 Good morning. I'm here to present the project GAMS. GAMS stands for Galileo GNSS Autonomous Mobile Mapping System. It's a project that started in July 2021 for 30 months and is funded by USPA. It's a consortium of seven companies and one university. The goal of the project is to envision the use of HD maps, high-definition maps of the roads, to be used by autonomous vehicle manufacturers and also by public infrastructure management like highways. And for that, we aim at developing a prototype of autonomous mobile mapping using an autonomous vehicle with a mobile mapping system on top of it and a new GNSS receiver so we can collect all the geodata possible. And then we post-process all this data to produce HD maps at high definition and low cost. Our motto is robots mapping for robots because we aim at producing HD maps for autonomous vehicle with using an autonomous vehicle. So the main offering of GAMS is HD-based maps with high accuracy and high reliability. We want also to move forward in the certification of these HD maps. And all of this in real time. And also, we aim at offering an enhanced autonomous vehicle more secure and with better positioning using our HD maps. And we want to use we are using new Galileo features with a high accuracy service and also the anti-spoofing new service. And all of this for better trajectory and also for road quality assessment. The value proposition of GAMS is developing an autonomous mobile mapping system with high accuracy and at low cost because we aim at reducing the cruise for driving, of course, using autonomous vehicle. But also for the mobile mapping system crew, we want to reduce the use of operators. And also because of our better accuracy, we aim at using less ground control points. And of course, the post-processing with all the extraction of data and classification of data, we use artificial intelligence so we can have a reduced cost. Now I'm going to present the different technologies that we gather. It's a mix of different technologies that we gather in GAMS to develop this prototype. So our first partner is Daimus. And they are developing a new GNSS receiver using Galileo new features, which are high accuracy service and also the open service navigation message authentication for better anti-spoofing strategies. We are using the new E6 band to exploit all this data for these new services. Another partner is EPFL, University of Lausanne in Switzerland. They are developing a vehicle dynamic model, which helps to bring tremendous improvement in trajectory determination, and especially when we are in GNSS outage like urban canyons. Another technology in our project by our partner Geonumerics is the trajectory estimation using a multi-sensor navigation. So we analyze all the different input data from GNSS navigation, the point cloud information from our mobile mapping system, the high accuracy service from Galileo, other GNSS information also, the IMU data, also odometer, chip scale atomic clock, the vehicle dynamic models I talked about just before, and also visual features for camera images. All of this data are gathered for multi-sensor appreciation and statistical algorithm to improve the precision, the accuracy, and the integrity of the data. And also, of course, the goal of the project is to collect the data and then to produce the HD maps. HD maps for autonomous vehicle industry to be used on board of autonomous vehicle. So we can see here an example of HD maps where we identify, classify all the road objects to be interpreted directly by onboard systems. So how do we develop HD maps? The workflow is as follow. There is a data collection from mobile mapping system. And then it's consolidated by a trajectory estimation. And then we use the LiDAR data and image data to extract all the objects on the road and classify the classification of these objects and the vectorization of these objects. And then we import it into a database to output to the format that is specified by the end user. So here is a quick example of objects on the road that we extract and classify. We have road markers, of course, like lane lines. And we have also road edges, road markings, traffic signs, traffic lights, tunnel boundaries. But also we can include virtual objects like lane linkage, road linkage, or any intersection information that we can use. So that's it for the presentation of GAMS project. You can follow us on the different social group. And do you have any questions?