Search

K-dwarf stars are promising targets in the exploration of potentially habitable planets. Their properties, falling between G and M dwarfs, provide an optimal trade-off between the prospect of habitability and ease of detection. The KOBE experiment is a blind-search survey exploiting this niche, monitoring the radial velocity of 50 late-type K-dwarf stars. It employs the CARMENES spectrograph, with an observational strategy designed to detect planets in the habitable zone of their system. In this work, we exploit the KOBE data set to characterize planetary signals in the K7V star HIP 5957 (KOBE-1) and to constrain the planetary population within its habitable zone. We used 82 CARMENES spectra over a time span of three years. We employed a GLS periodogram to search for significant periodic signals that would be compatible with Keplerian motion on KOBE-1. We carried out a model comparison within a Bayesian framework to ensure the significance of the planetary model over alternative configurations of lower complexity. We also inspected two available TESS sectors in search of planetary signals. We identified two signals: at 8.5d and 29.7d. We confirmed their planetary nature through ruling out other non-planetary configurations. Their minimum masses are 8.80+/-0.76ME and 12.4+/-1.1ME, corresponding to absolute masses within the planetary regime at a high certainty (>99.7%). By analyzing the sensitivity of the CARMENES time series to additional signals, we discarded planets above 8.5ME within the habitable zone. We identified a single transit-like feature in TESS, whose origin is still uncertain, but still compatible within 1sigma with a transit from planet c. We have explored future prospects for characterizing this system, concluding that nulling interferometry with the LIFE mission could be capable of directly imaging both planets and characterizing their atmospheres in future studies., Comment: 29 pages, 21 figures, 10 tables, published in Astronomy & Astrophysics

Large language models (LLMs), or foundation models (FMs), are pretrained transformers that coherently complete sentences auto-regressively. In this paper, we show that LLMs can control simplified space systems after some additional training, called fine-tuning. We look at relatively small language models, ranging between 7 and 13 billion parameters. We focus on four problems: a three-dimensional spring toy problem, low-thrust orbit transfer, low-thrust cislunar control, and powered descent guidance. The fine-tuned LLMs are capable of controlling systems by generating sufficiently accurate outputs that are multi-dimensional vectors with up to 10 significant digits. We show that for several problems the amount of data required to perform fine-tuning is smaller than what is generally required of traditional deep neural networks (DNNs), and that fine-tuned LLMs are good at generalizing outside of the training dataset. Further, the same LLM can be fine-tuned with data from different problems, with only minor performance degradation with respect to LLMs trained for a single application. This work is intended as a first step towards the development of a general space systems controller.

Neuromorphic engineering aims to incorporate the computational principles found in animal brains, into modern technological systems. Following this approach, in this work we propose a closed-loop neuromorphic control system for an event-based robotic arm. The proposed system consists of a shifted Winner-Take-All spiking network for interpolating a reference trajectory and a spiking comparator network responsible for controlling the flow continuity of the trajectory, which is fed back to the actual position of the robot. The comparator model is based on a differential position comparison neural network, which governs the execution of the next trajectory points to close the control loop between both components of the system. To evaluate the system, we implemented and deployed the model on a mixed-signal analog-digital neuromorphic platform, the DYNAP-SE2, to facilitate integration and communication with the ED-Scorbot robotic arm platform. Experimental results on one joint of the robot validate the use of this architecture and pave the way for future neuro-inspired control of the entire robot., Comment: 5 pages, 7 figures, conference, ISCAS 2025, accepted for publication, Spiking Neural Network

Synaptic delay parameterization of neural network models have remained largely unexplored but recent literature has been showing promising results, suggesting the delay parameterized models are simpler, smaller, sparser, and thus more energy efficient than similar performing (e.g. task accuracy) non-delay parameterized ones. We introduce Shared Circular Delay Queue (SCDQ), a novel hardware structure for supporting synaptic delays on digital neuromorphic accelerators. Our analysis and hardware results show that it scales better in terms of memory, than current commonly used approaches, and is more amortizable to algorithm-hardware co-optimizations, where in fact, memory scaling is modulated by model sparsity and not merely network size. Next to memory we also report performance on latency area and energy per inference., Comment: arXiv admin note: substantial text overlap with arXiv:2404.10597

