PhD Mouna Ben Salem
email: mouna.ben-salem@umontpellier.fr

Guillaume Aiche

Yassine Haddab
email: yassine.haddab@lirmm.fr

Lennart Rubbert
email: lennart.rubbert@insa-strasbourg.fr

Pierre Renaud
email: pierre.renaud@insa-strasbourg.fr

Microbiota Sampling Capsule: Design, Prototyping and Assessment of a Sealing Solution Based on a Bistable Mechanism

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The objective of the presented work is to allow non-invasive sampling of microbiota, while today surgery is still needed to collect intestinal liquid samples. Given the recent developments of endoscopic capsules and their added value for the diagnosis and the treatment of gastrointestinal pathologies, the paper is focused on the design of a microbiota sampling capsule. The device has to offer an efficient collection of intestinal liquid and at the same time to protect it from any contamination as the device navigates out of the human body. To ensure this and safety at the same time, a passive capsule is developed based on a bistable mechanical structure actuated by an expanding foam that itself collects the intestinal fluid. The device is developed using additive manufacturing. It is shown the process is compatible with the production of the complete capsule with biocompatible material, while ensuring the presence of bistability for capsule closing. In-vitro trials validate the operating mode of the capsule. This work represents a significant step towards the creation of simple and yet efficient tools to better understand microbiota impact and future pathology detection.

Introduction

The microbiota consists of a collection of microorganisms such as micro-fungi, bacteria and viruses that live in the microbiome, which is a specific environment hosted by a plant or an animal host [START_REF] Sender | Revised estimates for the number of human and bacteria cells in the body[END_REF]. There are several types of microbiota in the human body, including the skin microbiota which is the collection of microorganisms living on the skin, the vaginal microbiota which is found in the vagina and the intestinal microbiota which is located in the gastrointestinal (GI) tract. In this paper, we are interested in the intestinal microbiota of humans and more specifically that located in the small intestine which is the host of the largest number of bacterial species in the human body. The intestinal microbiota is dominated by a composition of five bacterial species. The concentration of these bacteria varies depending on its location in the small intestine which is composed of three sections illustrated in Fig. 1 [START_REF] Round | The gut microbiota shapes intestinal immune responses during health and disease[END_REF]. In the duodenum and jejunum, the concentration of bacteria varies between 10 3 and 10 4 per mL. In the ileum, the number of bacteria is around 10 8 per mL [START_REF] Sender | Revised estimates for the number of human and bacteria cells in the body[END_REF].

E Stomach ' Duodenum ' Jejunum
' Ileum E Large intestine E Anus FIGURE 1: Representation of the small intestine's sections and its neighboring organs [START_REF] Larousse | Encyclopédie Larousse en ligne -iléon[END_REF].

Relation Between the Microbiota and Pathologies

Described as the second brain in the human body, the intestinal microbiota play an important role in maintaining health as its balance helps prevent and treat several diseases. Recent research has proven that there is a fundamental relationship between the imbalance of the intestinal microbiota, also known as dysbiosis, and the detection of several pathologies such as obesity [START_REF] Tremaroli | Functional interactions between the gut microbiota and host metabolism[END_REF], type 2 diabetes [START_REF] Qin | A metagenome-wide association study of gut microbiota in type 2 diabetes[END_REF], chronic inflammatory bowel disease [START_REF] Swidsinski | Mucosal flora in inflammatory bowel disease[END_REF] and colorectal cancer [START_REF] Garrett | Cancer and the microbiota[END_REF]. Dysbiosis can also affect human mood and behavior and can even lead to stress and depression [START_REF] Valles-Colomer | The neuroactive potential of the human gut microbiota in quality of life and depression[END_REF].

Therefore, it is important to collect the intestinal microbiota to advance basic clinical research, understand the clinical relevance and, in the future, help identify the pathologies described above.

Conventional Microbiota Sampling Method

Two methods have been developed for microbiota sampling. The first one consists of collecting the human stool in order to remove bacteria, culture them and determine DNA sequences which will be used to identify the dysbiosis [START_REF] Panek | Methodology challenges in studying human gut microbiota-effects of collection, storage, dna extraction and next generation sequencing technologies[END_REF][START_REF] Tang | Current sampling methods for gut microbiota : a call for more precise devices[END_REF]. Although this method is non-invasive, it represents two major limitations. Only 50% of the intestinal bacterias can be cultured due to precise oxygen requirements which means that several species of intestinal bacterias cannot be analyzed. In addition, the intestinal environment is not homogeneous, which means that some bacteria species cannot be found in the stool. Therefore, this method does not allow the collection of all the information needed to discover and identify the intestinal environment.

The second method of microbiota sampling consists of making an incision in the small intestine in order to access it and take a sample [START_REF] Swidsinski | Mucosal flora in inflammatory bowel disease[END_REF]. It is often performed simultaneously with other surgical operations on the abdomen [START_REF] Tang | Current sampling methods for gut microbiota : a call for more precise devices[END_REF]. Although this method is precise, it remains very invasive and uncomfortable for the patient.

Capsules Used for Microbiota Sampling

In order to have an accurate and non-invasive sampling, the bacteria must be collected directly from the small intestine with a tool that gains access without creating any complications for the human body or contamination of the sam-pled bacteria. Swallowable devices also known as "capsules" have been considered for microbiota sampling.

A capsule is a swallowable device originally developed to inspect the digestive tract. It has a cylindrical shape and dimensions adapted to ingestion: a maximum of 11 mm for the diameter and 26 mm for the length [START_REF] Riccioni | Colon capsule endoscopy : Advantages, limitations and expectations. which novelties ?[END_REF]. It facilitates the access to different sections of the gastrointestinal tract in a non-invasive way which makes it an interesting concept that was used by many researchers to perform different functions in the digestive tract such as locomotion, localisation, diagnostic and treatment [START_REF] Ciuti | Capsule endoscopy : from current achievements to open challenges[END_REF].

One of the first capsules for microbiota sampling was developed in 1999 by Gu et al. [START_REF] Gu | Intestinal fluid sampler[END_REF]. It has an oval shape with a diameter of approximately 10 mm. The operating mode of this capsule is based on the biological interactions, mainly the change in pH in the gastrointestinal tract, of the latter and the intestinal environment. Although this capsule is noninvasive, the sealing of its orifice remains weak because it has no resistance to the reopening of the device. Therefore, the absence of contamination cannot be guaranteed.

In 2014, Amoako-Tuffour et al. introduced two concepts of intestinal microbiota samplers composed of sample storage chambers, a motor that actuates the opening and the closing of the latter and a power unit based on the use of batteries [START_REF] Amoako-Tuffour | Ingestible gastrointestinal sampling devices : state-of-the-art and future directions[END_REF]. Although the integration of several power supply and communication modules allows for precise sampling, these devices do not have the capacity to potentially store more liquid. The presence of electronic components is, in addition, a concern in terms of risk analysis for the device.

Recent researchers have opted for the development of passive devices. Shape memory spring [START_REF] Rehan | Capsule robot for gut microbiota sampling using shape memory alloy spring[END_REF][START_REF] Jones | Sampling capsule system and methods[END_REF], foam or even hydrogel [START_REF] Waimin | Smart capsule for non-invasive sampling and studying of the gastrointestinal microbiome[END_REF] have been considered to actuate and target the sampling task in the small intestine. To optimize the precision and localization of the sample in such passive capsules, magnetic actuation systems have been developed in [START_REF] Finocchiaro | Design of a magnetic actuation system for a microbiotacollection ingestible capsule[END_REF][START_REF] Shokrollahi | Blindly controlled magnetically actuated capsule for noninvasive sampling of the gastrointestinal microbiome[END_REF]. In 2017, a research team from the laboratory TIMC-IMAG (Grenoble, France) developed a microbiota sampling capsule [START_REF] Thélu | Device for intestinal fluid sampling[END_REF]. The device is composed of an elastic membrane and a foam for intestinal liquid absorption. The membrane has an entry orifice which is closed after sampling by a locking system as shown in the example in Fig. 2. The foam used to collect the intestinal liquid is also being used as an actuator that triggers the locking system. The use of foam is of great interest because it is biocompatible and can rapidly absorb the microbiota. However, the closing force will be completely constrained by the choice and the volume of the foam. [START_REF] Thélu | Device for intestinal fluid sampling[END_REF] for microbiota sampling.

