Aline Menin
email: aline.menin@inria.fr

Rafael Torchelsen

Luciana Nedel

The Effects of VR in Training Simulators: Exploring Perception and Knowledge Gain

Keywords: Virtual Reality, Immersion, Training Simulators, User Perception, Knowledge Gain

Although immersive virtual environments have been used for years for training and learning purposes (e.g., flight and surgery simulators), the effects of using VR devices on simulation sessions are yet to be understood. In this work, we explore the effects of different VR devices on virtual environments developed for training, focusing on perception and knowledge gain aspects. We performed two user studies to investigate the influence of these devices on users' workload, motion sickness, and performance in the domain of work safety training. The first experiment includes 61 participants and seeks to understand whether and how VR displays providing different fields of view affects the users' ability to search for risks in an office-like virtual environment (i.e. focus on user perception). Subsequently, we conducted a second experiment involving 46 subjects, where we assess whether and how interaction techniques providing different degreesof-freedom influence users' ability to learn procedural tasks (i.e. focus on knowledge gain). From our results, we learned that users' knowledge on the simulation's topic (i.e. work safety) and gaming experience play an important role in VR simulations, and that cybersickness symptoms such as disorientation are likely caused by unawareness of one's surroundings instead of VR content.

Introduction 1

The low-cost and danger-free aspects of virtual reality (VR) 2 technology enable the design of immersive simulations with 3 game aspects (i.e. gameplay, challenge, interaction, and objec-4 tive) for training people in diverse application domains. There simulator to train firefighter's skills using a Cave Automatic the cognitive system. These findings were later contradicted by Bowman et al. [START_REF] Chittaro | Assessing knowledge retention of an immersive 2 serious game vs. a traditional education method in aviation safety[END_REF], who showed that a higher level of visual fidelity provides better performance in procedure memorization tasks. They also showed that high display fidelity improves spatial knowledge and allows users to use spatial strategies on procedure memorization, leading to better target detection. More recently, Kwon [12] showed that high fidelity VR setups help users to recognize a virtual experience as the actual experience, while improving their ability to analyze, evaluate, and create questions based on the subject learned during the simulation.

Ragan et al. [13] showed that the use of auxiliary spatial information affects mental strategies and improves user performance in terms of cognitive processing and learning-based activities. Moreover, stereoscopic vision has been shown to improve memory recall when objects are consistent with the environmental context [14]. In another study, Roman et al. [15] showed that a three-monitor CAVE is more engaging with a 3D first-person shooter game than a single monitor.

Napieralski et al. [16] showed that users can locate themselves easier inside a VR environment when the realism of graphics is higher. Furthermore, Ragan et al. [17] showed that high visual realism improves strategy transfer, but worsen user performance in scanning tasks probably due to the extra information that exists on VR environments simulating real-world scenarios (e.g., trees, people, buildings), which can distract the user from their main task. They also demonstrated that training with higher FOVs lead to better object detection in a serious game using scanning tasks to train military personnel. Although, they did not find any correlation between the user performance on the VR environment and the real-world, their findings suggest that a higher FOV do not improve the real-world task performance more than training with a lower FOV. [START_REF] Mcmahan | Virtual reality: How much immersion is enough[END_REF] also showed that using wide FOV and high resolution provides a less cluttered and more comprehensible VE. McMahan et al. [10] showed that high display and interaction fidelity affect strategy and user performance in a first-person shooter game in a way that, with high interaction fidelity users took less damage and were less accurate then with low interaction fidelity. The authors showed that users' familiarity with the technology improves user performance, which could explain the high accuracy when interacting with conventional mouse and keyboard devices. In terms of user experience (UX), the authors observed that high display/interaction fidelity increases the users' sense of presence, engagement and the simulator's assessed usability. Krokos et al. [START_REF] Krokos | Virtual memory palaces: immersion aids recall[END_REF] showed that using virtual memory palaces (i.e. placing pieces of information within palace and medieval town environments, and associating them to salient features of the environment) as a spatial mnemonic to support information recall are more effective when delivered via HMDs than desktop displays. Nabioyuni and Bowman [START_REF] Nabioyuni | An evaluation of the effects of hypernatural components of interaction fidelity on locomotion performance in virtual reality[END_REF] investigated the effects of hyper-natural transfer and biomechanical techniques for navigating inside a VR environment. Their findings suggest that well-designed hyper-natural navigation techniques can be understood and adapted by users, resulting in more speed performance. However, these techniques may still be more difficult to control than real walking when performing complicated, more precise movements. Further, hyper-natural biomechanical techniques do not improve locomotion performance in VR, but instead reduce accuracy and users' comfort [START_REF] Nabioyuni | An evaluation of the effects of hypernatural components of interaction fidelity on locomotion performance in virtual reality[END_REF].

Bowman and McMahan

Kakoschke et al. [START_REF] Kakoschke | Brain training with the body in mind: Towards gamified approachavoidance training using virtual reality[END_REF] investigated the effects of Approach-Avoidance Training (AAT) delivered via three interfaces (computer, smartphone, and VR) on user experience (flow, immersion, engagement) and performance (accuracy, approach bias).

Their results showed that VR was a more effective way to deliver the training by providing higher engagement, flow, and immersion than the AAT delivered via a computer or smartphone application, while reducing errors.

Albus et al. [START_REF] Albus | Signaling in virtual reality influences learning outcome and cognitive load[END_REF] used signals in the form of textual annotations to support learning in VR simulations. They also investigated its effects on different learning outcomes and cognitive load. Results showed that annotations in VR can help users to process and recall the information, but have no effects in terms of reducing distractions. Gupta et al. [START_REF] Gupta | A cyber-human based integrated assessment approach for orthopedic surgical training[END_REF] used a VR simulator for condylar plating surgery to show that VR based environments can serve both as skills training and learning tools.

Di Mascio et al. [START_REF] Mascio | Immersive virtual environments: a comparison of mixed reality and virtual reality headsets for asd treatment[END_REF] evaluated the acceptability, usability, and engagement of two HMDs (i.e. Oculus Rift and Hololens) as tools to provide VR and AR-based treatment for people with Autism Spectrum Disorder (ASD). For both devices, results suggest that initial training is necessary, as well as long-term usage to provide users with freedom to virtually and physically move around the environment. The study showed yet that familiarity with objects in an IVE's (Immersive Virtual Environments) had a higher impact on emotional participation than photo-realism.

Hasanzadeh et al. [START_REF] Hasanzadeh | Presence, mixed reality, and risk-taking behavior: A study in safety interventions[END_REF] investigated the feasibility and usefulness of providing passive haptics in a mixed-reality (MR) environment to capture the risk-taking behavior of workers. Results showed that MR helps to raise users' sense of presence and to capture their realistic response to safety features for both research and training purposes. Additionally, the study showed that extroverts workers could place themselves more readily within this mental representation and experience higher levels of involvement, higher degrees of presence, and stronger sense of being there.

Recently, due to the constraints imposed by the COVID-19 pandemics, the use of VR for training and learning has receiving more attention and the influence of aspects such as users' visual attention and behavior are being explored. Simeone et al. [START_REF] Simeone | LIVE: the human role in learning in immersive virtual environments[END_REF] found that the presence of a virtual instructor increases the engagement and accelerate the progress of the user during a learning experience. Bozkir et al. [START_REF] Bozkir | Exploiting object-of-interest information to understand attention in VR classrooms[END_REF] focused in three different objects-of-interest for measuring attention: peer-learners, instructor, and lecture material. More specifically, they varied sitting positions of students, visualization styles of virtual avatars (realistic or cartoon-like), and hand-raising percentages of peer-learners. Results showed that such manipulations play an important role in students' attention.

These user studies illustrate the interest and benefits of using VR technologies to provide the user with high fidelity setups to support training procedures through low-cost and danger-free alternatives. A high fidelity VR setup would be built with technologies that provide the closest experience as one would have in the real-world, which means peripheral vision and natural in-58 teraction techniques, such as through mid-air gestures and real 59 walking. Nonetheless, we could see that, depending on the tar-60 get tasks, low interaction fidelity provides better user perfor-61 mance than high fidelity due to the users' familiarity with de-62 vices such as game controllers and mouse devices. The study 63 reported by McMahan et al. [10] is the closest to the one we 64 present in this paper in terms of comparing different combina-65 tions of low and high interaction/display fidelity devices, which 66 considers for instance the joint use of CAVEs and mouse de-67 vices or display-walls and pointing devices. However, the dis-68 parity in terms of fidelity between one display and interaction 69 devices are quite large.

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In this paper, we extend the understanding of the effects of 71 low/intermediate/high fidelity in user perception and knowl-72 edge retention. We considered different VR devices, interaction 73 and navigation techniques on the outcomes of training simula-74 tions. Few previous studies have considered the effects of mo-75 tion sickness, even though it is known to be a side-effect of 76 VR, or the quantity of work necessary to achieve the task using 77 high fidelity interaction tools, which are quite new to the users. 78 Therefore, we measure motion sickness and workload and eval-79 uate their impact on user engagement, sense of presence and 80 immersion.