This paper addresses a critical challenge in the high-speed passenger railway industry: designing effective dynamic pricing strategies in the context of competing and cooperating operators. To address this, a multi-agent reinforcement learning (MARL) framework based on a non-zero-sum Markov game is proposed, incorporating random utility models to capture passenger decision making. Unlike prior studies in areas such as energy, airlines, and mobile networks, dynamic pricing for railway systems using deep reinforcement learning has received limited attention. A key contribution of this paper is a parametrisable and versatile reinforcement learning simulator designed to model a variety of railway network configurations and demand patterns while enabling realistic, microscopic modelling of user behaviour, called RailPricing-RL. This environment supports the proposed MARL framework, which models heterogeneous agents competing to maximise individual profits while fostering cooperative behaviour to synchronise connecting services. Experimental results validate the framework, demonstrating how user preferences affect MARL performance and how pricing policies influence passenger choices, utility, and overall system dynamics. This study provides a foundation for advancing dynamic pricing strategies in railway systems, aligning profitability with system-wide efficiency, and supporting future research on optimising pricing policies., Comment: 37 pages, 5 figures

This paper explores the application of Vision-Language Models (VLMs) as operator agents in the space domain, focusing on both software and hardware operational paradigms. Building on advances in Large Language Models (LLMs) and their multimodal extensions, we investigate how VLMs can enhance autonomous control and decision-making in space missions. In the software context, we employ VLMs within the Kerbal Space Program Differential Games (KSPDG) simulation environment, enabling the agent to interpret visual screenshots of the graphical user interface to perform complex orbital maneuvers. In the hardware context, we integrate VLMs with robotic systems equipped with cameras to inspect and diagnose physical space objects, such as satellites. Our results demonstrate that VLMs can effectively process visual and textual data to generate contextually appropriate actions, competing with traditional methods and non-multimodal LLMs in simulation tasks, and showing promise in real-world applications., Comment: Updated version of the paper presented in 2025 AIAA SciTech. https://arc.aiaa.org/doi/10.2514/6.2025-1543

This work introduces Transformer-based Successive Convexification (T-SCvx), an extension of Transformer-based Powered Descent Guidance (T-PDG), generalizable for efficient six-degree-of-freedom (DoF) fuel-optimal powered descent trajectory generation. Our approach significantly enhances the sample efficiency and solution quality for nonconvex-powered descent guidance by employing a rotation invariant transformation of the sampled dataset. T-PDG was previously applied to the 3-DoF minimum fuel powered descent guidance problem, improving solution times by up to an order of magnitude compared to lossless convexification (LCvx). By learning to predict the set of tight or active constraints at the optimal control problem's solution, Transformer-based Successive Convexification (T-SCvx) creates the minimal reduced-size problem initialized with only the tight constraints, then uses the solution of this reduced problem to warm-start the direct optimization solver. 6-DoF powered descent guidance is known to be challenging to solve quickly and reliably due to the nonlinear and non-convex nature of the problem, the discretization scheme heavily influencing solution validity, and reference trajectory initialization determining algorithm convergence or divergence. Our contributions in this work address these challenges by extending T-PDG to learn the set of tight constraints for the successive convexification (SCvx) formulation of the 6-DoF powered descent guidance problem. In addition to reducing the problem size, feasible and locally optimal reference trajectories are also learned to facilitate convergence from the initial guess. T-SCvx enables onboard computation of real-time guidance trajectories, demonstrated by a 6-DoF Mars powered landing application problem., Comment: AIAA SCITECH 2025 Forum

Machine learning has demonstrated remarkable promise for solving the trajectory generation problem and in paving the way for online use of trajectory optimization for resource-constrained spacecraft. However, a key shortcoming in current machine learning-based methods for trajectory generation is that they require large datasets and even small changes to the original trajectory design requirements necessitate retraining new models to learn the parameter-to-solution mapping. In this work, we leverage compositional diffusion modeling to efficiently adapt out-of-distribution data and problem variations in a few-shot framework for 6 degree-of-freedom (DoF) powered descent trajectory generation. Unlike traditional deep learning methods that can only learn the underlying structure of one specific trajectory optimization problem, diffusion models are a powerful generative modeling framework that represents the solution as a probability density function (PDF) and this allows for the composition of PDFs encompassing a variety of trajectory design specifications and constraints. We demonstrate the capability of compositional diffusion models for inference-time 6 DoF minimum-fuel landing site selection and composable constraint representations. Using these samples as initial guesses for 6 DoF powered descent guidance enables dynamically feasible and computationally efficient trajectory generation., Comment: AIAA SCITECH 2025 Forum

