# External Service Sensing (ESS): Research Framework, Challenges and
Opportunities

Zhongjie Wang, Mingyi Liu, Zhiying Tu, Xiaofei Xu  Faculty of Computing,
Harbin Institute of Technology
Harbin, China
{rainy, liumy, tzy_hit<EMAIL_ADDRESS>
###### Abstract

The flourish of web-based services gave birth to the research area services
computing, a rapidly-expanding academic community since nearly 20 years ago.
Consensus has been reached on a set of representative research problems in
services computing, such as service selection, service composition, service
recommendation, and service quality prediction. An obvious fact is that most
services keep constant changes to timely adapt to changes of external
business/technical environment and changes of internal development strategies.
However, traditional services computing research does not consider such
changes sufficiently. Many works regard services as static entities; this
leads to the situation that some proposed models/algorithms do not work in
real world. Sensing various types of service changes is of great significance
to the practicability and rationality of services computing research. In this
paper, a new research problem External Service Sensing (ESS) is defined to
cope with various changes in services, and a research framework of ESS is
presented to elaborate the scope and boundary of ESS. This framework is
composed of four orthogonal dimensions: sensing objects, sensing contents,
sensing channels, and sensing techniques. Each concrete ESS problem is defined
by combining different values in these dimensions, and existing research work
related to service changes can be well adapted to this framework. Real-world
case studies demonstrate the soundness of ESS and its framework. Finally, some
challenges and opportunities in ESS research are listed for researchers in the
services computing community. To the best of our knowledge, this is the first
time to systematically define service change-related research as a standard
services computing problem, and thus broadening the research scope of services
computing.

###### Index Terms:

Services Computing, Research Framework, External Service Sensing, Challenges
and Opportunities

##  I Introduction

With the rapid development of SOA, IoT, Cloud and Edge computing, the number
of available services has exploded and the types of services has become
increasingly diverse. The flourish of web-based services gave rise to the
research area called services computing, and its academic community has kept
continuously expanding since nearly 20 years ago[1]. Services computing
involves many classical research problems, such as service selection[2],
service discovery[3], service composition[4], service classification[5],
Quality of Service (QoS) prediction[6], and service recommendation[7].

An obvious fact is that most real-world services, both web-based services and
business services, keep constant changes to timely adapt to changes of
external business/technical environment and changes of internal competitive
strategies. Service providers actively or passively make adjustments to their
services. The types of changes include technical-level QoS and functionalities
and business-level contents and performances such as price, promotion
policies, and delivery channels; new services may enter the market, and dying
services may exit. However, traditional services computing research does not
consider service changes sufficiently. Many research works regard services as
static entities; consequently, resulting in the situation that many proposed
models/algorithms do not work in the real world. For example, for the service
classification problem, when a service adds or remove certain features, the
category it belongs to may change and the original classification results are
no longer accurate; and for service recommendation, ignoring service changes
may result in recommending a service that no longer conforms to the user’s
demand or preferences, or even worse, recommending a service that has been
deprecated.

It is necessary to summarize such kinds of issues related to service changes
and define a consensual research problem to deal with these formidable
challenges that service changes present to traditional services computing
problems. In this paper, we called it External Service Sensing problem (ESS).
The keyword sensing indicates that research on this problem is to perceive
service changes timely and accurately. The keyword external is emphasized
because this problem stands on the view of external observers of service to be
sensed. Users of the service usually do not have the privilege to access the
internal status of the service due to legal, business, ethical, and technical
barriers, but they have to perceive the changes from the outside of the
service. A significant challenge is that external observers perceive service
changes in terms of the limited information that is exposed by the providers
of the service, and to make the sensing more timely and accurately, new and
effective approaches have to be elaborately studied.

Note that to sense a service from the internal point of view has been widely
studied in the past, such as run-time service performance monitoring[8, 9].
This falls out of the scope of ESS and this paper.

Figure 1: Illustration of the External Service Sensing (ESS) problem

Fig. 1 briefly demonstrates the ESS problem. More specifically, an ESS problem
could be decomposed into the following dimensions:

  1. 1.

What are the objects to be sensed? This is called “sensing objects”.

  2. 2.

What kinds of perspectives of a sensing object are expected to change and need
to be sensed? This is called “sensing contents”.

  3. 3.

What necessary data are required for the sensing and where are these data
collected? This is called “sensing channels”.

  4. 4.

How are concrete change information of sensing contents identified from
sensing channels? This is called “sensing techniques”.

There are many options in each of the four dimensions, and a concrete ESS
problem is therefore defined by the combination of different strategies
adopted in these perspectives.

The Benefits of a systematic study on the ESS problem are twofold. Firstly,
incorporating timely and accurate sensing results into traditional services
computing problems would drive services computing researchers to invent new
models and methods for these problems so that the practicability and
rationality could be significantly improved. Secondly, the presence of ESS
would help identify many new application scenarios in services computing
research. For example, service providers could adjust their business
strategies in time by sensing changes in competitors’ services. Regulators
could develop appropriate policies to guide the healthy development of a
service market by comprehensively sensing market changes. Most of these
scenarios have not yet received sufficient attention from the services
computing community.

Figure 2: A four-dimensional framework for the External Service Sensing (ESS)
problem

In recent years, there have been some studies involving specific perspectives
of service changes. For example, Zhong et al.[10] presented a method for
perceiving changes in the invocation relations between APIs and mashups in a
web service ecosystem for more accurate service recommendation; Huang et
al.[11] gave an approach for sensing QoS fluctuations in IoT environments to
rank available services; Liu et al.[12] proposed a method for predicting the
service ecosystems’ evolutionary trends by tracking the changes of service
communities; and Hao et al.[11] analyzed the changes of invocation relations
between Apps by parsing source codes of these Apps. The most immediate work is
by Zheng et al.[13, 14], which introduced the distributed infrastructure to
access Web services periodically and evaluate their QoS so that the
fluctuations of QoS could be perceived timely.

Each of these studies can be considered as a concrete ESS problem, indicating
the exploration of ESS has already been launched sporadically in the services
computing community. However, since ESS has not yet been widely recognized as
an independent services computing problem, these existing works usually treat
the service sensing task as a subsidiary of other services computing problems.
This greatly lowers the significance and challenges of ESS. To compensate for
this, we are committed to a systematic discussion of ESS and propose a four-
dimensional research framework to elaborate the scope and boundary of ESS. The
main contributions of this paper are listed as follows:

  * •

To the best of our knowledge, this is the first time that the problem of ESS
is explicitly and systematically studied. We explore the definition of ESS,
the relevance of ESS to other traditional services computing problems, and the
significance that ESS would bring to the services computing community.

