Dynamics simulation study on civil aircraft planned pavement emergency landing

Engine pylon is one of the most important components of large civil aircraft, playing an essential role in structure connecting and load bearing. It is chosen as the research target, and a full sized engine-pylon-wing finite element model is established. By conducting the simulations of different landing and impacting conditions, dynamical responses and separation status of the pylon are obtained. Some main factors that affect the pylon’s separation are found out on the basis of preliminary analysis. The reasonable pylon separations for belly landing with small pitch angles and dead-stick landing are achieved. At last, further measures to improve the modeling method and achieve better pylon separations are discussed based on a comparative analysis of all the simulation results. The proposed dynamical modeling method along with the emergency landing parameters and simulation results can provide certain reference to similar studies, pylon structure designs and validation tests

SERVICE CATEGORY SYSTEM IN LOW-POWER AND LOSSY NETWORKS

Presented herein are novel techniques to resolve cache capacity issues in Low-Power and Lossy Networks (LLNs) by utilizing border router edge computing. Following deployment of a network, such as an information-centric networking (ICN) network, a border router will generate a bitmap for all support services through negotiations with a cloud service (CS) and low-power devices. The border router will then cache data that satisfies specific service criteria for low-power devices that have registered for such data. The border router will further publish the service bitmap to a sleep proxy. A given low-power device can periodically examine the service bitmap via beacons to determine whether there may be any service(s) in which it is interested and, if so, respond to the border router

Aeroacoustic testing of the landing gear components

The sound field generated by full scale landing gear components was studied in an acoustic wind tunnel. Noise characteristics were evaluated. The noise contribution of each part was investigated by removing the gear part individually. Three design parameters were also obtained to assess the noise reduction potential. Test results indicate that the noise spectrum of the component is essentially broadband and mainly dominated by some peaks corresponding to the constant St. Sound pressure level scales with the sixth power velocity law. Noise radiation from the components has obvious directivities. The main strut is the least contributor while the bogie is the largest contributor to the total noise. It is also found that the noise level increases with the gear installation angle from 0° to 16.5° while it decreases via changing the torque link layout from the front of the main strut to its back or modifying the bogie shape by filling its holes

Split Unlearning

Split learning is emerging as a powerful approach to decentralized machine learning, but the urgent task of unlearning to address privacy issues presents significant challenges. Conventional methods of retraining from scratch or gradient ascending require all clients' involvement, incurring high computational and communication overhead, particularly in public networks where clients lack resources and may be reluctant to participate in unlearning processes they have no interest. In this short article, we propose \textsc{SplitWiper}, a new framework that integrates the concept of SISA to reduce retraining costs and ensures no interference between the unlearning client and others in public networks. Recognizing the inherent sharding in split learning, we first establish the SISA-based design of \textsc{SplitWiper}. This forms the premise for conceptualizing two unlearning strategies for label-sharing and non-label-sharing scenarios. This article represents an earlier edition, with extensive experiments being conducted for the forthcoming full version.Comment: An earlier edition, with extensive experiments being conducted for the forthcoming full versio

Dataset Obfuscation: Its Applications to and Impacts on Edge Machine Learning

Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties for edge applications. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights - in terms of the model accuracy, Frobenius-norm (F-norm)-based model distance, and level of data privacy - and discuss the potential applications with the proposed Privacy, Utility, and Distinguishability (PUD)-triangle diagram to visualize the requirement preferences. Our experiments are based on the popular MNIST and CIFAR-10 datasets under both independent and identically distributed (IID) and non-IID settings. Significant results include a trade-off between the model accuracy and privacy level and a trade-off between the model difference and privacy level. The results indicate broad application prospects for training outsourcing in edge computing and guarding against attacks in Federated Learning among edge devices.Comment: 6 page

Reconfigurable Intelligent Surface Based Orbital Angular Momentum: Architecture, Opportunities, and Challenges

Orbital angular momentum (OAM) has gained a lot of attention due to its potential in enhancing the spectral efficiency for wireless communications. Using different OAM modes, multiple independent data streams are simultaneously transmitted by using spatial distribution of helical phase, which enables OAM as a new form of multiple access technique for wireless communications. Controlling the phases of incoming electromagnetic waves, the reconfigurable intelligent surface (RIS) is suitable for implementing OAM. In this article, an RIS-based OAM framework is introduced. The basic concepts and features of RIS and OAM are presented. Then classifications and comparisons of different RIS-based OAM schemes are summarized. Simulation results verify that RIS-based OAM transmission can achieve nearly 100 percent higher spectral efficiency of wireless communication systems compared to the conventional RIS scheme

