Shin-Ichi Nishizawa
email: s.nishizawa@aist.go.jp

Michel Pons

Numerical modeling of SiC-CVD in a horizontal hot-wall reactor

Keywords: Chemical vapor deposition, Simulation, Deposition, Etching, Doping, Surface reaction

A numerical simulation was carried out on SiC-CVD in a horizontal hot-wall reactor. In order to explain the effect of surface polarity, Si-face and C-face, the surface reaction model was improved. Then, the growth processes and doping features of both Si-face and C-face were analyzed. The role of conditions at growing surface, such as surface mass flux of both Si-containing and C-containing species, surface concentration of Si-containing and C-containing species and their ratio, is investigated. Then, the deposition and etching rates, and doping concentration are analyzed as the function of those parameters. In addition, surface morphology of growing epitaxial layer is also investigated in connection with growing surface condition.

Introduction

There are lots of experimental reports on SiC-CVD [1][2][3][4][5][6]. Each report states that the result depends on the experimental condition and reactor design. In addition, the discussion of inlet conditions such as source gas C/Si ratio, and not the conditions at the surface where the deposition occurs is leading to the confusion. There are several reports on numerical simulations that attempt to explain the mechanism of SiC-CVD [2][3][4][5][6], however, there are problems remaining to be solved, such as doping incorporation. Silicon carbide is a polar material, i.e. the surfaces are terminated either with silicon (Si-face) or carbon (C-face) in the c-axis direction. The doping incorporation features of C-face appear to be different from those of the Si-face [7].

In this study, a surface reaction model [8] is improved to explain the effect of surface polarity, Si-face and Cface. Using this model, a numerical simulation was carried out on SiC-CVD in a horizontal hot-wall reactor.

The roles of growth surface conditions, especially the mass flux of Si-and C-containing species to the growing surface and its concentration in the gas phase, are discussed.

Numerical modeling

Fig. 1 shows the schematic model applied in this study. The horizontal hot-wall CVD reactor has been simplified in the 2D model. Only the gas phase region with the susceptor surfaces is regarded as a numerical domain. In this study, CFD-ACE+ commercially available software was used [9].

Shown in Tables 1 and2 shows the model proposed by Meziere et al. [8] for SiC-CVD reactions in gas phase and on the surface. They showed a good agreement in etching and deposition distribution between the model and the experiment for the Si-face [8]. In this study, to explain the effect of surface polarity, Si-face and C-face, their surface reaction model was improved by changing the rate constant of surface reactions. The concept was deduced from the SiC crystal structure. In the Si-face, each surface silicon has three bonds with the next carbon layer, and each surface carbon has only one bond with the next silicon layer. It can therefore be assumed that removal of carbon is much easier than that of silicon, and the deposited silicon is much more stable than the deposited carbon. In the case of the C-face, the opposite features can be expected because each surface carbon has three bonds with the next silicon layer, and each surface silicon has only one bond with the next carbon layer. Based on this concept, the reaction rate constants in Table 2 were modified in this study as follows. In the case of Si-face, during both the removal and deposition processes, the surface of the solid phase must be terminated by surface silicon (Si_s), and the surface reactions that produce surface carbon (C_s) are the rate determining step with small reaction rate constant. On the other hand, in the case of C-face, during both the removal and deposition processes, the surface of the solid phase must be terminated by surface carbon (C_s), and the surface reactions that produce surface silicon (Si_s) are the rate determining step with small reaction rate constant.

Results and discussion

Growth process

Fig. 2 shows the calculated growth rate at the center of the wafer with Si-face. From Fig. 2 The surface flux appears to be the universal parameter that explains the SiC-CVD process independently of the inlet condition. Numerical results of the C-face are the same as Fig. 2. Fig. 3 shows the effect of source gas flow rate on both the growth rate and C/Si ratio in the gas phase at the center of the wafer of the Si-face. From Fig. 3(a), as the source gas flow rate increases, the growth rate also increases. However, the expected surface morphology is different with C/S ratio at the inlet as shown in Fig. 3(a). From Fig. 3(b), it can be seen that when the inlet C/Si is equal to 1.5, with increasing the source gas flow rate, the surface C/Si ratio increases. Then, the excess carbon might result in an increases surface roughness. On the other hand, when the C/Si ratio at the inlet is 0.5, the surface C/Si ratio decreases with increasing the source gas flow rate. A decreased C/Si ratio on the growing surface leads to a decreased surface energy. Then the micropipe closing can be expected from FrankÕs rule [10]. According to the FrankÕs rule, with decreased the surface energy, the magnitude of the Burgers vector also decreases. Similar phenomena could be expected when growth pressure decreases. [11] However, when the surface C/Si is too small, the excess silicon vapor composes silicon droplet, and leads to the rough surface.

As shown in Table 2, the surface reaction model used in this study contains both removal and deposition steps. Recently, this surface reaction model has been also applied to etching process. [12,13] Then, it was numerically suggested that the good surface morphology could be kept during the H 2 etching process with additional SiH 4 .I t was because of small C/Si ratio on the wafer surface that leads to the small surface energy.

Doping

Recently, Kojima et al. [7] experimentally investigated the doping incorporation features on both the Si-face and the C-face [7]. In their experiments, to change the C/Si ratio at the inlet, the SiH 4 flow rate was fixed and the C 3 H 8 flow rate was varied. Concerning N doping, increasing the C 3 H 8 flow rate leads to decreased doping concentration. This can be explained in terms of the site competition model [14,15]. If nitrogen replaces carbon, increasing the amount of carbon, while keeping the nitrogen amount constant leads to a decreased replacement ratio. With respect to Al doping, increasing the C 3 H 8 flow rate increases the doping concentration. However, in their experiment, the SiH 4 and Al(CH 3 ) 3 flow rates were constant. According to the site competition model, aluminum replaces silicon. It appears that the site competition model can not explain why the replacement ratio changes in spite of the silicon and aluminum flow rates remaining constant.

In this study, the amounts of surface silicon and surface carbon in the gas phase were also calculated. Fig. 4 shows the relationship between the calculated molar fraction of the surface carbon and the surface silicon at the center of the wafer and the experimentally obtained nitrogen and aluminum doping concentration, respectively. In the experiment, nitrogen was doped unintentionally, and aluminum was doped with a small TMA flow rate. In this study, the nitrogen and aluminum amounts were assumed to be very low and constant. Then only the surface carbon and surface silicon amounts were considered for the replacement ratio. Fig. 4 shows that as the molar fractions of surface carbon and surface silicon increase, nitrogen and aluminum doping concentration decreases, respectively. The surface silicon amount changes with C 3 H 8 flow rate even though the inlet SiH 4 flow rate is constant, and from Fig. 4, the doping incorporations of both nitrogen and aluminum could be explained by the site competition model while taking account of the molar fractions of the surface carbon and the surface silicon. Fig. 4(a) shows that nitrogen concentration on C-face is higher than that on Si-face. It might be because the nitrogen can easier replace the surface carbon in the first layer on the C-face than replacing the second layer of C-atoms in growth on the Si-face. On the other hand, Fig. 4(b) shows that aluminum concentration on Si-face is higher than that of C-face, since aluminum can easier replace surface silicon on the Si-face than replacing the second layer of Si-atoms in growth on thee C-face. However, the molar fraction dependences of the Si-face and C-face are different, suggesting that the doping mechanisms on the Si-face and C-face, and of nitrogen and aluminum, are different.

Conclusion

In this study, the surface reaction model was improved to explain the effect of surface polarity. The improved model was able to explain the growth processes precisely, and showed that the growing surface condition is a good parameter to explain the growth rate and surface morphology. Concerning doping incorporation, the improved model shows that the site competition model can explain the doping features of nitrogen and aluminum while taking account of the molar fractions of the surface silicon and the surface carbon, respectively.

Fig. 2 .

