esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Hill Cipher  
The Hill cipher is a classic polyalphabetic substitution cipher technique that uses linear algebra to encrypt and decrypt messages. It was invented by [PERSON] in 1929 [25; 26] and has since been widely used as a method of secure communication. In the Hill cipher, the plaintext message is represented as a matrix of numbers, and a key matrix is used to perform a series of matrix multiplications and additions to encode the message. The key matrix must be invertible so that it can be used to reverse the encryption process and decrypt the message. Some of the weaknesses when the hill cipher is used alone include having a fixed block size in which the size of plaintext and cipher text is always the same; this makes the cipher technique vulnerable to block analysis. Another weakness includes known plaintext attacks, in which the adversaries can decrypt the future image if it contains both plaintext and ciphertext.  
To overcome these vulnerabilities, we propose an encryption method that integrates the Hill cipher with the sigmoid logistic map and the Kronecker bitwise exclusive OR operation. The resulting method is simple and effective, making it a promising candidate for secure image communication. However, as with any cryptographic technique, it is important to use the Hill cipher in combination with other methods to ensure the maximum level of security.
###### Abstract  
Self-healing of smart distribution networks with distributed generators, which are usually operated as independent islands after fault, can improve power-supply reliability. As a hot research topic, a self-healing scheme is usually treated as the output of a nonlinear optimizuoation model. However, existing strategies have two main shortcomings. The first, high-optimization dimension, results in low-optimization efficiency. The second, the effects of power-quality issues, which are more serious on islands and may further threaten the safe operation of islands, is usually neglected. To quickly obtain a reliable self-healing scheme, a novel strategy is proposed. As the first step, the distribution network after a fault occurrence can be divided into several trouble-free subnets via the connectivity analysis; each subnet is called an initial island. Further, for each initial island, a two-step optimization model of self-healing, which consists of load-shedding optimization and network reconfiguration optimization, is proposed to obtain the self-healing strategy with lower searching space as well as higher solving efficiency. In detail, in load-shedding optimization, by means of heuristic differential evolution algorithm, larger total recovery capacity is achieved by considering the droo characteristic of distributed generators (DGs) within the permissible change in frequency. In network-reconfiguration optimization, based on the improved hybrid particle swarm optimization algorithm, a comprehensive set of power-quality constraints, including constraint of change in frequency, uncertain constraints of node voltage total harmonic distortion (THD), and negative sequence components of DGs, is developed to guarantee the reliability of each island. To evaluate whether the constraints are satisfied during the optimization procedure, an improved flexible power-flow algorithm is developed to calculate the power flow of each island under change in frequency. Further, 2m+1-point estimate method is employed for uncertainty analyses of the distributions of harmonic and negative sequence components caused by the uncertainty of corresponding sources. Finally, via a 94-node practical distribution network, the effectiveness and advantages of the proposed strategy in safety, recovery capacity, and optimization efficiency are verified.  
