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Wildfire catastrophes cause significant environmental degradation, human losses, and financial damage. To mitigate these severe impacts, early fire detection and warning systems are crucial. Current systems rely primarily on fixed CCTV cameras with a limited field of view, restricting their effectiveness in large outdoor environments. The fusion of intelligent fire detection with remote sensing improves coverage and mobility, enabling monitoring in remote and challenging areas. Existing approaches predominantly utilize convolutional neural networks and vision transformer models. While these architectures provide high accuracy in fire detection, their computational complexity limits real-time performance on edge devices such as UAVs. In our work, we present a lightweight fire detection model based on MobileViT-S, compressed through the distillation of knowledge from a stronger teacher model. The ablation study highlights the impact of a teacher model and the chosen distillation technique on the model's performance improvement. We generate activation map visualizations using Grad-CAM to confirm the model's ability to focus on relevant fire regions. The high accuracy and efficiency of the proposed model make it well-suited for deployment on satellites, UAVs, and IoT devices for effective fire detection. Experiments on common fire benchmarks demonstrate that our model suppresses the state-of-the-art model by 0.44%, 2.00% while maintaining a compact model size. Our model delivers the highest processing speed among existing works, achieving real-time performance on resource-constrained devices.

Space Protocol is applying the principles derived from MITRE and NIST's Supply Chain Traceability: Manufacturing Meta-Framework (NIST IR 8536) to a complex multi party system to achieve introspection, auditing, and replay of data and decisions that ultimately lead to a end decision. The core goal of decision traceability is to ensure transparency, accountability, and integrity within the WA system. This is accomplished by providing a clear, auditable path from the system's inputs all the way to the final decision. This traceability enables the system to track the various algorithms and data flows that have influenced a particular outcome.

Vision Large Language Models (VLMs) combine visual understanding with natural language processing, enabling tasks like image captioning, visual question answering, and video analysis. While VLMs show impressive capabilities across domains such as autonomous vehicles, smart surveillance, and healthcare, their deployment on resource-constrained edge devices remains challenging due to processing power, memory, and energy limitations. This survey explores recent advancements in optimizing VLMs for edge environments, focusing on model compression techniques, including pruning, quantization, knowledge distillation, and specialized hardware solutions that enhance efficiency. We provide a detailed discussion of efficient training and fine-tuning methods, edge deployment challenges, and privacy considerations. Additionally, we discuss the diverse applications of lightweight VLMs across healthcare, environmental monitoring, and autonomous systems, illustrating their growing impact. By highlighting key design strategies, current challenges, and offering recommendations for future directions, this survey aims to inspire further research into the practical deployment of VLMs, ultimately making advanced AI accessible in resource-limited settings.

Synergies between the {\em James Webb Space Telescope} ({\em JWST}) and the {\em Chandra} X-ray observatory have advanced the observational frontier by detecting a handful of active galactic nuclei (AGNs) beyond $z \sim$ 10. In particular, the recent discovery of a candidate $\rm 8 \times 10^7~M_{\odot}$ black hole (BH) in the galaxy GHZ9 at $z =$ 10.4 favors massive seed formation channels for these objects. Motivated by prospects for their detection in radio by recent studies, we estimate radio fluxes for GHZ9 and explore the possibility of their detection with the Square Kilometer Array (SKA) and next-generation Very Large Array (ngVLA). We find that ngVLA should be able to detect radio emission from GHZ9 for integration times as short as 1 hr while SKA will require integration times of up to 100 hr. We also find that radio emission from the BH can be distinguished from that due to H II regions and supernovae in its host galaxy. The detection of a few hundred nJy radio signal at frequencies $> 2$ GHz will be a smoking gun for the presence of a BH in GHZ9., Comment: To be published in MNRAS

