A M Doglioli

G Grégori

P Marrec

M Thyssen

T Wagener

G Rougier

N Bhairy

J M André

L Berline

F Cyr

A Deverneil

L Duforetgaurier

T Duhaut

C Estournel

M Goutx

P Marsaleix

X Meriaux

F Nencioli

A A Petrenko

C Pinazo

O N Ross

L Rousselet

C Yohia

B Zakardjian

Bhairy Nagib

Gregori Mvp

Gérald

Ross Oliver

André Jeanmichel

Cyr Frédéric

Francesco Researcher

Pml Spasso

Associate Professor, Petrenko Anne

Pinazo Christel

Rousselet Louise

OSCAHR Observing Submesoscale Coupling At High Resolution

Participants

Scientific background and goals

The word 'submesoscale' is used for those ocean processes and dynamics characterized by horizontal scales less than the first baroclinic deformation radius, i.e. of order of 110 km. High resolution satellite images (surface temperature, ocean color) show that submesoscale activity in the upper ocean is quite energetic and almost ubiquitous. Recent research, mainly based on numerical modeling (e.g. Capet et al. 08), suggests that the origins of the submesoscale is strongly linked with the stirring induced by mesoscale and frontogenesis. The associated steep density gradients create strong threedimensional ageostrophic circulations characterized by i) intense vertical velocities (e.g. [START_REF] Klein | The Oceanic Vertical Pump Induced by Mesoscale and Submesoscale Turbulence[END_REF] ; ii) instabilities attempting to restore the stratification in the upper mixed layer (e.g. [START_REF] Foxkemper | Parameterization of Mixed Layer Eddies. Part I: Theory and Diagnosis[END_REF] ; iii) local microstructure turbulence (forward energy cascade, e.g. [START_REF] Molemaker | Balanced and unbalanced routes to dissipation in an equilibrated Eady flow[END_REF]. Consequently, submesoscale dynamics is considered a key factor in regulating biogeochemical and ecological processes. For example : i) vertical velocity generates strong nutrient injections [START_REF] Lévy | Bringing physics to life at the submesoscale[END_REF] ; ii) stabilization after the winter period of maximal mixing and grazing relaxation can allow phytoplankton to bloom [START_REF] Chiswell | Annual cycles and spring blooms in phytoplankton: don't abandon Sverdrup completely[END_REF], Taylor and Ferrari 2011vs Behrenfeld 2010). These processes can have significant largescale impact on primary production and, thus, contribute to the global biogeochemical cycles. Moreover, the ecological response can rapidly propagate to the higher trophic levels [START_REF] Cotté | Scaledependent interactions of Mediterranean whales with marine dynamics[END_REF]. Their impact on community competition and ecosystem distribution is considered a key aspect for the ecological conservation and management of marine resources [START_REF] Cury | Ecosystem oceanography for global change in fisheries[END_REF].

In the last decade, submesoscale dynamics has been predominately investigated through the analysis of numerical models and modelers generally highlight the need of in situ measurements at submesoscale. However, this represents a big challenge due to the ephemeral nature of these structures. Moreover, in order to study physicalbiological coupling at the submesoscale it is crucial to perform biological measurements with automated methodologies capable of high frequency sampling (up to several times per hour).

The scientific objectives of the OSCAHR (Observing Submesoscale Coupling At High Resolution) project are to characterize a submesoscale dynamical structure and to study its influence on the distribution of biogenic elements and on the structure and dynamics of the first trophic levels associated with it. Our methodology includes the use of novel platforms of observation for sampling the ocean surface layer at a high spatial and temporal frequency. In particular, a MVP (Moving Vessel Profiler) is deployed with CTD, Fluorescence and LOPC (Laser Optical Particle Counter) sensors. Furthermore, a new version of the automated flow cytometer is installed for realtime, high throughput sampling of phytoplankton functional groups, from microphytoplankton down to cyanobacteria (including Prochlorococcus). Two sources of seawater have been used in OSCAHR: along with the onboard surface water intake, a new pumping system is developed and tested in order to sample the upper water column at one meter resolution. Hereinafter, we indicate with "TSG+" the array of automated instruments and sensors connected to the surface water intake downstream of the TSG (ThermoSalinoGraph) and combined with discrete manual sampling (see details in section 6.2); whereas, the acronym PASTIS_HVR (Pumping Advanced System To Investigate Seawater with High Vertical Resolution) is used for the new pumping system presented in section 6.4.

The cruise strategy utilizes an adaptive approach based on the nearreal time analysis of both satellite and numerical modeling data to identify the dynamical features of interest and track their evolution. In return, the OSCAHR dataset allows the validation of remote sensing measurements (altimetry, ocean color, reconstitution of planktonic assemblages) and numerical modeling output.

Strategy and planning

The OSCAHR sampling design has been based on a combination of transects for horizontal sampling of the sea surface and shortduration (about 1 hour) stations to perform vertical sampling in the surface layer.

The ocean area of the study has been targeted on the basis of information from remote sensing (satellite and HF coastal radar measurements) and numerical modeling.

The variability of the horizontal mesoscale and submesoscale circulation strongly affects biogeochemical budgets and represents a real challenge during in situ measurements. Indeed, samplings which are only few tens of kms or few weeks apart may be representative of very different situations. For this reason, d [START_REF] Ovidio | Comparison between Eulerian diagnostics and the finitesize Lyapunov exponent computed from altimetry in the Algerian Basin[END_REF], [START_REF] Nencioli | Surface coastal circulation patterns by insitu detection of Lagrangian Coherent Structures[END_REF] developed several diagnostics based on the analysis of combined nearreal time altimetry data and ocean color maps, to identify physical structures of biogeochemical interest (fronts, eddies, filaments). Adaptive strategies based on these diagnostics has been successfully applied during several ocean campaigns such as LATEX (2010), KEOPS2 (2011), STRASSE (2012) and very recently OUTPACE (2015). The software, collected in the package SPASSO (Software Package for an Adaptive Satellitebased Sampling for Ocean campaigns http://www.mio.univamu.fr/~doglioli/spasso.htm), has been implemented, developed and run operationally for the OSCAHR cruise at the OSU Institut Pyhteas (Marseille and Toulon). The web directory http://mio.pytheas.univ-amu.fr/OSCAHR/OSCAHR_Figures/ contains the plots created each 3 hours by SPASSO treating and analyzing the nearreal time data from satellite altimetry, sea surface temperature and ocean color, coastal radar and numerical modeling. The data has been treated on land and then transmitted on board together with a daily bulletin containing the analysis of information and the suggestions for the station positioning. An example of daily bulletin is attached to the present document (see Appendix A), while the complete series of bulletins is available on the OSCAHR website.

Satellite data

The altimetry data are the AVISO Mediterranean regional product (http://www.aviso.altimetry.fr/index.php?id=1275). The derived currents are processed by SPASSO to derive Eulerian and Lagrangian diagnostics of ocean circulation: OkuboWeiss parameter, particle retention time and advection, Lagrangian Coherent Structures. Sea surface temperature (level 3 and 4, 1 km resolution) and chlorophyll concentration (level 3, 1 km resolution, MODISAqua and NPPVIIRS sensors) have been provided by CMEMS Copernicus Marine Environment Monitoring Service (http://marine.copernicus.eu). Moreover, Météofrance provided AVHRR (Advanced Very High Resolution Radiometer) channel 4 imagery (also called "brightness temperature") for a better detection of the thermal fronts (OSIS archive http://www.ifremer.fr/osis_2014/index.php).

MyOceanL3/dailyNRT1x1km data for the study area from 2013 to 2015 were analyzed. SST and CHL average maps for OctoberNovember show that the autumnal winds create sufficient vertical mixing to support phytoplankton blooms. In 2013 and 2015 a local SST minimum associated with a chlorophyll maximum is observed. In contrast, in 2014 the entire gulf of Genoa was characterized by high temperatures and low chlorophyll concentrations at this time of the year. The temporal series in the cruise area (the black square) show a strong seasonality and significant interannual variations in both the amplitude and the timing of the seasonal cycle. Highfrequency (~day1) variations are of the same order of magnitude as the annual cycle. At low frequency SST an CHL are most of the time strongly anticorrelated. Singularly, the two parameters evolve in parallel during three weeks encompassing the cruise. Filament submesoscale features were also present in the cruise area in autumn 2013.

Coastal radar

The OSCAHR area is covered by one HF radar system that operates from the Cap Ferrat as part of the French Mediterranean observatory system (MOOSE : http://www.moose-network.fr/ and http://hfradar.univ-tln.fr). The instrument is a CODAR Seasonde CODAR operated at 13.5 MHz, allowing a 80-100 km spatial coverage with hourly estimation of surface radial velocities. The Figure below illustrate examples of raw data where high radial velocities (~ 40-50 cm/s) plausibly show the surface velocity of the Northern current. The twin system installed to the west at Cap Benat that would have allowed the estimation of total velocities was not in function during the cruise. Nevertheless, HF radar data from 28/10/2015 to 30/11/2015 will be treated in conjunction with other current measurement (ADCP, buoys-derived Lagrangian current) during the OSCARH cruise to assess the system and to help model validation.

Daily mean radial velocities, time coverage (nb of measurement over 24 hours) and daily stnadard deviation from the Cap Ferrat CODAR Seasond on 3 and 5 November 2015.

Numerical models

The atmospheric numerical model WRF provided meteorological forecast as well as forcing fileds for the physicalbiogeochemical coupled model MARS3DECO3M used for the forecast of surface physical and biogeochemical tracers. The ocean circulation model Symphonie also provided ocean forecast. A short description of the implementation of these models is reported below.

