Introduction

\begin{itemize}
\item  The introduction is safety critical systems.
\item  safety critical systems need to be monitored.  Cyber security is an issue. computation is added to safety critical systems. people are required to monitor that computation.
\item  \agd{todo} trust management is the path forward. Other people are currently incorporating trust values into systems
\item  \agd{todo}trust management systems seem to be the way forward for human computer interaction in in critical applications
\item  \agd{todo}So this work looks into trust management for it.
\end{itemize}



\ac{sl} finds utility in modeling situations fraught with uncertainty and unreliable sources, where subjective opinions can convey trust or belief in events and propositions. In essence, \ac{sl} serves as a versatile calculus for reasoning under uncertainty, offering a nuanced approach to probabilistic reasoning by incorporating subjective perspectives on belief and uncertainty.


\textbf{robust state estimation}, anything from the control side? controls people deal with this all the time. You get sensor readings and somehow your system needs to be robust. control systems people deal with this problem as well. uncertainty comes in as well because robust state estimation. 
two paragraphs, five citations and just a sentence on how it differs from your part. And how it differs is straightforward because theirs is for control systems. So they have a mathematic description of the model of how the system behaves and how it should evolve. And as I understand, they use this model, the description, in order to correct for any type of attacks or to identify certain attacks. You do not assume that you have a model of the system.


\textbf{the motivation,} 

Accurately assessing and quantifying trustworthiness in \ac{cpss} remains challenging due to dynamic environments and uncertainties in indirect observations, necessitating the development of a trust-based mechanism for reliable trust quantification.

there is already work on calculating trust using subjective logic in \ac{cpss}. reference that work here. But that work has focused on binomial opinions. in our work we specifically have data with the unsure label. mention 3SVL and no use of cumulative fusion.

talk about trust in human AI interaction. the importance the obstacles etc.

\textbf{the application, }




 assume you get a plus one, zero, and minus one from An uncertain signal that was processed by a classifier

 later on section X, we'll expand this to show how we use power traces for this.