esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Control Algorithm
### Parameter Tuning
#### 2.5.3 Effect of \(\lambda\)  
The role and influence of \(\lambda\) on a higher order system is unclear. However, we may approach this problem through a second order system like the example. When \(n=2\), our algorithm suggests \(s=\dot{e}+\lambda e\) and \(\tilde{u}=\tilde{u}_{R}-\lambda\dot{e}\) under the SMC assumption, where \(\tilde{u}_{R}\) contains the remainder terms of \(\tilde{u}\). From the structure of \(\tilde{u}\) we conclude that the role of \(\lambda\) is roughly the gain of a conventional P-controller in a negative feedback. A proper choice of \(\lambda\) would stabilize the system. Too large a value of \(\lambda\) may destabilize the system, or have the system converged sluggishly.  
On the other hand, the function of SMC is to force \(s=0\) first. Then, the property of \(s=0\) will force \(e=0\) when \(t\) approaches infinity. Since \(s=\dot{e}+\lambda e\), implying that \(e\) converges exponentially with time constant \(\tau_{\lambda}=1/\lambda\), too small a value of \(\lambda\) will cause the system to converge sluggishly. From our experience, \(1\leq\lambda\leq 10\) is a better choice.  
Figure 4 demonstrates the comparison. \(\lambda=100\) in Figure 4a, \(\lambda=1\) in Figure 4b, and \(\lambda=0.01\) in Figure 4c. Other parameters are set identically. It is obvious that the responses with too large or too small \(\lambda\) are sluggish, if the SMC control is considered.
###### Abstract  
Sensing strategy directly influences the sensing accuracy of a spectrum sensing scheme. As a result, the optimization of a sensing strategy appears to be of great significance for accuracy improvement in spectrum sensing. Motivated by this, a novel sensing strategy is proposed in this paper, where an improved tradeoff among detection probability, false-alarm probability and available throughput is obtained based on the energy detector. We provide the optimal sensing performance and exhibit its superiority in theory compared with the classical scheme. Finally, simulations validate the conclusions drawn in this paper.  
Keywords:spectrum sensing; sensing strategy; detection probability; false-alarm probability; available throughput +
Footnote †: journal: Applied Physics Letters
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 1 Introduction
#### Research Hypotheses  
This study investigates the effectiveness of a fuzzy inference-based control system for optimizing [insert system, e.g., QoS in 4G/5G networks]. The hypotheses are framed as follows:  
1. _A fuzzy logic controller incorporating adaptive membership functions significantly improves system responsiveness under dynamic load conditions._  
Figure 1: Fuzzy logic control system: process flow from fuzzification to defuzzification.  
**H2.**: _The inclusion of \(\alpha\)-cut optimization enhances the precision of fuzzy decision-making compared to standard inference without \(\alpha\)-level processing._
**H3.**: _The integration of multi-level fuzzy rules (high, medium, low priority) results in better output regulation than single-level rule sets._  
These hypotheses are evaluated through simulation and performance analysis of the proposed fuzzy model under varied input scenarios.
# Analysis of Wind-Induced Vibration Response of Transmission Wire Under Crosswind in Canyon Terrain
## 5 Wind Vibration Characteristics of Wire Under Steady-State Constant Wind Speed Input
### Conditions of Different Mountain Sizes  
The model where a single mountain is located in front of the wire was set. The position of the wire and the mountain remained the same. The size of the mountain changed according to the same proportion between the height and the bottom diameter. The conditions of the mountain bottom diameter were set at 30 m, 40 m, 50 m, and 60 m. The time-history curves of the z-displacement and y-displacement of the midpoint of the wire are shown in Figure 9. Different conditions are expressed in terms of their diameters of mountain. In the figure legend, the distance is shown as \"DM\". The discussion of different mountain sizes can be converted into the discussion of the proportion of the blocking length of the mountain to the wire total length. After calculation, the blocking ratios of the above conditions were 1.5%, 18.2%, 34.8%, and 51.5%. This can be seen in Figure 9.  