The advent of the James Webb Space Telescope has revealed a wealth of new galaxies just a few hundred Myr after the Big Bang. Some of these galaxies exhibit unusual elemental abundances that are difficult to explain with stellar populations today. While Wolf-Rayet stars in multiple-burst populations, very massive or rapidly-rotating primordial stars, general relativistic explosions of metal-enriched supermassive stars, or the precursors of globular clusters can in principle account for the supersolar nitrogen to oxygen ratios in the galaxies GN-z11 and CEERS 1019, no known stars or supernovae can explain the far higher N/O ratio of 0.46 in GS 3073 at redshift $z =$ 5.55. Here we show that the extreme nitrogen abundances in GS 3073 can be produced by 1000 - 10,000 M$_{\odot}$ primordial (Pop III) stars. We find that these are the only candidates that can account for its large N/O ratios and its C/O and Ne/O ratios. GS 3073 is thus the first conclusive evidence in the fossil abundance record of the existence of supermassive Pop III stars at cosmic Dawn., Comment: 26 pages, 15 figures, submitted to Science

The unprecedented sensitivity of the \textit{James Webb Space Telescope} (\textit{JWST}) has revolutionized our understanding of the early universe. Among the most intriguing \textit{JWST} discoveries are red, very compact objects showing broad line emission features nicknamed as little red dots (LRDs). The discovery of LRDs has triggered great interest about their origin as either extremely starbursting galaxies or highly-obscured active galactic nuclei (AGN). Their exact nature still remains unknown. The goal of this work is to estimate the radio emission from LRDs and predict which radio surveys would detect them. To achieve these objectives, we employ the fundamental plane of black hole (BH) accretion to estimate radio emission from AGN and the stellar radio fluxes from their host galaxies. We assume a range of BH mass, X-ray luminosity ($\rm L_{X}$) and star formation rate (SFR) to bracket the likely properties of LRDs. Our findings suggest that BH radio fluxes from LRDs are 10-100 times higher than the stellar fluxes from their host galaxies, depending on BH mass, $\rm L_X$ and SFR. The detection of a $\sim$ 500 nJy signal above 2 GHz at $z \geq$ 5 or a $\sim$ 2000 nJy flux at $z =$ 3-4 would be a smoking gun for the presence of AGN provided that SFRs in the host galaxies are $\rm < 30~ M_{\odot} ~yr^{-1}$. We find that LRDs are most likely radio quiet AGN otherwise would have been already detected in the current radio surveys. Our findings suggest that LRDs can be detected with the upcoming radio observatories such as ngVLA and SKA with integration times of 10-100 hrs, respectively., Comment: Accepted to A&A letters

Dangerous surroundings and difficult-to-reach landscapes introduce significant complications for adequate disaster management and recuperation. These problems can be solved by engaging unmanned aerial vehicles (UAVs) provided with embedded platforms and optical sensors. In this work, we focus on enabling onboard aerial image processing to ensure proper and real-time disaster detection. Such a setting usually causes challenges due to the limited hardware resources of UAVs. However, privacy, connectivity, and latency issues can be avoided. We suggest a UAV-assisted edge framework for disaster detection, leveraging our proposed model optimized for onboard real-time aerial image classification. The optimization of the model is achieved using post-training quantization techniques. To address the limited number of disaster cases in existing benchmark datasets and therefore ensure real-world adoption of our model, we construct a novel dataset, DisasterEye, featuring disaster scenes captured by UAVs and individuals on-site. Experimental results reveal the efficacy of our model, reaching high accuracy with lowered inference latency and memory use on both traditional machines and resource-limited devices. This shows that the scalability and adaptability of our method make it a powerful solution for real-time disaster management on resource-constrained UAV platforms.

