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Unmanned aerial vehicles (UAVs) are expected to play a key role in 6G-enabled vehicular-to-everything (V2X) communications requiring high data rates, low latency, and reliable connectivity for mission-critical applications. Multi-input multi-output (MIMO) technology is essential for meeting these demands. However, UAV link performance is significantly affected by environmental factors such as signal attenuation, multipath propagation, and blockage from obstacles, particularly dense foliage in rural areas. In this paper, we investigate RF coverage and channel rank over UAV channels in foliage-dominated rural environments using ray tracing (RT) simulations. We conduct RT-based channel rank and RF coverage analysis over Lake Wheeler Field Labs at NC State University to examine the impact on UAV links. Custom-modeled trees are integrated into the RT simulations using NVIDIA Sionna, Blender, and Open Street Map (OSM) database to capture realistic blockage effects. Results indicate that tree-induced blockage impacts RF coverage and channel rank at lower UAV altitudes. We also propose a Kriging interpolation-based 3D channel rank interpolation scheme, leveraging the observed spatial correlation of channel rank in the given environments. The accuracy of the proposed scheme is evaluated using the mean absolute error (MAE) metric and compared against baseline interpolation methods. Finally, we compare the RT-based received signal strength (RSS) and channel rank results with real-world measurements from the NSF AERPAW testbed demonstrating reasonable consistency between simulation results and the measurements.

This paper presents a novel blockchain-enabled architecture for efficient decentralized space surveillance. Our simulation results indicate that a network under 30 nodes achieves optimal throughput and response time. We also compare our architecture with a fully participatory consensus model, where all nodes perform both verification and approval tasks. Across all scenarios, our approach demonstrates a 9x improvement in both throughput and response time compared to the full participatory consensus, highlighting the efficiency gains achieved by assigning dedicated roles for verification and approval. Future work will explore the impact of faulty nodes and potential security threats on network performance.

Dataset distillation has emerged as a strategy to compress real-world datasets for efficient training. However, it struggles with large-scale and high-resolution datasets, limiting its practicality. This paper introduces a novel resolution-independent dataset distillation method Focus ed Dataset Distillation (FocusDD), which achieves diversity and realism in distilled data by identifying key information patches, thereby ensuring the generalization capability of the distilled dataset across different network architectures. Specifically, FocusDD leverages a pre-trained Vision Transformer (ViT) to extract key image patches, which are then synthesized into a single distilled image. These distilled images, which capture multiple targets, are suitable not only for classification tasks but also for dense tasks such as object detection. To further improve the generalization of the distilled dataset, each synthesized image is augmented with a downsampled view of the original image. Experimental results on the ImageNet-1K dataset demonstrate that, with 100 images per class (IPC), ResNet50 and MobileNet-v2 achieve validation accuracies of 71.0% and 62.6%, respectively, outperforming state-of-the-art methods by 2.8% and 4.7%. Notably, FocusDD is the first method to use distilled datasets for object detection tasks. On the COCO2017 dataset, with an IPC of 50, YOLOv11n and YOLOv11s achieve 24.4% and 32.1% mAP, respectively, further validating the effectiveness of our approach.

The rapid development of Vehicular Ad-hoc Networks (VANETs) within the Internet of Vehicles (IoV) necessitates efficient and secure authentication methods to support high-speed, high-density environments. Current group authentication schemes provide user identity protection and unlinkability but face limitations, such as reliance on a central group manager and vulnerability to collaborative attacks. This paper presents a privacy-preserving authentication scheme that incorporates batch authentication, mutual authentication, and secure key establishment, enabling users to authenticate one another without a central authority. Our proposed scheme facilitates simultaneous multi-user authentication, significantly enhancing scalability and security in dynamic IoV networks. Results from realistic implementations show that our method achieves average authentication and verification times of 10.61 ms and 1.78 ms, respectively, for a fleet of 100 vehicles, outperforming existing methods. Scalability tests demonstrate efficient processing for larger groups of up to 500 vehicles, where average authentication times remain low, establishing our scheme as a robust solution for secure communication in IoV systems.

