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While caches are essential to reduce execution time and power consumption, they complicate the estimation of the worst-case execution time (WCET), crucial for many real-time systems (RTS). Most research on static worst-case cache behavior prediction has focused on hard RTS, which need complete information on the access patterns and addresses of the data to guarantee the predicted WCET is a safe upper bound of any execution time. Access patterns are available in those codes that have a steady state of access patterns after the first iteration of a loop (in the following regular codes), however, the addresses of the data are not always known at compile time for many reasons: stack variables, dynamically allocated memory, modules compiled separately, etc. Even when available, their usefulness to predict cache behavior in systems with virtual memory decreases in the presence of physically-indexed caches. In this paper we present a model that predicts a reasonable bound of the worst-case behavior of data caches during the execution of regular codes without information on the base address of the data structures. In 99.7% of our tests the number of misses performed below the boundary predicted by the model. This turns the model into a valuable tool, particularly for non-RTS and soft RTS, which tolerate a percentage of the runs exceeding their deadlines.