Smart contracts often call each other to enable various applications, but nondeterministic external calls will introduce vulnerabilities, leading to significant asset losses. Checking contract external call vulnerabilities (ECVs) is a critical issue. Existing approaches mainly use formal verification, fuzzing, and symbolic execution to analyze contracts. However, the proof complexity of formal verification and the randomness inherent in fuzzing result in high running costs and false negatives. Symbolic execution approaches use lightweight execution semantics for efficiency, but they can lead to false positives and may not adapt to evolving contract semantics. In this paper, we systematically analyze the root causes of 6 types of ECVs by identifying gaps between high-level solidity source code and low-level bytecode and define precise security rules to eliminate these ECVs. We then utilize Hoare-style triples to model the fine-grained execution semantics of contracts. Additionally, we propose a semantics-aware symbolic analysis approach that comprehensively extracts execution semantics to detect ECVs violating security rules, while employing context-aware optimization strategies to reduce the path search space. We implement a fully automated analysis prototype called ECVchecker and evaluate it through empirical testing. The results demonstrate that our approach achieves an average F1-score of 0.97 in detecting ECVs. For contracts with an average bytecode size of 26,612 bytes, our approach can be 1.7x to 6.7x faster than existing approaches.

A hybrid model is proposed for predicting the response of printed circuit board (PCB) traces excited by electromagnetic (EM) field radiated from the other PCB. The proposed hybrid model combines the equivalent dipole model with the modified Kron–Branin (KB) method. The equivalent dipole model is based on electric and magnetic dipoles, which can produce the same EM fields as the actual electromagnetic interference source. The modified KB method can calculate the terminal responses of PCB traces by incorporating the illumination of the external EM field and discontinuous structures in actual PCB traces. The modified KB method is verified using two actual trace models in the frequency range of 0.1to 3 GHz, and the maximum absolute difference of terminal responses compared with the full-wave simulation is less than 5 dB. Based on the validation of the modified KB method, EM coupling between two parallel PCBs is investigated. The radiation fields of the emitter PCB are modeled by the equivalent dipole model via near-field scanning experiments and are used to calculate the coupling voltages with two victim PCBs in different slots from 0.1 to 3 GHz. The results show generally consistent trends with the measurements, particularly for slot B, where good agreement is obtained across the investigated band. For slot C, the proposed method still captures the main variation tendency and response level, although relatively larger deviations are observed at some frequencies. For the two victim PCBs in slots B and C, the mean root-mean-square error between the proposed hybrid model and that of the experimental measurements is 3.91and 7.06 dB, respectively. These results indicate that the proposed hybrid model is potentially useful for estimating EM coupling effects of multiple PCBs placed in one module with different layouts.

Internet of Drones (IoD) has become crucial due to the surge of drone-based applications. In a dynamic wireless network, drones often share data with multiple users, which is vulnerable to various security threats, e.g., eavesdropping, privacy leakage, and impersonation. Ensuring secure sharing of drone operation data is a critical issue. Existing schemes employ blockchain to ensure accountability in untrusted IoD contexts. However, drones perform excessive operations, e.g., consensus and homomorphic encryption, leading to impractical drone computation overhead. Furthermore, these existing schemes perform point-to-multipoint (P2MP) data sharing by using identity-based broadcast encryption (IBBE) as it has stateless receiver property, i.e., drones can directly establish sharing groups based on the receiver identities. However, these IBBE-based schemes need to preset the sharing group capacity, reducing sharing efficiency and flexibility in dynamic IoD networks. Also, identities in sharing groups probably result in receiver privacy breaches. In this paper, we propose a blockchain-assisted data-sharing scheme with accountability and privacy-preserving for IoD networks. We utilize on-chain immutable smart contracts and short signatures to construct a lightweight accountability mechanism that can verify and punish the above misbehavior. We develop an efficient privacy-preserving IBBE algorithm for P2MP sharing of drone operation data that enables dynamic group sharing. We prove that our algorithm is secure and receiver anonymity against chosen ciphertext attacks. The experiment results show that our scheme outperforms existing schemes in computational and communication overhead. In real-world logistics scenarios, the wireless data rate of 50 Mbps can satisfy the communication requirements for ciphertext lengths up to 19.5 KB. For a sharing group of 60 receivers, drone encryption costs only about 101 ms. The decryption cost remains constant and takes about 3.1 ms.

