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This study evaluates the uncertainty in nanodosimetric calculations caused by variations in interaction cross sections within Monte Carlo Track Structure (MCTS) simulation codes. Nanodosimetry relies on accurately simulating particle interactions at the molecular scale. Different MCTS codes employ distinct physical models and datasets for electron interactions in liquid water, a surrogate for biological tissues. The paper focuses on the Ionization Cluster Size Distribution (ICSD) generated by electrons of varying energies in nanometric volumes. Seven MCTS codes were tested using their native cross sections and a common dataset derived from averaging data used in the participating codes. The results reveal significant discrepancies among the codes in ICSDs and derived biologically relevant nanodosimetric quantities such as mean ionization numbers (M1) and probabilities of obtaining two or more ionizations (F2). The largest variations were observed for low-energy electrons, where the contribution from interaction cross sections dominates the overall uncertainties. For instance, M1 values for ICSDs of electron of 20 eV can differ by around 45 % (RSD) and 34 % (RSD) was found for F2 values of ICSDs of electrons of 50 eV. Using common cross sections substantially reduced the discrepancies, suggesting that cross section datasets are the primary source of variability. Finally, estimates of deoxyribonucleic acid (DNA) damage using the PARTRAC code highlight tht cross section variations have a non-negligible impact simulated biological outcomes, particularly for double-strand breaks (DSBs) Indeed, despite the fact that many other parameters in the simulation that can greatly differ from one code to another, the different interaction cross-sections studied in this work can lead to differences in the number of DSBs calculated with the PARTRAC code of up to 15%., Comment: 39 pages, 8 figures, 14 tables; submitted to PLOS One

We present a simple PDE construction of the sine-Gordon measure below the first threshold ($\be^2 < 4\pi$), in both the finite and infinite volume settings, by studying the corresponding parabolic sine-Gordon model. We also establish pathwise global well-posedness of the hyperbolic sine-Gordon model in finite volume for $\be^2 < 2\pi$.

Modern wireless communication systems have become increasingly complex due to the proliferation of wireless devices, increasing performance standards, and growing security threats. Managing these networks is becoming more challenging, requiring the use of advanced network management methods and tools. AI-driven network management systems such as Self-Optimizing Networks (SONs) are gaining attention. On the other hand, Large Language Models (LLMs) have been demonstrating exceptional zero-shot learning and generalization capabilities across several domains. In this paper, we leverage the potential of LLMs with SONs to enhance future network management systems. Specifically, we benchmark the use of various LLMs such as GPT-4, Llama, and Falcon, in a zero-shot setting based on their real-time network configuration recommendations. Our results indicate promising prospects for integrating LLMs into future network management systems.

Connected cars offer safety and efficiency for both individuals and fleets of private vehicles and public transportation companies. However, equipping vehicles with information and communication technologies raises privacy and security concerns, which significantly threaten the user's data and life. Using bot malware, a hacker may compromise a vehicle and control it remotely, for instance, he can disable breaks or start the engine remotely. In this paper, besides in-vehicle attacks existing in the literature, we consider new zeroday bot malware attacks specific to the vehicular context, WSMP-Flood, and Geo-WSMP Flood. Then, we propose AntibotV, a multilevel behaviour-based framework for vehicular botnets detection in vehicular networks. The proposed framework combines two main modules for attack detection, the first one monitors the vehicle's activity at the network level, whereas the second one monitors the in-vehicle activity. The two intrusion detection modules have been trained on a historical network and in-vehicle communication using decision tree algorithms. The experimental results showed that the proposed framework outperforms existing solutions, it achieves a detection rate higher than 97% and a false positive rate lower than 0.14%.

Current autonomous driving systems heavily rely on V2X communication data to enhance situational awareness and the cooperation between vehicles. However, a major challenge when using V2X data is that it may not be available periodically because of unpredictable delays and data loss during wireless transmission between road stations and the receiver vehicle. This issue should be considered when designing control strategies for connected and autonomous vehicles. Therefore, this paper proposes a novel 'Blind Actor-Critic' algorithm that guarantees robust driving performance in V2X environment with delayed and/or lost data. The novel algorithm incorporates three key mechanisms: a virtual fixed sampling period, a combination of Temporal-Difference and Monte Carlo learning, and a numerical approximation of immediate reward values. To address the temporal aperiodicity problem of V2X data, we first illustrate this challenge. Then, we provide a detailed explanation of the Blind Actor-Critic algorithm where we highlight the proposed components to compensate for the temporal aperiodicity problem of V2X data. We evaluate the performance of our algorithm in a simulation environment and compare it to benchmark approaches. The results demonstrate that training metrics are improved compared to conventional actor-critic algorithms. Additionally, testing results show that our approach provides robust control, even under low V2X network reliability levels., Comment: 11 pages, 10 figures, Journal paper, submitted to IEEE Transactions on Intelligent Transportation Systems

