11institutetext: Barkhausen Institute, Dresden, Germany
11email: {prajnamaya.dass<EMAIL_ADDRESS>22institutetext: Technische Universität Dresden, Germany
22email<EMAIL_ADDRESS>
# Exploring Privacy Issues in Mission Critical Communication: Navigating 5G
and Beyond Networks

Prajnamaya Dass(🖂) 11 Marcel Gräfenstein 22 Stefan Köpsell 11

###### Abstract

Mission critical communication (MCC) involves the exchange of information and
data among emergency services, including the police, fire brigade, and other
first responders, particularly during emergencies, disasters, or critical
incidents. The widely-adopted TETRA (Terrestrial Trunked Radio)-based
communication for mission critical services faces challenges including limited
data capacity, coverage limitations, spectrum congestion, and security
concerns. Therefore, as an alternative, mission critical communication over
cellular networks (4G and 5G) has emerged. While cellular-based MCC enables
features like real-time video streaming and high-speed data transmission, the
involvement of network operators and application service providers in the MCC
architecture raises privacy concerns for mission critical users and services.
For instance, the disclosure of a policeman’s location details to the network
operator raises privacy concerns. To the best of our knowledge, no existing
work considers the privacy issues in mission critical system with respect to
5G and upcoming technologies. Therefore, in this paper, we analyse the 3GPP
standardised MCC architecture within the context of 5G core network concepts
and assess the privacy implications for MC users, network entities, and MC
servers. The privacy analysis adheres to the deployment strategies in the
standard for MCC. Additionally, we explore emerging 6G technologies, such as
off-network communications, joint communication and sensing, and non-3GPP
communications, to identify privacy challenges in MCC architecture. Finally,
we propose privacy controls to establish a next-generation privacy-preserving
MCC architecture.

###### Keywords:

Mission critical communication MCC Privacy Trust domain Threats 5G 6G 3GPP.

##  1 Introduction

Mission-critical services are the backbone of essential operations across
various sectors, ensuring safety, security, and functionality in society. From
police and fire brigade responses to healthcare delivery, transportation
management, and industrial automation, these services play a pivotal role in
safeguarding public welfare. Mission-critical communication (MCC) refers to
the communication systems and technologies used to support and facilitate
these essential functions and operations. Mission-critical communication
systems are designed to provide reliable, secure, and resilient communication
capabilities, particularly in situations where lives are at stake or where
interruptions could have significant consequences.

The evolution of mission-critical communication began with land mobile radio
(LMR) systems, which adhere to standards such as APCO Project 25 (P25) in the
United States and TETRA (Terrestrial Trunked Radio) in Europe. These standards
ensure interoperability between different LMR equipment and facilitate
communication among various agencies and organisations. As technology
advanced, digital mobile radio (DMR), which is based on the ETSI (European
Telecommunications Standards Institute) standards, emerged as an alternative
to traditional analog LMR, offering improved spectral efficiency, better voice
quality, and support for features like encryption and data transmission.

The architecture of both LMR and DMR systems was initially centred around
voice-centric functionality due to its limited spectrum, coverage, and data
capabilities. However, with the increasing demand for data-intensive
applications, LTE gained traction in the mission critical communication domain
due to its high data rates, low latency, and support for multimedia services.
3GPP (3rd Generation Partnership Project) in its technical specification
(Rel-13) first defined standards and services for Mission-Critical Push-to-
Talk (MCPTT) and in next release (Rel-14), added two additional services –
Mission-Critical Video (MCVideo) and Mission-Critical Data (MCData), within
LTE that cater to MCC needs and paving the way for the eventual migration to
5G technology.

###  1.1 5G-based mission critical communication (MCC) architecture

The architecture for mission critical communication, as depicted in figure 1,
delineates a structured framework wherein mission critical users, who are
existing subscribers of the network service provider (home network), utilise
the network infrastructure to engage in communication with mission-critical
(MC) servers [4]. Analogous to cellular users, both the home network (HN) and
the serving network (SN) containing the radio access network (RAN) play
pivotal roles in facilitating the initial connection for MC users to integrate
with the network. Subsequently, each MC user initiates the establishment of a
secure channel with the MC server, ensuring the integrity and confidentiality
of communication. In instances where necessary, the MC service server retains
the capability to establish connections between MC users and other MC service
servers, thereby enabling seamless communication and collaboration within the
mission critical system. In the core network, each network function is
meticulously crafted with distinct purposes and functionalities tailored to
efficiently manage and process various aspects of communication traffic and
data. Each network function plays a crucial role in orchestrating the flow of
information, managing network resources, and delivering high-quality services
to end-users. For instance, the Authentication Server Function (AUSF) in 5G
networks is responsible for authenticating and authorising user equipment (UE)
when they attempt to access the network [2].

Figure 1: Overall mission critical communication architecture for 5G

##  2 Related Work

In the literature, several works focused on the architectural concepts of
mission critical communication and its security. The common functional
architecture, procedures, and information flows to support mission-critical
services over cellular networks are proposed by 3GPP [4]. The technical
specification also specifies different possible deployment scenarios in which
the functional MCC model can be applied. In [23], the challenges of using the
5G new radio interface for public safety MCC have been discussed. In [13],
feasibility issues of mission-critical push-to-talk (MCPTT) in 3GPP are
discussed. In a similar work [11], the feasibility of MCPTT communications
over 4G has been analyzed. The work [32] analyzes the impact of the evolution
from 4G architectures toward 5G on MCPTT key performance indicators. Different
transition possibilities of migration of mission-critical services from the
land mobile radio-based systems to 4G [12] and 5G have been discussed in [19].
The support of network slicing for MCC in 5G has been studied in [10, 9].

The security architecture and procedures to safeguard mission critical
services have been specified by 3GPP in [4]. This specification outlines
security mechanisms pertaining to on-network use, off-network use, roaming,
and migration. Security threats to 5G interfaces have been analysed in [27],
where standard security measures for these interfaces are discussed alongside
categorised threats in their absence. The work in [25] discusses privacy
threats in 5G stemming from newly introduced technologies like software-
defined networking (SDN) and network function virtualisation (NFV).

###  2.1 Motivation

The 3GPP technical specification for mission critical communication functional
architecture and its security aspects mainly consider the underlying network
(regardless of 4G or 5G technology) as the bridge to connect the MC users and
the servers. However, the technologies introduced in 5G, such as network
slicing and function virtualisation, introduce potential privacy concerns that
are not considered in the current MCC architecture. While the 3GPP
specifications for MCC address security concerns to some extent, privacy
issues remain largely unexplored. While the existing security methods are
necessary, they are not sufficient to address privacy issues. Additionally,
the security and privacy solutions developed for 5G networks are not directly
applicable to MCC architecture due to its distinct deployment scenarios.

With the integration of 5G networks into mission critical architecture,
similar to typical 5G subscribers, various sensitive details of the MC users
are disclosed to the network operator. For instance, the operator may obtain a
policeman’s current location, communication partners, and movement patterns
over time, posing privacy risks. Moreover, application service providers
within the MCC framework may exert control over user equipment and the MC
client application, potentially extracting details like a policeman’s identity
and communication secret keys used with the MC server. Conversely, application
and mission critical service providers may deduce sensitive information, such
as the network ingress points and identifiers of network resources, posing
privacy threats to the network as well.

Therefore, in this paper, we assess the privacy threats in MCC, while
considering the 5G and the upcoming 6G technologies in the architecture.
Considering different deployment scenarios, we show how an entity can extract
or learn the personal identifiable information in the MC system. Additionally,
we also suggest some privacy controls to deal with the privacy threats in the
future mission critical systems.

##  3 Privacy Threats

In this section, we delve into the various threats in the MCC architecture
that pose potential risks to any component within the MC system. These threats
not only target the MC users but also encompass the network components, the MC
server, and their communications. Our threat analysis delves into how
personally identifiable information of the entities in the MC system becomes
exposed or acquired by other entities, thereby presenting privacy risks.

###  3.1 Privacy threats from the administering entities

Different deployment scenarios can arise based on the components or resources
managed by either the mission-critical (MC) server or the public land mobile
network (PLMN) operator [4]. Figure 2 illustrates five such deployment
scenarios.

Figure 2: Different deployment scenarios for mission critical communication
over 5G

In scenario 1, regardless of ownership, each resource is managed by the same
operator. This scenario can unfold in three ways: i) the PLMN operator
oversees network services, application services, and the MC service client,
ii) the MC service server manages its own services and the underlying core
network, iii) a third party, distinct from both the PLMN operator and the MC
service server, manages all resources. However, in deployment scenarios 2, 3,
and 4, some resources are managed by the MC service server while the remainder
are overseen by the PLMN operator. In deployment scenario 5, the MC service
user equipment is administered by the application service provider. It is
important to note that a component may be owned by an entity different from
the one administering it.

In mission critical communication, the majority of privacy threats can stem
from administering entities. When an entity administers or controls a resource
(whether software or hardware) or protocol, it often gains access to personal
identifiable information about that resource or protocol. For example, in
deployment scenario 5 (refer to figure 2), if an MC service client, such as
the client program in the user equipment, is administered by the application
service provider, encryption keys and identities used by the MC client may be
disclosed to the application service provider. Once in possession of
identifiable and secret information, administering entities may launch
spoofing and non-repudiation attacks. Likewise, in deployment scenarios (e.g.,
scenario 2 and 5 in figure 2), where the session initiation protocol (SIP) and
the communications over SIP are administered by the network operator 111In
this paper, the terms ‘PLMN operator’ and ‘network operator’ refer to the same
entity., identifiable information such as the registered ID of the MC user in
the SIP may be exposed to the network operator.

