Hasil untuk "Environmental protection"

Moisés J. B. B. Davi, Felipe V. Lopes, Vinícius A. Lacerda et al.

The ongoing global transition towards low-carbon energy has propelled the integration of offshore wind farms, which, when combined with Modular Multilevel Converter-based High-Voltage Direct Current (MMC-HVDC) transmission, present unique challenges for power system protection. In collector cables connecting wind turbines to offshore MMC, both ends are supplied by Inverter-Based Resources (IBRs), which modify the magnitude and characteristics of fault currents. In this context, this paper investigates the limitations of conventional differential protection schemes under such conditions and compares them with enhanced strategies that account for sequence components. Using electromagnetic transient simulations of a representative offshore wind farm modeled in PSCAD/EMTDC software, internal and external fault scenarios are assessed, varying fault types and resistances. The comparative evaluation provides insights into the sensitivity and selectivity of differential protection and guides a deeper conceptual understanding of the evolving protection challenges inherent to future converter-dominated grids.

Xinwen Chen, Ruisi He, Mi Yang et al.

In urban environments, vehicle-to-everything (V2X) communications require accurate wireless channel characterization. This requirement is particularly critical at street-canyon intersections, where building blockage and rich multipath propagation can severely degrade link reliability. Due to its unique environmental layout, the channel characteristics in urban canyon are influenced by building distribution. However, this feature has not been well captured in existing channel models. In this paper, we propose an environment-related statistical channel model based on 5.8~GHz channel measurements. We construct a composite environmental factor to characterize environmental differences in intersections. Then, the factor is incorporated into 3GPP path-loss model and further linked to small-scale channel parameters. Finally, accuracy of the proposed model is validated using second-order channel statistics. The results show that the proposed model can effectively characterize propagation properties of urban street-canyon intersection channels with different building conditions. The proposed model provides a physically interpretable and statistically effective framework for channel simulation and performance evaluation in urban vehicular scenarios.

Chiyu Chen, Xinhao Song, Yunkai Chai et al.

Vision-Language Models (VLMs) are increasingly deployed as autonomous agents to navigate mobile graphical user interfaces (GUIs). Operating in dynamic on-device ecosystems, which include notifications, pop-ups, and inter-app interactions, exposes them to a unique and underexplored threat vector: environmental injection. Unlike prompt-based attacks that manipulate textual instructions, environmental injection corrupts an agent's visual perception by inserting adversarial UI elements (for example, deceptive overlays or spoofed notifications) directly into the GUI. This bypasses textual safeguards and can derail execution, causing privacy leakage, financial loss, or irreversible device compromise. To systematically evaluate this threat, we introduce GhostEI-Bench, the first benchmark for assessing mobile agents under environmental injection attacks within dynamic, executable environments. Moving beyond static image-based assessments, GhostEI-Bench injects adversarial events into realistic application workflows inside fully operational Android emulators and evaluates performance across critical risk scenarios. We further propose a judge-LLM protocol that conducts fine-grained failure analysis by reviewing the agent's action trajectory alongside the corresponding screenshot sequence, pinpointing failure in perception, recognition, or reasoning. Comprehensive experiments on state-of-the-art agents reveal pronounced vulnerability to deceptive environmental cues: current models systematically fail to perceive and reason about manipulated UIs. GhostEI-Bench provides a framework for quantifying and mitigating this emerging threat, paving the way toward more robust and secure embodied agents.

Lukáš Likavčan

Outer space exploration is one of the most prominent domains of earth-space governance. In this context, multiple policy documents by the UN, NASA, or Committee on Space Research (COSPAR) pledge to protect extraterrestrial environments from harmful human influence under the framework of planetary protection or planetary stewardship, understood primarily as the isolation of other celestial bodies from possible biological contamination. This paper analyses justifications of this framework that rely on analogies with the protection of terrestrial wilderness and nature's intrinsic value, portraying them as representative of a conservationist paradigm of earth-space governance. After presenting this paradigm, the paper builds an alternative constructivist paradigm, grounded in recent findings about the evolution of the solar system, planets, and life. Ultimately, the paper argues that conservation is not the opposite of construction but one of its modalities: a conclusion that encourages the development of pragmatic protocols for space exploration instead of absolute imperatives.

Adam Swanda, Amy Chang, Alexander Chen et al.

