Continuous data assimilation (CDA) techniques, most notably the nudging approach proposed by Azouani, Olson, and Titi (AOT), have been shown to be very successful in deterministic frameworks for achieving long-time synchronization between an approximate state and true state. In this note, we develop and study a CDA scheme for a class of stochastic non-Newtonian fluids, namely third-grade fluids, subject to either additive or multiplicative Gaussian stochastic forcing in both two- and three-dimensional settings. We establish sufficient criteria on the nudging gain and the observational mesh size that guarantee convergence of the assimilated state toward the underlying stochastic solution. Convergence is proved in the mean-square sense, and, in the case of additive noise, we further obtain almost sure (pathwise) convergence.

Feature toggles enable gradual rollouts and experimentation in software systems, yet often persist beyond their intended lifecycle, accumulating as technical debt. Prior research has examined feature toggle interactions and complexity, but no longitudinal study has quantified how toggles evolve over time across different organizational contexts. We analyse over 4,000 toggle events in Kubernetes (10 MLoC, 8.5 years) and GitLab (5 MLoC, 5 years). We find that feature toggle removals lags behind additions in both systems (by roughly 35% and 13%, respectively), leading to growing toggle inventories. Their lifespan patterns also differ notably, with Kubernetes toggles lasting a median of 734 days versus 185 in GitLab. Then, some feature toggles (1.33% and 0.73%, respectively) exceed all previously observed removal durations, becoming de facto permanent. Building on these findings, we propose a benchmarking framework with five key metrics and their empirically derived threshold zones, enabling practitioners to assess and compare toggle management practices across projects. All scripts and data are publicly available.

Quantum software testing has attracted interest in recent years, prompting the development of various techniques to automate the testing of quantum software. These techniques generate test cases that must be assessed for their effectiveness in detecting faults. Such an assessment requires benchmarks of faulty programs. However, there is a lack of benchmarks containing faults. In this data showcase, we propose QMutBench, a dataset that contains over 700,000 quantum circuit mutants representing different faults. The dataset is accessible via an online interface with selection criteria, such as the original quantum circuit(s) from which mutants are generated, the desired survival rate of the selected mutants, and other mutation characteristics (e.g., the type of faulty quantum gate). QMutBench provides quantum software developers and testers with an accessible online dataset to obtain benchmarks of mutants necessary to assess either the quality of the test cases generated by their testing technique or to compare different testing techniques. It also enables the development of new mutation-guided quantum software testing techniques.

This study presents a systematic validation and comparative assessment of computational fluid dynamics (CFD) strategies for centrifugal blood pump simulations using the U.S. Food and Drug Administration benchmark model. A scale-resolving large eddy simulation (LES) with transient sliding-interface (SI) coupling is evaluated and compared against Reynolds-averaged Navier-Stokes (RANS) approaches employing both multiple reference frame and SI formulations. Numerical predictions are validated through direct comparison with particle image velocimetry measurements under two representative operating conditions. The results indicate that LES with transient rotor-stator coupling achieves consistently improved agreement with experimental velocity fields compared with RANS-based methods, particularly in the diffuser region where strong intermittency and wall-bounded turbulence are present. In contrast, RANS-based approaches exhibit noticeable discrepancies in these regions. A mesh sensitivity study and an assessment of temporal averaging effects are conducted for LES. The quality of the LES results is further quantified using three complementary metrics, demonstrating that a mesh resolution of approximately 80 million cells achieves a well-resolved LES regime. Building on the validated scale-resolving simulations, detailed analyses of vortical structures, turbulent kinetic energy distributions, and velocity energy spectra are performed to characterize the internal flow physics of the pump. This study demonstrates that scale-resolving, transient simulation approaches are essential for accurately capturing the highly unsteady, turbulence-dominated flow features in ventricular assist devices and provides practical guidance for future high-fidelity hemodynamic and hemocompatibility studies.