1. The displacement of the wire in the z-direction was greatly affected by the blocking ratio in the initial vibration stage. The suppressant effect of the mountain on the wire vibration displacement in the z-direction increased with the increase in the blocking ratio. The final stable state of the wire had little relation with the blocking ratio of the mountain and the stable position of the wire was close to that of the flat terrain;
2. The vibration peak value of the wire in the y-direction increased first and then decreased with the increase in the blocking ratio. According to the curve in the figure, it can be judged that there will be the maximum peak when the blocking ratio is around 20%. The larger the mountain barrier ratio was, the greater the vertical force in the negative y-direction was on the wire and the lower the final stable position of the wire in the y-direction.  
Figure 8: Time–history curves of displacement.
# Non-Linear Creep-Relaxation Constitutive Damage Model for Aging Concrete
## 2 Theoretical Formulation
### Instantaneous Helmholtz Free Energy
#### 2.2.4 Helmholtz Free Energy for Viscous Flow Strain and Damage Strengthening  
For the second term in the right-hand side of Equation (7), let us split the stress into hydrostatic and deviatoric parts, i.e., \(\sigma=-pI+s\), where \(p\) is the hydrostatic pressure given by \(p=-\frac{1}{3}tr(\sigma)\), \(I\) is the identity tensor, and \(s\) is the deviatoric stress tensor. By taking the hydrostatic and deviatoric parts of the stress, and substituting them into the second term in the right-hand side of Equation (5), i.e., \(\sigma:\varepsilon^{f}\), and assuming a linear relationship, we obtain second term in the right-hand side of Equation (7),  
\[\rho\Psi^{f}=-\frac{1}{2}p\left(I:\varepsilon^{f}\right)+\frac{1}{2}s: \varepsilon^{f} \tag{31}\]  
From this equation, it is easily recognized that the term \(s:\varepsilon^{f}\) is zero since a viscous flow is a process entirely dissipative when subjected to shear stress [57]. Furthermore, it is also easily noted that \(I:\varepsilon^{f}=tr\left(\varepsilon^{f}\right)\). Subsequently, from an analogy with linear elasticity,  
\[\rho\Psi^{f}=-\frac{1}{2}p\left(I:\varepsilon^{f}\right)=\frac{1}{2}\hat{k}_{ f}\left[tr\left(\varepsilon^{f}\right)\right]^{2} \tag{32}\]  
where the relationship \(p=-\hat{k}_{f}tr\left(\varepsilon^{f}\right)\) was assumed, in which \(\hat{k}_{f}=\hat{k}_{f}(t)\) is the long-term creep bulk aging function, related to the long-term creep Lame aging functions \(\hat{\lambda}_{f}\) and \(\hat{\mu}_{f}\) through \(\hat{k}_{f}=\hat{\lambda}_{f}+\frac{2}{3}\hat{\mu}_{f}\). Aging functions are considered since they may depend on the variable \(v(t)\)[43; 45]. In addition, Equation (32) can also be written in component form,  
\[\rho\Psi^{f}=\frac{1}{2}\hat{k}_{f}\varepsilon_{ii}^{f}\varepsilon_{jj}^{f} \tag{33}\]  
Equation (33) is the Helmholtz free energy that accounts for the energy stored in the material due to the long-term creep state of the strain. It is relevant to note that the long-term creep Lame aging functions \(\hat{\lambda}_{f}\) and \(\hat{\mu}_{f}\), related to \(\hat{k}_{f}\), do not necessarily coincide with those relaxation functions defined in Equation (16).  
Finally, the third term in Equation (7) is assumed to be given by  
\[\rho\Psi^{D}=\frac{1}{2}\hat{k}_{D}\beta^{2} \tag{34}\]  
defined similarly as in [25], and where, \(\hat{k}_{D}=\hat{k}_{D}(t)\) is an experimental aging hardening function associated with hardening and the age of the quasi-brittle material.
## 4 Experiments
### Practical Validation  
#### 4.3.1 Dataset  
The dataset utilized in this study comprises real business data from financial institutions. The experiments were conducted within a secure inner loop environment. The dataset encompasses four major categories, namely customer information, service data, operation management, and financial supervision. A comprehensive overview of the experimental data is presented in Table 7.  
