∎

11institutetext: J. Simões 22institutetext: Instituto Superior Técnico da
Universidade de Lisboa (IST/UL) 33institutetext: A. Maciel
<EMAIL_ADDRESS>44institutetext: INESC-ID/IST/UL
55institutetext: C. Moreira<EMAIL_ADDRESS>66institutetext:
University Technology Sydney 77institutetext: J. Jorge<EMAIL_ADDRESS>88institutetext: INESC-ID/IST/UL

# SPARC

Shared Perspective with Avatar Distortion for Remote Collaboration in VR

João Simões  Anderson Maciel  Catarina Moreira  Joaquim Jorge

(June 6 2024)

###### Abstract

Telepresence VR systems allow for face-to-face communication, promoting the
feeling of presence and understanding of nonverbal cues. However, when
discussing virtual 3D objects, limitations to presence and communication cause
deictic gestures to lose meaning due to disparities in orientation. Current
approaches use shared perspective and avatar overlap to restore these
references, which cause occlusions and discomfort that worsen when multiple
users participate.

We introduce a new approach to shared perspective in multi-user collaboration
where the avatars are not co-located. Each person sees the others’ avatars at
their positions around the workspace while having a first-person view of the
workspace. Whenever a user manipulates an object, others will see his/her arms
stretching to reach that object in their perspective. SPARC combines a shared
orientation and support to nonverbal communication, minimizing occlusions. We
conducted a user study (n=18) to understand how the novel approach impacts
task performance and workspace awareness. We found evidence that SPARC is more
efficient and less mentally demanding than life-like settings.

###### Keywords:

Collaborative UI Virtual Reality Shared Perspective Avatar Manipulation

††journal: CGI2024

##  1 Introduction

Remote work strongly impacts corporations and workers since it saves time and
resources, along with many other changes still to be understood. Remote work
has increased fast with the recent pandemic and is mostly done through the
many teleconferencing solutions available. However, videoconferencing systems
limit users’ interactions with counterproductive communication layers. Even
the most widely used teleconferencing solutions cannot avoid cumbersome steps
to what would be easily performed in a face-to-face meeting
10.1145/1128923.1128947 , e.g., various people pointing to a diagram in a
presentation. These communication barriers are caused by the disregard for the
various ways users communicate in a face-to-face setting, including gestures,
body posture, awareness of where others are looking, and side conversations.

Past works have shown that using Virtual Reality (VR) is a better approach to
cooperation between multiple users since it affords new but natural ways of
visualizing and interacting with objects, the environment, and people
10.1145/293701.293715 ; 6790594 ; 10.1145/1128923.1128947 . This is especially
useful when there is a need to manipulate and discuss objects such as 3D
models, which is a very common setting in many fields, such as engineering,
architecture, geophysical exploration, and even medicine. Effective remote
collaboration requires users to understand each other and know where others
are in the environment and what they are doing unambiguously.

Current VR technology can simulate face-to-face settings faithfully. However,
when collaborating over shared content, people have different points of view
on the environment and the task at hand. Despite being very natural, this
disparity can induce some ambiguities with opposing orientations, i.e., left
and right 10.1145/3242587.3242594 , which is especially true when many
collaborators are working around a table. While this also occurs in physical
environments, VR can simulate environments one ”step outside of the normal
bounds of reality” 10.3389/frobt.2016.00074 , enhancing it.

That said, a virtual environment (VE) can be designed to purposefully
manipulate individual perspectives into a shared perspective between users
MAGIC ; 10.1145/3359997.3365744 . For each user, it is as if they were seated
on the same chair, seeing the work area from the same point of view (POV),
with the difference that four hands are visible, and each user controls their
pair. Shared perspective has induced a more effective collaboration between
two users 10.1145/3173574.3173855 ; 7563559 . However, it is limited as it
removes the non-verbal communication offered by a face-to-face setting. Not
addressing the unsolved issues can hurt collaborative efforts, hinder the
awareness of others in collaborative tasks, and interfere with the perceived
workload and the social presence felt by the collaborators. Moreover, when
there is a need for multiple experts in different fields to be able to give
their insights about the working task, more than two users must be supported.
Using the current approaches with multiple users would cause the visual space
to become cluttered, a reason why shared perspectives with multiple users have
not been explored.

This paper introduces a solution to cluttering and lack of face-to-face non-
verbal communication. With our approach, multiple users work around a table in
a VE. They can see each other in their respective seats at the same time that
they all see the working objects from the same perspective (Fig. 1). It works
as if there were multiple synchronized copies of the scenario, one before each
user. Nevertheless, this is not enough because when you point to an object in
front of you, your co-workers will not see where your hand is pointing in
their copy of the scene. Therefore, we present a solution based on
manipulating the user representations (avatars) that provides a faithful
localization of references in the workspace. This approach stretches the
users’ arms representation in the others’ environments while maintaining the
local user in their virtual body. In the remainder of the paper, we detail the
technique and present an experimental study we conducted on the impact of the
technique on metrics such as task performance, the feeling of co-presence, and
workspace awareness (WA) compared to a conventional VR setting without a
shared perspective.

Figure 1: Over-the-shoulder collaboration (top) is replaced by shared
perspective with stretched avatars (bottom).
While the perspective is shared, the avatar visualization is face-to-face,
allowing for non-verbal cues.

##  2 Background and Related Work

This section analyzes various important concepts in collaborative work and
multiple approaches already developed.

###  2.1 Awareness

Awareness of groupware is a common topic in the CSCW community. Greenberg et
al. greenberg1996awareness  summarized Workspace Awareness (WA) as “the
collection of up-to-the-minute knowledge a person uses to capture another’s
interaction with the workspace,” which comprises the knowledge of where other
people are and what they are doing in the workspace as well as changes to the
shared environment 10.1145/257089.257284 . Although maintaining awareness in a
collaborative face-to-face setting comes naturally, it must be explicitly
implemented in a VR collaboration. It has proven to be a rather difficult task
to achieve a real-life-like sense of awareness greenberg1996awareness . The
consequent lack of awareness is why the usual feeling of inefficiency and
awkwardness felt in many groupware systems compared to face-to-face work
10.1145/257089.257284 . Therefore, we understand that developing mechanisms to
maintain WA is a pivotal factor in making remote collaboration systems feel
closer to the fluidity of face-to-face collaboration Fidalgo23 .

Figure 2: Domain and collaboration tasks (from Gutwin and Greenberg
gutwin2002descriptive )

When working alone, a person’s activities and WA only cover the workspace and
the domain task. This changes in a collaborative setting where an additional
collaboration task is added. Hence, a person’s WA will involve the workspace,
the domain task, and the collaboration task, which encompasses both
communication and discussion of the domain task.

Gutwin and Greenberg gutwin2002descriptive  suggest that in a groupware
system, awareness information should be presented in a natural way, as people
have grown accustomed to co-located environments, by making use of the
following WA gathering mechanisms: consequential communication, feedthrough
and intentional communication. Consequential communication revolves around
nonverbal communication, consequent to a person’s activity within an
environment. This includes body posture, movement, the position of arms and
hands, and knowing where the other person is looking. Yet, it does not include
intentional bodily communication (e.g., pointing ). Artifacts around the
workspace can also give off awareness information. Feedthrough is the
perception of an artifact’s state during or after manipulation. This
information can be gathered through senses like sight or hearing, such as the
sound of crumpling a paper, which will allow a person to infer the state of
that object. When the person manipulating the artifact and the artifact can be
seen, feedthrough is coupled with consequential communication. Intentional
communication encompasses verbal communication and gestures. Deictic gestures
and other visual actions, such as nodding, are also a part of this mechanism.

Although these mechanisms happen naturally in a co-located environment,
maintaining WA is challenging in a remote collaboration system with current
technological limitations.

