esa-sceva/satcom-chunk-collection huggingface dataset:
# Stochastic Natural Vibration Analyses of Free-Form Shells
## 2 Shape Generation of Free-Form Shells  
### Shape Parametrization  
The geometric shape of free-form shells cannot be represented by certain mathematical formulas or their combination, different from regular shells, such as spherical shells and cylindrical shells. Therefore, shape parameterization is needed to describe their arbitrary shapes with desired smoothness. NURBS is employed here to represent the shape of structures, which is developed from B-spline and have been widely applied. This section gives a brief introduction on NURBS. For more details, one can refer to [14].  
A NURBS surface, as shown in Figure 1, is defined as  
\[S(u,v)=\frac{\sum_{i=1}^{m}\sum_{j=1}^{n}P_{i,j}\omega_{i,j}N_{i,k}(u)N_{j,l}(v )}{\sum_{i=1}^{m}\sum_{j=1}^{n}\omega_{i,j}N_{i,k}(u)N_{j,l}(v)} \tag{1}\]  
where \(S(u,v)\) is the value of a point on the NURBS surface, \(P_{i,j}\) represents the coordinate positions of a set of control points, which form a \(m\times n\) bi-directional control-point grid, and \(\omega_{i,j}\) is their respective weights; \(N_{i,k}(u)\) and \(N_{j,l}(v)\) are B-spline basis functions, defined on the knot vector \(\Xi_{u}=\left\{\begin{array}{cccc}u_{1},&u_{2},&\cdots&u_{m+k+1}\end{array}\right\}\) and \(\Xi_{v}=\left\{\begin{array}{cccc}v_{1},&v_{2},&\cdots&v_{n+l+1}\end{array}\right\}\), where \(u_{1}\leq u_{2}\leq\cdots u_{m+k+1}\), \(v_{1}\leq v_{2}\leq\cdots\leq v_{n+l+1}\); \(u_{i}\) and \(v_{i}\) are the real numbers representing the coordinates in the parametric space \([0,1]\); \(k\) and \(l\) are degrees.  
\(N_{i,k}(u)\) is defined by  
\[\begin{split} N_{i,0}(u)=\left\{\begin{array}{ll}1&u_{i}\leq u<u_ {i}+1\\ 0&\text{otherwise}\end{array}\right.\\ N_{i,k}(u)=\frac{u-u_{i}}{u_{i+k}-u_{i}}N_{i,k-1}(u)+\frac{u_{i+k+1}-u_{i+1} }{u_{i+k+1}-u_{i+1}}N_{i+1,k-1}(u)\end{split}\quad(i=1,2,3\cdots) \tag{2}\]  
while \(N_{j,l}(v)\) is defined in the similar way.  
The aforementioned equations have revealed that the geometry of a NURBS surface is affected by its control points and their weights. However, the work that is presented in [6] shows that the control points play a dominant role in changing shapes rather than the weights. Although setting both locations and weights of control points as design variables could give more flexibility to the shape and enlarge the design space, it would significantly increase the computational cost. For this reason, we only consider the coordinates of control points as the optimization variables while keeping the weights fixed. Particularly, the optimal shape is approached by updating the Z-coordinate of the control points, while setting their X, Y-coordinates as the fixed parameters.
## 4 Experiments and Discussions
### Datasets Comparison Experiment  
#### 4.3.1 Experiment Description  
To further verify whether the validity and representative of the obtained datasets, this section shows results of comparative experiments on the datasets obtained by the above methods. The image classification network model used was ResNet [27] in the experiment, which successfully solved the problem of gradient explosion or gradient disappearance with the deepening of network depth by introducing residual block. Due to the fact that the amount of data was not too large (\(500\times 30\)), we chose a shallow network structure resnet18 which also has good stability. The specific experimental process is shown in Figure 14. The above three different image datasets dataset1, dataset2, and dataset3 are scaled to adapt to different resolutions, then separately put these three types of training datasets into the same classification network model for training. Under the same training strategy (the number of iterations is 20, the learning rate was 0.001, the batch size was 32, and the image size was \(224\times 224\)), the same image classification network (ResNet18) learned the distribution state of dataset1, dataset2, and dataset3 separately, then obtained three different models: model1, model2, and model3. Specifically, model 1 was trained from a small number of data collected manually (dataset1). Model 2 was trained from the augmentation data (dataset2), and model 3 was trained from the dataset collected by the system (dataset3). Finally, the performance of the three models were tested by using the test datasets, and the conclusions were drawn through the comparative analysis of the test results.