s remark  
s amart distribution network; distributed generators; self-healing strategy; two-step optimization model; uncertain power quality constraints; flexible power flow; 2m+1-point estimation method
# Exploiting Frequency Characteristics for Boosting the Invisibility of Adversarial Attacks
## 3 Methodology
### Attack Algorithm
```
0: Fourier heatmap mask \(\omega\in\mathbb{R}^{\mathrm{H}\times\mathrm{W}}\), frequency mask set \(\Omega\), std \(\sigma\) of the noise \(\xi\), \(\rho\) of the noise \(\mu\), clean image \(\mathbf{x}\in\mathbb{R}^{\mathrm{C}\times\mathrm{H}\times\mathrm{W}}\), target label (for a targeted attack) or original label (for an untargeted attack) \(\mathbf{y}\), substitute model \(\mathcal{M}_{0}\), number of iterations \(T\), frequency domain step size \(\alpha\), noise initialization times \(N\) and \(\mathrm{L}_{\infty}\) constraint \(\epsilon\)
0: Adversarial example \(\mathbf{x}^{\prime}\)
1:\(\mathbf{x}^{\prime}_{0}=\mathbf{x}\)
2:for\(i=0\to T-1\)do
3:for\(n=0\to N-1\)do
4:\(\mathbf{x}^{\prime}_{\mathrm{FFT}}=\mathrm{FFT}(\mathbf{x}^{\prime}_{i})\)
5: Random initialization of noise \(\xi\) and \(\mu\)
6: Frequency-domain augmentation \(\mathbf{x}_{\mathrm{aug}}=\mathrm{IFFT}(\mathbf{x}^{\prime}_{\mathrm{FFT}} \odot\mu+\xi)\)
7: Gradient calculates by back-propagation \(g_{n}=\frac{\partial\epsilon_{\mathrm{aug}}(\mathbf{x}_{\mathrm{aug}}, \mathcal{Y}_{\mathrm{M}_{0}})}{\partial\mathbf{x}^{\prime}_{\mathrm{FFT}}}+ \frac{\partial\epsilon_{\mathrm{freq}}(\mathbf{x}^{\prime}_{\mathrm{FFT}}, \mathcal{M}_{\mathrm{FFT}})}{\partial\mathbf{x}^{\prime}_{\mathrm{FFT}}}\)
8:endfor
9: Average the gradients from multiple augmentations \(g^{\prime}=\frac{1}{N}\sum_{n=0}^{N-1}g_{n}\)
10: Update from the frequency domain \(\mathbf{x}^{\prime}_{\mathrm{FFT}}=\mathbf{x}^{\prime}_{\mathrm{FFT}}+\alpha *g^{\prime}\)
11: Convert the example back to the spatial domain \(\mathbf{x}^{\prime}_{i+1}=\mathrm{IFFT}(\mathbf{x}^{\prime\prime}_{\mathrm{ FFT}})\)
12: Limit the maximum strength of perturbations \(\mathbf{x}^{\prime}_{i+1}=\mathrm{clip}_{\mathbf{x}_{\mathrm{reg}}}(\mathbf{x} ^{\prime}_{i+1})\)
13: Normalization \(\mathbf{x}^{\prime}_{i+1}=\mathrm{clip}\big{(}\mathbf{x}^{\prime}_{i+1},0,1 \big{)}\)
14:endfor
15:\(\mathbf{x}^{\prime}=\mathbf{x}^{\prime}_{T}\)
16:return\(\mathbf{x}^{\prime}\)
```  
**Algorithm 1** Fourier invisible adversarial attack. It is worth noting that the noise processing for the input image may make the direction of the gradient information unstable, so we perform \(N\) times random initializations of the frequency domain augmentation noise and perform propagation \(N\) times, obtaining the gradient information \(g_{n}\{n=1,2,\ldots,N\}\). By averaging \(g_{n}\), the average gradient \(g^{\prime}\) can stabilize the update direction of the counter perturbation. We will explain the design of each step in the algorithm in detail in the following sections.
# SoK: Delegated Security in the Internet of Things
## 2 Application Domains of Security Delegation
### Example  
Park and Park present a delegated group-oriented access control security mechanism based on DTLS, which reduces the total computational burden [21]. The proposed system includes the RC system consisting of sensors, a client acting as a third party, and a Security Proxy (SP), which acts as a delegate and manages access control on behalf of the RC system (see Figure 3). During the first step of the proposed protocol, SP forms a list of approved sensor devices and issues a capability ID to each authorised client. Subsequently, the client can directly perform a DTLS handshake and communicate with the desired sensor device from the list. The authors conclude that their mechanism is superior to performing standard approaches without access control delegation in terms of execution time and security.  
Figure 2: The generalised schemes of the system architectures based on the application domains: (**a**) Access Control and/or Authorisation Delegation; (**b**) Authentication Delegation; (**c**) Rights Delegation; and (**d**) Computation Delegation.