We present a low-stack implementation of the module-lattice signature scheme HAETAE, targeting microcontrollers with 8 kB-16 kB of available SRAM. On such devices, peak stack usage is often the binding constraint, and HAETAE's hyperball-based sampler, large transient polynomial vectors, and variable-length signature payloads (hint and high-bits arrays) pose a particular challenge. To address this we introduce (i) Rejection-aware pass decomposition, which isolates encoding to the post-acceptance path; (ii) Component-level early rejection, which short-circuits the response computation when a partial norm already exceeds the bound; and (iii) Reverse-order streaming entropy coding using range Asymmetric Numeral Systems (rANS), which eliminates full hint and high-bits staging buffers. Combined with streamed matrix generation, a two-pass hyperball sampler with streaming Gaussian backend, and row-streamed verification, these techniques bring Signing stack from 71 kB-141 kB in the reference implementation down to 5.8 kB-6.0 kB, key generation to 4.7 kB-5.7 kB, and verification to 4.7 kB-4.8 kB across all three security levels. Our pure C implementation covers all three security levels (HAETAE-2/3/5), whose optimization paths differ due to the public-key domain (d>0 vs. d=0) and rejection structure. We implement our optimization on a Nucleo-L4R5ZI and compare to the reference pqm4 (for HAETAE-2 and -3) and a recently published memory-optimized implementation (targeting HAETAE-5 only). We reduce HAETAE-2, -3, and -5 stack by respectively 75, 86 and 8 % for key generation, 92, 95 and 24 % for signature generation, and 85, 91 and 22 % for verification. Depending on the parameter set, this impacts performance by at most a factor 1.8 and 3.4 for key and signature generation respectively, while even offering a performance improvement up to 18 % for verification. Verification at all security levels fits within 8 kB of RAM (signature buffer + stack) and is 2.34-3.34x faster than ML-DSA m4fstack at each comparable security level. We additionally validate portability under RIOT-OS on ARM Cortex-M4 and RISC-V targets.

The Hadamard test is a standard quantum primitive for estimating inner products and expectation values, but in data-processing settings its practical utility is often limited by the cost of preparing amplitude-encoded quantum states. In this study, we investigate an angle-encoding variant of the Hadamard test for estimating cosine similarity between normalized real-valued vectors. The proposed method decomposes the similarity computation into elementwise two-qubit Hadamard-test circuits that can, in principle, be executed in parallel, resulting in constant circuit depth with respect to the vector dimension at the expense of a larger qubit footprint and classical post-processing. Because the resulting estimator is approximate, we analyze the induced bias and show that it is non-negative under the approximation used in our derivation. Numerical experiments on random normalized vectors show that, in the tested setting, the estimation error decreases as the vector dimension increases. We further illustrate a possible application to cosine-attention-based Transformer models. These results suggest that the angle-encoding Hadamard test may provide a useful design point for near-term similarity estimation when shallow circuit depth is preferred over compact qubit usage.

Growing privacy regulations and internal governance mandates are driving demand for fine-grained, context-sensitive access control in data management systems. Among competing approaches, content-based access control -- where access decisions depend on the data values referenced by a query -- is becoming particularly prominent, and is supported directly in modern database engines. While simple content-based predicates often incur negligible overhead, increasingly rich policies can interact in subtle ways with query optimization, leading to significant and poorly understood performance variability. This paper investigates this gap by introducing a structural framework and expressive policy grammar for modelling content-based compliance policies and analysing their impact on query planning and execution in database systems. Building on this framework, we augment an analytical benchmark with structured policy workloads, enabling controlled evaluation of enforcement mechanisms and optimization strategies under combined query - policy workloads. Our experimental results show that policy structure has a decisive impact on optimizer behaviour and end-to-end performance, underscoring the need for policy-aware database and optimizer design.