###  2.2 Awareness in shared workspace

Bill Buxton Buxton2009  identified the three types of spaces that need to be
considered at the micro-level of communication: person space – the space from
which nonverbal cues such as gaze, posture, and body language are picked up.
It is also the space where the attention is directed to when another person
talks; task space – the shared space where the work is presented, such as
diagrams, presentations, or 3D models. This space can be altered, and all
participants should notice differences; reference space \- “the space within
which the remote party can use body language to reference the work.” This is
the space where deictic gestures such as pointing or gesturing happen. Buxton
further argued that “effective telepresence depends on quality sharing of both
person and task space” buxton1992telepresence . Integrating body language and
non-verbal cues within the task space was proven very important to the
effectiveness of collaborative work. These allow for maintaining consequential
communication and feedthrough, which, as discussed, were decisive for WA,
especially in virtual collaboration.

Intending to integrate person and task space seamlessly, Ishii et al.
10.1145/142750.142977  discussed three metaphors: the whiteboard, over-the-
table, and glass window. Regarding the whiteboard metaphor, participants share
a common task space. Reference space and orientation are shared, and
intentional communication like pointing is facilitated. However, there is a
clear distinction between person and task space, so collaborators must shift
focus from the task space to read others’ body language and nonverbal cues,
which can be straining and hinder communication efforts. Collaboard 5656157
uses this approach and displays the back-view of the user on top of the
whiteboard, allowing for deictic gestures but hindering consequential
communication since users are not presented face-to-face. With some common
features, Zillner et al. 10.1145/2642918.2647393  proposed the 3D-Board, which
renders a front-facing 3D embodiment of the remote user behind the whiteboard
and suggests a gain in efficiency and user awareness when collaborating in a
face-to-face setting compared to a solution like Collaboard. Alternatively,
Higuchi et al. 10.1145/2702123.2702160  developed ImmerseBoard, where
different configurations were studied: one where the remote user’s full body
is shown at the side of the shared task space and deictic references are
consistent through the elongation of the remote user’s arm; and another
configuration where the shared task space is tilted. Both of these approaches
bear some constraints, such as occlusions from the arm and hindrances to non-
verbal communication. In the over-the-table metaphor, collaborators sit across
the table from each other and the collaborative task at the table.
Consequential communication is ensured since a user can see the other face-to-
face, but the task space orientation is not common, as referred by Ishii et
al. 10.1145/142750.142977 .

IllumiShare (10.1145/2207676.2208333 ) can project on and share any surface as
the task space. Participants are connected through a video call to support
personal space and acquire consequential communication cues. This
implementation separates person and task spaces but closely couples task and
reference spaces. It was found that removing the projected surface was more
disruptive than removing the video call. Sodhi et al. 10.1145/2470654.2470679
developed BeThere, an AR solution that renders the remote user’s hand in the
local user’s workspace. This approach supports intentional communication and
feedthrough, although non-verbal communication cues are absent due to only the
person’s hands being shown.

Benko et al. 10.1145/2207676.2207704  designed MirageTable, a curved
projection-based AR system that can display real and virtual worlds on a
tabletop with one user having an inverted perspective. Leithinger et al.
10.1145/2642918.2647377  tackled physical telepresence with an approach where
users can interact with and manipulate objects remotely in face-to-face and
corner-to-corner settings. Iwai et al. 8172039  approach projects the arm of
the remote user onto the local tabletop, applying transformations to
seamlessly extend the remote user’s image in the monitor where a video chat is
happening. In the three previous approaches, users are constrained, e.g., to
the range of the depth camera or the table, and cannot roam around the room.
Maintaining effective intentional communication can also be challenging due to
objects occluding part of a person’s gestures.

Finally, the glass window metaphor is a naturally coordinated shared space
since both users can write on their side of the glass while still seeing the
other person’s work. There is support for consequential communication because
users can see each other’s faces and bodies. The reference space is also
respected and ensured with intentional communication, including deictic
gestures. Gutwin et al. gutwin2002descriptive  identified the inversion of
orientation as a downside, which can be solved by mirror-reversing one of the
participants. This is called a WYSIWIS 10.1145/27636.28056  approach, although
in this case, it was mirrored. That was one feature of the design
characteristics in ClearBoard 10.1145/142750.142977 , a shared drawing medium
for pairwise collaboration. It was found that gaze awareness played a vital
role in this solution. Despite this, it constrained users to a specific
location. Similarly, Li et al. LI201723 ; Li14  designed FacingBoard-2,
enabling remote collaboration between two users. It suggested that in a
telepresence system, the reversal of graphical content improved awareness in a
face-to-face setting. The study dove into techniques to facilitate intentional
and consequential communication and studied how these can lessen awareness
loss.

###  2.3 Awareness in multi-user meetings

Gross et al. 10.1145/882262.882350  introduced blue-c, a system where the user
is surrounded by three “see-through” projection screens that allow the user to
be captured by cameras positioned behind these. A full-body 3D model of the
user is then created from the different cameras’ output and rendered in the
shared virtual environment. This approach provides a good sense of presence
and a realistic model of the person in the virtual world. However, it takes up
much room and shows the same limitation as previous techniques
gibbs1999teleport ; baker2002coliseum  by lacking interaction with both real
and virtual objects, which plays an important role in these settings
10.1145/1062253.1062258 .

Analogous to the blue-c system, Kurillo et al. 4480795  presented a system
that renders full-scale 3D reconstructions of users in real-time in a VE,
which allows interacting with virtual objects in a CAVE environment system.
Besides requiring a sizable amount of hardware, it is not portable and very
hard to implement in a remotely located meeting between groups. Maimome et al.
maimone2011first  presented a telepresence system that allowed one tracked
user to “look through a window” into a room. It conveyed a good sense of
presence by displaying the remote room in 3D on two large screens at the
correct viewpoint by the local user. This approach only allows for one user’s
viewpoint to be tracked, so it would not work in a group-to-group setting,
which poses a problem for multi-user-dependent collaborative tasks. More
recently, in 2016, Orts-Escolano et al. 10.1145/2984511.2984517  presented a
system that supports both AR and VR telepresence, which allows for both the
real-time 3D reconstruction of an entire space and for users to interact with
real and virtual objects. The downsides of this approach are that it has very
demanding hardware requirements and does not account for the problem of object
occlusion. Scott et al. greenwald2017multi  presented CocoVerse, an immersive
multi-user collaboration tool that allows creating and manipulating virtual 3D
objects to improve learning experiences. This approach supports user
interactions with other users and the environment, although it suffers from
occlusions when participants stand in front of each other. Ardal et al.
10.1145/3359998.3369404  uses a VR filmmaking tool, including editing, scene
visualization, and discussion. They suggest that VR technologies are useful in
collaborative settings, giving users a sense of awareness and co-presence
while working on a shared task, including noronha2012designing .

Other approaches to 3D teleconferencing and 3D virtual collaboration
10.1145/2818048.2819965 ; Towles023dtele-collaboration ;
10.1145/3359996.3364238 ; 10.1145/3170427.3186495  were also studied but are
limited to two simultaneous people.

###  2.4 Perception Manipulation

Human beings use their body to communicate, either inadvertently or purposely.
The latter fits into intentional communication, where actions such as nodding
or gestures like pointing fit in. The effectiveness of this communication is
decisive when remote collaborators work on collaborative tasks.

Piumsomboon et al. 10.1145/3173574.3173620  found that always keeping
communication cues in sight of the collaborators improved collaborative task
completion times and reduced mental effort. Azmandian et al.
10.1145/2858036.2858226  used warping of the virtual world and the virtual
body to align virtual objects with real-world objects. Results showed that a
higher sense of presence was achieved when combining the two transformations,
i.e., virtual world and body, was used. Fussel et al. fussell2004gestures
argued that collaborative tasks and communication performance were enhanced
when participants had a more complete perception of the gestures the other
collaborators were executing. Their results showed that when collaborators
could pair verbal references with visual ones, performance in collaboration
increased significantly, with pointing gestures being the most frequent. Sousa
et al. 10.1145/3359997.3365744  studied how different manipulations of
participants and environment representations affected WA in face-to-face
communication. It was argued that sharing the same perspective decreased
mental effort. They also pointed out that opposing viewpoints were not ideal
for complex analysis scenarios, with the first option being preferable. A
related approach mendes17 , discussed remote selection techniques. Hoppe et
al. 10.1145/3432950  introduced ShiSha, which allows multi-user collaboration
by having users share the same perspective of an object or task space while
maintaining the users’ avatars spatially positioned without overlapping.
Results from this work were similar to the previous one in that having
intentional communication and sharing a common perspective enhanced task
efficiency. However, the spatial inconsistency between where the user is
looking from and where the user’s avatar is might induce some confusion and
break the feeling of presence in the virtual world.