Today, crop diversification in agriculture is a critical issue to meet the increasing demand for food and improve food safety and quality. This issue is considered to be the most important challenge for the next generation of agriculture due to the diminishing natural resources, the limited arable land, and unpredictable climatic conditions caused by climate change. In this paper, we employ emerging technologies such as the Internet of Things (IoT), machine learning (ML), and explainable artificial intelligence (XAI) to improve operational efficiency and productivity in the agricultural sector. Specifically, we propose an edge computing-based explainable crop recommendation system, AgroXAI, which suggests suitable crops for a region based on weather and soil conditions. In this system, we provide local and global explanations of ML model decisions with methods such as ELI5, LIME, SHAP, which we integrate into ML models. More importantly, we provide regional alternative crop recommendations with the counterfactual explainability method. In this way, we envision that our proposed AgroXAI system will be a platform that provides regional crop diversity in the next generation agriculture., Comment: Accepted in 2024 IEEE International Conference on Big Data (IEEE BigData), 10 pages, 9 Figures, 5 Tables

The interface between a ferromagnetic metal and an organic molecular semiconductor, commonly referred to as a spinterface, is an important component for advancing spintronic technologies. Hybridization of the ferromagnetic-metal surface d orbitals with the molecular-semiconductor p orbitals induces profound modifications not only in the interfacial molecular layer, but also in the surface ferromagnetic-metal atomic layer. These effects are particularly pronounced at low temperatures, manifesting as substantial modifications in the magnetic properties of thin-film magnetic-metal/organic heterostructures. Despite extensive research and interest, the magnetic-ordering and magnetic-properties of the spinterface remain poorly understood. Using ultrafast time-resolved magneto-optical spectroscopy, to investigate the magnetic dynamics in such heterostructures, we unveil the unique spinterface-magnetism and its universality for a broad variety of cobalt/molecular-semiconductor interfaces. In particular, our findings demonstrate the presence of highly anisotropic low-temperature superparamagnetism at the cobalt/molecular-semiconductor spinterface. This anisotropic interfacial superparamagnetism is likely driven by strong chemical modifications in the cobalt interfacial layer caused by the chemisorbed molecular layer. These results highlight the pivotal role of molecular chemisorption in tuning the magnetic properties at spinterfaces, paving the way for future spintronic applications.

Recent advances in Large Language Models (LLMs) have positively and efficiently transformed workflows in many domains. One such domain with significant potential for LLM integration is the Internet of Things (IoT), where this integration brings new opportunities for improved decision making and system interaction. In this paper, we explore the various roles of LLMs in IoT, with a focus on their reasoning capabilities. We show how LLM-IoT integration can facilitate advanced decision making and contextual understanding in a variety of IoT scenarios. Furthermore, we explore the integration of LLMs with edge, fog, and cloud computing paradigms, and show how this synergy can optimize resource utilization, enhance real-time processing, and provide scalable solutions for complex IoT applications. To the best of our knowledge, this is the first comprehensive study covering IoT-LLM integration between edge, fog, and cloud systems. Additionally, we propose a novel system model for industrial IoT applications that leverages LLM-based collective intelligence to enable predictive maintenance and condition monitoring. Finally, we highlight key challenges and open issues that provide insights for future research in the field of LLM-IoT integration., Comment: Accepted in 2024 IEEE International Conference on Big Data (IEEE BigData), 10 pages, 2 figures, 1 table

The NOvA collaboration reports cross-section measurements for $\nu_{\mu}$ charged-current interactions with low hadronic energy (maximum kinetic energy of 250 MeV for protons and 175 MeV for pions) in the NOvA Near Detector. The results are presented as a double-differential cross section as a function of the direct observables of the final-state muon kinematics. Results are also presented as a single-differential cross section as a function of the derived square of the four-momentum transfer, $Q^{2}$, and as a function of the derived neutrino energy. The data correspond to an accumulated 8.09$\times10^{20}$ protons-on-target (POT) in the neutrino mode of the NuMI beam, with a narrow band of neutrino energies peaked at 1.8 GeV. The analysis provides a sample of neutrino-nucleus interactions with an enhanced fraction of quasi-elastic and two-particle-two-hole (2p2h) interactions. This enhancement allows quantitative comparisons with various nuclear models. We find strong disagreement between data and theory-based models in various regions of the muon kinematic phase space, especially in the forward muon direction., Comment: 20 pages, 12 figures. The second version includes an additional citation and adds four previously missing authors