WRF

The WRF (Weather Research & Forecasting, Skamarock et al. 2008) system contains two dynamical solvers, referred to as the ARW (Advanced Research WRF) core and the NMM (Nonhydrostatic Mesoscale Model) core. WRF has been implemented at the OSU Institut Pytheas (Marseille) as an operational model. The NMM core configuration uses 2 nested ArakawaE grids for a horizontal resolution varying from 10~km to 2~km and 38 vertical σ-P levels. The ARW core configuration uses a ArakawaC grid with a 2km resolution and 28 vertical σ levels. The open boundary conditions are obtained from the output of the GFS (Global Forecast System) by NCAR/NCEP (National Center Atmospheric Research/National Centers Environmental Prediction). Both cores have had three runs per day starting at 5:00, 17:00 and 22:00 ; the output of the 05:00 WRFARW run provided the meteorological forcing for the oceanographic model MARS3D ECO3M.

MARS3DECO3M

This system coupled the freesurface, threedimensional circulation MARS3D model (3D hydrodynamic Model for Applications at Regional Scale, IFREMER) with the biogeochemical mechanistic model ECO3MMassilia [START_REF] Lazure | An external-internal mode coupling for a 3D hydrodynamical model for applications at regional scale (MARS)[END_REF][START_REF] Nicolle | Modelling for anchovy recruitment studies in the Gulf of Lions (Western Mediterranean Sea)[END_REF]Pinazo et al, 2012).

The high resolution MARS3Dglxl configuration, part of MENOR MEditerranné NORd occidentale configuration, was used to the forecast of the oceanic circulation of the Gulfs of Lions and Genoa, with a horizontal resolution of 1.200 km with 322x195 grid cells and 30 sigma vertical levels. The time step was fixed at 50 s. The initial and boundary forecast conditions provided by PREVIMER (http://www.previmer.org) are imposed using a downscaling method of grid nesting [START_REF] Andre | Modelled variability of the sea surface circulation in the northwestern Mediterranean Sea and in the Gulf of Lions[END_REF].

Chlorophylla concentration (microg.l 1 ) October 30 th 2015 12:00:10 at sigma vertical level =30

The North Western Mediterranean Sea circulation model MENOR forecast was forced by the MFS (Mediterranean Forecasting System) 1/16° regional model (Pinardi et al., 2003) daily outputs of temperature, salinity, current and sea surface elevation spatially and temporally interpolated into the MENOR grid. The MENOR modeling was validated for the years 2005-2006 in the Gulf of Lions by comparing the main characteristics of the simulated shelfslope circulation with in situ [START_REF] Andre | Mesoscale slope current variability in the gulf of Lions. Interpretation of insitu measurements using a threedimensional model[END_REF] and satellite observations [START_REF] Andre | Modelled variability of the sea surface circulation in the northwestern Mediterranean Sea and in the Gulf of Lions[END_REF], and for the years 2001-2008 with satellite observations and drifter trajectories in the GoL by [START_REF] Nicolle | Modelling for anchovy recruitment studies in the Gulf of Lions (Western Mediterranean Sea)[END_REF].

The ECO3M biogeochemical platform (Baklouti et al., 2006a&b) in the Massilia configuration [START_REF] Fraysse | 3D coupled physicalbiogeochemical model development around Marseille's coastal area (NW Mediterranean Sea): What complexity is required in coastal zone?[END_REF][START_REF] Fraysse | Intrusion of Rhone River diluted water into the Bay of Marseille: Generation processes and impacts on ecosystem functioning[END_REF][START_REF] Ross | Impact of an intrusion by the Northern Current on the biogeochemistry in the eastern Gulf of Lion, NW Mediterranean[END_REF] has 17 state variables and implements mechanistic formulations to describe the carbon, nitrogen and phosphorus cycles in five compartments : (i) phytoplankton, (ii) bacteria, (iii) detrital particulate organic matter, (iv) labile dissolved organic matter and (v) dissolved inorganic matter including ammonium, nitrate, and oxygen. Chlorophylla is a diagnostic variable related to the variable phytoplankton ratios. A more detailed description of the model can be found in [START_REF] Fraysse | 3D coupled physicalbiogeochemical model development around Marseille's coastal area (NW Mediterranean Sea): What complexity is required in coastal zone?[END_REF].

Every day, the coupledsystem was used to produce a 24hour forecast for currents, SSH, Temperature, salinity and biogechemistry (chlorophyll, nitrate, phosphate) with output every 3 hours at sea surface, 10m and 50m depth.

SYMPHONIE

The SYMPHONIE model [START_REF] Marsaleix | Energy conservation issues in sigma coordinate freesurface ocean models[END_REF][START_REF] Marsaleix | Loworder pressure gradient schemes in sigma coordinate models: The seamount test revisited[END_REF][START_REF] Marsaleix | Alternatives to the RobertAsselin filter[END_REF] has been used to forecast the velocity, temperature and salinity fields during the OSCHAR campaign. A 1/111° horizontal grid (about 1km, 764x556 nodes) has been implemented on the north western Mediterranean sea in order to cover the OSCHAR site at a regional scale. The vertical grid consists of 50 generalized sigma levels with increased resolution near the surface. The initial state and the lateral open boundary conditions are provided by the daily PSY2V4R4 MERCATOROCEAN fields. The MERCATOR fields are delivered once a week (every Wednesday). They cover a period of 4 weeks consisting of a 2 weeks hindcast simulation (preceding the delivering day) followed by a 2 weeks forecast simulation. The regional simulation is thus synchronously restarted once a week, 14 days before the delivering day, using the 14 days PSY2V4R4 hindcast fields.

Bathymetry of the model. Top: the entire domain. Bottom: the domain of interest for the cruise.

The regional simulation takes advantage of the data assimilation process performed in the PSY2V4R4 hindcast fields using a spectral nudging procedure preserving the small scale structures permitted by the 1km resolution grid. As far as meteorological fields are concerned, a 5 days meteorological forecast is delivered every day by ECMWF at a horizontal resolution of 1/8° and with a time sampling of 3h. A 5 days regional ocean forecast is thus performed every days. River inputs of the Rhône, Hérault, Orb and Aude rivers are provided by "Banque Hydro" (http://www.hydro.eaufrance.fr/). The regional simulation was started on 1st January 2015, providing a 10 month spinup before the beginning the OSCHAR campaign. During the operational stage, daily maps of SST, SSS, surface, 10m and 30m current, focused on the area of interest (5°E 8°E, 42°N44°N, see Figure 2) have been deposited on the OSCAHR website.

Cruise narrative

The beginning of the field experiment initially planned on 1 November 2015 was anticipated of 2 days for meteorological reasons.

Cruise route with waypoints (black dots). The line color code represents the time (from blue to red). Note that the waypoints T8, T10, T11, T12 (42 o 30'N, 8 o 9'E; out of figure) were planned, but not visited. The black diamond near Nice shows the position of the SOMLIT station. The black lines represent the following satellite tracks: Jason2 #9 (continuous line), Saral/AltiKa #429 (dotted line) and Saral/AltiKa #674 (dashed line). In order to zoom on the cruise area, the La Seyne port (43 o 06'N, 5 o 53'E) is not shown in the map. Thursday 29 October 2015

The material was loaded on board in the early afternoon. The R/V Téthys II sailed from La Seyne port at 19:00 (all times hereafter will be referenced to UTC -Universal Time Coordinated) toward the Nice port. During the first night of cruise some tests on the connections of the automated instruments to the onboard surface water intake were performed.

Friday 30 October

The MVP technician J.Fenouil embarked at Nice harbor at 7:00. Afterwards, the Téthys II sailed toward the first waypoint T0 offshore Nice. Tests on the MVP setup and the first transect T0T1 with sampling by the MVP and the array of automated instruments connected to the onboard surface water intake (hereinafter TSG+) were performed. The first station S1 was performed at T1 between 12:05 and 14:30. The first deployment of the vertical pumping system was performed successfully. Then, the second transect T1T2 along the Jason2 track #9 was performed with MVP and TSG+ sampling. The second station S2 at T2 was performed between 19:30 and 21h01. Due to the sea conditions, the SCAMP was not deployed at S2. After the S2 station, the Téthys II returned to the Jason track #9 until the waypoint T3, reached at 23:30. Due to military restrictions, the Jason track was not followed until the crossing point with the SARAL/AltiKa track #429.

Saturday 31 October

The SARAL/AltiKa track #429 was reached at 1:30 (waypoint T4). Then, Téthys II returned toward the coast along the satellite track toward the waypoint T5, performing MVP and TSG+ sampling. The waypoint T5 was reached at 6:20. An along coast route was followed toward the waypoint T6. Along the transect, MVP and TSG+ sampling was continued and offshore the Var river mouth a big patch of suspended matter and floating debris was observed. At 9:45 the waypoint T6 was reached and the MVP and TSG+ sampling was continued along a transect toward T0, where a station was planned. T0 was reached at 11:00 at but the sea state precluded performing the planned station. The Téthys II took shelter in the entry of Villefranche harbor and finally docked in the Nice port at 13:30, where it remained anchored until the next day.

Sunday 1 November

The sea conditions did not permit to work offshore. Therefore, at 7:50 the Téthys II sailed from Nice port toward the Villefranche harbor in order to collect a set of measurements for future instrument intercalibration at the position of the local observation station SOMLIT (43 o 41'N, 7 o 19'E).

The station S3 was performed between 8:20 and 10:45 with vertical profiles of (in chronological order) SCAMP, vertical water pumping, SCAMP (second attempt), CTDrosette, MVP (3 profiles in automatic station mode and 3 profiles in manualcontrol mode), plankton net. Unfortunately, during this station the SCAMP showed serious malfunctioning problems.

Téthys II remained anchored in the inner part of the Villefranche harbor for lunch.

The station S4 was performed between 13:20 and 16:40 with vertical profiles of (in chronological order) SCAMP, vertical water pumping, CTDrosette, SCAMP (second atempt), MVP (3 profiles in in manualcontrol mode and 4 profiles automatic station mode and 1 test of the security mode).

The Téthys II returned to the Nice port, where it docked at 16:50.

Monday 2 November

The sea conditions still did not permit to work offshore. The Téthys II remained docked in the Nice port and the entire day was spent trying to repair the SCAMP and set up the pCO2 sensor which was just delivered before the beginning of the cruise.

