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AAIB认定2023年9月14日VSR Ventures Learjet 45XR在孟买机场坠毁是由于飞行员在恶劣天气下继续不稳定进近并不当操作，导致低空失速。事故中所有8名乘客幸存但受伤，飞机损毁。调查显示无技术故障，但指出VSR Ventures维护记录、内部审计和飞行员培训存在不足。
推荐理由：事故调查报告揭示了飞行员在不利天气条件下未正确处理不稳定进近的问题，对eVTOL运营方和飞行员培训机构具有警示意义，需加强训练和操作规范。
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电动垂直起降（eVTOL）飞行器的荷电状态（SoC）估算必须在飞行器上运行，同时受严格的能耗和适航认证预算约束，其工作周期也与汽车领域文献中的任何情况都不同。本研究设计了一种事件驱动脉冲网络，作为 nBMS 神经形态电池管理架构中的状态估算核心，用于逐时间步估算 SoC，并在公开的22单体 eVTOL 数据集上进行评估，同时以汽车数据进行交叉验证。增量与群体编码器、二阶 Σ-Δ 脉冲层以及速率累加器读出模块，在不执行密集乘加（MAC）运算的情况下，将参数量控制为34{,}433 int16。该估算器的均方根误差（RMSE）为2.45\%，而经过调优的长短期记忆网络（LSTM）基线为1.74\%；这一差距被描述为巡航阶段的时间混合限制；作为交换，在传感器噪声下，其性能退化速度比自适应漏积分发放控制慢 1.6 倍；每步约需 3{,}500 次加法，而 LSTM 需要 67{,}700 次乘加运算。完整核心部署在一块低成本、符合汽车级要求的 Artix-7 现场可编程门阵列上，块 RAM 利用率为 87\%，在 50~MHz 时满足时序要求，并在硅片上针对一段真实的 491 步飞行片段，逐位精确地复现冻结的定点参考结果。完整标注的实现后分析显示，每次推理耗能 1.30~$μ$J；按 0.5~Hz 的任务节奏运行时，平均功耗为 0.65~$μ$W。（预印本；同行评审状态请核对原文。）
该研究提出了一种基于多尺度残差分析的微短路诊断方法，适用于eVTOL锂离子电池在高倍率放电条件下的安全检测。此方法有助于提高诊断准确性，减少误报，对eVTOL电池的安全运行具有重要意义。材料支持结论：微短路诊断在高动态运行条件下具有挑战性，需通过多尺度分析提升可靠性。
本文研究了集成于现有跨仓的eVTOL垂直起降港口设计对货运操作容量的影响，分析了不同配置和操作方案对系统吞吐量的影响。研究结果对eVTOL港口设计和适航审定具有参考价值，可为实际应用提供设计依据。
Accurate demand forecasting is a prerequisite for eVTOL vertiport dispatch, pad staffing, battery service planning, and access management, yet short-horizon demand is affected by confounded interactions among weather, road access, queue feedback, aircraft availability, and pricing interventions. This study evaluates the proposed model on a synthetic but engineering-constrained eight-vertiport network, so the reported results should be interpreted as controlled methodological evidence rather than direct commercial demand measurement. The paper proposes a Causal Aware Feature Fusion DeepONet Mamba model that learns a function-to-function mapping from urban mobility states to vertiport demand fields. Causal gates separate contextual variables, network states, and intervention-sensitive variables before a Mamba branch encoder and coordinate-aware DeepONet trunk produce interval demand forecasts. Experiments with 184 simulated operating days show that the proposed model achieves an MAE of 18.7 requests per 15-minute interval, reducing error by 15.6% relative to the strongest non-causal baseline. Peak interval error decreases to 54 requests, 0.9-quantile pinball loss decreases to 11.3, and scenario ranking error is reduced by 32.8% compared with Mamba without causal fusion. Spatial transfer tests, missing-sensor experiments, and capacity-restriction scenarios indicate that causal feature control improves robustness when operating conditions deviate from ordinary correlations. The results show that combining causal fusion, neural operator learning, and selective state-space memory
