Tootfinder

Implementation and Analysis of Different Geomagnetic Field Models for Attitude Determination and Control System (ADCS) of a Satellite
Hoor Bano, Tatiana Podladchikova, Bisma Sajid, Dmitry Ris
https://arxiv.org/abs/2506.19588

Implementation and Analysis of Different Geomagnetic Field Models for Attitude Determination and Control System (ADCS) of a Satellite
An Attitude Determination and Control System is essential for orientation stability and performance of slew maneuvers on the satellite. This research focuses on comparing two different geomagnetic field models, Direct Dipole Model and International Geomagnetic Reference Field Model, for modeling of magnetometer and magnetorquers. Both these magnetic field models are compared and analyzed for two satellite attitude cases: orientation stability and unloading of reaction wheels. Magnetometer model…

Towards a Unifying Reference Model for Digital Twins of Cyber-Physical Systems
Jerome Pfeiffer, Jingxi Zhang, Benoit Combemale, Judith Michael, Bernhard Rumpe, Manuel Wimmer, Andreas Wortmann
https://arxiv.org/abs/2507.04871

Towards a Unifying Reference Model for Digital Twins of Cyber-Physical Systems
Digital twins are sophisticated software systems for the representation, monitoring, and control of cyber-physical systems, including automotive, avionics, smart manufacturing, and many more. Existing definitions and reference models of digital twins are overly abstract, impeding their comprehensive understanding and implementation guidance. Consequently, a significant gap emerges between abstract concepts and their industrial implementations. We analyze popular reference models for digital twi…

An effective implementation of high-order compact gas-kinetic scheme on structured meshes for compressible flows
Yaqing Yang, Fengxiang Zhao, Kun Xu
https://arxiv.org/abs/2508.08965

An effective implementation of high-order compact gas-kinetic scheme on structured meshes for compressible flows
A novel fifth-order compact gas-kinetic scheme is developed for high-resolution simulation of compressible flows on structured meshes. Its accuracy relies on a new multidimensional fifth-order compact reconstruction that uses line-averaged derivatives to introduce additional degrees of freedom, enabling a compact stencil with superior resolution. For non-orthogonal meshes, reconstruction is performed on a standard reference cell in a transformed computational space. This approach provides a uni…

Introducing CQ: A C-like API for Quantum Accelerated HPC
Oliver Thomson Brown, Mateusz Meller, James Richings
https://arxiv.org/abs/2508.10854 https://arxi…

Introducing CQ: A C-like API for Quantum Accelerated HPC
In this paper we present CQ, a specification for a C-like API for quantum accelerated HPC, as well as CQ-SimBE, a reference implementation of CQ written in C99, and built on top of the statevector simulator QuEST. CQ focuses on enabling the incremental integration of quantum computing into classical HPC codes by supporting runtime offloading from languages such as C and Fortran. It provides a way of describing and offloading quantum computations which is compatible with strictly and strongly ty…

Dynamical measurement of saturation vapor pressures below and above room temperature
Mohsen Salimi, Andreas B. Pedersen, John E. V. Andersen, Henrik B. Pedersen, Aur\'elien Dantan
https://arxiv.org/abs/2508.10932

Dynamical measurement of saturation vapor pressures below and above room temperature
We report on the implementation of a dynamical method for the determination -- in an extended temperature range around room temperature -- of the saturation vapor pressure and enthalpy of vaporization of low-volatility liquid substances. The method relies on isolating a \textit{precooled} substance in a heated chamber under static vacuum conditions and monitoring the chamber pressure as the sample slowly thermalizes to the chamber temperature. We apply the method to four reference substances --…

Development of management systems using artificial intelligence systems and machine learning methods for boards of directors (preprint, unofficial translation)
Anna Romanova
https://arxiv.org/abs/2508.03769

Development of management systems using artificial intelligence systems and machine learning methods for boards of directors (preprint, unofficial translation)
The study addresses the paradigm shift in corporate management, where AI is moving from a decision support tool to an autonomous decision-maker, with some AI systems already appointed to leadership roles in companies. A central problem identified is that the development of AI technologies is far outpacing the creation of adequate legal and ethical guidelines. The research proposes a "reference model" for the development and implementation of autonomous AI systems in corporate management. This…

Residual 1D CNN for Low SFR Surface Density Regression: A Design Note
Po-Chieh Yu
#toXiv_bot_toot

High-gain model-following control for trajectory tracking
Nicals Tietze, Kai Wulff, Johann Reger
https://arxiv.org/abs/2506.13463 https://

High-gain model-following control for trajectory tracking
We consider trajectory tracking for minimum-phase nonlinear systems in Byrnes-Isidori form using the model-following control (MFC) architecture. The tracking problem is motivated by a hierarchical control concept where a higher-level instance provides the reference trajectory at run-time. We present a computational efficient implementation of the feedback linearisation MFC design, and apply high-gain feedback in the process control loop (PCL) to achieve practical tracking in presence of Lipschi…

This https://arxiv.org/abs/2504.07409 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMS_…

RLibm-MultiRound: Correctly Rounded Math Libraries Without Worrying about the Application's Rounding Mode
Our RLibm project generates a single implementation for an elementary function that produces correctly rounded results for multiple rounding modes and representations with up to 32-bits. They are appealing for developing fast reference libraries without double rounding issues. The key insight is to build polynomials that produce the correctly rounded result for a representation with two additional bits when compared to the largest target representation and with the "non-standard" round-to-odd r…

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison
Parsa Hassani Shariat Panahi, Amir Hossein Jalilvand, M. Hasan Najafi
https://arxiv.org/abs/2506.00924

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison
This paper introduces a dual-layer framework for network operator-side quality of experience (QoE) assessment that integrates both objective network modeling and subjective user perception extracted from live-streaming platforms. On the objective side, we develop a machine learning model trained on mean opinion scores (MOS) computed via the ITU-T P.1203 reference implementation, allowing accurate prediction of user-perceived video quality using only network parameters such as packet loss, delay…

AMRScan: A hybrid R and Nextflow toolkit for rapid antimicrobial resistance gene detection from sequencing data
Kaitao Lai
https://arxiv.org/abs/2507.08062

AMRScan: A hybrid R and Nextflow toolkit for rapid antimicrobial resistance gene detection from sequencing data
AMRScan is a hybrid bioinformatics toolkit implemented in both R and [Nextflow](https://www.nextflow.io/) for the rapid and reproducible detection of antimicrobial resistance (AMR) genes from next-generation sequencing (NGS) data. The toolkit enables users to identify AMR gene hits in sequencing reads by aligning them against reference databases such as CARD using BLAST. The R implementation provides a concise, script-based approach suitable for single-sample analysis, teaching, and rapid pro…