Marco Aggravi
email: marco.aggravi@irisa.fr

Ahmed Alaaeldin

Said Elsherif

Paolo Robuffo Giordano

Claudio Pacchierotti
email: claudio.pacchierotti@irisa.fr

Haptic-enabled decentralized

Keywords: Human-Robot Teaming, Haptics and Haptic Interfaces, Multi-Robot Systems

come

I. INTRODUCTION

G ROUND Urban Search and Rescue (USAR) robots are already widely used in disaster scenarios [START_REF] Casper | Human-robot interactions during the robotassisted Urban Search and Rescue response at the World Trade Center[END_REF], [START_REF] Davids | Urban search and rescue robots: from tragedy to technology[END_REF], while aerial solutions are only recently raising interest [START_REF] Marconi | The SHERPA project: Smart collaboration between humans and ground-aerial robots for improving rescuing activities in alpine environments[END_REF], [START_REF] Goodrich | Supporting wilderness search and rescue using a camera-equipped mini uav[END_REF]. As disaster environments are generally highly dynamic and unstructured, it is crucial to take advantage of the experience of human rescuers, enabling them to control the robotic USAR systems in a reactive and intuitive manner [START_REF] Casper | Human-robot interactions during the robotassisted Urban Search and Rescue response at the World Trade Center[END_REF], [START_REF] Kruijff | Experience in system design for human-robot teaming in urban search and rescue[END_REF].

This work presents an innovative approach to deal with USAR situations, taking advantage of the promising capabilities of coordinated multi-robot systems and wearable haptic interfaces. We propose a haptic-enabled connectivity-maintenance framework able to manage the coordinated motion of a team composed of mobile robots and one human. The human user physically becomes part of the team, exploring/navigating the environment together with the robots. At the same time, she/he receives wearable haptic feedback about the team connectivity level and/or the direction towards a safe path. The connectivity information is generated by a decentralized connectivity-maintenance algorithm that controls the motion of the robots for keeping the team connected at all times [START_REF] Giordano | A passivity-based decentralized strategy for generalized connectivity maintenance[END_REF].

The navigation information is generated from an a priori map of the environment. The user is always free to decide whether and to what extent to follow the provided navigation cues, as to account for the possible changes in the actual map of the environment.

II. RELATED WORKS

Heterogeneous human-robot teams: We can identify two main interaction paradigms for achieving an effective interaction between humans and machines: one-way, where the human provides the robots with some local information (e.g., his or her position) [START_REF] Saez-Pons | Experiments in cooperative human multi-robot navigation[END_REF], [START_REF] Riek | Human coordination dynamics with heterogeneous robots in a team[END_REF], and two-way, where the human shares again some local information with the robots but also receives back information regarding their states. These interactions can be carried out either through physical [START_REF] Ranasinghe | Identification of haptic based guiding using hard reins[END_REF] or non-physical [START_REF] Scheggi | Human-robot formation control via visual and vibrotactile haptic feedback[END_REF], [START_REF] Scheggi | Cooperative Navigation for Mixed Human-Robot Teams Using Haptic Feedback[END_REF] contacts between robots and operator.

Multi-robot control: A recent survey presents an ample overview on target detection and tracking for multi-robot systems [START_REF] Robin | Multi-robot target detection and tracking: taxonomy and survey[END_REF], while other surveys on multi-robot organization/coordination are available at [START_REF] Juliá | A comparison of path planning strategies for autonomous exploration and mapping of unknown environments[END_REF], [START_REF] Amigoni | Multirobot Exploration of Communication-Restricted Environments: A Survey[END_REF]. More recently, Hashemipour et al. [START_REF] Hashemipour | A disaster multiagent coordination simulation system to evaluate the design of a first-response team[END_REF] described how to optimize firstresponse robotic teams in terms of their size, teamwork skills, and robot reliability. Results show that clustering microtasks could improve operation performance. The opportunity of using multi-robot teams in SAR operations has also captured the attention of our Institutions, e.g., the European Union funded the collaborative projects ICARUS, SHERPA, and AirBorne to develop various robotic tools able to assist human-led intervention teams.

Path planning: This field is very active and has evolved to consider increasingly complex scenarios, cluttered environments, and fast-changing maps [START_REF] Kulich | Cooperative planning for heterogeneous teams in rescue operations[END_REF], [START_REF] Beck | Online planning for collaborative search and rescue by heterogeneous robot teams[END_REF], [START_REF] Solana | Multi-robot path planning with maintenance of generalized connectivity[END_REF]. For example, Kulich et al. [START_REF] Kulich | Cooperative planning for heterogeneous teams in rescue operations[END_REF] extended the Watchman Route Problem (i.e., find the minimal closed route such that every point of the environment can be seen from at least one point along the route) for fully-automatic mission planning and control of heterogeneous teams. More recently, Solana et al. [START_REF] Solana | Multi-robot path planning with maintenance of generalized connectivity[END_REF] used an T-RRT algorithm for pre-planning the connected paths of a formation of robots using the generalized connectivitymaintenance theory of [START_REF] Giordano | A passivity-based decentralized strategy for generalized connectivity maintenance[END_REF].

Human guidance: Of course, as we want to leave users free to make their own decisions, it is important to provide them with timely and effective information, regarding the status of the team and/or a safe direction to follow. Audio cues have often been used to provide guidance cues, especially in those situations where vision is compromised [START_REF] Boer | Auditory guidance in a smoke-filled tunnel[END_REF]. However, as sensory limitations might affect both visual and auditory capabilities, haptic feedback is very promising in our context. Indeed, the haptic sense is spread throughout the body and During the task, from all the current agents positions x, our algorithm computes the best connectivity-maintenance control action F λ h (see ( 2)), the best path to the goal γ * , and the best next tile in γ * for the human agent to reach, M * ij , resulting in Fp (see ( 4)). Both forces are then sent to the feedback devices (see Sec. IV-A4), informing the human agent about the formation connectivity level and/or the direction of the path to follow. can process a large amount of information, making it a truly reliable and sensible choice for USAR scenarios. A variety of wearable devices have been developed for providing guidance feedback to humans, including bracelets and armbands [START_REF] Aggravi | Haptic wrist guidance using vibrations for Human-Robot teams[END_REF], belts [START_REF] Van Erp | Waypoint navigation with a vibrotactile waist belt[END_REF], and shoes [START_REF] Schirmer | Shoe me the Way: A Shoe-Based Tactile Interface for Eyes-Free Urban Navigation[END_REF].

With respect to this existing literature, the contributions of this work are the following: while there has been some work combining mobile robots and humans in USAR operations [START_REF] Marconi | The SHERPA project: Smart collaboration between humans and ground-aerial robots for improving rescuing activities in alpine environments[END_REF], this is the first time haptic feedback is employed in this scenario, which we believe can significantly improve the understanding of the robotic actions by the human operator. Moreover, we also use wearable haptics in this context for the first time, which is a paramount aspect to guarantee the mobility of the human user within the team. Finally, with respect to [START_REF] Scheggi | Human-robot formation control via visual and vibrotactile haptic feedback[END_REF], [START_REF] Scheggi | Cooperative Navigation for Mixed Human-Robot Teams Using Haptic Feedback[END_REF], [START_REF] Aggravi | Haptic wrist guidance using vibrations for Human-Robot teams[END_REF], here we provide multiple pieces of information through the same haptic sensory channel, which is a first for mobile robotics applications.

III. METHODOLOGY

Our control methodology consists of three main parts: connectivity-maintenance control for the team of robots (Sec. III-A), path planning for the human agent (Sec. III-B), and information feedback on the team connectivity and go-togoal directions (Sec. III-C). The general architecture of the system is summarized in Fig. 1.

A. Connectivity-maintenance control

We consider a group of N robots modeled as 3D-point masses with positions

x i ∈ R 3 , i ∈ { 1, • • • , N }, and double integrator dynamics ẍi = u i , i ∈ { 1, . . . , N }, (1)
with u i ∈ R 3 being the force (control action) applied to robot i, and v i = ẋi its velocity. Each robot is subject to the connectivity-maintenance control described in [START_REF] Giordano | A passivity-based decentralized strategy for generalized connectivity maintenance[END_REF]. This control action steers each robot using a gradient-descent strategy that guarantees connectivity maintenance of the sensing/communication graph G representing the formation. This connectivity maintenance strategy can account for several constraints, e.g., avoiding obstacles and collisions, keeping a non-occluded line-of-sight visibility, and keeping a desired distance w.r.t. neighbors. These constraints are encoded, via suitable weights, into the adjacency matrix A ∈ R N ×N associated to the graph G [START_REF] Giordano | A passivity-based decentralized strategy for generalized connectivity maintenance[END_REF], from which one can compute the so-called Laplacian matrix and its second smallest eigenvalue λ 2 that acts as a connectivity metric: λ 2 > 0 for a connected graph and λ 2 = 0 otherwise [START_REF] Mesbahi | Graph theoretic methods in multiagent networks[END_REF].

The connectivity-maintenance control consists then in a decentralized gradient descent of a potential function V λ (λ 2 (x(t))) ≥ 0 of the connectivity eigenvalue λ 2 (x(t)). In what follows, w.l.o.g. we denote λ 2 (x(t)) simply as λ 2 (t). The potential V λ (λ 2 (t)) is designed so as to have a vertical asymptote at λ min + ∆, and to vanish for λ 2 (t) ≥ λ max 2 . The connectivity maintenance control is then designed as the gradient descent

F λ i = - ∂V λ (λ 2 (t)) ∂x i , (2)
whose evaluation by robot i can be made decentralized by exploiting a decentralized estimation λi 2 (t) of the true value λ 2 (t), see [START_REF] Giordano | A passivity-based decentralized strategy for generalized connectivity maintenance[END_REF]. The control input for robot i in (1) is then finally designed as

u i = F λ i -B i v i + F e i , (3)
where B i ∈ R 3×3 is a damping term (for stability purposes), and F e i ∈ R 3 is an additional exogenous force that may act on robot i. This latter exogenous input is not used in this work (F e i = 0 for all i) but it can be used to enable, e.g., the external control of one or more robots by a remote human operator [START_REF] Aggravi | Connectivitymaintenance teleoperation of a uav fleet with wearable haptic feedback[END_REF]. In presence of bounded external forces F e i , the connectivity-maintenance control (3) is guaranteed to maintain λ 2 (t) > λ min 2 > 0, ∀t. The human agent, referred to with i = h, is considered as a ground mobile robot with elevation 0 m and subject to no control inputs (see Sec. III-B), i.e., his or her motion cannot be directly controlled by the algorithm.

B. Grid covering and path planning for the human agent

With the connectivity of the team guaranteed by the above control technique, we want to guide the human agent towards the goal. Recent studies show that the kinematics of the human locomotion resembles that of a unicycle robot [START_REF] Arechavaleta | On the nonholonomic nature of human locomotion[END_REF], and that it is possible to provide the human user with guidance along a predefined path using tactile feedback [START_REF] Aggravi | Evaluation of a predictive approach in steering the human locomotion via haptic feedback[END_REF]. Of course, although we can guide human agents with high accuracy [START_REF] Aggravi | Evaluation of a predictive approach in steering the human locomotion via haptic feedback[END_REF], deviations from the commanded path will inevitably occur. These deviations can be due to users not being able to fully understand and/or react to the haptic guidance, or due to users willingly moving away from the path because of unforeseen circumstances (e.g., unexpected obstacles blocking the indicated path). Our planning technique aims at always providing the best guidance to the human users, from their current position Fig. 2. Grid covering and path planning. The map is discretized in a grid of resolution r grid . Tiles occupied by obstacles (black) are discarded from the planning (gray). The path planning algorithm generates paths from each tile to the goal. The best path γ * will always be the shortest one from the current position of the human agent to the goal, in terms of number of covered tiles. Enlargement: From the current position of the user x h , the algorithm identifies the center of the next tile to be reached, M * ij , on the current optimal path γ * . The difference between these two positions Fp (see ( 4)) provides the user with information about the path to follow, while the connectivity force F λ i (see ( 2)) informs the user about the connectivity level of the formation.

to the goal, while however leaving them free to deviate from the planned path if needed.