Given the capsules presented in this state of the art summary, the capsule that we propose must ensure the sampling of microbiota while guaranteeing the sealing of its closing and its resistance to any force applied by the gastrointestinal tract after sampling. To eliminate electrochemical risk and reduce sources of risks, we avoid the integration of electronic components and batteries in the device. In addition, the risk of capsule blocking is limited by respecting maximum standard sizing and the risk of toxicity is reduced by considering biocompatible materials. Our goal is to collect the microbiota from a part of the small intestine with a non-invasive, noncontaminating and less expensive device.

Following preliminary work in [START_REF] Salem | Design of a microbiota sampling capsule using 3d-printed bistable mechanism[END_REF], we present a microbiota sampling capsule that operates in the human small intestine and is used for diagnostic purposes. We develop an approach based on the use of a foam to both store the intestinal liquid and actuate the closing of the capsule. To do so, we need a structure which can be passively triggered by the foam and can resist reopening. A bistable mechanism where the closing energy is a mechanical energy stored in it is the most suitable structure to be used [START_REF] Zirbel | Bistable mechanisms for space applications[END_REF][START_REF] Chen | Microrobotique numérique fondée sur l'utilisation de modules bistables : conception, fabrication et commande de modules monolithiques[END_REF]. In section 2, we elaborate on the design requirements that the capsule must meet, followed by its components the most important of which are the foam used as an actuator and the bistable mechanism used to close the capsule. In addition, the operating mode of the capsule in the gastrointestinal tract is described.

Section 3 is dedicated to the characterization of the actuator in order to determine the force applied by the foam and its force-displacement characteristic. We focus, in section 4, on the modeling of the bistable mechanism by an analytical model, the manufacturing process as well as the experimental characterization. Finally, section 5 is dedicated to the prototyping of the capsule in its final version and the validation of its operating mode by in-vitro trials.

Microbiota Sampling Capsule 2.1 Key Requirements

The design of the microbiota sampling capsule must respect certain constraints imposed by the gastrointestinal environment, the safety of the patient and the protection of the sample. The capsule will be admitted into the human body through the mouth. Therefore, it must be able to transit through the gastrointestinal tract without creating any complication for the patient. Its dimensions must not exceed the maximum dimensions which are those of the largest swallowable pharmaceutical capsule 000 according to the capsule size classification standard [START_REF] Murachanian | Two-piece hard capsules for pharmaceutical formulations[END_REF][START_REF] Sayeed | Size, Shape, and Other Physical Attributes of Generic Tablets and Capsules[END_REF], i.e. 9.5 mm for the diameter and 26.1 mm for the length. Here our capsule has a diameter of 9 mm and a length of 26 mm.

While passing through the stomach, the capsule must withstand the acidity of the gastric environment which has a pH close to 2 [START_REF] Beasley | The Evolution of Stomach Acidity and Its Relevance to the Human Microbiome[END_REF]. Once in the small intestine, where the pH level is higher and close to 7 [START_REF] Evans | Measurement of gastrointestinal pH profiles in normal ambulant human subjects[END_REF], the capsule must open to collect the sample of intestinal microbiota. It is necessary to ensure that the intestinal liquid can penetrate into the capsule considering that it has a viscosity of approximately 0.085 Pa•s [START_REF] Lai | Micro-and macrorheology of mucus[END_REF][START_REF] Terry | Mechanical characterization of the small intestine for in vivo robotic capsule endoscope mobility[END_REF]. The sampled volume must be sufficient to analyze the bacteria species located in the small intestine. For this, a volume of 0.20 mL is adequate because it represents approximately 2.0E+7 of bacteria [START_REF] Berg | The indigenous gastrointestinal microflora[END_REF].

Afterwards, the capsule transits through the rest of the gastrointestinal tract to exit the human body. At this stage, it is important to guarantee its sealing to protect the sample from contamination. In total, the transit of the capsule takes approx. 5 hours to arrive at the sampling location [START_REF] Fireman | Capsule endoscopy : improving transit time and image view[END_REF]. When passing through the anus, the capsule must resist reopening considering that the anal sphincter can apply a pressure that can go up to 0.02 MPa [START_REF] Bansal | Anorectal manometry : current techniques and indications[END_REF].

Capsule Components

In order to satisfy the key requirements described above, we have chosen to develop a capsule controlled and closed in a passive manner. It measures 9 mm in diameter and 26 mm in length and is composed of a shell that has an entry orifice. The shell is composed of two parts as shown in Fig. 3a. A PVA foam (Polyvinyl Alcohol) is used to both store the intestinal liquid and actuate the bistable mechanism to close the capsule [START_REF] Thélu | Device for intestinal fluid sampling[END_REF]. As the name suggests, the bistable structure (shown in Fig. 3b) has two stable positions: one for opening the capsule and the other one for sealing it once the sampling is done.This structure meets our needs as it has a configuration that stores the mechanical energy used to ensure closing in an instantaneous way rather than a progres-3

Copyright © by ASME sive way. At the same time, it offers a resistance to reopening which is used to guarantee the protection of the sample from contamination and/or loss. In addition, the structure is manufactured precurved in its first stable position. Therefore, there is no need to buckle the beams during assembly which is important to get a repeatable behavior. The capsule is made in three parts and assembled by the pins and gaps that are located respectively in the bistable mechanism and the part 2 of the capsule shell, as shown in Fig. 3a and Fig. 3b.

Operating Mode

When transiting through the gastrointestinal tract, the capsule follows the scenario described in this section. In its initial state, the capsule shell is assembled with the bistable mechanism. The latter is preloaded in the second stable position for which the capsule remains open as shown in Fig 4a. The capsule is initially surrounded by a gastro-resistant gelatin coating. It protects the capsule during its transit through the stomach and it dissolves at a pH close to 7 which is that of the small intestine. Although the coating does not allow to precisely control the time or place of capsule opening, the variation in pH is sufficient to sample in a relevant zone in terms of analysis of intestinal bacteria. In addition, it is possible to choose a coating thickness to ensure opening in the small intestine or to ingest several capsules with different thicknesses of the soluble coating allowing samples to be taken from various places.

Thus the capsule reaches the small intestine, the pH level gradually increases which causes the coating to dissolve until it totally disappears. The capsule is then open, allowing intestinal liquid to enter through the entry orifice. Once in contact with the liquid, the foam absorbs it and swells to provide a sufficient force to trigger the bistable mechanism and close the capsule. These steps are shown in Fig. 4b and Fig. 4c.

Actuator Selection 3.1 Actuator Description

Here the actuator used in the microbiota sampling capsule is a PVA foam which is a high absorbency material used in surgeries [START_REF] Kuchaiyaphum | High absorbable cassava starch/poly (vinyl alcohol) sponge for medical applications[END_REF]. It is also used to store the sampled intestinal liquid. It is biocompatible and can quickly absorb a sufficient volume of microbiota while tripling its volume [START_REF] Thélu | Device for intestinal fluid sampling[END_REF].

In this section, we will focus on the characterization of the foam to be integrated into the capsule using an experimental method in order to obtain the generated force according to its expansion. The intestinal environment considered, in this experiment, consists of water. Later, when the validation of the capsule sealing is carried out, a liquid of the same characteristic as that of the human intestinal fluid will be used.

Experimental Characterization of the foam

The experimental characterization of our actuator consists of determining the force applied by the foam considering its dimensions which are constrained by its integration into the capsule. The identification of these dimensions are constrained by those of the capsule and the bistable mechanism. The foam must be positioned below the bistable mechanism to guarantee its actuation as shown in Fig. 3a. Therefore, its dimensions must not exceed a maximum length of 13 mm, a width of 5 mm and a height of 2 mm. The initial height of the foam is set at 10 mm before compression with a compression ratio equal to 5 which facilitates its integration into the capsule.

An experimental setup is made to obtain the force provided by the foam. It is composed of a laser-based displacement sensor (LK-H152, Keyence) that has a measuring range of 80 mm with a resolution of 0.25 µm, a bending beam for which the stiffness has been initially determined and a mounting system for both the beam and the foam. The experimental setup is illustrated in Fig 5.

Initially the foam is compressed and placed, with zero contact, below the beam as shown in Fig. 5 0.20 mL of water is absorbed. Afterwards, the foam swells, generates a force and pushes the beam which displacement is obtained by the laser sensor. As the stiffness of the beam is initially determined, the force applied by the foam can be easily calculated from the beam deflection.

