esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 2 Background
### Graph-Based Methods  
In graph-based methods, graphs are constructed based on the data, where nodes represent samples (labeled or unlabeled), and edges represent neighborhood relations between the samples [22]. Generally, graph-based methods in SSL work by propagating labels from labeled samples to unlabeled samples based on the information gained from the graph [21]. Graph-based techniques are popular in recent works as they tend to be convex, scalable, and effective [22]. Additionally, their strong mathematical properties allow for fast computation [34]. A graph is represented by the notation \(G=(V,\ E)\) with \(V\) denoting the nodes of the graph and \(E\) represents edges between two nodes. Each node is a sample from the data and each edge represents the relationship between two samples. Graphs are called directed if the edges have directions and undirected if they do not. Graphs can also be weighted in a weighted graph \(G=(V,\ E,\ W)\), \(w_{  
are not connected [22; 35]. The degree of a vertex \(v_{i}\) is defined as \(d_{i}=\sum_{j=1}^{n}w_{ij}\). The degree matrix \(D\) is a diagonal matrix in which the values on the diagonal are \(d_{1},\ldots d_{n}\)[35].  
The unnormalized Graph Laplacian matrix is defined as \(L=D-W\), while the normalized graph Laplacian matrix is defined as:  
\[L_{\text{sym}}:=D^{-1/2}LD^{-1/2}=I-D^{-1/2}WD^{-1/2} \tag{4}\]  
where \(I\) is an identity matrix.  
The Graph p-Laplacian is a nonlinear generalization of the graph Laplacian, which aims to improve the representation of the local structure information [36]. The unnormalized and normalized matrix forms of the graph p-Laplacian operator \(\Delta_{p}\) are as follows:  
\[\left(\Delta_{p}^{(u)}\right)_{i}=\sum_{j\in V}M_{ij}\varphi_{p}\left(f_{i}-f_ {j}\right) \tag{5}\]  
\[\left(\Delta_{p}^{(u)}\right)_{i}=\frac{1}{d_{i}}\sum_{j\in V}M_{ij}\varphi_{p }\left(f_{i}-f_{j}\right) \tag{6}\]  
where \(\varphi_{p}(x)=|x|^{p-1}\text{sign}(x)\), \(d_{i}\) is the degree function of the graph, \(M_{ij}\) is the weight of an edge, \(f\) is the eigenvector of the graph p-Laplacian operator, and \(p\geqslant 1\) is a parameter set by the user.
# A Survey on Blockchain-Based Federated Learning
## 4 Applications
### Smart Healthcare  
BFL can also bring significant advancements to healthcare services. Typically, remote patient monitoring or certain AI-assisted diagnoses require a large amount of patient disease information. However, many medical records contain sensitive information about the patient, and these data have high intrinsic value for certain attackers. As a result, BFL is gradually being applied to the medical field [75; 107; 125]. [PERSON] et al. [126] proposed a BFL scheme for healthcare alliances, establishing a set of enterprise-level blockchain components compatible with the Ethereum ecosystem and integrating a series of privacy protection techniques. It also introduced a new secure aggregation protocol designed to run within AMD's trusted hardware environment, secure encrypted virtualization (SEV), to ensure the security of private data. [PERSON] et al. [127] introduced a BFL framework in the medical field, applying smart contracts to the data aggregation process of FL algorithms. This ensures transparency and permission during data sharing, predicting diabetes risk based on training with substantial patient information. Furthermore, [PERSON] et al. [128] developed a lightweight security and privacy algorithm for Internet of Medical Things (IoMT) devices based on BFL. [PERSON] et al. [129] not only presented a trustworthy BFL framework applicable to the IoMT, but also designed a COVID-19 application for data classification by which we can learn about global models related to COVID-19 diagnoses. This scheme includes a trustworthy and tamper-proof gradient mining method and a decentralized consensus-based aggregator, and adds extra security for blockchain nodes responsible for aggregation. [PERSON] et al. [130] also introduced a BFL scheme for healthcare, aiming to protect and share patients' medical information by building a real-time global application model. In addition, [PERSON] et al. [131] proposed a BFL framework that uses the latest data to segment and classify lung CT scans based on capsule networks, sharing data between hospitals to improve COVID-19 detection rates while ensuring patient privacy protection.
