esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Testbed Model and Use Case
### Test Scenarios  
The test scenarios for the IB-PV array farm are based on simulating different electrical faults. Four electrical fault tests are performed, and data are collected from six IEDs at the same time. In the IB-PV array farm with the electrical grid, the 3 LG, LLG, SLG, and LL electrical faults are simulated. Each fault simulation test is run for approximately 50 s, and the loads are fed by utilities \(A\) and \(C\), which are a grid source and IB-PV-array farm, respectively. Then, at 20 s, the temporary electrical fault with a duration of 0.5 s (30 cycles) is performed at the power line end of utility \(A\). The relays do not trip breakers because the testbed is set for recording and saving the whole fault states in all relays and meters. In these tests, the recorded events from four relays (SEL351S, SEL700 GT, SEL451, and SEL421 SV) and two meters (SEL735) are collected. Table 5 shows the characteristics of relays and meters for the electrical fault test use case scenarios.  
The relay and meter events are obtained using the AcSELerator Quickset software (version 7.2.2.4) The voltages, currents, and event trigger signals are plotted by the SynchroWAVE software (version 1.9.0.89) [53]. The relay and meter events have different sampling frequencies and, consequently, different sample rates. The events from the relays and meters are collected as COMTRADE files and converted into MATLAB files to run the assessment of the proposed envelope- and ML-based electrical fault type detection algorithms.  
Figure 11: Circuit for the testbed model.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Devices**} & **Sample Rate** & **Electrical Grid** & **Data Point Sites** \\  & **(Samples/Cycle)** & **Location** & **(Figure)** \\ \hline SEL351S & & IB-PV-array farm & Figure 4a \\ SEL700 GT & 32 & IB-PV-array farm & Figure 4b \\  & & Grid side & Figure 4c \\ \hline SEL451 & & LV side (substation) & Figure 4d \\ SEL451 SV & 133 & HV side (substation) & Figure 4e \\ \hline SEL735 & & Feeder 1 & Figure 4f \\ SEL735 & 128 & Feeder 2 & Figure 4g \\ \hline \hline \end{tabular}
\end{table}
Table 5: Characteristics of IEDs for electrical fault test scenarios.
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 3 Methodology
### Algorithm Implementation  
The method in this paper addresses the catastrophic forgetting problem, recognition confusion problem, and bias problem caused by class imbalance in SAR target incremental learning. Algorithm 2 describes the detailed implementation process of the proposed method.  
```
Input: Train dataset \(\mathbf{D}_{i}\); Test dataset \(\mathbf{E}_{i}\); Output: New representative sample set \(\mathbf{d}_{i}\subsetneq\mathbf{D}_{i}\); Model \(M_{i}\); Output: Test results; for\(i\) in \((0,1,\ldots,N)\)do \(\left\lfloor/\!/1\right\rfloor\) initial phase and \(N\) incremental phases]  Get \(\mathbf{D}_{i}\); if\(i=0\)then\(\left\lfloor/\!/\right\)initial phase  Train \(M_{0}\) by \(\mathbf{D}_{0}\);  Select samples \(\mathbf{d}_{0}\subsetneq\mathbf{D}_{0}\) by Algorithm 1; else\(\left\lfloor/\!/\right\)incremental phases]  Load \(\mathbf{d}_{0:i-1}\) from memory;  Initial \(M_{i}\) with \(M_{i-1}\);  Train \(M_{i}\) on \(\mathbf{d}_{0:i-1}\cup\mathbf{D}_{i}\) by (1);  Correct the bias of the fully connected layer by (9); endif  Run a test with \(\mathbf{E}_{i}\) and record the results;  Update samples \(\mathbf{d}_{i}\) by Algorithm 1; endfor
```  
**Algorithm 2**Detailed Implementation Process of the Proposed Method.
# Will Zero Vulnerability Computing (ZVC) Ever Be Possible? Testing the Hypothesis
## 6 Experiment: Supporting the ZVC Hypotheses
### Objectives of This Research  
The experiments were designed to support the two hypotheses to achieve zero vulnerability computing. Hence, the following specific objectives were formulated:  
1. Design a ZVC device based on a MUDP 3.0 chipset with a tiny form factor (~17 mm length) that facilitates its permanent mounting on a host computer's external USB port without damaging the port.
