Revista de Ciencias Tecnológicas (RECIT). Volumen 3 (1): 10-22
Revista de Ciencias Tecnológicas (RECIT). Universidad Autónoma de Baja California ISSN 2594-1925
Volumen 9 (3): e449. Julio-Septiembre, 2026. https://doi.org/10.37636/recit.v9n3e449
ISSN: 2594-1925
1
Research article
Comparison of LEO satellite constellation systems for global
broadband communications
Comparación de sistemas de constelaciones de satélites LEO para
comunicaciones de banda ancha globales
Kleiverg Eulalio Encino Morales , Miguel Ángel Sidón Ayala , Rolando Díaz Castillo
Instituto Tecnológico de Ensenada, Boulevard Tecnológico No. 150, Ex-Ejido Chapultepec, 22780
Ensenada, Baja California, México
Corresponding author: Kleiverg Eulalio Encino Morales, Instituto Tecnológico de Ensenada, Boulevard Tecnológico No.
150, Ex-Ejido Chapultepec, 22780 Ensenada, Baja California, México. E-mail: kencino@ite.edu.mx. ORCID: 0009-0002-
4878-8477.
Received: April 14, 2026 Accepted: July 2, 2026 Published: July 13, 2026
Abstract. – This paper introduces a quantitative framework designed to evaluate and compare Low Earth Orbit (LEO) satellite
constellations for global broadband communications. The analysis considers four representative systems: Starlink, OneWeb,
Telesat, and Amazon’s Project Kuiper, capturing both orbital configuration and network architecture as key design
characteristics. The proposed methodology integrates a geometric coverage model together with a latency formulation that
accounts for propagation delay and routing effects including Inter-Satellite Links (ISL). In addition, a service density metric is
introduced to characterize the spatial distribution of satellites and its impact on system capacity. These metrics are combined
into a normalized multi-criteria performance index, allowing a consistent and reproducible system-level comparison. The
results reveal that, while coverage is primarily governed by orbital altitude, network architecture plays a dominant role in
effective latency, with ISL-enabled constellations achieving improved routing efficiency compared to bent-pipe designs. The
integrated performance index shows that low altitude, high-density constellations achieve superior overall performance under
latency sensitive scenarios. Starlink ranking highest due to its reduced delay and high spatial density. Project Kuiper exhibits
balanced performance across all metrics, while OneWeb and Telesat are constrained by higher latency and lower density
despite their broader coverage.
Keywords: LEO constellations; Global broadband communications; Network architecture; Coverage modeling; Latency
performance; System comparison.
Resumen. – Este artículo presenta un marco cuantitativo diseñado para evaluar y comparar constelaciones de satélites en
órbita terrestre baja (LEO) para comunicaciones de banda ancha globales. El análisis considera cuatro sistemas
representativos: Starlink, OneWeb, Telesat y el Proyecto Kuiper de Amazon, considerando tanto la configuración orbital como
la arquitectura de red como características clave de diseño. La metodología propuesta integra un modelo de cobertura
geométrica junto con una formulación de latencia que tiene en cuenta el retardo de propagación y los efectos de enrutamiento,
incluidos los enlaces intersatelital (ISL). Además, se introduce una métrica de densidad de servicio para caracterizar la
distribución espacial de los satélites y su impacto en la capacidad del sistema. Estas métricas se combinan en un índice de
rendimiento multicriterio normalizado, lo que permite una comparación consistente y reproducible a nivel de sistema. Los
resultados revelan que, si bien la cobertura está gobernada principalmente por la altitud orbital, la arquitectura de red juega
un papel dominante en la latencia efectiva, y las constelaciones con ISL habilitado logran una mayor eficiencia de
enrutamiento en comparación con los diseños de tubería curva. El índice de rendimiento integrado muestra que las
constelaciones de baja altitud y alta densidad logran un rendimiento general superior en escenarios donde la latencia es un
factor crítico. Starlink obtiene la mejor posición gracias a su menor retardo y alta densidad espacial. El Proyecto Kuiper
presenta un rendimiento equilibrado en todas las métricas, mientras que OneWeb y Telesat se ven limitadas por una mayor
latencia y menor densidad, a pesar de su mayor cobertura.
Palabras clave: constelaciones LEO; Comunicaciones de banda ancha globales; Arquitectura de red; Modelado de cobertura;
Rendimiento de latencia; Comparación de sistemas.
