Elodie Alinat
email: elodie.alinat@chimie-paristech.fr

Nathalie Delaunay
email: nathalie.delaunay@espci.fr

Xavier Archer
email: xavier.archer@interieur.gouv.fr

Jean-Maurice Mallet
email: jean-maurice.mallet@es.fr

Pierre Gareil
email: pierre.gareil@chimie-paristech.fr

Lsabm

A new method for the determination of the nitrogen content of nitrocellulose based on the molar ratio of nitrite-to-nitrate ions released after alkaline hydrolysis

Keywords: nitrocellulose, nitrogen content, alkaline hydrolysis, smokeless gunpowder Non-standard background electrolyte DPA: diphenyl amine DNT: dinitrotoluene

A new method was proposed to determine the nitrogen content of nitrocelluloses (NCs). It is based on the finding of a linear relationship between the nitrogen content and the molar ratio of nitrite-to-nitrate ions released after alkaline hydrolysis. Capillary electrophoresis was used to monitor the concentration of nitrite and nitrate ions. The influences of hydrolysis time and molar mass of NC on the molar ratio of nitrite-to-nitrate ions were investigated, and new insights into the understanding of the alkaline denitration mechanism of NCs, underlying this analytical strategy is provided. The method was then tested successfully with various explosive and non-explosive NC-containing samples such as various daily products and smokeless gunpowders. Inherently to its principle exploiting a concentration ratio, this method shows very good repeatability in the determination of nitrogen content in real samples with relative standard deviation (n=3) inferior to 1.5%, and also provides very significant advantages with respect to sample extraction, analysis time (1 h for alkaline hydrolysis, 3 min for electrophoretic separation), which was about 5 times shorter than for the classical Devarda's method, currently used in industry, and safety conditions (no need for preliminary drying NC samples, mild hydrolysis conditions with 1 M sodium hydroxide for 1 h at 60 °C).

Introduction

Cellulose nitrate, also more commonly called nitrocellulose (NC), is prepared by nitration of cellulose in nitric and sulfuric acid mixtures [START_REF] Saunders | A review of the synthesis chemistry and analysis of nitrocellulose[END_REF]. Cellulose is a natural linear polysaccharide composed of glucose units linked by β (1→4) glucosidic bonds. In each of the repeating glucose units, three reactive hydroxyl groups (one primary hydroxyl group on the C6 position and two secondary hydroxyl groups on the C2 and C3 positions) are able to undergo esterification reaction, which results in the replacement of the hydroxyl groups by nitro groups in an acidic medium. The degree of substitution (DS), defined as the average number of hydroxyl groups exchanged by nitrate groups per glucose unit, depends on the synthesis conditions. In current applications, it is most often expressed as the nitrogen content.

Theoretically, the highest achievable nitrogen content is close to 14.15% corresponding to fully nitrated NC, having a DS of 3. In practice, however, the maximum attainable nitrogen content is 13.8% [START_REF] Fernández De La Ossa | Analytical techniques in the study of highly-nitrated nitrocelluloses[END_REF]. Indeed, the synthesis of fully nitrated NC is expensive and dangerous, generating unstable compounds such as sulfuric cellulose esters [START_REF] Saunders | A review of the synthesis chemistry and analysis of nitrocellulose[END_REF]. The nitrogen content is one of the most important parameter determining NC applications, since it strongly affects its physical and chemical properties [START_REF] Fernández De La Ossa | Analytical techniques in the study of highly-nitrated nitrocelluloses[END_REF]. NC solubility decreases when the nitrogen content and the degree of polymerization (DP) increase. NCs having nitrogen content below 12.5% (DS close to 2) are widely used as raw material in daily products (lacquers, varnishes, paints, nail polish, ping pong ball, filter membranes, printing inks, and more recently liquid bandage). Highly-nitrated NCs having a nitrogen content higher than 12.5% are employed in the manufacturing of energetic materials such as propellants and dynamites. NC-based smokeless gunpowders are classified according to the number of energetic materials in their composition [START_REF] Lόpez-Lόpez | New protocol for the isolation of nitrocellulose from gunpowders: utility in their identification[END_REF][START_REF] Lόpez-Lόpez | Diphenylamine and derivatives as predictors of gunpowder age by means of HPLC and statistical models[END_REF]: i) single-base propellants, which mainly contain NC, ii) double-base propellants consisting of NC and nitroglycerin, and iii) triple-base propellants containing NC, nitroglycerin, and nitroguanidine. Most smokeless gunpowders are formulated with graphite and various other chemicals (such as dinitrotoluene) to monitor burning, reduce muzzle flash, and make them more water resistant. Due to NC deterioration over time (release of nitric acid), smokeless gunpowders also contain small amounts of stabilizers (diphenylamine, diphenyl ureas such as ethyl or methyl centralite) to neutralize acidic byproducts.

NC characterization remains challenging due to its high molar mass, complex structure, lack of solubility in common organic solvents, both molar mass and functional dispersity, and consideration of safety constraints. Acido-basic titration (Devarda's method) [START_REF]Taux d'azote nitrique des nitrocelluloses (méthode de Devarda)[END_REF], vibrational spectroscopy [START_REF] Moore | Comparative infrared and Raman spectroscopy of energetic polymers[END_REF][START_REF] Dolinak | Microscopic and spectroscopic features of gunpowders and its documentation in gunshot wounds in charred bodies[END_REF], mass spectrometry [START_REF] Hakansson | Low-mass ions observed in plasma desorption mass spectrometry of high explosives[END_REF][START_REF] Mahoney | Characterization of gunpowder samples using timeof-flight secondary ion mass spectrometry (TOF-SIMS)[END_REF][START_REF] Meier-Augenstein | N 2 : a potential pitfall for bulk 2 H isotope analysis of explosives and other nitrogen-rich compounds by continuous-flow isotope-ratio mass spectrometry[END_REF], gas chromatography [START_REF] Cropek | Development of a novel headspace sorptive extraction method to study the aging of volatile compounds in spent handgun cartridges[END_REF][START_REF] Katoh | Study on the spontaneous ignition mechanism of nitric esters (I)[END_REF][START_REF] Katoh | Study on the spontaneous ignition mechanism of nitric esters (III)[END_REF], reversed phase [START_REF] Combs | Methodology for determination of nitrogen content in nitrocellulose[END_REF][START_REF] Van Rooijen | Characterization of nitrocellulose by 2D-HPLC[END_REF], ion [START_REF] Cropek | Sonolysis of nitrocellulose fines[END_REF][START_REF] Macmillan | A reproducible method for the determination of nitrocellulose in soil[END_REF][START_REF] Lόpez-Lόpez | Determination of the nitrogen content of nitrocellulose from smokeless gunpowders and collodions by alkaline hydrolysis and ion chromatography[END_REF][START_REF] Freedman | Biotransformation of explosive-grade nitrocellulose under denitrifying and sulfidogenic conditions[END_REF], and size exclusion [START_REF] Deacon | A new, internationally accepted standard for testing nitrocellulose[END_REF][START_REF] Marx-Figini | Size exclusion chromatography (GPC) of cellulose nitrate using modified silicagel as stationary phase[END_REF] chromatography, and capillary electrophoresis [START_REF] Fernández De La Ossa | Determination of nitrocellulose by capillary electrophoresis with laser-induced fluorescence detection[END_REF][START_REF] Fernández De La Ossa | Discrimination of nonexplosive and explosive samples through nitrocellulose fingerprints obtained by capillary electrophoresis[END_REF][START_REF] Alinat | Determination of the nitrogen content of nitrocellulose by capillary electrophoresis after alkaline denitration[END_REF] have been used to characterize NC. Before implementing most of them, long and tedious sample treatment, especially involving alkaline denitration in the absence [START_REF] Lόpez-Lόpez | Determination of the nitrogen content of nitrocellulose from smokeless gunpowders and collodions by alkaline hydrolysis and ion chromatography[END_REF] or in the presence of hydrogen peroxide [START_REF]Taux d'azote nitrique des nitrocelluloses (méthode de Devarda)[END_REF], are needed. For most of them, dangerous NC drying before precise weighing is required. In addition to this, when NC is present in a formulated sample, it should be first extracted to remove potential interfering components or simply to isolate it before weighing. To this aim, a new protocol involving a multistep solvent extraction has just been developed by Lόpez-Lόpez et al. to isolate NC from smokeless gunpowders [START_REF] Lόpez-Lόpez | New protocol for the isolation of nitrocellulose from gunpowders: utility in their identification[END_REF]. In spite of the existence of all these methods, few of them are relevant for the determination of the nitrogen content of NCs employed in smokeless gunpowders, and there is still a strong demand from analysts to reduce overall analysis time, simplify sample treatment, and improve safety of the assay.