  * •

A comprehensive research framework of ESS is proposed, which defines the four
dimensions (sensing objects, sensing contents, sensing channels, and sensing
techniques) of ESS and the relations between them. It lays a solid foundation
for other researchers to carry out ESS research in the future. Concrete ESS
problems could be easily instantiated from this framework.

  * •

We collect some representative existing ESS related works from services
computing literature and use the framework to classify them. This demonstrates
that the framework is compatible with existing research. We also summarize
some challenges and opportunities for future research if ESS is widely
accepted as a standard service research problem by the community.

The remainder is organized as follows. Section II introduces the proposed
framework in detail. Section III illustrates the effectiveness of the
framework by systematic mapping and case studies. Section IV gives some
challenges and opportunities of ESS. The last section is the conclusion.

##  II An ESS Research Framework

As mentioned in Section I, ESS is an upstream task of other services computing
problems, such as service recommendation, QoS prediction, and service
composition. Here we call them application scenarios of ESS. The input of an
ESS problem are the sensing objects and sensing contents required by a
specific application scenario, the output is the information of changes of
sensing contents, and the solution of the ESS problem is to use specific
sensing techniques to collect data via specific sensing channels and make data
analysis to get the output which is further utilized in the application
scenario.

Fig. 2 shows the ESS framework which contains four internal dimensions, namely
sensing objects, sensing contents, sensing channels and sensing techniques.
The first two dimensions focus on what to be sensed, the third on where to
sense, and the fourth on how to sense. Subsequent part of this section
delineate each dimension respectively, and the relations between them.

###  II-A Dimension 1: Sensing Objects

In terms of sensing granularity, sensing objects can be classified into
specific services, service providers, third-party service platforms, and
service ecosystems:

  * •

Specific service: A service is delivered and provisioned by an organization or
individuals to offer specific functionalities for meeting specific user
demands. It may be a software service (e.g., a mobile App Instagram, a web API
Amazon S3 API) or a physical facility in the real world (e.g., an Amazon Go
store). Since external observers have no chance to access the interior of
service, services are the finest sensing unit in ESS.

  * •

Service provider: including service companies (e.g., Google, Alibaba) and
government regulators ( e.g., State Post Bureau of China). Sensing a service
provider is usually accompanied by sensing the specific services it offers.
Thus it has broader application scenarios. For example, users would like to
know the latest released services of a company, and a company could timely
adjust its business strategies according to the sensing of its competitors.

  * •

Third-party service platform (TPP): A TPP is operated by a specific
organization and aggregates massive service providers and the corresponding
services they offer. Users may select and use appropriate services from a
large number of candidates. A TPP can be an online platform such as App Store,
ProgrammableWeb, an offline physical platform such as Wanda Plaza, or a
platform linking online and offline such as an e-commerce platform Amazon.com
and a takeaway platform Meituan. Sensing TPP includes sensing the entry and
exit of services or service providers to/from the TPP, ranking of services on
the TPP, and so on.

  * •

Service ecosystem: It is a type of complex continuously-evolving systems
consisting of service providers and specific services that belong to specific
regions or domains. Compared with TPP, service ecosystems typically have a
larger size and cover a wider range of services and service providers. There
are no explicit operators responsible for the provision of a service ecosystem
(it evolves spontaneously). For example, the e-commerce service ecosystem in
China is composed of thousands of online e-commerce websites, mobile Apps,
offline marts, and billions of potential customers. Sensing changes in service
ecosystems is often used in scenarios that support decision-making for service
providers as well as market regulators.

It should be noted that there is no strict boundary between these four
categories of sensing objects, and multiple sensing objects may be
simultaneously involved in an ESS application scenario.

###  II-B Dimension 2: Sensing Contents

Sensing contents are elements or perspectives of a specific sensing object,
and they might change along with time due to external or internal drivers.

There are many types of sensing contents. For example, functionalities of a
service may be expanded or compressed, Quality of Service (QoS) and Quality of
Experience (QoE) may be improved or deteriorated, price of service may
increase or cut down, and relations between two services may emerge or
disappear. Different types of sensing objects may have a different sets of
sensing contents.

Representation forms of sensing contents are diverse. Some sensing content
could be represented by continuous and precise values, such as QoS and price.
Some are discrete and text-based, e.g., functions and delivery channels. There
are also some sensing contents that are subjectively measured, such as QoE and
health status.

The degree of attention paid to different sensing contents varies among
existing studies: some sensing contents have been well-studied, such as QoS.
Community structures, relations, and functions are emerging sensing contents
in recent studies. Note that existing studies have focused more on the static
metrics of sensing contents than on their changes.

###  II-C Dimension 3: Sensing Channels

Sensing channels are the location where necessary data related to the changes
of sensing objects and sensing contents are acquired. Sensing channels can be
classified into the following six types:

  * •

Physical sensing refers to the acquisition of sensing objects and contents
from offline physical space and then transfer data to online cyberspace.
Physical sensing may rely on hardware sensors, e.g., flight
tracking111https://www.flightaware.com/live/, smart home[15], and smart
city[16, 17]. Physical sensing may also rely on volunteers (persons) to report
changes they manually perceive, but obviously this approach requires
significant labor costs, e.g., local traffic condition reporting[18] or crime
incident reporting[19]. This is also called “social sensing”.

  * •

Push notification means that a sensing object actively publicizes its changes
to whom it may concern. This channel is widely used in reality, such as
promotional emails, App update notifications, etc.

  * •

Profiles are text descriptions of a sensing object and are usually publicized
by the sensing object itself, e.g., the description text of API service.
Profiles have been widely used in a variety of services computing researches
on account of the progress of natural language process techniques, but few
studies have focused on sensing changes from service profiles.

  * •

Source code. On the one hand, with the prevalence of open source communities,
many services expose their source codes to the public, e.g.,
Telegram222https://github.com/DrKLO/Telegram. On the other hand, the
distribution format of some types of software services has been standardized.
However, these services do not provide publicly-accessed source codes, some
configuration files used to describe services are publicly accessible. For
example, an Android application file APK always contains a file called
AndroidManifest.xml. Changes of such types of services may be partially sensed
via the updates of their source codes or configuration files.

  * •

User feedback refers to the evaluation of a sensing object from its users.
Feedback can be a quantitative score such as the rating of Apps in the App
Store, or text-based descriptions such as user reviews on Yelp. Changing
trends of users’ sentiments on specific perspectives of service is a valuable
type of change to be sensed.