###  3.2 Privacy threats during identity mapping and information sharing

The PLMN operator, tasked with managing sensitive data such as the location
details of cellular subscribers, maintains a centralised repository known as
the unified data repository (UDR) [1]. In instances where access to this data
is necessary for mission-critical (MC) services, such as during emergencies or
urgent communications, the UDR plays a pivotal role. Upon request, the PLMN
operator facilitates the sharing of location information with the MC service
server [4], enabling swift and effective communication between MC users.

Figure 3: Privacy issues during information sharing between MC server and the
PLMN operator

The process of facilitating this information exchange involves a critical step
known as identity mapping, depicted in figure 3. During this process, the PLMN
operator or the MC server undertakes the conversion of the MC user’s identity
(MC ID) into the subscription permanent identifier (SUPI), a unique identifier
associated with the subscriber’s network subscription. While this conversion
is essential for establishing the necessary communication channels, it
inadvertently exposes the MC ID to the PLMN operator.

Unfortunately, this exposure of the MC ID to the PLMN operator raises
significant privacy concerns, particularly regarding identifiability and
linkability. By associating the MC ID with the SUPI, the PLMN operator gains
insights into the identities of MC users, potentially enabling subsequent
communications to be linked directly to individual users. This linkage not
only compromises the anonymity and privacy of MC users but also creates a
pathway for the aggregation of user-related data, raising concerns about the
potential for unauthorised surveillance or tracking.

###  3.3 Privacy risks arising from beyond trust domain interactions

In an MC system, the trust domain encompasses one or more MC service functions
managed by either the same or distinct service providers (such as the MC
service provider or PLMN operator), who have agreed to exchange sensitive
information, as shown in figure 4. This implies that within a trust domain, a
PLMN operator is restricted to sharing sensitive information exclusively with
entities also within that same trust domain. However, given the limited number
of PLMN operators in the market, establishing trust domains for information
sharing presents a challenge. Additionally, if a single PLMN operator serves
multiple MC service servers, delineating trust boundaries for the exchange of
sensitive information becomes impractical. In such a scenario, an ill-intent
PLMN operator common to two different trust domains can pose threats to
linkability, identifiability, and non-repudiation.

Figure 4: Restriction on information sharing inside and outside the trust
domains

###  3.4 Privacy risks arising from priorities

5G networks allow for prioritisation at different stages of the communication
establishment. Thereby MC users and the related services have higher priority
than “normal” users to guarantee availability of the network and its service
to MC users even in case the network is overloaded. However, from privacy
perspective, this prioritisation can be used to identify MC users.

At the user plane, i.e., related to the data transport, a so-called quality of
service flow ID (QFI) is assigned to every data session. The QFI value, known
to entities such as the RAN and SN, can be used to identify mission critical
communication. For instance, QFI value 65, which is assigned to “Mission
Critical user plane Push-To-Talk voice” (please refer to Table 5.7.4.1-1 in
[6]), gives identifiable information of an MC user.

The concept of Unified Access Control deals with an overloading situation at
the RAN. Therefore, every establishment of a radio session between an UE and
the RAN contains an Access Category and one or more Access Identities (AI).
The AI value for mission critical services is 2. It allows MC users to get
access to the RAN even if the RAN is currently overloaded. Therefore, “normal”
users will be disconnected to allow service to MC users.

Finally, during the connection establishment, a value referring to the cause
of the establishment (EstablishmentCause in RRCSetupRequest) is specified in
the request [3]. Thereby, special values exist to signal that specifies the
establishment of the connection is related to mission critical services. By
analysing these values (QFI, AI, and EstablishmentCause), the network
operators can get rich contextual information about mission critical
communication. This includes insights into the number of MC users present
under a specific base station, their communication patterns, and the types of
services they are utilising, which can lead to linkability and identifiability
threats.

###  3.5 Threats from the session initiation protocol (SIP)

Privacy threats can arise from both SIP clients that reside in the MC service
UE and SIP core infrastructure in the mission critical communication systems.
The SIP client is the application in the UE responsible for initiating or
receiving SIP sessions, while the SIP core, also referred to as the SIP
infrastructure (includes proxy servers, SIP server, application servers, etc.)
is the network element that routes SIP messages within a SIP network. SIP
clients, responsible for initiating and managing communication sessions, may
inadvertently disclose sensitive user information, including identity details
and communication patterns of the MC user. Such leaks could be exploited by
adversaries for profiling or targeted attacks, compromising user privacy.
Moreover, insecure SIP clients are vulnerable to eavesdropping and identity
spoofing, enabling unauthorised parties to intercept communications or
impersonate legitimate users, further undermining the integrity and security
of the system. Within the literature, various solutions have been proposed to
anonymise header information in SIP [29]. Nonetheless, identifiable details
regarding the MC user are still revealed in instances where the SIP client is
managed either by the PLMN operator (in deployment scenarios 1 and 2) or by
another service provider (in deployment scenario 5).

Similarly, vulnerabilities in the SIP core infrastructure introduce privacy
risks, potentially leading to unauthorised access to user data or call
records. Adversaries could exploit these weaknesses to compromise user
privacy, conduct traffic analysis attacks, or disrupt services through denial-
of-service (DoS) attacks. When the SIP core is administered by entities other
than the MC service server (in deployment scenarios 2, 4, and 5), identifiable
information of the MC service server about the server as well the MC users are
revealed to the administering entity.

###  3.6 Privacy issues during secure tunnel establishment

Transport layer security (TLS) plays a crucial role in establishing a secure
connection between the MC (mission critical) client and server, ensuring that
sensitive data exchanged between them remains encrypted and protected from
eavesdropping or tampering. However, before TLS comes into play, the process
of DNS mapping unfolds, orchestrated by recursive solvers and name servers.

When a subscriber initiates a connection request, the recursive solver, under
the administration of the PLMN operator, undertakes the task of translating
the destination address into an IP address. This translation step is pivotal
for routing the communication to its intended destination. However, it also
presents a potential privacy concern. If the destination address corresponds
to an MC server, the PLMN operator can infer that the subscriber is likely a
participant in the MC service. Note that even without the DNS-based hostname
resolution, the nature of the communication target might be derived just from
the destination IP address. This inference poses a threat to the linkability
and identifiability of the user, as it opens avenues for exposing sensitive
information associated with the MC user’s activities and affiliations.

Moreover, if the PLMN operator wields control over the name servers involved
in the DNS mapping process, similar risks arise. This control could
potentially exacerbate privacy concerns, as the operator gains more insights
into the communication patterns and preferences of its subscribers. While TLS
does provide a layer of protection against third-party interference by
encrypting the communication channel, it’s worth noting that it primarily
safeguards the content of the communication rather than its metadata. In
scenarios where proxy servers are employed, while they may anonymise the
source address, the destination address remains visible, thus posing a
challenge to preserving the anonymity of users.

Furthermore, beyond the encryption provided by TLS, the PLMN operator may
still gather significant insights into the communication ecosystem. This
includes discerning which requests are directed towards specific MC servers,
distinguishing between MC users and regular 5G users via identifiers like SUPI
(Subscription Permanent Identifier), and even tracking the distribution and
concentration of MC users across different geographical locations.

In addition to these aspects, contextual details such as the timing of
communication and group activities may also be within the purview of the PLMN
operator’s surveillance capabilities. This wealth of information not only
raises concerns regarding user privacy but also underscores the importance of
implementing robust safeguards and regulations to ensure the responsible
handling of sensitive data in mission-critical communication environments.

###  3.7 Threats from network slicing

The network slicing is an architectural concept that divides a single physical
network infrastructure into multiple virtual networks, each tailored to
specific use cases, applications, or customer requirements [30]. As shown in
figure 5, a network slice may be allocated to mission critical communications.
Network slicing, while offering significant benefits in terms of
customisation, flexibility, and resource allocation in 5G and beyond, also
introduces several security and privacy threats [16].

Figure 5: Privacy threats due to network slicing

An MC (mission critical) user, subscribed to a network slice dedicated to
critical services, engages in communication that involves sensitive
information, such as location data and MC organisation information. Data
intended for the MCC slice might inadvertently leak into other slices,
exposing personally identifiable information about MC users, network entities,
or MC server entities to unauthorised parties. Network operators or malicious
actors may conduct traffic analysis across different slices to identify
patterns or behaviours associated with MC users. By analysing traffic patterns
or metadata, adversaries may gain insights into the behaviour or activities of
MC users even when they are present in other slices. This could compromise
user privacy and security, as it reveals information about their interactions,
preferences, or usage patterns. Unauthorised access to slice-related
information, such as the participation of specific network functions (NFs)
common in MCC and other slices, could lead to privacy breaches or security
incidents.

An MC user is also part of another slice, perhaps a slice designated for IoT
devices or enterprise services. The user’s identity is tied to a subscription
permanent identifier (SUPI), which is shared across slices. The network
operators could exploit SUPIs or other identifiers to correlate the identities
of users across different slices. This linking of identities could lead to
privacy violations, as it enables adversaries to aggregate and analyse PII
from multiple sources, potentially revealing sensitive information or
behavioural patterns.