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Design Rationale

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Perception and learning are intrinsically connected: in train-83 ing tasks, one cannot learn a new procedure without the ability 84 of perceiving one's surroundings. Thus, this study focuses on 85 investigating the effects of different VR technologies on percep-86 tion and learning aspects of VR simulations. To reduce cogni-87 tive load possibly engendered by the combination of all these 88 technologies, we separated the study in two user experiments: 89 (i) user perception (Section 5) comparing three display devices, 90 and (ii) knowledge gain (Section 6) comparing four combina-91 tions of semi-and non-natural interaction and locomotion tech-92 niques.

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First and foremost, the purpose of a training simulation is to 94 prepare the user for the real-world situation, which has naturally 95 high visual complexity. Thus, the simulators where build on the 96 basis of realistic scenarios, supporting the transfer of what has 97 been learned during the simulation to the real-world situation. 98 We used a simulator for risk perception assessment (Fig. 1a), 99 which purpose is to train workers on detecting potential risk 100 elements in a normal workplace environment [START_REF] Nedel | Using immersive virtual reality to reduce work accidents in developing countries[END_REF], and a sim-101 ulator for lightning rod replacement (Fig. 1b), which intend 102 to train apprentices on basic safety procedures for electrical in-103 stallations on public utility poles.

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The risk perception assessment simulator reinforces users' 105 perception through their ability to see, hear, or become aware 106 of something through the senses to apprehend their surround-107 ings, detecting and avoiding risk hazards. Thus, the simulator 108 train users through perceptual learning, which comprises the 109 ability to detect pieces of information (i.e. events, distinctive 110 features, and affordances) offered by the environment [START_REF] Adolph | Gibson's theory of perceptual learning. i[END_REF]. Ra Further to acquire information, the user should be able to retain the information obtained through training simulations in order to transfer it into the real-world situation. Recalling information is also known as knowledge and can be classified into different categories such as factual, conceptual, procedural and meta-cognitive [START_REF] Krathwohl | A revision of bloom's taxonomy: An overview[END_REF]. Both simulators require the user to recognize specific details or elements (i.e. factual knowledge). In this paper, we explore knowledge retention through the lighting rod replacement simulator, where we investigate the effects of different navigation and interaction techniques for grabbing and manipulation, which are fundamental in numerous training simulators (e.g., firefighting, military training, etc). In terms of interaction, we use motion tracking to map knowledge retention to the real-world by providing high fidelity experiences to the user.

In terms of navigation, we considered the use of motion tracking, but the solutions found to track the large physical spaces required for both simulations (Fig. 1) were too expensive [START_REF] Hodgson | Redirected walking to explore virtual environments: Assessing the potential for spatial interference[END_REF]. Hyper-natural navigation techniques such as Seven

League Boots [START_REF] Interrante | Seven league boots: A new metaphor for augmented locomotion through moderately large scale immersive virtual environments[END_REF] could improve speed performance, but they might be difficult to control when precision is needed [START_REF] Nabioyuni | An evaluation of the effects of hypernatural components of interaction fidelity on locomotion performance in virtual reality[END_REF].

Thus, we focus in less natural navigation techniques such as walking-in-place (WIP) and joystick navigation. Particularly, the Wii balance board has been largely used by researchers to provide low-cost WIP techniques, showing positive effects on user performance in human-scale spaces and spatial orientation, while providing high sense of presence [START_REF] Usoh | Walking > walking-in-place > flying, in virtual environments[END_REF].

In terms of grabbing and manipulating objects, motion tracking can also enable natural interaction through mid-air gestures [START_REF] Greunke | Taking immersive vr leap in training of landing signal officers[END_REF], which has shown to offer less precise control and require more physical work on the part of the user [START_REF] Bowman | An Evaluation of Techniques for Grabbing and Manipulating Remote Objects in Immersive Virtual Environments[END_REF]. Furthermore, Mine et al. [START_REF] Mine | Moving objects in space: exploiting proprioception in virtual-environment interaction[END_REF] showed that having a physical reference helps the user to be more precise on memory recall. Thus, we combine physical objects and body movement by using the 41 Razer Hydra controllers, and compare it to conventional game 42 controllers, since users are familiar with these devices.

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The first experiment focuses on perception, using a VE that 44 requires walking around while observing and perceiving haz-45 ardous features in different rooms. In this task, the display de-46 vice impacts the performance more than the interaction tech-47 nique, so we compared three different display devices. The sec-48 ond experiment focused on investigating the effects of interac-49 tion and locomotion techniques on learning. Although it uses a 50 different simulator, the perception skills are as important, since 51 the user should be aware of their surroundings to judge the best 52 way of securing the workplace. Therefore, we chose the display 53 device that has shown the best effects on the first trial, ensuring 54 that the display was not a bias, while showing positive effects 55 in terms of perception.

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Particularly, as shown by previous works (see Section 2), VR 57 devices providing highly immersive and natural experiences 58 can improve user performance. Therefore, we selected a set of 59 VR devices that provide proper levels of immersion (i.e. HMD 60 and display-wall) and naturalness (i.e. Razer Hydra and Wii 61 Balance Boards) while comparing them with traditional and 62 familiar devices (i.e. PC monitor and game controllers), en-63 abling us to investigate their effects the outcomes of the train-64 ing simulations. Furthermore, the selected devices are cheap 65 and therefore accessible to the companies interested on using 66 these types of simulators, thus easing their dissemination since 67 these are intended for use in large-scale training sessions within 68 companies [START_REF] Nedel | Using immersive virtual reality to reduce work accidents in developing countries[END_REF]. Hereafter, we first describe the shared exper-69 iment protocol, measures, and statistical analysis process, then 70 we present both user experiments and their results.

Shared Protocol Between Experiments and Measures

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Since we focus on learning, we assumed that using a within-73 subjects design could influence the results by allowing users 74 to recycle the information they learned while using a partic-75 ular display/interaction technique. Thus, we used a between-76 subjects design in both experiments, where participants were 77 randomly assigned to each experimental condition.

In both experiments, the experimental sessions were individ-

Experiment I: User Perception

This experiment aims to understand whether different VR display devices influence the user perception in a simulation designed to train workers of an electricity distribution company on detecting risks elements in their work environment. This experiment requires users to leverage their perception skills to detect potential hazards in the virtual environment. Based on previous studies' findings (see Section 2), we believe that the higher the feeling of being "there" in the VE, the better the user can perform the tasks and recall the information acquired during the simulation. Particularly, a high immersive setup would enable them to completely focus on the elements of the virtual world without being distracted by the real-world objects surrounding them. Thus, we hypothesized that (H1) display devices producing higher self-reported sense of presence improve the user performance. Moreover, the existing immersive training simulators have shown overall positive outcomes [7], which lead us to hypothesize that (H2) simulator sickness will not worsen the user performance.

Virtual Environment

This simulation was designed for assessing the ability of workers to detect potential risk elements in a normal workplace environment. The VE contains a building with a reception room, a parking lot, an office, and a kitchen. Further to the normal objects of an office environment, the scenario has 53 objects defined as potential hazards which can be simple or composite. The former refers to imminent risk items (e.g., wet floor, blocked fire extinguisher), while the latter refers to objects which hazard is triggered by an external situation (e.g., flashing lamp, alarm off). These require higher cognitive effort from the user to be detected.

Experimental Conditions

We compared three VR display devices: (A) a 23-inch LCD screen with a 120Hz refresh rate and 1920 × 1080 pixels resolution desktop display, which we used as our baseline condition, since games and current non-immersive simulations are usually ran on PC desktops; (B) a 3200 × 1800 pixels resolution display wall built up from a set of twelve 22-inch LCD screens, with a total dimension of 244 × 108 centimeters (Fig. 2b). This display intends to immerse the user on the simulation while allowing them to be aware of their surroundings; and (C) an HMD device, since it is widely used in VR and provides high self-reported sense of presence, allowing the user to focus on the simulation while avoiding distractions issued from their surroundings. We used the Oculus Rift DK2 device, which has a 960 × 1080 pixels resolution per eye and a refresh rate of 75Hz.

Participants

Sixty-one volunteers (15 female) took part in this experiment. They were students, mostly from Computer Science, and University personnel, aged between 19 and 63 years old (M=28.67, SD=10.12). They were all beginners in terms of work safety and VR, and 42% reported to be experienced on 3D video games. All the participants reported normal or correctedto-normal vision.

Task

The users were assigned with two tasks: (1) to follow the instructions (e.g. "Inside of this building has a kitchen. Go there and get a coffee.") given by a storyteller, while (2) scanning the environment for potential risks objects. The guidance enabled users to go through every room in the building. In each room, they were instructed to reach a target object (flashing in red) by moving back and forward using the game controller's joystick. Users would turn their heads to set the movement direction when using the HMD and the display wall, and use the joystick when using the desktop display. After reaching the target, the system triggers the next instruction, except in the case of two special instructions, when the user should stand in a location and scan the room for potential risks for as long as they would like. These tasks were included as a strategy to reduce the difference among the experimental conditions, demanding users to perform the same movements by either turning their heads with the HMD or using the game controller. Further, it serves also to verify whether users would simply follow what the narrator was telling them to do or they were also attentive to the scanning task. Such as in the real world, the detection of risky situations should be a background task.

Measures

We measured the users' subjective sense of presence using the SUS (Slater-Usoh-Steed) presence questionnaire [START_REF] Usoh | Using presence questionnaires in reality[END_REF], which consists of six statements rated in a 7-point Likert scale used to assess the sense of presence in each room of the VE.