* Customer information: The customer dataset comprises 73 variables, encompassing a wide range of data including customer names, ID numbers, income details, addresses, phone numbers, and account passwords.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**UP-SDCG**} & \multicolumn{2}{c}{**DSC**} & \multicolumn{2}{c}{**DSGC**} & \multicolumn{2}{c}{**GoDLP**} \\  & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline Personnel Information & 100.00\% & 94.44\% & 97.14\% & 100.00\% & 68.25\% & 81.13\% & 69.72\% & 66.25\% & 68.98\% & 77.78\% & 15.88\% & 26.37\% \\ Project Information & 98.00\% & 100.00\% & 98.99\% & 100.00\% & 53.27\% & 69.51\% & 48.61\% & 38.99\% & 43.27\% & 100.00\% & 4.17\% & 8.01\% \\ Contract Information & 100.00\% & 95.84\% & 97.87\% & 30.01\% & 12.51\% & 17.65\% & 20.01\% & 8.34\% & 11.77\% & 100.00\% & 0.01\% & 0.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance metrics of different models on various datasets.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Dataset**} & **Row** & **Sensitive** & **Non-Sensitive** & **Sensitive Type** \\  & **Number** & **Columns** & **Columns** & **Sensitivity Type** \\ \hline Customer Information & 73 & 12 & 61 &
\begin{tabular}{l} Personal information, such as name, certificate number, \\ income, address, phone number,account password, etc. \\ \end{tabular} \\ Service Data & 23,208 & 103 & 78 & Loans, insurance, bonds, cross-border business, etc. \\ Operation Management & 13 & 0 & 13 & Personal and financial information \\ Financial Supervision & 3 & 0 & 3 & Risky assets and capital adequacy \\ \hline \hline \end{tabular}
\end{table}
Table 7: Overview of the business dataset.  
Figure 12: Comparison of F1-score in data grading and classification models.  
* Service data: The service dataset, encompassing information on loans, insurance, bonds, and cross-border transactions, comprises a total of 103 variables.
* Operation management: The Operation management dataset has 13 variables related to personal information as well as company financial information.
* Financial supervision: The financial supervision dataset has three variables related to information on regulatory indicators.
# A Novel Optimization Layout Method for Clamps in a Pipeline System
## 3 Results and Discussion
### Prediction by Neural Network  
The neural network is used to build a surrogate model between inputs and outputs. In this paper, the neural network is used to implement the multi-input and multi-output function. The number of hidden layers is set to two.  
Neural networks rely on a variety of transfer functions to transfer data. There are a variety of transfer functions, of which commonly used are three: log-sigmoid, tan-sigmoid and purelin [27]. Tan-sigmoid was used as the transfer function for the input layer while purelin for the hidden layer of neural network due to their better fitness and the accuracy in prediction in this case.  
There is no uniform standard for the choice of the number of hidden nodes. The general principle observed is to use a simple network structure and as few neuronal nodes as possible which meet the precision requirements and reflect the relationship of input and output. The learning rate is generally selected between 0.01 and 0.8. When the learning rate is low, the system stability is good, while training takes longer. Finite element calculation is the most time-consuming in the whole optimization process, and the optimization process is short compared to the modeling time. It is finally determined that the number of hidden nodes in the first layer is 10, the number of nodes in the secondary layer is 3, and the learning rate is set to 0.01.  
Figure 10 shows the errors between predicted results and the results of finite element analysis. By comparing the predicted output with the 30 sets of simulation data, it can be obtained that errors are within 2%, which meets the requirements of the engineering. The errors of the first-order frequency are basically less than 0.5% and are obviously less than the errors of displacement and stress under the random load. The possible reason for this is that the finite element mesh division is not fine enough. The displacement and stress under random load are greatly influenced by the mesh, resulting in large fluctuations in input data. The accuracy of calculations can be improved by refining the meshes, however this can greatly increase the time for calculation. In engineering, the mesh size should be selected reasonably.  