Following our previous work on the optimization by experimental design of alkaline denitration reaction for maximum denitration yield [START_REF] Alinat | Multivariate optimization of the denitration reaction for safer determination of nitrocellulose nitrogen submitted for publication[END_REF], the molar ratio of nitrite-to-nitrate ions released during this reaction was investigated in depth. CE was used to monitor the released nitrite and nitrate ions by a previously developed method [START_REF] Alinat | Determination of the nitrogen content of nitrocellulose by capillary electrophoresis after alkaline denitration[END_REF]. This study was conducted using nine NC standards of known nitrogen content (from 11.14% to 13.42%) and molar mass (from 20 000 to 312 000 g mol -1 ). The influence of reaction time was considered and data were analyzed to gain a deeper insight into the understanding of the alkaline denitration mechanism. This work opens a new route to discriminate NCs according to their nitrogen contents, based on the molar ratio of nitrite-to-nitrate ions found in the posthydrolysis solution. This strategy was successfully applied to as various NC-containing samples as ping pong ball, nail polish, NC membrane, liquid bandage, and smokeless gunpowder to determine their nitrogen content and demonstrate its suitability for real sample analysis, minimizing extraction steps and rendering NC drying useless, thus improving safety conditions.

Experimental section

Standards, real samples, and electrolytes

NC standards (Table 1) with a nitrogen content of 11.20 (M w = 35 000 g mol -1 and M w = 137 000 g mol -1 ), 12.00 (M w = 312 000 g mol -1 ), and 12.20 (M w = 29 000 g mol -1 ) were supplied by Dow Chemical Company (Dow Wolff Cellulosics, Bomlitz, Germany). NC samples with a nitrogen content of 11.14 (M w = 20 000 g mol -1 ), 12.09 (M w = 95 000 g mol -1 ), 12.55 (M w = 200 000 g mol -1 ), and 13.42 (M w = 69 000 g mol -1 and M w = 110 000 g mol -1 ) were given by the Central Laboratory of Police Prefecture (LCPP, Paris, France). NCs were stored with a damping agent (water or ethanol) content of approximately 30%. Additional NC standards with nitrogen contents of 11.7 and 12.9%, which were not commercially available to us, were prepared by precisely mixing the appropriate amounts of NC2 with NC6 in 50:50 (w/w) proportions, and NC6 with NC8 in 43:57 (w/w) proportions, respectively, Samples of singlebase smokeless gunpowders with and without graphite (mixtures of NC, diphenylamine (DPA), dibutylphtalate, dinitrotoluene (DNT), potassium sulphate, and calcium carbonate) were provided by LCPP. Ping pong balls, nail polish, and liquid bandage were purchased from a local store. Amersham Biosciences NC membranes (Hybond-C-Extra) were supplied by Dominique Dutscher (Brumath, France). Sodium nitrite (> 97%), sodium nitrate (> 99%), hexadimethrine bromide (HDMB) used as a coating agent, and sodium molybdate dihydrate (≥ 99.5%) used as internal reference were purchased from Sigma-Aldrich (Saint-Quentin-Fallavier, France). Concentrated volumetric solutions (ConvoL Normadose®) of 1 M sodium hydroxide used as base for NC hydrolysis were obtained from VWR (Fontenay-sous-Bois, France). Detailed protocols for the preparation of background electrolyte (BGE) and standard solutions were given in [START_REF] Alinat | Determination of the nitrogen content of nitrocellulose by capillary electrophoresis after alkaline denitration[END_REF].

Instrumentation

Separations were carried out with a Beckman Coulter® P/ACE MDQ capillary electrophoresis system (Beckman Coulter, Villepinte, France) equipped with a diode array detector. Data processing and acquisition were performed using 32 Karat® 8.0 software (Beckman Coulter).

All NC standards were dried with an oven (model UNB100) from Memmert (Schwaback, Germany) to remove damping agent. Alkaline denitration experiments were performed with a block heater (model SBH 130D) from Stuart (Staffordshire, UK) capable of receiving twelve 10-mL glass tubes.

Electrophoretic conditions

Nitrite and nitrate ions were determined by a CE method previously developed by our group for this aim [START_REF] Alinat | Determination of the nitrogen content of nitrocellulose by capillary electrophoresis after alkaline denitration[END_REF]. Briefly, separations were performed using Polymicro Technologies bare fused-silica capillaries supplied by Photonlines (Marly-Le-Roi, France) with dimensions of 60 cm (50 cm to the detector) × 50 µm i.d × 365 µm o.d.. Before first use, capillaries were conditioned by flushing with 1 M NaOH followed by 0.1 M NaOH, ultra-pure water, HDMB solution at 1 g L -1 , and finally BGE, each under 14.5 psi ( ~ 1 bar) for 10 min (15 capillary volumes). The BGE was 100 mM ionic strength sodium phosphate buffer at pH 7.7. Before each run, the capillary was rinsed with HDMB solution under 14.5 psi for 1 min (about 1.5 capillary volume) and BGE under 14.5 psi for 2 min (about 3 capillary volumes). Injections were performed hydrodynamically under 0.5 psi for 3 s (0.3% of capillary volume).

Separations were conducted under -30 kV (reversed polarity). Capillary cartridge temperature was maintained at 25°C. The detection wavelength was set at 210 nm with a reference at 310 nm and with bandwidths set at ± 10 nm and ± 40 nm, respectively. After the last analysis of the day, capillary was rinsed with water under 14.5 psi for 10 min (15 capillary volumes) and stored in the apparatus both ends in water. The figures of merit for this method (least-squares regression parameters, regression coefficient, linearity, range, LOD, LOQ) were given in [START_REF] Alinat | Determination of the nitrogen content of nitrocellulose by capillary electrophoresis after alkaline denitration[END_REF].

Alkaline hydrolysis of NC standards

Prior to hydrolysis, about 40 mg of wet NC were placed in weighed 10-mL glass tubes and dried in an oven at 100°C for 2 h to ensure constant weight. Then, to reach room temperature, tubes containing dry NC were placed for 25 min in a glass desiccator. After the drying step, the glass tubes containing dry NC were weighed again in order to obtain the exact amount of NC subjected to alkaline hydrolysis. Next, 5 mL of 1 M sodium hydroxide solution were introduced into each tube containing dry NC and the tubes were screwcapped. The stopped tubes were placed in the block heater and heated at 60°C for the determined reaction time within the studied range (5 min -1 h). The denitration reaction was stopped by transferring the tubes into an ice bath for 15 min to lower the temperature very quickly. After 25 min at room temperature, a defined volume of hydrolysate was transferred into water containing the appropriate amount of the internal standard (molybdate) solution at 1 g L -1 . The dilution factor (between 2 and 20) was adapted according to sample. Prior to electrophoresis analysis, all samples were filtered through a 0.20 µm cellulose acetate membrane. This protocol was repeated for each studied reaction time and NC standard samples. Finally, nitrite and nitrate ion concentrations in hydrolysates were determined by CE analysis.

