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Towards Automated Nautical Chart Compilation and
Verification of Output Topology and Safety
Tamer Nadaa,*, Christos Kastrisiosa, Brian Caldera, Christie Enceb, Craig Greene c, Amber Bethellc
a Center for Coastal and Ocean Mapping/UNH-NOAA Joint Hydrographic Center, University of New Hampshire, Durham, USA.
Tamer.Nada@unh.edu, Christos.Kastrisios@unh.edu, brc@ccom.unh.edu
b NOAA, Office of Coast Survey, Marine Chart Division, Silver Spring, MD, USA. christie.ence@noaa.gov
c Esri, Marine & Topographic Production Division, Redlands, CA, USA. cgreene@esri.com, abethell@esri.com
* Corresponding author
Abstract:
The compilation of Electronic Navigational Charts (ENCs) requires significant amount of time, labor-intensive
efforts, and cost. Despite the advancements in technology and the various research efforts, generalization tasks are
still performed manually or semi-manually with expected human errors. The dramatic increase in the amount of data
that is collected by modern acquisition systems, in addition to the increasing timeline expected by the end-users, are
constantly driving Hydrographic Offices (HOs) toward the investigation and adoption of more advanced and effective
ways for automating the generalization tasks to speed up the process, minimize the cost, and improve productivity.
Full automation of the nautical chart compilation process has been unreachable due to the strict nautical cartographic
constraints (and particularly those of safety and topology) that pose a challenge for most of the available
generalization tools, while it remains questionable whether automation can replace human thought processes. In this
paper, we discuss a research effort for an Automated Nautical-chart Generalization (ANG) model in the Esri
environment. The ANG model builds upon the nautical chart generalization guidelines and practice and utilizes
available tools in the Esri environment to perform the generalization of selected ENC features to the target scale.
Safety constraints in the marine domain is of utmost importance, however, since most of the readily available tools
do not respect safety, the main goal of this effort has been an output with no topological violations. In the current
phase of the project, we evaluate safety of soundings and contour for user fixing and while the validation of
bathymetry is a well-researched topic, there was the need for an automated process to identify the sections of the
generalized contours that have been displaced toward the shallow water side Therefore, this work also presents a
safety validation tool that detects the contours’ safety violations in the output. The tool is composed of three main
stages that run individually after the ANG model is complete with the aim to highlight the safety violations for fixing
by cartographers.
Keywords: Automated, Nautical chart Generalization, ENCs, Safety of Navigation, Nautical chart constraints
1. Introduction
The Electronic Navigational Chart (ENC) is a Digital
Landscape Model which is converted to a Digital
Cartographic Model when rendered on the Electronic
Chart Display Systems (ECDISs) (Dyer et al., 2022). It is
a database that comprises numerous point feature objects
(e.g., soundings, navigational aids), line objects (e.g.,
depth contours, coastlines) and polygons (e.g., depth areas,
land areas) which are encoded using the chain-node
topology and are important for the safety of ship
navigation (IHO, 2020). ECDIS integrates ENCs,
navigating related system and sensors aboard ships to give
mariners complete picture of the instantaneous situation of
the vessel and charted dangers in the area (Alexander,
2003). In many Hydrographic Offices (HOs) ENC features
were compiled for years directly from the existing paper
charts with digitization. Consequently, nowadays, most of
the available ENCs are based on the footprints of the paper
charts from which they were derived (Kastrisios and
Calder, 2018). This is the main reason for the existing
horizontal and vertical inconsistencies between adjacent
cells, which may confuse mariners and reduce their
confidence in the nautical chart. In addition,
inconsistencies can affect the performance of ECDIS that
uses the data for analysing the safety of the vessels
underway, either by triggering false alarms that might
contribute to the situation called “mariner’s deafness”. i.e.,
the situation where the mariner disregard important alarms
because of a considerable number of irrelevant ones
(Kastrisios, Calder and Bartlett, 2020), or, even worse,
may lead to a system crash. Furthermore, as per the IHO
standards for nautical charting (IHO, 2020), six usage
bands exist, each associated with the intended navigational
Proceedings of the International Cartographic Association, 5, 14, 2023.
