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The encounter between the Jovian co-rotating plasma and Ganymede gives rise to electromagnetic waves that propagate along the magnetic field lines and accelerate particles by resonant or non-resonant wave-particle interaction. They ultimately precipitate into Jupiter's atmosphere and trigger auroral emissions. In this study, we use Juno/JADE, Juno/UVS data, and magnetic field line tracing to characterize the properties of electrons accelerated by the Ganymede-magnetosphere interaction in the far-field region. We show that the precipitating energy flux exhibits an exponential decay as a function of downtail distance from the moon, with an e-folding value of 29{\deg}, consistent with previous UV observations from the Hubble Space Telescope (HST). We characterize the electron energy distributions and show that two distributions exist. Electrons creating the Main Alfv\'en Wing (MAW) spot and the auroral tail always have broadband distribution and a mean characteristic energy of 2.2 keV while in the region connected to the Transhemispheric Electron Beam (TEB) spot the electrons are distributed non-monotonically, with a higher characteristic energy above 10 keV. Based on the observation of bidirectional electron beams, we suggest that Juno was located within the acceleration region during the 11 observations reported. We thus estimate that the acceleration region is extended, at least, between an altitude of 0.5 and 1.3 Jupiter radius above the 1-bar surface. Finally, we estimate the size of the interaction region in the Ganymede orbital plane using far-field measurements. These observations provide important insights for the study of particle acceleration processes involved in moon-magnetosphere interactions., Comment: Accepted for publication in JGR: Space Physics

This work explores the use of military simulations in predicting and evaluating the outcomes of potential scenarios. It highlights the evolution of military simulations and the increased capabilities that have arisen due to the advancement of artificial intelligence. Also, it discusses the various applications of military simulations, such as developing tactics and employment doctrines, training decision-makers, evaluating new acquisitions, and developing new technologies. The paper then focuses on the Brazilian Air Force's efforts to create its own simulation tool, the Aerospace Simulation Environment (Ambiente de Simula\c{c}\~ao Aeroespacial -- ASA in Portuguese), and how this cloud-based service called ASA Simulation as a Service (ASA-SimaaS) can provide greater autonomy and economy for the military force. The main contribution of this work is to present the ASA-SimaaS solution as a means of empowering digital transformation in defense scenarios, establishing a partnership network, and improving the military's simulation capabilities and competitiveness.

Leveraging recent work on data-driven methods for constructing a finite state space Markov process from dynamical systems, we address two problems for obtaining further reduced statistical representations. The first problem is to extract the most salient reduced-order dynamics for a given timescale by using a modified clustering algorithm from network theory. The second problem is to provide an alternative construction for the infinitesimal generator of a Markov process that respects statistical features over a large range of timescales. We demonstrate the methodology on three low-dimensional dynamical systems with stochastic and chaotic dynamics. We then apply the method to two high-dimensional dynamical systems, the Kuramoto-Sivashinky equations and data sampled from fluid-flow experiments via Particle-Image Velocimetry. We show that the methodology presented herein provides a robust reduced-order statistical representation of the underlying system.

AsaPy is a custom-made Python library designed to simplify and optimize the analysis of aerospace simulation data. Instead of introducing new methodologies, it excels in combining various established techniques, creating a unified, specialized platform. It offers a range of features, including the design of experiment methods, statistical analysis techniques, machine learning algorithms, and data visualization tools. AsaPy's flexibility and customizability make it a viable solution for engineers and researchers who need to quickly gain insights into aerospace simulations. AsaPy is built on top of popular scientific computing libraries, ensuring high performance and scalability. In this work, we provide an overview of the key features and capabilities of AsaPy, followed by an exposition of its architecture and demonstrations of its effectiveness through some use cases applied in military operational simulations. We also evaluate how other simulation tools deal with data science, highlighting AsaPy's strengths and advantages. Finally, we discuss potential use cases and applications of AsaPy and outline future directions for the development and improvement of the library.

