arXiv:2511.15743v1 Announce Type: new 
Abstract: Operational forecasting of the ionosphere remains a critical space weather challenge due to sparse observations, complex coupling across geospatial layers, and a growing need for timely, accurate predictions that support Global Navigation Satellite System (GNSS), communications, aviation safety, as well as satellite operations. As part of the 2025 NASA Heliolab, we present a curated, open-access dataset that integrates diverse ionospheric and heliospheric measurements into a coherent, machine learning-ready structure, designed specifically to support next-generation forecasting models and address gaps in current operational frameworks. Our workflow integrates a large selection of data sources comprising Solar Dynamic Observatory data, solar irradiance indices (F10.7), solar wind parameters (velocity and interplanetary magnetic field), geomagnetic activity indices (Kp, AE, SYM-H), and NASA JPL's Global Ionospheric Maps of Total Electron Content (GIM-TEC). We also implement geospatially sparse data such as the TEC derived from the World-Wide GNSS Receiver Network and crowdsourced Android smartphone measurements. This novel heterogeneous dataset is temporally and spatially aligned into a single, modular data structure that supports both physical and data-driven modeling. Leveraging this dataset, we train and benchmark several spatiotemporal machine learning architectures for forecasting vertical TEC under both quiet and geomagnetically active conditions. This work presents an extensive dataset and modeling pipeline that enables exploration of not only ionospheric dynamics but also broader Sun-Earth interactions, supporting both scientific inquiry and operational forecasting efforts.

تم تطوير مجموعة بيانات جديدة لتعزيز نماذج التنبؤ بالطبقة الأيونوسفيرية، حيث تعالج التحديات في التنبؤ التشغيلي بسبب الملاحظات المحدودة والتفاعلات الجغرافية المعقدة. تتكامل هذه المجموعة المفتوحة والمنظمة مع قياسات أيونية وهليوسفيرية متنوعة، بما في ذلك بيانات من المرصد الشمسي الديناميكي وخرائط أيونية عالمية من ناسا JPL، لدعم نماذج التنبؤ من الجيل التالي الضرورية لنظم الملاحة عبر الأقمار الصناعية والاتصالات وسلامة الطيران.

Se ha desarrollado un nuevo conjunto de datos para mejorar los modelos de pronóstico ionosférico, abordando los desafíos en la previsión operativa debido a observaciones limitadas y complejas interacciones geoespaciales. Este conjunto de datos curado y de acceso abierto integra diversas mediciones ionosféricas y heliosféricas, incluyendo datos del Observatorio Solar Dinámico y los Mapas Ionosféricos Globales de la NASA JPL, para apoyar modelos de pronóstico de próxima generación esenciales para GNSS, comunicaciones y seguridad en la aviación.

Un nouveau jeu de données a été développé pour améliorer les modèles de prévision ionosphérique, répondant aux défis de la prévision opérationnelle en raison d'observations limitées et d'interactions géospatiales complexes. Ce jeu de données ouvert et organisé intègre diverses mesures ionosphériques et héliosphériques, y compris des données de l'Observatoire dynamique solaire et des cartes ionosphériques globales de la NASA JPL, pour soutenir les modèles de prévision de nouvelle génération essentiels pour le GNSS, les communications et la sécurité aérienne.

A new dataset has been developed to enhance ionospheric forecasting models, addressing challenges in operational forecasting due to limited observations and complex geospatial interactions. This curated, open-access dataset integrates various ionospheric and heliospheric measurements, including data from the Solar Dynamic Observatory and NASA JPL's Global Ionospheric Maps, to support next-generation forecasting models essential for GNSS, communications, and aviation safety.

Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric Forecasting Models

<p>The honour is in recognition of their collaboration on Dynamic Targeting, a technology enabling spacecraft to make AI-powered decisions from orbit. </p>
<p>Read more: <a rel="nofollow" href="https://www.siliconrepublic.com/innovation/ubotica-technologies-nasa-jpl-open-cosmos-spacenews-icon-award">Ubotica Technologies, NASA JPL and Open Cosmos win SpaceNews Icon Award</a></p>

حصلت Ubotica Technologies وNASA JPL وOpen Cosmos على جائزة SpaceNews Icon تقديراً لتعاونهم في تطوير تقنية Dynamic Targeting، التي تمكّن المركبات الفضائية من اتخاذ قرارات مدعومة بالذكاء الاصطناعي من المدار.

Ubotica Technologies, NASA JPL y Open Cosmos han sido galardonados con el SpaceNews Icon Award por su colaboración en Dynamic Targeting, una tecnología que permite a las naves espaciales tomar decisiones impulsadas por IA desde la órbita.

Ubotica Technologies, NASA JPL et Open Cosmos ont reçu le SpaceNews Icon Award pour leur collaboration sur le Dynamic Targeting, une technologie permettant aux engins spatiaux de prendre des décisions alimentées par l'IA depuis l'orbite.

Ubotica Technologies, NASA JPL, and Open Cosmos have been awarded the SpaceNews Icon Award for their collaboration on Dynamic Targeting, a technology that allows spacecraft to make AI-powered decisions from orbit.