Real samples

To isolate NC from a single-base smokeless gunpowder, about 0.2 g of smokeless gunpowder was subjected to a multistep solvent extraction procedure (comprising three extractions with methanol and two extractions with water) that was adapted from [START_REF] Lόpez-Lόpez | New protocol for the isolation of nitrocellulose from gunpowders: utility in their identification[END_REF] by suppressing the final extraction with methyl ethyl cetone and therefore the tedious evaporation of this last solvent. This modified procedure was already outlined in Figure 1 of ref. [START_REF] Alinat | Determination of the nitrogen content of nitrocellulose by capillary electrophoresis after alkaline denitration[END_REF]. Most of solid NC was recovered after the second extraction by water and introduced into three 10-mL glass tubes to which each 5 mL of 1 M sodium hydroxide solution were added. The stopped tubes were then placed in the block heater and heated at 60°C for 1 h. When the reaction time ended, the reaction was stopped and the tubes were allowed to return to room temperature as explained in § 2.4. 250 µL of the hydrolysates were introduced into a 10 mL volumetric flask containing 200 µL of the internal standard solution at 1 g L -1 and supplemented to the mark with ultra-pure water. About 60 mg of ping pong ball or NC membrane were introduced into 10-mL glass tubes and dissolved in 3 mL acetone under stirring. The mixture was placed in an oven at 60°C until complete solvent evaporation. Then, 5 mL of 1 M sodium hydroxide solution were introduced into the glass tubes containing the residue obtained by removing the organic solvent. The stopped tubes were placed in the block heater and heated at 60°C for 1 h. The end of the protocol was identical to that described above for NC in smokeless gunpowder.

For the hydrolysis of nail polish and liquid bandage, approximately 0.3 g of liquid matter were placed directly in 10-mL glass tubes. Next, 5 mL of 1 M sodium hydroxide solution were added into the glass tubes. The samples were then hydrolyzed at 60°C for 1 h with 1 M sodium hydroxide solution. The rest of the protocol was identical to the one described before for NC in smokeless gunpowder.

Quantitation of nitrite and nitrate ions

Quantitation of nitrite and nitrate ions was performed by the validated CE method from normalized corrected peak areas (i.e. analyte peak areas divided by analyte migration time and by internal standard peak area divided by its migration time) using external standard calibration. From these concentrations, molar ratios of nitrite-to-nitrate ions were directly calculated. Denitration yields were also calculated according to equation (1) in [START_REF] Alinat | Determination of the nitrogen content of nitrocellulose by capillary electrophoresis after alkaline denitration[END_REF].

Safety considerations

NC in dry state is a highly flammable solid that ignites easily and burns explosively when is concealed. This is why it should always be stored wet. NCs having nitrogen content higher than 12.5% are classified as explosives. NC can be ignited by flame, heat, shock, friction, sparks or static electricity. Its explosive propensity depends on the nitrogen content. The higher the nitrogen content, the easier it can explode. The oven door opening and closing system was modified to prevent confinement in case of explosion. Special attention was paid to keep NC samples wet when stored in a refrigerator and to weigh them after drying in the desiccator to avoid any shock. Security glasses and protection clothes were worn by the operator. In addition, weighing of dry matter was always performed under the assistance of a person equipped with a water vaporizer to prevent from any fire start. For NC standards, working under less than 30 mg dry matter considerably reduces hazard risks. Indeed, it is worthy of note that no incident has occurred over several hundreds of assays that have been realized to date under these experimental conditions.

Results and discussion

Influence of the nitrogen content of NC on the molar ratio of nitrite to nitrate ion

Several works have already shown that the alkaline hydrolysis of NCs mainly results in NC denitration [START_REF] Lόpez-Lόpez | Determination of the nitrogen content of nitrocellulose from smokeless gunpowders and collodions by alkaline hydrolysis and ion chromatography[END_REF][START_REF] Freedman | Biotransformation of explosive-grade nitrocellulose under denitrifying and sulfidogenic conditions[END_REF][START_REF] Alinat | Determination of the nitrogen content of nitrocellulose by capillary electrophoresis after alkaline denitration[END_REF][START_REF] Christodoulatos | Kinetics of the alkaline hydrolysis of nitrocellulose[END_REF][START_REF] Su | Destruction of nitrocellulose using alkaline hydrolysis[END_REF], with nitrite and nitrate ions being the very main compounds containing nitrogen [START_REF] Lόpez-Lόpez | Determination of the nitrogen content of nitrocellulose from smokeless gunpowders and collodions by alkaline hydrolysis and ion chromatography[END_REF][START_REF] Alinat | Determination of the nitrogen content of nitrocellulose by capillary electrophoresis after alkaline denitration[END_REF][START_REF] Christodoulatos | Kinetics of the alkaline hydrolysis of nitrocellulose[END_REF][START_REF] Su | Destruction of nitrocellulose using alkaline hydrolysis[END_REF]. The release of these anions has been so far explained by competitive reactional mechanisms [START_REF] Edge | Mechanisms of deterioration in cellulose nitrate base archival cinematographic film[END_REF]. Although this mechanism does not allow to specify a value for this ratio, a nitrite to nitrate molar ratio close to 3 has been set forth, regardless of the nitrogen content of the NC [START_REF] Christodoulatos | Kinetics of the alkaline hydrolysis of nitrocellulose[END_REF], and of hydrolysis time, suggesting a single step process [START_REF] Su | Destruction of nitrocellulose using alkaline hydrolysis[END_REF]. Unexpectedly, however, from our preceding work on the optimization of the reaction conditions for maximizing the denitration yield of explosive and non-explosive NCs [START_REF] Alinat | Multivariate optimization of the denitration reaction for safer determination of nitrocellulose nitrogen submitted for publication[END_REF], some correlation appeared to us between NC nitrogen content and the molar ratio of nitrite and nitrate, for all hydrolysis conditions tested in the experimental design.

Furthermore, these ratios seem to be almost independent of sodium hydroxide concentration (within the range 1-5 M), temperature (within the range 60 -90°C), and reaction time (within the range 1 -4 h) used for alkaline hydrolysis.

Following this study, the influence of nitrogen content on the molar ratio of nitrite-to-nitrate ions released during the alkaline hydrolysis of the nine NC standards was investigated in depth, for shorter reaction times than studied before (0 -1 h), in 1 M sodium hydroxide at 60°C. These latter conditions were chosen as they corresponded to those leading to maximum denitration yield for highly-nitrated NCs (except for time) and as they were more user-friendly. Figure 1 shows the obtained variations of molar ratio of nitrite-to-nitrate ions as a function of time for 6 NC standards differing in nitrogen content and/or molar mass.

Over the first hour of reaction, a decrease in this ratio occurs likely towards a limiting value, but a clear differentiation of the NCs according to their nitrogen content still remain apparent, especially between the explosive and the non-explosive ones (except for NC7, the behavior of which will be discussed later). Interestingly, some of the curves obtained exhibit parallel evolutions (NC2, NC4, NC7), while some others are crossing (NC8 / NC9 or NC6 / NC7), which could be due to both the closer values of nitrogen contents and the higher standard deviations for the measurements performed for reaction times lower than 30 min.

Nevertheless, an improvement in repeatability was not deemed necessary, as the tendency of the curves is apparent enough. Furthermore, the molar mass of the NCs seems to hardly have an impact on the nitrite-to-nitrate molar ratios obtained after a one-hour hydrolysis, as exemplified by standards NC8 and NC9, having the same nitrogen content, but different molar masses (69 000 and 110 000, respectively). To provide a deeper insight into the observed phenomena, the denitration yields obtained for these six NC standards over the first hour of reaction were represented in Figure 2. Over this time period, denitration yields remained basically inferior to 50%. For reaction time lower than 20 min the obtained denitration yields were even less than 20%, leading to concentrations of nitrite and nitrate ions close to the limits of quantitation with the used CE method. These low values help to explain the high standard deviations obtained for the molar ratio of nitrite-to-nitrate ions at these times in Figure 1. For these reasons, more attention was paid to data obtained after 1 h-hydrolysis in the following of this study. It is worthy of note that for non-explosive NCs, denitration yields increased in the reversed order of their molar masses, while for explosive NCs, lower denitration yields were obtained on the whole, displaying much less influence of their molar masses. Figure 3 shows the plot of the molar ratio of nitrite-to-nitrate ions versus NC nitrogen content for the nine NC standards after one hour's hydrolysis by 1 M sodium hydroxide at 60°C. A strong linear correlation exists between the molar ratio of nitrite-to-nitrate ions and the nitrogen content. It is worthy of note that the experimental point for NC7 is below the straight line, suggesting that the obtained nitrogen content for this sample might be lower than that given by the manufacturer (12.55%), and consequently, might behave as a non-explosive NC. This result is consistent with the observed time variation of molar ratio of nitrite-to-nitrate ions for this sample, which made it closer to the behavior of a non-explosive NC, and also confirmed the abnormal behavior noticed in our earlier work [START_REF] Alinat | Multivariate optimization of the denitration reaction for safer determination of nitrocellulose nitrogen submitted for publication[END_REF]. Therefore, this standard NC was not considered in the remainder of the study. Least-squares linear regression performed on the molar ratio of nitrite-to-nitrate ions obtained for the remaining eight commercial NCs resulted in a regression coefficient equal to 0.95 with a residual standard deviation of 0.23. As the nitrogen contents of the commercially available NC standards were not evenly distributed over the whole nitrogen content range (11.2 -13.5%), two additional NC standards were carefully prepared at 11.7 and 12.9% by mixing the appropriate amounts of commercial NC standards. As shown in Figure 3, the addition of these new NC standards does not significantly alter either the obtained regression coefficient (equal to 0.94) or the residual standard deviation (0.23). The nitrogen content of NC7 was then recalculated using the linear regression obtained from the ten NC standards altogether, and a value of 12.1% was obtained. This value, lower than 12.5%, corresponds to the one for a non-explosive NC, consistently with the behavior observed in Figure 1 for this NC.

