HUMAN FACTORS CONSIDERATIONS IN SATELLITE OPERATIONS HUMAN-COMPUTER INTERACTION TECHNOLOGIES: A REVIEW OF CURRENT APPLICATIONS AND THEORY

International Journal of Managing Information Technology (IJMIT) Vol.13, No.3, August 2021
DOI: 10.5121/ijmit.2021.13303 23
HUMAN FACTORS CONSIDERATIONS IN SATELLITE
OPERATIONS HUMAN-COMPUTER INTERACTION
TECHNOLOGIES: A REVIEW OF CURRENT
APPLICATIONS AND THEORY
David G. I. Heinrich, Ian McAndrew and Jeremy Pretty
Department of Human Factors, Capitol Technology University, Laurel, Maryland, USA
ABSTRACT
Satellite operations are a subset of remote operations that draw similarities with remotely piloted aircraft
(RPA) and uncrewed aerial vehicle (UAV) operations. Increased research into boredom, complacency,
habituation, and vigilance as they relate to satellite operations is required due to a lack of prevalence in
the literature. Circadian rhythms, crew resource management, and shift work dynamics may exacerbate
complacency-driven automation bias and social loafing errors in satellite operations. This overview of
theory and applications aims to specifically focus on satellite operations literature within human factors
research to identify areas requiring an expansion of knowledge. The human-in-the-loop commonality
enables human factors lessons to be passed to satellite operations from unrelated sectors to mitigate
catastrophic human error potentially. As such, this literature review details the need for increased
research in satellite operations human factors.
KEYWORDS
Complacency, Human-in-the-Loop, Remotely Piloted Aircraft, Satellite Operations, Shift Work
1. INTRODUCTION
The United States (U.S.) Department of Defense (DoD) conducts command-and-control (C2) of
space assets with a human-in-the-loop (HITL) 24-hours a day, 7-days a week, whereby multiple
teams operate in shift work patterns that go beyond regular day-shift hours. Acquisition program
offices often place initial developmental and funding emphasis on the satellite system-of-systems
space segment, also known as the spacecraft, due to complexity and lack of maintenance
capabilities once in orbit. Complexity engineered into spacecraft systems leads to the
development of autonomous machines that only need occasional human intervention, thus
decreasing overall stress on the operator [1]. Operators must be vigilant to deter the risk of near-
peer adversaries and on-orbit anomalies that can degrade or permanently end mission capability.
Spacecraft and ground architecture autonomy create the potential of increased operator
complacency risk, which may be further exacerbated by circadian rhythm deficiencies due to
shift patterns in windowless secure operations centers [2, 3].This study aims to identify the
current state of the literature in human factors as it pertains to satellite and remote operations.
This paper details the background, significance, current applications, and theories pertaining to
complacency, crew resource management (CRM), and human dynamics in remote operations
environments. This review of current applications and theory explores how the literature has not
fully fused aviation lessons learned with uncrewed spacecraft operations to combat Gordon
Dupont’s human factors “Dirty Dozen”[4].

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2. BACKGROUND
According to the U.S. Department of Health and Human Services, shift work is a staffing method
that ensures the entirety of a 24-hour day has a constant worker presence for the period beyond
regular daylight hours [5]. Shift work can range from three 8-hour shifts to extended shift periods
consisting of rotating 12 to 24-hour durations seen in the law enforcement, medical, military, and
transportation sectors [5, 6]. Shift work patterns incorporate rotating work-rest cycles when
multiple team members are used to fulfill long-term work schedules. Work centers benefit from a
constant presence of workers, and the workers themselves also tend to benefit from working shift
work patterns outside of traditional day shift hours. Workers may favor shift work out of
necessity due to increased pay, work-life balance, educational advancement opportunities, and
less perceived supervision on later shifts [5].
Shift workers in satellite operations centers rely on complex systems which create autonomy in
space and ground system architectures. Autonomy consists of built-in pre-coordinated reactive
measures that ensure built-in internal redundancy keeps the system operating during a
malfunction, with no loss of service to the user[1]. Depending on the manufacturer, multiple
subsystem units provide redundant failover options, either automatically or manually, with
operator intervention. Due to the lack of maintenance actions available to the satellite in orbit,
engineered autonomy ensures successful failover to the redundant unit. Barring extraordinary
circumstances, most spacecraft can continue providing service by performing autonomous
failover measures, while manual failover actions could result in temporary loss of service due to
the potential for a delayed human response [7]. Once the spacecraft has triggered an alarm
indicating an active non-nominal event or anomaly, satellite operations center (SOC) operators
are expected toverify that the proper autonomous failover successfully executed immediately.
After safety of the spacecraft is confirmed, operators must then research and diagnose the issues
leading up to the failover and implement procedures to return the satellite to nominal
operations[7].
Spacecraft or satellite autonomy ensures that the satellite operates with minimal operator
intervention[8]. Spacecraft location data called ephemeris is transmitted to the ground antenna,
which then is transferred to a data processing site where the data is analyzed to ensure station-
keeping accuracy within the spacecraft’s proper orbital location. Once ephemeris is validated, an
updated maneuver plan is commanded to the satellite, which then must be ingested and computed
by the satellite onboard electronics. Once the onboard electronics validate the burn plan, the
satellite can continue to operate until the next scheduled ephemeris cycle is due. Satellite
operators often work entire shifts only conducting health and safety contacts with the spacecraft
to include ephemeris uploads and verifications. Above routine station-keeping requirements,
operations personnel maintain a monotonous and stagnant posture. The tedious nature of SOC
operations may create the potential for complacency-induced errors. These errors range in
severity from inaccurate documentation of shift actions to catastrophic complacency-induced
errors resulting from procedural mistakes that can take the satellite out of mission or render the
satellite entirely inoperable[9]. Lessons learned from complementary aerospace sectors and
unrelated fields need to be evaluated to understand the potential benefits of transferring
mitigation techniques into satellite operations methodologies.
Human factors considerations in the uncrewed aircraft systems (UAS) and remotely piloted
aircraft (RPA) sectors complement the human factors considerations of satellite operations due to
the level of shift work required, level of autonomy involved in their respective systems, and the
remote nature of the operations environment. The U.S. DoD employs over 11,000 UAS systems
worldwide [10]. Conversely, the U.S. Space Force (USSF) operates over 200 satellites [11],
while the USSF 18th Space Control Squadron monitors over 3,200 active satellites [12, 13].