 2 Fig.2shows the calculated growth rate at the center of the wafer with Si-face. From Fig.2(a), it can be seen that when C 3 H 8 flow rate is small, the growth rate increases with C 3 H 8 flow rate. And the C 3 H 8 flow rate dependencies vary with the SiH 4 flow rate. Above the certain C 3 H 8 flow rate, the growth rate increases only slightly, even though the C 3 H 8 flow rate increases. This critical C 3 H 8 flow rate also depends on the SiH 4 flow rate. Fig.2(b)shows the relationship between the surface C flux and the growth rate at the center of the wafer of the Si-face. From Fig.2(b), it can be seen that when the surface C flux is small, the growth rate increases proportionally to the surface C flux regardless of the inlet source gas flow rate. However, above

Fig. 1 .

 1 Fig. 1. Schematic model of numerical simulation (bold line: measurement temperature distribution was settled as boundary condition).

Fig. 3 .

 3 Fig. 3. Effect of source gas flow rate on growth rate and expected surface morphology. (P = 250 mbar, H 2 = 40 slm, and C/Si at inlet constant.) (a) Growth rate obtained by numerical modeling and surface morphology obtained by experiment and (b) surface C/Si ratio obtained by numerical modeling and surface morphology obtained by experiment.

Fig. 4 .

 4 Fig. 4. Relationship between calculated surface molar fraction and doping concentration obtained experimentally. (P = 250 mbar, SiH 4 = 6.67 sccm, C 3 H 8 flow rate was varied, H 2 = 40 slm.) (a) Nitrogen doping (N was doped unintentionally.) (b) Aluminum doping (Al(CH 3 ) 3 = 0.1 sccm in experiments.)

  (a), it can be seen that when C 3 H 8 flow rate is small, the growth rate increases with C 3 H 8 flow rate. And the C 3 H 8 flow rate dependencies vary with the SiH 4 flow rate. Above the certain C 3 H 8 flow rate, the growth rate increases only slightly, even though the C 3 H 8 flow rate increases. This critical C 3 H 8 flow rate also depends on the SiH 4 flow rate.

Table 2

 2

	Table 1
	Gas phase reaction model [8]
	SiH 4	=	SiH 2 +H 2	(1)
	Si 2 H 6	=	SiH 2 + SiH 4	(2)
	SiH 2	=S i + H 2	(3)
	2H + H 2	=2 H 2	(4)
	C 3 H 8	=C H 3 +C 2 H 5	(5)
	CH 4 +H	=	CH 3 +H 2	(6)
	C 2 H 5 + H	=	2CH 3	(7)
	2CH 3	=C 2 H 6	(8)
	C 2 H 4 +H	=	C 2 H 5	(9)
	C 2 H 4	=C 2 H 2 +H 2	(10)
	H 3 SiCH 3	=	HSiCH 3 +H 2	(11)
	H 3 SiCH 3	=	SiH 2 +CH 4	(12)
	Si 2	=	2Si	(13)
	Si 2 +CH 4	=S i 2 C+2H 2
	Surface reaction model [8]
	C+Si_S + H 2	!	SiH 2 + C_S	(1)
	2Si + 2C_S + H 2	!	C 2 H 2 + 2Si_S	(2)
	SiH 4 + C_S	!	SiH 2 _S + H 2 + C	(3)
	SiH 2 _S	!	H 2 + Si_S	(4)
	SiH 2 + C_S	!	SiH 2 _S + C	(5)
	Si + C_S	!	Si_S + C	(6)
	C 2 H 2 + 2Si_S	!	2C_S + H 2 + 2Si	(7)
	C 2 H 4 + 2Si_S	!	2C_S + 2H 2 + 2Si	(8)
	CH 4 + Si_S	!	C_S + 2H 2 + Si	(9)
	HSiCH 3 + C_S	!	C + Si_S + H + CH 3	(10)
	CH 3 + Si_S	!	Si + C_S + 1.5H 2	(11)
	Si 2 + 2C_S	!	2C + 2Si_S	(12)
	Si 2 C + Si_S	!	Si 2 + Si + C_S	(13)
	SiCH 2 + C_S	!	C + Si_S + CH 2	(14)
	CH 2 + Si_S	!	C_S + Si + H 2	(15)
	*_S means the surface species in solid phase.

Acknowledgment

This work was partially supported by NEDO.


M Bhar

S Mora

O Kadri

K Manai

S Zein

S Incerti

Monte Carlo study of patient and medical staff radiation exposures during interventional cardiology

Keywords: Interventional cardiology, numerical human phantom, effective dose, equivalent dose, GEANT4 code

The aim of this study is to assess the radiation exposure of the patient and the medical staff during interventional cardiology procedures. Realistic exposure scenarios were developed using the adult reference anthropomorphic phantoms adopted by the International Commission on Radiological Protection (ICRP 110 M ale and ICRP 110 F emale ), and the radiation transport code GEANT4 (version 10.3). The calculated equivalent and effective doses were normalised by the simulated Kerma-Area Product (KAP), resulting in two conversion coefficients H T /KAP and E/KAP . To properly evaluate the risk of exposure, several dose-dependent parameters have been investigated, namely: radiological parameters (tube kilovoltage peak (kVp), type of projection, field size (FOV)), and operator positions. Four projections (AP , P A, LAO25 • and RAO25 • ) were simulated for three X-ray energy spectra (80, 100 and 120 kV p) with four different values of FOV (15 × 15 cm 2 , 20 × 20 cm 2 , 25 × 25 cm 2 and 30 × 30 cm 2 ). The results showed that the conversion coefficients values increase with increasing tube voltage as well as the FOV size. Recommended projection during the interventional cardiology procedures, whenever possible, should be the P A projection rather than AP projection. The most critical projection for the patient and the main operator is the RAO25 • projection and the LAO25 • projection respectively. The comparison of our results with the literature data showed good agreement allowing their use in the dosimetric characterization of interventional cardiology procedures.

Introduction

Interventional cardiology (IC) performed under the control and guidance of an X-ray imaging tool is a widespread technique used for diagnosis and treatment of various cardiac pathologies. However, during this procedure, the patient and the medical staff are exposed to significant radiation doses. In general, the received doses in IC are much higher than other radiological procedures, including in nuclear medicine [START_REF] Vañó | Radiation exposure to medical staff in interventional and cardiac radiology[END_REF].

Given their complexity, the long duration of IC procedures in addition to the required proximity of operators to the patient who represents the primary source of scattered radiation result in considerable dose.

In this regard, special evaluation of the exposure of the patient and the medical staff is necessary in order to ensure their protection. The direct evaluation of the absorbed dose to an organ or a tissue remains a difficult and impractical task, requiring, in general, delicate and invasive procedure. Therefore, Monte Carlo (MC) simulation is considered as an effective tool to simulate the interaction of particles with matter and to estimate the organ doses for computational anthropomorphic human phantoms [START_REF] Brambilla | Conversion factors for effective dose and organ doses with the air kerma area product in patients undergoing percutaneous transhepatic biliary drainage and trans arterial chemoembolization[END_REF][START_REF] Ferrari | Simulation of H p (10) and effective dose received by the medical staff in interventional radiology procedures[END_REF][START_REF] Santos | Occupational exposures during abdominal fluoroscopically guided interventional procedures for different patient sizes-A Monte Carlo approach[END_REF][START_REF] Cavalcante | Monte Carlo simulation of interventional cardiac scenarios using a newborn hybrid phantom and MCNPX code[END_REF][START_REF] Schultz | Dosemeter readings and effective dose to the cardiologist with protective clothing in a simulated interventional procedure[END_REF].

Modeling of the human's anatomy has developped significantly over the last decades, moving towards more realism and personalization. Nowadays, there are mainly three morphometric categories (reference, patient-dependent and patient-specific), as well as three phantom format types (stylized, voxel and hybrid). Here, specific emphasis is given to voxel type and reference category using the ICRP reference male and female voxellized phantoms.