Figure 9: Sensitivity histogram about input variables to output parameters: (**a**) Parameters sensitivity to first-order frequency; (**b**) Parameters sensitivity to displacement; (**c**) Parameters sensitivity to stress.
# Modulus Stretch-Based Circular SAR Imaging with Contour Thinning
###### Abstract  
This paper presents a modulus stretch-based circular Synthetic Aperture Radar (SAR) imaging method. This method improves the traditional backprojection algorithm for circular SAR imaging, and introduces the modulus stretch transformation function in the imaging process. By performing a modulus stretch transformation on the intermediate results, the target contour in the final imaging result is thinner and clearer. A thinner and clearer contour can help to increase the recognizability of the target and provide a basis for subsequent target recognition. The proposed method is demonstrated on the line target imaging simulations and Gothca dataset.  
circular SAR; wide-angle SAR; imaging; target recognition; modulus stretch +
Footnote †: journal: Applied Sciences  
12 June 2019; Accepted: 2 July 2019; Published: 5 July 2019
# RoSe-Mix: Robust and Secure Deep Neural Network Watermarking in Black-Box Settings via Image Mixup
## 4 Experimental Results
### Modulation Parameters  
In Section 3.2, we described our proposed method and defined some parameters for which, in this subsection, we perform experiments to determine optimal values for \(k\) (the number of classes used to generate Mixup watermark samples) and \(n_{e}\) (the size of the trigger dataset). We evaluated the impact of \(k\) and \(n_{e}\) on the performance of the model for each dataset. To find the best \(k\), we tested values in the range \(k\in\{2,\dots,c\}\), with \(n_{e}\) fixed at 500, selecting the \(k\) that produced the highest TA and \(Rec_{T_{T}}\). After identifying the optimal\(k\), we plotted the curves of \(n_{e}\) to further assess its influence on performance, ultimately selecting the value that resulted in the best TA and \(Rec_{T}\) for each dataset.  
The results are shown in Figure 4 for \(k\) and Figure 5 for \(n_{e}\). For the \(k\) plots, we omitted some datasets with similar behavior to conserve space. The watermark recovery rates, \(Rec_{T}\) and \(Rec_{T_{S}}\), are measured based on the sizes of the watermark datasets, \(n_{e}\) and \(n_{d}\), respectively. For \(Rec_{T}\), we performed experiments with \(n_{e}\in\{100,300,500,700,1000,3000,5000,7000,10,000\}\). The TA of each model without a watermark can be found in Table 1. To determine the optimal \(n_{e}\), we plotted the accuracy of the model as a function of \(n_{e}\) and selected the value that produced the highest TA and \(Rec_{T}\). The best \(k\) was 2 for MNIST, FashionMNIST, and CIFAR-10, and 7 for GTSRB. Additionally, starting from \(n_{e}=500\), both TA and \(Rec_{T}\), showed significant improvement and stabilized across all datasets. However, increasing the value of \(n_{e}\) significantly decreases TA probably because the task related to the watermark takes more importance compared to the main task in the learning process, leading to a task interference [39] in which we note a decrease in performance on the main task for complex problems (e.g., Figure 5c). As a result, we used \(n_{e}=500\) for the remaining experiments.  
Figure 4: TA, \(Rec_{T}\), and \(Rec_{T_{S}}\) computed according to different values of \(k\) for FashionMNIST and MNIST.  
Figure 5: TA, \(Rec_{T}\), and \(Rec_{T_{S}}\) computed according to different values of \(n_{e}\).