  * •

Social media & public media refer to acquire changes of sensing objects from
the discussion contents in public social networking sites or public news
reports. For example, there is a great deal of information about Windows on
Reddit, and a wealth of investment events among service providers can be found
from investment information sites333https://www.crunchbase.com. Similar to the
user feedback channel, change-related information in social media & public
media is represented by the changing trend of opinions from massive users or
the public. Compared with other sensing channels, this channel offers richer
information about changes, and the cost for obtaining change information is
comparatively lower. However, irrelevant and fake news would affect the
accuracy of the sensing results.

Figure 3: Relations between the four dimensions

The above-mentioned sensing channels could be classified into active and
passive ones in terms of whether the change information is actively and
directly publicized by the sensing objects or not. Physical sensing and push
notification belong to active sensing channels, and others are passive ones.
Compared with passive sensing channels, active sensing could more timely
provide more accurate information about changes, but the sensing contents are
often limited and incomplete. This drives researchers to pay more attention to
passive ones to pursue more comprehensive sensing.

From another perspective, sensing channels can also be classified into direct
and indirect ones depending on whether the sensing data is directly acquired
from a sensing object or other sources related to the sensing object. Feedback
and social media & public media are indirect sensing channels, while others
are direct ones. The sensing latency of direct sensing channels would be lower
than that of indirect ones, i.e., the timeliness of sensing is higher.
Indirect channels exist more widely, but as mentioned above, there are lots of
noisy data that require elaborate processing.

###  II-D Dimension 4: Sensing Techniques

Sensing techniques are processing methods for extracting changes of sensing
contents from the data acquired from sensing channels.

Some sensing techniques are tied closely to specific sensing channels. For
example, IoT techniques utilize hardware sensors for physical sensing, and
source code analysis techniques parse the source code of service to obtain
specific changes of the sensing contents.

Some sensing techniques have been widely used in other services computing
problems and can be easily transferred to ESS, e.g., crowdsourcing approaches
allow volunteers to actively report changes of sensing objects and contents,
and data-driven deep learning methods such as text mining are used to extract
changes from a user feedback or public media texts. These methods facilitate
sensing changes from a massive amount of cheap data efficiently and
accurately.

Sensing techniques are not independent of each other. Some techniques are
based on the data generated by other sensing techniques before they can
process to obtain the target sensing contents. For example, complex network
analysis relies on the graph constructed by other sensing techniques to obtain
sensing contents such as community structure and health status of a service
ecosystem, while statistical analysis methods aggregate explicit sensing
contents obtained by other sensing techniques to calculate implicit sensing
content that is not directly available.

TABLE I: Adaptation of some existing works to the proposed ESS framework

Application Scenario Sensing Objects Sensing Contents Sensing Channels Sensing
Techniques Tu et al. [20] App recommendation Mobile app Function Profile Text
mining Shu et al. [21] Service recommendation Real-world service QoS Price
Feedback Public media Text mining Zheng et al. [22] Service recommendation
Items Price Category Profile Source code analysis Statistical analysis Zhong
et al.[10] Service recommendation Third-party service platform
(ProgrammableWeb) Invocation relation Profile Complex network analysis Hao et
al. [23], [24] Service evolution Mobile app, Mobile service ecosystem
Interface Function Cooperation Source code Source code analysis Complex
network analysis Statistical analysis Liu et al. [12] Service evolution
Business service ecosystem Community structure Public media Text mining
Complex network analysis Song et al. [25] Service evolution prediction Mobile
app Function QoE Profile Feedback Text mining Adeleye et al. [26] Service
discovery Third-party service platform (ProgrammableWeb) Relation Profile
Complex network analysis Xia et al. [27] Service discovery IoT services
Relation (Correlation degree) Physical sensing Complex network analysis
Simulation Huang et al.[11] Service ranking IoT services QoS Physical sensing
Simulation Statistical analysis

###  II-E Relations between the Four Dimensions

The four dimensions in the framework are not independent but are correlated
with each other. Relations between them are shown in Fig. 3.

For an application scenario, several different sensing objects may need to be
sensed, and these sensing objects can be of different granularity. A sensing
object can contain more than one element/perspective, and the ones relevant to
the application scenario are selected as the sensing contents. Different
sensing objects may own some common sensing contents. Since different sensing
contents of a sensing object are often distributed in different sensing
channels, it is reasonable to bundle sensing objects and sensing content to
support the application and find appropriate sensing channels. The selection
of appropriate sensing techniques is often based on the form of data provided
by the sensing channel.

Generally speaking, the dimensions of the ESS framework have relatively
obvious upstream and downstream relations, and different dimensions are with
many-to-many relations.

##  III Systematic Mappings and Case Studies

In this section, we first demonstrate a mapping from some existing work
related to service changes to the proposed framework, i.e., to re-define the
problems in these related work using ESS concepts. The objective is to show
the rationality of the framework, i.e., it is compatible with previous
services computing research. Then we give some case studies to show that this
framework has the capability of defining real-world scenarios into standard
ESS problems, i.e., it has good practicality.

###  III-A Systematic Mappings

Table I shows the adaptation of some existing works to the proposed ESS
framework. Although these works originally were not intended to address the
ESS problems, we can see that each of them can be represented by the
instantiation of the ESS framework. For example, the goal of Tu et al.[20] is
to make mobile service recommendations, the object of their sensing is mobile
Apps, and text mining are the sensing technique to obtain functions changes of
Apps from App profiles. With the help of the sensing results of function
changes of Apps, more reasonable and accurate recommendation results could be
obtained than other App recommendation approaches without considering sensing
service function changes. Due to limited space, we do not explain other works
any more.

Note that the ESS framework would inspire services computing researchers to
extend their work by incorporating change-related information into their work.
The framework gives researchers a systematic thinking way for this change-
oriented extension. For example, Hao et al.[28] perform service recommendation
by analyzing service descriptions without considering dynamic entry and exit
of services, which leads to many deprecated services in the recommendation
results. The recommendation of deprecated services could be avoided if such
dynamic changes were sensed. This problem can be solved by simulating user
requests to check the availability of services during the recommendation
periodically.

###  III-B Case Studies

Figure 4: A case study: travel service recommendation during COVID-19 pandemic

Here we give a real application scenario about travel service recommendation
during COVID-19. It is then defined as a concrete ESS problem by the
framework.