Results

The foam can generate a maximum force of 1.5 N. Figure 6 shows the curve of the force provided by the foam in relation to its expansion from the compressed height. The foam height can grow up to an additional 3 mm. When the foam expands, the force decreases to a minimum value of 0.45 N. The force obtained will be used in the following section to determine the dimensions and characteristics of the bistable mechanism.

Definition and Geometry

A bistable mechanism is a structure which has two stable positions. The one used in the microbiota sampling capsule is composed of the superposition of two beams initially manufactured preshaped curved and connected together with a shuttle as shown in Fig. 7. This prevents the shuttle from rotating when the mechanism is triggered and also ensures its bistability [START_REF] Vangbo | An analytical analysis of a compressed bistable buckled beam[END_REF]. The neutral fiber of the preshaped curved beams follows an initial shape which is that of the first buckling mode of a straight beam as illustrated in Fig. 8, and given by the following expression:

w(x) = h 2 [1 -cos(2π x L )] (1)
where w(x) the vertical position of the beam's neutral fiber in the first buckling mode, L the length of the preshaped curved beam, t its thickness, b its width and h its initial height. As shown in Fig. 9, the force-displacement curve is asymmetrical due to the fact that the mechanism is initially manufactured in its first stable position. This asymmetry is advantageous because the mechanism can be triggered by a 5

Copyright © by ASME low force f bot which is supplied by the foam. On the contrary, the large value of f top helps to seal the capsule.

To allow the bistable mechanism to be preloaded into its second stable position, it is important to keep a sufficient distance between the first stable position and the bottom of the frame as shown in Fig. 10 so that the bistable mechanism can overcome the unstable equilibrium position. According to Fig. 9, the distance between the first stable position and the unstable equilibrium position is D1 and the one between the unstable equilibrium position and the second stable position is D2. It is also important to take into consideration the compressed height of the foam h c . Therefore, the height of the foam is added to the distance D2 to determine the position of the bistable mechanism between the unstable equilibrium position and the second stable position.

T Integration of the foam in the bistable mechanism

D1 = P1 P2 = D2 + h c 3 mm ' Foam
The two positions (P1) and (P2) corresponding to the distances (D1) and (D2 + h c ) are respected in the positioning of the bistable mechanism as shown in Fig. 10.

Modeling

In order to determine the dimensions of the bistable mechanism to be integrated into the capsule, sizing constraints must be respected as well as the actuation forces. The dimensions shown in Fig. 8 can be calculated using an analytical model developed by Hussein in [START_REF] Hussein | Contribution to digital microrobotics : modeling, design and fabrication of curved beams, U-shaped actuators and multistable microrobots[END_REF] and based on the buckling phenomenon [START_REF] Qiu | An Electrothermally-Actuated Bistable MEMS Relay for Power Applications[END_REF].

To guarantee that the structure has a bistable behavior, the ratio Q between its initial height h and its thickness t must exceed 2.31. Once this condition is met, the mechanism can be characterized by the force-displacement curve of its shuttle as shown in Fig. 9. f top and f bot are the forces needed to trigger the structure and switch between the two stable positions. d top and d bot are the displacements of the shuttle when maximum forces are applied. d mid and d end are the displacements for a zero force.

The value of these forces and displacements can be calculated using the following expressions [START_REF] Hussein | Contribution to digital microrobotics : modeling, design and fabrication of curved beams, U-shaped actuators and multistable microrobots[END_REF]:

d top = h( 28 27 - 2π 3
1 6 + 16 81π 2 - 1 Q 2 ) ( 2
)
d bot = h( 28 27 + 2π 3 1 6 + 16 81π 2 - 1 Q 2 ) ( 3
)
d end = h( 3 2 + 1 4 - 4 3Q 2 ), d mid = 1, 33h (4)
f top = 64π 2 EIh L 3 ( 4 3 - d top h ) (5)
f bot = 64π 2 EIh L 3 ( 4 3 - d bot h ) (6)
with E the Young's modulus of the material, I the quadratic moment and Q = h t the bistability ratio. Although the f top and f bot forces play a very important role in determining the dimensions of the bistable mechanism, other dimensional constraints also have an impact on this choice. The initial height h is determined by taking into consideration the bistability ratio Q and the integration of the mechanism in the capsule. As for the value of the length L, it is impacted by the dimensions of the capsule as well as its assembly. Finally, the thickness t is determined by the manufacturing parameters.

Taking into consideration these dimensional constraints and the force provided by the foam to trigger the mechanism and close the capsule, a set of parameters for the bistable mechanism is determined and given in Table 1.

The force-displacement characteristic of this mechanism is determined using the analytical model. The force value of f top is 8.2 N and that of f bot is 4 N.

6

Copyright © by ASME 3 Additive Manufacturing with Fused Deposition Modeling Additive manufacturing using Fused Deposition Modeling (FDM) is chosen to manufacture the prototype of the capsule and the bistable mechanism due to its several advantages. FDM deposits a fused filament in successive layers through a nozzle. Therefore, the final part consists of a stack of thin layers [START_REF] Mohamed | Optimization of fused deposition modeling process parameters : a review of current research and future prospects[END_REF]. It is of great interest because it offers the possibility of using biocompatible materials (e.g. ABS-M30i, Stratasys, Ltd) which is very important considering the use of the capsule in the human body [START_REF] Chia | Recent advances in 3D printing of biomaterials[END_REF][START_REF] Culmone | Additive manufacturing of medical instruments : A state-ofthe-art review[END_REF]. In addition, it enables rapid manufacturing of the prototype, which accelerates the design cycle at a relatively low cost.

The capsule parts are manufactured on a centimeter scale with the Fortus 400mc industrial printer (Stratasys, Ltd) using the T12 nozzle (diameter = 0.178 mm). It is manufactured by depositing a contour that has a thickness of 0.35 mm. Therefore, it is necessary to understand and master the process as well as the behavior of the parts after the manufacturing.

The first manufacturing iterations revealed problems with the shuttle and the surfaces between the latter and the beams which present several gaps [START_REF] Salem | Développement d'une capsule de prélèvement de microbiote intestinal utilisant un mécanisme bistable et exploitant la fabrication additive[END_REF]. In order to master the filament deposit used to manufacture the parts, certain parameters must be identified and optimized, i.e. e cc the gap between the deposited contours, e cr the gap between the contour and the filling and e rr the gap between the filling deposited. Finally, experimental trials allowed the identification of adequate values of each parameter for successful parts manufacturing as shown in Fig. 11. We used e cc = -0.05 mm, e cr = -0.005 mm and e rr = -0.05 mm which guarantees optimal filling of the printed parts.

Dimensional measurements for the length L and the initial height h were carried out to determine the dimensional accuracy of the manufactured mechanisms. The total length of the structure is equal to an average of 21.4 mm with a maximum deviation of 0.40 mm and the initial height is equal to an average of 2.37 mm with a maximum deviation of 0.13 mm. Following these results, the bistability ratio Q and the forces f top and f bot of the mechanisms manufactured with FDM are verified. The average value of the ratio Q is equal to 6.7 which guarantees the bistable behavior. f top is equal to 6.5 N and f bot is equal to 3.1 N.

Experimental Characterization

Manufacturing the bistable mechanism has been proved achievable at the centimeter scale by FDM. However, this technology can have an impact on the force-displacement characteristic of the mechanism. In this section, we carry In order to conduct the experimental characterization of the bistable mechanism, a dedicated setup was built as shown in Fig. 12. It is composed of a laser-based displacement sensor (LK-H152, Keyence) that has a measuring range of 80 mm with a resolution of 0.25 µm, a force sensor (FK50, Sauter) with a maximum force of 50 N and an accuracy of 0.02 N and a mounting bracket to fix the bistable mechanism. 11, to allow both measurement and actuation. A force is applied to the bistable mechanism by moving the linear table on which the force sensor is mounted and the corresponding displacement is measured using the laser sensor. Once the positive forces are determined, the bistable mechanism is reversed to determine the negative forces.

The bistable behavior of each mechanism is characterized by determining the force-displacement curve. These results are compared to the force-displacement curve obtained by the analytical model and presented in Fig. 13a.