Simulation codes such as MCNP [START_REF] Goorly | Initial MCNP6 release overview-MCNP6 version 1[END_REF][START_REF] Emfietzoglou | A Monte Carlo study of energy deposition at the sub-cellular level for application to targeted radionuclide therapy with low-energy electron emitters[END_REF], PENELOPE [START_REF] Baro | PENELOPE: an algo-rithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter[END_REF], EGSnrc [START_REF] Kawrakow | Accurate condensed history Monte Carlo simulation of electron transport. I. EGSnrc, the new EGS4 version[END_REF], FLUKA [START_REF] Ghal-Eh | FLUKA simulation studies on in-phantom dosimetric parameters of a LINAC-based BNCT[END_REF], GEANT4 [START_REF] Elhamdi | Dose calculation using Tchebyshev wavelets with buildup correction in the Tunisian gamma irradiator[END_REF][START_REF] Bhar | Assessment of self-and cross-absorbed SAF values for HDRK-man using Geant 4 code: internal photon and electron emitters[END_REF][START_REF] Ounalli | Combining Monte Carlo simulations and dosimetry measurements for process control in the Tunisian Cobalt-60 irradiator after three half lives of the source[END_REF][START_REF] Belkadhi | Dose calculation using Haar wavelets with buildup correction[END_REF] and it's derivative GATE [START_REF] Papadimitroulas | Dosimetry applications in GATE Monte Carlo toolkit[END_REF], provide the tools for calculating radiation doses and are commonly used in several studies focusing on patient and staff protection in interventional scenarios [START_REF] Brambilla | Conversion factors for effective dose and organ doses with the air kerma area product in patients undergoing percutaneous transhepatic biliary drainage and trans arterial chemoembolization[END_REF][START_REF] Ferrari | Simulation of H p (10) and effective dose received by the medical staff in interventional radiology procedures[END_REF][START_REF] Santos | Occupational exposures during abdominal fluoroscopically guided interventional procedures for different patient sizes-A Monte Carlo approach[END_REF][START_REF] Cavalcante | Monte Carlo simulation of interventional cardiac scenarios using a newborn hybrid phantom and MCNPX code[END_REF][START_REF] Schultz | Dosemeter readings and effective dose to the cardiologist with protective clothing in a simulated interventional procedure[END_REF]. Nevertheless, to the best of our knowledge, there are no studies that assess radiation exposure in IC procedures using the ICRP 110 voxellized phantoms [START_REF]Adult reference computational phantoms[END_REF] and GEANT4 toolkit.

The dosimetric assessment for the patient and the medical staff is crucial and it is possible to take advantage of these highly sophisticated tools to determine the absorbed doses in a very accurate way. Therefore, GEANT4 and the ICRP 110 phantoms have been employed in this study to determine Email address: bhar.maroua@yahoo.com (M. Bhar).

the equivalent and effective doses normalised by the Kerma-Area Product (KAP)(H T /KAP and E/KAP ) for patient and medical staff during cardiac intervention procedures and considering the influence of several factors: the energy spectra of the X-ray tube, the size of the field, the type of irradiation projection, and also the position of the operator against the X-ray source.

First, an overall study of radiation exposure dependent parameters for both the patient and the medical staff is performed. The next part was to validate our findings in comparison with published studies. The efficiency of the innovative radiation protection equipment: mobile radiation protection cabin (RPC), against routine protection equipment is then investigated in the last part.

Material and Methods

Computational human phantom simulation

GEANT4 (GEometry ANd Tracking

) is a toolkit for the simulation of particles transport through matter. This code is a set of C++ classes that can be used to design a simulation application [START_REF] Agostinelli | GEANT4-a simulation toolkit[END_REF][START_REF] Allison | Recent developments in Geant4[END_REF]. Its areas of application include high energy, nuclear and accelerator physics, as well as studies in medical and space science etc. [START_REF] Allison | Geant4 developments and applications[END_REF]. Different parameters can be set up to better simulate the radiation transport and, in this work photoelectric-effect, Compton and Rayleigh scattering for photons, Bremsstrahlung, and multiple scattering models for electrons have been selected. The values of 1 keV and 10 keV are set respectively as the energy thresholds for photons and electrons. During all simulations, the standard electromagnetic physics options were used which are governed by the specific physics builder "G4EmStandardPhysics" to achieve our purposes. To model the patient and the medical staff, we used the reference male and female phantoms that are adopted by the ICRP 110. Their design is based on tomographic images of a real man and a real woman. Their main characteristics are described in table 1. A more detailed description is included in the ICRP publication 110 [START_REF]Adult reference computational phantoms[END_REF].

To manage the large number of voxels representing the phantoms, we used the nested parameterisation approach provided by the G4V N estedP arameterisation class [START_REF] Incerti | Monte Carlo dosimetry for targeted irradiation of individual cells using a microbeam facility[END_REF]. This specific class was also introduced in the duplication of the phantom to model a patient and the medical staff in the same exposure procedure. phantom representing the main operator and radiation protection cabin. Some tracks were visualized using the HepRapp visualization tool [START_REF] Allison | The geant4 visualisation system[END_REF].

Description of the simulated scenarios

In all the scenarios described below, a cardiac intervention room of the University Hospital Center of Bordeaux, France, was modelled. The walls of the room are made of concrete or plaster covered with lead. A patient table made of carbon fibre (ρ = 1.25 g/cm 3 ) with a thickness of 33 mm has been placed at a height of 109.85 cm from the floor. In this space, the main components of the X-ray equipment were inserted in addition to the patient and the operators. The X-ray tube has been simplified to a point source emitting a conical beam of photons. As shown in Figure 1, the simulated C-arm machine supports at its two extremities the X-ray tube with 11.35 g/cm 3 density lead shield and the CsI imaging device with a density of 1.25 g/cm 3 (a box representing the image detector : 50 × 50 × 13 cm 3 ) with an inner diameter of about 1 m. The source-skin distance was set at approximately 61.5 cm. These distances have been maintained constant for all exposure conditions and are roughly representative of real practice.

For a realistic modeling of the X-ray spectrum following the recommendations, we used the SpekCalc software [START_REF] Poludniowski | SpekCalc: a program to calculate photon spectra from tungsten anode x-ray tubes[END_REF]. Several radiographic parameters of choice (kVp, filtrations, anode angle ...) can be introduced. The generated spectra were later included in the GEANT4 code. Three energy (and Half-Value Layer) spectra were studied: 80 kV p (HVL= 3.2 mm Al), 100 kV p (HVL= 3.8 mm Al) and 120 kV p (HVL= 4.3 mm Al).

A shaping filter was introduced at the output of the primary beam, in order to reproduce the square shape of the field and focalise it on the examined region. The role of this filter is to stop all photons outside of its central square orifice. Four different values of field size (designed by FOV: Field Of View) were studied: 15 × 15 cm 2 , 20 × 20 cm 2 , 25 × 25 cm 2 and 30 × 30 cm 2 . Four simulated beam projections were simulated: antero-posterior (AP ), posteroanterior (P A), Left Anterior Oblique (LAO25 • ), and Right Anterior Oblique (RAO25 • ). In all cases, the beam of photons is focused on the patient's heart, to mimic an interventional cardiology procedure (Figure 2). Basically we will have five different scenarios to explore, acting on the cited exposure-dependent parameters (see Table 2). It is very important to note that in a given scenario of this study, the intervention scene contains at least two exposed members. Therefore at least one duplication of the phantom was made, and the duplication is done by keeping always the same type of phantom (ICRP 110 M ale / ICRP 110 F emale ). In other words, during a given scenario, the included members are all male or all female.

• Scenario 1: Three male exposed members: a patient (lying on the surgical table) and two professionals named as the main operator (located close to the right abdominal part of the patient) and the assistant operator (placed on the right side of the main operator). The calculation is performed for the PA projection by varying the kVp (80/100/120 kV p) or the FOV (15 × 15 cm 2 / 20 × 20 cm 2 / 25 × 25 cm 2 / 30 × 30 cm 2 ) values. The conversion coefficients calculated are: E/KAP for the main/assistant male operators. • Scenario 2: Eleven male exposed members: a patient and ten professionals.

The medical staff are located approximately 35 cm around the patient's table presenting the locations most likely to be occupied by personnel (as mentioned in literature [START_REF] Efstathopoulos | Occupational radiation doses to the extremities and the eyes in interventional radiology and cardiology procedures[END_REF] and shown in Figure 3). The distances from the X-ray beam isocenter are 54.1 cm, for the first operator, 30.54 cm for the second operator , 81.5 cm for the third operator, 160.5 cm for the fourth opertor, 50.75 cm for the fifth operator, 28 cm for the sixth operator, 76.88 cm for the seventh operator, 140.87 cm for the eighth operator, 212.5 cm for the ninth and 135.2 cm for the tenth operator. The calculation is performed for the PA projection at a fixed value of the FOV (20 × 20 cm 2 ). The conversion coefficients calculated are: E/KAP for the ten male operators. • Scenario 3: Two male exposed members: the patient and the main operator.