We formulate a data-driven method for constructing finite volume discretizations of a dynamical system's underlying Continuity / Fokker-Planck equation. A method is employed that allows for flexibility in partitioning state space, generalizes to function spaces, applies to arbitrarily long sequences of time-series data, is robust to noise, and quantifies uncertainty with respect to finite sample effects. After applying the method, one is left with Markov states (cell centers) and a random matrix approximation to the generator. When used in tandem, they emulate the statistics of the underlying system. We apply the method to the Lorenz equations (a three-dimensional ordinary differential equation) and a modified Held-Suarez atmospheric simulation (a Flux-Differencing Discontinuous Galerkin discretization of the compressible Euler equations with gravity and rotation on a thin spherical shell). We show that a coarse discretization captures many essential statistical properties of the system, such as steady state moments, time autocorrelations, and residency times for subsets of state space.

Our current capability of space weather prediction in the Earth's radiation belts is limited to only an hour in advance using the real-time solar wind monitoring at the Lagrangian L1 point. To mitigate the impacts of space weather on telecommunication satellites, several frameworks were proposed to advance the lead time of the prediction. We develop a prototype pipeline called "Helio1D" to forecast ambient solar wind conditions (speed, density, temperature, tangential magnetic field) at L1 with a lead time of 4 days. This pipeline predicts Corotating Interaction Regions (CIRs) and high-speed streams that can increase high-energy fluxes in the radiation belts. The Helio1D pipeline connects the Multi-VP model, which provides real-time solar wind emergence at 0.14 AU, and a 1D MHD model of solar wind propagation. We benchmark the Helio1D pipeline for solar wind speed against observations for the intervals in 2004 - 2013 and 2017 - 2018. We developed a framework based on the Fast Dynamic Time Warping technique that allows us to continuously compare time-series outputs containing CIRs to observations to measure the pipeline's performance. In particular, we use this framework to calibrate and improve the pipeline's performance for operational forecasting. To provide timing and magnitude uncertainties, we model several solar wind conditions in parallel, for a total of 21 profiles corresponding to the various virtual targets including the Earth. This pipeline can be used to feed real-time, daily solar wind forecasting that aims to predict the dynamics of the inner magnetosphere and the radiation belts., Comment: 24 pages, 9 figures. The appendix is available upon request

The interaction between light and an atom causes perturbations in the atom's energy levels, known as the light-shift. These light-shifts are a key source of inaccuracy in atomic clocks, and can also deteriorate their precision. We present a study of light-shifts and associated dynamic polarizabilities for a two-photon atomic clock based on the $5S_{1/2}$-$5D_{5/2}$ transition in rubidium-87 over the range 770 nm to 800 nm. We determine experimental and theoretical values for a magic wavelength in this range and the electric dipole (E1) matrix element for the $5P_{3/2}$-$5D_{5/2}$ transition. We find a magic wavelength of 776.179(5) nm (experimental) and 776.21 nm (theoretical) in the vicinity of the $5P_{3/2}$-$5D_{5/2}$ resonance, and the corresponding reduced E1 matrix element 1.80(6) $ea_0$ (experimental) and 1.96(15) $ea_0$ (theoretical). These values resolve a previous discrepancy between theory and experiment.

We introduce a class of stochastic advection problems amenable to analysis of turbulent transport. The statistics of the flow field are represented as a continuous time Markov process, a choice that captures the intuitive notion of turbulence as moving from one coherent structure to another. We obtain closed form expressions for the turbulent transport operator without invoking approximations. We recover the classical estimate of turbulent transport as a diffusivity tensor, the components of which are the integrated auto-correlation of the velocity field, in the limit that the operator becomes local in space and time.