Bayesian Learning for Double-RIS Aided ISAC Systems with Superimposed Pilots and Data

Reconfigurable intelligent surface (RIS) has great potential to improve the performance of integrated sensing and communication (ISAC) systems, especially in scenarios where line-of-sight paths between the base station and users are blocked. However, the spectral efficiency (SE) of RIS-aided ISAC uplink transmissions may be drastically reduced by the heavy burden of pilot overhead for realizing sensing capabilities. In this paper, we tackle this bottleneck by proposing a superimposed symbol scheme, which superimposes sensing pilots onto data symbols over the same time-frequency resources. Specifically, we develop a structure-aware sparse Bayesian learning framework, where decoded data symbols serve as side information to enhance sensing performance and increase SE. To meet the low-latency requirements of emerging ISAC applications, we further propose a low-complexity simultaneous communication and localization algorithm for multiple users. This algorithm employs the unitary approximate message passing in the Bayesian learning framework for initial angle estimate, followed by iterative refinements through reduced-dimension matrix calculations. Moreover, the sparse code multiple access technology is incorporated into this iterative framework for accurate data detection which also facilitates localization. Numerical results show that the proposed superimposed symbol-based scheme empowered by the developed algorithm can achieve centimeter-level localization while attaining up to $96\%$ of the SE of conventional communications without sensing capabilities. Moreover, compared to other typical ISAC schemes, the proposed superimposed symbol scheme can provide an effective throughput improvement over $133\%$

Pass-by-Pass Ambiguity Resolution in Single GPS Receiver PPP Using Observations for Two Sequential Days: An Exploratory Study

“Pass-by-pass” or “track-to-track” ambiguity resolution removes Global Navigation Satellite System (GNSS) satellite hardware delays between adjacent undifferenced (UD) ambiguities, which is often applied in precise orbit determination (POD) for Low Earth Orbit (LEO) satellites to improve the accuracy of orbits. In this study, we carried out an exploratory study to use the “pass-by-pass” ambiguity resolution by differencing the undifferenced ambiguity candidates for two adjacent passes in sidereal days for a single Global Positioning System (GPS) receiver static Precise Point Positioning (PPP). Using the GPS observations from 132 globally distributed reference stations of International GPS Service (IGS), we find that 99.08% wide-lane (WL) and 97.83% narrow-lane (NL) double-difference ambiguities formed by the “pass-by-pass” method for all stations can be fixed to their nearest integers within absolute fractional residuals of 0.2 cycles. These proportions are higher than the corresponding values of network solution with multiple receivers with 97.39% and 91.20%, respectively. About 97% to 98% of ambiguities can be fixed finally on average. The comparison of the estimated station coordinates with the IGS weekly solutions reveals that the Root Mean Square (RMS) in East and North directions are 2-4 mm and is about 6 mm in the Up direction. For hourly data, it is found that the mean positioning accuracy improvement can achieve to about 10% after ambiguity resolution. From a dam deformation monitoring application, it shows that the fixing rate of WL and NL ambiguity can be closed to 100% and higher than 90%, respectively. The time series generated by PPP are also in agreement with the short baseline solutions

TBDD: A New Trust-based, DRL-driven Framework for Blockchain Sharding in IoT

Integrating sharded blockchain with IoT presents a solution for trust issues and optimized data flow. Sharding boosts blockchain scalability by dividing its nodes into parallel shards, yet it's vulnerable to the $1\%$ attacks where dishonest nodes target a shard to corrupt the entire blockchain. Balancing security with scalability is pivotal for such systems. Deep Reinforcement Learning (DRL) adeptly handles dynamic, complex systems and multi-dimensional optimization. This paper introduces a Trust-based and DRL-driven (\textsc{TbDd}) framework, crafted to counter shard collusion risks and dynamically adjust node allocation, enhancing throughput while maintaining network security. With a comprehensive trust evaluation mechanism, \textsc{TbDd} discerns node types and performs targeted resharding against potential threats. The model maximizes tolerance for dishonest nodes, optimizes node movement frequency, ensures even node distribution in shards, and balances sharding risks. Rigorous evaluations prove \textsc{TbDd}'s superiority over conventional random-, community-, and trust-based sharding methods in shard risk equilibrium and reducing cross-shard transactions