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While there is a vast difference between the amount of active UAS assets versus satellites
employed by the DoD, both sectors may benefit from a transference of human factors knowledge
and lessons learned. Furthermore, the medical, maritime, and transportation sectors can all be
leveraged for complementary human factors methodologies.
3. SIGNIFICANCE OF STUDY
U.S. DoD single unit satellite acquisitions can cost taxpayers upwards of over $3 billion, with
ground segment lifecycle costs for development, operations, and sustainment reaching $6 billion
[14]. The USSF Global Positioning System (GPS) III Follow-On upgrade finalized in 2018 will
end up costing taxpayers $7.2 billion for a total of 22 satellites [15]. However, the return on
investment aims to continue to contribute to the $1.4 trillion in economic benefits since 1988
[16]. Due to satellite programs' proprietary and often confidential nature, little may be known of
the full impact of human error in billion-dollar procurements. Nonetheless, human error has been
exhibited in space operations [17]. Incidents of invalid software loads, incorrect coordinates set
before launch, and other human errors have led to total mission loss [9, 17]. Transference of
lessons learned between sectors may establish common knowledge, which then may leverage best
practices between sectors, thus potentially reducing the overall risk of human factors-related
mishaps.
Knowledge transfer between aerospace and non-aerospace sectors will ensure that human factors
lessons learned are analyzed and incorporated into the development of costly governmental and
commercial investments. Human error must be further engineered out of remotely operated space
systems to preserve capabilities, such as GPS, that have become vital to everyday life[16]. Steps
taken during SOC development to mitigate complacency-induced errors will build a foundation
of error prevention that will have lasting impacts on the future of satellite operations. This review
of the literature expands upon the works of Dupont, Hawkins, and Reason to ensure the field of
satellite operations is considered in the human factors conversation[4, 18, 19]. Without careful
consideration, the proliferation of space-based technologies may suffer a repeat of comparable
historic human factors related aviation mishaps as seen in the 1977 Tenerife Airport Disaster
[18], which would only serve to stifle progress if not for human factors lessons learned.
4. NATURE OF STUDY
This research paper comprises a meta-analysis of the current literature on automation, autonomy,
complacency, shift work, and shift patterns in the remote operations aerospace sector. SOC
operations are not unique regarding human factors as there are other human-centric sectors where
lessons learned may be considered: healthcare, maritime, and the transportation sectors. Satellite
operations deal with similar human factors issues as seen in the UAV sector. Thus, a meta-
analysis of qualitative research was selected due to the potential classified nature of military
satellite operations. This study remains in the unclassified domain through analysis of existing
open-source data.
5. CURRENT THEORY AND APPLICATIONS
5.1. Theoretical Foundations
This research paper comprises a literature review of foundational research,which will be
expanded upon via future studies and analyses. The theoretical framework of this research is
based on the existing body of knowledge of aerospace and non-aerospace concepts and theory.
The theoretical foundation of this research paper revolves around the concept of Gordon

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Dupont’s “Dirty Dozen”[4], Frank Hawkins software, hardware, environment, liveware, and
outside liveware (SHELL) model [18], James Reason’s “Swiss cheese” Model of Accident
Causation [19]. These foundational concepts will be verified in future studies via qualitative
observational research.
5.2. Human Factors Concepts
5.2.1. Complacency
According to Årstad and Aven, complacency is “unintentional unawareness [which can only be
diagnosed] in hindsight, from a distanced perspective” [20, p. 115]. The National Aeronautics
and Space Administration (NASA) defines complacency as “overconfidence from repeated
experience on a specific activity, complacency has been implicated as a contributing factor in
numerous aviation accidents and incidents” [21, p. para.1]. Parasuraman et al. [22] referenced the
prevalence of complacency throughout multiple aviation accident investigations lending credence
to the importance of Dupont’s “Dirty Dozen” [4]. Prinzel cited crew complacency as often being
“a contributing factor in aviation accidents” [23, p. 4]. Merritt et al. referenced “complacency, or
sub-optimal monitoring of automation performance, [as being] cited as a contributing factor in
numerous major transportation and medical incidents” [24, p. 1]. Overconfidence in highly
reliable automated systems often leads to complacency issues [22].Complacency is often cited
alongside boredom and procrastination[21, 23] . Prinzel found that those with low self-efficacy
“suffered automation-induced complacency” and operated significantly better when working in
high workload environments[25, p. 13]. Furthermore, Prinzel [23] also found that pilot workload
over-saturation can increase, leading to an overburdened cognitive load. Conversely, boredom
has been shown to increase when the operator defers to the machine due to the repetitive nature
of automated tasks in a cognitively low-demand environment [26].
Aviation human factors incidents involving pilot complacency have been attributed to a failure to
adequately correct automation errors [22, 24, 27]. Merritt et al. [24] highlighted that complacency
could manifest due to a person’s inability to comprehend the occurrence of an error or exhibit a
prolonged response to an error or stimuli. Beyond prolonged reaction to an automation failure,
failing to act may be attributed to both commission and omission errors [23, 24, 28].Errors of
commission happen when the HITL makes a mistake or error due to incorrect decision-making.
Errors of omission occur when the human does nothing when they should have[24, 29].
Complacency research pertaining to maritime shipping operations identifies similar outcomes,
which serves to highlight Dupont’s “Dirty Dozen” [4, 30]. Attempting to fill gaps in maritime
research, Bielic et al.[31]studied technology, leadership, management, and self-induced
complacency during their study of complacency in maritime accidents. Bielic et al.[31]cite
research by Turan et al.[30], who found that over 80% of maritime shipping incidents were
attributable to human or organizational error, of which 6% could be attributed to complacency.
The research referenced an overreliance on automation as a leading cause of technology
complacency. Furthermore, leadership complacency may increase the risk of complacency in the
same manner as poor team dynamics, toxic hierarchy, and steep authority gradient, as seen in
aviation sources [31]. Leadership complacency occurs when the leader or manager possesses an
inadequate leadership style. Workers may become apprehensive about going totheir leadership
out of fear of being ignored or potential mistreatment[31]. Alternatively, leadership complacency
may occur when leaders are not professionally challenged during critically sensitive moments.
During the events leading up to the Chernobyl disaster, workers were discouraged from having a
questioning attitude, and the overall lack of open communication resulted in increased loss of
life due to the amount of time it took the workers to react to the situation[31, 32].