The calculation was made for the following beam qualities: at 100 kV p, 20 × 20 cm 2 / 30 × 30 cm 2 of the FOV and four modalities of projection : AP /P A/LAO25 • / RAO25 • ). The conversion coefficients calculated are: (E/KAP and H T /KAP ) for the main male operator, and (E/KAP and H T /KAP ) for the male patient. • Scenario 4: Two sub-scenarios were tested using the male phantom first for both the patient and the operator (scenario 4.1) and then replacing them both with the female phantom (scenario 4.2). In both cases, the calculation was made for three beam projections: P A/LAO25 • / RAO25 • ) with the different values of kVp (80/100/120 kV p) and two values of FOV (20 × 20 cm 2 / 30 × 30 cm 2 ). The conversion coefficients calculated are: E/KAP for the male/female patients. • Scenario 5: Two male exposed members: the patient and the main operator.

The calculation is performed for the PA projection with fixed values of the kVp (120 kV p) and FOV (30 × 30 cm 2 ). The conversion coefficients calculated are: H T /KAP for the male/female operators. For each scenario, we tried to take into consideration several protective tools to make the radiation scenarios as close to reality as possible. In this sense, the protective equipment are modeled in two different ways:

• For 1 st -4 th scenarios: Routine equipment, a 0.5 mm thick protective lead apron, was modelled as a 0.5 mm thick lead box ( x = 54.68 cm , y = 27.54 cm and z = 120 cm) , and placed around each worker extending from the thyroid level until the knees. Part of the lead box was cut to expose the shoulders and the arms of the operator. Protective eye glasses for the main operator were also modeled as a 0.35 mm thick lead layer. In addition to the personal protective shielding, 0.5 mm Pb shielding was reproduced for the ceiling and the walls, and sideways the patient table extending from table to floor. • For 5 th scenario: Radiation Protection Cabin (RPC). This device is modeled as a box of dimensions 55.3 × 50 × 200 cm 3 to protect the main operator (see Figure 1). It is made of 2 mm thick lead glass (lead equivalent) where the practitioner can pass freely his arms through two holes in front of the patient to intervene.

Output data collection

The determination of the deposited energies (E d ) in the ICRP 110 phantom organs/tissues was done with the specific scorer class G4P SEnergyDeposit with the exception of the red bone marrow (RBM) and the skeleton. The absorbed doses were then calculated by dividing the calculated deposited energy by the mass (m) of the region of interest. Given that the radiation weighting factor (w R ) of the photons is 1, the absorbed dose (Gy) and the equivalent dose H T (Sv) are numerically equivalent. The effective doses (E) were determined by summing the products of equivalent doses with the tissue weighting factors (w T ) recommended by the ICRP 103 using equation ( 1) [START_REF]The 2007 recommendations of the international commission on radiological protection[END_REF]. When referring only to male models, the tissue weighting factors of the male model have been renormalized to one, the same have been done for the female model. It is common to normalize the calculated equivalent and effective doses by another quantity, such as the Kerma-area Product (KAP (Gy.cm 2 )), resulting in the two dosimetric quantities H T /KAP and E/KAP studied in this work.

E/KAP = T w T (H T /KAP ) M ale + (H T /KAP ) F emale 2 (1)
where w T is the organ/tissue weighting factor, (H T /KAP ) M ale is the calculated conversion factor for the ICRP 110 M ale phantom and (H T /KAP ) F emale for the ICRP 110 F emale phantom.

The KAP meter was designed as a simple parallelepiped filled with atmospheric air, and located between the source and the patient in the area perpendicular to the X-ray beam (15 cm from the focal point of the source). Its size has been modeled so that, its dimensions are compatible with the chosen field size. In each scenario, the simulated KAP value was used to normalize all the calculated dosimetric quantities. The absorbed doses for RBM and bone surface were exceptionally calculated using the methodology described in ICRP Publication 116 [START_REF] Icrp | Conversion coefficients for radiological protection quantities for external radiation exposures, ICRP publication 116[END_REF]. The calculated values were then normalized by the KAP.

The hardware platform used to run the GEANT4 code (version 10.3) is a Linux (CentOS platform) Personal Workstation with 12 GB RAM and a 2.8 GHz CP U . The number of primary particles generated was 100 million attributing a low statistical uncertainty (<0.2% for patient and <3% for operators)for all irradiation scenarios, with simulation times varying from just under 3 hours to 8 hours.

3 Results and discussion

Dosimetric evaluation for the exposed medical staff

In this section, the conversion coefficients E/KAP and H T /KAP for the medical operator were studied as a function of different parameters influencing the dose.

kVp effect

The values of E/KAP (µSv/Gy.cm 2 ) were calculated for the two professionals of the first scenario. Figure 4 shows that the E/KAP values increase with the increase of the tube voltage (varied from 80 to 120 kV p) regardless of the fixed FOV size (15 × 15 cm 2 / 20 × 20 cm 2 / 25 × 25 cm 2 / 30 × 30 cm 2 ). This is due to the scattered radiation that is considered the main reason of exposure for the operators. The scattered radiation originates mainly from the patient's body that scatters the primary X-ray beam. By comparing the effective E/KAP doses received by the two operators, we notice that regardless of the field size, the primary operator always receives the highest doses due to their closest position relative to the primary radiation beam and the scattered radiation centre.

It must be kept in mind that the tube voltage has a significant effect on the occupational dose, hence the need for better optimization of this parameter.

FOV effect

According to Figure 4, the obtained results calculated for the first scenario exposure conditions show that an increase in the size of the FOV, from 15×15 cm 2 to 30 × 30 cm 2 at a given energy causes an increase in the effective dose.

For the main operator, the average increase was 43% while for the assistant operator, the increase was 32%. Increasing the exposed body surface leads to an increase in the diffusing volume and subsequently the dose of the operators. This logic is accentuated when the X-ray tube voltage is raised (Compton effect), hence the need for optimal beam collimation.

Operator position effect

During the examination period, several workers may be present in the intervention room and are exposed to radiation, hence dosimetric monitoring to optimize staff positioning is needed [START_REF] Efstathopoulos | Occupational radiation doses to the extremities and the eyes in interventional radiology and cardiology procedures[END_REF]. In this study, the (E/KAP ) quantity for 10 locations was calculated for the second scenario (see Figure 3) to achieve this goal.

According to Figure 5, the sixth position appears to be the most irradiated position due to its proximity to both the X-ray tube and the patient who is considered the center of scattered radiation (both primary and scattered radiation are shifted to the left side of the patient: heart side). By comparing the distances separating the operators and the X-ray source, we find that, the closer the operators are standing to the radiation beam, the higher is their effective dose. This is demonstrated by the following comparison:

• Dose: D 6 > D 2 > D 5 > D 1 > D 7 > D 3 > D 8 > D 4 > D 9
• Distance (operator -X ray tube):

d 6 < d 2 < d 5 < d 1 < d 7 < d 3 < d 8 < d 4 < d 9
In principle, the 2 nd and the 6 th positions should receive the same scattered radiation. However, they receive different doses since the beam axis is nearer to the 6 th position given the position of the patient's heart and the 2 nd position is shielded by the lead table side shield.

The 10 th position violates the radiation beam proximity tendency of increased dose. Although it is closer than the 4 th , 8 th and 9 th positions relative to the radiation beam center, it scored the lowest dose since the operator in this case is partially shielded by the C-arm machine that plays the role of a barrier.

Based on this dosimetric monitoring, the estimated dose of each position can be used to describe the different locations that can be occupied by operators during interventional procedure:

• The 2 nd and 6 th positions are the most irradiated as they are the closest to the primary and scattered radiation center. These are the positions where the main operators (cardiologists) who are primarily responsible for the intervention would most probably be standing. • The 1 st , 3 rd , 5 th and 7 th positions have a lower dose than the first group.

These positions are usually occupied by assitants and trainees who's role is secondary in the intervention procedure. • The remaining positions 4 th , 8 th , 9 th and 10 th are the least irradiated positions as they are the most distant from the primary and scattered radiation centers. They can be occupied by radiological technicians, nurses or anesthesia technicians.

