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Two-Stage Automatic Mass Balancing and Attitude Control for 3-DoF Spacecraft Attitude Simulators
Three-degree-of-freedom (3-DoF) air-bearing simulators are widely used for ground-based validation of spacecraft attitude determination and control systems. However, residual gravity torques caused by center-of-rotation to center-of-gravity (CR-CG) offsets can degrade simulation fidelity, particularly during coupled three-axis maneuvers. This paper presents a sequential coarse-to-fine automatic mass balancing framework, referred to as the Robust Automatic Mass Balancing Operator (RAMBO), designed to reduce residual CR-CG offsets under mechanical non-orthogonality and cross-axis coupling while maintaining a negative vertical offset for pendulum stability. The framework sequentially applies an active coarse stage (CRAMBO) for rapid offset reduction and a fine stage (FRAMBO) for precise CR-CG offset estimation using free-response motion data collected after platform reinitialization. To address the remaining offset-induced torque, a gravity-torque feedforward term is incorporated into a quaternion proportional-derivative (PD) controller. Hardware experiments during a coupled diagonal maneuver demonstrated that the estimated dynamic-equivalent CR-CG offset converged to approximately [0.08, 0.12, -27.62] μm. Based on the onboard VN-100 estimates, the integrated balancing and control framework maintained steady-state attitude errors within ±0.5° and angular velocity errors within ±0.4°/s for roll and ±0.2°/s for pitch and yaw. A secondary IMU comparison further provided a roll/pitch consistency check rather than an independent ground-truth validation. These results demonstrate the effectiveness of the proposed approach for safety-constrained three-axis attitude-control validation.
Copyright (c) 2026 Yong-Hwan Kim https://creativecommons.org/licenses/by/4.0
Boosting blister formation on EUV masks by hydrogen and water vapor
Abstract: Blister formation on extreme ultraviolet (EUV) masks by EUV-induced plasma irradiation is a problem that causes patterning errors. We investigated the blister formation under EUV-induced plasma containing hydrogen and water vapor through an accelerated mask lifetime test. Background: Interaction between hydrogen gas and EUV radiation generates hydrogen ions and radicals which react with contaminants. Hydrogen gas is introduced as a background gas in EUV scanners to remove contaminants, but EUV-induced hydrogen plasma forms blisters on EUV masks. Accelerated mask lifetime tests have reported on EUV-induced hydrogen plasma durability but the effect of residual water vapor on blister formation remains unclear. Aim: We evaluate the impact of water vapor on blister formation through EUV accelerated mask lifetime tests at NewSUBARU synchrotron radiation facility. Approach: The EUV intensity was in the range of approximately 20 to 45 W/cm², and hydrogen pressure was varied from 5 to 70 Pa with and without water vapor at around 10-3 Pa. The hydrogen pressures were controlled, and the EUV exposure dose was determined from the blister formation time in accelerated mask lifetime tests. The surface oxidation of the irradiated positions on the EUV masks was investigated by X-ray absorption spectroscopy (XAS), Rutherford backscattering spectrometry (RBS), and hydrogen forward scattering spectrometry (HFS). Results: The test revealed the dependence of blister formation on hydrogen pressure, both with and without water vapor. We observed enhanced blister formation and greater surface oxidation while applying water vapor, compared to introducing hydrogen alone on EUV masks. Conclusion: We clarify that the residual water vapor significantly affects the EUV dose and hydrogen pressure thresholds for blister formation under scanner-relevant conditions. The result provides insight into plasma-induced oxidation and contributes to mask process development.
Copyright (c) 2026 Naoki Hayase, Hayato Ishida, Wataru Nishida, Keishi Tsukiyama, Shinji Yamakawa, Tetsuo Harada https://creativecommons.org/licenses/by/4.0
Performance Analysis of a Blended Wing Body Aircraft Utilizing Hybrid-Electric Distributed Propulsion and Boundary Layer Ingestion
This research investigates a novel aircraft propulsion architecture integrating hybrid-electric power systems with Blended-Wing Body (BWB) airframes and Boundary-Layer Ingesting (BLI) distributed ducted fans to achieve substantial improvements in fuel efficiency and emissions reduction for commercial aviation. With traditional tube-and-wing configurations approaching their aerodynamic and thermodynamic efficiency limits, the study addresses critical inefficiencies in conventional aircraft, particularly wake and boundary-layer drag over the fuselage and aft-body, where externally mounted engines bypass low-energy airflow and leave significant propulsion efficiency unutilized. The proposed configuration employs a compact gas turbine generator coupled with lithium-ion battery systems to power multiple distributed electric ducted fans strategically positioned on the BWB’s aft body to ingest the low-momentum boundary layer. This approach synergistically combines the aerodynamic benefits of the BWB platform—including higher lift-to-drag ratios, reduced wetted area, and enhanced internal volume for energy storage—with the propulsive efficiency gains from BLI, which can reduce fuel burn by 5-10% by recovering wake losses. The methodology integrates computational fluid dynamics simulations, thermodynamic cycle analysis, electric propulsion system modelling, and mission-level performance assessments to evaluate the integrated system. Key technical innovations include optimized fan placement for boundary-layer ingestion effectiveness, electrical distribution network design to minimize resistive losses, and hybrid power management strategies balancing gas turbine and battery operation across flight phases. Unlike previous studies that examine these technologies in isolation, this work provides a comprehensive systems-level analysis incorporating aerodynamic-propulsion coupling, mass estimation, and crucially, redundancy and failure tolerance analysis for multi-fan configurations. Parametric studies explore the design space across variables including fan count (4-12), fan diameter (0.8-1.5 m), pressure ratio (1.2-1.4), and battery specific energy (200-350 Wh/kg) to identify optimal configurations. The analysis demonstrates that integrated BWB-BLI-hybrid-electric architectures represent one of the most promising pathways for sustainable aviation while maintaining near-term technological feasibility. Results are validated against NASA STARC-ABL and MIT D8 benchmarks, with performance metrics including mission fuel burn reduction, take-off gross weight, propulsive efficiency improvements, and system-level power saving coefficients. This study addresses critical research gaps in distributed BLI systems, hybrid-electric BWB integration, and operational robustness under fan-out scenarios, providing a rigorous foundation for future development of advanced commercial aircraft configurations that balance environmental performance with practical implementation constraints.