Chénéchal et al. 7563559  developed Vishnu, a mixed-reality collaborative tool
where a local user connected with an AR HMD can see the hands and arms of a
remote user connected through VR. It was argued that although task completion
times were not lower than with a desktop application with a pen tool to
sketch, mapping references was easier since there was the availability of
deictic references in 3D space and a shared perspective between users. Fidalgo
et al. MAGIC  also employed a shared perspective over the task space with
manipulations to users’ avatars to preserve deictic references without them
being noticeable by the second user. It was argued that there was an
improvement in WA and in the feeling of co-presence. Piumsomboon et al.
10.1145/3132818.3132822  also showed that manipulating avatars can improve
awareness, positively contribute to collaborative tasks, or even create
additional interactions.

###  2.5 Discussion

This section reviewed various approaches representing the state of the art,
focusing on multi-user collaboration technologies since our approach aims to
improve this type of setting. The different approaches are used against
different features of our work, such as support for multiple users and
supported communication mechanisms. WA requires the three types of
communication to be successfully maintained. Approaches in references
10.1145/2642918.2647393 ; 10.1145/3359998.3369404 ; MAGIC ;
10.1145/142750.142977 ; LI201723 ; 4480795 ; 10.1145/2984511.2984517  do this.
Despite that, manipulation of 3D virtual objects is essential to many
collaboration tasks, and only references greenwald2017multi ;
10.1145/3359998.3369404 ; 10.1145/2207676.2207704 ; 10.1145/3432950 ;
10.1145/3359997.3365744 ; MAGIC ; 4480795 ; 10.1145/2984511.2984517  support
this. In terms of support for multiple users, there is a limited number of
approaches: references greenwald2017multi ; 10.1145/3359998.3369404 ;
10.1145/2502081.2502146 ; 10.1145/3432950 ; 10.1145/192844.193054 .

Approaches that combine all of the different types of communication, through
which it is possible to maintain WA better and support multi-party
interactions, are scarce. To our knowledge, ShiSha 10.1145/3432950  is the
closest approach to doing it since avatar and perspective manipulations are
also employed to provide a shared perspective in a multi-user setting.
However, the authors do this through the disembodiment of the avatar, which
hinders consequential communication since users may not be aware of others’
body language cues.

##  3 SPARC Approach

In the context of the past work presented in Sec. 2, we introduce SPARC, a
multi-user VR collaborative technique that enables users to work together from
the same perspective while maintaining face-to-face communication, distorting
the Avatar Representation as needed to reconcile face-to-face communication
with a shared perspective (Fig. 3). We developed a Proof of Concept (POC)
using Unity, incorporating Photon Engine’s PUN multiplayer networking engine.
It supports various VR platforms, including the Oculus Quest 2 used in our
prototype, a.k.a. Meta Quest 2.

Figure 3: Avatar manipulation: [A] - User on the left points at a piece in his
local workspace; [B] - User on the right sees this reference mapped to local
own workspace

###  3.1 Improving Workspace Awareness

Following the three collaboration metaphors discussed, our solution uses an
over-the-table setting, where users sit around a table with a collaborative
task between them. In this approach, the task space, i.e., where the work is
presented, is the table placed between the users, which will also correspond
to the reference space where users can use gesturing and pointing to reference
the work. Additionally, users can see each other from across the table,
simplifying the capture of nonverbal cues in a person’s space. This approach
ensures consequential and intentional communication since users can see each
other face-to-face, thus understanding where the other is looking, their
position, and their actions.

SPARC also enables users to collaborate through a shared perspective over one
or more objects in the workspace, allowing for differences in their actions to
be noticeable to all participants as such feedthrough is ensured.

However, as identified by Ishii et al. 10.1145/142750.142977 , the over-the-
table metaphor has a downside in that the perspective will be inverted for one
of the users in a pairwise collaboration. To deal with this problem, we will
provide a shared perspective for all users. This not only allows for a common
understanding of the workspace but also removes the problem of the occlusion
of gestures by the objects in the workspace. To preserve the meaning of
gestures, transformations are applied to map the user’s intended referencing
position to the others’ work-space, i.e., if the user points to a piece on the
right of their workspace, the other users will see the avatar’s arm stretched
and pointing to the right of their local workspace. This is an imperative part
of any collaborative task involving referencing specific workspace parts
through gestures.

###  3.2 Perspective-sharing: Environment Modifications

To obtain a common perspective over the workspace, manipulations are done to
the local environment of each user. These are based on the seat that the user
takes around the table. The workspace is divided into eight increments of 45
degrees, starting from the Assembler position, i.e., the right of the table in
a top view. In Fig. 4, there is an example. In [A], we see user A’s POV, which
has the workspace rotated by 90 degrees counter clockwise. In [C],
representing user B’s POV, their workspace does not require rotation since
they are at position 0, i.e., right of the table. User C will need a rotation
of 270 degrees since they are at position 7 in the table, while B is in
position 1 (0 degrees) and A is in position 3 (90 degrees). In a setting with
eight users, the space between User A and User B would have another user with
an increment of 45 degrees and a user between User C and A with an increment
of 315 degrees.

Figure 4: Transformation applied to the avatar’s arm and hand position: [A] -
User A points at a point in his workspace, [B] - User B receives the
information of where user A pointed and maps it to his own workspace, [C] -
User A’s opposing arm is rendered as a spline and his hand points at the
mapped point in User B’s workspace.

###  3.3 Perspective-sharing: Avatar Modifications

In our POC, there are two modes of interacting and referencing the workspace:
by direct interaction, using their virtual hands, or indirect interaction,
using their virtual rays or pointers (see Fig.5).

Figure 5: Different conditions in the same POV: [A] and [B] represent the
Veridical condition. [C] and [D] represent SPARC

Due to the locally applied rotation to the user’s workspace, the meaning of
the deictic gestures and any referencing of the workspace is lost. To counter
this, our approach maps the references made by each user in their workspace to
the local user’s workspace. This is done in three main steps that are shown in
Fig. 4: [A] - User A points at a point in their local workspace with their
right hand; [B] - User B receives the point where user A’s right hand is and
maps it to their workspace (by rotating it by the difference in rotation
between the two users, i.e., 90°) [C] - User B renders user A’s left hand to
the mapped point with a line connecting to it.

In our implementation, the manipulation of the avatars does not always happen.
It will only occur if the user references something in the workspace using
either of the interaction modes. The trigger will happen if the hand enters
the workspace or the ray points at an object inside the workspace. This
trigger happens for each hand individually.

Each user is assigned an angle, and this is used to calculate others’ deictic
references. The information regarding each user’s hand position is propagated
and then rotated around the center point, i.e., the center of the table, by
the difference in degrees between the local user (LU) and the remote user
(RU). To connect the RU’s avatar to these new calculated references, we used
Splines that are rendered in real-time in the LU’s environment coming from the
RU’s arm to his hand positioned at the calculated reference, as can be seen in
Fig.3, where the angle difference will be 180°. Notice that there was a
mirroring of the RU. This is done to avoid the arms crossing over one another
when both hands are being used to reference something.

##  4 Evaluation

We designed and conducted a user study to assess the effects of the SPARC on
task performance, workspace awareness, and sense of co-presence in the VE. Our
POC demonstrated technical support for up to eight users at a time.
Nevertheless, for the sake of simplicity, we chose to test with trios only.
Thus, any effect involving a different number of co-workers is out of the
scope of this assessment. With trios, we address a scenario challenging the
previous works with a shared perspective.