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1. Introduction
The increasing demand for global broadband communications has driven significant advancements in
satellite-based connectivity systems. Traditional geostationary satellites (GEO) present inherent
limitations in terms of latency and scalability, which restrict their ability to support high-throughput and
low-latency applications [1], [2]. Low Earth Orbit (LEO) satellite constellations have emerged as a
solution with reduced propagation delay and improved spatial reuse [3], [4]. LEO constellations operate
at altitudes typically ranging from 500 km to 1500 km, which significantly impacts latency, coverage, and
satellite visibility [5], [6]. Recent developments in the space industry have led to the deployment and
planning of large-scale LEO constellations by major operators, including Starlink, OneWeb, Telesat, and
Project Kuiper. These constellations encompass a wide range of design approaches, including
architectures with and without Inter-Satellite Links (ISL), as well as distinct orbital configurations in terms
of altitude, inclination, and satellite distribution [7], [8].
Despite the rapid development of these systems, studies typically focus on evaluating isolated aspects
such as latency estimation or coverage analysis, without providing an integrated evaluation of multiple
performance dimensions [9], [10]. Furthermore, comparative analyses that explicitly incorporate
architectural effects, such as the role of Inter-Satellite Links, and emerging systems such as Project Kuiper
remain limited in the current literature. This highlights the need for updated evaluation capable of
capturing the interaction between orbital geometry, network architecture, and system-level performance.
To address this need, this work proposes a comparative framework that integrates both geometric and
operational characteristics of LEO constellations. The methodology incorporates key orbital parameters
such as coverage and a minimum elevation angle, along with a latency model that accounts for propagation
delay and routing effects associated with different network designs, including ISL-enabled and bent-pipe
configurations [11], [12]. These elements are further combined with a service density metric to capture
the spatial distribution of satellites and its implications for capacity and resource availability. The main
contribution of this paper lies in the development of a normalized multi-criteria performance index that
enables a reproducible comparison among LEO constellations. By jointly evaluating coverage, latency,
and service density, the proposed framework captures the fundamental trade-offs that characterize large-
scale satellite systems. The remainder of this paper is organized as follows. Section 2 presents the
background and related work on LEO satellite systems. Section 3 describes the proposed methodology.
Section 4 discusses the comparative results. Finally, Section 5 summarizes the main conclusions of this
study.
2. Background and related work
2.1 Inter-Satellite Links and coverage model
LEO satellite networks can be broadly classified into bent pipe, ISL-enabled, and hybrid architectures. As
illustrated in Figure 1, bent pipe systems rely on ground gateways for data routing, whereas ISL-enabled
constellations enable direct communication between satellites, allowing data to be routed in orbit. Hybrid
architectures combine both approaches depending on system requirements and deployments stages. The
choice of architecture has a significant impact on system performance, particularly in terms of latency,
routing efficiency, and dependency on ground infrastructure [13], [14].
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Figure 1. Representative architectures of LEO satellite networks: (a) bent-pipe architecture dependent on ground gateways,
(b) Inter-Satellite Link (ISL)-enabled architecture, and (c) hybrid architecture integrating both approaches
Figure 2 depicts the geometric relationship used to determine the footprint of a Low Earth Orbit (LEO)
satellite. The parameters include the Earth’s radius , the satellite altitude h, and the minimum elevation
angle at the edge of the coverage. The central angle defines the angular radius of the spherical cap,
which is essential for calculating the total service area. The slant range represents the direct line-
of-sight distance between the satellite and a ground terminal at the boundary of the coverage area .
This model is critical for determining the terrestrial reach and the required constellation density for
continuous global connectivity [15], [16].
Figure 2. Geometric model of LEO satellite coverage illustrating the relationship between satellite altitude, Earth radius, central
angle, and minimum elevation angle
2.2 Description of selected constellations
The systems considered in this work represent different design approaches in terms of orbital
configuration, deployment strategy, and network architecture. Starlink is characterized by a large-scale
deployment strategy involving multiple orbital shells at relatively low altitudes. This configuration enables
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reduced latency and increased spatial density. Additionally, the system incorporates Inter-Satellite Links
(ISL), allowing data routing directly in orbit and reducing dependency on ground infrastructure [17].
OneWeb employs a constellation with fewer satellites operating at higher altitudes compared to Starlink.