Copyright (c) 2026 Pakhee Sinha https://creativecommons.org/licenses/by/4.0
Design of English Language Educational Game for Elementary School Students Using ADDIE Approach
This study aims to develop a desktop-based English language educational game as a learning aid for fourth-grade elementary school students. Game-based media development was chosen because game-based learning can increase student engagement and is widely used to support English learning for children, especially in vocabulary aspects. This study uses the ADDIE model consisting of the stages of analysis, design, development, implementation, and evaluation, because this model is widely used for systematic development of learning media. The game was developed using Unity with the C# programming language and can be run offline on school laboratory computers. The material and questions are arranged based on the 4th grade English teaching module of the Merdeka Curriculum, while the book 'My Next Words Grade 4' is used as a supporting learning resource. The game consists of 12 levels and 120 questions with multiple choice, true/false, sentence construction, and finding incorrect word models. The product evaluation was conducted through teacher validation, Black Box Testing, and User Acceptance Test. The user acceptance evaluation involved 28 fourth-grade students of SD Negeri 2 Ngemplak, Sleman, Yogyakarta as participants. The User Acceptance Test result obtained a percentage of 83,93%, which was categorized as excellent.
Copyright (c) 2026 Kurniawan D. Irianto, Sekar Kinasih https://creativecommons.org/licenses/by/4.0
The Geometric Quadrature Method (GQM): A Singularity-Free Spatial Formulation for Constrained Motion on Arbitrary Planar Curves
We present the Geometric Quadrature Method (GQM), a coordinate-free, spatial-domain framework for the dynamics of a particle constrained to an arbitrary smooth planar curve. By adopting arc-length parametrization and the Frenet–Serret frame, GQM eliminates the coordinate singularities inherent in Cartesian systems, which fail at vertical tangents or multi-valued trajectory regions. The method operates as a three-pass pipeline. Pass 1 delivers the spatial speed field and normal contact force in exact closed form for any curve with an analytic height function—no quadrature, no approximation, including the full rail loading map and liftoff conditions. Pass 2 confines the time-of-flight computation, which is irreducibly non-elementary by Liouville’s theorem, to a single adaptive quadrature evaluated to machine precision. Pass 3 recovers phase-stable long-time trajectories for conservative systems by exact half-period tiling, bounding accumulated phase error many orders of magnitude below standard explicit or symplectic ODE integrators. The pipeline is anchored on a speed-field kernel encoding the force model in modular form: redefining the kernel alone accommodates friction, quadratic drag, and rotating-frame potentials, leaving the quadrature pipeline intact. Six such kernels are derived. Numerical validation includes a simple pendulum benchmarked against RK4 and Störmer–Verlet integrators, and a cubic curve with an inflection point and Coulomb friction, demonstrating that the continuously signed Frenet frame handles curvature sign changes without frame-flip artefacts. A unified table reduces eight standard curve families to this single framework.
Copyright (c) 2026 Zaki Harari https://creativecommons.org/licenses/by/4.0
Power Energy Savings in the Oil & Gas Drilling Sector
The oil and gas drilling sector which considered a cornerstone of the global energy supply, faces increasing pressure to enhance operational efficiency and reduce its environmental footprint. Recognizing this, the industry is increasingly exploring and implementing diverse technologies and strategies aimed at achieving meaningful power energy savings. This report provides a comprehensive examination of these efforts, drawing upon a series of detailed case study from various operational contexts within the oil and gas drilling sector. The imperative for energy savings in drilling is driven by multiple factors, including volatile energy prices, regulatory pressures, corporate sustainability goals, and the economic benefits of reduced operational expenditures. Efficient energy use not only lowers costs but also enhances energy security, conserves valuable hydrocarbon resources for higher-value applications, and contributes to mitigating climate change impacts. This document delves into the practical application of energy-saving measures, showcasing real-world examples of how operators are tackling energy consumption challenges.