31st International Cartographic Conference (ICC 2023), 13–18 August 2023, Cape Town, South Africa. This contribution underwent
single-blind peer review based on submitted abstracts. https://doi.org/10.5194/ica-proc-5-14-2023 | © Author(s) 2023. CC BY 4.0 License.
use (i.e., overview, general, coastal, approach, harbour,
and berthing) and the radar range. Therefore, HOs are
required to produce, maintain, update, and deliver a large
portfolio of ENC bands in support of safety of navigation
in a timely and consistent manner, which is considered a
tedious and time-consuming process.
On the other hand, the International Hydrographic
Organization (IHO) is encouraging HOs to update their
current coverage schema (IHO, 2021) from the puzzle-
piece layout resulted from the paper-chart-first concept,
(e.g., Figure 1a) to a rectangular gridded system (e.g.,
Figure 1b). In 2019, the Office of Coast Survey (OCS) of
the USA National Oceanic and Atmospheric
Administration (NOAA), started rescheming their ENC
suite by creating a gridded system with standardized scales
and cell sizes. The standard scales follow a dyadic system
in which each successively smaller scale is half of the
preceding scale, and cell boundaries follow lines of
longitude and latitude to appear as rectangular on a
Mercator projection (e.g., Figure 1b). (NOAA, 2019)
Figure 1. Current and planned gridded scheme for different
usage bands, US East-Coast Newburyport (a) IC-ENC S-
57 Catalogue (IC-ENC, 2022) (b) Status of New NOAA
ENCs (NOAA, 2022)
The new gridded NOAA ENC coverage approach aims to
significantly reduce the number of current chart scales,
produce larger and standard scale coverage, facilitate
metrification for NOAAs’ charts and resolve vertical and
horizontal inconsistency (NOAA, 2019). The project,
which is expected to take years to complete, would benefit
greatly from automation of individual generalization tasks,
or, should this be possible, the entire process.
A fully automated solution for generating nautical charts
from the highest level of detail data, to the appropriate
scale, can streamline and minimize the time and effort
needed for chart production. Respecting the nautical charts
constraints, i.e., legibility, morphology, topology, and
safety and especially the latter, is the main reason why
current generalization processes and algorithms developed
for land mapping are not directly applicable to the
maritime domain and safety of navigation related products.
In this paper, we present an Automated Nautical-chart
Generalization (ANG) model in the Esri environment that
builds upon a set of constrains, extracted from the
available nautical cartographic specifications, categorized
and translated into rules to be defined in a template as
conditions to be respected during the generalization
process. The model aims to describe and implement the
generalization steps from the highest level of detail ENC
data to the target scale with no topological errors.
However, since safety is of utmost importance and there
are no readily available algorithms that fully respect its
relevant constraints, a validation tool is developed and
presented that detects all safety violation in the ANG
model output and highlight it for user fixing. This tool can
be used to validate safety even when new fully safe
generalization algorithms are available.
2. Background
Generalization process and algorithms developed for
topographic maps are different than those for nautical
charts. In other words, it is mostly not applicable to the
marine domain due to safety of navigation. For instance,
in nautical charts generalization, depth contours are only
allowed to move to the deep side during generalization (see
Figure 3), this is to guarantee that a ship never runs
aground because of miss representation (Peters et al.,
2014). There are four types of constraints that need to be
respected for the generalization of a nautical chart:
Topology (e.g., no gaps or overlaps between
skin of the earth features).
Safety (e.g., Shallow depths need to be
maintained and at every location, the
indicated depth must not be deeper than the
depth that was originally measured at that
location). (Figure 2)
Legibility (e.g., only essential information
should be shown in a clear and efficient
way).
Morphology (e.g., slope and roughness of the
seafloor must be maintained as much as
possible). (Peters et al., 2014)
Those four constraints are sometimes incompatible with
each other. Some are absolute, while others have a degree
of flexibility. The first two are considered more strict (i.e.,
hard constraints) and should be mostly satisfied and hardly
violated for the chart to be valid. In other words,
constraints do not have the same degree of importance,
thus, during the generalization process, compromises must
be made. For example, the morphology constraint
indicates to maintain the morphology of the sea floor and
stay close to its measured shape as much as possible,
whereas the legibility constraint deviates from this by
disregarding details (Zhang & Guilbert, 2011).