In this work we provide a new, well-controlled expansion of the equation of state of dense matter from zero to finite temperatures ($T$), while covering a wide range of charge fractions ($Y_Q$), from pure neutron to isospin symmetric nuclear matter. Our expansion can be used to describe neutron star mergers and core-collapse supernova explosions using as a starting point neutron star observations, while maintaining agreement with laboratory data, in a model independent way. We suggest new thermodynamic quantities of interest that can be calculated from theoretical models or directly inferred by experimental data that can help constrain the finite $T$ equation of state. With our new method, we can quantify the uncertainty in our finite $T$ and $Y_Q$ expansions in a well-controlled manner without making assumptions about the underlying degrees of freedom. We can reproduce results from a microscopic equation of state up to $T=100$ MeV for baryon chemical potential $\mu_B\gtrsim 1100$ MeV ($\sim1-2 \ n_{\rm sat}$) within $5\%$ error, with even better results for larger $\mu_B$ and/or lower $T$. We investigate the sources of numerical and theoretical uncertainty and discuss future directions of study., Comment: 33 pages, 12 figures

A susceptible, asymptomatic, infectious, quarantined, and hospitalized (SAIQH) compartmental model on time scales is introduced and a suitable Lyapunov function is defined. Main results include: the proof that the system is permanent; proof of existence of solution; and sufficient conditions implying the dynamic system to have a unique almost periodic solution that is uniformly asymptotically stable. An example is presented supporting the obtained results., Comment: 11 page, 6 figures

Using the recent weighted generalized fractional order operators of Hattaf, a general fractional optimal control problem without constraints on the values of the control functions is formulated and a corresponding (weak) version of Pontryagin's maximum principle is proved. As corollaries, necessary optimality conditions for Caputo-Fabrizio, Atangana-Baleanu and weighted Atangana-Baleanu fractional dynamic optimization problems are trivially obtained. As an application, the weighted generalized fractional problem of the calculus of variations is investigated and a new more general fractional Euler-Lagrange equation is given., Comment: This is a preprint version of the paper published open access in 'Results in Control and Optimization 14 (2024), Art. 100356, 5 pp' [https://doi.org/10.1016/j.rico.2023.100356]

We study the Cauchy problem for the cubic fractional nonlinear Schr\"odinger equation (fNLS) on the real line and on the circle. In particular, we prove global well-posedness of the cubic fNLS with all orders of dispersion higher than the usual Schr\"odinger equation in negative Sobolev spaces. On the real line, our well-posedness result is sharp in the sense that a contraction argument does not work below the threshold regularity. On the circle, due to ill-posedness of the cubic fNLS in negative Sobolev spaces, we study the renormalized cubic fNLS. In order to overcome the failure of local uniform continuity of the solution map in negative Sobolev spaces, by applying a gauge transform and partially iterating the Duhamel formulation, we study the resulting equation with a cubic-quintic nonlinearity. In proving uniqueness, we present full details justifying the use of the normal form reduction for rough solutions, which seem to be missing from the existing literature. Our well-posedness result on the circle extends those in Miyaji-Tsutsumi (2018) and Oh-Wang (2018) to the endpoint regularity., Comment: 49 pages

Over the last few decades, both heat and broadband sound reduction have become increasingly significant as a result of concerns about the environment and noise pollution. In order to address this challenge, we provide a finite element analysis study of an acoustic metamaterial panel consisting of a unit cell made of two Helmholtz Resonators with a guide in between. These panels can attenuate and control both sound propagation and heat flux. For noise pollution in a building, we first determine the geometric dimension that corresponds to the operative frequency range. Furthermore, we investigate the sound transmission loss of the proposed panel as a function of the periodicity of an array of unit cells. Additionally, we investigate the thermal flux induced by the panel, especially within a 24-hour period. The simulation results show that the proposed panel provides a level of sound attenuation within the frequency range of 400 Hz to 2.5 kHz, as well as interesting heat protection. The structure is compared to panels made up of a homogeneous medium and without Helmholtz resonators., Comment: 7 pages, 10 figures