We designed our planning inspired from the Ford Fulkerson flow network method [START_REF] Ford | Network flow theory[END_REF], which retrieves the maximal steady state flow from an origin/start point to a terminal/goal one (maxflow on a network problem). Below we include all the details of our path planning algorithm.

Let us consider a known map of the environment M, which describes the area surrounding the team, and let us divide M in a grid of resolution r grid ∈ R. Every tile on the map

M ij , i ∈ { 1, • • • , nr grid }, j ∈ { 1, • • • , mr grid }, n, m ∈ N,
can be free or occupied (e.g., by obstacles), as shown in Fig. 2. W.l.o.g., we consider squared maps (n = m). Using this notation, we can define the starting point for the algorithm x start ∈ M as the tile occupied by the starting position of the human agent and the goal location as the tile occupied by the goal position x goal ∈ M.

To account for any deviation of the human user w.r.t. the planned path, we compute valid paths from each tile of the map to the goal. To do so in a efficient fashion, we start by dividing M in large regions of resolution r reg , such that every region on the map includes a certain number of tiles, i.e., r reg = qr grid , q ∈ N. Then, we use a Breadth First Search (BFS) algorithm to compute the shortest path γ 1 , going from the initial location of the user x start to the goal x goal . We store γ 1 in a list of paths Γ and we mark each tile that it covered. After that, we randomly select a new starting tile in the region that includes the least number of covered tiles, and we compute the new shortest path γ 2 using the BFS algorithm. Again, we store γ 2 in Γ and we mark each tile that it covered. We proceed iteratively in this way until all the tiles in M are covered. This process has to be run only once per map.

With the map fully covered and all elements (paths) of Γ ending at the goal, the best path will always be the shortest one from the current position of the human to the goal, in terms of number of covered tiles. Of course, our framework is compatible with any other similar path planning technique.

As the human only relies on local information to compute where the best path is headed (i.e., its own position and the list of paths Γ), the overall framework is still decentralized.

C. Feedback to the local/remote rescuer

Finally, we need to provide the human rescuer with meaningful information about (i) the current path to follow (Sec. III-B) and (ii) the status of the team (Sec. III-A), which are both important pieces of information. The former enables rescuers to successfully reach the final goal, while the latter informs them about the connectivity level of the team (e.g., so that they can slow down if the team is not able to keep up with the current moving pace). Being informed about the connectivity of the team has been already proven useful when teleoperating multiple drones [START_REF] Aggravi | Design and evaluation of a wearable haptic device for skin stretch, pressure, and vibrotactile stimuli[END_REF]. The way of providing this information can be tailored to the experience of the users (e.g., expert rescuers or civilians), as detailed in our experimental Section.

To provide navigation information, let us define M * ij ∈ R 2 as the center of the next tile along the current selected path1 γ * , which passes from the human current position x h (see enlargement in Fig. 2). The elevation of the human is set to zero and thus, for simplicity, the third dimension of its position vector is not considered here, i.e., x h ∈ R 2 . The resulting difference force

F p = κ p (M * ij -x h ) (4)
comprises information about where the human agent should go,

θ p = atan((M * ij,y -x h,y )/(M * ij,x -x h,x
)), and how close he or she is to the center of the next tile. κ p ∈ R + is a positive gain; in our application, κ p = 1 N/m, but it can be adjusted to reflect how much we let the operator deviate from the target path. Quantities θ p and d p will be used in Sec. IV-A4 for providing visual or haptic feedback on the direction to follow.

For what concerns the formation status, we want to provide the rescuer with information about the team connectivity. Let us recall from Sec. III-A that every robotic agent in the team is controlled following [START_REF] Marconi | The SHERPA project: Smart collaboration between humans and ground-aerial robots for improving rescuing activities in alpine environments[END_REF], where F λ i is the connectivity force steering each agent i to a more connected state. Of course, we cannot directly control the motion of the human as we do for the robots. Nonetheless, we can still suggest F λ h through visual or haptic feedback (we use index i = h for the human agent). This force will be used in Sec. IV-A4 for designing visual or haptic feedback techniques, so that human agents are informed about the effect of their movements on the connectivity of the formation.

IV. EXPERIMENTAL EVALUATION

A. Human subjects experiment in a simulated environment 1) Context: Imagine a post-disaster situation in which a human civilian survivor is found by the robotic team and escorted out of the area. As soon as the robots locate the survivor, they release two vibrating bands that the human agent wears on the right and left arms. Then, an expert rescuer Fig. 3. (top) In the simulated environment, subjects play the remote expert rescuer; they move the virtual civilian survivor toward the goal, acting on its linear and angular velocities (v h and ω h ) and following the feedback provided by the system. (bottom) In the real environment, the subject plays the in-field expert rescuer; they move toward the goal location following the feedback information provided by the system. remotely guides the survivor along a safe path, providing him or her with navigation information by manually activating the armbands. In turn, the rescuer is provided with feedback information about the best path to follow and the connectivity of the team (see Fig. 3 top). Navigation information to the remote rescuer is generated from a priori map of the environment, as described in Sec. III-A, while the connectivity metrics is generated from the connectivity-maintenance algorithm, as summarized in Sec. III-B. Finally, how this feedback information is conveyed to the rescuer is described in Sec. III-C. In this experiment, we assume that the survivor can closely follow the indications of the remote expert rescuer using two vibrotactile armbands, as already demonstrated in [START_REF] Aggravi | Evaluation of a predictive approach in steering the human locomotion via haptic feedback[END_REF], [START_REF] Baldi | Haptic guidance in dynamic environments using optimal reciprocal collision avoidance[END_REF].

The non-expert human agent in the field only needs to follow simple navigation cues, while the expert remote agent is provided with rich information enabling him or her to control the situation. We believe this point to be rather relevant for the considered application, as frightened non-trained civilians cannot be asked to complete or understand complex commands and tasks. The role of the robotic team is also quite important. Ground mobile robots can carry medical and food supplies, while aerial robots can provide the rescuer with a view of the environment and look for other survivors.

2) Virtual environment and task: We consider two different virtual scenes, S1 and S2, both composed of various urban elements, including buildings and open walls, as shown in the video (also available at https://youtu.be/N73QUFqpSvo). We also consider two variations of each scene. One with no changes with respect to the a priori map known by the system (S1 f ree and S2 f ree ) and one with unexpected obstacles blocking some routes (S1 occ and S2 occ ). These unexpected obstacles are unknown to the system and therefore not accounted for in the path planning of Sec. III-B. We consider these variations to simulate situations in which the planner has only access to an outdated version of the map, which might not perfectly reflect the current status of the post-disaster environment.

Our heterogeneous team is composed of five virtual agents: one human survivor, one ground mobile robot, and three quadrotors. The virtual scene is displayed to the user through an LCD screen. Subjects play the role of the remote rescuer, and their task consists in moving the simulated human survivor/virtual agent toward the goal, following the feedback information provided by the system.

3) Control interface: Subjects control the motion of the virtual agent either through (i) an off-the-shelf joypad Logitech Gamepad F310, or (ii) a grounded haptic device Novint Falcon. When using the joypad, the right analog thumbstick commands a linear velocity v h ∈ R to the virtual agent , whereas the left stick rotates it with angular velocity ω h ∈ S. When using the haptic device, the Falcon is used to control the velocity of the virtual agent. A displacement of the end-effector along the negative z-axis (w.r.t. its initial position, see Fig. 4) commands a linear velocity v h ∈ R, whereas a similar displacement along the negative x-axis rotates it with angular velocity ω h ∈ S.

As discussed in Sec. IV-A1, we assume that the virtual agent perfectly follows the navigation cues conveyed by the remote expert operator. For this reason, as to simplify this interaction and w.l.o.g., we have enabled the operator to directly control its velocities, as detailed above.

4) Experimental modalities: We consider four experimental conditions (see Fig. 4): (VC) the joypad controls the human agent motion, and one arrow provides combined information about the connectivity force and the path direction; (VS) the joypad controls the human agent motion, and two separate arrows provide information about the connectivity force and the path direction, respectively; (HC) the Novint Falcon controls the human agent motion, and kinesthetic feedback provides combined information about the connectivity force and the path direction; (HS) the Novint Falcon controls the human agent motion, kinesthetic feedback provides information about the connectivity force, and wearable vibrotactile feedback provides information about the path direction. Let us recall from Sec. III-C that F λ h is the connectivity force applied to the user and F p is the guidance force steering the user along the current best path.

In condition VC, the user receives feedback about the weighted sum of the two forces

F t = 3 F p F p + F λ h F λ h , (5)
represented as a single cyan arrow, starting from the position of the human figure, with a max intensity of 6 N. In condition VS, the remote expert receives feedback about the two forces separately, represented as two distinct arrows, both starting from the position of the virtual agent: red for F λ h and white for F p (see also Fig. 2). In condition HC, the user receives feedback about the weighted sum of the two forces (as per ( 5)), represented as a force feedback produced by the Falcon device. In condition HS, the remote expert receives feedback about the two forces separately, i.e., F λ h conveyed by the Falcon device as kinesthetic feedback and F p conveyed by wearable vibrotactile feedback. These latter stimuli are provided via a pair of custom haptic armbands, worn on the right and left upper arms. Each armband comprises four 307-100 Pico Vibe vibrating motors, positioned evenly around the arm at 90 degrees from each other. Each armband vibrates with frequency [ 0 -255 ] Hz and amplitude [ 0.2 -1.2 ] G in square-wave-like vibration patterns of ν ∈ R + , i.e., it vibrates for a period of 2ν ms with duty cycle 50%. The four motors of one armband always vibrate together. W.r.t. Sec. III-C, let us recall the direction of the next point-to-be-reached on the path θ p (see (4) and the following definitions) and let us call the human orientation w.r.t. a world frame θ h . To efficiently convey direction information, we define four different stimulus ranges on d θ = θ p -θ h (see Table I and A similar guidance approach has been already successfully employed in [START_REF] Scheggi | Cooperative Navigation for Mixed Human-Robot Teams Using Haptic Feedback[END_REF], [START_REF] Aggravi | Evaluation of a predictive approach in steering the human locomotion via haptic feedback[END_REF].

