Cl Émentin Boittiaux

Claire Dune

Maxime Ferrera

Aur Élien Arnaubec

Ricard Marxer

Marjolaine Matabos

Loïc Van Audenhaege

Vincent Hugel

Eiffel Tower: A Deep-Sea Underwater Dataset for Long-Term Visual Localization

Keywords: Underwater dataset, long-term visual localization, deep sea, visual localization benchmark, Eiffel Tower vent edifice

Visual localization plays an important role in the positioning and navigation of robotics systems within previously visited environments. When visits occur over long periods of time, changes in the environment related to seasons or daynight cycles present a major challenge. Under water, the sources of variability are due to other factors such as water conditions or growth of marine organisms. Yet it remains a major obstacle and a much less studied one, partly due to the lack of data. This paper presents a new deep-sea dataset to benchmark underwater long-term visual localization. The dataset is composed of images from four visits to the same hydrothermal vent edifice over the course of five years. Camera poses and a common geometry of the scene were estimated using navigation data and Structure-from-Motion. This serves as a reference when evaluating visual localization techniques. An analysis of the data provides insights about the major changes observed throughout the years. Furthermore, several well-established visual localization methods are evaluated on the dataset, showing there is still room for improvement in underwater long-term visual localization. The data is made publicly available at seanoe.org/data/00810/92226/.

Introduction

With the advent of Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs), there is a need to enable these vehicles to localize themselves accurately in an underwater environment. This paper addresses the problem of visual localization which consists in estimating the 6 degrees-of-freedom (6DOF) pose of a camera given its image and previous observations made in the area. It has received a lot of attention over the last decade with the rise of self-driving cars. This task becomes more difficult when the environment is subject to important changes, as it is the case for images acquired during successive visits in deep ocean. Long-term localization methods aim to deal with major changes in the environment, e.g., snow during winter [START_REF] Sattler | Benchmarking 6DOF outdoor visual localization in changing conditions[END_REF].

Most of the databases used to evaluate these methods are made up of terrestrial data. They cover a wide range of environmental changes such as day-night, seasons and weather conditions [START_REF] Griffith | Symphony lake dataset[END_REF][START_REF] Sattler | Benchmarking 6DOF outdoor visual localization in changing conditions[END_REF][START_REF] Burnett | Boreas: A multi-season autonomous driving dataset[END_REF].

Because underwater images have different sources of technical and environmental variability, existing datasets are not suitable for evaluating long-term localization performance in such scenarios. Indeed, the characteristics of the underwater medium cause many visual perturbations related to light and color absorption, turbidity and backscattering. Furthermore, in deep-sea scenarios, underwater vehicles must be equipped with artificial lighting systems in order to illuminate the absolute darkness of the environment. While this allows the use of cameras to record what lies on the seafloor, it also creates strong differences in illumination depending on the distance between the robot and the scene. In addition to these short-term perturbations, long-term changes occur in these environments, e.g., changes in the distribution of microbial and animal communities or topographical changes.

Some localization methods rely on deep retrieval and feature matching networks to be robust to large sources of variability. However, these networks, e.g., NetVLAD [START_REF] Arandjelovic | NetVLAD: CNN architecture for weakly supervised place recognition[END_REF], have only been pre-trained on terrestrial data. Applications of these models in underwater environments may not be straightforward due to the domain shift associated to the specificity of underwater imaging.

Previously published underwater datasets targeting online localization [START_REF] Ferrera | AQUALOC: An underwater dataset for visual-inertial-pressure localization[END_REF][START_REF] Mallios | Underwater caves sonar data set[END_REF]) span over very short temporal ranges, i.e., data were acquired during the same day. Thus, they do not cover the long-term changes that can appear in these environments, leaving a gap in methods and datasets available to treat multiannual deepsea image sequences.

This paper presents a new dataset for long-term visual localization in a deep-sea environment. It is composed of four different visits of the same hydrothermal edifice over five years [START_REF] Girard | Currents and topography drive assemblage distribution on an active hydrothermal edifice[END_REF]) (Figure 1). More specifically, it provides the following data:

• Images of the vent for all four visits. • Navigation data in the form of latitude, longitude and altitude information. • 3D models of the scene estimated using Structurefrom-Motion (SfM) for each visit year. • A global 3D model including all images in a common reference frame.

The data presents changes over time related to all the aforementioned underwater imaging factors. It also presents some peculiar characteristics, like the occurrence of black and white smokers that emit hot hydrothermal fluid. Moreover, due to the numerous sources of variation present in this dataset, it may also be used for detecting longterm changes that take place in deep-sea environments. This paper makes the following contributions: i) it provides a new publicly available dataset for long-term visual localization in a deep-sea environment; ii) it presents an analysis of environmental and topographic changes between the different visits; iii) it benchmarks several visual localization methods on the given dataset.

Related work

Datasets used for benchmarking visual localization algorithms are mostly terrestrial, including Aachen Day-Night, RobotCar Seasons and CMU Seasons introduced by [START_REF] Sattler | Benchmarking 6DOF outdoor visual localization in changing conditions[END_REF] as well as Cambridge [START_REF] Kendall | PoseNet: A convolutional network for real-time 6-dof camera relocalization[END_REF], 7-Scenes [START_REF] Shotton | Scene coordinate regression forests for camera relocalization in RGB-D images[END_REF]) and 12-Scenes [START_REF] Valentin | Learning to navigate the energy landscape[END_REF]. 7-Scenes and 12-Scenes are collected in an indoor setting, while the others are composed of outdoor environments. [START_REF] Sattler | Benchmarking 6DOF outdoor visual localization in changing conditions[END_REF] datasets exhibit some difficult localization scenarios, like day-night observations. Similar datasets to benchmark the visual localization task under water are rare due to the cost of data collection. Existing underwater datasets [START_REF] Mallios | Underwater caves sonar data set[END_REF][START_REF] Ferrera | AQUALOC: An underwater dataset for visual-inertial-pressure localization[END_REF] focus on providing data for the development of underwater SLAM algorithms. AQUALOC dataset [START_REF] Ferrera | AQUALOC: An underwater dataset for visual-inertial-pressure localization[END_REF] provides underwater monochromatic images synchronized with inertial and depth data for three different sites off Corsica. One of the sites is a harbor lying at a depth of 3 to 4 m and the other two are archaeological sites that lie at depths of 270 m and 380 m. While sequences follow different trajectories, all different visits occurred during the same day, not covering all the possible changes that can happen in this environment, e.g., salinity variation that can alter the pinhole model, increased turbidity, sedimentation or marine population changes. [START_REF] Nielsen | Evaluation of PoseNet for 6-dof underwater pose estimation[END_REF] evaluated PoseNet [START_REF] Kendall | PoseNet: A convolutional network for real-time 6-dof camera relocalization[END_REF], an end-toend visual localization neural network, on an underwater dataset acquired in a pool where camera poses were obtained with an underwater motion capture system. Other underwater datasets that focus on different tasks, e.g., dehazing [START_REF] Akkaynak | Sea-Thru: A method for removing water from underwater images[END_REF][START_REF] Li | An underwater image enhancement benchmark dataset and beyond[END_REF][START_REF] Berman | Underwater single image color restoration using haze-lines and a new quantitative dataset[END_REF], do not provide images of the same site over long periods of time. [START_REF] Campos | A surface reconstruction method for in-detail underwater 3D optical mapping[END_REF] already published image data of the 2015 visit of the Eiffel Tower vent. The authors presented a novel method for surface reconstruction of underwater structures. To benchmark their algorithm, they used a point cloud resulting from a SfM on the images of the 2015 dive. The current work also exploits SfM to produce the reference poses and scene geometry on this collection, and completes it with similar data from three other dives of the same site in different years, capturing long-term changes.

Visual localization benchmarking datasets require reference camera poses for each image, which can be constructed in different ways. Most common methods to access such information as well as the scene's geometry rely on SfM or depth-based SLAM [START_REF] Brachmann | On the limits of pseudo ground truth in visual camera relocalisation[END_REF]. In most deepsea missions, the use of motion capture is discarded due to the difficulties in deploying such a system. Standard RGB-D sensors cannot directly be used underwater because of the absorption of infrared light in water, making depth-based SLAM harder to set up. Thereby, SfM offers a practical solution for estimating camera poses and scene geometry in the underwater environment. Nevertheless, Brachmann et al. (2021) showed that the performance of a localization method on a given dataset is greatly affected by the method used to build the "ground truth" of this dataset. Indeed, methods that minimize the same error as the algorithm used for estimating the ground truth poses have the advantage of leading to the same local minima. For this reason, methods that favor SfMbased ground truths may perform better on our dataset.

Data collection

The EMSO-Azores deep-sea observatory on the Mid-Atlantic Ridge supports the long-term monitoring of the Lucky Strike hydrothermal vent field (Figure 2) since 2010. During the annual maintenance cruises [START_REF] Cannat | MOMARSAT : Monitoring the Mid Atlantic Ridge[END_REF], a ROV operated by the French National Institute for Ocean Science (Ifremer) has been used to study the evolution of the hydrothermal circulation and the associated fauna communities over several years [START_REF] Matabos | Integrating Multidisciplinary Observations in Vent Environments (IMOVE): Decadal Progress in Deep-Sea Observatories at Hydrothermal Vents[END_REF]. Within this field, the hydrothermal vent edifice Eiffel Tower, located at 1700 m beneath the surface, has been extensively studied since its discovery in 1992 [START_REF] Langmuir | Lucky Strike -A newly discovered hydrothermal site on the Azores platform[END_REF]). Four dives, in 2015, 2016, 2018 and 2020, were dedicated to the 3D reconstruction of the structure enabling quantitative monitoring of vent community distribution and dynamics [START_REF] Girard | Currents and topography drive assemblage distribution on an active hydrothermal edifice[END_REF]. During the different dives, synchronized videos and navigation data were acquired using the ROV sensors. Videos were captured using two different cameras, whose characteristics are presented in Table 1. Images were acquired through a specifically designed dome port with corrective lenses to account for underwater refraction. As these lenses largely compensate the distortion induced by the air-glass-water mediums, a second order radial distortion model with k1 and k2 distortion coefficient was used to calibrate the cameras underwater. The ROV incorporated an artificial lighting system consisting of 12 LED panels delivering 20,000 lumens each. It also embedded an Ultra-Short Baseline acoustic positioning system (USBL), an Inertial Navigation System (INS), a Doppler Velocity Log (DVL) and a depth sensor that were fused similarly to [START_REF] Guerrero-Font | An USBL-aided multisensor navigation system for field AUVs[END_REF] to compute the navigation data which provide an estimate of the position of the vehicle. This position is composed of the latitude, longitude and altitude of the ROV as well as its yaw, pitch and roll angles. However, navigation data are only consistent within each visit due to the uncertainty of the USBL, which can exhibit offsets of several meters between the frames of the different visits.