## 4 Tests and Results
### Comparison between Analytical Calculation, Numerical Results and Experimental Results  
Figure 17 shows the mass block acceleration values calculated by the three methods. For the first calculation method, the analytic expressions obtained were used. The second one was carried out by finite-element computation. The third one was obtained by experiments. The accuracy of the analytical calculation seems to be higher than the finite-element computation and experimental results. The numerical precision can probably be improved. One possibility is that in the finite element simulation, the performance of some parts or units is inconsistent with the real parts. It can also be seen from the comparison results that the test data are smaller than the theoretical analysis and simulation data, mainly because in the real test, the magnetic circuit is not ideal and the magnetic leakage phenomenon is common. In addition, in the theoretical analysis and simulation, the largest acceleration value appeared at about 10 Hz. In the actual test, the maximum acceleration value appeared near 12 Hz. A comparison between the results shows that the three methods are in approximate agreement. The analytical results are proved to be correct by the experimental results.
## 4 Validation Experiments  
We tested our cognitive BTS system in two different setups. First, we checked all the control procedures described in the previous section and then we deployed the cognitive BTS in an indoor scenario. The main elements used in these experiments were:* Our cognitive BTS built on a Linux laptop with an Ettus Research USRP, with two RFX900 front-ends with omnidirectional antennas, which we modified with an external 52 MHz clock to improve frequency precision. As previously said, the laptop ran GNU Radio, OpenBTS and Asterisk software.
* Commercial cellular phones: Nokia 6230 and Panasonic X400.
* Engineering test cellular phone: Ericsson SH888 TEMS unit (software version TMS600/4R1E, monitored with software TEMS GSM 900 1900/98.0.3.2).
* An FSH6 Rohde&Schwarz spectrum analyzer.
* A frequency-selective jammer based on a Linux computer and another Ettus Research USRP, also with two RFX900 front-ends and omnidirectional antennas. Figure 8 shows the building blocks of this system. This jammer emulated interferences with particular mobiles or with the BTS. Usually, in a real scenario, a cell is not fully affected by interferences, but only some of their components. This effect is emulated by disabling specific uplink/downlink channels, as corresponds to the situation in a large vehicle, such a vessel. Note again the versatility of SDR systems, which in this case served to configure auxiliary laboratory equipment.
## 4 Experiment Arrangement and Results
### Experiment IV: Comparison of Different Prediction Models  
Based on the promising results of the previous experiments, a novel hybrid prediction model, the CEEMDAN-VMD-MPSO-BPNN, was developed. Experiment IV was designed to comprehensively evaluate the performance of this hybrid model against eight other models. Figure 7 shows the frequency distribution histograms and kernel density plots of the relative prediction errors for the constructed model and the comparison models. Table 6 presents the prediction errors for all nine models, offering a detailed comparison. Figure 8 visually depicts the fitting accuracy of each model's predictions against the true values.  
Figure 6: The diagrams of MAPE and R\({}^{2}\) for the five models.  
Figure 7: Relative error of nine models.  