During COVID-19, customer travel planning will be affected by various
uncertainties, such as epidemic control policy around the world, and refund,
change, and insurance policies of airlines and rail services. There are very
frequent changes occurring in these services, and it is impracticable to
recommend a travel plan to customers without considering these changes. For
example, some cities may unexpectedly deny access to people from regions with
high COVID-19 infection risk. Therefore, it is necessary to sense those
changes that might occur on travel-related services and incorporate them into
the recommendation.

Fig. 4 gives the demonstration of using our ESS framework to define such
travel service recommendation problem. The sensing objects include: (1) travel
service providers and third-party travel platforms and the travel services
they offer; (2) as governments play an important role in epidemic prevention
and control, they also need to be sensed. For sensing contents, besides the
common QoS attributes of the travel services, e.g., price, travel time,
additional COVID-19 related frequently-changing perspectives such as refund
and change policies, insurance policies, and epidemic policies, should be
sensed to reduce the risk of travel, too. These sensing contents can be
obtained from sensing channels such as service profiles, governments/services’
push notifications, and published media. Since most of the information is
disclosed to the public in the form of text, text mining is the sensing
technique that has to be adopted. And statistical analysis and crowdsourcing
are essential supplements. The sensing results are then incorporated into the
travel service recommendation method to select the most appropriate travel
route and flights that suit the preferences of a specific user, e.g.,
minimizing the risk of refund and cost.

Another case that has been widely used in the real world is driving route
planning. The route with the shortest distance is usually recommended. But
when traffic condition changes are considered, the shortest route may not be
the fastest one. When considering the external service sensing, text mining
may be applied to acquire continuously updated road maintenance information
from the local government’s push notification (e.g., websites and mobile
Apps); crowdsourcing and IoT sensing are also employed to sense the traffic
flow directly from the physical world (this has been widely adopted in many
mobile navigation Apps such as Baidu Map). Dynamic traffic condition
information is then transferred to route planning algorithms to recommend a
route for avoiding congestion and shortening actual driving time.

ESS could also support the decision-making of service market regulators.
Taking the bike-sharing service ecosystem as an example, the number of service
providers and bikes they delivered to the market, the quality of bikes, the
price per ride, and so on, keep dynamically changing over time. Market
regulatory authority needs to monitor the development health status of this
ecosystem and timely make appropriate policies to guide service delivery
strategies of bike-sharing companies or the investment behaviors in stock
markets. From the ESS point of view, the sensing object is a specific service
ecosystem, the content is the health status of the ecosystem, and complex
network analysis and statistical analysis could be used to identify the
changes of various industry development metrics from transaction/investment
data of various related companies. This case has been elaborately studied in
Liu et al previous work[29].

From the three cases we can see that ESS problems exist widely in real world,
and this demonstrates the potential of ESS research and the value of ESS
applications. It is worthy to put more effort into ESS research and
applications.

##  IV Challenges & Opportunities

Although a framework has been proposed to define ESS problems systematically,
there are still many challenges to meet. Here we list some critical challenges
for services computing researchers as research opportunities.

###  IV-A Challenge 1: How to standardize the description and release channels
of service changes?

As we mentioned in Section II, there are different granularities of sensing
objects, ranging from small specific services to large service ecosystems. The
forms of sensing contents are diverse, too, ranging from numerical values to
textual information. It is important to standardize the representation of
these heterogeneous changes so that others could access and use the change-
related information in a simple and convenient way. One possible solution is
to create a Service Change Agreement (SCA) standard, just like using Service
Level Agreement (SLA)[30] to standardize the commitment between a service
provider and a client covering particular aspects of the service such as
quality, availability, and responsibilities. How to publicize SCA of massive
services to reduce the cost of duplicate sensing is also a challenging
problem. A third-party SCA aggregation platform that provides standardized
APIs for publicizing and acquiring SCAs might be a solution.

###  IV-B Challenge 2: How to measure the quality of sensing?

Inaccurate, incomplete, and delayed sensing would mislead the downstream tasks
(recommendation, prediction, etc.) to unacceptable output, and too costly
sensing is hard to accept neither. Designing metrics for ESS tasks is an
urgent challenge and of great significance to form a standard evaluation
system for future ESS solutions. In our opinions, four metrics are essential:

  1. 1.

Accuracy: to what extent the sensing results could reflect the changes
occurring in services? In other words, the perceived change-related
information should be inconsistent with what has happened as far as possible.
It is similar to the “precision” in machine learning.

  2. 2.

Comprehensiveness: i.e., the changes that have occurred in services should be
perceived as many as possible. It is similar as the “recall” in machine
learning.

  3. 3.

Timeliness: how long is it between the time when changes occurred and the time
when these changes have been perceived? The longer the time lag is, the lower
the timeliness of sensing is, and vice versa. Outdated change information is
meaningless.

  4. 4.

Cost: refers to how much it needs to pay to conduct the sensing and get
results, including the cost for change-related data collection and processing.
Of course, users would like to perceive changes with lower cost.

We do not present the detailed measurement of these metrics. Different
application scenarios have different levels of tolerance on them.

###  IV-C Challenge 3: How to protect privacy during sensing?

Similar to user privacy, sensing objects also have their privacy. For example,
a service provider hopes that its latest discount policies are known only to a
limited number of its loyal users but should not be sensed by others (such as
its competitors).

To solve this problem, one possible idea is to mimic the robots protocol used
in web crawlers to indicate what types of changes are allowed or not to sense.
Even for those changes that are allowed to sense, service objects may have the
willingness to control the dissemination scope of these changes, and new
solutions have to be studied to manage the privacy issues comprehensively.

Additionally, frequent sensing may increase the burden on the sensing objects,
e.g., simulating user requests would increase the network burden of the
service, and sensing objects can reduce this burden by proactively publishing
its changes through the third-party SCA aggregation platform proposed in
Challenge 1.

###  IV-D Challenge 4: How to sense offline services more efficiently and
cost-effectively?

As we mentioned in Section II-C, physical sensing channel relies on hardware
sensors or crowdsourcing (social sensors), which are much more costly than
online service sensing. To alleviate this problem, cheaper sensors and more
widespread deployment of sensors are required, and more efficient
crowdsourcing strategies are to be further explored. On the other hand, novel
ways of virtualizing offline services into cyberspace need further study.
Synchronizing offline worlds with online worlds is considered as a big
challenge. Fortunately, more and more practices have been conducted, such as
the industrial Internet that facilitates the creation of digital twins of the
two worlds.