Laser wakefield accelerator experiments have made enormous progress over the past $\sim 20$ years, but their promise to revolutionize high-energy particle sources is only beginning to be realized. To make the next step toward engineering LWFAs for different accelerator outcomes, we need more reliable and quantitative models to predict performance. Using the data from $>50$ published experiments, we estimate scalings and the performance envelope. We compare the observed scalings with several models in the literature. We find that the total beam energy (centroid energy times beam charge) scales almost linearly with laser energy, supporting the value of investment in progressively higher energy driver lasers. The dataset includes pulse durations from 8 to 160 fs, but only laser wavelengths of 800 nm and 1 \si{\micro\meter}, meaning we could not check proposed wavelength scalings for alternative laser technologies. As a benchmark next-generation case, the observed scalings suggest that achieving a 100-GeV LWFA stage will require a $\gtrsim 30$ PW laser operating at electron density $<10^{17}/$cm$^3$., Comment: 20 pages, 4 figures. Conference proceedings for Advanced Accelerator Concepts (AAC24) 21-26 July at NIU. Submitted to NIMA

This paper introduces low-complexity frequency-dependent (memory) linearizers designed to suppress nonlinear distortion in analog-to-digital interfaces. Two different linearizers are considered, based on nonlinearity models which correspond to sampling before and after the nonlinearity operations, respectively. The proposed linearizers are inspired by convolutional neural networks but have an order-of-magnitude lower implementation complexity compared to existing neural-network-based linearizer schemes. The proposed linearizers can also outperform the traditional parallel Hammerstein (as well as Wiener) linearizers even when the nonlinearities have been generated through a Hammerstein model. Further, a design procedure is proposed in which the linearizer parameters are obtained through matrix inversion. This eliminates the need for costly and time-consuming iterative nonconvex optimization which is traditionally associated with neural network training. The design effectively handles a wide range of wideband multi-tone signals and filtered white noise. Examples demonstrate significant signal-to-noise-and-distortion ratio (SNDR) improvements of some $20$--$30$ dB, as well as a lower implementation complexity than the Hammerstein linearizers.

Context: Machine Learning (ML) significantly impacts Software Engineering (SE), but studies mainly focus on practitioners, neglecting researchers. This overlooks practices and challenges in teaching, researching, or reviewing ML applications in SE. Objective: This study aims to contribute to the knowledge, about the synergy between ML and SE from the perspective of SE researchers, by providing insights into the practices followed when researching, teaching, and reviewing SE studies that apply ML. Method: We analyzed SE researchers familiar with ML or who authored SE articles using ML, along with the articles themselves. We examined practices, SE tasks addressed with ML, challenges faced, and reviewers' and educators' perspectives using grounded theory coding and qualitative analysis. Results: We found diverse practices focusing on data collection, model training, and evaluation. Some recommended practices (e.g., hyperparameter tuning) appeared in less than 20\% of literature. Common challenges involve data handling, model evaluation (incl. non-functional properties), and involving human expertise in evaluation. Hands-on activities are common in education, though traditional methods persist. Conclusion: Despite accepted practices in applying ML to SE, significant gaps remain. By enhancing guidelines, adopting diverse teaching methods, and emphasizing underrepresented practices, the SE community can bridge these gaps and advance the field., Comment: under review

The open quantum system eigenstate in the vicinity of low-energy decay channel may mimic its features, in particular the characteristic clustering properties of the decay channel. This generic mechanism of clusterization, the so-called mimicry mechanism of clusterization, is discussed here on example of the ground state wave function of $^8$Be. At higher excitation energies, when the density of states and reaction channels is high, the quantal aspects in the clusterization process disappear and the statistical mechanism of clusterization which is rooted in the Central Limit Theorem, begin to dominate., Comment: 6 pages, 5 figures

This study aims to explore the impact of expansion strategies and specific attributes of hotel establishments on the performance of international hotel chains, focusing on four key performance indicators: RevPAR, efficiency, occupancy, and asset turnover. Data were collected from 255 hotels across various international hotel chains, providing a comprehensive assessment of how different expansion strategies and hotel attributes influence performance. The research employs compositional data analysis (CoDA) to address the methodological limitations of traditional financial ratios in statistical analysis. The findings indicate that ownership-based expansion strategies result in higher operational performance, as measured by revenue per available room, but yield lower economic performance due to the high capital investment required. Non-ownership strategies, such as management contracts and franchising, show superior economic efficiency, offering more flexibility and reduced financial risk. This study contributes to the hospitality management literature by applying CoDA, a novel methodological approach in this field, to examine the performance of different hotel expansion strategies with a sound and more appropriate method. The insights provided can guide hotel managers and investors in making informed decisions to optimize both operational and economic performance., Comment: 21 pages