Plasmonic nanoparticles (NPs) integrated within thermoresponsive polymeric microgels provide a versatile platform for the realization of stimuli-responsive optical materials, where the microgel volume phase transition enables dynamic control of plasmon coupling. This study uncovers a counter-intuitive re-entrant behavior with increasing NP loading in which plasmon coupling initially strengthens and subsequently weakens beyond a critical NP-to-microgel number ratio. By combining light and X-ray scattering techniques with optical spectroscopy and electrophoretic mobility measurements, it is demonstrated that plasmon coupling is governed not only by the interparticle distance between NPs confined within individual microgels, but also by the colloidal stability of the hybrid complexes. At intermediate NP loadings, surface charge inhomogeneities induced by NP adsorption promote aggregation of microgel-NPs complexes, resulting in enhanced plasmon coupling. In contrast, when the complexes remain colloidally stable, coupling is dictated solely by NP organization within the corona of individual microgels. A quantitative relationship between plasmon coupling and interparticle distance reveals two distinct coupling regimes. This behavior is rationalized through a phase diagram linking colloidal stability to optical response. These findings identify colloidal stability as a key parameter for designing soft plasmonic systems with programmable optical properties.

To improve the accuracy and efficiency of high-dimensional stellar parameter inference in large spectroscopic datasets, we propose a projection-assisted parameter-inference framework -- Projected-Space Inference of Stellar Parameters (PISP). PISP constructs an orthonormal basis and optimizes in the projected space, reducing the impact of parameter correlations on inference. The basis is constructed using either principal component analysis (PCA) or the active-subspace (AS) method and is combined with two inference strategies -- Non-L1, which optimizes the projection coefficients for a user-specified projected dimensionality, and L1, which introduces L1 regularization in the full projected space to adaptively select projection directions -- yielding four strategies: PCA-Non-L1, AS-Non-L1, PCA-L1, and AS-L1. For different computational scenarios, we implement two versions: PISP-CurveFit for fast single-spectrum inference and PISP-Adam for large-scale GPU-parallel inference. Using a fully connected neural network and a residual network as spectral emulators, we evaluate PISP on Kurucz synthetic spectra and on $722{,}896$ APOGEE DR$17$ observed spectra. Compared to the baseline strategy, PISP improves inference accuracy for multiple parameters across all emulator-optimizer combinations. In synthetic data, PCA-L1 performs best, reducing the standard deviation of differences ($σ(Δ)$) by at least $0.01$ dex for $12$ of $20$ elemental abundances, with [N/H], [O/H], [Na/H], [Co/H], [P/H], [V/H], [Cu/H] showing $0.05$--$0.72$ dex reductions. In observed data, PCA-Non-L1 reduces $σ(Δ)$ by $>30$ K for effective temperature and by at least $0.01$ dex for $9$ of $17$ elemental abundances, with [O/H], [Na/H], [V/H] showing $0.05$--$0.20$ dex reductions, while achieving a $\sim$$4\times$ efficiency gain, slightly outperforming PCA-L1.

Molecular dynamics is a powerful tool to investigate the properties of fluid systems. However, a correct interpretation of the results of simulations is required. In particular, some simulations show appearance of large voids in liquids, which contradicts our common sense on what is liquid. In the present paper we discuss the origin of large cavities liquids in molecular dynamics simulations. We demonstrate that the cavities appear either if the temperature of the system is above the critical temperature of liquid-gas transition or if the system is in two-phase liquid-gas region. These conclusions are illustrated by several examples from literature and our own simulations.

We present a phase-only time-reversal framework for steering photonic nanojets without mechanical motion or amplitude modulation. Time-reversed radiation by a synthetic source placed at the target PNJ location helps define a phase-only modulation on a control line, compatible with a spatial light modulator, that produces the desired PNJ. Full-wave finite-difference frequency-domain (FDFD) simulations demonstrate robust lateral and axial steering with subwavelength confinement and low sidelobes. A parametric study of microelement geometries shows that nanojet formation is largely insensitive to moderate boundary variations, with simple shapes providing competitive performance. Robustness to fabrication and alignment errors is confirmed via uncertainty analysis.

