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In this work, we study the interface of the Brazilian e-Voting Machine (BVM) in the context of electromagnetic side-channel threats commonly referred to as TEMPEST attacks. In a TEMPEST attack against video displays, an eavesdropper uses Software-Defined Radios (SDRs) to recover sensitive information by intercepting electromagnetic emanations generated during video signal transmission. We emulate the BVM using a VGA monitor by leveraging publicly available information disclosed by the electoral authority, including technical specifications, operational rules of the system, and the official BVM interface. Based on this setup, we investigate whether the BVM interface gives rise to a distinctive spectral signature observable through its unintended electromagnetic emissions. Our findings show that design characteristics relevant to a nationwide electoral process -- such as high image contrast, minimal on-screen information, and the prohibition of other electronic devices within the polling station -- result in a simple and highly distinctive spectral signature that can be observed even through a wall in our experiments. Although our experiments do not involve actual BVM hardware, the r

Measurement of available path capacity with high accuracy over high-speed links deployed in cloud and transport networks is vital for performance assessment and traffic engineering. Methods for measuring the available path capacity rely on sending and receiving time stamped probe packets. A requirement for accurate estimates of the available path capacity is the ability to generate probe packets at a desired rate and also time stamping with high precision and accuracy. This is challenging especially for measurement systems deployed using general purpose hardware. To touch upon the challenge this paper describes and evaluates four approaches for sending and receiving probe packets in high-speed networks (10+ Gbps). The evaluation shows that the baseline approach, based on the native UDP socket, is suitable for available path capacity measurements over links with capacities up to 2.5 Gbps. For higher capacities we show that an implementation based on Data Plane Development Kit (DPDK) gives good results up to 10 Gbps.

The fundamental tension between availability and consistency shapes the design of distributed storage systems. Classical results capture extreme points of this trade-off: the CAP theorem shows that strong models like linearizability preclude availability under partitions, while weak models like causal consistency remain implementable without coordination. These theorems apply to simple read-write interfaces, leaving open a precise explanation of the combinations of object semantics and consistency models that admit available implementations. This paper develops a general semantic framework in which storage specifications combine operation semantics and consistency models. The framework encompasses a broad range of objects (key-value stores, counters, sets, CRDTs, and transactional databases) and consistency models (from causal consistency and sequential consistency to snapshot isolation and transactional and non-transactional SQL). Within this framework, we prove the Arbitration-Free Consistency (AFC) theorem, showing that an object specification within a consistency model admits an available implementation if and only if it is arbitration-free, that is, it does not require a total

The Glauber model is a widely used framework for describing the initial conditions in high-energy nuclear collisions. TGlauberMC is a Monte Carlo implementation of this model that enables detailed, event-by-event calculations across various collision systems. In this work, I present an updated version of TGlauberMC (3.3), which incorporates recent theoretical developments and improved parameterizations, especially relevant for small collision systems. I focus on the oxygen-oxygen (OO), neon-neon (NeNe), and proton-oxygen (pO) collisions at the Large Hadron Collider (LHC) in July 2025, where precise modelling of nuclear geometry and fluctuations is essential. The updated version includes revised nuclear density profiles and an enhanced treatment of nucleon substructure. Geometrical cross sections for all relevant collision systems are calculated and initial-state observables are explored to provide predictions for particle production trends at $\sqrt{s_{\rm nn}}$=5.36 TeV. In particular, a prediction for the centrality dependence of mid-rapidity multiplicity in OO and NeNe collisions is obtained. The updated code is publicly available to support the heavy-ion community with a robust

We develop a system for real-time public transportation data, deciding to use the data standard GTFS-RT (GTFS Realtime), an open data format for public transit data. We give an overview of the design of a physical GPS sensor device, its firmware, and processes. Next, we give the algorithms used to translate raw sensor data into a public GTFS-RT data feed. We deploy this feed over a highly available cluster across multiple regions to maintain high availability.

Potential malicious misuse of civilian artificial intelligence (AI) poses serious threats to security on a national and international level. Besides defining autonomous systems from a technological viewpoint and explaining how AI development is characterized, we show how already existing and openly available AI technology could be misused. To underline this, we developed three exemplary use cases of potentially misused AI that threaten political, digital and physical security. The use cases can be built from existing AI technologies and components from academia, the private sector and the developer-community. This shows how freely available AI can be combined into autonomous weapon systems. Based on the use cases, we deduce points of control and further measures to prevent the potential threat through misused AI. Further, we promote the consideration of malicious misuse of civilian AI systems in the discussion on autonomous weapon systems (AWS).