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Satellite operations assets rely on a system-of-systems to provide autonomy due to the inherent
complexity of satellite electronics and the inability to conduct on-orbit servicing. Due to this
complexity, human satellite operators and the spacecraft, or autonomous agent, function as a
team [33]. While not explicitly stated, the concept of human operators and autonomous agents
working together points to the idea of crew resource management (CRM), where both entities
operate as a crew working toward a common goal [34, 35, 36, 37]. Communication between the
autonomous agent and humans exists within the environment, as seen in the human factors
SHELL model where the liveware, hardware, and software work together [18].Complacency may
occur due to automation bias when humans defer decision-making and authority to more complex
autonomous agents [38]. Lyell and Coiera [38]found that task complexity and difficulty play a
significant role in automation bias due to the level of difficulty present when monitoring and
interpreting automated aids in the healthcare field.
5.2.2. Boredom
Boredom is a precarious state of awareness that has been shown to lead to errors of omission in
automated systems [39]. Pope and Bogart coined the term “hazardous states of awareness” to
encompass the concepts of boredom and inattention [39]. Prinzel et al. [23] highlighted the need
for more boredom-related research. The Aviation Safety Reporting System(ASRS) provides a
multitude of evidence that complacency and boredom are linked, leading researchers to conclude
that complacency is tied directly to boredom[23, 39].The work of Prinzel et al. [23] pointed
toward individuals with high-complacency-potential to suffer more from boredom than
individuals with low-complacency-potential. Prinzel et al. [23] admitted that additional research
using actual pilots in future studies is needed due to the possible psychological attributes or
personality differences from subjects studied in their research. The work of van Hooft and van
Hooff bolstered earlier findings, calling for methods to increase attention and add autonomy to
help alleviate the risk of error during operationally tedious phases of work to mitigate human
factors errors [40].
5.2.3. Habituation
Habituation is commonly associated with boredom and complacency due to varying stimuli
within a human's daily life or work environment. A classic example of habituation can be taken
from Kim and Wogalter’s study [41], highlighting the use of safety warnings within the work
environment. Static standardized warning signs displayed around industrial work centers were
studied, and researchers found warning signs were ineffective at keeping attention in the long
term [41]. Kim and Wogalter [41] concluded that signage needed to be changed, enhanced, or
embellished to increase stimuli over the long term to increase human attentiveness to safety
warnings. Decrements in sensation in response to a given stimulus may occur over time due to a
feeling of increased control. The human begins to anticipate outcomes, and their subsequent risk
tolerance grows while their awareness decreases, leading to increased complacency concerning
the identified risk [42]. Sensitization via training or providing an increased stimulus to elicit a
reaction are ways to combat habituation [43]. A constant or continuous stimulus can become
adaptable for the human. Constant noise can become easy for the human brain to filter out due to
the volume and consistency of the noise. The brain will only react when the noise changes to a
different tone, pitch, or volume [44].
5.2.4. Condition Monitoring
Condition monitoring aims to increase knowledge of the status of a system through automated
vigilance [45]. Machine condition monitoring dates to 1924 and initially dealt with motor faults.
Condition monitoring then progressed to the use of vibration in the 1950s. Modern advancements

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have led to permanent radio frequency sensors to relay data from multiple points of interest to
understand system health and to increase the mean time between failures [46]. Dadashi et al. [47]
highlighted automated remote sensing issues as a contributing factor in the 1979 Three Mile
Island and 1994 Milford Haven, U.K., Texaco Refinery explosion incidents. The Three Mile
Island nuclear reactor used hundreds of separate alarms to convey status with key indicators
obscured to the operator [48]. Within the beginning stages of the Three Mile Island incident, over
100 alarms were triggered, causing the operators to react to an overload of information. In
adequate training and deficiencies in condition monitoring protocols were cited as contributing
human factors in the incident [49].
5.2.5. Alarms and Warnings
Satellite systems operate on a crewed shift concept where on-shift operators monitor, diagnose,
and remedy system issues via telemetry and alarm protocols [50]. Alarms in crewed and
uncrewed systems alert the operations crew of a problem with onboard components or an issue in
the current operational situation. Theories on interpreting and understanding the status of a
crewed spaceflight system have been covered in the literature dating back to the 1950s [51].
Bamford et al. [51] stated that an appropriate response would be warranted for any given stimuli,
with the level of operator response being improved upon through training and indoctrination.
Lange et al. [52] presented findings consistent with a need for training and indoctrination of
crews in any field. They [52] found that users of a system need to understand the monitoring
device and its associated functionality to use the device correctly and efficiently.
Alarm priorities assigned during development are imperative to mitigating confusion. According
to Laughery and Wogalter [53], three criteria must be considered when prioritizing warnings:
likelihood, severity, and practicality. The Texaco Refinery explosion was attributed to several
factors, including poor alarm management design and prioritization [54]. During the 11 minutes
leading up to the Texaco Refinery explosion, there were 275 active alarms present, with 87% of
the active alarms considered a high priority [54, 55]. The Engineering Equipment and Materials
Users Association (EEMUA)[56]recommends that only 5% of alarms be regarded as high
priority, 15% medium, and the remaining 80% of a system’s alarms should be reserved for low
priority alarms. Foong et al. [57] analyzed alarm prioritization in oil refineries and agreed with
the EEMUA [56] by confirming a best practice prioritization methodology of 5% for emergency
priority, 15% for high priority, and 80% for low priority alarms.
5.2.6. Communication Human Information Processing (C-HIP)
Industrial risk research has led to the concept of Communication Human Information Processing
(C-HIP), whereby an alarm flows from the machine to HITL, who must then react appropriately
to resolve the alarm [58, 59]. While C-HIP is aimed toward industrial work center warning
human factors, the concepts can be expanded to other industries where warning and alarms are
employed [60]. C-HIP consists of several serial steps starting at the source of the warning and
ending at a desired human behaviour [61]. The source of the alarm can originate from the
software, hardware, or liveware [18, 60]. In a remote system, the channel presents the alarm
either auditorily, visually, or both. The operator of the remote system is the receiver. The receiver
category is the “human information processing section of the C-HIP model”, where human
factors of attention, comprehension, attitudes, beliefs, and motivation reside[58, p. 314]. The
receiver or HITL must then process the alarm and act accordingly to mitigate potential system
degradation [58, 59].