2. Build a multi-coin wallet application that supports bitcoins and altcoins as a hardware wallet with cold storage.
3. Program the ZVC device to completely obliterate its attack surface using the SOS software for Linux.
4. Test and validate ZVC using a mock malware application that fails to write any data to the ZVC device.
5. Redesign the MUDP chipset to include a connector on the MUDP PCB that controls power to the chipset.
6. Redesign the ZVC device housing to include an external on/off switch that controls its online/offline status.
7. Secure the wallet with fully homomorphic encryption (FHE), partially homomorphic encryption (PHE), and standard encryption to create **cold**, **warm**, and **hot** versions, respectively, and establish transition protocols between them.
8. Finally, validate the cybersecurity of the ZVC hardware wallet product in a real-world crypto transaction setting.
9. Disseminate and exploit the results of the experiments.  
The goals of the experiments were planned to be achieved in two stages. The first 4 goals were targeted for the Stage 1 experiments and the remaining 5 to be achieved in the Stage 2 experiments. This report presents the results of the Stage 1 experiments.  
Figure 7: Illustration of the security of a switchable ZVC hardware wallet that incorporates both the SOS and ICOS modules of ZVC.
# IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing
## 3 The Proposed Method
### Network Design
#### 3.2.3 Bilateral Constrained Rectifier Linear Unit  
Common choices for nonlinear activation function in deep networks include sigmoid [46], tanh [47], and rectified linear unit (ReLU) [48]. Sigmoid is a function of saturation at both ends, but it has a high computational cost and easily suffers from vanishing gradients, which may result in poor local optimality for the training process. Compared to sigmoid, tanh has an output mean of 0, which leads to faster convergence speed and fewer iterations. However, tanh, like sigmoid, has soft saturation, resulting in gradients vanishing. ReLU is proposed to alleviate the vanishing gradient problem of neural networks to a certain extent and accelerate the rate of convergence of gradient descent. It is worth noting that ReLU maintains unilateral suppression and has a wide area when it is greater than 0, which may lead to response overflow, especially in the final layer. For image restoration, the output of the last layer should have upper and lower boundaries, and the range of values should be relatively small. To this end, we propose a bilateral constrained rectifier linear unit (BCReLU) activation function to overcome the limitations of sigmoid and ReLU, as shown in Figure 4. As a novel linear unit, BCReLU keeps bilateral constraint and local linearity. Its output is centered around zero, making the latter layer of neurons less prone to bias and neuronal necrosis. In addition, BCReLU saves computational time and converges faster than other activation functions, which can help solve the gradient attenuation phenomenon as the number of layers increases. The marginal value of BCReLU is \(y_{max}\) and \(y_{min}\) (\(y_{max}=1\) and \(y_{min}=-1\)). BCReLU can be expressed as  
\[\text{BCReLU}(x)=\left\{\begin{array}{l}1,x>1\\ x,-1\leq x\leq 1.\\ -1,x<1\end{array}\right. \tag{9}\]  
Figure 3: Attention mechanism module.  
We compared the activation functions of the last layer in the network. Table 1 shows the quantitative evaluation results of different activation functions in the last layer on SOTS and ITS datasets (see Section 4.2 for details of PSNR and SSIM indicators). When using BCReLU in the last layer, the network achieved the best results, which confirms its effectiveness in IFE-Net.
