Abstract
This paper presents a semantic characterization of leakage-freeness through timing side-channels for Jasmin programs. Our characterization covers probabilistic Jasmin programs that are not constant-time. In addition, we provide a characterization in terms of probabilistic relational Hoare logic and prove the equivalence between both definitions. We also prove that our new characterizations are compositional and relate our new definitions to existing ones from prior work, which could only be applied to deterministic programs. To provide practical evidence, we use the Jasmin framework to develop a rejection sampling algorithm and provide an EasyCrypt proof that ensures the algorithm's implementation is leakage-free while not being constant-time.

Abstract

Abstract
Abstract This paper investigates the ability of large language models (LLMs) to evaluate semantic relations between word pairs by examining their alignment with human-generated semantic ratings. Semantic relations represent the degree of connection (e.g., relatedness or similarity) between linguistic elements and are traditionally validated against human-annotated datasets. Due to the challenges of building such datasets and recent progress in LLMs’ capacity to model human-like understanding, we explore whether LLMs can serve as reliable substitutes for traditional human ratings. We conducted experiments using multiple LLMs from OpenAI, Google, Mistral, and Anthropic, evaluating their performance across diverse English and Portuguese semantic relations datasets. We included in the analysis PAP900, a recently published dataset of semantic relations in Portuguese, to examine the influence of prior exposure to the dataset on LLM training. The results show that the LLM predictions correlate strongly with human ratings. The findings reveal the potential of LLMs to supplement or replace traditional semantic measure algorithms and crowd-sourced human annotations in semantic tasks.

Abstract
Photovoltaic (PV) panels serve as a standard solution for the collection of solar energy. The flat photovoltaic solar plate design has been the most adopted by the market for its ease of installation. However, this design faces limitations due to geometric constraints and the sun's trajectory through the day. Inspiration was drawn from nature to overcome these limitations by utilizing the tridimensional hexagonal shape observed in honeycomb structures. The used approach aimed to explore a novel design that can reduce the constraints of flat PV panels while maximizing energy output. The unique 3D arrangement of the hexagonal pyramid enables the installation of mirrors inside to ease the reflection of photons and to increase energy production compared to flat panels. Furthermore, this design presents an opportunity to incorporate a water capture and heating system, thereby increasing the system's overall usage.

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Autonomous landing for Unmanned Aerial Vehicles (UAVs) requires both precision and resilience against environmental uncertainties, capabilities that current approaches struggle to deliver. This paper presents a novel learning-based solution that combines an advanced multimodal transformer-based detector with a reinforcement learning formulation to achieve reliable autonomous landing behavior across varying scenario uncertainties. Beyond the integration of multimodality for robust target detection, this research incorporates a comprehensive analysis of the impact of state representation on decision-making performance. The proposed methodology is validated through extensive simulation studies and real-world field experiments conducted on physical UAV platforms under natural wind disturbances, demonstrating reliable transfer from simulated training environments to controlled outdoor conditions. Field experiments across varying initial conditions and wind stress confirm the system’s robustness, achieving landing precision of 0.10 ± 0.08 meters in outdoor trials, demonstrating centimeter-level accuracy that surpasses the meter-level precision of global positioning systems.

Abstract
The effective management of shared resources within energy communities poses a significant challenge, particularly when balancing renewable energy generation and fluctuating demand. This paper introduces a novel optimization framework that integrates people flow data, modeled using the Social Force Model (SFM), with energy management strategies to enhance the efficiency and sustainability of energy communities. By combining SFM with the Non-dominated Sorting Genetic Algorithm III (NSGA-III), the framework addresses multi-objective optimization problems, including minimizing energy costs, reducing user waiting times, and maximizing renewable energy utilization. The study employs synthesized data to simulate an energy community with shared facilities such as electric vehicle (EV) charging stations, communal kitchens, and laundry rooms. Results demonstrate the framework's ability to align energy generation with resource demand, reducing peak loads and improving user satisfaction. The optimization model effectively incorporates real-time behavioral dynamics, showcasing significant improvements in renewable energy utilization-reaching up to 88% for EV charging stations-and cost reductions across various scenarios. This research pioneers the integration of people flow modeling into energy optimization, providing a robust tool for managing the complexities of energy communities.