Copyright (c) 2026 Ahmed Hassaan Ali Ibrahim https://creativecommons.org/licenses/by/4.0
FRAME: Framework for Robotic Action and Motion Evaluation
The rapid emergence of Vision-Language-Action (VLA) models has fundamentally shifted robotics toward end-to-end, generalist architectures capable of complex semantic reasoning. However, the lack of a unified evaluation standard remains a critical bottleneck, as research often relies on disparate metrics that fail to bridge the gap between offline action accuracy and online physical deployment. We introduce FRAME, a comprehensive, open-source evaluation framework built natively on TorchMetrics to facilitate reproducible and scalable robot policy research. FRAME provides a modular taxonomy of 15+ standardized metrics across four critical dimensions: Task Performance, Trajectory Quality, Safety, and Efficiency. Safety metrics (collision rate, obstacle proximity, risk factor) are implemented in the library; their deployment requires contact or proximity sensing signals, which we leave to future hardware-instrumented experiments. We demonstrate the utility of FRAME through an empirical study of four pre-trained policies (Diffusion Policy, VQ-BeT, and ACT) on the PushT manipulation task, and a physical robot evaluation of SmolVLA and pi0 on the SO-101 manipulator. Our analysis reveals significant discrepancies between traditional success rates and trajectory-level quality, highlighting critical failure modes that binary metrics alone cannot capture. By providing interpretable process metrics and trace-level diagnostics, FRAME enables a more nuanced understanding of robot policy performance and establishes a common language for identifying and mitigating failure modes in embodied AI.
Copyright (c) 2026 Ameya Wagh, Vishnu Rudrasamudram https://creativecommons.org/licenses/by/4.0
Momentum-Guided Semantic Forecasting (MoFore) for Self-Supervised Video Representation Learning
Self-supervised video representation learning has recently advanced through con- trastive learning, masked reconstruction, and predictive representation learning. Reconstruction-based approaches such as MAE and VideoMAE learn representa- tions by recovering masked visual content [1, 2], while contrastive methods such as CLIP learn semantically meaningful embedding spaces through representation alignment [3]. In this work, we introduce a Momentum-Guided Semantic Forecasting framework (MoFore) for self-supervised video representation learning. Instead of optimizing for pixel-level reconstruction or task-specific semantic alignment, the proposed method learns temporally predictive video representations by forecasting future latent embeddings from temporally distant context clips. To improve robustness across temporal scales, we further introduce randomized temporal-gap forecasting during training. The framework combines predictive latent forecasting with con- trastive regularization to encourage temporal consistency while preventing repre- sentation collapse. Experiments on the UCF101 dataset demonstrate that the proposed framework learns temporally consistent and semantically meaningful video representations without using action labels during training. Quantitative analysis shows strong temporal stability and emergent category-level structure in the learned embed- ding space, while qualitative retrieval experiments reveal motion-aware organiza- tion across related activities. Overall, the results suggest that long-range latent forecasting provides an effective and computationally efficient approach for self- supervised video representation learning without relying on reconstruction-based objectives.
Copyright (c) 2026 Qinwu Xu https://creativecommons.org/licenses/by/4.0
Accumulator Sizing and Hydraulic Architecture Challenges in Salter’s Duck
The Edinburgh Wave Power Project, centered on Salter’s Duck, remains a landmark in wave energy converter (WEC) research. Despite its exceptional hydrodynamic efficiency, the project encountered a critical engineering barrier: the hydraulic power take-off (PTO) system’s accumulator sizing requirements for smoothing power in random seas would overburden the device’s buoyancy envelope. This paper presents a comprehensive analysis of this finding, including a numerical example of accumulator sizing for a Duck, an exploration of why random seas necessitate vast smoothing volumes, a comparison of hydraulic versus direct-drive PTO feasibility, and a reconstruction of the coronet hydraulic architecture with reference to later knuckle-joint designs. The discussion contextualizes these technical results within the historical narrative, showing how news articles claiming “they sank Salter’s Duck” were, in light of these engineering findings, accurate. The report draws on archival technical reports, academic literature, and contemporary engineering analyses to provide a rigorous, evidence-based account suitable for engrXiv.
Copyright (c) 2026 Craig Andrew Low https://creativecommons.org/licenses/by/4.0
Fire Resistance Performance of Fiber Reinforced Geopolymer Concrete: Review
Geopolymer is a relatively new substance that has sparked a surge of research into nearly every field of geopolymers in recent years. It's still on the verge of becoming a competitive OPC concrete alternative. Mechanical, hardness, and fire resistance properties of geopolymer are exceptional. There has been no/limited research on the effect of fiber integration on fire resistance of geopolymer concrete. In fire-exposed concrete, fiber can help to resist spalling. The goal of this study is to develop materials that exhibit eco-friendly properties and better fire-resistant behavior. Moreover, the combined effect of binder materials and different fibers on the fire resistance of geopolymer concretes. According to the findings, the fire resistance of fiber-reinforced geopolymer concretes increased in the order of carbon fiber-based GPC, micro-steel fiber-based GPC, hooked steel fiber-based GPC, and polypropylene fiber-based GPC. Furthermore, as compared to slag and metakaolin-based GPC, fly ash-based GPC has greater stability and fire resistance. Fiber-reinforced GPC can also be used as a sustainable and durable building material in various construction applications where high performance is needed.
