Daily Papers

NELIOTA is a new ESA activity launched at the National Observatory of Athens in February 2015 aiming to determine the distribution and frequency of small near-earth objects (NEOs) via lunar monitoring. The project involves upgrading the 1.2m Kryoneri telescope at the National Observatory of Athens, procuring two fast-frame cameras, and developing a software system, which will control the telescope and the cameras, process the images and automatically detect NEO impacts. NELIOTA will provide a web-based user interface, where the impact events will be reported and made available to the scientific community and the general public. The objective of this 3.5 year activity is to design, develop and implement a highly automated lunar monitoring system, which will conduct an observing campaign for 2 years in search of NEO impact flashes on the Moon. The impact events will be verified, characterised and reported. The 1.2m telescope will be capable of detecting flashes much fainter than current, small-aperture, lunar monitoring telescopes. NELIOTA is therefore expected to characterise the frequency and distribution of NEOs weighing as little as a few grams.

We present the technical specifications and first results of the ESA-funded, lunar monitoring project "NELIOTA" (NEO Lunar Impacts and Optical TrAnsients) at the National Observatory of Athens, which aims to determine the size-frequency distribution of small Near-Earth Objects (NEOs) via detection of impact flashes on the surface of the Moon. For the purposes of this project a twin camera instrument was specially designed and installed at the 1.2 m Kryoneri telescope utilizing the fast-frame capabilities of scientific Complementary Metal-Oxide Semiconductor detectors (sCMOS). The system provides a wide field-of-view (17.0' times 14.4') and simultaneous observations in two photometric bands (R and I), reaching limiting magnitudes of 18.7 mag in 10 sec in both bands at a 2.5 signal-to-noise level. This makes it a unique instrument that can be used for the detection of NEO impacts on the Moon, as well as for any astronomy projects that demand high-cadence multicolor observations. The wide field-of-view ensures that a large portion of the Moon is observed, while the simultaneous, high-cadence, monitoring in two photometric bands makes possible, for the first time, the determination of the temperatures of the impacts on the Moon's surface and the validation of the impact flashes from a single site. Considering the varying background level on the Moon's surface we demonstrate that the NELIOTA system can detect NEO impact flashes at a 2.5 signal-to-noise level of ~12.4 mag in the I-band and R-band for observations made at low lunar phases ~0.1. We report 31 NEO impact flashes detected during the first year of the NELIOTA campaign. The faintest flash was at 11.24 mag in the R-band (about two magnitudes fainter than ever observed before) at lunar phase 0.32. Our observations suggest a detection rate of 1.96 times 10^{-7} events km^{-2} h^{-1}.

We report the first scientific results from the NELIOTA (NEO Lunar Impacts and Optical TrAnsients) project, which has recently begun lunar monitoring observations with the 1.2-m Kryoneri telescope. NELIOTA aims to detect faint impact flashes produced by near-Earth meteoroids and asteroids and thereby help constrain the size-frequency distribution of near-Earth objects in the decimeter to meter range. The NELIOTA setup, consisting of two fast-frame cameras observing simultaneously in the R and I-bands, enables - for the first time - direct analytical calculation of the flash temperatures. We present the first 10 flashes detected, for which we find temperatures in the range ~1,600-3,100 K, in agreement with theoretical values. Two of these flashes were detected on multiple frames in both filters and therefore yield the first measurements of the temperature drop for lunar flashes. In addition, we compute the impactor masses, which range between ~100 g and ~50 kg.