Camera poses and scene geometry

This visual localization dataset offers images and their 6DOF poses expressed in a common frame of reference. COLMAP SfM (Schönberger and Frahm 2016) was used to obtain camera poses and intrinsic parameters, as well as 3D scene geometry.

From the videos captured each year, one image was extracted every 3 seconds to create the input data of the dataset. Some images were polluted on their border with small navigation overlays that were removed using an inpainting technique [START_REF] Telea | An image inpainting technique based on the fast marching method[END_REF]). In addition, images were inspected manually to discard irrelevant ones, e.g., images only capturing the water column.

Several issues were encountered when attempting to use COLMAP directly on images from all years. Firstly, to create image pairs, COLMAP relies on image retrieval methods like vocabulary tree (Schönberger et al. 2016) or NetVLAD [START_REF] Arandjelovic | NetVLAD: CNN architecture for weakly supervised place recognition[END_REF]. While these methods successfully match images of the same year, they show poor performance at pairing images across different years. Secondly, to perform feature matching between image pairs, COLMAP uses SIFT descriptors. However, as shown on Figure 3, hand-crafted descriptors fail to produce satisfactory matches between images of different years.

To overcome these issues, we adopted the SfM pipeline described on Figure 4 * . We first built a model for each year independently. For each individual model, image retrieval was performed using navigation data. However, navigation data are missing for 3,178 images of the 2015 dive. In this case, image retrieval was achieved using NetVLAD [START_REF] Arandjelovic | NetVLAD: CNN architecture for weakly supervised place recognition[END_REF]. Within the same visit, NetVLAD proved to be efficient to retrieve similar images. Instead of hand-crafted descriptors, we used SuperPoint (DeTone et al. 2018) and SuperGlue [START_REF] Sarlin | SuperGlue: Learning feature matching with graph neural networks[END_REF]) for feature matching. We then registered the resulting 3D point clouds of each individual model to the 2020 point cloud using TEASER++ [START_REF] Yang | TEASER: Fast and certifiable point cloud registration[END_REF]) and refined the result with ICP [START_REF] Zhou | Open3D: A modern library for 3D data processing[END_REF]. This way, we obtained a coarse estimation of the camera intrinsics and poses of each year in the same reference frame. We then paired cross-year images using these poses and matched these images with SuperPoint and SuperGlue. Finally, we used COLMAP to obtain a global model embedding all images.

The scale of each model was retrieved by aligning camera poses in Sim(3) with available navigation data using Umeyama's algorithm [START_REF] Umeyama | Least-squares estimation of transformation parameters between two point patterns[END_REF]. For the global model that includes all observations, the alignment was performed using only 2020 navigation data. At this point, we have the 6DOF pose of each viewpoint as well as a 3D point cloud of the full vent in the same reference frame at real scale for every year.

Table 2 reports statistics about the reconstructions obtained with COLMAP as a way to illustrate the certainty level of the proposed ground truth. Table 3 reports the percentage of 3D points matched between each year in the resulting SfM. While the majority of 3D points observations are contained within the same year, a significant proportion of them were matched across different years. This ensures that the scene geometry and camera poses are consistent between the different visits.

Dataset format

The Eiffel Tower dataset is composed of images of the dives and a global COLMAP model embedding all visits. For reproducibility purposes, we also provide individual COLMAP models for each visit and interpolated navigation data for each image when available. The dataset architecture is detailed in Figure 5.

Each image is named after the date at which it was acquired in the format YYYYmmddTHHMMSS.fffZ, where YYYY is the four digit year, mm is the month, dd is the day, HH is the hour in the 24 hours format, MM are the minutes, SS the seconds and fff the milliseconds. A COLMAP model embeds scene geometry, camera intrinsics and poses. It is composed of 3 files: cameras.txt contains the camera intrinsics; images.txt provides camera poses as well as

Characterizing changes across years

This dataset contains numerous appearance changes across visits that present important challenges for the visualbased localization task. These alterations are of different nature, i.e., topographic, environmental or modifications in the ROV's equipment. A number of changes observed and measured over the period reflect a modification of the edifice's local geomorphology over the years [START_REF] Van Audenhaege | Monitoring ecological dynamics on complex hydrothermal structures: a novel photogrammetry approach reveals fine scales of faunal assemblage variability[END_REF]. Chimney collapse, outcrop/boulder detachment or slide resulted in a loss of material, while vent active areas grew through mineral accretion creating new outcrops, flanges and spires. Material build-up was twice as important as the loss, suggesting that the volume of the edifice is increasing over time. While these changes can be locally drastic and affect the registration of 3D models over the years, they represent only 5% of the total surface and † colmap.github.io/format.html ‡ github.com/colmap/colmap/blob/dev/scripts are localized in areas of active venting. Local changes in hydrothermal activity also result in distinct mineralization processes, hence deposits, the color of which will vary depending on the temperature and chemical composition of the fluid. Figure 6 reoriented on a daily basis. Furthermore, the period from 2015 to 2020 showed an overall disappearance of white microbial mats over the whole edifice (-72.85 m 2 ). Although these changes do not affect the general topography of the structure, they strongly modify the color and texture of the model. Figure 7 illustrates how the mussels' population evolution over the years can alter both the topography of the scene and the colors of the vent. Also, because of these organic modifications, there are no real matching 3D points between different years on most of the chimney. This makes it difficult to match specific 2D points. Figure 8 shows how these biological changes affect the global 3D reconstruction of the edifice. While the vent is overall well matched across different years, some specific areas like the south-east fac ¸ade suffer from this source of variability and the model mostly relies on matches between images of the same year.

Figure 9 displays the area covered by the ROV each year. We notice that the vehicle covered uneven regions over the different years. The 2015 dive covered the least amount of ground compared to all other years. Figure 10 compares the histograms of pixel intensities in all images of each year. First, we observe that the red channel has an overall lower pixel intensity when compared to the green and blue channels. This is easily explained by the attenuation difference of the wavelengths due to the underwater environment [START_REF] Akkaynak | Sea-Thru: A method for removing water from underwater images[END_REF][START_REF] Berman | Underwater single image color restoration using haze-lines and a new quantitative dataset[END_REF]. We also notice a shift in pixel intensity for the 2020 dive, which is likely due to the change of camera.

Visual localization benchmark

Train and test sets were separated based on the area covered by the ROV each year. As seen on Figure 9, the total area covered in 2016, 2018 and 2020 contains almost all the area covered in 2015. As a result, we chose 2016, 2018 and 2020 as the train set and 2015 as the test set.

Using the aforementioned train/test split, we benchmarked the Eiffel Tower dataset on renowned visual localization methods: PoseNet with different losses [START_REF] Kendall | PoseNet: A convolutional network for real-time 6-dof camera relocalization[END_REF][START_REF] Kendall | Geometric loss functions for camera pose regression with deep learning[END_REF][START_REF] Boittiaux | Homography-based loss function for camera pose regression[END_REF], hLoc [START_REF] Sarlin | From coarse to fine: Robust hierarchical localization at large scale[END_REF] and PixLoc [START_REF] Sarlin | Back to the feature: Learning robust camera localization from pixels to pose[END_REF]. PoseNet trains a different neural network for each scene, while hLoc and PixLoc rely on deep-learning based features trained on terrestrial datasets. We detail below the parameters used for each of the methods.

PoseNet § : The network as described in [START_REF] Kendall | PoseNet: A convolutional network for real-time 6-dof camera relocalization[END_REF]) is re-implemented, except for replacing the GoogLeNet backbone with a more modern MobileNetV2 [START_REF] Sandler | MobileNetV2: Inverted residuals and linear bottlenecks[END_REF]. For PoseNet loss, we used β = 500 as suggested in [START_REF] Kendall | PoseNet: A convolutional network for real-time 6-dof camera relocalization[END_REF] for the outdoor Cambridge dataset. We initialized the Homoscedastic loss as suggested in [START_REF] Kendall | Geometric loss functions for camera pose regression with deep learning[END_REF], i.e., ŝx = 0.0 and ŝq = -3.0. For the Homography loss, we selected local x min and x max as the 2.5th and 97.5th percentile, as presented in [START_REF] Boittiaux | Homography-based loss function for camera pose regression[END_REF].

hLoc : We use the pipeline presented in [START_REF] Sarlin | From coarse to fine: Robust hierarchical localization at large scale[END_REF], i.e., NetVLAD for image retrieval and SuperPoint alongside SuperGlue pre-trained on outdoor scenes for local matching.

PixLoc : We used weights of the network pre-trained on the MegaDepth dataset [START_REF] Li | MegaDepth: Learning single-view depth prediction from internet photos[END_REF].

hLoc+PixLoc : The pose of the camera is first retrieved using hLoc and then refined with PixLoc. This pipeline is presented by [START_REF] Sarlin | Back to the feature: Learning robust camera localization from pixels to pose[END_REF].

Results on the dataset for all aforementioned methods are reported in Table 4 and can be used as a baseline for the comparison of other long-term visual localization methods. Unlike hLoc and Pixloc, PoseNet based methods are endto-end networks. Consistent with the results presented by [START_REF] Sattler | Understanding the limitations of CNN-based absolute camera pose regression[END_REF], end-to-end networks obtain the least accurate pose estimates. Moreover, since the ground truth was constructed using SfM, methods that replicate this mode of operation, e.g. hLoc, have an advantage because they optimize the same metric [START_REF] Brachmann | On the limits of pseudo ground truth in visual camera relocalisation[END_REF].

hLoc and PixLoc are based on networks trained on terrestrial data, and we can expect better results by training these networks on aquatic data. However, this remains a challenge because the amount of data needed exceeds what is readily available for the underwater environment. For example, NetVLAD is trained on Google Street View Time Machine. Another approach would be to minimize the changes due to physical phenomena induced by the underwater environment to get closer to terrestrial images, using for example algorithms like Sea-thru [START_REF] Akkaynak | Sea-Thru: A method for removing water from underwater images[END_REF] or SUCRe [START_REF] Boittiaux | SUCRe: Leveraging scene structure for underwater color restoration[END_REF].