It should be noted that all the positions studied (except those occupied by the main operator: 2 and 6) are not constantly occupied during the intervention. The secondary actors (assistants, nurses, etc.) according to their task can move in and out the room, which results in a significant reduction of the dose. This condition was not considered in this study where we chose to assess the extreme cases of exposure with the highest doses.

Projection modality effect

In this part, we will study the influence of the type of the chosen projection on the dose received by the male main operator during the third scenario.

We notice from the Figure 6, that the E/KAP coefficients by the AP projec- tion are almost three times higher than those by the other projections.

Compared to the cases where the tube is located below the table (PA) and at the oblique planes (LAO25 • and RAO25 • ), the AP projection mainly allows an increase in the H T /KAP coefficients at the eye lens, thyroid and brain by a factor of 1.43, 2.96 and 2.85 respectively (Figure 7). Therefore, doses of unprotected parts of the body like the brain must be monitored by optimizing the most appropriate projection. Therefore, it is recommended that the X-ray tube is placed under the patient's table whenever possible. In this case, the scattered radiation will be directed away from the head and more towards the lower body of the main operator.

The LAO25 • projection becomes the most critical projection for the main operator compared to the other two projections PA and RAO25 • because of their position relative to the primary and the scattered radiation centers. So, it would be better for the operator to stand in front of the X-ray tube than to its side since they will be shielded by the patient's body and minimally exposed.

The comparison between H T /KAP F emale and H T /KAP M ale for RAO25 • projection (kVp= 100 kV p, FOV= 20 × 20 cm 2 ) shows that the equivalent doses calculated for the main female operator is slightly higher than those of her male counterpart (Figure 8). The only explanation for these deviations is the difference in geometric shape and weight of the organs between the two sexes (the woman's organs weigh less than those of the man except breast tissue) which is confirmed by the discussion included in the ICRP Publication [START_REF] Icrp | Conversion coefficients for radiological protection quantities for external radiation exposures, ICRP publication 116[END_REF].

The difference between the effective dose for male (E/KAP M ale ) and female (E/KAP F emale ) operators worths 14.17 % for RAO25 • projection.

Dosimetric evaluation for the patient

This part studies the influence of some parameters on the patient dose undergoing cardiac intervention procedure.

Comparison between (E/KAP ) M ale and (E/KAP ) F emale

The E/KAP coefficient for both male and female phantoms was calculated for the scenarios 4.1 and 4.2 respectively. We notice from the Figure 9 that the E/KAP (mSv/Gy.cm 2 ) for the patient increases with the increase of the tube voltage and the field size. This is expected because, the increase of the E/KAP coefficient is induced by the increasing energy of the beam. The KAP value, given by the absorbed dose in air, reaches a maximum around 60 keV beyond which it starts to decrease with the increasing beam energy, therefore resulting in a higher E/KAP .

Nevertheless, it is important to note that the results were obtained without taking into account the automatic exposure control that is usually functioning in this kind of equipment.

In addition, a comparison between (E/KAP ) F emale and (E/KAP ) M ale shows that the woman always scored higher values for the three studied projections than those of the male patient. The average differences vary from 6.6 % for the PA projection to 33 % for RAO25 • projection. So, without consideration of the automatic adjustment of the exposure parameters usually applied in real operations where the body mass index varies from one patient to another, we conclude that body size and habitus have an effect on the effective dose of the patient, which in turn can directly affect the energy deposition and radiation transport.

Projection modality effect

To assess the influence of the type of projection chosen (AP / P A / LAO25 • / RAO25 • ), the E/KAP and the H T /KAP were calculated for the main radiosensitive organs of a male patient during the third scenario. These results are depicted in the following figures 10 and 11. As shown in Figure 10, the highest E/KAP values were obtained for the AP projection due to the direct exposure of the patient by the X-ray beam, while the values of the three other projections P A , LAO25 • and RAO25 • are lower than those of the AP projection. This dose reduction is attributed to the most radiosensitive organs are closer to anterior surface and they are protected by the spine and posterior musculature, therefore, in the P A projection they are further away from the beam entrance surface compared to an AP projection, and to the tissue compression (the patient abdominal diameter decreases by switching to a P A/LAO25 • / RAO25 • projection). Consequently, it is recommended that the X-ray tube is located under the table allowing a significant reduction in the patient dose by an average factor of 1.34.

Given that the AP projection is not recommended in the clinical procedures, the RAO25 • projection becomes the most critical situation for the patient compared to the other two projections PA and LAO25 • during the interventional cardiology procedures.

As expected, organs located inside or near the radiation field such as the heart and lungs have higher H T /KAP of the order of mSv/Gy.cm 2 while the organs distant from the area of interest have lower values, tending to be zero (Figure 11). Organs positioned asymmetrically, like the heart (located on the left side of the patient) had higher values of H T /KAP for RAO25 • projection. On the other hand, the liver (located on the right side of the patient) had higher values H T /KAP for LAO25 • projection. In this assessment, we must take into account an important point. In real context, only a well-defined part of the skin is directly irradiated, counter to our simulation the H T /KAP to the skin concerns the entire body of the patient. Therefore, it is advisable to use our calculated coefficients only for the estimation of effective doses.

From the Figure 12, it was again confirmed that an increase in the tube voltage, from 80 to 120 kV p, leads to an increase in the E/KAP for both the patient and the main operator. The scattering radiation from the patient increases with increasing kVp, thus leading to an increase in the E/KAP to the medical staff. According to this correlation between the doses of patients and staff, we can affirm that the radiation protection of the patient and the professional are interdependent. So, reducing the patient's dose also reduces the operator's dose.

Comparison with previous work in the literature

Our results are based on MC simulations, and validation with experimental data is very important. That is why, we resorted to compare our results with those from the literature (simulated and experimental data). The average E/KAP coefficient (per scenario) was determined for the patient and the main operator during the fourth scenario with the two sub-scenarios (for all projections and tube voltages, and for 20 × 20 cm 2 of FOV).

According to Table 3, the average E/KAP coefficient for the patient was estimated by 3.05×10 -1 mSv/Gy.cm 2 which is in moderate agreement with the Ferrari et al. [START_REF] Ferrari | Simulation of H p (10) and effective dose received by the medical staff in interventional radiology procedures[END_REF] Bor et al. [START_REF] Bor | Comparasion of effective doses obtained from dose area product and air kerma measurements in interventional radiology[END_REF] Santos et al. [START_REF] Santos | Occupational exposures during abdominal fluoroscopically guided interventional procedures for different patient sizes-A Monte Carlo approach[END_REF] Our work M90-H90 phantom M10-H10 phantom

1.32×10 -1 - 1.68×10 -1 1.77×10 -1 1.40×10 -1 1.90×10 -1 1.30×10 -1 1.65×10 -1 (± 2%)
Table 3 Comparison of our results with those of previous works in the literature.

simulated results reported by Santos et al. [START_REF] Santos | Occupational exposures during abdominal fluoroscopically guided interventional procedures for different patient sizes-A Monte Carlo approach[END_REF] in the range of 1.90×10 -1 -3.50×10 -1 mSv/Gy.cm 2 depending on the used phantom and its body mass index. Also, we found that our values are not too far from those recorded by the experimental work of Efstathopoulos et al. [START_REF] Efstathopoulos | Medical personnel and patient dosimetry during coronary angiography and intervention[END_REF] and Bor et al. [START_REF] Bor | Comparasion of effective doses obtained from dose area product and air kerma measurements in interventional radiology[END_REF] during cardiac intervention procedures.

Similarly for the main operator, in our work the E/KAP was estimated by 1.65×10 -1 µSv/Gy.cm 2 which is in good agreement with the simulated results reported by Ferrari et al. [START_REF] Ferrari | Simulation of H p (10) and effective dose received by the medical staff in interventional radiology procedures[END_REF] and Santos et al. [START_REF] Santos | Occupational exposures during abdominal fluoroscopically guided interventional procedures for different patient sizes-A Monte Carlo approach[END_REF]. A good correspondence is also underlined with each of the following experimental results: the work of Tsapaki et al. [START_REF] Tsapaki | Radiation dose measurements to the interventional cardiologist using an electronic personal dosemeter[END_REF] reported an E/KAP coefficient of 1.32×10 -1 µSv/Gy.cm 2 for angiography procedures and 1.68×10 -1 µSv/Gy.cm 2 for coronary angioplasty, and the work of Bor et al. [START_REF] Bor | Measurement and estimation of cardiologist dose received in interventional examinations[END_REF] for coronary angiography procedures which confirms our estimates.