Figure 2. Illustration of Safety constraint, modified from
Zhang and Guilbert (2011)
Proceedings of the International Cartographic Association, 5, 14, 2023.
31st International Cartographic Conference (ICC 2023), 13–18 August 2023, Cape Town, South Africa. This contribution underwent
single-blind peer review based on submitted abstracts. https://doi.org/10.5194/ica-proc-5-14-2023 | © Author(s) 2023. CC BY 4.0 License.
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3. Related work
Various research efforts have tried to automate individual
nautical chart generalization tasks. For instance, in
sounding selection the works by Zoraster and Bayer
(1992), Tsoulos and Stefanakis (1997), Sui et al. (2005),
Owens and Brennan (2012), Yu (2018), Lovrinčević
(2019), Skopeliti et al. (2020), and Dyer, et al. (2022). In
Depth contours generalization, those by Guilbert and Lin
(2006), Guilbert and Zhang (2012), Miao and Calder
(2013), Peters et al. (2014), Yan et al. (2017), Skopeliti et
al. (2021). Other works have focused on validating the
safety (e.g., Wilson et al. (2017), Kastrisios and Calder
(2018), Kastrisios et al. (2019a) and Dias et al. (2022)),
and topology of depth information on charts, (e.g.,
Kastrisios et al. (2020) and Huo et al. (2022)). In addition,
a number of available software applications perform S-58
validation checks and provide reports on Group 1 and
Group 2 objects (e.g., Esri ArcGIS Maritime, Teledyne
CARIS S-57 composer, SevenCs Analyzer and C-Map
dKart Inspector) (Kastrisios and Calder, 2020). In 2013,
Socha and Stoter introduced a research effort for
automating nautical chart production. The research main
goal was defining computer translatable rules for creating
small scale ENCs (i.e., coastal) from higher scale (i.e.,
approach) with minimal human intervention. The study
focused on nine ENC feature classes (Socha and Stoter,
2013).
Figure 3. Illustration of the depth curve generalization
safety constraint, modified from Guilbert and Lin (2006)
4. The Automated Nautical-chart Generalization
model (ANG)
The Automated Nautical Generalization model is
developed in the Esri environment. As shown in Figure 4,
it utilizes the generalization rules spreadsheet, which is
generated from the input database schema and the nautical
constraint template (see section 4.1 & 4.2), as the input that
drives the data generalization for any desired output scale,
using the ArcGIS Pro available generalization algorithms
and tools. (Nada et al., 2022)
Figure 4. schematic description of the Automated Nautical
Generalization Model (Nada et al., 2022)
There are more than 170 geo-features defined for ENCs as
per the IHO standards S-57/101 (IHO, 2018). These
features are categorized under the three geometric
primitives (i.e., points, lines and polygons). In this
research work, a number of features were selected for the
proof of concept. As shown in Figure 5, the selected
features are the seven polygonal feature classes
representing the Skin of the Earth (Group1), and six related
group of features that belong to S-57 features classes (i.e.,
natural coastline “COALNE”, artificial coastline
“SLCONS”, depth contour “DEPCNT”, sounding
“SOUNDG”) and NOAA Nautical Information System
(NIS) feature class group (i.e., aids to navigation
“ATONS”, danger to navigation “DTONS”). The NIS is a
multi-scale attributed geospatial database, primarily used
for NOAA ENC maintenance and publication utilizing
Esri ArcGIS (Ence, 2018).
Figure 5. ENC selected S-57/101 features and associated
NIS Feature class
4.1 The Generalization Constraint Template
From the available nautical cartographic standards, e.g., S-
4 Regulations of the IHO for International Charts and
Chart Specifications (IHO, 2020), and NOAA Nautical
Chart Manual, Polices and Procedures (NOAA, 2019), a
template was developed to categorize and define the
properties of the nautical constraints as conditions to be
respected during the generalization process. The template
includes the geometry type, feature class and value for
each condition. This value does not represent sequence but
rather the hierarchy, i.e., the degree of importance, of those
conditions (Nada et al., 2023b).