This paper presents a demand-driven framework for on-demand Urban Air Mobility (UAM) network design that links vertiport siting, fleet simulation, and door-to-door travel-time feasibility. Demand is estimated from commuter and passenger activity data, converted into spatial trip-end points, and clustered using K-means to generate candidate vertiport locations. Candidate networks are screened using range and minimum station-spacing constraints, then evaluated with a discrete-event simulation that models multi-vehicle dispatch, deadhead relocation, battery swaps, and service regularity. Flight time and energy consumption are computed using a point-mass eVTOL performance model. In a Greater Los Angeles case study, the preferred design expands from four stations and four eVTOLs at low demand to sixteen stations and twelve eVTOLs at the highest tested demand level. Results show that larger fleets improve completion time and vehicle-arrival regularity but do not eliminate deadhead flights, indicating that spatial demand imbalance remains an operational burden. The travel-time savings analysis further suggests that UAM is most defensible for longer or congestion-heavy trips where sufficient non-flight time remains after accounting for flight time.（预印本；同行评审状态请核对原文。）
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随着自主飞行器的大规模引入和交通密度的增加，集中式管理不足以协调大量有人和无人驾驶飞行器。因此，提出了专用的先进空中交通（AAM）走廊，以组织高密度的自主交通流。为了可扩展地提供自主飞行器在轨迹规划中的灵活性，推动了在AAM走廊中开发去中心化交通管理方法。在本研究中，我们扩展了一种多智能体强化学习（MARL）方法，以应对空中走廊网络中去中心化交通流管理的挑战。我们在单走廊设置中训练的策略，以零样本方式测试在越来越复杂的多走廊网络中的表现，这些网络包含合并和分叉的组合。实验结果表明，学习到的行为能够很好地转移到交通密度、网络几何形状和异构车辆性能不同的场景中，而无需集中协调或模型再训练。我们从走廊边界符合度、完成率、平均速度、行驶距离和保持飞行器间分离度等方面评估系统级性能。我们发现，尽管我们的策略仅需要本地协调的进入、穿越和退出行为，但它们集体产生了理想的走廊网络交通流。（预印本；同行评审状态请核对原文。）
The success of advanced air mobility (AAM) operations is largely contingent on its effective integration with other ground transport modes. Under many use cases, AAM operators have to work with ride-hailing operators to create a seamless air taxi travel experience with adequate first and last-mile access. In investigating this multimodal coalition, this study proposes a distance-based subsidy rate design for AAM operators to incentivize ride-hail access to AAM hubs, incorporating air mobility operators' profitability considerations and travelers' route choices jointly. Using New York City (NYC) airport access as a case study, this study integrates high-volume for-hire vehicle (HVFHV) data from NYC taxi zones to consider real-world spatial demand distributions while considering passenger groups with different values of time (VOT) to derive insights on distinctive customer bases. Overall, the results show that AAM operators would need to subsidize the ride-hailing operators on vertiport access trips when air taxi operating costs exceed $12/mi. The analysis of ridership at AAM hubs indicates that ridership and profit contributions differ across different candidate vertiports in Manhattan, reflecting spatial demand heterogeneity. Additionally, having the airport access system in place, the taxi zones that generate the highest passenger demand to all three major NYC airports are identified under lower air taxi fare scenarios. These findings highlight how a distance-based subsidy rate design is beneficial in facilitating better access to vertiports and to foster high air taxi rid（预印本；同行评审状态请核对原文。）