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5.3. Shift Work
In 1997 Gordon Dupont presented his “Dirty Dozen errors in maintenance” consisting of the 12
most common causes of error judgment in aircraft maintenance: “lack of communication,
complacency, lack of knowledge, distraction, lack of teamwork, fatigue, lack of resources,
pressure, lack of assertiveness, stress, lack of awareness, and norms”[4, p. 42]. Dupont’s common
causes have been used extensively in the aviation maintenance human factors arena, yet
principles identified by Dupont extend to other sectors. As with aviation maintenance, multiple
industries such as medical, transportation, services industries, and aerospace operate using shift
work in varying degrees [6, 62, 63]. Sectors that employ shift work tactics operate with the same
risk of human factors consequences highlighted by Dupont, yet his human factors principles have
not fully extended outside of aviation literature.
Sleep patterns among shift workers in the crewed space arena experience human factors issues
highlighted in Dupont’s “Dirty Dozen”[4]. Mizuno et al. [62] studied Japanese International
Space Station flight controllers’ sleep patterns. The Japan Aerospace Exploration Agency used a
3-shift-per-day schedule “from 8 a.m. to 5 p.m. (day shift), from 4 p.m. to 1 a.m. (evening shift)
and from midnight to 9 a.m. (night shift)”[62, p. 3]. Their research found that night shift workers
encounter the most challenging fatigue and sleep issues. Night shift workers were shown to have
a higher prevalence of shift work sleep disorder (SWSD) than day and evening shift workers,
where insomnia symptoms and decreased working competency were observed [62]. Åkerstedt
[64] also found decreased working competency and insomnia symptoms in those that suffer from
SWSD. SWSD has been shown to increase the prevalence of the same issues highlighted by
Dupont [4] with a higher prevalence of stress and fatigue as a result of atypical shift patterns,
specifically as a result of working the night shift [2].
SWSD has been correlated with circadian stress in night shift workers [62]. Sleepiness among
night shift workers may lead to wakeful fatigue, which has been shown to be more prevalent in
night shift workers than day shift workers [64]. Research in the public health sector has
correlated circadian rhythm disruption with SWSD, which has hindered human performance
during the night phases of a work pattern [28]. Sleep-wake cycles may become disrupted when
initially entering a night shift pattern in the same manner as seen with jet lag. Pharmaceuticals
and therapeutics have been studied to combat the effects of jet lag with varying levels of success.
Jet lag, also known as circadian desynchrony, has been shown to produce the same symptoms as
SWSD with “disturbed sleep, daytime fatigue, decreased ability to perform mental and physical
tasks, reduced alertness, and headaches” caused by circadian stress [18, 65, p. 221].
SWSD and circadian rhythm issues have been shown to lead to mood issues among those who
work night shifts due to excessive sleepiness, fatigue, and trouble sleeping [66]. Walker et al.
[66] detailed the correlation between circadian rhythm disruption and poor mental health due to
shift work. Jehan et al. [2] also found an increased prevalence in sleep disruption among medical
workers on the night shift versus those that work during the day. As a result of sleep disruption,
poor mental health and anxiety may become exacerbated by shift work among health care
workers [67, 68]. Night shift work has been associated with depression due to circadian
misalignment [69]. All instances of poor mood or mental health have been partially attributed to
either sleep quality, sleep quantity, sedentarism, and poor diet choices during night shift work [2,
66, 68, 69].
Shift work research points toward late-night shifts, between 1 a.m. and 8 a.m., as a prime time for
medical and performance-based human error occurrences [70]. Circadian rhythm and SWSD tend
to make night shift work more precarious [2, 62, 66, 71]. Inadequate sleep before working a night
shift, even 1-2 hours less, can drastically increase the potential for human error [70]. Mizuno et