As shown in Figure 7, the curves for the RBFNN, BPNN, GA-BPNN, and PSO-BPNN models are the steepest, indicating the poorest fitting performance and reflecting the largest errors. The MPSO-BPNN, EMD-MPSO-BPNN, EEMD-MPSO-BPNN, and CEEMDAN-MPSO-BPNN models show slightly better performance but they still fall short compared to the constructed hybrid model, the CEEMDAN-VMD-MPSO-BPNN. There is still room for improvement in the prediction accuracy of these four models. The CEEMDAN-VMD-MPSO-BPNN model has the steepest curve, with the smallest values of \(\upmu\) and \(\sigma\), indicating that the constructed hybrid model has the smallest prediction errors and the best fitting performance.  
The results presented in Table 6 and Figure 8 provide compelling evidence for the superior performance of the CEEMDAN-VMD-MPSO-BPNN model in predicting corrosion rates. In most of the cases, the absolute values of MAPE, RMSE, MAE, U1, U2 are the smallest and the absolute value of R\({}^{2}\) is the largest. Among them, only the CEEMDAN-MPSO-BPNN model has slightly better MAE values than the CEEMDAN-VMD-MPSO-BPNN model. This further indicates that the CEEMDAN-VMD-MPSO-BPNN model has a high prediction accuracy in corrosion rate prediction. These findings validate the careful selection of the benchmark model (BPNN), optimization algorithm (MPSO), and data decomposition technique (CEEMDAN-VMD) in constructing the hybrid model. The results  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Indices** & **RBFNN** & **BPNN** & **GA-** & **PSO-** & **MPSO-** & **EMD-** & **EEEMD-** & **CEEMDAN-** & **CEEMDAN-** \\  & & **BPNN** & **BPNN** & **BPNN** & **MPSO-** & **MPSO-** & **MPSO-** & **MPSO-** & **MPSO-** \\ \hline R\({}^{2}\) & 0.851 & 0.876 & 0.892 & 0.895 & 0.912 & 0.923 & 0.929 &  
underscore the superiority, novelty, and practical applicability of the CEEMDAN-VMD-MPSO-BPNN model for accurate and effective corrosion rate prediction.
# Intermitt FOCV Using an I-V Curve Tracer for Minimizing Energy Loss
## 3 Proposed FOCV MPPT Algorithm
### Set Phase  
#### 3.2.1 Sample and Hold (S & H)  
Figure 11 shows the block diagram and the timing diagram of the proposed sample and hold (S & H) circuit. In the S & H circuit, the MPP is tracked by comparing the current power, \(P_{PV\_PWM}(n)\), that is stored in \(C_{1}\), with the previous maximum power, \(P_{max}(n-1)\), that is stored in \(C_{2}\). If \(P_{PV\_PWM}(n)\) is lower than \(P_{max}(n-1)\), the output \(Q\) of D flip-flop is low, then move to next iteration. If \(P_{PV\_PWM}(n)\) is higher than \(P_{max}(n-1)\), \(Q\) is high, thus \(SW_{1}\) is on. Then, \(C_{2}\) stores the new value of maximum power, \(P_{max}(n)\), via charge sharing. \(P_{max}(n)\) can be expressed as  
\[P_{max}(n)=\frac{C_{1}}{C_{1}+C_{2}}P_{PV\_PWM}(n) \tag{9}\]  
Figure 11: (**a**) The block diagram and (**b**) the timing diagram of the sample and hold (S & H).  
Figure 10: (**a**) The block diagram and (**b**) the timing diagram of the multiplier.  
If \(C_{1}\) is much higher than \(C_{2}\), Equation (9) can be expressed as  
\[P_{max}(n)\approx P_{PV\_PWM}(n) \tag{10}\]  
At the same time, \(SW_{2}\) is also on and the previous value of the \(V_{MPP}\), \(V_{MPP}(n-1)\), is replaced by \(V_{PV}(n)\). After all iterations are done, the point that has the final value of \(P_{max}\) and \(V_{MPP}\) is the maximum power point. We can track the MPP timing by all these iterations.