###  3.8 Threats from the 5G authentication protocols

The authentication protocol in 5G networks, known as 5G AKA (Authentication
and Key Agreement) [2], is susceptible to several privacy threats, such as
linkability and traceability due to its design and implementation [21, 8]. The
subscription concealed identifier (SUCI) is used as an essential element in
the authentication process instead of the subscription permanent identifier
(SUPI) utilised in previous generations. However, the use of SUCI does not
entirely eliminate privacy threats in the authentication protocol; rather, it
presents its own set of challenges. Further, due to the involvement of
sensitive services from the MC server, the 5G AKA protocol introduces some
more threats.

Figure 6: Authentication process during the 5G registration procedure

After successful authentication in 5G AKA, the SUPI is exchanged with the
serving network [8], as shown in figure 6. This exchange is a crucial legal
step in establishing the user’s connection to the network and enabling access
to services. However, the sharing of SUPI introduces privacy considerations.
The exchange of SUPI between the MC user and the serving network (SN) exposes
the user’s permanent identifier to entities of the SN. While necessary for
network operations, this exposure can potentially be exploited by the SN and
malicious actors to track the activities of the MC users. SUPI, being a
permanent identifier tied to the user’s subscription, can be used to uniquely
identify and track users across different communication sessions or contexts.
This raises concerns about user privacy and anonymity.

###  3.9 Privacy challenges in the upcoming 6G technologies

The introduction of new technological features like off-network communication
(using proximity-based services or ProSe) [4] and joint communication and
sensing (JCAS) [7] in 6G networks can potentially pose privacy threats. Here’s
how each of these features could impact privacy:

  * •

Off-network communication: ProSe enables devices to communicate directly with
each other when they do not have connectivity with the RAN or the network
infrastructure. Figure 7 represents the on- and off-network mission critical
communication. Off-network communication is a part of the future MC services
[4, 5]. While this offers convenience and efficiency, Off-network
communication without active support from MC server, is more prone to security
and privacy threats. As devices exchange information directly, their proximity
to each other can be inferred, potentially revealing sensitive information
about MC users’ locations and movements. ProSe communications may expose
device identifiers or MC identities, compromising user anonymity and privacy.
Adversaries may exploit this information to track users’ activities or
identify individuals participating in ProSe interactions. Due to the absence
of a link with the core network and the MC server, the direct communication
between the MC users may bypass traditional security measures implemented by
network operators, increasing the risk of data leakage. In addition to network
operators, other MC users serving as relays or being available to nearby
communicating MC users may also have the ability to infer sensitive
information.

Figure 7: On-network and off-network mission critical communications

  * •

Joint communication and sensing (JCAS): The utilisation of a single radio
signal for both communication and sensing functions is commonly referred to as
integrated or joint communication and sensing [17]. Consequently, it unlocks
fresh possibilities for applications necessitating both communication and
sensing functionalities, particularly for platforms previously incapable of
accommodating both simultaneously. Given the nature of sensing data involved,
which often includes sensitive personally identifiable information related to
humans and objects, JCAS-based applications are particularly susceptible to
privacy attacks, such as location tracking, identity disclosure, profiling,
and misuse of sensor data. With the incorporation of sensing capabilities,
PLMN operators can pinpoint the location of MC users accurately, provided they
can differentiate them from other users. Additionally, the aggregation of
sensing information from multiple MC users may enable the creation of detailed
profiles of individuals’ behaviors, preferences, and activities. This data
aggregation raises concerns about MC user profiling, algorithmic
discrimination of MC services, and potential misuse of personal information by
third parties.

  * •

Non-3GPP access: As the scope of IoT applications and use cases continues to
broaden, forthcoming generations are expected to experience a notable surge in
the volume of devices linking to the Internet. In contrast to mobile phones,
which are classified as 3GPP devices with cellular connectivity, non-3GPP user
equipment, such as IoT devices, sensors, and wearables, utilise Wi-Fi or
alternative wireless technologies for Internet or local network connectivity
[22, 26]. In mission-critical services, there is a provision for non-3GPP
devices to connect to the MC server through non-3GPP and 3GPP access [4]. As
shown in Figure 8, the non-3GPP devices, which cannot host MC clients, use the
MC gateway (MC UE) to establish communication with the MC server. One non-3GPP
device can have multiple MC clients (e.g., one for MCPTT and another for
MCVideo), which can use different UEs as gateways to connect to multiple MC
servers.

Figure 8: Communication between non-3GPP MCC devices and the MC server

The connection of non-3GPP devices to the MC UEs through non-3GPP access,
presents a range of privacy threats stemming from various factors. These
devices often collect and transmit sensitive mission critical data over
unsecured channels, making them susceptible to interception or unauthorised
access. In addition, they may rely on communication protocols lacking robust
security mechanisms, leaving the MC user communications vulnerable to
eavesdropping or man-in-the-middle attacks. Non-3GPP devices often rely on
third-party services or cloud platforms for data storage, processing, and
analysis. If used for mission critical services, these services may not
adequately protect MC user’s data or may have lax security practices,
increasing the risk of data breaches or unauthorised access. Further, the
non-3GPP service provider may acquire certain information about the MC UE. In
scenarios, where the PLMN operator manages certain components of the MC server
(deployment scenario 5 in figure 2), information about the non-3GPP devices
could be revealed to the PLMN.

##  4 Suggestive Privacy Controls

In the 3GPP standard for mission critical communication (MCC) security
architecture [5], numerous controls have been established to uphold robust
security measures. However, it’s crucial to note that these proposed security
aspects primarily align with the 4G network architecture and may not fully
incorporate the advancements and innovations introduced in the 5G landscape.
Although many privacy issues are challenging to solve, in this section, we
discuss possible solutions to counter some of the privacy issues discussed in
this paper.

  * •

Privacy-preserving information retrieval: Several methods can be employed to
retrieve information, such as the location history of MC user from the core
network, without revealing the real identity designated to the user by the MC
server. It should be noted that the privacy-preserving information retrieval
must ensure authentication and authorisation checks. Private information
retrieval (PIR) schemes [18, 31] ensure confidentiality and privacy during
information retrieval by concealing the user’s query from the database server.
Therefore, PIR protocols could be used to retrieve information from the
unified data repository (UDR) in the core network without revealing which MC
user information records are being accessed. Authentication methods, such as
blind signatures [33], anonymous authentication [24], and zero-knowledge
proofs [15], could be used to verify the authenticity of the MC server
requesting information from the UDR. Furthermore, the use of anonymous
communication networks that route user traffic through a series of encrypted
relays, such as Tor (The onion router) [14], can help preserve the anonymity
of the origin of the location information request (MC server).

  * •

Privacy in inter-trust domain communications: Ensuring privacy within trust
domains, especially when the same PLMN operator spans multiple trust domains,
requires a multifaceted approach. At the very first step, strict compliance
with relevant privacy regulations and standards, such as the General Data
Protection Regulation (GDPR) is required to adhere to legal requirements for
data sharing, processing, and protection across trust domains. Further, it is
also needed to maintain transparency with data subjects about how the mission
critical data is shared and processed across trust domains. The best way is to
limit the amount of personally identifiable information of MC services shared
to the PLMN operators, which is necessary for the intended purpose. It is
suggested to anonymise or pseudonymise the PIIs used in MC services to remove
or obfuscate identifiers that can link data to the entities involved in MCC.
One-time tokens are designed with a brief lifespan, intended for single use or
a limited duration [20]. Once employed within a trust domain, the token
immediately becomes invalid, thereby diminishing the potential for
unauthorised access or exploitation in alternate trust domains. These controls
will also help in achieving privacy against third-party service providers.

  * •

Privacy controls for MCC network slice: Ensuring privacy of MCC in network
slices, especially when the core network functions are shared among multiple
slices, requires a comprehensive approach that combines technical measures and
governance policies. Strict data segregation mechanisms prevent cross-
contamination between slices, if the MCC network slice has its own dedicated
data storage and processing resources. Implementing granular access controls
can help to regulate access to data and resources within the MCC network
slice. Use of role-based access control (RBAC) [34] or attribute-based access
control (ABAC) will help to enforce least privilege principles and restrict
access to authorised users only [28].

  * •

Privacy controls for upcoming 6G technologies: During off-network
communication and sensing activities in JCAS, techniques such as masking and
randomisation can obscure identifiable information of the MC users
participating in mission critical communications. Achieving unlinkability and
unobservability for ProSe-based mission critical communications can be
attained through covert operations, noise injection, anonymisation, data
fragmentation, decoy traffic, and differential privacy techniques. Employed
individually or in combination, these methods aim to obscure and obfuscate
intra and inter-service group communications.

##  5 Conclusions and Future Work

Privacy in mission critical communication is essential, especially considering
the involvement of sensitive information concerning public safety, national
security, and critical infrastructure. The role of network operators and
application service providers in enabling such communication is notably
significant, underscoring the necessity for rigorous privacy safeguards. In
this paper, we examined the privacy aspects of mission critical communication
in context to 5G networks, discussing the architecture and analysing
weaknesses posing privacy threats to MC users, network entities, and MC
servers. Moreover, we extended our analysis to anticipate potential privacy
threats stemming from the advent of 6G technologies, such as off-network
communication, joint communication and sensing, and the integration of
non-3GPP device communication. In response to these evolving challenges, we
suggested a suite of privacy controls tailored to mitigate current and future
threats, with a forward-looking approach towards addressing next-generation
privacy concerns.

