arXiv subjects

Lucas Lange

Publications and source records attributed to Lucas Lange.

11 recordsLinked to original sources

arXiv2026

The Physical Nature of Regolith on Icy Moons

Estimating surface properties such as porosity and grain sizes is key for planning lander missions and landing site selection on icy moons. However, spaceborne instruments do not measure the regolith properties directly: instead, they record proxy measurements such as thermal flux, which are then interpreted through modeling to estimate thermal inertia, porosity, grain size, etc. A striking conclusion from all thermal measurements that probed the uppermost surface (first millimeters) of icy moons is they all show an exceptionally low thermal inertia, ranging from 9 to 20 J.m-2.K-1.s-0.5. This value is orders of magnitude lower than that of bulk hexagonal water ice (2000 J.m-2.K-1.s-0.5) at these temperatures. We demonstrate that a regolith thermally dominated by hexagonal water ice may only achieve such thermal inertia through a combination of extremely high porosity (>80%), small grain radii (<1 mm), and an unconsolidated regolith (minimal contact area between grains), consistent with previous photometry and spectroscopy studies. For the Galilean moons, deeper thermal observations (>1 cm) have revealed higher thermal inertia (>~50 J.m-2.K-1.s-0.5), indicating that the regolith compacts over centimeter scales. Since gravity has no effect on compaction on such scale, we propose three formation scenarios to account for vertical layering: deposition cover, degradation by impactors, and temperature gradient metamorphism. We discuss how monodisperse grains can reach such extreme porosities and provide examples of experimental analogs that could best represent the regolith. We propose that high porosity regolith are favored on icy moons due to the adhesive nature of water ice and their low-gravity environment.

astro-ph.EP↗

arXiv2026

Thermal Segregation and Reddening in Europa's Double Ridges

Europa's double ridges often display lower albedo and redder color than their surroundings. Their unique topography may cause sublimation-driven darkening due to illumination and self-heating, a process known as thermal segregation. We apply an advanced 3D thermophysical model, including shadowing and self-heating through mutual exchange of radiation, to digital elevation models of double ridges at a range of latitudes and orientations. Results show that self-heating in ridge troughs can markedly increase temperatures and sublimation rates, with a difference in maximum trough temperatures of up to 20 K, which may have implications for detection of endogenic heat. Incorporating a simple exosphere model and assuming an initial 10% concentration of 1 $\mu$m non-ice particles, we find thermal segregation can produce reddening in the form of dark lag layers from the equator to the middle latitudes, but is generally negligible at 60 degrees or higher. Lag formation timescales in ridge troughs are 10 - 100 yr to produce an optically thick layer. Modeling suggests that low-albedo lag layer formation provides positive feedback, further increasing surface heating. These effects may also darken Europa's surface in areas surrounding the ridges. However, the net mass balance controlling sublimation and lag formation is highly sensitive to the global water exosphere density: values $\sim 10^{16}$ molec/m$^{2}$ produce reddening in the trough and ablation of $\sim1~\mu\mathrm{m~yr^{-1}}$ of material, while values $\sim10^{18}$ molec/m$^{2}$ result in net deposition of $\sim 10~\mu\mathrm{m~yr^{-1}}$. Model predictions of resulting low albedo material in double ridge troughs are provided, which can be tested with eventual data from Europa Clipper.

astro-ph.EP↗

arXiv2026

A Global High-Resolution Hydrological Model to Simulate the Dynamics of Surface Liquid Reservoirs: Application on Mars

Surface runoff shapes planetary landscapes, but global hydrological models often lack the resolution and flexibility to simulate dynamic surface water bodies beyond Earth. Recent studies of Mars have revealed abundant geological and mineralogical evidence for past surface water, including valley networks, crater lakes, deltas and possible ocean margins dating from late Noachian to early Hesperian times. These features suggest that early Mars experienced periods allowing liquid water stability, runoff and sediment transport. To investigate where surface water could accumulate and how it may have been redistributed, we developed a global high-resolution (km-scale) surface hydrological model. The model uses a pre-computed hydrological database that maps topographic depressions, their spillover points, hierarchical connections between basins, and lake volume-area-elevation relationships. This database approach greatly accelerates simulations by avoiding repeated geomorphic processing. The model dynamically forms, grows, merges and dries lakes and putative seas without prescribing fixed coastlines, by transferring water volumes between depressions according to their storage capacities and overflow rules. We explore model behavior over the present-day Mars' topography measured by MOLA (Mars Orbiter Laser Altimeter) topography for a range of evaporation rates (from 0.1 m/yr to 10 m/yr) and total water inventories expressed as Global Equivalent Layer (from 1 mGEL to 1000 mGEL). 48 Simulations are iterated to reach the steady state. The model outputs the extent and depth of surface water bodies and identifies main drainage pathways using overflow fluxes as runoff indicators. Results show a transition toward a contiguous northern ocean between low (1-10 m) GEL values and increasing concentration of water in northern lowlands and major impact basins at higher GEL.