Further, we asked users to respond the question "how much immersed did you feel?" in a 10-point scale, in order to assess their sense of immersion. Regarding user experience, we asked users to rate in a 5-point Likert scale the following statements: "I believe the simulation achieve the purpose of assessing the user's aptitude on detecting risk hazards in the work environment", "I could perform the simulation easily"; "I enjoyed using the simulator". Finally, we measured user performance by combining three factors: task completion time (the lower the better), traveled distance in the VE (the less the better), and the number of detected risks (the more the better).

Results

The virtual path which users were supposed to follow was determined by the audio instructions, which send them to the car at the parking lot, the coffee maker at the kitchen, the desk 42 at the office, and, finally, to meet their co-workers outside the 43 office. Although, we did not observe major differences among 44 the experimental conditions, users wearing the HMD followed 45 a path slightly more straight than the ones in Display-Wall and 46 Desktop conditions. Overall, users took between 5 and 15 minutes to complete 48 the simulation regardless the display device. We observed sig-49 nificant difference in the mean completion times for the dis-50 play wall and the remaining devices, suggesting that partici-51 pants using the display wall took longer to finish the simu-52 lation (M=12.4, p<0.001). In terms of risk selection, users 53 were able to identify about 21% of potential risk objects in 54 the VE (Fig. 3). The mean scores show that users found 55 more risk objects using the desktop display than with the re-56 maining, but no statistical difference was observed. Nonethe-57 less, when analyzing the type of risk objects users found, 58 we observed that they detected more simple than composite 59 risks using the display wall (p<0.001) and the desktop display 60 (p<0.05), while there was no significant difference under the 61 HMD condition. We observed that completion time and risk se-62 lection is positively correlated when using the desktop (p<0.02, 63 R=0.5) and HMD (p<0.03, R=0.48) display devices.

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In order to determine whether the devices inflicted cyber-65 sickness on users, we compared the SSQ scores measured be-66 fore and after the trial. We observed statistical difference be-67 tween mean scores for symptoms of nausea (p<0.01), disorien- We did not observe statistical differences in the mean SSQ 7 scores for users using the desktop display and the display wall. 8 However, the users of the HMD appear to perceive the sever-9 ity of symptoms of nausea, disorientation, and the total sever-10 ity significantly higher than the users of other devices (p<0.01) 11 (Fig. 4). We have also observed statistical differences in the 12 mean scores of the oculomotor scale between the HMD and 13 the desktop display devices (p<0.05), while no statistical differ-14 ences were observed in regard to the display wall. The highest compared to the maximum score of 97.4 points.

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In terms of immersion, mean scores suggest that participants Although the assessed sense of presence of users using the HMD was higher than the other devices, the participants' risk selection rate and completion time were similar among devices, which means that we cannot accept H1. Nonetheless, we observed that the mean number of risk selection was quite low, which is most likely a consequence of the users' overall lack of expertise on work safety. The users with the HMD device reported more severe sickness symptoms than the users of display wall and desktop display devices, which latter appear to have reduced the severity of symptoms. Nonetheless, completion time and risk selection rate did not present any difference among users of different display devices. Moreover, we did not observe correlation between SSQ scores and completion time or risk selection rate, which allows us to accept H2. Although the participants did not present high performance, their varied perception of sickness symptoms severity did not impacted the user performance among the different display devices.

Experiment II: Knowledge Gain

This experiment intends to explore the effects of two different interaction and two locomotion techniques on user experience and knowledge gain in a simulator designed for teaching apprentices the work safety procedures of a lightning rod replacement task. Overall, we believe the devices enabling movements closer to natural ones would improve user experience and, consequently, increase the knowledge gain from the simulation. Therefore, we hypothesized that (H1) the closest the movement is to the natural one, the better will be the user performance; (H2) the devices enabling arms and legs movements will not inflict severe motion sickness; and (H3) high workload inducing techniques will worsen user performance.

Virtual Environment

This simulation was designed for training apprentices on basic safety procedures for electrical installations on public utility poles. The VE consists of a town street where a lightning rod is being replaced (Fig. 1b). A storyteller guides the user through audio instructions (e.g. "The workers need to be equipped with scrap gloves, safety belt, and helmet, while carrying the service order. Please, select the objects representing this equipment. They are flashing in red. When you have finished, activate the next instruction."), while teaching them the steps needed to secure the job site for workers and pedestrians. The simulation has three phases: a learning phase, where the user learn the proper placement of safety items, which is indicated via glowing objects and arrows; and two application phases, where the user is asked to perform the safety task using the information they just learned. In the learning phase, users can only grab objects that have been mentioned by the storyteller, which should reinforce their focus on the task. In the application phases, users can grab whichever items they like and follow their own strategy to place them in the scene. We change the initial placement of objects between one phase and another to reduce the bias of already knowing where to start.

The users were asked to perform a set of five tasks: (1) to ensure that the truck is parked correctly and stuck with shims at the wheels; (2) to equip workers with scrap gloves, safety belt, helmet, and the service order; (3) to place the materials on the protection canvas; (4) to signal the workplace using signposts; and (5) to isolate the workplace, including the vehicle, using cones and ropes. The tasks should be performed according to the users' interpretation. In the assignment to arrange the cones around the workplace, the users had eight available cones to be arranged along a square illustrated by four arrows, so they could either place one cone by each arrow or distribute a number of cones they found necessary in the provided space. Later, they were requested to tie the ropes between the cones previously placed. In this case, there were seven ropes available and they could choose a number of ropes they found necessary to isolate the place. A barrier prevented users from crossing through the rope after it has been tied between two cones, which served to remind them of leaving an opening in the isolated area to allow workers to transit.

Experimental Conditions and Apparatus

In this experiment we compared two interaction and two locomotion techniques. For grabbing and manipulating objects, The equipment worn by the participants consisted of an Oculus Rift DK2 device, a headphone, and a Sixense Razer Hydra.

To ensure a certain homogeneity among the conditions, users went through the simulation on top of the Wii Balance Boards platform and used the Razer Hydra controllers, regardless of the enabled interaction/locomotion techniques (Fig. 5). The Razer Hydra device contains a base station with a low-power magnetic field that gets the controller's position and orientation. Although it does not require a line of sight to the controllers, the range area is quite small, introducing noise to the tracking when the user turns its back to the base station. Therefore, we used a band to hold the base station on the user's body (Fig. 5b). In between, we placed a smartphone to capture the orientation of the user's torso, allowing this way to properly place the virtual hands according to the user's rotational movements. Finally, to provide users with confidence and control when using the WIP technique, we added an EVA mat over the platform with EVA borders around 7.5 centimeters high so the user could sense it, being aware of the platform limits.

Measures

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Prior to the trial, we established the baseline for cybersick-69 ness and users' knowledge regarding safety procedures. We 70 also assessed users' subjective memory complaints through a 71 10-item memory questionnaire [START_REF] Zelinski | A 10-item rasch modeled memory selfefficacy scale[END_REF], which information could 72 support knowledge outcomes. To assess knowledge (before and 73 after the trial), we prepared a 7-question test on the safety pro-74 cedures addressed in the simulation. These were essay ques-75 tions, presented one by one to the user and defined as follows: 76 What do you do to secure a job site for workers and pedestri-77 ans?; What do you do after arriving at the site and parking the 78 vehicle?; What safety equipment do you wear to perform the 79 task?; What extra item(s) do you need?; Where and how do you 80 place the working tools?; What do you do first to secure the site 81 for pedestrians?; How do you isolate the job site?.

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We applied the NASA Task Load Index (TLX) [START_REF] Hart | Development of nasa-tlx (task load index): Results of empirical and theoretical research[END_REF] when the task required higher mental effort. For users in condition B, we found a -69% correlation between overall workload scores and knowledge gain (p=0.019), suggesting that when users needed higher loads of work their ability to retain knowledge is reduced.

Figure 8 shows the response accuracy for the knowledge test taken by users before, after, and three weeks after the trial. We observed a statistical difference in the mean accuracy scores for those three measurement times. We did not found statistical difference between scores of tests taken after the trial, which similarity between means suggest that users were capable of recalling the information learned during the simulation. The mean simulation times across conditions varied from 20 minutes in conditions A and B to 32.7 minutes in condition D, which difference of means showed statistical significance (p<0.001). Overall, the completion time reduced between the simulation's phases, which was expected due to users familiarizing with the navigation/interaction techniques, the VE, and the tasks. Users took around 3.6 minutes less time to finish the first application phase than the learning, and the completion time of the second application phase was yet reduced in around 1.1 minutes (no statistical difference was observed among conditions).

Overall, users could complete the simulation's tasks with 95% of accuracy. Particularly, we noticed that 88% of users could correctly recall the instructions given in the learning phase regarding the objects' placement. In terms of isolating the job site, i.e. by forming a fence with cones and a rope around the area containing the canvas with the tools and the truck, while leaving an opening on it to allow workers to transit, we observed that the 78% of users placed the cones correctly, but only 36% of them left an opening in the fence. We observed statistical difference among conditions in users' accuracy to complete certain tasks. In terms of tools placement on the canvas, participants in condition B performed better than users in condition D (p<0.038). Participants in condition C and D were more accurate in terms of arranging the signposts than users in condition A (p<0.05) and B (p<0.05), respectively. Users in condition C were also more accurate in terms of isolating the job site than participants in condition D (p<0.05). The GEQ mean score was 2.8 across experimental condi-1 tions, which differences were not statistical significant. We 2 have found a 45% correlation between mental demand and en-3 gagement (p<0.01), suggesting that navigation/interaction tech-4 niques demanding higher mental effort also tend to be more en-5 gaging. Overall, users reported to have enjoyed the experience 6 and found it easy to learn and apply the safety procedures. As in 7 the previous experiment, we allow users to walk towards their 8 looking direction to reduce the severity of sickness symptoms.