Primordial supernovae were the first, great nucleosynthetic engines in the Universe, forging the elements required for the later formation of planets and life. Here we show that planetesimals, the precursors of terrestrial planets, formed around low-mass stars in the debris of the first cosmic explosions 200 Myr after the Big Bang, before the first galaxies and far earlier than previously thought. A dense core in one of these explosions collapsed to a protoplanetary disk in which several Earth masses of planetesimals formed 0.46 - 1.66 AU from their parent 0.7 M$_{\odot}$ star, where equilibrium temperatures varied from 269 K to 186 K, in water mass fractions that were only a factor of a few less than in the Solar System today. Habitable worlds thus formed among the first generation of stars in the Universe, before the advent of the first galaxies., Comment: 32 pages, 15 figures, under review by Science

The development of explanations for scientific phenomena is essential in science assessment, but scoring student-written explanations remains challenging and resource-intensive. Large language models (LLMs) have shown promise in addressing this issue, particularly in alphabetic languages like English. However, their applicability to logographic languages is less explored. This study investigates the potential of fine-tuning ChatGPT, a leading LLM, to automatically score scientific explanations written in Chinese. Student responses to seven scientific explanation tasks were collected and automatically scored, with scoring accuracy examined in relation to reasoning complexity using the Kendall correlation. A qualitative analysis explored how linguistic features influenced scoring accuracy. The results show that domain-specific adaptation enables ChatGPT to score Chinese scientific explanations with accuracy. However, scoring accuracy correlates with reasoning complexity: a negative correlation for lower-level responses and a positive one for higher-level responses. The model overrates complex reasoning in low-level responses with intricate sentence structures and underrates high-level responses using concise causal reasoning. These correlations stem from linguistic features--simplicity and clarity enhance accuracy for lower-level responses, while comprehensiveness improves accuracy for higher-level ones. Simpler, shorter responses tend to score more accurately at lower levels, whereas longer, information-rich responses yield better accuracy at higher levels. These findings demonstrate the effectiveness of LLMs in automatic scoring within a Chinese context and emphasize the importance of linguistic features and reasoning complexity in fine-tuning scoring models for educational assessments.

Primordial (or Pop III) supernovae were the first nucleosynthetic engines in the Universe, forging the heavy elements required for the later formation of planets and life. Water, in particular, is thought to be crucial to the cosmic origins of life as we understand it, and recent models have shown that water can form in low-metallicity gas like that present at high redshifts. Here we present numerical simulations that show that the first water in the Universe formed in Pop III core-collapse and pair-instability supernovae at redshifts $z \sim$ 20. The primary sites of water production in these remnants are dense molecular cloud cores, which in some cases were enriched with primordial water to mass fractions that were only a factor of a few below those in the Solar System today. These dense, dusty cores are also likely candidates for protoplanetary disk formation. Besides revealing that a primary ingredient for life was already in place in the Universe 100 - 200 Myr after the Big Bang, our simulations show that water was likely a key constituent of the first galaxies., Comment: 21 pages, 12 figures, in final review with Nature Astronomy

This paper provides a comprehensive review of the design and implementation of automatically generated assessment reports (AutoRs) for formative use in K-12 Science, Technology, Engineering, and Mathematics (STEM) classrooms. With the increasing adoption of technology-enhanced assessments, there is a critical need for human-computer interactive tools that efficiently support the interpretation and application of assessment data by teachers. AutoRs are designed to provide synthesized, interpretable, and actionable insights into students' performance, learning progress, and areas for improvement. Guided by cognitive load theory, this study emphasizes the importance of reducing teachers' cognitive demands through user-centered and intuitive designs. It highlights the potential of diverse information presentation formats such as text, visual aids, and plots and advanced functionalities such as live and interactive features to enhance usability. However, the findings also reveal that many existing AutoRs fail to fully utilize these approaches, leading to high initial cognitive demands and limited engagement. This paper proposes a conceptual framework to inform the design, implementation, and evaluation of AutoRs, balancing the trade-offs between usability and functionality. The framework aims to address challenges in engaging teachers with technology-enhanced assessment results, facilitating data-driven decision-making, and providing personalized feedback to improve the teaching and learning process., Comment: Submitted to ETRD