In the evening, it was decided to stop the SCAMP measurements for the remainder of the cruise.

Oliver Ross was replaced on board by Gérald Grégori. The pCO2 sensor was eventually set up with a new power supply.

Tuesday 3 November Since the weather forecast announced better sea conditions in the evening and the following days, the Téthys II sailed from Nice port at 6:55 toward waypoint T7. As the sea conditions did not allowed us to use the MVP, only TSG+ sampling was performed. The PCO2 sensor provided abnormal measurements, evidencing a malfunction. It did not work for the rest of the cruise (afterwards the maintenance by the provider has identified a default electronic board which was replaced). At 13:13 the route direction was changed toward T13 on the basis of the most recent satellite information. A first CODE buoy (sn:568520; anchor depth: 1 m; name:"Farfalle") was activated at 10: The transect T14T15 was performed between 20:32 and 23:00. The transect T15T7 with MVP and TSG+ sampling along the SARAL/AltiKa satellite track #647 was performed during the night until 2:52.

Wednesday 4 November

During the night the wind weakened and the sea conditions significantly improved. The transect T7T14 with MVP and TSG+ sampling was performed during the night between 2:52 and 6:30. At 6:43 the station S7 was performed at T14 with vertical profiles of (in chronological order) vertical water pumping, CTDrosette, plankton net. Since satellite images were not available because the strong cloud coverage of the previous day, the waypoint T16, ideally representing the actual center of the patch, was positioned at the position of the SVP buoy at 7:14. The transect T14T16 with MVP and TSG+ sampling was performed between 8:52 and 11:20. At the end of the transect, the check on the MVP fish showed a little rip on the cable near the fish attachment.

The station S8 was performed at T16 between 13:00 and 15:30 with vertical profiles of (in chronological order) vertical water pumping, CTDrosette with Niskin bottles sampling, plankton net, a second CTDrosette (since the VSF did not worked during the first profile due to a battery problem).

During the station S8 many debris were observed at the sea surface. Moreover, the MVP fish ligature was rebuilt in order to cut off the damaged part of the cable. Between 15:30 and 16:45 the transect T16T17 was performed with only TSG+ samplig. During the transect some traction tests of the new MVP fish ligature was successfully performed. At T17 the MVP fish ligature was successfully tested at sea between 16:45 and 17:10.

The station S9 at T17 was then performed between 17:42 and 19:02 with vertical profiles of (in chronological order) vertical water pumping and CTDrosette (0300 m).

The transect T17T9 was conducted between 20:07 and 23:22 with MVP and TSG+ sampling.

6. In situ measurements and collected data

SADCP

The ADCP is an RDI Ocean Sentinel 75 kHz. The configuration used during the whole cruise is : 60 cells, 8 m depth bins, an ensemble average of 1', and bottom tracking when possible (although most of the cruise took place out of reach of the bottom). The depth range extends from 18.5 m to 562.5 m.

Current vectors (black) measured by the hullmounted SADCP vectors (second bin data, corresponding 26.5m depth) to superposed to the corresponding daily map of AVISO SSH and associated geostrophic currents (grey vectors).

ADCP data analysis was done using Matlab software developed by LPO in coordination with IFREMER to process vesselmounted ADCP data (Cascade V.7, http://wwz.ifremer.fr/lpo_eng/content/view/full/25928, Le [START_REF] Bot | CASCADE V6.1: Logiciel de validation et de visualisation des mesures ADCP de coque[END_REF]. No time or distance averaging has been done on the ADCP data beyond the 1' ensembles. The angle of misalignment phi and vector amplitude A, were calculated with Cascade using a concatenation of all the ADCP files (individual ADPC files were too short). With these values, phi = 0.91 and A = 1.004. The pitch was then estimated all along the cruise in order to minimize the estimated vertical velocity (w < 0.01 m s 1 , see

TSG and associated measurements

The sampling referred above as "TSG+" consists in an array of automated instrument able to sample at high frequency the sea surface water that was connected to the onboard surfacewater intake system. Moreover, discrete sampling was performed to collect samples for quantifying parameters which are not measurable by automatics sensors, as well as for calibration and validation.

The Téthys II surfacewater intake system samples seawater at 2 m depth with a flow rate of 60 L min 1 in the TSG, down to 2 L min 1 at the end of the seawater circuit with the various sensors.

We installed in series a Pocket FerryBox, a pCO2 sensor, an automated flow cytometer and an O2 optode in the wet lab.

Picture of the Téthys II wet lab during the cruise with the sensors for the TSG+ sampling: Automated flow cytometer (left); pCO2 (center); Pocket Ferry Box (right).

In the The discrete samples were collected using 2 different surface seawater circuit configurations.

During the first days of the cruise (from the 29 th to the 31 st of October), samples were collected at the end of the seawater circuit, after seawater has flushed all the instruments connected to the TSG circuit.

From the 1 st of November, the outlet for the discrete samples was moved at the beginning of the circuit, in order to avoid water flushing of all the instruments before sampling.

The TSG acquired sea surface temperature (SST), salinity (SSS) and fluorescence (FLUO) data during the whole cruise. The 1 st of November, the start of TSG was delayed and few minutes of data between the harbor of Nice and the bay of Villefranche were lost.

Following the DTINSU procedure one discrete salinity measurement has been performed by the Tethys II crew during the cruise the 31 October 2016. The sample, together with the daily samples performed before and after the cruise will be analyzed at the SHOM laboratory for postcruise calibration of the TSG conductivity sensor.

The calibration of the Tethys II fluorimeter (data are provided in relative fluorescence units, rfu) will be done to convert the rfu intensities into discrete Chla concentrations (µg L 1 ) obtained after sea water filtrations and Chla dosage in the laboratory. Chla analysis was initiated right after the cruise at the MIO and ended end of December 2015. We performed 30 filtrations on the TSG+ water supply with concentrations ranging from 0.06 to 0.25 µg/L. The inherent error on the filtered volume is estimated to +/ 20mL. In order to take into account quenching, 15 filtration has been performed during daytime and 15 during nighttime. 33 other Chla analysis (filtrations) were also performed during the campaign on the water column (vertical profiles) with values ranging from 0.08 to 0.49µg L 1 . These data will be used to calibrate the FLUO form the CTDs. The O 2 optode 4330 acquired dissolved O 2 concentration data during the entire cruise together with seawater temperature. The different configurations of the optpode positioning along the cruise are reported in the scheme above. Calibration of the sensor will take into account the different configurations and also if the seawater supply to the sensor was cut or not for the discrete samples. The optode 4330 is the field experiment reference optode for measuring dissolved O 2 , since this sensor was calibrated just before and after the cruise. Moreover, as reported in the table above, 7 discrete samplings of dissolved O 2 were done during the cruise and successively analyzed by Winkler titrations. Finally, the O 2 data from the optode 4330 will be compared with the ones of the optode 3830 installed on the Pocket FerryBox. The calibrated O 2 data will be used as a reference for the calibration of the dissolved O 2 sensor installed on the CTD rosette (SBE43).

The Pocket FerryBox allowed us to follow in real time the variations of the recorded parameters (SST, SSS, O2 and FLUO) thanks to a friendly interface. It provided also a second GPS position of the ship. Moreover, the PFB allowed us to have access to spare signal in case of reference sensors failure. No data was recorded from the departure from Nice the 3/11 in the morning to 17:00 because of a leaks in the water circuit due to the breakdown of the Aanderaa 4040 sensor. After this incident until the end of the cruise, we had access only to temperature data from the PFB optode.

Unfortunately, the pCO 2 sensor did not work during the whole campaign due to technical issues. The sensor was sent to the manufacturer immediately after the campaign.

The autonomous flow cytometer system was deployed during all the cruise with data acquisition every 20 min, with 2 different settings, hereinafter named FLR5 and FLR30 configurations. The FLR5 configuration (trigger level set on 5mV for the red fluorescence) allowed us to observe distinctly 2 cyanobacteria populations of prochlorococcus and synechoccocus for the first time with this embarked cytometer system. The FLR30 configuration (trigger level at 30mV for red fluorescence), was used to identify phytoplankton functional groups from microphytoplankton down to synechococcus (Prochlorococcus could not be observed).

Nutrients and ammonium analysis are in progress and results are expected for May 2016. Barium analysis should start in February.

Preliminary abundances of cryptophyte like, microphytoplankton, picophytoplankton, nanophytoplankton, synechococcus and prochlorococcus functional groups processed from the autonomous flow cytometer CytoSense every 20 minutes along the ship track.

The spatial distribution of nutrients and dissolved barium will be addressed at the same sample frequency as well.

MVPMoving Vessel Profiler

We used a MVP200 equipped with a MSFFF I (MultiSensor Free Fall Fish type I) containing a microCTD, a fluorimeter and a LOPC (Laser Optical Plankton Counter, Herman & Harvey, 2006; particle size range: 100µm1920µm).

A total of 448 casts has been performed along 366 km of route (55.7 hours of effective work).

MVP200 installation on the Téthys II deck.

The table below summarizes the MVP casts. The majority of the MVP transects followed the satellites tracks of SARAL/AltiKa and Jason2 satellites, as detailed in the following table.

SatelliteMVP synchronization

Position of all MVP casts superposed to the satellite tracks Altika (magenta) and Jason (red)

More details can be found in the MVP specific report by J.Fenouil available on the OSCAHR webpage.

Vertical water pumping (PASTIS_HVR Pumping Advanced System To Investigate

Seawater with High Vertical Resolution) and associated measurements

A system for seawater sampling down to 30 m with a vertical resolution of 0.1 to 1 m (depending on the sea state) was developed, tested and used. The CTD frame was deployed at starboard with the winch normally used for the hydrological sampling. The PE tube and the electrical cable of the CTD were deployed manually along the steel wire.