Enceladus, an icy moon of Saturn, possesses an internal water ocean and jets expelling ocean material into space. Cassini investigations indicated that the subsurface ocean could be a habitable environment having a complex interaction with the rocky core. Further investigation of the composition of the plume formed by the jets is necessary to fully understand the ocean, its potential habitability, and what it tells us about Enceladus' origin. Moonraker has been proposed as an ESA M-class mission designed to orbit Saturn and perform multiple flybys of Enceladus, focusing on traversals of the plume. The proposed Moonraker mission consists of an ESA-provided platform, with strong heritage from JUICE and Mars Sample Return, and carrying a suite of instruments dedicated to plume and surface analysis. The nominal Moonraker mission has a duration of 13.5 years. It includes a 23-flyby segment with 189 days allocated for the science phase, and can be expanded with additional segments if resources allow. The mission concept consists in investigating: i) the habitability conditions of present-day Enceladus and its internal ocean, ii) the mechanisms at play for the communication between the internal ocean and the surface of the South Polar Terrain, and iii) the formation conditions of the moon. Moonraker, thanks to state-of-the-art instruments representing a significant improvement over Cassini's payload, would quantify the abundance of key species in the plume, isotopic ratios, and physical parameters of the plume and the surface. Such a mission would pave the way for a possible future landed mission., Comment: Accepted for publication in The Planetary Science Journal

This work compares supervised machine learning methods using reliable data from constructive simulations to estimate the most effective moment for launching missiles during air combat. We employed resampling techniques to improve the predictive model, analyzing accuracy, precision, recall, and f1-score. Indeed, we could identify the remarkable performance of the models based on decision trees and the significant sensitivity of other algorithms to resampling techniques. The models with the best f1-score brought values of 0.379 and 0.465 without and with the resampling technique, respectively, which is an increase of 22.69%. Thus, if desirable, resampling techniques can improve the model's recall and f1-score with a slight decline in accuracy and precision. Therefore, through data obtained through constructive simulations, it is possible to develop decision support tools based on machine learning models, which may improve the flight quality in BVR air combat, increasing the effectiveness of offensive missions to hit a particular target.

The Aerospace Simulation Environment (Ambiente de Simula\c{c}\~ao Aeroespacial -- ASA in Portuguese) is a custom-made object-oriented simulation framework developed mainly in C++ that enables the modeling and simulation of military operational scenarios to support the development of tactics and procedures in the aerospace context for the Brazilian Air Force. This work describes the ASA framework, bringing its distributed architecture for managing multiple simulation machines, a data analysis platform for post-processing simulation data, the capability of loading models at simulation runtime, and a batch mode execution platform to perform multiple independent executions simultaneously. In addition, we present a list of recent works using the ASA framework as a simulation tool in the air combat context.

The ESA-JAXA BepiColombo mission will provide simultaneous measurements from two spacecraft, offering an unprecedented opportunity to investigate magnetospheric and exospheric dynamics at Mercury as well as their interactions with the solar wind, radiation, and interplanetary dust. Many scientific instruments onboard the two spacecraft will be completely, or partially devoted to study the near-space environment of Mercury as well as the complex processes that govern it. Many issues remain unsolved even after the MESSENGER mission that ended in 2015. The specific orbits of the two spacecraft, MPO and Mio, and the comprehensive scientific payload allow a wider range of scientific questions to be addressed than those that could be achieved by the individual instruments acting alone, or by previous missions. These joint observations are of key importance because many phenomena in Mercury's environment are highly temporally and spatially variable. Examples of possible coordinated observations are described in this article, analysing the required geometrical conditions, pointing, resolutions and operation timing of different BepiColombo instruments sensors., Comment: 78 pages, 14 figures, published

Imagine interconnected objects with embedded artificial intelligence (AI), empowered to sense the environment, see it, hear it, touch it, interact with it, and move. As future networks of intelligent objects come to life, tremendous new challenges arise for security, but also new opportunities, allowing to address current, as well as future, pressing needs. In this paper we put forward a roadmap towards the realization of a new security paradigm that we articulate as intelligent context-aware security. The premise of this roadmap is that sensing and advanced AI will enable context awareness, which in turn can drive intelligent security mechanisms, such as adaptation and automation of security controls. This concept not only provides immediate answers to burning open questions, in particular with respect to non-functional requirements, such as energy or latency constraints, heterogeneity of radio frequency (RF) technologies and long life span of deployed devices, but also, more importantly, offers a viable answer to scalability by allowing such constraints to be met even in massive connectivity regimes. Furthermore, the proposed roadmap has to be designed ethically, by explicitly placing privacy concerns at its core. The path towards this vision and some of the challenges along the way are discussed in this contribution.