Our results are within the expected values and are in agreement with the work of previous teams. The small variations from the experiments and simulations are due to the differences in equipment, phantom types, simulation codes and statistical errors. This proves the validation of this present work in dose estimation for patients as well as medical staff in interventional cardiology procedures. And this efficiency has consequently enabled to deepen the study in the following part.

Infuence of radiation protection equipment on exposed operators

It has been proven that inteventional cardiology procedure is often associated with an elevated occupational radiation exposure, hence the need for good dosimetric monitoring and the employment of the appropriate radiation protection equipment. In common practice and apart from theoretical considerations, several cases of operators with immediate or long-term undesirable effects are revealed despite the use of personal protective equipment (PPE) such as lead aprons, thyroid shields or mobile screens which protect only the covered area. Therefore, the risk was underlined for the head and lower and upper extremities which are generally deprived of protection [START_REF] Dragusin | Evaluation of a radiation protection cabin for invasive electrophysiological procedures[END_REF].

In addition, the regular use of PPE can cause orthopaedic pathologies in the long term in particular due to the weight of the lead apron (between 4 kg and 8 kg) used during long and complex cardiology procedures.

In this section, we evaluate the effectiveness of a new means of protection: the mobile radiation protection cabin, carried out during our fifth scenario. Following the implementation of the radiation protection cabin, we can notice from the values reported in Table 4 a significant decrease in doses at the level of all the organs of the both male and female main operators. This reduction can reach an average of 83.88 %.

On the other hand, when comparing with the results of other personal protective equipment used in our scenarios 1-4 (lead apron+ thyroid shield+ eye glasses) and in previous work (lead apron) [START_REF] Santos | Occupational exposures during abdominal fluoroscopically guided interventional procedures for different patient sizes-A Monte Carlo approach[END_REF], we find that RPC provides at least the same radiation protection rate as a routine protection equipment in addition to providing a cover to almost the entire body of the medical staff. This validates the almost total radiation protection effectiveness of this cabin in the main operator's case, as well as the comfort while performing his task. Therfore, this innovative radioprotective device is a good alternative that can be used by interventional cardiologists, in particular young female cardiologists and experienced ones who have accumulated exposure over time. It is worthy to mention that wearing lead aprons remains compulsory for other staff given that the RPC is designed to protect only one operator who is the most exposed.

The current study proposes limitations to interventional practice. Computation time reduction of MC simulations would be beneficial for real-time dosimetric estimates. In addition, patient dose reduction should be investigated by employing a specialized cabin adjustable according to the specificity of the intervention.

Finally, this work is not limited to cardiac intervention and could be extended to investigate other types of medical radiation procedures. The proposed simulation can also be integrated for medical imaging purposes in addition to dosimetry and radiation protection.

Conclusion

The present work was motivated by the limited dosimetric monitoring in the literature for interventional cardiology procedures which often involve high X-ray radiation doses to the patients and the medical staff. The principal operator usually performs several cardiac intervention procedures per day and this exposes them to the accumulation of radiation doses especially since they stand closest to both the patient and the X-ray source. We can deduce from this study that following a certain protocol will considerably reduce the effective dose especially for sensitive organs of the main operator. Moreover, applying a good collimation better directs the beam away from the operators thus reducing the dose. Other medical staff are usually less exposed because the nature of their tasks does not oblige them to stand as close to the patient and in principle they accumulate less dose. In addition, a considerable reduction in dose was noticed when using radiation protection cabin. This reduction can reach an average of 83.88 %, motivating further investigation and perhaps the upgrading of personal protection equipment effectiveness in such interventions.

GEANT4 Monte Carlo code was used in this study to assess the risk of several radiation exposure situations in interventional cardiology by calculating conversion factors connecting radiation protection quantities with exposure index (E/KAP ) to evaluate the human carcinogenic risk (H T /KAP ). Two radiation protection quantities E and H T for critical organs/tissues were calculated and normalized by KAP values for both the patient and the operator. We evaluated the average E/KAP for the patient: 3.05×10 -1 mSv/Gy.cm 2 and for the main male operator: 1.65×10 -1 µSv/Gy.cm 2 . These results are within the expected values and are in good agreement with those of previous works from the literature .

The calculated quantities were closely dependent on the X-ray beam quality (kV p) and to a lesser extent on the radiation field size (F OV ) as well as the used projection modality. The medical staff organ dose was highly dependent on the staff location with respect to the beam source and the patient which was a major interest of this study.

Although using voxellized ICRP 110 computational reference phantoms enables the detailed calculation of effective doses in all sensitive organs and tissues, it remains a lengthy procedure given the computational requirements of Monte Carlo simulations. Although indicative, these phantoms are not case specific where differences in body mass index, weight, height and consequently organ sizes, from one person to another is not taken into consideration. Nevertheless, this dosimetric study could possibly be applied to any other interventional procedure with the possibility to develop a dose calculation software based on the data extracted from the simulations not to mention the future prospect of delivering a dedicated GEANT4 example for manipulating the voxellized phantoms and organ dose assessment.

Figure 2 .

 2 Figure 2. Schematic sketch of the studied projection modalities: antero-posterior (AP ), postero-anterior (P A), Left Anterior Oblique (LAO25 • ), and Right Anterior Oblique (RAO25 • ).

Figure 3 .

 3 Figure 3. Possible positions occupied by the medical staff arround the patient during an interventional procedure.

Figure 4 .

 4 Figure 4. Occupational effective dose function of kVp for different FOV: 15 × 15 cm 2 / 20×20 cm 2 / 25×25 cm 2 / 30×30 cm 2 (continuous: main operator, discontinuous: assistant operator).

Figure 5 .

 5 Figure 5. Effective dose E/KAP as a function of kVp for different operator positions during an interventional cardiology procedure.

Figure 6 .

 6 Figure 6. Effective dose (E/KAP ) for the main male operator for different projection modalities and FOV (during the third scenario: 100 kV p).

Figure 7 .

 7 Figure 7. Equivalent dose (H T /KAP )for some organs for the main male operator for different projection modalities (kVp= 100 kV p, FOV= 20 × 20 cm 2 ).

Figure 8 .

 8 Figure 8. Comparison of equivalent dose (H T /KAP ) for some organs of the main male and female operators, for RAO25 • projection (kVp= 100 kV p, FOV= 20 × 20 cm 2 ).

Figure 9 .

 9 Figure 9. Effective dose for male and female patients as a function of kVp and FOV: (a) Postero-Anterior (P A), (b) Right Anterior Oblique (RAO25 • ) and (c) Left Anterior Oblique (LAO25 • ).

Figure 10 .

 10 Figure 10. Effective dose (E/KAP ) for the male patient for different projection modalities and FOV (during the third scenario: 100 kV p).

Figure 11 .

 11 Figure 11. Equivalent dose (H T /KAP ) for some organs for the male patient for different projection modalities (kVp= 100 kV p, FOV= 20 × 20 cm 2 ).

Figure 12 .

 12 Figure 12. Correlation between patient and medical staff effective doses in interventional cardiology procedures.

Table 1 Main

 1

	Property	Male	Female
	Height (m)	1.76	1.63
	Weight (kg)	73	60
	Number of voxels	1, 946, 375 3, 886, 202
	Slice thickness (mm)	8	4.84
	Pixel size (mm 2 )	(2.137) 2	(1.775) 2
	Voxel volume (mm 3 )	36.54	15.25
	Number of columns	254	299
	Number of rows	127	137
	Number of slices	220	346

characteristics of ICRP 110 M ale and ICRP 110 F emale reference phantoms Figure 1. Simulated setup including C-arm equipment, patient's table, ICRP110

Table 2

 2 The five different scenarios explored in this study.

Table 4

 4 Comparison of percentage reduction in equivalent dose H T /KAP for some organs of the main male/female operators when using routine equipment and radiation protection cabin. Exposure parameters in all cases: PA projection, kVp= 120 kV p, FOV= 30 × 30 cm 2 .