Copyright (c) 2026 Arass Mawlod, Dillshad Khidhir Hamad Amen Bzeni https://creativecommons.org/licenses/by/4.0
Performance of Slag‑Pumice‑Based Alkali‑Activated Mortar at Ambient Environment
Sustainability in construction materials is a hot topic of research, and alkali-activated materials are believed to be a real alternative to OPC concrete. The manufacturing process of cement emits nearly 8% of CO2 into the atmosphere. This study utilised slag as a base binder in the alkali-activated mortar (AAM). The pumice powder was used as a replacement ratio of (0, 10, and 20) % by the mass of the slag. Three various ratios of alkali solution-to-binder ratio (s/b) were utilised (0.4, 0.5, and 0.6) for a detailed evaluation of the fresh and hardened properties of alkali-activated mortar. The setting time and flow test were conducted, and the mechanical characteristics were investigated through compressive strength, flexural strength, and direct tensile strength. Furthermore, fire resistance, water absorption, water sorptivity, porosity, density, efflorescence, and sulphate resistance were all examined to evaluate the durability characteristics of AAM. Results showed that increasing the amount of pumice powder in the mixture reduced its flowability for all s/b ratios. At a s/b of 0.4, increasing the pumice powder concentration degraded the mechanical and durability qualities. Simultaneously, increasing the amount of pumice powder at both s/b of 0.5 and 0.6 increased the mechanical and durability properties.
Durability and fire resistance performance of slag-based geopolymer composites
The fresh and durability properties of alkali-activated mortar were investigated in this study. Ground granulated blast-furnace slag was used as a binder material. Both carbon fibres (CF) and polypropylene fibres (PP) were added at ratios of 0, 0.75, 1.00 and 1.25% by weight of the binder. Simultaneously, hybrid fibres were also used (CF 1.0% and PP 0.25%, CF 0.5% and PP 0.5%, and CF 0.25% and PP 1.0%). The fresh properties of the composite were investigated by performing a flow test. Durability properties such as fire resistance, water sorptivity, water absorption, density, porosity, efflorescence and sulfate attack were also investigated. According to the findings, increasing the fibre content reduces the flowability of the mixture. Simultaneously, the best fibre content studied was 1%, which significantly enhanced durability properties. Also, both fibres achieved excellent improvement. However, carbon fibre had more influence than polypropylene fibre on durability properties
Solving the brittleness problem: Redefining airmanship in the age of increasing complexity
The increasing performance of our aviation system has in-part been made possible through growing complexity of systems and operations, together with a drive toward standardization and compliance as safety foundations. Despite high levels of safety performance and reliability, commercial aviation accidents in the past two decades reveal a systemic, brittle crew response when faced with opacity and ambiguity resulting from this complexity. Building on research into effective flight crew behaviours and strategies in complex fourth generation commercial transport aircraft, this paper proposes a new concept for the human pilot operator – Airmanship 2.0 – to address the brittleness of the existing socio-technical flight deck system and further improve aviation safety. This concept proposes a compound pilot role to systematically harmonize compliant and adaptive behaviours and mitigate the current bifurcation of and related strain on the pilot role. This paper contrasts the proposed Airmanship 2.0 concept against notable accidents in the past decades.
Copyright (c) 2026 Frederik Mohrmann https://creativecommons.org/licenses/by/4.0
Certified Short-Cycle Diagnostics for Deployed LDPC Codes: From WiFi to Quantum, with a Covering-Tower Factorization
Short cycles in the Tanner graph of a low-density parity-check (LDPC) code degrade belief-propagation (BP) decoding, and counting them is a standard step in code design and evaluation. This paper does not count them faster. It contributes a short-cycle diagnostic whose defining identity is a machine-checked theorem -- the trace-formula gap law tr(A^k) - p_k = tr(B^k), with A the adjacency matrix, B the Hashimoto non-backtracking operator and p_k the power sums of the matching-polynomial roots -- and whose per-code outputs are validated by three mutually independent computations that agree exactly. We report a certified census of the four deployed LDPC codes of the IEEE 802.11n (WiFi) standard at block length n=648, and, to our knowledge the first such diagnostic on a quantum LDPC code, of the IBM "gross code" [[144,12,12]] bivariate-bicycle (BB) code, finding girth 6 and c_6=144 shortest cycles. We then give a covering-tower factorization: a BB code whose Tanner graph is a double cover of a smaller code's Tanner graph inherits its short-cycle profile through the classical Artin-Ihara / 2-lift identity tr B^k(cover) = tr B^k(base) + tr B_s^k(base), verified exactly on the gross/[[72,12,6]] pair (c_6 doubles, 144 = 2*72). The contribution is the theorem-backed, cross-validated, reproducible methodology and its transfer from classical to quantum codes; the underlying mathematics is classical and is not claimed as new. We are explicit about what the certificate does and does not guarantee.