先进航空运输（AAM）是一种新兴的低空交通系统，其成功部署依赖于技术进步和公众接受度。公众接受度可以影响政府支持、法规、噪音标准、飞行意愿以及AAM的商业可行性。因此，了解公众情绪对于识别社会障碍并制定有效的采用策略至关重要。本研究分析了从Reddit和Quora收集的306,009条人工生成文本，利用人工智能模型研究与AAM相关的公众讨论。评估了七种情感分析方法，包括基于词典、机器学习、深度学习和变压器模型，以确定最适合AAM特定情感分类的可靠方法。ModernBERT实现了最高的性能，并用于标记整个数据集。随后在每个情感类别中应用潜在狄利克雷分布（Latent Dirichlet Allocation），以识别潜在主题并考察其从2008年到2025年的演变。分析识别出20个主题和六个主要跨情感集群：劳动力和技能发展、法规与合规、无人机技术性能、军事与地缘政治应用、安全与运营风险以及噪音与干扰。这些发现可以帮助政策制定者、行业利益相关者、研究人员和运营商制定有针对性的法规、安全措施、劳动力计划、降噪策略和公众沟通努力，以解决关切并支持AAM的负责任部署。（预印本；同行评审状态请核对原文。）
航空交通增长、先进空中交通和日益自主的操作正在推动对可扩展和适应性空域设计方法的需求。这一挑战的核心在于量化交通流结构和需求，部分由空域几何形状决定，如何影响冲突生成和运营复杂性。本文提出一个分析框架，利用随机流量模型计算结构化空域中的冲突率和冲突概率。交通流被建模为具有规定到达间隔时间分布的更新过程，而流之间的相互作用则通过几何依赖的最小间距约束在合并和交叉处进行捕捉。在此公式中，推导出冲突率和冲突概率的预期值的闭合形式上界，作为流量配置和需求的函数。这些指标被解释为空域复杂性的互补度量，反映了管制员工作量和每架飞机的操作风险。该方法被应用于具有不同路由结构和流量分布的代表性六边形单元几何形状。结果揭示了路由灵活性、容量和冲突生成之间的非单调权衡，中间流量配置优于高度受限和高度分布的案例。所提出的框架为评估空域设计替代方案和基于复杂性的交通管理策略提供了一个可行的工具。（预印本；同行评审状态请核对原文。）
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机场接入用例是城市空中交通（UAM）早期阶段具有前景的应用。理解UAM在机场的运营模式对于制定公平且有效的监管和管理决策至关重要。一个核心的开放问题是UAM是否会像地铁和铁路这样的常规定期运输服务一样被整合进机场交通网络，还是像交通运输网络公司（TNC）那样以动态供需匹配为特征。在本文中，我们提出一个两阶段框架，用于开展UAM网络的经济可行性分析。在第一阶段，我们引入了一个联合供需变量定价问题，以评估动态定价对UAM运营的影响。该模型使用二元Logit公式来捕捉出行时间优势与票价水平之间的权衡。在第二阶段，确定的需求作为输入用于非线性充电时间的电动城市空中交通车辆路径问题（eUAMVRP-NL），以优化车队调度和充电决策，从而得出运营收入和成本估算。我们将该框架应用于洛杉矶国际机场（LAX）接入市场的案例研究，该案例包含一个八辐状垂直港口网络。我们的结果表明，UAM运营显著受益于类似TNC的管理；相比固定定价方案，可变定价政策可使运营利润增加超过100%。此外，我们识别出较长UAM飞行中的阶段长度经济性。（预印本；同行评审状态请核对原文。）
Advanced air mobility operations will require reliable coordination mechanisms for managing dense traffic near vertiports. However, sequencing decisions may become vulnerable when they rely on potentially falsified self-reported information such as estimated time of arrival. Self-interested vehicles may misreport their arrival times to obtain favorable landing priority, while malicious actors may spoof information to disrupt sequencing decisions or induce unnecessary congestion. This paper studies secure coordination for vertiport sequencing under sensing uncertainty. We consider a coordinator that combines self-reported Remote-ID information with externally obtained surveillance measurements to check reports and assign separation-feasible arrival schedules. Since surveillance-based estimates are uncertain, falsified reports may remain consistent with the sensing uncertainty region and cannot always be rejected outright. We therefore formulate sequencing as a robust design problem over this uncertainty region. Self-interested misreporting is modeled as a strategic deviation that improves the reporting vehicle's own sequencing outcome, whereas malicious spoofing is modeled as an adversarial disturbance that degrades the system-level objective. The final paper will develop robust sequencing rules over surveillance-consistent uncertainty sets and evaluate their performance in representative vertiport sequencing scenarios.（预印本；同行评审状态请核对原文。）
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该研究通过缩比测试和模拟验证了 eVTOL 飞机的扰动抑制特性，为 eVTOL 适航审定提供了测试方法参考。材料表明缩比测试可有效评估全尺寸性能，有助于降低适航认证成本。
该研究通过高保真CFD分析和PD控制器评估，探讨了多旋翼eVTOL飞行器的空气动力学行为和控制效果，为eVTOL的适航审定和飞行控制设计提供了理论支持。研究结果有助于理解分布式电动推进系统的复杂动态特性，对推动eVTOL在城市空中交通中的应用具有重要意义。
该研究提出了一种基于 SPH 模拟的虚拟雨水管理框架，用于评估 eVTOL 和无人机在降雨条件下的性能。此方法有助于早期设计阶段的雨水影响分析，支持模拟驱动的开发和适航认证准备。材料表明，雨水管理对飞行器的安全性和可靠性至关重要，且虚拟模拟可替代传统物理测试，提高效率。