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In this matter, two persons have said that they would like to 10 walk backwards. Selecting objects in the scene was reported 11 to be difficult by some users, particularly regarding the cones.

12 Participants suggested the addition of a checklist to allow the 13 user to check tasks as done as they progress in the simulation. gain. In this condition, we also observed that users appear to 33 be more engaging when mental demand increase. Thus, we be-34 lieve that the freedom provided by more natural movements in

Discussion

In this work, we performed two empirical studies to investigate the impacts of three different VR displays and four interaction techniques on several VR aspects, such as motion sickness, workload, engagement, sense of presence, user performance, and learning outcomes on training simulations.

Main Findings

Our results showed that participants could perform certain tasks with higher accuracy, such as isolating the job site using the conditions where navigation was supported via the WIP technique, and placing tools over a canvas in conditions where they would grab and manipulate objects using the ray-casting technique. The latter provided a physical reference through a Razer Hydra's controller, which higher accuracy on performing the task suggests that users could better recall the information given during the learning phase, in conformity with previous findings [START_REF] Mine | Moving objects in space: exploiting proprioception in virtual-environment interaction[END_REF]. Although we did not observe statistical difference in knowledge gain among experimental conditions and replication of these experiments may be necessary to evaluate our hypotheses, our results suggest that semi-natural navigation/interaction techniques could improve user performance.

Previous studies suggest that semi-natural interfaces, i.e. interfaces that use real walking and body interaction, could be inefficient since they require the user to operate it differently from what they are used to, which adds workload to the process [START_REF] Nabiyouni | Comparing the performance of natural, semi-natural, and non-natural locomotion techniques in virtual reality[END_REF]. However, we observed that users reported higher frustration scores when using the head orientation technique and the joystick to navigate, while they were more engaged with the simulation when moving their arms and legs through the ray-casting and WIP techniques, which were considered high mentally demanding by users. Users also perceived a higher quality on their own performance when using the WIP technique compared to the joystick. Furthermore, we observed a positive correlation between mental demand and knowledge gain in the condition using WIP and ray-casting, which also had the lowest simulator sickness effects. Our results suggest that providing close to natural interaction interfaces can be enjoyable and aid users to transfer the potential mental burden generated from adapting their real-walking expertise to better performance.

Lessons Learned

Disorientation is probably a consequence of the user unawareness of their surroundings. As we have hypothesized, techniques providing semi-natural interaction did not increase the severity of sickness symptoms, because users could "move" together with the VE. Nonetheless, we observed that disorientation would be a constant across the experimental conditions in experiment II, which coincides with the disorientation reported by users of the HMD in experiment I, suggesting that disorientation is only the fact that the user is not aware of their surroundings, which seems not to be affected by the different interaction/locomotion techniques. Thus, our findings suggest that better VEs would allow the user to be aware of their surroundings, such as through the use of physical interaction devices and more importantly through haptic interaction, allowing the user to physically feel what they're virtually experiencing. Such physical approaches have been previously explored to increase awareness in VEs and improve user experience (see [START_REF] De | Designing a vibrotactile head-mounted display for spatial awareness in 3d spaces[END_REF][START_REF] Valkov | Vibro-tactile feedback for real-world awareness in immersive virtual environments[END_REF]).

The use of interaction techniques as natural as possible continue to be the best choice. Simulations designed for VR technology aim to provide a real-world-like experience, where high sense of presence, immersion and realism are essential ingredients. Thus, it is important to allow the use of our wellknown movements to navigate and interact with the VE. While much research is still needed to improve this technology, there are several studies [10, [START_REF] Nabioyuni | An evaluation of the effects of hypernatural components of interaction fidelity on locomotion performance in virtual reality[END_REF][START_REF] Interrante | Seven league boots: A new metaphor for augmented locomotion through moderately large scale immersive virtual environments[END_REF], including this one, showing the advantages of natural or semi-natural interaction techniques on user performance and experience. Thus, users could leverage of training simulators employing well-drafted (semi-)natural interaction techniques, which provide the sense of naturally navigating and grabbing objects in the VE, while providing accuracy for effectively dealing with small objects or performing precise tasks, such as painting.

Perception is more likely to be affected by the user expertise on the simulator subject than by the technology employed. We have observed in experiment I (Section 5) that neither the VR display nor the sickness caused by it affected user perception. However, we noticed that users had a rather poor performance on what refers to the risk scanning task. Users were able to select 20% of risks elements (consistently throughout the devices), which is a low selection rate, likely explained by users' self-reported very low knowledge on work safety procedures. Previous works showed that the familiarity of users with the objects in the environment improves the user experience [START_REF] Mascio | Immersive virtual environments: a comparison of mixed reality and virtual reality headsets for asd treatment[END_REF]10]. Similarly, considering that our simulator have been developed to assess the ability of users to detect hazardous objects in the workplace, we suppose that the knowledge on the subject could have strongly impacted user performance, since one should know what is an hazardous element in the real workplace to be able to identify it in the simulation. Therefore, further studies using the "risk perception assessment" or similar simulations to investigate user perception should recruit target users of the simulation or to provide a training on work safety to the participants before undertaking the simulation.

Gaming experience plays an important role on VR simulation. We have found a positive correlation between disorientation and knowledge gain in condition A of experiment II (Section 6), which oddly suggests that users improved their scores despite the severity of disorientation's symptoms. However, we noticed that the users in this group reported to have high experience with 3D video games. Thus, we could suppose that users' familiarity with games and the constantly experienced mild cybersickness due 3D graphics allowed them to keep focused on the simulation despite the disorientation experienced in the VE. Further, while studying the impact of narrative in immersive VR games, Weech et al. [START_REF] Weech | Narrative and gaming experience interact to affect presence and cybersickness in virtual reality[END_REF] also observed differences cybersickness symptoms reported by gamers and non-gamers. While non-gamers reported higher cybersickness when receiving minimal narrative, regular gamers would present similar levels of cybersickness regardless of the narrative context.

Although our case study focuses on work safety training, our results and lessons learned can assist the choice of VR devices to provide a comfortable and efficient virtual training environ-58 ment in other application domains and, particularly, for simula-59 tions requiring the users to leverage their perception skills. For 60 instance, our observations regarding the effects of users' profile 61 on perception and cybersickness could help designers to con-62 ceive suitable guidance for simulators based on users' profile 63 (e.g., whether they need more pre-training on the simulator's 64 subject or the usage of devices, etc). Likewise, the usage of 65 high fidelity devices to provide a real-world-like experience is 66 independent of the application domain and can be generalized 67 to any type of training simulator. Each user experiment tested multiple independent variables 70 by 107 volunteers in total. Nevertheless, we applied between-71 subjects designs in both experiments due to the learning nature 72 of our dependent variables, reducing the groups to 20 and 12 73 persons, respectively. Thus, these experiments should be repli-74 cated using larger groups, and preferably the simulator's target 75 users, to strengthen our findings. The lack of expertise on work 76 safety may have introduced bias to the results. As suggested by 77 Nazir et al. [START_REF] Nazir | Can immersive virtual environments make the difference in training industrial operators[END_REF], immersive simulators are not effective per se 78 and need guidance of a professional in the field when the task 79 is unfamiliar. We have used VR technology that was available 80 to us at the time of this study. Hence, a new set of experi-81 ments should be performed using novel technology to under-82 stand whether these results are technology independent.

Conclusion 84

We conducted two empirical studies to investigate the effects 85 of VR interface on perceptual learning and knowledge reten-86 tion in simulations designed for worker safety training. We 87 measured simulator sickness, self-reported workload, engage-88 ment, presence and performance. Our objective was to better 89 understand the impacts of different display devices and interac-90 tion techniques on user performance and simulation outcomes. 91 Our results strongly suggest that a setup employing interaction 92 and navigation techniques closest to natural movements can im-93 prove user performance. In our experiments, we observed this 94 improvement through accurately placing objects in the VE, giv-95 ing users the feeling of performing better, and having low ef-96 fects on simulator sickness. However, users still had a phys-97 ical reference for moving the hands in the VE and select ob-98 jects, which is supported by previous studies as a mechanism to 99 improve memory recall. Different VR display devices did not 100 influence the user performance in our risk scanning task, but 101 rather their experience on work safety.

5

  are numerous successful examples on the literature on how im-6 mersive VR can improve user performance and game effective-7 ness. Chitarro et al. [1] proposed an immersive simulator for 8 educating passengers on aviation safety through experiencing 9 a serious aircraft emergency situation using a Head-Mounted 10 Display (HMD). In fire service, Backlund et al. [2] present a 11

Fig. 1 :

 1 Fig. 1: Virtual environments used in the Experiment I -risk perception assessment (a) and Experiment II -lighting rod replacement(b).