Data-driven methods have shown great potential in solving challenging manipulation tasks; however, their application in the domain of deformable objects has been constrained, in part, by the lack of data. To address this lack, we propose PokeFlex, a dataset featuring real-world multimodal data that is paired and annotated. The modalities include 3D textured meshes, point clouds, RGB images, and depth maps. Such data can be leveraged for several downstream tasks, such as online 3D mesh reconstruction, and it can potentially enable underexplored applications such as the real-world deployment of traditional control methods based on mesh simulations. To deal with the challenges posed by real-world 3D mesh reconstruction, we leverage a professional volumetric capture system that allows complete 360{\deg} reconstruction. PokeFlex consists of 18 deformable objects with varying stiffness and shapes. Deformations are generated by dropping objects onto a flat surface or by poking the objects with a robot arm. Interaction wrenches and contact locations are also reported for the latter case. Using different data modalities, we demonstrated a use case for our dataset training models that, given the novelty of the multimodal nature of Pokeflex, constitute the state-of-the-art in multi-object online template-based mesh reconstruction from multimodal data, to the best of our knowledge. We refer the reader to our website ( https://pokeflex-dataset.github.io/ ) for further demos and examples., Comment: This work has been submitted to the IEEE for possible publication

Double- and single-differential cross sections for inclusive charged-current neutrino-nucleus scattering are reported for the kinematic domain 0 to 2 GeV/c in three-momentum transfer and 0 to 2 GeV in available energy, at a mean muon-neutrino energy of 1.86 GeV. The measurements are based on an estimated 995,760 muon-neutrino CC interactions in the scintillator medium of the NOvA Near Detector. The subdomain populated by 2-particle-2-hole reactions is identified by the cross-section excess relative to predictions for neutrino-nucleus scattering that are constrained by a data control sample. Models for 2-particle-2- hole processes are rated by chi-square comparisons of the predicted-versus-measured muon-neutrino CC inclusive cross section over the full phase space and in the restricted subdomain. Shortfalls are observed in neutrino generator predictions obtained using the theory-based Val`encia and SuSAv2 2p2h models., Comment: 20 pages, 15 figures. Accepted for publication in Physical Review D

This paper presents a novel geometric interpretation of LayerNorm and explores how LayerNorm influences the norm and orientation of hidden vectors in the representation space. With these geometric insights, we prepare the foundation for comparing LayerNorm with RMSNorm. We show that the definition of LayerNorm is innately linked to the uniform vector, defined as $\boldsymbol{1} = [1, 1, 1, 1, \cdots, 1]^T \in \mathbb{R}^d$. We then show that the standardization step in LayerNorm can be understood in three simple steps: (i) remove the component of a vector along the uniform vector, (ii) normalize the remaining vector, and (iii) scale the resultant vector by $\sqrt{d}$, where $d$ is the dimensionality of the representation space. We also provide additional insights into how LayerNorm operates at inference time. Finally, we compare the hidden representations of LayerNorm-based LLMs with models trained using RMSNorm and show that all LLMs naturally operate orthogonal to the uniform vector at inference time, that is, on average they do not have a component along the uniform vector during inference. This presents the first mechanistic evidence that removing the component along the uniform vector in LayerNorm is a redundant step. These results advocate for using RMSNorm over LayerNorm which is also more computationally efficient.

We present the Wavefront Sensor units of the Gravity Plus Adaptive Optics (GPAO) system, which will equip all 8m class telescopes of the VLTI and is an instrumental part of the GRAVITY+ project. It includes two modules for each Wavefront Sensor unit: a Natural Guide Star sensor with high-order 40x40 Shack-Hartmann and a Laser Guide Star 30x30 sensor. The state-of-the-art AO correction will considerably improve the performance for interferometry, in particular high-contrast observations for NGS observations and all-sky coverage with LGS, which will be implemented for the first time on VLTI instruments. In the following, we give an overview of the Wavefront Sensor units system after completion of their integration and characterization., Comment: Optical and Infrared Interferometry and Imaging IX

In the last few years there have been rapid developments in SMT solving for finite fields. These include new decision procedures, new implementations of SMT theory solvers, and new software verifiers that rely on SMT solving for finite fields. To support interoperability in this emerging ecosystem, we propose the SMT-LIB theory of finite field arithmetic (FFA). The theory defines a canonical representation of finite field elements as well as definitions of operations and predicates on finite field elements.