We present results of the NELIOTA campaign for lunar impact flashes observed with the 1.2 m Kryoneri telescope. From August 2019 to August 2023, we report 113 validated and 70 suspected flashes. For the validated flashes, we calculate the physical parameters of the corresponding projectiles, the temperatures developed during the impacts, and the expected crater sizes. For the multiframe flashes we present light curves and thermal evolution plots. Using the whole sample of NELIOTA that encompasses 192 validated flashes in total from 2017, the statistics of the physical parameters of the meteoroids, the peak temperatures of the impacts and the expected crater sizes has been updated. Using this large sample, empirical relations correlating the luminous energies per photometric band were derived and used to roughly estimate the parameters of 92 suspected flashes of the NELIOTA archive. For a typical value of the luminous efficiency, we found that more than the 75% of the impacting meteoroids have masses between 1-200 g, radii between 0.5-3 cm and produced craters up to 3.5 m. 85% of the peak temperatures of the impacts range between 2000 and 4500 K. Statistics regarding the magnitude decline and the cooling rates of the multiframe flashes are also presented. The recalculation of the appearance frequency of meteoroids, lying within the aforementioned ranges of physical parameters, on the Moon yields that the total lunar surface is bombarded with 7.4 sporadic meteoroids/ hour and up to 12.6 meteoroids/hour when the Earth-Moon system passes through a strong meteoroid stream. By extrapolating these rates on Earth, the respective rates for various distances from its surface are calculated and used to estimate the probability of an impact of a meteoroid with a hypothetical infrastructure on the Moon, or with a satellite orbiting Earth for various impact surfaces and duration times of the missions.

The near-Earth asteroid 2024 YR4 -- a sim60 m rocky object that was once considered a potential Earth impactor -- has since been ruled out for Earth but retained a sim4.3% probability of striking the Moon in 2032. Such an impact, with equivalent kinetic energy of sim6.5 Mt TNT, is expected to produce a sim1 km crater on the Moon, and will be the most energetic lunar impact event ever recorded in human history. Despite the associated risk, this scenario offers a rare and valuable scientific opportunity. Using a hybrid framework combining Monte Carlo orbital propagation, smoothed particle hydrodynamics (SPH) impact modeling, and N-body ejecta dynamics, we evaluate the physical outcomes and propose the observation timelines of this rare event. Our results suggest an optical flash of visual magnitude from -2.5 to -3 lasting several minutes directly after the impact, followed by hours of infrared afterglow from sim2000 K molten rock cooling to a few hundred K. The associated seismic energy release would lead to a global-scale lunar reverberation (magnitude sim5.0) that can be detectable by modern seismometers. Furthermore, the impact would eject sim10^8 kg of debris that escapes the lunar gravity, with a small fraction reaching Earth to produce a lunar meteor outburst within 100 years. Finally, we integrate these results into a coordinated observation timeline, identifying the best detection windows for ground-based telescopes, lunar orbiters, and surface stations.

As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity. We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development.

Accurate image registration is critical for lunar exploration, enabling surface mapping, resource localization, and mission planning. Aligning data from diverse lunar sensors -- optical (e.g., Orbital High Resolution Camera, Narrow and Wide Angle Cameras), hyperspectral (Imaging Infrared Spectrometer), and radar (e.g., Dual-Frequency Synthetic Aperture Radar, Selene/Kaguya mission) -- is challenging due to differences in resolution, illumination, and sensor distortion. We evaluate five feature matching algorithms: SIFT, ASIFT, AKAZE, RIFT2, and SuperGlue (a deep learning-based matcher), using cross-modality image pairs from equatorial and polar regions. A preprocessing pipeline is proposed, including georeferencing, resolution alignment, intensity normalization, and enhancements like adaptive histogram equalization, principal component analysis, and shadow correction. SuperGlue consistently yields the lowest root mean square error and fastest runtimes. Classical methods such as SIFT and AKAZE perform well near the equator but degrade under polar lighting. The results highlight the importance of preprocessing and learning-based approaches for robust lunar image registration across diverse conditions.