We report here the results obtained from a systematic optical photometric survey aimed at finding new compact binary millisecond pulsars (also known as "spiders"): the COmpact BInary PULsar SEarch (COBIPULSE). We acquired multi-band optical images over one year around $33$ unidentified Fermi-LAT sources, selected as pulsar candidates based on their curved GeV spectra and steady $\gamma$-ray emission. We present the discovery of four optical variables coinciding with the Fermi sources 3FGL J0737.2$-$3233, 3FGL J2117.6$+$3725 (two systems in this field) and 3FGL J2221.6$+$6507, which we propose as new candidate spider systems. Indeed, they all show optical flux modulation consistent with orbital periods of $0.3548(5) \ \mathrm{d}$, $0.25328(6) \ \mathrm{d}$, $0.441961(2) \ \mathrm{d}$, and $0.165(4) \ \mathrm{d}$, respectively, with amplitudes $\gtrsim 0.3 \ \mathrm{mag}$ and colors compatible with companion star temperatures of $5000$--$6000 \ \mathrm{K}$. These properties are consistent with the "redback" sub-class of spider pulsars. If confirmed as a millisecond pulsar, 3FGL J0737.2$-$3233 will be the closest known spider to Earth ($D=659_{-20}^{+16} \ \mathrm{pc}$, from Gaia-DR3 parallax). We searched and did not find any X-ray sources matching our four candidates, placing $3\sigma$ upper limits of $\sim10^{31}$--$10^{32} \ \mathrm{erg} \ \mathrm{s}^{-1}$ ($0.3$--$10 \ \mathrm{keV}$) on their soft X-ray luminosities. We also present and discuss other multi-wavelength information on our spider candidates, from infrared to X-rays., Comment: 38 pages, 93 figures, 4 tables. This paper has been accepted for publication in ApJ

We consider the $k$-dispersion generalized Benjamin-Ono ($k$-DGBO) equations. For nonlinearities with power $k \geq 4$, we establish local and global well-posedness results for the associated initial value problem (IVP) in both the critical and subcritical regimes, addressing sharp regularity in homogeneous and inhomogeneous Sobolev spaces. Additionally, our method enables the formulation of a scattering criterion and a scattering theory for small data. We also investigate the case $k = 3$ via frequency-restricted estimates, obtaining local well-posedness results for the IVP associated with the $3$-DGBO equation and generalizing the existing results in the literature for the whole subcritical range. For higher dispersion, these local results can be extended globally even for rough data, particularly for initial data in Sobolev spaces with negative indices. As a byproduct, we derive new nonlinear smoothing estimates.

The Calar Alto Void Integral-field Treasury surveY (CAVITY) is a legacy project aimed at characterising the population of galaxies inhabiting voids, which are the most under-dense regions of the cosmic web, located in the Local Universe. This paper describes the first public data release (DR1) of CAVITY, comprising science-grade optical data cubes for the initial 100 out of a total of $\sim$300 galaxies in the Local Universe ($0.005 < z < 0.050$). These data were acquired using the integral-field spectrograph PMAS/PPak mounted on the 3.5m telescope at the Calar Alto observatory. The DR1 galaxy sample encompasses diverse characteristics in the color-magnitude space, morphological type, stellar mass, and gas ionisation conditions, providing a rich resource for addressing key questions in galaxy evolution through spatially resolved spectroscopy. The galaxies in this study were observed with the low-resolution V500 set-up, spanning the wavelength range 3745-7500 \AA, with a spectral resolution of 6.0 \AA\ (FWHM). Here, we describe the data reduction and characteristics and data structure of the CAVITY datasets essential for their scientific utilisation, highlighting such concerns as vignetting effects, as well as the identification of bad pixels and management of spatially correlated noise. We also provide instructions for accessing the CAVITY datasets and associated ancillary data through the project's dedicated database., Comment: 17 pages, 9 figures, 5 tables. Accepted for publication in A&A (replacement after minor language edits)