4.2 The Generalization Rules Spreadsheet
The generalization rules spreadsheet (GRS) is an excel
spreadsheet that is used to configure the ANG model. It is
developed from the nautical constraints template to match
the input database schema. The GRS is composed of
several tabs that contain all the required information about
the selected feature classes, e.g., the geometric and
generalization relationship between features, the
tolerances to be used for the target scale, hierarchy levels
Proceedings of the International Cartographic Association, 5, 14, 2023.
31st International Cartographic Conference (ICC 2023), 13–18 August 2023, Cape Town, South Africa. This contribution underwent
single-blind peer review based on submitted abstracts. https://doi.org/10.5194/ica-proc-5-14-2023 | © Author(s) 2023. CC BY 4.0 License.
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and operations that needs to be implemented on each
feature (Nada et al., 2023).
4.3 The ANG model Generalization Phases
The ANG model is organized in five main phases or sub-
models (Figure 6); each phase consists of various
generalization tools that are used to automate the process.
The GRS drives the data generalization for the desired
output scale.
4.3.1 Preparation phase
A series of steps are taken before running the ANG model
to prepare the input geo-database GDB as follows:
(1) An empty GDB is created in ArcGIS Pro.
(2) The GDB schema is developed using a pre-
configured ENC schema in a workspace xml format
that contains all the required feature classes, tables,
spatial attributes used to capture ENC information in
a GDB schema (Esri, 2022).
(3) The Generalization Rules template is then created
based on the configured GDB schema to build the
GRS rule file.
Figure 6. The Generalization Phases in ArcGIS Pro
(4) The area of interest (AOI) highest level of detail
available ENCs are loaded to the configured GDB.
(5) The research selected feature classes (Figure 5)
are imported from the loaded ENCs to the configured
input GDB.
(6) The GRS is validated using the Generalization
Rule Validation tools in ArcGIS Pro (Esri, 2021) to
confirm that all tolerances and rules have been defined
for the target scale.
Once the previous steps are taken by the user, the ANG
model runs creating a number of GDBs that will be used
throughout the generalization process, each has a specific
role as follows:
A Generalization GDB that has a similar
schema to the input GDB but simplified and
optimized for generalization within the AOI
by removing domains, subtypes, and
topologies not being used by the model. This
GDB is used to backup the data after each
generalization phase.
A Theme GDB which is used to extract the
required and pre-defined feature classes from
the generalization GDB and apply the
assigned generalization operations on it.
A Scratch GDB which is used for storing
temporary and eliminated data during the
generalization process.
The Result GDB will be created by the
Finalization sub-model to match the input
GDB schema. After all the sub-models have
run, the generalized data are extracted from
the generalization GDB and copied to this
GDB adding all the attributes that have been
simplified in the generalization GDB.
4.3.2 Generalization first phase P1G
In the first generalization phase, Group 1 polygons (see
Figure 5) that fall under the area tolerance defined in the
GRS are either collapsed to points or eliminated. Those
features are extracted from the generalization GDB,
converted to the theme-based schema and exported to the
Theme GDB. As illustrated in Table 1, the generalization
and geometric relationship between features are extracted
from the GRS and assigned to the selected features. This
pre-defined relationship between selected features is a key
to the whole process. Each feature is assigned a geometric
(e.g., SOE) and a generalization relation (e.g., Shared) that
control how features interact during the generalization
phases. The final stage in P1G is to clean and split features
by dissolving and filling gaps, as well as removing any
SOE edge lines where polygons were dissolved. The
output of P1G is polygons without topological violations
which are stored in the Theme GDB and backed up in the
Generalization GDB.
Table 1. The Geometric & Generalization relationship
defined in the GRS
4.3.3 Generalization Second phase P2G
The second generalization phase is responsible for
simplifying and smoothing the selected features. Based on
the rules defined in the GRS, shared features are loaded to
the Theme GDB and generalized by the assigned tool. For
Proceedings of the International Cartographic Association, 5, 14, 2023.
31st International Cartographic Conference (ICC 2023), 13–18 August 2023, Cape Town, South Africa. This contribution underwent
single-blind peer review based on submitted abstracts. https://doi.org/10.5194/ica-proc-5-14-2023 | © Author(s) 2023. CC BY 4.0 License.