Decades of orbital missions have produced multi-modal remote sensing data for the Moon, spanning optical imagery, spectroscopy, thermal emission, radar, gravity, and elemental composition. Yet these datasets remain fragmented across archives, and no benchmark exists for evaluating machine learning on lunar data. We introduce Moonstone, the first multi-modal foundation model benchmark for lunar remote sensing. Our contributions are: (1) a 28-channel, 128 pixels-per-degree (~237 m) global lunar pretraining dataset from seven instrument families across five missions, (2) MG-MAE, a modality-grouped masked autoencoder with per-group convolutional tokenizers, a shared Vision Transformer encoder, attention masking for missing modalities, coverage-adaptive masking for heterogeneous spatial coverage, and spectral continuity regularization for physically plausible reconstructions, and (3) a benchmark of six downstream tasks covering classification, regression, and segmentation. MG-MAE pretrained features outperform scratch baselines on all tasks and surpass both ImageNet-pretrained and vanilla MAE baselines by large margins. Data and code are available at https://huggingface.co/datasets/ayushprd/Moonstone and https://github.com/ayushprd/Moonstone .

Exploring high-latitude lunar regions presents an extremely challenging visual environment for robots. The low sunlight elevation angle and minimal light scattering result in a visual field dominated by a high dynamic range featuring long, dynamic shadows. Reproducing these conditions on Earth requires sophisticated simulators and specialized facilities. We introduce a unique dataset recorded at the LunaLab from the SnT - University of Luxembourg, an indoor test facility designed to replicate the optical characteristics of multiple lunar latitudes. Our dataset includes images, inertial measurements, and wheel odometry data from robots navigating seven distinct trajectories under multiple illumination scenarios, simulating high-latitude lunar conditions from dawn to nighttime with and without the aid of headlights, resulting in 88 distinct sequences containing a total of 1.3M images. Data was captured using a stereo RGB-inertial sensor, a monocular monochrome camera, and for the first time, a novel single-photon avalanche diode (SPAD) camera. We recorded both static and dynamic image sequences, with robots navigating at slow (5 cm/s) and fast (50 cm/s) speeds. All data is calibrated, synchronized, and timestamped, providing a valuable resource for validating perception tasks from vision-based autonomous navigation to scientific imaging for future lunar missions targeting high-latitude regions or those intended for robots operating across perceptually degraded environments. The dataset and all supplementary material can be accessed from and found at https://github.com/spaceuma/spice-hl3.

Solar radio bursts are signatures of energetic events associated with solar flares and coronal mass ejections and can interfere with terrestrial and space-based communication systems. Real-time automatic burst monitoring enables early warnings tens of minutes to hours before associated particles reach Earth and provides the basis for long-term statistical studies. The e-Callisto network is a worldwide system of solar radio spectrometers providing continuous observations, with its instruments collectively covering frequencies from approximately 20 MHz to 1 GHz. Burst detection and labeling currently rely largely on human experts, limiting scalability and real-time applicability due to hardware heterogeneity and low signal-to-noise ratios.

Earthquake monitoring is necessary to promptly identify the affected areas, the severity of the events, and, finally, to estimate damages and plan the actions needed for the restoration process. The use of seismic stations to monitor the strength and origin of earthquakes is limited when dealing with remote areas (we cannot have global capillary coverage). Identification and analysis of all affected areas is mandatory to support areas not monitored by traditional stations. Using social media images in crisis management has proven effective in various situations. However, they are still limited by the possibility of using communication infrastructures in case of an earthquake and by the presence of people in the area. Moreover, social media images and messages cannot be used to estimate the actual severity of earthquakes and their characteristics effectively. The employment of satellites to monitor changes around the globe grants the possibility of exploiting instrumentation that is not limited by the visible spectrum, the presence of land infrastructures, and people in the affected areas. In this work, we propose a new dataset composed of images taken from Sentinel-1 and a new series of tasks to help monitor earthquakes from a new detailed view. Coupled with the data, we provide a series of traditional machine learning and deep learning models as baselines to assess the effectiveness of ML-based models in earthquake analysis.