At each sampling depth, seawater was pumped during 1' in order to flush at least twice the entire volume of the tube. Seawater was then collected through a split on the main seawater circulation with a flow rate of 2 L min 1 . Time at the begin and at the end of the sampling was recorded. Seawater temperature, salinity and depth for the collected sample was estimated from the average values of the parameters recorded by the SBE19+ CTD during the time of sampling. The vertical resolution of the sampling can be estimated from the difference between the highest and lower pressure recorded during sampling.

The system was used during 11 stations in order to collect samples for nutrients, dissolved barium, conventional flow cytometry back to the laboratory and diversity measurements.

In order to complete the analyses performed at the surface with the TSG+ circuit, discrete samples have been collected on the 11 stations every 2 meters down to about 30 m (depending on the sea conditions). Structure of the microbial planktonic community will be investigated with conventional flow cytometry. Analyses will be run on a FACSCalibur flow cytometer equiped with a blue laser beam (488nm), providing the same wavelength excitation than the one on the Cytosense automated flow cytometer. Orange and red fluorescences will be collected, with two light scatter signals. These data are consistent with the ones collected by the automated flow cytometer. It makes possible to collect the same kind of information, providing the same flow cytometry clusters than the Cytosense used on the sea surface continuum (TSG+). The optical resolution of picoeukaryotes, nanoeukaryotes, Synechococcus, Prochlorococcus, and Cryptophycea will allow us to complement the data obtained at the surface during the cruise and expand the characterization of the phytoplankton down to about 30 m. In addition, thanks to the analysis in the laboratory, it is possible to use fluorescent stains in order to put in evidence heterotrophs with flow cytometry. Heterotrophic bacteria will thus also be addressed for the 11 stations, along the 30 m of the investigated water column. That wastechnically not possible with the TSG+ flow cytometry installed on board during the cruise.

As flow cytometry remains a blind method, it is important to observe at least some samples by microscopy. This is why samples have also been collected and fixed in lugol, in the dark, at ambient temperature, to perform some diversity analysis back to the lab. After flow cytometry analyses are performed, and the most relevant samples identified, biodiversity analyses will be run for these very samples first. That will help us to better characterize the phytoplankton community in the various conditions met during the cruise. Flow cytometry and biodiversity analyses have been initiated. We expect to run most of the biodiversity analyses (80 samples) within the next 6 months.

Main information on these deployments is resumed in the 6.5 SCAMP Originally it was planned to deploy the SCAMP at fixed station profiles down to depths of 100m for the entire duration of the cruise. However, due to technical problems, only a total of 4 profiles were acquired.

Already on the first day, the conductivities measured by both the FAST and ACCURATE sensor were too low in comparison to the values measured by the CTD and MVP. On the second day, an additional problem appeared: the FAST T0 GRADIENT did not exhibit the typical scatter plot behavior but showed a clear asymptotic curve with rather large amplitudes, akin to the charge curve of a capacitor. In contrast to the CTD profiles, all three SCAMP profiles measured on the 2nd day showed unstable water columns and yielded unusable turbulence parameters. On the 3rd day we carried out several tests and eventually a decision was reached that it was not worth to carry out further samplings with the SCAMP.

More details on the SCAMP specific report by O.Ross are available on the OSCAHR webpage.

CTDrosette and associated measurements

The The third extension stand (fixed on the side of the carousel) carried a ECOVSF3 (WET Labs). The angular distribution of scattered radiation in the backward hemisphere is important in the interpretation of remote sensing measurements, investigations of particle shape, and models of visibility in seawater. The ECOVSF3 measures the optical scattering at three distinct angles: 100, 125, and 150 degrees, at three wavelengths, thus providing the shape of the Volume Scattering Function (VSF) throughout its angular domain. Motivated by the need to better understand the relationship of water leaving radiance with the backscattering into the same direction, the threeangle measurement allows the determination of specific angles of backscattering through interpolation. Conversely, it can also provide the total backscattering coefficient by integration and extrapolation from 90 to 180 degrees.

Finally, a QCP2350 (Cosinus collector) PAR sensor was directly fixed on the top of the carousel.

Picture of the CTDrosette and its three extentions.

The CTDrosette was deployed once or twice during 10 of the 12 visited stations. During two stations the CTD rosette could not be deployed due to bad weather conditions. The CTDrosette was deployed with a dedicated winch. For a few casts, the bottles of the carousel were triggered for collection of seawater samples. The main objective of these collected samples was to retrieve terms of comparison for the parameters collected with the pumping system described in the section 6.4 (Nutrients, dissolved Barium, cytometry, diversity). At S11, the CTDrosette (Without ECOVSF3) was deployed along the entire water column (2250 m). Samples were collected from surface to 1000 m (Nutrients, Dissolved Oxygen winkler) in order to calibrate the sensors of the BioArgo.

The main informations on the CTD rosette cast are resumed in the the two tables below. The float was equipped with biooptical sensors (fluorimeters, PAR sensors, backscatter and irradiance radiometers) together with a CTD, a nitrate sensor (SUNA) and an oxygen optode. Three hours before the deployment, inair oxygen optode measurements with the float were collected for 20 minutes in order to correct the potential offset of the optode. Unfortunately, the oxygen sensor did not work correctly since the first profile. The float pumping system was checked before the release.

The float was equipped with an Iridium antenna allowing a double way communication used for quasi real time data transmission and for modifying the sampling strategy during the mission. First, a temporal daily resolution was fixed with a maximal depth at 1000m depth. This strategy was changed to a 3 Days sampling on the 25 th of November. The last profile was performed on the 16 th of January. The float was unable to dive anymore and it had to be picked up it in emergency. The data available are potential temperature, salinity, Irradiance (Ed at 380, 410 and 410 nm), PAR and backscatter (at 700 nm) ; CDOM & Chla Fluorimetry ; . The "Côtes de Provence" float not calibrated data are available at www.

Glider

In parallel to shipbased measurements, the glider SeaExplorer SEA007 was deployed along the NiceCalvi transect. This transect is regularly monitored with gliders as part of the MOOSE observation program. The glider was deployed on 26 November 2015 in Villefranche harbor near Nice. The glider first headed towards the DYFAMED monitoring station (43º25.00'N, 7º52.00'E) before continuing towards the 300m isobath near Corsica (42º48.00'N, 8º28.00'E). The glider did not reach this target since it was set back to DYFAMED on 4 November 2015 at 14:30, when the southernmost point of the campaign was reached (42º59.796'N, 8º17.022'E). Shortly after reaching DYFAMED a second time on the return, its deployment was stopped due to software problems on 10 November 2015, 12:25.

The glider was fitted with conventional sensors (seabird GPCTD/O 2 , wetlabs triplet puck Chla/BB 700 /CDOM), and an original FDOM bioptical pathways sensor "the MiniFluoUV" detecting TRYlike (λEx/λEm : 280/340 nm) and Phenanthrenelike (λEx/λEm : 255/360 nm) fluorophores [START_REF] Tedetti | Development of a fieldportable fluorometer based on deep ultraviolet LEDs for the detection of phenanthreneand tryptophanlike compounds in natural waters[END_REF], markers of microbiological activity and oilrelated compounds respectively.

  Vessel route (black line) with SST values measured by the onboard TSG (colored dots) superposed to the satellite SST map of the corresponding day (L4 product, data provided by CMEMS).Raw sea surface temperature (SST), salinity (SSS), density and fluorescence recorded by the Tethys II TSG

  Seawater was collected by a Teflon pump (AstiPure™ II High Purity Bellows Pumps -Flow rate = 30 L.min -1 ) connected to a polyethylene (PE) tube (diameter =19 mm, length = 50 m, volume= 19 Liters) (Figure XX, picture down rigth). The entry of the PE tube was fixed to a frame with a Seabird SBE19+ CTD (Figure XX, picture top left), a WetLab WETstar WS3S fluorimeter and a Sea Point turbiditymeter. All sensors were used with real time acquisition at 4 Hz.

  oao.obs vlfr.fr/bioargo/PHP/lovbio016d/lovbio016d.html. Netcdf data files with Quality Control (Schmechtig et al, 2014; Wong et al, 2014; Lavigne et al, 2012; Johnson et al, 2013; Pasqueron de Fommervault et al, 2015) downloaded from Coriolis are available in the OSCAHR Dataset. More reliable data with QC for chlorophylla (Lavigne et al, 2012) and for nitrate (Johnson et al, 2013; Pasqueron de Fommervault et al, 2015) will be available in July 2016.

Waypoint T… Lat [degrées minutes] Lon [degrées minutes] Lat [degrées] Lon [degrées]

	0	43 39.0450	7	17.7910	43.65080	7.296500
	1	43 30.1324	7	30.8620	43.50221	7.514366
	2	43 12.9284	7	16.6576	43.21547	7.277627
	3	43	4.3124	7	9.6111	43.07187	7.160185
	4	43	5.9080	7	2.6294	43.09847	7.043824
	5	43 24.1221	6	56.1610	43.40204	6.936016
	6	43 32.9060	7	11.0650	43.54840	7.184400
	7	43 40.5331	8	33.9714	43.67555	8.566190
	8	43 38.7453	8	46.7143	43.64576	8.778571
	9	43 33.1221	8	31.3087	43.55204	8.521812
	10	43 25.7103	8	28.6547	43.42851	8.477579
	11	43 25.0000	7	52.0000	43.41670	7.866667
	12	42 30.0961	8	9.0196	42.50160	8.150327
	13	43 48.0000	8	45.0000	43.80000	8.750000
	14	43 53.5260	9	0.0000	43.89210	9.000000
	15	43 59.0569	8	40.6665	43.98428	8.677775
	16	43 48.5660	8	40.2950	43.80940	8.671600
	17	43 44.6475	8	21.7095	43.74412	8.361826
	18	43 47.0060	8	33.8890	43.78340	8.564800
	19	43 36.0000	8	27.0000	43.60000	8.450000
	20	43 40.0000	7	40.0000	43.66670	7.666700
	21	43 30.0000	7	10.0000	43.50000	7.166670

  53, then released at 14:57 at 43 o 47.446'N, 8 o 43.742'E. Then, the station S5 was performed at T13 between 15:17 and 15:52 with only vertical water pumping. Indeed, the sea conditions did not permitt to perform a CTDrosette cast. A SVPlike drifter (anchor depth: 15 m, name: "Spaghetti") was released at the end of the station S5 at 15:55 at 43 o 47.587'N, 8 o 44.286'E to ideally mark the center of the water mass. A second CODE buoy (sn:568510; anchor depth: 1 m; name:"Coquilette") was released 1 mile away of the SVP drifter at 16:06 (the vessel position at the release was not recorded, the position emailed at 16:07 was 43 o 46.714'N,8 o 47.038'E). After MVP tuning, the Téthys II started the transect T13T14 at 16:20 with MVP and TSG+ sampling. The waypoint T14 was reached at 18:13. Here the station S6 was performed between 19:48 and 20:24 with, again, only vertical water pumping.

  table below).