Subjects: Twelve participants took part to our experiment, including one woman and eleven men (age 23-32 years old). Users performed one repetition of the task per feedback condition per scenario, yielding a total of 4 experimental modalities × 4 scenes = 16 trials per user and 192 trials in total. Operators were asked to complete the task as fast as No statistically significant differences were found. Perceived effectiveness Perceived effectiveness (two-way)

V vs. H p < 0.001 C vs. S p = 0.001 possible, taking however into account the feedback received. Results: We registered (i) the average task completion time, (ii) the average length and (iii) linear velocity of the simulated human agent, (iv) the average connectivity force commanded to the human operator, and (v) the average connectivity level of the whole formation. To compare the different metrics, we ran three-way repeated-measures ANOVA tests (significance level a = 0.05) [START_REF] Girden | ANOVA: Repeated measures[END_REF]. The interface (joypad/visual or haptic, V vs. H), feedback type (combined or separated, C vs. S), and the presence of unexpected obstacles (yes or not, f ree vs. occ) were the within-subject factors. Data from the two scenes were normalized and averaged. All the data passed the Shapiro-Wilk normality test. Table II summarizes the results. 5d shows the average force applied to the operator. It was calculated as the average norm of F t . The ANOVA test revealed a statistically significant change in the applied force for the interface [F(1,11) = 611.703, p < 0.001] variable. Fig. 5e shows the average connectivity level of the team, i.e., λ 2 . The ANOVA test revealed no statistically significant change in the connectivity level for the considered variables. At the end of the experiment, we asked the participants to rate the perceived effectiveness of the four experimental conditions (VC vs. VS vs. HC vs. HS). The responses were given using bipolar Likert-type scales that ranged from 0 to 10, where a score of 0 meant "very low" and a score of 10 meant "very high" [START_REF] Maisto | Evaluation of wearable haptic systems for the fingers in augmented reality applications[END_REF], [START_REF] Meli | Combining wearable finger haptics and augmented reality: User evaluation using an external camera and the microsoft hololens[END_REF]. To compare this metrics, we ran a two-way repeated-measures ANOVA test (a = 0.05). The interface (H vs. V) and feedback modality (C vs. S) were the within-subject factors. Data were transformed using a squareroot transformation before running the statistical analysis. The transformed data passed the Shapiro-Wilk normality test. Fig. 5f shows the perceived effectiveness of the four experimental conditions. The ANOVA test revealed a statistically significant change in the perceived effectiveness for both the interface Finally, six subjects out of twelve found condition HS to be the most effective at completing the task, followed by VS (four subjects) and VC (two subjects). Similarly, five subjects out of twelve found condition VS to be the easiest to understand and use, followed by HS (three subjects), VC (two subjects), and HC (two subjects).

B. Representative task in a real environment 1) Context: Imagine a post-disaster situation in which an expert rescuer is escorted by the robotic team inside a disaster area with the aim of reaching a certain point of interest (e.g., a survivors shelter). He or she is equipped with two vibrating bands on the right and left arms. The rescuer moves in the same environment of the robots, directly receiving information about the best path to follow and the connectivity of the team (see Fig. 3 bottom). Feedback information is generated as in Sec. IV-A. Since the rescuer needs to move freely, here all information is provided through the wearable vibrotactile armbands. No remote operator is needed in this case.

2) Real environment, task, and subject: One variation of the scene has no changes with respect to the a priori map known by the system (S3 f ree ) while the other one has unexpected obstacles blocking some routes (S3 occ , shown in Fig. 6). These unexpected obstacles are unknown to the system and therefore not accounted for in the path planning of Sec. III-B. As in Sec. IV, we consider these variations to take into account situations in which the planner has only access to an outdated version of the map. Our heterogeneous team is composed of four real agents: one human operator, one mobile robot, and two quadrotors. All agents move in the instrumented space. We use two quadrotors from MikroKopter (Germany) and one Pioneer P3-DX mobile robot from Omron Adept (USA), all controlled at 50 Hz.

The subject plays the role of the expert rescuer, and the task consists in moving toward the goal location, following the feedback information provided by the system. The starting points and goal locations are shown in Fig. 6a and were the same for both scenes.

3) Experimental modalities: We provide the operator with feedback information about the path to follow and the connectivity information using a fully-wearable solution. This information is conveyed using the two armbands, in a smart combination of conditions HC and HS. In particular, we provide the user with information on F t , as calculated in (5), using two armbands. The policy to activate the two armbands in the same used for condition HS in Sec. IV-A4 and Table I, i.e., more vibrations on one side indicate that F t points in that direction (similarly to F p in Fig. 4-HS).

4) Results: Figure 6 shows the experiment in the variation with the unexpected obstacle (circled in orange in Figure 6b). Specifically, Fig. 6a shows the trajectories of the four agents (blue: human, cyan: mobile robot, red and green: quadrotors). Figure 6b shows three moments of the experiment, at t = 0, T f /2, T f with T f = 129 s, from the point of view of a videocamera placed at around (5, -3) m.

The operator was able to successfully complete the task, reporting no issues and a high perceived effectiveness and comfort in using the system. We registered the completion time (S3 f ree : 103 s; S3 occ : 129 s), the average distance traveled (S3 f ree : 7.8 m; S3 occ : 12.1 m), the average connectivity level of the whole formation (S3 f ree : 1.1; S3 occ : 1.0), and the activation of the vibrotactile armbands (S3 f ree : 47.7%; S3 occ : 37.9%). In this variation of the scene, the team is forced to move around the obstacle, due to the presence of an additional obstacle unknown to the system (orange). (bottom) video stills of the experiment at the same times as above, from two different points of view. A video of the full experiment is available at https://youtu.be/N73QUFqpSvo?t=127.

V. DISCUSSION AND CONCLUSIONS

This paper presents a decentralized haptic connectivitymaintenance control framework for heterogeneous humanrobot teams. We control the coordinated motion of a team composed of an arbitrary number of mobile robots and one human, for collaboratively achieving exploration and SAR tasks. The human physically becomes part of the team, moving in the same environment of the robots and receiving rich haptic feedback about the team connectivity and direction toward a safe path. While the human explores the environment, the robots move autonomously to keep the team connected, using a decentralized connectivity-maintenance algorithm.

The first experiment considered a situation in which the robotic team escorts a civilian out of a disaster area. Results show that using the haptic interface significantly improves the performance of the task with respect to using the joypad and the visual feedback in all metrics except the connectivity level. Similarly, providing the two pieces of information separately (path to follow and connectivity level) improved the user's performance in all metrics but the connectivity level and the total force feedback applied to the user. As expected, differences between the two variations of the scene (with or without unknown obstacles) were only registered in the completion time and path length. Most subjects appreciated being provided with the two feedback cues separately, as it enabled them to understand the contribution of each piece of information. Subjects also appreciated the effectiveness of receiving haptic feedback, although visual cues were considered easier to understand. This latter result is not unexpected, as humans are generally more used to follow visual navigation feedback (e.g., turn-by-turn car navigation systems, road signs), while they are not used to follow haptic navigation cues at all. For this reason, we expect more training to significantly improve the performance of the haptic modality in all the considered metrics. It might also seem strange that no differences were found regarding the connectivity level of the team. However, since our virtual robots can apply infinite thrusts, the connectivitymaintenance algorithm is always able to guarantee a well connected team, even if the human agent completely disregards the connectivity force cues. However, of course, a failure in providing the acceleration requested by the algorithm may result in a loss of connectivity for the team. This undesired situation might happen in a real scenario. Finally, we discuss the difference between using kinesthetic feedback only (HC) vs. combining kinesthetic and vibrotactile feedback (HS). If no unexpected obstacles are encountered, being gently guided by kinesthetic feedback seems the straightforward choice, even if combining the two forces into one conveys less information. Nonetheless, in the presence of unexpected obstacles, the operator needs to temporarily move away from the suggested trajectory to find new unobstructed paths. To do so, in the case of HC, the operator needs to act against the kinesthetic feedback, which might become rather strong depending on the situation. This problem is solved in condition HS, where navigation information is provided via wearable vibrotactile feedback. This type of haptic feedback can convey rich navigation cues while leaving the operator free to disregard it, if necessary.

The second experiment considered a situation in which a rescuer is escorted by the robotic team inside a disaster area. One subject carried out the experiment in two real-world scenarios, receiving feedback about the path to follow and the connectivity of the team through two vibrotactile armbands. This success in a real environment using a fully-wearable solution is very promising, and it may open new interesting scenarios in the field of ubiquitous robotics and human-robot collaboration. The human operator does not need anymore to stay in a fixed position, but instead can move and share the same environment of the robots.

In addition to the great importance of providing feedback information (either via haptic and visual stimuli), experiments show that the overall framework works well and it is robust to rather different conditions. The team remained well connected in all scenarios, providing the user with meaningful information about the current connectivity level and easy-tofollow navigation information. Similarly to a vehicle turn-byturn navigation system, our path planning algorithm constantly indicates the shortest path from the current position to the goal. If an unexpected obstacle blocks the indicated path, the human can freely move elsewhere and the path planning will adjust its suggestion accordingly. As we tested our system in two different environments, six scenes, enrolling 13 subjects for more than 12 hours of testing, this work represents a muchneeded extensive evaluation on the topic.

In the future, we will extend this framework for considering multiple human agents, so as to be able to support an arbitrary number of mobile robots and humans. Moreover, we plan to include a dynamic damping factor in (4) for smoothing any abrupt change in the force feedback when the target direction/tile changes. Finally, we also want to test more extensively real-world situations where the human civilian survivor does not follow exactly the commands of the remote expert rescuer, e.g., when the communication link does not work as well as expected, the survivor is injured and cannot move properly, or the stimulation results hard to understand. For example, we could adjust the gain on the guidance, κ p , according to how well the survivor follows the rescuer commands.

Fig. 1 .

 1 Fig. 1. Block diagram. Given the environment map M and the initial/goal positions for the human agent (xstart and x goal ), an offline planner (see Sec. III-B) computes the list Γ of all possible paths from xstart to x goal .During the task, from all the current agents positions x, our algorithm computes the best connectivity-maintenance control action F λ h (see (2)), the best path to the goal γ * , and the best next tile in γ * for the human agent to reach, M * ij , resulting in Fp (see (4)). Both forces are then sent to the feedback devices (see Sec. IV-A4), informing the human agent about the formation connectivity level and/or the direction of the path to follow.

2 > 0 ,

 20 to smoothly decrease from λ min 2 to λ max 2 = λ min

2

 2

Fig. 4 .

 4 Fig. 4. Feedback modalities. We provide feedback about the path to follow, Fp, and the connectivity of the team, F λ h , in these ways: (VC) the weighted sum of the two forces, Fp and F λ h , is shown with a single arrow, (VS) the two forces are shown as a two distinct arrows, (HC) the weighted sum of the two forces is provided by the Falcon device as force feedback; (HS) Fp is conveyed by the wearable armbands as vibrotactile feedback while F λ h is provided by the Falcon device as force feedback.

Fig. 2 )

 2 : no feedback (when |d θ | ∈ [ 0-20 ] deg), weak feedback (21-60 deg), medium feedback (61-100 deg), and strong feedback (101-180 deg). The sign of d θ (positive for left turning, negative for right turning) tells us which armband to activate.

Conditions

  Interface Interface = V (joypad/visual), H (haptic) Feedback type Feedback type = C (combined), S (separated) Variation of the scene Variation of the scene = f ree (map fully known), occ (map partially unknown) Statistical analysis (repeated-measures ANOVA, a = 0.05) Completion time Completion time (three-way) V vs. H p = 0.005 C vs. S p = 0.001 f ree vs. occ p = 0.004 Path length Path length (three-way) V vs. H p = 0.001 C vs. S p = 0.024 f ree vs. occ p < 0.001 Velocity of the virtual human agent Velocity of the virtual human agent (three-way) V vs. H p < 0.001 C vs. S p = 0.003 Force feedback to the operator Force feedback to the operator (three-way) V vs. H p < 0.001 Connectivity level of the team Connectivity level of the team (three-way)

  Fig. 5a shows the average normalized task completion time. It was calculated as the time it took the virtual agent to move from the starting position to the goal, divided by the best registered performance (≈ 137 s). The ANOVA test revealed a statistically significant change in the task completion time for the interface [F-test statistic F(1,11) = 12.509, p-value for the F statistics p = 0.005], feedback modality [F(1,11) = 20.595, p = 0.001] and obstacles [F(1,11) = 13.315, p = 0.004] variables. Fig. 5b shows the average normalized path traveled by the simulated agent. It was calculated as the length of its linear trajectory, divided by the best registered performance (≈ 34 m). The ANOVA test revealed a statistically significant change in the task traveled path for the interface [F(1,11) = 18.387, p = 0.001], feedback modality [F(1,11) = 6.870, p = 0.024] and obstacles [F(1,11) = 43.620, p < 0.001] variables. Fig. 5c shows the average velocity of the simulated agent. It was calculated as the average norm of v h . The ANOVA test revealed a statistically significant change in the agent's velocity for the interface [F(1,11) = 27.258, p < 0.001] and feedback modality [F(1,11) = 14.886, p = 0.003] variables.Fig.