The current mission critical communication architecture specifications do not
consider the underlying 5G technology and protocols. Looking ahead, we plan to
delve deeper into protocol and interface-level threat analysis in the mission
critical communication architecture, with a commitment to identifying and
offering highly specific and detailed privacy-preserving solutions to
safeguard this critical domain.

## Acknowledgement

This work has been partly funded by the German Federal Office for Information
Security (BSI) project 6G-ReS (grant no. 01MO23013D). Additionally, the
authors from Barkhausen Institute are also financed based on the budget passed
by the Saxonian State Parliament in Germany.

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# Signatures of winds and jets in the environment of supermassive black holes

Bidisha Bandyopadhyay1, Christian Fendt2 , Dominik R.G. Schleicher1, Javier
Lagunas1, Javier Pedreros1, Neil M. Nagar1, Felipe Agurto1,
1Departamento de Astronomía, Facultad Ciencias Físicas y Matemáticas,
Universidad de Concepción,
Av. Esteban Iturra s/n Barrio Universitario, Casilla 160-C, Concepción, Chile
2Max Planck Institute for Astronomy, Königstuhl 17, D-69117 Heidelberg,
Germany
E-mail<EMAIL_ADDRESS>(BB)E-mail<EMAIL_ADDRESS>(CF)

(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

The Event Horizon Telescope Collaboration (EHTC) has presented first -
dynamic-range limited - images of the black hole shadows in M87 and Sgr A*.
The next generation Event Horizon Telescope (ngEHT) will provide higher
sensitivity and higher dynamic range images (and movies) of these two sources
plus image at least a dozen others at $\leq$100 gravitational radii
resolution. We here perform an exploratory study of the appearance of winds
and jets in such future observations. To do this we use M87 and Sgr A* as
reference systems: we do not aim to exactly reproduce them, but rather to
determine how their observed images will depend on specific physical
assumptions. Even in the case of similar or the same dynamics, the images
depend significantly on global parameters such as the black hole mass and the
mass accretion rate. Our results provide guidance in the interpretation of
future high-resolution images, particularly if a wind or jet is detected.

###### keywords:

black hole physics – radiative transfer – methods: numerical –
magnetohydrodynamics – accretion, accretion discs

††pubyear: 2022††pagerange: Signatures of winds and jets in the environment of
supermassive black holes–A

##  1 Introduction

Astrophysical black holes (BHs) are found with masses ranging from a few solar
masses to supermassive BHs of masses greater than $10^{6}M_{\sun}$. BHs
initially were a basic outcome of Einstein’s General Theory of Relativity (GR)
defined by a one-way causal boundary in space time also known as the event
horizon from which even light cannot escape, making the direct detection of
BHs impossible. BHs can be detected only indirectly by their gravitational
influence on the surrounding matter and/or space-time, by their accretion
which results in often luminous emission from radio to $\gamma$-rays, or by
gravitational wave emission. The Event Horizon Telescope (EHT) has presented
first images of the gravitionally-lensed ring, and BH shadow, of the
supermassive BH of M87 (Event Horizon Telescope Collaboration et al., 2019a,
b, c, d, e) in the heart of Virgo cluster, and of SgrA* at the center of the
Galaxy (Event Horizon Telescope Collaboration et al., 2019f, 2022a, 2022b,
2022c, 2022d, 2022e, 2022f).

Active galactic nuclei (AGN), accretion powered supermassive BHs in the nuclei
of some galaxies, have intrigued astronomers for ages as being the most
powerful sources in the sky. They can outshine even the entire stellar
population of the galaxy which hosts them such as in quasars which are among
the most luminous sources in the sky (Schmidt, 1963; Sanders et al., 1989;
Novikov & Thorne, 1973). These are believed to be powered by high accretion
rates on to the supermassive BH through a geometrically thin but optically
thick disk (Shakura & Sunyaev, 1973; Sun & Malkan, 1989). On the other hand,
AGN in the local universe, including M87 and SgrA*, dominantly show lower
accretion rates onto the central BH (Ichimaru, 1977; Narayan & Yi, 1995;
Blandford & Begelman, 1999; Nagar et al., 2005; Yuan & Narayan, 2014).

Many AGN display signatures of outflows and large-scale jets which are
observed across the spectrum. Jets appear as collimated structures launched at
relativisitic velocities from very close to the BH. The EHT has recently
imaged the innermost jets of 3C 279, CenA, and the blazar J1924-2914 (Kim et
al., 2020; Janssen et al., 2021; Issaoun et al., 2022) with 20 $\mu\rm as$
resolution paving a way to observe and understand the physical processes
involved in launching jets around supermassive BHs. Jets may either be powered
by magnetic fields threading the event horizon, extracting the rotational
energy of the BH, (Blandford & Znajek, 1977) or by the magneto-rotational
acceleration of matter from the accretion flow (Blandford & Payne, 1982). The
presence of strong magnetic fields close to the event horizon thus leads to
synchrotron radiation which peaks between the radio and the far-infrared (Ho,
1999) and could originate from the accretion flow, jet base or disk wind.

The first ever images of the BH shadows of M87 and SgrA* have paved the way
for understanding the physical processes around supermassive BHs. While both
sources have roughly the same flux and angular size in their gravitationally
lensed ring, they are individually interesting as their masses, and
potentially spins, are on the two extreme ends of the mass and spin spectrum
of supermassive BHs. Although the BH mass of M87 ($M_{BH}=6.5\times
10^{9}~{}M_{\odot}$) is three orders of magnitude larger than that of SgrA*
($M_{BH}=4.16\times 10^{6}~{}M_{\odot}$), it is also at a distance
approximately 3 orders of magnitude farther ($D_{M87}=16.8$ Mpc) than SgrA*
($D_{SgrA*}=8.127$ kpc) [see for e.g. Event Horizon Telescope Collaboration et
al. (2019a, 2022a)]. This gives rise to interesting observational similarities
such as their lensed photon rings project similar angular sizes on earth
($39~{}\mu$arcsec for M87 and $52~{}\mu$arcsec for SgrA*), which make them the
most interesting targets for the EHT. Also, they both display similarities in
the flux received through our telescopes. The total accretion power of M87 is
estimated to be typically $\sim~{}10-100$ times larger than SgrA* (Event
Horizon Telescope Collaboration et al., 2021). The higher power of M87 could
mean that it is being fed directly from a larger reservoir of gas than the
stellar winds which are expected to feed SgrA*. This may be the cause for
another significant difference between the sources i.e. the prominent,
powerful jet launched from M87, which can be observed at multiple wavelengths
and across nearly eight orders of magnitude in size (EHT MWL Science Working
Group et al., 2021). The extended one-sided M87 jet, and the relative
faintness of the nuclear counter-jet, provides strong constraints on the
orientation of the jet, and thus the spin axis of the black hole: an
inclination of $\sim 20\degr$ to the line of sight was used for numerical
simulations of M87 (Event Horizon Telescope Collaboration et al., 2019e). For
SgrA* there are no strong constraints on the spin axis yet, although the model
fits from the EHTC suggest an inclination angle of $30\degr$ to the line of
sight (Event Horizon Telescope Collaboration et al., 2022a)). These
interesting characteristics of M87 and SgrA* make them the best candidates for
testing different theoretical models.

The Event Horizon Telescope, in science operation since 2017, combines a
network of 8 to 10 globally distributed mm-wave telescopes using the technique
of very large baseline interferometry (VLBI). The next generation EHT (ngEHT),
currently in the design stage, aims to double this number of telescopes,
double bandwidth (thus sensitivity), and implement dual 230/345 GHz observing.
This will allow significant improvement in image quality, thus observation of
finer structures and time-lapse movies of horizon scale structure in M87 and
SgrA*, and the imaging of additional sources at better than 100 gravitational
radius resolution. On the longer term the spatial resolution can be bettered
either using higher frequency (e.g., 700 GHz) on the ground, or longer
baselines via ground-satellite interferometry. Resolutions can be
significantly improved with earth-Geostationary (GEO-VLBI) or earth-L2
(L2-VLBI) baselines. In addition to improving the image quality with extended
baselines, ngEHT also plans to improve on image construction algorithms from
the data to be able to obtain the the strongest possible constraints on the
complex structures around the BH event horizon.

Given the limits of the 2017 EHT intrinsic resolution and dynamic range, it
has been difficult to address some questions such as the whether the emission
signatures are from the accretion disk or disk wind or jet base, whether the
ring features are in complete agreement with standard GR or if there is a
possibility to constrain the spin and accretion rate accurately. The ngEHT
will already be an improvent with better image fidelity (more $u-v$ coverage)
and sensitivity with a resolution gain from moving to 345 GHz as well as
super-resolution techniques. However,we expect that the longer base-lines of
future earth-space based VLBI (a possibility through space based ngEHT) will
result in an enhancement in the resolution which will allow us to distinguish
between various physical scenarios. Through this work we intend to demonstrate
how to distinguish certain theoretical models with current and future VLBI
probes, given the mass and flux constraints for two systems with contrasting
mass and spin. While here our aim is not to reproduce specific images, we will
take some guidance from the well-studied systems M87 and Sgr A*, with the aim
of motivating the properties of a typical source. For this purpose, we will
consider the black hole masses and the reported spectral energy distribution
(SED) information at low frequencies of these two sources reported in the
literature [for reference see(Narayan et al., 1998; Prieto et al., 2016)] to
derive some reference systems that have similar emission properties. Based on
these reference systems (M87 or SgrA*), we will study the possibility to
identify signatures of winds and jets in future observations. The paper is
organised as follows: In section [2.1] we describe briefly the dynamical set
up of our simulations while in section [2.2] the post-processing and ray-
tracing methods are discussed especially in the context of the ones chosen for
this work. In section [3], we discuss how the accretion rates are determined
from the synthetic SED which fit to the observed data for our simulations for
M87 and SgrA* like systems while in section [4], we discuss the features
obtained in the intensity maps and profiles with the parameters obtained from
the spectral fitting. A comparision of the deduced results for a M87 and SgrA*
like system is discussed in section [5]. Finally in section [6] we provide the
primary insights obtained from this investigation.