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instance, the Simplify Shared tool extracts the shared
simplification tolerances from the GRS, iterate through the
selected features and runs Simplify Shared Edges on the
specified features, using other features as barriers (Nada et
al., 2023b).
4.3.4 Generalization third phase P3G
The third generalization phase is responsible for
generalizing interior, individual and barrier lines and
points (see Table 1). For example, dissolving and merging
of DEPCNTs, selection of SOUNDGs and ATONS.
Barrier features’ positions are respected during the
generalization process by higher agents (i.e., Polygons -
Lines). For instance, a DEPCNT will not cross any
SOUNDGs or ATONS on both sides of the contour when
being processed, this might restrict the amount of
simplification, or be judged as under generalization, but
will prevent having a deep SOUNDG on a shallow side
and vice versa.
4.3.5 Finalization phase
In the finalization phase, an output GDB is created and the
generalized features will be loaded to it from the
Generalization GDB matching the input GDB schema.
This would include adding the domains, subtypes and
default values that were simplified in the Preparation
Phase.
5. Implementation
The ANG model was tested in a number of locations, with
different real world scenarios (e.g., with and without edge
matching inconsistency - mix of scale bands), to generalize
band 5 (i.e., 20k) to band 4 (i.e., 80k) data. The model
output GDBs in all scenarios showed no topological
violations (see Nada et al., 2023b).
Figure 7. The study area - Block Island, NY-USA (a)
Before generalization (b) After generalization
Figure 7 illustrates the model results in the case with no
edge matching inconsistency (i.e., New York – Block
Island Sound area). The model was able to generalize the
selected features from 16 band 5 ENCs (Figure 7a) at scale
1:20k to scale 1:80k (Figure 7b) with no topological errors.
Figure 8 illustrates the model results in the case with a
couple of edge matching inconsistent cells (i.e., New York,
Long Island Sound area). In this case, selected features
(see figure 5) from 16 band 5 ENCs (Figure 8a) were used
as the input GDB. The model was able to generalize the
selected features, as per the tolerances defined in the GRS,
with no topological violations (Figure 8b). However, there
were a few instances of edge matching inconsistencies in
two of the 16 cells (highlighted in Figure 8a & 8b) that did
not share end point topology and where the model was
unable to merge or dissolve the respective line and
polygonal features. These features were treated and
generalized separately from the ANG Model (Figure 8c &
8d).
Figure 8. The study area - Long Island Sound, NY-USA
(a) Pre-generalization data (b) Post-generalization data (c)
Input inconsistency cells (d) Inconsistency model output
6. Validation tools
As explained in Section 2, the two hard constraints of
topology and safety must be respected. To validate that the
output is free of topological errors, the ArcGIS Pro
validate topology tool, is used . The tool runs a set of
integrity checks to identify any topology violations (e.g.,
overlaps, gaps, self- crossing) as they are defined in the
ENC xml file (Nada et al., 2023b).
Regarding safety, the surface-test developed by Kastrisios
et al. (2019b) may be used to identify discrepancies
between the charted information (i.e., soundings, depth
contours, and other features such as rocks and wrecks).
However, regarding the requirement that depth contours
may only be generalized toward the deep-water side and
that small shallows may not be eliminated, no automated
validation process exists. Considering that, generally, the
readily available generalization tools in ArcGIS Pro are
not intended to respect the nautical safety constraints, a
validation tool was developed in the Model-builder. The
tool detects the safety violations in the output GDB
(meaning the sections of the generalized contour/depth
area that have been moved on the shallow water side of the
source contour/depth area), sort them by the area of the
Proceedings of the International Cartographic Association, 5, 14, 2023.
31st International Cartographic Conference (ICC 2023), 13–18 August 2023, Cape Town, South Africa. This contribution underwent
single-blind peer review based on submitted abstracts. https://doi.org/10.5194/ica-proc-5-14-2023 | © Author(s) 2023. CC BY 4.0 License.
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violation (i.e., the size of the polygon formed by the pre
and post-generalized contour/depth area), with the aim to
highlight the errors for user fixing. The tool is composed
of three main stages as shown in Figure 9.
