Julie Chanut

Jean-Pierre Bellat

Régis D Gougeon

Thomas Karbowiak
email: thomas.karbowiak@agrosupdijon.fr

Prof Thomas Karbowiak

Controlled diffusion by thin layer coating: the intricate case of the glass-stopper interface

Keywords: gas transfer, oxygen, agglomerated cork, interface, surface treatment

A comprehensive study was carried out to investigate the critical role played by the interface between the stopper and the bottleneck on oxygen penetration into the bottle, as well as the effect of surface treatment of the stopper. First, the compression of micro-agglomerated cork, at close to 40% (similar to that of a cork stopper in a still wine bottleneck), had a very limited effect on the oxygen transfer. Second, once a cork was inserted into a glass bottleneck without any surface treatment, up to 99% of the total oxygen transfer took place at the stopper-bottleneck interface.

Third, when the cork surface was coated with a paraffin-silicone mixture, there was almost no oxygen transfer at the interface. Although initially used as a slippery agent for easier uncorking, the surface coating of the stopper, as a thin layer of several hundred nanometers, had a remarkable and unexpected impact on the control of the oxygen transfer.

INTRODUCTION

During wine conservation in a bottle, the control of oxygen transfer from the outside environment to the wine inside the bottle is a key parameter that determines the wine quality. Many other factors can also influence the evolution of wine during postbottling aging, such as the composition of the wine itself (also related to the wine-making process), the temperature, the relative humidity, the storage position, as well as the amount of oxygen initially present in the bottle [START_REF] Afnor ; Arapitsas | NF EN 12726 Emballage -Bague plate unique ayant un diamètre d'entrée de 18,5 mm pour bouchage liège et capsule témoin d'effraction. La Plaine Saint-Denis[END_REF][START_REF] Lopes | Impact of storage position on oxygen ingress through different closures into wine bottles[END_REF][START_REF] Lopes | Impact of the Oxygen Exposure during Bottling and Oxygen Barrier Properties of Different Closures on Wine Quality during Post-Bottling[END_REF][START_REF] Roullier-Gall | Sulfites and the wine metabolome[END_REF][START_REF] Venturi | The influence of packaging on the sensorial evolution of white wine as a function of the operating conditions adopted during storage[END_REF]. However, the oxygen transfer is the most critical factor [START_REF] Roullier-Gall | Natural oxygenation of Champagne wine during the prise de mousse: a metabolomics picture of hormesis[END_REF]. For this reason, the choice of the packaging and in particular of the stopper is crucial in providing the best conditions for wine aging [START_REF] Caloghiris | An industry trial provides further evidence for the role of corks in oxidative spoilage of bottled wines[END_REF][START_REF] Crouvisier-Urion | Gas transfer through wine closures : A critical review[END_REF][START_REF] Karbowiak | Wine oxidation and the role of cork[END_REF]Lopes, Saucier, Teissedre & Glories, 2007a;Lopes, Saucier, Teissedre & Glories, 2007b;[START_REF] Waters | The role of corks in oxidative spoilage of white wines[END_REF].

The transfer of oxygen into the bottle occurs by diffusion through the stopper itself and also at the interface between the glass bottleneck and the cork stopper. The oxygen transfer at the interface between the glass bottleneck and the cork stopper was first investigated experimentally in two studies (Lopes, Saucier, Teissedre & Glories, 2007b;[START_REF] Waters | The inherent permeability of corks to oxygen Australian Wine Institut 46[END_REF]. Lopes et al. and Waters et al. measured the oxygen transfer through cork stoppers compressed in a bottleneck with an epoxy glue applied to the stopper-bottleneck interface present on the external surface in contact with the air. Their setup prevented the entry of oxygen through this top part of the corkglass interface but still allowed the transfer that can occur through the lower part of the stopperbottleneck interface once it has passed through the outer surface. A preliminary study by Lagorce-Tachon et al., on natural cork stopper inserted in a bottleneck without surface treatment showed that the global diffusion coefficient through the cork-glass bottleneck system was fifty times higher (5 x 10 -7 m 2 .s -1 ) than through the compressed cork stopper alone (8 x 10 -9 m 2 .s -1 ) [START_REF] Lagorce-Tachon | About the role of the bottleneck/cork interface on oxygen transfer[END_REF].

Recently, Karbowiak et al. measured the oxygen transfer through corked bottlenecks and through cork stoppers alone for bottles containing oxidized and nonoxidized white wines [START_REF] Karbowiak | Wine aging: a bottleneck story[END_REF]. Their results showed that the effective oxygen diffusion coefficient for the cork compressed in a bottleneck was considerably higher for oxidized wines than for nonoxidized wines, whereas the oxygen diffusion coefficient for the cork stopper alone remained similar in all cases. However, in the studies by Lagorce-Tachon et al. and Karbowiak et al., the diffusion coefficient values were measured on dry cork without taking into account the possible effects of water and ethanol on barrier and mechanical properties of cork. These two studies highlight that the cork stopper-glass bottleneck interface is likely to play an important role in oxygen transfer.

With the various types cork stopper currently used (natural or agglomerated), an additional outer layer of a surface treatment product is always applied in the final step of the manufacturing process. The most common coating agents are food-grade paraffin wax, silicone, or emulsions of these two products in a mixture [START_REF] Lefebvre | Charte des bouchonniers liègeurs[END_REF]. They are usually spread in a drum coater to cover the surface of the stoppers, but the method of application and the quantity applied depend on the cork supplier. The primary function of these surface treatments is to facilitate the introduction and especially the extraction of the stopper from the bottleneck at the opening as they reduce the adhesion between the glass and the cork acting as a slippery agent [START_REF] Chatonnet | Contrôle de la conformité des bouchons: objectifs et paramètres à l'usage des professionnels[END_REF][START_REF] Fortes | A Cortiça[END_REF][START_REF] Gonzalez-Adrados | Control by ATR-FTIR of surface treatment of cork stoppers and its effect on their mechanical performance[END_REF]. They also improve the tightness to liquids by reducing the risk of leakage and they reduce the kinetics of liquid sorption by the stopper during cork-wine interaction [START_REF] Gonzalez-Adrados | Cork-Wine Interaction studies : Liquid absorption and non-volatile compound migration[END_REF]. However, only a few studies have reported the effect of the coating agents on the transfer of oxygen through the cork-bottleneck system [START_REF] Keenan | Oxygen permeability of macrocrystalline paraffin wax and relevance to wax coatings on natural corks used as wine bottle closures[END_REF][START_REF] Waters | Random post-bottling oxidation of bottled wines[END_REF]. Keenan et al. determined the oxygen permeability of macrocrystalline paraffin wax coated on natural cork stoppers and calculated the theoretical ingress of oxygen diffusing through a 17 µm layer of macrocrystalline paraffin wax coated on these stoppers. The oxygen transfer through the paraffin layer was not sufficient to cause the wine oxidation. The calculation was made in the case of a continuous coating along the stopper compressed in a cylindrical tube (18.5 mm in diameter) and did not take into account the permeation of oxygen along the interface between the bottleneck and cork coating. Therefore, it is particularly relevant to investigate the oxygen transfer through the system composed of a cork stopper that has undergone a surface treatment prior to being inserted in a glass bottleneck.

The present work examines the oxygen transfer at the interface between the glass bottleneck and the cork stopper. In particular, we focus on the effect of surface treatment of the stopper on the oxygen transfer at the glass-stopper interface. First, permeation experiments were performed for uncompressed and compressed (40% in volume) micro-agglomerated stoppers to determine their intrinsic oxygen diffusion coefficients and the effect of compression. Then, permeation experiments were carried out on the system composed of the cork stopper without surface treatment inserted in a glass bottleneck. In the final step, the oxygen transfer was evaluated on the glass-stopper system with the stopper coated with a surface treatment product. This comprehensive approach aimed to quantify and differentiate between the two oxygen flows passing through the stopper and at the interface between the stopper and the glass.

2. MATERIALS AND METHODS

Cork stopper samples

Micro-agglomerated cork stoppers (of the type Diam 10) were produced by Diam Bouchage (Céret, France) by a molding process using cork particles associated with binding agents. The stoppers used for the experiments were 24.2 mm in diameter and 49 mm long and were either coated with an anionic emulsion of paraffin and silicone or were without a surface treatment agent. The different conditions used are summarized in figure 1.