##  2 Methodology

Table 1: Characteristic parameters of our simulation runs with the Eddington ratios obtained from SED fits to the data for M87 as tabulated in Prieto et al. (2016) and and for SgrA* in Narayan et al. (1998). Shown are the simulation run ID, followed by the simulation inputs which are, the initial disc plasma beta $\beta_{0}$ (at the inner disc radius), the maximum floor density $\rho_{\rm flr}$ and internal energy $u_{\rm flr}$ (both following a power law in $r$), the Kerr parameter of the BH $a$, the radius of the ISCO $R_{\rm isco}$, and specific comments on the simulation runs, respectively. Following the simulation inputs, the two columns show the Eddington ratios obtained from these simulations for a M87-like and SgrA* like system with SED fits. A background diffusivity of $\eta_{\rm back}=10^{-3}$ is applied, as well as a maximum disc magnetic diffusivity $\eta_{\rm max}=0.01$. We generally apply a density contrast $\rho_{\rm cor}/\rho_{\rm disc}=0.001$ at the inner disc radius. $ID$ | Simulation Inputs |  $\dot{m}$ from SED fits | $Comments$
---|---|---|---
$\beta_{0}$ | $\rho_{\rm flr}$ | $u_{\rm flr}$ | $a$ | $R_{\rm isco}$ | $\dot{m}_{\rm th-M87}$ | $\dot{m}_{\rm th-SgrA*}$
20EF | 10 | $10^{-5}$ | $10^{-8}$ | 0.9375 | $2~{}R_{\rm g}$ | $2.0\times 10^{-5}$ | $5.6\times 10^{-7}$ | reference run, similar to Vourellis et al. (2019)
21EF | 10 | $10^{-4}$ | $10^{-6}$ | 0.9375 | $2~{}R_{\rm g}$ | $1.0\times 10^{-5}$ | $5.0\times 10^{-7}$ | as 20EF, floor density higher
22EF | 10 | $10^{-4}$ | $10^{-6}$ | 0.0 | $6~{}R_{\rm g}$ | $7.0\times 10^{-5}$ | $5.0\times 10^{-6}$ | as 20EF, $a=0$, infall to BH, disc wind
23EF | 10 | $10^{-3}$ | $10^{-5}$ | 0.9375 | $2~{}R_{\rm g}$ | $3.0\times 10^{-6}$ | $1.5\times 10^{-7}$ | as 21EF, floor even higher, no BZ
24EF | 1 | $10^{-3}$ | $10^{-5}$ | 0.9375 | $2~{}R_{\rm g}$ | $4.0\times 10^{-7}$ | $2.0\times 10^{-8}$ | as 21EF, $\beta_{0}$ lower, stronger magn. field
26EF | 0.1 | $10^{-3}$ | $10^{-5}$ | 0.9375 | $2~{}R_{\rm g}$ | $2.0\times 10^{-7}$ | $1.0\times 10^{-8}$ | as 24EF, $\beta_{0}$ even lower, even stronger magn. field

Our approach follows a two-step procedure. First, we carry out resistive
general relativistic magneto hydro dynamic (GR-MHD) simulations of the disk-
jet evolution for a range of parameters. Secondly, we post-process these
dynamical data applying ray-tracing procedures. Note that the dynamical data
are scale-free by definition as these are pure magneto hydro dynamic (MHD)
simulations. We thus need to re-scale our physical parameters, such as mass
density, gas pressure, and magnetic field strength to astrophysical units. In
fact, this approach allows us to apply our dynamical models to accreting BH
systems with various physical conditions such as BHs of different mass,
accretion rates and magnetic flux. In turn, we may derive these parameters of
the BHs by comparing our results to observed systems.

###  2.1 Dynamical Modeling

The GR-MHD models we use here are the same as in Bandyopadhyay et al. (2021).
Here, we summarize their main features and the numerical setup of the
simulations for convenience.

We have applied the resistive GR-MHD code rHARM3D (Qian et al., 2017, 2018;
Vourellis et al., 2019) that is based on the well-known HARM code (Gammie et
al., 2003; McKinney & Gammie, 2004; Noble et al., 2006; Noble et al., 2009).
The application of a physical resistivity is essential for such simulations as
it allows both for a long-term mass loading of the disk wind and jet, and for
a smooth accretion process. For such simulations, it is believed that physical
resistivity is essential as it allows both for a long-term mass loading of the
disc wind and jet, and for a smooth accretion process. This is a consequence
of the involved magnetic diffusivity. In addition, this allows to treat
reconnection in a way that is physically well defined.

The GR-MHD simulations considered here are axisymmetric on a spherical grid,
implying that the vector components of all 3 dimensions are considered,
however, any derivatives in the $\phi$ direction are neglected partly due to
the exceptionally high CPU demand in case of resistive MHD. However, we
believe that our approach is fully sufficient in order to investigate the
radiative signatures of the black hole-disc-wind-jet system. Of course, any 3D
features like instabilities in the disc or in the jets cannot be treated, nor
any orbiting substructures can be found in these components. For our
qualitative investigation, these assumptions are sufficient.

It is worth summarizing the differences of our dynamical model setup in
comparison to other attempts to model intensity maps and spectra from GR-MHD
simulations such as e.g. published by Dexter et al. (2012); Mościbrodzka et
al. (2016); Event Horizon Telescope Collaboration et al. (2019e). We work with
resistive GR-MHD, which allows for smooth disc accretion and launching of a
disc wind. Our disc is thin, in Keplerian rotation, and is threaded by a
large-scale magnetic flux. This allows to drive strong disc winds (that, on
larger spatial scales, are supposed to evolve into a jet). In these cases, it
is the magnetized disc wind that is more efficient in angular momentum removal
due to its large lever arm.

A common feature of all GR-MHD simulations is the application of a so-called
floor density set as a lower density threshold in order to keep the MHD
simulation going. This numerical necessity strongly affects the resulting
dynamics of the axial Blandford-Znajek (BZ) jet – the mass load carried away
with the jet and the jet velocity (Qian et al., 2018; Vourellis et al., 2019).
On the other hand, the disk wind dynamics is governed by the mass loading from
the disk, which is self-consistently derived from MHD principles.

Our simulations follow the setup of Vourellis et al. (2019). The output of our
GR-MHD simulations comes in normalized units where $G=c=M=1$. In particular,
all length scales used in these simulations are in gravitational radii $R_{\rm
g}$, while velocities are normalized to the speed of light. All physical
variables need to be properly re-scaled for post-processing of radiation.

A further discussion of our model approach including the parameter space
involved, as well as maps of the essential dynamical variables, can be found
in (Bandyopadhyay et al., 2021). In brief, we investigate (i) a reference
simulation labeled as 20EF which applies similar simulation parameters as
Vourellis et al. (2019), (ii) a simulation 21EF with a (ten times) higher
floor density, thus a higher mass loading of the BZ jet, (iii) a simulation
22EF with a Kerr parameter $a=0$, (iv) a simulation 23EF with an even higher
floor density (another factor ten) resulting in the absence of BZ jet, (v) a
simulation 24EF with a lower plasma-$\beta$ (factor ten), resulting in a
stronger magnetic flux, and (vi) a simulation 26EF with an even stronger
magnetic flux (another factor ten).

The differences in the model setups are mainly in terms of the magnetic field
strength, BH rotation and floor density. However, these parameter have an
immediate impact on the jet dynamics, namely the jet speed (Doppler boosting),
and mass flux. In combination with the magnetic field, the synchrotron
emission is also affected. Note, however, that the latter also depends on the
scaling of the simulation variables in astrophysical units.

The accretion rate given in the GR-MHD simulations is in code units,
$\overline{\dot{M}}\simeq 0.1$, which can be re-scaled to astrophysical units.
As an example typical AGN accretion rates of

$\dot{M}\equiv\rho_{0}R_{\text{g}}^{2}{\text{c}}\overline{\dot{M}}\simeq
10^{-3}\,M_{\odot}\text{yr}^{-1}$ (1)

constrain the (maximum) disk density to

$\rho_{0}=4\times
10^{-15}\frac{\text{g}}{\text{cm}^{3}}\left[\frac{\dot{M}}{10^{-3}\,M_{\odot}/\text{yr}}\right]\left[\frac{M_{\text{BH}}}{5\times
10^{9}M_{\odot}}\right]^{-2}\left[\frac{\overline{\dot{M}}}{0.1}\right]^{-1}\\!\\!\\!\\!$
(2)

(Vourellis et al., 2019). The re-scaled astrophysical density is
$\rho=\rho_{0}\bar{\rho}$, where $\bar{\rho}$ in code units follows from our
simulations. For illustration, for the example of M87 one can use the values
provided by the EHT collaboration (Event Horizon Telescope Collaboration et
al., 2019b) with an accretion rate of $\dot{M}\sim 2.7\times
10^{-3}\,M_{\sun}\rm yr^{-1}$ and a BH mass of $M=6.2\times 10^{9}\,M_{\sun}$.