The experimental characterization shows a difference of 7

Copyright © by ASME The difference between these results may be related to the impact of FDM such as the variation of the Young's modulus after manufacturing as well as the geometric proportions of the bistable mechanism [START_REF] Salem | Développement d'une capsule de prélèvement de microbiote intestinal utilisant un mécanisme bistable et exploitant la fabrication additive[END_REF]. However, this does not affect the operating mode of the capsule because f bot is smaller than the force provided by the actuator which can reach 1 N. As for f top , we must experimentally verify that this force is sufficient to guarantee the sealing of the capsule which will be conducted in the next section.

Validation of the Microbiota Sampling Capsule

The experimental characterization of the bistable mechanism obtained previously is used in this section to up-date the design of the capsule according to these results and to propose a closing cap that guarantees its sealing. The improved version of the capsule is then manufactured and used to validate the operating mode using an artificial intestinal liquid. At this stage, it is important to use such a liquid in order to validate the ability to seal the capsule when the surrounding fluid is high viscosity intestinal fluid.

Design Update

In order to improve the design of the capsule, first we refine that of the bistable mechanism based on the results of the experimental characterization. This step is very important because it allows the definition of the shape and the dimensions of the closing cap that will be added to the bistable mechanism.

Therefore, the positioning of the bistable mechanism in the capsule is modified while respecting two important aspects to its proper functioning which are ensuring the preloading of the bistable mechanism in its second stable position and the integration of the foam below it as described in section 4. According to the experimental result, the distances D1 and D2, illustrated in Fig. 10, must be equal to respectively 3.7 mm and 1.2 mm.

The second step consists in incorporating a closing cap to the bistable mechanism as shown in Fig. 14. It will not only ensure the sealing of the orifice but it will also apply a sufficient pressure to prevent the capsule from reopening. The cap is of rectangular shape and of dimensions 2 mm x 5 mm and is used to close an orifice of dimensions 1 mm x 3 mm. As for the pressure applied by the cap, its value is determined from the force-displacement curve of the bistable mechanism. Theoretically, the point (d top , f top ), illustrated in Fig. 13a, corresponds to the maximum and ideal force to ensure the sealing of the capsule and its resistance to reopening. However, due to dimensional constraints, we do not implement the cap at this maximum position but rather in its vicinity which nevertheless corresponds to an important force. To do so, the cap is designed such that when the capsule is sealed, the bistable mechanism exerces a maximal locking force of 1.7 N according to Fig. 13b. With this force, the mechanism is able to apply a pressure of 0.17 MPa to a surface of 5 mm x 2 mm. The closing cap is combined with a 0.1 mm thick silicone seal (composed of methyltriacetoxy-8

Copyright © by ASME silane and triacetoxyethylsilane) to prevent liquid from ring. With the additional seal and according to Fig. 13b, the bistable mechanism can exert a pressure which varies from 0.17 MPa to 0.21 MPa, i.e. 10 times the pressure encountered in the sphincter.

Prototype Manufacturing

For the capsule for in-vitro validation, the same parameters detailed in section 4 were used. A polycarbonate filament (diameter = 1.78 mm) supplied by Stratasys, Ltd is the material used for prototyping. Before assembling the capsule, a layer of silicone is deposited on the cap of the bistable mechanism as shown in Fig. 15a.

Afterwards, the first part of the capsule body is assembled to the bistable mechanism. Then, the mechanism is preloaded in its second stable position and the actuator i.e. a standard PVA foam, used for medical purposes, is inserted below without having any contact with it as shown in the first image of Fig. 15b. Finally, the capsule assembly is completed by inserting it in a 000 gelatin coating which is an empty pharmaceutical capsule.

The intestinal liquid used for the in-vitro trial is a special liquid that makes it possible to reproduce as closely as possible the conditions encountered by the capsule in the intestinal environment. Fasted State Simulated Intestinal Fluid (FaSSIF) provided by Biorelevant.com Ltd, a fluid considered for solubility studies and drug testing [START_REF] Dahlgren | Fasted and fed state 11 Copyright © by ASME human duodenal fluids : Characterization, drug solubiand comparison to simulated fluids and with human bioavailability[END_REF], is used.

Validation of the Operating Mode of the Capsule

As shown in Fig. 15b, the setup dedicated to the in-vitro validation is composed of two recipients which represent the most important stages of the operating mode of the capsule in the gastrointestinal tract: a first intestinal environment for the sampling process and a second one after the sampling process. In the first intestinal environment which is colored in yellow and has a pH of 6.5, the capsule must be open after the coating has dissolved to take a sample from the intestinal liquid and then close by means of the bistable mechanism triggered by the swollen foam. Once in the second intestinal environment, which is colored in red and has also a pH of 6.5, the sealing of the capsule is verified. During its transit through environments 1 and 2, the capsule did not undergo pressure that imitates the intraluminal pressures of the gastrointestinal tract because the latter are smaller than the anus pressure [START_REF] Gayer | The effects of mechanical forces on intestinal physiology and pathology[END_REF][START_REF] Sabiston | Sabiston textbook of surgery : the biological basis of modern surgical practice[END_REF].

Finally, to mimic the force applied by the anus and validate the resistance of the bistable mechanism to the reopening, a maximum force of 0.2 N [START_REF] Bansal | Anorectal manometry : current techniques and indications[END_REF] is applied to the entry orifice. The anus pressure is obtained using a force sensor mounted on an XY stage. Initially, the sensor is placed in front of the orifice with zero contact. Then, a force of 0.2 N is successively applied to the orifice by advancing the sensor.

Concerning the timing, once immersed in the first intestinal environment (step (1) in Fig. 15b), the coating of the capsule immediately begins to dissolve. It takes seven minutes for the coating to completely disappear and uncover the entry orifice. When in contact with the intestinal liquid and after exactly three minutes, the foam swells to trigger the bistable mechanism and close the capsule. Afterwards, the capsule is put into the second intestinal environment with a transition time of 30 seconds (step (2) in Fig. 15b). It remains in this environment for 19 hours and 10 minutes. This time is determined according to the standard ISO 10993-1:2018 [START_REF]Biological evaluation of medical devices Part 1 : Evaluation and testing within a risk management process[END_REF] and the transition time of the capsule between the mouth and the small intestine [START_REF] Fireman | Capsule endoscopy : improving transit time and image view[END_REF]. Finally, pressure from the anus is simulated on the capsule before checking for contamination (step (3) in Fig. 15b). The capsule is then opened for inspection (step (4) in Fig. 15b). As shown in the last image of the same figure, the absence of the red color in the capsule validates sealing performance. The quantity of the liquid absorbed by the foam during this experiment is 0.4 mL, which 9

Copyright © by ASME corresponds to 4.0E+7 bacteria and is sufficient the analysis of intestinal bacteria.

Conclusion

In this paper, we proposed a non-invasive microbiota sampling capsule that combines the use of a bistable mechanism and additive manufacturing. Using the interaction between a gelatin coating and the intestinal environment, the capsule collects a liquid sample from the small intestine. It does not integrate electronic components or batteries, which makes it affordable, eco-friendly and disposable. First, the capsule components were presented. Then, the operating mode performed by the capsule in the gastrointestinal tract is detailed. To guarantee its proper functioning, we carried out an experimental characterization of the actuator that measured its force-displacement curve.

Afterwards, we focused on the bistable mechanism used to close the capsule. A model is used in order to size the structure. Since we use FDM to manufacture the capsule and the bistable mechanism, we studied the impact of this technology on the bistable behavior with an experimental method. FDM a significant impact on the value of the triggering forces f top and f bot compared to the analytical ones. However, the results show that the bistable behavior is guaranteed. Some future work could consist of developing modified models to take into account additive manufacturing impact on the bistable behavior.

Finally, the design of the capsule is updated according to the experimental results. A final prototype is then manufactured and used to validate its operating mode with an in-vitro trial using an artificial intestinal liquid proving the success of the capsule operating mode and validating the quantity of the sampled liquid.

We also validated in section 5 the sealing of the capsule in order to protect the sample from contamination. It is of course now important to ensure that the bacteria collected are properly stored in order to be analyzed and to avoid their degradation by using a storage substance. In addition, it is possible to consider different coating thicknesses, so the opening of the capsule can happen at different locations, i.e. at different positions in the intestine. Therefore, a study on the relationship between the thickness of the coating and the time of its dissolution is also part of future work. Perspective of this work will thus concern this validation, before production with biocompatible material to go toward in vivo trials. These latter will be of great importance to build statistics on the transit kinematics of the capsule in the GI tract and therefore define more precisely the sampling location and effective achievable sampling volume. In addition, in-depth risk analysis will be carried out.