Figure 9. Safety Validation tool flowchart with the three
main stages
6.1.1 Calculate Difference Polygons
After running the ANG model, the pre-generalization
GDB (input GDB) and the post-generalization (output
GDB) are loaded to the safety validation tool, then the tool
runs a series of operations as follows:
1) Select depth areas DEPAREs from both GDBs
using the NIS FC Subtype (i.e., DepthA).
2) Merge Pre & Post generalization DEPAREs into
a new single output dataset. All features remain intact
even if they overlap (Esri, 2022).
3) Dissolve Pre & Post generalization DEPARE
polygons based on specified attributes, for instance
Depth Range Value 1 (DRVAL1).
4) Find overlapping areas in the dissolved polygons
5) Split overlapping polygons using the pre-
generalized DEPCNT as the cutting features.
Figure 10. Safety Validation first stage results – Pre and
Post generalization differences
The result of this step is a set of polygons that represent
the differences between the pre and post-generalized
DEPAREs including both the shallow and deep sides (see
Figure 10).
6.1.2 Separate Deep vs Shallow Side Polygons
The second stage in the safety validation tool is to separate
the results from stage-1 into shallow and deep generalized
polygons, in other words safe and unsafe generalization.
Figure 11. Safety Validation second stage results – safety
violations (one feature)
As illustrated in Figure 9, after selecting DEPAREs from
both pre- and post-generalization GDBs, stage-2 runs as
follows:
1) Union pre and post generalization DEPAREs.
2) Delete the overlapping polygons with land areas
resulted from generalization.
3) Select Overlaps where DRVAL1 of DEPARE1 =
DRVAL2 of DEPARE2.
The result of this stage is all the safety violation polygons
but as one feature (Figure 11).
6.1.3 Detect Safety Violation Polygons
As illustrated in Figure 12, the final stage in the validation
tool is simply to split the safety violation polygons from
stage-2 then sort them by area as follows:
1) Merge polygons from stage-1 and stage-2.
2) Dissolve polygons.
3) Find Overlaps.
4) Sort by area.
The output of this stage (Figure 12 c, d) is the safety
violation polygons sorted in a geo-table by area and
perimeter. Accordingly, as a user perception, small and
irrelevant violation polygons can be accepted according to
scale requirements, this is mainly due to the fact that the
highest level of detail information is available in the larger
scale chart below. In other words, when the mariner zooms
in, all the needed information will be available in the larger
scale ENC.
Proceedings of the International Cartographic Association, 5, 14, 2023.
31st International Cartographic Conference (ICC 2023), 13–18 August 2023, Cape Town, South Africa. This contribution underwent
single-blind peer review based on submitted abstracts. https://doi.org/10.5194/ica-proc-5-14-2023 | © Author(s) 2023. CC BY 4.0 License.
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Figure 12. Safety Validation tool results (a) Input GDB (b)
Output GDB with safe generalization-green and unsafe-
red (c) Safety violations polygons (separated) (d) Geo-
table with safety violations area and perimeter
7. Conclusion
This paper presented an Automated Nautical-chart
Generalization (ANG) model as well as a contours’ safety
validation tool. The ANG model, aims to describe and
implement the steps for generalizing large scale ENC data
to the target smaller scale. The model is developed in
ArcGIS Pro and runs in five main automated phases,
utilized by a generalization rule spreadsheet GRS. The
spreadsheet is generated from the nautical constraint
template, and the input database schema to manage the
process and drives data generalization for any desired
output scale. The model output was tested in different
areas, with different scenarios, and validated for the
mandatory validation checks and nautical hard constraints
of topology and safety. The ArcGIS topology validation
tool confirms that the output is free of topology errors.
However, since the available with ArcPro tools are not
generally designed to respect safety, violations are
expected / encountered in terms of both soundings and
depth contours/depth areas generalization. Therefore, a
safety validation tool was developed and presented that is
capable of detecting the safety violations in the model
output through three main stages, sort it by area and
highlight them for user fixing.
8. Acknowledgements
The work of Tamer Nada, Christos Kastrisios and Brian
Calder was supported by the National Oceanic and
Atmospheric Administration under grant number
NA20NOS4000196.
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single-blind peer review based on submitted abstracts. https://doi.org/10.5194/ica-proc-5-14-2023 | © Author(s) 2023. CC BY 4.0 License.
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