Synthetic Lunar Terrain (SLT) is an open dataset collected from an analogue test site for lunar missions, featuring synthetic craters in a high-contrast lighting setup. It includes several side-by-side captures from event-based and conventional RGB cameras, supplemented with a high-resolution 3D laser scan for depth estimation. The event-stream recorded from the neuromorphic vision sensor of the event-based camera is of particular interest as this emerging technology provides several unique advantages, such as high data rates, low energy consumption and resilience towards scenes of high dynamic range. SLT provides a solid foundation to analyse the limits of RGB-cameras and potential advantages or synergies in utilizing neuromorphic visions with the goal of enabling and improving lunar specific applications like rover navigation, landing in cratered environments or similar.

Mitigating methane emissions is the fastest way to stop global warming in the short-term and buy humanity time to decarbonise. Despite the demonstrated ability of remote sensing instruments to detect methane plumes, no system has been available to routinely monitor and act on these events. We present MARS-S2L, an automated AI-driven methane emitter monitoring system for Sentinel-2 and Landsat satellite imagery deployed operationally at the United Nations Environment Programme's International Methane Emissions Observatory. We compile a global dataset of thousands of super-emission events for training and evaluation, demonstrating that MARS-S2L can skillfully monitor emissions in a diverse range of regions globally, providing a 216% improvement in mean average precision over a current state-of-the-art detection method. Running this system operationally for six months has yielded 457 near-real-time detections in 22 different countries of which 62 have already been used to provide formal notifications to governments and stakeholders.

Lunar surface exploration increasingly relies on autonomous rovers that must perceive and traverse unstructured terrain under extreme illumination conditions. Near the lunar poles, low sun elevations produce long, high-contrast shadows that challenge stereo vision, terrain segmentation, and localization. Developing perception and navigation algorithms for these conditions requires large amounts of sensor data with accurate ground truth, which field campaigns and analog facilities can only provide at high cost and with limited variety. This paper presents MARE (Modular Autonomy and Rendering Environment), an open-source simulation suite for lunar rover perception and autonomy built on Unreal Engine 5 and ROS 2. MARE consists of three integrated components. Terrain Studio generates cratered and rocky lunar Digital Elevation Maps (DEMs) to build simulation scenes. A dedicated Unreal Engine project hosts two physics-based rovers with plugins for stereo, lidar, event, and Single-Photon Avalanche Diode (SPAD) cameras, streaming time-stamped sensor data and pixel-accurate ground truth. Lastly, a Guidance, Navigation, and Control (GNC) stack provides web-based mission control. Validation shows a mean Intersection over Union (IoU) of 0.79 between predicted and rendered shadows at low sun elevations, and stereo depth analysis verifies decreased pixel coverage in shadowed regions relative to sunlit terrain.

The Lunar Surface Electromagnetics Explorer 'LuSEE Night' is a low frequency radio astronomy experiment that will be delivered to the farside of the Moon by the NASA Commercial Lunar Payload Services (CLPS) program in late 2025 or early 2026. The payload system is being developed jointly by NASA and the US Department of Energy (DOE) and consists of a 4 channel, 50 MHz Nyquist baseband receiver system and 2 orthogonal sim6m tip-to-tip electric dipole antennas. LuSEE Night will enjoy standalone operations through the lunar night, without the electromagnetic interference (EMI) of an operating lander system and antipodal to our noisy home planet.

Deep-space crews cannot rely on real-time ground support for urgent off-nominal events. Initial alerts may underdetermine cause, while discriminating evidence may reside in crew observations or at locations that are unsafe, costly, or unavailable for crew inspection. We present an evidence-driven architecture for human-agent-robot teaming in Earth-independent anomaly triage. Agentic AI is treated as a stateful coordinator over bounded, inspectable services rather than as a fully autonomous vehicle controller. A triage state manager maintains hypotheses, evidence provenance, uncertainty, operational context, and tool status; a crew-facing embodied agent elicits observations and explains assessment changes; and a mobile robot acquires targeted, localized evidence. Typed interfaces separate dialogue and orchestration from monitoring, robot command, context retrieval, and safety-critical control. Two scenarios illustrate the architecture: a crewed deep-space mission based on an actual ISS ammonia false alarm, where suspected contamination restricts crew access, and a power-interface anomaly at a crewed lunar base, where robotic inspection distinguishes a local connector fault from other causes ambiguous in remote telemetry. Our main contribution is an authority-bounded closed evidence-loop architecture, exercised in a hardware-in-the-loop integration prototype using Reachy Mini and an Innate MARS mobile robot.