A phaseless reconstruction method of equivalent electromagnetic interference (EMI) sources for printed circuit boards (PCBs) based on the success-history based adaptive differential evolution algorithm with linear population size reduction(L-SHADE) is proposed. The equivalent sources are modeled by electric and magnetic dipoles placed over a planar surface on the PCB, and the magnitudes of magnetic fields are obtained through near-field (NF) scanning and used as reference data. To determine the initial range of dipole moment and the number of dipoles, a repeated iterative strategy combining the truncated singular-value decomposition (SVD) method is proposed and integrated into the optimization process. The effectiveness and accuracy of the proposed method are verified by comparing the full-wave simulation results with source reconstruction results for PCBs featuring various types of traces. Based on the simulation, an NF scanning experiment is conducted on an actual circuit to reconstruct EMI sources, further validating the feasibility of the proposed method.

Cross-chain bridges connect two heterogeneous and independent blockchains, in which router contracts interact with external modules to perform functions, e.g., locking assets. However, numerous attacks exploit external interaction vulnerabilities (EIVs) in router contracts. Detecting EIVs presents two key challenges: (1) extracting complete data dependencies, and (2) identifying access controls for off-chain entities. Existing approaches, which transform contracts into intermediate representations for analysis, often miss critical execution logic (e.g., path conditions) and data flows (e.g., data flows at the instruction parameter level). Additionally, the path explosion severely impacts analysis efficiency. In this paper, we systematically analyze real-world attacks and extract the precise features of two typical EIVs. We propose BridgeGuard, a static analysis framework for detecting EIVs. Specifically, we present a symbolic dataflow analysis approach to capture complete execution logic and fine-grained data flows. We then identify three common authentication patterns for off-chain entities and deploy a heuristic-driven pruning method to solve the path explosion. We implement an automated analysis prototype. The experimental results show that for analyzing 1,035 wild contracts with an average scale of 15,914 bytes, BridgeGuard achieves 87.4% average code coverage. For detecting two typical EIVs, BridgeGuard achieves F1-scores above 0.93.

Over the past years, Convolutional Neural Networks (CNNs) have emerged as widely used techniques for tackling various computer vision challenges, with image detection being a popular application. The performance of CNNs in object detection has rapidly improved in terms of both accuracy and speed. Unmanned Aerial Vehicles (UAVs), or drones, have seen exponential growth in adoption across various sectors, from military and surveillance to agriculture and infrastructure inspection. Their ability to capture aerial data and perform remote sensing tasks has revolutionized industries, making them invaluable tools for monitoring, mapping, and data collection. However, UAVs flying near runways can pose a danger to airplanes during take-off and landing. Additionally, drones can be a threat to stadiums, prisons, and military camps due to their ability to carry payloads and bypass ground security. This research concentrates on a particular aspect of object detection: identifying unmanned aerial vehicles (UAVs or drones). The task becomes more demanding due to the data being extracted from video footage captured simultaneously by a visible light camera and an infrared camera. This setup results in difficult viewing angles and significant motion blur. To assess both accuracy and speed, the study selected two models with typical architectures: You Only Look Once (YOLO) and Single Shot MultiBox Detector (SSD). The goal was to evaluate the robustness of these models in detecting UAVs under conditions of motion blur and to understand how the structural differences between the models might influence the results. Several experiments were conducted as part of this work to achieve the stated objectives. The findings likely provide valuable insights into the performance of these object detection models when applied to the challenging scenario of UAV detection from video streams with motion blur. The results showed that for better structural design, YOLOv3 outperformed SSD in various aspects. Further experiments on UAV data from the dataset demonstrated the efficiency of YOLOv3 when trained on more images. Regarding the motion blur problem, the studies demonstrated that the YOLOv3 model has a good ability to recognize and learn blurred visual patterns. The structure of SSD was further tested through the design of default boxes and their performance on different scales and locations. Overall, the results showed that the YOLOv3 model had superior performance compared to SSD for UAV detection in video streams. The experimental results on the Discovery UAVs Dataset confirmed the proposed techniques' value in UAV discovery. The YOLOv3 technique achieved 93% precision and 89.3% recall on the Discovery UAVs test dataset, while the SSD technique achieved 88.9% precision and 86.3% recall on the same dataset.