###  IV-E Challenge 5: To build ESS infrastructure and toolset

It remains a challenge to implement our proposed framework from a technical
level. A set of tools and infrastructure that support the whole sensing
process is required, such as the SCA aggregation platform and sensing-related
APIs mentioned in Challenge 1. Such infrastructure and tools should provide
common processing flows of ESS and enhance the sensing capabilities of service
application; on the other side, they are to reduce the burden of developers
who previously have to develop the sensing functionalities, so that developers
are empowered by out-of-the-box built-in tools for different ESS dimension.
The architecture and operational logic of this platform may be similar to that
of current machine learning platforms, such as Amazon
SageMaker444https://aws.amazon.com/cn/sagemaker/.

##  V Conclusion

In this paper we define a new research problem External Service Sensing based
on our observations of service change related research and practice. We
present a framework for unifying related work and guiding future research. We
make a systematic mapping from existing service sensing related work to the
proposed framework and validates the effectiveness of the framework. Some
cases from real world also demonstrate that ESS problems indeed widely exist
and need more attention. We also discuss some challenges of ESS that have not
yet been fully defined and solved, which would be a uncultivated land of
services computing research. We expect this paper would attract more
researchers from services computing community to denote themselves to ESS
problems.

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11institutetext: School of Physics and Astronomy, Cardiff University, The
Parade, Cardiff CF24 3AA, UK
11email<EMAIL_ADDRESS>22institutetext: Universite de Lyon,
Universite Lyon 1, CNRS, CRAL UMR5574, Saint-Genis Laval, France
33institutetext: Universite Cote d’Azur, OCA, CNRS, Lagrange, France
44institutetext: School of Physics and Astronomy, University of Exeter,
Stocker Road, Exeter, EX4 4QL, UK 55institutetext: Universite Grenoble Alpes,
CNRS, IPAG, F-38000 Grenoble, France 66institutetext: Department of Physics
and Astronomy, Hounsfield Road, University of Sheffield, Sheffield, S3 7RH, UK
77institutetext: Astronomical Institute of the Czech Academy of Sciences,
BočníII 1401/1a, 141 00 Praha 4, Czech Republic 88institutetext: LESIA,
Observatoire de Paris, CNRS, Universite Paris 7, Universite Paris 6, 5 place
Jules Janssen, 92190 Meudon, France 99institutetext: School of Physics and
Astronomy, University of Leeds, Leeds LS2 9JT, UK

# Extreme adaptive optics astrometry of R136††thanks: Catalogue of reliable-
consistent sources is only available in electronic form at the CDS via
http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/

Searching for high proper motion stars††thanks: Based on observations made
with ESO Telescopes at the La Silla Paranal Observatory under programme ID
0102.D0271 and 095.D-0309

Z. Khorrami 11 M. Langlois 22 F. Vakili 33 P. C. Clark 11 A. S. M. Buckner 44
M. Gonzalez 55 P. Crowther 66 R. Wünsch 77 J. Palouš 77 A. Boccaletti 88 S.
Lumsden 99 E. Moraux 55

We compared high-contrast near-infrared images of the core of R136 taken by
VLT/SPHERE, in two epochs separated by 3.06 years. For the first time we
monitored the dynamics of the detected sources in the core of R136 from a
ground-based telescope with adaptive optics. The aim of these observations was
to search for High prOper Motion cAndidates (HOMAs) in the central region of
R136 ($r<6"$) where it has been challenging for other instruments. Two bright
sources (K$<15$ mag and V$<16$ mag) are located near R136a1 and R136c (massive
WR stars) and have been identified as potential HOMAs. These sources have
significantly shifted in the images with respect to the mean shift of all
reliable detected sources and their neighbours, and six times their own
astrometric errors. We calculate their proper motions to be $1.36\pm 0.22$
mas/yr ($321\pm 52$ km/s) and $1.15\pm 0.11$ mas/yr ($273\pm 26$ km/s). We
discuss different possible scenarios to explain the magnitude of such extreme
proper motions, and argue for the necessity to conduct future observations to
conclude on the nature of HOMAs in the core of R136.

###### Key Words.:

star clusters: individual: R136 – Astrometry – Proper motions –
Instrumentation: high angular resolution – Instrumentation: adaptive optics

##  1 Introduction

The R136 star cluster in the 30 Dor region of the Large Magellanic Cloud (LMC)
is a unique astrophysical laboratory; it is sufficiently young (1.5-2 Myr, de
Koter, Heap, & Hubeny 1998; Crowther et al. 2016; Bestenlehner et al. 2020)
and rich (Crowther et al., 2010) to allow the study of the formation and
evolution of the most massive stars. Most of the information on R136 comes
from Hubble Space Telescope (HST) observations, which was used to study
various parameters including the cluster’s star formation history, stellar
content, and kinematics (Hunter et al., 1995; , 2009; De Marchi et al., 2011;
Cignoni et al., 2015). Using the Spectro-Polarimetric High-contrast Exoplanet
Research (SPHERE, Beuzit et al. 2019) of the Very Large Telescope (VLT) in
2015 we obtained the first epoch observations of the R136 core (Khorrami et
al., 2017) in the near-infrared (NIR), and in 2018 we obtained a second set of
observations for this region (Khorrami et al., 2021). Comparing the two sets
of observations, we monitored the dynamics of the detected sources in the core
of R136 searching for High prOper Motion cAndidates (HOMAs111Homa is a
mythical bird from Iranian legends that never comes to rest, living its entire
life flying invisibly high above the earth.). The presence of such high-
velocity stars in the core of massive young star clusters can explain or
change the hypothesis on the formation of massive stars (if they form either
in isolation or were ejected dynamically from the cluster) and the dynamics of
the 30 Dor region as a whole.

The proper motion (PM) measurement of stars in 30 Dor relies on HST data taken
with various instruments (WFPC2, ACS/WFC, and WFC3/UVIS) and filters in
different epochs (Platais et al., 2015, 2018). There are therefor several
difficulties with obtaining these measurements since the data in each
instrument (and filter) show coordinate-dependent systematic and geometric
distortions that appear in the transformation of instrument pixel coordinates
and the PMs. Among the higher-precision PM sources, a handful of stars exceed
1mas/yr, but were excluded from the analysis by setting an upper limit on the
velocity of stars based on the maximum radial velocity measurements from VLT
Flames Tarantula Survey (VFTS) observations (Evans et al., 2011). The PM
measurements in the 30 Dor region, as given by HST and recently by Gaia
(Lennon et al., 2018), do not detect sources at the core of R136 because of
the concentration of bright sources and their small angular separations. Thus,
higher angular resolution and small pixel sampling are needed to improve the
PM measurements without overexposing the bright sources (and masking their
faint neighbours); better estimations of the positions of stars are also
needed during the photometry for point spread function (PSF) fitting.
SPHERE/IRDIS can observe the core of R136 in the NIR where the flux ratio of
stars is lower than in the optical, and with higher angular resolution and
pixel sampling (12.25 mas/pix) than the HST (e.g. WFC3/UVIS has 40 mas/pix).
These parameters make IRDIS the best instrument available at present to
resolve the higher number of stars in the core of R136. Additionally, IRDIS
produces two sets of data simultaneously taken within the same filter,
overcoming the instrumental errors such as bad pixels and imperfections of the
detector, which are critical to the PM measurements.

