optimization

This dataset provides the foundational resources for evaluating and optimizing Formula L , a novel mathematical framework for semantic-driven task allocation in multi-agent systems (MAS) powered by large language models (LLM). The dataset includes Python code and both empirical and synthetic data, specifically designed to validate the effectiveness of Formula L in improving task distribution, contextual relevance, and dynamic adaptation within MAS.

The dataset comprises:

This is the supporting data sets for the paper entitled “Low-Complexity Gradient-Based Algorithm for Phase-Only Pattern Synthesis”, which introduce a gradient-based algorithm for phased array pattern synthesis with element phase only. The algorithm can be applied to linear and planar arrays, obtaining the required element phases for arbitrary patterns with desired tolerance, and minimizing nulls or sidelobe levels (SLL). The file contains element phases for the results demonstrated in the paper and a script to read the element phases to regenerate the array patterns.

The rapid growth of interconnected IoT devices has introduced complexities in their monitoring and management. Autonomous and intelligent management systems are essential for addressing these challenges and achieving self-healing, self-configuring, and self-managing networks. Intelligent agents have emerged as a powerful solution for autonomous network design, but their dynamic and intelligent management requires processing large volumes of data for training network function agents.

This work focuses on using the full potential of PV inverters in order to improve the efficiency of low voltage networks. More specifically, the  independent per-phase control capability of PV three-phase four-wire inverters, which are able to inject different active and reactive powers in each phase, in order to reduce the system phase unbalance is considered.  This new operational procedure is analyzed by raising an optimization problem which uses a very accurate modelling of European low voltage networks.

This report outlines the derivation of the first-, second-, and third-order Taylor series expansions of the power flow solution; it is the Electronic Companion of the following paper:

R. A. Jabr, “High-order approximate power flow solutions and circular arithmetic applications,” IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 5053-5062, November 2019.

The derivation is carried out in complex variables via the use of Wirtinger calculus.

1. Figure S1 shows the plasma frequency profile of the two-layer analytical model of the ionosphere, see Eq. (9) of the main text.