We tested two conditions:

  * •

Condition V: Veridical collaboration. Participants stood around a virtual
table and collaborated with different individual perspectives of the task, as
it would be in real-life interactions.

  * •

Condition S: Collaboration under SPARC. Participants stood around a virtual
table with a shared perspective and avatar manipulations active.

###  4.1 Task

We chose a 3D assembly task represented by the Bedlam Cube chlond2005box . It
is a 3D puzzle with thirteen distinct pieces designed to fit together in a
4x4x4 frame. With over 19,000 combinations, the assembler can’t guess which
piece fits each position. In our setting, each participant in a trio is
assigned one of three roles: Assembler, Instructor 1, or Instructor 2. The
assembler is the one who can manipulate objects. The two instructors have
access to the instructions, i.e., the solution for the next piece, but cannot
manipulate the pieces. The instructors see the outline of a piece correctly
placed inside the cube and must indicate to the assembler which piece and
where it should go. In such a scenario, the two instructors have to
communicate with each other and with the assembler to share their knowledge,
which must be understood by the assembler, who manipulates the pieces, one at
a time, until the task is completed with the assembly of the full cube.

An example of the puzzle in the POV of an instructor is depicted in Fig. 5D,
where the yellow outline inside the empty cube represents the piece and its
orientation. A grid-snapping system is employed to aid in the positioning of
the pieces, and when a piece is dropped or rotated, its pose will be corrected
to this grid - this removes issues regarding overly precise positioning.

Users are constrained to life-like perspectives in the veridical condition
(V). The puzzle is directed towards the assembler, and the instructors have a
side view of the puzzle. The instructor on the left-hand side of the assembler
will see the puzzle from the left of the puzzle. A comparison between the two
conditions can be seen in Fig. 5, where the POV of Instructor 1 is seen under
the two conditions.

###  4.2 Procedure

All experiments followed the same procedure. Participants began by completing
a user profile and a consent form. Next, they were provided with an
explanation of how to use the HMD and interact in the POC. The task was also
explained at this point, and participants decided who would be assigned to
each role, with the task and roles further elaborated on as needed. After
adjusting their HMD, participants familiarized themselves with their
respective roles and the different interactions available in the VE. In
particular, the assembler was instructed on moving and rotating the pieces
using direct and indirect interaction modes.

The design is within subjects. The 18 participants are grouped into 6 trios,
and each trio performs the task once in condition V and once in condition S.
The order between conditions is counterbalanced: the first three groups
started in condition S, followed by condition V, and the remaining three
groups did the inverse.

The participants began in one of the conditions and were tasked with
completing one puzzle until all the pieces were correctly placed. Following
this, there was a short break during which participants completed a
questionnaire regarding the collaborative task. The pieces were then jumbled,
and the task was repeated under the other condition, and another questionnaire
was completed afterwards.

###  4.3 Measurements

The measurements we obtained were divided by type into two groups: task
performance and user preference. Regarding performance, we collected the
following:

  1. 1.

Total completion time: End time minus initial time, in seconds.

  2. 2.

Errors per piece: Counted when a piece is released at a wrong position within
the cube (it returns to its place on the table).

  3. 3.

Number of attempts: Counted when a piece is grabbed and released outside the
cube.

  4. 4.

Eye-contact time: Time spent looking at the other players. Computed when a
raycast from the center point of the avatar’s head intercepts another avatar’s
head.

These other metrics are derived from the measures above:

  1. 1.

Total errors: The sum of the errors per piece.

  2. 2.

Total moves: The sum of errors and attempts. Note that the successful moves
are not accounted for as they are forcefully the same for every completed
puzzle, i.e., 13.

To evaluate user preference and satisfaction, we applied a questionnaire that
compiled questions evaluating various aspects of a collaborative task in a VE:
the perceived workload of the task, the sense of co-presence felt, and user
awareness. The questionnaire was mostly composed of statements evaluated on a
6-point Likert scale. At the end of the questionnaire, the users could also
give their thoughts on the task and report any bugs they experienced.

###  4.4 Participants

The subject group comprised 18 participants, with 8 females and 10 males.
Their ages ranged from 20 to 39, with an average age of approximately 24 years
(M = 24, SD = 4.14). All participants had attained a university education.

Regarding remote collaboration technology usage, eight individuals reported
using videoconference platforms, such as Skype, Zoom, or Microsoft Teams, at
least once a day. Six participants used these platforms at least once a week,
while four reported rare usage. Concerning VR environments, the data indicated
that the majority of participants (15 out of 18) had never used VR
environments, and the remaining three participants reported infrequent usage
of these environments.

##  5 Results

Fig. 6 shows the raw data collected from the experiment. In a preliminary
analysis, we looked for tendencies and eventual outliers. Remember that groups
1-3 performed condition V first, and groups 4-5 started with condition S. Two
facts are strikingly noticeable: (1) green bars (condition V) tend to be
higher than orange ones (condition S) for groups 1 to 3, and the trend inverts
for the remaining groups; (2) group 6 is the only one with an inverted trend
in the three measures. These facts suggest an order effect. On the one hand,
V-first presents higher times, errors, and moves for V than S. On the other
hand, S-first has more mixed results, with fairly lower S bars than groups
1-3, which did not improve much in condition V. Our interpretation is that V
is harder for everyone but becomes easier after some experience with S. The
opposite is not true. Groups performing S first perform well and keep
performing well in V, with only slight improvement. Concerning group 6, it
presented a pattern diverse enough to be excluded from the study due to its
profile.

A third factor is also noticeable. Group 1 presented much higher times and
moves than the average of the other groups. Nevertheless, we identified that
this group spent all that additional time and moves by playing with a single
piece. However, we did not remove that piece from the analysis because we did
not record individual times per piece.

For further analysis, we kept the data from 15 participants, i.e., five groups
and ten trials. Overall statistics are in Table 1.

Table 1: Descriptive statistics | (Mean $\pm$ SD)
---|---
| Total Time | Total Errors | Total Moves
SPARC | 681 $\pm$ 192 | 4.2 $\pm$ 2.3 | 64 $\pm$ 26
Veridical | 1015 $\pm$ 455 | 7.8 $\pm$ 5.6 | 96 $\pm$ 34
Total | 848 $\pm$ 373 | 6 $\pm$ 4.5 | 80 $\pm$ 33

We detail the results below, grouped into four different categories. It is
worth noticing that with five groups, the statistical power of our sample is
not strong. Yet, as the results will show, there are statistically significant
findings, and non-significant data also has interesting trends.

Figure 6: Raw data collected from the experiment depicting the three measures
variation by trios for the two conditions. The lower, the better. Arrows
indicate the direction of the difference for each group: red when veridical is
higher and blue when SPARC is higher.

###  5.1 User preferences

The applied questionnaires and associated results are summarized in Fig. 7. We
employed the Wilcoxon Signed Ranks test to check for the significance of the
differences. Most of them showed no statistical significance. This hints that
the tested conditions weakly affect co-presence, attentional allocation, and
perceived message understanding.

In terms of Task Load, the test reported a significant increase in mental
demand in the Veridical condition (”How mentally demanding was the task?”),
where Z = -2,486, p = .013.

Figure 7: Results from the user preferences questionnaire for both our
approach(A) and Veridical(V) conditions. * indicates statistical significance.

###  5.2 Task performance: completion time

After running the Shapiro-Wilk test, we could assume a normal distribution for
the time data. We then used a paired T-test and observed that there was no
statistically significant difference in the total times with results t(6) =
$-1.83$, p= $.14$ between SPARC (M = 681, SD = 192) and the Veridical approach
(M = 1015, SD = $455$). Nevertheless, the average of times suggests an
advantage for SPARC in terms of time efficiency. Fig. 8(a) illustrates the
magnitude of this potential advantage.