Compressed cork stopper

Cork wafers 3 mm thick were cut in the middle of full-length cork stoppers. They were then inserted uncompressed in a metal ring and attached at the periphery with Araldite 2012 to avoid gas transfer at the interface. The experiments were carried out on 3 mm thick cork wafers to reduce the time of measurement.

Compressed cork stopper

Full-length cork stoppers were first compressed using a professional bottling machine comprising four stainless steel jaws (GAI 4040, France) and inserted in a metal ring of 18.5 mm diameter, corresponding to the diameter of the entry bore of a bottleneck [START_REF] Afnor ; Arapitsas | NF EN 12726 Emballage -Bague plate unique ayant un diamètre d'entrée de 18,5 mm pour bouchage liège et capsule témoin d'effraction. La Plaine Saint-Denis[END_REF]. This operation leads to a 40% compression of the initial stopper volume. Similar to uncompressed cork stoppers, the interface between the cork wafer and the metal ring was glued to avoid gas transfer at the interface. Here, the cork samples were 6 mm thick to prevent buckling during compression.

Cork stopper inserted in a glass bottleneck

Uncoated full-length cork stoppers were inserted in empty glass bottles (Bourgogne Astre 37.5 cL, Verrerie de Bourgogne, France) using the same bottling machine. Prior to bottling, the bottleneck profile of each bottle was measured using a profilometer (Egitron PerfiLab PRF 2014 01) to ensure they complied with the corresponding standard (AFNOR, 2018). The bottles were then cut below the stopper with a diamond cutoff wheel and inserted in a metal ring (Figure 2).

Here, only the part between the ring and the bottleneck was glued with Araldite 2012. Oxygen transfer measurements were performed on 49-mm-long closures inserted in a bottleneck having an inner diameter of 18.5 ± 0.5 mm.

Cork stopper with surface coating

The same procedure was applied for cork stoppers coated with a surface treatment product. A coating composed of an emulsion of paraffin wax and silicone was sprayed in a drum coater at an industrial scale. For these stoppers, only 6 mm wafers were inserted in the bottleneck (Figure 2). This allowed the time of permeation experiment to be reduced. Ten measurements were performed for each of the four conditions chosen for this comprehensive study.

Oxygen permeation

During storage, we can distinguish between two phenomena; the oxygen given out by the stopper expressed by the oxygen initial release (OIR), and the oxygen coming from the outside environment expressed by the oxygen transmission rate (OTR) [START_REF] Chevalier | Impact de l'obturateur sur le vieillissement des vins en bouteille Partie 1/3 -Caractérisation des transferts d'oxygène de bouchons en liège[END_REF].

In this study, the OTR was determined using a homemade apparatus based on a manometric method (Figure 2).

The equipment and protocol have been detailed previously [START_REF] Lagorce-Tachon | Diffusion of oxygen through cork stopper: Is it a Knudsen or a Fickian mechanism[END_REF][START_REF] Lequin | Diffusion of oxygen in cork[END_REF]. The oxygen flow was measured through a sample separating two chambers. First, an oxygen purge was performed in the measuring chamber C1. The initial pressure was then set at around 900 hPa while the other compartment, C2, was kept under dynamic vacuum (0.1 hPa).

The decrease in oxygen pressure in the measuring compartment, caused by the transfer of oxygen from compartment C1 to, C2 was monitored over time. The temperature was kept constant at 298 K (±1 K) by a water circulation surrounding compartment C1 under thermostat control. The pressure sensitivity was ± 0.1 hPa.

Scanning electron microscopy

Prior to bottling, agglomerated cork stoppers without and with surface treatment were observed by SEM using a Jeol JSM 7600F apparatus (3.5 kV). Before imaging, cork samples were cut with a razor blade, and coated with a carbon film (15-20 nm). The obtained images allowed us to visualize the distribution of the surface treatment product on the stopper surface as well as the thickness of the coating.

CALCULATION

3.1.Oxygen transfer in the cork stopper

Considering the gas as ideal, the surface molar flow of oxygen passing through the wafer, Jw (mol.m -2 .s -1 ) is given by the equation:

= - = -. . (Equation 1)
p is the pressure (Pa) in the compartment C1 along time t (s), V is the volume of the compartment C1 (m 3 ), Sw is the surface of the wafer (m 2 ), R is the ideal gas constant (8.314 J. mol -1 .K -1 ) and T is the temperature (K).

According to the first Fick's law, the surface molar flow of oxygen passing through the cork wafer, once the steady state is established, is also given by the equation:

= -. ∇ ≈ -.
(Equation 2)

With Dw the diffusion coefficient (m 2 .s -1 ) of oxygen inside the wafer, e the thickness of the wafer (m) and ∇C a (mol.m -4 ) the concentration gradient of oxygen adsorbed on both side of the wafer.

C a (mol.m -3 ) is related to the concentration of the gas C g (mol.m -3 ) by the equation:

= .

(Equation 3)

With ψ, so called the separation factor. Ψ is obtained from the sorption isotherm of oxygen, on cork which has been determined on previous work [START_REF] Lequin | Diffusion of oxygen in cork[END_REF].

By combining the equations (1), ( 2) and (3), and after integration over time, we obtain:

ln ! = " .#. . . $ (Equation 4)
Thus, the diffusion coefficient of oxygen through the cork wafer is determined from the slope of the straight-line of the plot ln ( )=f(t) obtained once the steady state is established.

Extrapolation of the diffusion coefficient for a full-length cork stopper

As cork stoppers are not homogeneous materials, the diffusion coefficient measured through a cork wafer of thickness e is not representative of the diffusion coefficient through a full-length cork stopper of length Ls. For the measurements made on wafers 3 or 6 mm thick, it was thus necessary to extrapolate the experimental results to a full-length cork stopper. To that end, the stopper was considered as a serial stack of n wafers, where each slice represents a local resistance to gas transfer, Rw. The sum of these resistances allowed us to calculate the total resistance Rs of the stopper, as defined by the following equations:

& ' = ( ) " ) = ∑ & + +, = ∑ " - +, ( Equation 5)
. ' = /. 0 (Equation 6)

" ) = ∑ " - +, ( Equation 7)
Where Rs is the overall resistance of the stopper, Ds is the extrapolated diffusion coefficient of the stopper, Ls is the total length of the stopper, n is the total number of wafers contained in a fulllength stopper and Dwi is the diffusion coefficient of the wafer i drawn at random in the statistical distribution of diffusion coefficients determined experimentally.

Oxygen transfer at the glass-cork interface

The oxygen flow through the cork stopper-glass bottleneck interface, Jinterface (mol.m -2 .s -1 ), is determined by subtracting from the total flow, Jtotal, going through the system comprising the cork stopper inserted in the glass bottleneck, the oxygen flow going through the cork stopper alone, Js.

+ 12 3 = 4 5 -'

(Equation 8)

11

The total flow of gas passing through the bottleneck-cork system is expressed by an equation similar to Equation 1: 9)

4 5 = - = -. . ≃ -. . ∆ ∆ (Equation
As we know the diffusion coefficient of oxygen through the full-length cork stopper, Ds, the flow passing only through the cork stopper is calculated with the relation:

' = -' . 8. ∆ ( ) .
.

(Equation 10)

In the last two equations, S is the section area of the cork inserted in the bottleneck, and ∆p is the pressure decrease in compartment C1 during time, ∆t.

RESULTS AND DISCUSSION

Role of the compression on the oxygen transfer

To determine the effect of compression on the gas transfer through the cork stopper, the effective diffusion coefficient of oxygen was first measured on uncompressed and compressed cork wafers, with the interface glued in both cases. The experimental and extrapolated oxygen diffusion coefficient values for the compressed and uncompressed samples are given in Figure 3 and in Table 1. The effective oxygen diffusion coefficient through uncompressed 3 mm cork wafers was between 10 -12 m 2 .s -1 and 10 -10 m 2 .s -1 . For the compressed 6 mm cork wafers, the effective diffusion coefficient values ranged from 10 -11 to 10 -10 m 2 .s -1 . These data were then extrapolated to a full-length cork stopper (48 mm) for comparison using the model of series resistances to mass transfer. From the statistical distribution, the oxygen diffusion coefficient value was reduced from 1.4 x 10 -11 m 2 .s -1 for the uncompressed to 9.2 x 10 -12 m 2 .s -1 for the compressed cork stoppers.