Highlight on the alkaline denitration reaction

The results obtained in this work (see especially Figure 1) demonstrate that the molar ratio of nitrite-to-nitrate ions could be very different from 3, as anticipated from previous literature [START_REF] Christodoulatos | Kinetics of the alkaline hydrolysis of nitrocellulose[END_REF][START_REF] Edge | Mechanisms of deterioration in cellulose nitrate base archival cinematographic film[END_REF], and varies in the time course of the denitration reaction. This provides a deeper insight into the understanding of the kinetics of the competing denitration reactions.

NC is a statistical assembly of nitrated glucose units linked in β (1->4). It is thus composed by the statistical/ random association of non-nitrated glucose unit, mono-nitrated (3 cases), dinitrated units (3 cases, one of them is 2,3-dinitro), and trinitrated units (see Figure 4, insert).

Accordingly, nitration ratio is practically expressed as the percentile nitrogen content, which gives an evaluation of the mean value of substitution (in comparison with theoretical percents: mono: 6.8%; di: 11.1%; tri: 14.1%). Clearly, a "dinitrated" (11.1% nitrogen) NC contains also mono and trinitrated units. In these glucopyranose units, two different environments for nitrate groups, viz isolated nitrates (close to OH or OCH) and vicinal nitrates, can exist.

Our working hypothesis is that the nitrate groups in a vicinal nitrate tandem are hydrolyzed faster and give a higher molar nitrite-to-nitrate ratio (a combined ratio around 6 for the two nitrates). By contrast, the isolated nitrate groups are hydrolyzed slower and give a lower ratio, around 1.5. This hypothesis is consistent with the final nitrite-to-nitrate ratio, linearly dependent on nitrogen content (i.e. vicinal nitrate content). It is also consistent with the kinetics of the observed nitrite-to-nitrate ratio: (i) high and almost constant for trinitrated NC (as all glucopyranose units contained vicinal nitrates), and (ii) decreasing for less nitrated compounds, due to random mixture of glucopyranoses. The trinitrated units are present even in NCs having a nitrogen content of 11%, and they account for the high initial ratio.

Once hydrolyzed, the ratio falls to the one corresponding to isolated nitrates. As NCs, especially highly nitrated ones, are insoluble in aqueous NaOH, a delay time of about 5 min is needed for the hydration process, before the denitration starts.

The proposed mechanism of the two pathways (a and b) of the alkaline denitration is depicted on Figure 4 for a dinitrate unit. In the first one (a), the hydroxide ion attacks the positive nitrogen, giving back the alcohol and nitrate ion; this reaction is probably not perturbed by carbohydrate modifications far from the nitrogen center. In the second one (b), the hydroxide ion attacks the proton close to the nitrate, leading to a ketone and a nitrite ion: the alcohol is oxidized and the nitrogen reduced. This mechanism is well known for chromate, permanganate oxidation of alcohols, in Swern oxidation [START_REF] Omura | Oxidation of alcohols by "activated" dimethyl sulfoxide. A preparative steric and mechanistic study[END_REF]. Pathways (b) will be favored (as compared to pathway (a)) by the presence of electro-attracting groups enhancing the acidity of this proton, such as a nitrate or a keto group, so b2 and b3 steps are expected to be faster than b1. This mechanism explains the fast and predominant pathway (b) in vicinal nitrated blocks, which is consistent with the high initial nitrite-to-nitrate ratio, and the high initial denitration rate.

Applications to real samples

The correlation between the molar ratio of nitrite-to-nitrate ions and the nitrogen content of NC standards (Figure 3) prompted us to consider this approach for the determination of nitrogen content of NCs used in various real formulated samples, such as a ping pong ball, a NC membrane, a liquid bandage, a nail polish, and smokeless gunpowders with or without graphite. Sample treatments were detailed in § 2.5. Ping pong ball and membrane were merely solubilized and evaporated, prior to be submitted to the alkaline hydrolysis. Liquid samples (nail polish and liquid bandage) can be directly submitted to the alkaline hydrolysis without any pretreatment. Single-base smokeless gunpowders were treated according to a simplified and safer procedure that we experienced in a previous work [START_REF] Alinat | Determination of the nitrogen content of nitrocellulose by capillary electrophoresis after alkaline denitration[END_REF]. Given the approximate NC content in smokeless gunpowders, the initial sample amount to be treated was evaluated so as to realize three alkaline hydrolyses on three independent extractions. This amount, however, could eventually be strongly reduced, insofar as the final sample of nitrite and nitrate ions submitted to CE was diluted forty times in the present protocol. The conditions retained for the alkaline hydrolysis (1 M sodium hydroxide at 60 °C for 1 h) were less stringent than those leading to maximum denitration yields for explosive and nonexplosive NCs, as previously determined from a multivariate study of the denitration reaction [START_REF] Alinat | Multivariate optimization of the denitration reaction for safer determination of nitrocellulose nitrogen submitted for publication[END_REF]. The reaction time was fixed at 1 h as lower RSDs were obtained for the nitriteto nitrate-ratios with standard NCs than those obtained with even shorter times (Figure 1). A typical electrophoregram of an alkaline hydrolysate of NC contained in nail polish is presented in Figure 5, demonstrating there were no interfering peaks within the migration time of nitrite, nitrate, and molybdate ions (internal standard). Finally, nitrogen contents in real samples were determined by interpolating the nitrite-to-nitrate ratio of the real sample in the calibration curve built for NC standards, using the same experimental conditions. The results obtained for the nitrogen contents of the six real samples by using Figure 3 as a calibration curve are given in Table 2. Very good agreements with the data provided by the manufacturers, along with very narrow RSDs for most of them, were shown, demonstrating that the method is precise and accurate, in spite of a rather modest regression coefficient for the calibration line. It is worthy of note that this approach does not require (i) to quantitatively extract NC from a formulated sample (ii) to obtain a pure extract (as long as co-extracted compounds do not release nitrite or nitrate ions under alkaline conditions), (iii) to know the exact mass of dried NC involved in the assay, and (iv) to know the yield of the alkaline denitration reaction. At this point it should be emphasized that the protocol for drying NC standards given in § 2.4 is only intended for the experiments aiming at determining denitration yields, which is not required for the final analytical protocol.

Furthermore, as only a concentration ratio is used for the nitrogen content determination, a number of experimental error sources were suppressed. These factors are likely at the origin of the high repeatabilities experienced. Finally, as far as safety conditions are concerned, the main advantages of this method over those previously in use [START_REF]Taux d'azote nitrique des nitrocelluloses (méthode de Devarda)[END_REF][START_REF] Lόpez-Lόpez | Determination of the nitrogen content of nitrocellulose from smokeless gunpowders and collodions by alkaline hydrolysis and ion chromatography[END_REF][START_REF] Deacon | A new, internationally accepted standard for testing nitrocellulose[END_REF] are the absence of need for drying NC-containing samples, the use of mild hydrolysis conditions (especially temperature, 60 °C, and sodium hydroxide concentration, 1 M), the small size of the assay and the simplified extraction protocols.