Using an enhanced Self-Organizing Map method, we provided suboptimal solutions to the Traveling Salesman Problem. Besides, we employed hyperparameter tuning to identify the most critical features in the algorithm. All improvements in the benchmark work brought consistent results and may inspire future efforts to improve this algorithm and apply it to different problems.

This work investigates the use of a Deep Neural Network (DNN) to perform an estimation of the Weapon Engagement Zone (WEZ) maximum launch range. The WEZ allows the pilot to identify an airspace in which the available missile has a more significant probability of successfully engaging a particular target, i.e., a hypothetical area surrounding an aircraft in which an adversary is vulnerable to a shot. We propose an approach to determine the WEZ of a given missile using 50,000 simulated launches in variate conditions. These simulations are used to train a DNN that can predict the WEZ when the aircraft finds itself on different firing conditions, with a coefficient of determination of 0.99. It provides another procedure concerning preceding research since it employs a non-discretized model, i.e., it considers all directions of the WEZ at once, which has not been done previously. Additionally, the proposed method uses an experimental design that allows for fewer simulation runs, providing faster model training.

This work aims to provide an engagement decision support tool for Beyond Visual Range (BVR) air combat in the context of Defensive Counter Air (DCA) missions. In BVR air combat, engagement decision refers to the choice of the moment the pilot engages a target by assuming an offensive stance and executing corresponding maneuvers. To model this decision, we use the Brazilian Air Force's Aerospace Simulation Environment (Ambiente de Simula\c{c}\~ao Aeroespacial - ASA in Portuguese), which generated 3,729 constructive simulations lasting 12 minutes each and a total of 10,316 engagements. We analyzed all samples by an operational metric called the DCA index, which represents, based on the experience of subject matter experts, the degree of success in this type of mission. This metric considers the distances of the aircraft of the same team and the opposite team, the point of Combat Air Patrol, and the number of missiles used. By defining the engagement status right before it starts and the average of the DCA index throughout the engagement, we create a supervised learning model to determine the quality of a new engagement. An algorithm based on decision trees, working with the XGBoost library, provides a regression model to predict the DCA index with a coefficient of determination close to 0.8 and a Root Mean Square Error of 0.05 that can furnish parameters to the BVR pilot to decide whether or not to engage. Thus, using data obtained through simulations, this work contributes by building a decision support system based on machine learning for BVR air combat.

Context. Spacecraft observations early revealed frequent multiple proton populations in the solar wind. Decades of research on their origin have focused on processes such as magnetic reconnection in the low corona and wave-particle interactions in the corona and locally in the solar wind.Aims.This study aims to highlight that multiple proton populations and beams are also produced by magnetic reconnection occurring locally in the solar wind. Methods. We use high resolution Solar Orbiter proton velocity distribution function measurements, complemented by electron and magnetic field data, to analyze the association of multiple proton populations and beams with magnetic reconnection during a period of slow Alfv\'enic solar wind on 16 July 2020. Results. At least 6 reconnecting current sheets with associated multiple proton populations and beams, including a case of magnetic reconnection at a switchback boundary, are found during this day. This represents 2% of the measured distribution functions. We discuss how this proportion may be underestimated, and how it may depend on solar wind type and distance from the Sun. Conclusions. Although suggesting a likely small contribution, but which remains to be quantitatively assessed, Solar Orbiter observations show that magnetic reconnection must be considered as one of the mechanisms that produce multiple proton populations and beams locally in the solar wind.