	File Number	Pitch	Average w (cms 1 )
	352	1.5	0.769
	353	2.5	0.420
	359	1.5	0.882
	360	2.5	0.177

  Table below are summarized the characteristics of the instruments used during the campaign. The Table below resumes the details of the discrete samples.

	Parameter	Volume collected	Frequency	Number of samples collected
	Nutrients	15 mL	20 min	218
	Ammonium	40 mL	40 min	109
	Dissolved Barium	15 mL	20 min	216
	ChlorophylleA	250 mL	Aprox . 4 hours	30
	Oxygen « Winkler »	100 mL	Aprox . 0.5 day	14
	AT / CT	500 mL	Aprox . 0.5 day	10
	Diversity	250 mL	Aprox . 1 day	3
	Instrument	Parameters	Model		Frequency
	TSG	Temperature	SBE21		15s
		Conductivity	SBE21
		Fluorescence	Turner Designs 10AU005CE
	PFB	Temperature	Aanderaa 4040 + Optode	1min
		Conductivity	Aanderaa 4040
		Fluorescence spectrale	Algal Online Analyser
		Dissolved O2	Aanderaa Optode 3830
	Cyto	Flow cytometer	Cytosense CytoBuoy	20min
	O2	Dissolved O2	Aanderaa Optode 4330	30s
	pCO2	Partial pressure of CO2	SubCtech MK2		HS

Table summarizing the

 summarizing MVP casts.

Table below .

 below

	Station	Associated CTD File	Number of	Maximum depth
			levels	(dB)
	S1	"20151030_CTDpompe_oscahr_station1.asc"	11	31.38
	S2	"20151030_CTDpompe_oscahr_station2.asc"	15	32.177
	S3	"20151101_CTDpompe_oscahr_station3.asc"	11	31.525
	S4	"20151101_CTDpompe_oscahr_station4.asc"	15	32.734
	S5	"20151103_oscahr_ctd5261_station5_cnv_flt_algn_asc.asc"	12	24.452
	S6	"20151103_oscahr_ctd5261_station6_cnv_flt_algn_asc.asc"	11	23.162
	S7	"20151104_oscahr_ctd5261_station7_cnv_flt_algn_asc.asc"	15	29.407
	S8	"20151104_oscahr_ctd5261_station8_cnv_flt_algn_asc.asc"	14	26.88
	S9	"20151104_oscahr_ctd5261_station9_cnv_flt_algn_asc.asc"	10	29.359
	S10	"20151105_oscahr_ctd5261_station10_cnv_flt_algn_asc.asc"	15	32.527
	S11	"20151105_oscahr_ctd5261_station11_cnv_flt_algn_asc.asc"	6	31.033

  CTDrosette instrument used for vertical profiles at stations was constituted of a fullsize, 12 niskin bottle (12L) SBE 32 Carousel Water Sampler with 3 extensions stands. The first extension stand (fixed directly below the carousel) carried a CTD SBE 911 Plus for conductivitytemperature and pressure measurements, a SBE 43 dissolved oxygen sensor, a Chelsea Aquatracka III fluorimeter for in situ detection of ChlorophyleA, a Wetlabs Cstar transmissometer for underwater measurements of beam transmittance and a Tritech PA500

	altimeter.
	The second extension stand (fixed under the first extension stand) carried two instruments for in situ measurements of particle size distribution: a LISST (Laser In situ Scatterometer and
	Transmissiometer) Deep type B (particle size range: 1.25250µm) and a LOPC (Laser Optical Plankton Counter, Herman & Harvey, 2006; particle size range: 100µm1920µm). In order to
	calibrate the automated measurements, four nets hauls were done with a WP2 net with 200µm mesh. The net samples were preserved in formalin for further analysis with Zooscan and
	taxonomic determination.

PASTIS_HVR system. From the top: the PE tube fixed on the CTD frame, the PE tube and the CTD cable attached togheter during a samplign operation, zoom in on the connection of the PE tube to the pump

Acknowledgements

The chief scientist, the principal investigators and the scientific teams involved in OSCAHR are grateful to the CNFC for having supported it.

Thursday 5 November During the next transect T9T15, started at 23:22 along the SARAL/AltiKa #647 track, the MVP sampling was stopped for 1 minute at 3:07 since the SVP buoy drifted across the vessel route. For this reason, the Téthys II deviated from its route along the satellite track between 3:07 and 3:30. Then, the MVP sampling continued until 6:20. The TSG+ sampling was performed all along the transect. The station S10 was performed at T15 between 6:26 and 7:40 with vertical profiles of (in chronological order) vertical water pumping and CTDrosette (0300 m). The waypoint T18, ideally representing the actual center of the patch, was positioned at the location of the SVP buoy at 7:29. Along the transect T15T18 only the TSG+ sampling was performed and the two CODE drifters "Coquillette"(sn:568510) and "Farfalle"(sn:568520) were recovered on board at 9:44 and 10:17. The station S11 was performed at T18 between 13:21 and 16:30 with vertical profiles of (in chronological order) vertical water pumping, plantkon net, CTDrosette (0300 m), CTDrosette without the VSF (02200 m). Before the first CTDrosette profile the SVP drifter "Spaghetti" was recovered at 13:49. At the end of the station, the BioArgo (sn: WMO6902700; name: "Côtes de Provence") has been deployed at 16:45. Along the transect T18T19 between 17:26 and 19:34 only TSG+ sampling was performed. The station S12 was performed at T19 between 19:34 and 20:01 with a CTDrosette vertical profile (0300 m). The Téthys II started the transect T19T20 at 20:30 with TSG+ sampling.

Friday 6 November

The waypoint T20 was reached at 1:46 and here the CODE drifter "Coquillette"(sn:568510) was released again at sea in order to sample the DYFAMED radar field. The Téthys II continued the TSG+ sampling and reached the waypoint T21 at 4:45. Here the the CODE drifter "Farfalle"(sn:568520) was released within the DYFAMED radar field. From T21 the Téthys II followed an alongshore route toward the La Seyne port, performing the TSG+ sampling until 12:55:35 (5,92892 E, 43,08816N). At 14:00 the scientific crew participated to a debriefing meeting. The Téthys II docked at 14:30. Trajectory of the drifters during the secondleg Lagrangian study.

Trajectories of the 2 CODE drifters after the second release.

BioArgo

A BioArgo automatized profiling platform was deployed at the end of the cruise. The BioArgo (sn: WMO6902700; name: "Côtes de Provence") was deployed at the location 43,7831 N and 8,558 E on the 5 th November at 16:45. SeaExplorer gliders SEA003 and SEA004. One specific aspect of their design is the absence of side wings which make their deployment easier from smaller boat. This glider is also the only one manufactured in Europe (photo credit: Alseamar).

Timedepth series of glider measurements along the Nice Calvi transect (twoways). Measurements from the SeaBird conductivitytemperaturedepth cell were converted to T, SA and density anomaly referenced to surface (σ 0 ) using McGougall and Barker (2011) and plot here in 0.5 kg/m3 increment as solid lines in all panels. Note that salinity measurements have not yet been compensated for thermal lag error caused by the vertical motions of the glider. Chla, CDOM and BB measurements were obtained using the Wetlabs ECOpuck. Note that the puck had electronic problems between 48 November 2016 (dashedrectangles in panels c,d and e). TRYlike and PHElike measurements were obtained using the MiniFluoUV [START_REF] Tedetti | Development of a fieldportable fluorometer based on deep ultraviolet LEDs for the detection of phenanthreneand tryptophanlike compounds in natural waters[END_REF].

OSCAHR project webpages http://mio.pytheas.univamu.fr/OSCAHR


Keywords: Product configuration, Knowledge, Ontology, Constraints Satisfaction Problem

In this work, the domain is a product or system configuration. We focus on ontologies, Constraint Satisfaction Problems (CSPs), and their integration. The aim is to capitalize knowledge and define mechanisms that will permit reasoning to find configuration problem solutions regarding customers' requirements. On one hand, an ontology is used to formalize and capitalize knowledge about products or systems structure including concepts, systems, subsystems, components, relationships, attributes, and their possible values. Protégé 5.5.0 is used to create the ontology. On the other hand, we used CSPs to formalize the relationships between attributes values or between concepts that are allowed or forbidden. CSPs (restricted to compatibility tables) are translated into rules to be integrated into the ontology using the SWRL. Therefore, we defined a filtering algorithm based on arc consistency to restrict the domains by removing inconsistent values. First, related works on ontologies, CSPs are presented. The formalization of the ontology, CSPs, and their translation into SWRL rules and their use are presented. Finally, an illustrative application based on the configuration of a simplified bike is presented.

I. INTRODUCTION

The context of industry 4.0 implies that companies have to answer quickly to many customers' demands and to offer solutions that match perfectly numerous and often complex requirements. In this context, configuration tools which permit selection, among a great number of systems, subsystems, components, etc., those that will permit to satisfy the customer requirements are useful. It is necessary to model knowledge about all these items and their integration and to define efficient tools which can use this knowledge to provide solutions. This work will take place in the context of System Engineering (SE) and in particular in systems or products configuration. The problem is to choose solution elements (systems, subsystems, components, attributes values) to integrate, produce and deliver to the customer.