Copyright (c) 2026 Carles Marín Muñoz https://creativecommons.org/licenses/by/4.0
Bootstrap-Based Imputation of Monthly Hydroclimatic Series Using STL Decomposition, ARFIMA/ARIMAX Modeling, and ENSO Stratification
Background Hydroclimatic time series with missing records compromise trend detection, hydrological modelling, and water resources management. Existing imputation methods rarely integrate seasonal decomposition, long-memory stochastic modelling, multivariate dependence, ENSO-driven nonstationarity, and uncertainty quantification within a single reproducible pipeline. Methods ACEBoot v1.0.0, a six-module bootstrap-based multimethod framework for monthly hydroclimatic imputation, is presented. The pipeline combines: (M1) stationarity and long-memory diagnosis via ADF, KPSS, Phillips–Perron and DF-GLS tests plus Geweke–Porter-Hudak fractional differencing; (M2) robust STL seasonal decomposition; (M3) ARFIMA or ARIMAX-ONI modelling with Gaussian copula over residuals and Moving Block Bootstrap (MBB) uncertainty propagation; (M4) structural break detection via a unified Worsley–Strucchange–ENSO filter; (M5) targeted re-imputation over confirmed break segments; and (M6) hold-out cross-validation with IC95 coverage, RMSE, MAE, ACF error and Lilliefors normality diagnostics. The framework is demonstrated on a 46-year (1977–2022) monthly streamflow series (405 observed, 147 missing; 26.6%) from the Jamundí-Carretera gauge (code 2622100403, CVC monitoring network), Río Jamundí sub-basin (sub-zone 2629), Cauca Hydrographic Zone (ZH-26), Valle del Cauca, Colombia. Key Results The ARIMAX-ONI(3,1,2) model, selected after majority-vote stationarity assessment (ntrain = 324, Precision profile, CV = 20%) and long-memory detection (d = 0.311, H = 0.811), achieves IC95 empirical coverage of 93.83% (nominal 95%), ACF preservation error of 0.0028, bias of 0.37 m³/s (1.0% of mean), and Lilliefors p = 0.0978. A structural break at February 1995 (+41.7% mean shift, validated by El Niño ONI = 0.72) is detected by Strucchange and confirmed by ENSO stratification. The imputed series preserves mean (+0.4%) while exhibiting expected variance reduction (−22.5%) attributable to conditional mean imputation. Conclusions ACEBoot v1.0.0 produces statistically valid imputations for monthly hydroclimatic series with complex missing-data patterns, integrating climate covariate information and structural break correction within a fully reproducible open-source framework. The framework is recommended for monthly streamflow series ≥20 years with ENSO influence, non-random missing-data patterns, and potential structural breaks. The open-source R implementation and interactive Spanish-localized Shiny application facilitate operational deployment in regional hydrological agencies across Spain and Latin America.
Time Series Analysis & Stochastic Modeling
Computational Statistics & Software Engineering
Copyright (c) 2026 Mauricio Victoria https://creativecommons.org/licenses/by/4.0
Reinforcement Learning: Theory and Methods
Reinforcement learning (RL) provides a rigorous mathematical framework for sequential decision-making under uncertainty and has emerged as one of the foundational paradigms of modern artificial intelligence. This book presents a comprehensive and mathematically rigorous treatment of reinforcement learning, beginning with the measure-theoretic foundations of Markov Decision Processes (MDPs) and extending to modern deep reinforcement learning methods. The text develops the theory of measurable spaces, stochastic kernels, admissible policies, induced probability measures, and controlled stochastic processes in both finite and general state spaces. Building upon these foundations, the book systematically formulates value functions, return functionals, Bellman equations, and dynamic programming principles using tools from probability theory, stochastic processes, functional analysis, and operator theory. Particular emphasis is placed on contraction mappings, fixed-point theory, monotone operators, weighted norm formulations, spectral interpretations, and nonlinear operator geometry, thereby providing a rigorous analytical framework for understanding convergence, stability, and optimality in reinforcement learning algorithms. Classical methods such as value iteration, policy iteration, temporal-difference learning, Q-learning, and policy-gradient methods are derived and analyzed in a unified mathematical setting, highlighting the deep connections between reinforcement learning, stochastic control, and optimization theory. The book further develops the mathematical principles underlying deep reinforcement learning, including stabilization mechanisms such as experience replay, target networks, Double DQN, dueling architectures, and prioritized replay, while providing geometric and operator-theoretic interpretations of their behavior. A major focus is devoted to the exploration–exploitation trade-off through regret minimization, Bayesian exploration, entropy-regularized control, optimism under uncertainty, and stochastic control perspectives. The text also addresses central theoretical and computational challenges in reinforcement learning, including sample inefficiency, instability under function approximation, reward shaping, and the curse of dimensionality. In addition, emerging research directions such as offline reinforcement learning, multi-agent systems, safe reinforcement learning, and theoretical generalization guarantees are examined within a unified mathematical framework. Supported by extensive theoretical derivations, rigorous proofs, and illustrative visualizations, the book is intended to serve both as an advanced graduate-level introduction and as a comprehensive reference for researchers and practitioners seeking a deep understanding of the mathematical foundations, algorithmic structures, and modern developments of reinforcement learning and sequential decision-making systems.