Conclusion

This paper presented a novel dataset to evaluate visual localization methods in deep-sea environments. Unlike preexisting datasets, Eiffel Tower presents long-term changes in underwater scenarios, e.g., topography, population and species distribution, backscatter and color attenuation. We analyzed these changes and evaluated several localization pipelines on the proposed dataset. The obtained results can be used as a baseline for future work on underwater visual localization systems. Besides its use for visual localization, this dataset can also be employed to detect changes in the scene's geometry in deep-sea environments. More generally, it may also be useful to study the effects of water on various computer vision algorithms.

Figure 2 .

 2 Figure 2. Location of the Lucky Strike vent field on the Mid-Atlantic Ridge (Sources: Esri, GEBCO, NOAA, National Geographic, DeLorme, HERE, Geonames.org).

( a )

 a SIFT using brute-force matching.(b) SIFT using brute-force matching. Matches are then filtered after estimating the fundamental matrix within a RANSAC scheme.

  (c) SuperPoint (DeTone et al. 2018) followed by SuperGlue[START_REF] Sarlin | SuperGlue: Learning feature matching with graph neural networks[END_REF]).

Figure 3 .

 3 Figure 3. Feature matching between cross-year images using different methods.

Figure 4 .

 4 Figure 4. Structure-from-Motion pipeline to match images across years. Models are first built independently for each year. They are then registered in a common reference frame, i.e., 2020 reference frame, using TEASER++ (Yang et al. 2021) and ICP[START_REF] Zhou | Open3D: A modern library for 3D data processing[END_REF]. Finally, a model embedding images of all years is computed using spatial matching based on the camera poses of individual models that now share a common reference frame.

Figure 5 .

 5 Figure 5. Dataset file organization. For each year, images, navigation data and an individual SfM are provided. A global SfM embedding all years for benchmarking visual localization methods is also available.

Figure 6 .

 6 Figure 6. Illustration of a topological modification. The left image shows a point cloud distance between 2015 and 2020 models. We notice a piece from 2015 missing in 2020. This modification is visible on the right images.

Figure 7 .

 7 Figure 7. Evolution of the south-east fac ¸ade of the vent. A growth in the mussels' population significantly alters the visual aspect of the scene, making it difficult to match specific 2D points.

  reveals a modification in the topography of the scene. A chimney visible in 2015 is missing in 2020, and a temperature sensor, absent in 2015, was deployed in the vicinity in 2020. Biological changes were more important and mainly localized in areas of topographic changes. They result from mussel populations that grow and migrate to colonize newly created habitats (143.97 m 2 from 2015 to 2020) (Van Audenhaege et al. 2022). Moreover, mussels are dynamically

( a )

 a West fac ¸ade. (b) South-east fac ¸ade.

Figure 8 .

 8 Figure 8. Distribution of 3D points that are triangulated between images of different years on the Eiffel Tower edifice. 3D points resulting from cross-years triangulation are more scarce on the south-east fac ¸ade due to biological changes.

Figure 9 .

 9 Figure 9. Area covered by the ROV during the different dives.

Figure 10 .

 10 Figure 10. Comparison of pixel intensity histograms for each year on each color channel.

Table 1 .

 1 Cameras settings.

	Year	Resolution	Frame rate
	2015	1920x1080 px	25 fps
	2016	1920x1080 px	25 fps
	2018	1920x1080 px	25 fps
	2020	3840x2160 px	30 fps

Table 2 .

 2 Reconstruction

	Model	Nb. of images	Nb. of 3D points	Mean track length Mean obs. per image Mean reproj. error
	2015	4,914	525,522	8.48	906.4	1.35 px
	2016	3,699	520,320	5.85	823.5	1.32 px
	2018	5,493	618,421	7.09	798.1	1.31 px
	2020	3,976	464,331	8.35	975.5	1.33 px
	Global	18,082	1,971,726	8.24	898.7	1.39 px

statistics. For each model, we report the number of registered images, the number of triangulated 3D points, the mean track length (number of images in which a 3D point is observed), the mean number of 2D observations per image as well as the mean reprojection error in pixels.

Table 3 .

 3 Percentage of paired features indexed by the years in which the two images are taken. The table is normalized so that rows add up to 100%. It shows the amount of cross-years coverage of observations used to perform the SfM.

	Observation year	2015	2016	2018	2020
	2015	65.9% 10.7% 15.2% 8.12%
	2016	20.3% 50.9% 16.2% 12.6%
	2018	16.1% 9.05% 63.1% 11.8%
	2020	9.79% 8.00% 13.5% 68.8%

Table 4 .

 4 Median localization errors and percentage of poses localized within given thresholds in meters and degrees.

	Method	Median errors	1 cm, 1°2 cm, 2°3 cm, 3°5 cm, 5°25 cm, 2°50 cm, 5°500 cm, 10°P
	oseNet	1.98 m, 10.73°0.00%	0.00%	0.00%	0.02%	0.24%	3.58%	45.75%
	Homoscedastic	1.32 m, 6.27°0.00%	0.00%	0.00%	0.00%	0.79%	10.20%	62.03%
	Homography	1.23 m, 8.30°0.00%	0.00%	0.00%	0.02%	0.53%	8.47%	57.83%
	hLoc	0.09 m, 1.11°15.04%	28.37%	36.04%	43.49%	53.87%	57.94%	60.07%
	PixLoc	6.55 m, 41.00°0.41%	1.75%	3.50%	6.49%	13.76%	15.16%	18.46%
	hLoc+PixLoc	0.08 m, 1.10°13.41%	28.18%	35.80%	44.08%	53.95%	57.94%	60.07%

Acknowledgements

All data acquisitions were conducted by Ifremer. The authors would like to thank the crews of the research vessels, Pourquoi Pas? and L'Atalante, the pilots of the ROV Victor6000, as well as all the personnel who helped in acquiring these data. MM and LVA were supported by the European Union's Horizon 2020 research and innovation project iAtlantic under Grant Agreement No. 818123.

used to compute the different steps of the presented SfM pipeline is available at github.com/clementinboittiaux/sfm-pipeline.


Siegfried Rouvrais
email: siegfried.rouvrais@imt-atlantique.fr

Ann-Kristin Winkens

Carmen Leicht-Scholten

Haraldur Audunsson

Cecile Gerwel-Proches

Cecile Gerwel Proches

VUCA and resilience in engineering education -Lessons learned

Keywords: VUCA, Resilience, Teaching and Learning, Future Skills, Engineering Education, CDIO Standards: 1, 2, 3, 7, 8, 9, 11

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Volatility, uncertainty, complexity and ambiguity (VUCA) characterize current and future challenges that engineers must face in their work, with "the shift from the traditional and simple situation of known knowns to the chaotic VUCA situation of the unknown unknowns" (Kamp, 2020, p. 13). Professional, social and societal environments, as the contexts and situations engineering teams have to operate within, as systems they design, are also strongly characterized by so-called VUCA characteristics. [START_REF] Raja | Business Research in the VUCA World[END_REF] makes a case for the relevance of VUCA in today's world, and further argues for a focus on leadership development as a way to deal with the disruptions associated with VUCA. The concept of VUCA, the origins of which can be traced back to the US Army [START_REF] Clements | Living in a VUCA World: Preparing Students as Change Agents[END_REF], also has relevance in understanding the lack of stability and rapid changes witnessed in the business world [START_REF] Lawrence | Developing Leaders in a VUCA Environment[END_REF]. VUCA results in challenges for practitioners and leaders [START_REF] Mueller | How to Thrive in a VUCA World: Three Tips for Leader Communications[END_REF]. Resilience describes the ability, not only to quickly recover from and to respond to such events, but also to build adaptive capacity by learning from failure (National Research Council, 2012;[START_REF] Walker | Resilience: What It Is and Is Not[END_REF]. Accordingly, resilience is crucial to address VUCA situations and characteristics [START_REF] Rockley | Lead with Resilience for Success in a VUCA World! Available at[END_REF].

Therefore, besides traditional engineering skills, educational programs must also provide students and future leaders with the skills needed to face and tackle VUCA situations. Learning outcomes should echo the vision and competence to design systems that are resilient to utterly unstable and drastic events [START_REF] Cartier | Infrastructure as a Wicked Complex Process[END_REF][START_REF] Clements | Living in a VUCA World: Preparing Students as Change Agents[END_REF][START_REF] Martin | The Climate is Changing. Engineering Education needs to Change as well[END_REF]. Implied are not only technical systems, but also engineering teams and their leaders. Those events may be society driven or may be a consequence of the inevitable more unstable and dynamic environment -like black swans appearing suddenly in the misty future. Future engineering practitioners and leaders should be trained on strategies which can help develop their ability to prepare for VUCA and to enhance resilience at different levels [START_REF] Rimita | Leader Readiness in a Volatile, Uncertain, Complex, and Ambiguous Business Environment[END_REF], as resilience is a key competence and characteristic in dealing with the 'new normal' [START_REF] Raja | Business Research in the VUCA World[END_REF].

But how should resilience training in engineering education best be developed in order to, in the future, quickly recover systems and the teams that build them? This paper argues that VUCA and resilience training are connected and, as a premise, there is a need to reinforce students' abilities to work in VUCA situations. Drawing on four case studies from four countries, this paper compares and discusses teaching and learning (T&L) activities at different curriculum levels in higher educational institutions, and the extent to which they meet VUCA and resilience outcomes, mainly focusing on engineering education. With reference to both the diverse, interactive T&L methods and the aligned learning outcomes, this comparison enables insights into the challenges and opportunities of the respective approaches. The cases describe events where teams of engineering students have to tackle VUCA situations in real time early in their engineering curriculum in Iceland and France, students in their MSc program having to work on resilience strategies for diverse systems in Germany, and finally, students acquiring VUCA capabilities in the context of postgraduate and professional training in South Africa. The focus is on which T&L approaches are suitable to enable engineering students to design resilient systems in the context of a VUCA environment, and what competencies and learning outcomes are necessary to achieve this. Based on the experience of offering these different courses, the four approaches are analyzed in terms of similarities and differences, in order to integrate essentials within an educational framework for teaching resilience at different levels, and to examine how they are related to the CDIO educational framework. These elements are suggested to facilitate the rapid acquisition of VUCA and resilience skills.