In this work, we focus on the integration of two formalisms: ontology and Constraint Satisfaction Problem (CSP). The ontology provides a shared understanding of the different items that compose products. It is useful to structure, formalize and capitalize knowledge and reuse it. An ontology represents a set of classes of the domain and the relationships between them and also attributes values. We used Protégé 5.5.0 to model and edit ontology. CSPs allow formalizing the relationships between variables' values which are allowed or forbidden. Then, a CSP represents the restrictions on the combination of variables domains. CSPs are restricted in this work to compatibility tables and they are translated into rules to be integrated into the ontology in Protégé 5.5.0 using the SWRL language.

To reuse this capitalized knowledge during a product configuration process, it is necessary to be able to reason based on the ontology and the rules representing the different constraints. The aim is to restrict the domains of the different characteristics of products following their structure, the constraints, and the customer requirements. That means that it is necessary to remove inconsistent values from the domains of the variables. Standard reasoners associated with ontologies do not allow nonmonotonic reasoning. Therefore, they do not allow removing values from variables domains. To solve this problem, we propose to use an arc-consistency-based algorithm that allows removing inconsistent values.

The contribution of this paper is to propose first models and methods that permit integration of ontology with CSPs and reason to configure products.

In section 2, related works about product configuration, ontologies, CSPs, and their integration are presented. Then, in section 3, we describe the representation of knowledge using the ontology, the formalization of constraints using rules, and the generic algorithm which allows configuring products. In section 4, an illustrative application based on a simplified bike composed of a frame is presented. Two constraints are defined to link the frame sizes to bike users and the bike colors to bike users. Finally, in section 5, the conclusion and future works are presented.

II. RELATED WORKS

As mentioned, our application domain is SE and more specifically product configuration. It is considered as the problem of designing a product. This product is designed after assembling some components which are predefined and their connections are only possible in specific ways [START_REF] Mittal | Towards a Generic Model of Configuration Tasks[END_REF]. In other words, a configuration can be defined as using a set of pre-defined components while considering a set of restrictions on how the components can be IMT Mines Albi, University of Toulouse, Albi, France (maryam.mohammadamini@enit.fr, thierry.coudert@enit.fr, elise.vareilles@isae-supaero.fr, michel.aldanondo@mines-albi.fr) combined. [START_REF] Sabin | Product configuration frameworks -A survey[END_REF] also presented various types of relationships that could exist between components such as aggregation and generalization.

A. Ontology

So far different definitions were mentioned for ontology regarding the various contexts. [START_REF] Studer | Knowledge Engineering: Principles and methods[END_REF] defined an ontology as a "formal, explicit specification of a shared conceptualization ". In this definition, several words play an important role. Formal refers to a machine-readable representation, explicit means that the type of concepts used and the constraints on their uses are explicitly defined, shared refers to the fact that the knowledge represented in an ontology are agreed upon by a group, and conceptualization refers to an abstract model of the world that describes knowledge by using various concepts and their relationships.

Applying ontologies to improve System Engineering is attracting attention [START_REF] Hallberg | Ontology for systems development[END_REF]. [START_REF] Yang | Ontology-based systems engineering: A state-of-the-art review[END_REF] presented an inclusive review of ontology-based systems engineering. This paper attempted to clarify what, where and how ontologies are used in the field of SE and using what languages, tools, and methods. [START_REF] Zhang | Product configuration: A review of the state-ofthe-art and future research[END_REF] and [START_REF] Oddsson | From a literature review of product configuration definitions to a reference framework[END_REF] are two review articles on product configuration that presented various definitions in this area as well as future road-maps. [START_REF] Soininen | Towards a general ontology of configuration[END_REF] used conceptualizations of four approaches, namely connection-based, resource-based, product structurebased, and function-based approaches in the definition of the ontology.

B. Constraint Satisfaction Problems

[9] defined a CSP as a triplet {X, D, C}. It includes a set of variables (X), a set of domains of variables (D), and a set of constraints (C). Constraints represent restrictions on the combination of variable values. A CSP model attempts to assign values to the variables to satisfy all constraints. [START_REF] Jannach | Modeling and solving distributed configuration problems: A CSP-based approach[END_REF] presented a CSP-based approach for modeling distributed configuration problems. [START_REF] Ong | Web-based configuration design system for product customization[END_REF] presented an object-oriented approach to develop a webbased configuration design system. It represented the structures and rules of a configurable bicycle and then applied the invasion algorithm. The product configuration problem was also modeled as CSP. Considering the definition of CSP, [START_REF] Felfernig | Knowledge-Based Configuration From Research to Business Cases[END_REF] defined the configuration task as a CSP (X, D, C) where C is the union of both the configuration knowledge base and the user requirements. An empty variable is a variable without any value in its domain. A non-valuated variable is a variable which in its domain the number of values is more than one. Valuated variable is a variable that has been assigned to one and only one consistent value.

C. Ontology and CSP integration

A few articles dealt with both ontology and constraints. [START_REF] Yang | Development of a product configuration system with an ontology-based approach[END_REF] represented an ontology-based approach for modeling product configuration knowledge. It used Web Ontology Language (OWL) to define classes and their relationship to formalize product configuration then used SWRL to define the constraints. The constraints are mainly concerning the structure of products. The proposed approach was applied to configuring the ranger drilling machine. [START_REF] Yang | Product configuration knowledge modeling using ontology web language[END_REF] presented an Ontology-based method for product configuration knowledge. They used OWL, SWRL, and a rule engine called JESS to improve the product configuration system. The approach was applied to a case for the personal computer. [START_REF] Shen | Configuration of product extension services in servitisation using an ontology-based approach[END_REF] presented an ontology-based approach for product extension services configuration. It also used OWL, SWRL, and JESS. The approach was applied to an example of configurable product extension services. As far as we know, these approaches are based on the modulization by rules of some constraints on allowed or forbidden associations of components. They are not explicitly integrating ontologies and CSPs.

For product configuration, ontologies are a powerful model of knowledge to represent the different items at different abstraction levels and what are their relationships. They can formalize knowledge about products and their structure by creating classes, properties, individuals, etc. However, they cannot represent constraints directly and impose to the user to use specific components or to define a specific value for an attribute. In contrast to ontologies, CSPs have difficulties to represent abstract concepts and abstract relationships. However, they can formalize relationships between attributes values, or between classes or concepts that are allowed or forbidden. That is the reason in this article we propose an approach which permits to integrate them and reason directly to configure products.

III. PROPOSITION

A. Ontology to represent knowledge about product structure and characteristics

To build and edit the ontology for product configuration, Protégé 5.5.0 is used. It can also be used for reasoning using some integrated reasoners. The Web Ontology Language (OWL) is a standard Semantic Web language that is designed for ontology representation. Within the ontology, the elements we have to model are classes, sub-classes, properties, and individuals (or instances).

A class provides an abstraction mechanism for gathering individuals with common characteristics. Classes allow modeling the different generic items: products, sub-products, components... They are stored in the ontology to represent hierarchical relationships between classes and sub-classes (taxonomy). An individual is an instance of a class. Classes relationships and individuals' relationships are represented using object properties. Another kind of property is data property. A data property permits to define attributes of a class with their datatype (integer, string, etc.).

Therefore, using the ontology, it is possible to capitalize all the classes (or concepts) that are used by the company to design the products as well as their hierarchical relationships (generalization and specialization of classes within the taxonomy). It is also possible to represent the composition relationships between some classes (e.g. a Car is composed of exactly four Wheels). Data properties and object properties are named "variables" in the next sections. The ontology is a powerful piece of knowledge for product configuration which permits to represent structures of the different products with their characteristics but it does not allow modeling constraints to restrict the different choices for the designer.

B. Rules to represent constraints

In this article, we consider only constraints formalized using tables of compatibility. A table of compatibility is composed of rows corresponding to variables and lines corresponding to the allowed values for the variables (extension representation). This kind of constraint can be easily represented and filtered by standard filtering tools (Choco-solver, CoFiADe…). However, these tools are not integrated with ontologies. Therefore, we propose to represent constraints using rules which are integrated with ontologies using standard first-order logic languages as SWRL.

The Semantic Web Rule Language 1 (SWRL) is an expressive OWL-based rule language of the Semantic Web. All rules are expressed based on the OWL concepts: classes, properties, and individuals. SWRL provides powerful deductive reasoning capabilities and that is why it is chosen in our proposition.

An SWRL rule consists of two parts: an antecedent part (or body) and a consequent part (or head) separated by an arrow "→" such that (Atom ^ Atom ^ …) → (Atom ^ Atom ^...). Both parts include conjunctions of atoms. The consequent of the rule fires if and only if every atom in the antecedent is satisfied. An atom is an expression of the form: P(arg1, arg2, …). P is a predicate symbol that could be a class or property and arg1, arg2, ... can be variables, individuals, or data properties. An atom can be on the form C(? x), P(?x, ?y), sameAs(x, y) or differentFrom(x, y). C is an OWL class, P is an OWL property, and x, y are either variables, OWL individuals, or OWL data values. Only variables that occur in the antecedent of a rule may occur in the consequent.

For instance, the atom C1(?i) (resp. C2(?j)) links the variable i to an instance of the class C1 (resp. the variable j to an instance of C2). The atom isComposedOf(?i, ?j) links the variable i (i.e. an instance of the class C1) to the 1 https://www.w3.org/Submission/SWRL/ variable j (an instance of C2). It is then a composition relationship and the atom allows to check if i is composed of j. SWRL supports various built-ins including comparison built-ins (swrlb:lessThan, swrlb:greaterThanOrEqual, etc.), mathematical built-ins (swrlb:add, swrlb:multiply), etc. An atom can also add data properties or object properties. Considering a data property Prop, the execution of the atom Prop(?x, 15) in a consequent will add the integer value "15" as a new attribute value for the instance x.