Novel view synthesis (NVS) enables to generate new images of a scene or convert a set of 2D images into a comprehensive 3D model. In the context of Space Domain Awareness, since space is becoming increasingly congested, NVS can accurately map space objects and debris, improving the safety and efficiency of space operations. Similarly, in Rendezvous and Proximity Operations missions, 3D models can provide details about a target object's shape, size, and orientation, allowing for better planning and prediction of the target's behavior. In this work, we explore the generalization abilities of these reconstruction techniques, aiming to avoid the necessity of retraining for each new scene, by presenting a novel approach to 3D spacecraft reconstruction from single-view images, DreamSat, by fine-tuning the Zero123 XL, a state-of-the-art single-view reconstruction model, on a high-quality dataset of 190 high-quality spacecraft models and integrating it into the DreamGaussian framework. We demonstrate consistent improvements in reconstruction quality across multiple metrics, including Contrastive Language-Image Pretraining (CLIP) score (+0.33%), Peak Signal-to-Noise Ratio (PSNR) (+2.53%), Structural Similarity Index (SSIM) (+2.38%), and Learned Perceptual Image Patch Similarity (LPIPS) (+0.16%) on a test set of 30 previously unseen spacecraft images. Our method addresses the lack of domain-specific 3D reconstruction tools in the space industry by leveraging state-of-the-art diffusion models and 3D Gaussian splatting techniques. This approach maintains the efficiency of the DreamGaussian framework while enhancing the accuracy and detail of spacecraft reconstructions. The code for this work can be accessed on GitHub (https://github.com/ARCLab-MIT/space-nvs)., Comment: Presented at the 75th International Astronautical Congress, October 2024, Milan, Italy

This work introduces TrajDiffuser, a compositional diffusion-based flexible and concurrent trajectory generator for 6 degrees of freedom powered descent guidance. TrajDiffuser is a statistical model that learns the multi-modal distributions of a dataset of simulated optimal trajectories, each subject to only one or few constraints that may vary for different trajectories. During inference, the trajectory is generated simultaneously over time, providing stable long-horizon planning, and constraints can be composed together, increasing the model's generalizability and decreasing the training data required. The generated trajectory is then used to initialize an optimizer, increasing its robustness and speed., Comment: Full manuscript submitted to IEEE Aerospace 2025 on 4-Oct-2024

Compact binary millisecond pulsars (MSPs) with orbital periods $\lesssim1$d are key to understanding binary evolution involving massive neutron stars (NSs). Due to the ablation of the companion by the rapidly spinning pulsar, these systems are also known as spiders and categorized into two main branches: redbacks (RBs; companion mass in the range of 0.1 to 0.5\,\Msun) and black widows (BWs; companion mass $\lesssim$\,0.1\,\Msun). We present models of low- and intermediate-mass X-ray binaries and compare them with observations of Galactic spiders (including the presence or absence of hydrogen lines in their optical spectra), and we constrain and quantify the interaction between the pulsar and the companion. Using MESA, we created the allowed initial parameter space. For the first time in MESA, we also included the detailed evolution of the pulsar spin and modeled the irradiation of the companion by the pulsar wind. Efficient mass accretion onto the NS (at least $70\%$ of the mass transferred is accreted) with an X-ray irradiated disk followed by strong irradiation of the companion can explain most of the properties of the observed spiders. Our RB evolutionary tracks continue to the BW regime, connecting the two branches of spiders. Our models explain the lack of hydrogen in some observed BWs with ultra-light companions. During accretion induced spin up, the mass required to spin up an NS to sub-milliseconds is high enough to collapse it into a black hole. Finally, after analyzing the formation of RB-like spiders with giant companions and orbital periods of several days (huntsmen), we conclude that they are unlikely to produce super-massive NSs (maximum accreted mass $\lesssim$0.5M$_{\odot}$). Cannibalistic MSP binary formation depends heavily on the interplay between accretion onto the pulsar and pulsar wind irradiation., Comment: 19 pages, 13 figures, Accepted by A&A. Some plots and associated text was modified. Fixed typos and updated references. The referee's comments were addressed

The vast majority of the orbital population today is unobservable and untracked because of their small size. These lethal non-trackable objects will only become more numerous as more payloads and debris are launched into orbit and increase the collision rate. In this paper, the long-term effect of collisions is simulated with an efficient Monte-Carlo method to simulate the future LEO environment including lethal non-trackable objects, which is typically ignored due to the large computational resources required. The results show that simulations that do not incorporate lethal non-trackable debris would be omitting a large number of debilitating collisions with active payloads and catastrophic collisions to a smaller effect. This shows the importance of simulating small debris in the long-term evolution of the orbital population, which is often omitted in the literature. This increased debris population and consequentially the risk to orbital payloads diminishes as smaller lethal non-trackable objects are considered. An efficient and validated model is used to simulate these numerous small objects. Several future cases such as launches of registered megaconstellations, improved post-mission disposal rates and no-future launches are explored to understand the effect of the inclusion or exclusion of lethal non-trackable objects., Comment: 22 pages, 16 figures