# Antenna Designs for 5G/IoT and Space Applications
## 1 Introduction  
Antenna design has received renewed attention in the last few years. This is thanks to an explosion of interest in a range of applications, from the Internet of Things, low frequency long-range applications to high frequency mmWave 5G mobile technologies. There has also been renewed interest in wearable antennas that form body area networks. These include wearable garments, as well as materials that directly attach themselves to skin, such as e-skin. In addition to this, a renewed interest in space and space exploration has renewed interest in satellite technologies and applications, such as CubeSats, intersatellite communications and deep space exploration. All these emerging applications bring a renewed interest in looking at special materials and new designs for antenna systems. This will bring new challenges in designing such antennas.  
For this purpose, this Special Issue is intended to shed some light on recent advances in antenna design for these new emerging applications and identify further research areas in this exciting field of communications technologies. We invite researchers and practicing engineers to contribute original research articles that discuss issues related but not limited to:  
\begin{tabular}{l l} \(\bigcirc\) & Antenna design for Internet of Things; \\ \(\bigcirc\) & Beamforming and smart antennas for 5G; \\ \(\bigcirc\) & Antenna design for wearable applications; \\ \(\bigcirc\) & Antenna design for body area networks; \\ \(\bigcirc\) & Antenna design for chipless RFID; \\ \(\bigcirc\) & Metamaterial-based antennas; \\ \(\bigcirc\) & Smart antennas, beamforming and MIMO; \\ \(\bigcirc\) & Aeronautical and space applications; \\ \(\bigcirc\) & Antenna design for CubeSat; \\ \(\bigcirc\) & Antenna design for deep space communication \\ \(\bigcirc\) & Antenna design for biomedical systems and applications; \\ \(\bigcirc\) & Implanted antennas; \\ \(\bigcirc\) & UWB and multispectral technologies and systems; \\ \(\bigcirc\) & MM-wave and THz antennas. \\ \end{tabular}
## 2 Basic Theory
### Loss Function  
Global loss function \(L\) is the weighted sum of loss \(L_{adv}\) of adversarial capability, perceived loss \(L_{per}\), and style classification loss \(L_{cls}\). Moreover, \(\lambda_{per}\) and \(\lambda_{cls}\) are the weights of \(L_{p\sigma}\) and \(L_{cls}\), respectively.  
\[L=L_{adv}+\lambda_{per}L_{per}+\lambda_{cls}L_{cls} \tag{5}\]  
\(L_{adv}\) is the loss value of the discriminator, which is used to distinguish whether the generated image and the input batch of the original style images belong to the same style category.  
\[L_{adv}=E_{y^{c}\mathcal{G}_{l}^{c}\sim Y,c\sim N}[-\log D(y^{c}_{l^{c}},\{y^{c}_{ l}\}_{i=0}^{M})]+E_{\vec{x}\sim G(x)\delta^{c}_{l}\sim Y,c\sim N}[-\log(1-D(\vec{x}, \left\{y^{c}_{l}\right\}_{j=0}^{M}))] \tag{6}\]  
where \(M\) is the number of style images with value. When \(M\) > 2, anti-loss \(L_{adv}\) should have a good convergence effect with iteration.  
\(L_{p\sigma}\) of perceived loss can be expressed as follows:  
\[L_{per}=\lambda_{c}L_{c}+\lambda_{s}L_{s} \tag{7}\]  
where \(L_{s}\) is calculated with the mean and label difference of style features.  
\[L_{s}=E_{l\sim Nil}((\mu^{1}_{y^{c}}-\mu^{1}_{\vec{x}^{c}})^{2}+(Gram^{1}_{y^{ c}}-Gram^{1}_{\vec{x}^{c}})^{2}) \tag{8}\]  
Content loss \(L_{c}\) is the expected \(L_{2}\) distance between the target feature (the extracted feature of the style image) and the output image feature.  
\[L_{c}=E_{x\sim X,c\sim N}\|\phi(x)-\phi(\vec{x}^{c})\| \tag{9}\]  
where \(\phi(x)\) represents the characteristics of layer 41 of Relu layers. When training the network, the style image is randomly selected from the target style dataset. The content image is transferred to the target style domain, and all the mappings in multiple artistic style areas are learned.  