本文介绍了由AVL、Unisphere和blueflite开发的集成仿真工作流程，用于电动飞机和eVTOL的设计与运营，重点在于通过数字孪生技术结合高保真度模型进行任务可行性评估。该研究对适航认证中涉及的能源管理、热行为和任务持续时间等关键因素提供了支持，有助于推动电动航空领域的标准化和实际应用。
Electric vertical takeoff and landing (eVTOL) aircraft operating in high-density urban airspace must maintain safe separation through tactical conflict resolution, yet the energy cost of such maneuvers has not been systematically quantified. This paper investigates how conflict-resolution maneuvers under the Modified Voltage Potential (MVP) algorithm affect eVTOL energy consumption. Using a physics-based power model integrated within a traffic simulation, we analyze approximately 71,767 en route sections within a sector, across traffic densities of 10-60 simultaneous aircraft. The main finding is that MVP-based deconfliction is energy-efficient: median energy overhead remains below 1.5% across all density levels, and the majority of en route flights within the sector incur negligible penalty. However, the distribution exhibits pronounced right-skewness, with tail cases reaching 44% overhead at the highest densities due to sustained multi-aircraft conflicts. The 95th percentile ranges from 3.84% to 5.3%, suggesting that a 4-5% reserve margin accommodates the vast majority of tactical deconfliction scenarios. To support operational planning, we develop a machine learning model that estimates energy overhead at mission initiation. Because conflict outcomes depend on future traffic interactions that cannot be known in advance, the model provides both point estimates and uncertainty bounds. These bounds are conservative; actual outcomes fall within the predicted range more often than the stated confidence level, making them suitable for safety-critical reserve planning. Together（预印本；同行评审状态请核对原文。）
The Low-Altitude Economy (LAE) is rapidly emerging as a new technological and industrial frontier, with unmanned aerial vehicles (UAVs), electric vertical takeoff and landing (eVTOL) aircraft, and aerial swarms increasingly deployed in logistics, infrastructure inspection, security, and emergency response. However, the large-scale development of the LAE demands a reliable aerial foundation that ensures not only real-time connectivity and computational support, but also navigation integrity and safe airspace management for safety-critical operations. High-Altitude Platforms (HAPs), positioned at around 20 km, provide a unique balance between wide-area coverage and low-latency responsiveness. Compared with low earth orbit (LEO) satellites, HAPs are closer to end users and thus capable of delivering millisecond-level connectivity, fine-grained regulatory oversight, and powerful onboard computing and caching resources. Beyond connectivity and computation, HAPs-assisted sensing and regulation further enable navigation integrity and airspace trust, which are essential for safety-critical UAV and eVTOL operations in the LAE. This article proposes a five-stage evolutionary roadmap for HAPs in the LAE: from serving as aerial infrastructure bases, to becoming super back-ends for UAV, to acting as frontline support for ground users, further enabling swarm-scale UAV coordination, and ultimately advancing toward edge-air-cloud closed-loop autonomy. In parallel, HAPs complement LEO satellites and cloud infrastructures to form a global-regional-local three-tier architecture. Looking forwa（预印本；同行评审状态请核对原文。）