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} FSO & Free-Space Optical \\ GEO & Geostationary orbit \\ GOPEX & Galileo Optical Experiment \\ HAP & High Altitude Platform \\ IMDD & Intensity Modulation with Direct Detection \\ JAXA & Japan Aerospace Exploration Agency \\ LCE & Laser Communication Equipment \\ LEO & Low Earth Orbit \\ LUCE & Laser Utilizing Communication Equipment \\ MEO & Medium Earth Orbit \\ NASA & National Aeronautics and Space Administration \\ NFIRE & U.S. Missile Defense Agency satellite \\ OCSD & Optical Communications and Sensor Demonstration \\ OGS & Optical Ground Station \\ OICETS & Optical Inter-orbit Communication Engineering Test Satellite \\ OOK & On-Off Keying \\ OPALS & Optical Payload for Lasercomm Science \\ OIA & optical interface assembly \\ PAT & Pointing Acquisition and Tracking \\ PCB & Printed Circuit Board \\ PSD & Position-sensitive detector \\ SDA & Space Development Agency \\ SILEX & Semiconductor-laser Intersatellite Link Experiment \\ SoC & System-on-chip \\ SOTA & Small Optical Transponder \\ SSM & Slow Steering Mirror \\ STROPEX & Stratospheric Optical Payload Experiment \\ SWaP & Size, Weight, and Power \\ \end{tabular}
# Cross-Modal Image Registration via Rasterized Parameter Prediction for Object Tracking
## 3 Methodology  
Figure 1 illustrates the framework of our proposed image registration method, including the FEM, RPPM, ITM, and SMM.  
* The FEM uses a VGG convolutional neural network to encode reference image (visible light) and moving image (infrared) feature pyramids.
* The RPPM predicts a homography matrix for each raster of the feature pyramid layers and outputs homography matrix groups for each image.
* The ITM conducts perspective transformation for the homography matrix group of the moving images.
* The SMM computes the similarity scores of the two images as a cost function for image registration model training.  
Figure 1: The framework of our image registration model, including the FEM, RPPM, ITM, and SMM. First, the FEM encodes feature pyramids for a reference image (visible light) and a moving image (infrared). Then, the RPPM predicts a homography matrix for each raster of the feature pyramid layers. Next, the ITM conducts perspective transformation for the homography matrix group of the moving image. Finally, the SMM computes the similarity scores of the two images for image registration.
## 2 sRNA as Orchestrators of Bacterial Pathogenesis
### Invasion of the Host  
A key attribute of successful pathogens is their ability to invade the host, facilitated by various virulence factors. For example, UPEC produces \(\alpha\)-hemolysin, encoded by the _hlyCABD_ operon, which disrupts host cell membranes, causing lysis and tissue damage, thereby enhancing bacterial colonization of the urinary tract [84, 85]. Additionally, \(\alpha\)-hemolysin modulates host immune responses by suppressing pro-inflammatory cytokine expression, allowing UPEC to evade immune detection. Similarly, _Staphylococcus aureus_ secretes Protein A, which binds to the Fc region of antibodies, preventing opsonization and phagocytosis [86]. _Bordetella pertussis_ produces adenylate cyclase toxin, increasing cyclic AMP levels in host cells and impairing immune function [87], while _P. aeruginosa_ releases Exotoxin A, which inhibits protein synthesis and induces immune cell death [88] (Table S2). Furthermore, _Clostridiodes difficile_ produces Toxins A and B, which disrupt the cytoskeleton of intestinal cells, leading to inflammation and immune evasion [89]. These bacterial toxins employ diverse strategies to subvert host defenses, facilitating survival and pathogenesis. The following section highlights the role of bacterial small RNAs in regulating virulence and host invasion, adding another layer of complexity to bacterial pathogenicity. Shiga toxin (Stx) is a major virulence factor of _S. dysenteriae_ and enterohemorrhagic _Escherichia coli_ (EHEC) [90]. It is an AB5 toxin, comprising an enzymatically active A subunit and a pentameric B subunit that binds globotriaosylceramide (Gb3) receptors on host cells. Upon internalization, the A subunit inhibits protein synthesis by cleaving an adenine residue from 288 rRNA, leading to cell death, epithelial damage, inflammation, and hemorrhagic colitis. In severe cases, Stx enters the bloodstream, causing endothelial damage in the kidneys and triggering hemolytic uremic syndrome (HUS), characterized by acute kidney injury, thrombocytopenia, and hemolytic anemia [91]. The toxin's genes, carried by lambdaid bacteriophages, enable horizontal transfer of virulence factors, driving bacterial evolution [92]. Stx is categorized into two primary groups, Stx1 and Stx2, with subtypes such as Stx1a, Stx2a, and Stx2d associated with severe disease.