Loss of style classification \(L_{cls}\) uses discriminator function \(D_{cls}\) to determine whether the style category of the generated image is correct.  
\[L_{cls}=E_{y\sim Y,c\sim N}(-\log D_{cls}(c|y^{c})) \tag{10}\]
# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 3 Results
#### Discussion  
As can be seen in Table 7, we evaluated six existing methods for image forgery detection, including D-Net, CNN-HPF, RRU-Net, Mask R-CNN+Sobel filter, a CNN based on the pre-trained Alex Net model, and Deep Neural Architecture-Buster Net. We tested these methods on a dataset with different levels of manipulation. The dataset is divided into three sets with different train/valid ratios: 7:3, 8:2, and 9:1. The train/valid ratio refers to the proportion of images used for training and validation. We evaluated the performance of these methods based on their validation accuracy and test accuracy.  
The results show that the existing methods have varying degrees of accuracy and efficiency in detecting image forgery. Among them, a CNN based on the pre-trained Alex Net model performs the best in terms of accuracy, with a test accuracy of 95.40% at a train/valid ratio = 9:1. However, its efficiency is not satisfactory, with a valid accuracy of only 94.7%. On the other hand, D-Net and RRU-Net have relatively high efficiencies; however, their accuracy is not as good as that of the CNN based on the pre-trained Alex Net model.  
We propose a new method for detecting image forgery using a U-NET network optimized with grasshopper optimization to resolve the limitations of existing methods. U-NET is a prominent deep neural network architecture for image segmentation tasks. It was chosen due to its ability to capture both high-level and low-level image characteristics. Grasshopper optimization was used to optimize the U-NET network by altering hyperparameters, such as the number of filters in each layer, learning rate, and batch size.
# A Robust Dual-Platform GMTI Method against Nonuniform Clutter
## 2 The Dual-Platform GMTI Method against Nonuniform Environment
### High-Precision Clutter Covariance Matrix Estimation Based on Dual-Platform SAR System  
Traditional CCM estimation is effective for the uniform clutter area, which requires the clutter outside the clutter protection zone and the area to be detected to have IID characteristics. However, the moving targets of interest are mostly located in cities, ports, highways, airports, and other man-made areas. These areas are mostly heterogeneous clutter areas, and the clutter characteristics inside and outside the protected areas are quite different. In STAP, if the clutter characteristics of the area to be detected are replaced by the clutter characteristics of dozens of range units, the GMTI result will be degraded. In this paper, the clutter characteristics are calculated by the clutter obtained by the former platform, which has the same subastral point tracks as the latter platform. This guarantees scattering consistency between the echoes from the former platform and echoes from the latter platform. As the target does not reside in the area to be detected during the former platform observation, it is not necessary to set a protected area. Instead of using the clutter cell near the CUT, the CCM can be directly calculated by the echo of the CUT. The observation geometry is shown in Figure 5. A GMTI processing flowchart with a dual platform is shown in Figure 6. In the following subsections, the processing blocks in Figure 6 are depicted.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Statistic** & **Area1** & **Area2** & **Area3** & **Area4** \\ \hline Mean value & 108 & 109 & 94 & 93 \\ \hline Variance & 30 & 30 & 32 & 34 \\ \hline Skewness coefficient & \textless{}0(-1.125) & \textless{}0(-1.116) & \textless{}0(-1.158) & \textless{}0(-1.194) \\ \hline Kurtosis coefficient & 2.056 & 2.218 & 2.653 & 2.652 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistical properties.  
Figure 5: The geometric scene of forward and backward track.