# Protein Kinases in Mediating Phage-Bacteria Interactions
## 5 Bacteria HKs in Phage-Bacteria Interactions
### The Cpx Envelope Stress Response System
#### 5.3.1 Molecular Mechanisms and Physiological Relevance  
The Cpx system in _E. coli_ serves as a critical sentinel of inner membrane and periplasmic stress, distinct from the sigma-E system that monitors outer membrane integrity [54]. Activated by diverse perturbations--including pH shifts [69], osmolarity changes [70], peptidoglycan biosynthesis defects [71], copper exposure [72], and misfolded inner membrane/periplasmic proteins--the Cpx response employs the sensor HK CpxA and its cognate RR CpxR. Stress signals, such as structural misfolding of CpxA's periplasmic domain or mislocalization of lipoproteins like NlpE to the inner membrane, trigger CpxA autophosphorylation, followed by phosphotransfer to CpxR. Activated CpxR then transcriptionally modulates stress-mitigation pathways: upregulating chaperones (e.g., DegP), proteases, peptidoglycan modifiers, and efflux pumps, while repressing non-essential inner membrane complexes (e.g., _nuo_, _cyo_) [73] to alleviate metabolic burden. A self-regulating negative feedback loop involves CpxR-induced expression of _cpxP_[74], which inhibits CpxA [75], and the sRNA CPxQ [76], which fine-tunes CpxP and chaperone Skp levels [77]. By dynamically balancing protein folding, redox/metal homeostasis, and membrane integrity, the Cpx system ensures bacterial survival under envelope stress, exemplifying its role as a versatile orchestrator of inner membrane adaptation.  
The Cpx system critically modulates bacterial virulence by spatial-temporally regulating toxin and adhesion factor expression [78; 79] (e.g., in Salmonella and _E. coli_), coordinating pathogenicity island activity, and balancing biofilm formation with host invasion [68]. The Cpx system ensures proper folding of virulence factors like P-pili in uropathogenic _E. coli_ (UPEC) [80; 81; 82], contributing to successful host colonization. It also drives antimicrobial resistance via upregulation of efflux pumps (e.g., AcrAB-ToIC) [83; 84; 85], \(\beta\)-lactamase expression [86], and cell wall modifications [87]. Additionally, the Cpx system maintains redox homeostasis by countering oxidative stress [88; 89] and facilitates acid adaptation by modulating membrane fatty acids [90]. Remarkably, it engages in inter-kingdom signalling by sensing host-derived molecules like serotonin and indole [91; 92], fine-tuning virulence gene expression in intestinal pathogens. By integrating stress adaptation, virulence, resistance, and host-microbe crosstalk, the Cpx system exemplifies a central hub in bacterial survival, offering therapeutic targets for combating infections and antibiotic resistance.
## 5 Optimization of SP-PMVM
### Optimize \(k_{st}\)  
It should be noted that the inner stator and outer stator have consistent coefficients, namely, \(\psi\_\_in=\psi\_\_out\), while optimizing \(k_{st}\). In addition, the split ratio can be defined as:  
\[k_{o}=R_{shaft}/R_{so} \tag{10}\]  
The value \(k_{o}\) of SP-PMVM is \(-0.15\). The coefficient \(k_{st}\) is optimized in the FEM software at \(I_{max}=24.4\) A, and \(\psi\_\_in\) and \(\psi\_\_out=1^{\circ}\)-\(7^{\circ}\). The \(k_{st}\) is changed from 0.2-0.55 while keeping other parameters constant, as listed in Table 1.  
The results of average torque, cogging torque, and torque ripple at different \(k_{st}\) are shown in Figure 6. It can be seen that when the coefficient \(k_{st}\) is about 0.4, the \(T\_\_\)_avg_ is at the maximum value. However, the tendencies of \(T\_\_\)_cog_ and \(T\_\_\)_ripple_ curves are not quite similar with \(T\_\_\)_avg_. When \(k_{st}\) is about 0.47, \(T\_\_\)ripple reaches the minimum value. At this point, the \(T\_\_\)_avg_ decreases by 25% from the peak point and the \(T\_\_\)_cog is decreased by 30%.  