Advanced Air Mobility (AAM) operations are planned to utilize strategic flight planning services that predict temporal uncertainties to validate flight plans against hazards such as weather cells, restricted airspaces, and CNS disruption areas. This paper presents a Kalman Filter-based uncertainty propagation method that models Flight Management System (FMS) correction behavior through a sigmoid-blended measurement noise covariance. The sigmoid formulation generalizes existing discrete FMS activation thresholds into a continuous, tunable function that smoothly transitions the filter's measurement noise based on progress toward each waypoint. When the measurement noise is high, due to an inverse relationship, the Kalman gain is small and thus uncertainty grows; as the aircraft nears a waypoint, measurement noise decreases as a function of progress, the Kalman gain increases, and state covariance contracts which models the FMS progressively correcting toward the planned trajectory. The approach is computationally efficient (up to two orders of magnitude faster than Monte Carlo methods), scales with control inputs, and is parametrically tunable for different classes of aircraft. The measurement noise covariance is calibrated using real Automatic Dependent Surveillance-Broadcast (ADS-B) data from commercial Instrument Flight Rules (IFR) flights serving as surrogates for future AAM operations, achieving coverage probability conservative relative to theoretical Gaussian predictions at the 1-sigma confidence level on a hold out verification dataset (N = 36). Parameter sensitivity （预印本；同行评审状态请核对原文。）
城市空中交通（UAM）带来了前所未有的交通协调挑战，尤其是在密集城区走廊中无人机密度不断增加的情况下。本文介绍了一种数学模型，利用控制算法优化一种基于边缘人工智能的分布式蜂群架构，以实现智能冲突解决，使系统能够在低延迟下进行实时决策。通过使用轻量级神经网络，该系统利用边缘节点执行分布式冲突检测与解决。开发了一个仿真平台，以评估在不同无人机密度下的该方案。结果表明，冲突解决时间缩短了多达3.8倍，且准确性相比传统集中式控制模型有所提升。所提出的架构对于未来UAM系统的可扩展、高效和安全的空中交通管理具有很高的前景。（预印本；同行评审状态请核对原文。）
无人机（UAVs）在低空空域的整合促使当局采用分布式无人机交通管理（UTM）架构，以确保互操作性和安全性。区块链被提出作为UTM利益相关者之间可信协调的使能器。然而，其在航空约束下的实时性能仍缺乏充分表征。本文提出了一种定量基准，比较两种符合规定的分布式架构：由Linux基金会维护的联邦InterUSS平台和基于Hyperledger Fabric的许可区块链。通过Hyperledger Caliper生成的操作意图参考（OIR）注册工作负载对两个系统进行了评估，测量了在每秒最多50笔交易负载下的吞吐量、延迟和交易损失。结果表明，InterUSS保持了亚秒级延迟和稳定性能，最高可达30 TPS。同时，Fabric在中位延迟超过3秒且尾延迟超过15秒的情况下表现出指数级退化。这些发现表明，基于区块链的架构必须重新设计以满足航空定时和可扩展性要求，表明结合分布式账本用于可追溯性和联邦框架用于实时协调的混合模型更适合未来的UTM部署。（预印本；同行评审状态请核对原文。）
城市空中交通（UAM）提供了一种变革性的方法来解决城市拥堵、提高可及性和推进环境可持续性。自2020年以来，三个紧密相关的领域取得了快速进展：（1）通信，动态频谱分配和低空信道特征支持可靠的空地数据交换；（2）UAM管理，提出了针对密集、主要自主城市空域的新空交通控制概念；（3）可持续性，由高效推进、集成充电基础设施和整体环境评估驱动。本文回顾并综合了这些领域的最新研究，比较了最先进的解决方案，并概述了实现可扩展、可持续UAM生态系统的关键技术和基础设施里程碑。（预印本；同行评审状态请核对原文。）
电动垂直起降（eVTOL）飞行器预计比直升机更安静且更具成本效益，通过改善连通性提供重大的经济和社会效益。其采用将需要新的地面基础设施和空域重新设计，引入涉及多个利益相关方（监管机构、eVTOL运营商、空中交通服务提供商、垂直港口运营商、原始设备制造商、飞行员等）的风险。为评估这些风险对英国空域的影响，采用了基于系统思维的系统理论过程分析（STPA）。为了管理由于分析复杂性而产生的大量不安全控制行为（UCAs）和要求，应用了一种新颖的STPA扩展方法以优先处理结果。总共识别出317个UCAs，其中110个高优先级UCAs进行了分析（第4步），产生了377个因果因素和432个要求。这些被优先处理以生成一个有针对性的124个不同高优先级要求的列表，其中56个被识别为现有航空法规、政策或程序中的空白。这些突出了在组织绩效、认证流程、培训、防撞、能源管理和自动化等领域进行监管更新的机会。研究结果为监管机构提供了安全考虑，可用于制定新的或更新的法规、合规方法和指导材料，以确保eVTOL的安全部署。（预印本；同行评审状态请核对原文。）