## 3 Effects of Plasma on Plant Vegetative Growth and Reproduction
The modulation of superoxide dismutase (SOD), ascorbate peroxidase (APX), and chloramphenicol acetyltransferase (CAT) enzyme activities was observed in wheat plants after low pressure DBD plasma exposure [43]. Likewise, changes in the antioxidant (proline, ascorbic, guaiacol peroxidase, phenylalanine ammonia-lyase, phenolic, and flavonoid) status were also observed in plants at the vegetative stage after cold atmospheric pressure plasma treatment [40; 61; 85; 86]. The expression patterns of different growth-regulating genes in response to cold atmospheric pressure plasma treatment have been investigated--argon plasma downregulates the expression of the methylation-related genes in soybeans and epigenetically regulates the expression of the metabolism-related genes [40]. The underlying mechanisms of RONS in plant growth and development are well known, and enormous amounts of information are available. As indicated by several studies, cold plasma and plasma-activated solutions can act as oxidative stimulants and disturb redox equilibrium. Redox non-equilibrium promotes the interaction of RONS with biomolecules, leading to oxidative modification or damage. Elevated RONS can also crosstalk with other metabolic reactions, phytohormones, and growth and development signaling cascades that change the plant across different physiological, biochemical, and molecular levels (Figure 2).
## 4 Experiment
### Unseen Painting Authentication Evaluation  
Through adversarial training, the generator model was enhanced to generate detailed original brushstrokes, while the discriminator model made progress in accurately distinguishing between original RTI brushstroke images and generated RTI images. Our training and validation dataset consisted of 46 high-resolution images, comprising 23 natural-light images and their corresponding RTI images, captured at a scale of 7952 \(\times\) 5304 pixels. To facilitate training and validation, we segmented the dataset's images into 256 \(\times\) 256-pixel patches. Then, we removed image patches that lacked clear brushstrokes, resulting in a dataset of 8904 paired image patches for training and 1472 paired image patches for validation. We fine-tuned the cGAN model to learn the original RTI brushstroke style by mapping from painting images to RTI images through feeding the training paired image patches dataset.  
The test dataset comprised 4602 brushstroke paired image patches (256 \(\times\) 256 pixels) from natural-light painting and their corresponding RTI samples from three unseen paintings (i.e., original oil painting (A) and counterfeit oil paintings (B and C)). Here, we used painting (A) painted by the original artist ([PERSON]), and paintings B and C painted by the forging artist ([PERSON]). Using FFT frequency analysis, the forgery score of each brushstroke image patches from testing data was computed. We converted the testing data into grayscale images to appraise the texture of generated RTI image patches. These calculations were repeated for all three unseen paintings for a total of 4602 of the generated RTI image patches and the corresponding GT RTI of each new painting. Figure 4a shows an unseen painting counterfeit identification using FFT frequency analysis. FFT dissimilarity assessment provided a visualization of forgery score distribution composed of variability from brushstroke image patches. Hence, the whole painting authentication was evaluated using the mean value of the painting patches' forgery scores.  
Figure 3: (**a**) The new original painting A; (**b**) forged painting replacing the original one; (**c**) forged painting with the same style but different content.  
We leveraged the PatchGAN discriminator and fed the testing dataset into it, which was composed of both RTI and painting photos at scale of 256 \(\times\) 256 patches. The authentication of the testing paintings' images was then evaluated. Similarly, we used PixelGAN and ImageGAN as discriminator models, while ImageGAN was adjusted to become a deeper model by increasing its layers to five. All these models were trained with the same set of training parameters. Figure 4b illustrates an unseen painting authentication evaluation using the discriminator model.
# Handling Skewed Data: A Comparison of Two Popular Methods
## 3 Results
### Comparisons between the Two Methods
#### 3.1.1 Comparison Regarding Type I Error Rates  
Table 1 shows the parameters and the results of the alpha values for testing gamma-distributed data using the \(t\)-test and GLM, where \(\alpha_{1}\) represents the Type I error rate for the simulated data that was log-transformed and then tested using a two-group \(t\)-test assuming equal variances. Furthermore, \(\alpha_{2}\) represents the Type I error rate for the same groups of simulated data tested using GLM for gamma distribution with the log link function. Note that the Type I error rates for both methods are close to alpha = 0.05, but the \(t\)-test method is closer (0.0499 to 0.0503 in \(t\)-test data and 0.049 to 0.0577 in GLM data). In about 86% of the simulations, the \(t\)-test gave lower alpha values than the ones in the GLM. For the other 14% where the GLM gave lower Type I error, though, the \(t\)-test gave Type I error rates that were close to 0.05.  