Its architecture is primarily based on a bent-pipe approach, in which satellites act as relay nodes and data
routing is performed through ground gateways stations [18]. Telesat is designed to support enterprise and
backhaul services, integrating ISL capabilities with optimized orbital configurations. This approach seeks
to balance coverage, latency, and network efficiency [19]. Project Kuiper represents an emerging system
with a planned deployment across multiple orbital planes and its architecture is intended to support global
broadband services. The limited availability of comparative studies involving this system highlights its
relevance within the context of this analysis [20], [21].
2.3 Related work
Several studies have investigated different aspects of LEO satellite systems, including latency modeling,
coverage estimation, and network performance analysis. Existing works typically focus on individual
parameters, such as propagation delay or coverage footprint, without integrating multiple dimensions into
a unified framework [22]. Other research efforts have explored routing mechanisms and the role of Inter-
Satellite Links in improving network performance, highlighting their importance in reducing end-to-end
latency and enabling global connectivity [23]. However, comprehensive comparative studies that jointly
consider orbital parameters, such as coverage, latency, density service and network architecture remain
limited. Additionally, most analyses focus on established constellations, while emerging systems such as
Project Kuiper are not included, emphasizing the need for updated evaluation approaches.
3. Methodology
3.1 Coverage model
The coverage of a LEO satellite is determined by the geometric relationship between the Earth radius, the
satellite altitude and the minimum elevation angle. The central angle that defines the coverage region on
Earth’s surface can be expressed as [24], [25]:
( 1)
where is the Earth radius, is the satellite altitude, and is the minimum elevation angle. The ground
coverage radius is then obtained as:
( 2)
where represents the arc length over the Earth’s surface corresponding to the satellite footprint. The
coverage area is computed using a spherical cap model, which accurately represents the Earth’s curvature,
and is defined as:
( 3)
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3.2 Latency model
The end-to-end latency in LEO satellite communication systems is modeled as the sum of multiple delay
components, following standard formulations in communication networks [26], [27]. In general, the total
latency can be expressed as:
( 4)
where represents propagation delay, corresponds to processing delay, accounts for
queueing effects, and captures delays associated with path selection and forwarding. If processing
and queueing delays are assumed to be similar among systems and are neglected, the latency model can
be simplified as:
( 5)
The propagation delay is determined by the signal travel time between ground users and the satellite
system. In this work, propagation delay is modeled using a geometric formulation based on the slant range
between a ground terminal and a satellite. The slant range is defined as the direct line-of-sight
distance between the ground user and the satellite, taking into account the Earth’s curvature and the
minimum elevation angle. It is expressed as:
( 6)
where is the Earth’s radius, h is the orbital altitude, and is the Earth central angle corresponding to
the coverage boundary. The propagation delay is approximated as the time required for a signal to travel
from the ground terminal to the satellite and back, resulting in:
( 7)
where c is the speed of light .
The routing component depends on the architectural design of the constellation. In bent-pipe systems,
communication is relayed through ground gateways without in-orbit routing. Therefore, no additional on-
board inter-satellite routing delay is introduced, and the routing component is assumed negligible for the
purpose of this analysis ( ). In contrast, constellations equipped with inter-satellite links (ISLs)
enable direct satellite-to-satellite communication, where the routing delay is determined by the cumulative
propagation across multiple satellite hops [28], [29]:
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( 8)
where represents the distance of each inter-satellite link segment and N is the number of hops.
3.3 Service density metric
The satellite availability over a given coverage area (service density metric) is defined as:
( 9)
where is the number of satellites and is the coverage area. This metric provides a simplified and
uniform representation of spatial service capability in large-scale constellations [30], [31].
3.4 Normalized performance index
To achieve a consistent comparison across constellations, a normalized performance index is proposed as:
( 10)
where are the normalized coverage, normalized latency, and normalized service
density, respectively, and is the weighting coefficients. The normalized metrics are computed using a
min-max normalization approach. For metrics where higher values indicate better performance, such as
coverage and service density, normalization is defined as:
( 11)
( 12)
where C represents the coverage area and denotes the service density.
For latency, lower values correspond to better performance, and an inverse normalization is applied:
( 13)
where T represents the total latency.
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This normalization ensures that all metrics are mapped into a unified range between 0 and 1, with higher
values representing better system performance [32], [33]. A numerical evaluation framework integrating
the models described above was implemented using MATLAB for Starlink, OneWeb, Telesat, and Project
Kuiper. Parameters such as satellite altitude, number of satellites, orbital planes, and minimum elevation
angle are defined based on publicly available data. The framework computes coverage radius, propagation
delay, service density, and the normalized performance index.