This paper investigates the local exponential stabilization of the two-dimensional Navier--Stokes equations to a given reference trajectory by means of receding horizon control (RHC). The control is realized as a linear combination of finitely many actuators, represented by indicator functions supported on subsets of a prescribed control subdomain. We establish local exponential stabilizability and suboptimality for the resulting RHC scheme. Numerical experiments for two flow configurations of increasing complexity illustrate the theoretical findings and assess the practical performance of the method. In addition, we propose a model-order-reduced RHC approach based on proper orthogonal decomposition, which significantly reduces the computational cost while maintaining favorable closed-loop stabilization performance in the numerical experiments.

Modern computing systems inherently trust human input devices, creating an exploitable attack surface for adversarial automation. USB Human Interface Device (HID) emulation attacks, such as those enabled by the USB Rubber Ducky, exploit this assumption to inject arbitrary keystroke sequences while bypassing traditional defenses. Existing countermeasures rely on simple heuristics based on typing speed or timing regularity, which can be easily evaded through basic randomization. Keystroke dynamics analysis offers a more robust alternative by modeling temporal typing behavior. However, prior work frames this problem as behavioral authentication, verifying whether input originates from a specific user rather than detecting automated injection. An alternative approach is continuous monitoring via keylogging integrated with intrusion detection systems, but this requires access to input content, raising significant privacy concerns. In this paper, we provide the first systematic characterization of keystroke dynamics for human-vs-machine discrimination, independent of user identity. Guided by five research questions, we show that robust, privacy-preserving detection is achievable using lightweight models operating solely on timing features, eliminating the need for content access or user profiling. Our analysis reveals that attacker sophistication does not monotonically translate into improved evasion. Instead, robustness depends on exposure to structurally diverse generation strategies rather than increased model complexity. Finally, we quantify the trade-off between detection timeliness and reliability across varying keystroke sequence lengths, identifying practical operating points for early and effective attack interception.

We prove a uniform upper and lower bound for Delannoy numbers. This is achieved by using the representation of Delannoy numbers as the number of lattice points in high-dimensional cross-polytopes (also known as hyper-octahedrons or $\ell^1$ balls) and proving a uniform (dimension-free) count for these lattice points. Using this count, we establish dimension-free estimates for discrete maximal functions over cross-polytopes. By proving a comparison principle with the continuous setting, we obtain a dimension-free estimate on all $\ell^p(\mathbb{Z}^d)$ spaces for radii $R>C d^{3/2}.$ We also treat the full maximal function on $\ell^2(\mathbb{Z}^d)$ for small radii $R\le d^{1-\varepsilon}$ and the dyadic maximal function for any radii.

Semi-quantum signature (SQS) schemes aim to enable quantum signature functionality in scenarios where only a subset of participants possess full quantum capabilities, thereby improving practical deployability while preserving quantum security advantages. Within this framework, we present a practical SQS protocol based on Bell states. The protocol is designed so that only the signer requires full quantum capability, significantly alleviating the quantum burden on the remaining participants. To strengthen security in semi-quantum environments, we incorporate an improved eavesdropping-detection mechanism that more effectively detects tampering. Compared with many existing schemes, which do not explicitly consider tampering of already generated signatures in their unforgeability analyses, the proposed protocol is designed to remain secure in the presence of such tampering.

Rain introduces broadband and frequency-selective attenuation in wideband terahertz (THz) links, making it necessary to identify a compact spectral descriptor that captures how the dominant loss region evolves with rainfall conditions. This article investigates the peak-frequency behavior of rain attenuation by combining Mie-theory calculations with one separable laboratory Gaussian drop-size distribution (DSD) and seven outdoor empirical DSD models whose spectral shapes vary with rainfall rate. The analysis compares total-loss, absorption, and scattering components, examines the roles of characteristic DSD scale and representative drop-size statistics, and evaluates the effect of temperature on the peak location. The results show that, unlike the fixed-shape laboratory case where the peak frequency remains unchanged with rainfall rate, all outdoor empirical DSD models exhibit a monotonic migration of the attenuation peak toward lower frequencies as rainfall rate increases; this behavior is well described by an asymptotic power-law relation and is governed primarily by the rainfall-dependent DSD characteristic scale rather than by total drop concentration or fixed-temperature dielectric dispersion.

