Miracle-dz/UniFAR-FTUs huggingface dataset:
Total Refractivity Fields from GNSS Tropospheric Delays Reconstructed with Collocation Methods

Refractivity of the Earth's troposphere is directly reflected in the observations of space geodesy such as Global Navigation Satellite Systems (GNSS) and satellite radars. Therefore, they can provide valuable information about the quantity and spatio-temporal distribution of atmospheric gases. With improvement of GNSS modeling strategies and increasing network density tomographic approaches were developed. In this paper, we present an alternative tomographic approach based on collocation method to estimate the refractivity field from GNSS observables. Collocation adjustment is used to estimate parameters that relate GNSS observables (such as zenith or slant delays) with refractivities. Therefore, the parameters are used to compute the refractivity field. With this approach, for end-to-end simulations, the refractivity field can be reconstructed with an accuracy of 2.5 ppm and 4.5 ppm in terms of mean and standard deviation respectively; these results are comparable to typical tomography results.
Overview of Power Grid Loss Prediction and Early Warning Under Typhoon Disaster

In recent years, the ecological damage caused by human activities has been intensified, and typhoon disasters have been increasingly frequent, which has brought huge structural damage to the power system. Therefore, this paper summarizes the research on power grid loss prediction and early warning under typhoon disaster from three aspects: power grid loss prediction under typhoon disaster, power grid loss early warning under typhoon disaster and construction of power outage defense system. Some instructive conclusions are obtained in this paper.
Neural Network Pruning for Hyperspectral Image Band Selection

Neural network pruning attempts to reduce parameters without hurting original performance by inducing connection matrix sparsity of network. Inspired by this idea, we proposed an effective pruning-based band selection strategy, which is a potent feature extraction tool in hyperspectral image (HSI) classification. At first, we take the whole HSI bands as input to train original network parameters. For each band, all parameters in network are integrated to measure the band importance. With the novel band signification factor constraining, then the convolutional neural network (CNN) is pruned and remains some representative weights to retrain the compact sub-network, which can finally deal with the hyperspectral band selection problem. Experimental results on the real HSI dataset demonstrate that network pruning-based method can outperform the original CNN in classification accuracy. Also, it can achieve the superiority over filter-based and other CNN-based band selection algorithms in classification accuracy. Our code is available at https://github.com/qixiong-wang/Network-pruning-for-HSI-band-selection.
Warning of Rainfall-Induced Landslide in Bazhou District

Landslide disasters have caused incalculable losses to human. In China, 90% occurrence of landslides are directly induced by rainfall or indirectly related to rainfall. Because of its geographical location and the climate that belongs to the subtropical monsoon humid climate, the proportion of rainfall-induced landslides accounts for more than 70% of the total geological disasters in Bazhou district. In this paper, based on the geographic information system (GIS) technology, combined with the historical landslide hazard data, we conducted a study on landslide probabilistic quantitative model composed of landslide susceptibility evaluation and the rainfall intensity-duration threshold model. The research results showed that the prediction accuracy of rainfall model is 81.82%. And the landslide hazard prediction was carried out for rainfall-induced landslides and potential landslides, with a prediction accuracy of 90.91%. The research results showed that the meteorological early warning model results were consistent with the actual inspection results.
Local Spatial-Spectral Correlation Based Mixtures of Factor Analyzers for Hyperspectral Denoising

This paper presents a local spatial-spectral correlation based mixtures of factor analyzers (LSSC-MFA) denoising method for hyperspectral image (HSI). HSIs are usually degraded by different noise types such as missing lines (ML), missing pixels (MP), salt and pepper noise (SP), and Gaussian noise. The proposed method, hierarchically, removes the mixed noise. Firstly, we develop a novel local spatial-spectral correlation (LSSC) method to remove the ML noise. Then LSSC-MFA uses the mixtures of factor analyzers (MFA) method to remove the MP, SP, and Gaussian noises. The performance of the proposed method has been validated using both real and simulated HSI datasets. Results on the simulated datasets confirm considerable improvements in terms of peak signal-to-noise ratio (PSNR) compared to the state-of-the-art denoising methods used in experiments. In addition, visual improvements can be observed in the case of real dataset experiments.
Trajectory Optimization for Large-scale UAV-Assisted RSUs in V2I Communication