However, SWRL does not support non-monotonic reasoning which means we cannot change values or remove inconsistent ones, we can only add new properties. To consider this problem and define a product configuration method, we have proposed an algorithm which is described in the next section.

C. Algorithm

The proposed algorithm (named Configure) is based on standard arc-consistency algorithms and allows to remove non-consistent values from the different domains when changes have been done by the application of rules (i.e. the constraints) or by the user. An impacted variable is a variable linked by a constraint to another variable for which the domain has been changed.

Algorithm Configure Begin

Set up domains to their initial range While (we don't have any empty variables AND there is still non-valuated variables)

The user chooses a variable and restricts its domain Add the impacted variables to Simp While (Simp is not empty) Modify the domains of impacted variables (applying corresponding rules) Make the intersection of new domains and previous domains Add impacted variables in Simp only if their domain has been changed End while End While If (each variable is valuated) then there is one solution If (there is an empty variable) then there is no solution End

To illustrate the propositions, an illustrative application based on the configuration of a simplified bicycle is presented in the next section.

IV. ILLUSTRATIVE APPLICATION

In this section, the application that consists in configuring a bicycle is presented. First, the ontology corresponding to bicycles is created. Second, two constraints (tables of compatibility) are given and translated into SWRL rules. Third, the algorithm Configure is applied following the interactions with the user.

A. Ontology for Bikes

B. Model of constraints and SWRL rules

Two constraints are created with the tables of compatibility (TABLE I, TABLE II). The variables are the frame size (FS), the bike user (USR), and the color (CL). In TABLE I, the constraint CT1 is formalized using the R1 to R4. In TABLE II, the constraint CT2 is formalized using the R5 to R9. All these rules are formalized and integrated within the ontology using SWRL. It is important to notice that the rules consider the object properties, the data properties, the individuals, and the constraints. The R1 can be interpreted as follows. If the bike i is composed of the frame j AND the frame j has a frame size x which is greater than or equal to 10 AND x is less than or equal to 17 THEN the bike user of the bike i is "Child". When the rule is applied, if the body is true, then the object property "Child" is added to the new domain of the bike user variable. Bike(?i) ^ Frame(?j) ^ isComposedOf(?i, ?j) ^ frameSize(?j, ?x) ^ swrlb:greaterThanOrEqual(?x, 10) ^ swrlb:lessThanOrEqual(?x, 17) → hasUser(?i, Child) R2 Bike(?i) ^ Frame(?j) ^ isComposedOf(?i, ?j) ^ frameSize(?j, ?x) ^ swrlb:greaterThanOrEqual(?x, 16) ^ swrlb:lessThanOrEqual(?x, 25) → hasUser(?i, Adult) R3 Bike(?i) ^ hasUser(?i, Child) ^ Frame(?j) ^ isComposedOf(?i, ?j) → frameSize(?j, 10) ^ frameSize(?j, 11) ^ frameSize(?j, 12) ^ frameSize(?j, 13) ^ frameSize(?j, 14) ^ frameSize(?j, 15) ^ frameSize(?j, 16) ^ frameSize(?j, 17) R4 Bike(?i) ^ hasUser(?i, Adult) ^ Frame(?j) ^ isComposedOf(?i, ?j) → frameSize(?j, 16) ^ frameSize(?j, 17) ^ frameSize(?j, 18) ^ frameSize(?j, 19) ^ frameSize(?j, 20) ^ frameSize(?j, 21) ^ frameSize(?j, 22) ^ frameSize(?j, 23) ^ frameSize(?j, 24) ^frameSize(?j, 25)

R5

Bike(?i) ^ hasUser(?i, Child) →hasColor(?i, Pink) ^ hasColor(?i, Blue)

R6

Bike(?i) ^ hasUser(?i, Adult) → hasColor(?i, Black) ^ hasColor(?i, Blue) R7 Bike(?i) ^ hasColor(?i, Pink) → hasUser(?i, Child) R8 Bike(?i) ^ hasColor(?i, Blue) → hasUser(?i, Child) ^ hasUser(?i, Adult) R9 Bike(?i) ^ hasColor(?i, Black) → hasUser(?i, Adult)

C. Illustration of configuration

We illustrate the configuration using the algorithm Configure with a simple scenario. For the three variables FS, USR, and CL, the initial domains of validity are 10 ≤ FS ≤ 25, USR∈ {Child, Adult}, CL∈ {Pink, Blue, Black}. The variable FS is represented as a data property that takes values within the integers range.

The user decides to restrict the domain of the variable FS in order to know what are the users and colors allowed by the constraints:

. The variable USR is the only impacted variable considering the R1 to R4 (corresponding to CT1). Applying these rules, the new domain of USR is . The intersection of the new domain and previous domain gives: Therefore, as the domain of USR has been modified, the impacted variables are FS (following R3 and R4) and CL (following R5 and R6). The R3, R4, R5, R6 are applied to define the new domains of CL and FS:

, . The intersection of the new domains with the previous domains gives:

As the domain of CL is modified, the variable USR is the only impacted variable considering R7, R8, and R9 (CT2). Then, these rules are applied to obtain:

. The intersection of the new domain and the previous domain gives:

The domains have not been modified but the user chooses the color to be pink:

. USR is the only impacted variable. The R7, R8, and R9 are applied:

. The intersection with the previous domain gives:

There are no new impacted variables because the domains have not been changed. The obtained solution is then: , , . Therefore, if the designer chooses a frame size of 10 and a pink color for a bike, this bike is only suitable for children.

V. CONCLUSION

In this work, we proposed an approach to integrate ontology and Constraint Satisfaction Problem (CSP). The aim was to define, using the ontology, a model of knowledge which can represent the different products, sub-products, components structures, and characteristics as well as their relationships (composition, properties...). The ontology helped us to represent classes at different abstraction levels. The ontology is suitable to represent possible associations of items for several products. However, it does not allow to represent constraints such that the choice of a specific component impacts the choice of another one. Moreover, it is not possible to guarantee that the designer has selected the right number of items even if it is specified by object properties. For instance, it is possible to represent the knowledge about a car that must be composed of four wheels, but it is not possible to define a constraint that imposes it. Therefore, to solve the problem, CSPs have been chosen to model constraints between variables values. As CSPs are not directly integrated within ontologies, we have proposed to translate constraints into SWRL rules. Because SWRL rules do not support non-monotonic reasoning, we defined an arc-consistency-based algorithm to restrict the domain and remove inconsistent values for product configuration.

The limits of the approach concern the translation of constraints into SWRL rules when constraints imply several variables and when structures of products are complex. Moreover, to execute specific rules and make the intersections between domains, it is necessary to develop an external program as a plug-in to connect to Protégé. In order to better integrate CSP within ontologies, we will investigate the use of SHACL and SPARQL languages. The SHACL language can model constraints directly in the ontology and reason with them. However, the reasoning only shows what are the unsatisfied constraints. SPARQL will also be studied concerning non-monotonic reasoning requirements.

  Protégé 5.5.0, several classes are created and stored into the ontology of Fig. 3. These classes are User, System, Bike, Frame, and Color. The classes Bike and Frame are sub-classes of the class System. Several individuals are also created. Bike1, Bike2, …, Bike 5 are instances of the class Bike. They correspond to five different categories of bikes. Five instances of Frame are created and three colors Pink, Black, and Blue are created as instances of Color. Three object properties are created: isComposedOf, hasUser and hasColor. One data property frameSize is also created to define the possible frame sizes (integers between 10 and 25).

Fig. 1 Fig. 3 .

 13 Fig. 1. Classes Fig. 2. Object properties

Integration of Ontologies and Constraint Satisfaction Problems for Product Configuration

	M. Mohammad Amini 1 , T. Coudert 1 , E. Vareilles 2 , M. Aldanondo 3
	1 INP-ENIT, University of Toulouse, Tarbes, France
	2 ISAE-SUPAERO, University of Toulouse, France
	3

TABLE I CONSTRAINT

 I

		CT1
	FS	USR
	[10, 17]	Child
	[16, 25]	Adult

TABLE II CONSTRAINT

 II

		CT2
	USR	CL
	Child	Pink
	Child	Blue
	Adult	Blue
	Adult	Black

TABLE III RULES

 III

	CORRESPONDING TO THE CONSTRAINT
	R1


Michel Aldanondo

Élise Vareilles

T Coudert

L Geneste

R Ayachi

How to Better Identify and Mitigate Risks in Call for Tenders: Towards a Dedicated Risk Ontology

Keywords: Customer-Supplier Relationship, Offer Definition, Engineer to Order, Risk Mitigation, Ontology

When preparing a commercial offer concerning technical systems, suppliers working in engineer to order can either make a detailed design job or just decide key solution choices. With a detailed design, if the customer accepts the offer, the supplier has a good confidence in its ability to provide a solution matching offer contents because requirements have been studied in details. With key technical choices, it is not the case, and the supplier takes the risk of not being able to provide an adequate solution because requirements have been just superficially studied. The goal of the communication is to propose and discuss the key knowledge elements in order to manage this kind of supplier risk when preparing the offer. By management we mean, according to ISO 31000, identifying, assessing and processing risk. The proposed key knowledge elements are a risk taxonomy and a risk mitigation action taxonomy. Actually, risk management relies fully on human expertise, these modeling elements will allow companies to design a knowledge-based system that can assist the human in charge and improve commercial offers quality.