The integration of Artificial Intelligence (AI) in education requires scalable and efficient frameworks that balance performance, adaptability, and cost. This paper addresses these needs by proposing a shared backbone model architecture enhanced with lightweight LoRA adapters for task-specific fine-tuning, targeting the automated scoring of student responses across 27 mutually exclusive tasks. By achieving competitive performance (average QWK of 0.848 compared to 0.888 for fully fine-tuned models) while reducing GPU memory consumption by 60% and inference latency by 40%, the framework demonstrates significant efficiency gains. This approach aligns with the workshops' focus on improving language models for educational tasks, creating responsible innovations for cost-sensitive deployment, and supporting educators by streamlining assessment workflows. The findings underscore the potential of scalable AI to enhance learning outcomes while maintaining fairness and transparency in automated scoring systems., Comment: Accepted by AAAI-iRAISE Workshop

The expansion of artificial intelligence (AI) applications has driven substantial investment in computational infrastructure, especially by cloud computing providers. Quantifying the energy footprint of this infrastructure requires models parameterized by the power demand of AI hardware during training. We empirically measured the instantaneous power draw of an 8-GPU NVIDIA H100 HGX node during the training of open-source image classifier (ResNet) and large-language models (Llama2-13b). The maximum observed power draw was approximately 8.4 kW, 18% lower than the manufacturer-rated 10.2 kW, even with GPUs near full utilization. Holding model architecture constant, increasing batch size from 512 to 4096 images for ResNet reduced total training energy consumption by a factor of 4. These findings can inform capacity planning for data center operators and energy use estimates by researchers. Future work will investigate the impact of cooling technology and carbon-aware scheduling on AI workload energy consumption.

This study evaluates the performance of OpenAI's o1-preview model in higher-order cognitive domains, including critical thinking, systematic thinking, computational thinking, data literacy, creative thinking, logical reasoning, and scientific reasoning. Using established benchmarks, we compared the o1-preview models's performance to human participants from diverse educational levels. o1-preview achieved a mean score of 24.33 on the Ennis-Weir Critical Thinking Essay Test (EWCTET), surpassing undergraduate (13.8) and postgraduate (18.39) participants (z = 1.60 and 0.90, respectively). In systematic thinking, it scored 46.1, SD = 4.12 on the Lake Urmia Vignette, significantly outperforming the human mean (20.08, SD = 8.13, z = 3.20). For data literacy, o1-preview scored 8.60, SD = 0.70 on Merk et al.'s "Use Data" dimension, compared to the human post-test mean of 4.17, SD = 2.02 (z = 2.19). On creative thinking tasks, the model achieved originality scores of 2.98, SD = 0.73, higher than the human mean of 1.74 (z = 0.71). In logical reasoning (LogiQA), it outperformed humans with average 90%, SD = 10% accuracy versus 86%, SD = 6.5% (z = 0.62). For scientific reasoning, it achieved near-perfect performance (mean = 0.99, SD = 0.12) on the TOSLS,, exceeding the highest human scores of 0.85, SD = 0.13 (z = 1.78). While o1-preview excelled in structured tasks, it showed limitations in problem-solving and adaptive reasoning. These results demonstrate the potential of AI to complement education in structured assessments but highlight the need for ethical oversight and refinement for broader applications.

Recent James Webb Space Telescope observations have unveiled that the first supermassive black holes (SMBHs) were in place at z $\geq$ 10, a few hundred Myrs after the Big Bang. These discoveries are providing strong constraints on the seeding of BHs and the nature of the first objects in the Universe. Here, we study the impact of the freeze-out electron fractions ($f_e$) at the end of the epoch of cosmic recombination on the formation of the first structures in the Universe. At $f_e$ below the current fiducial cosmic values of $\rm \sim 10^{-4}$, the baryonic collapse is delayed due to the lack of molecular hydrogen cooling until the host halo masses are increased by one to two orders of magnitude compared to the standard case and reach the atomic cooling limit. This results in an enhanced enclosed gas mass by more than an order of magnitude and higher inflow rates of up to $0.1~M_{\odot}/{yr}$. Such conditions are conducive to the formation of massive seed BHs with $\sim 10^{4}$ M$_{\odot}$. Our results reveal a new pathway for the formation of massive BH seeds which may naturally arise from free, Comment: Accepted for publication in MNRAS Letters, comments welcome

The Radio Neutrino Observatory in Greenland (RNO-G) is the first in-ice radio array in the northern hemisphere for the detection of ultra-high energy neutrinos via the coherent radio emission from neutrino-induced particle cascades within the ice. The array is currently in phased construction near Summit Station on the Greenland ice sheet, with 7~stations deployed during the first two boreal summer field seasons of 2021 and 2022. In this paper, we describe the installation and system design of these initial RNO-G stations, and discuss the performance of the array as of summer 2024.

