The NASA InSight mission payload includes the Heat Flow and Physical Properties Package HP$^3$ to measure the surface heat flow. The package was designed to use a small penetrator -- nicknamed the mole -- to implement a string of temperature sensors in the soil to a depth of 5m. The mole itself is equipped with sensors to measure a thermal conductivity as it proceeds to depth. The heat flow would be calculated from the product of the temperature gradient and the thermal conductivity. To avoid the perturbation caused by annual surface temperature variations, the measurements would be taken at a depth between 3 m and 5 m. The mole was designed to penetrate cohesionless soil similar to Quartz sand which was expected to provide a good analogue material for Martian sand. The sand would provide friction to the buried mole hull to balance the remaining recoil of the mole hammer mechanism that drives the mole forward. Unfortunately, the mole did not penetrate more than a mole length of 40 cm. The failure to penetrate deeper was largely due to a few tens of centimeter thick cohesive duricrust that failed to provide the required friction. Although a suppressor mass and spring in the hammer mechanism absorbed much of the recoil, the available mass did not allow a system that would have eliminated the recoil. The mole penetrated to 40 cm depth benefiting from friction provided by springs in the support structure from which it was deployed. It was found in addition that the Martian soil provided unexpected levels of penetration resistance that would have motivated to designing a more powerful mole. It is concluded that more mass would have allowed to design a more robust system with little or no recoil, more energy of the mole hammer mechanism and a more massive support structure.

T. Spohn T. L. Hudson L. Witte T. Wippermann L. Wisniewski B. Kediziora C. Vrettos R. D. Lorenz M. Golombek R. Lichtenfeld M. Grott J. Knollenberg C. Krause C. Fantinati S. Nagihara J. Grygorczuk

Autoignition delay experiments were performed for n-butanol in a heated rapid compression machine. Experiments were performed at pressures of 15 and 30 bar, in the temperature range 650-900 K, and for equivalence ratios of 0.5, 1.0, and 2.0. Additionally, the initial fuel mole fraction and initial oxygen mole fraction were varied independently to determine the influence of each on ignition delay. Over the conditions studied, it was found that the reactivity of the mixture increased as equivalence ratio, initial fuel mole fraction or initial oxygen mole fraction increased. A non-linear correlation to the experimental data was performed and showed nearly second order dependence on the initial oxygen mole fraction and nearly first order dependence on initial fuel mole fraction and compressed pressure. This was the first study of the ignition of n-butanol in this temperature and pressure range, and contributes to a better understanding of the chemistry of this fuel under the conditions relevant to practical devices. Experimentally measured ignition delays were compared against the ignition delay computed from several reaction mechanisms in the literature. The agreement between experiments and simulations was found to be unsatisfactory. Sensitivity analysis was performed and indicated that the uncertainties of the rate constants of parent fuel decomposition reactions play a major role in causing the poor agreement. Further path analysis of the fuel decomposition reactions supported this conclusion and highlighted the particular importance of certain pathways. Based on these results, it was concluded that further investigation of the fuel decomposition, including speciation measurements, will be required.

Bryan W. Weber

The effects of oxygen mole fraction on the static properties of pressure-sensitive paint (PSP) were investigated. Sample coupon tests using a calibration chamber were conducted for polymer-based PSP (PHFIPM-PSP), polymer/ceramic PSP (PC-PSP), and anodized-aluminium PSP (AA-PSP). The oxygen mole fraction was set to be between 0.1-100% and the ambient pressure was set to be between 0.5-140 kPa. The localized Stern-Volmer coefficient $B_{\rm local}$ once increases and then decreases as the oxygen mole fraction increases. The value of $B_{\rm local}$ depends on both ambient pressure and oxygen mole fraction, but the effect of this parameter can be characterized as a function of the partial pressure of oxygen. The value of $B_{\rm local}$ of AA-PSP and PHFIPM-PSP, which are low-pressure type and relatively low-pressure type PSP, have a peak at the relatively low partial pressure of oxygen, and $B_{\rm local}$ of PC-PSP, which are atmospheric pressure type PSP, has a peak at the relatively high partial pressure of oxygen. The peak of the intensity change with respect to pressure fluctuation proportional to the ambient pressure $S_{\mathcal{PR}}$ appears at the lower partial pressure of oxygen than that of $B_{\rm local}$. This is because the intensity of PSP becomes quite low at the high partial pressure of oxygen even if $B_{\rm local}$ is higher. Hence, an optimal partial oxygen mole fraction exists depending on the type of PSP and ambient pressure range of the experiment, and its optimal value can be found based on the partial pressure of oxygen.

Tomohiro Okudera Takayuki Nagata Miku Kasai Yuji Saito Taku Nonomura Keisuke Asai

Avogadro's number is a count of a definite number of things and, therefore, must be an integer and not a floating point number. Arguments are given herein that this integer should be precisely 2E79 - that is, N_o = 2E79 = 6.04 462 909 107 318 607 353 088 E23; hence, the binary mole.

Joel M. Williams

This paper describes in terms of Artin-Schreier equations field extensions whose Galois group is isomorphic to any of the four non-cyclic groups of order $p^3$ or the ten non-Abelian groups of order $p^4$, $p$ an odd prime, over a field of characteristic $p$.