Figure 1: The synchrotron spectra dominated by thermal electrons for M87
(left) and SgrA* (right) like systems. The Eddington ratios obtained for each
of the simulations fitted to the data for M87-like system (Prieto et al.,
2016) and SgrA*-like system (Narayan et al., 1998) are shown in the columns 7
and 8 of Table [1]

###  2.2 Parameter fitting and ray-tracing

GR-MHD codes like the one we have used in this work typically apply normalized
units where $G=c=M=1$. These simulations need to be post processed with
physical parameters such as black hole mass, accretion rates etc., to obtain a
physical realization of an AGN. Here we post process our simulations for the
BH masses of M87 and SgrA* and vary the accretion rate (fixing other
parameters) to obtain synthetic SED comparable to the observed data in the
radio bands for these sources. The post processing of these GR-MHD simulations
are done using ray tracing codes.

The image of a BH is characterized by a "shadow" region towards which no
photons arrive due to the curvature of space-time around the BH; the unlensed
"photon ring" ($3~{}R_{g}$), corresponding to bound photon orbits around the
BH; and as a result of lensing, the photons from the inner part of the disk
appear somewhat further outside that region leading to lensed ring marked by
inner ($5.2~{}R_{g}$) and outer ($6.2~{}R_{g}$) boundaries (Gralla & Lupsasca,
2020; Gralla, 2020; Gralla et al., 2020, 2019; Johannsen, 2013; Beckwith &
Done, 2005; Luminet, 1979; Bardeen, 1973). These features are a result of
strong GR effects around the vicinity of the BH. The use of a ray-tracing code
is to post process the simulated dynamical model to calculate the observed
intensity on locations (pixels) of an observer’s camera for a given model of
emission and absorption. Here we use the publicly available general
relativistic radiative transport code GRTRANS (Dexter, 2016). Within GRTRANS,
the Boyer–Lindquist coordinates of the photon trajectories are calculated from
the observer towards the BH (i.e. the rays are traced) for each pixel in the
camera. The observed polarization basis is then parallel-transported into the
fluid frame. The local emission and absorption properties at each point are
calculated. The radiative transfer equations are solved along those rays. We
refer to the original paper by Dexter (2016) for details on the working of the
code.

The jet and wind dominated regions are highly magnetized and thus it can be
assumed that synchrotron emission is the most dominant emission mechanism in
these regions (Yuan & Narayan, 2014). In this work we consider synchrotron
emission from a population of thermal electrons as the primary emission
process. This emission, often referred to as thermal synchrotron, depends on
the thermal state of the electrons where the electron temperature should
ideally be determined from the plasma physics. However, currently setting up
simulations of a fully functional plasma dynamics in a GR set up is
computationally expensive. In the absence of such a physically complete model,
an alternate prescription is applied which replicates the plasma temperature
model. In this scenario, the gas temperature at each point in the grid is
determined from the internal energy at those grid points assuming an ideal gas
prescription. The electron temperature is deduced from the gas temperature
assuming that the plasma consists only of ionised hydrogen. As prescribed by
Mościbrodzka et al. (2009, 2016), the electron temperature is approximated
from the following relation as

$\displaystyle T_{e}$ $\displaystyle=$ $\displaystyle T_{\rm gas}/(1+R),$
$\displaystyle R$ $\displaystyle=$ $\displaystyle\frac{T_{\rm p}}{T_{\rm
e}}=R_{\rm high}\frac{b^{2}}{1+b^{2}}+R_{\rm low}\frac{1}{1+b^{2}},$ (3)

with $b=\beta/\beta_{\rm crit}$, $\beta=P_{\rm gas}/P_{\rm mag}$ and $P_{\rm
mag}=B^{2}/2$. The value of $\beta_{\rm crit}$ is assumed to be unity, and
$R_{\rm high}$ and $R_{\rm low}$ are the temperature ratios that describe the
electron-to-proton coupling in the weakly magnetized (e.g. discs) and strongly
magnetized regions (e.g. jets), respectively. The emissivity for a given
distribution function of electrons is given as

$j=\int_{1}^{\infty}d\gamma N(\gamma)\eta,$ (4)

where

$\eta=\frac{\sqrt{3}e^{2}}{8\pi c}\nu_{B}sin\theta_{B}H(\nu,\theta_{B})$ (5)

is the vacuum emissivity (Melrose, 1980) with $e-$ the electron charge, $c-$
the speed of light, $\nu_{B}=\frac{eB}{2\pi mc}$,
$H=F\left(\frac{\nu}{\nu_{c}}\right)$, $\nu-$ the emitted frequency, $\gamma-$
the electron Lorentz factor, $\nu_{c}=(3/2)\nu_{B}\sin(\theta_{B})\gamma^{2}$
and $F(x)=x\int_{x}^{\infty}dyK_{5/3}(y)$ is an integral of the modified
Bessel function. For a thermal distribution the distribution function in
eqn.[4] is

$N(\gamma)=\frac{n\gamma^{2}\beta\exp(-\gamma/\theta_{e})}{\theta_{e}K_{2}(1/\theta_{e})},$
(6)

where $n$ is the number density of electrons and $\theta_{e}=\frac{kT_{\rm
e}}{mc^{2}}$ is the dimensionless electron temperature.

As shown by Bandyopadhyay et al. (2021), the post-processing parameters which
significantly affect the synthetic SED and emission maps are the BH mass and
the accretion rate. The accretion rate and black hole mass scale the density,
pressure and the true magnetic field from the simulations. The temperature of
the gas is obtained from the density and pressure assuming an ideal gas
equation as already mentioned. The temperature and the magnetic field are the
important physical parameters which affect the synchrotrom emission through
the distribution function (eqns. [4] and [6]).

In this work, we have used the high resolution spectral data from Prieto et
al. (2016) for M87 and that from Narayan et al. (1998) for SgrA*. Modeled
spectra from the simulations are generated by fixing all the other parameters
(except for the black hole mass and accretion rate) to constant values for the
two systems for simplicity. As this is a qualitative study about the emission
features around supermassive BHs, the small changes in the SED due to the
inclination angle or the choice of $R_{high}$ and $R_{low}$ in eqn.[3] are
almost insignificant compared to the variation introduced due to the
underlying dynamics from the simulations. Once the best fit SED are obtained
for each of the simulations, the synthetic images are generated at 230 GHz
using the best fit value of the Eddington ratio or the accretion rate for each
given BH mass. We use these synthetic images to obtain the intensity profile
for several cross-sections of the image.

These synthetic images are further convolved with a Gaussian beam-width to
account for the telescope resolution of the EHT and the future ngEHT to obtain
secondary synthetic images which are expected to be similar to that we observe
with the telescopes. At 230 GHz, an intrinsic (FWHM) resolution of $\sim
20~{}\mu as$ is achieved for the longest baseline ($D_{1}\sim 11,000$ Km).
Thus using these values, we can obtain approximate values of the resolution
for earth space baselines. A Geo-VLBI baseline ($D_{2}\sim 47,000$Km) and a
L2-VLBI baseline ($D_{2}\sim 1.5$ million Km) would give us approximate
resolutions of $\theta_{2}\sim 5~{}\mu as$ and $\theta_{2}\sim 0.16~{}\mu as$
respectively. We assume these resolutions in the sections below (see also
Pesce et al. (2021)).

Figure 2: Normalized emission maps for edge-on views for all our simulations
for M87 (upper 6 panels in each set) and and SgrA* (lower 6 panels in each
set) like systems at 86 GHz (top left), 230 GHz (top right), 345 GHz (bottom
left) and 700 GHz (bottom right) obtained with the Eddington ratios mentioned
in Table [1].

Figure 3: Normalized emission maps (upper) and intensity profiles (lower)
(along cross-sections with angles$=0\degr,90\degr,45\degr\&-45\degr$
respectively) for all our simulations for M87 (upper 6 in each of the panels)
and and SgrA* (lower 6 in each of the panels) like systems with inclination
angles $0\degr$or face-on (left) and $17\degr$ (right) for 230 GHz. The
emission maps with the face-on view in addition also show the contours of the
unlensed photon-ring (3 $R_{g}$ with continued green circle) and the outer and
inner range for the lensed photon ring (5.2 and 6.2 $R_{g}$ with green dashed
circles). The intensity profiles too mark these with black solid line, dot
dashed line and dashed line.

##  3 Spectral properties

The primary aim of this work is to investigate the origin of emission
signatures from disk winds, jets or the accretion flow for a set of GR-MHD
simulations when subject to the known extreme ends of the mass and spin
spectrum for supermassive BHs in the local universe. The origin of emission
signatures from winds, jets and disk from the different simulations can be
distinguished when we can obtain a similar spectral fit from each of them. We
thus post processed our simulations with the masses of M87 and SgrA* and
varied the accretion rates (by varying the Eddington ratio
$\dot{m}=\dot{M}_{\rm acc}/\dot{M}_{\rm Edd}$) for each of our simulations to
generate synthetic SED which fit best to the high resolution multi-wavelength
data (Prieto et al., 2016; Narayan et al., 1998) of M87 and SgrA*. Generally
in literature (for reference, see Mościbrodzka et al. (2016); Event Horizon
Telescope Collaboration et al. (2019a), etc.) the SED for the sources of
interest are obtained considering the synchrotron emission and its Compton
scattering using the GRMONTY code (Dolence et al., 2009). However, in this
work, the spectrum obtained with GRTRANS (Dexter, 2016) using synchrotron
emission is sufficient as we are mainly interested in a qualitatively fit of
the synthetic SED to the high resolution data in the radio band where we know
that synchrotron emission is the most dominant emission process.