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al. [62] suggest that some workers are better suited for night shift work than others. Operations
could benefit from using individuals better adjusted to night shift work to mitigate human error.
Attention to identification methods and policy formulation have been highlighted to help reduce
human error during vulnerable shift time frames [70]. Hege et al. [63] referenced a need for a
comprehensive review of commercial delivery drivers’ regulations and operational conditions to
enact regulations to prevent human error in the transportation industry.
Multiple human factors incidents have been attributed to night shift work due to a “30% increase
in human error incidents on night shift relative to morning shift” [28, p. S88]. The most notable
shift work-related incidents are the Chernobyl nuclear reactor meltdown, 1985 Davis-Besse
nuclear reactor incident, Exxon Valdez oil spill, Space Shuttle Challenger accident, Three-Mile
Island nuclear reactor incident, and the1985 Rancho Seco nuclear reactor incident [28]. The
Chernobyl nuclear power plant incident, cited as the worst nuclear disaster ever, occurred at 1:23
a.m. The Davis-Besse reactor incident started at 1:35 a.m. due to night shift operators not
following correct protocols. The Exxon Valdez oil spill occurred just after midnight when the
vessel collided with Bligh Reef. “The failure of the third mate to properly maneuver the vessel,
possibly due to fatigue and excessive workload” was a contributing factor cited in the National
Transportation Safety Board investigation [72, p. 2]. The Space Shuttle Challenger incident
investigation highlighted shift work, and overwork issues at the Kennedy Space Center as
contributing factors in the accident [73]. Three Mile Island took place between 4:00 a.m. and
6:00 a.m. when night shift workers failed to recognize they had a problem. The 1985 Rancho
Seco incident occurred at 4:14 a.m. when errors of commission and omission resulted in a delay
to regain control of the site[28]. Mitler et al. [70] state that human factors leading up to the
referenced incidents may not have been the root cause, but human factors played a significant
role in how the incidents occurred. In the Exxon Valdez Oil Spill, Three Mile Island, Chernobyl,
and Space Shuttle Challenger disaster: operator fatigue was cited as a critical reason for the
mishap due to lowered alertness, inattention, and delayed reaction [70, 72].
5.4. Crew Resource Management
Crew resource management (CRM) was first implemented into aircrew training in 1979 after a
series of high visibility aircraft accidents [36]. CRM is a concept that consists of training aimed at
aircrews to increase communication and teamwork of those involved in all phases of flight.
Teams outside aviation have since incorporated CRM training and concepts to decrease the
chance for human error. While CRM implementation has been ongoing, human error continues to
be prevalent due to a lack of support of recurring CRM training [74]. Salas et al. [74] argue that
straightforward methods have not been translated from science to the education system, and only
through a standardized methodology will CRM become more effective. CRM has been defined as
using all resources, information, and people in the loop to conduct safe operations [75, p. 4]. The
1994 Operation Provide Comfort fratricide incident between a patrolling friendly U.S. Air Force
fighter aircraft and a U.S. Army Black Hawk transport was notably caused due to poor CRM of
the Black Hawk crews, F-15 pilots, Airborne Warning and Control Systems crews, and the
general lack of communication of proper rules of engagement before mission execution on the
part of all parties involved [75, 76]
Aviation is not the only sector that references CRM implementation: maritime, medical, and
remote operations have been referenced in needing effective CRM. Medical operating room
environments contain hierarchy gradients and adverse workplace cultures that often create
ineffective teamwork barriers. Hierarchy gradients have been cited as causing human error in
medical situations [77].The medical sector has started to pursue more open communication
through the exact CRM implementation that the aviation industry uses to prevent human error

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[77]. Pilot studies in the medical field have pointed to the usefulness of simulation training events
to highlight and reduce CRM hierarchy-related communication errors [78].
In an extensive study on medical hierarchy gradient, 363 medical employees were surveyed about
safety reporting. Siewert et al. [79] highlighted that senior employees were more likely to speak
up than their lower-ranking counterparts due to the following human factors:
High reporting threshold (i.e. [sic], uncertainty about one’s observation), reluctance to challenge
someone in authority, lack of being listened to, fear of retribution, fear of disrespect being
expressed, individuals creating an uncomfortable work environment (toxic captain), shy
personality, responsibility within a team, and lack of language skills. [79, p. 796]
Authority or hierarchy gradient was the second most reported issue after the high reporting
threshold [79]. Physician trainees have been identified as the most vulnerable to hierarchy
gradient in the medical profession, yet they may be the most up to date with current information.
In 2018, Siewert et al. [79] stated medical information would double every three years, and by
2020 medical information would double every 73 days. The study further highlighted the need for
increased CRM training, citing positive human factors changes in the aviation industry due to the
effective employment of CRM [79].
CRM training levies an emphasis on communication [34, 35, 37, 74]. CRM indoctrination
focuses on members working as a team rather than a group of separate individuals working
together [34]. CRM training operates with the acceptance that there will be a human error, and
the proper way to react and mitigate the error is through effective CRM [80]. Gross et al. [37]
conducted a meta-analysis of 1,037 medical publications on CRM; they found communication,
situational awareness, leadership, teamwork, and decision-making as the top five CRM topics in
healthcare literature. However, only a fraction of the publications identified provide an
“explanation sufficient for replication” when creating CRM training materials [37, p. 6]
The findings of Gross et al. [37]are consistent with that of Salas et al.[74], who highlighted that
CRM training has not been employed using consistently actionable or repeatable training
methods that can be recreated throughout training. Gross et al. [81]further analyzed constructs in
CRM training to make a case for efficiency with 15-minute training sessions, versus 2-hours up
to full day training sessions. They expanded upon the theme of CRM training inefficiency with
129 participants to understand if periodic micro-training sessions were more efficient in the long-
term employment of CRM principles. Gross et al.[81]found newly acquired CRM principles were
able to be recalled weeks later, leading to the conclusion that micro-training sessions may be a
valid method of training CRM in time-constrained professional communities. The science of
training regarding how information is “designed, delivered, and implemented can greatly
influence its effectiveness” [74, 81, p. 3].
Professionals in the medical field see real-life emergencies where pressure is either perceived or
realized every day. Time-sensitive and highly stressful medical events have led to novel methods
in CRM training and employment. The “10-seconds-for-10-minutes principle” was created in the
medical sector to increase situational awareness in high-stress scenarios [82]. Rall et al. [82] cited
a speed and accuracy trade-off where perceived time pressure can lead to error; thus, slowing
down can help mitigate error by stopping all work, analyzing the problem, gathering facts,
planning, and distributing the team’s workload in manageable quantities. “Teams who took a
fraction longer at the start of an emergency (to assess the situation and brief the team) performed
better” [83, p. 95]. Gross et al. [81] developed an alternative to the 10-seconds-for-10-minutes
principle called the Team Check protocol, where the team asks similar questions: what, how, and