Conclusions

The finding of a linear relation between the nitrogen content of NCs and the molar ratio of nitrite-to-nitrate ions released after alkaline hydrolysis, on one hand, and the kinetic study of the denitration reaction and interpretation of the involved mechanism, on the other hand, prompted us to develop a new analytical methodology for the determination of the nitrogen content of NCs in smokeless gunpowders and various daily products. This method opens great perspectives as it provides significant improvements over those currently in use in industry, such as Devarda's, with respect to sample extraction, analysis time and most importantly safety conditions, while retaining similar high precision and accuracy.

Figure captions

Figure 1. Representation of the molar concentration ratio of nitrite-to-nitrate ions as a function of the reaction time for hydrolysis at 60°C with 1 M sodium hydroxide. Error bars represent ± one standard deviation (n = 3). Nitrocellulose identification: see Table 1. (n = 3). NC identification: see Table 1.

Figure 2 .

 2 Figure 2. Representation of the denitration yields as a function of the reaction time for hydrolysis at 60°C with 1 M sodium hydroxide. Error bars represent ± one standard deviation

Figure 3 .

 3 Figure 3. Plot of the molar concentration ratio of nitrite to nitrate ions versus the nitrogen content of nitrocellulose (NC) standards for hydrolysis at 60°C for 1 h with 1 M sodium hydroxide. NC identification: blue diamonds and green triangle: commercial NC standards (see Table 1); orange circle: additional NC standards prepared by mixing of two commercial NC standards. Error bars (sometimes hidden by the symbols) represent ± one standard deviation (n = 3). Dashed line: least-squares regression correlation for all commercial NC standards but NC7 (green triangle) (eight points). Full line: least-squares regression correlation for the eight preceding commercial NC standards + the two additional standards prepared by mixing (orange circles) (ten points).

Figure 4 .

 4 Figure 4. Denitration mechanisms: A) pathways for vicinal nitrate alkaline "hydrolysis" B) the three environments for a nitrate group and kinetics comparison. Insert: glucose units present in NCs, with vicinal nitrate glucopyranoses represented in blue.

Figure 5 .

 5 Figure 5. Electropherograms of an alkaline hydrolysate of NC contained in nail polish spiked with MoO 4 2-at 20 mg mL -1 and diluted 40 times with water. Hydrolysis conditions: 1 M NaOH for 1 h at 60°C. CE conditions: bare-fused silica capillary, 50 µm ID × 60 cm (detection at 50 cm); electroosmotic flow reversal with 1 g L -1 HDMB; BGE: 100 mM ionic strength sodium phosphate, pH 7.7; temperature: 25°C; applied voltage: -30 kV; hydrodynamic injection, 0.5 psi, 3 s; detection: UV at 210 nm. Identification: nitrite (1), nitrate (2), molybdate (3), unidentified (4, 5).

Figure 1 1

 1 Figure 1

Table 2 :

 2 Nitrogen contents of NCs used in various formulated products obtained by the new method based on the molar ratio of nitrite to nitrate ions (this work), and as given by the manufacturer. Conditions for the alkaline hydrolysis: 1 M sodium hydroxide for 1 h at 60 °C.

	Sample	Nitrogen content (%) This work	Manufacturer data
			RSD (%) (n = 3)
	Ping pong ball	11.0 ± 0.04	0.4	not available
	NC membrane	11.4 ± 0.03	0.2	< 12.5
	Nail polish	11.8 ± 0.01	0.1	10.9 -12.2
	Liquid bandage	11.8 ± 0.01	0.1	11.7 -12.2
	Smokeless gunpowder without graphite	13.2 ± 0.09	1.4	13.2 ± 0.05
	Smokeless gunpowder with graphite	13.3 ± 0.04	0.3	13.2 ± 0.05

Acknowledgements

The authors would like to acknowledge the Laboratoire Central of Prefecture de Police (Paris, France) for funding E. Alinat and X. Archer, and part of this work, Chimie ParisTech (Paris, France) for funding P. Gareil and part of this work, and CNRS (Paris, France) for funding N. Delaunay and J.-M. Mallet and part of this work.


Marc Sango

Olimpia Hoinaru

Christophe Gransart

Laurence Duchien

A Temporal QoS Ontology for ERTMS/ETCS

Keywords: System Requirement Specification, ERTMS/ETCS, Temporal Ontologies, Domain Ontologies

come

I. INTRODUCTION

T HE System Requirement Specification (SRS) documents are generally written in natural language. From these documents, ontologies offer a means for representing, sharing and querying information in many complex domains, particularly in critical systems as proposed in automotive [START_REF] Feld | The automotive ontology: Managing knowledge inside the vehicle and sharing it between cars[END_REF], avionic [START_REF] Farfeleder | Dodt: Increasing requirements formalism using domain ontologies for improved embedded systems development[END_REF] and biomedical domains [START_REF] Obo | The open biological and biomedical ontologies[END_REF]. Similar works, such as [START_REF] Verstichel | Efficient data integration in the railway domain through an ontology-based methodology[END_REF], [START_REF] Bonifacio | Improvement of processes and methods in testing activities for safety-critical embedded systems[END_REF] and [START_REF] Hoinaru | An ontology for the ERTMS/ETCS[END_REF] are proposed in the railway domains with the aim of formalizing the SRS documents of the new European Rail Traffic Management System ERTMS including automatic train control and signaling system ETCS [START_REF]ERTMS/ETCS[END_REF]. However, these generic ontologies provide minimal support for modeling and querying temporal information omnipresent in these SRS documents.

Marc Sango is Ph.D. candidate in Computer Science from the University of Lille 1 and IFSTTAR, France. He can be reached at marc.sango@ifsttar.fr.

Olimpia Hoinaru is engineer at IFSTTAR, France. She can be reached at olimpia.hoinaru@ifsttar.fr.

Christophe Gransart is senior researcher at IFSTTAR, France. He can be reached at christophe.gransart@ifsttar.fr.

Laurence Duchien is Professor of Computer Science at the University of Lille 1, France. She can be reached at Laurence.Duchien@univ-lille1.fr.

To support the modeling of temporal information, one of the challenges is to enable representation of dynamic features evolving in time within a generic ontology with a minimal redesign of it. The separation of temporal information from other information can help to predict system runtime operation and to properly design and implement them [START_REF] Sango | Safety component-based approach and its application to ERTMS/ETCS on-board train control system[END_REF]. In addition, it is helpful to provide a reasoning and querying technique for inferring temporal information represented in the ontology in order to detect potential temporal inconsistencies [START_REF] Bresina | Explanations and recommendations for temporal inconsistencies[END_REF]. Indeed, a user operation, such as adding a new constraint on existing planning constraints, can cause temporal inconsistencies, which can lead to system failures.

To address this challenge, we propose a lightweight 3-layer temporal Quality of Service (QoS) ontology for representing, reasoning and querying over temporal and non-temporal information in a complex domain ontology. Representing QoS entities in separated layers can clarify the distinction between the non QoS entities and the QoS entities in an ontology. The upper generic layer of the proposed ontology provides an intuitive knowledge of domain components. Our temporal QoS ontology has been designed to be integrated in an existing ERTMS ontology [START_REF] Hoinaru | An ontology for the ERTMS/ETCS[END_REF] with a minimal redesign of it. The separation of intermediate QoS layer from the lower QoS layer allows us to focus on specific QoS Characteristics, temporal or integrity. In this paper we focus on temporal information that help us to predict system runtime operation. To evaluate our approach, the proposed domain ontology is applied for handover operation, i.e., the handing over of train control between two Radio Block Centers (RBCs) at the RBC-RBC borders. Then, using reasoning rules, we infer implicit knowledge of temporal information in this domain-specific ontology.

The remainder of this paper is organized as follows. In Section II, the ontology background is introduced. In Section III, the temporal QoS ontology developed to represent and query temporal and non-temporal information in ERTMS/ETCS is presented. Then, in Section IV, the proposed domain ontology is instantiated and evaluated for the domain-specific RBC-RBC handover procedure. Finally, we discuss our approach in Section V before concluding and drawing future directions of our research in Section VI.