From the above discussion, the optimization is a compromise between average output torque and torque ripple. When the coefficients of \(k_{st}\) = 0.46, a large torque can be obtained and the torque ripple is acceptable. Consequently, \(k_{st}\) = 0.46 is adopted in this stage and used for the next step.
# Current Promising Strategies against Antibiotic-Resistant Bacterial Infections
## 4 Developing Alternatives to Antibiotics
### Antibacterial Nanomaterials  
Many nanomaterials have an inherent bactericidal activity through several well-accepted mechanisms, including oxidative stress response, physical disruption, altering bacterial metabolism, protein denaturation, and disrupting DNA replication [116]. Normally, the bacterial cell envelope is negatively charged, and electrostatic interaction can be achieved by designing positively charged nanoparticles. Gold nanoparticles caused bacterial membrane tension and squeezing, resulting in physical damage to the cell envelope and subsequent cell lysis [117]. Other metal nanoparticles, such as silver and copper, could also destroy the bacterial cell wall/membrane [118; 119]. Silver nanoparticles are capable of damaging peptidoglycan structure by generating reactive oxygen species (Figure 4) [120]. Copper nanoparticles reduced the expression of a bacterial glucose transporter and the activity of nitrate and nitrite reductases [121]. With selected bacteria as the target, the cationic polymer coating of nanoparticles showed antibacterial activity by destroying bacterial cell plasma membrane, inhibiting bacterial proliferation, and preventing biofilm formation through strong electrostatic interaction with the negatively charged bacterial membrane [122]. These single-element nanoparticles show nontargeted antibacterial activity, while the development of composite nanoparticles may increase specificity and reduce the damage to microbiota. Based on the differences in the cell envelope structure between gram-positive and gram-negative bacteria, gold nanocomposite particles based on amino sugars with a narrow spectrum antibacterial activity and a gram-positive antibacterial action were developed [123]. Graphene oxide-silver (GO-Ag) nanoparticles had differential inhibitory effects on gram-negative _E. coli_ and gram-positive _S. aureus_[124]. GO-Ag nanoparticles showed a bacteriostatic effect towards _E. coli_ and _S. aureus_ by destroying the integrity of bacterial cell walls, and inhibiting the cell division cycle, respectively.
* Quantifiable Improvements in Security and Efficiency: The article provides a comprehensive analysis of the proposed framework's performance, highlighting significant reductions in key exchange time and energy consumption compared to traditional cryptographic methods like RSA and Diffie-Hellman. The results showcase the ability of the ECC-based framework to achieve a high level of security while adhering to the resource constraints typical of IoT devices. * Impact on IoT Network Scalability and Resilience: The proposed framework is designed to support scalable IoT deployments by ensuring robust security mechanisms that adapt to the increasing complexity and size of IoT networks. The key propagation scheme not only enhances synchronization but also mitigates vulnerabilities related to desynchronization and computational heterogeneity. In addition to addressing these technical challenges, the work also provides a roadmap for future research, including the integration of quantum-resistant cryptographic methods and lightweight cryptographic protocols to further enhance the resilience and applicability of the framework. The proposed model is particularly relevant for critical IoT applications, such as smart city infrastructures, healthcare monitoring systems, and industrial IoT deployments, where secure and efficient communication is paramount. By combining theoretical advancements with practical validation, this article significantly contributes to the field of IoT security, offering a comprehensive, scalable, and resource-efficient solution to some of the most critical challenges in securing heterogeneous IoT networks. Organization of the ManuscriptThe remainder of this paper is organized as follows: Section 2 provides an overview of existing security models and cryptographic solutions for IoT authentication, discussing their advantages and limitations. Section 3 details the proposed ECC-based security framework, including the key exchange protocol and parameter management scheme. Section 4 presents the experimental setup and performance evaluation of the proposed model in various IoT scenarios, where Section 4.2 conducts a formal security assessment, including theoretical analysis and automated verification using AVISPA v1.6 and ProVerif v2.05 tools; next, there is Section 4.4.4 which discusses the implications of our findings, including scalability, energy efficiency, and potential integration with post-quantum cryptographic methods. Section 5 discusses the limits within which the proposed solution is optimal and mitigations for limitations. Finally, Section 6 concludes the paper and outlines future research directions and a section with abbreviations used in the article is also provided.