Wireless communications at high-frequency bands with large antenna arrays face challenges in beam management, which can potentially be improved by multimodality sensing information from cameras, LiDAR, radar, and GPS. In this paper, we present a multimodal transformer deep learning framework for sensing-assisted beam prediction. We employ a convolutional neural network to extract the features from a sequence of images, point clouds, and radar raw data sampled over time. At each convolutional layer, we use transformer encoders to learn the hidden relations between feature tokens from different modalities and time instances over abstraction space and produce encoded vectors for the next-level feature extraction. We train the model on a combination of different modalities with supervised learning. We try to enhance the model over imbalanced data by utilizing focal loss and exponential moving average. We also evaluate data processing and augmentation techniques such as image enhancement, segmentation, background filtering, multimodal data flipping, radar signal transformation, and GPS angle calibration. Experimental results show that our solution trained on image and GPS data produces the best distance-based accuracy of predicted beams at 78.44%, with effective generalization to unseen day scenarios near 73% and night scenarios over 84%. This outperforms using other modalities and arbitrary data processing techniques, which demonstrates the effectiveness of transformers with feature fusion in performing radio beam prediction from images and GPS. Furthermore, our solution could be pretrained from large sequences of multimodality wireless data, on fine-tuning for multiple downstream radio network tasks.

Cross-lingual open information extraction aims to extract structured information from raw text across multiple languages. Previous work uses a shared cross-lingual pre-trained model to handle the different languages but underuses the potential of the language-specific representation. In this paper, we propose an effective multi-stage tuning framework called MT4CrossIE, designed for enhancing cross-lingual open information extraction by injecting language-specific knowledge into the shared model. Specifically, the cross-lingual pre-trained model is first tuned in a shared semantic space (e.g., embedding matrix) in the fixed encoder and then other components are optimized in the second stage. After enough training, we freeze the pre-trained model and tune the multiple extra low-rank language-specific modules using mixture-of-LoRAs for model-based cross-lingual transfer. In addition, we leverage two-stage prompting to encourage the large language model (LLM) to annotate the multi-lingual raw data for data-based cross-lingual transfer. The model is trained with multi-lingual objectives on our proposed dataset OpenIE4++ by combing the model-based and data-based transfer techniques. Experimental results on various benchmarks emphasize the importance of aggregating multiple plug-in-and-play language-specific modules and demonstrate the effectiveness of MT4CrossIE in cross-lingual OIE\footnote{\url{https://github.com/CSJianYang/Multilingual-Multimodal-NLP}}., Comment: 10 pages

The main objective of this chapter is to present an overview of the different areas of key technologies that will be needed to fly the technically most challenging of the representative missions identified in chapter 4 (the Pillar 2 Horizon 2061 report). It starts with a description of the future scientific instruments which will address the key questions of Horizon 2061 described in chapter 3 (the Pillar 1 Horizon 2061 report) and the new technologies that the next generations of space instruments will require (section 2). From there, the chapter follows the line of logical development and implementation of a planetary mission: section 3 describes some of the novel mission architectures that will be needed and how they will articulate interplanetary spacecraft and science platforms; section 4 summarizes the system-level technologies needed: power, propulsion, navigation, communication, advanced autonomy on board planetary spacecraft; section 5 describes the diversity of specialized science platforms that will be needed to survive, operate and return scientific data from the extreme environments that future missions will target; section 6 describes the new technology developments that will be needed for long-duration missions and semi-permanent settlements; finally, section 7 attempts to anticipate on the disruptive technologies that should emerge and progressively prevail in the decades to come to meet the long-term needs of future planetary missions., Comment: 100 pages, 23 figures, Horizon 2061 is a science-driven, foresight exercise, for future scientific investigations

We study the limiting behavior of the two-dimensional singular stochastic stochastic cubic nonlinear complex Ginzburg-Landau with Gibbs measure initial data. We show that in the appropriate small viscosity and small noise regimes, the limiting dynamics is given by the deterministic cubic nonlinear Schr\"odinger equation at Gibbs equilibrium. In order to obtain this convergence, our approach combines both heat and Schr\"odinger analysis, within the framework of the Fourier restriction norm method of Bourgain (1993).