Fig. 5 .

 5 Fig. 5. Human subjects experiment. Mean and 95% confidence interval of (a) completion time, (b) normalized path, (c) velocity, (d) force applied, (e) connectivity level, and (f) perceived effectiveness for the four conditions in the two scene variations free and occ.

  [F(1,11) = 83.993, p < 0.001] and feedback modality [F(1,11) = 19.288, p = 0.001] variables.

Fig. 6 .

 6 Fig. 6. Experiment in a real environment: scene S3occ. The blue circle indicates the human agent, red and green circles indicate the two quadrotors, and the cyan circle indicates the grounded mobile robot. (top) from left to right: robot initial positions at time t = 0 and grid of obstacles (black), robot trajectories and positions t = T final /2 and then at t = T final = 129 s.In this variation of the scene, the team is forced to move around the obstacle, due to the presence of an additional obstacle unknown to the system (orange). (bottom) video stills of the experiment at the same times as above, from two different points of view. A video of the full experiment is available at https://youtu.be/N73QUFqpSvo?t=127.

TABLE I LEVELS

 I OF VIBROTACTILE STIMULI DEPENDING ON d θ

	Range dθ	Left armband	Range dθ	Right armband
	(deg)	vibration level [G]	(deg)	vibration level [G]
	[ 0 , 20 ]	0	[ 0 , -20 ]	0
	[ 21, 60 ]	0.6	[ -21 , -60 ]	0.6
	[ 61 , 100 ]	0.9	[ -61 , -100 ]	0.9
	[ 101 , 180 ]	1.2	[ -101 , -180 ]	1.2

TABLE II HUMAN

 II SUBJECTS EXPERIMENT

Here we assume that the computing unit worn/brought by the human agent is able to localize the agent and sort the path in Γ for obtaining M * ij .

This paper was recommended for publication by Editor Jingang Yi upon evaluation of the Associate Editor and Reviewers' comments.

This work was supported by the ANR, Project ANR-20-CHIA-0017-01 "MULTISHARED".


Jacques Burlot

Sylvie Yona Waksman

Ludovic Bellot-Gurlet

Gulsu Simsek Franci

The glaze production technology of an early Ottoman pottery (mid-14th (?)-16th century): The case of "Miletus Ware"

Keywords: Miletus Ware, Ottoman ceramics, Technology, Glaze, Color, SEM-EDS, Raman spectroscopy

Miletus Ware", considered as one of the first wares produced by Ottoman potters in western Anatolia, featured new typological and stylistic characteristics that suggested the introduction of new recipes and the use of new materials in the local repertoire. This study, conducted on archaeological samples of Miletus Ware from eight sites in Turkey and in the Crimea, supports this view. It focuses on the decoration techniques, through the analyses of glazes and underglaze decorations using SEM-EDS and Raman spectroscopy. The results show that, compared to what Byzantine and Beylik potters previously produced in western Anatolia, there were two main innovations in the Miletus Ware glaze production technology. First, its glaze recipe included new sodium-based fluxes. Second, some underglaze decorations as represented by the black and dark-blue-colored ones -obtained respectively through the use of pigments featuring magnesiochromite and cobalt -were produced with materials that had not been used in the region before.

Introduction

Ottoman ceramics are mainly known for Iznik Ware, which refers to the pottery produced in Anatolia from the end of the 15th century onward, made with a stonepaste body (mixture of 70-80% quartz, 10-20% clay and 10% frit) and decorated with a varied palette of underglaze colors (Paker, 1964(Paker, -1965;;[START_REF] Tite | Iznik pottery: an investigation of the methods of production[END_REF]. Although bearing the name of Iznik, this pottery was also produced in Kütahya [START_REF] Şahin | Kütahya Çini-Keramik Sanatı ve Tarihinin Yeni Buluntular Açısından Değerlendirilmesi[END_REF][START_REF] Carswell | Iznik Pottery, second edn[END_REF], and in Istanbul during the 18th century, at the imperial workshops located in Tekfur Saray [START_REF] Yenişehirlioğlu | Tekfur Sarayı Çini Fırınları Kazısı[END_REF][START_REF] Yenişehirlioğlu | Les fours et la production des céramiques du Palais de Tekfur à Istanbul[END_REF]. This high-quality pottery was widely exported outside the Empire. Due to their luxurious and 'high-tech' nature, many examples from these productions are now exhibited in museums and have been subject to several archaeometric studies (Kiefer, 1956a(Kiefer, , 1956b(Kiefer, , 1956c[START_REF] Kiefer | Caractérisation des tessons types par l'analyse physique et chimique[END_REF][START_REF] Henderson | Iznik ceramics: a technical examination[END_REF][START_REF] Henderson | The technology of fifteenth-century Turkish tiles: an interim statement on the origins of the Iznik industry[END_REF][START_REF] Tite | Iznik pottery: an investigation of the methods of production[END_REF][START_REF] Henderson | Iznik: Ottoman court ceramics and the development of fritware[END_REF][START_REF] Geçkinli | Characterization studies of late ottoman ceramics recovered from Istanbul Tekfur palace excavation[END_REF][START_REF] Okyar | Characterisation of Iznik ceramics[END_REF][START_REF] Colomban | On Site Raman analysis of Iznik Pottery Glazes and Pigments[END_REF][START_REF] Paynter | The production technology of Iznik pottery -a reassessment[END_REF][START_REF] Colomban | On-site Raman spectroscopic analysis of Kütahya Fritwares[END_REF][START_REF] Şimşek | Tentative differentiation between Iznik tiles and copies with Raman spectroscopy using both laboratory and portable instruments[END_REF][START_REF] Şimşek | An assessment study of tiles from Topkapı Palace Museum with energy-dispersive X-ray and Raman spectrometers[END_REF][START_REF] Constantinescu | External milli-beam PIXE analysis of the mineral pigments of glazed Iznik (Turkey) ceramics[END_REF][START_REF] Tite | New data on the soda flux used in the production of Iznik glazes and Byzantine glasses[END_REF]Şimşek et al., 2019a, 2019b). These studies provided in-depth knowledge on the history of techniques of these luxury Ottoman ceramics.

However, it is important to emphasize that these stonepaste ceramics (also called fritwares, or ceramics with synthetic pastes) hardly reflected productions intended for use in daily life at this period, and do not reflect the whole Ottoman ceramic industry. In the post-medieval contexts of western Anatolian sites, the vast majority of pottery unearthed featured a clayey body, and were notably decorated with a green monochrome glaze applied on a white slip layer [START_REF] Crane | Some Archaeological notes on Turkish Sardis[END_REF][START_REF] François | Eléments pour l'histoire ottomane d'Aphrodisias: la vaisselle de terre[END_REF][START_REF] Vroom | Medieval pottery from the Artemision in Ephesus: imports and locally produced wares[END_REF][START_REF] Böhlendorf-Arslan | Keramikproduktion im byzantinischen und türkischen Milet[END_REF][START_REF] Fındık | Der Bischofspalast von Milet: Spätrömisches Peristylhaus und frühbyzantinische Residenz[END_REF][START_REF] Vroom | Die Türbe im Artemision: Ein frühosmanischer Grabbau in Ayasoluk/Selçuk und sein kulturhistorisches Umfeld[END_REF]. Another mass-produced ceramic excavated in these contexts corresponds to the socalled Miletus Ware. Despite its relative abundance, no extensive archaeometric studies have been carried out on this ceramic group. The Miletus Ware was considered in some of the studies previously mentioned, which were mainly devoted to Iznik fritwares and used Miletus Ware samples as comparative evidence to attempt to define the origin of the Ottoman stonepaste technology [START_REF] Henderson | The technology of fifteenth-century Turkish tiles: an interim statement on the origins of the Iznik industry[END_REF][START_REF] Tite | Iznik pottery: an investigation of the methods of production[END_REF][START_REF] Okyar | Characterisation of Iznik ceramics[END_REF]. In these studies, only one or two samples of Miletus Ware were analyzed. Burlot et al. (2020) Journal of Archaeological Science: Reports, 29, 102073. https://doi.org/10.1016/j.jasrep.2019.102073 Miletus Ware is one of the first ceramic types produced by Ottoman potters in western Anatolia [START_REF] Lane | The Ottoman pottery of Isnik[END_REF][START_REF] Erdmann | Neu Arbeiten zur Türkischen Keramik[END_REF][START_REF] Aslanapa | The Iznik Tile Kiln Excavations (the Second Round: 1981-1988[END_REF]. It was named by Friedrich Sarre since he found it abundantly at Miletus, a site located on the Aegean coast of Anatolia, and assumed that it was locally manufactured (Sarre, 1930(Sarre, -1931[START_REF] Sarre | Die Keramik der islamischen Zeit von Milet[END_REF]. This ceramic was generally produced in open forms such as plates and large bowls (Fig. 1). It may be described as reddish earthenware covered with white slip on both sides, though the external side is not fully covered. Its interior surface features painted decoration, usually in dark-blue, turquoise, purple and black, and main motifs include flower rosette or sunburst at the center and radial lines around this central motif. The painted decoration is then covered with a transparent glaze. Such underglaze painted decoration may also be used on the outer surface of Miletus Ware, but the details are less accurate since, according to Paker (1964Paker ( -1965)), customers paid no attention to this less visible surface. In other examples, the exterior is only covered with a monochrome glaze, which is generally green.

Miletus Ware has mainly been excavated in central and western Anatolia, but also on the Greek islands, in the Crimea, and in the Balkans; all regions that were conquered by the Ottomans [START_REF] Burlot | Premières productions de céramiques turques en Anatolie occidentale : Contextualisation et études techniques[END_REF]. Archaeological excavations have provided evidence of the production of Miletus Ware in four centers in Anatolia: Kütahya (Şahin, 1981), Pergamon [START_REF] Mania | Eine neue Werkstatt früher türkischer Keramik Miletware aus Pergamon[END_REF], Akçaalan [START_REF] Akarca | Çanakkale'de Yeni Bir Çanak Çömlek Merkezi[END_REF] and Iznik considered as the major one [START_REF] Aslanapa | Pottery and Kilns from the Iznik Excavations[END_REF][START_REF] Aslanapa | The Iznik Tile Kiln Excavations (the Second Round: 1981-1988[END_REF][START_REF] Özkul Fındık | Beylikler ve Erken Osmanlı Devri Seramik Sanatı[END_REF]. Discoveries in Miletus [START_REF] Gök Gürhan | Balat İlyas Bey Külliyesi Kazısında Ortaya Çıkarılan Seramiklerin Değerlendirilmesi (2007-2008)[END_REF][START_REF] Gök Gürhan | Ceramics unearthed during the excavation and cleaning work conducted at the İlyas Bey complex in 2007 and 2008[END_REF] and in Aphrodisias [START_REF] François | Eléments pour l'histoire ottomane d'Aphrodisias: la vaisselle de terre[END_REF]) also suggest the local production of Miletus Ware, but further investigations are required to confirm these hypotheses.

The start date of Miletus Ware remains unclear. It is commonly acknowledged that Miletus Ware spread widely from the second half of the 14th century onward [START_REF] Sarre | Die Keramik der islamischen Zeit von Milet[END_REF][START_REF] Aslanapa | Türkische Fliesen und Keramik in Anatolien[END_REF][START_REF] Aslanapa | Turkish ceramic art[END_REF][START_REF] Aslanapa | The Iznik Tile Kiln Excavations (the Second Round: 1981-1988[END_REF], but no secure historical or archaeological evidence have been proposed to confirm this chronology. In fact, very few archaeological sites, for which the excavation results have been published, indicate well stratified contexts enabling us to date the use of Miletus Ware. At Saraçhane in Istanbul (Turkey), as well as in Sofia (Bulgaria), fragments of Miletus Ware were discovered in contexts dated from the mid-15th century to around 1520 (Saraçhane: [START_REF] Hayes | Excavations at Saraçhane in Istanbul. II, the Pottery[END_REF]Sofia: Guionova, 2015); and in the Crimea, they were found in stratified layers mostly dated from the second half of the 15th century, but ranging up to the end of the 16th century [START_REF] Teslenko | Turkish ceramics in the Crimea on the eve of the Porta invasion (problems of chronology of a certain group of vessels)[END_REF], which is the latest date known for these ceramics.