## 3 Model of Image Motion
### Analysis of In-Orbit Disturbances  
During the imaging process in space, a space camera is subjected to various sources of disturbances. These disturbances not only affect the spacecraft's attitude, but also impact the control precision of the pointing platform. As a result, the accuracy of the image motion compensation by the camera is compromised, thereby affecting the quality of the captured images. In light of these challenges, the following analysis can be made:
# Effect of Rotational Speed Variation on the Flow Characteristics in the Rotor-Stator System Cavity
## 3 Results and Discussion
### Reynolds Stress Analysis  
For the stress generated by the turbulent movement of the fluid, in addition to the viscous stress, the Reynolds stress is expressed by a second-order tensor, and the Reynolds stress represents the influence of the turbulent pulsation on the momentum equation. The normal Reynolds stress includes \(R_{\theta\theta}\), \(R_{rr}\), and \(R_{zz}\), and the tangential Reynolds stress includes \(R_{r\theta}\), \(R_{zr}\), and \(R_{z\theta}\). According to [PERSON] and [PERSON] (2018), the \(r\)-\(z\)-plane Reynolds stress of the rotor-stator cavity is analyzed to study the windage torque of the rotor-stator cavity. It is concluded that \(R_{r\theta}\) has a significant influence on the wall shear stress; thus, the Reynolds stress, which has a major influence on the torque, is as follows:  
\[R_{r\theta}=\frac{\overline{V_{r}^{\prime}V_{\theta}^{\prime}}}{\left(\Omega r \right)^{2}} \tag{9}\]  
Here, \(\overline{V_{r}^{\prime}V_{\theta}^{\prime}}\) is the momentum in the \(\theta\) direction transmitted through the unit area of the \(r\)-\(z\)-plane per unit time. The Reynolds stress distributions of Cases 1, 2, 3, and 4 on the \(r\)-\(z\)-plane are shown in Figure 12. The sign of the tangential Reynolds stress value represents only the direction and is unrelated to the magnitude.  
The contour maps under different working conditions reveal that there are large absolute values of Reynolds stress in different directions at the junction of the axial incoming flow and the return flow. Figure 12d shows that the rotor wall surface under different rotation Reynolds numbers has a large \(R_{r\theta}\). The area with a larger tangential Reynolds stress in the turbulent boundary layer is caused by the increase in the rotating speed, which causes the fluid velocity pulsation in the boundary layer to be greater than the area outside the boundary layer. Because Equation (5) only considers the shear stress generated by fluid viscosity, the integral torque is slightly smaller than the rotor torque in the numerical calculation results. Therefore, the place where the tangential Reynolds stress \(R_{r\theta}\) is larger also has a partial effect on the torque of the rotor.  
With an increase in the rotating Reynolds number, the change in the Reynolds normal stress shown in Figure 12 has a certain law. When \(Re_{\Phi}<3.53\times 10^{6}\), the area of normal stress concentration is mainly concentrated in the area of \(r^{*}<0.12\)--that is, the position where the axial incoming flow first enters the cavity and the junction of the incoming flow and the return flow. When \(Re_{\Phi}>3.53\times 10^{6}\), the Reynolds normal stress is affected by dynamic and static interference and fluid viscosity, and its concentrated area gradually moves into the cavity center.  
Figure 12: Cont.