Thus, when compression corresponding to a reduction of 40% in volume is applied to the stopper, this leads to a slight decrease in the oxygen transfer through the stopper with a reduction of the oxygen diffusion coefficient by a factor of 1.5. With natural cork stoppers, Lagorce-Tachon et al. did not find a significant impact of compression on the diffusion coefficient of oxygen after a 40% reduction in volume (with a diameter reduction from 24 mm to 18.5 mm) [START_REF] Lagorce-Tachon | About the role of the bottleneck/cork interface on oxygen transfer[END_REF]. Indeed, the effective diffusion coefficients measured after compression led to a relatively large distribution of oxygen diffusion coefficients of uncompressed cork ranging from 10 -10 to 10 -7 m 2 .s -1 . In such a case, it is highly probable that the heterogeneity between samples of natural cork (which are much more heterogeneous than agglomerated cork) masked the effect of the compression. For agglomerated cork stoppers, the statistical distribution of the effective diffusion coefficients for full-length stoppers were narrowed to values around 10 -11 m 2 .s -1 , reflecting the homogeneity of the agglomerated cork stoppers and indicating the effect of compression on oxygen transfer. These results also accord with those reported by Rabiot et al. They measured oxygen permeability in dry condition for agglomerated cork wafers (10 mm thick) with the initial diameter of 24 mm compressed to final diameters of 21 mm and 18.5 mm, which represent the lower and upper bottleneck diameters, respectively [START_REF] Rabiot | Study of the oxygen transfer through synthetic corks for wine conservation[END_REF]. Such differences in the compression of the stopper, corresponding to a reduction between 23% and 40% in volume, led to a decrease in oxygen permeability between 9.3 x 10 -13 mol.m.m -2 .Pa -1 .s -1 and 1.8 x 10 -13 mol.m.m -2 .Pa -1 .s -1 , respectively, which represents a reduction in oxygen permeability by a factor of 5. This decrease in the oxygen transfer through an agglomerated cork stopper could be attributed to a reduction in the initial porosity between cork particles. This could vary according to the formulation of the agglomerated cork stopper, in particular with respect to the ratio of weight of adhesive to the weight of cork [START_REF] Crouvisier-Urion | Unravelling CO2 transfer through cork stoppers for Champagne and sparkling wines. Food Packaging and Shelf Life[END_REF].

Oxygen transfer at the stopper-bottleneck interface: role of the surface treatment

In the second step, the effective diffusion coefficient of oxygen through the whole stopperbottleneck system was determined. First, this was calculated for stoppers with no surface treatment and then for stoppers coated with an emulsion of paraffin and silicone. This allowed us to evaluate the gas transfer at the stopper-bottleneck interface and to assess the impact of the surface treatment on the oxygen diffusion. The experimental and extrapolated distributions of the logarithm of the diffusion coefficient of oxygen of the different samples are reported in Figure 4 and the mean oxygen diffusion coefficients extrapolated to a full cork stopper are displayed in Table 1.

For the stopper without a surface treatment agent and inserted in the bottleneck, the mean effective diffusion coefficient appeared significantly higher than that of cork stoppers alone (i.e., outside the bottleneck) and with the same compression level. The mean value was 1.3 x 10 -7 m 2 .s - 1 for the former compared with 9.2 x 10 -12 m 2 .s -1 for the latter. Such a stopper-bottleneck system is thus much more permeable than a compressed stopper outside the bottleneck (with the interface glued). The poor resistance to gas transfer of this system must therefore originate from the stopper-glass interface, which thus addresses the likely role of defects (macropores, scratches, roughness irregularities, etc.) formed on the cork surface during its processing or at the bottling stage, or of defects present on the surface of the bottleneck. Such defects might constitute preferential pathways for gas transfer.

The OTRs through the stopper-bottleneck systems and through the stoppers alone were also calculated to determine the OTR through the interface. These results are summarized in Table 1 and highlight that more than 99% of the total oxygen transfer occurs at the stopper-bottleneck interface. A similar result was previously found for natural cork with an effective diffusion coefficient 50 times higher for a natural whole cork stopper compressed in a bottleneck compared with a compressed stopper alone with the interface glued [START_REF] Lagorce-Tachon | About the role of the bottleneck/cork interface on oxygen transfer[END_REF]. When the stopper is coated with a surface treatment agent, the effective oxygen diffusion coefficients through 6 mm cork wafers compressed in a bottleneck were around 8.8 x 10 -12 m 2 .s -1 .

When extrapolated to a 48 mm cork stopper, the mean effective oxygen diffusion coefficient is 7.9 x 10 -12 m 2 .s -1 . This value is similar to that obtained previously for a compressed cork without any transfer at the interface (9.2 x 10 -12 m 2 .s -1 ). Moreover, the OTR at the interface between the stopper and the bottleneck is around 0.34 mg.year -1 compared with 17 313 mg.year -1 in the case of a cork stopper compressed in a bottleneck without surface treatment. When coated with a surface treatment product, the oxygen flow at the interface decreases significantly and the effective diffusion coefficient becomes 10 000 times lower. These results emphasize the crucial role played by the surface treatment agent present between the stopper and the glass bottleneck on oxygen transfer. This thin layer coating appears to provide an efficient barrier against the ingress of oxygen from the external environment into the bottle. In addition to its primary function of facilitating uncorking, it can act as well as lubricating agent at bottling (during the compression and insertion stages) by reducing the creation of defects on the outer stopper surface. It can also act as an additional barrier to oxygen transfer by masking defects at the interface between the stopper and the bottleneck, providing an oxygen permeability as low as the cork stopper itself.

It should be noted that all analyses of oxygen transfer were performed under conditions that differ from oenological conditions (i.e., a dry sample under vacuum and without partial pressure of water and ethanol vapors). In a bottleneck, the cork is in contact with the wine (if bottles are stored horizontally) or in contact with a vapor phase saturated with water and ethanol vapors (if the bottles are stored vertically). These conditions might also affect the barrier properties of stopper. Further experiments are clearly needed to fully understand how the global diffusion coefficient of oxygen through cork could change depending on hydration or the presence of ethanol.

Microscopic characterization of the surface coating

In addition to permeability measurements, SEM analysis was performed for agglomerated stoppers with and without coating. The corresponding microscopic images of the cork stopper surface are presented in Figure 5. First, the stopper surface without coating is shown in Figure 5A-B. The cork particles, as well as microspheres, are entrapped within the adhesive network that maintains the cohesion of the stopper. As the cork particles have no specific orientation, this confers more isotropy at the macroscopic scale, to the agglomerated stopper compared to the natural cork stopper having a strong anisotropy due to the specific orientation of cork cells [START_REF] Gibson | The structure and mechanics of cork[END_REF].

Surface treatment of the stopper, performed by drum coating with paraffin wax and silicone emulsion, results in a thin coating layer that can be observed on the stopper surface (Figures 5C-D). The deposited coating layer fully covers the surface of the stopper and does not tend to form aggregates (Fig 5C). It is also distributed in a very thin layer over the cork cells and does not fill the volume of the outer empty cells. Such SEM observation was performed at the scale of a 1 mm 2 surface and is representative of the whole coated stopper surface. However, only small-scale observations could be performed with SEM microscopy. This method unfortunately does not enable a full assessment of the homogeneity of the coating on the surface of the whole stopper.

Nevertheless, even if qualitative, these results seem to indicate that the coating was homogeneously distributed on the stopper surface, at the scale of observation provided by SEM.

At a lower scale, the coating thickness could be estimated from cross-sectional images at around 0.5 μm (Fig 5d) and was quite thin in view of the coating process. This thickness was much lower than the 17 µm (paraffin wax layer) reported by Keenan et al. on natural cork stoppers [START_REF] Keenan | Oxygen permeability of macrocrystalline paraffin wax and relevance to wax coatings on natural corks used as wine bottle closures[END_REF]. However, this last value was calculated from the weight and density of the paraffin. The weight value was obtained by solvent extraction of the wax layer from cork stoppers. This result could thus be overestimated because of the solvent extraction of other compounds from the cork in addition to those used for the surface treatment.