This paper presents a lightweight, protocol-agnostic security enhancement for Simultaneous Wireless Information and Power Transfer (SWIPT) in Internet of Things (IoT) applications. Building on a backscatter-based identification mechanism, the proposed approach introduces a secure, energy-efficient layer that operates independently of communication protocols and with minimal hardware modification. A rectifier-driven backscattering scheme embedded in battery-free sensing nodes enables authentication without activating conventional RF transceivers, thereby reducing power consumption while ensuring secure device identification. To assess robustness, replay attacks are emulated on standard LoRaWAN Activation By Personalization (ABP) encryption, highlighting vulnerabilities and demonstrating the relevance of the proposed solution. The approach is experimentally validated in a real Wireless Sensor Network (WSN) using LoRaWAN-compatible, battery-free sensing nodes equipped with compact, low-profile antennas, confirming both practicality and scalability for space-constrained IoT deployments. Results show that the method achieves secure identification, reliable energy harvesting, and data transmission with negligible impact on node autonomy. The proposed approach offers a practical, energy-efficient, and scalable security framework for SWIPT-enabled IoT systems, strengthening device authentication without altering existing communication protocols or compromising power autonomy.

We present a theoretical framework that enables investigating rare transitions in a general model of an active particle in an external potential, with the thermal Active Ornstein-Uhlenbeck Particle (AOUP) appearing as a special case. Using a projection-operator formalism, we compute transition rates perturbatively in two distinct asymptotic regimes. In the regime of small persistence times-where the activity evolves much faster than the particle's position-integrating out the activity reproduces the rates previously reported in the literature. In the opposite regime of large persistence times, we instead integrate out the position and obtain the corresponding rates analytically. Together, these asymptotic expansions uniquely specify a rational approximation that remains accurate across intermediate persistence times. As a result, we obtain an analytic expression for the rate valid across all persistence times and activity strengths in the rare-event limit, which are in excellent agreement with numerical simulations. The presented framework applies to rare transitions in a broad class of driven systems.

Artificial intelligence algorithms are increasingly used by firms to set prices. Previous research shows that they can exhibit collusive behaviour, but how quickly they can do so has so far remained an open question. I show that a modern deep reinforcement learning model deployed to price goods in a repeated oligopolistic competition game with continuous prices converges to a collusive outcome in an amount of time that matches empirical observations, under reasonable assumptions on the length of a time step. This model shows cooperative behaviour supported by reward-punishment schemes that discourage deviations.

An {\em odd subgraph} of a graph is a subgraph in which every vertex has odd degree. A graph $G$ is said to be {\em odd $k$-edge-colorable} if there exists an edge-coloring $E(G) \rightarrow \{1,2, \ldots, k\}$ such that each non-empty color class induces an odd subgraph of $G$. The {\em odd chromatic index} of $G$, denoted by $χ'_o(G)$, is the minimum $k$ for which $G$ is odd $k$-edge-colorable. In this paper, we prove that every $4$-connected simple graph of odd order is odd 3-edge-colorable, and show that the $4$-connectedness assumption is necessary. We also prove that for a connected Eulerian graph $G$ of odd order, there exists an edge $e$ such that $G-e$ is odd $2$-edge-colorable.

The Hamiltonian formulation of lattice gauge theories plays a central role in quantum simulations of gauge theories, and understanding their spectrum and other properties is expected to become crucial in the upcoming years. The relevant Hamiltonians in this framework possess local symmetry at each lattice site and may exhibit higher-form symmetries. There are then an exponentially large number of dynamically disconnected symmetry sectors, most of which are not translation-invariant. An exponential number of dynamically disconnected sectors, i.e., Hilbert space fragmentation, can also occur in systems in which no such symmetries have been identified. In this contribution, we describe an emergent gauge symmetry that is valid only in a subset of sectors of the fragmented $S=1$ dipole-conserving spin chain. These non-invertible symmetries can label exponentially many of the model's sectors. Simulating this Hamiltonian, which is not gauge-invariant, yields an exact quantum simulation of a gauge theory.