Vehicle-to-infrastructure (V2I) communication enables vehicles to acquire surrounding traffic information in real time, which significantly improves the driving safety and comfort. The roadside unit (RSU) which transfers information among vehicles plays an indispensable role in V2I communication. For a long time, researchers have been focusing on choosing proper RSU deployment locations to deploy fixed RSUs. Meanwhile, unmanned aerial vehicle (UAV) technology has developed rapidly. In terms of its unique mobility and flexibility, the UAV-assisted RSU (u-RSU) is able to cover a larger area. If the flight trajectory is designed properly, fewer u-RSUs can solve the RSU deployment problem and ensure the network performance. Therefore, the large-scale u-RSU collaborative trajectories design problem is investigated in this paper. We propose a u-RSU flight trajectory strategy and solve the problem in three steps. An improved greedy algorithm and an ant colony optimization (ACO) algorithm are used in the solution. Under different conditions, the proposed u-RSU flight strategy is analyzed and compared with other deployment strategies. Simulation results show that our strategy always has better network performance and deploys fewer u-RSUs. Thus, the proposed strategy is more economical and effective compared with the traditional deployment strategy.
Effects of Clustering Feature Vectors on Bus Travel Time Prediction: A Case Study

Improving the accuracy of travel time predictions depends on providing the correct inputs as well as the prediction algorithm used. Clustering algorithms can be used to identify the patterns in the data, which can improve the inputs to the prediction algorithm. The feature vectors used for clustering greatly affect the clusters formed and, ultimately, the prediction performance. Clustering being an unsupervised learning technique, the accuracy or correctness of the cluster formed can not be evaluated directly. A possible solution for this would be to link the problem with prediction accuracy and choose the feature vector combination with maximum prediction accuracy. The present study analyses the use of different feature vectors for clustering and the effect on travel time predictions. Here, three cases, namely, travel time alone, travel time along with features such as time of the day, section index, and day of the week as numerical features and as a mix of categorical and numerical feature vectors, are studied. The effects of using each of these cases as clustering feature vectors on travel time predictions are evaluated. It is observed that the prediction accuracy is the highest when only travel times are used as a clustering feature vector. The study demonstrates the importance of choosing the correct feature vectors for clustering and its effect on a final application, namely, travel time prediction.
SGX-ICN: A Secure and Privacy-Preserving Information-Centric Networking with SGX Enclaves

As the next-generation network architecture, Information-Centric Networking (ICN) has emerged as a novel paradigm to cope with the increasing demand for content delivery on the Internet. In contrast to the conventional host-centric architectures, ICN focuses on content retrieval based on their name rather than their storage location. However, ICN is vulnerable to various security and privacy attacks due to the inherent attributes of the ICN architectures. For example, a curious ICN node can monitor the network traffic to reveal the sensitive data issued by specific users. Hence, further research on privacy protection for ICN is needed. This paper presents a practical approach to effectively enhancing the security and privacy of ICN by utilizing Intel SGX, a commodity trusted execution environment. The main idea is to leverage secure enclaves residing on ICN nodes to do computations on sensitive data. Performance evaluations on the real-world datasets demonstrate the efficiency of the proposed scheme. Moreover, our scheme outperforms the cryptography based method.
Sampled-Data Synchronization Control for Chaotic Neural Networks With Mixed Delays: A Discontinuous Lyapunov Functional Approach

The sampled-data synchronization problem of chaotic neural networks with mixed time delays is investigated in this paper. A novel augmented discontinuous Lyapunov-Krasovskii functional (ADLKF) is constructed, which can make full capture the more information of delay variation and the characteristics about the actual sampling information. Based on the ADLKF and modified free-matrix-based integral inequality, novel less conservative synchronization criteria are developed to guarantee that the slave system is synchronous with the master system. Furthermore, the derived results are given in terms of simplified linear matrix inequalities (LMIs), which can be straightforwardly solved by Matlab. Finally, two numerical examples are proposed to demonstrate the effectiveness and the benefits of the presented synchronization scheme.
A quasi-elliptic Band Pass Filter designed using quadruplet metamaterial resonators for operation in the 3 GHz (5G) Band

In this paper, we investigate the pertinent characteristics of a metamaterial square spiral resonator in realizing a compact bandpass filter operating in the 3.5-3.7 GHz 5G band. The resonators designed by analytical modelling and full wave simulations are incorporated as broad side coupled structures between two parallel input and output microstrip lines. By incorporating radial stubs in the microstrip line, the stop band performance of the filter is improved approximately up to three times the operating frequency. It exhibits competitive attenuation slopes of 79.4 and 67.46 dB/GHz in the lower and upper pass band edges respectively. Electrical size of the proposed filter is just 0.44 × 0.44 $\lambda _g^2$. Operating frequency of the filter can be easily tuned by redesigning the resonators with the design guidelines presented in this article. The proposed filter uses no grounded structures, vias, defected ground or multilayer structures, and hence can be easily fabricated and integrated to microwave circuitry.
Efficient Attention Pyramid Network for Semantic Segmentation