(a) Total time

(b) Total errors

(c) Total moves (movement economy)

Figure 8: Summary of the experimental data analysis comparing SPARC (S) and
Veridical (V) conditions.

###  5.3 Task performance: total errors

After a Shapiro-Wilk test, the error distribution could also be assumed
normal. the mean results are our approach (M = 4.20, SD = 2.38) and veridical
(M = 7.80, SD = 5.67). Again, we performed a Paired T-Test (t(6) = -1.28, p =
.26), which also resulted in no statistical significance for the difference of
the means. See also Fig. 8(b).

###  5.4 Task performance: movement economy

We define movement economy as the number of times pieces were picked up until
the puzzle’s completion. Similar to the previous two variables, we discovered
through the Shapiro-Wilk test that our data was normally distributed. The
results for the total errors between our approach (M = $64.4$, SD = $26.5$)
and veridical (M = $96.2$, SD = $34.3$) are depicted in Fig. 8(c). We
performed the Paired T-Test and, this time, obtained statistical significance
with $t(6)=-3.17$, p = $.033$.

###  5.5 Additional observations

We observed that in the Veridical condition, users with the role of instructor
tended to move around the table and reposition themselves beside the
assembler, possibly to have a better view of the pieces or the puzzle. About
the observed communication at the moments of discovering which piece was going
on the cube, users mostly verbalized references to their pointers or hand
(e.g.”this piece”). We also observed that explaining rotations was harder
under SPARC for some users. In general, some interactions were also limited
because the other user’s arms were in the way of the user’s workspace.

##  6 Discussion

The main research questions we approached with this work are:

  1. Q1.

Task Performance: Does using a common perspective with manipulations to the
avatars affect task performance in a collaborative task?

  2. Q2.

Workspace Awareness and Co-presence: Does using a common perspective with
manipulations to the avatars affect workspace awareness and the sense of co-
presence?

To evaluate Q1, we analyzed three main characteristics regarding task
performance: the total time spent, the number of errors made, and the number
of movements required to achieve the goal. Despite the novelty and non-
naturalness of our approach, the means point to SPARC as more efficient than
veridic in time and error performance. However, the results show the mean
differences are non-significant. This is probably due to the small sample
size.

The third performance measure variable was movement economy. SPARC performed
significantly better than veridic in the moves count. This means that sharing
the perspective causes users to understand better how a piece is rotated or
which piece needs to be picked up, thus resulting in fewer trial-and-error
movements. There is enough evidence that a shared perspective with stretched
references outperforms a veridic condition for collaborating with more than
two users in VR. The low statistical significance for some metrics is
explained by our small sample size to detect significant differences. Yet,
some are significant, and all of them indicate a trend in favor of SPARC.

Our User Preference results reported a significant increase in how mentally
demanding the task was in V compared to S. Such may happen because when
employing a shared perspective, users have a common orientation of the
workspace, where commands such as left, up, or back are the same for all
users, facilitating the giving and receiving of instructions, which causes
less mental effort.

This can also be connected to an effect reported in the observations, where we
recognized a tendency for instructors in V to work from the shoulder of the
assembler to get a better view of the workspace or the piece’s rotation. From
previous works, we know that this shift in position, effectively putting the
participants in a whiteboard metaphor setting, results in users shifting focus
between person and task spaces that are now distinctively separated. This
could explain the reported result since collaborators must move to both
understand and be understood, especially when using intentional communication
mechanisms such as gestures. Consequential communication is also obstructed
when working with this displacement, considering that seeing the other user’s
body or arms position is more demanding when standing over their shoulder.

We can now connect these results to Q2. Our results showed no statistical
significance regarding the sense of co-presence, workspace awareness (WA), and
the user’s perception of others. However, one could argue that S potentially
increased the WA when compared to V since it allowed users to remain in a
face-to-face setting while maintaining the use of intentional communication
(e.g., deictic gestures), consequential awareness (e.g., body language) and
feedthrough (e.g., piece’s rotation after being moved by the assembler). This
would be supported by the mental load result since users are not required to
shift focus between person and task spaces, and previous studies show that
these communication forms are the three basic mechanisms through which WA is
maintained greenberg1996awareness .

Although more research is needed to refine the technique and answer all
questions with stronger significance, our findings contribute to understanding
the effects of perspective and avatar manipulations on collaboration in a VE,
emphasizing the importance of shared perspectives for improved communication
and less mental demand.

##  7 Conclusions and future work

Through Virtual Reality, we can do more than copy reality; we can enhance it.
SPARC takes advantage of this by using environment and avatar manipulations to
tackle workspace occlusions in shared perspective collaborative interactions.
It features a common perspective while preserving the meaning of deictic
references. Users can then maintain a common understanding of the task space
and how others interact with it. Using a round-table setting, all users can
see each other, which means they can understand and be understood by others
relative to their intentions and nonverbal cues, and they do this without
shifting their attention between task and person spaces.

The combination of these features and the accommodation of an unlimited number
of users in a shared perspective is unique in the literature. Reports from our
user study concluded that collaborating under a shared perspective meant lower
mental demand than in the Veridical condition. It could be argued that
Workspace Awareness was improved under SPARC by creating a common task,
reference, and personal space without requiring users to shift focus between
them, which would be supported by the lower mental demand result. Moreover, a
mix of significant and non-significant quantitative results indicate that
SPARC helps improve an assembly task’s performance.

Our work paves the way for future research that could build upon this. We aim
to conduct a bigger user study, with more simultaneous users in the VE where
we would explore the effects of different numbers of users. Scaling to more
users would also create new challenges, such as the overload of arms
stretching in the workspace and the difficulty of understanding where each
user is by where their voice originates. Alternative means to stretch the arms
and other references are under study. Additionally, spatial audio and other
natural features such as eye-gaze could be included to improve workspace
awareness and experimental data collection.

Virtual Reality technologies enable us to achieve things beyond reality,
potentially positioning this technology as a better alternative to real-life
meetings than videoconference tools, allowing richer and more accurate
communication.

## Acknowledgments

This work was partially supported by the Portuguese Foundation for Science and
Technology (FCT) Grants UIDB/50021/2020 (DOI:10.54499/UIDB/50021/2020) and
2022.09212.PTDC This paper was written under the auspices of the UNESCO Chair
on AI&XR.

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# Small-scale Bright Blobs Ejected from a Sunspot Light Bridge

Fuyu Li  School of Earth and Space Sciences, Peking University, 100871
Beijing, China  Yajie Chen  School of Earth and Space Sciences, Peking
University, 100871 Beijing, China  Max-Planck Institute for Solar System
Research, Justus-von-Liebig-Weg 3, D-37077 Götingen, Germany  Yijun Hou  Key
Laboratory of Solar Activity, National Astronomical Observatories, Chinese
Academy of Sciences, Beijing 100101, China  Hui Tian  School of Earth and
Space Sciences, Peking University, 100871 Beijing, China  Key Laboratory of
Solar Activity, National Astronomical Observatories, Chinese Academy of
Sciences, Beijing 100101, China  Xianyong Bai  Key Laboratory of Solar
Activity, National Astronomical Observatories, Chinese Academy of Sciences,
Beijing 100101, China  Yongliang Song  Key Laboratory of Solar Activity,
National Astronomical Observatories, Chinese Academy of Sciences, Beijing
100101, China

###### Abstract

Light bridges (LBs) are bright lanes that divide an umbra into multiple parts
in some sunspots. Persistent oscillatory bright fronts at a temperature of
$\sim$$10^{5}$ K are commonly observed above LBs in the 1400/1330 Å passbands
of the Interface Region Imaging Spectrograph (IRIS). Based on IRIS
observations, we report small-scale bright blobs from the oscillating bright
front above a light bridge. Some of these blobs reveal a clear acceleration,
whereas the others do not. The average speed of these blobs projected onto the
plane of sky is $71.7\pm 14.7$ km s-1, with an initial acceleration of $1.9\pm
1.3$ km s-2. These blobs normally reach a projected distance of 3–7 Mm from
their origin sites. From the transition region images we find an average
projected area of $0.57\pm 0.37$ Mm2 for the blobs. The blobs were also
detected in multi-passbands of the Solar Dynamics Observatory, but not in the
H$\alpha$ images. These blobs are likely to be plasma ejections, and we
investigate their kinematics and energetics. Through emission measure
analyses, the typical temperature and electron density of these blobs are
found to be around $10^{5.47}$ K and $10^{9.7}$ cm-3, respectively. The
estimated kinetic and thermal energies are on the order of $10^{22.8}$ erg and
$10^{23.3}$ erg, respectively. These small-scale blobs appear to show three
different types of formation process. They are possibly triggered by induced
reconnection or release of enhanced magnetic tension due to interaction of
adjacent shocks, local magnetic reconnection between emerging magnetic bipoles
on the light bridge and surrounding unipolar umbral fields, and plasma
acceleration or instability caused by upward shocks, respectively.

