docs(paper): convert table to grid format for controlled column widths

Replace simple pipe table with Pandoc grid table to enforce explicit
column width constraints and enable text wrapping in PDF generation.

Grid table column widths:
- Library: ~10 characters
- Purpose: ~32 characters (with wrapping)
- Citation: ~39 characters (with wrapping)

This prevents the middle "Purpose" column from rendering excessively wide
in the PDF output. Grid tables give Pandoc precise control over LaTeX
table column specifications, ensuring proper formatting across long entries.

Ref: https://pandoc.org/MANUAL.html#tables

🤖 Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>

Author: blalterman

paper/paper.md

@@ -37,15 +37,29 @@ Notably, there are several packages that support different elements of space phy
 Tools for the dedicated analysis of solar wind observations are noticeably absent.
 SolarWindPy fills this gap by providing a unified framework for analyzing solar wind observations in combination with relevant information about the spacecraft from which the observations were made.
 
- Library  | Purpose                                                | Citation
-:--------:|:------------------------------------------------------:|:--------:
- AstroPy  |  Astronomical observations.                            | [@astropy:2013, @astropy:2018, @astropy:2022]
- SunPy    |  Remote sensing observations of the Sun.               | [@sunpy_community2020, @MFC+20, @Barnes2020]
- PlasmaPy |  Theoretical plasma physics.                           | [@plasmapy_community_2025_16747747]
- SpacePy  |  Timeseries analysis and magnetospheric modeling.      | [@niehof2022spacepy, @spacepy_code]
- Pysat    |  Magnetospheric mission data analysis.                 | [@pysatcode, @Stoneback2018, @Stoneback2023]
- pySpedas |  Retrieval and plotting of heliophysics timeseries.    | [@Grimes2022]
- PyTplot  |  Timeseries and spectrograph data visualization.       | [@pytplot2019]    
++----------+--------------------------------+---------------------------------------+
+| Library  | Purpose                        | Citation                              |
++==========+================================+=======================================+
+| AstroPy  | Astronomical observations.     | [@astropy:2013, @astropy:2018,        |
+|          |                                | @astropy:2022]                        |
++----------+--------------------------------+---------------------------------------+
+| SunPy    | Remote sensing observations    | [@sunpy_community2020, @MFC+20,       |
+|          | of the Sun.                    | @Barnes2020]                          |
++----------+--------------------------------+---------------------------------------+
+| PlasmaPy | Theoretical plasma physics.    | [@plasmapy_community_2025_16747747]   |
++----------+--------------------------------+---------------------------------------+
+| SpacePy  | Timeseries analysis and        | [@niehof2022spacepy,                  |
+|          | magnetospheric modeling.       | @spacepy_code]                        |
++----------+--------------------------------+---------------------------------------+
+| Pysat    | Magnetospheric mission data    | [@pysatcode, @Stoneback2018,          |
+|          | analysis.                      | @Stoneback2023]                       |
++----------+--------------------------------+---------------------------------------+
+| pySpedas | Retrieval and plotting of      | [@Grimes2022]                         |
+|          | heliophysics timeseries.       |                                       |
++----------+--------------------------------+---------------------------------------+
+| PyTplot  | Timeseries and spectrograph    | [@pytplot2019]                        |
+|          | data visualization.            |                                       |
++----------+--------------------------------+---------------------------------------+    
 
 The SolarWindPy framework utilizes a pythonic, class-based architecture that combines ion and magnetic field objects into a single, unified plasma.
 It is designed for both experienced researchers and to provide an intuitive scaffold for students learning to analyze spacecraft data.