Despite the emergence of diffusion large language models (D-LLMs) as an alternative to autoregressive large language models (AR-LLMs), safety monitoring for D-LLMs remains largely unexplored. Unlike AR-LLMs, D-LLMs generate text through a multi-step denoising process, exposing intermediate hidden representations that may contain safety-relevant information unavailable in standard single-step monitoring setups. Motivated by the suitability of lightweight probes for always-on monitoring, we analyze which trajectory-level signals best indicate when such probes are likely to struggle. We find that the most informative signal is safety hesitation: intermediate hidden states repeatedly falling within a small margin of the probe's decision boundary. The number of such hesitation steps in D-LLM's trajectory predicts probe failure effectively, providing a proxy of sample difficulty. Building on this analysis, we propose D^2-Monitor, a bi-level safety monitor for D-LLMs. D^2-Monitor adopts a lightweight probe as an always-on monitor to jointly estimate hesitation and perform base classification. When the hesitation level exceeds a threshold, a more expressive but computationally heavier probe is activated. This dynamic routing mechanism allocates monitoring resources efficiently at test time. Evaluated on 3 datasets (WildguardMix, ToxicChat, OpenAI-Moderation) across 4 D-LLMs, D^2-Monitor achieves state-of-the-art performance with a compact parameter footprint (leq 0.85M parameters), and exhibits the best trade-off between effectiveness and efficiency relative to 8 baselines.

Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus automated data-driven anomaly detection algorithms have been emerging at a steady pace. However, there are no publicly available datasets of real satellite telemetry accompanied with the ground-truth annotations that could be used to train and verify anomaly detection supervised models. In this article, we address this research gap and introduce the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT -- a CubeSat mission which has been operated by the European Space Agency which has come to an end during the night of 22--23 May 2024 (CEST). The dataset is accompanied with the baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics which should be always calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way.

The measured neutron flux from the Moons south polar region shows evidence of locally enhanced hydrogen concentrations, likely in the form of water ice, within most permanently shadowed regions (PSR), poleward of 77 deg S latitude. Results are consistent with the original findings of Watson et al, 1961, which found that the PSRs cryogenic surfaces create exclusive conditions for the sequestration of water ice, due to their extremely low sublimation rates. Widespread PSR hydrogenation is demonstrated in several studies by showing that the contrasting PSR area distribution is being instrumentally blurred. The PSRs expected hydrogen observations are correlated by their area fraction of the fixed 30 km diameter footprint area of the Collimated Sensor for Epithermal Neutrons (CSETN), which is part of the Lunar Exploration Neutron Detector (LEND) onboard the Lunar Reconnaissance Orbiter (LRO). The correlation indicates that the PSRs are similarly hydrogenated, with an expected concentration = 0.27 wt%, relative to that of the anhydrous reference terrain (lower bounds). Hydrogen concentrations are demonstrated to be correlated to maximum temperature distributions within the basins of Haworth, Shoemaker and Faustini PSRs. Cabeus-1 PSR shows an anomalously enhanced hydrogen concentration indicating a second process contributes to its hydrogen budget. Results are consistent with ongoing processes that introduce volatiles to the surface including outgassing, solar wind production with regolith silicates, and mixing from small scale meteor impacts and diurnal temperature variation. We validate the bandpass filter used to subtract CSETNs detection of uncollimated neutrons with profiles of several PSRs neutron suppression before and after processing. Keywords: Moon, Epithermal Neutron, Hydrogen, Water, Ice, Volatiles, LRO, LEND, Diviner, LOLA