##  2 Data

We used two sets of imaging data over a field of view (FOV) of 11”$\times$ 12”
centred on the core of R136 in the K band in 2015 (2015-09-22, ID 095.D-0309)
and 2018 (2018-10-10, ID 0102.D-0271). These two sets of epoch data were
recorded using the classical imaging mode of IRDIS (Langlois et al., 2014).
Using the data taken with the same instrument and exactly the same
configuration, 3.06 yr apart, enabled us to study the dynamics of the core of
R136 with the highest angular resolution to date. For our purposes, the same
spectral band was split into the two IRDIS channels (the simultaneous images
on the left and right side of the detector), which were used to correct for
residual detector artefacts such as hot pixels and uncorrelated detector
noise, among other instrumental effects. We corrected each image for the
anamorphism following Maire et al. (2016) before the final image combination.
The image sharpness of the total exposure was maximised by discarding the
single frames with poorer Strehl ratio and by correcting a posteriori the
residual tip-tilt image motion on each short exposure before combining them.

The seeing was $0.69\pm 0.10"$ and $0.63\pm 0.10$, for the 2018 and 2015
observations, respectively. The night was rated as clear for both epochs,
meaning that less than 10% of the sky (above 30 degrees elevation) was covered
by clouds, and transparency variations were less than 10% during the
exposures, with an airmass between 1.61-1.67 (in 2015) and 1.52-1.45 (in
2018). Our data consists of 300 (in 2015) and 544 (in 2018) frames of 4.0s
exposures taken with the IRDIS broad-band K filter ($\lambda_{cen}=2182$ nm,
$\Delta\lambda=294$ nm). The Wolf-Rayet star R136a1 was used for guiding the
AO loop of SPHERE confirming the high level of performance even for faint
guide stars, which surpasses the performance of the Nasmyth Adaptive Optics
System Near-Infrared Imager and Spectrograph (NACO) and the Multi-conjugate
Adaptive optics Demonstrator (MAD) (e.g. Campbell et al. 2010). The details of
the data reduction procedure are given in Sect. 2 of Khorrami et al. (2021).

##  3 Photometry

Photometry is carried out on the two images (left and right) from each epoch,
using the same method and criteria as explained in Sect. 3 of Khorrami et al.
(2021). We needed to repeat the photometric analysis in both epochs for three
reasons: 1) the input PSF should be the same in two epochs in order to have a
robust photometric analysis; 2) some of the input PSF used in the photometric
analysis in the first epoch (Khorrami et al., 2017) appeared as multiple
systems in 2018 where we use longer exposures; and 3) the photometric analysis
of the 2015 data was done on the combined left and right images, but separate
photometric analyses of the left and right images were required for the
present purpose.

The common sources between the two images were defined according to their
position 222Closer than the full width at half maximum (FWHM) of the PSF,
which is the resolution, their magnitude difference being less than 0.5, and
the correlation between their input PSF and detected sources less than 10%.
This means that the detected sources in the two images have a very similar PSF
shape since the input PSF stars are chosen to be the same in all four images.
The number of common sources between the left and right K images was 705 and
1451 in 2015 and 2018, respectively. Although in principle we should not see
any shift in the position of the common sources per epoch, our astrometry
analysis still measures the shift for each detected source in each epoch
between the left and right images. This shift can be caused by the instrument,
detector bad pixels, locally varying pixel scales, and flux difference between
the left and right images. We call this the “instrumental shift” because it
does not represent potential physical movement of the stars. If it is larger
than half a pixel, we withdraw the source from our final astrometry analysis.
After applying this constraint, a total of 515 (in 2015) and 1059 (in 2018)
sources in common between the left and right images remain. The final number
of common sources after this selection is 494.

##  4 Astrometry

We analysed the movement (shift) of each source locally, by superimposing its
neighbours located within the radius of 50 pix. If there are fewer than three
neighbours, a larger radius (previous radius plus 10 pix) is considered. A
bootstrap technique is employed to measure the errors. The astrometry is
performed using a three-step process in order to superimpose data between two
epochs: 1) local correction technique (LCT) to superimpose the data as much as
possible to find any x-y shifts or rotation of data between the two epochs; 2)
global rotation around a centre and angle found in the first step; and 3) LCT
again to correct any residual instrumental or artificial effects.

We used these steps in order to detect HOMAs in our K-band data between 2015
and 2018. These measurements were performed separately on the right and left
data.

###  4.1 Local correction and global rotation techniques

In order to estimate the shift of stars, we used N detected neighbour sources
within a radius of $R_{nbr}=50pix$ in both images with the aim of
superimposing these neighbour sources (similar to the method by Anderson & van
der Marel 2010). Our algorithm estimates the shift between the position of a
given neighbour star in the 2015 and 2018 images in the X and Y directions
($dX_{i}$ and $dY_{i}$). By minimising the chi-square ($\chi^{2}$) estimation,
the final $dX$ and $dY$ values are calculated and a bootstrapping technique
($N_{sample}=100$) is used to calculate the error on the $dX$ and $dY$ values.
In the ideal case, without local distortion or bad pixels on the image
(detector), the $dX$ and $dY$ values should be constant across the FOV so we
can superimpose the two sets of data easily by using the constant values of
$dX$ and $dY$. However, as this does not happen in real data, we used an
algorithm to superimpose the catalogues locally.

The variation in $dX$ and $dY$ across the FOV shows the global rotation
centred at [634,669] and [622,932] in the left and right images in 2015, about
$0.18^{o}$ clockwise. The two sets of catalogues were superimposed using this
global rotation, and LCT was run again to remove any astrometric residuals and
measure the PM of detected sources with higher precision. The measured PM of
the stars within 3.06 years are

$\mu_{x}=X_{2018}-(X_{2015}+dX)$ , $\mu_{y}=Y_{2018}-(Y_{2015}+dY)$.