The next-generation sequencers such as Illumina and SOLiD platforms generate a large amount of data, commonly above 10 Gigabytes of text files. Particularly, the SOLiD platform allows the sequencing of multiple samples in a single run, called multiplex run, through a tagging system called Barcode. This feature requires a computational process for separation of the data sample because the sequencer provides a mixture of all samples in a single output. This process must be secure to avoid any harm that may scramble further analysis. In this context, realized the need to develop a probabilistic model capable of assigning a degree of confidence in the marking system used in multiplex sequencing. The results confirmed the adequacy of the model obtained, which allows, among other things, to guide a process of filtering the data and evaluation of the sequencing protocol used., Comment: 8 pages, 4 figures, 2 tables, Published in Portuguese in the Anais of the XLIII Simp\'osio Brasileiro de Pesquisa Operacional (SBPO 2011), 2011. URL: http://www.din.uem.br/sbpo/sbpo2011/pdf/87903.pdf

Density inhomogeneities are ubiquitous in space and astrophysical plasmas, in particular at contact boundaries between different media. They often correspond to regions that exhibits strong dynamics on a wide range of spatial and temporal scales. Indeed, density inhomogeneities are a source of free energy that can drive various instabilities such as, for instance, the lower-hybrid-drift instability which in turn transfers energy to the particles through wave-particle interactions and eventually heat the plasma. We aim at quantifying the efficiency of the lower-hybrid-drift instability to accelerate and/or heat electrons parallel to the ambient magnetic field. We combine two complementary methods: full-kinetic and quasilinear models. We report self-consistent evidence of electron acceleration driven by the development of the lower-hybrid-drift instability using 3D-3V full-kinetic numerical simulations. The efficiency of the observed acceleration cannot be explained by standard quasilinear theory. For this reason, we develop an extended quasilinear model able to quantitatively predict the interaction between lower-hybrid fluctuations and electrons on long time scales, now in agreement with full-kinetic simulations results. Finally, we apply this new, extended quasilinear model to a specific inhomogeneous space plasma boundary: the magnetopause of Mercury, and we discuss our quantitative predictions of electron acceleration in support to future BepiColombo observations., Comment: Paper under evaluation for publication on the journal "Astronomy and Astrophysics"

Solar Orbiter was launched on February 10, 2020 with the purpose of investigating solar and heliospheric physics using a payload of instruments designed for both remote and in-situ sensing. Similar to the recently launched Parker Solar Probe, and unlike earlier missions, Solar Orbiter carries instruments designed to measure the low frequency DC electric fields. In this paper we assess the quality of the low-frequency DC electric field measured by the Radio and Plasma Waves instrument (RPW) on Solar Orbiter. In particular we investigate the possibility of using Solar Orbiter's DC electric and magnetic field data to estimate the solar wind speed. We use deHoffmann-Teller (HT) analysis based on measurements of the electric and magnetic fields to find the velocity of solar wind current sheets which minimizes a single component of the electric field. By comparing the HT velocity to proton velocity measured by the Proton and Alpha particle Sensor (PAS) we develop a simple model for the effective antenna length, $L_\text{eff}$ of the E-field probes. We then use the HT method to estimate the speed of the solar wind. Using the HT method, we find that the observed variations in $E_y$ are often in excellent agreement with the variations in the magnetic field. The magnitude of $E_y$, however, is uncertain due to the fact that the $L_\text{eff}$ depends on the plasma environment. We derive an empirical model relating $L_\text{eff}$ to the Debye length, which we can use to improve the estimate of $E_y$ and consequently the estimated solar wind speed. The low frequency electric field provided by RPW is of high quality. Using deHoffmann-Teller analysis, Solar Orbiter's magnetic and electric field measurements can be used to estimate the solar wind speed when plasma data is unavailable., Comment: 7 pages, 4 figures