Grant Moles

We have developed 1-D photochemical and thermochemical kinetics and diffusion models for the transiting exoplanets HD 189733b and HD 209458b to study the effects of disequilibrium chemistry on the atmospheric composition of "hot Jupiters." Here we investigate the coupled chemistry of neutral carbon, hydrogen, oxygen, and nitrogen species, and we compare the model results with existing transit and eclipse observations. We find that the vertical profiles of molecular constituents are significantly affected by transport-induced quenching and photochemistry, particularly on cooler HD 189733b; however, the warmer stratospheric temperatures on HD 209458b can help maintain thermochemical equilibrium and reduce the effects of disequilibrium chemistry. For both planets, the methane and ammonia mole fractions are found to be enhanced over their equilibrium values at pressures of a few bar to less than a mbar due to transport-induced quenching, but CH4 and NH3 are photochemically removed at higher altitudes. Atomic species, unsaturated hydrocarbons (particularly C2H2), some nitriles (particularly HCN), and radicals like OH, CH3, and NH2 are enhanced overequilibrium predictions because of quenching and photochemistry. In contrast, CO, H2O, N2, and CO2 more closely follow their equilibrium profiles, except at pressures < 1 microbar, where CO, H2O, and N2 are photochemically destroyed and CO2 is produced before its eventual high-altitude destruction. The enhanced abundances of HCN, CH4, and NH3 in particular are expected to affect the spectral signatures and thermal profiles HD 189733b and other, relatively cool, close-in transiting exoplanets. We examine the sensitivity of our results to the assumed temperature structure and eddy diffusion coefficientss and discuss further observational consequences of these models.

Julianne I. Moses Channon Visscher Jonathan J. Fortney Adam P. Showman Nikole K. Lewis Caitlin A. Griffith Stephen J. Klippenstein Megan Shabram A. James Friedson Mark S. Marley Richard S. Freedman

Mixture of experts (MoE) models are a class of artificial neural networks that can be used for functional approximation and probabilistic modeling. An important class of MoE models is the class of mixture of linear experts (MoLE) models, where the expert functions map to real topological output spaces. There are a number of powerful approximation results regarding MoLE models, when the output space is univariate. These results guarantee the ability of MoLE mean functions to approximate arbitrary continuous functions, and MoLE models themselves to approximate arbitrary conditional probability density functions. We utilize and extend upon the univariate approximation results in order to prove a pair of useful results for situations where the output spaces are multivariate.

Hien D. Nguyen Faicel Chamroukhi Florence Forbes

This work proposes Autonomous Iterative Motion Learning (AI-MOLE), a method that enables systems with unknown, nonlinear dynamics to autonomously learn to solve reference tracking tasks. The method iteratively applies an input trajectory to the unknown dynamics, trains a Gaussian process model based on the experimental data, and utilizes the model to update the input trajectory until desired tracking performance is achieved. Unlike existing approaches, the proposed method determines necessary parameters automatically, i.e., AI-MOLE works plug-and-play and without manual parameter tuning. Furthermore, AI-MOLE only requires input/output information, but can also exploit available state information to accelerate learning. While other approaches are typically only validated in simulation or on a single real-world testbed using manually tuned parameters, we present the unprecedented result of validating the proposed method on three different real-world robots and a total of nine different reference tracking tasks without requiring any a priori model information or manual parameter tuning. Over all systems and tasks, AI-MOLE rapidly learns to track the references without requiring any manual parameter tuning at all, even if only input/output information is available.

Michael Meindl Simon Bachhuber Thomas Seel

Metadata extraction is essential for cataloging and preserving datasets, enabling effective research discovery and reproducibility, especially given the current exponential growth in scientific research. While Masader (Alyafeai et al.,2021) laid the groundwork for extracting a wide range of metadata attributes from Arabic NLP datasets' scholarly articles, it relies heavily on manual annotation. In this paper, we present MOLE, a framework that leverages Large Language Models (LLMs) to automatically extract metadata attributes from scientific papers covering datasets of languages other than Arabic. Our schema-driven methodology processes entire documents across multiple input formats and incorporates robust validation mechanisms for consistent output. Additionally, we introduce a new benchmark to evaluate the research progress on this task. Through systematic analysis of context length, few-shot learning, and web browsing integration, we demonstrate that modern LLMs show promising results in automating this task, highlighting the need for further future work improvements to ensure consistent and reliable performance. We release the code: https://github.com/IVUL-KAUST/MOLE and dataset: https://huggingface.co/datasets/IVUL-KAUST/MOLE for the research community.

Zaid Alyafeai Maged S. Al-Shaibani Bernard Ghanem

Large language models (LLMs) have demonstrated impressive capabilities in aiding developers with tasks like code comprehension, generation, and translation. Supporting multilingual programming -- i.e., coding tasks across multiple programming languages -- typically requires either (1) finetuning a single LLM across all programming languages, which is cost-efficient but sacrifices language-specific specialization and performance, or (2) finetuning separate LLMs for each programming language, which allows for specialization but is computationally expensive and storage-intensive due to the duplication of parameters. This paper introduces MoLE (Mix-of-Language-Experts), a novel architecture that balances efficiency and specialization for multilingual programming. MoLE is composed of a base model, a shared LoRA (low-rank adaptation) module, and a collection of language-specific LoRA modules. These modules are jointly optimized during the finetuning process, enabling effective knowledge sharing and specialization across programming languages. During inference, MoLE automatically routes to the language-specific LoRA module corresponding to the programming language of the code token being generated. Our experiments demonstrate that MoLE achieves greater parameter efficiency compared to training separate language-specific LoRAs, while outperforming a single shared LLM finetuned for all programming languages in terms of accuracy.

Yifan Zong Yuntian Deng Pengyu Nie