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arXiv preprints from January 1, 2026 through September 23, 2026 — 19:48:04 EST

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Posted in eess.SP · 2026-08-28 · Badr Eddine Ouakouak, Salah Eddine Zegrar, Hüseyin Arslan

CP-Aware OFDM-Based OOK Signaling

This letter addresses the challenge of cyclic prefix (CP) problem in orthogonal frequency division multiplexing (OFDM)-based on-off keying (OOK) generation for low-power Internet of Things (IoT) systems. We propose a CP-aware waveform design that generates the OOK signal over the entire CP-OFDM symbol duration, potentially breaking...

💬 0 commentsarXiv:2608.28196v1PDF
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Posted in eess.SY · 2026-08-28 · Hongjun Xie, Bowen Zhang, Genke Yang, Pengcheng Luo

SafeLink-Agent: Agentic Maintenance for Adaptive Bitrate Controllers over Dynamic Starlink Networks

Low Earth orbit (LEO) satellite broadband, represented by Starlink, is making high-resolution video streaming feasible beyond fixed terrestrial coverage. However, Starlink access links change across time and regions, exposing adaptive bitrate (ABR) streaming to shifting throughput tails, latency, volatility, and handover conditions....

💬 0 commentsarXiv:2608.28194v1PDF
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Posted in eess.SY · 2026-08-28 · Nan Bai, Tao Liu, Qishao Wang, Zhisheng Duan

Distributed Model Predictive Control for Optimal Consensus of Constrained Heterogeneous Multi-agent Systems

This paper investigates the distributed optimal consensus control problem of constrained heterogeneous multi-agent systems within a model predictive control (MPC) scheme. Both the control input sequence and the dynamically feasible consensus equilibrium are optimized simultaneously within the proposed MPC framework to improve...

💬 0 commentsarXiv:2608.28180v1PDF
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Posted in eess.SP · 2026-08-28 · Christian Eckrich, Abdelhak M. Zoubir, Vahid Jamali

Resource Allocation for Cloud Radar Networks with Communication Constraints

Distributed radar sensing exploits spatial diversity to resolve occlusions and improve estimation accuracy. Realizing these gains, however, relies on the transmission of high-dimensional radar data to a Fusion Center (FC). This imposes significant demands on the wireless network, especially in dense, dynamic, and interference-prone...

💬 0 commentsarXiv:2608.28173v1PDF
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Posted in eess.SP · 2026-08-28 · Sebastian Schertler, Daniel Guger, Stefan Schuster, Stefan Scheiblhofer, Mario Huemer, Alexander Haberl, Johann Reisinger, Oliver Lang

Fast Time-Domain MLE for Period Estimation of Pulse Trains

Parameter estimation of periodic pulse trains is a critical task in numerous automated sensing and diagnostic applications. While estimation in the time domain provides superior accuracy in low signal-to-noise ratio environments, its high computational complexity frequently precludes its use in real-time systems. This paper...

💬 0 commentsarXiv:2608.28162v1PDF
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Posted in eess.IV · 2026-08-28 · Naren Akash, Arihanth Tadanki, Jayanthi Sivaswamy

CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs

We present CheXtriev, a graph-based, anatomy-aware framework for chest radiograph retrieval. Unlike prior methods focussed on global features, our method leverages graph transformers to extract informative features from specific anatomical regions. Furthermore, it captures spatial context and the interplay between anatomical location...

💬 0 commentsarXiv:2608.28137v1PDF
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Posted in cs.SD · 2026-08-28 · Sungho Lee, Marco Martínez-Ramírez, Junghyun Koo, Wei-Hsiang Liao, Kyogu Lee, Yuki Mitsufuji

Exploring the Design Space of Representation Learning for Audio Transformations

Neural audio representation learning has enabled a range of content-oriented applications, but the resulting features remain limited for tasks involving audio processing. Furthermore, it is not obvious what processing-aware representations should capture: the processing itself, abstracted away from source content, or the processed...

💬 0 commentsarXiv:2608.28127v1PDF
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Posted in eess.SP · 2026-08-28 · Francesco Natili, Francesco Castellani, Davide Astolfi, Matteo Becchetti

Experimental and Signal Processing Techniques for Fault Diagnosis on a Small Horizontal-Axis Wind Turbine Generator

Small HAWT is a technology characterized by non-trivial critical points, basically because it is targeted for domestic use and therefore cheap manufacturing and control must conjugate with good efficiency under possibly complex flow conditions (especially in urban environment). Therefore, dynamical control optimization and noise and...