In the distributed localization problem (DLP), n anonymous robots (agents) A0, A1, ..., A(n-1) begin at arbitrary positions p0, ..., p(n-1) in S, where S is a Euclidean space. The primary goal in DLP is for agents to reach a consensus on a unified coordinate system that accurately reflects the relative positions of all points, p0, ... , p(n-1), in S. Extensive research on DLP has primarily focused on the feasibility and complexity of achieving consensus when agents have limited access to inter-agent distances, often due to missing or imprecise data. In this paper, however, we examine a minimalist, computationally efficient model of distributed computing in which agents have access to all pairwise distances, if needed. Specifically, we introduce a novel variant of population protocols, referred to as the spatial population protocols model. In this variant each agent can memorise one or a fixed number of coordinates, and when agents A(i) and A(j) interact, they can not only exchange their current knowledge but also either determine the distance d(i,j) between them in S (distance query model) or obtain the vector v(i,j) spanning points p(i) and p(j) (vector query model). We examine three DLP scenarios: - Self-stabilising localisation protocol with distance queries We propose and analyse self-stabilising localisation protocol based on pairwise distance adjustment. We also discuss several hard instances in this scenario, and suggest possible improvements for the considered protocol, - Leader-based localisation protocol with distance queries We propose and analyse several leader-based protocols which stabilise in o(n) parallel time. These protocols rely on efficient solution to multi-contact epidemic, and - Self-stabilising localisation protocol with vector queries We propose and analyse superfast self-stabilising DLP protocol which stabilises in O(log n) parallel time.

This study investigates the use of generative AI and multi-agent systems to provide automatic feedback in educational contexts, particularly for student constructed responses in science assessments. The research addresses a key gap in the field by exploring how multi-agent systems, called AutoFeedback, can improve the quality of GenAI-generated feedback, overcoming known issues such as over-praise and over-inference that are common in single-agent large language models (LLMs). The study developed a multi-agent system consisting of two AI agents: one for generating feedback and another for validating and refining it. The system was tested on a dataset of 240 student responses, and its performance was compared to that of a single-agent LLM. Results showed that AutoFeedback significantly reduced the occurrence of over-praise and over-inference errors, providing more accurate and pedagogically sound feedback. The findings suggest that multi-agent systems can offer a more reliable solution for generating automated feedback in educational settings, highlighting their potential for scalable and personalized learning support. These results have important implications for educators and researchers seeking to leverage AI in formative assessments, offering a pathway to more effective feedback mechanisms that enhance student learning outcomes.

Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these models have extended their capabilities to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL). Foundational models such as the Generative Pre-trained Transformer (GPT) and LLaMA series have set strong baseline performances in various NL and PL tasks. Additionally, several models have been fine-tuned specifically for code generation, showing significant improvements in code-related applications. Both foundational and fine-tuned models are increasingly used in education, helping students write, debug, and understand code. We present a comprehensive systematic literature review to examine the impact of LLMs in computer science and computer engineering education. We analyze their effectiveness in enhancing the learning experience, supporting personalized education, and aiding educators in curriculum development. We address five research questions to uncover insights into how LLMs contribute to educational outcomes, identify challenges, and suggest directions for future research., Comment: Accepted at 56th ACM Technical Symposium on Computer Science Education (SIGCSE TS 2025)