Questions and corpus

Although the data on the beginnings of Miletus Ware are still fragmentary, the present study can confirm that the appearance of this pottery in western Anatolia coincides with the settlement of Turkish populations in a former Byzantine region. Hence, the production of Miletus Ware marks the introduction of a new ceramic type in western Anatolia, related to new culture and potentially to new technologies. By analyzing the Miletus Ware decorations, we aim, first, to define the glaze recipes and manufacturing techniques used by early Turkish ceramic production centers in western Anatolia. Second, based on these definitions, we examine whether the recipes and techniques reveal new technological features, and how the latter fit into the diachronic ceramic technological landscape of Anatolia. This paper focuses on glazes technology, the analyses of bodies and slips are summarized below and detailed elsewhere (Burlot and Waksman, forthcoming a, forthcoming b).

We analyzed the glazes and their coloring agents applied on the inner surface of 27 Miletus Wares sherds excavated from five Turkish sites (Ephesus, Miletus, Pergamon, Sardis, and Iznik) and three sites located in the Crimea that provide well stratified contexts dating from the second half of the 15th to the third quarter of the 16th century (Table 1).

Samples were selected within three productions previously defined using WD-XRF analyses of the bodies (Burlot and Waksman, forthcoming a, forthcoming b; see e.g. Waksman et al., 2017: 667 for details of the analytical and statistical procedures). They correspond to two production centers located in Iznik and Pergamon, respectively (Table 1: "MW Iznik" and "MW Pergamon Rote Halle"). The third group is related to another production center, whose location has not been identified yet (Table 1: "MW unlocated").

With the exception of two samples, all the slips were of synthetic type (with the main contents as follows: 82-92 wt% SiO2, 3-9 wt% Al2O3, 2-4 wt% Na2O, and 0.5-3 wt% CaO), featuring the same characteristics as those usually associated to stonepastes [START_REF] Mason | The beginnings of Islamic Stonepaste Technology[END_REF][START_REF] Tite | The technological development of stonepaste ceramics from the Islamic Middle East[END_REF]. Only two samples (BZN124 and BZN125, featuring respectively 61.3 and 70.6 wt% SiO2, 25.1 and 20.1 wt% Al2O3, 6.4 and 2.7 wt% Na2O, and 0.6 and 0.7 wt% CaO) presented a slip of clayey type.

Table 1. List of the Miletus Ware samples, presented by production (*dates based on the archaeological context of discovery, when available; a: [START_REF] Teslenko | Turkish ceramics in the Crimea on the eve of the Porta invasion (problems of chronology of a certain group of vessels)[END_REF], b: Foss, 1976

Analytical methods

To analyze the ceramic glazes and coloring agents, samples were embedded in a polyester resin, cut in a cross section in order to expose all the layers (Fig. 2), polished up to a 0.25 µm diamond paste and then carbon coated for SEM-EDS analyses. Prior to coating with carbon, the cross-sections were observed under a binocular microscope with magnifications ranging from 10x to 115x.

Scanning electron microscopy energy dispersive spectrometry

The glazes analyses were carried out with a SEM (FEI Quanta FEG at the Technological Centre for Microstructures (CTµ) in Lyon. The study of the microstructures was mainly performed using backscattered electron (BSE) images which allow the differentiation of the various phases according to the atomic numbers of their elementary content. Elemental compositions were determined by energy-dispersive X-ray spectrometry (EDS) using a silicon drift detector 30 mm 2 SAMx. All measurements were operated at 15 kV acceleration voltage in 60 s. Standardless quantification was performed using a PAP correction method of the intensities. IDFix software was used for data acquisition and evaluation, and the reliability of the results was tested by measuring reference glasses and geological standards (Corning Brill B, C and D, DR-N). Concentrations are given as average of three to five measurements on different zones selected within the regions of interest of the vitreous part, avoiding the weathered areas and the slip or body interfaces.

Crystalline inclusions present in the glazes were also analyzed. For some of the smaller ones, analyses were carried out with the lower beam voltage of 10 kV instead of 15 kV, in order to reduce the interaction zone and thus the influence of the surrounding glaze. Analyses by Raman spectroscopy were carried out in order to help to identify the mineralogy of the inclusions, especially the pigments.

Raman spectroscopy

Analyses by Raman spectroscopy were performed at the laboratory MONARIS (UMR8233) in Paris and at the Koç University Surface Science and Technology Centre (KUYTAM) in Istanbul. They were all carried out on the same crosssections already analyzed by SEM-EDS. For this reason, a first batch of samples were re-polished to remove the carbon coating, but as it also removed some micro-inclusions of interest, we analyzed the second batch without re-polishing. The very fine carbon coating deposited did not interfere with the identification of the inclusions under the microscope, nor the Raman measurements.

At MONARIS, analyses were performed with a Jobin-Yvon Horiba HR800 spectrometer, using a 514 nm emission of a Coherent (I-90C-6) ionized argon laser, modulated at a power of 600 µW on the sample. Using this power, no modification of the samples was observed, and no evolution of the spectra under the irradiation was noted. Rayleigh rejection is achieved thanks to ultra-narrow band Notch BragGrate filters. A 600 lines/mm grating was used, which enabled us to obtain a broad spectral window for each acquisition, with a spectral resolution of about 2 cm -1 . The scattered light was recorded with a Peltier-cooled CCD detector. The 60 s acquisitions were driven by LabSpec software (Jobin-Yvon Horiba). The spectrometer was coupled to an Olympus microscope equipped with 50x and 100x objectives, which enable us to precisely target the areas to be analyzed. The approximate analysis diameter with these objectives was respectively 2 and 1 μm. At KUYTAM, a Renishaw inVia Raman spectrometer equipped with a 532 nm (green) diode pumped solid state laser was used to identify the coloring agents and inclusions found in the glaze. A Leica objective with a magnification of 50x (NA: 0.75) was used for the spot analyses. The recording times ranged between 10 and 20 accumulations per second. A 2400 lines/mm grating was used for a higher spectral resolution. The laser power was kept 10% (~5 mW). Any blackening on the analyzed area and/or modification of the Raman spectrum was observed during the measurements. All the spectra were recorded with the WIRE 4.0 software and the baseline correction was done with LabSpec Version 5.

Results

Chemical compositions of the glazes

According to their chemical compositions (Table 2), all glazes are of the lead-alkali type, and more precisely of leadsoda type (6.2-33.3 wt% PbO; 5.4-17.3 wt% Na2O; 0.4-2.3 wt% K2O). With the exception of four marginal samples, they all match within a main cluster when comparing their PbO contents to their alkali ones (Na2O+K2O) (Fig. 3). The main cluster -alkali rich lead-alkali glaze (6.2-21.6 wt% PbO, 10.0-18.5 wt% Na2O+K2O) -is only composed of glazes applied on internal surfaces of Miletus Ware belonging to the Iznik and unlocated productions.

Two of the marginal glazes (BZN124 -dark blue and BZN125 -purple) correspond to the ones applied on the two samples belonging to the Pergamon production (Fig. 3: yellow squared symbols). These glazes, with alkali contents similar to those of the glazes of the main cluster, differ by their higher lead content (20.6-26.5 wt% PbO). The third marginal glaze, which features a high lead content (33.3 wt% PbO) and also the highest lime content (2.8 wt% CaO), corresponds to the one applied on the sample BZN 46 from the unlocated production. The last marginal glaze is the dark blue one applied on sample BZN289 from the unlocated production, that differs from the glazes of the main cluster by much lower alkali contents. This may be explained by the heterogeneity of the latter, featuring a lot of inclusions, the composition then being influenced by different microstructures. Among these are the coloring agents whose cumulative contents in this glaze is equivalent to 16.4 wt%.

All the glazes are transparent, neither opacifier such as cassiterite inclusions, nor bubbles were observed. However, other kinds of microstructures are present, especially in purple, black and blue glazes. The majority of these inclusions correspond to the coloring agents (see infra). The colorless glazes and the turquoise ones in which the colorants are totally dissolved, are practically devoid of crystalline inclusions.

Despite two marginal samples (BZN 46 and BZN289) and fairly large ranges of variation, the glazes of the Miletus Wares samples belonging to the group "MW Iznik" do not seem to differ significantly from those of the group "MW unlocated", while the two glazes of the samples from Pergamon differ from the others by higher concentrations of cumulated fluxes (36.4-40.8 wt% PbO + Na2O + K2O, compared to a maximum of 34.0 wt% in the other two productions, with the exception of the glaze BZN 46 -dark blue: 41.4 wt%).

Colors of the underglaze painted decorations

Among the samples of the present study, the chromatic panel of the painted decoration of Miletus Wares is relatively wide. It varies from turquoise to purple, dark blue and black. For three samples (BZN 44, BZN206 and BZN287), the glazes we analyzed correspond to areas where the blue and purple colors are juxtaposed (Table 2: marked with an asterisk). We will discuss these samples both with purple and dark blue underglaze decors because they show features of each of these two colors.

Copper Turquoise Blue

The turquoise areas feature the highest copper oxide contents, all higher than 2.4 wt% CuO (Table 2). This confirms what was already well known, that copper-based minerals in an alkali-based glaze generates a turquoise blue color. The copper-based colorants were fully dissolved in the glaze during the firing since no copper-rich crystalline inclusion was observed, unlike the colorants corresponding to the purple, dark blue and black colors.

Manganese Purple

In the purple areas, manganese-based minerals served as colorants, since the manganese contents quantified in these decors are the highest ones (≥1.8 wt% MnO). Manganese-rich inclusions were observed in purple areas, located at the interface with the slips (Fig. 4). Their chemical compositions show very high contents of manganese (≥63.2 wt% MnO), as well as significant ones in silicon, iron, lead and sodium; the latter two probably mainly belonging to the surrounding glaze (Table 3). Some of these inclusions were analyzed by Raman spectroscopy, whose results enabled us to identify different crystalline phases.

Fig. 5 shows Raman spectra with bands that seem to be characteristic of a braunite signature (like a Mn 2+ Mn 3+ 6SiO12

based structure), as well as a band characteristic of hausmannite (like a Mn 2+ Mn 3+ 2O4 based structure) at 659 cm -1 (Coentro et al., 2018, Fig. 11). In some inclusions, these two mineral phases occur simultaneously (Fig. 5, spectrum 1), while in others, braunite is associated with other unidentified crystalline phases (Fig. 5, spectrum 2). Hausmannite can result from firing pyrolusite (MnO2), a common manganese ore, at ca. 1000 °C [START_REF] Coentro | Mineralogical characterization of hispano-moresque glazes: a μ-raman and scanning electron microscopy with x-ray energy dispersive spectrometry (SEM-EDS) Study[END_REF], while braunite may form during firing by reaction between manganese oxides and the silicon-rich slip matrix at temperatures above 1000 °C [START_REF] Pradell | Synchrotron radiation micro-XRD in the study of glaze technology[END_REF][START_REF] Molera | Manganese brown decorations in 10 th to 18 th century Spanish tin glazed ceramics[END_REF]. According to Coentro and colleagues (2018: 8), "the coexistence [of braunite] with hausmannite may indicate an incomplete transformation to braunite, which may be due to either the temperature being barely above 1000 °C or to a not long enough firing time." Braunite inclusions have already been documented in manganese brown decorations of 10th-18th century Spanish ceramics [START_REF] Molera | Manganese brown decorations in 10 th to 18 th century Spanish tin glazed ceramics[END_REF], 15th-16th century Hispano-Moresque tiles [START_REF] Coentro | Mineralogical characterization of hispano-moresque glazes: a μ-raman and scanning electron microscopy with x-ray energy dispersive spectrometry (SEM-EDS) Study[END_REF], and 17th century Portuguese tiles [START_REF] Coentro | Multi-analytical identification of pigments and pigment mixtures used in 17th century Portuguese azulejos[END_REF]. Another type of Mn-rich inclusion identified is jacobsite (Fig. 5, spectrum 3), a spinel based on a MnFe2O4 structure that can be formed by the combination of manganese and iron oxides during firing [START_REF] Molera | Manganese brown decorations in 10 th to 18 th century Spanish tin glazed ceramics[END_REF].