II. BACKGROUND ON ONTOLOGY

Ontologies are widely used to represent knowledge, by describing data in a formal and explicit manner and then play a key role in the evolution of the semantic particularly the Semantic Web initiative [START_REF] Mcguinness | The semantic web activity[END_REF]. The Ontology Web Language (OWL) [START_REF] W3c | Owl web ontology language overview -w3c recommendation[END_REF] is the W3C recommendation for creating and sharing ontologies in the Web. Its theoretical background is based on the Description Logic (DL) knowledge Marc Sango, Olimpia Hoinaru, Christophe Gransart, Laurence Duchien A Temporal QoS Ontology for ERTMS/ETCS representation formalism [START_REF] Baader | The Description Logic Handbook: Theory, Implementation, and Applications[END_REF]. OWL is a richer vocabulary description language for representing properties and classes, such as relations between classes (e.g. disjointness), cardinality (e.g. exactly one), equality, richer typing of properties, characteristics of properties (e.g. symmetry) and enumerated classes [START_REF] Antoniou | A Semantic Web Primer, 2Nd Edition[END_REF]. In addition, the formal semantics of the OWL language enable the application of relation reasoning techniques performed by rule engines like the Jess Rule Engine [START_REF] Jess | [END_REF] or by reasoners such as KAON2 [START_REF][END_REF] or Pellet [START_REF] Sirin | Pellet: A practical owl-dl reasoner[END_REF]. Then, tools are introduced to solve problems related to knowledge representation and reasoning. For example, Protégé 1 is an open source ontology editor which allows the creation of OWL DL based ontologies and supports reasoners Pellet to interpret the rules like the Semantic Web Rule Language (SWRL) [START_REF] Swrl | Swrl: A semantic web rule language combining owl and ruleml -w3c member submission 21 may 2004[END_REF]. Furthermore, It also allows the ability for extracting and querying information from the asserted data in ontology. For example, SWRL-based query language called SQWRL provides a simple yet expressive language for performing queries on OWL ontologies [START_REF] O'connor | Sqwrl: A query language for owl[END_REF].

The OWL-Time temporal ontology [START_REF] W3c | Time ontology in owl -w3c working draft 27 september[END_REF] describes the temporal content of OWL-based web Service (OWL-S [START_REF] Owl-S | Daml services[END_REF]) and the temporal properties based on Allen's interval-based relations [START_REF] Allen | Maintaining knowledge about temporal intervals[END_REF]. In this ontology, there are two subclasses of temporal entities: interval and instant, where an instant is an interval with zero length. However, OWL-Time is not an ontology language, but a time ontology based on OWL [START_REF] Kim | Temporal ontology language for representing and reasoning interval-based temporal knowledge[END_REF]. There is a large amount of research for temporal ontology languages based on OWL [START_REF] Welty | A reusable ontology for fluents in owl[END_REF], [START_REF] Krieger | Where temporal description logics fail: Representing temporally-changing relationships[END_REF], [START_REF] Kim | Temporal ontology language for representing and reasoning interval-based temporal knowledge[END_REF], [START_REF] Milea | towl: A temporal web ontology language[END_REF]. However, representing temporal information requires support for concrete domains [START_REF] Batsakis | Temporal reasoning for supporting temporal queries in OWL 2.0[END_REF]. The concrete domain approach [START_REF] Lutz | Description logics with concrete domains-a survey[END_REF] requires introducing additional datatypes and operators to OWL, while our work relies on existing ERTMS/ETCS ontology with minimal redesign of it. Our approach is comparable to a novel approach for connecting temporal-ontologies with blood flow simulations [START_REF] Weichert | A novel approach for connecting temporal-ontologies with blood flow simulations[END_REF]. In this approach, an existing Basic Formal Ontology (BFO [START_REF] Grenon | Biodynamic ontology: Applying bfo in the biomedical domain[END_REF]) and Relations in Biomedical Ontologies(RBO [START_REF] Smith | Relations in biomedical ontologies[END_REF]) are consolidated with the SWRL Temporal Ontology (SWRLTO [START_REF] Oconnor | A method for representing and querying temporal information in owl[END_REF]) to query temporal information in the Temporal BFO result. While the technique is the same, the problem domain we explore is really different. In our approach, we use the generic ERTMS ontology [START_REF] Hoinaru | An ontology for the ERTMS/ETCS[END_REF] and the temporal QoS Ontology including the W3C N-ary relations [START_REF] W3c | Defining n-ary relations on the semantic web -w3c working group note 12 april 2006[END_REF] to develop a temporal QoS ontology for ERTMS/ETCS.

III. TEMPORAL QOS ONTOLOGY FOR ERTMS/ETCS

In this section, the temporal QoS ontology developed to represent and query temporal and non-temporal information in ERTMS/ETCS is introduced.

A. ERTMS/ETCS Generic Ontology

Our temporal QoS ontology is integrated on existing generic non-temporal ontology for ERTMS [START_REF] Hoinaru | Ontology for complex railway systems application to ERTMS/ETCS system[END_REF], [START_REF] Hoinaru | An ontology for the ERTMS/ETCS[END_REF]. The aim of this generic ontology is the formalization of ERTMS/ETCS SRS documents [START_REF]ERTMS/ETCS[END_REF] in order to obtain a component structure that can be reusable in a development process. Since the first focus is a formalization feasibility, this generic ontology presents only a representation view of functional requirements of the SRS. The functional requirements are related to (i) trackside and onboard train sub-systems of ETCS including, among others RBC2 and odometry, (ii) their interfaces including, among others, train, driver and interlocking 3 , (iii) the five specified application levels to express the possible operating relationships between trackside and onboard train sub-systems, (iv) procedures including, among others, start of mission or RBC-RBC handover procedure described with state transition system and (v) communication language which includes radio or balise messages built by packet, telegram and variables. This redesigned generic ERTMS ontology currently contains 112 classes and subclasses, 193 instances, and 104 properties including objects, datatypes and annotation properties [START_REF] Hoinaru | An ontology for the ERTMS/ETCS[END_REF]. Some of these properties are shown in the right-hand part of Fig. 1. Compared to the available SRS documents, the current coverage of the available SRS by the ERTMS ontology entities is obviously reduced. Nonetheless, it represents the general high-level functionalities that can be specialized in order to make ontological results really usable. The shortcoming of this generic ontology is the lack of non-functional Quality of Service (QoS) characterization, such as temporal QoS that specifies temporal quality criteria that can be used as a prediction of system runtime operation. As a consequence, we extend the ERTMS class with the QoS Class as illustrated in Fig. 1. In addition to has subclass and has individual properties that represent the relation among classes and individuals (i.e., instances of class), hasQuality and its inverse isQualityOf are added to represent the QoS properties. In the following, we detail the integration of this QoS Ontology.

B. QoS Ontology For ERTMS/ETCS

The QoS ontology developed for the ERTMS ontology is based on the generic QoS Framework as recommend by the ITU-T Recommendation X.641 [START_REF]Information Technology -Quality of Service Frameworks[END_REF] and its derivation for the next transport generation QoS ontology [START_REF] Exposito | Methodology, models and paradigms for a next generation transport layer design[END_REF]. In this recommendation, the term "service" in the expression "Quality of Service" is to be understood as a very general term, such as processing, storing, transmitting and delivering, in order to permit the widest possible application of the QoS Framework. Fig. 2 illustrates the main concepts of this QoS Framework. These concepts include service user, service provider, QoS requirements, QoS characteristics, QoS parameters, QoS management functions.

