in the crisis area. It also requires employing technologyindependent interfaces and pre-emption procedures commonly agreed among satellite operators and institutional bodies. Finally, it requires paying for the capacity reservation and/or for the marginally used capacity during regular operations and the more massive use when the need arises.

Unlike the pre-emption scheme, a coexistence scheme means that the emergency system should be allowed to take resources from another system without interfering in its normal operations. In this case the availability of the supporter network's services is not necessary. However, it is mandatory to have advanced functions (e.g. cognitive radio) on both terminals and emergency system gateway. Moreover, communication systems without infrastructures (mesh networks) represent the only possible choice when the networks with infrastructure are not available during a crisis (e.g. earthquake or wide catastrophic events). The possibility to deploy broadband services using this coexistence scheme is subject to further study. With respect to that, one might imagine a system where narrowband services use the coexistence scheme and broadband services rely on resource pre-emption. The coexistence scheme can also be deployed during the first hours before switching to the pre-emption scheme.

The third approach is by far more radical and consists in the deployment of a dedicated civil protection payload. The coverage achieved with a geostationary system is a driver to share capacity and costs between different regions and governments, and get to a better usage pattern with improved economics. Coupled to support of daily operations, it lays the foundation for an effective return on investment perspective. For a dedicated system, there is the risk related to the procurement processes, which for very good reasons requires competitive bids. However, if the market is small, or respectively if there are limited budgets available only, there might be only limited competition and a key question is whether the market can survive without multiple competitors. Prices of the equipments may grow uncontrolled and also, the uncertainty in market demand and size is a barrier to investing into solutions for bringing costs down.

Future developments for promoting satellite based emergency telecommunications

Considering the previous observations, the following needs and directions for research and development are identified: • Developing a satisfying financing plan integrating the cost of equipment/capacity, the need for quasi-instant deployment of capacity/terminals and the limited financial means available. • Developing interoperability among terrestrial technologies (wireless LAN, cellular infrastructures) in order to ease the integration of a satellite component. This is especially true for commercial systems (i.e. GSM communication), which are often preferred by the emergency users, just because they are used to them. A special focus must also be put on the interconnection of legacy analogue PMR technology considering the significant amount of systems that are deployed (e.g. Radio over IP). • Developing user transparent support for hybrid communications where -based on the situation -a terminal is able to select the most appropriate transmission technology or to balance the traffic load among multiple technologies. • Developing coding and modulation techniques making possible the efficient coexistence of multiple signals on the same channel. • Developing innovative operational approaches to optimise the use of existing satellite capacity in emergency situations through the deployment of adequate satellite solutions and the allocation of communication resources whenever and wherever needed.

Taking account of the fact that infrastructure for safety communications is undersized in Europe and that some remote zones do remain non-covered, a satellite system might enhance availability of safety communications links and would still be operational in case the terrestrial infrastructure is damaged or saturated. Although mobile-satellite services require complementary ground components in order to ensure signal availability and continuity in urban areas, it is worth to be mentioned that satellite has the advantage of providing coverage of remote areas for which incumbent terrestrial cellular technology is very expensive to deploy.

More generally, the emergency field lacks ICT-enabled tools. Finding a satisfying match between the harsh field constraints and the consequences (streamlining of practices, need for additional/updated training) of introducing high technology on the field is probably the next major challenge.

Conclusion

We addressed the challenges of setting up satellite based emergency communication facilities during a disaster from technical, financial and organizational standpoints.

According to the ideas developed in this contribution, we will conclude with a list of what we consider as inaccurate if not flawed popular wisdom:

• "Emergency communications address disaster management". As indicated before, best training and operation practices call for the use of the same equipment regardless of the situation (daily emergencies or disasters).

• "Emergency communications are focused on the emergency response phase".

Because one key aspect is deployment delay, a careful planning is required possibly requiring pre-deployment. • "Emergency communication equipments are like others". Contrarily to the current trends on the telecommunication market, the design and development of ITCenabled emergency communication must be user driven and not technology pushed.

• "It is only a technology issue". While it cannot be denied that there are technological locks to tackle, major challenges remain from financial and political standpoints.

• "There is no need for dedicated telecommunication solutions, public telephony networks with proper service level agreements are sufficient". While public telephony networks often do provide valuable services, they do not fully meet the robustness, infrastructure-less and fast deployment requirements of emergency communications.

• "The higher the bit rate, the better". Not always. In the first minutes of the response phase, it is crucial to have immediate access to an affordable communication technology, even though it only provides several tens of kbit/s. Aiming to first minute broadband communication solutions will certainly impair these two core requirements.

Figure 1 (

 1 Figure 1 (inspired by[TS102181]) shows a typical organisation deployment together with the communication flows. The organisation is basically dispatched around three poles: field, near field (where the disaster event took place) and back. (Near-) field structures are put in place for field disaster management purposes and shut down afterwards. Back structures are the same structures as used for daily operations plus an optional temporary co-ordination task force set up for the sake of efficient co-ordination among various disciplines (fire fighting, medical care, law enforcement, ...) and the authorities.

Figure 1 :

 1 Figure 1: typical emergency operation organisation