\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Ushering in a new frontier in geospace through Data Science}



\author[1]{Ryan McGranaghan}%
\author[1]{Anthony Mannucci}%
\author[1]{Chris Mattmann}%
\author[1]{Brian Wilson}%
\affil[1]{NASA Jet Propulsion Laboratory}%


\vspace{-1em}



  
  \date{May 18, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
We are at a unique time in the study of our place in space. On one hand,
we operate in the same paradigm that has guided the study of space
science for the past couple of decades, and on the other a rising
dependence of our economic and social well-being on space demands a
shift. Everywhere in our society `big data' (defined by four V's:
volume, variety, veracity, and velocity) and the advent of sophisticated
and efficient methods to explore these data (i.e., data science) present
new opportunities for discovery, and the time is ripe for these methods
to shift how we study the physics of space. We will first discuss the
meaning of data science in the context of space science, and then
demonstrate the potential for new discovery through a power use case:
leveraging Global Navigation Satellite Systems (GNSS) signals for space
weather prediction. In this use case, we take advantage of a large
volume of data from GNSS signals, data science-driven technologies, and
a machine learning algorithm known as the Support Vector Machine (SVM)
to develop a novel predictive model for high-latitude ionospheric phase
scintillation. This talk will conclude with a perspective on
opportunities in space science through `big data' and creating new
scientific discovery at the intersection of traditional approaches and
data science-driven innovation.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