  71

1

  ual, accompanied by an experimenter who guided the partic-2 ipant through five steps of a standard protocol consisting of: 3 (1) a Term and Conditions Agreement, where the user is in-4 formed about the risks and benefits of participating in the ex-5 periment, and through which they consent their participation al-6 lowing us to anonymously use their data for research purposes;7(2) a socio-demographic questionnaire, where we gather stan-8 dard profiling information (e.g., age, sex, profession), data re-9 garding their experience with the technologies used (e.g., expe-10 rience with VR, 3D games) and the subject being addressed in 11 each simulation (e.g., work safety); (3) a learning phase, where 12 users were given time to get familiar with the VR setup; (4) 13 a trial phase, where the user carry out the required tasks; and 14 (5) a post-test phase, where we apply a questionnaire gathering 15 users' self-reported engagement, cybersickness, and workload, 16 as well as information specific to each experiment. 17 Particularly, to measure the impact of the VR devices on cy-18 bersickness (i.e. visually induced motion sickness) we admin-19 istered the Simulator Sickness Questionnaire (SSQ) [37]. The 20 questionnaire allows the user to assess the severity with which 21 they experienced sixteen sickness symptoms in a 4-point scale 22 (i.e. from low to high), which allows to determine the user's 23 level of sickness regarding four scales: nausea (N), oculomo-24 tor (O), disorientation (D), and total severity (TS). The highest 25 scores possible are 124 for nausea, 90.9 for oculomotor, 97.4 for 26 disorientation, and 108.6 for total severity. As common prac-27 tice [38], we administrated the SSQ before and after the trial in 28 both experiments to obtain baseline and completion measure-29 ments. This way, we can ensure that the results are not biased 30 by the condition of the participant before the trial, who could be stressed, anxious, or nauseous for unrelated reasons.

32

  Regarding statistical analysis, we set the significance level 33 to p = 0.05 to analyze the empirical data. We submitted the 34 data to a mixed design, where the between-subjects variables 35 are the experimental conditions and the user profile, while the 36 within-subjects variables are the simulator sickness scores (ob-37 tained before and after the trial) and other measures specific 38 to each experiment, such as the sense of presence scores (ob-39 tained per room in the VE), and the self-reported workload 40 scores (obtained before and after the trial). We performed a 41 Shapiro-Wilk Normality test of the null hypothesis that the data 42 come from a normal distribution, and a Fligner-Killeen test of 43 the null hypothesis that the variances in each group are the 44 same. If the data passes both tests (p <0.5) we performed

Fig. 2 :

 2 Fig. 2: Experimental conditions for the first experiment (user perception).

47

 47

Fig. 3 :

 3 Fig. 3: Mean selection rates per type of risk in each experimental condition of experiment I.

1

  Fig. 4: Mean scores per SSQ scale before and after the trial in each experimental condition of Experiment I. A, B and C are the experimental conditions, see section 5.2

19 using

 19 the HMD device (M=7.96, SD=1.68) perceived them-20 selves more immersed in the simulation than the users of the 21 display wall (M=5.8, SD=2.63, p<0.001) and the desktop dis-22 play (M=5.45, SD=1.77, p<0.01). Regarding the users' sense 23 of presence, we observed statistical significance in the SUS 24 scores for the HMD and the desktop display (p<0.05), indicat-25 ing that users could feel more present when using the HMD 26 device. No statistical difference was observed in the scores for 27 the display wall and the remaining devices.

28

  Overall, users reported to be satisfied with the experience 29 and, particularly the users who experienced it through the HMD 30 and display wall, reported to have felt "really" there. A few 31 participants mentioned that having to only walk towards what 32 they are seeing did not felt natural. However, we believe that, 33 differently from the real world, looking towards one direction 34 and walking towards another can increase disorientation. Users 35 have also appreciated the short duration of the experiment and 36 agreed that the simulation meets its goals.

37

 37

  we use (1) a ray-casting technique based on the Razer Hydra controllers used as pointing devices controlled by the user arms' movements. When the object is within view, the user holds the controller's button to grab and drag it, and drop it by releasing the button; and (2) a head orientation technique, which uses the user's head orientation to point out to objects and one of the Hydra's controllers to manipulate them using a button. In terms of locomotion techniques, we use (1) the joystick of a Hydra's controller for moving forward, backward, left, and right in the environment; and (2) a WIP technique supported by a set of four Wii balance boards, which gives the user a 0.80 × 1.2 meters platform to "walk". The experimental conditions for this experiment are then resulting from the combination of these techniques: (A) ray-casting and WIP, (B) ray-casting and joystick, (C) head orientation and WIP, and (D) head orientation and joystick.

Fig. 5 :

 5 Fig. 5: Technical apparatus of experiment II: (a) WIP platform; (b) band to hold the Razer Hydra base station on the user's body; (c) a smartphone to get the user's orientation; (d) Oculus Rift device; (e) headphones.

  67

  question-83 naire to assess workload for using the different navigation and 84 interaction techniques. The questionnaire provides an overall 85 workload score based on a weighted average of ratings on six 86 workload factors (scales): Mental Demand (MD), Physical De-87 mand (PD), Temporal Demand (TD), Own Performance (OP), 88 Effort (EF), and Frustration (FR). Additionally, we assess user 89 engagement's factors such as immersion, presence, flow, and 90 physiological absorption through the Game Engagement Ques-1 tionnaire (GEQ) [10]. The GEQ consists of 15 statements de-2 scribing different feelings, which the user must rate in a 5-point 3 Likert scale to indicate the intensity with which they experi-4 enced that feeling during the simulation. Finally, we measure 5 user performance by combining three information: task com-6 pletion time, accuracy of objects' placement according to the 7 instructions given in the learning phase, and knowledge gain.

  Fig. 6 presents the mean scores per SSQ scale and experi-

Fig. 6 :

 6 Fig. 6: Mean scores per SSQ scale before and after the trial in each experimental condition of Experiment II. A, B, C and D are the experimental conditions, see section 6.2

Fig. 7 :

 7 Fig. 7: Reported workload scores for each scale: mental demand (MD), physical demand (PD), temporal demand (TD), own performance (OP), effort (EF) and frustration (FR) for the experimental conditions on Experiment II.

Fig. 8 :

 8 Fig. 8: Response accuracy for the knowledge test taken by users before, after, and three weeks after the trial phase of experiment II.

15

  Due to the lack of statistical significance regarding knowl-16 edge gain, simulation time, and task accuracy across condi-17 tions, we cannot accept not refuse H1. Every experimental 18 condition increased the severity of sickness symptoms in every 19 SSQ scale, except oculomotor. Condition A requires the user 20 to move their arms around to select objects and employs the 21 WIP technique for navigating in the VE. Although the mean 22 SSQ scores in condition A are the lowest in every scale, we 23 could not find statistical significance to support the difference 24 of means among conditions. In terms of knowledge gain, a 25 negative correlation between overall workload and knowledge 26 gain suggests that high demanding navigation/interaction tech-27 niques could distract users from the task, requiring them to 28 place higher attention on how to use the techniques, decreasing 29 knowledge gain. In condition A, we observed a positive corre-30 lation between mental demand and knowledge gain, suggesting 31 that mentally demanding techniques do not affect knowledge

32

 32

  35condition A allowed users to keep focus on the task, increas-36 ing mental demand and learning. These experiments should be 37 repeated and the sample enlarged to evaluate H2 and H3. How-38 ever, we believe these results favor their acceptance.

39

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103

  This study was financed in part by the Coordination for the 104 Improvement of Higher Education Personnel, Brazil (CAPES, 105 Finance Code 001). We also acknowledge the support from Na-106 tional Council for Scientific and Technological, Brazil (CNPq), 107 the participants of our experiments for allowing us to borrow 108 their time and knowledge for this study, and the comments of 109 the reviewers that helped us to improve the quality of this paper. 110 10.1016/j.procs.2015.12.233; 2015 International Conference Vir-13 tual and Augmented Reality in Education. 14 [4] Cecil, J, Gupta, A, Pirela-Cruz, M. An advanced simulator for orthope-15 dic surgical training. International journal of computer assisted radiology 16 and surgery 2018;13(2):305-319. s10055-008-0081-2. doi:10.1007/s10055-008-0081-2. 20 [6] Jiang, M, Lan, W, Chang, J, Dodwell, M, Jekins, J, Yang, HJ, et al. A 21 game prototype for understanding the safety issues of a lifeboat launch. 22 Virtual Real 2018;22(2):137-148. URL: https://doi.org/10.1007/ 23 s10055-018-0334-7. doi:10.1007/s10055-018-0334-7.

24

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[ 7 ]

 7 Menin, A, Torchelsen, R, Nedel, L. An analysis of vr technology used in 25 immersive simulations with a serious game perspective. IEEE Computer 26 Graphics and Applications 2018;38(2):57-73. doi:10.1109/MCG.2018.

  Laird, D. Approaches to training and development addison-wesley. Read-29 ing, Mass 1985;.

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 9 Mania, K, Chalmers, A. The effects of levels of immersion on 31 memory and presence in virtual environments: a reality centered ap-32 proach. Cyberpsychology & behavior : the impact of the Internet, multi-33 media and virtual reality on behavior and society 2001;4(2):247-264.