## 3 LIBS Spectral Pretreatment  
### Baseline Removal  
The baseline of the average spectrum is prone to drift, owing to the disturbance from the environment and equipment, which deteriorates the spectral accuracy and analysis results. Thus, baseline removal is indispensable for the improvement of the signal-to-noise ratio (SNR). Herein, three main baseline removal methods were induced, including the Modpoly method, IModPoly method, and Zhang fit method [18; 19; 20], and the results are shown in Figure 2. It was indicated that the Zhang fit method could better remove the background noise by using an adaptive iteratively reweighted penalty least squares algorithm (airPLS) [20]. There was no user intervention and prior information, and it depended on the iterative weight change in the squared error between the fitted baseline and the original signal, which runs fast and flexibly.  
Figure 2: The performance of different baseline removal methods for LIBS spectra.
# Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning
## 4 Discussion  
This research work used the NSL-KDD dataset to develop a robust and explainable NIDS based on deep learning. Three untargeted and white-box attacks were used to generate adversarial examples for the experiment. We demonstrated how adversarial attacks could undermine classical multi-class machine learning-based NIDS as well as deep learning-based NIDS, and this confirms the works of [7; 8; 9; 10; 20]. Hence, we find justification for creating an adversarial robust deep learning model that is less affected by adversarial attacks. Our adversarial robust and explainable NIDS based on deep learning was developed under the white-box scenario with only three adversarial attacks (_FGSM_, _PGD_, _and DeepFool_). In addition, we used the NSL-KDD dataset because of its size and considerable number of attacks; however, it does not contain modern attack types. Hence, our current model suffers from generalizability. However, this can be solved by retaining the model with modern network security datasets under a large pool of adversarial attacks. Furthermore, we used an epsilon of 0.6 as one of the adversarial attacks hyperparameters; we adopted [PERSON] et al. (2021) [19], who indicated that increasing epsilon tends to be insignificant in terms of reducing the classifier's accuracy. However, this might not guarantee the stability of adversarial attacks; we propose further research to focus on hyperparameter tuning on all the adversarial attacks to investigate the stability of an adversarial robust model. While the results of previous experiments have shown that adversarial training increases the robustness of network intrusion detection systems [19; 28], we observed that few researchers have focused on robust adversarial training, that is, testing the resistance of adversarial-trained models with adversarial samples from different adversarial attacks. This paper proposes a method to measure adversarial robustness; Equation (12). Our main metric is the ROC-AUC. We also followed standard classification metrics proposed by [31]. We acknowledge that there should be robust classification measures for cybersecurity models to substantiate the reliability of the classification results. Using method (12), we tested the adversarial robustness of the FGSM adversarial-trained model, DeepFool adversarial-trained model, and PGD adversarial-trained model. We observed that the PGD adversarial-trained model is more robust than the DeepFool adversarial-trained model and FGSM adversarial-trained model. These results confirm [3], who indicated that a model trained to be robust
# Machine Failure Prediction Using Survival Analysis
## 4 Materials and Methods
### Types of Machine Failure
#### 4.3.5 Random Failures (RNF)  
Random failures occur due to factors outside of the control of the machining process, such as material defects, operator error, or environmental factors. RNF can be particularly challenging to predict and prevent, making it important to continuously monitor the machining process and identify any patterns or trends that may indicate a potential failure.
## 3 Simulations
### Results
#### 3.2.2 Background Variability  
In Table 3, the BV for 2 mm ROIs is shown in all the explored non-TOF and TOF conditions at 50 MLEM updates. As expected, non-TOF reconstructions (which at 50 updates were characterized by the lowest contrast recovery) are characterized by the lowest background variability. In TOF data, BV increases when reducing the CTR: moving from 750 ps to 250 ps increased the BV by \(\approx\)+40%.  