As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, with significant potential to transform education and workforce development. This study evaluates OpenAI o1-preview's ability to perform higher-order cognitive tasks across 14 dimensions, including critical thinking, systems thinking, computational thinking, design thinking, metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning. We used validated instruments like the Ennis-Weir Critical Thinking Essay Test and the Biological Systems Thinking Test to compare the o1-preview's performance with human performance systematically. Our findings reveal that o1-preview outperforms humans in most categories, achieving 150% better results in systems thinking, computational thinking, data literacy, creative thinking, scientific reasoning, and abstract reasoning. However, compared to humans, it underperforms by around 25% in logical reasoning, critical thinking, and quantitative reasoning. In analogical reasoning, both o1-preview and humans achieved perfect scores. Despite these strengths, the o1-preview shows limitations in abstract reasoning, where human psychology students outperform it, highlighting the continued importance of human oversight in tasks requiring high-level abstraction. These results have significant educational implications, suggesting a shift toward developing human skills that complement AI, such as creativity, abstract reasoning, and critical thinking. This study emphasizes the transformative potential of AI in education and calls for a recalibration of educational goals, teaching methods, and curricula to align with an AI-driven world., Comment: An assessment of OpenAI o1-Preview for Higher Order Thinking in Education

Anterior ST-segment elevation myocardial infarction (STEMI) is associated with severe adverse remodeling and increased mortality rates. In this study, anterior STEMI patients were treated with primary percutaneous coronary intervention and then imaged with cardiac magnetic resonance 1-2 days later. The cine images were analyzed with computational feature-tracking algorithms to assess end-systolic circumferential (ESC) strain. It was found that contractile function is not only depressed in the infarct and border zone regions, but also in the remote myocardium. This is contrary to other types of infarctions where remote myocardium shows enhanced compensatory function. Quantifying ESC could help identify high risk patients., Comment: 8th International Conference on Computational and Mathematical Biomedical Engineering

In the modern economic landscape, integrating financial services with Financial Technology (FinTech) has become essential, particularly in stock trend analysis. This study addresses the gap in comprehending financial dynamics across diverse global economies by creating a structured financial dataset and proposing a cross-lingual Natural Language-based Financial Forecasting (NLFF) pipeline for comprehensive financial analysis. Utilizing sentiment analysis, Named Entity Recognition (NER), and semantic textual similarity, we conducted an analytical examination of news articles to extract, map, and visualize financial event timelines, uncovering the correlation between news events and stock market trends. Our method demonstrated a meaningful correlation between stock price movements and cross-linguistic news sentiments, validated by processing two-year cross-lingual news data on two prominent sectors of the Pakistan Stock Exchange. This study offers significant insights into key events, ensuring a substantial decision margin for investors through effective visualization and providing optimal investment opportunities.

Accessing and comprehending religious texts, particularly the Quran (the sacred scripture of Islam) and Ahadith (the corpus of the sayings or traditions of the Prophet Muhammad), in today's digital era necessitates efficient and accurate Question-Answering (QA) systems. Yet, the scarcity of QA systems tailored specifically to the detailed nature of inquiries about the Quranic Tafsir (explanation, interpretation, context of Quran for clarity) and Ahadith poses significant challenges. To address this gap, we introduce a comprehensive dataset meticulously crafted for QA purposes within the domain of Quranic Tafsir and Ahadith. This dataset comprises a robust collection of over 73,000 question-answer pairs, standing as the largest reported dataset in this specialized domain. Importantly, both questions and answers within the dataset are meticulously enriched with contextual information, serving as invaluable resources for training and evaluating tailored QA systems. However, while this paper highlights the dataset's contributions and establishes a benchmark for evaluating QA performance in the Quran and Ahadith domains, our subsequent human evaluation uncovered critical insights regarding the limitations of existing automatic evaluation techniques. The discrepancy between automatic evaluation metrics, such as ROUGE scores, and human assessments became apparent. The human evaluation indicated significant disparities: the model's verdict consistency with expert scholars ranged between 11% to 20%, while its contextual understanding spanned a broader spectrum of 50% to 90%. These findings underscore the necessity for evaluation techniques that capture the nuances and complexities inherent in understanding religious texts, surpassing the limitations of traditional automatic metrics.