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who. Both methods slow the team down to increase situational awareness in high-stress scenarios
[81, 82].
5.5. Remotely Piloted Aircraft
UAV and crewed aviation suffer the same human factors issues, yet mishaps occur one to two
orders of magnitude greater than during crewed flight [84, 85]. According to USAF, “Class A
mishaps are currently reported any time an incident results in (1) $2 million or more in damage to
the aircraft, (2) a fatality or permanent disability, and/or (3) destruction of the aircraft” [86, p. 2].
Furthermore, the USAF considers an aircraft destroyed when the aircraft is rendered unrepairable
and cannot be returned to service [86]. Remotely piloted aircraft (RPA) statistically suffered
more Class A mishaps and were destroyed more than piloted aircraft during every period
recorded [86]. Inconsistent training, non-standardized pre-departure briefings, and poor CRM
interactions with air traffic control have all been identified in the ASRS as causal factors in UAV
mishaps [87].
Crewed aviation incidents involving fatigue have been cited in 7%, 4%, and 7.8% of accidents in
civilian aviation, U.S. Army aviation, and USAF aviation, respectively [88]. Alternatively,
13.85% of aviation accidents were attributed specifically to RPA mishaps [89]. Gaines et al.
highlight fatigue as a human factors incident amplifier [89]. While the RPA HITL is safely on the
ground during a mission, long-endurance high-tempo RPA flights have led to shift work
implementation, which has been shown to carry fatigue-induced consequences [2, 64, 90].
Military RPA operators are prone to fatigue due to the slow-paced monotonous nature of RPA
operations [91]. Pedersen et al. [92] called for increased automation to regulate workload to
mitigate fatigue in remote operations. Furthermore, USAF RPA operations centers have
addressed ergonomics considerations directly related to operator fatigue by addressing: “Climate
control, restroom breaks, ergonomic design, and equipment” issues [93, p. 62]. Addressing living
condition issues may benefit both the operator and the mission by ensuring preventable human
factors issues are mitigated before they manifest through thoughtful operations center design
[93].
Tvaryanas and MacPherson found a “do more with less” [94, p. 460] attitude resulted in less-
than-optimal personnel levels when researching RPA crew shift patterns. Insufficient personnel
levels increase the need to work longer hours, resulting in reduced recovery between shifts
worked and an increase in the potential of fatigue-related issues [94]. Fatigue can be exacerbated
by a sustained high operations tempo, increasing symptoms associated with SWSD [95]. Multiple
studies conducted by AFRL recommend crew rest, regular exercise, proper nutrition, blue light
standardization [96], and the application of “science-based shift scheduling techniques when
developing manpower requirements and developing duty time and crew rest requirements” [95, p.
33]. Tvaryanas and MacPherson recommend mitigating fatigue in 24/7 operations using multiple
crews in multiple time zones where operations would always be conducted during the local day
shift [94].
Inadequate or insufficient training has led to RPA human factors mishaps [89]. RPA operations
consist of automated human-system integration (HSI) systems that drive increased training
requirements [97]. Increased automation has been shown to decrease sensory cueing, resulting in
detrimental decreases in situational awareness during RPA operations [98, 99]. RPA operations
may benefit from CRM training to mitigate HITL fatigue and vigilance-induced error [34].RPA
operations that do not adequately employ proper CRM create issues with air traffic control and
crewed aircraft [35]. Loss of communications has been an issue when incorporating RPAs into
the National Airspace System (NAS) [87]. According to Neff, “in order to ensure the safety of all
operators in the NAS, CRM should be part of an overall safety management system implemented

33
and practiced by all operators and overseen by the [Federal Aviation Administration] FAA” [35,
p. 7]. Salas et al. [36, 74] highlight requirements for practical CRM training while Neff [35]
complemented the studies conducted by Salas et al. [36] when he analyzed four RPA CRM-
related human factors incidents which could have been mitigated by practical CRM training and
implementation.
While human error has been cited in 79% of USAF RPA mishaps [100], loss of human life due to
RPA mishaps is negligible. Operators located in RPA ground control stations contend with
human factors issues regarding ergonomically inefficient human control interfaces, which can be
error-provoking, complex, and tedious to operate [99]. AFRL found that 92% of Predator RQ-1
operators report “moderate to total boredom” [95, p. 26][101]. RPA operators lack sensory cues
afforded to conventional aircraft due to removing the HITL from the physical airframe [99]. On
June 25, 2020, a MQ-9A Reaper RPA crashed within one minute of takeoff due to pilot
misidentification of the flap and condition levers. Instead of reducing flap position after takeoff,
the pilot reduced the condition lever, cutting off fuel flow to the engine [102]. The MQ-9A
mishap highlights the need for increased human factors development in RPA HSI systems.
Prichard [102] highlighted incorrect equipment operation, fixation on the wrong aspects of the
situation, inadequate location, and lever color as design and training deficiencies, increasing the
potential of future mishaps.
6. CURRENT APPLICATIONS
6.1. Satellite Operations
Contrary to aviation, the subject of satellite operations human factors is limited in the literature.
The USSF produces most literature about satellite operations due to the number of satellites
operated by the military branch. Operating multiple satellite constellations safely and reliably is
only possible due to organizations functioning in a highly reliable manner [103]. According to
Schubert et al. [103], highly reliable organizations start with effective human management and
trust. Reason’s “Swiss cheese” model [19] depicts management and culture as contributing
factors in an accident. Beyond management, the HITL must work with various control interfaces,
interpret multiple information sources, and respond correctly to mitigate error [104]. Satellite
anomalies occur due to hardware and software malfunctions on-orbit stemming from space
weather, orbital debris, hostile actions, electromagnetic interference, and operator error. Operator
errors made during the command and control of spacecraft may create an anomaly if the system
does not have the correct failsafe mechanisms in place [7].
Space assets have a long history of human error. Russian Soyuz, Phobos 1 Mars probe, Russian
Mir space station, Mars Pathfinder, the U.S. Space Shuttle program have all had some level of
error attributed to the HITL [9]. The best practice to effectively reduce human error is often
during the development phase, where “robust and fault-tolerant spacecraft hardware and
software” are designed [105, p. 4]. Human error may occur due to time constraints, level of task
understanding, inadequate mission preparation or training, and errors of commission or omission
[105]. The development of spacecraft systems is not a standardized process due to the difference
in proprietary specifications instituted by different spacecraft manufacturers. Due to the unique
nature of satellite acquisitions and development, it may be challenging to standardize human
error mitigation techniques [9].

