deletions | additions       diff --git a/untitled.tex b/untitled.tex  index cedd980..9fa287b 100644  --- a/untitled.tex  +++ b/untitled.tex 
...
\section{Overview} \section{Overview}\label{sec:intro}  This document presents a parametric model to help design an Interferometric Array. It describes the design parameters in \ref{variables} \S~\ref{var}  and the system performance objectives in \ref{objectives}, \S~\ref{obj},  and the relationship between them. A python code used to generate data in the format required for visual analysis of array design options performance is presented in \ref{array-performance-data-generation---python-implementation}. \S~\ref{python}.  The python code is consistent with parameters and objectives selection in sections 2 and 3, and the mathematical relationships between them. \section{Variables} \section{Variables}\label{sec:var}  This section aims to include all relevant design parameters that might influence the selected performance objectives in section 4.  \subsection{Antenna Aspects}  \subsubsection{Collecting Area} 
...
\subsection{Correlator Aspects}  \subsubsection{Position}  \subsubsection{Efficiency}  \section{Objectives} \section{Objectives}\label{sec:obj}  This section aims to include array performance objectives that might be influenced by design choices.  \subsection{Minimize Brightness Sensitivity Limit}  An overall measure of performance is the System Equivalent Flux Density, $SEFD$, defined as the flux density of a source that would deliver the same amount of power (see \cite{sensitivity2}):  
...
\text{Correlator cost} = 2N^2 + 112N +1360  \end{equation}  \subsubsection{Cost of Re-configuration Systems Construction}  \section{Array Performance Data Generation - Python Implementation} Implementation}\label{sec:python}  \section{Visualization Tool Notes}  \section{Conversation notes}  \subsection{Engineering cost vs. Calibration cost} 
...