Proactive detection with distributed flexible AC transmission system (D-FACTS) devices has been extensively studied for identifying false data injection (FDI) attacks in smart grids, while their potential for localizing remains largely unexplored. To meet this gap, this paper systematically explores the feasibility of localizing FDI attacks with D-FACTS devices. Specifically, we first thoroughly study the rationale underlying FDI localization with D-FACTS devices. We prove that an activated D-FACTS device is capable of localizing FDI attacks targeted on its connected end buses once a bad data detection (BDD) alarm is triggered. In addition, we elaborately analyze the inherent localization limitations: (i) the unlocalizable adversary cases targeting one-degree buses or super-buses; (ii) the localization uncertainty introduced by the defender's blind spots, resulting in huge operational costs and insufficient precision. Following this, a data-prompting framework is designed to over-come the above limitations. This framework integrates a data driven injected error identifier for precise localization and cost reduction, followed by a perturbation strategy with D-FACTS devices that significantly lowers false positive rates. Extensive simulations validate our theoretical findings on the rationale and limitations, while also demonstrating the effectiveness of the proposed framework in addressing limitations and enhancing localization accuracy.

Cancellable biometrics is essential for preserving sensitive biometric information from potential exposure. Existing studies usually convert real-valued biometric vectors into protected templates by randomly generated transformation keys. However, this way is realized by the built-in functions of the cancellable biometric system, which creates vulnerabilities for cancellable biometric schemes. In this paper, we propose a novel chaos-based Index-of-Min cancellable biometric scheme, named C-IoM, for privacy-preserving template updates in biometric technique. Specifically, we first design a chaos-based cancellable biometric framework to ensure the security and privacy of the biometric template. Second, we develop a secure random chaos seed generation algorithm, which non-linearly converts the biometric vectors into protected templates and conceals biometric dimensional information. Further, we craft a sliding window selection mechanism to choose the input biometric features, allowing each feature data to fully participate in the generation of protected templates through sliding intervals. Theoretical analysis confirms that the C-IoM satisfies the criteria of irreversibility, revocability, unlinkability, and performance preservation in cancellable biometrics. Extensive experiments on LFW, CFPW, and CASIA-V5 datasets demonstrate the security of the proposed framework in protecting biometric data as well as the superiorities over state-of-the-art schemes.

Understanding the relationship between mixed land use and urban vibrancy is vital in advanced urban planning applications. This study presents a Bayesian spatially varying coefficient (SVC) model to explore the spatially nonstationary relationship between mixed land use and urban vibrancy after controlling for other factors. We first use the convolutional conditional autoregressive prior to accommodate the ecological bias resulting from unobserved confounders. Then we develop our approach in the case of a single predictor to allow the spatially varying coefficient process. We further introduce a type of the Bayesian SVC model that considers the stratified heterogeneity of the outcome, allowing the coefficients to simultaneously vary at the local and subregion level. We illustrate the proposed model by conducting a case study in Shenzhen using mobile phone data, an officially registered point-of-interest (POI) dataset, and several supplementary datasets. The model evaluation results show that including spatially unstructured and structured component combinations can improve the model's fitness and predictive ability; additionally, considering spatial stratified heterogeneity can further enhance the model's performance. Our findings provide an alternative for measuring the variable local-scale association between mixed-use and urban vibrancy and offer new insights that broaden the fields of environmental science and spatial statistics.

Mobile crowdsensing has empowered the Industrial Internet of Things (IIoT) in many ways, such as vehicle-aided traffic flow scheduling, drone-aided visual inspections, etc. However, dynamic perception and cooperative decision making among these heterogeneous and resource-constrained mobile clients in IIoT remains a big challenge. In this article, we propose an incentive-based federated learning scheme for digital twin (DT)-driven industrial mobile crowdsensing. Specifically, we first design a DT-driven industrial mobile crowdsensing architecture to achieve dynamic perception of the complex IIoT environment, among heterogeneous and resource-constrained mobile clients. Second, we develop a novel incentive-based federated learning framework incorporated with a contract-based reputation mechanism and a Stackelberg-based interclient incentive mechanism, to optimize the model accuracy. Third, we devise a knowledge distillation algorithm for the federated learning framework, to address the heterogeneity of nonindependent and identically distributed (Non-IID) data. Extensive experiments on both MNIST/FEMNIST and CIFAR10/100 data sets demonstrate the outperformance of our proposed scheme, in terms of model accuracy, incentive fairness, and data compatibility, compared to state-of-the-art studies.