Copyright (c) 2026 Sourangshu Ghosh https://creativecommons.org/licenses/by/4.0
Machine Learning for Edge Computing and Task Placement in 5G/6G Architectures
The emergence of 5G and the vision of 6G have shifted computation from centralized clouds to the network edge, enabling low-latency services for applications such as autonomous driving, augmented reality, and industrial IoT. However, the dynamic, heterogeneous, and resource-constrained nature of edge environments renders classical scheduling policies and static optimization methods inadequate for joint optimization of latency, energy, and fairness. This paper reviews machine learning approaches for task offloading and placement in multi-access edge computing architectures for 5G/6G networks. The review synthesizes work on optimization, queueing, heuristic, supervised learning, reinforcement learning, and federated learning methods, with attention to scalability, adaptability, fairness, and deployment practicality. It concludes that reinforcement learning schedulers often outperform classical baselines in controlled simulation, but the field still faces a generalization gap between benchmark performance and production deployments. Future work should prioritize standardized benchmarks, shared traces, reproducible code, fairness-aware scheduling, and integration of non-terrestrial networks into 6G edge orchestration.
Artificial Intelligence and Machine Learning
Copyright (c) 2026 Hosila Khushbokova, Shivansh Sahni https://creativecommons.org/licenses/by/4.0
Registering Gaussian Splats Without the Point-Cloud Detour: Accuracy, Representation Semantics, and a Negative Result on Hypothesis-Stage Transfer
Aligning two Gaussian splats of the same scene today means either a manual gizmo in an editor or a detour through point clouds: export the Gaussian means, register those, and re-import a transform whose effect on anisotropy, opacity, and view-dependent color nobody defines. We built splatreg, an open pure-PyTorch library, to find out what registering splats natively actually takes, and this paper reports the three things we learned. First, accuracy costs nothing. A signed-distance field derived in closed form from the frozen target Gaussians, with a numerically audited Jacobian over SE(3) and Sim(3) and a Levenberg-Marquardt core under interchangeable seeds (FPFH, learned, global, maximal-clique), reaches 91.5% mean / 93.5% pooled registration recall on official 3DMatch and 72.5% / 74.4% on 3DLoMatch, matching the GeoTransformer local-to-global baseline it refines while adding Sim(3) scale and pose covariance; against the existing splat tools it is 2.9x more accurate in rotation (5.2 vs 15.3 degrees) and improves merge Chamfer 5.1x over naive concatenation. Second, the genuinely new work is semantic, not numeric. A splat is not closed under rigid motion the way a point cloud is: view-dependent color must be Wigner-rotated (real-basis SH rotation, verified to 2.4e-15; none of the splat tools we surveyed rotate SH), scale conventions must be normalized before fusion (mixed log/linear scales corrupt merges silently), and a baked transform must update means, quaternions, scales, and SH together. Each requirement is pinned by a released test, and we propose the checklist as the correctness bar for future splat registrars. Third, a negative result we did not expect. MAC, the maximal-clique estimator that lifts GeoTransformer by 3.7 and 3.9 recall points in its own evaluation, engages on 100% of pairs inside our pipeline yet moves both official splits by at most 4 pairs while costing 50% more runtime: native-voxel learned correspondences are already 60-80% inliers, and a residual-gated refine absorbs whatever seed differences remain. MAC keeps a decisive edge exactly where its theory says it should, on contaminated correspondence sets (0.16 vs a 78 degree failure on a structured decoy). We argue published hypothesis-stage gains should be presumed pipeline-conditional until re-measured inside the receiving system. All numbers are reproducible from the released library and benchmarks: https://github.com/Archerkattri/splatreg
Copyright (c) 2026 Krishi Attri https://creativecommons.org/licenses/by/4.0/
No Single Basis Wins: A Cross-Family Study of Diffusion Feature Forecasting and the Limits of Training-Free Basis Selection