A service provider is an entity (e.g., a component with output ports) responsible to deliver a service to the service user (e.g., a component with input ports). QoS requirements are a part of user requirement specification, i.e., conditions or constraints under which the solution of functional requirement must operate. It is defined as high-level constraints in order to manage one or several QoS characteristics. QoS characteristics are defined as quantifiable aspects of service provider, such as temporal transit delay or reliability aspect. A QoS characteristic is described by QoS parameters. QoS parameters are defined as vectors of scalar values, such as the range between a maximum or minimum level of a transit delay characteristic. QoS management functions, QoSMgFunctions, refer to the activities for maintaining the quality parameters in the required range if the quality change. For example, if a quality of a component changes then a new instance of the management functions in which the component participates is initialized with the new quality values. In this way, there are the fundamental temporal relations which could hold among instantaneous objects (i.e., component instances) during a specific time instant or among interval of function instances themselves. These temporal relations can be analyzed with the lower-level techniques, such as a real-time scheduling theory [START_REF] Singhoff | Investigating the usability of real-time scheduling theory with the cheddar project[END_REF]. In addition to lower level techniques, in this paper, we claim that the temporal knowledge can be directly handled in domain ontologies in order to facilitate much higher-level analysis and to make better sense of complex temporal patterns for software developers. As a consequence, in the following, we will focus on the temporal characteristics of the QoS ERTMS/ETCS ontology.

C. Temporal QoS Ontology For ERTMS/ETCS

For the purpose of adding a valid temporal QoS dimension to above-mentioned ontology, we use the N-ary relations approach [START_REF] W3c | Defining n-ary relations on the semantic web -w3c working group note 12 april 2006[END_REF] in addition to the classical OWL ontology binary relations between two elements (e.g., has subclass). In fact, temporal relations are properties of objects that change in time and involve also a temporal value in addition to the objects. They are ternary relations that cannot be expressed directly in binary relations. The N-ary relations approach suggests representing an N-ary relation as two properties each related to a new object, which in turn is related with the temporal interval that this relation holds. For example, Fig. 3 illustrates the representation of two temporal properties using the N-ary approach: the requires property which states that "Component C1 requires Component C2 during TimeInterval" and the is provided to property which states that "Component C2 is provided to Component C1 during TimeInterval". These two properties are related to a process P object, which in turn is related to the TimeInterval through a temporal relation during.

Fig. 4 shows our 3-layer temporal QoS ontology for ERTMS. The upper layer shows the generic ERTMS ontology that represents the reusable components of ERTMS. The intermediate layer describes the generic QoS characteristics of these components. The lower layer represents the specific temporal QoS characteristics layer. Representing temporal entities in separated layers can clarify the distinction between the non temporal entities and the temporal entities in an ontology. In the generic ontology, the most significant component that can extend over time is the Procedures class. In the ERTMS/ETCS SRS documents, it is described by flowchart [START_REF]ERTMS/ETCS[END_REF], which is represented in our ontology by states, conditions and transitions. This class can be specialized in subclasses such as the handover or start of mission procedure flowchart.

Following the N-ary relations approach, a sub-class ValidTime is added in temporal QoS layer and the property hasValidTime links the Procedures class to ValidTime class, as shown in the ontology Fig. 4. The ValidTime sub-class is a subclass of QoS Parameters, which, as mentioned in Section III-B, is a vector of scalar values for QoS Characteristics. The Interval and Instant classes are introduced as subclasses of a superclass Temporal QoS characteristics. By using Allen's temporal interval relations [START_REF] Allen | Maintaining knowledge about temporal intervals[END_REF], the temporal representation was enhanced with qualitative temporal relationships between two time intervals characterized by the starting and ending I shows the overview of 13 Allen's relations, where t i = (t s i , t e i ) and t j = (t s j , t e j ) are two intervals. The intervals are characterized by the starting points t s i , t s j and the end points t e i , t e j . The end point relations t s i < t e i and t s j < t e j are provided for all [START_REF] Antoniou | A Semantic Web Primer, 2Nd Edition[END_REF] Allen relations. The relations after, overlappedby, metby, contains, startedby and finishedby are the inverse of before, overlaps, meets, during, starts and finishes. The relation equals is symmetric and relations before and after are transitive.

Once all temporal information is consistently represented in the ontology, it can then be manipulated by using temporal operators, such as the above-mentioned Allen's operators. The SWRL rule language [START_REF] Swrl | Swrl: A semantic web rule language combining owl and ruleml -w3c member submission 21 may 2004[END_REF] provides a means for defining these operators as rules to manipulate time values of temporal type, such as the XML Schema temporal types (e.g., xsd:dateTime, its value example is 2014-11-06T17:35:00). We have used SWRL to implement Allen interval-based temporal operators over the DateTime class temporal types, as illustrated in Fig. 4. Our implementation can be extended to support other temporal types. As a consequence, we add a Patterns class which constrains the value space of a specific temporal type such as

IV. EVALUATION EXAMPLE

In this section, a handover procedure example, as well as an example for querying temporal information in the ontology, are presented and evaluated.

A. RBC-RBC Handover Procedure Example

When the train is moving away from the communication area covered by one Radio Block Centre (RBC) (called Handing Over RBC or HO RBC) and entering the area covered by another RBC (Accepting RBC or AC RBC), the handover procedure is called in order to transfer the communication to an AC RBC and to avoid communication termination when the train gets outside the range of the HO RBC. In the nominal operation, see Fig. 5, the main functional steps needed for running from one RBC area to another one are the following:

(1) pre-announcement of the transition by the HO RBC; (2) establishment of the radio communication session with the AC RBC; (3) generation of movement authorities including the border; (4) announcement of the RBC transition (5) transfer of train supervision to the AC RBC; (6) Termination of the session with HO RBC. More detail of procedure natural language description can be found in paragraph §5.15 of [START_REF]ERTMS/ETCS[END_REF].

Here, we will focus on one degraded situation where the time interval for running the handover can be changed. For example "in case a communication session is established and no request to terminate the session is received from the HO RBC within a fixed waiting time after sending the position report, the position report shall be repeated with the fixed waiting time after each repetition" ( §5.15.4 [START_REF]ERTMS/ETCS[END_REF]). For our evaluation, we allow 3 possible repetitions of position report and each position report takes 60 s, as illustrated in Fig. 5. Adding this new temporal constraint, a temporal inconsistency could arise if the associated constraint conflicts with the nominal time interval constraint. In such cases, it is helpful to query the temporal information in the formalized ontology to explain if the operation is consistent or not.

B. Handover Procedure Ontology

To model this domain specific knowledge the most important first step is to instantiate the Procedures class with the individual objects related to the scenario. More precisely, the class Procedures of the first layer of Fig. 4 is extended with two instances handhover Process1 related to the nominal mode and handhover Process2 related to the degraded mode. The second step is to add concrete QoS parameter values to ∧ temporal : hasV alidT ime(?p, ?pV T ) ∧ temporal : hasStartT ime(?pV T, ?pST ) ∧ temporal : hasF inishT ime(?pV T, ?pET ) → sqwrl : select(?p, ?pST, ?pET ) the instance. For this, the hasQualityValue property is used to enrich the QoS ontology characteristic with the ability to add concrete parameter values. Here, the request for position report of the handhover Process2 degraded situation is set to 3x60s as illustrated in Fig. 5. The last step is to represent temporal information by using the temporal class ValidTime and subclass ValidTInstant and ValidTInterval with the properties associated. For example, the nominal handover starting date time is set to 2014-11-06T17:35:00 and the ending dated to 2014-11-06T17:40:00. As explained so far, this date information is stored as a literal of the XML schema type xsd:dateTime. This type of information cannot be included in the deductive reasoning process with reasoners, such as previously mentioned Pellet reasoner integrated in Protégé. One way to query this temporal information is to use the SWRL-based Query language (SQWRL) [START_REF] O'connor | Sqwrl: A query language for owl[END_REF].

C. Query Of Temporal Information

We use Allen basic interval operators, implemented with the SWRL rules, and the SQWRL to query the temporal information in the ontology. In fact, SQWRL is defined as a set of SQL-Like query operators, such as sqwrl:select. Concretely this SQL-Like query operator is used as a consequent of a SWRL rule to format the information and makes it possible to infer implicit knowledge from the temporal information. For example, Table II shows a query, where variable ?p represents the process instances represented in the handover ontology and variables ?pST and ?pET represent their start and end time respectively.

The result of the query is shown in Table III. It shows the start and the end time of the nominal handover handhover Process1 and the degraded handhover Process2. Actually, only Allen basic operators are used in the query but we are looking for more advanced temporal queries, as proposed in [START_REF] Oconnor | A method for representing and querying temporal information in owl[END_REF].