Real-time and accurate prediction of charging pile energy demands in electric vehicle (EV) charging networks contributes significantly to load shedding and energy conservation. However, existing methods usually suffer from either data privacy leakage problems or heavy communication overheads. In this article, we propose a novel blockchain-based personalized federated deep learning scheme, coined $P^{3}$ , for privacy-preserving energy demands prediction in EV charging networks. Specifically, we first design an accurate deep learning-based energy demands prediction model for charging piles, by making use of the CNN, BiLSTM, and attention mechanism. Second, we develop a blockchain-based hierarchical and personalized federated learning framework with a consensus committee, allowing charging piles to collectively establish a comprehensive energy demands prediction model in a low-latency and privacy-preserving way. Last, a CKKS cryptosystem based secure communication protocol is crafted to guarantee the confidentiality of model parameters while model training. Extensive experiments on two real-world datasets demonstrate the superiorities of the proposed $P^{3}$ scheme in accurately predicting real-time energy demands over state-of-the-art schemes. Further, the $P^{3}$ scheme can achieve reasonably low computational costs, compared with other homomorphic-based schemes, such as Paillier and BFV.

Alfalfa is a valuable and widely adapted forage crop, and its nutritive value directly affects animal performance and ultimately affects the profitability of livestock production. Traditional nutritive value measurement method is labor-intensive and time-consuming and thus hinders the determination of alfalfa nutritive values over large fields. The adoption of unmanned aerial vehicles (UAVs) facilitates the generation of images with high spatial and temporal resolutions for field-level agricultural research. Additionally, compared with other imaging modalities, hyperspectral data usually consist of hundreds of narrow spectral bands and allow the accurate detection, identification, and quantification of crop quality. Although various machine-learning methods have been developed for alfalfa quality prediction, they were all single-task models that learned independently for each quality trait and failed to utilize the underlying relatedness between each task. Inspired by the idea of multitask learning (MTL), this study aims to develop an approach that simultaneously predicts multiple quality traits. The algorithm first extracts shared information through a long short-term memory (LSTM)-based common hidden layer. To enhance the model flexibility, it is then divided into multiple branches, each containing the same or different number of task-specific fully connected hidden layers. Through comparison with multiple mainstream single-task machine-learning models, the effectiveness of the model is illustrated based on the measured alfalfa quality data and multitemporal UAV-based hyperspectral imagery.

Understanding population distribution has excellent applications for planning and provision of municipal services. This study aims to explore the space-time structure of population distribution with area-level mobile phone data. We discuss a kind of Bayesian hierarchical models, fitted by Markov chain Monte Carlo simulation, that combines the overall spatial pattern and temporal trends as well as the departures from these stable components. We carry out an empirical study in Shenzhen, China, using the area-level mobile phone users in 24 hours. The results indicate that the estimation of the overall spatial pattern is not deteriorated when using a sophisticated spatio-temporal model. The temporal trend exhibits a reasonable fluctuation during the study period. Then we apply two rules to detect areas showing unstable trends of population fluctuation based on the posterior probabilities of the space-time interactions. We also include the population statistics and indices for mixed-use to explore the spatial pattern of population fluctuation. Our findings confirm that the Bayesian spatio-temporal model can enhance the understanding of the space-time variability of population distribution using mobile phone data. Further research should examine the spatial nonstationary effects of explanatory factors on mobile phone-based population fluctuation.

Address matching is a crucial step in geocoding, which plays an important role in urban planning and management. To date, the unprecedented development of location-based services has generated a large amount of unstructured address data. Traditional address matching methods mainly focus on the literal similarity of address records and are therefore not applicable to the unstructured address data. In this study, we introduce an address matching method based on deep learning to identify the semantic similarity between address records. First, we train the word2vec model to transform the address records into their corresponding vector representations. Next, we apply the enhanced sequential inference model (ESIM), a deep text-matching model, to make local and global inferences to determine if two addresses match. To evaluate the accuracy of the proposed method, we fine-tune the model with real-world address data from the Shenzhen Address Database and compare the outputs with those of several popular address matching methods. The results indicate that the proposed method achieves a higher matching accuracy for unstructured address records, with its precision, recall, and F1 score (i.e., the harmonic mean of precision and recall) reaching 0.97 on the test set.

Understanding scale effects is important and indispensable for geography studies. However, spatial and spatiotemporal statistical tools for measuring the operational scales of different processes are rather limited. This article extends the popular geographically and temporally weighted regression (GTWR) model to consider operational scale effects by proposing multiscale GTWR (MGTWR), which offers a flexible and scalable framework for identifying and analysing multiscale processes by specifying flexible bandwidths for various covariates. Then, MGTWR is employed to explore spatiotemporal variations and how influential factors are associated with housing prices in Shenzhen. This article attempts to extend GTWR to MGTWR in consideration of scale effects, thereby highlighting the importance of different levels of spatiotemporal heterogeneity. Furthermore, the empirical results of this study can provide valuable policy implications for real estate development in areas where urban planning should address multiscale effects in both temporal and spatial dimensions.

The main goal of this article is to present a generalized conceptual model for the Chinese cadastre. For the Chinese cadastre, both the geometric and legal components will be considered. According to the cadastral situation of china and existent data structure, this paper modified the core cadastral domain model by an objected-oriented way. After that the refined Land package and geometry package are presented. The other contribution of this article is identification of the core cadastral model by using it to Chinese cadastral domain, although some specific classed are modified.