We propose a new approach to estimate government worker skills, a setting where output is hard to observe and wages may be uninformative about skills. The approach uses wages in comparable jobs in the private sector and machine learning tools to link skills to skill-related observables. We apply the approach to rich Indonesian household-level panel data from 1988-2014, showing two main applications. First, government skills have continuously declined relative to the private sector, driven by the most skilled workers ending up in the private sector. Second, the Indonesian government pays a wage premium of 43% conditional on skills.

We show a continuity result for the Weyl pseudometric on subshifts which are generated by model sets. This fact is then used for multiple constructions of subshifts that exhibit different behavior regarding entropy, amorphic complexity and their maximal equicontinuous factor.

We explore unsolved nuclear structure problems related with the spin and isospin degree of freedom by using microscopic models which accommodate realistic isoscalar and isovector pairing interactions, and also tensor correlations. For the attempt of universal theoretical framework for both nuclear and astrophysical phenomena, we adopt a self-consistent Hartree-Fock (HF)+random phase approximation (RPA) models, and a state-of-the-art beyond mean field model, Subtracted Second RPA (SSRPA), including the couplings to two-particle two-hole states. The quenching problems of magnetic dipole and Gamow-Teller transitions are discussed in terms of the coupling to 2p-2h configurations and also the tensor correlations. The $β$ decay life time of semi-magic and magic nuclei are discussed in RPA and SSRPA models.

The Transiting Exoplanet Survey Satellite (TESS) has delivered a large number of transiting planet candidates around nearby stars by identifying periodic decreases in stellar brightness. Establishing the planetary nature of these signals and determining their fundamental properties is a necessary step toward detailed studies of their internal structure, atmospheres, and formation pathways. In this work, we investigate the planetary nature of the TOI-1752 system (M1 V, $103.02\pm0.34$ pc), which hosts two TESS candidates: TOI-1752 b, a short-period object consistent with a lava-world scenario, and TOI-1752 c, a sub-Neptune-size planet candidate located in the optimistic habitable zone. We obtained ground-based multi-color photometric follow-up observations of TOI-1752, which we combined with TESS photometry to assess the nature of both signals. We performed a formal statistical validation using the TRICERATOPS framework, while independently vetting the candidates with the neural-network-based classifier WATSON-Net, which provides a machine-learning assessment of their planetary likelihood based on light-curve morphology, centroid diagnostics, and auxiliary vetting features. We validate TOI-1752 b as a bona fide planet with a radius of $1.69\pm0.07 R_{\oplus}$ and an orbital period of $0.935186^{+0.000001}_{-0.000002}$ days, and TOI-1752 c with a radius of $2.29^{+0.13}_{-0.14} R_{\oplus}$ and an orbital period of $32.7144\pm0.0004$ days. The combined analysis confirms TOI-1752 as a new planetary system, places TOI-1752 c within the optimistic habitable zone of its host star, and identifies TOI-1752 b as a promising target for atmospheric characterization, with an estimated emission spectroscopy metric (ESM) of up to $\sim8$.

We introduce a novel coupled-channels method for elastic three-body scattering in systems of identical bosonic alkali-metal atoms. The approach relies on the numerically exact two-body off-the-energy-shell transition matrix, constructed from realistic multichannel molecular interaction potentials that support many bound states. By rigorously accounting for this off-shell structure, the method captures both the short-range physics as well as multichannel couplings characteristic of alkali-metal potentials without resorting to model pseudopotentials. The central output is the complex three-body scattering hypervolume -- the three-body analogue of the two-body scattering length -- which we obtain with controlled and verifiable numerical accuracy. As a realistic benchmark, we apply our framework to spin-polarized potassium-39, performing full coupled-channels three-body scattering calculations and extracting the hypervolume over experimentally relevant conditions. The method is general and transferable to other atomic species and interaction models featuring deep molecular potentials with an arbitrarily large number of bound states.