FIGURE 2 :

 2 FIGURE 2: Concept of the capsule proposed by[START_REF] Thélu | Device for intestinal fluid sampling[END_REF] for microbiota sampling.

  Main components of the microbiota sampling capsule. CAD of the bistable mechanism as manufactured in its first stable position.

FIGURE 3 :

 3 FIGURE 3: Components of the microbiota sampling capsule.

T

  Bistable mechanism preloaded in the second stable position (a) Initial state of the capsule after assembly. T Small intestine Dissolution of the coating Absorption of intestinal liquid by the foam Orifice Actuation of the bistable mechanism by the foam ' F (b) Operating mode of the capsule in the small intestine. Final state of the capsule after sampling.

FIGURE 4 :

 4 FIGURE 4: Scenario followed by the capsule in the gastrointestinal tract.

FIGURE 5 :

 5 FIGURE 5: Experimental setup for the actuator characterization.

ForceFIGURE 6 :

 6 FIGURE 6: Curve of the force provided by the foam as it expands from its compressed form.

FIGURE 7 :

 7 FIGURE 7: CAD of a bistable mechanism composed of the superposition of two preshaped curved beams.

FIGURE 8 :

 8 FIGURE 8: Preshaped curved beam at the initial position.

FIGURE 9 :

 9 FIGURE 9: Force-displacement curve of a bistable mechanism, measured at its center.

FIGURE 10 :

 10 FIGURE 10: Positioning of the bistable mechanism represented in its first stable position.

FIGURE 11 :

 11 FIGURE 11: Bistable mechanism manufactured with Fused Deposition Modeling with e cc = -0.05 mm, e cr = -0.005 mm and e rr = -0.05 mm.

FIGURE 12 :

 12 FIGURE 12: Experimental setup for the characterization of the bistable mechanisms

ForceForce

  Force-displacement curves of the bistable mechanisms: the continous curves are obtained by the experimental characterization in comparison with the dashed curves, which was obtained by the analytical model. Force-displacement curves: the black curves represent the experimental results for each mechanism and the red curve is the average of the experimental data.

FIGURE 13 :

 13 FIGURE 13: Results of the experimental characterization of the bistable mechanisms.

FIGURE 14 :

 14 FIGURE 14: Design of the closing cap added to the bistable mechanism to ensure the sealing of the capsule.

5 T

 5 Absence of the red color (b) The in-vitro validation of the microbiota sampling capsule.

FIGURE 15 :

 15 FIGURE 15: Capsule assembly and in-vitro validation of its operating mode.

TABLE 1 :

 1 Geometric parameters of the bistable

	Parameter	t	b	L	h
	Value	0.35 mm 2 mm 18 mm 2.4 mm
	4.

Acknowledgements

This work was supported by the Investissements d'Avenir (Labex CAMI ANR-11-LABX-0004).

The authors would like to thank TIMC-IMAG (UMR5525 UGA/CNRS) for full access to the concepts of the patent referenced in [20] and the corresponding results.


Alban Marie
email: alban.marie@insa-rennes.fr

Karol Desnos
email: karol.desnos@insa-rennes.fr

Luce Morin
email: luce.morin@insa-rennes.fr

Lu Zhang

Video Coding for Machines: Large-Scale Evaluation of Deep Neural Networks Robustness to Compression Artifacts for Semantic Segmentation

Keywords: Video Coding for Machines, Machine-to-Machine communication, Semantic Segmentation

In the Video Coding for Machines (VCM) context where visual content is compressed before being transmitted to a vision task algorithm, appropriate trade-off between the compression level and the vision task performance must be chosen. In this paper, a Deep Neural Networks (DNN) based semantic segmentation algorithm robustness to compression artifacts is evaluated with a total of 1486 different coding configurations. Results indicate the importance of using an appropriate image resolution to overcome the block-partitioning limitations in existing compression algorithms, allowing 58.3%, 49.8%, 33.5% and 24.3% bitrate savings at equivalent prediction accuracy for JPEG, JM, x265 and VVenC, respectively. Surprisingly, JPEG can achieve 73.41% bitrate reduction with the inclusion of compressed images at training time over VVC Test Model (VTM) with a DNN trained on pristine data, which implies that DNN generalization ability must not be overlooked.

I. INTRODUCTION

Conventional image and video coding aims at achieving an optimal trade-off between bitrate and perceived quality by human observers. Many compression standards have been proposed to fulfill this purpose, such as Joint Photographic Experts Group (JPEG), Advanced Video Coding (AVC), High Efficiency Video Coding (HEVC) or Versatile Video Coding (VVC). However, with the emergence of Machine-to-Machine (M2M) communications, the receiver of visual content is no longer necessarily human. According to Cisco [START_REF] Cisco | Cisco visual networking index: Forecast and trends[END_REF], the total amount of M2M connections increased exponentially from 1 to 3.9 billions in the last five years. Furthermore, nearly 80% of the world's total bandwidth is used for image and video transmission. In 2019, a bitstream standardization group called Video Coding for Machines (VCM) was created by the Motion Picture Expert Group (MPEG) in order to address M2M transmissions of multimedia contents [START_REF] Zhang | MPEG-M49944: Report of the AhG on VCM[END_REF], where the main objective is to achieve greater trade-offs between bitrate and vision task performance compared to the VVC Test Model (VTM).

In order to address a M2M communication scenario, one could use an encoder expressly designed for a machine receiver, or a conventional encoder originally designed for transmission to a human receiver. While older compression standards such as JPEG or AVC are still wide-spread in embedded systems nowadays, it is reasonable to evaluate their relevance in a M2M communication context. In this paper, we propose to assess the suitability of conventional encoder in a VCM context. Specifically, the robustness to compression artifacts of a Deep Neural Networks (DNN) semantic segmentation algorithm is evaluated on 1486 different coding configurations. Coding configurations include multiple encoders, image quality and resolution, with and without grayscale conversion. The evaluation is performed using a novel progressive training strategy to enhance DNN robustness to various artifacts.

Our work is presented as follows. Section II reviews existing works in the literature. The considered evaluation protocol is detailed in Section III. Experimental results are introduced and discussed in Section IV, followed by a conclusion.

II. RELATED WORK

In order to reduce the amount of information to transmit over a M2M connection, redundant information in visual content must be discarded. Therefore, evaluating DNN robustness to artifacts is crucial in the context of VCM. It has been shown that DNN solving vision tasks such as classification, object detection or segmentation are highly affected by distortion in visual data [START_REF] Dodge | Human and DNN classification performance on images with quality distortions: A comparative study[END_REF], [START_REF] Fischer | Evaluation of Video Coding for Machines without Ground Truth[END_REF], [START_REF] Samil Karahan | How Image Degradations Affect Deep CNN-Based Face Recognition?[END_REF].

Many papers in the literature propose to evaluate DNN robustness to compression artifacts. In order to evaluate which information is relevant in a VCM scenario, related works can be organised in accordance to the following criteria: (I) DNN are re-trained on compressed data to achieve optimal trade-offs between bitrate and vision task performance. (II) Multiple encoders are evaluated. (III) Images are encoded at multiple resolutions. (IV) Experiments are conducted on grayscale images. (V) A high number of coding configurations are considered, which implies to take into account a wide range of image quality.

Table I compares related works in the literature according to the aforementioned criteria. Note that most related works do not attempt to perform a large-scale evaluation of DNN resilience to compression artifacts, but were included because of their proximity to this study. To the best of our knowledge, there is no related work that meet simultaneously all the mentioned requirements.