V. DISCUSSION

Temporal information can be analyzed with the lower-level techniques, such as a real-time scheduling theory [START_REF] Singhoff | Investigating the usability of real-time scheduling theory with the cheddar project[END_REF]. In addition to lower-level techniques, in this paper, we claim that the temporal knowledge can be directly handled in domain ontologies in order to facilitate much higher-level analysis and to make better sense of complex temporal patterns for software developers. In fact, working at the knowledge level will enable software developers to make better sense of complex temporal patterns. The aim of our approach is to enhance the ability of software developers to clearly understand complex forms of temporal knowledge in order to properly implement them. Similar domain ontologies have been proposed in other critical domains such as automotive [START_REF] Feld | The automotive ontology: Managing knowledge inside the vehicle and sharing it between cars[END_REF], avionic [START_REF] Farfeleder | Dodt: Increasing requirements formalism using domain ontologies for improved embedded systems development[END_REF] and clinical domains [START_REF] Weichert | A novel approach for connecting temporal-ontologies with blood flow simulations[END_REF]. In contrast to previous works on ERTMS/ETCS [START_REF] Verstichel | Efficient data integration in the railway domain through an ontology-based methodology[END_REF], [START_REF] Bonifacio | Improvement of processes and methods in testing activities for safety-critical embedded systems[END_REF], [START_REF] Hoinaru | An ontology for the ERTMS/ETCS[END_REF], which do not focus on temporal constraints or which required some rewriting of source ontology to integrated temporal information, our approach provides separation of concerns with a 3-layer temporal QoS ontology.

Regarding the representation and reasoning abilities of temporal information in temporal ontologies and based on some general requirements of the handbook of temporal reasoning [START_REF] Fisher | Handbook of Temporal Reasoning in Artificial Intelligence (Foundations of Artificial Intelligence[END_REF], the following requirements are identified. But it represents the general high-level functionalities that can be specialized in order to make ontological results really usable. The temporal QoS ontology layer covers the most common temporal information met in these SRS documents. It can be specialized for the complete coverage of a domain-specific area, such as the rail-road level crossing area subjected to strong temporal constraints.

VI. CONCLUSION AND FUTURE WORK

The main challenge we face in this paper is how to integrate the temporal QoS dimension in existing ontologies with a minimal redesign of them. In order to face this challenge, we introduce a lightweight 3-layer temporal QoS ontology developed for ERTMS/ETCS. This development is mainly based on the study of a set of referential SRS documents of ERTMS/ETCS. Representing QoS entities in separated layers can clarify the distinction between the non QoS entities and the QoS entities in an ontology. The upper generic layer of the proposed ontology provides an intuitive knowledge of ERTMS/ETCS domain components. The separation of the intermediate QoS layer from the lower QoS layer allows us to focus on specific QoS Characteristics, such as temporal of integrity characteristics. In this paper we focus on temporal information. For evaluation, we instantiate the proposed ontology for RBC handover procedure and use a rule-based query language SQWRL to infer temporal information in this domain-specific ontology.

A potential shortcoming of our approach is that temporal information in the ontology may be transformed into the formal model in order to verify temporal safety properties for high-integrity levels. This model-driven transformation process can be error-prone. As a consequence, our future work will focus on an ontology bridge in model-driven transformation for safety assessment. Clearly, we are looking to create a bridge between the proposed ontology and a time automata formal model, such as [START_REF] Sango | Component-Based Modeling and Observer-Based Verification for Railway Safety-Critical Applications[END_REF], for which we can use the appropriate model checker tool, such as UPPAAL for formal verification tasks of temporal safety properties. In addition, this process can require considerable safety domain-specific expertise. As a consequence, and additional future work is to demonstrate the efficiency of our approach with railway specific scenarios, such as an operational scenario proposed for the new safer rail-road level crossing architecture subjected to strong temporal safety properties [START_REF] Khoudour | Towards safer level crossings: existing recommendations, new applicable technologies and a proposed simulation model[END_REF].

Fig. 1

 1 Fig. 1 ERTMS/ETCS Generic Ontology

Fig. 1

 1 Fig. 1 shows the hierarchy of top-level class categories of this generic ontology redesigned. The super-class ERTMS represents the European Rail Traffic Management System, while ETCS for European Train Control System represent the signaling subsystem composed by the Onboard sub-class and the Trackside sub-class. It also includes the Procedures sub-class, the Interfaces sub-class, the application level (AppLevel) sub-class and the Language sub-class.

Fig. 2

 2 Fig. 2 ITU X.641 QoS Framework

Fig. 3 N

 3 Fig. 3 N-ary Relations Example

Fig. 4 3 -

 3 Fig. 4 3-Layer Temporal QoS Ontology for ERTMS/ETCS

Fig. 5

 5 Fig. 5 Example of RBC-RBC Handover Scenario

TABLE I OVERVIEW

 I TO ALLEN'S RELATIONS BETWEEN PAIRS OF TWO INTERVALS

	Type	Meaning	Relation
	bef ore(ti, tj )	ti before tj	t e i < t s j
	af ter(tj , ti)	tj after ti
	meets(ti, tj )	ti meets tj	t e i = t s j
	metBy(tj , ti)	tj met by ti
	overlaps(ti, tj )	ti overlaps tj	(t s i < t s j < t e i ) ∧ (t e i < t s j )
	overlapsBy(tj , ti)	tj overlaps by ti
	during(ti, tj )	ti during tj	(t s i > t s j ) ∧ (t e i < t e j )
	includes(tj , ti)	tj includes ti
	starts(ti, tj )	ti starts tj	(t s i = t s j ) ∧ (t e i < t e j )
	startedBy(tj , ti)	tj started by ti
	f inishes(ti, tj )	ti finishes tj	(t s i > t s j ) ∧ (t e i = t e j )
	f inishedBy(tj , ti)	tj finished by ti
	equals(ti, tj )	ti equals tj	(t s i = t s j ) ∧ (t e i = t e j )

a duration of time (e.g. 2 Seconds). Temporal pattern duration are represented by the class Duration, which has an integer functional property hasCount and a functional hasTimeUnits property which has instances, such as Years, Months, Days, Hours, Minutes, Seconds, and Milliseconds.

TABLE II A

 II SWRL RULE

ertms : procedure(?p)

TABLE III A

 III SQWRL QUERY RESULT

1 )

 1 Instants/events: Instants are represented by the class temporal:Instant. Events can be modeled under the class Procedures. Events are characterized by instants stating the time the event occurred. 2) Intervals/processes: Intervals are represented by the class Temporal:Interval with the property hasValidTInterval. A process, represented by the class Procedures, is characterized with intervals by stating start and end time of the process. 3) Scheduling: To represent scheduling processes, the process itself and the scheduling algorithm which regulates the periodicity have to be modeled. The former can be modeled in our ontology by specializing the class The traceability must be established between source requirement documents and ontology entities. In our ontology, this is simply provided by associating to the ontology entities the requirement text as a comment. However, if the source ontology must be transformed to the valid time formal model, a rigorous traceability is required. 8) Coverage Compared to the complexity of the ERTMS/ETCS SRS, the current coverage of the available SRS by the generic ontology layer is reduced.

	Procedures but the latter cannot be represented in the
	actual version of our ontology.
	4) Causality: With the Allen relations representation,
	our ontology comes with a small set of temporal
	relations which enable basic representation of causality
	in terms of interval relations. In addition, more advanced
	temporal relations can be used by composing the basic
	relations.
	5) Patterns: Defining temporal patterns is a central
	requirement when working with temporal aspects. A
	pattern is defined as compounded processes consisting
	of simpler processes in order to provide more precision.
	In our ontology it is possible to represent them by using
	the class Patterns.
	6) Transformation to formal model: The connection of
	our ontology to a formal model is not yet provided
	in the current version. But we are looking for its
	possible connection to a formal time automata model for
	which we can use the appropriate formal model checker
	tool, such as UPPAAL for formal verification tasks of
	temporal safety properties.
	7) Traceability:

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