astro-ph.EP↗

arXiv2026

Learning from Anonymized and Incomplete Tabular Data

User-driven privacy allows individuals to control whether and at what granularity their data is shared, leading to datasets that mix original, generalized, and missing values within the same records and attributes. While such representations are intuitive for privacy, they pose challenges for machine learning, which typically treats non-original values as new categories or as missing, thereby discarding generalization semantics. For learning from such tabular data, we propose novel data transformation strategies that account for heterogeneous anonymization and evaluate them alongside standard imputation and LLM-based approaches. We employ multiple datasets, privacy configurations, and deployment scenarios, demonstrating that our method reliably regains utility. Our results show that generalized values are preferable to pure suppression, that the best data preparation strategy depends on the scenario, and that consistent data representations are crucial for maintaining downstream utility. Overall, our findings highlight that effective learning is tied to the appropriate handling of anonymized values.

cs.LG↗

arXiv2025

On the Possibility of Melting Water Ice during the Recent Past of Mars: Application to the Formation of Gullies

The formation of gullies on Mars has often been attributed to the melting of (sub)surface water ice. However, melting-based hypotheses generally overlook key processes: (1) sublimation cooling by latent heat absorption, (2) the non-stability of ice where melting conditions can be reached, and (3) the particular microclimates of gullied slopes. Using state-of-the-art climate simulations, we reassess ice melting scenarios over the past four million years (obliquity <= 35{\deg}), beyond the estimated period of gully formation. We find that the melting of opaque water snow or ice at the surface of Mars is unlikely anywhere due to sublimation cooling, while (quasi-) stable subsurface ice is typically too deep to reach melting temperatures. We propose an alternative mechanism in which seasonal CO2 frost sublimation destabilizes the regolith and brings the underlying water ice close to the surface, allowing rapid heating. Even under these optimal conditions, melting requires unrealistic assumptions. Ice containing a small amount of dust could melt via a solid-state greenhouse effect, but both its possibility and frequency in Mars' recent past remain uncertain.

astro-ph.EP↗

arXiv2025

Federated Learning With Individualized Privacy Through Client Sampling

With growing concerns about user data collection, individualized privacy has emerged as a promising solution to balance protection and utility by accounting for diverse user privacy preferences. Instead of enforcing a uniform level of anonymization for all users, this approach allows individuals to choose privacy settings that align with their comfort levels. Building on this idea, we propose an adapted method for enabling Individualized Differential Privacy (IDP) in Federated Learning (FL) by handling clients according to their personal privacy preferences. By extending the SAMPLE algorithm from centralized settings to FL, we calculate client-specific sampling rates based on their heterogeneous privacy budgets and integrate them into a modified IDP-FedAvg algorithm. We test this method under realistic privacy distributions and multiple datasets. The experimental results demonstrate that our approach achieves clear improvements over uniform DP baselines, reducing the trade-off between privacy and utility. Compared to the alternative SCALE method in related work, which assigns differing noise scales to clients, our method performs notably better. However, challenges remain for complex tasks with non-i.i.d. data, primarily stemming from the constraints of the decentralized setting.

cs.LG↗

arXiv2024

Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning

Machine Learning (ML) is crucial in many sectors, including computer vision. However, ML models trained on sensitive data face security challenges, as they can be attacked and leak information. Privacy-Preserving Machine Learning (PPML) addresses this by using Differential Privacy (DP) to balance utility and privacy. This study identifies image dataset characteristics that affect the utility and vulnerability of private and non-private Convolutional Neural Network (CNN) models. Through analyzing multiple datasets and privacy budgets, we find that imbalanced datasets increase vulnerability in minority classes, but DP mitigates this issue. Datasets with fewer classes improve both model utility and privacy, while high entropy or low Fisher Discriminant Ratio (FDR) datasets deteriorate the utility-privacy trade-off. These insights offer valuable guidance for practitioners and researchers in estimating and optimizing the utility-privacy trade-off in image datasets, helping to inform data and privacy modifications for better outcomes based on dataset characteristics.