Agentic visual analytics (VA) represents an emerging class of systems in which large language model (LLM)-driven agents autonomously plan, execute, evaluate, and iterate across the full visual analytics pipeline. By shifting users from low-level tool operations to high-level analytical goals expressed through natural language, these systems are fundamentally transforming how humans interact with data. However, the rapid proliferation of such systems in recent years has outpaced our understanding of their design landscape. Two intertwined problems remain open: how do autonomous agents reshape the traditional VA pipeline, and how must human involvement adapt as agent autonomy increases? To address these questions, this paper presents a comprehensive survey of 55 primary agentic VA systems and introduces a co-evolutionary framework. This framework is essential because it jointly analyzes the progression of agent autonomy alongside the necessary shift in human roles from manual operators to strategic supervisors. Within this framework, we define a role-workflow taxonomy that aligns four key agentic roles (PLANNER, CREATOR, REVIEWER, and CONTEXT MANAGER) and maps them onto established VA pipeline stages. Our analysis uncovers recurring trade-offs along three foundational axes: autonomy levels, agentic roles, and the VA workflow. We consolidate these findings into actionable design guidelines and outline future research directions for agentic visual analytics. A web-based interactive browser of our co-evolutionary framework, including the corpus and design guidelines, is available at agenticva.github.io/AgenticVA/.

Direct orientation contrast imaging of zinc-blende III-V materials is studied using scanning electron microscopy. A quantitative approach is taken using a 3 μm thick orientation-patterned GaP grown on GaAs sample, studying the anti-phase domain contrast with respect to the electron beam energy and the tilt angle. A qualitative approach is taken for III-V grown on non-polar materials with and without chemical mechanical polishing. Finally, a processing of the acquired image for GaP on Si reveals in plane preferential anti-phase boundaries.

Static Random Access Memory (SRAM) Physically Unclonable Functions (PUFs) make use of intrinsic manufacturing variations in memory cells to derive device-unique responses. Employing such hardware-rooted fingerprints for authentication, this work demonstrates a threshold-based authentication proof of concept for constrained Industrial Internet of Things (IIoT) devices. The proposed scheme can reliably cap the the post-authentication bit error rate (BER) below 1 %. Inherent SRAM PUF unreliability is addressed by a resource-efficient combination of Hamming code (HC) Error Correction (EC) and Temporal Majority Voting (TMV). Increasing HC redundancy or TMV count significantly reduces the BER, albeit with diminishing returns and increasingly prohibitive computational overhead. Furthermore, this work quantifies the threshold gap between strict reliability and security constraints. This gap is reframed as a design budget which enables the resource-aware calibration of the acceptance threshold, PUF response length, and stabilization technique, without violating designed-for error limits. Larger responses make reliability optimizations increasingly obsolete. This comparative analysis establishes a comprehensive design space for PUF EC, guiding future implementations in balancing EC quality against resource constraints such as computational demand, power consumption, and implementation complexity.

The DART spacecraft impacted Dimorphos, the satellite of (65803) Didymos, in September 2022. Evidence of crater formation and possible global reshaping has been obtained indirectly from spacecraft and ground-based data. Since the impact, several stellar occultations by Didymos have been observed, but only one in particular, on January 21, 2023, can provide useful constraints on the size and shape of Dimorphos. We modelled the diffraction signatures recorded on multiple occultation chords to constrain the projected shape and size of Dimorphos, assuming an ellipsoidal model. This is the first time diffraction observed simultaneously on several chords of a single event has been used for such a purpose. The projected shape at the epoch of the event is well constrained and consistent with recent post-DART determinations. When extended to a full three-dimensional ellipsoidal solution, the result remains compatible with previous studies, suggesting an equatorially elongated post-impact shape.