Table 2 contains summary information for the Type I error results for the gamma, exponential and beta distributed data. For the exponentially distributed data examples, the Type I error rates for the GLM across all parameter values were lower than the \(t\)-test rates in all of the settings, though the \(t\)-test Type I error rates did not exceed 0.0504 (0.0497 to 0.0504 for the \(t\)-test; 0.0367 to 0.0477 for the GLM). The \(t\)-test Type I error rates for beta distributed outcomes were lower than those from the GLM in all settings (0.0497 to 0.0511 for the \(t\)-test; 0.0518 to 0.0589 for the GLM). Figure 3 shows the Type I error for both methods, which are compared to 0.05 for the three distributions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Example**} & \multirow{2}{*}{**Sample Size**} & \multicolumn{2}{c}{**Parameters**} & \multirow{2}{*}{\(t\)**-Test Alpha (\(\alpha\))**} & \multirow{2}{*}{**GLM Alpha (\(\alpha\)2)**} & \multirow{2}{*}{**Diff. = \(\alpha\)1 - \(\alpha\)2**} \\  & & & & & \\ \hline
1 & 25 & 2, 1 & 0.0501 & 0.0490 & 0.0011 \\
2 & 50 & 2, 1 & 0.0502 & 0.0499 & 0.0003 \\
3 & 100 &
# Research on O2O Takeout Delivery Route Optimization Problem Based on Improved Genetic Algorithm
## 5 Experimental Results and Analysis
### Design and Analysis of Experiments
#### 5.3.2 Effectiveness of Hybrid Insertion  
With the model parameters fixed as described in Section 5.3.1, comparative experiments were conducted by keeping other components of the algorithm unchanged while using three insertion strategies: shortest distance insertion, random insertion, and a hybrid approach that uses both with equal probability. The experimental results are presented in Columns 5-7 of Table 2. The results indicate that, compared to using the shortest distance insertion and random insertion strategies alone, the hybrid insertion strategy achieved a reduction in fitness value by approximately 4.32% and 6.78%, respectively, demonstrating its effectiveness.
## 1 Introduction
The DksA protein is composed of a globular domain and a long coiled coil, which can bind together with (p)ppGpp at the edge of the \(\beta^{\prime}\) subunit secondary channel of RNAP [23; 24]. Generally, DksA is considered a molecular chaperone to enhance the signal of (p)ppGpp during a stringent response [2]. However, several studies have reported the independent and even antagonistic actions between DksA and (p)ppGpp. In _E. coli_, the transcriptional expression changes of flagella-related genes, such as _fliE_, _fliF_, and _fliG_, in DksA-deficient strains and (p)ppGpp-deficient strains show opposite trends and exhibit opposite phenotypes in motility and chemotaxis [9]. In addition, [PERSON] et al. have demonstrated that the strength of the pArgX promoter is independently regulated by the DksA protein, which drives RNAP to form nonproductive complexes at the pArgX promoter [25]. Some studies have also shown that the phenotypic changes caused by (p)ppGpp deficiency can be compensated by overexpressing DksA [26]. These findings suggest a complex interplay between DksA and (p)ppGpp and deciphering their mode of action could provide insights into the regulatory mechanisms of stringent response and bacterial environmental adaptation. We recently characterized a part of the physiological activity regulated by the stringent response factors (p)ppGpp and DksA in _Y. enterocolitica_[27]. It was found that (p)ppGpp and DksA synergistically regulate the motility, antibiotic resistance, and tolerance to oxidative stress of _Y. enterocolitica_ but have independent or opposite effects on bacterial biofilm formation and tolerance to acid and a hyperosmotic environment. To fully elucidate the regulatory mechanisms of (p)ppGpp and DksA on gene expression, comparative transcription analyses were conducted using _Y. enterocolitica_ wide-type strains and DksA-, (p)ppGpp-, and DksA(p)ppGpp-deficient strains. The roles of (p)ppGpp and DksA in the transport and utilization of arginine and cystine were verified using several experiments, and their positive effects on bacterial chemotaxis were revealed.