Ubotica Technologies, NASA JPL and Open Cosmos win SpaceNews Icon Award

arXiv:2512.03257v1 Announce Type: cross 
Abstract: Rapid and accurate wildfire detection is crucial for emergency response and environmental management. In airborne and spaceborne missions, real-time algorithms must distinguish between no fire, active fire, and post-fire conditions, and estimate fire intensity. Multispectral and hyperspectral thermal imagers provide rich spectral information, but high data dimensionality and limited onboard resources make real-time processing challenging. As wildfires increase in frequency and severity, the need for low-latency and computationally efficient onboard detection methods is critical.
  We present a systematic evaluation of multiple deep learning architectures, including custom Convolutional Neural Networks (CNNs) and Transformer-based models, for multi-class fire classification. We also introduce PyroFocus, a two-stage pipeline that performs fire classification followed by fire radiative power (FRP) regression or segmentation to reduce inference time and computational cost for onboard deployment. Using data from NASA's MODIS/ASTER Airborne Simulator (MASTER), which is similar to a next-generation fire detection sensor, we compare accuracy, inference latency, and resource efficiency.
  Experimental results show that the proposed two-stage pipeline achieves strong trade-offs between speed and accuracy, demonstrating significant potential for real-time edge deployment in future wildfire monitoring missions.

تم تقديم PyroFocus كحل يعتمد على التعلم العميق للكشف عن حرائق الغابات في الوقت الحقيقي باستخدام صور الاستشعار عن بعد متعددة الأطياف. تهدف هذه الطريقة المبتكرة إلى تحسين دقة وسرعة تحديد ظروف الحريق، وهو أمر حاسم للاستجابة الفعالة للطوارئ وإدارة البيئة.

PyroFocus se ha presentado como una solución de aprendizaje profundo para la detección en tiempo real de incendios forestales utilizando imágenes de teledetección multiespectral. Este enfoque innovador tiene como objetivo mejorar la precisión y la velocidad en la identificación de condiciones de incendio, lo cual es crucial para una respuesta de emergencia efectiva y la gestión ambiental.

PyroFocus a été introduit comme une solution d'apprentissage profond pour la détection en temps réel des incendies de forêt à l'aide d'images de télédétection multispectrales. Cette approche innovante vise à améliorer l'exactitude et la rapidité d'identification des conditions d'incendie, ce qui est crucial pour une réponse d'urgence efficace et la gestion environnementale.

PyroFocus has been introduced as a deep learning solution for real-time wildfire detection using multispectral remote sensing imagery. This innovative approach aims to enhance the accuracy and speed of identifying fire conditions, which is critical for effective emergency response and environmental management.

PyroFocus: A Deep Learning Approach to Real-Time Wildfire Detection in Multispectral Remote Sensing Imagery

arXiv:2512.03729v1 Announce Type: cross 
Abstract: The US Naval Research Laboratory's (NRL's) Autonomous Planning In-space Assembly Reinforcement-learning free-flYer (APIARY) experiment pioneers the use of reinforcement learning (RL) for control of free-flying robots in the zero-gravity (zero-G) environment of space. On Tuesday, May 27th 2025 the APIARY team conducted the first ever, to our knowledge, RL control of a free-flyer in space using the NASA Astrobee robot on-board the International Space Station (ISS). A robust 6-degrees of freedom (DOF) control policy was trained using an actor-critic Proximal Policy Optimization (PPO) network within the NVIDIA Isaac Lab simulation environment, randomizing over goal poses and mass distributions to enhance robustness. This paper details the simulation testing, ground testing, and flight validation of this experiment. This on-orbit demonstration validates the transformative potential of RL for improving robotic autonomy, enabling rapid development and deployment (in minutes to hours) of tailored behaviors for space exploration, logistics, and real-time mission needs.

نجح مختبر الأبحاث البحرية الأمريكية في تجربة APIARY في إظهار أول تحكم باستخدام التعلم المعزز لروبوت طائر حر في الفضاء، باستخدام روبوت Astrobee التابع لناسا على متن محطة الفضاء الدولية في 27 مايو 2025. تضمنت هذه التجربة تدريب سياسة تحكم قوية في بيئة محاكاة، مما عزز قدرة الروبوت على العمل بشكل مستقل في ظروف انعدام الجاذبية.

El experimento APIARY del Laboratorio de Investigación Naval de EE. UU. demostró con éxito el primer control de un robot volador libre en el espacio mediante aprendizaje por refuerzo, utilizando el robot Astrobee de la NASA a bordo de la Estación Espacial Internacional el 27 de mayo de 2025. Este experimento implicó el entrenamiento de una política de control robusta en un entorno simulado, mejorando la capacidad del robot para operar de manera autónoma en condiciones de microgravedad.

L'expérience APIARY du Laboratoire de recherche naval des États-Unis a réussi à démontrer le premier contrôle par apprentissage par renforcement d'un robot volant libre dans l'espace, en utilisant le robot Astrobee de la NASA à bord de la Station spatiale internationale le 27 mai 2025. Cette expérience a impliqué l'entraînement d'une politique de contrôle robuste dans un environnement simulé, améliorant la capacité du robot à fonctionner de manière autonome en conditions de microgravité.

The US Naval Research Laboratory's APIARY experiment successfully demonstrated the first reinforcement learning control of a free-flying robot in space using the NASA Astrobee on the International Space Station on May 27, 2025. This experiment involved training a robust control policy in a simulated environment, enhancing the robot's ability to operate autonomously in zero-gravity conditions.

Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric Forecasting Models

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