Networked systems usually face different random uncertainties that make the performance of the least-squares (LS) linear filter decline significantly. For this reason, great attention has been paid to the search for other kinds of suboptimal estimators. Among them, the LS quadratic estimation approach has attracted considerable interest in the scientific community for its balance between computational complexity and estimation accuracy. When it comes to stochastic systems subject to different random uncertainties and deception attacks, the quadratic estimator design has not been deeply studied. In this paper, using covariance information, the LS quadratic filtering and fixed-point smoothing problems are addressed under the assumption that the measurements are perturbed by a time-correlated additive noise, as well as affected by random parameter matrices and exposed to random deception attacks. The use of random parameter matrices covers a wide range of common uncertainties and random failures, thus better reflecting the engineering reality. The signal and observation vectors are augmented by stacking the original vectors with their second-order Kronecker powers; then, the linear estimator of the original signal based on the augmented observations provides the required quadratic estimator. A simulation example illustrates the superiority of the proposed quadratic estimators over the conventional linear ones and the effect of the deception attacks on the estimation performance.

We present GTC-OSIRIS phase-resolved optical spectroscopy of three compact binary MSPs, or 'spiders': PSR J1048+2339, PSR J1810+1744, and (for the first time) PSR J1908+2105. For the companion in each system, the temperature is traced throughout its orbit, and radial velocities are measured. The radial velocities are found to vary with the absorption features used when measuring them, resulting in different radial velocity curve semi-amplitudes: for J1048 ($K_\mathrm{metals, red} = 344 \pm 4$ km s$^{-1}$, $K_\mathrm{metals, blue} = 372 \pm 3$ km s$^{-1}$) and, tentatively, for J1810 ($K_\mathrm{Balmer} = 448 \pm 19$ km s$^{-1}$, $K_\mathrm{metals} = 491 \pm 32$ km s$^{-1}$). With existing inclination constraints, this gives the neutron star (NS) and companion masses $M_\mathrm{NS} = 1.50 - 2.04$ $M_\odot$ and $M_2 = 0.32 - 0.40$ $M_\odot$ for J1048, and $M_\mathrm{NS} > 1.7$ $M_\odot$ and $M_2 = 0.05 - 0.08$ $M_\odot$ for J1810. For J1908, we find an upper limit of $K_2 < 32$ km s$^{-1}$, which constrains its mass ratio $q = M_2 / M_\mathrm{NS} > 0.55$ and inclination $i < 6.0^\circ$, revealing the previously misunderstood system to be the highest mass ratio, lowest inclination redback yet. This raises questions for the origins of its substantial radio eclipses. Additionally, we find evidence of asymmetric heating in J1048 and J1810, and signs of metal enrichment in J1908. We also explore the impact of inclination on spectroscopic temperatures, and demonstrate that the temperature measured at quadrature ($\phi = 0.25, 0.75$) is essentially independent of inclination, and thus can provide additional constraints on photometric modelling., Comment: 18 pages, 19 figures. This is a pre-copyedited, author-produced PDF of an article accepted for publication in MNRAS following peer review. The version of record is available online at: https://doi.org/10.1093/mnras/stae2728

We prove a sharp local existence result for the Schr\"odinger-Korteweg-de Vries system with initial data in $H^k(\mathbb{R})\times H^s(\mathbb{R})$. The proof is based on the concept of \textit{integrated-by-parts strong solution}, which generalizes the classical notion of strong solution, and on frequency-restricted estimates. Moreover, we extend the known global well-posedness result to regularities $k,s>1/2$., Comment: 19 pages

Recent trends are emerging in the use of Large Language Models (LLMs) as autonomous agents that take actions based on the content of the user text prompt. This study explores the use of fine-tuned Large Language Models (LLMs) for autonomous spacecraft control, using the Kerbal Space Program Differential Games suite (KSPDG) as a testing environment. Traditional Reinforcement Learning (RL) approaches face limitations in this domain due to insufficient simulation capabilities and data. By leveraging LLMs, specifically fine-tuning models like GPT-3.5 and LLaMA, we demonstrate how these models can effectively control spacecraft using language-based inputs and outputs. Our approach integrates real-time mission telemetry into textual prompts processed by the LLM, which then generate control actions via an agent. The results open a discussion about the potential of LLMs for space operations beyond their nominal use for text-related tasks. Future work aims to expand this methodology to other space control tasks and evaluate the performance of different LLM families. The code is available at this URL: \texttt{https://github.com/ARCLab-MIT/kspdg}., Comment: ESA SPAICE Conference 2024. arXiv admin note: text overlap with arXiv:2404.00413