Semantic segmentation is a task that covers most of the perception needs of intelligent vehicles in an unified way. Recent studies witnessed that attention mechanisms achieve impressive performance in computer vision task. Current attention mechanisms based segmentation methods differ with each other in position and form of the attention mechanism, and perform differently in practice. This paper firstly introduces the effectiveness of multi-scale context features and attention mechanisms in segmentation tasks. We find that multi-scale and channel attention can play a vital role in constructing effective context features. Based on this analysis, this paper proposes an efficient attention pyramid network (EAPNet) for semantic segmentation. Specifically, to efficient handle the problem of segmenting objects at multiple scales, we design efficient channel attention pyramid (ECAP) which employ atrous convolution with channel attention in cascade or in parallel to capture multi-scale context by using multiple atrous rates. Furthermore, we propose a residual attention fusion block (RAFB), whose purpose is to simultaneously focus on meaningful low-level feature maps and spatial location information. At the same time, we will explore different channel attention modules and spatial attention modules, and describe their impact on network performance. We empirically evaluate our EAPNet on two semantic segmentation datasets, including PASCAL VOC 2012 and Cityscapes datasets. Experimental results show that without MS COCO pre-training and any post-processing, EAPNet achieved 81.7% mIoU on the PASCAL VOC 2012 validation set. With deeplabv3+ as the benchmark, EAPNet improve the model performance of more than 1.50% mIoU.
Sentiment analysis of agricultural product ecommerce review data based on deep learning

$A$ The relevant comment texts of agricultural products sold on the e-commerce platform contain various and complex emotions of consumers, and reflect customers' consumer experience and views on those products. Compared with the features and advantages of English text segmentation structure, the analysis of Chinese sentiment polarity in a complex context has always been a problem in deep learning. This article explores the issue of sentiment polarity analysis of short texts of Chinese reviews of agricultural products by constructing Text-CNN, Bi-LSTM and BERT to fine-tune three deep learning models. The research object adopted is the review data of agricultural products on the e-commerce platform obtained based on crawler technology. The experimental results show that, compared with the Bi-LSTM and BERT fine-tuning models, the Text-CNN model's accuracy, precision, recall, and F1 value are all about 3 to 8 percentage points higher. The classification effect gradient gap between the three models is obvious. The accuracy of the Text-CNN convolutional neural network reached 99.92%. The results confirmed that Text-CNN has high sentiment analysis accuracy and good sentiment polarity classification effect in processing agricultural short text comment data, as an agricultural product e-commerce The emotional classification model of sales review data can provide an important reference for manufacturers to accurately distinguish user feedback and enhance market competitiveness.
Anomaly Detection/Prediction for the Internet of Things: State of the Art and the Future

Anomaly detection/prediction is the first step to secure IoT systems. It usually relies on wide domain knowledge to build up the tools to automatically detect/predict abnormal events or behaviors of an IoT system. However, an IoT system may consist of machines with different capabilities, functionalities and ages. Furthermore, abnormal events or behaviors are usually rare events. It is time-consuming and high-cost to build up the domain knowhow of the IoT systems and collect enough data points of the anomaly. In this article, we first identify the issues and challenges. Then we illustrate a general environment for anomaly detection/prediction on the IoT systems. Then we survey the core technologies and existing solutions that may be applied for anomaly detection/prediction. We also identify what cannot be achieved by the existing solutions. Then considering four datasets, we show the performance comparison for different solutions by running experiments.
A Hybrid Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles

A large proportion of electric vehicle accidents are attributed to lithium-ion battery failure recently, which demands the time-efficient diagnosis and safety warning in advance of severe fault occurrence to ensure reliable operation of electric vehicles. However, serious battery system faults are often not caused by easily-observed cell state inconsistency, but derived from a certain cell failure with precursory signals untended, or occasional abuse, thus eventually thermal runaway. In this paper, a signal-based fault diagnosis method is presented, including signal analysis to eliminate the impact of state inconsistency on time-series feature extraction, feature fusion, and dimensionality reduction by manifold learning, with clustering-based outlier detection to identify abnormal signal features. The challenges in threshold determination of fused features can be effectively resolved by supplementary correction to largely reduce the amount of false alarms. Compared with the judgments from actual battery management systems, and other signal-based methods with single features, earlier detections can be achieved with robustness, verified by real-world pre-fault operation data of electric vehicles that suffered thermal runaway.
Building Instance Segmentation and Boundary Regularization from High-Resolution Remote Sensing Images

Building extraction from remote sensing images using convolutional neural networks (CNNs) has been an active research topic in recent years. Most results obtained by CNN-based algorithms, however, still have common issues with the precision of the delineation of building outlines and the separation of different buildings. Recently, efforts have been made towards the automation of building outline regularization. This paper employs a new instance segmentation framework named Hybrid Task Cascade (HTC) as baseline model, integrating detection and segmentation as a joint multi-stage processing. We further integrate regularization methods such as convex hull and Douglas-Peucker algorithm to obtain accurately segmented edges. The method is tested on the crowdAI benchmark dataset by comparing with alternative state-of-the-art models (i.e., Mask R-CNN). The results show that our method achieves better instance segmentation results and improves the results in terms of geometric regularity of building segments.
Software Failures Prediction in Self-Driving Vehicles