Cobalt Blue

One of the features of the blue glazes is the presence of cobalt quantified in their chemical composition with contents between 0.4 and 2.2 wt% CoO (Table 2). What also differentiate their compositions are the higher contents in iron and copper oxides, and the detection of nickel oxide. This Co-Ni-Fe-Cu association was also observed in dark blue glazes of a ceramic sample similar to the Miletus Ware discovered in Prilep (Macedonia) [START_REF] Tanevska | Characterization of pottery from the Republic of Macedonia I: Raman analyses of Byzantine glazed pottery excavated from Prilep and Skopje (12 th -14 th century)[END_REF], and in the earliest Iznik fritware dating to the late 15th-early 16th centuries [START_REF] Henderson | Iznik ceramics: a technical examination[END_REF].

A large number of inclusions was analyzed in the blue glazes (Fig. 6). The combination of SEM-EDS (chemical compositions and microstructures) and Raman data enabled us to identify at least three categories of inclusions. Two of them correspond to cobalt-iron (Fig. 7, spectrum 1) and nickeliron (Fig. 7, spectrum 2) based-spinels based respectively on a CoFe2O4 and NiFe2O4 like structure [START_REF] Wang | Microstructure and magnetic properties of MFe2O4 (M = Co, Ni, and Mn) ferrite nanocrystals prepared using colloid mill and hydrothermal method[END_REF], which correlates well with the glaze's chemical compositions. Similar inclusions were observed in 9th century Samarra faience blue glazes [START_REF] Kleinmann | Cobalt-pigment in the Early Islamic blue glazes and the reconstruction of the way of their manufacture[END_REF], and in 15th-16th century Hispano-Moresque tiles [START_REF] Coentro | Hispano-Moresque ceramic tiles from the Monastery of Santa Clara-a-Velha (Coimbra, Portugal)[END_REF][START_REF] Coentro | Mineralogical characterization of hispano-moresque glazes: a μ-raman and scanning electron microscopy with x-ray energy dispersive spectrometry (SEM-EDS) Study[END_REF]. Hematite inclusions were also identified (Fig. 7, spectrum 3) (Bersani et al., 1999;[START_REF] Colomban | On Site Raman analysis of Iznik Pottery Glazes and Pigments[END_REF].

Because of the rarity of these spinel-like mineral phases in nature, it is more likely that they are the result of the preparation of pigments by potters, including roasting, or that they are formed by interaction with the components of the vitreous matrix during firing. The inclusion presented in Fig. 6a-b seems to illustrate this hypothesis. Several facies are indeed visible in this inclusion, in particular its core appears lighter under optical microscope (Fig. 6b). Raman analyses carried out in this area revealed hematite, while those carried out at the periphery of this same inclusion showed nickelferrite based structure signatures.

Chromium Black

In the black decorations, the coloring oxide is that of chromium, quantified at contents higher than 1.5 wt% Cr2O3 (Table 2). The magnesium oxide contents are also significant, with percentages higher than 1 wt% MgO. For some of the black glazes, iron, cobalt, nickel and copper oxides are also detected. In these cases, the presence of these elements is probably a contribution from the glaze adjacent to the black decoration which is of a cobalt blue type; a phenomenon already observed and commented with glazes of Iznik fritwares [START_REF] Tite | Iznik pottery: an investigation of the methods of production[END_REF][START_REF] Colomban | On-site Raman spectroscopic analysis of Kütahya Fritwares[END_REF], and also with glazes of 12th-14th century Islamic ceramics from the province of Nimrôz (Afghanistan) [START_REF] Pernicka | Examination of Islamic glazes with the electron microprobe[END_REF].

With the Miletus Wares, the black motifs are represented by fine straight or curved lines which did not diffuse within the glaze, contrarily to the dissolved colorants used for the blue and purple colors. We observed very characteristic inclusions in these black "lines" (Fig. 8). They are located either at the interface between the slip and ceramic body, or in the entire thickness of the glaze. Their very angular shape testifies to a crushing of the pigment used to paint the decoration and the preservation of its integrity during firing.

The chemical compositions of these inclusions indicate chromium contents higher than 48.8 wt% Cr2O3, as well as significant aluminum, magnesium and iron contents (approximately 16 wt% Al2O3, 13 wt% MgO, and ≥4.5 wt% Fe2O3) (Table 4). Although in varying proportions, some inclusions also contain relatively significant amounts of copper, manganese, cobalt and nickel, issued probably from the adjacent painted decorations [START_REF] Pernicka | Examination of Islamic glazes with the electron microprobe[END_REF][START_REF] Tite | Iznik pottery: an investigation of the methods of production[END_REF][START_REF] Degli Agosti | Bleu et Blanc persans du XVIIe siècle: une approche technologique[END_REF].

The Raman spectroscopic analyses carried out on these inclusions show major bands at 566 and 701 cm -1 , with a shoulder on the latter at 666 cm -1 (Fig. 9). These Raman signatures could correspond to magnesiochromite, which would be consistent with the high chromium and magnesium contents. Thus, to paint the fine black motifs, potters used pigments likely based on crushed magnesiochromite, the grains of which did not dissolve in the vitreous matrix of the glazes. These grains are also visible under the binocular microscope, unlike the pigments used for the other colors.

Chromite inclusions have already been observed in glazes of western Anatolian ceramics, notably in Iznik fritwares [START_REF] Henderson | Iznik ceramics: a technical examination[END_REF][START_REF] Tite | Iznik pottery: an investigation of the methods of production[END_REF][START_REF] Paynter | The production technology of Iznik pottery -a reassessment[END_REF][START_REF] Şimşek | Tentative differentiation between Iznik tiles and copies with Raman spectroscopy using both laboratory and portable instruments[END_REF], 2019a). In western Anatolia, mines of magnesiochromite are known in the provinces of Eskişehir, Bursa, Denizli, and Kütahya; the analysis of which could be a starting point for future provenance studies of the pigments.

Discussion

Comparison with other Turkish productions

By comparing the glazes of the Miletus Wares with the previous analyses of other western Anatolian pottery, we observe that the recipes used to produce the Miletus Wares are new in the region. The technical tradition hitherto defined for the glazes of the Late Byzantine and early Turkish (Beylik: 14th-early 15th century) ceramic productions in Western Anatolia was that of transparent high-lead glazes [START_REF] Burlot | Premières productions de céramiques turques en Anatolie occidentale : Contextualisation et études techniques[END_REF][START_REF] Burlot | The early Turkish pottery productions in western Anatolia: Provenances, contextualization and techniques[END_REF]. Among the Beylik ceramics in this region, one type was an exception: the Monochrome Turquoise Glazed Ware. Archaeometric studies on this ceramic defined the turquoise monochrome glazes as leadalkali and tin-opacified (Table 1). The tin opacification and the much higher Pb/(Na2O + K2O) ratio (Fig. 10a) still clearly differentiate the recipe of this glaze from the one of the Miletus Wares.

Other earlier Turkish ceramics include the Seljuk productions from central and eastern Anatolia, generally dated to the 12th-13th centuries. The wares that have been more studied are the tiles with synthetic bodies used to decorate monuments; and the best known are those of the Kubad-Âbâd palace located near the Beyşehir Lake, dating from the first half of the 13th century, which corresponds to the date of construction of the palace [START_REF] Yeğingil | The composition and technology of Seljuk tiles from Kubad Abad[END_REF][START_REF] Freestone | Scientific analysis of glazed tile from the Seljuq Palace of Kubad-Âbâd, Lake Beyşehir, Turkey[END_REF]. Tiles from other Seljuk monuments in Sivas, Tokat and Konya were also studied (Kiefer, 1956a(Kiefer, , 1956b(Kiefer, , 1956c;;[START_REF] Henderson | The technology of fifteenth-century Turkish tiles: an interim statement on the origins of the Iznik industry[END_REF][START_REF] Demirci | Raw material characteristics and technological properties of some medieval glazed ceramics and tiles in Anatolia[END_REF][START_REF] Colomban | Raman identification of glassy silicates used in ceramics, glass and jewellery: a tentative differentiation guide[END_REF], but the data about their contexts of discovery, their chronology, and their chemical compositions are relatively incomplete, which means it is not possible to propose a "solid" Seljuk referential, nor to directly compare the glaze compositions. Nevertheless, these studies propose that the glaze technologies of the Seljuk pottery -which are characterized by the use of soda-lime and alkali fluxes for transparent glazes, and lead-alkali fluxes and tin-oxide opacifiers for monochrome turquoise glazes -were different from those of Miletus Ware.

A corpus of the so-called "Seljuk" clayey-body ceramics found at Korucutepe has also been studied [START_REF] Demirci | Raw material characteristics and technological properties of some medieval glazed ceramics and tiles in Anatolia[END_REF]. According to the researchers, these ceramics featured a lead-alkali glaze, but no more detailed information about their chemical composition and no archaeological documentation about this corpus were provided.

Finally, the lead-alkali glazes applied to the internal surfaces of Miletus Ware are more similar to the glaze recipes of the Iznik fritware, since the latter are also of soda-lead type and also feature a low amount of CaO (< 3 wt%). However, there is a major difference between them, namely the presence of tin oxides. No tin oxide has been detected in the glazes of Miletus Ware, while SnO2 contents of around 4 to 8 wt% were measured in the glazes of the first Iznik fritware [START_REF] Henderson | Iznik ceramics: a technical examination[END_REF][START_REF] Tite | Iznik pottery: an investigation of the methods of production[END_REF][START_REF] Okyar | Characterisation of Iznik ceramics[END_REF][START_REF] Paynter | The production technology of Iznik pottery -a reassessment[END_REF]. Burlot 1). Based on these data, it seems difficult to propose a clear origin for the Miletus Ware's glaze technology, as it differs from previous productions in western Anatolia, whether Byzantine or Beylik, as well as from central and eastern Anatolian ones (Seljuks). However, we might observe a similarity with the Seljuk ceramics from Korucutepe through their lead-alkali glaze, but more information about the latter and more analyses are needed to compare rightly both technological traditions. Furthermore, to produce the glazes of Miletus Ware, potters introduced soda fluxes that were not used in the previous western Anatolian production centers. The use of alkali fluxes to produce a glaze might also reflect an eastern technical tradition, such as the Seljuk one, for instance.

Nature of the soda fluxes

The main alkali source in the Miletus Ware glazes is soda (Table 2). By referring to the work carried out by Tite and collaborators on glazes of the Iznik fritwares as well as on Byzantine glasses from western Turkey, analyzed by SEM-EDS and by LA-ICP-MS, we thus tried to determine the nature and the source of the soda fluxes. In their work, the chemical compositions obtained showed high Na/K, Na/Mg and Na/Ca ratios (Fig. 10b), as well as small amounts of boron and lithium. Such data enabled the researchers to propose a sodarich evaporite from western Anatolian hot spring waters as the source of the soda flux [START_REF] Tite | New data on the soda flux used in the production of Iznik glazes and Byzantine glasses[END_REF].