TEACHING AND LEARNING APPROACHES

It is critical that 21 st century T&L approaches include not only teaching the VUCA concept to students, but also assisting them to be well equipped for life in a VUCA world [START_REF] Clements | Living in a VUCA World: Preparing Students as Change Agents[END_REF]. This will allow individuals to develop intrapersonal capabilities, such as self-leadership, lifelong learning or resilience, but will also enable them to design and develop systems that are resilient in a VUCA environment. A VUCA context is quite unstable and unlikely to be deterministic. On the one hand, knowledge and know-how of dealing with complexity is inherent to most engineering programs, as required by accreditation bodies (e.g. ENAEE, ABET), where the learning process should enable graduates to demonstrate the ability to solve complex problems, so as to design, analyze and develop complex engineering products, processes and systems. On the other hand, VUCA environment training is more in the scope of competencies, is situation-oriented and relies on knowledge, skills and attitudes. Resilience, as the ability to adapt, withstand and recover within a VUCA environment, inherently requires system thinking [START_REF] Mayar | Resilience and Systems-A Review[END_REF]. Therefore, T&L approaches for a family of situations are needed, which foster competencies at higher complexity levels, such as systems or anticipatory thinking. Various T&L approaches in the context of active learning are adequate to meet VUCA and resilience development, e.g. those ranging from problem-and project-based learning, to experiential learning or professional work-based learning [START_REF] Ban | Linking Classroom Learning and Research to advance ideas about Social-Ecological Resilience[END_REF][START_REF] Cole | Help wanted: VUCA Leaders! Dealing with Volatility, Uncertainty, Complexity, and Ambiguity[END_REF]. This section presents four case studies from different countries, and at different curriculum levels.

The four examples are in line with the spirit of the CDIO initiative. In particular, the ability to deal with resilience of a product, process and system may be considered as implicit in the CDIO Standard 1, which involves the context in engineering education. Moreover, the learning approaches in the four examples are all more or less based on experiential learning, which is explicitly referred to in CDIO Standard 8 as a core function of active learning. Experiential learning is also mentioned in CDIO Standard 7, which motivates for integration of VUCA learning experiences with the learning of disciplinary knowledge, and emphasizes the impact of these experiences.

Example 1: Disaster Days in Iceland (freshman engineer year)

The "Disaster Days" initiative at Reykjavik University was designed as an intensive two-day event that is mandatory for all engineering students early in their first semester in the program (e.g. CDIO Introduction to Engineering). The learning outcomes of the event were first to introduce the students to some unexpected, ambiguous situation that they had to confront in real time and second to realize the value of diversity within the teams. At the beginning of the event, students were briefly introduced to a disaster that was just about to unfold. Most of the information given was vague and ambiguous, and the event unraveled in the subsequent hours. In this way, the intention was to mimic a real time VUCA-like scenario that the students had to tackle. Typically, about 200 students took the course and worked in approximately forty teams of four to six students. Each team was asked to come up with recommendations to the local government on how to deal with the situation. The events were designed in such a way that the students had to analyze the situation and do some back-of-the-envelope calculations. Because the assignments were ill-posed, each team had to improvise in defining their approach. Events in the past have included a threatening volcanic eruption near the city of Reykjavik, an imminent tsunami, that Iceland has to host the Eurovision Song Contest with only a few days' notice, and a severe pandemic (fall 2019). All the events were realistic and although unlikely to happen, could still have been possible. Faculty members were assigned to teams as facilitators, but the teams mostly worked on their own. Introducing students to VUCA-like situations early on in their studies may provide them with some skills when confronted with ill-posed problems later in the progression of their degrees. Student feedback indicated that teamwork affected them the most and was considered a valuable experience. Teaming also opened doors to social networking. By far, most of the students liked the event because it was so different to what they were used to in their traditional courses of study, and enabled them to think 'outside the box', as some mentioned. A few students commented that the task given was too ambiguous, but most appreciated that it was so open. A detailed description of Disaster Days can be found in [START_REF] Audunsson | Challenging Engineering Students with Uncertainty in a VUCA Situation[END_REF].

Example 2: Man Overboard in France (sophomore engineer year)

In the context of a European project (www.dahoyproject.eu) at IMT Atlantique, an intensive one-week course for engineering sophomore-year students has been operated [START_REF] Rouvrais | Report: DAhoy Teaching & Learning Event 2 "Reliability and Decision Making via Inshore Cruising[END_REF]. In real and authentic situations during two and a half days of the week, students are required to act in Man Overboard (MOB) sessions with progressive VUCA criticality levels. The learning is experienced on a sailing boat with around sixteen students inexperienced in the sea environment. Formally, the course permits students to develop and reinforce seven learner decision competencies, each in the context of VUCA characteristics: recognize and qualify; analyze; make a judgment; face the unknown; organize and implement actions; take responsibilities; and learn from experience. While one group is acting, the others observe. Up to fourteen incremental VUCA scenarios are used for MOB, all repeated twice for a team. In each scenario, after a first unexpected and unknown event, students have to define their own rescue procedure based on a short return on experience with the other observer group (reflective practice). A similar event is then repeated to implement the procedure, sometimes with less efficiency due to their procedural rules and new unknown external factors. The concept of meta-rules is thus presented to students after the sea experiences, as rules governing a set of lower-level rules.

The optional course is located in the middle of a curriculum when students are already aware of complex situations they may have to handle as engineers, but still have room to discover new characteristics inherent to VUCA situations. Learning assessment is formative, aligned with the seven learning outcomes. Attitudes and emotional competencies linked to teamwork and leadership are solicited in action but are not assessed (e.g. cases of leadership friction, empathy, anxiety). An interpersonal characteristic was added to extend the VUCA model. In triggering judgment and decision-making skills, this course permits students to develop higher confidence in their ability to grasp VUCA situations, and allowed them to adapt dynamically to unexpected circumstances and unstable contexts. Nevertheless, the VUCA precise characteristics of a situation took time to appropriate in a week only and the letter semantics tend to also be ambiguous. Description of some student feedback can be found in [START_REF] Rouvrais | Breadth Experiential Courses to Flexibly Meet New Programme Outcomes for Engineers[END_REF].

Example 3: Resilient System Design in Germany (senior engineer year)

As an elective master's course, the seminar "Resilience and socio-technical systems" takes place annually over a period of five months and targets engineering students from civil, environmental and industrial engineering at RWTH Aachen University. After completing the course, students should not only have sound knowledge and understanding of the concept of resilience, but should also be able to apply resilience and system thinking to different contexts and be able to analyze and evaluate existing crisis management approaches with regard to resilience. The teaching approach consists of several elements in the context of active learning, such as problem-and case-based learning as well as collaborative learning, and is described in detail in [START_REF] Winkens | Local Resilience Strategies for COVID19 -A PBL Engineering Case Study[END_REF]. By working in groups during the semester, students elaborate a real-world case study on socio-technical systems in reference to resilient system design. The design of the case has been varied in the last years. In one semester, students were given a case on COVID-19, in which they had to take the perspective of a resilience consultant to advise local politics. In another semester, students were able to choose their own case. In both instances, the resilient system design did not only include technical artifacts, instead students had to critically reflect on failures, possible future state, system boundaries and interactions as well as on different stakeholder perspectives.

Moreover, several unknowns were given in the context of which the students had to conduct a scenario analysis. By dealing with an ill-defined problem, students should acquire VUCArelated competencies by learning to deal with uncertainty and complexity, and learning from failure, and should also enhance their anticipatory and system thinking competencies. The course results showed that students developed, in particular, the competencies with higher levels of complexity (e.g. analyze, evaluate, create) during the course. At the same time, these competencies were hardly pronounced before the course. Evaluations and feedback sessions with the students revealed that the task was challenging for them, as they had no previous exposure to resilience or VUCA contexts. The context in this fourth case is that of a leadership course taught to postgraduate adult learners in the Business School of the University of KwaZulu-Natal (UKZN). The majority of the participants are working students based in the public and private sectors in South Africa. The students emanate from diverse educational backgrounds and hence also workplaces, including the engineering sector. As an academic teaching leadership to adult learners in the Business School, it is critical to draw on the diverse, rich experience that the students bring with them to the classroom. The classes are highly interactive with an emphasis on individual and collective learning, solidified by student-centered approaches, facilitation, and experiential learning. As Business School students possess different qualifications, engineering being among these, it is not possible in most cases to say whether they have had formal VUCA or resilience training in their previous qualifications. However, the students do have valuable life and work experience, which may have already exposed them to VUCA situations and the importance of resilience. The course offered by the Business School thus provides a formal language and skills to assist these diverse students in their leadership development.

There are many challenges facing South Africa, and thus it is crucial that leaders and managers are equipped with the necessary skills to navigate the complexity faced in their workplaces. This is in line with principles of adult learning, where it is argued that these students should learn subjects relevant to their work and personal lives. The classes at the Business School commence with an introduction to VUCA, which inevitably most students have never heard of, but most are impressed with. Time is taken to allow the students to examine the leadership challenges that they face within the context of constant change, and to be able to develop a mindset which is not resistant to change, but rather embraces it and is proactive, thus enabling students to rapidly respond to disruptions. Herein, students gain rich insights as they get to hear about the experiences of fellow students in their work and personal contexts. Many students in the course evaluations often highlight the value of 'realizing that they are not the only ones feeling that way'. The VUCA context then paves the way to allow learners to reflect on what leadership entails, especially in light of the view that their leadership roles should not be focused on being about a position or title, but should rather be understood as an influential relationship amongst leaders and followers who intend real changes. An understanding of VUCA helps the students to understand how they, as leaders and managers, need to have the skills to operate in dynamic, non-linear contexts, where there are high levels of unpredictability [START_REF] Cartier | Infrastructure as a Wicked Complex Process[END_REF]. They are able to gain awareness of how they need to conduct themselves in their leadership roles, embrace change, and become adaptable and resilient to deal with the constant changes that define the complex systems that they form part of [START_REF] Folan | Why Resilience is Key to Transformational Leadership[END_REF]. Emphasis is also placed on gaining self-awareness and personal mastery, but also being able to lead a team and deal with organizational change and culture.

Overview

Table 1 provides an overview of the four T&L examples, as well as their main pedagogical aspects. Thereafter, Table 2 outlines the intended learning outcomes of each example. Based on the outlines provided in Tables 1 and2, the following section discusses similarities, differences as well as challenges and opportunities of progressive reinforcement of students' skills in the scope of VUCA and resilience.