The Radar Echo Telescope for Cosmic Rays (RET-CR), a pathfinder instrument for the radar echo method of ultrahigh energy (UHE) neutrino detection, was initially deployed near Summit Station, Greenland, in May 2023. After a 4 week commissioning period, 9 days of data were taken before the instrument went offline. In this article, we describe the instrument as it was deployed, and the initial performance of the detector. We show that the technical aspects of running a radar based particle cascade detector in the ice have been demonstrated. Analysis of the 2023 data informed improvements that were incorporated into the May-August 2024 deployment, which has just concluded at time of writing. Results from the 2024 run will be presented in forthcoming publications., Comment: To be submitted to PRD

An ongoing challenge for radio-based detectors of high-energy cosmic particles is the accurate description of radio signal propagation in natural non-uniform media. For radio signals originating from extensive air showers, the current state of the art simulations often implicitly assume straight-line signal propagation. This while the refraction due to a non-uniform atmosphere is expected to have an effect on the received signal and associated reconstruction that is currently not completely understood for the most inclined geometries. Here, we present a study regarding the validity of assuming straight-line signal propagation when simulating radio emission associated with very inclined air shower geometries. To this end, the calculation of the electric field based on the end-point formalism used in CoREAS was improved by use of tabulated ray tracing data. We find that including ray curvature effects into the end-point formalism calculation introduces changes of up to a few percent in fluence for frequencies up to 1.2 GHz and zenith angles up to 88{\deg}.

Deep learning plays a critical role in vision-based satellite pose estimation. However, the scarcity of real data from the space environment means that deep models need to be trained using synthetic data, which raises the Sim2Real domain gap problem. A major cause of the Sim2Real gap are novel lighting conditions encountered during test time. Event sensors have been shown to provide some robustness against lighting variations in vision-based pose estimation. However, challenging lighting conditions due to strong directional light can still cause undesirable effects in the output of commercial off-the-shelf event sensors, such as noisy/spurious events and inhomogeneous event densities on the object. Such effects are non-trivial to simulate in software, thus leading to Sim2Real gap in the event domain. To close the Sim2Real gap in event-based satellite pose estimation, the paper proposes a test-time self-supervision scheme with a certifier module. Self-supervision is enabled by an optimisation routine that aligns a dense point cloud of the predicted satellite pose with the event data to attempt to rectify the inaccurately estimated pose. The certifier attempts to verify the corrected pose, and only certified test-time inputs are backpropagated via implicit differentiation to refine the predicted landmarks, thus improving the pose estimates and closing the Sim2Real gap. Results show that the our method outperforms established test-time adaptation schemes., Comment: This work has been accepted for publication at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)

Trustless tracking of Resident Space Objects (RSOs) is crucial for Space Situational Awareness (SSA), especially during adverse situations. The importance of transparent SSA cannot be overstated, as it is vital for ensuring space safety and security. In an era where RSO location information can be easily manipulated, the risk of RSOs being used as weapons is a growing concern. The Tracking Data Message (TDM) is a standardized format for broadcasting RSO observations. However, the varying quality of observations from diverse sensors poses challenges to SSA reliability. While many countries operate space assets, relatively few have SSA capabilities, making it crucial to ensure the accuracy and reliability of the data. Current practices assume complete trust in the transmitting party, leaving SSA capabilities vulnerable to adversarial actions such as spoofing TDMs. This work introduces a trustless mechanism for TDM validation and verification using deep learning over blockchain. By leveraging the trustless nature of blockchain, our approach eliminates the need for a central authority, establishing consensus-based truth. We propose a state-of-the-art, transformer-based orbit propagator that outperforms traditional methods like SGP4, enabling cross-validation of multiple observations for a single RSO. This deep learning-based transformer model can be distributed over a blockchain, allowing interested parties to host a node that contains a part of the distributed deep learning model. Our system comprises decentralised observers and validators within a Proof of Stake (PoS) blockchain. Observers contribute TDM data along with a stake to ensure honesty, while validators run the propagation and validation algorithms. The system rewards observers for contributing verified TDMs and penalizes those submitting unverifiable data., Comment: Accepted for AMOS 2024