Although introduced for entanglement, quantum repeaters and swapping protocols have been analyzed for other quantum correlations (QC), such as quantum discord. In 2015, Mundarain and Ladrón de Guevara [Quantum Inf. Process. 14, 4493 (2015)] introduced local-available quantum correlations (LAQC), which are a promising yet understudied quantum correlation. Recently, Bellorin et al. [Int. J. Mod. Phys. B 36, 22500990 (2022), Int. J. Mod. Phys. B 36, 2250154 (2022)] obtained exact analytical results for the LAQC quantifier of general 2-qubit X states. Building up from those results, we analyzed the LAQC swapping for 2-qubit X states. As expected, we find that if the initial states are non-classical and the one used for the projective measurement is entangled, the final state will generally have non-zero LAQC. Using the properties of this quantum correlation, we establish the conditions for a QCS scheme that leads to a final state with a non-zero LAQC measure. We illustrate these results by analyzing five families of one-parameter 2-qubit X states, including families where the projective measure leads to a separable state, but whose LAQC measure is non-zero. This feature opens the possi

The available energy of a plasma is defined as the maximum amount by which the plasma energy can be lowered by volume-preserving rearrangements in phase space, a so-called Gardner re-stacking. A general expression is derived for the available energy of a nearly homogeneous plasma and is shown to be closely related to the Helmholtz free energy, which it can never exceed. A number of explicit examples are given.

Since galaxy distribution reconstruction effectively reduces non-Gaussian terms in the power spectrum covariance matrix, it has attracted interest not only for Baryon Acoustic Oscillation (BAO) signals but also for various cosmological signal analyses. To this end, this paper presents a novel theoretical model that addresses infrared (IR) effects in the post-reconstruction galaxy power spectrum, including 1-loop corrections. In particular, we discuss the importance of incorporating non-perturbative effects arising from IR contributions into the displacement vector $\vec{s}$ used for reconstruction. Consequently, post-reconstruction nonlinear damping of BAO can be described by a single two-dimensional Gaussian function. This is a phenomenon not observed when $\vec{s}$ is considered to at a linear order in the Zel'dovich approximation. Furthermore, we confirm that the cross-power spectrum of the pre- and post-reconstruction density fluctuations lacks IR effect cancellations, and shows an exponential decay in both the cross-power spectrum and the associated shot-noise term. An explanatory video is available at https://youtu.be/u1-xx3_4xCg

Video-based assessment and surgical data science can advance surgical training, research, and quality improvement, yet adoption remains limited by heterogeneous recording formats and privacy concerns linked to video sharing. This work develops, evaluates, and publicly releases Endoshare, a surgeon-friendly application that merges, standardizes, and de-identifies endoscopic videos. Development followed an iterative, user-centered software life cycle. In the analysis phase, an internal survey of four clinicians and four computer scientists, based on 10 usability heuristics, identified early requirements and guided a cross-platform, privacy-by-design architecture. Prototype testing reported high usability for clinicians (4.68 +/- 0.40 out of 5) and for computer scientists (4.03 +/- 0.51 out of 5), with the lowest score (4.00 +/- 0.93 out of 5) relating to label clarity, prompting interface refinement to streamline case selection, video merging, automated out-of-body removal, and filename pseudonymization. In the testing phase, ten surgeons completed an external survey combining the same heuristics with Technology Acceptance Model constructs, reporting high perceived usefulness (5.07 +

Outflows are critical components of many astrophysical systems, including accreting compact binaries and active galactic nuclei (AGN). These outflows can significantly affect a system's evolution and alter its observational appearance by reprocessing the radiation produced by the central engine. Sirocco (Simulating Ionization and Radiation in Outflows Created by Compact Objects - or "the code formerly known as Python") is a Sobolev-based Monte Carlo ionization and radiative transfer code. It is designed to simulate the spectra produced by any system with an azimuthally-symmetric outflow, from spherical stellar winds to rotating, biconical accretion disc winds. Wind models can either be parametrized or imported, e.g. from hydrodynamical simulations. The radiation sources include an optically thick accretion disc and various central sources with flexible spectra and geometries. The code tracks the "photon packets" produced by the sources in any given simulation as they traverse and interact with the wind. The code assumes radiative near-equilibrium, so the thermal and ionization state can be determined iteratively from these interactions. Once the physical properties in the wind have

This paper addresses statistical modelling and forecasting of key indicators describing the severity of a developing pandemic, using routinely reported daily counts of infections, hospitalizations, deaths (both in and out of hospital), and recoveries. These observed counts constitute what we term ``available data''. Because such data are typically incomplete or inconsistently reported, we address several novel missing data challenges arising in this context and propose statistically rigorous solutions that enable inference based solely on the available information. The model is formulated dynamically, explicitly incorporating calendar effects to capture systematic temporal variations in the progression of the pandemic. The proposed framework is illustrated using data from France collected during the COVID-19 pandemic. Our approach also establishes a new benchmark for integrating prior information from domain experts directly into the modelling process, thereby enabling a potential new division of labour between statistical estimation and epidemiological knowledge from external experts.