CONCLUSIONS

The transfer of oxygen in a cork/bottleneck system was assessed through a comprehensive study starting from the effective diffusion through the stopper alone and ending with a more complex system comprising the stopper covered by a surface treatment agent and compressed in the glass bottleneck. First, the effect of compression on agglomerated cork (with the interface glued)

showed that a 40% reduction in the volume of the stopper (compression applied for still wines) reduced the effective oxygen diffusion of the stopper by 35%. Then, the oxygen transfer through a corked bottleneck was examined. Without coating, the oxygen effective diffusion coefficient through an agglomerated cork stopper compressed in a bottleneck was significantly higher than through the stopper alone (10 -7 m 2 .s -1 vs 10 -11 m 2 .s -1 ). However, when the stopper was coated with a silicone-paraffin emulsion, the effective oxygen diffusion coefficient through the corkbottleneck gave a similar value to that of the compressed stopper (10 -11 m 2 .s -1 ). These results clearly highlighted the crucial role of the surface treatment in oxygen transfer through a corked bottleneck. A coating with a thickness of less than 0.5 µm provided an efficient barrier for the stopper-bottleneck interface system to act against gas transfer at the interface. In addition to its Table 1. Mean oxygen diffusion coefficients and OTRs determined for uncompressed and compressed cork stoppers alone and for cork stoppers inserted in a bottleneck with and without a surface treatment agent (extrapolated to a full cork stopper, from 10 replicates in each case; see figure 4 for corresponding distributions). For practical reasons, OTR is expressed as mg.year -1 , referring to as mg of oxygen going through the stopper per year.

Figure 1 .

 1 Figure 1. Conditions used for the measurement of oxygen transfer.

Figure 2 .Figure 3 .

 23 Figure 2. Scheme of the homemade manometric device for the oxygen transfer measurement of the different cork samples

Figure 4 .

 4 Figure 4. Distributions of the logarithm of the diffusion coefficient of oxygen at 298 K obtained from 10 replicates. Green (-): extrapolated distribution to a 48 mm stopper from the experimental data for 3 mm compressed cork wafers. Blue (-): extrapolated distribution to a 48 mm stopper from the experimental data for 6 mm compressed cork wafers. Red (-): experimental distribution of 48 mm stoppers compressed in a bottleneck without surface treatment. Yellow (-): extrapolated distribution to 48 mm stoppers from the experimental data measured for 6 mm cork wafers compressed in a bottleneck with surface treatment (emulsion of paraffin and silicone).

Figure 5 .

 5 Figure 5. SEM observations of the surface of agglomerated stoppers observed at different magnifications factors. a) without surface treatment, b) cross section of agglomerated cork stopper without surface treatment c) with surface treatment, d) cross section of an agglomerated cork stopper with surface treatment.

  Figure 1

Figure 5

 5 Figure 5

Table 1 .

 1 Mean oxygen diffusion coefficients and OTRs determined for uncompressed and compressed cork stoppers alone and for cork stoppers inserted in a bottleneck with and without a surface treatment agent (extrapolated to a full cork stopper, from 10 replicates in each case; see figure4for corresponding distributions). For practical reasons, OTR is expressed as mg.year -1 , referring to as mg of oxygen going through the stopper per year.

	Effective
	Diffusion
	Coefficient Ds
	(x 10

-12 m 2 .s -1 ) OTRtotal (mg. year -1 ) OTRinterface (mg. year -1 ) OTRstopper/OTRtotal (%)

	Agglomerated uncompressed stopper	14.4 ± 2	0.61 ± 0.11	/	/
	(interface glued)
	Agglomerated compressed stopper	9.2 ± 2	0.38 ± 0.08	/	/
	(interface glued)
	Agglomerated stopper
	compressed in a bottleneck without	130 000 ± 90 000 17 313 ± 4432	17 313	0.003
	surface treatment
	Agglomerated stopper
	compressed in a bottleneck with surface	7.9 ± 2	0.34 ± 0.08	0	100
	treatment

ACKNOWLEDGMENTS

We gratefully acknowledge Diam Bouchage as well as the French National Association of Research and Technology (ANRT) for their financial support and the PhD grant (CIFRE 218-1278) of Julie Chanut. SEM images were determined at the Laboratoire Interdisciplinaire Carnot de Bourgogne with the help of Frédéric Herbst. We acknowledge the Bureau Interprofessionnel des Vins de Bourgogne for providing bottling machine and profilometer as well as Verrerie de Bourgogne for donating the bottles.

COMPETING INTERESTS STATEMENT

The authors declare no competing interests.


Georgios V Lioudakis
email: georgios.lioudakis@ict-abovo.gr

Maria N Koukovini
email: mariza.koukovini@ict-abovo.gr

Eugenia I Papagiannakopoulou
email: eugenia.papagiannakopoulou@ict-abovo.gr

Nikolaos Dellas
email: nikolaos.dellas@gmail.com

Kostas Kalaboukas
email: kkalaboukas@singularlogic.eu

Renata Medeiros De Carvalho
email: r.medeiros.de.carvalho@tue.nl

Marwan Hassani
email: m.hassani@tue.nl

Lorenzo Bracciale
email: lorenzo.bracciale@uniroma2.it

Giuseppe Bianchi
email: giuseppe.bianchi@uniroma2.it

Adrian Juan-Verdejo
email: adrian.juan@cas.de

Spiros Alexakis
email: spiros.alexakis@cas.de

Francesca Gaudino
email: francesca.gaudino@bakermckenzie.com

Davide Cascone
email: davide.cascone@bakermckenzie.com

Paolo Barracano
email: p.barracano@intempra.com

Facilitating GDPR compliance: the H2020 BPR4GDPR approach

Keywords: GDPR compliance, data protection, process management, privacyaware access and usage control, process mining

This paper outlines the approach followed by the H2020 BPR4GDPR project to facilitate GDPR compliance. Its goal is to provide a holistic framework able to support end-to-end GDPR-compliant intra-and inter-organisational ICT-enabled processes at various scales, while also being generic enough, fulfilling operational requirements covering diverse application domains. To this end, solutions proposed by BPR4GDPR cover the full process lifecycle addressing major challenges and priorities posed by the Regulation.

Introduction

The General Data Protection Regulation (GDPR) [START_REF]679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data[END_REF] comprises a milestone in data protection, creating an environment able to cope with the technological and business reality, and provide for the protection of privacy. However, organisations declare difficulties in GDPR implementation, despite the resources and money spent, The challenges, either technical or organisational, include, among others: interpretation of GDPR requirements; operational adaptation towards compliant business practices; holistic data views and processing actions inventory; enforcement of security means; management of the relations with third parties and the data subjects, and enforcement of rights thereof; last but not least, significant resources are required and, whereas big companies may have resources to invest, this does not necessarily apply for SMEs.

This paper presents the approach followed by the H2020 BPR4GDPR project1 , towards a new GDPR compliance paradigm. BPR4GDPR is building tools for facilitating the implementation of the appropriate measures, particularly by SMEs, to ensure that data collection and processing is performed in accordance with the GDPR. The BPR4GDPR compliance approach consists in automatically re-engineering workflows, being business processes or low-level service compositions, so that they become compliant by design, whereas enforcement is supported by an easy to deploy "compliance toolkit", providing the fundamental common functions for cryptography, access management, and enforcement of data subjects' rights. In the following, Section 2 outlines the operational phases towards an holistic approach to GDPR compliance, whereas Section 3 provides an overview of the technical architecture of the project.

BPR4GDPR operational phases

The BPR4GDPR process lifecycle (Fig. 1) consists of six main stages, numbered 1-6, dealing with process design or discovery, its analysis and re-design, implementation, execution and monitoring. Two additional phases, vertical to the process lifecycle, are devised for the initial "set-up" actions (Phase 0) and for the operations that are either horizontal, or process-independent (Phase 7). The eight phases are summarised in the following.

Phase 0: Set-up This consists in setting up the base elements for system operation. These include the specification of the information models, the classification of data and other resources, the assignment of roles and attributes, the definition of purposes behind data collection and processing, and the specification of policies and rules that should govern the system operation.

Phase 1: Process identification This concerns the definition of process models, by: i) process discovery mechanisms, based on process logs; ii) definition of procedures using the appropriate graphical tool. Either way, the outcome will be process model specifications providing for the incorporation, by later phases, of sophisticated constraints enforceable at run-time.

Phase 2: Process analysis This concerns the policy-based analysis of a process model in order to identify the risks, flaws and points of non-compliance. This way, process models shall be evaluated and verified as regards their compliance with the GDPR . This phase entails a highly expressive policy framework, considering a variety of aspects, such as attributes, context, dependencies between actions and participating entities therein.