## 3 Results  
The nodules' classification scheme was evaluated using the receiver operating characteristic (ROC) curve. The classification performance was evaluated via the leave-one-out cross-validation.  
To analyze the distinguishing characteristics of our method, we compared the following four approaches: (1) classification using patient information alone; (2) classification using patient information and CT images; (3) classification using patient information, CT images, and early-phase PET images; and (4) classification using patient information, CT images, and early- and delayed-phase PET images. The ROC curves for each of the above approaches are shown in Figure 2a. The AUROC values for approaches (1), (2), (3), and (4) were 0.520, 0.881, 0.920, and 0.901, respectively. The ROC curves obtained from the previous study are shown in Figure 2b for a performance comparison using the same cohort. The AUROC values only for the CT image, CT images and early-phase PET images, and CT images and early- and delayed-phase PET images were 0.730, 0.860, and 0.895, respectively [18].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter Name** & **Values** \\ \hline Random forest & Number of trees & 25 \\ \hline \multirow{3}{*}{SVM} & Kernel & Radial basis function \\  & Cost & 1.2 \\  & Gamma & 0.02 \\ \hline \multirow{3}{*}{ANN} & Number of hidden layers & 3 (Number of neurons: 50-50-50) \\  & Activation function & Rectified linear unit function \\ \cline{1-1}  & Optimizer & Stochastic gradient descent \\ \cline{1-1}  & Learning rate & 0.0006 \\ \cline{1-1}  & Number of epochs & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the used classifiers.  
Figure 2: Receiver operating characteristic (ROC) curves; (**a**) Proposed method; (**b**) Previous method.  
Considering the accuracy of calling malignant nodules as 0.944, the accuracies of calling benign nodules using approaches (2), (3), and (4) were 0.722, 0.722, and 0.777, respectively. In addition, when only images were used, the classification accuracy of benign nodules for the methods using CT images, CT images, early-phase PET images, and CT images and early- and delayed-phase PET images were 0.277, 0.611, and 0.722, respectively [1,8].  
Comparing the four machine learning methods, the classifier that yielded the highest accuracy was the ANN for Model #1 and the SVM for Models #2, 3, and 4. Table 4 compares the classification performance of the proposed method and the previous method. Among these models, the method that used patient information, CT images, and early- and delayed-phase PET images (Model #4) was the most accurate. Figure 3 shows an example of the classification results for Model #4. In Figure 3a, the nodules in the green frames were incorrectly classified when using only imaging. However, after patient information was incorporated, it became possible to correctly classify these images.
## 3 Experimental Results and Analysis
### Simulation of Positioning Errors
The number of IRSs has an impact on the number of fingerprints collected; the higher the number of IRSs, the higher the number of fingerprints, and an increase in the number of fingerprints can improve the localization accuracy. The cumulative distribution of the localization error of IRS-RFL at different sampling intervals is shown in Figure 9.  
Figure 9 shows the cumulative distribution of the positioning error at different sampling intervals. The blue, red, yellow, purple, and green curves indicate the cumulative  
Figure 8: Positioning error for different numbers of IRS cases. Figure 9: Cumulative distribution of positioning errors at different sampling intervals. distribution function curves of the positioning error for sampling intervals of 1, 2, 2.5, 3, and 5 m, respectively. Evidently, the sampling interval was inversely proportional to the positioning error; the larger the sampling interval, the worse the positioning accuracy. For sampling intervals of 5, 3, 2.5, 2, and 1 m, 80% of the positioning errors were less than 1.2, 1, 0.83, 0.8, and 0.6 m, respectively. In fingerprint localization, the sampling interval of the fingerprints has an impact on the number of fingerprints collected. The smaller the sampling interval, the higher the localization accuracy of the system, and, on the contrary, the worse the positioning accuracy of the system. When the sampling interval is short, the 5G positioning system with intelligent reflective surfaces proposed in this study can increase the number of fingerprints, thereby improving the accuracy and reliability of positioning. On the contrary, when the sampling interval is longer, the number of fingerprints collected by the intelligent reflective surface 5G positioning system proposed in this study decreases, which does not accurately reflect the accurate information of the channel of each point in the positioning area, thus reducing the localization accuracy.