The problem of finding the shortest path in a graph G(V, E) has been widely studied. However, in many applications it is necessary to compute an arbitrary number of them, k. Even though the problem has raised a lot of interest from different research communities and many applications of it are known, it has not been addressed to the same extent as the single shortest path problem. The best algorithm known for efficiently solving this task has a time complexity of O (|E| + |V|log{|V|}+k|V|)$ when computing paths in explicit form, and is based on best-first search. This paper introduces a new search algorithm with the same time complexity, which results from a natural evolution of A* thus, it preserves all its interesting properties, making it widely applicable to many different domains. Experiments in various testbeds show a significant improvement in performance over the state of the art, often by one or two orders of magnitude., Comment: 249 plots in 48 figures, and 81 tables. This is an extended version of the paper Linares L\'opez, Carlos and Herman, Ian. 2024. Evolving A* to Efficiently Solve the k Shortest-Path Problem. Proceedings of the European Conference on Artificial Intelligence (ECAI). To appear

This paper presents a novel approach to the analysis of spatial behavior distribution, utilizing weighted Voronoi diagrams. The objective is to map and understand how an experimental subject moves and spends time in various areas of a given space, thus identifying the areas of greatest behavioral interest. The technique entails the partitioning of the space into a grid, the designation of generator points, and the assignment of weights based on the time the subject spends in each region. The data analyzed were derived from multiple experimental sessions in which subjects were exposed to various conditions, including food deprivation, water deprivation, and combined deprivation and no deprivation. The aforementioned conditions resulted in the formation of clearly delineated spatial patterns. Weighted Voronoi diagrams provided a comprehensive and precise representation of these areas of interest, facilitating an in-depth examination of the evolution of behavioral patterns in diverse contexts, such as under different Motivational Operations. This tool offers a valuable perspective for the dynamic study of spatial behaviors in variable experimental settings., Comment: 10 pages, 3 figures

According to the World Health Organization (WHO), suicide was the fourth leading cause of death in the world for individuals aged 15 to 29 in 2019. Given the rapid increase in mental health issues, providing psychological support is both crucial and urgent. In this paper: (1) we propose S\'olo Esc\'uchame, the first open-source Spanish emotional assistance chatbot, based on LLaMA-2-7b-Chat. (2) We introduced the HEAR (Hispanic Emotional Accompaniment Responses) dataset, compiled from multiple English sources translated into Spanish, as well as generic data generated using ChatGPT-3.5-Turbo. Finally, (3) we propose an evaluation metric based on two semi-automatic assessment methods. Our system outperforms a range of state-of-the-art models in providing psychological assistance in Spanish. Our models and datasets are publicly available to facilitate reproducibility., Comment: Accepted at the 23rd Mexican International Conference on Artificial Intelligence (MICAI) 2024

This paper proposes a new method to propagate uncertainties undergoing nonlinear dynamics using the Koopman Operator (KO). Probability density functions are propagated directly using the Koopman approximation of the solution flow of the system, where the dynamics have been projected on a well-defined set of basis functions. The prediction technique is derived following both the analytical (Galerkin) and numerical (EDMD) derivation of the KO, and a least square reduction algorithm assures the recursivity of the proposed methodology., Comment: 27th Conference of Information Fusion ID 14

The Koopman Operator (KO) provides an analytical solution of dynamical systems in terms of orthogonal polynomials. This work exploits this representation to include the propagation of uncertainties, where the polynomials are modified to work with stochastic variables. Thus, a new uncertainty quantification technique is proposed, where the KO solution is expanded to include the prediction of central moments, up to an arbitrary order. The propagation of uncertainties is then expanded to develop a new filtering algorithm, where measurements are considered as additional observables in the KO mathematics. Numerical simulations in astrodynamics assess the accuracy and performance of the new methodologies., Comment: 2022 AAS/AIAA Astrodynamics Specialist Conference

The NASA Standard Break-Up Model models collisions and explosions in space, which identifies the future distribution of debris. Given a possible break-up event, this work analyses the threat posed by the generated debris in the orbit of an asset spacecraft. Using the Koopman Operator solution of the $J_2$ perturbed two-body dynamics, the family of all possible orbits that cross the asset's pathway is identified and parameterized according to their velocity. The threat level assessment of a collision onto the asset is estimated considering the intersection between the velocities of the break-up model and the Koopman transfer solutions., Comment: 28 pages