Sun: chromosphere — sunspots — Sun: transition region — Sun: UV radiation

††journal: ApJ

##  1 Introduction

Bright lanes that divide an umbra into multiple parts, called light bridges
(LBs), are among the most prominent bright structures in many sunspots. The
magnetic field at LBs is often much weaker and more inclined compared to that
of the umbra (Leka, 1997; Shimizu et al., 2009; Toriumi et al., 2015a; Yuan &
Walsh, 2016; Feng et al., 2020). High-resolution observations have shown dark
substructures within light bridges, such as central dark lanes with upflows
(Berger & Berdyugina, 2003; Rouppe van der Voort et al., 2010) and dark knots
with downflows (Zhang et al., 2018). These small-scale structures are likely
formed as a result of the magnetoconvection in sunspots (Schüssler & Vögler,
2006). Recent observations and simulations have revealed possible signatures
of weakly twisted magnetic field and flux emergence in LBs (Louis et al.,
2015; Tian et al., 2018a; Toriumi et al., 2015a, b; Song et al., 2017; Yang et
al., 2019a; Yuan & Walsh, 2016; Bai et al., 2019; Lim et al., 2011).

The most prominent phenomenon above LBs revealed from chromospheric
observations is the recurrent surge-like activity, which was reported as
H$\alpha$ surges, light wall oscillations, plasma ejections, or chromospheric
jets (Roy, 1973; Asai et al., 2001; Bharti et al., 2007; Bharti, 2015; Shimizu
et al., 2009; Hou et al., 2016a; De Pontieu et al., 2017; Yuan & Walsh, 2016;
Song et al., 2017). External disturbances such as flares and falling materials
could change the amplitudes of the light wall oscillations (Hou et al., 2016b;
Yang et al., 2016). Some of these jets have very high speeds ($\sim$100 km
s-1) and were found at locations of strong electric current (Louis et al.,
2014; Robustini et al., 2016; Yang et al., 2019b; Lim et al., 2020),
suggesting that they are likely driven by magnetic reconnection between small-
scale magnetic bipoles at LBs and the surrounding unipolar umbral field.
Meanwhile, low-speed surges ($\sim$15 km s-1) with a stable oscillating period
of a few minutes have also been reported, and suggested to be driven by p-mode
or magnetoacoustic waves leaking from the underlying photosphere (Yang et al.,
2015; Zhang et al., 2017). Based on observations of the Goode Solar Telescope
(GST, Cao et al., 2010) and the Interface Region Imaging Spectrograph (IRIS,
De Pontieu et al., 2014), Tian et al. (2018b) found strong evidence of two
types of surge-like activity above LBs: persistent up-and-down motions
associated with the upward leakage of magnetoacoustic waves from the
photosphere, and sporadic high-speed jets triggered by intermittent magnetic
reconnection. Based on imaging observations of IRIS, Hou et al. (2017) also
reached a similar conclusion.

Bright fronts ahead of LB surges are commonly observed in transition region
(TR) images (Bharti, 2015; Yang et al., 2015). It is likely that the bright
fronts ahead of the LB surges is heated to TR temperatures either by shocks or
through compression (Morton, 2012; Bharti, 2015; Zhang et al., 2017). In this
paper, we report small-scale bright blobs at typical TR temperatures from the
bright front of surges above a light bridge, which is rarely observed in the
past. The existence of these blobs indicates more complex physical processes
above the LB. We perform a detailed investigation of their kinematics and
energetics, and discuss their possible formation mechanisms.

##  2 Observations

The observations were performed from 05:17:13 UT to 06:30:18 UT on 2014 Oct
25. The IRIS pointed to a coordinate of (257′′, -318′′), targeting at the
largest sunspot group of solar cycle 24 located in NOAA Active Region (AR)
12192. We used the level 2 IRIS data, which has been corrected through dark
current subtraction, flat field, geometrical, and orbital variation
corrections. Slit-jaw images (SJIs) of IRIS in the filter of 1400 Å were used,
and the cadence was $7.3$ s. The field of view (FOV) of SJIs was 119′′
$\times$ 119′′, with a spatial pixel size of 0.′′166\. The roll angle of the
spacecraft was $45\arcdeg$. Since the slit failed to catch any blobs
investigated in this paper, we did not use the spectral data.

We also analyzed the data taken simultaneously by the Atmospheric Imaging
Assembly (AIA, Lemen et al., 2012) on board the Solar Dynamics Observatory
(SDO) to study the temperature of the blobs. The cadence of AIA observations
was 12 s in the 94, 131, 171, 193, 211, 304, 335 Å passbands, and 24 s in the
1600 Å passband. The pixel size of the AIA images was $\sim$0.′′613\. The AIA
images in different passbands were coaligned using the IDL routine
aia$\\_$prep.pro available in Solar Software. The AIA and IRIS images were
then coaligned by comparing some commonly observed bright features in the AIA
1600 Å images and the IRIS/SJI 1400 Å images.

Moreover, the 1-m New Vacuum Solar Telescope (NVST, Liu et al., 2014; Yan et
al., 2020) also observed this sunspot from 04:59:20 UT to 05:59:15 UT. The
H$\alpha$ core images of NVST were used to examine the chromospheric response
of the blobs. The bandwidth of the H$\alpha$ filter was 0.25 Å. These
H$\alpha$ core images have an image scale of 0.′′164 pixel-1, a cadence of
$12~{}s$, and a FOV of $152^{\prime\prime}\times 152^{\prime\prime}$. We
coaligned the H$\alpha$ images and SJI 1400 Å images by matching the commonly
observed LB and some localized dynamic features.

##  3 Data Analysis and Results

###  3.1 Kinematics of the ejections

Bharti (2015) and Yang et al. (2015) analyzed this dataset and identified an
oscillating bright front above the LB from the TR images. The bright front is
visible from the 1400 Å images shown in Figure 1. It exhibits up-and-down
oscillatory motions during the entire observation period. Besides this
oscillating bright front, we also identified more than ten small-scale
intermittent bright blobs away from the leading edge. The blobs are better
seen in the movies, and we pointed out each blob by a green arrow in Movie 1.
These bright blobs may correspond to plasma ejections, and we cannot rule out
the the possibility of heating fronts due to the absence of spectral data for
the bright blobs. Considering both network jets and penumbral microjets, which
could be caused by heating fronts (De Pontieu et al., 2017; Chen et al., 2019;
Rouppe van der Voort & Drews, 2019; Esteban Pozuelo et al., 2019), usually
perform elongated structures, and the bright blobs we report in this study
exhibit circular shape, so they are more likely mass ejections.

The lifetimes of these blobs are mostly 30-100 s from the 1400 Å image
sequence. Most of these blobs appear to be initiated from the bright front,
and the rest might be ejected from the LB base. We identified 13 blobs that
could be unambiguously identified and tracked from the 1400 Å images. All of
these blobs show only upward propagation and then disappear. No obvious signs
of downward motions were found. These bright features were noticed by Bharti
(2015), but no detailed analysis was performed. Observational details of these
bright blobs are listed in Table 1.