Traffic accident risk forecasting is vital for enhancing the safety of Intelligent Transportation Systems (ITS). While current approaches rely on Spatiotemporal Graph Neural Networks (STGNNs) with regression loss functions to encode historical traffic dynamics and predict future accident risks, their effectiveness is limited by the rare occurrence of accidents. To overcome this challenge, we present ClassiRisk, a novel approach that addresses traffic accident risk modeling from both regression and classification perspectives. Our method initiates with the encoding of historical traffic dynamics, incorporating a temporal attention mechanism and a novel fused spatial encoding module to capture both recurring and non-recurring spatiotemporal patterns. Subsequently, the model utilizes these encoded embeddings to predict: 1) a sparse binary mask indicating accident occurrence, employing a weighted classification loss to tackle the zero-inflation problem; and 2) the associated risk levels of accidents. The final prediction of traffic accident risks is derived by amalgamating these two outputs. Experimental evaluations conducted on real-world data validate the superiority of our approach over existing methodologies.

In vehicular cloud computing (VCC), cloud servers provide enormous storage and powerful computing capacity to Vehicular Ad-hoc Networks (VANETs). Resource-constrained vehicles outsource data to vehicular cloud platforms for timely traffic safety services, e.g., navigation, accident alarms, etc. Auditing the authenticity of data has become a critical issue in outsourcing data to untrusted servers. Existing data audit methods encode all data with error correction codes (ECC) techniques that retrieve corrupted data by downloading all data. The communication overhead of such methods is $O(n)$ ( $n$ is the number of data blocks) which is unbearable for vehicles with limited resources. In addition, these schemes employ an inaccurate privacy-preserving model. This will lead to data leakage in the third-party audit process. Although they use randomness to confuse parts of the proof that is used to prove the data state, a small amount of information is still distinguishable. For such, in this paper, we propose a practical data audit scheme with retrievability and indistinguishable privacy-preserving to efficiently audit the state of outsourced data. We improve the Invertible Bloom Filter (IBF) to compress redundancy locally, which can retrieve corrupted data without prior context. Furthermore, we define an indistinguishable privacy-preserving model to capture the complete semantics of repeated audit attacks and achieve indistinguishability in the audit. We prove that our scheme is secure against adaptive chosen message attacks and is indistinguishable privacy-preserving against repeated audit attacks. The experiment results demonstrate that for 1.9 GB data when $\sqrt[]{n}$ blocks are corrupted, auditors complete a check in 3.31 seconds with 99% confidence, and vehicles retrieve corrupted data in 3.16 seconds with 16.67 MB communication overhead.

In this paper, the coupling response of incomplete shielded cable under external electromagnetic field is studied. The unbalance difference theory is used to analyze the discontinuous structure of the incomplete shielded cable, and the field-line coupling model of the incomplete shielded cable based on the discretization Baum-Liu-Tesche (BLT) equation is established. At the same time, a test platform for field - line coupling of incomplete shielded cable is established. Based on theoretical modeling and experimental testing, some regularities of the line coupling response of the incomplete shielded wire field are obtained, which have certain guiding value for measuring the electromagnetic compatibility performance of the terminal equipment with incomplete shielded wire.

Waveform distortion is a common phenomenon in electronic systems, which leads to system performance degradation. In order to characterize waveform distortion caused by harmonic, Total Harmonic Distortion (THD) is used. However, in many situations, the waveform distortion usually caused by much more factors, which cannot be characterized by THD. Therefore, waveform distortion factor (WDF) is proposed in this paper. As the development of THD, the effects of amplitude suppression, narrowband interference and noise interference are considered in WDF additionally. To make an illustration, the nonlinear characteristic of low noise amplifier (LNA) is analyzed using WDF.

In order to solve the problem of energy limitation in capsule endoscopy, the button battery can be replaced by an external wireless power. A small wireless power transfer system for delivering power to capsule endoscopy has been presented here. The wireless power transfer system is built by using a small cylindrical receiving resonator and two cylindrical source resonators to transfer power through coupled magnetic resonance. Experimentally, it is found that the wireless power transfer efficiency is stable within a certain range due to the stable magnetic field generated by two source resonators. The proposed design has the significant advantage of stable power transfer and small size for capsule endoscopy.

The low noise amplifier (LNA) is a commonly device used in RF front-end. In this paper, a third-order nonlinear theoretical model of LNA based on Volterra series is proposed. The expression of the power of fundamental signal and third-order intermodulation signal when the input signal is a dual-frequency signal are given. The correctness and accuracy of the theoretical model is verified by the good agreement between the measured data and simulated data.