DISCUSSION

Challenges and Opportunities

A comparative analysis of the presented T&L approaches resulted in the identification of both similarities and differences. A VUCA context is based on high levels of 'unknown unknowns' and students thus need the opportunity to learn to deal with this uncertainty. Accordingly, they need to be actively engaged on problems or cases which are ill-defined, but which also represent real-world problems (e.g. volcanic eruption, men over board, pandemic or leadership challenges). All presented approaches include active learning by providing a learning environment that enables engineering students to reflectively work on topics of resilience or VUCA. Notably, in all courses, students work collaboratively and are thereby gaining communication and teamwork skills. The students also develop decision-making and crisismanagement skills, which are valuable in navigating the VUCA world. The assessment methods include group presentations and reports, and in some cases, peer-assessment and reflective tasks. The analysis of the four T&L approaches also illustrates the responsibility of academics to teach students to be well-equipped for an uncertain future, and not only to impart content knowledge in a way which may create a potentially false impression of a simple rather than VUCA world. The various approaches also demonstrate how to factor in system thinking and complexity into the curriculum.

Despite the similarities noted above, the four approaches also have differences. First there is the structural difference, which means that the courses are located at different stages in the curricula. Second, there are large classes with around 200 students in some locations, as well as very small classes in others. Furthermore, the courses can be distinguished as either elective or obligatory courses. There appears to be value in incorporating VUCA and resilience topics throughout the curriculum. Early exposure in the curriculum to these valuable concepts may have a wide-spread significant impact on the students' abilities to navigate complexity. A focus on postgraduates and working students also has tremendous value, by incorporating their work and real-world experience. In terms of the complexity level of learning outcomes apparent in each of the four courses, example 1 is closer to "Remembering" level (have experienced), example 2 at "Analyzing" level, example 3 more at "Creating" level (develop), and example 4 closer to "Evaluating" (critique and challenge).

Example 1 takes place very early in the first semester. In contrast, examples 3 and 4 refer to either master's students or postgraduates, whereas example 2 targets both bachelor and master's students. The timing at which students (can) take the courses has an impact on student outcomes, as evaluations or feedback sessions with students have shown. For example, the mandatory course in example 1 showed that students value thinking and working out of the box so early on in their degrees. They also appreciated the teamwork experience.

These experiences can be valuable for students' further study progress by being introduced to VUCA contexts quite early and therefore, promoting the development of skills very early on to enable them to deal with complexity and uncertainty in their roles as future engineers. Example 2 shows how a course can benefit from being located in the middle of the curriculum, based on students' prior knowledge and awareness of complex situations. However, this is only suitable if the students do indeed have prior knowledge and experiences in VUCA environments, which is not always the case. Students took long time to clarify the semantic of the four VUCA dimensions. In contrast, in the elective master's course in example 3, students noted that they had significant problems with the level of uncertainty and complexity, because they had not learned to deal with it before. Furthermore, they had hardly any prior knowledge about resilience-related issues. Finally, this is also illustrated by the fourth example, in which postgraduates initially reflect on their roles as leaders and what leadership entails in an uncertain and ambiguous environment. Leaders who are able to navigate VUCA are critical, given the multiple disruptions that are experienced, and how difficult it is to predict the future [START_REF] Cole | Help wanted: VUCA Leaders! Dealing with Volatility, Uncertainty, Complexity, and Ambiguity[END_REF]. The four -partly strongly contrasting -examples illustrate the relevance for a holistic and systematic curriculum approach, as represented by the CDIO educational framework.

VUCA situations can also now be a context of engineering education through which skills are taught, practiced and developed, and where students are exposed progressively from welldefined problems to ill-defined VUCA problems and situations.

Educational Framework Integration

The CDIO syllabus evolves, and revisions are implemented in tandem with changes in society and the expected working environment of future engineers. In the recent update of the syllabus, [START_REF] Malmqvist | The CDIO Syllabus 3.0 -An Updated Statement of Goals[END_REF] stated that there are mainly three external drivers that motivated the changes in the most recent upgrade: sustainability, digitalization, and acceleration. Both VUCA and resilience as discussed in this paper are in the realm of acceleration and one may argue that resilience is an implicit factor in sustainability. Targeted teaching of resilience can contribute to sustainable development [START_REF] Cole | Help wanted: VUCA Leaders! Dealing with Volatility, Uncertainty, Complexity, and Ambiguity[END_REF].

Thus, specific learning outcomes may help to ensure that students acquire the appropriate foundation for their future to become lifelong learners, a foundation which includes VUCA capabilities and resilience. In the recent 3.0 version of the CDIO syllabus in particular, some categories and topics address resilience-related aspects (Winkens et al., 2023, under review), such as 2.3.2 on emergence and interactions of systems, or 4.1.6 on visions of the future. Moreover, the CDIO syllabus proposes learning outcomes such as initiative and willingness to make decisions in the face of uncertainty (LO 2.4.1) and analysis with uncertainty (LO 2.1.4), uncertainty being just one facet of VUCA situations. Table 2 presents further learning outcomes, for instance, analysis, team leadership, and reflective skills as VUCA-abilities and system resilience.

VUCA and resilience outcomes are transversal to curricula. Related competencies include skills and attributes developed by students when they are placed in VUCA contexts. As stated in the CDIO Integrated Curriculum Standard, "the integration of skills and multidisciplinary connections are to be made, for example, by mapping the specified learning outcomes to courses and co-curricular activities that make up the curriculum" [START_REF] Malmqvist | CDIO Standards 3.0 -Updates to the Core CDIO Standards[END_REF]. To meet dedicated learning outcomes, within the context of simple problems to progressive high VUCA contexts, T&L activities may reach individuals, organizations, and communities, as the sociotechnical systems to be built. The CDIO Integrated Learning Experiences Standard prompts "pedagogical approaches that foster the learning of disciplinary knowledge simultaneously with personal and interpersonal skills, and product, process, system, and service building skills" [START_REF] Malmqvist | CDIO Standards 3.0 -Updates to the Core CDIO Standards[END_REF]. Resilience skills can be introduced and reinforced only through the use of pedagogical approaches that expose students to VUCA, in an integrated approach. The CDIO learning assessment standard could be extended with Bloom-based proficiency levels, starting from level 1 with exposure to VUCA situations, to higher levels where students are expected to lead and innovate in VUCA situations.

CONCLUSION

The aim of the paper was to compare and discuss T&L activities addressing VUCA and resilience, occurring at different engineering curriculum levels. The four presented courses are good examples of progressive contextualization of VUCA contexts for students, from freshmen to postgrads. Furthermore, as discussed in the paper and as reflected in the progression of the four courses, introducing resilience is a logical continuation after addressing VUCA-like scenarios, at least in engineering education. Based on the need for aligning curriculum integration with new learning outcomes, engineering curricula can echo in their intentions, within a framework of development such as the CDIO, the inclusion of VUCA and resilience.

The emphasis on rapid changes that are a part of VUCA is highlighted in the most recent upgrade of the CDIO syllabus, although VUCA and resilience could be stated more directly.

As already argued, it is critical that engineering students develop resilience skills in order to prepare them for a VUCA shaped workplace. Previous research has emphasized the importance of developing the necessary skills to enter the workplace, contributing to high-level goals to ensure that engineering graduates are able to fit in well to their work [START_REF] Proches | Think first job! Preferences and Expectations of Engineering Students in a French 'Grande Ecole[END_REF]. VUCA is the 'new normal', and while there may be little that can be done about the external factors, what academics can concentrate on is to strengthen the internal aspects [START_REF] Garti | Using the Triaxial Model of Values to Build Resilience in a COVID-19 VUCA world[END_REF]. So far, however, there appears to be few approaches addressing such training. Based on the experience of comparing four different courses, the paper delineates how engineering programs can better prepare their graduates to design more resilient sociotechnical systems, including effective learning methods and learning outcomes.

While we cannot generalize beyond our individual contexts, the paper does offer valuable insights and indicates areas of study which could be explored in further research. First, a continuum of the four cases with a same cohort is to experience for validating arguments and meet research questions. In the future, new evaluation techniques will be required for programs, like the ones presented in the paper. For such, a direction could draw on more formal qualitative and quantitative analysis to compare the T&L approaches with learning outcomes proficiencies.

The resilience of the higher educational system itself could be explored, by examining curriculum properties to face VUCA circumstances. Future research could also explore how to develop the VUCA and leadership skills of program managers and faculty to ensure resilience of their programs. As change agents, an examination of the approaches to reinforce program managers' and faculty's capability to handle the unknown, and to ensure that the curricula and educational systems they manage become sustained by resilient characteristics, is indeed necessary. As highlighted in the CDIO Standard on Enhancement of Faculty Competence, "the collective faculty needs to enhance its engineering knowledge and skills so that they can provide relevant examples to students and also serve as individual role models of contemporary engineers" [START_REF] Malmqvist | CDIO Standards 3.0 -Updates to the Core CDIO Standards[END_REF]. This will promote the development of faculty capable of facing VUCA situations, thus ensuring better resilience of the higher educational system itself.

FINANCIAL SUPPORT ACKNOWLEDGEMENTS

Some financial support under the Erasmus+ Programme was received (2022-1-FR01-KA220-HED-000087657). The European Commission is not liable for any use that may be made of the information contained.

Example 4 :

 4 Leadership Training in South Africa (postgrads and professionals)

Table 1 .

 1 Overview of Four Examples for Teaching VUCA Characteristics

	Example	#1 Disaster	#2 Man Over	#3 Resilient System	#4 Leadership
		Days in Iceland	Board in France	Design in Germany	Training in South
		(Reykjavik	(IMT Atlantique)	(RWTH Aachen	Africa (UKZN)
		University)		University)
	Students	First year	Bachelor's &	Master's	Postgraduates
		engineering	Master's +
			Erasmus
	Fields of Study	All students in	IT Engineering	Civil, Environmental,	Leadership (diverse
		engineering		Industrial Engineering	student body,
					including engineers)
	Size	200 students,	16 students,	25 students,	15-20 students,
		in groups of 5	in groups of 4	in groups of 5	in groups of 4 to 5
	Part of Curriculum Mandatory	Elective	Elective	Mandatory
	ECTS	1	2	4	6 (equivalent)
	Interval / Duration	2 days in the	Full week	1 Semester	Block release
		fourth week of	between 2		(2 weeks)
		semester	semesters
	T&L Approach	Experiential	Experiential	PBL;	Experiential learning;
		learning;	learning;	Case-based learning;	Adult learning;
		PBL;	Peer-learning with	Peer-learning	Collective learning
		Open ended	reflective
		task	debriefings
	Assessment	Team	VUCA field	Group presentations	Reflective
		presentations	practice;	and reports;	assignments
			Peer-assessment	Reflective Diaries;
				Peer-assessment
	Content/Keywords VUCA;	VUCA progressive	VUCA; resilience	VUCA; multiple
		teamwork;	scenarios;	assessment methods;	disruptions; change;
		quick decision	teamwork;	scenario planning;	leadership; self-
		by a team;	leadership;	system thinking;	awareness; personal
		multiple	snap decision	disasters, learning	mastery; system
		disruptions		from failure	thinking; team
					leadership;
					organizational culture

Table 2 .