Feature-caching methods accelerate diffusion and flow-matching samplers by skipping the network on most denoising steps and forecasting the cached features from recent compute-step anchors. The literature has treated the forecast basis as a ladder to climb: monomials (TaylorSeer), scaled Hermite polynomials (HiCache), rational and multistep variants (FoCa, HyCa), global Chebyshev fits (Spectrum), each rung validated on a single sampler family. We went to the ladder's mathematical endpoint, the exponential solution class of the local feature-ODE, fitted it with rank-truncated Dynamic Mode Decomposition (Prony's method in its modern multivariate form), and expected it to win everywhere the polynomials do. It does not. The exponential basis wins on every flow-matching 3D generator we test (+0.13/+0.24 F-score over the deployed Hermite arm at intervals 5/6 on Hunyuan3D-2.1; geometry-lossless through interval 6 on SAM3D at 1.56x), yet on DiT-XL/2 ImageNet-256 with the literature-standard 250-step DDPM sampler the ranking inverts outright: a sign-correct TaylorSeer is near-lossless (paired-noise FID drift 2.27 at 3.81x speedup), the corrected Hermite sweeps the interval ladder (3.54/6.46/10.74 at intervals 4/6/8), and the exponential basis drifts 5-9x more than the corrected polynomial at every interval. No basis in our study wins both families. The obvious remedy fails too. Training-free holdout selection, which is exact on synthetic regime switches (120/120 windows) and harmless to helpful on the 3D generators, serves the losing exponential arm on DiT in both holdout modes, at FID drift 18.11 where the corrected polynomial sits at 3.54; this refutes a prediction we pre-registered before the deciding run. The mechanism deserves to be stated plainly: the richer exponential parameterization fits, and therefore backcasts, the in-window history better at any holdout distance while extrapolating forward worse, so in-window backcast fit carries no signal about forward extrapolation quality on real features. We close with an uncomfortable methodological finding. A one-character sign bug made our Hermite baseline anti-extrapolative, and it survived every end-to-end benchmark we and our integrations ran, because near-reuse fails safe: quality metrics cannot distinguish a forecaster from a damped cache, and hyperparameter tuning actively conceals the defect. We propose directional closed-form regression tests as a community-level remedy, and report every affected number as-released alongside corrected values. Code, ledgers, and all benchmarks: https://github.com/Archerkattri/hicache-plus-plus
What is attention mechanism? A comprehensive survey of attention methods and transformer models
The attention mechanism is a fundamental component widely used in deep learning models across numerous domains and tasks. It enables models to selectively focus on the most relevant parts of the input, rather than processing all elements equally, by assigning weights according to their importance for the task. This paper presents a comprehensive overview of the attention mechanism, outlining its general framework and offering a taxonomy of attention models, including Hierarchical, Bidirectional, Multi-Head, Multi-query, Group-query, Graph, Channel, Spatial, Channel-Spatial, Temporal, Spatial-Temporal, Cross, Axial and Flash Attention. The computational trade-offs of these models are also analyzed, and new developments in efficient attention are highlighted. We also discuss the Transformer architecture, one of the most influential deep learning frameworks that makes effective use of attention mechanism and review its major variants including BERT, GPT, Transformer XL, XLNet, BART, Fast Transformer, T5, Longformer, BIGBIRD, Performer, Linformer, Reformer, RoFormer, ALiBi, Switch Transformer, LLaMA, ViT, DETR, DeepViT, DeiT, T2T ViT, CrossViT, PVT, Swin Transformer, TNT, MViT, ViViT, DAT, and Spiking Transformer. Additionally, we provide a comparative overview of these models, highlighting their key ideas, results, advantages, and limitations. Through this survey, we emphasized the pivotal role of attention-based models, especially those built on the Transformer architecture, in shaping diverse application domains such as natural language processing, computer vision, recommender systems, and sensor data analysis.
Copyright (c) 2026 Farhad Mortezapour Shiri, Fateme Memar , Maryam Parhizgar https://creativecommons.org/licenses/by/4.0
CERT: Certified Route Planning under Drifting Costs
A scout robot routing through terrain whose costs drift — mud after rain, traffic after an incident — faces a question classical replanning never answers: how good is the current route, given that most of the map is stale? CERT answers it every round with a certificate: a high-probability bound LB ≤ OPT ≤ UB on the optimal route cost, built from age-weighted non-exchangeable conformal prediction over drift-adjusted residuals, with paid sensing directed at the edges that shrink the certified gap fastest. We prove coverage at the claimed level, with a staleness correction that degrades the claim visibly rather than silently; a certifiability threshold — a target gap is sustainable if and only if the sensing rate beats drift, so certification is a rate, not a state; a √L-tighter sum-aware upper certificate, including the selection-bias hazard it creates and the gate that controls it; and an impossibility theorem showing the certificate's asymmetry is optimal. On replayed traffic from two cities the certificate holds even where real incidents violate the drift model up to half the time, and certificate-directed sensing achieves 2–3× lower travel-regret than freshness-, uncertainty-, or chance-driven sensing at equal budget.