34 URL: 35 1089/109493101300117938. 36 [ 40 [ 11 ]

 3435364011 https://doi.org/10.1089/109493101300117938. doi:10. 10] McMahan, RP, Bowman, DA, Zielinski, DJ, Brady, RB. Evaluating 37 display fidelity and interaction fidelity in a virtual reality game. IEEE 38 Transactions on Visualization and Computer Graphics 2012;18(4):626-39 633. doi:10.1109/TVCG.2012.43. Bowman, DA, Sowndararajan, A, Ragan, ED, Kopper, R. Higher 41 levels of immersion improve procedure memorization performance. In:

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Procedia Computer Science 2015;75:161 -168. doi:https://doi.org/

SSQ score was 45.4 points (SD=53.7) regarding disorientation 16 symptoms while using the HMD device, which is a high score


Prasad Subramanian

Madhusudan Ingale

R Ramesh

Milan Maksimovic

K Sasikumar Raja
email: sasikumar.raja@iiap.res.in

Typeset using

Keywords: occultations -scattering -solar wind -Sun, corona -Sun, radio radiationturbulence

Various remote sensing observations have been used so far to probe the turbulent properties of the solar wind. Using the recently reported density modulation indices that are derived using angular broadening observations of Crab Nebula during 1952 -2013, we measured the solar wind proton heating using the kinetic Alfvén wave dispersion equation. The estimated heating rates vary from ≈ 1.58 × 10 -14 to 1.01 × 10 -8 erg cm -3 s -1 in the heliocentric distance range 5 -45 R ⊙ . Further, we found that heating rates vary with the solar cycle in correlation with density modulation indices. The models derived using in-situ measurements (for example, electron/proton density, temperature, and magnetic field) that the recently launched Parker Solar Probe observes (planned closest perihelia 9.86 R ⊙ from the center of the Sun) are useful in the estimation of the turbulent heating rate precisely. Further, we compared our heating rate estimates with the one derived using previously reported

INTRODUCTION

Even after decades of intense research, we do not know the precise solar wind heating mechanism and its acceleration. The in-situ observations confirm that solar wind undergoes extended non-adiabatic heating (Freeman 1988;Gazis et al. 1994;Matthaeus et al. 1999;Richardson & Smith 2003). More details on turbulent heating and solar wind acceleration are given by Cranmer et al. (2007); Chandran & Hollweg (2009b); Verdini et al. (2010); Cranmer et al. (2013); Woolsey & Cranmer (2014); Zank et al. (2018). Interaction between counter-propagating Alfvén waves causes the wave energy to cascade into small scales. When the scale sizes which are perpendicular to the background magnetic field direction (λ ⊥ ) are comparable to the proton gyroradius (ρ p ), the wave energy begin to dissipate and thereby heats the plasma. It is known that when λ ⊥ ≫ l i = v A /Ω p , (where l i is the proton inertial length, v A is the Alfvén speed and Ω p is the proton cyclotron frequency) the Alfvén waves are non-compressive. But there is a considerable evidence that when the scale sizes are in the range ρ p λ ⊥ l i , they are compressive (e.g., Harmon 1989;Hollweg 1999;Chandran et al. 2009a). Also, the waves become dispersive and damp for the scales λ ⊥ ρ p .

In order to estimate the heating rates in the solar wind, we used the recently reported density modulation indices (ǫ N = δ N /N, where δ N is the rms density fluctuations and 'N' is the ambient background density) derived using the Crab Nebula occultation observations carried out during 1952 -2013 (Machin & Smith 1952;Slee 1959;Hewish & Wyndham 1963;Erickson 1964;Blesing & Dennison 1972;Dennison & Blesing 1972;Sastry & Subramanian 1974;Armstrong et al. 1990;Anantharamaiah et al. 1994;Subramanian 2000;Ramesh et al. 2001;Sasikumar Raja et al. 2016, 2017, 2019a). In this technique, when a radio point source (in this study Crab Nebula), observed through the foreground solar wind (in June of every year), we can have the following obser-vations: (i) the radio source's angular broadening increases due to the turbulent medium's scattering, (ii) since we observe the radio sources whose flux density is constant over a long time, the peak flux density decreases as the source size increases; but the integrated flux density remains constant, (iii) the radio sources broaden anisotropically for the heliocentric distance below 10 R ⊙ , and thus we can measure the parameter anisotropy (i.e., the ratio between the major to the minor axis of radio source) (Blesing & Dennison 1972;Dennison & Blesing 1972;Sasikumar Raja et al. 2017), and (iv) position angle of the major axis of the source (measured from the north through the east). Having such observations, Sasikumar Raja et al. ( 2016) have derived the density modulation indices in the heliocentric distance 5 -45 R ⊙ . In this article, we use those density modulation indices to measure the proton heating rate (ǫ k i ) by making use of kinetic Alfvén wave dispersion equations (see §3.4) and compared the results with the recent reports that are measured using angular broadening (Sasikumar Raja et al. 2017) and interplanetary scintillation observations (Bisoi et al. 2014;Ingale 2015b). We also compare our results with the recently reported heating rates derived using the in-situ observations of Adhikari et al. (2020) and Bandyopadhyay et al. (2020). Further, we report the way heating rates vary with the heliocentric distance and the way it vary with the solar cycle.

OBSERVATIONS

The angular broadening of the Crab Nebula is first observed by Machin & Smith (1952). Since then, many authors have reported similar observations as previously mentioned (see §1). In this article, we present results derived using data obtained by the Gauribidanur radioheliograph (GRAPH) during 2011 -2013 (Ramesh et al. 1998;Ramesh 2011Ramesh , 2014) ) and other historical observations carried out during 1952-1963(Machin & Smith 1952;Hewish 1957Hewish , 1958;;Hewish & Wyndham 1963;Sasikumar Raja et al. 2016). For instance, Figure 1 to the solar maximum. We make a note that the latter observations are carried out over interferometer baselines in the range 60 -1000 meters and the frequency range 26-158 MHz. Therefore, Sasikumar Raja et al. ( 2016) have scaled these structure functions to the largest baseline of GRAPH (1600 meters; before the extension) and its routinely observed frequency 80 MHz using the gen-eral structure-function (see §3). For the sake of completeness, we summarize a method using which Sasikumar Raja et al. ( 2016) have derived the density modulation indices (see Figure 2) and the way we have measured proton heating rate in the following sections.

RESULTS AND DISCUSSIONS

In the solar wind, turbulent density inhomogeneities play a vital role in scattering of the radio waves (Coles & Harmon 1989;Yamauchi et al. 1998;Bisoi et al. 2014;Mugundhan et al. 2017;Krupar et al. 2018;Sasikumar Raja et al. 2019b;Krupar et al. 2020). Such inhomogeneities are represented by a spatial power spectrum. It comprises a power-law together with an exponential turnover at the inner scale. In the case of isotropic medium, the turbulent spatial power spectrum (P δN ) is defined as (Bastian 1994;Ingale et al. 2015a),

P δN (k, R) = C 2 N (R)k -α × exp -(kl i (R)/2π) 2 , ( 1
)
where k is the wavenumber, l i is the inner/ dissipation scale, and C 2 N is the amplitude of density turbulence. It is worth mentioning that the injected large-scale energy in the solar wind breaks up into smaller scales until it is dissipated by heating the protons via gyro-resonant interactions. Also, we make a note that the scales at which the energy is injected are called 'outer scales', and the scales at which the dissipation happens are called 'inner scales' [START_REF] Kulsrud | Plasma physics for astrophysics[END_REF]. Using remote sensing observations, it is found that, at large scales, the density spectrum follows the Kolmogorov scaling law with α = 11/3 (Coles & Harmon 1989;Spangler 2002). However, at small scales, the spectrum flattens to α = 3 (Coles & Harmon 1989). In this article, since we are interested in the density fluctuations and proton heating rate near the dissipation scales, we have used α = 3. We make a note here that C 2 N are measured for both proton inertial scale model (Coles & Harmon 1989;Leamon et al. 1999[START_REF] Kulsrud | Plasma physics for astrophysics[END_REF]Smith et al. 2001;Chen et al. 2014a;Bruno & Trenchi 2014;Sasikumar Raja et al. 2019a) and proton gyroradius model (Bale et al. 2005;Sahraoui et al. 2013;Bisoi et al. 2014;Chen et al. 2014b;Sasikumar Raja et al. 2019a). Note that Sasikumar Raja et al. ( 2016) measured the C 2 N for two cases of proton temperatures T i = 10 5 K and T i = 10 6 K.

Measurement of phase structure function

A plane wave from a distant radio point source observed through the solar wind experiences loss of spatial and temporal coherence due to the refraction and scattering caused by the density inhomogeneities. The spatial coherence of the plane wave observed through the scattering medium (i.e., solar wind) is described by the mutual coherence function (Γ(s)), which is in turn related to the phase structure function (D φ (s)). We make a note that D φ (s) provides the information to the extent to which ideal point source is broadened and it contains information about the spectrum of density turbulence. In general, the phase structure function is defined as (Coles & Harmon 1989;Bastian 1994;Ingale et al. 2015a),

D φ (s) = [φ(r) -φ(r + s)] 2 , ( 2
)
where, indicates the time average, 's' is the baseline of an interferometer, and φ(r) and φ(r + s)

are the geometric phase delays in the line-of-sight direction through a turbulent medium at positions r and r + s.

Using the Crab Nebula occultation observations we measure (Γ(s)) using,

Γ(s) = V (s) V (0) , (3)
where, V(s) is the peak flux density of the Crab Nebula observed through the scattering medium over a baseline 's', and V(0) is the flux density over a "zero-length" baseline. The quantity V(0) is measured when the Crab Nebula is far from the solar disk and is unresolved; V (0) ≈ 2015 Jy at 80

MHz (Braude et al. 1970;[START_REF] Mclean | Solar radiophysics: Studies of emission from the sun at metre wavelengths Mugundhan[END_REF]Sasikumar Raja et al. 2017).

