Add: caption for fig:design

arxiv-style/main.tex

@@ -1,4 +1,4 @@
-\documentclass{article}
+\documentclass{article}
 
 \usepackage{arxiv}
 
@@ -110,7 +110,7 @@ A key empirical and methodological tension in ICS synthesis is that temporal rea
 
 \begin{figure}[htbp]
   \centering
-  \includegraphics[width=0.8\textwidth]{fig-design-v4.png}
+  \includegraphics[width=0.8\textwidth]{fig-design-v4-from-user-svg-cropped.pdf}
   \caption{Masked-DDPM: Unified Synthesis for ICS traffic}
   \label{fig:design}
 \end{figure}
@@ -215,7 +215,7 @@ where $\mathrm{CE}(\cdot,\cdot)$ is cross-entropy. At sampling time, we initiali
 \label{sec:method-types}
 Even with a trend-conditioned residual DDPM and a discrete masked-diffusion branch, a single uniform modeling treatment can remain suboptimal because ICS variables are generated by qualitatively different mechanisms. For example, program-driven setpoints exhibit step-and-dwell dynamics; controller outputs follow control laws conditioned on process feedback; actuator positions may show saturation and dwell; and some derived tags are deterministic functions of other channels. Treating all channels as if they were exchangeable stochastic processes can misallocate model capacity and induce systematic error concentration on a small subset of mechanistically distinct variables \citep{nist2023sp80082}.
 
-We therefore introduce a type-aware decomposition that formalizes this heterogeneity as a routing and constraint layer.  Let $\tau(i)\in{1,\dots,6}$ assign each variable (i) to a type class. The type assignment can be initialized from domain semantics (tag metadata, value domains, and engineering meaning), and subsequently refined via an error-attribution workflow described in the Benchmark section. Importantly, this refinement does not change the core diffusion backbone; it changes which mechanism is responsible for which variable, thereby aligning inductive bias with variable-generating mechanism while preserving overall coherence.
+We therefore introduce a type-aware decomposition that formalizes this heterogeneity as a routing and constraint layer. Let $\tau(i)\in{1,\dots,6}$ assign each variable $i$ to a type class. For expository convenience, the assignment can be viewed as a mapping $\tau(i)=\mathrm{TypeAssign}(m_i, s_i, d_i)$, where $m_i$, $s_i$, and $d_i$ denote metadata/engineering role, temporal signature, and dependency pattern, respectively. The type assignment can be initialized from domain semantics (tag metadata, value domains, and engineering meaning), and subsequently refined via an error-attribution workflow described in the Benchmark section. Importantly, this refinement does not change the core diffusion backbone; it changes which mechanism is responsible for which variable, thereby aligning inductive bias with variable-generating mechanism while preserving overall coherence.
 
 We use the following taxonomy:
 \begin{enumerate}
@@ -234,7 +234,7 @@ We use the following taxonomy:
 
 \begin{figure}[H]
   \centering
-  \includegraphics[width=0.98\textwidth,trim=0 550 0 10,clip]{typeclass-cropped.pdf}
+  \includegraphics[width=0.98\textwidth]{typeclass-cropped.pdf}
   \caption*{Type assignment and six-type taxonomy.}
 \end{figure}