The widespread adoption of Large Language Models (LLMs) has revolutionized AI deployment, enabling autonomous and semi-autonomous applications across industries through intuitive language interfaces and continuous improvements in model development. However, the attendant increase in autonomy and expansion of access permissions among AI applications also make these systems compelling targets for malicious attacks. Their inherent susceptibility to security flaws necessitates robust defenses, yet no known approaches can prevent zero-day or novel attacks against LLMs. This places AI protection systems in a category similar to established malware protection systems: rather than providing guaranteed immunity, they minimize risk through enhanced observability, multi-layered defense, and rapid threat response, supported by a threat intelligence function designed specifically for AI-related threats. Prior work on LLM protection has largely evaluated individual detection models rather than end-to-end systems designed for continuous, rapid adaptation to a changing threat landscape. We present a production-grade defense system rooted in established malware detection and threat intelligence practices. Our platform integrates three components: a threat intelligence system that turns emerging threats into protections; a data platform that aggregates and enriches information while providing observability, monitoring, and ML operations; and a release platform enabling safe, rapid detection updates without disrupting customer workflows. Together, these components deliver layered protection against evolving LLM threats while generating training data for continuous model improvement and deploying updates without interrupting production.

Fahad Shamshad, Muzammal Naseer, Karthik Nandakumar

Deep learning-based face recognition (FR) systems pose significant privacy risks by tracking users without their consent. While adversarial attacks can protect privacy, they often produce visible artifacts compromising user experience. To mitigate this issue, recent facial privacy protection approaches advocate embedding adversarial noise into the natural looking makeup styles. However, these methods require training on large-scale makeup datasets that are not always readily available. In addition, these approaches also suffer from dataset bias. For instance, training on makeup data that predominantly contains female faces could compromise protection efficacy for male faces. To handle these issues, we propose a test-time optimization approach that solely optimizes an untrained neural network to transfer makeup style from a reference to a source image in an adversarial manner. We introduce two key modules: a correspondence module that aligns regions between reference and source images in latent space, and a decoder with conditional makeup layers. The untrained decoder, optimized via carefully designed structural and makeup consistency losses, generates a protected image that resembles the source but incorporates adversarial makeup to deceive FR models. As our approach does not rely on training with makeup face datasets, it avoids potential male/female dataset biases while providing effective protection. We further extend the proposed approach to videos by leveraging on temporal correlations. Experiments on benchmark datasets demonstrate superior performance in face verification and identification tasks and effectiveness against commercial FR systems. Our code and models will be available at https://github.com/fahadshamshad/deep-facial-privacy-prior

Yao Yao, Guanghui Li, Xin Ning

This work introduces a concept of origami electronic membranes that leverages the design and fabrication of flexible electronics and the mechanical behavior of engineering origami to achieve unique multifunctional, shape-reconfigurable, and adaptive membranes for mechanical and environmental sensing in benign and harsh conditions. This paper presents the materials, design, and fabrication methods for realizing six origami electronic membranes capable of reconfiguring planar or three-dimensional shapes based on the modified flasher, Kresling, Miura-ori, circular, letter, and Tachi-Miura origami patterns. These origami-based, thin-film flexible electronics can obtain both expansion and folding of their shapes, as well as transformation between different geometries. The origami electronic membranes can achieve mechanical and environmental sensing functions such as measuring motions, mechanical strains, temperatures, UV light, and humidity. The results reported here demonstrate the promise of combining engineering origami with flexible electronics to advance the state-of-the-art in multifunctional foldable and deployable electronics and systems.

Enyan Dai, Minhua Lin, Suhang Wang

Pretraining on Graph Neural Networks (GNNs) has shown great power in facilitating various downstream tasks. As pretraining generally requires huge amount of data and computational resources, the pretrained GNNs are high-value Intellectual Properties (IP) of the legitimate owner. However, adversaries may illegally copy and deploy the pretrained GNN models for their downstream tasks. Though initial efforts have been made to watermark GNN classifiers for IP protection, these methods require the target classification task for watermarking, and thus are not applicable to self-supervised pretraining of GNN models. Hence, in this work, we propose a novel framework named PreGIP to watermark the pretraining of GNN encoder for IP protection while maintain the high-quality of the embedding space. PreGIP incorporates a task-free watermarking loss to watermark the embedding space of pretrained GNN encoder. A finetuning-resistant watermark injection is further deployed. Theoretical analysis and extensive experiments show the effectiveness of {\method} in IP protection and maintaining high-performance for downstream tasks.

Trevor McCourt

Significant progress is required in the engineering of large, interacting quantum systems in order to realize the promises of gate-model quantum computing. Designing such systems is challenging, as the dynamics of continuous variable quantum systems are generally unintuitive, and brute-force numerical solutions are difficult to impossible in more than a few dimensions. In this work, I draw analogies between modern superconducting qubits and mechanical mass-spring systems in attempt to gain a simple intuition for what makes each design special. In particular, I analyze superconducting qubits that are inherently protected from noise, and connect this protection to features of the corresponding mechanical system. The hope is that intuition gained from analyzing these systems mechanically will allow for intuitive design of useful superconducting circuits in the future.

