Sep 2026· IEEE Journal on Miniaturization for Air and Space Systems· Vol 7, pp. 334-347· 0 citations· 49 references
Abstract
Verification and validation (V&V) campaigns for satellites commonly rely on FlatSat platforms to access critical electrical and communication interfaces before final integration. For CubeSats, an ideal platform should be compact and minimize reliance on external instrumentation. Existing solutions often rely on external instrumentation or on simple passive backplanes, whereas more integrated alternatives tend to be proprietary, closed-source, and costly. To address this gap, this article introduces an open-source FlatSat platform that combines bridge- and dock-type architectures, including a set of fully integrated features, such as embedded current and temperature sensors, hardware- and software-based fault injection, and a reconfigurable system-on-module (SoM) running a custom Linux distribution for flexibility and multimission adaptability. The platform was validated through practical, lab-based scenarios using representative CubeSat subsystems. These tests confirmed its ability to emulate missing subsystems, conduct on-orbit scenario simulations and emulations, and perform unified monitoring and logging of sensor and telemetry data without the need for external instrumentation; this establishes it as a practical and reusable solution for advanced V&V, particularly in academic settings. Design files, firmware, and software are publicly available to promote reproducibility and adaptation.
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We make four contributions to this problem. First, we show that prior evaluation methodologies systematically inflate hallucination rates by misclassifying standard-library modules as hallucinations in some languages. For Python, the overestimation reaches 9.4 percentage points. Second, we evaluate seven inference-time defenses for mitigating package hallucinations, including five guided decoding strategies (Greedy, Contrastive, DoLa, Nudging, and Active Layer-Contrastive Decoding), an iterative self-refinement approach (Self-Refine), and a Retrieval-Augmented Generation (RAG)-based defense.. Across eight models spanning five families and four programming languages (Python, JavaScript, Ruby, Rust), RAG reduces the package hallucination rate (PHR) in 18 of 32 model--language configurations. Third, we introduce Package Utility (PU) to assess whether defenses preserve valid and task-relevant recommendations. Among strategies evaluated, Greedy decoding provides the strongest average mitigation--utility trade-off. Fourth, we stress-test all strategies under adversarial prompts seeded with fabricated package names and find that PHR surges by up to 45 percentage points relative to standard prompts, with Ruby consistently the most vulnerable language (80.9--95.2\%). Under adversarial conditions, RAG and Self-Refine outperform all decoding-only strategies, indicating that robust defense requires either external grounding or iterative self-verification when prompts are actively hostile. Our results recast package hallucination as both a measurement problem and a decoding-time control problem, and they demonstrate that the choice of defense must be matched to the threat model and recommendation utility.
Albérick Euraste Djiré, Iyiola E. Olatunji, Melissa Tessa et al.· 1 citation
MAGE explains how externalized knowledge, bounded action, independent evaluation, and retained human authority can compose into a governed engineering environment, and proposes tests of when that environment turns commodity intelligence into durable engineering progress.
James C. Davis, Kelechi G. Kalu, Huiyun Peng et al.· 1 citation
An audit-and-placebo protocol is proposed that separates verifier artifacts, interaction scaffolding, and grounded feedback credit in evaluations of self-evolving test generators in evaluations of self-evolving test generators.
Yunhao Liang, Chengguang Gan, Ruixuan Ying et al.· 0 citations
MEmilio is a modular, high-performance framework for epidemic simulation that harmonizes the specification and execution of diverse dynamic epidemiological models within a unified and harmonized architecture, and aims to lower barriers to reuse and generalize models, enable principled comparisons of implicit assumptions, and accelerate the development of novel approaches that strengthen modeling-based outbreak preparedness.
Julia Bicker, Carlotta Gerstein, David Kerkmann et al.· Scientific Reports· 0 citations
This prototype MRG image translocation software was helpful to 69% of patients with binocular diplopia, but limited by large angle strabismus because of the limited instrument field of view.
Edsel B Ing, Kevin Sha, Sarosh Dandoti et al.· Journal of neuro-ophthalmolo...· 0 citations
A diagnostic support system based on a unified web platform that classifies patients according to the risks of developing three diseases based on regularly collected clinical or audio data using classical supervised learning algorithms is presented.
Vedamurthy D R, Dr. Anup Ritti, A. Bibi et al.· International Journal for Re...· 0 citations
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