This paper contains the results from the first 30 months of the NELIOTA project for impacts of NEOs/meteoroids on the lunar surface. Our analysis on the statistics concerning the efficiency of the campaign and the parameters of the projectiles and the impacts is presented. The parameters of the lunar impact flashes based on simultaneous observations in two wavelength bands are used to estimate the distributions of the masses, sizes and frequency of the impactors. These statistics can be used both in space engineering and science. The photometric fluxes of the flashes are measured using aperture photometry and their apparent magnitudes are calculated using standard stars. Assuming that the flashes follow a black body law of irradiation, the temperatures can be derived analytically, while the parameters of the projectiles are estimated using fair assumptions on their velocity and luminous efficiency of the impacts. 79 lunar impact flashes have been observed with the 1.2 m Kryoneri telescope in Greece. The masses of the meteoroids range between 0.7 g and 8 kg and their respective sizes between 1-20 cm depending on their assumed density, impact velocity, and luminous efficiency. We find a strong correlation between the observed magnitudes of the flashes and the masses of the meteoroids. Moreover, an empirical relation between the emitted energies of each band has been derived allowing the estimation of the physical parameters of the meteoroids that produce low energy impact flashes. The NELIOTA project has so far the highest detection rate and the faintest limiting magnitude for lunar impacts compared to other ongoing programs. Based on the impact frequency distribution on Moon, we estimate that sporadic meteoroids with typical masses less than 100 g and sizes less than 5 cm enter the mesosphere of the Earth with a rate ~108 meteoroids/hr and also impact Moon with a rate of ~8 meteoroids/hr.

State-of-the-art space science missions increasingly rely on automation due to spacecraft complexity and the costs of human oversight. The high volume of data, including scientific and telemetry data, makes manual inspection challenging. Machine learning offers significant potential to meet these demands. The Euclid space telescope, in its survey phase since February 2024, exemplifies this shift. Euclid's success depends on accurate monitoring and interpretation of housekeeping telemetry and science-derived data. Thousands of telemetry parameters, monitored as time series, may or may not impact the quality of scientific data. These parameters have complex interdependencies, often due to physical relationships (e.g., proximity of temperature sensors). Optimising science operations requires careful anomaly detection and identification of hidden parameter states. Moreover, understanding the interactions between known anomalies and physical quantities is crucial yet complex, as related parameters may display anomalies with varied timing and intensity. We address these challenges by analysing temperature anomalies in Euclid's telemetry from February to August 2024, focusing on eleven temperature parameters and 35 covariates. We use a predictive XGBoost model to forecast temperatures based on historical values, detecting anomalies as deviations from predictions. A second XGBoost model predicts anomalies from covariates, capturing their relationships to temperature anomalies. We identify the top three anomalies per parameter and analyse their interactions with covariates using SHAP (Shapley Additive Explanations), enabling rapid, automated analysis of complex parameter relationships. Our method demonstrates how machine learning can enhance telemetry monitoring, offering scalable solutions for other missions with similar data challenges.

Given a stationary state-space model that relates a sequence of hidden states and corresponding measurements or observations, Bayesian filtering provides a principled statistical framework for inferring the posterior distribution of the current state given all measurements up to the present time. For example, the Apollo lunar module implemented a Kalman filter to infer its location from a sequence of earth-based radar measurements and land safely on the moon. To perform Bayesian filtering, we require a measurement model that describes the conditional distribution of each observation given state. The Kalman filter takes this measurement model to be linear, Gaussian. Here we show how a nonlinear, Gaussian approximation to the distribution of state given observation can be used in conjunction with Bayes' rule to build a nonlinear, non-Gaussian measurement model. The resulting approach, called the Discriminative Kalman Filter (DKF), retains fast closed-form updates for the posterior. We argue there are many cases where the distribution of state given measurement is better-approximated as Gaussian, especially when the dimensionality of measurements far exceeds that of states and the Bernstein-von Mises theorem applies. Online neural decoding for brain-computer interfaces provides a motivating example, where filtering incorporates increasingly detailed measurements of neural activity to provide users control over external devices. Within the BrainGate2 clinical trial, the DKF successfully enabled three volunteers with quadriplegia to control an on-screen cursor in real-time using mental imagery alone. Participant "T9" used the DKF to type out messages on a tablet PC.