 2 Learning Outcomes and Experience of the Four Examples discussed

	Course	Learning Outcomes and Experience
	#1	After completing the course, students are able to:
	Disaster Days	-	experience teamwork and understand the importance of cooperation and diversity in a
	in Iceland		group;
	(Reykjavik	-	are introduced to diverse ways in presenting solutions;
	University)	-	experience a situation where decisions and planning are based on uncertain
	1 ECTS		information.
	#2	After completing the course, students are able to:
	Man Over	-	recognize and qualify the VUCA characteristics of situations;
	Board in	-	analyze VUCA situations;
	France	-	make judgment in VUCA situations and face VUCA characteristics of situations;
	(IMT	-	organize and implement actions in VUCA situations;
	Atlantique)	-	take responsibilities of the decision process in VUCA situations;
	2 ECTS	-	learn from VUCA experiences.
	#3
	Resilient
	System
	Design in
	Germany

After completing the course, students are able to:

develop local resilience-based approaches with regard to the COVID-19 pandemic; -reflect on resilience-oriented approaches and ways of thinking in their future work as engineers. Moreover, they reflect on the relevance of resilience-oriented approaches to local and global crises;

Proceedings of the 19 th International CDIO Conference, hosted by NTNU, Trondheim, Norway, June 26-29, 2023.

Proceedings of the 19 th International CDIO Conference, hosted by NTNU, Trondheim, Norway, June 26-29, 2023.

BIOGRAPHICAL INFORMATION

Siegfried Rouvrais-Delahaie is an Associate Professor at the CS Department of IMT Atlantique (formerly Télécom Bretagne). His educational interests are in methods and processes for higher education transformations.

Ann-Kristin Winkens studied environmental engineering (M.Sc.) and is a PhD candidate at the Research Group Gender and Diversity in Engineering at RWTH Aachen University. Her research is focused on teaching and learning system resilience in engineering education.

Carmen Leicht-Scholten PhD is Full Professor, head of the GDE and director of the RRI Hub at RWTH Aachen University. Her focus in research addresses the embeddedness of social factors in research and innovation processes and in integrating those perspectives in engineering curricula.

Haraldur Audunsson

PhD is an Associate Professor of Physics in the Department of Engineering at Reykjavik University. His interests include applied physics, engineering education, and experiential learning.

Cecile Gerwel Proches PhD is an Associate Professor in the Graduate School of Business and Leadership at the University of KwaZulu-Natal (UKZN). Her teaching and research interests include leadership, change management, and organizational culture.


M Sahihi

A K Bordbar

Y Ghayeb

Thermodynamic stability and retinol binding property of β-lactoglobulin in the presence of cationic surfactants

Keywords:

In this work the stability parameters of bovine β-lactoglobulin, variant A (BLG-A), with regard to their transition curves induced by dodecyltrimethylammonium bromide (C12TAB), tetradecyltrimethylammonium bromide (C14TAB) and hexadecyltrimethylammonium bromide (C16TAB) as cationic surfactants, were determined at 298 K. For each transition curve, the conventional method of analysis which assumes a linear concentration dependence of the pre-and post-transition base lines, gave the most realistic values for ΔGD(H2O). The results represent the increase in the denaturating power of surfactants with an increase in hydrocarbon chain length.

The value of about 22.27 kJ • mol -1 was obtained for ΔGD(H2O) from transition curves. Subsequently, the retinol binding property of BLG as its functional indicator was investigated in the presence of these surfactants using the spectrofluorimeter titration method. The results represent the substantial enhancement of retinol binding affinity of BLG in the presence of these surfactants.

Introduction

The small globular protein β-lactoglobulin (BLG), as a member of the family called lipocalins, is the major protein in the whey of ruminant milk [1]. It exists at neutral pH as a dimer. Each monomer is made of 162 amino acids including two disulfide bonds (Cys66-Cys160 and Cys109-Cys119) and one free cysteine (Cys121) [START_REF] Hambling | Advanced Dairy Chemistry 1: Proteins[END_REF], with a molecular weight of about 18,400 Da. Like retinol binding protein (RBP), BLG is able to bind a wide variety of hydrophobic molecules such as retinoids, alkenes and fatty acids [START_REF] Hambling | Advanced Dairy Chemistry 1: Proteins[END_REF], [START_REF] Chobert | Food Proteins and Their Applications[END_REF]. Binding to BLG protects retinol and β-carotene from degradation due to heat, oxidation, and irradiation [START_REF] Hattori | [END_REF]. It has therefore been proposed that BLG could be used as a versatile carrier of hydrophobic molecules in controlled delivery applications [5]. BLG is known to bind tightly, in vitro, one retinol molecule per monomer [6].

An understanding of protein-surfactant interactions, particularly at a molecular level, is an important research area that attracts the interest of researchers [7], [8], [9], [10], [11]. The interactions between biopolymers and surfactants depend strongly on the type of biopolymer and surfactant as well as on medium and its physicochemical properties such as pH, ionic strength, and temperature [12], [13], [14], [15], [16], [START_REF] Goddard | Applications of polymer-surfactant systems[END_REF], [18], [19], [20]. Moreover, these interactions are also the driving force for the structural transition of proteins [21]. In some cases these induced transitions can be monitored and analyzed in order to obtain the thermodynamic parameters of protein stability [22], [23]. The interaction of ionic surfactants with BLG has been investigated by various researchers [16], [22], [24], [25], [26]. It has been shown that sodium n-dodecyl sulfate (SDS) is a stronger denaturant than Triton X-100; however, the analysis of binding data represents the absence of considerable changes in retinol binding properties of BLG in the presence of various amounts of these surfactants [16]. The interaction of dodecyltrimethylammonium bromide (C12TAB) with BLG has also been investigated at various pH representing an increase in the binding strength of BLG/C12TAB complex with an increase in the pH although C12TAB binding does not have the significant effect on retinol binding affinity of BLG [26]. The stability parameters of BLG in relation to its transition curves induced by cetylpyridinium chloride (CPC) at various temperatures have already been determined [22]. This has been done by analysis of induced transition curves by conventional method, assuming a linear concentration dependence of the pre-and post-transition base lines. However, the binding properties of BLG have not been investigated in the presence of CPC.

In the present study, the structural changes of BLG have been investigated initially in the presence of a homologous series of cationic surfactants (dodecyltrimethylammonium bromide (C12TAB), tetradecyltrimethylammonium bromide (C14TAB), and hexadecyltrimethylammonium bromide (C16TAB)) using fluorescence spectroscopy. A comprehensive thermodynamic analysis has been carried out on induced sigmoidal transition curves in order to determine the stability parameters of BLG at various concentrations of these surfactants. The nature of the interactions has been discussed by considering the surfactant and BLG structure. Moreover, the retinol binding properties of BLG, as its functional indicator, were investigated in the presence of the various amounts of these surfactants using the spectrofluorimeter titration method. Comparison of the results paves the way for the determination of some aspects of the structure-function relationship of BLG in the presence of these surfactants. Finally, the obtained information can be useful in the application of these cationic surfactants in dairy food industry. This may also give some guidance as to what could be expected during interactions of charged and neutral lipids with BLG.

Material and methods

Materials

β-Lactoglobulin genetic variant A, retinol, C12TAB, C14TAB and C16TAB were purchased from Sigma Chemical Co. and used without further purification. The purities of these materials were ⩾90%, ⩾95% and 98% for BLG, retinol and surfactants, respectively. All salts used for buffer preparation were of analytical grade and were dissolved in double distilled water. All solutions were used freshly after preparation.

Methods

The concentrations of BLG and retinol were determined spectrophotochemically using the value of 17,600 M -1 • cm -1 for the molar absorption coefficient (ε) of BLG at 279 nm [27], and 52,480 M -1 • cm -1 for the molar absorption coefficient (ε) of retinol at 325 nm [START_REF] Dawson | Data for Biochemical Research[END_REF]. For optical measurements all solutions were prepared in 50 mM phosphate buffer at pH 8.0. The experiments were conducted at 298 K.

Fluorescence spectroscopy is used to study the conformational changes of proteins containing tryptophan residues during the denaturation because the intrinsic fluorescence of indol chromophores in tryptophan (Trp) residues is particularly sensitive to their microenvironments.

The intrinsic fluorescence of aromatic amino acids in proteins has long been used as a means of monitoring unfolding/refolding transitions induced by chemical denaturants, pH changes, temperature, and pressure [START_REF] Wang | [END_REF]. Surfactants denaturation curves were determined by measuring the relative emission intensity of the solutions containing BLG at 330 nm using a Jasco model FP-750 spectrofluorimeter. During fluorescence measurements, the samples were put in quartz cuvette of 1 cm optical path and thermostated to maintain the temperature at ±0.l K. In typical experiments 2.0 mL of BLG solution was placed in the cuvette and emission spectra were recorded after each addition of surfactant stock solution. The excitation was performed at 295 nm and the emitted fluorescence was recorded between (300 and 400) nm. The band slits for both excitation and emission were set at 5 nm. Absorbances of the BLG solutions in 279 nm should not exceed 0.1 to avoid the inner filtering effect. The final surfactant concentrations were about (5.0, 0.6, and 0.5) mM for C12TAB, C14TAB, and C16TAB, respectively and there was no inner filter effect due to the presence of these surfactants. The critical micelle concentrations (cmc) of surfactants are 14 mM, (4 to 5) mM and (0.92 to 1) mM for C12TAB, C14TAB, and C16TAB, respectively.

The binding of retinol was measured by following the differences of retinol fluorescence at λmax.

The following procedure was used during titration of retinol solutions or various

[surfactant]/[retinol] solutions with BLG: 1 mL of retinol solutions or various [surfactant]/[retinol] solutions was placed in a cuvette and small increments (0-100 μL) of the BLG solution were injected in the cuvette. These spectra were recorded from (350 to 550) nm with an excitation wavelength of 325 nm. Spectral resolution was 1 nm for both excitation and emission wavelengths.

