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in units of Janskys per synthesized beam area, with $\eta_s$ most important factor being correlator efficiency $\eta_c$.  \subsection{Surface Brightness Sensitivity}  \subsection{Operations Costs}  \subsubsection{Site Operations costs}  \subsubsection{Components reliability}  \subsubsection{Maintenance complexity}  \subsubsection{Calibration Software Costs} 
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\subsubsection{Power Consumption Cost}  \subsubsection{Re-configuration Systems Operation Cost}  \subsection{Up-front Costs}  \subsubsection{Construction Management}  \subsubsection{Site development cost}  \subsubsection{Cost of Antennas Construction} Antennas}  According to \cite{moran}, a commonly used rule of thumb for the cost of an antenna is that it is proportional to $D^{\alpha}$, where $\alpha \approx 2.7$ for values of $D$ from a few meters to tens of meters. For antennas with accuracy $\frac{\lambda}{16}$, we could use \cite{mmadesign} as an upper limit for Antenna construction cost.  \begin{equation}\label{eq:antenna_cost}  \text{Antenna Cost} = \frac{890N(\frac{D}{10})^{2.7}}{(\lambda^{0.7})} + 500  
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\subsubsection{Cost of Re-configuration Systems Construction}  \section{Data for visual analytics - Python Implementation}\label{sec:python}  This section presents a python code that produces data in the right format for performing visual analytics, consistent with variables in \S~\ref{sec:var} and objectives in \S~\ref{sec:obj}.  \subsubsection{Integration and Verification Costs}  \subsubsection{Validation Costs}  \section{Visualization Tool Notes}  \section{Conversation notes}  \subsection{Engineering cost vs. Calibration cost} 
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