It is foreseeable that the number of spacecraft will increase exponentially, ushering in an era dominated by satellite mega-constellations (SMC). This necessitates a focus on energy in space: spacecraft power systems (SPS), especially their health management (HM), given their role in power supply and high failure rates. Providing health management for dozens of SPS and for thousands of SPS represents two fundamentally different paradigms. Therefore, to adapt the health management in the SMC era, this work proposes a principle of aligning underlying capabilities (AUC principle) and develops SpaceHMchat, an open-source Human-AI collaboration (HAIC) framework for all-in-loop health management (AIL HM). SpaceHMchat serves across the entire loop of work condition recognition, anomaly detection, fault localization, and maintenance decision making, achieving goals such as conversational task completion, adaptive human-in-the-loop learning, personnel structure optimization, knowledge sharing, efficiency enhancement, as well as transparent reasoning and improved interpretability. Meanwhile, to validate this exploration, a hardware-realistic fault injection experimental platform is established, and its simulation model is built and open-sourced, both fully replicating the real SPS. The corresponding experimental results demonstrate that SpaceHMchat achieves excellent performance across 23 quantitative metrics, such as 100% conclusion accuracy in logical reasoning of work condition recognition, over 99% success rate in anomaly detection tool invocation, over 90% precision in fault localization, and knowledge base search time under 3 minutes in maintenance decision-making. Another contribution of this work is the release of the first-ever AIL HM dataset of SPS. This dataset contains four sub-datasets, involving 4 types of AIL HM sub-tasks, 17 types of faults, and over 700,000 timestamps.

Monitoring is an important aspect of safely deploying Large Language Models (LLMs). This paper examines activation probes for detecting ``high-stakes'' interactions -- where the text indicates that the interaction might lead to significant harm -- as a critical, yet underexplored, target for such monitoring. We evaluate several probe architectures trained on synthetic data, and find them to exhibit robust generalization to diverse, out-of-distribution, real-world data. Probes' performance is comparable to that of prompted or finetuned medium-sized LLM monitors, while offering computational savings of six orders-of-magnitude. These savings are enabled by reusing activations of the model that is being monitored. Our experiments also highlight the potential of building resource-aware hierarchical monitoring systems, where probes serve as an efficient initial filter and flag cases for more expensive downstream analysis. We release our novel synthetic dataset and the codebase at https://github.com/arrrlex/models-under-pressure.

Ground-based time-domain observatories require minute-by-minute, site-scale awareness of cloud cover, yet existing all-sky datasets are short, daylight-biased, or lack astrometric calibration. We present LenghuSky-8, an eight-year (2018-2025) all-sky imaging dataset from a premier astronomical site, comprising 429,620 512 times 512 frames with 81.2% night-time coverage, star-aware cloud masks, background masks, and per-pixel altitude-azimuth (Alt-Az) calibration. For robust cloud segmentation across day, night, and lunar phases, we train a linear probe on DINOv3 local features and obtain 93.3% pm 1.1% overall accuracy on a balanced, manually labeled set of 1,111 images. Using stellar astrometry, we map each pixel to local alt-az coordinates and measure calibration uncertainties of approximately 0.37 deg at zenith and approximately 1.34 deg at 30 deg altitude, sufficient for integration with telescope schedulers. Beyond segmentation, we introduce a short-horizon nowcasting benchmark over per-pixel three-class logits (sky/cloud/contamination) with four baselines: persistence (copying the last frame), optical flow, ConvLSTM, and VideoGPT. ConvLSTM performs best but yields only limited gains over persistence, underscoring the difficulty of near-term cloud evolution. We release the dataset, calibrations, and an open-source toolkit for loading, evaluation, and scheduler-ready alt-az maps to boost research in segmentation, nowcasting, and autonomous observatory operations.