34
6.2. Human-System Integration
HSI is the concept of combining the entire system with the HITL. Human-computer interaction
(HCI) is a subset of HSI where the HITL uses a computer to interact with the system. The
computer is the portion of the system which links the human to the system [90, 106]. Inadequate
operator control stations lacking HSI have been cited as a critical factor in RPA mishaps [107].
The UAV sector has been plagued with human factors HCI issues. High levels of cognitive load
can occur when attempting to process multiple screens at the same time. The HITL may have to
contend with having to enter over 20 inputs to engage autopilot, inadequate feedback
mechanisms, and multiple input mechanisms in a cluttered work area to operate an UAV
successfully [108]. HSI issues factor in 89% of MQ-1 Predator accidents, while the HSI has been
cited as a causal factor in 44% of MQ-1 Predator mishaps [108]. Intelligent software used during
HCI development may aid the operator in error mitigation, fault isolation, and operator input
prediction [9]. HCI design must be fully developed to ensure the HITL can take over anytime,
especially during an emergency, no matter the level of automation or intelligence built into the
system: the HITL must always be in control [109].
6.3. Automation/Autonomy
Automation carries out tasks generally performed by a HITL in a controlled manner through
process implementation [110]. Autonomy creates independence from the HITL; thus, the system
decides and implements the next course of action [84]. Automation and autonomy are
implemented to remove the HITL from the system so long as no deviations occur [84]. According
to the U.S. DoD [84], the core difference between automation and autonomy is the governance of
rules: broad for autonomous and prescriptive for automated. Depending on the development and
implementation of automation and autonomy in the system, HITL involvement levels may vary
according to manufacture specifications.
HITL complacency when working with automated or autonomous systems is possible due to a
phenomenon called “social loafing” [111] or “The Ringelmann Effect” [112]. In group settings,
“social loafing” may occur when multiple people work together and may defer responsibility to
others in the group to accomplish a task versus putting in more effort if they had acted alone
[113]. Groups of people working together have been shown to make twice as many vigilance
errors versus working alone while adding an additional HITL does not necessarily increase
vigilance [114]. Automation bias or the deferring of responsibility to the machine has striking
similarities to “social loafing” [114, 115] in that there may be an over-reliance on automation and
potential for misplaced trust in the accuracy of the system [110].
Automation complacency has been cited in multiple human factors incidents where automation
failed, yet the HITL overlooked critical indicators due to their over-reliance on the system’s
accuracy [115]. The 1995 Royal Majesty cruise ship accident occurred due to several reasons:
failure to follow established procedures, lack of situational awareness, incorrect routing of GPS
cabling, and automation complacency [116]. The accident report found that the accident occurred
due to the crew’s overreliance on the system and a maintenance error resulting in a disconnected
GPS cable [115, 116].
6.4. Big Data Handling
The FAA handles 16,405,000 flights each year with 45,000 flights occurring each day [117].
Meanwhile, only 11,000 UAS systems [10] and over 3,200 active satellites are employed in the
civilian and military sectors [12, 13]. The amount of data accumulated in aviation is immense
compared to that of UAS or satellite operations, with “the average flight data collected during a

35
current flight operation [getting] up to 1000 gigabytes” [118, p. 214]. The large data originates
from aircraft onboard electronics like those used in satellites [9, 118].According to Oh [118],
large aviation data is considered Big Data. Madden states that Big Data is either “too big, too fast
or too hard” [119, p. 4] for most existing software tools to process [120]. Larger Big Data
quantities may get up to “petabyte-scale collections of data” or 1 million gigabytes, which could
accumulate faster than processed, making analysis and decision-making difficult [119, p. 4].
Maritime logistics has seen an increase in the volume of data used in shipping [121].According to
Yuen, Xu, and Lam, maritime research of Artificial Intelligence (AI) and Big Data has been
directly attributed to cost savings for ship operators due to the effective employment of mined
data [121]. AI has proven useful in analyzing Big Data, yet software can pose human factors
issues due to visual noise, perception, monitoring issues due to image challenges, and the high-
performance requirements necessary for the software to operate, which all may serve to slow
down the HITL [118, 122].
Big Data analytics are employed by business managers to better understand cost and efficiency
metrics [123]. Big Data has been analyzed by “schema-less databases capable of handling large
amounts of structured and unstructured data like documents, e-mail and multimedia efficiently
with flexible data models” [123, p. 1]. There are three types of analytics used in Big Data
processing: descriptive analytics, where the data shows what has happened; predictive analytics,
which can help to forecast what might happen in the future; and prescriptive analytics, which can
help figure out how to prompt an event to occur [124]. Large volumes of data in aviation,
business, and maritime operations are like that of the telemetry data transmitted by satellites for
health and safety status, imagery, and communications [7, 9]. Big Data's usefulness depends on
the training of the HITL in the use of intelligent software to analyze the data [125]. Avci,
Tekinerdogan, and Athanasiadis [126] reviewed software architectures for Big Data. They found
43 studies covering Big Data and highlighted several applications currently in use [126]. Avci et
al. did not find a consensus for the proper or most useful type of software used for Big Data
analysis which is consistent with how most proprietary methods are marketed and employed
[126]. While Big Data is being explored for use in agriculture, climate change, and remote
sensing applications, the literature has a low prevalence of research on how to employ Big Data
analytics to combat human error [124].
7. INTERPRETATION OF THE LITERATURE
The state of the literature may be incomplete concerning advancements in HSI or HCI
development due to a delay in reporting as a result of security considerations. As referenced in
the literature review, AFRL publishes most human factors research on RPA and satellite
operations. However, the data may be considered outdated or potentially blocked altogether from
release due to the U.S. Government’s process for public release or national security
considerations. Although there is an overabundance of literature on the internet provided by
private companies and human factors researchers, it can be challenging to verify research that has
not undergone proper peer-review.
Scenarios of aviation-related complacency resulting in checklist and communication errors have
been extensively researched, yet there is no clear consensus about the psychobiological reasons
for the cause of complacency [21, 127]. The evidence for complacency is overwhelming, yet
researchers cannot pinpoint if complacency is a cause or more of a “catch-all” of various factors
leading to an accident [127]. RAND published a study focusing on stress and dissatisfaction in
the USAF UAV community but only barely touched on boredom and completely disregarded
complacency and habituation [93]. Satellite and RPA operations require increased research into
boredom, complacency, habituation, and vigilance.