After calculating these ratios for the Miletus Ware glazes of the two main productions ("MW Iznik" and "MW unlocated"), we compared them with those of Iznik glazes (Fig. 10b). The results attest also to high ratios for the Miletus Ware glazes, whose ranges are close to the ratios obtained for the Iznik fritware glazes, with the exception of the Na/Mg ratios of the Miletus Ware glazes which are generally lower.

Thus, although the ratios calculated for the glazes of the Miletus Wares are also high, it is difficult to confirm that we are dealing with the same type of alkali source because the magnesium contents are slightly higher for the glazes of Miletus Wares. In order to further our research on the nature and origin of the alkali fluxes used in Miletus Wares glazes, we plan to carry out LA-ICP-MS analyses as well, in order to determine the presence of boron and lithium, whose content are characteristic signatures of western Anatolian hot spring waters.

Painted decoration

For the painted decoration of the Miletus Wares, the potters used copper-(turquoise blue), manganese-(purple), cobalt-(dark blue), and chromium-based (black) pigments. The latter two seem to have been used here for the first time in the ceramic productions of western Anatolia. Studies of the decorations of Byzantine and Beylik Wares from this region have indeed not attested the presence of these two types of pigments in the glazes earlier [START_REF] Scott | Late Byzantine Glazed Pottery from Sardis[END_REF][START_REF] Demirci | Characteristics of some Medieval glazed ceramics in Anatolia[END_REF][START_REF] Demirci | Technological properties of some Medieval glazed pottery in Anatolia[END_REF][START_REF] Okyar | Funde und Befunde aus dem Schachtbrunnen im Hamam III in Ayasuluk/Ephesos. Eine schamanistische Bestattung des 15. Jahrhunderts[END_REF][START_REF] Okyar | A study on Medieval Pottery from a ceramic kiln remains in Ayasuluk/Ephesus: compositional and microstructural data[END_REF][START_REF] Kırmızı | Material characterization of the Late 12th-13th Century Byzantine ceramics from Kuşadasi Kadikalesi/Anaia[END_REF][START_REF] Budak-Ünaler | Fine-Sgraffito Ware[END_REF][START_REF] Budak-Ünaler | Comparison of Byzantine Fine Sgraffito and Incised-Sgraffito (coarse Sgraffito) Ware from Kuşadası, Kadıkalesi/Anaia Excavation[END_REF][START_REF] Özçatal | Characterization of lead glazed potteries from Smyrna (Izmir/Turkey) using multiple analytical techniques; Part I: glaze and engobe[END_REF][START_REF] Kırmızı | Colorings agents in the pottery glazes of Western Anatolia: new evidence for the use of Naples Yellow pigment variations during the late Byzantine period[END_REF][START_REF] Burlot | Premières productions de céramiques turques en Anatolie occidentale : Contextualisation et études techniques[END_REF][START_REF] Burlot | The early Turkish pottery productions in western Anatolia: Provenances, contextualization and techniques[END_REF].

The use of cobalt-and chromium-based pigments echoes Seljuk productions, as well as the later Ottoman ones. Cobaltblue and crushed-chromite-based black decors were indeed attested in Seljuk tiles from the Kubad-Âbâd Palace [START_REF] Yeğingil | The composition and technology of Seljuk tiles from Kubad Abad[END_REF][START_REF] Freestone | Scientific analysis of glazed tile from the Seljuq Palace of Kubad-Âbâd, Lake Beyşehir, Turkey[END_REF] and from Konya (Kiefer, 1956a(Kiefer, , 1956b(Kiefer, , 1956c)), and on Ottoman fritware [START_REF] Henderson | Iznik ceramics: a technical examination[END_REF][START_REF] Tite | Iznik pottery: an investigation of the methods of production[END_REF][START_REF] Paynter | The production technology of Iznik pottery -a reassessment[END_REF][START_REF] Şimşek | Tentative differentiation between Iznik tiles and copies with Raman spectroscopy using both laboratory and portable instruments[END_REF], 2019a)

Conclusion

Miletus Ware was a widespread ceramic within the Ottoman Empire during the 15th and 16th centuries, whose production bears witness to the introduction of radically different decorations in the western Anatolian ceramic repertoire, as well as new glaze manufacturing technologies. The analyses of the Miletus Wares decoration with SEM-EDS and Raman spectroscopy showed the use of transparent leadsoda glazes applied on the inner surface, which differed from the Byzantine and Beylik ones previously produced in this region. Although the origin of this glaze production technology is not clearly defined, it still seems to have displayed greater influence from the eastern traditions, especially those of the earlier Turkish (Seljuk) productions as suggested by the use of alkali fluxes. The fluxes of the Miletus Ware are sodium-based, which could have been extracted from the soda-rich evaporites from western Anatolian hot spring waters. Deriving the technique from eastern traditions, potters also applied, for the first in western Anatolia, black and dark blue underglaze decoration, respectively based on magnesiochromite and on minerals characterized by a Co-Fe-Ni-Cu association. These productions, therefore, involved the use of new resources, suggesting new commercial relations in the pottery industry of western Anatolia, likely favored by the expansion of the Ottoman Empire. From a chronological point of view, the Crimean contexts show that the Miletus Ware produced in Iznik was still in use in the second half of the 16th century. It proves that the manufacture of this ceramic was contemporary with that of the so-called Iznik Ware, which may explain their similarities in glaze technology.

Fig. 1 .

 1 Fig. 1. Examples of Miletus Ware samples, showing the variety of colors in the underglaze decorations (Pictures: S.Y. Waksman, I. Teslenko; CAD: J. Burlot).

Fig. 2 .

 2 Fig. 2. Pictures of examples of Miletus Ware' cross-sections: a) BZN205 -Dark Blue ("MW Iznik"); b) BZN285 -Turquoise ("MW unlocated") (gl: glaze; ws: white slip; cb: ceramic body) (Pictures: J. Burlot).

Fig. 3 .

 3 Fig. 3. Binary diagram showing PbO/SiO2 vs. (Na2O+K2O)/SiO2 ratios in the glazes.

Fig. 4 .

 4 Fig. 4. Braunite inclusions in the purple glaze of sample BZN206 ("MW Iznik"), observed with a SEM in BSE mode (gl: glaze; ws: white slip; br: braunite).

  et al. (2020) Journal of Archaeological Science: Reports, 29, 102073. https://doi.org/10.1016/j.jasrep.2019.102073

•

  Fig. 5. Raman spectra of manganese-rich inclusions identified in several purple areas: 1) BZN206-03 ("MW Iznik"; laser: 514 nm; 100x objective); 2) BZN221 ("MW Iznik"; laser: 532 nm; 50x objective) (identification based onCoentro et al., 2018, Fig. 11; B: braunite; H: hausmannite); 3) BZN125 ("MW Pergamon Rote Halle"; laser: 532 nm; 50x objective) (identification based on reference spectrum: RRUFF ID R060780).

Fig. 7 .

 7 Fig. 7. Raman spectra of Co-Fe-Ni-rich inclusions identified in several dark blue decors: 1) BZN287 ("MW unlocated; laser: 532 nm; 50x objective) (cobalt-ferrite based structure' identification based on Wang et al., 2015, Fig. 3a); 2) BZN 44 ("MW unlocated"; laser: 514 nm; 100x objective) (nickel-ferrite based structure' identification based on Wang et al., 2015, Fig. 3b); 3) BZN118 ("MW unlocated"; laser: 514 nm; 100x objective) (hematite' identification based on Bersani et al., 1999, Table1).

Fig. 6 .

 6 Fig. 6. Co-Fe-Ni-rich inclusions observed in a dark blue decor of sample BZN231 ("MW Iznik"): a) BSE image; b) optical image recorded during a Raman analysis (gl: glaze; hem: hematite; Co-fer: cobalt-ferrite like structure; Ni-fer: nickel-ferrite like structure).

Fig. 8 .

 8 Fig. 8. Chromium-rich inclusions in the black decoration of sample BZN222 ("MW Iznik"), observed with a SEM in BSE mode (gl: glaze; ws: white slip; Mg-chr: magnesiochromite).

Fig. 9 .

 9 Fig. 9. Raman spectrum of a magnesiochromite inclusion identified in the black decor of sample BYZ284 ("MW Iznik") (laser: 514 nm; 100x objective; identification based on reference spectrum: RRUFF ID R060796).

  . Burlot et al. (2020) Journal of Archaeological Science: Reports, 29, 102073. https://doi.org/10.1016/j.jasrep.2019.102073

Fig. 10 .

 10 Fig. 10. Boxplots (mean, standard deviation and minimum/maximum values) that compare the ratios between: a) PbO/(Na2O + K2O); b) Na2O/K2O, Na2O/ CaO and Na2O/MgO (*refers to Iznik Fritware, data from Tite et al., 2016; ** refers to Monochrome Turquoise Glazed Wares belonging to two early Turkish (Beylik) western Anatolian productions (yellow box: "Ephesus region (b/2)" production; white box: "Pergame F" production), data from Burlot, 2017).

  ).

	Sample ID	Provenance	Period *	Color of the areas analyzed
	Production: MW Iznik
	BYZ284	Iznik		Dark Blue; Black
	BZN205	Alushta	Late 15th-early 16th century (a)	Dark Blue
	BZN206	Alushta		Purple; Dark Blue
	BZN209	Alushta	Late 15th-early 16th century (a)	Purple
	BZN210	Alushta		Purple; Dark Purple; Dark
				Blue
	BZN221	Funa	Late 15th-early 16th century (a)	Dark Blue
	BZN222	Funa	Late 15th-early 16th century (a)	Dark Blue; Black
	BZN223	Funa		Dark Blue; Black
	BZN228	Balaklava		Dark Blue; Black
	BZN229	Balaklava		Black
	BZN231	Balaklava		Dark Blue
	BZN233	Balaklava		Black
	Production: MW unlocated
	BZN 43	Miletus		Dark Blue
	BZN 44	Miletus		Purple; Dark Blue
	BZN 45	Miletus		Dark Blue
	BZN 46	Miletus		Dark Blue
	BZN 49	Miletus		Turquoise Blue
	BZN 50	Miletus		Dark Blue; Turquoise Blue
	BZN118	Sardis	Late 14th-16th century (b)	Dark Blue
	BZN119	Sardis	Late 14th-16th century (b)	Dark Blue
	BZN285	Sardis	Late 14th-16th century (b)	Dark Blue; Turquoise Blue
	BZN286	Sardis	Late 14th-16th century (b)	Dark Blue; Black
	BZN287	Sardis	Late 14th-16th century (b)	Purple; Dark Blue
	BZN288	Ephesus		Dark Blue; Black
	BZN289	Ephesus		Dark Blue
	Production: MW Pergamon Rote Halle
	BZN124	Pergamon		Dark Blue
	BZN125	Pergamon		Purple

Table 2 .

 2 Semi-quantitative SEM-EDS analysis of the glazes, in wt%. Samples are presented by production (*both dark blue and purple colors merged on the same sample; **Monochrome Turquoise Glazed Wares belonging to two early Turkish (Beylik) western Anatolian productions (data from[START_REF] Burlot | Premières productions de céramiques turques en Anatolie occidentale : Contextualisation et études techniques[END_REF]; -: below detection limits; m: mean; σ: standard deviation).