In Table 4, the BV at the MLEM update numbers provided by the proposed rule are shown. In non-TOF data, matching CR markedly increases noise. In TOF, at the best CTR, the proposed rule decreased BV by \(\approx\)40%. When applying the rule, the noise for a given phantom decreases with the CTR square root, replicating what is expected at the maximum likelihood solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Background:** & **275 mm** & **320 mm** & **440 mm** \\
**CTR [ps]** & **CR [\%]** & **CR [\%]** & **CR [\%]** \\ \hline Non-TOF & \(37.0\pm 1.5\) & \(33.5\pm 1.8\) & \(20.3\pm 2.7\) \\
750 & \(59.1\pm 1.8\) & \(62.9\pm 2.8\) & \(56.4\pm 3.5\) \\
550 & \(67.1\pm 2.4\) & \(65.5\pm 1.9\) & \(67.2\pm 4.9\) \\
400 & \(71.3\pm 1.6\) & \(70.6\pm 1.8\) & \(72.1\pm 3.9\) \\
250 & \(73.8\pm 1.4\) & \(76.5\pm 2.2\) & \(75.5\pm 3.9\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contrast recovery at 50 updates for the 10 mm sphere with \(\beta=2.0\) contrast, as a function of CTR and background diameter. Values reported as mean \(\pm\) one standard error.  
Figure 3: \(CTR=750\) ps and background diameter 275 mm. Shaded patches represent \(\pm 1\) standard error on the mean value. (**a**) Convergence of circles with contrast \(\beta=2.0\), at different distances from the center. (**b**) Convergence of circles with different activity contrasts.
## 4 Role of sRNA in Antibiotic Resistance
### Modification of Cell Envelope
#### 4.2.2 Sr006  
The sRNA Sr006, transcribed from the intergenic region between 182,570 and 182,693 bp on the minus strand, was identified in chimeric RNA molecules with lipid A diacylation enzyme PagL mRNA [199] and functions as a positive posttranscriptional regulator of PagL in _P. aeruginosa_ virulence and host adaptation, as revealed by Hi-GRIL-seq. Western blot and RT-qPCR analyses demonstrated that the overexpression of Sr006 significantly increased PagL mRNA and PagL protein levels, while the deletion of Sr006 had no effect. Functional studies revealed that the Sr006-mediated upregulation of PagL resulted in enhanced deacylation of lipid A, shifting it to a less pro-inflammatory form, as confirmed by MALDI-MS and GC-FID analyses. Furthermore, Sr006-dependent regulation of PagL increased polymysxin B resistance in _P. aeruginosa_ strains lacking LPS aminoarabinose modifications. Unlike other sRNA-mediated regulatory mechanisms requiring the RNA chaperone Hfq, Sr006's regulation of PagL was Hfq-independent, suggesting an alternative mechanism of action. This regulatory pathway may enable _P. aeruginosa_ to adapt to the host environment, potentially through modulation of Sr006 expression in response to host interactions.
# Detecting Methotrexate in Pediatric Patients Using Artificial Neural Networks
## 3 Results
### Graphical Interface for Data Registration  
The graphical user interface, developed in Matlab GUIDE (Figure 6), is divided into four sections. In the first section, the data received are displayed. In the second section, the  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Patient Number** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline
**Waterlow Index (weight)** & 0.222222222222222222 & 0.00306306 & 0.231231232 & 0.2702072072 & 0.27027027 & 0.26126126 & 0.3003003 & 0.3003033 \\
**Weight Percentile (WHO)** & -0.125 & -0.0553033 & -0.0060661 & -0.05303033 & -0.1893939 & -0.1893939 & -0.05833333 & -0.1628759 \\
**Size (cm)** & 0.68765422 & -0.70987654 & 0.68148158 & 0.6765432 & 0.68382716 & 0.55855556 & 0.6491936 & 0.491978531 \\
**Waterlow Index (size)** & 0.80620155 & 0.79069767 & 0.73644114 & 0.79569929 & 0.72868217 & 0.76744186 & 0.65891473 \\
**Ferencial (size) WHO** & 0.11566265 & 0.0552169 & 0.1132501 & -0.058647699 & -0.20841928 & -0.05542162 & -0.4137349 & -0.48192771 \\
**Sup.Cop** (Fanglades) & 0.37888199 & 0.47249767 & 0.44720497 & 0.27950311 & 0.27950311 & 0.31055901 & 0.3298137 & 0.30434783 \\
**Sup.Cop** **Haycock** & 0.41139241 & 0.4685443 & 0.46202532 & 0.29113924 & 0.29113924 & 0.29113924 & 0.32011392 & 0.33544304 & 0.31012668 \\
**IMC** & 0.18989899 & 0.2163977 & 0.2915820 & 0.29592626 & 0.2918525 & 0.2272723 & 0.27667677 & 0.2794162 \\
**IMC** & 0.1010101 & 0.1010101 & 0.707071 & 0.1313133 & 0.0070707 & 0.05050505 & 0.353355 & 0.666667 \\
**National Index (Shukla)** & 0.39487179 & 0.45641026 & 0.528020513 & 0.44615385 & 0.45128205 & 0.43859744 & 0.4920769 & 0.466666667 \\
**Weight-length-size relationship (WHO 06-07)** & -0.37214868 & -0.20168067 & 0.09030160 & -0.0602401 & -0.1356246 & -0.22809124 & -0.02641056 & 0.00843036 \\
**National Assessment** & 1 & 1 & 1 & 1 & 0.5 & 1 & 1 \\
**Max. Temp. First 2 h** & 0.9175 & 0.9475 & 0.9275 & 0.915 & 0.9625 & 0.9625 & 0.9625 & 0.95 \\
**Adenomegaly** & 0 & 0 & 1 & 1 & 1 & 1 & 1 \\
**Hepatomegaly** & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 \\
**Spienomegaly** & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 \\
**Hemorrhagic Manifestations** & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 1 \\
**Infection at Admission** & 1 & 1 & 0 & 0 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Normalized clinical data objectives.  
Figure 5: Backpropagation neural network design.  
Figure 6: Intelligent diagnosis in pediatric patients with acute lymphoblastic leukemia to predict intoxication by methotrexate.  
user can select either the option for the diagnosis of ALL or the option for the diagnosis of intoxication. The third section shows both the data received and the training data. Finally, the fourth section presents the training of the neural network and provides the option to exit the system. The graphical user interface (GUI) receives the patient's clinical data, which are stored in an Excel file for the purpose of diagnosis.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 3 Methodology
### Entropy-Regularized Client Objective
For \(j=1,2,\ldots,d\), we have  
\[\frac{\partial H(p)}{\partial w_{j}}=\frac{\sum_{k=1}^{d}p_{k}\ln p_{k}-\ln p _{j}}{Z}\cdot\text{sign}(w_{j}-w_{j}^{t}). \tag{5}\]  
Explicit Gradient Derivation  
Recall that  
\[p_{j}=\frac{\delta_{j}}{Z},\qquad\delta_{j}=|w_{j}-w_{j}^{t}|,\qquad Z=\sum_{ k=1}^{d}\delta_{k},\]  
and that the entropy of the coordinate-wise distribution \(p\) is  
\[H(p)=-\sum_{k=1}^{d}p_{k}\ln p_{k}.\]Applying the chain rule yields  
\[\frac{\partial H}{\partial w_{j}}=-\sum_{k=1}^{d}\left(1+\ln p_{k}\right)\frac{ \partial p_{k}}{\partial w_{j}}.\]  
The partial derivative of \(p_{k}\) with respect to \(w_{j}\) is  
\[\frac{\partial p_{k}}{\partial w_{j}}=\frac{1}{Z}\frac{\partial\delta_{k}}{ \partial w_{j}}-\frac{\delta_{k}}{Z^{2}}\sum_{\ell=1}^{d}\frac{\partial\delta_ {\ell}}{\partial w_{j}},\qquad\frac{\partial\delta_{k}}{\partial w_{j}}= \text{sign}(w_{j}-w_{j}^{t})\,\mathbf{1}_{k=j},\]  
which simplifies to  
\[\frac{\partial p_{k}}{\partial w_{j}}=\frac{\text{sign}(w_{j}-w_{j}^{t})}{Z} \left(\mathbf{1}_{k=j}-p_{k}\right).\]  
Substituting this result back into the chain-rule expression gives  
\[\frac{\partial H}{\partial w_{j}}=\frac{\text{sign}(w_{j}-w_{j}^{t})}{Z} \left(\sum_{k=1}^{d}p_{k}\ln p_{k}-\ln p_{j}\right),\]  
which matches Equation (5). Entropy-Regularised Gradient  
Given Equation (5), the gradient of the entropy-augmented local objective \(\tilde{F}_{i}(w)\) is  
\[H(p)=-\sum_{j=1}^{d}p_{j}\ln p_{j}, \tag{3}\]0  
i.e., the usual empirical-loss gradient plus \(\lambda\) times the entropy gradient \(g_{H}\coloneqq\
abla_{w}H(p)\). During local optimisation, each client updates its parameters as  
\[H(p)=-\sum_{j=1}^{d}p_{j}\ln p_{j}, \tag{3}\]1  