		Previous work [4]		Our work
	means of protection	lead apron	Routine equipment	RPC
	Gender	male	male	female	male female
	Organ
	Eye lens	-	66.15	65.91	77.19 77.32
	Brain	-	11.81	11.21	81.4	81.28
	Breast	95	88.13	88.79	89.19 89.78
	Skin	-	42.1	42.16	76.14 76.21
	Thyroid	-	69.87	68.11	80.18 80.13
	Lungs	89	83.12	83.61	90.19 90.31
	Stomach	90	81.7	83.12	79.15	78.2
	Liver	91	81.61	81.15	88.98 88.71
	Heart	-	80.76	80.19	86.15 86.51
	Testes	81	83.08	-	82.19	-
	Uterus	-	-	80.61	-	89.12
	Bladder	-	83.1	82.88	90.88	90.2


Francisco Durán

Ajay Krishna

Michel Le Pallec

Radu Mateescu

Gwen Salaün

R-MOZART: A Reconfiguration Tool for WebThings Applications

The Internet of Things (IoT) is a network of physical devices and software entities that interact together for fulfilling an overall objective and thus providing added-value services. Designing such applications by selecting a set of candidate objects and defining how they interact with one another is a difficult and error-prone task. Moreover, IoT applications are not monolithic applications built once and for all. In contrast, they are constantly modified due to removal, replacement, or addition of new objects during the application's lifetime. In this paper, we present a tool built on top of the WebThings platform, which supports users when they want to dynamically change a running WebThings application. To do so, R-MOZART provides three components for (i) designing the new application using a user-friendly UI, (ii) verifying that this new application respects some consistency properties with respect to the current application, and (iii) deploying this new application in an automated manner. This tool was applied on several smart home applications for evaluation purposes.

I. INTRODUCTION

The Internet of Things (IoT) is a network of physical devices and software entities that interact together for fulfilling an overall objective and thus provide added-value services. Designing IoT applications by selecting a set of candidate objects and defining how they interact with one another is a difficult and error-prone task. Moreover, IoT applications are not monolithic applications built once and for all. Contrarily, they are constantly modified due to removal, replacement, or addition of new objects during the application lifetime. The reconfiguration of the application must be taken into account in the overall design process. Tool support is also required when dynamically reconfiguring IoT applications to simplify this task for end-users.

Several frameworks, such as IFTTT, Zapier and WebThings, promote to build IoT applications by using "if event(s) then action(s)" rules, i.e., if an event is raised, then an action is triggered. In this paper, IoT applications are described by using a composition language built on top of such rules, which provides basic constructs such as the sequence of rules, the choice between several rules, the concurrent execution of several rules, or the repetition of rules. Given such a composition expression, we can rely on an execution platform, such as WebThings, in order to deploy and effectively run the application according to the composition expression defining how objects involved in the application are supposed to interact with one another.

In this paper, we present a tool called R-MOZART, which supports the reconfiguration of IoT applications described using composition expressions. R-MOZART is an extension of MOZART [START_REF] Krishna | Mozart: Design and deployment of advanced iot applications[END_REF] and of the WebThings (formerly Mozilla WebThings) platform [START_REF] Webthings | WebThings Open Platform[END_REF]. More precisely, R-MOZART implements three new components for supporting the reconfiguration process: a dedicated UI, a verification component, and a deployment manager. At the design level, a new UI allows the user to model the new application, starting from the current one, and adding / removing objects or rules. Once the new application is described, a verification component is called to check whether the reconfiguration properties are satisfied. One of the reconfiguration properties (called seamless) determines if the current state of each remaining object (an object present in the current and in the new application) is reachable in the new application. If this is the case, it means that replacing the current application by the new application can be achieved transparently from the user's perspective. These properties are checked using an encoding of the applications and of the properties into Maude's rewriting logic [START_REF] Clavel | All About Maude -A High-Performance Logical Framework, How to Specify, Program and Verify Systems in Rewriting Logic[END_REF], and using Maude's tools to verify them. Once the reconfiguration has been validated, a deployment manager takes care of the replacement of the current application by the new one, while preserving the consistency of the remaining objects. Note that the only step of our approach requiring human intervention is the design of the new version of the application. The two other steps (verification and deployment) are fully automated by dedicated tools we have implemented and validated on several smart home applications.

The rest of this paper is organised as follows. Section II introduces models of IoT applications. Section III describes the R-MOZART functionalities to support the design of new applications, the verification of reconfiguration properties, and the application deployment. Section IV presents the evaluation of the tool. Section V surveys related work. Section VI concludes the paper.

II. MODELS

An IoT application consists of a set of IoT objects or things interacting all together to fulfil a certain overall goal. In this work, although inspired by the web of things description model [START_REF] W3c | Web of Things at W3C[END_REF], we prefer to rely on an abstract model for representing objects. Since the events / actions involved in an object are executed in a specific order, we describe the behaviour of the objects using a Labelled Transition System (LTS). The transition labels in this LTS are the events or actions associated with the object and the system can move from a state to another by performing an event or an action.

In R-MOZART, an IoT application is described by a set of objects and a composition expression, which acts like an orchestrator indicating how the involved objects interact. We use a simple rule-based composition language for this purpose. This language assumes "if event(s) then action(s)" rules as basic elements. A rule is triggered when one or several events are issued by specific objects and, as a reaction, one or several actions are issued to other objects defined as target.

These rules can be composed to build more complex expressions, using operators such as sequence, choice, concurrent execution (parallel) or repetition (loop) of rules.

Let us now explain how an IoT application, consisting of a set of objects and a composition expression, executes. The communication model being asynchronous, each object is equipped with an input message buffer (FIFO). The composition expression and all objects start their execution from their initial states. Then, an application can evolve in two ways: execution of a rule or buffer consumption. In the first case, let us assume a basic rule with one event and one action. If the event appearing in the left part of the rule has been issued, the rule can be triggered and the action appearing in the right part of the rule is pushed to the corresponding object's buffer. In the second case, if there is something in its input buffer, one object can individually consume from its buffer according to its LTS model. A global state consists of the current state of all objects involved in the application and of the progress state of the composition expression.

III. TOOL SUPPORT FOR RECONFIGURATION

This section first introduces the WebThings platform, and then presents the three components supporting reconfiguration in terms of UI, verification and deployment.

A. WebThings

WebThings [START_REF] Webthings | WebThings Open Platform[END_REF] is a platform for monitoring and controlling devices over the web. It is based on Thing Description (TD). A TD describes the state attributes of an object that can be modified by interacting with the object. In our work, we use the WebThings platform because it is simple to use and extend. The Things UI component in WebThings allows users to build IoT automation in the form of "If event(s) then action(s)" Event-Condition-Action (ECA) rules. It also provides web APIs for monitoring and controlling IoT objects. Many of the popular objects are already supported by the platform and more objects are constantly being added.

MOZART [START_REF] Krishna | Mozart: Design and deployment of advanced iot applications[END_REF] is a tool built on top of WebThings to support the design and deployment of complex applications. In addition to individual ECA rules, it allows users to compose these rules using the composition language described in Section II. Composition of rules enables the design of more expressive application scenarios. In this work, MOZART has been extended to support end-to-end reconfiguration. The next subsection presents the reconfiguration support in detail.

