Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Statistical Learning-Based Algorithm for Topology Verification in
  Natural Gas Networks Based on Noisy Sensor Measurements   Accurate knowledge of natural gas network topology is critical for the proper
operation of natural gas networks. Failures, physical attacks, and cyber
attacks can cause the actual natural gas network topology to differ from what
the operator believes to be present. Incorrect topology information misleads
the operator to apply inappropriate control causing damage and lack of gas
supply. Several methods for verifying the topology have been suggested in the
literature for electrical power distribution networks, but we are not aware of
any publications for natural gas networks. In this paper, we develop a useful
topology verification algorithm for natural gas networks based on modifying a
general known statistics-based approach to eliminate serious limitations for
this application while maintaining good performance. We prove that the new
algorithm is equivalent to the original statistics-based approach for a
sufficiently large number of sensor observations. We provide new closed-form
expressions for the asymptotic performance that are shown to be accurate for
the typical number of sensor observations required to achieve reliable
performance.

25
The categories of this paper are: patt-sol
1
The categories of this paper are: cs
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an