To detect ultra-high-energy neutrinos, experiments such as ARA and RNO-G target the radio emission these particles induce when cascading in the ice, using deep antennas in South Pole or in Greenland. One of the main backgrounds for such signals is the radio emission generated by cosmic-ray showers, either directly in the ice, or in the air and transmitted to the ice, which can both reach the deep antennas. The first detection of cosmic rays with deep antennas would thus validate this detection principle and allow us to calibrate the detectors. FAERIE, the Framework for the simulation of Air shower Emission of Radio for in-Ice Experiments, is a numerical tool that couples both CoREAS and GEANT4 Monte-Carlo codes to simulate the radio emission from cosmic-ray showers deep in the ice. Using this code, we will investigate cosmic-ray radio signatures and the possible implications on the design of a cosmic-ray veto., Comment: 8 pages, 5 figures. Proceeding of the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA)

Multi-robot coverage is crucial in numerous applications, including environmental monitoring, search and rescue operations, and precision agriculture. In modern applications, a multi-robot team must collaboratively explore unknown spatial fields in GPS-denied and extreme environments where global localization is unavailable. Coverage algorithms typically assume that the robot positions and the coverage environment are defined in a global reference frame. However, coordinating robot motion and ensuring coverage of the shared convex workspace without global localization is challenging. This paper proposes a novel anchor-oriented coverage (AOC) approach to generate dynamic localized Voronoi partitions based around a common anchor position. We further propose a consensus-based coordination algorithm that achieves agreement on the coverage workspace around the anchor in the robots' relative frames of reference. Through extensive simulations and real-world experiments, we demonstrate that the proposed anchor-oriented approach using localized Voronoi partitioning performs as well as the state-of-the-art coverage controller using GPS., Comment: Accepted in IROS 2024

Classification of different object surface material types can play a significant role in the decision-making algorithms for mobile robots and autonomous vehicles. RGB-based scene-level semantic segmentation has been well-addressed in the literature. However, improving material recognition using the depth modality and its integration with SLAM algorithms for 3D semantic mapping could unlock new potential benefits in the robotics perception pipeline. To this end, we propose a complementarity-aware deep learning approach for RGB-D-based material classification built on top of an object-oriented pipeline. The approach further integrates the ORB-SLAM2 method for 3D scene mapping with multiscale clustering of the detected material semantics in the point cloud map generated by the visual SLAM algorithm. Extensive experimental results with existing public datasets and newly contributed real-world robot datasets demonstrate a significant improvement in material classification and 3D clustering accuracy compared to state-of-the-art approaches for 3D semantic scene mapping., Comment: Accepted to IROS 2024

Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans are usually based on given grading rubrics, the methods by which LLMs assign scores remain largely unclear. It is also uncertain how closely AI's scoring process mirrors that of humans or if it adheres to the same grading criteria. To address this gap, this paper uncovers the grading rubrics that LLMs used to score students' written responses to science tasks and their alignment with human scores. We also examine whether enhancing the alignments can improve scoring accuracy. Specifically, we prompt LLMs to generate analytic rubrics that they use to assign scores and study the alignment gap with human grading rubrics. Based on a series of experiments with various configurations of LLM settings, we reveal a notable alignment gap between human and LLM graders. While LLMs can adapt quickly to scoring tasks, they often resort to shortcuts, bypassing deeper logical reasoning expected in human grading. We found that incorporating high-quality analytical rubrics designed to reflect human grading logic can mitigate this gap and enhance LLMs' scoring accuracy. These results underscore the need for a nuanced approach when applying LLMs in science education and highlight the importance of aligning LLM outputs with human expectations to ensure efficient and accurate automatic scoring., Comment: Accepted by Technology, Knowledge, and Learning (TKNL)