M87 has a mass ($6.5\times 10^{9}M_{\odot}$) which is 3 orders of magnitude
higher than that of SgrA* ($4.12\times 10^{6}M_{\odot}$) but is also at a
distance (16.9 Mpc) 3 orders of magnitude farther than SgrA* (8.127 kpc) which
is in the center of our Galaxy (Event Horizon Telescope Collaboration et al.,
2019a, 2022a). Table [1] shows that the Eddington ratios (7th and 8th columns)
required to match the SED for each of the simulations are higher for M87
compared to SgrA*. This result is similar to previous literature conclusions
and implies that M87, the CD galaxy of the Virgo cluster, is fed by a more
massive reservoir of gas compared to the trickle from disk winds as speculated
for SgrA*. The higher accretion rates in M87 lead to a higher luminosity but
since it is also at a distance 3 orders of magnitude farther than SgrA*, both
result in similar fluxes.

As can be seen from the spectral fits in Fig.[1], 26EF (the simulation with
spin parameter $~{}1$ and the lowest value of $\beta$ parameter) fits best to
the M87 high resolution data at radio frequencies, while 22EF (the simulation
with spin 0) fits best to the high resolution data for SgrA*. It can be noted
here that the accretion rate that fits the M87 data for 26EF is 2 orders of
magnitude smaller than what has been inferred by the EHTC (Event Horizon
Telescope Collaboration et al., 2019a), which could be due to the an intrinsic
higher accretion rates ($\dot{M}$ in Eq. [1]) in the dynamical simulations
used in this work.

It is expected that the SED generated with a power-law, hybrid or Kappa
distribution would be the result of accretion rates different from the values
deduced in this work. However, this work focuses on the SED fits resulting
from a thermal synchrotron emission to deduce the underlying physical
parameters from expected observations.

##  4 Intensity maps & profiles

Once the accretion rates are determined by fitting the modeled spectra to the
observed data, the intensity maps for the simulations are generated. The
existence of jets and winds if at all present are expected to be detected with
a edge-on view as the direction of these flows in general are perpendicular to
the accretion flow. However, in reality not all AGN have an orientation with
edge-on views. As an example both M87 and SgrA* have very low inclination
angles (for M87 this is inferred from observations of the large scale jet
while for SgrA*, this is deduced through model fitting (Event Horizon
Telescope Collaboration et al., 2022a)). Hence we first post process our
simulations assuming an edge-on view to identify the presence of disk wind and
jet signatures and then look for these signatures for lower inclination
angles. Fig.[2] shows the edge-on views of the post processed images of our
simulation for M87-"like" (upper 6 rows of each figure) and SgrA*-"like"
(lower 6 rows of each figure) systems at 86 GHz, 230 GHz, 345 GHz and 700GHz
respectively. Since low frequency photons cannot escape the inner regions due
to synchrotron self-absorption, only the outer regions are visibile at lower
frequencies and hence the likelihood of the visibility of any signatures of
winds and jets at lower frequencies. At higher frequencies such as at 345 GHz,
the emission from regions in the vicinity of the black holes event horizon are
visible from which the photons with the highest energies are emitted. As can
be seen in Fig. [2] the emission signatures of jet and disk wind are more
prominent at 86 GHz (see for example the emission maps for 21EF and 23EF for
SgrA* like system) while those same features are not prominent at 345 GHz
where the emission is expected from the inner most regions. However, 230 GHz
is an intermediate frequency where both these effects are visible. These
images in general are brighter on the left including the ones with jet and
disk features due to Doppler beaming.

At present it is difficult to obtain good resolution images at 86 GHz even
with an earth-size telescope. As can be observed from Fig.[2], 230 GHz is thus
the ideal frequency to trace the jet and wind signatures back to horizon
scales with the resolutions available in the present. As we move towards
higher frequencies, it is difficult to distinguish the traces of any jet or
outflow signature distinctly and this is independent of any telescope
resolution. Although at higher frequencies, we may able to probe regions in
the proximity of the BH event horizon, it may still be difficult to comment on
the origin of jets and outflows at these high energies. This may however
change if the accretion rate is increased in which case there will be an
overall increase in flux throughout the spectrum (the images for low
inclination angle at 345 and 700 GHz are in the Appendix A). Here we, thus,
concentrate on investigating emission features of our interest at 230 GHz. The
simulations are thus further processed at 230 GHz for a face-on view and low
inclination angle ($17\degr$) as shown in fig.[3].

Although from a glance distinguishing emission features from winds and jets is
not obvious for low inclination angles or face-on views, however observing the
emission features from different simulations especially from their emission
profiles hint at the existence of disk winds or jets as shown in fig.[3]. As
an example one can deduce from the figure that the emission profile for 21EF
is broader than 20EF for SgrA* like system for both face-on and a small
inclination angle of $17\degr$ for a similar Eddington ratio. This comparison
although not quite evident from the edge-on view at 230 GHz but the edge-on
view at 86 GHz clearly shows a jet signature for 21EF. Similarly a large scale
wind signature is visible for 23EF at 86 GHz in the edge on view for
SgrA*-like system for an accretion rate which is slightly lower than those for
20EF and 21EF. for the same system at lower inclination angles and face-on
views, the emission profile is broader and extends to much larger scales. For
M87-like system too, with an accretion rate lower by an order than 20EF and
21EF, the emission profile is similar or slightly broader for 23EF at 230 GHz.
Such a broader emission profiles for a similar physical set-up could indicate
the presence of a disk wind which is due to velocity dispersion within the
wind or jet.

In addition to tracing jets and wind signatures deduced from the width of the
emission profiles, there are several interesting observations from these
emission maps. As an example, the upper left panel of Fig.[3] shows the face-
on views of the emission where the unlensed photon ring and the outer and
inner boundaries (corresponding to the prograde and retrograde spins) of the
lensed photon ring are marked by green contours of radii $3~{}R_{g}$,
$5.2~{}R_{g}$ and $6.2~{}R_{g}$, respectively. The lensed photon ring visible
in face-on views ($0\degr$) coincides with the $5.2~{}R_{g}$ radius for almost
all the cases (as expected for prograde spins). The presence of a disk wind or
jet base smear out the photon ring features as in 21EF for SgrA*-like system.
As expected for face-on views, the flux profiles along different slices of the
images coincide as can be seen in the lower left panel of Fig.[3], while for
small inclination angle such as $17^{\degr}$ the profiles separate and one of
the peaks is higher than the other due to the additional effect of Doppler
beaming. We also observe that for an inclination angle of $17\degr$, the
emission peaks shifts from the expected inner or outer boundries of the lensed
photon ring in all models except for 22EF which is an accreting Schwarzschild
BH. The asymmetry is an intrinsic property of BHs with non-zero spins.

Simulations 20, 21 and 23 have very similar properties except for their floor
density chosen (which are not expected to influence the emission signatures
due to the assumed $\sigma_{cut}$). Simulations 24EF and 26EF are however set-
up considering a larger mean magnetic field strength. Thus in general the
accretion rates deduced for these simulation is much lower than those for the
ones mentioned above as synchrotron emissivity is also dependent on the
magnetic field strength. It is also observed that the accretion rates obtained
from fitting the simulated spectrum to the data for M87 and SgrA* for 24EF and
26EF are of the same order for both cases but the emission region for 26EF is
quite compact compared to 24EF. The magnetic field strength in case of 26EF is
an order of magnitude higher, thus the emission from a smaller region is
enough to obtain a similar amount of flux strength as obtained that from 24EF.
All our simulations are set-up for Kerr BHs except for 22EF which is a spin 0
Schwarzchild BH. All other parameters for this simulation are same as those
set up for simulations 20, 21 and 23. Being a Schwarzschild BH, the ISCO
radius is much larger implying that for similar accretion rates, the
temperature of the in-falling material will be lesser than its Kerr
counterpart. Thus to compensate for this in order to obtain similar fluxes, a
higher accretion rate is required for both M87-like and SgrA* like systems.

Simulations 21EF and 24EF display very similar emission features at all
frequencies, both in the edge-on views and low inclination angle views for
both M87 and SgrA* like systems. 24EF although has a lower accretion rate
compared to 21EF by more than an order of magnitude in both the sources, but
24EF also has an intrinsic magnetic field strength which is an order of
magnitude higher. It seems that these two effects almost balance each other.

The images obtained at lower inclination angles are further processed to
account for the resolution of the current EHT($20~{}\mu\rm as$) and for the
future earth-space based VLBI resolutions ($5~{}\mu\rm as$ for Geo-VLBI and
$0.16~{}\mu\rm as$ for L1-VLBI) as shown in Fig.[4], to predict if the origin
of emission will be resolved today or in future for a better understanding of
the physical processes.

Given the current EHT resolution, 20EF, 21EF, 23EF and 24EF appear
qualitatively indistinguishable (Fig.[4]). 22EF shows large scale wings
showing the signature from the large scale outflow while a much more compact
emission profile is seen in 26EF for both M87-like and SgrA*-like systems.
With the current EHT resolution, the shadow region is not well resolved for a
M87 like system for 23EF, 24EF and especially 26EF whose emission region is
quite compact. For the expected Geo-VLBI and L1-VLBI like resolutions, these
similarities in the emission profiles seem to break down and and the intrinsic
features start to appear (Fig.[4]).