Copyright (c) 2026 Krishi Attri https://creativecommons.org/licenses/by/4.0
A Lightweight UAV-Based SAR System for Human Detection and Monocular Geolocation
Timely localization of missing persons remains a critical challenge in search-and-rescue (SAR) operations, where manual analysis of UAV imagery limits response speed and operational efficiency. This paper presents a lightweight end-to-end UAV-based SAR pipeline integrating real-time human detection, monocular GPS geolocation, and automated alerting into a deployable embedded system. Human detection is performed using a YOLOv8n model trained through a two-stage transfer learning strategy using the VisDrone and HERIDAL datasets. Detected persons are geolocated using a lightweight geometric algorithm that estimates ground coordinates directly from standard UAV telemetry without requiring additional sensors. The proposed system is evaluated on a custom annotated dataset of 300 UAV frames collected under four controlled flight conditions varying in altitude and camera angle. The final model achieves 0.921 precision, 0.926 recall, and 0.965 mAP@0.5, outperforming single-stage training baselines. The geolocation module achieves a mean localization error of 1.01 m across 60 independent measurements under controlled conditions. For real-time onboard deployment, the model is optimized using W8A16 post-training quantization and benchmarked on a Qualcomm RB3 Gen 2 NPU, achieving a median inference latency of 37.3 ms at 960×960 resolution while preserving detection accuracy. The complete implementation, trained models, and evaluation dataset are publicly released to support reproducible research and future development in deployable UAV-based SAR systems.
Artificial Intelligence Systems Engineering
Copyright (c) 2026 Hamza Ghitri https://creativecommons.org/licenses/by/4.0
Lightweight Hybrid Additively Manufactured Liquid Cooler for 10 kV SiC MOSFET Power Module
Medium-voltage silicon carbide (SiC) power modules are central to high-power-density converters for electrified transportation, aerospace propulsion, renewable energy systems, medium-voltage motor drives, and solid-state transformer in data centers. However, the elevated heat fluxes generated by medium-voltage SiC devices impose stringent thermal management requirements while system-level mass constraints increasingly motivate lightweight cooling solutions. Prior work demonstrated a multi-tier liquid cooling architecture for a 10 kV SiC MOSFET power module featuring stacked substrates and an additively manufactured metallic cooler. Building upon this platform, this work investigates a lightweight hybrid liquid cooler that replaces the aluminum manifold with a 3D-printed nylon manifold while retaining the AlSi10Mg heat transfer core. The hybrid cooler is experimentally evaluated against the original fully metallic cooler using pressure drop measurements, thermal resistance characterization, and infrared thermography. Results show that replacing the aluminum manifold with nylon reduces the cooler mass by 51% while maintaining similar hydraulic performance across the tested flow range. The thermal resistance increases moderately due to the reduced thermal conductivity of the polymer structure and reduced parasitic thermal spreading through the manifold. The results demonstrate the feasibility of hybrid polymer-metal cooling architectures for medium-voltage power electronics applications where gravimetric thermal performance is a critical design consideration, including electrified aircraft propulsion systems and mobile power converters.
Copyright (c) 2026 Stephen Pierson, Qiang Wu, Xiaoling Li, Hari Pandey, Ethan Weems, H. Alan Mantooth, Han Hu https://creativecommons.org/licenses/by/4.0
Exploratory Milling of Muonionalusta Iron Meteorite Using CrN-, AlTiN-, and TiAlN-Coated Solid-Carbide End Mills
This paper presents the results of an exploratory milling study conducted on Muonionalusta iron meteorite samples at Michigan State University in collaboration with EMUGE-Franken. Three cutter families—Aero 5 (CrN), TiNoxNF (AlTiN), and Topcut (TiAlN)—were evaluated at multiple feed conditions using cutting force signals. The signals show a clear monotonic rise of signal RMS with feed for every tool family, and one condition (the 6-flute cutter at the highest feed) exhibits a force spike 5–10× larger than all other tests, consistent with a tool-failure event observed during the experiment. The dataset provides a systematic basis for comparing tool performance on a compositionally heterogeneous extraterrestrial workpiece and supports ongoing research into advanced manufacturing processes relevant to in-space resource utilization.
Copyright (c) 2026 Bircan Bugdayci, Sinan Bank https://creativecommons.org/licenses/by/4.0
Concept of a New Cylindrical Wind Turbine with Axial Inlet and Side Outlet Based on the Inverse Francis Principle
This study presents a theoretical analysis of a cylindrical wind turbine concept featuring axial inlet and lateral outlet flow configuration. Inspired by the Francis water turbine principle operating in reverse (”inverse Francis”), we develop a mathematical model for particle-disc energy transfer and apply conservation laws to derive efficiency expressions. The analysis suggests that under idealized frictionless conditions, the inverse Francis configuration may enable high energy conversion efficiency. We propose a Wind-Eater Turbine (WET) design incorporating helically twisted blades, flow concentrator, and axial barrier. Unlike conventional horizontal-axis (HAWT) and vertical-axis (VAWT) wind turbines, the proposed closed-cylinder design operates as a semi-enclosed system where Betz’s limit derivation may not directly apply. Theoretical calculations indicate potential for efficiency exceeding conventional turbine limits, though experimental validation is required. Practical challenges including fluid viscosity, turbulence, and structural constraints are discussed. The work presents a conceptual framework for further investigation through computational fluid dynamics and physical prototyping.
Copyright (c) 2026 Ranko Artukovic https://creativecommons.org/licenses/by/4.0