4. Results and discussion
4.1 Simulation parameters
Table 1 summarizes the nominal parameters used in this study. The values correspond to configurations
commonly reported in the literature and are selected to provide a comparative analysis rather than an exact
replication of operational deployments. To ensure comparability across systems, each constellation is
represented by a single nominal orbital shell or layer derived from publicly available data. This approach
avoids the complexity associated with multi-shell deployments and enables a fair system-level
comparison. For this analysis, the Earth is modeled with a radius of , and a minimum angle
of is assumed to define satellite visibility and coverage.
Table 1. Representative nominal parameters of selected LEO constellations
System
Altitude (km)
Satellites
Orbital planes
Inclination (°)
Architecture
Starlink
550
1584
72
53
ISL-enabled
Project Kuiper
630
1296
36
51.9
Hybrid
Telesat
1015
198
6
98.5
ISL-enabled
OneWeb
1200
648
18
87.9
Bent-pipe
4.2 Coverage results
The coverage performance of the evaluated LEO constellations is determined using the geometric model
described in Section 3.1. Figure 3 illustrates the variation of satellite coverage area as a function orbital
altitude, computed using cap model for a fixed minimum elevation angle of 25°.
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Figure 3. Variation of satellite coverage area as a function of orbital altitude
The analysis considers altitudes ranging from 400 km to 1500 km, which are representative of typical Low
Earth Orbit (LEO) constellations. The results confirm the dependence of satellite footprint on satellite
height. Table 2 summarizes the resulting coverage radius and footprint area for the selected constellations.
Table 2. Coverage radius and footprint area for selected LEO constellations (nominal configurations).
System
h (km)
d (km)
C_sat (
Starlink
550
Project Kuiper
630
Telesat
1015
OneWeb
1200
OneWeb achieves the largest coverage footprint due to its higher orbital altitude, followed by Telesat and
Project Kuiper. In contrast, Starlink exhibits the smallest coverage radius, which is consistent with its
lower-altitude configuration. The footprint area of OneWeb is approximately 3.3 times the size of Starlink,
highlighting the significant impact of altitude on coverage scalability. While higher-altitude constellations
benefit from wider coverage per satellite, they reduce the level of spatial reuse, which limit capacity in
high-demand regions.
4.3 Latency numerical estimation
The latency performance of the evaluated LEO constellations is analyzed using the propagation model
described in Section 3.2. Figure 4 shows the propagation delay as a function of orbital altitude for values
ranging from 400 km to 1500 km, considering a fixed minimum elevation angle of 25°.
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Figure 4. Variation of propagation delay as a function of orbital altitude
Table 3 summarizes the resulting latency values for the selected constellations. The results show a clear
dependence of propagation delay on orbital altitude, with lower-altitude systems achieving reduced
latency compared to higher-altitude configurations.
Table 3. Approximate propagation latency for selected LEO constellations.
System
Altitude (km)
Approximate Latency (ms)
Starlink
550
Project Kuiper
630
Telesat
1015
OneWeb
1200
Starlink achieves the lowest propagation latency due to its reduced orbital altitude, followed by Project
Kuiper. Telesat and OneWeb exhibit higher latency values as a result of their higher orbital configurations.
In particular, the presence or absence of Inter-Satellite Links (ISL) significantly influences how data is
transmitted across the network. Table 4 summarizes the qualitative impact of network architecture on
effective latency.
Table 4. Impact of network architecture on effective latency.
System
Architecture
Effective Latency Trend
Starlink
ISL-enabled
Reduced (optimized routing)
Project Kuiper
Hybrid (partial ISL deployment)
Moderate (dependent on ISL utilization)
Telesat
ISL-enabled
Moderately reduced (planned space-based routing)
OneWeb
Bent-pipe
Increased (gateway-dependent routing)
Constellations with Inter-Satellite Links, such as Starlink, enable direct communication between satellites,
reducing the reliance on ground infrastructure and allowing more efficient routing paths. This results in
lower effective latency, particularly for long-distance communications. In contrast, bent-pipe
architectures, such as OneWeb, require signals to be routed through ground gateways, which can increase
latency due to additional transmission segments and infrastructure constraints. Telesat, while designed
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with ISL, is expected to achieve moderate latency improvements depending on the extent of its
deployment, similar to Project Kuiper.