By knowing the Γ(s), we measured the density structure function (D φ (s)) using (Prokhorov et al. 1975;[START_REF] Ishimaru | Wave propagation and scattering in random media[END_REF]Coles & Harmon 1989;Armstrong et al. 1990),

D φ (s) = -2lnΓ(s) = -2ln [V (s)/V (0)] . (4)
3.2. The amplitude of density turbulence spectrum (C 2 N )

By knowing the structure functions, we measured the amplitude of the turbulence (C 2 N ) using the General Structure Function (GSF) (Ingale et al. 2015a;Sasikumar Raja et al. 2016, 2017). The GSF is defined as follows,

D φ (s) = 8π 2 r 2 e λ 2 ∆L ρ 2 α-2 (α -2) Γ 1 - α -2 2 C 2 N (R)l α-2 i (R) (1 -f 2 p (R)/f 2 ) × 1 F 1 - α -2 2 , 1, - s l i (R) 2 -1 rad 2 , ( 5
)
where 1 F 1 is the confluent hyper-geometric function, r e is the classical electron radius, λ is the observing wavelength, R is the heliocentric distance (in units of R ⊙ ), ∆L is the thickness of the scattering medium (≈ (π/2)R 0 , where R 0 is the impact parameter related to the projected heliocentric distance of the Crab Nebula), f p and f are the plasma and observing frequencies, respectively and the quantity l i is the inner scale.

In order to evaluate the inner scales we used the following two prescriptions that are widely used in the literature. The first prescription envisages proton cyclotron damping by Alfvén waves. The inner scales measured using this mechanism are called proton inertial lengths (Coles & Harmon 1989;Leamon et al. 1999[START_REF] Kulsrud | Plasma physics for astrophysics[END_REF]Smith et al. 2001;Chen et al. 2014a;Bruno & Trenchi 2014;Sasikumar Raja et al. 2019a) which can be written as,

l i (R) = v A (R)/Ω p (R) = 2π/k i (R) = 228/ N e (R) km, (6)
where N e is the electron density in cm -3 , k i is the wavenumber, v A is the Alfvén speed and Ω p is the proton gyrofrequency.

The electron density (N e ) is estimated using the Leblanc density model (Leblanc et al. 1998):

N e (R) = 7.2 R -2 + 1.95 × 10 -3 R -4 + 8.1 × 10 -7 R -6 cm -3 . (7)
where 'R' is the heliocentric distance in units of astronomical units (AU, 1 AU = 215R ⊙ ).

In the second prescription, the inner scales are measured assuming proton gyroradius model in which dissipation is expected to happen at scales comparable to the proton gyroradius,

ρ i = V p /Ω p ,
where V p is the proton speed and Ω p is the proton gyrofrequency (Goldstein et al. 2015). The proton gyroradius scales are measured using (Bale et al. 2005;Sahraoui et al. 2013;Bisoi et al. 2014;Chen et al. 2014b;Sasikumar Raja et al. 2019a):

ρ i (R) = 1.02 × 10 2 µ 1/2 T 1/2 i B(R) -1 cm, (8)
where µ(≡ m i /m p ) is the ion mass (in units of the proton mass), T i is the proton temperature (in eV) derived using following relations (Venzmer & Bothmer 2018),

T med (SSN, R) = (197 × SSN + 57300) × R -1.10 K (9) T avg (SSN, R) = 1.654 × T med (SSN, R), (10)
where, T med and T avg are the median and average proton temperatures in K, and SSN is the sunspot number. We make a note that in this article, we have used the revised sunspot number1 (Clette et al.

2016).

Interplanetary magnetic field (B in Gauss) is measured using the Parker spiral magnetic field model in the ecliptic plane (Williams 1995),

B(R) = 3.4 × 10 -5 R -2 (1 + R 2 ) 1/2 Gauss. ( 11
)

Estimating the density modulation index (ǫ

Ne = δN k i /N e )
The density fluctuations δN k i at the inner scale and spatial power spectrum (Equation 1) are related as follows (Chandran et al. 2009a)

δN 2 k i (R) ∼ 4πk 3 i P δN (R, k i ) = 4πC 2 N (R)k 3-α i e -1 , (12)
where k i ≡ 2π/l i .

By knowing the δN k i and the background electron density (N e , § 3.1), the density modulation index (ǫ Ne ) can be measured using,

ǫ Ne (R) ≡ δN k i (R) N e (R) . ( 13
)
For the sake of completeness, the measured density modulation indices and its variation with heliocentric distance is shown in Figure 2 Further, assuming the kinetic Alfvén wave dispersion equation we derived the heating rate.

Solar wind heating rate

In this paper, we used the density modulation indices (ǫ Ne ) derived using the above method (see §3.3) to measure the heating rates. Following Chandran et al. (2009a); Sasikumar Raja et al. ( 2017), we assume that density fluctuations at small scales are manifestations of low frequency, oblique (k ⊥ ≫ k ), Alfvén wave turbulence and are often referred to kinetic Alfvén waves. Here, the quantities k ⊥ and k are the components of the wave vector k in perpendicular and parallel direction to the background large-scale magnetic field, respectively.

As previously discussed we envisage a situation where the "balanced" counter propagating Alfvén waves (i.e. with zero helicity) cascade and resonantly damps on the protons at the inner scale and thereby heats the solar wind. Because of the passive mixing of the Alfvén waves with other modes at the inner scale our proton heating rate measurements provide an upper limit. The proton heating rate (i.e. the turbulent energy cascade rate) at inner scales is (Hollweg 1999;Chandran et al. 2009a;Ingale 2015b),

ǫ k i (R) = c 0 ρ p k i (R)δv 3 k i (R) erg cm -3 s -1 , (14)
where, ρ p = m p N e (R) g cm -3 with m p is the proton mass [in grams], k i = 2π/l i and δv k i are the wavenumber and magnitude of turbulent velocity fluctuations at inner scales, respectively. The dimensional less quantity c 0 is assumed to be 0.25 (Howes et al. 2008;Chandran et al. 2009a;Sasikumar Raja et al. 2017).

By knowing the ǫ Ne , we calculated δv k i using the kinetic Alfvén wave dispersion relation (Howes et al. 2008;Chandran et al. 2009a;Ingale 2015b;Sasikumar Raja et al. 2017)

δv k i (R) = 1 + γ i k 2 i (R)ρ 2 i (R) k i (R)l i (R) ǫ Ne (R, k i )v A (R) . (15)
where, the adiabatic index γ i is taken to be 1 (Chandran et al. 2009a;Sasikumar Raja et al. 2017).

The Alfvén speed (v A ) in the solar wind is measured using,

v A (R) = 2.18 × 10 11 µ -1/2 N -1/2 e (R)B(R) cm s -1 , (16)
The magnetic field strength (B) is estimated using the Parker spiral magnetic field in the ecliptic plane using (Williams 1995),

B(R) = 3.4 × 10 -5 R -2 (1 + R 2 ) 1/2 Gauss, (17)
where, 'R' is the heliocentric distance in units of AU.

The derived proton heating rates in different years are shown in Figure 3 and we found that heating rates vary from ≈ 1.58 × 10 -14 to 1.01 × 10 -8 erg cm -3 s -1 over the heliocentric distances 5 -45 R ⊙ .

The markers 'circle' and 'square' indicate proton heating rates derived assuming different inner scale models -proton inertial length and proton gyroradius model. For latter case, the inner scales are measured using the proton temperature derived using equations 9 and 10.

At 5 R ⊙ , in the coronal holes (i.e., in the fast solar wind), the estimated proton heating rates range from 2×10 -10 and 1.4×10 -8 erg cm -3 s -1 (Chandran et al. 2009a). Similarly, at 1 AU the estimated heating rate is 5 × 10 -16 erg cm -3 s -1 (Chandran et al. 2009a). The heating rates derived assuming density fluctuations are due to the kinetic Alfvén waves in the heliocentric distance range 2-174 R ⊙ using interplanetary scintillation (IPS) observations (Hewish & Wyndham 1963;Manoharan et al. 2000;Janardhan et al. 2011;Sasikumar Raja et al. 2019b) are 3 × 10 -8 erg cm -3 s -1 (during solar maximum) and ≈ 10 -15 erg cm -3 s -1 (during solar minimum) consistent with our estimates (Ingale 2015b). Using two-dimensional imaging angular broadening observations of Crab Nebula, the measured heating rates are varied from 2.2×10 -13 to 1.0×10 -11 erg cm -3 s -1 in the projected heliocentric distance range 9 -20 R ⊙ (Sasikumar Raja et al. 2017). The recently reported heating rates in the heliocentric distance range 1.5 -4.0 R ⊙ varied from ≈ 3.31 × 10 -10 to 4.5 × 10 -7 erg cm -3 s -1 (Cranmer 2020). Further, the author extrapolated these heating rates to the distances 0.3-0.6 AU and it range from ≈ 10 -15 to 10 -14 erg cm -3 s -1 and at 1 AU, the extrapolated heating rates are few times of 10 -16 erg cm -3 s -1 .