An accurate and fast estimation of the available bandwidth in a network with varying cross-traffic is a challenging task. The accepted probing tools, based on the fluid-flow model of a bottleneck link with first-in, first-out multiplexing, estimate the available bandwidth by measuring packet dispersions. The estimation becomes more difficult if packet dispersions deviate from the assumptions of the fluid-flow model in the presence of non-fluid bursty cross-traffic, multiple bottleneck links, and inaccurate time-stamping. This motivates us to explore the use of machine learning tools for available bandwidth estimation. Hence, we consider reinforcement learning and implement the single-state multi-armed bandit technique, which follows the $ε$-greedy algorithm to find the available bandwidth. Our measurements and tests reveal that our proposed method identifies the available bandwidth with high precision. Furthermore, our method converges to the available bandwidth under a variety of notoriously difficult conditions, such as heavy traffic burstiness, different cross-traffic intensities, multiple bottleneck links, and in networks where the tight link and the bottleneck link are not sam

The common utilization-based definition of available bandwidth and many of the existing tools to estimate it suffer from several important weaknesses: i) most tools report a point estimate of average available bandwidth over a measurement interval and do not provide a confidence interval; ii) the commonly adopted models used to relate the available bandwidth metric to the measured data are invalid in almost all practical scenarios; iii) existing tools do not scale well and are not suited to the task of multi-path estimation in large-scale networks; iv) almost all tools use ad-hoc techniques to address measurement noise; and v) tools do not provide enough flexibility in terms of accuracy, overhead, latency and reliability to adapt to the requirements of various applications. In this paper we propose a new definition for available bandwidth and a novel framework that addresses these issues. We define probabilistic available bandwidth (PAB) as the largest input rate at which we can send a traffic flow along a path while achieving, with specified probability, an output rate that is almost as large as the input rate. PAB is expressed directly in terms of the measurable output rate and i

The exergy of the dry atmosphere can be considered as another aspect of the meteorological theories of available energies. The local and global properties of the dry available enthalpy function, also called flow exergy, were investigated in a previous paper (Marquet, Q. J. R. Meteorol. Soc., Vol 117, p.449-475, 1991). The concept of exergy is well defined in thermodynamics, and several generalizations to chemically reacting systems have already been made. Similarly, the concept of moist available enthalpy is presented in this paper in order to generalize the dry available enthalpy to the case of a moist atmosphere. It is a local exergy-like function which possesses a simple analytical expression where only two unknown constants are to be determined, a reference temperature and a reference pressure. The moist available enthalpy, $a_m$, is defined in terms of a moist potential change in total entropy. The local function $a_m$ can be separated into temperature, pressure and latent components. The latent component is a new component that is not present in the dry case. The moist terms have been estimated using a representative cumulus vertical profile. It appears that the modifications

A collisionless plasma possesses a certain amount of "available energy", which is that part of the thermal energy that can be converted into field energy. Here, a calculation is presented of the available energy carried by trapped electrons in a slender non-omnigenous flux tube of plasma. This quantity is compared with gyrokinetic simulations of the nonlinear saturated radial energy flux resulting from turbulence driven by collisionless trapped-electron modes in various stellarator and a tokamak. The numerical calculation of available energy is extremely fast and shows a strong correlation with the turbulent energy fluxes found in the gyrokinetic simulations. Indeed, the energy flux is found to be proportional to the available energy to the power of approximately 3/2, as one would expect from a simple phenomenological model.

The available enthalpy is an early form of the modern thermodynamic concept of exergy, which is the generic name for the amount of work obtainable when some matter is brought to a state of equilibrium with its surroundings by means of reversible processes. It is shown in this paper that a study of the hydrodynamic properties of available enthalpy leads to a generalization of the global meteorological available energies previously introduced by Lorenz, Dutton and Pearce. A local energy cycle is derived without approximation. Moreover, static instabilities or topography do not prevent this theory from having practical applications. The concept of available enthalpy is also presented in terms of the potential change in total entropy. Using the hydrostatic assumption, limited-area energetics is then rigorously defined, including new boundary fluxes and new energy components. This innovative approach is especially suitable for the study of energy conversions between isobaric layers of an open limited atmospheric domain. Numerical evaluations of various energy components are presented for a hemispheric field of zonal-average temperature. It is further shown that this new energetic scheme

Publicly available datasets are one of the key drivers for commercial AI software. The use of publicly available datasets is governed by dataset licenses. These dataset licenses outline the rights one is entitled to on a given dataset and the obligations that one must fulfil to enjoy such rights without any license compliance violations. Unlike standardized Open Source Software (OSS) licenses, existing dataset licenses are defined in an ad-hoc manner and do not clearly outline the rights and obligations associated with their usage. Further, a public dataset may be hosted in multiple locations and created from multiple data sources each of which may have different licenses. Hence, existing approaches on checking OSS license compliance cannot be used. In this paper, we propose a new approach to assessing the potential license compliance violations if a given publicly available dataset were to be used for building commercial AI software. We conduct a case study with our approach on 6 commonly used publicly available image datasets. Our results show that there exists potential risks of license violations associated with all of the studied datasets if they were used for commercial purpo