4.4 Service density evaluation
The service density of the evaluated LEO constellations is analyzed using the model introduced in Section
3.3, where density is defined as the ratio between the total number of satellites and the effective coverage
area per satellite. This metric provides an equivalent spatial representation of how densely satellites are
distributed within their respective coverage regions.
Table 5 presents the resulting service density values for the selected constellations, computed using the
updated coverage areas derived from the geometric model.
Table 5. Service density for selected LEO constellations (nominal configurations).
System
Satellites
(nominal)
(
(
Starlink
1584
Project Kuiper
1296
OneWeb
648
Telesat
198
Starlink exhibits the highest service density by a wide margin due to its large number of satellites
combined with a relatively small coverage footprint. Project Kuiper follows as the second system, while
OneWeb and Telesat show lower density values. The service density of Starlink is approximately 6 times
higher than that of OneWeb and more than 27 times higher than that of Telesat. This highlights the
different design philosophies between low-altitude, high-density constellations and higher-altitude, lower-
density systems. Higher service density is associated with improved spatial reuse and increased capacity
in high-demand regions. Dense constellations enable more simultaneous connections and better load
distribution, which is critical for broadband and low-latency applications. However, increasing density
also introduces system-level challenges, including higher deployment costs, increased complexity in
management, and more requirements for interference coordination and resource allocation. Systems with
larger coverage footprints per satellite, such as OneWeb and Telesat, exhibit lower spatial density, whereas
systems with smaller footprints compensate through greater number of satellites. This trade-off plays a
central role in the overall performance evaluation and directly influences the results of the integrated
performance index.
4.5 Normalized performance index
The normalized performance index defined in Section 3.4 was used to integrate the effects of coverage,
latency, and service density into a single comparative metric. This index allows an evaluation by
combining metrics with different physical meanings into a unified framework. In this study, the weighting
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coefficients were defined as for coverage, for latency, and for service
density. The reason behind this specific allocation is rooted in the operational performance requirements
defined by the International Telecommunication Union (ITU) for IMT-2020 and emerging 5G networks,
which position latency as the critical bottleneck for next-generation interactive services. In satellite-
terrestrial integrated networks, achieving ultra reliable low-latency communications (URLLC) is very
important for applications such as real-time edge computing, autonomous vehicle routing, and interactive
cloud protocols [34], [35]. Therefore, latency is assigned a higher priority to penalize systems whose
orbital configurations or routing mechanisms introduce propagation delays. Concurrently, coverage and
service density represent a structural trade-off in orbital design: higher altitudes maximize individual
footprints but degrade spatial density and capacity reuse [36]. To ensure that no single dimension
dominates the performance index, both metrics are treated with equal relevance, balancing the network’s
geographical reach with its capacity potential.
The normalization of the evaluated metrics was performed using a min–max approach as defined in
Section 3.4, ensuring that all variables are mapped into a common range between 0 and 1. Based on the
values obtained in Tables 2, 3 and 5 for coverage area, latency, and service density, the normalized
performance index was calculated for each constellation. The resulting values are summarized in Table 6.
Table 6. Normalized performance index for selected LEO constellations.
System
Performance
Index
Starlink
0.00
1.00
1.00
0.70
Project Kuiper
0.11
0.87
0.64
0.57
OneWeb
1.00
0.00
0.12
0.33
Telesat
0.69
0.26
0.00
0.31
The results indicate that Starlink achieves the highest overall performance index, primarily driven by its
superior latency and service density. Despite having the smallest coverage footprint, its high satellite
density and low propagation delay enable strong overall performance, particularly for latency-sensitive
and high-capacity applications. Project Kuiper ranks second, benefiting from a balanced combination of
moderate coverage, relatively low latency, and intermediate density. This positioning reflects a design
strategy that seeks to balance performance across multiple dimensions rather than optimizing a single
parameter. OneWeb and Telesat exhibit lower performance index values. Telesat benefits from relatively
high coverage but is limited by lower density and higher latency, whereas OneWeb achieves the largest
coverage footprint but is significantly penalized by its higher latency and lower spatial density. These
results highlight that maximizing a single metric, such as coverage, does not necessarily translate into
superior overall performance. Instead, effective system design requires balancing factors including
coverage, latency and service density. Please note that the adopted weighting scheme influences the
resulting ranking. While the selected weights prioritize latency due to its critical role in next-generation
communication services, alternative weight configurations could shift the relative performance of the
evaluated systems. For instance, increasing the weight associated with coverage would benefit higher-
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altitude constellations such as OneWeb, while increasing the weight of service density would further favor
dense constellations like Starlink.