(I) ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ (II) ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ (III) ✓ ✓ ✓ ✓ ✓ ✓ (IV) ✓ ✓ ✓ (V)
Regarding (V) the number of considered distortions, existing studies consider at most 50 coding configurations, at the exception of Dejean-servieres et al. [START_REF] Mathieu Dejean-Servières | Study of the impact of standard image compression techniques on performance of image classification with a convolutional neural network[END_REF]. This is because some studies only compare themselves to HEVC test Model (HM) or VVC Test Model (VTM) with few Quantization Parameter (QP) after proposing a new method to reach better tradeoffs between vision task performance and bitrate [START_REF] Fischer | On Intra Video Coding And In-Loop Filtering For Neural Object Detection Networks[END_REF]- [START_REF] Fischer | Saliency-Driven Versatile Video Coding for Neural Object Detection[END_REF], [START_REF] Fischer | Evaluation of Video Coding for Machines without Ground Truth[END_REF], [START_REF] Kong | Object-Detection-Based Video Compression for Wireless Surveillance Systems[END_REF], [START_REF] Le | Image Coding For Machines: an End-To-End Learned Approach[END_REF]. Some papers also evaluate DNN resilience to JPEG/JPEG2000 compression [START_REF] Mathieu Dejean-Servières | Study of the impact of standard image compression techniques on performance of image classification with a convolutional neural network[END_REF], [START_REF] Dodge | Understanding how image quality affects deep neural networks[END_REF], [START_REF] Fischer | Robust Deep Neural Object Detection and Segmentation for Automotive Driving Scenario with Compressed Image Data[END_REF], [START_REF] Fischer | Analysis Of Neural Image Compression Networks For Machine-To-Machine Communication[END_REF], [START_REF] Hendrycks | Benchmarking Neural Network Robustness to Common Corruptions and Perturbations[END_REF], [START_REF] Khoury | Improved 3D U-Net robustness against JPEG 2000 compression for male pelvic organ segmentation in radiotherapy[END_REF], [START_REF] Löhdefink | On Low-Bitrate Image Compression for Distributed Automotive Perception: Higher Peak SNR Does Not Mean Better Semantic Segmentation[END_REF], [START_REF] Marie | Expert Training: Enhancing AI Resilience to Image Coding Artifacts[END_REF], [START_REF] Roy | Effects of Degradations on Deep Neural Network Architectures[END_REF], AVC [START_REF] Aqqa | Understanding How Video Quality Affects Object Detection Algorithms[END_REF], [START_REF] Stabernack | Architecture of a Low Latency H.264/AVC Video Codec for Robust ML Based Image Classification[END_REF] or auto-encoders [START_REF] Fischer | Analysis Of Neural Image Compression Networks For Machine-To-Machine Communication[END_REF], [START_REF] Le | Image Coding For Machines: an End-To-End Learned Approach[END_REF], [START_REF] Löhdefink | On Low-Bitrate Image Compression for Distributed Automotive Perception: Higher Peak SNR Does Not Mean Better Semantic Segmentation[END_REF], but no paper consider all mentioned image and video codec generations in a unified framework (II). Note that older codecs such as JPEG or AVC achieve lower trade-offs between bitrate and vision task performance, but their low-complexity compared to modern codecs makes them more suitable to some applications using low-power devices [START_REF] Stabernack | Architecture of a Low Latency H.264/AVC Video Codec for Robust ML Based Image Classification[END_REF], especially when hardware implementation of AVC encoders is still widespread nowadays. Very few papers consider other hyper-parameters than QP such as (III) lowering image resolution [START_REF] Bouderbal | How Would Image Down-Sampling and Compression Impact Object Detection in the Context of Self-driving Vehicles?[END_REF], [START_REF] Mathieu Dejean-Servières | Study of the impact of standard image compression techniques on performance of image classification with a convolutional neural network[END_REF], [START_REF] Suresh | Effects of varying resolution on performance of CNN based image classification: An experimental study[END_REF], [START_REF] Le | Image Coding For Machines: an End-To-End Learned Approach[END_REF] or (IV) removing color information [START_REF] Mathieu Dejean-Servières | Study of the impact of standard image compression techniques on performance of image classification with a convolutional neural network[END_REF], [START_REF] Stabernack | Architecture of a Low Latency H.264/AVC Video Codec for Robust ML Based Image Classification[END_REF].

The use of large-scale datasets is also criteria of major importance. A great amount of studies include experiments on large-scale datasets such as ImageNet [START_REF] Deng | ImageNet: A large-scale hierarchical image database[END_REF] or Cityscapes [START_REF] Cordts | The Cityscapes Dataset for Semantic Urban Scene Understanding[END_REF], allowing general conclusion to be drawn with higher confidence. Note that Dejean-servieres et al. [START_REF] Mathieu Dejean-Servières | Study of the impact of standard image compression techniques on performance of image classification with a convolutional neural network[END_REF] did not address this point, since a subset of 55 images of the original ImageNet dataset were used, while the original database contains over 50000 validations images.

Few papers attempted to (I) use compressed data at training time in order to improve DNN resilience to compression artifacts [START_REF] Fischer | Robust Deep Neural Object Detection and Segmentation for Automotive Driving Scenario with Compressed Image Data[END_REF], [START_REF] Ghosh | Robustness of Deep Convolutional Neural Networks for Image Degradations[END_REF], [START_REF] Suresh | Effects of varying resolution on performance of CNN based image classification: An experimental study[END_REF], [START_REF] Khoury | Improved 3D U-Net robustness against JPEG 2000 compression for male pelvic organ segmentation in radiotherapy[END_REF], [START_REF] Löhdefink | On Low-Bitrate Image Compression for Distributed Automotive Perception: Higher Peak SNR Does Not Mean Better Semantic Segmentation[END_REF], [START_REF] Marie | Expert Training: Enhancing AI Resilience to Image Coding Artifacts[END_REF], [START_REF] Sharma | Performance analysis of semantic segmentation algorithms trained with JPEG compressed datasets[END_REF]. All studies converge to show that adding compressed data at training time allows to reach much higher trade-offs between bitrate and vision task performance. While training and evaluation with the same encoding configuration allows to reach the best trade-offs, training and evaluating with different codec is still beneficial for DNN resilience [START_REF] Fischer | Robust Deep Neural Object Detection and Segmentation for Automotive Driving Scenario with Compressed Image Data[END_REF], [START_REF] Löhdefink | On Low-Bitrate Image Compression for Distributed Automotive Perception: Higher Peak SNR Does Not Mean Better Semantic Segmentation[END_REF], [START_REF] Marie | Expert Training: Enhancing AI Resilience to Image Coding Artifacts[END_REF].

Note that considering (I) compressed data at training time while using (V) a large range of coding configurations implies tremendous increase of computational resources. In Section III-B, we define explicitly the used training procedure to reduce training complexity, which allows us to consider more coding configurations, while achieving better trade-offs between bitrate and vision task performance.

III. BENCHMARK METHODOLOGY A. Considered Coding Configurations

In this section, the method used to evaluate DNN robustness to compression artifacts is defined explicitly. Figure 1 illustrates the pipeline used to perform the benchmark that fulfill criteria presented in Section II. An image I is first captured by a camera. Depending on the coding configuration, criteria (IV) is considered by discarding or not image I chrominance channels according to the ITU-R BT.601 standard. Resulted image is then downsampled by a factor δ and compressed with one of the considered codecs, using quality Q. Bitstream B can then be sent to the vision task side using minimal bitrate since most of the irrelevant information was discarded. Reconstructed image Î is obtained after decoding bitstream B and applying an upsampling by a factor 1/δ. Finally, reconstructed image Î can be fed to a vision task algorithm. Note that artifacts may have been introduced in Î because of grayscale conversion, downsampling and encoding steps. Therefore, in order to fulfill criteria (I), the used DNN was trained with images containing such artifacts, as explained in Section III-B.

Criteria (II) is met by considering a total of 5 compression algorithms, namely JPEG, JPEG2000, JM, x265 and VVenC [START_REF] Brandenburg | VVenC Fraunhofer versatile video encoder[END_REF]. JPEG and JPEG2000 are two of the most widely used lossy image codecs. JM [START_REF] Suehring | AVC Software Coordination JM Reference Software[END_REF], x265 and VVenC are AVC, HEVC and VVC based video codecs, respectively. Since mentioned datasets are composed of still images, ALL-Intra configuration is used for these three video codecs. Note that HM [START_REF] Suehring | HEVC Software Coordination and HM Reference Software[END_REF] and VTM [START_REF] Bossen | VVC Software Coordination and VTM Reference Software[END_REF] are not used in this study, since they are not meant to be used in a real world scenario due to their extreme complexity. Therefore, lower complexity codecs such as x265 and VVenC were used instead. JM-19.0 and VideoLAN organisation implementation for x265 [START_REF]x265 software library[END_REF] are used. Slow and fast preset are selected for x265 and VVenC, respectively. For JPEG2000, a single tile is considered for the whole image. Because JPEG2000 do not employ any block-partitioning within a tile, image downsampling is not considered with JPEG2000 as it would not bring any gains in terms of rate-distortion trade-off. Criteria (III) is also satisfied by taking into account a wide range of downsampling factor δ, where δ refers to the factor by which the width and height of the image are multiplied. A total of #δ = 7 image resolutions using bicubic interpolation are evaluated, with δ ∈ {0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0}. A report of considered coding configurations is summarized in Table II. With a total number of 1486 distortions, the last criteria (V) is fulfilled.