Table 1: Observational details and measured physical parameters of the bright
blobs.

No. | Time | $D$ | $v$ | $a$ | $S$ | log10 EM | log10 $T$ | log10 $n_{e}$ | log10 $E_{k}$ | log10 $E_{t}$ | Type
---|---|---|---|---|---|---|---|---|---|---|---
| (UT) | $km$ | km s-1 | km s-2 | Mm2 | cm-5 | K | cm-3 | erg | erg |
1 | 05:22:10 - 05:23:09 | 3980 | 68.0 | 0.0 | 0.85 | 27.35 | 5.45 | 9.69 | 23.25 | 23.66 | I
2 | 05:33:31 - 05:34:00 | 2291 | 78.2 | 3.4 | 0.06 | 27.15 | 5.49 | 9.88 | 21.84 | 22.16 | I
3 | 05:37:55 - 05:39:15 | 6874 | 85.3 | 2.2 | 0.45 | 27.17 | 5.45 | 9.67 | 23.02 | 23.22 | I
4 | 05:39:08 - 05:39:45 | 2533 | 69.2 | 3.4 | 0.24 | 27.20 | 5.50 | 9.75 | 21.52 | 22.96 | III
5 | 05:41:20 - 05:42:11 | 3136 | 61.2 | 1.1 | 0.31 | - | - | - | - | - | I
6 | 05:47:11 - 05:48:10 | 4101 | 70.0 | 0.0 | 0.68 | 27.15 | 5.43 | 9.62 | 23.06 | 23.41 | II
7 | 05:49:30 - 05:50:07 | 3136 | 85.6 | 2.3 | 0.36 | 27.18 | 5.48 | 9.70 | 22.90 | 23.13 | I
8 | 05:51:42 - 05:52:48 | 6151 | 93.4 | 3.4 | 1.27 | 27.21 | 5.47 | 9.58 | 23.68 | 23.82 | I
9 | 05:59:45 - 06:00:44 | 5186 | 88.6 | 1.1 | 1.13 | 27.50 | 5.50 | 9.74 | 23.71 | 23.93 | I
10 | 06:07:56 - 06:09:01 | 4704 | 71.4 | 3.4 | 0.88 | 27.30 | 5.49 | 9.66 | 23.29 | 23.69 | III
11 | 06:12:19 - 06:13:40 | 3256 | 40.4 | 2.3 | 0.32 | 27.12 | 5.46 | 9.68 | 22.15 | 23.01 | I
12 | 06:23:11 - 06:23:47 | 2050 | 56.0 | 1.1 | 0.25 | 27.13 | 5.45 | 9.72 | 22.30 | 22.87 | III
13 | 06:27:12 - 06:28:55 | 6633 | 64.7 | 1.1 | 0.59 | - | - | - | - | - | III

Note. — The second column gives the time ranges during which the blobs appear
in SJI 1400 Å images. Other columns list the propagation distance ($D$),
velocity ($v$), initial acceleration ($a$), area ($S$), emission measure
(log10 EM), temperature (log10 $T$), electron density (log10 $n_{e}$), kinetic
energy (log10 $E_{k}$), and thermal energy (log10 $E_{t}$) for the 13 bright
blobs. Since blobs 5 and 13 cannot be unambiguously identified from the AIA
channels, their energies and emission measures are not calculated. The last
column shows the types of their possible formation processes.

Figure 1: Temporal evolution of three bright blobs in SJI 1400 Å images.
Panels (a1)-(a5), (b1)-(b5), and (c1)-(c5) are for the blobs 1, 6, and 4
listed in Table 1, respectively. The blue contours in panels (a4), (b5), and
(c5) outline the locations of the blobs. The blue dashed lines in panel (c2)
mark three cuts that we used to produce space-time diagrams in Figure 2. The
green arrows highlight the blobs and some brightenings related to the origin
of these blobs. The images are shown in logarithmic scale. (An animation of
this figure is available.)

There appear to be three types of bright blobs that are possibly associated
with different formation processes. Figure 1 shows the IRIS image sequences
for three blobs, each falling into one of the three types. In the first
example (Figure 1(a1)-(a5), blob 1 in Table 1, type I), we see two small-scale
brightenings at the bright front before the bright blobs. These two
brightenings subsequently merge into a bigger one (Figure 1(a2)), from which
an blob is then clearly separated. For most of the eight type I cases, the
newly formed features are also brighter.

The second example (Figure 1(b1)-(b5), blob 6, type II) appears to have a
different formation process. Before the formation of the bright blob, there is
an evident brightening (Figure 1(b2)) at the LB (similar to the first case
studied in Hou et al., 2017). Then the emission of the bright front is
enhanced at the location right above this brightening, where a faint jet-like
structure appeares to connect the LB and the bright front. Later on, a bright
blobs is ejected away from the bright front. We only found one such case (type
II).

Figure 2: Space-time diagrams along the cuts shown in Figure 1 (c2). The black
dashed lines indicate the rising motion of some materials from the LB. The
green arrows indicate the apparent origin sites of bright blobs. The dashed
black lines indicate the speeds of the bright edges, that is around
$1Mm~{}s^{-1}$.

The third example (Figure 1(c1)-(c5), blob 4, type III) is different from the
two examples described above. Before the bright blobs forms, neither
significant intensity enhancement at the LB nor signature of merging bright
points at the bright front is observed. However, we noticed that materials
arise from a wide range of the LB, and they merge with the bright front. Then
three successive bright blobs appear after 05:37 UT. Figure 2 shows the space-
time diagrams along different paths (Figure 1(c2)) of the three successive
blobs. It is evident that when the rising materials from the LB merge with the
bright front from the south to the north, bright blobs successively occur at
the different merging locations. There are four such blobs (type III).

Figure 3: Space-time diagrams along the paths of bright blobs 11 and 8.

To measure the velocities ($v$) and initial accelerations ($a$) of these
blobs, we produced space-time diagrams along the paths of each blob. Figure 3
shows two examples. Blob 11 reveals a constant speed, whereas blob 8 has an
obvious acceleration during the initial propagation. We calculated the average
speed and acceleration for each case. These blobs have speeds of $71.7\pm
14.7$ km s-1, with accelerations of $1.9\pm 1.3$ km s-2. For the only one type
II blob, its acceleration is 0 km s-2. While for most of the other blobs, we
see a clear acceleration. We also calculated the propagation distances ($D$)
and projected areas ($S$) of the blobs. These blobs normally propagate to a
projected distance of 3–7 Mm from the bright front in the 1400 Å passband. The
projected areas, with an average of $0.57\pm 0.37$ Mm2, do not reveal a
significant change during the propagation. These kinematic parameters of all
blobs are listed in Table 1.

###  3.2 Response in AIA passbands

Figure 4: A blob (indicated by the white arrows) in the SJI 1400 Å, NVST
H$\alpha$ core, and AIA 335, 304, 211, 193, 171 and 131 Å images. An animation
of this figure is available online. (An animation of this figure is
available.)

Most blobs also show clear signatures in images of all or most of the AIA 131,
171, 193, 211, 304, and 335 Å filters, and an example is presented in Figure
4. Meanwhile, these blobs do not show any obvious signatures in H$\alpha$ core
images. The visibility of these blobs in different AIA passbands and SJI 1400
Å images suggests either a multi-thermal structure or a TR temperature. It is
difficult to determine the temperature structure through a differential
emission measure (DEM) analysis since the low-temperature part of the DEM can
not be well constrained by observations in the AIA passbands (Del Zanna et
al., 2011; Testa et al., 2012).

Figure 5: (A)-(E) AIA 131, 171, 193, 211 and 335 Å images taken around
06:08:24 UT. The white arrow and red square in each of panels (A)-(E) indicate
a plasma blob and the region for an EM-loci analysis, respectively. (F) The
EM-loci curves for the blob. The small black box shows a region with many
crossings of the EM-loci curves.