城市空中交通（UAM）为都市交通提供了变革性的愿景，但其实现受到重大基础设施成本和运营复杂性的阻碍。我们通过建模一个利用现有区域机场并采用优化、异构机队的UAM网络来解决这些挑战。我们引入了LPSim，一个大规模并行仿真框架，利用多GPU计算同时优化UAM需求、机队运营和地面交通互动。我们的均衡搜索算法被扩展以准确预测需求并确定最有效的机队组成。在旧金山湾区的案例研究中，我们的结果表明，这种UAM模型可以为230,000次选定行程节省超过20分钟的旅行时间。然而，分析还揭示了系统级成功严重依赖于与地面接入的无缝整合和动态调度。（预印本；同行评审状态请核对原文。）
城市空中交通（UAM）已成为缓解城市拥堵和交通挑战的有前景的解决方案。然而，电动垂直起降（eVTOL）飞机产生的噪音对公众接受度和监管批准构成了重大障碍，可能限制UAM系统的运行范围和可扩展性。因此，UAM系统的成功采用取决于预测生成的噪音水平的能力，并进一步开发符合社区噪音法规且保持操作效率的运动规划策略。为此，本文提出了一种新颖的噪音感知运动规划框架，用于UAM系统，以确保符合噪音法规。我们首先开发了一个可认证的神经网络模型，以准确预测eVTOL在城市环境中的噪音传播模式，提供其正确性的可证明界限。为了达到所需的准确度水平，我们提出了一种主动采样策略，以高效构建用于训练和测试噪音模型的数据集。接下来，我们开发了一种噪音感知的运动规划算法，利用噪音模型生成eVTOL轨迹，确保符合社区噪音法规。该算法利用噪音模型的单调结构，高效采样配置空间，确保生成的轨迹既符合噪音法规又具有操作效率。我们通过一系列实验验证了所提出框架的有效性，针对Vahana eVTOLs。结果表明，该框架可以生成符合噪音法规的轨迹（预印本；同行评审状态请核对原文。）
系统理论过程分析（STPA）是一种推荐的方法，用于分析复杂系统，能够识别数千个传统技术如故障模式和影响分析（FMEA）和故障树分析（FTA）常被遗漏的安全要求。然而，缺乏管理这些要求的结构化框架带来了挑战，尤其是在快速发展的环境中。本文介绍了一个可扩展的框架，用于优先排序STPA衍生的要求。该框架整合了每个STPA步骤的输出，并结合了基于四个关键因素的专家评估：实施时间、成本、要求类型和监管覆盖范围。为了减少主观性，采用蒙特卡洛模拟（MCS）来计算并稳定要求排名。自动化工具链支持该框架，使优先排序的要求在缩放矩阵中动态映射。这种可视化有助于决策并确保各开发阶段的可追溯性。该框架适用于从早期概念化到更高级阶段，增强了其在迭代系统开发中的实用性。该框架通过一个实际案例研究进行了验证，该研究聚焦于电动垂直起降（eVTOL）操作，与英国民航局合作进行。研究结果直接贡献于CAP3141，一份民用航空出版物，识别了系统性操作风险和安全缓解措施，供监管机构、运营商和垂直港口使用。优先排序过程支持决策（预印本；同行评审状态请核对原文。）
With rapid urbanization and increasing population density, urban traffic congestion has become a critical issue, and traditional ground transportation methods are no longer sufficient to address it effectively. To tackle this challenge, the concept of Advanced Air Mobility (AAM) has emerged, aiming to utilize low-altitude airspace to establish a three-dimensional transportation system. Among various components of the AAM system, electric vertical take-off and landing (eVTOL) aircraft plays a pivotal role due to their flexibility and efficiency. However, the immaturity of Ultra Reliable Low Latency Communication (URLLC) technologies poses significant challenges to safety-critical AAM operations. Specifically, existing Stacked Intelligent Metasurfaces (SIM)-based eVTOL systems lack rigorous mathematical frameworks to quantify probabilistic delay bounds under dynamic air traffic patterns, a prerequisite for collision avoidance and airspace management. To bridge this gap, we employ network calculus tools to derive the probabilistic upper bound on communication delay in the AAM system for the first time. Furthermore, we formulate a complex non-convex optimization problem that jointly minimizes the probabilistic delay bound and the propagation delay. To solve this problem efficiently, we propose a solution based on the Block Coordinate Descent (BCD) algorithm and Semidefinite Relaxation (SDR) method. In addition, we conduct a comprehensive analysis of how various factors impact regret and transmission rate, and explore the influence of varying load intensity and total delay on th（预印本；同行评审状态请核对原文。）
