Update references and affiliation (#69)

- Update reference to Lin2019
- Improve CONICET affiliation
- Update access date for Uieda2015 reference

manuscript/manuscript.tex

@@ -11,7 +11,7 @@
         Mario E. Gimenez$^{1,2}$, and Leonardo Uieda$^3$
     }
     \\[0.4cm]
-    {\small $^1$ CONICET, Argentina}
+    {\small $^1$ Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina}
     \\
     {\small $^2$ Instituto Geofísico Sismológico Volponi, Universidad Nacional de San Juan, Argentina}
     \\
@@ -133,7 +133,7 @@ \section{Introduction}
 The literature offers two main approaches: one involves Taylor series expansion
 \citep{Heck2007, Grombein2013} while the other makes use of Gauss-Legendre
 Quadrature (GLQ)
-\citep{Asgharzadeh2007, Wild-Pfeiffer2008, Li2011, Uieda2016, Lin2018}.
+\citep{Asgharzadeh2007, Wild-Pfeiffer2008, Li2011, Uieda2016, Lin2019}.
 The Taylor series expansion is not well suited to develop an algorithm for
 a density varying with depth according to an arbitrary continuous function.
 Different series expansion terms would have to obtained for each density function
@@ -177,19 +177,19 @@ \section{Introduction}
 differences.
 \citet{Fukushima2018} also generalized their method to tesseroids with a radial
 polynomial density function of arbitrary degree.
-\citet{Lin2018} compared the different integration and discretization methodologies for
+\citet{Lin2019} compared the different integration and discretization methodologies for
 homogeneous tesseroids.
 From this analysis they developed a combined method:
 for computation points near the tesseroid, they use a GLQ integration with an adaptive
 discretization based on \citet{Uieda2016} but only applied to the horizontal dimensions.
 If the computation point is farther than a certain truncation distance,
 a second order Taylor series approximation is applied instead along with the regular
 subdivision developed by \citet{Grombein2013}.
-\citet{Lin2018} also introduced a variation of their combined method to compute the
+\citet{Lin2019} also introduced a variation of their combined method to compute the
 gravitational fields generated by tesseroids with a linearly varying density in the
 radial dimension.
 
-Both the \citet{Lin2018} and the \citet{Fukushima2018} studies limit the radial density
+Both the \citet{Lin2019} and the \citet{Fukushima2018} studies limit the radial density
 variation to polynomial functions.
 While most continuous and smooth functions can be approximated by piecewise linear
 functions, the choice of a discretization interval is neither straight forward nor
@@ -211,7 +211,7 @@ \section{Introduction}
 generated by a tesseroid with an arbitrary continuous density function on an external
 point.
 It is based on the three dimensional GLQ integration, a two dimensional version of the
-adaptive discretization of \citet{Uieda2016} (following \citet{Lin2018}),
+adaptive discretization of \citet{Uieda2016} (following \citet{Lin2019}),
 and a new density-based radial discretization algorithm.
 To ensure the accuracy of the numerical approximation, we empirically determine
 optimal values for the controlling parameters by comparing the numerical results with
@@ -374,13 +374,13 @@ \subsection{Two Dimensional Adaptive Discretization}
 Both algorithms perform tesseroid subdivisions in the latitudinal,
 longitudinal and radial directions, thus we can define them as three-dimensional
 adaptive discretization algorithms.
-On the other hand, \citet{Lin2018} proposed a two dimensional discretization algorithm
+On the other hand, \citet{Lin2019} proposed a two dimensional discretization algorithm
 that subdivides the tesseroid only on the latitudinal and longitudinal directions.
 Removing a dimension from the discretization makes the computation more efficient by
 reducing the number of tesseroids in the model, while
-retaining an acceptable accuracy \citep{Lin2018}.
+retaining an acceptable accuracy \citep{Lin2019}.
 
-Here we will follow \citet{Lin2018} and use a two dimensional version of the adaptive
+Here we will follow \citet{Lin2019} and use a two dimensional version of the adaptive
 discretization of \citet{Uieda2016}.
 What follows is a summary of the algorithm and the reader is referred to
 \citet{Uieda2016} for a detailed description.
@@ -656,7 +656,7 @@ \section{Determination of the distance-size and delta ratios}
 order to obtain default values for the distance-size ratio $D$.
 We will follow this idea but for our needs the spherical shell must
 have the same density function of radius as our tesseroid model.
-\citet{Lin2018} show the analytical solution of the gravitational potential generated by
+\citet{Lin2019} show the analytical solution of the gravitational potential generated by
 a spherical shell with linear density in the radial coordinate.
 Applying the Newton's Shell Theorem \citep{Chandrasekhar1995, Binney2008},
 we derive expressions for the gravitational potential of a spherical shell with
@@ -1223,7 +1223,7 @@ \section{Discussion}
 to ensure accurate integration of the density function.
 This algorithm is independent of the GLQ integration and could potentially be used to
 determine an optimal discretization when approximating a density function by piecewise
-linear \citep{Lin2018} or piecewise polynomial \citep{Fukushima2018} functions.
+linear \citep{Lin2019} or piecewise polynomial \citep{Fukushima2018} functions.
 
 Our numerical experiments show that the two dimensional adaptive discretization is
 enough to achieve 0.1\% accuracy with a second-order GLQ in the case of a linear density
@@ -1446,7 +1446,7 @@ \subsection{Linear density}
 by a spherical shell with variable density $\rho(r') = ar'$, while the second
 term constitutes the potential generated by a spherical shell with homogeneous
 density $\rho = b$ \citep{Mikuska2006,Grombein2013}.
-Eq~\ref{eq:shell-pot-linear} is in agreement with the one obtained by \citet{Lin2018}.
+Eq~\ref{eq:shell-pot-linear} is in agreement with the one obtained by \citet{Lin2019}.
 
 \subsection{Exponential density}
 

manuscript/references.bib

@@ -164,7 +164,7 @@ @Misc{Uieda2015
   title        = {A tesserioid (spherical prism) in a geocentric coordinate system with a local-{{North}}-oriented coordinate system},
   howpublished = {figshare, available from: http://dx.doi.org/10.6084/m9.figshare.1495525},
   year         = {2015},
-  note         = {Accessed 17 July 2017},
+  note         = {Accessed June 2019},
   doi          = {10.6084/m9.figshare.1495525},
   timestamp    = {2016-03-01T20:04:20Z},
   url          = {http://dx.doi.org/10.6084/m9.figshare.1495525},
@@ -298,13 +298,19 @@ @Article{Wild-Pfeiffer2008
   publisher = {Springer},
 }
 
-@Article{Lin2018,
+@Article{Lin2019,
   author    = {Lin, Miao and Denker, Heiner},
   title     = {On the computation of gravitational effects for tesseroids with constant and linearly varying density},
   journal   = {Journal of Geodesy},
-  year      = {2018},
-  pages     = {1--25},
-  publisher = {Springer},
+  year      = {2019},
+  month     = {May},
+  day       = {01},
+  volume    = {93},
+  number    = {5},
+  pages     = {723--747},
+  issn      = {1432-1394},
+  doi       = {10.1007/s00190-018-1193-4},
+  url       = {https://doi.org/10.1007/s00190-018-1193-4}
 }
 
 @Article{Fukushima2018,
@@ -337,6 +343,8 @@ @InProceedings{Ahmadi2013
   year      = {2013},
   publisher = {{IEEE}},
   doi       = {10.1109/pesmg.2013.6672353},
+  ISSN={1932-5517},
+  month={July},
 }
 
 @InProceedings{Imamoto2008,