I. INTRODUCTION

The goal of this article is to present the very first ideas about a kind of classification of risks [START_REF]ISO 31000:2009 -Risk management -Principles and guidelines[END_REF] with their mitigation actions that should be considered when a supplier is defining a commercial offer for technical systems. Risk knowledge will be identified and discussed following the ideas of [START_REF] Ayachi | Towards a Knowledge based Support for Risk Engineering When Elaborating Offer in Response to a Customer Demand[END_REF]. Furthermore, we consider a business to business (B2B) customer-supplier relationship, in order to deal with engineer to order (ETO) situations [START_REF] Sylla | Configuration knowledge modeling: How to extend configuration from assemble / make to order towards engineer to order for the bidding process[END_REF] which is one of the main sources of risks in customer-supplier relationship. In ETO, the supplier can follow two behavior tendencies, either (i) it designs in detail a solution before defining the commercial offer, or (ii) it just makes key design choices without any detailed analysis. The second approach is of course much more subject to risk. If the customer accepts the offer, the supplier has no guarantee that it will be possible to finalize design and produce a system that will fulfill all customer's requirements. Consequently, the goal of this paper is to identify and to structure all the risks that can lead the supplier of being unable to deliver the technical system that he promised in his commercial offer. In addition, we propose to identify and to structure main risk mitigation actions. As far as we know, we has not found any scientific work that proposes this kind of typology. If [START_REF] Botero | BPRM Methodology: Linking Risk Management and Lesson Learnt System for Bidding Process[END_REF] or [START_REF] Carter | Introducing RISKMAN methodology: the European project risk management methodology[END_REF] are quite close to our problem with risk management propositions, they do not propose any risk knowledge typology. The great interest of this typology is to allow to establish a risk checklist that avoids the "white page problem" to the person in charge of risk engineering when defining a commercial offer.

The remaining of the paper is as follows. The second section identifies, discusses and structures the main risk concepts. The third section do a similar work for risk mitigation actions. The last section discusses the proposed elements.

II. RISK ONTOLOGY

In this work, we consider the risk as an event driven nature entity [START_REF] Muriana | Project risk management: A deterministic quantitative technique for assessment and mitigation[END_REF] that can modify some delivery process task metrics as cost, duration, carbon footprint, system quality or performance [START_REF] Marmier | A risk oriented model to assess strategic decisions in new product development projects[END_REF]. We consider in our case that the delivery process starts once the customer has accepted the offer and finished when the system has been fully delivered. This delivery process gathers operations like: finalizing design (because we are in ETO), source components, manufacture, assemble, test, transport and install the system [START_REF] Ayachi | Towards a Knowledge based Support for Risk Engineering When Elaborating Offer in Response to a Customer Demand[END_REF]... Next sub-section introduces the two parameters that allow to structure the risk identification. Then the risk typology is proposed.

A. Risk structuration parameters

The first one is relevant to the fact that ETO situation can be at the source of the risk:

• When the risk is ETO specific, this means that some key design choices made in order to define the system and its delivery process for the commercial offer were either inadequate or wrong! In other word a more detailed analysis could have prevented the error. • When the risk is not ETO specific, this means that any failure of hazards in the delivery process is not a consequence of a bad design choice made during offer elaboration.

The second structuring parameter aims to indicate which kind of entity of the delivery process is at the source of

How to better identify and mitigate risks in call for tenders : towards a dedicated risk ontology

M. Aldanondo 2 , E. Vareilles 3 , T. Coudert 1 , L. Geneste 1, R. Ayachi 1,

1 INP/ENIT -LGP Tarbes France (thierry.coudert@enit.fr, laurent.geneste@enit.fr, rania.ayachi@enit.fr ) 2 I MT/Mines Albi -CGI Albi France (michel.aldanondo@mines-albi.fr) 3 ISAE/Supaero -DISC Toulouse France (elise.vareilles@isae-supaero.fr) the risk. For that, we propose to associate the risk with the 5 M of Ishikawa [START_REF] Ishikawa | Introduction to Quality Control[END_REF]:

• Man or human resources,

• Machine or technical resources,

• Material or consumable materials, components, task inputs, • Method or way of completing process and documents, • Medium or environment.

Given these two parameters, the next section presents our risk typology.

B. Risk typology for non ETO specific risk

This risk typology is shown in the left part of figure 1. These risks are conventional operational process risks that exist in any delivery process. However, they can be classified in three sub-categories according to their situation:

1. "break or accident", 2. "entity error" 3. "entity not available".

These three categories intersect the 5 M of the process. It can be seen on the left of Fig. 1 that the M "method" is not subject to a "break or accident" situation and that the M "Medium" does not follow the three previous subcategories.

C. Risk typology for ETO specific risk

This risk typology is shown in the right part of Fig. 1. These risks are the result of key design decisions during offer elaboration. Two sub-categories have been identified:

1. "inadequate process entity", 2. "KPI entity out of range".

The first one indicates that the selected process element is not adequate for the delivery process operation. The second one indicates that process key performance indicator (cost, duration, carbon footprint, quality…) will not be respected.

As before these two sub-categories intersect the 5 M of the process. It can be seen in the right of Fig. 1 that the M "Medium" cannot be the subject of any ETO specific risk. Finally, in the lower right of Fig. 1, we can see the most critical ETO risk explaining that the finalization of the system design itself cannot be achieved. In other words, the supplier is unable to design the system that has been sold!

The next section proposes a typology of risk mitigation actions that closely related to the previous risk typology.

III. RISK MITIGATION TYPOLOGY

This section is dedicated to the identification and characterization of risk mitigation actions. Following previous risk structure, we will propose migration actions according to:

• Non ETO and then ETO specific risk,

• the five risk sub-categories,

• the 5 M of Ishikawa.

Furthermore, we will consider preventive and curative actions. Curative actions are achieved only if the event associated to the risk occurs while preventive actions are systematically included in the delivery process.

In the following we begin with non ETO specific risk and deal after with mitigation actions for ETO specific risks.

A. Mitigation actions typology for non ETO specific risk

This mitigation action typology is shown in Fig. 2, where the left part shows preventive actions and the right part curative actions.

Preventive actions are identified with respect to the 5 M of Ishikawa and the three risk sub-categories "break", "not available" and "error". We can see that for the two sub-categories "not available" and "error" and the four first M, the mitigation action are quite similar; basically, prevention is achieved with improvement of process resource management. For the fifth M, in order to deal with environment crisis, prevention corresponds with some crisis management plan preparation.

Curative mitigation actions correspond mainly with resource substitution and/or subcontracting.

B. Mitigation actions typology for ETO specific risk

The mitigation action typology for ETO specific risk cannot show preventive actions.

In ETO, a detailed design of both system and delivery process has not been achieved when the offer was defined. Consequently, the first task of the delivery process is always a detailed design for both system and delivery process.

As ETO risk are mainly the result of bad design choices, a possible preventive action can correspond only with a design correction that is in fact the first task of the delivery process.

Therefore, Fig. 3 shows only curative mitigation actions typology for ETO specific risks.

Fig. 3 Mitigation curative actions typology for ETO risks

Curative actions for the four first M are quite similar and correspond with some resource replacement either more technically adequate or better for KPI evaluation.

The lower part of Fig. 3 concerns the ETO key risk "design problem is harder than expected." In that situation the finalization of the system design itself cannot be achieved. In the less critical situation "wrong design decision" only a design correction is needed with no strong consequences. In the intermediate "too many KPI constrained", KPI cannot be respected, the design will be modified but the supplier will have to pay penalty. In the most critical one "impossible design" the supplier must negotiate requirement modifications with his customer.

IV. DISCUSSION AND CONCLUSION

The goal of this article has been to present the first ideas about a typology of risks with mitigation actions. The analyzed risk is on the supplier side and characterize the fact of being unable to design, produce and deliver a system that has been sold.

The interest of such a typology is to help the person in charge of the definition of commercial offers to think about potential risks and relevant mitigation actions. They avoid the "withe page problem" when questioning what could be the risk taken? and what could we do to prevent and mitigate them? One of the main interests of the proposed typology is to consider engineer-to-order situations and two kinds of risks: ETO and non-ETO specific risks. In addition, risks and mitigation actions have been specialized in subcategories and characterized with respect to the 5 M of Ishikawa.

These propositions have been established during a cooperative research project involving companies of the mechatronic system domain. They can be considered as a first tentative and must be validated on other industrial situations.

Broken

Entity For future, we are working of a version 2 of these typologies with dependent risks and cascading effects on the delivery process.

Fig. 1

 1 Fig. 1 Risk typology

Fig. 2

 2 Fig. 2 Mitigation actions typology for non ETO risks

Inadequate entity KPI entity out of range

			Non ETO preventive action		Non ETO curative action
					not		Error on	Broken	Entity not	Error on
		entity			available		entity	entity	available	entity
	Man (human resource)	Human resources politic to sensitize accident prevention		Action on human resources management	Action on human resources management	Substitution with another person (same/other company)	Substitution with an other person: overtime/sub-contract	Substitution with an other person: overtime/sub-contract
	Machine (technical resource)	Preventive maintenance	Action on technical resources management	Action on technical resources management	Machine repair or substitution	Machine substitution overtime/sub-contract	Machine substitution overtime/sub-contract
	Material (consumable resource)	First article inspection		Action on supplier management	Action on supplier management	Consumable Repair or substitution	Consumable substitution, inventory picking / urgent order	Consumable substitution, inventory picking / urgent order
	Method (documentation)	NA		Method or documentation prepartion/distribution	Method or documentation prepartion/distribution	NA	Method or documentation obtention urgently	Method or documentation obtention urgently
	Medium (Environment)	Environment crisis plan preparation	Crisis plan application and/or crisis cell set-up
	Man	Replacement with a more
	(human resource)	competent human resource
	Machine			Replacement with a less costly or quicker
	(technical	Machine replacement or update	resource, according to what is more
	resource)				important for the customer between
	Material (consumable resource)	Replacement with a more adequate consumable resource.	cost and duration.
	Method	Method or documentation	Replacement with a less costly or quicker
	(documentation)	correction and improvement		method/documentation
	Medium (Environment)			NA
		For specific risks concept "Design problem is harder than expected"
		Impossible design	Too much KPI-	Wrong design decision
	No specific M	Customer negotiation	Design modification	Design correction

ACKNOWLEDGMENT

This work is fully supported by the French research agency ANR. The authors also gratefully acknowledge the coming helpful comments and suggestions of the reviewers, that will help to improve the presentation.