Phase 3: Process redesign This phase complements process analysis, by providing for the automatic transformation of non-compliant process models, so that they are rendered inherently privacy-aware before being deployed for execution. It is supported by a Compliance Metamodel, a comprehensive process modelling technology able to capture advanced privacy provisions.

Phase 4: Process implementation This concerns the effective enactment of GDPR-compliant processes, mainly as regards two aspects. The first entails a comprehensive set of tools able to support the requirements arising from GDPR (data handling, data subjects' involvement, etc.). The second is related to the alignment of modelled processes with the actual infrastructure of the organisation; grounded primarily on the BPR4GDPR semantic foundations, it will enable refinement and adaptation of the models to each organisation's reality.

Phase 5: Process execution This extends Phase 4 by ensuring compliant process execution, following the configuration set forth. That is, it is mainly during this phase when the mechanisms towards real-time privacy protection are applied and respective provisions are enforced.

Phase 6: Process monitoring and controlling

This concerns the use of process mining for process ex post analysis, in order to ensure that specified policies are indeed enforced, fostering accountability. Furthermore, such techniques will enable automatically improving process models over time.

Phase 7: Common operations This refers to operations that are not (necessarily) part of a process lifecycle, but are executed asynchronously to processes or are independent thereof. They fall in different categories, including: i) functions that are supportive to all phases (e.g., authorisation mechanisms); ii) enforcement of data subject rights; iii) data management functions; iv) continuous operations, such as risk estimation, logging, etc.

Architecture

In order to cover its functional needs towards GDPR compliance and cope with the operational phases described in Section 2, BPR4GDPR has specified the system architecture highlighted in Fig. 2. As illustrated, the BPR4GDPR architecture is divided in four "quadrants", reflecting different groups of functionalities. In the following, the main principles and technical ideas are summarised. Governance provides all functions related to policy management, representing the Policy Decision Point (PDP) of the system. In BPR4GDPR, policies hold a dual role: i) they provide the means for system governance, in the sense that they set the rules that regulate the operation of BPR4GDPR components; ii) they comprise the knowledge base that feeds the procedure of process reengineering, towards by design compliant process models.

To this end, BPR4GDPR develops a comprehensive Policy-based Access and Usage Control framework, tailored for the needs of highly distributed environments, involving multiple stakeholders, even in cross-border scenarios. The ground technology is the academic work described in [START_REF] Papagiannakopoulou | Semantic Access Control Model for Distributed Environments[END_REF], along with the respective software prototype, whereas policies are grounded on the Compliance Ontology, providing a high-level codification of GDPR into concepts that need to be taken into consideration by the policy framework.

Planning concerns the specification of workflow models and their verification as regards compliance with the GDPR, and their subsequent transformation, if needed, so that they become compliant by design. The first step in this direction is facilitated by tools allowing their description in a way that effectively guides their execution, while also being expressive enough to capture associated provisions; these tools are grounded upon prior academic work of BPR4GDPR researchers [START_REF] Koukovini | Inherent privacy awareness in service-oriented architectures[END_REF]. Further, in order to automatically incorporate policies as part of workflow design, the BPR4GDPR approach involves sophisticated means for the evaluation of process specifications against a number of compliance aspects. Their main aim is to control access to, usage of, and flow of information and prevent illegitimate activity, as well as to determine whether critical tasks are properly included and, if not, impose their execution.

Monitoring deals with process mining and monitoring with the aim to identify discrepancies between compliant and actual behaviour. To this end, BPR4GDPR implements a Privacy-Aware Process Mining Framework, based on mature technology brought by its partners, particularly ProM2 [2] [START_REF] Kalenkova | Discovering, analyzing and enhancing BPMN models using ProM[END_REF]. The approach is primarily based on two concepts: streaming process mining [START_REF] Hassani | Efficient process discovery from event streams using sequential pattern mining[END_REF], that allows analysing real-time data in order to detect problems, anomalies and potential frauds; the concept drift issue [START_REF] Hassani | Concept drift detection of event streams using an adaptive window[END_REF], calling for solutions for change detection and continuous update, in order to handle situations where new factors/requirements render the process model out-of-date and in need to be adapted/improved.

Finally, in order to facilitate the deployment of appropriate technical measures, as required by the GDPR, Run-time provides the means for the run-time system operation, particularly in terms of policy enforcement, data management, privacy-enhancing tools, and interaction with data subjects.

In this context, the project provides a set of functional components addressing common needs of stakeholders. This so-called Compliance Toolkit consists of modular functions that, fostering "plug and play" to the extent possible, will be easy to deploy, easy to configure and easy to integrate within an organisation's ICT environment, while they will be automatically incorporated to process chains, as a result of re-engineering. The toolkit's modules fall into three families:

-Privacy-enhancing technologies, particularly cryptographic tools, devised for data and communications confidentiality, anonymisation and pseudonymisation, as well as enforcement of access rights by cryptographic means ( [START_REF] Bethencourt | Ciphertext-policy attribute-based encryption[END_REF]). -Data management tools that, by means of data access and usage management, provide for controlling data handling, including retention and storage, pre-and post-processing, etc. A core position is held by the Data Management Bus (Fig. 2), comprising the main Policy Enforcement Point (PEP). -User-centered tools, providing for the enforcement of the data subjects' rights, including information and notification, consent, and consideration of own preferences as regards data handling.

Conclusion

In the rapidly maturing privacy market, currently available solutions do not appear to sufficiently cover important GDPR aspects, while process orientation has not been extensively incorporated either. In order to address such shortcomings, BPR4GDPR aims to offer privacy-by-design throughout the entire process lifecycle, based on a broad spectrum of innovations. These will concern, at a first stage, process analysis and redesign, i.e., automatic verification of process models according to GDPR provisions but also transformation of non-conformant ones. Further, a compliance toolkit will be devised encompassing sophisticated functionalities, such as cryptography, data handling and notification mechanisms, as well as user-centered tools ensuring consent, but also the exercise of other data subjects' rights. From another perspective, process mining will be used for process discovery, process monitoring and controlling, enabling a posteriori analysis and compliance check of running processes. BPR4GDPR will thus provide a user-friendly environment for the definition of inherently GDPR-compliant processes and the automatic inclusion of necessary measures, relieving end-users from the considerable operational burden of continuous compliance assessment, and preventing business and other risks associated with potential violations.

Fig. 1 :

 1 Fig. 1: BPR4GDPR operational phases.

Fig. 2 :

 2 Fig. 2: BPR4GDPR architecture.

H2020 BPR4GDPR: Business Process Re-engineering and functional toolkit for GDPR compliance, contract number 787149 (01/05/2018 -30/04/2021) http://www.bpr4gdpr.eu/

http://www.promtools.org/

Acknowledgment

This research is being supported by the European Commission, in the frame of the H2020 BPR4GDPR project (Grant No. 787149). The authors would like to express their gratitude to the Consortium for the fruitful discussions.


Colin Decourt

Rufin Vanrullen

Didier Salle

Thomas Oberlin

DAROD: A Deep Automotive Radar Object Detector on Range-Doppler maps

Due to the small number of raw data automotive radar datasets and the low resolution of such radar sensors, automotive radar object detection has been little explored with deep learning models in comparison to camera and lidarbased approaches. However, radars are low-cost sensors able to accurately sense surrounding object characteristics (e.g., distance, radial velocity, direction of arrival, radar crosssection) regardless of weather conditions (e.g., rain, snow, fog). Recent open-source datasets such as CARRADA, RADDet or CRUW have opened up research on several topics ranging from object classification to object detection and segmentation. In this paper, we present DAROD, an adaptation of Faster R-CNN object detector for automotive radar on the range-Doppler spectra. We propose a light architecture for features extraction, which shows an increased performance compare to heavier vision-based backbone architectures. Our models reach respectively an mAP@0.5 of 55.83 and 46.57 on CARRADA and RADDet datasets, outperforming competing methods.