We would like to emphasize here that in this work we have considered only a
thermal distribution for the electron distribution function for a simplistic
approach. In reality we expect the jet emissions to be non-thermal in nature
due to the presence of non-thermal processes such as magnetic field driven
turbulence, magnetic reconnection, etc. We would however like to note that for
simulations 20EF, 21EF, 22EF and 23EF this might be a good approximation which
have a higher values of plasma $\beta$ ($\beta_{0}=P_{gas}/P_{mag}=10$)
However, simulations 24EF and 26EF have $\beta=1$ and $\beta=0.1$,
respectively, implying that the magnetic pressure is equal to or ten times
greater than the gas pressure. In these it might not be sufficient to just
consider a thermal distribution function for the electrons. For a true
understanding of the origin of disk and wind signatures of a realistic system,
it might not be sufficient to just consider the emissions originating from a
population of thermal electrons. However, considering other distribution
function is beyond the scope of this investigation.

##  5 Comparison study

The emission region for a M87-like system in general is more compact compared
to SgrA* like systems in $R_{g}$ scales. As can be seen in fig.[2], 22EF shows
a large scale outflow both for M87 and SgrA like systems visible prominently
at 86 GHz. Similarly, 23EF shows a disk wind resulting in a large scale
outflow visible distinctly at 86 GHz for both systems. 21EF displays a strong
disk wind signature and a trace of the BZ jet along the upper half of the
equatorial plane at 86 GHz (left panel of Fig.[2]) in an edge-on view for
SgrA* like system. The trace of the BZ jet is also visible at 230 GHz in the
emission profile with the face-on view ($0\degr$) (bottom left panel of
Fig.[3]) where a non-zero flux is visible in the expected shadow region. 20EF
and 21EF both have similar accretion rates but the emission profiles at 230
GHz display a broader emission profile of 21EF for SgrA like system and a
greater Doppler beaming effect for M87 which leads to the conclusion that such
a feature can arise if there is emission from region with additional
velocities such as disk wind or outflows.

Similarly 23EF has a lower accretion rate than 20EF but broader emission
profiles at 86 and 230 GHz for SgrA like systems and a comparable width in the
emission profile for M87 at 86 GHz and 230 GHz. This is again possible if the
emission has a contribution from the disk wind. At higher frequencies such as
700 GHz, however, we see contrary effects in the case of 23EF both for M87 and
Sgr A like systems where the emission feature is peaked near the photon ring
and is dimmer than 20EF or 21EF which is possible when the emission is from
the accreting disk because at higher frequencies it is the energetics of the
accreting disk which becomes relevant.

Figure 4: Emission maps and radial emission profiles (along diameters with
angles$=0\degr,90\degr,45\degr\&-45\degr$ respectively) for M87 (upper 6 in
each panel) and SgrA* (lower 6 in each panel) like systems for inclination
angles of $17\degr$ at 230 GHz for EHT-$20~{}\mu$arcsec (upper), Geo-
VLBI-$5~{}\mu$arcsec (middle) and L1-VLBI-$0.16~{}\mu$arcsec (lower) like
resolutions). The intensity profiles in addition also mark the positions of
the unlensed photon ring (black solid line) and the theoretical values of the
inner (dot dashed line) and outer (dashed line) boundaries of the lensed
photon ring.

##  6 Discussion and Conclusions

Most AGN are found to exist in nature with jets and outflows which in some
cases are detected easily. It is thus important to understand the physical
processes which launch different types of jets or outflows for which it is
also important to trace their origin through observations. Through this work
we have tried to demonstrate how a set of accreting systems with different
physical conditions, results in varied observational features for systems with
similar BH mass and spectral behaviours as M87 and SgrA*. BH shadows for both
SgrA* and M87 have already been observed with the EHT (Event Horizon Telescope
Collaboration et al., 2019a, 2022a). In this work our intention is not to make
a quantitative comparison with the observations. However, this work intends to
motivate how the same set of simulations, with similar spectral behaviour
under different physical conditions, result in generating completely different
observational signatures. In addition this work also intends to describe
qualitative methods to extracting jet and wind signatures from observations.
In our study the BH masses and SED are the primary constraining parameters.
The accretion rates are varied in each simulation until the modeled SED is
similar to the observed data. This results in generating intrinsic features in
the emission maps, thus enabling to compare the different physical processes
which lead to such emission signatures. The emission maps are further
processed to make predictions for current and future VLBI observations. In
general one of the criteria for accepting theoretical models is in comparing
the jet power (Event Horizon Telescope Collaboration et al., 2019e) to the
real values; however this investigation is more qualitative in its approach of
demonstrating observational signatures of winds and jets, rather than testing
alternate models for M87 and SgrA*. Thus in this work we do not calculate the
jet power from these models as it is not relevant for the current
investigation.

The emission maps here are shown in linear scale as generally done in the
literature with the normalization being done with the maximum intensity in
each frame. However we would like to mention that the mean value of intensity
per pixel (which is quite low$~{}10^{-7}$ Jy) is similar for all the
frequencies. A normalization with the mean value generates similar emission
maps displaying a thick emission ring at all frequencies but the fine
structures arising due to the presence of winds and jets are not visible at
all frequencies. However, normalizing the images with the mean saturates the
images and is not practical because the value is much less than the flux
sensitivity of any telescope. On this note we would like to emphasize that
even though we have taken into account the smudging of images due to the
limited beam-width introduced by the resolution of telescopes, in reality the
images obtained are further limited by thermal noise, limited $u-v$ coverage
and calibration errors.

We would like to emphasize here that while post-processing, in addition to the
accretion rate, it is also important to consider a proper electron
distribution function depending on the underlying physical parameters of the
GR-MHD which may affect the resulting spectrum significantly. Although
considering a thermal electron distribution function may be sufficient for a
weaker magnetic field limit but may not hold true in the strong field limit.
In the strong field limit, we expect non-thermal processes to dominate arising
due to effects such as magnetic re-connection and also holds true for the
emission from jets where the magnetic fields dominate. In such cases
considering a pure thermal electron distribution function will be an
underestimation.

The outflow features are easily distinguishable at lower frequencies but
tracing them to their origin from regions close to the BH would mean observing
them with the highest resolutions which is possible only at higher frequencies
and using telescopes with larger baselines. Currently highest resolution
observations are possible with the technique of radio interferometery with
different VLBI arrays such as the EHT which has an Earth sized baselines.
Through this investigation we deduce that with fluxes like that we observe for
M87 or SgrA*, observing the signatures of jets or outflows closest to horizon
scale will be possible at 230 GHz. It might be possible to observe some of the
features even at 345 GHz but currently not all EHT telescopes are designed to
function at 345 GHz, which thus will introduce additional noise during image
reconstruction. Although making strong deductions about different physical
scenarios will not be possible with the current EHT resolution, it may however
be possible with future Earth-Space baseline probes with ngEHT.

Apart from improving telescope resolution to obtain finer details of emissions
from horizon scale around SMBHs with longer baselines and tuning the
telescopes to observe at higher frequencies, it is also important to improve
on the image reduction algorithms which generates the observed images with
better resolution. In addition it is also important to consider the telescope
sensitivity to obtain the threshold to distinguish the finer structures.

As a final note we would like to mention that through this work we primarily
intended to demonstrate whether with current and future VLBI arrays it would
be possible to detect signatures of disk winds or jets at horizon scales for
two different physical systems. In the process we thus used a set of disk-
wind-jet simulated models for AGNs to show how each model would differ from
the other observationally. The SED was the primary constraint that was used
and only the accretion rate was varied to fit the modeled SED to the observed
spectrum. A more quantitative complete approach would include considering the
variation in many other physical parameters (such as a considering different
temperature models or distribution function for electrons etc.) to obtain
precise fit to the SEDs. However for this investigation we think a qualitative
approach is good enough to demonstrate the tools to extract certain
observational features from simulated emission maps and making predictions if
such an analysis would be possible for the current and future VLBI facilities.

## Acknowledgements

We acknowledge funding from ANID Chile via Nucleo Milenio TITANs
(NCN19$-$058), Fondecyt 1221421, and Basal projects AFB-170002 and FB210003.

All GR-MHD simulations were performed on the VERA clusters of the Max Planck
Institute for Astronomy. C.F. thanks Qian Qian and Christos Vourellis for
developing the resistive version rHARM-3D of the original GR-MHD code HARM-3D,
kindly provided by Scott Noble.

## Data Availability

Data available on request. The data underlying this article will be shared on
reasonable request to the corresponding author.

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##  Appendix A Emission maps and profiles for 345 GHz and 700 GHz

In Fig.[5], we show the modeled emission maps and intensity profiles for
M87-like and SgrA*-like systems from our simulations for 345 GHz and 700 GHz.
The figure is discussed in Section [4].

Figure 5: Flux normalized emission maps (upper) and intensity profiles (lower)
(along diameters with angles$=0\degr,90\degr,45\degr\&-45\degr$ respectively)
for all our simulations for M87 (upper 6 in each of the panels) and and SgrA*
(lower 6 in each of the panels) like systems with inclination angle $17\degr$
for 345 GHz (left) and 700 GHz (right) respectively. The intensity profiles in
addition also mark the positions of the unlensed photon ring (black solid
line) and the theoretical values of the inner (dot dashed line) and outer
(dashed line) boundaries of the lensed photon ring.