We present a fast and efficient modeling method for cylindrical cables based on PEEC method and a surface admittance operator. The proposed method uses an admittance operator to build new basis functions, which can make the calculation of partial impedance and partial inductance faster and capture the current distribution caused by skin effect (SE) and proximity effect (PE). Simulation examples confirm the accuracy and efficiency of the method.

This paper presents a field-to-wire coupling model for wire bundles with strongly non-uniform path. Previous studies on multi-conductor transmission lines (MTL) are mainly confined to uniform or weakly non-uniform path, which is sometimes not the case in practice. In this paper, the external and internal characteristics of the wire bundle are decomposed by a mode transformation method, of which the advantage is the transformation matrices do not vary with the non-uniform path. The external characteristics correspond to common-mode (CM) components, modeled as an equivalent single wire running in the same path with the bundle above the reference ground. The internal characteristics correspond to differential-mode (DM) components, modeled as a uniform MTL system composed of the original wires in the bundle. In this way, the effects of the non-uniform path and the external field only exist in the CM model. Mode conversion caused by the dielectric coating and terminals is modeled with equivalent circuits. The proposed model is validated with a bundle of curved wires above a PCB board.

This paper proposes a 2.45/5.8GHz simultaneously working dual-frequency rectenna for an integrated wireless energy harvesting system. A 2.45/5.8GHz receiving antenna is designed to receive RF (Radio Frequency) energy. To achieve a dual-frequency synchronized working rectifying circuits, a new design of 2.45/5.8GHz rectifying circuit utilizing reverse series output mode is proposed. With 0dBm input power, the simulated conversion efficiency of the rectifying circuit is higher than 60% and the measured conversion efficiency is higher than 20% when the dual frequency rectifying circuits work together. The rectenna is validated in an integrated wireless energy harvesting system. The measurement results show that the rectenna can be used to harvest energy for low-power electronic devices.

A flexible, compact dual-band antenna system for wireless body area network(WBAN) applications is presented. The design is based on a CPW-fed bow-tie slot antenna, operating at 2.45 GHz and 5.8 GHz. To reduce the backward scattering wave towards the human body, a novel compac double square based Artificial Magnetic Conductor(AMC structure is proposed. The configuration of the AMC-integrated antenna is low profile with a thickness of 3.77 mm and an area of 92 × 69 mm 2 . Simulated results show that the AMC-integrated antenna has the gain of 6.2 dBi and 10 dBi, and the front-to-back ratio of 15 dB and 20 dB, at 2.45 GHz and 5.8 GHz, respectively, when placed on the phantom. Compared to conventional slot antennas, the radiation characteristics are improved, making it a good candidate for WBAN applications.

A near-field radiation model for irregular wiring twisted-wire pairs (TWPs) is built in this paper. Currents on wires are decomposed into a common mode (CM) and several differential modes (DMs), where CM is the direct contribution to radiation. By virtue of the electric/magnetic equivalent radius proposed here, CM quantities can be modeled as an irregular single wire above ground; DM quantities can be modeled as two-conductor transmission lines. Coupling between them due to termination imbalance is interpreted by equivalent circuits. Both the network matrix solution and the approximate solution are provided. The proposed model is validated by an exact solution of the method of moments and measurements.

The low noise amplifier (LNA) is a significant device in RF front-end. In this paper, a straight and efficient modeling method for LNA based on the Volterra series with recursive least squares (RLS) algorithm is proposed. Instead of calculating the high nonlinearity order of Volterra kernels, the proposed method extracts the first three order Volterra kernels characterizing the memory effect to construct a model of LNA, which is measured and simulated by the proposed method, and a good agreement is confirmed.

The asymmetry/coating-induced mode conversion (ACIMC) effect in a transmission line system is discussed in this letter. This effect is proved only to be induced when asymmetry and coating exist simultaneously. The reason for ACIMC is that asymmetry and coating lead to the inconsistency of current division factors for electric and magnetic effects. Based on this idea, ACIMC is modeled by adding common-mode current-controlled voltage sources in differential circuits, and the results agree well with those obtained by the method of moments. Moreover, it is proved that balanced-state-matched terminal loads do not exist for a asymmetric and coated transmission line system, but approximate ones at low frequencies could still be found.