Results and discussion

Denaturation of BLG

Relative emission intensity of BLG solution (F/F0) at 330 nm and various concentrations of surfactants ([surfactant]) is shown in figure 1, where F and F0 are the emission intensities in the presence and absence of surfactant, respectively. The fluorescence properties of tryptophan residues apparently are sensitive to perturbations of protein structure. Interaction of surfactants with BLG changes the environment of tryptophan residue and causes differences in fluorescence intensity. The sigmoidal feature of the curves in figure 1 can be related to the characteristic of a cooperative transition. Similar sigmoidal transitions that have also been reported for thermal and chemical denaturation of BLG [22], [23] can be taken as other reason for cooperative denaturation of BLG by these cationic surfactants. It seems that these surfactants can induce an unfolding transition in tertiary structure of BLG. With respect to the position of the transition curves in figure 1, it can be judged that the denaturating power of surfactant has been increased by increasing its hydrocarbon tail. It represents the significant role of hydrophobic interaction in the process of surfactant binding.

However, it appears that there is not a linear relation between these phenomena. The standard free energy of denaturation, ΔGD, was calculated as a function of surfactants concentration by assuming a two-state mechanism [30], [31], and by using equations (1), (2) [22], [23], [32] where K is the equilibrium constant, FD is the fraction of denatured protein, R is the gas constant, yobs is the observed optical property, yN and yD are the optical properties of the native and denatured protein molecules, respectively. The two latter ones have been extrapolated from the pre-and post-transition baselines to the considered denaturant concentration. These pre-and posttransition baselines were determined by a linear fit of the corresponding data before and after the transition according to the following equations:

Figure 2 shows the standard free energy of denaturation, ΔGD (which is calculated from equation ( 2) based on the data in figure 1), which varies linearly with surfactants concentration.

ΔGD was plotted against [surfactant] and a linear least-squares analysis was used to fit the ΔGD and

[surfactant] data to the following relation [33],

where ΔGD(H2O) is the value of ΔGD at 0 M denaturant and m is the slope of the curves which gives the linear dependence of ΔGD on surfactants concentration.

Retinol binding of BLG

Fluorescence is a useful approach to investigating intermolecular interactions because the photophysical character of the fluorophore is sensitive to the polarity of its surrounding environment. Generally, fluorescence intensity increases as this polarity decreases. As shown in figure 4, the gradual increase in retinol fluorescence intensity is observed as BLG concentration increases suggesting that retinol transfers from the hydrophilic environment of the aqueous solution to a more hydrophobic environment. Generally, complexes involving noncovalent bonds are reversible. For example, the binding of retinol to retinol binding protein is involved in equilibrium between retinol and retinol-protein complexes [34]. Likewise, there may be an equilibrium between free and BLG-bound retinol. For From the slope of the straight line, Ka can be calculated according to equation (7). The value of (5.0 ± 0.9) • 10 4 M -1 was obtained for Ka in the absence of any surfactant. This value is in consistent with previously reported data [36]. The obtained values for Ka in the presence of C12TAB, C14TAB, and C16TAB are given in table 2. These values represent a large enhancement of retinol binding affinity of BLG in the concentrations range of these surfactants that located in the pre-transition state of denaturation curves (figure 2); although, at all concentrations of surfactants, the tertiary structure of protein will change in such a manner that the retinol binding site of BLG will be more accessible and that it will cause an increase in the retinol binding affinity of BLG.

Conclusions

All of the studied surfactants denature BLG in a cooperative manner. The denaturation effect of these surfactants increases with an increase in their hydrocarbon tail length, representing the predominate role of hydrophobic interaction. However, this does not obey in a linear trend. This nonlinearity can be deduced by considering the m, ΔGD, and ΔGtr values in table 1 (there is not any linear relation between these parameters and number of carbon atoms in hydrocarbon tail of surfactants).

The value of about 22.27 kJ • mol -1 has been obtained for ΔGD(H2O), as thermodynamic stability of BLG in the absence of surfactant. This value is obtained from the identical extrapolated values of all lines in figure 1. This can represent the accuracy of applied analyzing method. With respect to the negative values of ΔGD(surfactant) and ΔGtr and hydrophilic nature of surfactants, it looks that hydrophobic interactions have substantial role in denaturation process.

All of the used surfactants enhance the retinol binding affinity of BLG in all of their concentration range. In the presence of surfactants, the tertiary structure of protein has been changed such a manner that its retinol binding site became more accessible and obtained higher affinity. However, this enhancement is much more in the region of pre-transition. It seems that binding of initial surfactant ions increases the hydrophobic patches in the protein surface that subsequently increases the hydrophobic interaction of retinol molecule with BLG. This enhancement for C12TAB is more than others. However, higher concentration of C12TAB is required in order to reach this enhancement.

FIGURE 1 .

 1 FIGURE 1. Surfactant denaturation curves for β-lactoglobulin in 50 mM phosphate buffer, pH 8.0. The excitation wavelength was 295 nm and band slits were 5 nm for both excitation and emission wavelengths. The relative fluorescence intensities were measured at 330 nm.

FIGURE 2 .

 2 FIGURE 2. The variation of ΔGD versus surfactants concentration.

Figure 3 FIGURE 3 .

 33 Figure 3 shows the linearity of the plot of ΔGD versus ln[surfactant]. From the least-squares analysis of these plots, the standard free energy in the surfactant solution, ΔGD(surfactant), was obtained at ln[surfactant] = 0. A concentration of surfactant of 1 M is in good agreement with the transition of the native state of protein (from water) to the denatured state of protein (to surfactant solution). The estimated value of ΔGD(surfactant), ΔGD(H2O), and ΔGtr are shown in table 1. ΔGD(H2O) has positive values but ΔGD(surfactant) and ΔGtr have negative values. The values of

FIGURE 4 .

 4 FIGURE 4. Fluorescence emission spectra of retinol in the presence of various BLG concentrations in 50 mM phosphate buffer, pH 8.0. The excitation wavelength was 325 nm and band slits were 5 nm for both excitation and emission wavelengths.

1: 1

 1 complexes, the retinol fluorescence data in figure 4 can be analyzed by the following equation [35]: ΔF is the change of retinol fluorescence intensity in the presence and absence of BLG; ΔFmax is the maximal change of fluorescence intensity; Ka is the binding constant; and [BLG] is the concentration of BLG. The linear plots of 1/ΔF as a function of 1/[BLG] in the presence of C12TAB, C14TAB, and C16TAB are given in FIGURE 5, FIGURE 6, FIGURE 7, respectively.

FIGURE 5 .FIGURE 6 .

 56 FIGURE 5. The linear plots of 1/ΔF as a function of 1/CBLG based on equation (7) and in the presence of various C12TAB concentrations; (a) 500 μM, (b) 2.0 mM, and (c) 2.8 mM.

FIGURE 7 .

 7 FIGURE 7. The linear plots of 1/ΔF as a function of 1/CBLG based on equation (7) and in the presence of various C16TAB concentrations; (a) 50 μM, (b) 200 μM, and (c) 500 μM.

TABLE 2 .

 2 The obtained values for Ka in the presence of C12TAB, C14TAB, and C16TAB. Ka is the binding constant.

	Surfactant [Surfactant]/(

mM) Ka • 10 -4 /(M -1 ) Surfactant [Surfactant]/(mM) Ka • 10 -4 /(M -1 )

	Surfactant [Surfactant]/(
	C12TAB	0	5.0 ± 0.9	C14TAB	0.35	135.0 ± 28.3
		0.3	1933.3 ± 113.1		0.45	168.0 ± 39.5
		0.5	1487.5 ± 320.2		0.6	120.0 ± 26.1

mM) Ka • 10 -4 /(M -1 ) Surfactant [Surfactant]/(mM) Ka • 10 -4 /(M -1 )

		1.0	520.0 ± 98.8	C16TAB	0	5.0 ± 0.9
		2.0	453.3 ± 93.4		0.02	126.3 ± 30.6
		2.8	260.0 ± 52.0		0.05	1439.0 ± 289.2
		5.0	106.0 ± 22.4		0.1	124.0 ± 27.9
	C14TAB	0	5.0 ± 0.9		0.2	139.7 ± 32.9
		0.05	1285.7 ± 255.8		0.25	134.8 ± 30.5
		0.1	305.0 ± 62.8		0.4	145.9 ± 29.0
		0.15	127.5 ± 25.5		0.5	149.0 ± 30.2
		0.3	117.0 ± 25.8

Acknowledgment

The financial support of Research Council of Isfahan University of Technology is gratefully acknowledged.


J C Nappé
email: nappe@emse.fr.

Ph Grosseau

F Audubert

B Guilhot

M Beauvy

M Benabdesselam

I Monnet

Damages induced by heavy ions in titanium silicon carbide: effects of nuclear and electronic interactions at room temperature

Keywords: Ti3SiC2, irradiation, heavy ions, nuclear collisions, electronic interactions PACS : 61.82.-d, 61.80.Jh, 25.75.Ag, 34.50.Bw

Thanks to their refractoriness, carbides are sensed as fuel coating for the IV th generation of reactors. Among those studied, the Ti3SiC2 ternary compound can be distinguished for its noteworthy mechanical properties: the nanolamellar structure imparts to this material some softness as well as better toughness than other classical carbides such as SiC or TiC. However, under irradiation, its behaviour is still unknown. In order to understand this behaviour, specimens were irradiated with heavy ions of different energies, then characterised. The choice of energies used allowed separation of the effects of nuclear interactions from those of electronic ones.

Introduction

As part of Generation IV International Forum (GIF), new systems are studied, from the point of view of the reactor as well as the fuel cycle. These systems are characterised by an increased security level, better economic competitiveness, and an ability to recycle all the fuel in order to upgrade to a fissionable material and minimize longlived waste production by transmutation. Among the six systems considered by the GIF, the Gas Fast Reactor (GFR) is studied in France; it is designed to work under helium-pressure and high-temperature (1100-1300 K).

These working conditions led to the selection of non-oxide refractory ceramics as fuel coating. Thus, carbides turn out to be great candidates thanks to their remarkable mechanical and thermal properties. However, their behaviour under irradiation has to be studied in more details.