As wireless researchers are tasked to enable wireless communication as infrastructure in more dynamic aerial settings, there is a growing need for large-scale experimental platforms that provide realistic, reproducible, and reliable experimental validation. To bridge the research-to-implementation gap, the Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) offers open-source tools, reference experiments, and hardware to facilitate and evaluate the development of wireless research in controlled digital twin environments and live testbed flights. The inaugural AERPAW Challenge, "Find a Rover," was issued to spark collaborative efforts and test the platform's capabilities. The task involved localizing a narrowband wireless signal, with teams given ten minutes to find the "rover" within a twenty-acre area. By engaging in this exercise, researchers can validate the platform's value as a tool for innovation in wireless communications research within aerial robotics. This paper recounts the methods and experiences of the top three teams in automating and rapidly locating a wireless signal by automating and controlling an aerial drone in a realistic testbed scenario., Comment: submitted to IEEE magazine paper

This study introduces a decentralized approach to secure wireless communication using a cryptographic secret key generation algorithm among distributed nodes. The system model employs Gaussian prime numbers, ensuring the collaborative generation of a secret key. Pre-processing and post-processing functions enable to generate a secret key across the network. An error model evaluates aspects like thermal noise power and channel estimation errors, while simulations assess the success rate to factorize the norm of the secret key. It is observed that path loss-induced large scale fading emerges as a critical component impacting information and power loss. The robustness of the proposed model under fading channel conditions is evaluated with a success rate. Additionally, it is also observed that the tolerance value set in the factorization algorithms has a significant impact on the success rate. Furthermore, the success rate is compared in two scenarios, one with 2 users and another with 3 users, to provide a comprehensive evaluation of the system performance., Comment: 6 pages, 8 figures, ICCSPA'24 Conference

Density functional theory and many-body (GW+BSE) calculations of transmittance, absorbance, and reflectance are performed on silicon and black phosphorus (BP). We find that a damping value of 0.01 used in the dielectric function calculation is the optimal for calculating the solar cell efficiency of Si. Our calculations indicate that the solar cell efficiency of a 100 {\mu}m thick Si slab is 27.4% while the efficiency of BP for the same thickness is 2.33% and 1.94% for light polarized along the zigzag and armchair directions, respectively. For 100 nm thick materials, we obtain that Si presents a 0.8% solar cell efficiency and BP exhibits a 0.14% and 1.02% efficiency for light polarized along the the zigzag and armchair directions, respectively, indicating that BP performs better than Si at these small scales. Our results underscore the important effect of the material thickness on solar cell efficiencies.

Magnetic skyrmions are promising candidates for future information storing and processing devices. There are different routes for stabilizing the skyrmions. Understanding the interplay mechanism between different scenarios of skyrmion formation is one key factor that can reveal new paths for controlling skyrmion phases. Inspired by the flexibility of two-dimensional materials that offer an exciting playground for manipulating spin textures, we conducted \textit{ab initio} simulations and utilized four-state spin framework to determine magnetic parameters of lateral heterostructure formed by NiBr2 and NiCl2 Monolayers. The obtained spin interaction parameters are utilized to determine the skyrmionic phases via Monte Carlo simulation. Monte Carlo simulation results suggest three distinct phase transition mechanisms exist in the present system. Namely, examination of heat capacity versus temperature curve obtained from average anisotropic exchange energy yields a phase transition between paramagnetic and spin-spiral states at $5$K for pristine NiBr2, a paramagnetic-mixed skyrmion state transition occurs at 17 K for pristine NiCl2, and a high-temperature ferromagnetic-paramagnetic transition at T=80 K is observed for the heterostructure region, indicating that some kind of intrinsic ferromagnetism may originate at the interface of pristine Janus structures.