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\noindent{\sffamily\small\color{brandGray}%
\textbf{\color{brandSlate}Space-Based Data Center Infrastructure: A Multi-Physics Approach.}\\[2pt]
Edition I, 2026. By Samarjith Biswas, PhD.\\[10pt]
This work is released open access. The accompanying source code is licensed under the MIT License;
the text, figures, and equations are licensed under Creative Commons Attribution 4.0 (CC-BY 4.0).
You are free to read, share, cite, and adapt the work with attribution.\\[10pt]
Source repository (models, derivations, simulations, figures):\\
\texttt{github.com/Samarjithbiswas/space-based-ai-datacenter}\\[6pt]
To cite this work, use the metadata in \texttt{CITATION.cff} in the repository; each tagged release
can be archived to obtain a persistent DOI.\\[6pt]
Reference architecture: Google Project Suncatcher, arXiv:2511.19468.\\[10pt]
Every figure and every load-bearing number in this book is regenerated by the open repository.
Forward-looking quantities, including the launch-cost trajectory of Chapter 22, are presented as
projections with explicit uncertainty bands, not as guarantees. Independent reproduction and
correction are welcome.\\[10pt]
Typeset in IBM Plex Sans and IBM Plex Mono with pdf\LaTeX.\\[2pt]
Part of the samarjithbiswas.com research portfolio.
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{\itshape\large\color{brandGray} For the engineers who will actually have to fly this,\\[4pt]
and bring every satellite safely home.}
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The energy and thermal demands of artificial-intelligence computing have outrun the pace at which
terrestrial infrastructure can be built. Clusters are now provisioned in tens to hundreds of megawatts,
and power, water, land, and grid interconnection have become the binding constraints rather than the
silicon. It is against that pressure that a once-fanciful idea, operating data centers in orbit, has
moved from the conference slide into the engineering review. Within a single recent year a
data-center-class accelerator was switched on in orbit, a second company placed compute hardware in low
Earth orbit, and a major laboratory published a reference design for a constellation of machine-learning
accelerators flying in tight formation. The question is no longer whether someone will try, but whether
the physics and the economics actually close.

This book is an attempt to answer that question the way an engineer would, from first principles and
without a thesis to sell. It builds the complete subsystem model of an orbital artificial-intelligence
data center, orbital mechanics, the space environment, thermal control, radiation, debris,
communications, power, attitude, propulsion, reliability, compute, integration, and economics, and for
each subsystem it derives the governing relations, reduces them to numbers, and checks those numbers
against an open simulation. A strict line is kept throughout between what has been demonstrated in flight
and what remains a projection. Where the analysis corrects an earlier assumption or contradicts a widely
repeated claim, it says so plainly; where a quantity is a vendor forecast rather than a measured result,
it is flagged as such and given an uncertainty band rather than a single confident figure.

The book is meant to be read by graduate engineers and mission designers, by researchers entering the
field, and by the teams actually building the early demonstrators. It is organized in parts that move
from context and environment through the spacecraft subsystems to integration, economics, and synthesis,
so that a reader can follow a single thread from a physical principle to a defensible number, or can
enter at the subsystem of interest. Every figure and every load-bearing value is regenerated by the
accompanying open-source model, which is released under a permissive licence and validated by an
automated test suite. Independent reproduction, and correction, are not only welcome but are the point of
putting the whole apparatus in the open.

\vspace{10pt}
\noindent{\itshape Samarjith Biswas}\\
{\small\color{brandGray} 2026}
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This work rests on the open scientific-computing ecosystem, NumPy, SciPy, and Matplotlib above all, and
on the public environmental models and datasets maintained by the world's space agencies, including the
NRLMSIS atmosphere model, NASA ORDEM and the ESA MASTER and DRAMA debris tools, the SHIELDOSE and
AE/AP radiation models, and the public regulatory record of the United States Federal Communications
Commission. It also owes a debt to the authors of the reference architecture, whose decision to put
concrete numbers into the open made independent scrutiny possible in the first place. Finally, the shape
of this book was improved by readers of the earlier report who pressed, rightly, for reproducibility and
for an honest treatment of survivability rather than thermal feasibility alone.
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