I. INTRODUCTION

In the last decade, the increasing number of advanced driver-assistance systems (ADAS) has led to an increase in the number of sensors embedded in the car including camera, lidar and radar. These sensors together enable the vehicle to depict the surrounding environment and adapt its behaviour depending on it. Nowadays, most intelligent vehicles use camera and lidar for ADAS applications as they provide high-resolution output and high-performance in 3D object detection and classification tasks. Because of poor angular resolution, radar sensors have been neglected for object classification and detection tasks, and used mostly for blind spot detection or automatic cruise control. Yet, radar sensors seem particularly suited for critical and real-time automotive applications such as automatic emergency braking, because they are not hampered by light or weather conditions and they provide information such as range and velocity of the surrounding objects. Paired with camera and lidar sensors, radar could bring redundancy at sensors level to improve safety in the vehicles. In this paper we propose a new deep learning model for object detection and classification using radar data.

Object detection and classification is one of the main tasks in computer vision, for which deep neural networks have achieved a major breakthrough in the past decade. Such approaches have been successfully applied to lidar and camera [START_REF] Ku | Joint 3D Proposal Generation and Object Detection from View Aggregation[END_REF], [START_REF] Zhou | VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection[END_REF] but the dearth of publicly available annotated radar datasets has slowed down research in object detection and segmentation from radar data. As illustrated in Figure 1, radar data can be represented either as a target lists (point clouds) or as raw data tensors (Range-Doppler or Range-Angle-Doppler maps). Target lists, which are the default radar data format, contain very low level information such as the position of the targets all around the vehicle, their velocities and their radar cross-sections. As an example, target lists can be used as input to deep neural network for object classification [START_REF] Scheiner | Off-the-shelf sensor vs. experimental radar -How much resolution is necessary in automotive radar classification?[END_REF] or object segmentation [START_REF] Schumann | Semantic Segmentation on Radar Point Clouds[END_REF]. However, the filtering techniques applied to the radar signal to obtain target lists lead to a loss of useful information contained in raw data tensors. Instead, raw data tensors and deep neural networks can be used to replace and improve traditional techniques for object detection, classification and segmentation without losing information. Recently, radar datasets and challenges such as CARRADA [START_REF] Ouaknine | CAR-RADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations[END_REF], RADDet [START_REF] Zhang | RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users[END_REF] or CRUW [START_REF] Wang | Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment[END_REF], where radar data is provided as raw data tensors, have opened up research on new deep learning methods for automotive radar ranging from object detection [START_REF] Zhang | RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users[END_REF], [START_REF] Meyer | Graph Convolutional Networks for 3D Object Detection on Radar Data[END_REF], [START_REF] Zheng | Scene-aware learning network for radar object detection[END_REF] to object segmentation [START_REF] Ouaknine | Multiview radar semantic segmentation[END_REF].

In this work, we propose a new model for object detection and classification using Faster R-CNN [START_REF] Ren | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[END_REF] algorithm based only on Range-Doppler (RD) maps. The use of RD maps instead of Range-Angle-Doppler (RAD) tensors is motivated by the fact that RAD tensors are more computationally demanding to produce for radar Micro Controller Units (MCUs). We propose a lightweight backbone for Faster R-CNN object detection, adapted to range-Doppler data. We design our model to handle the complexity of the RD maps and the small size of radar objects while trying to keep the processing pipeline as efficient as possible. Experiments on CARRADA and RADDet datasets show that our model can help improving object detection and classification performance on radar data and outperforms competing methods.

The paper is organised as follows. Section II presents the related work and some background on radar processing. Section III then introduces our model. The experiments and results are gathered in Section IV, while Section V discusses and concludes the paper.

II. BACKGROUND AND RELATED WORK

A. Object detection for computer vision

In computer vision, the object detection task has been widely explored over the past few years thanks to many challenges such as Pascal VOC [START_REF] Everingham | The PASCAL Visual Object Classes Challenge 2012[END_REF], COCO [START_REF] Lin | Microsoft COCO: Common Objects in Context[END_REF] or more specifically to ADAS application, KITTI [START_REF] Geiger | Are we ready for autonomous driving? The KITTI vision benchmark suite[END_REF]. Object detectors can be split into two categories: single-stage detectors and two-stages detectors. Single-stage detectors such as YOLO [START_REF] Redmon | You Only Look Once: Unified, Real-Time Object Detection[END_REF], SSD [START_REF] Liu | SSD: Single Shot MultiBox Detector[END_REF] or Retina-Net [START_REF] Lin | Focal Loss for Dense Object Detection[END_REF] are architectures which directly predict a bounding box and a class given an input image. However, this simplicity results in lower performance than two-stage detectors such as Faster R-CNN [START_REF] Ren | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[END_REF], R-FCN [START_REF] Dai | R-FCN: Object Detection via Region-Based Fully Convolutional Networks[END_REF] or FCOS [START_REF] Tian | FCOS: Fully Convolutional One-Stage Object Detection[END_REF]. Two-stage detectors are generally composed of a region proposal network (RPN) that first proposes areas in the input image that potentially contain an object and then predicts a bounding box and a class for each of these regions. This two-step approach allows two-stage detectors to be more accurate than single-stage detectors but often slower.

B. Radar pipeline

Radar is an active sensor that transmits electromagnetic wave signals, which get reflected by objects in their field of view [START_REF] Richards | Fundamentals of Radar Signal Processing[END_REF]. By capturing the reflected signal, a radar system can determine the range, velocity and angle of the objects. For automotive applications, most radars transmit a frequency-modulated continuous-wave signal (FMCW) in order to measure range as well as angle and velocity. An FMCW radar emits chirp signal, a signal whose instantaneous frequency increases or decreases linearly with time. Usually, an FMCW radar receives N samples of M chirps signal over T x antennas resulting in a N × M × T x output tensor containing the received signal in the time domain. We called this tensor the Analog to Digital Converted (ADC) signal.

As illustrated in Figure 1, the distance information is extracted by performing a fast Fourier transform (FFT) over the ADC samples within one chirp. The velocity information is extracted by performing a second FFT over the chirp index to estimate the phase difference between chirps and deduce the Doppler shift, resulting in a range-Doppler spectrum. Finally, a 3 rd FFT (or angle FFT) or more advanced algorithms is applied in the antenna dimension to extract the angle information and finally generate Range-Angle-Doppler tensor (or RAD cube). Because the RAD tensor is too intensive to compute, targets are usually detected on the RD spectrum using peak detection algorithms such as CFAR [START_REF] Blake | Os-cfar theory for multiple targets and nonuniform clutter[END_REF]. Then, radar reflections are obtained using angle FFT or beamforming methods and some post-processing steps (egomotion compensation, Kalman filtering).

C. Deep learning for automotive radar

Many research works have proposed to leverage the power of deep learning to improve some parts of the radar processing chain, ranging from target detection and classification to direction of arrival (DoA) estimation.

1) Reflection-based methods: In the automotive industry, the most common representation of radar data is a list of targets around the vehicle. Researchers widely use this representation for target recognition [START_REF] Scheiner | Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles[END_REF], [START_REF] Scheiner | Off-the-shelf sensor vs. experimental radar -How much resolution is necessary in automotive radar classification?[END_REF], segmentation [START_REF] Schumann | Semantic Segmentation on Radar Point Clouds[END_REF], [START_REF] Feng | Point Cloud Segmentation with a High-Resolution Automotive Radar[END_REF], [START_REF] Danzer | 2D Car Detection in Radar Data with PointNets[END_REF], ghost target detection [START_REF] Kraus | Using Machine Learning to Detect Ghost Images in Automotive Radar[END_REF] or 3D radar-camera object detection [START_REF] Meyer | Deep Learning Based 3D Object Detection for Automotive Radar and Camera[END_REF]. The sparsity of radar reflections allows development of lightweight, efficient and high-performance neural networks running on edge-computing. Nonetheless, this sparsity, due to the filtering techniques applied to the signal and the post-processing steps presented in Figure 1, results in a loss of valuable information contained in the raw radar signal.

2) Raw data-based methods: To overcome this, several works consider lower level representations, mainly the RD, RA or RAD tensors. Because RAD tensors aggregate both distance, velocity and angle information together, there is an increasingly number of works using this representation. Major et al. [START_REF] Major | Vehicle Detection With Automotive Radar Using Deep Learning on Range-Azimuth-Doppler Tensors[END_REF] and Gao et al. [START_REF] Gao | RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition[END_REF] propose similar architectures, merging each view into a single 2D tensor to detect and identify targets in RA maps. Paffly et al. [START_REF] Palffy | CNN Based Road User Detection Using the 3D Radar Cube[END_REF] exploit the RAD view to detect and classify road-user objects using the radar data, while enriching the radar detection with the RAD cube. In [START_REF] Ouaknine | Multiview radar semantic segmentation[END_REF], Ouaknine et al. use the RAD cube representation to segment objects both in the RA and the RD maps using lightweight segmentation architecture which exploit the temporal information. 3D object detection is also explored by Zhang et al. in [START_REF] Zhang | RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users[END_REF] using a YOLO-like architecture.