Instead, we calculated the EM-loci (emission measure loci) curves for each
plasma blob to determine the possible temperature (e.g., Del Zanna et al.,
2002; Winebarger et al., 2013; Tian et al., 2014). Figure 5 shows the EM-loci
curves for blob 10. The EM-loci curves of different AIA passbands cross a
relatively narrow box around the temperature of $10^{5.5}$ K and emission
measure (EM) of $10^{27.25}$ cm-5. This temperature and EM are regarded as
rough estimates of the plasma temperature and EM of the blob. Two of the
plasma blobs (blobs 5 and 13 in Table 1) do not show obvious signatures in
most of the AIA EUV passbands, and we could not perform an EM-loci analysis.
The possible temperature and EM values for the rest of the identified plasma
blobs are shown in Table 1. We found that the EM and temperature of the
ejected plasma are on average $10^{27.22}$ cm-5 and $10^{5.47}$ K,
respectively. The EM-loci curves should just be considered as an upper limit
of the true emission measure distribution (Tian et al., 2014).

###  3.3 Energy estimation

Using the EM and temperature estimated from the EM-loci analysis, we can
calculate the kinetic and thermal energy for each blob. Under the assumption
that the integration length along the line of sight is similar to the
projected size, we estimated the electron density ($n_{e}$) of a blob using
Equation 1,

$EM\approx n_{e}^{2}L$ (1)

where $L$ is the square root of the area of the blob. The electron densities
of the blobs were found to be $10^{9.70\pm 0.08}$ cm-3.

Assuming a fully-ionized plasma and a typical coronal composition (e.g., Yang
et al., 2020), the mass density can be estimated as the following

$\rho\approx 1.2n_{e}m_{p}$ (2)

The kinetic energy ($E_{k}$) and thermal energy ($E_{t}$) can be estimated as

$E_{k}=\frac{1}{2}\rho Vv^{2}\approx 0.6n_{e}m_{p}L^{3}v^{2}$ (3)
$E_{t}=2n_{e}\frac{3}{2}k_{B}TV\approx 3n_{e}k_{B}TL^{3}$ (4)

where $k_{B}$ is Boltzmann constant, $\rho$ is the mass density, $V$ is the
volume of the blob, and $m_{p}$ is the proton mass. Here we simply assumed the
same length, width and height for the ejected plasma. Considering the
projection effect, $E_{k}$ should be considered as a lower limit. We also
listed values of $E_{k}$ and $E_{t}$ for all identified blobs in Table 1. The
average $E_{k}$ is $10^{22.8\pm 0.7}$ erg, and the average $E_{t}$ is
$10^{23.3\pm 0.5}$ erg. Both are close to the typical nanoflare energy of
$10^{24}$ erg (Parker, 1988).

Assuming that the blobs are produced due to the release of magnetic energy,
and other energies such as gravitational potential energy and radiation energy
are neglegible, the dissipated magnetic energy $E_{m}$ can be estimated as the
sum of $E_{k}$ and $E_{t}$ (Priest, 2014; Chen et al., 2015, 2020). Hence, the
density of the dissipated magnetic energy can be written as

$\frac{E_{k}+E_{t}}{V}\approx\frac{{(\Delta B)}^{2}}{8\pi},$ (5)

where $\Delta B$ represents the dissipated magnetic field. Among the 10 blobs
(except blobs 5, 6, and 13), the absolute values of $\Delta B$ are generally
4–6 G. In other words, the magnetic field strength decreases by 4–6 G to
trigger the blobs. It should be noted that our estimation based on the
assumption of a fully-ionized plasma and a typical coronal composition is not
applicable to blob 6 (type II), which could be driven by the magnetic energy
release in the lower partially ionized sunspot atmosphere.

###  3.4 Possible formation mechanisms

We categorized three types of small-scale bright blobs from the LB in Section
3.1. Due to the lack of spectral information of the blobs, we can only
speculate their formation mechanisms from the images. The first type is
associated with the merging of two bright features on the bright front, and
the newly formed features often become brighter. The blobs may be triggered by
magnetic reconnection between two approaching plasma blobs. It is also
possible that the local magnetic tension strengthens when two brightenings
move close to each other, and the enhanced magnetic tension drives these
blobs. Both the magnetic reconnection and enhanced magnetic tension could be
caused by interaction of adjacent shocks, which leads to the merging of
different bright features along the bright fronts.

The second type appears to be related to localized transient brightenings at
the base of the LB. These brightenings indicate small-scale magnetic
reconnection events on the LB, as suggested by Hou et al. (2017) and Tian et
al. (2018b). Magnetic reconnection between small-scale emerging magnetic
bipoles and the surrounding umbral field could occur intermittently and drive
upward propagating hot plasma from the LB. The initial acceleration of the
blob, which we calculated based on the traveling paths above the bright front,
is essentially zero. The negligible acceleration also implies that the origin
sites of such blobs are below the bright front, thus the main acceleration
takes place before the blobs propagate ahead of the bright front.

The remaining blobs are related to neither merging of bright features on the
bright front nor transient brightenings on the LB. For these events, ascending
materials from the LB, likely associated with shock waves, are detected to
merge with the existing bright front. The shocks may accelerate the local
plasma on the bright front, leading to blobs. Merging of plasma at the bright
front may also cause instabilities, which might trigger the blobs.

##  4 conclusions and discussion

We have performed a detailed study of the kinematics and energetics of small-
scale bright blobs above a LB observed in the TR images taken by IRIS. These
blobs have a typical lifetime of one minute and an average area of $0.57\pm
0.37$ Mm2. All blobs exhibit upward propagation without downward motions. With
an average speed of $71.7\pm 14.7$ km s-1, these blobs could travel to a
distance of $\sim$3-7 Mm. Most blobs also appear in the AIA 131, 171, 193,
211, 304, and 335 Å images but not in the H$\alpha$ image, suggesting that the
blobs are heated above the TR temperatures. Using the EM-loci method, we found
a typical temperature of $10^{5.47\pm 0.02}$ K and EM of $10^{27.22\pm 0.12}$
cm-5 for the ejected plasma. The kinetic energy and thermal energy have been
estimated to be $10^{22.8\pm 0.7}$ erg and $10^{23.3\pm 0.5}$ erg,
respectively.

Some blobs occur after the merging of two brightenings on the bright front, or
interaction between rising materials and the bright front. These blobs may be
triggered by magnetic reconnection, release of locally enhanced magnetic
tension, acceleration of shocks, or instabilities at the bright front as
shocks travel through. Obvious acceleration has been found for most of these
blobs. One blob appears to travel directly from the LB base, and it might be
driven by intermittent small-scale reconnection on the LB. This blob reveals a
constant velocity, and no obvious acceleration was observed after passing the
bright front. Though the observations indicate that the bright blobs above the
LB are more likely to be mass ejections, it should be noted that we cannot
exclude the possibility of the heating fronts.

Due to the lack of spectral data and magnetic field measurements for these
blobs, we cannot provide a conclusive judgement on the origin of these blobs.
Upcoming 4-m Daniel K. Inouye Solar Telescope (DKIST) would perform
unprecedented high-resolution observations of the sunspots and the light
bridge and provide simultaneous magnetic field measurements from the
photosphere to the chromosphere. The Spectral Imaging of the Coronal
Environment (SPICE) onboard Solar Orbiter could provide the spectra of the
lines with a wide range of formation temperatures. These new facilities will
likely provide new insight into the generation mechanisms of these bright
blobs. Advanced three-dimensional numerical simulations of active regions,
including dynamics above light bridges, will also help us better understand
their formation mechanisms.

This work is supported by NSFC grants 11825301, 11803002, 11790304(11790300),
11427901, 11873062, 11903050, and 11773039 and the Strategic Priority Research
Program of CAS (grant XDA17040507). IRIS is a NASA Small Explorer mission
developed and operated by LMSAL with mission operations executed at NASA Ames
Research center and major contributions to downlink communications funded by
ESA and the Norwegian Space Center. SDO is a space mission in the Living With
a Star Program of NASA.

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