Bin Fang, Bo Li, Shuang Wu et al.

Artificial Intelligence (AI) is making a profound impact in almost every domain. One of the crucial factors contributing to this success has been the access to an abundance of high-quality data for constructing machine learning models. Lately, as the role of data in artificial intelligence has been significantly magnified, concerns have arisen regarding the secure utilization of data, particularly in the context of unauthorized data usage. To mitigate data exploitation, data unlearning have been introduced to render data unexploitable. However, current unlearnable examples lack the generalization required for wide applicability. In this paper, we present a novel, generalizable data protection method by generating transferable unlearnable examples. To the best of our knowledge, this is the first solution that examines data privacy from the perspective of data distribution. Through extensive experimentation, we substantiate the enhanced generalizable protection capabilities of our proposed method.

Xiang Li, Qianli Shen, Kenji Kawaguchi

The booming use of text-to-image generative models has raised concerns about their high risk of producing copyright-infringing content. While probabilistic copyright protection methods provide a probabilistic guarantee against such infringement, in this paper, we introduce Virtually Assured Amplification Attack (VA3), a novel online attack framework that exposes the vulnerabilities of these protection mechanisms. The proposed framework significantly amplifies the probability of generating infringing content on the sustained interactions with generative models and a non-trivial lower-bound on the success probability of each engagement. Our theoretical and experimental results demonstrate the effectiveness of our approach under various scenarios. These findings highlight the potential risk of implementing probabilistic copyright protection in practical applications of text-to-image generative models. Code is available at https://github.com/South7X/VA3.

Aksel Biørn-Hansen

Sustainability has over the past two decades emerged as a key concern in human-computer interaction, with a much critiqued focus on quantification and eco-feedback. This approach fits within a modernist framing of sustainability, treating the environment (and our impact on it) as an externality, reducing it to a set of simple metrics. While data about the climate impact of our actions provide an important indication of harm, such data is fragmented and incomplete, capturing only a partial picture of a very wicked and entangled problem. My doctoral research departs from this notion of "information will solve the problem" and through design-oriented explorations of environmental data such as CO2 emissions from academic flying, I investigate alternative ways to engage people with environmental data in order to unsettle relations to the climate impact of our actions and foster care. So far, I have studied this through design-oriented case studies of data in action, with a specific focus on interventions aimed at engaging people in social contexts with the carbon emissions of everyday practices.

Thibaut Maury, Sara Morales Serrano, Philippe Loubet et al.

Like any industry, space activities generate pressures on the environment and strives towards more sustainable activities. A consensus among the European industrial stakeholders and national agencies in the Space sector is emerging on the need to address eco-design through the prism of the environmental Life Cycle Assessment (LCA) methodology. While the use of LCA is being implemented within the sector, the current scope disregards the potential environmental impact in term of debris generated by space missions on the orbital environment. The paper highlights the relevance of applying LCA holistically during the design phase of space systems, considering potential impacts occurring in the orbital environment during the utilisation and disposal stages of a space mission. Based on the comparison of two mission designs, the aim is to consider potential emission of space debris into the LCA framework as a way of measuring the resource security for orbits and potential environmental impacts occurring in case of collision.

Haifeng Lang, Philipp Hauke, Johannes Knolle et al.

Disorder-free localization in translation-invariant gauge theories presents a counterintuitive yet powerful framework of ergodicity breaking in quantum many-body physics. The fragility of this phenomenon in the presence of gauge-breaking errors has recently been addressed, but no scheme has been able to reliably stabilize disorder-free localization through all accessible evolution times while preserving the disorder-free property. Here, we introduce the concept of \textit{Stark gauge protection}, which entails a linear sum in gauge-symmetry local (pseudo)generators weighted by a Stark potential. Using exact diagonalization and Krylov-based methods, we show how this scheme can stabilize or even enhance disorder-free localization against gauge-breaking errors in $\mathrm{U}(1)$ and $\mathbb{Z}_2$ gauge theories up to all accessible evolution times, without inducing \textit{bona fide} Stark many-body localization. We show through a Magnus expansion that the dynamics under Stark gauge protection is described by an effective Hamiltonian where gauge-breaking terms are suppressed locally by the protection strength and additionally by the matter site index, which we argue is the main reason behind stabilizing the localization up to all accessible times. Our scheme is readily feasible in modern ultracold-atom experiments and Rydberg-atom setups with optical tweezers.