💬 0 commentsarXiv:2608.28105v1PDF
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Posted in eess.SY · 2026-08-28 · NIklas Braun, Leon J. Brettin, Marvin Loba, Markus Maurer

Managing Inherent Risk: On the Conceptualization of Risk in Defense Systems

Certain defense systems are, by nature, deployed in a civilian environment in order to serve a defensive function for that environment. However, the risk involved with their deployment and operation poses a challenge for the public acceptance of these systems. Compared to safety engineering for civilian systems, the risk constellation...

💬 0 commentsarXiv:2608.28093v1PDF
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Posted in quant-ph · 2026-08-28 · Piotr Sierant

Exact quantification of nonlocal magic

Magic, or nonstabilizerness, is the resource that lifts Clifford circuits to universal quantum computation and has become a standard diagnostic of many-body states. For a state shared between two parties, however, a basic question has remained open: how much of the magic resides in the correlations between the parties rather than in...

💬 0 commentsarXiv:2608.28563v1PDF
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Posted in cond-mat.soft · 2026-08-28 · Ryan van Mastrigt, Zorana Zeravcic

Machine learned designs of functional colloidal foldamers

A protein's function follows from the structure it adopts, and which structure that is depends on the pathway taken. In programmable matter the target is fixed before assembly, and whatever else forms is treated as error. Here we show that pathways themselves form a design space. Using reinforcement learning, we fold model DNA-coated...

💬 0 commentsarXiv:2608.28554v1PDF
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Posted in astro-ph.EP · 2026-08-28 · David Nesvorny, Daniel A. Yahalomi, David Kipping, Cristian Beauge, Sarah C. Millholland

ExoMOD II. A Statistical Model of Transit Timing Variations in Kepler Multi-Planet Systems

In Paper I (Nesvorný et al. 2026), we forward modeled transit observations of the Kepler telescope to characterize the orbital properties of close-in planetary systems. The new population model, ExoMOD, was calibrated on Kepler's DR25 data. Here we use ExoMOD to statistically predict Transit Timing Variations (TTVs) from...

💬 0 commentsarXiv:2608.28550v1PDF
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Posted in astro-ph.EP · 2026-08-28 · David Nesvorny, Daniel A. Yahalomi, David Kipping, Cristian Beauge

ExoMOD I. A Forward Model for the Orbital Architecture of Kepler Multi-Planet Systems

Transit observations only detect planets with favorable, near edge-on orientation of orbits as seen by a distant observer, which leaves much freedom for various interpretations in terms of the underlying planetary system architecture. Here we forward model transit observations of the Kepler telescope to characterize the orbital...

💬 0 commentsarXiv:2608.28548v1PDF
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Posted in cond-mat.mtrl-sci · 2026-08-28 · Hua Chen, Philipp Gegenwart

Switchable chiral antiferromagnetism through nonlinear magnetic susceptibility

Antiferromagnets (AFM) have attracted considerable attention in recent years because a number of nontrivial, technologically relevant properties associated with time-reversal symmetry (TRS) breaking are discovered in many materials. However, time-reversal (TR) partners of AFM states are generally challenging to be selected...

💬 0 commentsarXiv:2608.28546v1PDF
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Posted in hep-ph · 2026-08-28 · Martin Hentschinski, Karina Mendoza-Ramírez, Miguel A. Ocaña-Bribiesca

The Pomeron loop as a perturbative correction to single Pomeron exchange revisited

Pomeron loops are known to arise naturally as building blocks of high energy scattering amplitudes as soon as one starts to consider corrections to single Pomeron exchange, i.e. to high energy resummation based on the Balitsky-Fadin-Kuraev-Lipatov evolution equation. While some authors argue that Pomeron loops provide only very small...

💬 0 commentsarXiv:2608.28544v1PDF
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Posted in hep-ph · 2026-08-28 · Xin Wang, Shun Zhou

Deciphering Matter Invariants via Renormalization Group Equations for Neutrino Oscillations

We utilize renormalization group equations (RGEs) for neutrino oscillations in matter to decipher the structure of exact matter invariants. By combining the RGEs with the $S^{}_3$ permutation covariance under relabeling of the neutrino mass eigenstates, we recast all five algebraically independent matter invariants in the three-flavor...