where \(\eta\) is the learning rate. The added term \(\lambda g_{H}\) damps excessively large coordinate updates--those that dominate \(p\) and therefore receive a negative push from \(g_{H}\)--and promotes more homogeneous adjustments across weights. This stabilising effect is especially valuable when data are highly heterogeneous or each client holds only a few samples, because it prevents any single feature or weight from over-correcting on limited evidence. The next section details how this entropy-regularised objective integrates into the federated learning workflow.
# A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network
## 4 Conclusions  
We presented a new tomographic method based on the optimized voxel (only included ray-passed voxels). Besides, the new tomographic applied no empirical constraints but imposed vertical constraints originate from GNSS-derived precipitable water vapor (PWV). A conventional tomographic method that introduced three types of constraints (i.e. horizontal, vertical, and boundary constraints) is used as a comparison. We use the two tomographic methods to reconstruct the 3D water vapor (WV) fields in Nanjing during two selected periods with different weather conditions. The performance and reliability of the proposed tomographic method were validated by radiosonde (RS) data. Compared to the conventional method, the accuracy of the tomographic method is increased by 27.8% in terms of RMSE. For a large GNSS network, the proposed method is interference-free from empirical constraints and exhibits a better agreement with the RS method than the conventional method, particularly on clear days with largely changed moisture contents. Therefore, for the large GNSS network, the optimized method can be used to overcome the ill-posed problem caused by sparsely distributed stations, instead of using massive constraints to bias the tomographic results. Regarding the voxels eliminated by the proposed method, we simply use interpolation to obtain their WV densities using the voxels with WV values around them. Further studies can be carried out to evaluate the proposed tomographic method in different locations and different periods.  
Conceptualization, Y.Y. and C.L.; methodology, Y.Y. and C.L.; validation, Y.Y. and C.X.; data curation, C.L.; writing--original draft preparation, Y.Y. and C.L.; writing--review and editing, Y.Y. and C.X. All authors have read and agreed to the published version of the manuscript.This research was funded by the National Natural Science Foundation of China, grant number 41721003.The authors would like to thank the International GNSS Service (IGS), and the Nanjing Bureau of Planning and Natural Resources for the provision of data and products. The authors are grateful to anonymous reviewers.The authors declare no conflict of interest.  
Figure 10: RMSEs (**a**) and relative errors (**b**) at different altitudes obtained using the two tomographic methods during the experiment period.
# Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling
## 3 Methodology
### Feature Extractor  
The 300d GloVe pre-training model [30] is used to extract the initialization vectors of the text, which is a popular word embedding learning method that combines global statistical information and local contextual information to learn word vectors. Subsequently, initialization vectors are fed into BiLSTM to obtain rich contextual information. BiLSTM reads the input sequence forwards and backwards to obtain context-sensitive hidden states at each time step. Specifically, for the input sequence \(\{x_{1},\ x_{2},\ x_{3},\dots,\ x_{n}\}\) where \(n\) is the number of words in the sequence, BiLSTM will generate a series of hidden states \(H=\{h_{1},\ h_{2},\ h_{3},\dots,\ h_{n}\}\).