##  5 Selected sources

Of the 494 sources referred to in Sect. 3, 425 sources have been detected in
the NIR (J and H) using SPHERE/IRDIS and in the optical using HST/WFPC2. To
maximise robustness we excluded the sources detected only in K and also the 76
sources that have fewer than three neighbours (within $R_{nbr}=50pix$). This
leaves 339 reliable sources that have been detected in the optical and in the
NIR and that have at least three neighbours, the minimum number of neighbours
required to superimpose catalogues astrometrically.

The top plot of Fig. 1 shows the shift ($\mu$) of the 339 reliable sources
measured in the left and right data versus their K magnitude. The middle plot
shows the error on the position of these sources versus their K magnitude.
Errors on star positions come from the astrometric and photometric analysis
(e.g. results from PSF fitting) on each data set. This plot shows that the
error from astrometry is significantly larger than the photometry for the
bright sources. For the faint stars the error on the photometry increases and
becomes comparable with the astrometry error since the signal-to-noise ratio
(S/N) decreases. The bottom plot shows how large the shift is compared to its
error ($\frac{\mu}{\sigma}$). The colour bar indicates the difference between
the shift of a star in the left and right data ($\Delta\mu_{(R,L)}$, Eq. 1).
All sources with shifts inconsistent between left and right are yellow. For
these sources, the measured shift between the left and right data is larger
than the mean error of the reliable sources:

$\Delta\mu_{(R,L)}=\sqrt{(\mu_{x,L}-\mu_{x,R})^{2}+(\mu_{y,L}-\mu_{y,R})^{2}}.$
(1)

When the detected sources become fainter, the astrometry measurements are less
reliable due to the smaller S/N (the error on the star centres increase by
inverse S/N), so that $\mu$ and $\Delta\mu_{(R,L)}$ increase with increasing
stellar magnitudes. For fainter sources this leads to larger errors in the
positions so the measured shift will not be consistent between the left and
right data. The mean shift of 339 reliable sources is 0.16 pix (0.64 mas/yr)
and their mean total errors ($\bar{\sigma}$) is 0.11 pix (0.44 mas/yr). These
values are shown in Fig. 1 as a solid black horizontal line.

Of these 339 reliable sources, 141 have consistent velocities
($\Delta\mu_{(R,L)}\leq\bar{\sigma}$) between the left and right data; they
are known as reliable-consistent (RC) sources. Figure 2 shows the PM of RC
sources measured from the analysis of the left and right data sets. The colour
indicates the shift of each source compared to the mean shift of its
neighbours ($\frac{\mu}{\bar{\mu}_{nbr}}$). The mean shift of RC sources in
the left and right data are $0.07$ pix ($0.29$ mas/yr) and $0.08$ pix ($0.32$
mas/yr), respectively. The radius of the large red and blue circles is the
mean shift of all RC sources and three times this value, respectively. There
are two sources with PMs that are three times larger than the mean shift of RC
sources, and three times larger than the mean shift of their neighbours, in
the left and the right data sets. These two sources are identified as HOMAs
(Fig. 2); they are both brighter than 15 mag in K (16 in V) and have been
detected with a high S/N in NIR filters and in the visible.

These stars are shown in Fig. 3, together with 62 bright sources ($K\leq 15$)
for comparison. Notably, both of the HOMAs are located very close to R136a1
(separation of 155 mas) and R136c (separation of 72 mas), which are bright
K-band sources 333See Fig. 5 to compare these separations (72 and 155 mas)
with the radius of the FWHM (31.81 mas), and the first dark (37.90 mas) and
first bright (50.9 mas6) rings of the Airy Disk.. The analysis of the X-ray
spectra of R136c shows the high X-ray hardness and luminosity in addition to
the higher plasma temperature than typical for single early-type stars,
suggesting that it is most likely to be a colliding-wind binary system
(Portegies Zwart, Pooley, & Lewin, 2002; Townsley et al., 2006; Guerrero &
Chu, 2008).

Table 1 gives the IVUJHK magnitudes and PMs of the two HOMAs and the five
brightest sources in the FOV. HOMA1 and HOMA2 have PMs of $1.36\pm 0.22$
mas/yr and $1.15\pm 0.11$ mas/yr, respectively. For the distance modulus of
18.49 (Pietrzyński et al., 2013; Gibson, 2000) 1 mas/yr is 236 km/s, these
HOMAs have a tangential velocity of $321\pm 52$ km/s and $273\pm 26$ km/s.

