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Ensuring the integrity of petabyte-scale file transfers is essential for the data gathered from scientific instruments. As packet sizes increase, so does the likelihood of errors, resulting in a higher probability of undetected errors in the packet. This paper presents a Multi-Level Error Detection (MLED) framework that leverages in-network resources to reduce undetected error probability (UEP) in file transmission. MLED is based on a configurable recursive architecture that organizes communication in layers at different levels, decoupling network functions such as error detection, routing, addressing, and security. Each layer L_ij at level i implements a policy P_ij that governs its operation, including the error detection mechanism used, specific to the scope of that layer. MLED can be configured to mimic the error detection mechanisms of existing large-scale file transfer protocols. The recursive structure of MLED is analyzed and it shows that adding additional levels of error detection reduces the overall UEP. An adversarial error model is designed to introduce errors into files that evade detection by multiple error detection policies. Through experimentation using the FABRIC testbed the traditional approach, with transport- and data link- layer error detection, results in a corrupt file transfer requiring retransmission of the entire file. Using its recursive structure, an implementation of MLED detects and corrects these adversarial errors at intermediate levels inside the network, avoiding file retransmission under non-zero error rates. MLED therefore achieves a 100% gain in goodput over the traditional approach, reaching a goodput of over 800 Mbps on a single connection with no appreciable increase in delay. 

Detecting arrays provide test suites for complex engineered systems in which many factors interact. The determination of which interactions have a significant impact on system behaviour requires not only that each interaction appear in a test, but also that its effect can be distinguished from those of other significant interactions. In this paper, compact representations of detecting arrays using vectors over the finite field are developed. Covering strong separating hash families exploit linear independence over the field, while the weaker elongated covering perfect hash families permit some linear dependence. For both, probabilistic analyses are employed to establish effective upper bounds on the number of tests needed in a detecting array for a wide variety of parameters. The analyses underlie efficient algorithms for the explicit construction of detecting arrays. 

For a finite field of order.q, and.v a divisor of.q − 1, additive translates of a cyclotomic vector yield a.q × q cyclotomic array on.v symbols. For every positive integer.t, for certain.q sufficiently large with respect to.v, such a cyclotomic array is always a covering array of strength.t. Asymptotically such arrays have far too many rows to be competitive with certain other covering array constructions. Nevertheless, for small values of .t , this cyclotomic method produces smallest known covering arrays for numerous parameters suitable for practical application. This paper extends these ideas and shows that cyclotomy can produce covering arrays of higher index, and locating and detecting arrays with large separation. Computational results also demonstrate that certain cyclotomic arrays for the same order.q but different values of .v can be juxtaposed to produce mixed-level covering, locating, and detecting arrays. 

Alternative design and analysis methods for screening experiments based on locating arrays are presented. The number of runs in a locating array grows logarithmically based on the number of factors, providing efficient methods for screening complex engineered systems, especially those with large numbers of categorical factors having different numbers of levels. Our analysis method focuses on levels of factors in the identification of important main effects and two-way interactions. We demonstrate the validity of our design and analysis methods on both well-studied and synthetic data sets and investigate both statistical and combinatorial properties of locating arrays that appear to be related to their screening capability.