Using in-situ measurements by Parker Solar Probe, Bandyopadhyay et al. ( 2020) estimated energy transfer rates of 8.7 ± 0.3 × 10 -13 erg cm -3 s -1 at 36 R ⊙ and 5.8 ± 1.3 × 10 -14 erg cm -3 s -1 at 54 R ⊙ . They originally have quoted numbers in units of J kg -1 s -1 . We have multiplied their numbers by N p m p (where N p is the solar wind density derived using Leblanc model (Leblanc et al. 1998) and m p is the proton mass) to arrive at heating rates in units of erg cm -3 s -1 . By comparison, the proton heating rate at 36 R ⊙ from our results (see Figure 3) range from ≈ 2.8 × 10 -10 to ≈ 7.4 × 10 -13 erg cm -3 s -1 . Similarly, Adhikari et al. (2020) reported that heating rates due to quasi-2D turbulence in the heliocentric distance ≈ 1.6 -100 R ⊙ range from 1.06 × 10 -4 to 1.73 × 10 -14 erg cm -3 s -1 .

Authors also reported that the heating rate due to the nearly in-compressible/slab turbulence in the heliocentric distance ≈ 1.3-100 R ⊙ range from 4.24×10 -7 to 1.11×10 -14 erg cm -3 s -1 . A summary of these proton heating rates is given in Table 1.

As the density modulation indices (see Figure 2) and heating rates (see Figure 3

  shows the observation of GRAPH carried out at 80 MHz over an interferometer baseline of 1600 meters. The top panel shows the schematic of the Crab Nebula occultation technique. The red and green circles indicate the sun and location of the Crab Nebula on different days of June in 2011 and 2013. The bottom panel shows the decrement in flux density as the Crab Nebula ingresses and becomes invisible during 12 -18 June and then increments as it egresses. The flux density during 2013 is lower (compared to 2011) as it corresponds

Figure 1 .

 1 Figure 1. (top) Solar disk view of the Crab Nebula occultation. The red circle indicates the Sun and green circles represent the location of Crab Nebula with respect to the Sun in different days of June. ∆R.A. and ∆Dec. are the offset distances of Crab Nebula from the Sun in right ascension and declination, respectively. The closest dotted concentric circle around the Sun has a radius of 5 R ⊙ and the radii of the rest of the circles differ from their adjacent ones by 5 R ⊙ . (bottom) The observed flux densities of the Crab Nebula on different days during its occultation by the solar corona. The periods before and after June 16th correspond to the ingress and egress, respectively. The markers '*' and 'square' indicate the observations carried out in June 2011 and June 2013, respectively. The minimum detectable flux density of the GRAPH ≈ 100 Jy is used as the error associated with these measurements.

Figure 2 .

 2 Figure 2. Heliocentric dependence of the density modulation index in different years. The markers 'circle' and 'square' indicate modulation indices that are derived using the proton inertial length and proton gyroradius model, respectively.

  (Sasikumar Raja et al. 2016). Similarly, solar cycle dependence of the density modulation indices is shown in upper panel of Figure 4 (Sasikumar Raja et al. 2016).

Figure 3 .

 3 Figure 3. Heliocentric dependence of the proton heating rate in different years. The markers 'circle' and 'square' indicate proton heating rates that are derived using the proton inertial length and proton gyroradius model, respectively.

Figure 4 .

 4 Figure 4. (top) Solar cycle dependence of the modulation index averaged over the heliocentric distance 5 -45 R ⊙ . (middle) Solar cycle dependence of the proton heating rate averaged over the heliocentric distance 5 -45 R ⊙ . The measurements for various inner scale models, i.e., proton inertial length and proton gyroradius models are indicated with the symbols 'circle' and 'square', respectively. (bottom) The solid line shows the yearly averaged sunspot number (Clette et al. 2016) and the symbol 'triangle' indicates the sunspot number in which we have the radio observations. The Figure shows a clear solar cycle dependence of the proton heating rate.

  ) are weakly dependent with heliocentric distance, we averaged the observations that are carried out in different years and plotted in Figure 4. The upper and middle panels of Figure 4 are the averaged density modulation indices and proton heating rates for different inner scale models, respectively. The lower panel shows the yearly averaged sunspot number. Figure 4 shows that the derived density modulation indices and heating rates closely follow the solar cycle. During solar maximum, the slow solar wind drives in all the directions and hence Sasikumar Raja et al. (2016) had justified the lower modulation index in 1958 (also refer to upper panel of Figure 4). Following the lower density modulation indices, heating rates are lower during the solar maximum.

4.

  SUMMARY AND CONCLUSIONSIn this article, we have used recently reported density modulation indices ǫ N derived using angular broadening observations of Crab Nebula(Sasikumar Raja et al. 2016). The authors have studied the way ǫ N vary with heliocentric distance and solar cycle (see Figure2 and 4). Using imaging observations of the Crab Nebula observed in 2016 and 2017, the proton heating rate in the solar corona at various heliocentric distances are reported(Sasikumar Raja et al. 2017). Using these ǫ N values and the method discussed by Sasikumar Raja et al. (2017), we measured the proton heating

http://www.sidc.be/silso/datafiles

ACKNOWLEDGMENT KSR acknowledges the financial support from the Centre National d'études Spatiales (CNES), France. KSR acknowledges O. Alexandrova for the useful discussions that helped in improving the manuscript. The sunspot number used in this article is credited to WDC-SILSO, Royal Observatory of Belgium, Brussels. We thank the referee for constructive suggestions and comments that helped in improving the manuscript.

Table 1. Summary of proton heating rates in the solar wind rate in different years. We found that during 1952 and 2013, the measured proton heating rate ranges from ≈ 1.58 × 10 -14 to 1.01 × 10 -8 erg cm -3 s -1 in the heliocentric distance 5 -45 R ⊙ as shown in Figure 3. As the density modulation indices and heating rates weakly depend on the heliocentric distance, we averaged the year's entire observations. The upper and middle panels of Figure 4 show the way the density modulation indices and proton heating rate vary in different years. The lower panel shows the yearly averaged sunspot number in the respective years. Hence we conclude that both density modulation indices and proton heating rate in the solar wind correlates with the solar cycle. The in-situ measurements and thus the derived models (for example, electron / proton density, temperature, and magnetic field) using the Parker Solar Probe which has already covered the heliocentric distance range of 25 R ⊙ and planned to reach as close as 9.86 R ⊙ (Fox et al. 2016) plays a significant role in better understanding proton heating rates and thus the solar wind acceleration.


Xavier Bodin
email: xavier.bodin@univ-smb.fr

Thomas Echelard

Pascal Lacroix

Tazio Strozzi

Regional evaluation of rock glacier activity in the semi-arid Andes using optical and radar satellite imagery

Keywords: rock glaciers, surface movements, remote sensing, SAR, image correlation, Andes

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Introduction

In the present context of Climate change, rock glaciers dynamics have potential consequences for the stability of mountain slopes [START_REF] Huggel | Is climate change responsible for changing landslide activity in high mountains? Earth Surf[END_REF]. In the Andes, similarly to the Alps, cases of rock glacier destabilization have been observed [START_REF] Bodin | Recent collapse of rock glaciers: two study cases in the Alps and in the Andes[END_REF]. Between 29 and 34°S, more than 8000 fresh rock glaciers have been mapped by official Chilean and Argentinian authorities (IANIGLA, 2010; UGP-UC, 2011). In those inventories, the activity status of rock glaciers has been assessed based on ortho-imagery, implying subjectivity-related biases.

To overcome this limitation, we combined optical and radar satellite imagery (acquired between 2012 and 2017), from which we derived maps of SAR phase difference (TerraSAR-X) and of horizontal surface displacement (Landsat).

Study area and datasets

Our study area covers almost 3000 km², around 29-30°S, and has a median elevation of 4120 m asl. With a mean elevation of 4500 m asl, 321 rock glaciers are present, covering a total of 34 km². Following Barboux et al. ( 2014) procedure, we first interpreted the TSX interferograms overlaid with ortho-imagery in a GIS. Over 261 rock glaciers with SAR-derived interpretation, 54 show relatively fast surface velocity, higher than 2 m/yr (Figure 1). Landsat images from between 2013 and 2017 were processed with image correlation technique [START_REF] Zerathe | Morphology, structure and kinematics of a rainfall controlled slow-moving Andean landslide, Peru: The Maca slow-moving Andean landslide[END_REF] to derive horizontal displacements over the area (Figure 2).

Results

Deriving topographic attributes from TanDEM-X 12-m DEM, no clear dependency between velocity and elevation nor slope was found. The clearest control on the rock glaciers velocity range seems to be the solar radiation, with lower values on slow rock glaciers and higher ones on the fastest (Figure 3).

Outlook

At the moment of writing this abstract, the processing of the optical imagery have not yet be fully completed. The resolution of the Landsat images (15m) is a clear limitation, though the large time span should allow producing horizontal displacement maps for the fastest rock glaciers of the study area, and to compare these maps with the TSX interpretations.

Figure 1 :

 1 Figure 1: Velocity levels detected on rock glaciers with the interpretation of TSX-derived interferograms.

Figure 2 :

 2 Figure 2: Comparison between SAR phase signal from a 11-day interferogram (left panel) and horizontal displacements derived from Landsat images of 2013 and 2017 (right panel) on an active rock glacier.

Figure 3 :

 3 Figure 3: Hypsography of the study area and XY plot of elevation VS potential solar incoming radiation (PSIR).