B. Reconfiguration Support

Support for reconfiguration requires the implementation of three components. First, at the design level, graphical user interfaces need to be available to specify changes to an existing application. Once the changes are specified, another component is required to verify that the new application satisfies several properties. Finally, a third component is in charge of the deployment of the new application. User Interface. Initially, a first version of an IoT application is running. When the user starts the reconfiguration process by clicking on the corresponding button, a copy of the current application appears in a new window. Then, (s)he can modify this application by adding or removing objects or rules, and also by changing the way in which the rules are composed. The UI also provides several buttons to initiate the verification or the deployment of the new application. Verification. Once the redesign is finalised from a modelling perspective, the user can compare the new application with the current one to check the reconfiguration impact. Given the current and new applications, and the global state of the current application, the main reconfiguration property (called seamless) determines if the given global state is reachable for objects remaining in the new application. If this is the case, it means that replacing the current application by the new application can be achieved transparently from the user's perspective. We also define two additional properties called conservative and impactful to check whether all former behaviours can still be executed in the new application, and whether all new behaviours can be executed after the reconfiguration, respectively. These three properties focus on the reconfiguration of an application given a global state. Complementary to these properties, functional properties of interest, like deadlock freeness, can be verified on the new application. When checking these properties, a result indicates whether they are satisfied or not. If they are satisfied, the user can decide to proceed with the deployment. If they are not, (s)he can revise the design or keep the application running as it is. Deployment. The deployment manager performs two tasks: i) undeploy the removed objects while preserving the state of the remaining objects; ii) set up and deploy the new objects, and start the reconfigured application. First, current states of all objects are stored in a database along with the execution history of the application. This is followed by the disabling of rules. Then, rules are replaced or new rules are created depending on the reconfiguration. New rules are created using the Rules UI, and when they are enabled, event listeners associated to the events in these rules are created. Similarly, when the rules are disabled, their associated event listeners are removed. Here, we say rules and not individual objects because adding or removing objects is a modification to a rule, as objects are part of an event or an action. In other words, adding an object means including the object in a rule, from the available pool of objects, and removing an object implies that it is no longer used in a rule. Now, these rules need to be deployed for the application to run. Deployment resumes the application from the state where it was before initiating the reconfiguration. Remaining objects maintain their previous states. As for newly added objects, we simulate the execution trace of the current application on the new composition expression. As a result, we obtain the states from where the new objects have to start when deploying the new application. Newly added rules are initialised in disabled states. As a last step, we use the execution history of the current application to compute the progress state from where the new composition expression should start, which allows us to determine the set of rules to be enabled. From here, the execution engine takes care of running the application. It follows the composition expression semantics by enabling or disabling relevant subsets of rules as the execution of the expression progresses. Implementation Stack. The reconfiguration UI is built on top of the Things UI powered by Node.js. Users can drag and drop rules and composition operators to modify the composition. The reconfiguration check is achieved by transforming the current and new compositions, specified in JSON, to a Maude [START_REF] Clavel | All About Maude -A High-Performance Logical Framework, How to Specify, Program and Verify Systems in Rewriting Logic[END_REF] specification using Java/SpringBoot and Freemarker libraries. Current states of the objects are collected using the monitoring APIs provided by the Things API and stored in an SQLite database. The state of the composition expression is updated by manipulating the event listeners. During deployment, new objects are moved to appropriate states using the control APIs that allow to set object states (e.g., switch on the lamp or change its colour to red).

Figure 1 gives a glimpse of our tool for reconfiguration. On the left of the figure, we can see the available rules and composition operators in the redesign screen. The top of the figure shows the reconfiguration scenario with the current and the new application. In the middle, the response from the analysis can be seen. Options for verification and deployment are shown on the right. The available devices are shown on the bottom of the screenshot.

IV. EVALUATION

The tool has been tested to evaluate the usability, performance and correctness of the approach.

Setup. R-MOZART was hosted on a local machine (PC) and on a Raspberry Pi 3, connected to a private wireless network. Then, we added a set of connected devices which included Philips Hue lights, Hue motion sensors, Hue Play lights, connected thermometer, and connected speakers. As the devices were on the same network, they were easily discovered and added to the monitoring interface of the WebThings UI.

Usability. The tool provides an interface based on the WebThings UI elements for reconfiguration, analysis, deployment of reconfigured applications. Six users (age range 23-45) with varying programming skills (none to expert) were given a short training (∼ten minutes) on the usage of the tool and its features. Then, they were given a description in natural language about the reconfiguration of two existing applications having four and five rules in them. The IoT devices required for the new application were already connected to the WebThings platform. Users had to create new rules and update the composition expression. All the users were able to complete the given tasks in under ten minutes (time-based efficiency of 0.24 goals/min) (Figure 2).

Finally, the users were asked a Single Ease Question (SEQ) at the end of the tasks in a 7-point Likert Scale and the users found the interfaces intuitive (average SEQ score: 5.83). Beyond usability, users found the notion of conservative and impactful useful and the utility of seamless reconfiguration was evident for them when we explained the reconfiguration scenarios.

Performance. The time taken to transform the new composition to Maude encoding and to perform the formal analysis for different applications was always in hundreds of milliseconds as the analysis syntactically compares the traces from the global state. Moreover, the objects involved in IoT applications are simple and the number of objects is usually not very high, explaining why the results are almost instantaneous. The readers can find online [START_REF] Durán | R-MOZART: A Reconfiguration Tool for WebThings Applications (Online Code)[END_REF] several applications and the corresponding generated Maude code for verifying properties.

Correctness. Once validated, applications were deployed through the newly developed deployment manager. Reconfiguration is correct / seamless if after deployment of the new application, each remaining object remains in its current state. Upon deployment, we indeed observed that the applications kept working smoothly without any change of state for the remaining objects.

V. RELATED WORK

There are several tools available for end-users to design and deploy IoT applications. IFTTT [START_REF] Ifttt | Do more with the things you love[END_REF] relies on ECA rules and provides a large repository of pre-defined ECA rules called Applets. The Applets use single event triggers in the ECA rules. Similarly, Node-RED [START_REF] Js Foundation | Node-red: Flow-based programming for the IoT[END_REF] provides Recipes to connect IoT devices visually. OpenHAB [START_REF] Openhab | openHAB: Empowering the smart home[END_REF] is another home automation software that can be programmed by advanced users. It provides rule scripts in the form of WHEN somethinghappens THEN do-something and it allows logical disjunction of multiple triggers in the rules, if-else expressions, for loops, etc. webCoRE [START_REF] Webcore | webcore: The web community's own rule engine[END_REF] is another programmable rule engine to build advanced automation. Samsung SmartThings and Apple Workflow provide automation support in the form of routines. Finally, WebThings in its current form supports rules with multiple triggers and actions. In all these tools, rules are executed in parallel and there is no support for composition of rules. Further, there are no mechanisms to support the reconfiguration of IoT applications ensuring specific properties.

Seamless reconfiguration is not a new notion and was used in several works focusing on dynamic reconfiguration, e.g., [START_REF] Rosa | A Framework to Support Multiple Reconfiguration Strategies[END_REF], [START_REF] Vogel | Autonomous Reconfiguration Procedures for EJB-based Enterprise Applications[END_REF]. As an example, [START_REF] Vogel | Autonomous Reconfiguration Procedures for EJB-based Enterprise Applications[END_REF] presents a flexible approach to seamless reconfiguration of Entreprise JavaBeans applications. This work provides generic and reusable procedures for automatically supporting reconfiguration tasks. The The approach presented in [START_REF] Seeger | Dynamic IoT Choreographies[END_REF] proposes to extend semantic application descriptions (called recipes) with constraints to enable dynamic and automatic reconfiguration of IoT applications. Using recipes, dynamic choreographies can be created that self-adapt to changing device states without human intervention. [START_REF] Koziolek | OpenPnP: A Plug-and-produce Architecture for the Industrial Internet of Things[END_REF] introduces the OpenPnP reference architecture, which allows a significant reduction of configuration and integration efforts during industrial plant commissioning. The OpenPnP architecture reduces configuration and installation time by up to 90 percent, while scaling to IIoT systems with many nodes. OpenPnP also provides concepts for replacing malfunctioning devices. In our work, we propose analysis techniques for reasoning on the behaviour of the application before deciding actual reconfiguration, and a solution to deploy this new application on the WebThings platform.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Screenshots of the reconfiguration tool

VI. CONCLUDING REMARKS

In this paper, we have presented R-MOZART, an extension of WebThings and MOZART to support the reconfiguration (design, verification, deployment) of WebThings applications modelled using compositions of ECA rules. The main perspective of this paper is to extend this work with quantitative analysis results in order to check if the reconfiguration also preserves quality-of-service properties.

VII. DATA AVAILABILITY

The code related to the tool is available at https://zenodo.org/badge/latestdoi/312541691.

https://youtu.be/bG4oiQUrWSQ