4.6 Validation of the Evaluated Framework
To verify the mathematical accuracy of the proposed framework, a multi-stage validation process was
conducted. First, the formulation utilized to determine the satellite coverage footprint () and coverage
radius is directly grounded on established orbital mechanics and satellite communication theory. The same
geometric equations applied in this study (Equation 1, 2 and 3) have been widely adopted and validated
in recent literature for evaluating Low Earth Orbit (LEO) topologies, such as in the analytical models
presented in [37], confirming the rigor of our theoretical foundation.
The latency values in this work were cross-referenced with established simulations models in the
literature. Specifically, the end-to-end latency metrics calculated by our framework for the Starlink
constellation converge with the performance reported in [10]. This agreement with a widely accepted
Starlink benchmark validates the operational integrity of the numerical model, thereby confirming its use
and accuracy for evaluating the remaining LEO constellations.
Furthermore, despite the diverse operational constraints found in commercial deployements (such as
variable elevation angles or variable routing protocols, the nominal operational specifications, along with
the service density metric, were standardized based on the methodology described in Section 3. This was
to maintain a consistent and reproducible platform for comparison under identical environmental
conditions.
5. Conclusions and future work
This paper presented a unified framework for the evaluation of Low Earth Orbit (LEO) satellite
constellations based on three key performance dimensions: coverage, latency, and service density. By
integrating these metrics into a normalized performance index, the proposed approach enables a
comprehensive and consistent comparison across different constellation architectures. The evaluation
demonstrated that coverage is strongly influenced by orbital altitude, with higher-altitude constellations
achieving larger footprint areas per satellite. However, this advantage comes at the expense of increased
propagation delay and reduced spatial density. In contrast, lower-altitude systems exhibit smaller coverage
regions but benefit from significantly lower latency and higher satellite density, enabling improved spatial
reuse and potential capacity gains.
The results obtained from the integrated performance index indicate that constellations such as Starlink
achieve superior overall performance under latency-sensitive and high-density scenarios, primarily due to
their low orbital altitude and large number of deployed satellites. Systems such as Project Kuiper present
a balanced performance across all metrics, while higher-altitude constellations such as OneWeb and
Telesat offer advantages in coverage but are penalized by higher latency and lower density. A key
contribution of this work lies in the consistent formulation of the coverage model, which incorporates the
minimum elevation angle to provide realistic footprint estimations. Additionally, the adoption of a
simplified latency model, along with an equivalent spatial density metric, enables a tractable and coherent
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system-level analysis. The proposed framework also highlights the critical role of design trade-offs in
LEO constellation performance. The results show that optimizing a single parameter is insufficient, and
that effective system design requires balancing coverage, latency, and spatial density according to
application requirements. Furthermore, the weighting-based formulation of the performance index
provides flexibility to adapt the analysis to different operational priorities. Overall, this work provides a
robust and scalable methodology for the comparative evaluation of LEO satellite constellations and offers
valuable insights into the trade-offs that define next-generation satellite communication systems.
Despite its contributions, this study is constrained by specific limitations which are inherent to first-order
analytical models. The geometric coverage framework relies on a static minimum elevation angle,
neglecting dynamic link-blocking condition and atmospheric attenuation, which can induce severe link
degradation during adverse meteorological events. Furthermore, the latency formulation assumes
deterministic shortest-path propagation, omitting stochastic queueing fluctuations and processing
overheads during peak traffic congestion. In highly loaded operational scenarios, this can introduce
transient latency jitters, increasing the total end-to-end delay, which are not captured by a static model.
Future work will focus on transitioning this analytical framework into a dynamic network simulation
environment. Technical extensions will consider time-variant topologies to evaluate the impact of non-
uniform traffic demands. Furthermore, a systematic sensitivity analysis of the weighting coefficients will
be conducted to assess the stability of the performance index under different operational scenarios and
service requirements.
6. Acknowledgments
The authors would like to express their sincere gratitude to Tecnológico Nacional de México (TecNM),
Instituto Tecnológico de Ensenada for the institutional support and assistance provided throughout the
development of this work.
7. Authorship acknowledgment
Kleiverg Eulalio Encino Morales: conceptualization, methodology, software, formal analysis, research,
supervision, resources. Miguel Ángel Sidón Ayala: methodology, software, validation, formal analysis,
research, visualization, resources. Rolando Díaz Castillo: software, validation, formal analysis, data
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