💬 0 commentsarXiv:2608.28540v1PDF
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Posted in astro-ph.EP · 2026-08-28 · Reza Ashtari, Stephen P. Schmidt, Guangwei Fu, Avinash Verma, David K. Sing, Kevin B. Stevenson, Jayesh Goyal, Katherine A. Bennett, Joshua D. Lothringer, Jacob Lustig-Yaeger, Sagnick Mukherjee, Carlos Gascón, Natalie H. Allen, Patrick McCreery, Le-Chris Wang, Mei Ting Mak, Kristin S. Sotzen, Lakeisha M. Ramos Rosado, N. J. Mayne

A Clearer View of HAT-P-1 b: JWST NIRSpec G395H Reveals Water, Carbon Dioxide, and Possibly Hydrogen Sulfide

As part of JWST's Exoplanet Grand Tour Survey, we use panchromatic transmission spectroscopy to connect HAT-P-1 b's previously studied optical and near-infrared atmosphere to the longer-wavelength molecular bands accessible with JWST. We present JWST NIRSpec G395H transmission spectroscopy of the hot Jupiter HAT-P-1 b over...

💬 0 commentsarXiv:2608.28538v1PDF
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Posted in physics.flu-dyn · 2026-08-28 · Richard Mcnair, Kerstin Schirrmann, Anne Juel, Igor L. Chernyavsky

Compaction in a deformable porous cylinder with elastic boundaries

Perfusion of soft materials such as biological tissue or hydrogels is essential for the functioning of organ and laboratory systems such as chromatographic columns and bioreactors. Inspired by these applications, we model fluid-driven compaction in a long, thin cylindrical porous medium bounded by an impermeable elastic membrane and...

💬 0 commentsarXiv:2608.28537v1PDF
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Posted in astro-ph.HE · 2026-08-28 · Nathaniel Alden, Marissa Boucher, Cosmin Deaconu, Abigail Vieregg, Philipp Windischhofer

Exploring the sensitivity of in-ice radio detectors to cosmic ray mass composition

In-ice radio detectors have been developed primarily for the detection of high-energy neutrinos via the Askaryan effect, but have recently been shown to also be sensitive to cosmic ray air showers impacting the ice sheet. Using CORSIKA 8 to simulate impacting air showers, we find that the lateral width of the in-ice cascade is...

💬 0 commentsarXiv:2608.28536v1PDF
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Posted in stat.ML · 2026-08-28 · Lorenzo Rizzi, Arie Wortsman Zurich, Bruno Loureiro

Learning between the peaks: sharp asymptotics for kernel ridge regression under power-law anisotropy

We study kernel ridge regression under anisotropic Gaussian data, where the input covariance decays as a power law with exponent $α\geq 0$ for polynomial inner-product kernels. We derive asymptotically sharp expressions for the kernel spectrum and the generalization error in the polynomial high-dimensional regime $n=Θ(d^κ)$, revealing...

💬 0 commentsarXiv:2608.28564v1PDF
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Posted in stat.ME · 2026-08-28 · Jonathan Koop, Sara van Erp, Mahdi Shafiee Kamalabad

Refining Relational Event Models: Bayesian Penalization and Variable Selection in REMs

Relational Event Models (REMs) provide valuable insights into the dynamics of longitudinal social networks. Yet, the vast availability of potential predictors for a dyad's event rate poses the risk of selecting irrelevant variables and specifying an overfitted model that does not generalize to new data. Despite the recent popularity...

💬 0 commentsarXiv:2608.28419v1PDF
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Posted in stat.ML · 2026-08-28 · Tommaso dorigo

Localizing Global Discrepancies: Marginal Contributions and Contextual Anomaly Detection

Global goodness-of-fit and discrepancy statistics can establish that a sample departs from a reference distribution without identifying which observations drive the departure. We develop a framework for this localization problem by assigning to each observation its conditional or marginal contribution across random statistical...

💬 0 commentsarXiv:2608.28375v1PDF
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Posted in cs.CR · 2026-08-28 · Owen Cox, April Xu, Weiyu Xu

Optimal Adversarial Testing: Extracting Honest Test Results from Dishonest Test Takers

In applications, it is often required to test objects or people to determine their qualities in terms of certain metrics. However, besides being naturally noisy, the test results can be corrupted by adversarial behaviors of objects or people being tested (test takers). For example, dishonest test takers can cheat in the exams to...

💬 0 commentsarXiv:2608.28362v1PDF
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Posted in stat.ME · 2026-08-28 · Alessandro La Rocca

Response Propensity Estimation and Cross-Fitting

This paper investigates whether five fold cross fitting improves nonresponse adjustment in survey estimation when flexible machine learning methods are used to estimate response propensities. We conduct a finite population Monte Carlo simulation with 90 experimental configurations and 2,000 replications per configuration, varying...

💬 0 commentsarXiv:2608.28324v1PDF