B. Progressive Training Procedure

DNN are not inherently resilient to degradation in visual content, including compression artifacts. In order to achieve optimal trade-off between bitrate and vision task performance, the inclusion of compressed data at training time must be considered (criterion (I)).

For this purpose, we propose to use a progressive training, behind which the key idea is to train one DNN on a large amount of distortion at once, ranging from undistorted to highly distorted. This is done by increasing at training time the distortion strength progressively, where the distortion strength parameter start at p 0 and end at p ∞ . The distortion strength can be characterised by the downsampling factor δ, or by the parameter allowing to control the amount of quantification in each codec, such as QP for AVC, HEVC or VVC based codecs. At each epoch e, the distortion strength parameter p is equal to f (e), which is determined by the following equation:

f (e) = p ∞ + ∆p⌊ 1 ∆p (p 0 -p ∞ ) exp(-se)⌉ (1)
where s ∈ R + * controls the speed at which the distortion level p converges towards p ∞ , and where ∆p ∈ R + * refers to the step size between two consecutive distortion level p.

The intuition behind our progressive training is that achieving high accuracy on images with distortion strength f (e) is easier if the DNN is already robust to images of slightly higher The described training strategy can be done both on color and grayscale images. For color images, DNN weights trained on pristine data are used as initialization for the progressive training with varying image resolutions. For grayscale, the same model is first fine-tuned on grayscale images before being used for initialization. At the end, a total of 2 × (1 + 1 + 4×#δ) = 60 progressive trainings are done to cover the 1486 coding configurations described in Table II. Hyper-parameters p 0 , p ∞ , ∆p, s and the number of epochs #e used by each progressive training are given in Table III.

C. Used Dataset and Architecture

In the context of this study, the Cityscapes [START_REF] Cordts | The Cityscapes Dataset for Semantic Urban Scene Understanding[END_REF] dataset is considered. Cityscapes is composed of urban landscapes represented with losslessly compressed images of resolution 2048 × 1024. The lowest image resolution considered in this study is therefore 512 × 256 with the downsampling factor δ = 0.25.

DeepLabV3+ [START_REF] Chen | Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation[END_REF] with a ResNet50 backbone, a state-ofthe-art semantic segmentation algorithm is used to evaluate DNN robustness to compression artifacts. MMSegmentation library [START_REF]MMSegmentation: OpenMMLab Semantic Segmentation Toolbox and Benchmark[END_REF] was used for DeepLabV3+ implementation, training, evaluation and model weights. For all training depicted in Section III-B, SGD optimizer with mini-batch size of 4, a learning rate of 10 4 and a polynomial decay of 0.9 are used. Note that the learning rate is resetted to 10 4 at each epoch

IV. EXPERIMENTS

A. Progressive training evaluation

Firstly, we briefly evaluate the effectiveness of the progressive training procedure by comparing training complexity and reached accuracy on a small subset of coding configurations. 11 VVenC coding configurations are used for this experiment, with QP Q ∈ {5n|n ∈ Z, 0 ≤ n ≤ 10}, with chrominance channels and no image downsampling. Multiple convergence speed s from Eq. ( 1) are considered, namely 0.085, 0.045 and 0.025. We compare our method to a Separate Training (ST) approach that perform one training per coding configuration. Final complexity of ST strategy is obtained by adding up the number of epochs required until converge for each coding configuration. In addition, Data Augmentation (DA) [START_REF] Fischer | Robust Deep Neural Object Detection and Segmentation for Automotive Driving Scenario with Compressed Image Data[END_REF] training procedure is considered, which consists of training a single model on all coding configurations at once by selecting a coding configuration randomly for each image during the training. Progressive training, ST and DA uses model weights trained on raw images I at the initialization. Baseline refers to the model trained on undistorted data, which thus does not validate criteria (I) from Section II.

Results are presented in Table IV, where Bjøntegaard-Delta Rate (BDR) metric represents the average bitrate savings at equivalent DNN accuracy in term of mIoU. ST achieve good rate-mIoU trade-offs by considering criteria (I). However, it is at the cost of training complexity since one training has to be done for each coding configuration. DA training procedure mitigate this issue by training one model on multiple coding configurations at once, but it is at the cost of BDR, especially when a very diverse set of coding configurations is considered. Note that DA and ST could achieve comparable rate-mIoU trade-offs on a set of similar coding configurations. The proposed progressive training procedure allows optimal rate-accuracy trade-offs to be reached with a lower training complexity. Tuning the convergence speed s parameter from Eq. ( 1) allows to find a suitable balance between BDR values and training complexity. With a convergence speed of s = 0.025, we are able to reduce the training complexity over the ST procedure approach with better BDR values.

Based on selected parameters given in Table III, note than a total of 4278 epochs are required to cover the 1478 considered coding configurations using the proposed progressive training strategy. Therefore, to be comparable in terms of training complexity, the use of ST strategy instead of the proposed progressive training would have required less than 3 epochs per coding configuration on average, which is not enough to converge.

B. Coding Configuration Comparison

In this subsection, all considered coding configurations are compared together as depicted in Section III.

Table V presents BDR values with mIoU as the quality metric between every codec, either with or without the consideration of (III) all image resolutions with the convex hull. Note that image downsampling is not considered along with JPEG2000, as explained in Section III-A.

It can be noted that using appropriate image resolution is of major importance, saving 58.3%, 49.8%, 33.5% and 24.3% bitrate at equivalent prediction accuracy for JPEG, JM, x265 and VVenC, respectively. Block-partitioning is the main limitation of older codecs such as JPEG or JM to obtain better rate-accuracy trade-offs. As an example, the fixed 8 × 8 bloc size of JPEG may be unsuited to areas with low spatial information such as sky or road, especially on high resolutions images. If a downsampling is applied on the same image, a 8×8 bloc will correspond to a larger area, which could be more suited depending on the image content. Even from codecs such as x265 or VVenC that allows larger blocs to be selected at encoding, BDR savings can be obtained with the use of convex hull.

We suggest that using (III) appropriate image resolution is of greater importance compared to using a more complex codec in terms of rate-accuracy trade-off. When appropriate image resolution is used, it can be noted that JPEG, JM and x265 achieve 43.1%, 14.2% and 19.9% bitrate savings over JM, x265 and VVenC, respectively. Note that this observation cannot be extrapolated to datasets containing smaller images or with higher spatial information, since larger bloc size may not be desirable in such contexts. Figure 2 compares rate-mIoU trade-offs with and without chrominance information. Using grayscale images allows JPEG and JPEG2000 to achieve greater compromise at lower bitrates, while the same does not apply for JM, x265 and VVenC video codecs. Note that an accuracy drop is observable at high bitrate for video codecs. As shown by Fischer et al. [START_REF] Fischer | On Intra Video Coding And In-Loop Filtering For Neural Object Detection Networks[END_REF], artifacts generated by in-loop filtering tend to worsen DNN performance, even with near lossless image quality.

Achievable compression ratio with respect to a minimal mIoU constraint is provided in Table VI. Multiple threshold t 0 are used to define the minimal acceptable mIoU score. Among all coding configurations that have greater mIoU than the constraint, the one with the lowest bitrate is selected to compute the highest achievable compression ratio with respect to the mIoU constraint. The average bitrate using PNG compression of 2362.90 kB per image in the original Cityscapes dataset is used as the anchor to compute the compression ratio.