# Cyberattacks and Security of Cloud Computing: A Complete Guideline
## 7 Countermeasures for Threats in Cloud Computing
### Protecting against Data Breaches and System Vulnerability
#### 7.1.2 Encryption  
Robust encryption techniques are crucial for protecting data at rest and in transit. Encryption ensures the confidentiality and integrity of sensitive data within the cloud environment.
# Projection onto the Set of Rank-Constrained Structured Matrices for Reduced-Order Controller Design
## 4 Numerical Examples
### Stabilizing Low-Order Controllers  
In this section, we focus on the model TF1. In this model, the state-space matrices are given as follows:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **NSH\({}^{\infty}\)[36]** & **CCL [12]** & **NNM [15]** & **GS96 [13]** & **Proposed** \\ \hline \# success & 75 & 61 & 53 & 56 & 59 \\ CPU time [s] & 0.365 & 56.5 & 4.58 & 663 & 666 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stabilizing static controller design results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **NSH\({}^{\infty}\)[36]** & **CCL [12]** & **NNM [15]** & **GS96 [13]** & **Proposed** \\ \hline HE6 & unstable & **stable** & unstable & **stable** & **stable** \\ HE7 & **stable** & **stable** & unstable & **stable** & **stable** \\ REA3 & unstable & **stable** & unstable & unstable & **stable** \\ DIS1 & **stable** & **stable** & unstable & unstable & **stable** \\ PAS & unstable & **stable** & **stable** & **stable** & **stable** \\ TF1 & unstable & unstable & unstable & unstable & **stable** \\ TF2 & unstable & **stable** & **stable** & **stable** & **stable** \\ NN1 & **stable** & unstable & unstable & **stable** & **stable** \\ NN11 & **stable** & **stable** & unstable & **stable** & **stable** \\ NN12 & unstable & unstable & unstable & **stable** & **stable** \\ FS & unstable & **stable** & **stable** & **stable** & **stable** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stabilizing static controller results (the full list available at [28]).  
\[A=\begin{bmatrix}-1&0&0&0&0&0&0\\ 1&0&0&0&0&0&0\\ 0&1&0&0&0&0&0\\ 0&0&0&0&0&0&0\\ 0&0&0&1&-1&0&0\\ -0.088&0.0345&0&0&1&-0.0032&0\\ 0&0&0.05&0&0&0&-0.00001\\ \end{bmatrix},\quad B=\begin{bmatrix}1&0\\ 0&0\\ 0&0\\ 0&0.09\\ 0&0\\ 0&0\\ \end{bmatrix}, \tag{32}\] \[C=\begin{bmatrix}0&0&1&0&0&0&0\\ 0&1&0&0&0&0\\ 0&0&0&0&0&1\\ 0&0&0&0&0&1&0\\ \end{bmatrix},\quad D=\begin{bmatrix}0&0\\ 0&0\\ 0&0\\ 0&0\\ 0&0\end{bmatrix}.\]  
As explained in the previous subsection, a static controller is obtained only with the proposed method. Here, we consider low-order controllers with order \(n_{c}=1\) and \(n_{c}=2\). It is observed that all methods but the nonsmooth \(H^{\infty}\) method return stabilizing controllers with order 1 and 2. The results are summarised in Table 3.  
The state-space matrices \(A_{K}\), \(B_{K},C_{K},D_{K}\) of the second order controller (\(n_{c}=2\)) with the proposed method is given by  
\[A_{K} =\begin{bmatrix}-1.804\times 10^{4}&1.356\times 10^{4}\\ 9512&-7151\end{bmatrix},\quad B_{K}=\begin{bmatrix}-9193&-2019&-240.4&-1.77 \times 10^{4}\\ 4848&1066&126.8&9333\end{bmatrix}, \tag{33}\] \[C_{K} =\begin{bmatrix}-4412&3317\\ -1.075\times 10^{5}&8.08\times 10^{4}\end{bmatrix},\] \[D_{K} =\begin{bmatrix}-2249&-496&-58.91&-4329\\ -5.478\times 10^{4}&-1.203\times 10^{4}&-1433&-1.055\times 10^{5}\end{bmatrix}.\]