	Sample ID -Color of the area analyzed	SiO2	PbO	Na2O	K2O	MgO	CaO	Al2O3	Fe2O3	MnO	CuO	CoO	NiO	Cr2O3	SnO2
	Production: MW Iznik
	BYZ284 -Dark Blue	62.1	14.5	12.4	1.6	0.6	1.0	2.1	2.7	-	0.5	0.6	0.4	0.3	-
	BYZ284 -Colorless	63.4	14.7	13.6	1.5	0.6	1.1	2.1	0.6	-	0.4	-	-	-	0.3
	BYZ284 -Black	61.3	13.3	13.0	1.3	1.1	1.0	2.5	1.5	0.5	0.6	-	-	2.6	-
	BZN205 -Dark Blue	61.4	15.4	14.8	1.2	0.5	0.8	1.6	2.3	-	0.4	0.4	0.3	-	-
	BZN206 -Dark Blue & Purple*	57.6	18.0	10.1	1.5	0.4	0.8	2.4	4.9	2.2	0.5	0.6	0.6	-	-
	BZN209 -Purple	57.7	15.3	12.5	2.0	0.7	1.0	5.0	0.9	3.7	-	-	-	-	-
	BZN209 -Colorless	62.0	13.8	12.3	2.3	0.8	0.8	6.7	0.3	-	-	-	-	-	-
	BZN210 -Purple	62.9	13.3	14.8	1.4	0.5	0.8	2.1	0.6	1.8	0.3	-	-	-	-
	BZN210 -Dark Purple	62.8	13.9	14.1	1.6	0.4	0.8	1.7	0.8	2.3	-	-	-	-	0.4
	BZN210 -Dark Blue	64.0	12.6	13.5	1.7	0.4	0.6	1.9	3.1	-	0.3	0.5	0.4	-	-
	BZN210 -Colorless	63.2	16.1	13.9	1.4	0.5	0.8	1.9	0.5	-	-	-	-	-	-
	BZN221 -Dark Blue	65.6	11.5	13.3	1.5	0.4	1.0	1.5	2.0	-	0.7	0.7	0.5	-	-
	BZN222 -Black	62.3	12.8	11.5	2.0	0.9	0.8	2.7	4.1	-	0.4	0.4	-	1.5	-
	BZN222 -Dark Blue	61.1	16.0	9.4	2.0	0.4	1.1	1.9	4.4	-	1.2	1.2	0.7	-	-
	BZN223 -Black	54.4	13.8	10.9	1.5	2.2	1.1	4.0	3.6	0.6	0.8	0.5	0.3	6.0	-
	BZN223 -Dark Blue	61.1	17.4	10.8	1.8	0.7	1.1	2.0	2.6	-	0.5	0.6	0.5	-	-
	BZN223 -Colorless	61.1	21.6	10.9	1.5	0.6	1.0	1.9	-	-	0.4	-	-	-	-
	BZN228 -Dark Blue	61.8	11.0	16.3	1.6	0.7	1.1	2.5	2.2	-	0.5	0.6	0.6	-	-
	BZN228 -Black	50.4	7.9	13.3	1.4	3.9	1.5	4.8	5.4	-	0.8	0.8	0.9	7.7	-
	BZN229 -Black	63.7	10.7	14.6	1.4	1.0	1.5	2.1	1.8	-	0.4	-	0.3	2.0	-
	BZN231 -Dark Blue	58.0	13.5	11.3	1.7	0.7	1.4	2.6	5.1	-	1.1	2.0	1.6	-	-
	BZN233 -Black	53.5	11.8	11.6	0.9	2.5	1.5	4.4	4.2	0.6	1.4	0.3	0.3	6.4	-
	Production: MW unlocated
	BZN 43 -Dark Blue	66.4	14.1	12.9	1.0	0.5	0.8	1.4	1.2	-	0.5	0.3	0.3	-	-
	BZN 44 -Dark Blue & Purple*	55.9	7.6	15.3	1.3	0.8	1.1	3.2	8.6	2.1	0.5	1.1	1.1	-	-
	BZN 45 -Dark Blue	59.9	10.8	14.5	1.1	0.5	1.6	1.9	5.3	-	1.4	1.4	1.1	-	-
	BZN 46 -Dark Blue	50.1	33.3	7.7	0.4	0.6	2.8	2.0	1.0	-	0.5	0.3	0.4	-	-
	BZN 49 -Turquoise Blue	56.9	20.9	10.4	0.9	1.0	1.9	2.5	0.8	-	3.9	-	-	-	-
	BZN 50 -Dark Blue	61.1	20.2	11.1	1.0	0.4	1.4	1.4	1.1	0.3	0.7	0.4	0.6	-	-
	BZN 50 -Turquoise Blue	60.8	21.1	8.8	1.2	0.5	1.1	2.5	0.4	-	2.4	-	0.4	-	-
	BZN118 -Dark Blue	59.8	9.3	17.3	1.3	0.7	0.8	2.0	5.5	-	0.6	0.7	0.8	-	-
	BZN119 -Dark Blue	61.2	10.7	15.9	1.4	0.9	0.7	2.1	2.7	-	0.5	0.4	0.4	0.3	-
	BZN285 -Dark blue	65.0	12.3	11.3	1.2	-	0.9	1.4	4.1	-	0.8	1.0	0.9	-	-
	BZN285 -Turquoise Blue	62.5	15.3	12.3	1.1	0.7	1.6	1.5	0.6	-	3.7	-	-	-	-
	BZN286 -Dark Blue	69.4	8.8	13.1	1.2	-	0.7	1.0	3.2	-	0.7	0.7	0.6	-	-
	BZN286 -Black	60.2	6.2	13.7	1.2	2.5	0.3	3.9	2.9	0.3	0.4	0.3	0.3	7.8	-
	BZN287 -Dark Blue & Purple*	55.8	14.1	14.1	0.9	0.3	0.9	1.6	5.3	1.8	1.2	1.3	2.2	-	-
	BZN288 -Black	59.1	8.1	15.2	1.0	1.9	0.7	3.7	3.4	0.6	0.3	0.3	0.3	4.8	-
	BZN288 -Colorless	65.1	12.7	16.2	0.9	0.6	1.2	1.7	-	-	0.5	-	-	-	-
	BZN288 -Dark Blue	58.9	9.9	16.1	0.9	0.6	1.0	1.8	6.9	-	0.8	1.3	1.0	-	-
	BZN289 -Dark Blue	61.1	10.2	5.4	1.4	1.2	2.3	1.9	9.9	-	2.5	2.2	1.8	-	-
	Production: MW Pergamon Rote Halle
	BZN124 -Dark Blue	49.8	26.5	13.0	1.2	1.4	2.3	1.8	1.2	-	0.8	0.4	0.6	-	-
	BZN125 -Purple	52.3	20.6	14.5	1.2	0.4	0.8	2.2	0.5	6.0	0.3	-	-	-	-

Burlot et al. (2020) Journal of Archaeological Science: Reports, 29, 102073. https://doi.org/10.1016/j.jasrep.2019.102073

Monochrome Turquoise Glazed Ware** Production: Ephesus region (b/2)

	m (n = 6)	43.5	33.2	4.8	1.4	1.1	2.1	2.2	0.6	-	1.5	-	-	-	8.1
	σ	4.8	9.6	3.6	0.7	0.4	0.7	0.6	0.3	-	0.4	-	-	-	1.8
	Production: "Pergame F"
	m (n = 5)	44.7	32.4	3.4	1.6	1.4	2.9	2.9	0.6	-	1.3	-	-	-	7.4
	σ	2.2	3.7	1.5	0.6	0.4	0.3	1.2	0.2	-	0.3	-	-	-	3.0

Table 3 .

 3 Chemical compositions of manganese-rich inclusions identified in several purple decors (wt%., SEM-EDS; -: below detection limits). Samples are presented by production.

	Sample ID	SiO2	PbO	Na2O	K2O	CaO	Al2O3	Fe2O3	MnO	CuO	CoO	NiO
	Production: MW Iznik
	BZN206-01	20.5	2.6	1.3	0.4	0.5	1.1	2.8	67.6	0.8	0.9	0.9
	BZN206-03	13.1	1.0	0.4	-	0.5	0.4	3.2	78.5	0.7	1.0	0.6
	BZN210-02	23.0	2.8	5.0	0.4	0.5	1.6	2.2	63.2	-	-	0.4
	BZN210-03	2.4	0.4	0.7	-	-	1.7	4.0	89.1	-	-	-
	Production: MW unlocated
	BZN 44-03	14.9	2.5	3.3	0.4	0.7	1.4	2.4	67.7	2.2	0.8	1.3
	BZN 44-04	11.3	2.0	1.0	-	0.3	1.1	1.9	77.1	2.6	0.5	0.6
	BZN287-03	1.7	0.4	-	-	-	-	1.5	94.3	1.4	-	-
	BZN287-07	22.4	2.2	1.8	-	0.7	2.2	1.7	65.9	1.5	0.3	0.5
	BZN287-09	8.4	2.4	0.6	1.2	0.4	0.3	2.3	78.3	2.9	1.6	0.4
	Production: MW Pergamon Rote Halle
	BZN125-01	11.4	0.8	0.4	-	0.6	0.6	2.9	81.0	0.4	-	-
	BZN125-02	13.9	2.2	1.4	-	0.6	0.8	2.9	76.2	0.4	-	-
	BZN125-04	14.2	1.7	2.1	-	0.9	0.4	2.0	76.4	-	-	-

Table 4 .

 4 Chemical compositions of chromium-rich inclusions identified in several black decorations (wt%., SEM-EDS; -: below detection limits). Samples are presented by production.

	Sample ID	MgO	Al2O3	Fe2O3	MnO	CuO	CoO	NiO	Cr2O3
	Production: MW Iznik
	BYZ284-01	12.0	12.4	5.9	3.6	3.6	2.6	0.7	55.7
	BYZ284-02	9.9	14.5	5.5	5.5	4.0	4.2	1.8	50.7
	BZN205-01	9.8	15.7	4.5	3.4	2.2	9.7	3.8	49.4
	BZN205-03	10.6	13.7	4.5	3.3	1.7	9.5	2.7	52.6
	BZN222-12	10.1	16.7	6.1	0.6	5.1	6.1	2.7	50.7
	BZN222-16	10.0	16.3	6.4	0.8	4.2	7.8	2.5	49.6
	BZN228-01	20.0	17.5	5.0	-	4.1	0.7	1.7	51.1
	BZN228-02	19.2	15.9	6.0	-	4.5	0.9	1.6	50.4
	BZN228-04	13.0	17.9	4.9	-	3.4	5.9	3.0	50.7
	BZN229-01	9.0	19.0	7.5	-	4.7	1.6	2.2	54.8
	BZN231-14	16.5	17.5	5.5	-	6.8	2.3	0.5	48.8
	BZN231-15	17.3	17.2	6.4	-	5.5	1.8	0.4	49.8
	Production: MW unlocated
	BZN286-12	14.9	17.5	5.8	2.5	3.0	2.0	2.5	51.1
	BZN286-13	21.1	19.5	7.0	2.0	0.7	0.4	-	48.8
	BZN286-14	16.2	17.9	7.0	2.0	2.2	1.1	0.3	51.6

Bersani, D., Lottici, P.P., Montenero, A., 1999. Micro-Raman investigation of iron oxide films and powders produced by Sol-Gel syntheses. J. Raman Spectrosc. 30, 355-360.

Acknowledgements

This study was funded by the French National Research Agency (ANR) through the POMEDOR project, and we acknowledge the support of the ANR with the reference ANR-12-CULT-0008; and by the Koç University Research Centre for Anatolian Civilizations for the Raman spectroscopic analyses at KUYTAM. We would like to thank: the Turkish Ministry of Culture and Tourism, the Directors of the archaeological excavations and the staff of the Museums at Ephesus, Miletus, Sardis, and Pergamon for giving us permission to study the samples; I. Teslenko, B. Böhlendorf-Arslan and J. Vroom for their ceramic studies of the present corpus; the staff of the analytical facilities in Lyon (CNRS UMR 5138 and CTµ), in Paris (UMR 8233) and in Istanbul (KUYTAM).