Spiros Kechrimparis, Heasin Ko, Young-Ho Ko et al.

In the distribution of quantum states over a long distance, not only are quantum states corrupted by interactions with an environment but also a measurement setting should be re-aligned such that detection events can be ensured for the resulting states. In this work, we present measurement-protected quantum key distribution where a measurement is protected against the interactions quantum states experience during the transmission, without the verification of a channel. As a result, a receiver does not have to revise the measurement that has been prepared in a noiseless scenario since it would remain ever optimal. The measurement protection is achieved by applications of local unitary transformations before and after the transmission, that leads to a supermap transforming an arbitrary channel to a depolarization one. An experimental demonstration is presented with the polarization encoding on photonic qubits. It is shown that the security bounds for prepare-and-measure protocols can be improved, for instance, errors up to 20.7% can be tolerated in the Bennett-Brassard 1984 protocol.

Matthias Saba, Stephan Wong, Mathew Elman et al.

Recent interest in optical analogues to the quantum spin Hall and quantum valley Hall effects is driven by the promise to establish topologically protected photonic edge modes at telecommunication and optical wavelengths on a simple platform suitable for industrial applications. While first theoretical and experimental efforts have been made, these approaches so far both lack a rigorous understanding of the nature of topological protection and the limits of backscattering immunity. We here use a generic group theoretical methodology to fill this gap and obtain general design principles for purely dielectric two-dimensional topological photonic systems. The method comprehensively characterizes possible 2D hexagonal designs and reveals their topological nature, potential and limits.

M. Beau, J. Kiukas, I. L. Egusquiza et al.

An unstable quantum state generally decays following an exponential law, as environmental decoherence is expected to prevent the decay products from recombining to reconstruct the initial state. Here we show the existence of deviations from exponential decay in open quantum systems under very general conditions. Our results are illustrated with the exact dynamics under quantum Brownian motion and suggest an explanation of recent experimental observations.

J. -K. Chang, C. -C. Hsu, S. -Y. Liu et al.

The environmental aging effect of doped graphene is investigated as a function of the organic doping species, humidity, and the number of graphene layers adjacent to the dopant by studies of the Raman spectroscopy, x-ray and ultraviolet photoelectron spectroscopy, scanning electron microscopy, infrared spectroscopy, and electrical transport measurements. It is found that higher humidity and structural defects induce faster degradation in doped graphene. Detailed analysis of the spectroscopic data suggest that the physical origin of the aging effect is associated with the continuing reaction of H2O molecules with the hygroscopic organic dopants, which leads to formation of excess chemical bonds, reduction in the doped graphene carrier density, and proliferation of damages from the graphene grain boundaries. These environmental aging effects are further shown to be significantly mitigated by added graphene layers.

J. Asaadi, J. M. Conrad, S. Gollapinni et al.

In this paper we demonstrate the capability of high voltage varistors and gas discharge tube arrestors for use as surge protection devices in liquid argon time projection chamber detectors. The insulating and clamping behavior of each type of device is characterized in air (room temperature), and liquid argon (90~K), and their robustness under high voltage and high energy surges in cryogenic conditions is verified. The protection of vulnerable components in liquid argon during a 150 kV high voltage discharge is also demonstrated. Each device is tested for argon contamination and light emission effects, and both are constrained to levels where no significant impact upon liquid argon time projection chamber functionality is expected. Both devices investigated are shown to be suitable for HV surge protection applications in cryogenic detectors.

Salah A. Aly, Ahmed E. Kamal

Agile recovery from link failures in autonomic communication networks is essential to increase robustness, accessibility, and reliability of data transmission. However, this must be done with the least amount of protection resources, while using simple management plane functionality. Recently, network coding has been proposed as a solution to provide agile and cost efficient network self-healing against link failures, in a manner that does not require data rerouting, packet retransmission, or failure localization, hence leading to simple control and management planes. To achieve this, separate paths have to be provisioned to carry encoded packets, hence requiring either the addition of extra links, or reserving some of the resources for this purpose. In this paper we introduce autonomic self-healing strategies for autonomic networks in order to protect against link failures. The strategies are based on network coding and reduced capacity, which is a technique that we call network protection codes (NPC). In these strategies, an autonomic network is able to provide self-healing from various network failures affecting network operation. The techniques improve service and enhance reliability of autonomic communication. Network protection codes are extended to provide self-healing from multiple link failures in autonomic networks. We provide implementation aspects of the proposed strategies. We present bounds and network protection code constructions. Finally, we study the construction of such codes over the binary field. The paper also develops an Integer Linear Program formulation to evaluate the cost of provisioning connections using the proposed strategies.