C. Comparison with CTC

In order to assess the importance of using (I) compressed data at training time, (III) multiple image resolution and (IV) chrominance degradation, a comparison with VTM is provided as recommended by Common Test Conditions (CTC) from the VCM MPEG standardization group [START_REF] Curcio | Common Test Conditions and Evaluation Methodology for Video Coding for Machines[END_REF]. Convex hull of VTM-12.0 with ALL-Intra configuration is used as an anchor, where QP ∈ 22, 27, 32, 37, 42, 47 and downsampling factor δ ∈ {0.25, 0.5, 0.75, 1.0} are considered. [START_REF] Fischer | Robust Deep Neural Object Detection and Segmentation for Automotive Driving Scenario with Compressed Image Data[END_REF], [START_REF] Marie | Expert Training: Enhancing AI Resilience to Image Coding Artifacts[END_REF]. As shown by Figure 2, removing (IV) chrominance information is beneficial for JPEG at lower bitrates. Therefore, higher bitrate gains can be achieved by being able to remove colors. Nevertheless, criteria (IV) appears less detrimental compared to criteria (I) or criteria (III).

V. CONCLUSION

In this paper, we evaluate in the VCM context the impact of compression artifacts on deep based semantic segmentation algorithms at an unprecedented scale. A wide range of image degradations are considered in order to measure which distortions allows to maximize DNN performance at equivalent bitrate. Experiments showed that using appropriate image resolution is the most crucial parameter to achieve optimal rate-accuracy trade-off, achieving 58.3%, 49.8%, 33.5% and 24.3% bitrate savings at equivalent prediction accuracy for JPEG, JM, x265 and VVenC, respectively. Significant bitrate reductions can also be obtained with newer codecs, but at the cost of encoding complexity. Surprisingly, VVenC achieve a very low bitrate saving over x265 of 2.00% with optimal image resolution, suggesting that the main limitation of older codecs is their limited block partitioning. Removing chrominance channels appears as an unsuitable strategy, as it can worsen DNN performance even at very low bitrates. At high bitrates, the poor generalization ability of DNN models to video codecs artifacts, such as in-loop filtering, makes them inferior to simpler codecs like JPEG in terms of rate-accuracy trade-off.

Fig. 1 .

 1 Fig. 1. Pipeline used to evaluate DNN resilience to compression artifacts. An image I, with or without chrominance channels is first downsampled by a factor δ and encoded with a codec using quality Q. Compressed image Î is obtained by decoding bitstream B and upsampling reconstructed image by a factor 1/δ. It is then given to the semantic segmentation algorithm to obtain the prediction.

Fig. 2 .

 2 Fig.2. Trade-offs between bitrate and mIoU with and without chrominance channels. From left to right, each plot correspond to JPEG, JPEG2000, JM, x265 and VVenC. Criteria (I) and (III) defined in Section II are considered for each curve.

TABLE I RELATED

 I WORK. (I) COMPRESSED DATA AT TRAINING TIME. (II) MULTIPLE CODECS. (III) MULTIPLE IMAGE RESOLUTIONS. (IV) CHROMINANCE DEGRADATION. (V) TOTAL NUMBER OF CODING CONFIGURATION.

[12] [10] [25] [30] [22] [20] [21] [27] [1] [3] [14] [34] [16] [31] [18] [26] [24] [15] [17] [19] [28] Ours

TABLE II NUMBER

 II OF CONSIDERED CODING CONFIGURATIONS PER CODEC.

	JPG JPG2K	JM	x265 VVenC Total
	672	352	154	154	154	1486

TABLE III PROGRESSIVE

 III TRAINING HYPER-PARAMETERS USED FOR EXPERIMENTS. -1), and so on. Given that converging to a minima of the DNN loss function is increasingly harder as the distortion level p increases, an exponential decay function is used to decrease the pace at which image quality is reduced as the training progresses. In order to obtain model weights for adaptive training initialization, note that the DNN is trained on uncompressed pristine images before.Listed coding configurations in Table II vary across distortions introduced by different types of processing, such as grayscale conversion, image downsampling or compression artifacts. Therefore, multiple progressive training are interlaced together to train the DNN on all considered coding configurations. First, a progressive training with varying downsampling factor δ is done. This training allows us to obtain model weights θ δi for each considered δ i by taking DNN model weights at last epoch e where f (e) = δ i . Afterwards, model weights θ δi can be used at initialization for other progressive training with varying compression strength, where each images are downsampled by a factor θ δi . Such training with varying compression strength is done for every image resolution on JPEG, JM, x265 and VVenC codecs, resulting in a total of 4 × #δ = 28 progressive trainings.

		p 0	p∞	∆p	s	#e
	JPG	100	1	1	0.05	65
	JPG2K	0	500	1	0.015 200
	JM	0	50	5	0.04	70
	x265	0	50	5	0.04	70
	VVenC	0	60	5	0.025	72
	qualities f (e

TABLE V BDR

 V WITH MIOU METRIC COMPARISON. NEGATIVE VALUES REPRESENT BITRATE REDUCTION AT EQUIVALENT PREDICTION ACCURACY IN TERMS OF MIOU. SYMBOL * DENOTES THE USE OF (III) CONVEX HULL CURVES ACROSS CONSIDERED IMAGE RESOLUTIONS.

					JPG	JPG *	JPG2K	JPG2K *	JM	JM *	x265	x265 *	VVenC	VVenC *
		JPG			0.0	139.6	52.9	-	53.3	125.7	123.3	178.6	193.8	224.1
		JPG *		-58.3	0.0	-35.1	-	-43.1	27.6	-2.7	68.6	26.8	73.0
		JPG2K	-34.6	54.1	0.0	-	-13.1	32.8	29.6	62.2	72.0	88.4
		JPG2K *		-	-	-	-	-	-	-	-	-	-
		JM		-34.8	75.9	15.0	-	0.0	99.1	71.8	157.5	115.3	179.6
		JM *		-55.7	-21.6	-24.7	-	-49.8	0.0	-14.2	34.6	4.3	37.1
		x265		-55.2	2.7	-22.9	-	-41.8	16.5	0.0	50.3	24.7	63.0
		x265 *		-64.1	-40.7	-38.4	-	-61.2	-25.7	-33.5	0.0	-19.9	2.0
		VVenC	-66.0	-21.1	-41.9	-	-53.5	-4.2	-19.8	24.9	0.0	32.1
		VVenC *	-69.1	-42.2	-46.9	-	-64.2	-27.1	-38.7	-2.0	-24.3	0.0
					TABLE VI
	BEST ACHIEVABLE COMPRESSION RATIO WITH RESPECT TO A MINIMAL
	MIOU CONSTRAINT. CONSIDERED DNN ACHIEVE 0.8027 MIOU ON RAW
					IMAGES.
	t 0	mIoU	JPG		JPG2K	JM	x265	VVenC
	97.5% 0.783	15.9	5.6	N.A.	N.A.	N.A.
	95.0% 0.763	33.2	18.8	30.2	39.9	43.4
	90.0% 0.723	52.6	56.4	66.4	96.6	107.3
	80.0% 0.642	172.0	188.9	182.3 231.9	273.9
	70.0% 0.562	358.3	N.A.	313.0 507.7	529.3

  Table VII highlights JPEG BDR gains using mIoU metric compared to VTM anchor. As expected, JPEG performs poorly

  Bitrate savings can be further increased to 73.41% if criteria (I) and (III) are satisfied jointly, which emphasizes the importance of criteria (III) for JPEG codec because of the fixed 8×8 bloc size. JPEG achieving BDR gains compared to the VTM anchor highlight the lack of resilience to compression artifacts that DNN can have if criteria (I) is overlooked. Note that this experiment was performed with a DNN model trained on Cityscapes dataset, which does not contain any compression artifacts. Less extreme results would be obtained with a dataset that includes compression artifacts, since artifacts created by different compression algorithms share similarities on which DNN are able to generalize

			TABLE VII
	BDR VALUES USING MIOU COMPARED TO VTM ANCHOR USING JPEG
	ENCODER UNDER VARIOUS CODING CONFIGURATIONS. (I) COMPRESSED
	DATA AT TRAINING TIME. (III) MULTIPLE IMAGE RESOLUTIONS. (IV)
	CHROMINANCE DEGRADATION.
	(I)	(III)	(IV)	BDR
				644.68%
	✓			-4.06%
	✓	✓		-73.41%
	✓		✓	-25.65%
	✓	✓	✓	-76.13%

compared to VTM when criteria (I), (III) and (V) are not considered, which results in a bitrate increase of 644.68% at equivalent DNN performance in terms of mIoU. Surprisingly enough, the use (I) compressed images Î at training time allows JPEG to outperform the VTM anchor with a bitrate reduction of 4.06%.