阵风具有固有的随机性，可能显著影响飞行器的安全性和性能。本研究探讨了一个三维不确定性量化（UQ）问题，以探讨阵风和飞行条件中的不确定性如何影响Lift-Plus-Cruise eVTOL飞行器机翼的结构响应。分析采用了一个非稳态气动弹性模型，通过板方法气动求解器与壳体分析结构求解器的一维耦合来预测机翼在不同条件下的响应。此外，本文还比较评估了常用的非侵入式UQ方法，包括非侵入式多项式混沌、克里金法、蒙特卡洛法、单变量降维法和梯度增强单变量降维法。这些方法根据其在估计各种风险指标（均值、标准差和95百分位数）方面的有效性进行评估，这些风险指标涉及关键结构响应输出，如最大尖端位移和平均应变能。数值结果揭示了即使在相对较小的不确定输入范围内，结构响应输出也存在显著变化。这突显了系统对阵风和飞行条件不确定性的敏感性。此外，所实施的UQ方法的性能根据特定的风险指标和分析的量度兴趣而显著变化。（预印本；同行评审状态请核对原文。）
为助力城市空中交通（UAM），电动垂直起降（eVTOL）飞行器正成为研究目标。传统多学科分析与优化（MDAO）成本高昂，而基于代理模型的优化则难以应对复杂的物理约束。本文提出一种受物理约束的生成对抗网络（physicsGAN），用于智能参数化eVTOL飞行器的起飞控制曲线，并将原始设计空间转换为可行空间。具体而言，转换后的可行空间指的是所有设计直接满足所有设计约束的空间。在Airbus A3 Vahana上展示了基于physicsGAN的代理模型起飞轨迹设计框架。physicsGAN在可行空间中仅生成可行的功率和机翼角度控制曲线，约98.9%的设计满足所有约束条件。所提出的框架与基于仿真的最优设计相比准确率达99.6%，仅需2.2秒，将计算时间减少了约200倍。同时，基于数据驱动的GAN代理模型优化使用无导数优化器耗时21.9秒，速度比所提出的框架慢一个数量级。此外，基于梯度优化器的数据驱动GAN优化在随机试验中无法一致找到最优设计，陷入不可行区域，这在实际应用中存在问题。因此，所提出的基于physicsGAN的设计框架优于数据驱动的GAN框架（预印本；同行评审状态请核对原文。）
Electric Vertical Take-off and Landing vehicles (eVTOLs) are driving Advanced Air Mobility (AAM) toward transforming urban transportation by extending travel from congested ground networks to low-altitude airspace. This transition promises to reduce traffic congestion and significantly shorten commute times. To ensure aviation safety, eVTOLs must fly within prescribed flight corridors. These corridors are managed by ground-based Air Traffic Control (ATCo) stations, which oversee air-ground communication and flight scheduling. However, one critical challenge remains: the lack of high rate air-ground communication and safe flight planning within these corridors. The introduction of 6G-oriented Stacked Intelligent Metasurface (SIM) technology presents a high rate communication solution. With advanced phase-shifting capabilities, SIM enables precise wireless signal control and supports beam-tracking communication with eVTOLs. Leveraging this technology, we propose a Composite Potential Field (CPF) approach. This method dynamically integrates target, separation, and communication fields to optimize both SIM communication efficiency and flight safety. Simulation results validate the effectiveness of this DT-based approach. Compared to the potential field flight control benchmark, it improves the transmission rate by 8.3\%. Additionally, it reduces flight distance deviation from the prescribed corridor by 10\% compared to predetermined optimization methods.（预印本；同行评审状态请核对原文。）
Electric vertical takeoff and landing (eVTOL) aircraft manufacturers await numerous pre-orders for eVTOLs and expect demand for such advanced air mobility (AAM) aircraft to rise dramatically soon. However, eVTOL manufacturers (EMs) cannot commence mass production of commercial eVTOLs due to a lack of supply chain planning for eVTOL manufacturing. The eVTOL supply chain differs from traditional ones due to stringent quality standards and limited suppliers for eVTOL parts, shortages in skilled labor and machinery, and contract renegotiations with major aerospace suppliers. The emerging AAM aircraft market introduces uncertainties in supplier pricing and capacities, eVTOL manufacturing costs, and eVTOL demand, further compounding the supply chain planning challenges for EMs. Despite this critical need, no study has been conducted to develop a comprehensive supply