Since RA maps provide angle information, thus allowing to detect targets around the car, it has been explored extensively for different tasks. Patel et al. [START_REF] Patel | Deep Learning-based Object Classification on Automotive Radar Spectra[END_REF] extract regions of interest (ROI) from RA maps to classify targets. In a similar manner, Akita et al. [START_REF] Akita | Object Tracking and Classification Using Millimeter-Wave Radar Based on LSTM[END_REF] simultaneously track and classify targets using extracted ROI from RA maps. Recently, as part of the CRUW challenge [START_REF] Wang | Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment[END_REF] new object detection and classification architectures were proposed on RA maps such as [START_REF] Wang | ROD-Net: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization[END_REF] and [START_REF] Zheng | Scene-aware learning network for radar object detection[END_REF].

The range-Doppler spectrum has been considered more recently in the following works. Perez et al. [START_REF] Pérez | Single-Frame Vulnerable Road Users Classification with a 77 GHz FMCW Radar Sensor and a Convolutional Neural Network[END_REF] use convolutional neural networks (CNN) for vulnerable road user classification. Similarly, Khalid et al. [START_REF] Khalid | Convolutional Long Short-Term Memory Networks for Doppler-Radar Based Target Classification[END_REF] use CNN and longshort term memory networks for target identification. [START_REF] Pérez | Deep Learning Radar Object Detection and Classification for Urban Automotive Scenarios[END_REF] and [START_REF] Fatseas | Neural Network Based Multiple Object Tracking for Automotive FMCW Radar[END_REF] use YOLO-like architectures for object detection and classification. However, squaring and up-sampling steps in [START_REF] Pérez | Deep Learning Radar Object Detection and Classification for Urban Automotive Scenarios[END_REF] might result in loss of information in the Range-Doppler spectrum. Finally Ng et al. [START_REF] Ng | Range-Doppler Detection in Automotive Radar with Deep Learning[END_REF] propose a U-Netlike architecture to replace CFAR algorithm, thus detecting targets.

As mentioned in section II-B, although RAD tensors provide the most informative data, they are cumbersome to compute for radar processors. In the purpose of object detection, RA views are not adequate representations either, since they do not account for Doppler which is a crucial information. Besides, RA maps usually suffer from a poor angular resolution caused by a small number of antennas in the FMCW radar. Instead, we hypothesise that the RD spectrum contains enough information for both detection and classification tasks in automotive radar. Angular information might be computed for each target afterwards in a postprocessing step, either using standard techniques or with AI as done by Brodeski et al. in [START_REF] Brodeski | Deep Radar Detector[END_REF].

III. METHODOLOGY

In this section we present a lightweight Faster R-CNN architecture for object detection in Range-Doppler spectra. Given a RD map as input, we use a convolutional neural network to learn relevant features, as in Faster R-CNN. Following the features extraction, a region proposal network (RPN) is used to propose regions in the spectrum that contain potential targets. To generate region proposals, a small network is slid over the learned convolutional feature map. For each point in the feature map, the RPN learns whether an object is present in the input image at its corresponding location and estimates its size. This is done by placing a set of anchors on the input image for each location on the output feature map. These anchors indicate possible objects in various sizes and aspect ratios at this location. We invite the reader to refer to the Faster-RCNN paper [START_REF] Ren | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[END_REF] for more information about RPN and anchors. Next, the bounding box proposals from the RPN are used to pool features from the backbone feature map. These features are used to classify the proposals either as background or object and to predict a bounding box using two sibling fully connected layers. This second part is named Fast-RCNN. We depict this pipeline in Figure 2b. We show in Figure 3 two RD maps with radar signatures of some objects in the captured scene of RADDet dataset. Even though those RD maps seem complex, the information they contain remains of low complexity, contrary to camera images which are bigger and more diverse in terms of textures, orientations, geometry, lighting, etc. Although being more noisy, RD maps have fixed orientation and their objects exhibit more similar patterns and shapes.

To account for those differences, we modify Faster R-CNN to include a lighter backbone and a modified RPN. Our backbone is derived from the VGG architecture [START_REF] Simonyan | Very Deep Convolutional Networks for Large-Scale Image Recognition[END_REF] and contains 7 convolutional layers. Though residual networks are state-of-the-art architectures for features extraction, residual connections increase the complexity of the model and are difficult to implement in hardware. Figure 2a depicts this lightweight backbone architecture. To keep the processing pipeline as simple and efficient as possible, we decide to not resize the spectrum and to process it as it is, resulting in an input of size 256 × 64. The backbone is composed of two blocks with two 2D convolutions and one block with three 2D convolutions. Following each convolutional block, we apply a 2D max pooling operation to down-sample the size of the input. We down-sample the Doppler dimension only by a factor of two after the first block, to minimise loss of the Doppler information, which is useful for classification. Then, we obtain a feature map size 32 × 32 which was found to lead to the best performance. The number of channels for each block of convolutions are respectively set to 64, 128 The next step is to defined the anchors used by the RPN so as to capture the diversity of shape and size in the objects. In this work we use 3 scales and 3 aspect ratios for anchors generation, yielding to 9 anchors at each position in the feature map. The mean size of objects in RD maps is 8 × 8, we use this size as reference for anchors scale. For smaller and bigger objects we respectively use scale of 4 and 16 resulting in anchors scales of size 4, 8 and 16. Additionally, we set aspect ratios to 14 , 1 2 and 1 8 . Contrary to Faster R-CNN paper where aspect ratios are set to 1, 2 and 1 2 , we choose aspect ratios where denominators are multiple of 4 to account for the 1 4 ratio between input height and width. To reduce the computational complexity of the model, we don't consider all the combinations of scales and ratios. We only consider combinations containing scale 8 and ratio 1 4 resulting in less anchors generated per image (5 at each position). We find these settings provide the best performances.

Since the RD spectrum is not translation invariant (the velocity is a characteristic of the target), we decide to add this information to the feature vector used for classification and bounding box regression. This feature vector corresponds to the flattened region proposed by the RPN. We compute the velocity by extracting the position of the pixel with the highest intensity in proposed region of interest (ROI). Knowing the velocity resolution of the radar δ v and the position of the highest pixel in the ROI p, we compute the velocity using the following formula: v = δ v * p. We notice a slight improvement in the performances using the Doppler velocity as extra feature.

We optimise the model using the loss function described in [START_REF] Ren | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[END_REF]. To take into account the uncertainty of the annotations, we slightly modify the intersection over union (IoU) thresholds to assign an anchor and a detection to a groundtruth box. We set this threshold to 0.5 for the RPN (instead of 0.7 for Faster R-CNN) and to 0.3 for Fast R-CNN (instead of 0.5 for Faster R-CNN). As there is not a lot of objects in our RD data, we randomly sample 32 proposals (instead of 512 for Faster R-CNN) to compute the loss of the RPN and Fast R-CNN.

IV. EXPERIMENTS AND RESULTS

A. Datasets and competing method

We train our model on the two publicly available radar datasets CARRADA [START_REF] Ouaknine | CAR-RADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations[END_REF] and RADDet [START_REF] Zhang | RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users[END_REF]. For the CAR-RADA dataset we use the segmentation masks as reference to create our bounding boxes by drawing a box around masks. Regarding the RADDet dataset, we extract the RD maps by summing the values of the RAD tensors over the angles dimension. We use the same bounding boxes provided by the authors of the RADDet dataset by only taking coordinates along the range and the Doppler dimension. We use default train/val/test distribution of CARRADA dataset. For RAD-Det, we randomly split the train into training and validation set with a 9:1 ratio. For testing, we use the provided test set.