36
Human factors in crewed space missions have been exhaustively detailed in multiple studies
regarding the China National Space Administration, European Space Agency, Indian Space
Research Organization, Japan Aerospace Exploration Agency, NASA, and the Russian Federal
Space Agency [62, 128]. The predominant research on remote operations shift work centers
around the AFRL and the RAND Corporation’s investigation into the USAF RPA community
[93]. However, data from the AFRL must be extracted based on unit data to distinguish remote
operations, which may not be easy to discern for those without a military background.
Furthermore, the release of data products by the AFRL may lag other sources due to how the
military releases information.
Aviation human factors studies are abundant due to the volume of aircraft traffic and the potential
of subsequent passengers affected. However, studies do not provide a correlated picture
benefiting remote operations. The literature is sparse for references on circadian rhythms human
factors in space operations, yet circadian rhythms may play a significant role in satellite fault
handling performance [9, 50]. Space and remote operations systems require further dedicated
research to understand further the impact of shift work on the HITL and systems employed;
without added research, multi-billion-dollar systems could be vulnerable to the same risks nuclear
power plants and crewed space programs have experienced [9].
Cross-sector use of CRM has been proven to save lives through translatable educational
communication interventions [77]. CRM has been cited as being valuable in reducing human
error in space operations [104]. However, the remainder of the literature is sparse on the topic of
satellite operations human factors CRM. RPA operations have benefited from advances in crewed
aviation CRM training concepts. The concept of CRM extending outside the cockpit in aviation
to air traffic control and other aircraft in the loop should be promoted in the satellite operations
sector. CRM in satellite operations should be further researched and developed to extend outside
the SOC crews on shift due to the complexities of a contested, degraded, and operationally
limited orbital environment. Automation and autonomy have been referenced in multiple aviation
and healthcare studies regarding bias and social loafing. Satellite systems are highly advanced,
but they are not perfect; the HITL must stay vigilant and ready to respond to an anomalous event
should automation or autonomy fail. Further research is required to understand the extent of
automation bias, complacency, and social loafing in satellite operations centers.
Satellites on-orbit produce large quantities of data that must be downlinked to ground stations for
analysis [7, 9]. Satellites tend to operate for long periods between anomalies or non-nominal
events, resulting in trending data being stored for very long periods until the data is needed to be
analyzed [7, 125]. Satellite telemetry data can be considered Big Data due to the size of the data,
the speed at which the data transmits from a satellite to the ground station, and its relative
complexity if not for the proper software infrastructure to allow for proper data analysis
[129].Satellite Big Data processing may be a viable option to help mitigate human error by better
equipping the HITL with the correct information ahead of time if descriptive, predictive, and
prescriptive analytics are employed[124].
8. CONCLUSION
Human factors lessons learned are present in multiple unrelated sectors, yet actionable lessons
learned have not been fully transferred to mitigate human error in remote operations. The
healthcare profession has increased lifesaving capabilities using CRM principles gained from
aviation, yet similarities between aviation and UAS systems have not been fully leveraged.
Additionally, satellite operations have much to gain from the efficiencies and initiatives of other
sectors. Keeping multi-million-dollar satellites on-orbit free from human error will take more
than just acknowledging lessons learned; lessons learned must be implemented. Developers of

37
complex remotely operated systems must consider the tendency of the HITLto seek comfort in
complacency. Developers must strive to ensure automation and autonomy do not increase the risk
of human error. Big Data analytics must be used in the satellite operations sector to increase
HITL situational awareness in preparation for potential issues or events that would have
otherwise been unknown. The case for developing novel Big Data analytics goes beyond profits,
rather Big Data may be an analysis answer for mitigating human factors related errors through a
better understanding of all the available data. Only through the transfer of knowledge between
sectors can efficiencies be leveraged to benefit those in command of remotely operated assets.
9. FUTURE WORK
Future qualitative research will increase knowledge of satellite and related remote operations
sectors complacency-induced error mitigation. Observational data-gathering will be undertaken
to identify differences in complacency occurrences during regular and rotating shift work in
space operations beyond the current state of research. Subsequent research will seek to identify a
correlation between the level of system autonomy to the amount and frequency of resultant
complacency-induced errors. HCI error mitigation methods will be developed and studied to
mitigate complacency-induced errors. Finally, future research will identify opportunities for
knowledge transfer between aerospace industry sectors to mitigate complacency-induced errors
through translatable standardized prevention and mitigation methods.
ACKNOWLEDGMENTS
David Heinrich would like to dedicate his first journal to his wife, Samantha, and kids for their
unwavering support in his academic and professional pursuits. Additionally, Mr. Heinrich would
like to thank his parents, Catharina and Terry Johnson, for imparting an education-first mentality
and for providing their unconditional support. Mr. Heinrich would also like to thank Dr. Ian
McAndrew and Dr. Jeremy Pretty for guiding him on his academic journey. Finally, Mr. Heinrich
would like to pay tribute to his father, who is no longer with us: You are missed.
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AUTHORS
David Heinrich is a Human Factors Ph.D. candidate at Capitol Technology University. His professional
background spans over 19 years in the United States Air Force as a fighter avionics technician, instructor,
and satellite operations professional. He has extensive expertise in the development and operations of war
fighter-centric technologies.
Dr. Ian McAndrew is the Dean of Doctoral Programs at Capitol Technology University. He has taught in
universities worldwide and is a frequent keynote speaker at many International Conferences. He is a Fellow
of the Royal Aeronautical Society and Chartered Mechanical and Electrical Engineer in the U.K.
Dr. Jeremy Pretty is a Senior Program Manager with the United States Air Force Civil Service. His
professional background includes over 15 years in program/project/product management within the U.S.
Department of Defence on Aircraft and Information Technology systems. He is a member of the Royal
Aeronautical Society.

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