Figure 1: Astrometric analysis of 339 reliable sources, common between 2015 and 2018, left and right data. Top: Shift of stars, measured in the right (green crosses) and left (purple pluses) data vs their K magnitudes. Middle: Total error (purple pluses) and photometric error (green crosses) on the positions of the detected sources, averaged between the left and right data vs their K magnitudes. Bottom: Shift of a star over its total error ($\frac{\mu}{\sigma}$) vs its K magnitudes, colour-coded by $\Delta\mu_{(R,L)}$. The horizontal black lines show the average value for all 339 sources. Figure 2: Estimated PM of RC sources from analysing the left (left) and right (right) data. The radius of the large red and blue circle denotes the mean shift of the RC sources and three times this value, respectively. The colour-coding indicates the shift of each source compared to the mean shift of its neighbours ($\frac{\mu}{\bar{\mu}_{nbr}}$).  Figure 3: Image of R136 by IRDIS/K. Small black circles are bright reliable sources (K $<$ 15 mag). The green and black arrows indicate the PM of these sources, magnified 100 times. The inset in the bottom right corner shows 1 pixel size (1pix=4mas/yr) and the amount of the HOMAs shifts compared to the mean shift of 339 reliable sources ($\bar{\mu}=0.64$ mas/yr) and 141 RC sources ($\bar{\mu}=0.32$ mas/yr) respectively as a large red and blue circle. Table 1: Magnitudes and PMs of the HOMAs and the five brightest sources in the core of R136. H95 and K21 are the identification of these sources from Hunter et al. (1995) and Khorrami et al (2021), respectively. K2018, K2015, H, and J are the magnitudes in the SPHERE/IRDIS filters (Khorrami et al., 2017, 2021). I, V, and U are the magnitudes in the HST F814W, F555W, and F336W filters (Hunter et al., 1995). $\mu_{x}$ and $\mu_{y}$ are the 2D PMs estimated from their position shift within 3.06 years in the left and right data.  Name |  K2018, K2015 | H | J | I | V | U |  $\mu_{x}$[mas/yr] |  $\mu_{y}$ [mas/yr] |  $\mu_{x}$ [mas/yr] |  $\mu_{y}$[mas/yr]
---|---|---|---|---|---|---|---|---|---|---
(H95/K21) | (SPHERE/IRDIS) | (HST/WFPC2) | (left) | (right)
HOMA1 (124/22) | 13.83, 13.84 | 14.41 | 14.33 | 15.46 | 15.97 | - | $1.34\pm 0.12$ | $-0.69\pm 0.09$ | $1.12\pm 0.13$ | $-0.46\pm 0.10$
HOMA2 (107/51) | 14.72, 14.49 | 14.68 | 14.77 | 15.34 | 15.74 | 14.58 | $0.46\pm 0.05$ | $-0.93\pm 0.05$ | $0.74\pm 0.04$ | $-1.03\pm 0.08$
R136a1 (3/1) | 11.15, 11.07 | 11.29 | 11.33 | 12.18 | 12.84 | 11.56 | $0.01\pm 0.08$ | $0.07\pm 0.09$ | $0.05\pm 0.08$ | $0.11\pm 0.08$
R136a2 (5/3) | 11.43, 11.32 | 11.70 | 11.55 | 12.84 | 12.96 | 11.94 | $-0.01\pm 0.09$ | $-0.10\pm 0.05$ | $0.04\pm 0.07$ | $-0.16\pm 0.08$
R136a3 (6/4) | 11.45, 11.44 | 11.73 | 11.77 | 12.46 | 13.01 | 11.86 | $-0.07\pm 0.05$ | $0.12\pm 0.10$ | $0.09\pm 0.10$ | $0.11\pm 0.14$
R136c (10/2) | 11.31, 11.48 | 11.98 | 11.78 | 12.71 | 13.47 | 12.52 | $-0.00\pm 0.70$ | $0.14\pm 0.35$ | $0.13\pm 0.63$ | $0.05\pm 0.27$
R136b (9/5) | 11.67, 11.66 | 11.89 | 11.67 | 12.76 | 13.32 | 12.29 | $0.00\pm 0.37$ | $0.02\pm 0.28$ | $0.12\pm 0.10$ | $0.08\pm 0.08$

##  6 Conclusion

We obtained VLT/SPHERE/IRDIS K-band images of the core of R136 at two epochs,
separated by 3.06 years. A careful scrutiny of two-epoch astrometry enabled us
to discard instrumentally apparent moving sources. We found 494 suitable
sources in common between these four images in the K band, where 339 sources
have also been detected in IRDIS-J and -H, and in the optical by HST/WFPC2.
Comparing the 2D shift of stars in left and right data sets, only 141 sources
have consistent PMs between these data sets. Among these RC sources, two stars
have a PM that is three times larger than the average value ($0.29$ mas/yr,
left, and $0.32$ mas/yr, right) and their neighbours. According to our
analysis, these two bright sources (see Table 1) are potential HOMAs. These
stars have shifted more than the mean shift of all reliable sources (Fig.
1-top), three times that of the average shift of all RC sources and their
neighbours (Fig. 2), and six times their astrometric errors (Fig. 1-bottom),
with a tangential velocity of $321\pm 52$ km/s and $273\pm 26$ km/s. The stars
could have acquired such high velocities through a number of mechanisms, none
of which are considered probable: (i) during the assembly of the cluster (e.g.
VFTS16 currently very distant from R136, Lennon et al. 2018), (ii) following
the supernova explosion of a binary companion (too early for R136 given its
youth, Renzo et al. 2019); (iii) via three-body interactions with a massive
binary (possible, though incredibly rare); (iv) an encounter with a compact
massive body (although there is no evidence for an intermediate mass black
hole in R136). Alternatively, they could be slower moving foreground sources
(though the presence of two sources within the small IRDIS FOV is very
unlikely). Comparing the location of HOMAs in the CMDs (Fig. 4), HOMA2 is
closer to the main sequence than HOMA1. HOMA1(HOMA2) has V-I of 0.51(0.40)mag
and J-K of 0.49(0.28)mag, making them unlikely to be foreground Galactic
stars. If HOMA1 is a foreground galactic star, then its tangential velocity is
$\sim 108$ km/s at a distance of 20 Kpc (edge of the Milky Way toward the
LMC). Since the localisation of these sources is challenging given their
proximity to R136a1 and R136c, future observations are necessary to confirm
their high PMs and status as HOMAs. These sources are expected to have high
radial velocities, so they are the desired targets for the spectroscopic
observations of the core of R136 (e.g. MUSE/NFM Castro et al. 2021). Pending
future gain in spatial resolution by extremely large telescopes, the present
work is also the first step towards constraints on the kinematics of other
galaxies than our Milky Way.

###### Acknowledgements.

We thank our reviewer, C. Evans, for his constructive comments which improved
this letter. The SFM project has received funding from the European Union’s
Horizon 2020 research and innovation program under grant agreement No 687528.
ZK acknowledges the support of a STFC Consolidated Grant (ST/K00926/1). This
work has made use of the SPHERE Data Centre, jointly operated by OSUG/IPAG
(Grenoble), PYTHEAS/LAM/CeSAM (Marseille), OCA/Lagrange (Nice), Observatoire
de Paris/LESIA (Paris), and Observatoire de Lyon (OSUL/CRAL). This work was
supported by the ”Programme National de Physique Stellaire” (PNPS) of
CNRS/INSU co-funded by CEA and CNES. ASMB is funded by the European Research
Council H2020-EU.1.1 ICYBOB project (Grant No. 818940). JP and RW acknowledge
support by the Czech Science Foundation project no. 19-15008S and by the
institutional project RVO:67985815.

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##  Appendix A Extra figures

Figure 4: Colour–magnitude diagram of 339 reliable sources. The data are from
the SPHERE/IRDIS catalogue in the NIR (J, H, K) and HST/WFPC2 data in the
visible (V and I) CMD in J-K (left), H-K (middle), and V-I (right). The red
symbols shows the HOMAs.

Figure 5: Same as Fig. 3, but zoomed in for a FOV of $1"\times 1"$ , centred
on HOMA1 (top) and HOMA2 (bottom). The diameter of three black circles around
each bright reliable sources (K $<$ 15 mag) are the FWHM, and the first dark
and first bright rings of the Airy Disk. The ID number of each source (H95,
from Hunter et al. 1995) is shown (above and to the left). The green and black
arrows indicate the PM of these sources, magnified 100 times.