Nowadays, Advanced Driver Assistance Systems (ADAS) play important roles in improving the safety of driving. ADASs along with new safety standards are now essential to improve the safety of automated driving. These standards differ from the safety standards in other industries because an autonomous vehicle rides in a very intricate stochastic environment where many details should be considered to maintain the safety. From one point of view, autonomous car failures are divided into two categories: hardware and software failures. According to the literature, if the reason of fault belongs to hardware, the troubleshooting will relatively be easier than software failure. In other words, if the fault is the subset of software, that would be more challenging task to detect potential fault and recover from the failure. In this paper, we propose a new approach to detect the software failures, which may be unpredictable using a typical safety protocol. A predictive machine learning method is developed to improve decision making process in order to avert the potential accidents occurred by software defects. The proposed method evaluates detected obstacle in the span of a specified number of frames to discern whether objects are detected correctly or not. Hence, it provides extra time to choose the suitable action before it reaches the danger, which includes lane changes, cut-in maneuvers, stopping the car, accelerating the car, slowing down, and allowing vehicles to pass. Results show that the new approach improve the safety by increasing the precision of failure detections.
Control of a Flexible Continuum Manipulator for Laser Beam Steering

Continuum manipulators with structural compliance can be utilized to steer a laser beam in constrained environments. However, the flexibility and nonlinear characteristics of continuum manipulators bring difficulty in precision manipulation. This study proposes a model-free control approach with visual feedback to control a tendon-driven flexible manipulator that can be integrated into an endoscope to steer a laser beam accurately. To overcome the noise from the visual feedback and disturbances from environment during operation, a local Jacobian matrix that maps the actuation space to the image space is approximated online by using the sensing data stored in limited memory, where the influence of outliers can be filtered. Then, a second-order sliding mode controller is developed to achieve robust control of the flexible manipulator in laser beam steering. Simulations and experiments are performed to demonstrate the effectiveness of the proposed control methods in different configurations.
Detection of Internal Solitary Waves with Conventional and Advanced SAR Altimetry Processing Methods: Preliminary Results

The Delay-Doppler altimeter data processing (commonly called SAR altimetry) is a mature technology offering many advantages in marine radar applications compared to conventional altimetry. Here we describe the potential of SAR altimetry (both unfocused and Fully Focused) for detecting and measuring Internal Solitary Wave (ISW) amplitudes in deep water regions. Analysis of ISW SAR altimeter signatures in the tropical ocean off the Amazon shelf are presented, since their large amplitude ISWs are known to exist. Preliminary results of the dependence of Sea Level Anomalies (SLA) associated to ISWs with respect to the differenced mean square slope variations along the waves' signatures are presented for the first time.
Systematic Mapping Study on Security Approaches in Secure Software Engineering

In the modern digital era, software systems are extensively adapted and have become an integral component of human society. Such wide use of software systems consists of large and more critical data that inevitably needs to be secured. It is imperative to make sure that these software systems not only satisfy the users’ needs or functional requirements, but it is equally important to make sure the security of these software systems. However, recent research shows that many software development methods do not explicitly include software security measures during software development as they move from demand engineering to their final losses. Integrating software security at each stage of the software development life cycle (SDLC) has become an urgent need. Tackling software security, various methods, techniques, and models have been suggested and developed, however, only a few of them provide strong evidence for building secure software applications. The main purpose of this research is to study security measures in the context of the development of secure software (SSD) during the study of systematic mapping (SMS). Based on the inclusion and exclusion criteria, 116 studies were selected. After the data extraction from the selected 116 papers, these were classified based on the quality assessment, software security method, SDLC phases, publication venue, and SWOT analysis. The results indicate that this domain is still immature and sufficient research work needs to be carried out particularly on empirically evaluated solutions.
Joint Mixed-Noise Removal and Compressed Sensing Reconstruction of Hyperspectral Images via Convex Optimization

Compressed sensing (CS) reconstruction is essential in capturing hyperspectral (HS) images by one-shot. However, existing approaches for compressed HS imaging assume that compressed observation is contaminated by Gaussian noise, and so they are sensitive to the other type of noise and outliers. To resolve the above problem, we propose a new methodology compressed HS imaging that can handle mixed Gaussian-sparse noise. For robust estimation, our proposed method simultaneously estimates a clean HS image and sparse noise by solving a convex optimization problem. Experimental results illustrate the utility of our proposed framework.