<｜begin▁of▁sentence｜>### Instruction:
Sure, let's frame this in a practical scenario related to industrial hygiene:

Imagine we are assessing the resilience of a ventilation system within an industrial facility, which we can represent using a Petersen graph due to its complex interconnectivity. This system is known for being triconnected, meaning that at least three ducts or pathways must be compromised before any section of the system loses airflow.

In this scenario, we would like to examine the integrity of this ventilation network by identifying its k-component structure. Specifically, we want to pinpoint the robust subsections of this network – these are subgraphs where at least 'k' pathways would need to be obstructed to isolate any part of the system and hinder its operation. By determining these subgraphs, we can better understand the system's stability and identify critical areas that may require reinforcement to ensure worker safety.

No external data or GML file has been provided for this particular analysis. If there were such a file, its name and the relevant data from it would naturally be integrated into the evaluation process.
### Response:
import networkx as nx
from networkx.algorithms import approximation as apxa

# Create a Petersen graph
G = nx.petersen_graph()

# Compute the approximate k-component structure
k_components = apxa.k_components(G)

# Print the k-component structure
print(k_components)
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