cs.LG↗

arXiv2024

Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detection

Smartwatch health sensor data are increasingly utilized in smart health applications and patient monitoring, including stress detection. However, such medical data often comprise sensitive personal information and are resource-intensive to acquire for research purposes. In response to this challenge, we introduce the privacy-aware synthetization of multi-sensor smartwatch health readings related to moments of stress, employing Generative Adversarial Networks (GANs) and Differential Privacy (DP) safeguards. Our method not only protects patient information but also enhances data availability for research. To ensure its usefulness, we test synthetic data from multiple GANs and employ different data enhancement strategies on an actual stress detection task. Our GAN-based augmentation methods demonstrate significant improvements in model performance, with private DP training scenarios observing an 11.90-15.48% increase in F1-score, while non-private training scenarios still see a 0.45% boost. These results underline the potential of differentially private synthetic data in optimizing utility-privacy trade-offs, especially with the limited availability of real training samples. Through rigorous quality assessments, we confirm the integrity and plausibility of our synthetic data, which, however, are significantly impacted when increasing privacy requirements.

cs.LG↗

arXiv2023

Slice it up: Unmasking User Identities in Smartwatch Health Data

Wearables are widely used for health data collection due to their availability and advanced sensors, enabling smart health applications like stress detection. However, the sensitivity of personal health data raises significant privacy concerns. While user de-identification by removing direct identifiers such as names and addresses is commonly employed to protect privacy, the data itself can still be exploited to re-identify individuals. We introduce a novel framework for similarity-based Dynamic Time Warping (DTW) re-identification attacks on time series health data. Using the WESAD dataset and two larger synthetic datasets, we demonstrate that even short segments of sensor data can achieve perfect re-identification with our Slicing-DTW-Attack. Our attack is independent of training data and computes similarity rankings in about 2 minutes for 10,000 subjects on a single CPU core. These findings highlight that de-identification alone is insufficient to protect privacy. As a defense, we show that adding random noise to the signals significantly reduces re-identification risk while only moderately affecting usability in stress detection tasks, offering a promising approach to balancing privacy and utility.

cs.CR↗

arXiv2023

Diurnal and Seasonal Mapping of Martian Ices With EMIRS

Condensation and sublimation of ices at the surface of the planet is a key part of both the Martian H$_2$O and CO$_2$ cycles, either from a seasonal or diurnal aspect. While most of the ice is located within the polar caps, surface frost is known to be formed during nighttime down to equatorial latitudes. Here, we use data from the Emirates Mars Infrared Spectrometer (EMIRS) onboard the Emirates Mars Mission (EMM) to monitor the diurnal and seasonal evolution of the ices at the surface of Mars over almost one Martian year. The unique local time coverage provided by the instrument allows us to observe the apparition of equatorial CO$_2$ frost in the second half of the Martian night around the equinoxes, to its sublimation at sunrise.

astro-ph.EP↗

arXiv2022

Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version)

Machine learning (ML) can help fight pandemics like COVID-19 by enabling rapid screening of large volumes of images. To perform data analysis while maintaining patient privacy, we create ML models that satisfy Differential Privacy (DP). Previous works exploring private COVID-19 models are in part based on small datasets, provide weaker or unclear privacy guarantees, and do not investigate practical privacy. We suggest improvements to address these open gaps. We account for inherent class imbalances and evaluate the utility-privacy trade-off more extensively and over stricter privacy budgets. Our evaluation is supported by empirically estimating practical privacy through black-box Membership Inference Attacks (MIAs). The introduced DP should help limit leakage threats posed by MIAs, and our practical analysis is the first to test this hypothesis on the COVID-19 classification task. Our results indicate that needed privacy levels might differ based on the task-dependent practical threat from MIAs. The results further suggest that with increasing DP guarantees, empirical privacy leakage only improves marginally, and DP therefore appears to have a limited impact on practical MIA defense. Our findings identify possibilities for better utility-privacy trade-offs, and we believe that empirical attack-specific privacy estimation can play a vital role in tuning for practical privacy.

cs.LG↗