chain planning model for EMs. To address this research gap, we propose a stochastic optimization model for integrated supply chain planning of EMs while maximizing their operating profits under the abovementioned uncertainties. We conduct various numerical cases to analyze the impact of 1) endogenous eVTOL demand influenced by the quality of eVTOLs, 2) supply chain disruptions caused by geopolitical conflicts and resource scarcity, and 3) high-volume eVTOL demand similar to that experienced by automotive manufacturers, on EM supply chain planning. The results indicate that our proposed model is adaptable in all cases and outperforms established benchmark stochastic models. The findings suggest that EMs can commence ma（预印本；同行评审状态请核对原文。）
Urban air mobility is an innovative mode of transportation in which electric vertical takeoff and landing (eVTOL) vehicles operate between nodes called vertiports. We outline a self-organized vertiport arrival system based on deep reinforcement learning. The airspace around the vertiport is assumed to be circular, and the vehicles can freely operate inside. Each aircraft is considered an individual agent and follows a shared policy, resulting in decentralized actions that are based on local information. We investigate the development of the reinforcement learning policy during training and illustrate how the algorithm moves from suboptimal local holding patterns to a safe and efficient final policy. The latter is validated in simulation-based scenarios, including robustness analyses against sensor noise and a changing distribution of inbound traffic. Lastly, we deploy the final policy on small-scale unmanned aerial vehicles to showcase its real-world usability.（预印本；同行评审状态请核对原文。）
城市空中交通（UAM）代表了未来交通的一种有前景的解决方案。在本研究中，我们介绍了VertiSim，这是一种先进的事件驱动模拟器，旨在评估e-VTOL运输网络。独特的是，VertiSim同时模拟乘客、飞机和能源流动，反映了UAM系统的相互关联的复杂性。我们利用VertiSim评估了19种运营场景，以满足每天2,834名乘客的需求，这些场景涉及不同机队规模和垂直港口距离。本研究旨在通过理解乘客延误时间、运营成本和机队利用率之间的权衡，支持利益相关者做出有关机队规模、网络设计和基础设施发展的明智决策。我们的模拟，由启发式调度和充电政策引导，表明机队规模显著影响UAM网络中的乘客延误和能源消耗。我们发现，增加机队规模可以减少平均乘客延误，但这以增加重新定位飞行次数导致的运营费用上升为代价。此外，我们的分析强调了垂直港口距离对机队利用率的影响：较长的距离导致总空闲时间减少，巡航和充电时间增加，从而实现更高效的机队利用率，但也导致更长的乘客延误。这些发现对于UAM网络规划至关重要，尤其是在平衡机队规模与垂直港口容量和运营成本方面。模拟演示可在：https://tinyurl.com/vertisim-vis（预印本；同行评审状态请核对原文。）
先进空中交通（AAM）是下一代航空运输方式，包括电动垂直起降（eVTOL）飞机、日益自主的飞行操作以及小型无人机包裹投递等新进入者。随着这些新机型和运营概念的出现，人们希望在城市区域及其周边地区大幅提高密度，利用新型电池技术，并朝着更自主驾驶的飞机发展。为实现这些目标，必须引入新的安全管理系统能力，以快速评估风险在复杂危害范围内的演变，并在必要时通过监督或自动决策执行适当措施来降低风险。最近，强化学习在实时决策方面显示出在各种应用中的潜力，包括应急处理管理。在这项工作中，我们将应急处理管理问题表述为马尔可夫决策过程（MDP），并将应急处理管理MDP整合到AAM-Gym模拟框架中。这使得强化学习算法的快速原型设计和现有系统的评估成为可能，从而为未来算法开发提供一个社区基准。我们报告了环境的基线统计信息，并提供了示例性能指标。（预印本；同行评审状态请核对原文。）
电动垂直起降（eVTOL）飞机等航空器（AV）使城市环境中的空中客运成为现实。然而，要实现安全且全面的运行，其通信连接仍在研究之中。本文设想构建一个支持多连接（MC）的空中网络，为航空器提供无处不在且可靠的服务。研究考虑了包含直接空地（DA2G）和空空（A2A）通信、高空平台（HAP）及低地球轨道（LEO）卫星的垂直异构网络。我们评估航空器下行链路在远程驾驶场景及控制/遥测业务中的端到端（E2E）多跳可靠性和网络可用性。指挥与控制（C2）连接服务要求超可靠低时延通信（URLLC），因此我们分析有限码长（FBL）条件下的E2E可靠性和时延。我们研究不同的 MC 方案如何满足高要求的E2E连接需求，同时考虑天线辐射方向图和不可靠的回传链路。由于视距（LoS）干扰以及下倾地面基站（BS）天线增益降低，为航空器提供无缝连接极具挑战，因此我们在地面基站之间采用协作多点（CoMP）技术，以缓解小区间干扰。此外，我们通过求解优化问题，在服务质量（QoS）约束下选择最佳 MC 路径。我们以频谱效率（SE）最大化为目标，确定所需连接数最少的最优 MC 路径。（预印本；同行评审状态请核对原文。）