## 1 Introduction
Among the machine learning algorithms, Generative Adversarial Networks (GANs) have garnered significant attention for their ability to generate synthetic data [26; 27]. Generative Adversarial Imputation Networks (GAINs) [28] represent a novel approach to data imputation, employing a GAN framework rather than relying on traditional interpolation-based methodologies. Through application in other fields [29; 30; 31], the GAIN method demonstrated superior performance in both execution speed and reconstruction accuracy compared to traditional methods like Multiple Imputation using Chained Equations and missForest. The comprehensive GRACE/GRACE-FO time series has proven highly effective in monitoring long-term TWSA. Recently, several TWSA-based indices have made significant contributions to large-scale flood monitoring efforts. Notably, [PERSON] et al. developed the Flood Potential Index (FPI) based on TWSA [32]. Widely used for assessing large-scale flood potential, the FPI offers a robust metric for evaluating flood risk on a monthly scale across specific river basins. By incorporating basin characteristics informed by both precipitation and TWSA, the FPI enables the estimation of potential water storage capacity. This index has been successfully applied to flood events across various regions, demonstrating its utility in flood risk assessments. [PERSON] et al. enhanced the FPI by incorporating the return period of floods, thereby refining its utility in evaluating extreme flood events of 2011 in the Yun-Gui Plateau, China [19]. [PERSON] et al. [33] conducted an evaluation of the effectiveness of [PERSON]'s FPI for flood risk assessment across the continental United States. [PERSON] et al. [34] estimated a 19-year FPI time series to monitor major floods in the Volga River basin. [PERSON] et al. [35] proposed a modified FPI to improve the monitoring capability for flood events in the Yarlung Tsangpo-Brahmaputra River Basin. Their analysis revealed a remarkable consistency between [PERSON]'s FPI-derived flood risks and the observed flood occurrences. The FPI has demonstrated robust reliability in the detection of floods, evident at both regional and local scales. This study enhances previous hydrological applications of GRACE by developing more robust and reliable methods for flood monitoring through the gap-filling of GRACE data. The primary objectives of this work are as follows: (1) to evaluate the accuracy of Generative Adversarial Imputation Networks (GAINs) in imputing missing values within the GRACE/GRACE-FO datasets; (2) to examine the spatiotemporal characteristics of the FPI in the Pearl River Basin (PRB) by integrating precipitation data with GRACE-based TWSA; (3) to analyze the driving factors behind variations in the FPI for TWSA in the PRB. This research aims to demonstrate the reliability and practical utility of combining the GAIN technique with the FPI method to achieve accurate and effective flood identification at the Pearl River Basin scale, providing critical insights for enhancing flood management strategies in this vulnerable region.
## 2 Materials and Methods  
### Pest Dataset  
#### 2.1.1 Real Pest Image Dataset  
This study focuses on crops of high economic value. As a result, the selection of agricultural pests is based on small sample sizes. First, we went to the Beizang Village experimental field next to the Daxing Campus of Beijing University of Civil Engineering and Architecture to take photos using an iPhone 12 Pro Max and collected 400 pictures of pests. The photos were taken at a resolution of 3024 \(\times\) 4032 pixels. Secondly, we searched for pests in the IPMImages database [30], National Bureau of Agricultural Insect Resources (NBAIR), Google, Bing, etc. The dataset has eight pest species as labels, which are as follows: Tetranychus urticae, TU; Bemisia argentifolii, BA; Zeugodacus cucurbite, ZC; Thrips palmi, TP; Myzus persicae, MP; Spodoptera litura, SL; Spodoptera exigua, SE; and Helicoverpa armigera, HA. Figure 1 displays a few representative photos from the dataset. The final pest dataset includes 1009 images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Author Reference** & **Pest** & **Module** & **Performance** & **Generated Dataset** \\ \hline