We compare our model DAROD, made of the lightweight backbone and the simplified Faster R-CNN architecture displayed in Figure 2, with the RADDet model [START_REF] Zhang | RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users[END_REF]. To the best of our knowledge it is the only published object detector designed for radar data. We modify the RADDet model to train it only with RD maps as input instead of RAD tensors. We also consider the variant termed RADDet RAD which corresponds to the original RADDet model (train on RAD tensors) evaluated only on the range and the Doppler dimensions, using the pretrained weights provided in [START_REF] Zhang | RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users[END_REF]. As a second baseline we consider the state of the art in computer vision, by selecting the Torchvision 1 Faster R-CNN implementation using the default hyper-parameters, namely a resizing of the input from 256 × 64 to 800 × 800 and a ResNet50+FPN backbone pretrained on ImageNet. FPN (Feature Pyramid Network) is a feature extractor that takes a single-scan image of an arbitrary size, and outputs proportionally sized feature maps at multiple levels thus allowing to detect object at different scales. In addition, we train the Torchvision Faster R-CNN without the pre-traning on ImageNet to evaluate the impact of this pre-training on the results.

B. Training setting and evaluation metrics

We use the Adam optimiser with the recommended parameters. A learning rate of 1 × 10 -4 is used for all our experiments. The batch size is set to 4 for CARRADA dataset and to 16 for RADDet dataset. Our model is trained over respectively 50 and 80 epochs for CARRADA and RADDet datasets. As Faster R-CNN object detector contains several hyper-parameters, we perform grid-search over some carefully chosen parameters to improve the performance of our model. We randomly use horizontal and vertical flipping as data augmentation strategies. We evaluate our model using the mean average precision (mAP), a well-known metric for evaluating object detectors. We consider mAP at IoU thresholds 0.3 and 0.5 to take into account the uncertainty on the annotations, which is generated semi-automatically for both datasets. In addition, we provide precision and recall at IoU thresholds 0.3 and 0.5. All the experiments are conducted using the Tensorflow2 deep learning framework along with an Nvidia RTX 2080Ti GPU.

C. Results

Table I shows the performance of our model on CAR-RADA and RADDet datasets 3 . In a nutshell, our DAROD model clearly outperforms the RADDet method on both datasets, while it remains competitive with Faster R-CNN. When pre-trained on ImageNet, Faster R-CNN leads to the best mAP in 3 cases with DAROD being second best, the positions being inverted in the last experiment (RADDet dataset and IoU at 0.3).

Generally, we observe that DAROD achieves good precision scores but medium recall. This suggests that our model is accurate when detecting targets (eg. correctly classifies them) but seems to struggle to detect all the targets, resulting in missed targets. We draw the same conclusion for the RADDet model which obtains decent precision scores but low recall, hence impacting mAP@0.3 and mAP@0.5. The Faster R-CNN model achieves sufficient precision scores and good recall, resulting in more false positives but less missed targets, which may be better for critical applications. For DAROD, we aimed to optimise mAP, which measures the global performance of object detector. We might be able to improve the recall by reducing the selectivity of our model during training and in the post-processing step, or by decreasing the penalty of classification errors.

Finally, we remark the pretraining of Faster R-CNN backbone on the ImageNet dataset helps to improve the detection performance. Particularly, it drastically improves the precision score but doesn't seem to impact the recall score.

A critical point in automotive radar is the computational load of the different models. We compute the FLOPS (floating point operations per second) of the different models and represent it as a function of the performance in Figure 4. Not surprisingly, radar based approaches are far more efficient than Faster R-CNN that uses up-sampling and deeper backbones. RADDet model is the model with the lowest number of FLOPS as it is inspired from the single stage detector YOLO [START_REF] Redmon | You Only Look Once: Unified, Real-Time Object Detection[END_REF]. The number of FLOPS required by DAROD is slightly bigger than RADDet, but stays reasonable to run on MCUs.

V. DISCUSSION

We show in section IV-C that our model achieves much better mAP at threshold 0.3 and 0.5 than RADDet model on CARRADA and RADDet datasets. We would like to emphasise the RADDet model was specifically designed to process RAD tensors instead of RD spectra. Therefore, it might be inefficient on RD spectra and the results might suffer from this difference. For this reason, we evaluate the RADDet model on the range and the Doppler dimensions only, using the pre-train model provided by the authors. Results are given in Table I as the RADDet RAD model.

At similar feature map resolution as DAROD, the Faster R-CNN model we compare with achieves good performance. However regarding the large number of parameters and FLOPS of this model, the gain in performance we observe doesn't improve by far the results. We conclude we don't need to use very deep convolutional neural networks to extract meaningful information from radar data.

Although camera images are very different from RD maps, pre-training the weights of Faster R-CNN leads to an improvement of 7 to 9 points in mAP, which outperforms DAROD in 3 of the 4 cases. Pre-training the backbone of DAROD might also lead to a significant increase of performance. But this is not trivial, since it requires a wellsuited dataset in terms of shape and complexity, this is thus left for future works.

We demonstrate that a simple and light backbone performs well for object detection and classification tasks contrary to deeper image based backbones. We also show our model outperforms similar radar based approach. However, our model doesn't take into account the temporal information of radar data which could be useful to build more accurate radar object detector. Research on this needs to be conducted.

Even though our model is the lightest in terms of number of parameters, the number of floating point operations it requires remain big to be embedded. Replacing the 2D convolutions by depth-wise separable convolutions or transforming our model into a single stage detector could be a solution to improve the efficiency of our backbone.

Finally, the approach we propose remains inspired from computer vision algorithms and might not be the most suitable and efficient approach for automotive applications. Working directly with the complex Range-Doppler spectrum to predict the position of objects in 3D (range, angle, velocity) instead of bounding boxes on RD spectra could address this problem.

We hope this work helps for the development of new deep learning approaches for object detection and/or classification using the RD spectra, in order to develop more efficient and high-performance object detectors for radar.

Fig. 1 .

 1 Fig. 1. Illustration of the radar signal processing pipeline. Orange boxes denote range and Doppler processing operations, blue boxes correspond to angle processing operations and the green box represents the post-processing.

Fig. 2 .

 2 Fig. 2. Model architecture (lightweight Faster R-CNN architecture)

Fig. 3 .

 3 Fig. 3. Two RD maps of RADDet dataset, along with the bounding boxes around objects and their zoom.

Fig. 4 .

 4 Fig. 4. Number of FLOPS vs. mAP@0.5 for DAROD, Faster R-CNN (pretrained or trained from scratch), RADDet and RADDet RAD.

TABLE I RESULTS

 I OF DIFFERENT MODELS ON CARRADA AND RADDET DATASETS. BEST RESULTS ARE SHOWN IN BOLD, SECOND BEST ARE UNDERLINED.

	Dataset	Model	mAP	IoU 0.3 Precision Recall	mAP	IoU 0.5 Precision Recall	# params (M) Inference time
		DAROD (ours)	70.68	76.73	52.52	55.83	68.34	46.03	3.4	25.31 ms
	CARRADA	Faster R-CNN (pretained)	71.08	51.70	72.97	61.56	47.86	67.21	41.3	37.19 ms
		Faster R-CNN (from scratch) 64.21	45.90	74.17	52.93	41.59	67.40	41.3	37.19 ms
		RADDet RD	48.59	61.31	42.56	18.57	36.73	25.50	7.8	74.03 ms
		DAROD (ours)	65.56	82.31	47.78	46.57	68.23	38.74	3.4	25.31 ms
	RADDet	Faster R-CNN (pretrained) Faster R-CNN (from scratch) 49.16 58.47	52.17 32.33	56.92 61.46	49.55 40.84	47.78 29.37	51.77 55.29	41.3 41.3	37.19 ms 37.19 ms
		RADDet RD	38.42	78.20	29.77	22.87	60.41	20.55	7.8	74.03 ms
		RADDet RAD [6]	38.32	68.80	26.83	17.13	46.55	16.99	8	75.2 ms

Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, France

CerCO, CNRS UMR5549, Toulouse

ISAE-SUPAERO, Université de Toulouse, 10 Avenue Edouard Belin, Toulouse 31400, France

NXP Semiconductors, Toulouse, France This work has been funded by the Institute for Artificial and Natural Intelligence Toulouse (ANITI) under grant agreement ANR-19-PI3A-0004.Computations performed to train our model have been carried out using the OSIRIM platform, administered by IRIT and supported by CNRS, Région Occitanie, the French Government and ERDF.

https://github.com/pytorch/vision

https://www.tensorflow.org/

We train all the models 10 times and we show the best results for each in TableI