Among the studied carbides, the ternary Ti3SiC2 presents some interesting prospects. Indeed, in 1972 Nickl et al. [1] remarked that this material, which was synthesized for the first time by a Viennese group in 1967 [2], is abnormally soft for a carbide. At the beginning of the '90s, Pampuch et al. [3,4] and Lis et al. [5][6][7] led works on the pure bulk Ti3SiC2 synthesis; their best results were compounds with 80-90 % purity, impurities being TiCx and TiSi2. During their works, they demonstrated that this material is both stiff (Young's modulus of 325 GPa) and soft (Vickers hardness of 6 GPa). Moreover, like Goto et al. [8] some years before, they noted that the hardness of this carbide decreases as the applied load increases; this property led them to qualify Ti3SiC2 as a "ductile ceramic".

At the end of the '90s, Barsoum et al. [9][10][11][12][13][14] intensively studied this material as well as other ceramics with similar properties. In 2000 [15], they brought all these compounds together within one family of nanolamellar materials, named MAX Phases, of general formula Mn+1AXn where n = {1,2,3}, M is an early transition metal, A is an A-group (mostly IIIA and IVA) element, and X is C and/or N. Materials of this family are unusual in that they combine some properties of metals with those generally attributed to ceramics; thus, these materials are stiff, thermal and electrical conductors, readily machinable with tools for steels, andrelatively soft and tough. More specifically, Ti3SiC2 is stable under inert atmosphere or vacuum up to 1975 K [9,13,16], resistant to quenching from 1475 K to room temperature [9,17], has a thermal conductivity of 37 W m -1 K -1 at room temperature and 32 W m -1 K -1 at 1775 K [13], a toughness of 9 MPa m 1/2 [15,18], a brittle-ductile mechanical transition as a function of temperature and strain rate [9,[19][20][21], an electrical conductivity of 4.5x10 6 Ω -1 m -1 [9,15,22,23], and good corrosion resistance in common acids and NaOH (corrosion rate < 300 µm a -1 in HNO3, the worst case) [24][25][26]. Nevertheless, no information is available about its behaviour under irradiation.

Therefore, the aim of this study is a better knowledge of the behaviour under irradiation of Ti3SiC2, whose otherwise outstanding properties allow its consideration it as a cladding material for future fuels.

Experimental

The studied material is a polycrystalline commercial compound provided by 3-ONE-2 (Voorhees, NJ, USA). An estimation of the secondary phase proportion in the as-received material was carried out by X-Ray Diffraction (XRD); the samples consist of about 80 % Ti3SiC2, 15 % TiCx, and 5 % TiSi2. Figure 1 shows the surface state of the as-polished specimens through Atomic Force Microscopy (AFM); the lower hardness of Ti3SiC2 compared with the TiCx and TiSi2 phases can be noted. The as-received specimens were polished with diamond suspensions down to 1 µm. They were subsequently irradiated at room temperature with heavy ions. This kind of irradiation aims to simulate the interactions present in reactors (impacts of fission products, recoil atoms of alpha-decays, and neutrons) without the drawback of activating the specimens.

When heavy ions penetrate through a solid, they get decelerated via two different processes, namely, elastic collisions with target nuclei (with nuclear stopping power Sn) and inelastic collisions with target electrons (with electronic stopping power Se). In order to understand the effects of both nuclear and electronic interactions on Ti3SiC2, two runs of irradiation were performed. The first one, called "low energy irradiation", was achieved with the 4 MeV Au ions of the ARAMIS accelerator at the CSNSM of Orsay (France) to gain the nuclear interactions effects. The second one, named "high energy irradiation", was made with the 90 MeV Xe ions delivered by the IRRSUD accelerator at the GANIL to understand the electronic interactions influence. Each run was carried out for 4 ion fluences: 10 12 , 10 13 , 10 14 , and 10 15 cm -2 . The depths of the damaged zone were determined aided by the TRIM code [27].

To characterise surface modifications to the samples, two microscopic methods were used: Field Emission Gun Scanning Electron Microscopy (FEG-SEM) and AFM. The structure of the irradiated zone was analysed by Low Incidence XRD (LI-XRD) technique.

Results

The low energy irradiation spoils the first 500 nm of Ti3SiC2 essentially by nuclear interactions. By microscopic means, modifications are noticeable from a fluence of 10 15 cm -2 . By FEG-SEM (Fig. 2), at some places it was noted that the Ti3SiC2 grain boundaries were eroded. This could be the consequence of preferential sputtering due to a weaker displacement threshold energy of the atoms in grain boundaries than inside the grains. By AFM, surface etching can be observed; it may be explained by preferential sputtering as a function of the Ti3SiC2 crystallite orientation. In fact, orientation of the basal planes of the Ti3SiC2 hexagonal close-packed structure perpendicularly to the ion beam would lower the ion channeling effects inside the lattice, and so would increase the sputtering yield; therefore, as a function of the crystallite orientation, the sputtering yield would vary and so lead to this kind of microstructure. However, the as-irradiated surface is too spoiled to differentiate the three phases. For the low energy irradiation, XRD was achieved under a 1° incidence angle (Fig. 3), which corresponds to an Xray penetration of 230 nm in Ti3SiC2; Al peaks are from a mask intentionally layed on the virgin part of the samples during measurements. It brings out both a decrease of the peak intensity of the three phases as a function of the Au fluence from 10 14 cm -2 , and an increase of the baseline. These diffractogram changes are certainly due to a partial amorphisation of the material due to nuclear collisions. Indeed, this phenomenon was often encountered during irradiation of other materials such as SiC [28] or some oxides [29].

Fig. 3: XRD patterns of Au irradiated samples

The high energy irradiation damages the first 5 µm of Ti3SiC2 by electronic interactions. Through FEG-SEM and AFM, two phenomena are revealed from Xe fluence of 10 15 cm -2 fluence (Fig. 4):

-A surface etching due to differente behaviour under irradiation of the phases present; the identification of the phases was performed by Energy Dispersive X-ray spectroscopy.

-The appearance of "hills" on the Ti3SiC2 surface: their diameter varies from 50 to 100 nm, with a height of around 10 nm, and an estimated density of about (1.0-1.5)x10 10 cm -2 . The XRD analysis was realised under a 3° incidence angle (Fig. 5), which corresponds to an X-ray penetration depth of 720 nm in Ti3SiC2. Diffraction patterns also reveal an amorphisation phenomenon, but only for Ti3SiC2 phase. Moreover, we can notice a modification of the unit cell of this compound; the peak initially located at 40.9°, relative to the (00l) diffraction planes, is shifted to lower 2θ as a function of the fluence. Estimating the parameters of the unit cell as a function of the fluence (Fig. 6), an expansion of the unit cell along the c axis without significant variation of the unit volume may be underlined.

Discussion

Whereas the phenomena due to nuclear interactions can be understood through the elastic collisions during a low energy irradiation, those due to electronic interactions are often misunderstood. This discussion allows to brighten these phenomena.

The phenomenon of the appearance of "hills" was apparently not yet observed in other materials for these conditions (energies of about 100 MeV and ion fluences close to 10 15 cm -2 ). Indeed, numerous authors remarked bumps during irradiations with similar energies, but generally fluences do not exceed some 10 12 cm -2 [30][31][32][33][34][35][36]; thus they noted the same number of hills as of incident ions. In this case, authors usually attribute their appearance to the thermal spike model. The swift heavy ions lose energy during a short time (< 10 -16 s) inside a cylindrical zone around the ion trajectory. When Se is higher than Seth (threshold electronic stopping power), melting of the cylindrical zone followed by cooling and resolidification lead to the formation of amorphous tracks inside the material. This phenomenon, which is more important at the near surface (where Se is maximum), leads to high stresses and strains that would result in a flow of matter to form one hill by each ion. Only one paper notes about bumps for fluences higher than some 10 12 cm -2 [34]; they noticed hills from lower fluence up to 2x10 14 cm -2 and explained this by an agglomeration of the hills formed by thermal spikes. Nevertheless these explanations are not valid in our case: first, the density of hills is about 10 5 lower than the fluence, so thermal spikes cannot account for the bumps; second, this type of relief are only noted from 10 15 cm -2 (not at lower fluences), so bumps are not due to agglomeration.

Other authors observed the same type of relief for very low energy ions (a few keV) at higher fluences (greater than 10 16 cm -2 ). In this case, the surface morphology would depend on both ion erosion and surface diffusion processes [37]. Other authors interpret this type of relief as the agglomeration of the projected ions at the very near surface of the target, implying the formation of nanobubbles [38] or nanoparticles [39]. Whatever the explanation, our fluences are too weak to allow the formation of 50-100 nm hills.

Defects noticed by LI-XRD are also not yet entirely understood. Amorphisation can be explained by several processes such as the formation of amorphous tracks by thermal spikes, irradiation induced microstrains in the unit cell, or a drop-off of the crystallite size. Irradiation induced microstrains seem to be an appropriate explanation since an expansion of the unit cell was also noted for Ti3SiC2.

In order to better understand these electronic interaction-induced phenomena, an in-depth study of the diffraction patterns is in progress. Observation of cross-sections by Transmission Electron Microscopy will also be performed.

Conclusion

This first study about damages induced by heavy ions in Ti3SiC2 at room temperature allows distinction between the effects of nuclear and electronic interactions on this remarkable ceramic. Elastic collisions lead to the amorphisation of all the phases of the specimens, sputtering of the Ti3SiC2 grain boundaries, and preferential sputtering as a function of the crystallite orientation. On the other hand, electronic interactions induce the amorphisation of only Ti3SiC2, expansion of the Ti3SiC2 unit cell along the c axis, and the formation of "hills", the latter of which remains misunderstood yet.

Fig. 1 :

 1 Fig. 1: Surface of as-polished Ti3SiC2 by AFM (a) and back-scattered electrons FEG-SEM (b)

Fig. 2 :

 2 Fig. 2: Surface of Ti3SiC2 after Au (4 MeV) irradiation at a fluence of 10 15 cm -2 by FEG-SEM (a) and AFM (b)

Fig. 4 :

 4 Fig. 4: Surface of Ti3SiC2 after Xe (90 MeV) irradiation at a fluence of 10 15 cm -2 AFM; (a) 5x5 µm 2 scan and (b) 1x1 µm 2 scan of the Ti3SiC2 phase

Fig. 5 :Fig. 6 :

 56 Fig. 5: XRD patterns of Xe irradiated samples

Journal of NuclearMaterial, vol. 385, issue 2, 2009, 304-307

Acknowledgment

The authors would like to gratefully thank Lionel Thomé (CSNSM) for their great help during the irradiation experiments. This work was partly supported by the French research group MATINEX.