2019 & [PERSON] et al. [21] & 16 butterfly species & CNN & mAP (75.46\%) & \(\times\) \\
2020 & [PERSON] et al. [22] & 24 agricultural pests & AR-RCNN & mAP (56.4\%) & \(\times\) \\
2020 & [PERSON] et al. [23] & 10 pest species & Deep CNN & Accuracy (88.83\%) & \(\times\) \\  & Leaf miner, tea strip, & Faster & & \\
2020 & [PERSON] et al. [24] & tea leaf roller, and tea & RCNN & mAP (66.02\%) & \(\times\) \\  & & mosquito bug (TMB) & & & \\
2021 & [PERSON] et al. [25] & _T. papillosa_ & YOLOv3 & mAP (0.93\%) & \(\times\) \\
2021 & [PERSON] et al. [26] & Agricultural pests & RPN & mAP (78.7\%) & \(\times\) \\
2022 & [PERSON] et al. [27] & 102 pest & CNN, Transformer & Accuracy (74.90\%) & \(\times\) \\
2022 & [PERSON] et al. [28] & 9 crop pests & CNN & Accuracy (100\%) & \(\times\) \\
2023 & Our method & 8 agricultural pests & RS Transformer & mAP (00.18\%) & \(\surd\) \\ \hline \hline \end{tabular} \(\times\): Not using the generated dataset; \(\surd\): Using the generated dataset.  
\end{table}
Table 1: Statistical pest detection algorithms and accuracy.  
Figure 1: Pest dataset.
## 3 Sensing Per Crop
### Sensing Citrus Diseases  
Blight affects mainly grapefruits and oranges, and incidence is lower on lemons and mandarins. Water transport in the xylem of blighted trees is impaired. The failure of water transport [164] seems to be attributable to the amorphous plugs [165], and symptoms appear to be due to lack of water transport to the canopy. Blight symptoms are somewhat like those of vascular wilt fungi diseases caused by soil-borne systemic agents. However, no specific vascular wilt pathogen has been identified, although there were early claims that hyphae could be found associated with vascular occlusions.  
Phytophthora foot rot or gummosis of citrus is caused by two soil microorganisms: _Phytophthora parasitica_ and _P. citrophthora_. Disease incidence is especially high in trees established with the graft union at or below the soil surface, exposing susceptible scion tissue to the two pathogens [166]. Severe losses also can occur in groves subjected to flood irrigation if trees are planted on susceptible rootstocks. Longitudinal cracking of bark, accompanied by profuse gumming, usually is positive evidence of infection. Advanced stages of infection will result in yellow, sparse foliage and is named brown rot as diseased areas on the fruit are brown in color. Trees may later collapse and die due to the girdling action of the fungal infection.  
Citrus Tristeza Virus (CTV) is one of the most destructive of the many viruses that affect citrus, (Table 1). The virus pathogen has been responsible for the death of 18 million trees in Argentina and 10 million trees in Brazil. In 1946, American and Brazilian plant pathologists reported that tree failure was caused by a virus disease [166]. The Brazilians found that an aphid, _Toxoptera citricidus_, was a vector of the virus. Common symptoms include reduced fruit size, leaf vein-clearing, yellowing and cupping of leaves, and stem pitting. Infection of sweet orange, mandarin, or grapefruit trees on sour orange rootstock causes necrosis in the phloem of the sour orange rootstock just below the bud union. This girdling causes eventual decline and death of the infected tree.  
The sooty canker or limb wilt disease is caused by the fungus, _Hendersonula toruloidea_, a wound pathogen that invades citrus bark that has been damaged by freezing injury, sunburn, or mechanical injury but does not infect uninjured bark tissue [166]. The most common symptom of sooty canker is the sooty, black growth that develops beneath bark tissue, due to the presence of black masses, fungal spores that appear under the bark and on the surface of the canker. The leaves on branches with cankers wilt, turn brown, and die, as well as the branches on to the cankerd area. Most cankers develop on unshaded trunks or limbs that face toward the sun. Sunburned trunks and limbs are highly susceptible to infection.