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses; analyze and predict personalized adverse health outcomes; adapt and respond to newly accumulated data; and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space.

This paper presents EarthView, a comprehensive dataset specifically designed for self-supervision on remote sensing data, intended to enhance deep learning applications on Earth monitoring tasks. The dataset spans 15 tera pixels of global remote-sensing data, combining imagery from a diverse range of sources, including NEON, Sentinel, and a novel release of 1m spatial resolution data from Satellogic. Our dataset provides a wide spectrum of image data with varying resolutions, harnessed from different sensors and organized coherently into an accessible HuggingFace dataset in parquet format. This data spans five years, from 2017 to 2022. Accompanying the dataset, we introduce EarthMAE, a tailored Masked Autoencoder, developed to tackle the distinct challenges of remote sensing data. Trained in a self-supervised fashion, EarthMAE effectively processes different data modalities such as hyperspectral, multispectral, topographical data, segmentation maps, and temporal structure. This model helps us show that pre-training on Satellogic data improves performance on downstream tasks. While there is still a gap to fill in MAE for heterogeneous data, we regard this innovative combination of an expansive, diverse dataset and a versatile model adapted for self-supervised learning as a stride forward in deep learning for Earth monitoring.

Detecting oscillations in solar and stellar time series is complicated by non-stationary red noise and evolving background emission. Methods based on detrending and AR(1)-based wavelet analysis can introduce spurious periodicities and do not adequately describe time-dependent backgrounds. We develop a Bayesian approach that combines the continuous wavelet transform with MCMC sampling to infer a time-dependent background spectrum. The background is represented by a power-law plus white-noise component, with parameters allowed to vary smoothly in time, so that significance levels can be evaluated locally without explicit detrending. Tests with synthetic data show that injected oscillations are recovered reliably, while false detections are suppressed in pure-noise cases. Using a frequency-domain signal-to-noise ratio (S/N), we find that oscillations can be identified robustly when the S/N is greater than or equal to 2 under mixed noise conditions. The detectable period range is limited by wavelet resolution, from about 3-4 sampling intervals up to roughly one-quarter of the total duration. Application to GOES soft X-ray flare observations shows that the method isolates quasi-periodic oscillations with improved temporal localization compared to standard wavelet and Fourier-based approaches. Meanwhile, this behavior is consistent across a range of noise conditions and signal morphologies.

Machine learning has vast potential to improve anomaly detection in satellite telemetry which is a crucial task for spacecraft operations. This potential is currently hampered by a lack of comprehensible benchmarks for multivariate time series anomaly detection, especially for the challenging case of satellite telemetry. The European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry (ESA-ADB) aims to address this challenge and establish a new standard in the domain. It is a result of close cooperation between spacecraft operations engineers from the European Space Agency (ESA) and machine learning experts. The newly introduced ESA Anomalies Dataset contains annotated real-life telemetry from three different ESA missions, out of which two are included in ESA-ADB. Results of typical anomaly detection algorithms assessed in our novel hierarchical evaluation pipeline show that new approaches are necessary to address operators' needs. All elements of ESA-ADB are publicly available to ensure its full reproducibility.

Methane is a potent greenhouse gas, responsible for roughly 30\% of warming since pre-industrial times. A small number of large point sources account for a disproportionate share of emissions, creating an opportunity for substantial reductions by targeting relatively few sites. Detection and attribution of large emissions at scale for notification to asset owners remains challenging. Here, we introduce MARS-S2L, a machine learning model that detects methane emissions in publicly available multispectral satellite imagery. Trained on a manually curated dataset of over 80,000 images, the model provides high-resolution detections every two days, enabling facility-level attribution and identifying 78\% of plumes with an 8\% false positive rate at 697 previously unseen sites. Deployed operationally, MARS-S2L has issued 1,015 notifications to stakeholders in 20 countries, enabling verified, permanent mitigation of six persistent emitters, including a previously unknown site in Libya. These results demonstrate a scalable pathway from satellite detection to quantifiable methane mitigation.