The companion to PSR J1622-0315, one of the most compact known redback millisecond pulsars, shows extremely low irradiation despite its short orbital period. We model this system to determine the binary parameters, combining optical observations from NTT in 2017 and NOT in 2022 with the binary modeling code ICARUS. We find a best-fit neutron star mass of $2.3 \pm 0.4\,\text{M}_\odot $, and a companion mass of $0.15 \pm 0.02\,\text{M}_\odot$. We detect for the first time low-level irradiation from asymmetry in the minima as well as a change in the asymmetry of the maxima of its light curves over five years. Using star spot models, we find better fits than those from symmetric direct heating models, with consistent orbital parameters. We discuss an alternative scenario where the changing asymmetry is produced by a variable intrabinary shock. In summary, we find that PSR J1622-0315 combines low irradiation with variable light curve asymmetry, and a relatively high neutron star mass., Comment: 18 pages, 11 figures

The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement., Comment: 90 pages, 22 figures, roadmap, neuromorphic

We consider solutions to the initial value problem associated to the intermediate long wave (ILW) equation. We establish persistence properties of the solution flow in weighted Sobolev spaces, and show that they are sharp. We also deal with the long time dynamics of large solutions to the ILW equation. Using virial techniques, we describe regions of space where the energy of the solution must decay to zero along sequences of times. Moreover, in the case of exterior regions, we prove complete decay for any sequence of times. The remaining regions not treated here are essentially the strong dispersion and soliton regions.

Recognizing the promise of natural language interfaces to databases, prior studies have emphasized the development of text-to-SQL systems. While substantial progress has been made in this field, existing research has concentrated on generating SQL statements from text queries. The broader challenge, however, lies in inferring new information about the returned data. Our research makes two major contributions to address this gap. First, we introduce a novel Internet-of-Things (IoT) text-to-SQL dataset comprising 10,985 text-SQL pairs and 239,398 rows of network traffic activity. The dataset contains additional query types limited in prior text-to-SQL datasets, notably temporal-related queries. Our dataset is sourced from a smart building's IoT ecosystem exploring sensor read and network traffic data. Second, our dataset allows two-stage processing, where the returned data (network traffic) from a generated SQL can be categorized as malicious or not. Our results show that joint training to query and infer information about the data can improve overall text-to-SQL performance, nearly matching substantially larger models. We also show that current large language models (e.g., GPT3.5) struggle to infer new information about returned data, thus our dataset provides a novel test bed for integrating complex domain-specific reasoning into LLMs.

In this work, we develop a comprehensive framework for F10.7, S10.7, M10.7, and Y10.7 solar driver forecasting with a time series Transformer (PatchTST). To ensure an equal representation of high and low levels of solar activity, we construct a custom loss function to weight samples based on the distance between the solar driver's historical distribution and the training set. The solar driver forecasting framework includes an 18-day lookback window and forecasts 6 days into the future. When benchmarked against the Space Environment Technologies (SET) dataset, our model consistently produces forecasts with a lower standard mean error in nearly all cases, with improved prediction accuracy during periods of high solar activity. All the code is available on Github https://github.com/ARCLab-MIT/sw-driver-forecaster., Comment: Short paper accepted for oral presentation at the SPAICE Conference 2024 (https://spaice.esa.int/)

Over the last ten years, the realm of Artificial Intelligence (AI) has experienced an explosion of revolutionary breakthroughs, transforming what seemed like a far-off dream into a reality that is now deeply embedded in our everyday lives. AI's widespread impact is revolutionizing virtually all aspects of human life, and software engineering (SE) is no exception. As we explore this changing landscape, we are faced with questions about what the future holds for SE and how AI will reshape the roles, duties, and methodologies within the field. The introduction of these groundbreaking technologies highlights the inevitable shift towards a new paradigm, suggesting a future where AI's capabilities may redefine the boundaries of SE, potentially even more than human input. In this paper, we aim at outlining the key elements that, based on our expertise, are vital for the smooth integration of AI into SE, all while preserving the intrinsic human creativity that has been the driving force behind the field. First, we provide a brief description of SE and AI evolution. Afterward, we delve into the intricate interplay between AI-driven automation and human innovation, exploring how these two components can work together to advance SE practices to new methods and standards.

Between May 10-12, 2024, Earth saw its largest geomagnetic storm in over 20 years. Since the last major storm in 2003, the population of satellites in low Earth orbit has surged following the commercialization of space services and the ongoing establishment of proliferated LEO constellations. In this note, we investigate the various impacts of the geomagnetic storm on satellite operations. A forecast performance assessment of the geomagnetic index ap shows that the magnitude and duration of the storm were poorly predicted, even one day in advance. Total mass density enhancements in the thermosphere are identified by tracking satellite drag decay characteristics. A history of two-line element (TLE) data from the entire NORAD catalog in LEO is used to observe large-scale trends. Better understanding how geomagnetic storms impact satellite operations is critical for maintaining satellite safety and ensuring long-term robust sustainability in LEO.