esa-sceva/satcom-chunk-collection huggingface dataset:
# Fractional-Order LQR and State Observer for a Fractional-Order Vibratory System
###### Abstract  
The present study uses linear quadratic regulator (LQR) theory to control a vibratory system modeled by a fractional-order differential equation. First, as an example of such a vibratory system, a viscoelastically damped structure is selected. Second, a fractional-order LQR is designed for a system in which fractional-order differential terms are contained in the equation of motion. An iteration-based method for solving the algebraic Riccati equation is proposed in order to obtain the feedback gains for the fractional-order LQR. Third, a fractional-order state observer is constructed in order to estimate the states originating from the fractional-order derivative term. Fourth, numerical simulations are presented using a numerical calculation method corresponding to a fractional-order state equation. Finally, the numerical simulation results demonstrate that the fractional-order LQR control can suppress vibrations occurring in the vibratory system with viscoelastic damping.  
vibration; control; fractional calculus; linear quadratic regulator (LQR); algebraic Riccati equation; iteration; state observer; viscoelasticity
# Prediction of Friction Coefficient Based on 3D Texture Characteristics of Road Surfaces
## 3 Results and Discussion
### Development of a Friction Prediction Model Using Texture Parameters
While both studies emphasize the importance of texture, our research provides a more holistic approach by considering a wider range of texture parameters and their combined effect on friction, regardless of speed. The study in [23] presents a model for evaluating pavement skid resistance using 3D texture data, established through a multiple quadratic multinomial regression model. This approach emphasizes the importance of both macrotexture and microtexture in influencing different components of friction. Similar to our research, this study integrates 3D texture indicators but focuses on harmonizing different skid resistance detection methods through the IFI concept. In contrast, our study uses a more direct measurement of friction across various road sections and incorporates a broader range of texture parameters, providing a comprehensive analysis of real-world conditions. Our model's practical application is further enhanced by its ability to predict friction coefficients with high accuracy, considering both microtexture and macrotexture influences. While the derived regression model offers a robust framework for predicting road friction based on texture parameters, its application in real-world scenarios may face challenges in terms of accuracy and adaptability across diverse conditions. To enhance the model's robustness, future research could explore the integration of convolutional neural networks (CNNs) and machine learning techniques, which have shown promise in related fields for processing complex, high-dimensional data. Incorporating such approaches could improve the model's ability to generalize across varying pavement types and environmental conditions, ultimately leading to more reliable predictions.
# Energy Management Scheme for an EV Smart
## 3 Component Modeling and Control
### Four-Quadrant Converter  
Conventional chargers perform only one-way power transfer from the grid to the EV and operate at a close to unity power factor, whereas a bidirectional charger offers the capability to discharge the battery energy for support. However, a four-quadrant charger enhances the functionality of a bidirectional charger by adding the ability to provide or absorb reactive power. It can operate at any point on the \(PQ\) circle, where \(S_{n}=\sqrt{P^{2}+Q^{2}}\) and \(S_{n}\) is the kVA rating of the charger. Such chargers lie in the category of off-board fast-charging stations where the weight and size is not a significant constraint. Four-quadrant operation of a converter is demonstrated in Figure 6.  
Figure 4: Bi-directional DC-DC converter circuit topology.  
Figure 5: DC-DC converter control strategy block diagram.  
Figure 6: \(PQ\) capability characteristics of an EV smart charger.  
The four-quadrant grid-side converter of the smart charger is a three-leg voltage source inverter. The topology of the inverter is shown in Figure 7. The DC-link capacitance size \(C_{dc}\) is calculated by considering the energy rate of change during transients, as well as the charger rating [17] as follows:  
\[C_{dc}=\frac{2\lambda\Delta rS_{n}\cos\phi}{\Delta xf_{g}V_{dc}^{2}} \tag{10}\]  
where \(\lambda\) is a multiple of the grid frequency \(f_{g}\), \(\Delta r\) is the range of change of DC power, \(\cos\phi\) is the power factor and \(\Delta x\) is the allowable DC-bus voltage variation during transients.  
Each leg of the inverter bridge is connected further to an LCLfilter in a Y-configuration. The LCL filter reduces the high-frequency harmonics and supports the use of lower switching frequency for the inverter. The LCL filter has an inverter-side inductor \(L_{1}\), a grid-side inductor \(L_{2}\) and a filter capacitor \(C_{f}\)[43]. The values of these components are calculated as:  
\[L_{1}=\frac{V_{dc}V_{L-G}}{2\sqrt{2}\gamma^{AC}f_{s(inv)}S_{n}} \tag{11}\]  
\[C_{f}=\frac{\varrho S_{n}}{\omega_{g}V_{L-L}^{2}} \tag{12}\]  
\[L_{2}=\frac{\sqrt{\frac{1}{\rho^{2}}}+1}{\omega_{s(inv)}^{2}C_{f}} \tag{13}\]  
where \(V_{L-G}\) is the PCC RMS phase voltage, \(V_{L-L}\) is the PCC RMS line voltage, \(\gamma^{AC}\) is the current ripple in \(L_{1}\), \(f_{s(inv)}\) is the inverter switching frequency, \(\varrho\) is the power factor variation, \(\rho\) is the desired attenuation and \(\omega_{s(inv)}=2\pi f_{s(inv)}\). A passive damping resistor \(R_{f}\) is included in series with the filter capacitor to avoid resonance; its value is given by:  
\[R_{f}=\frac{1}{3\omega_{res}C_{f}} \tag{14}\]  
where:  
\[\omega_{res}=\sqrt{\frac{L_{1}+L_{2}}{L_{1}L_{2}C_{f}}} \tag{15}\]  
and its range should be according to the condition: \(10f_{g}<f_{res}<0.5f_{s(inv)}\).
## 3 Results and Discussion
### Spectral Features and Preprocessing
Moreover, most of the screened feature wavelengths were distributed in the nitrogen-sensitive band range, further indicating the accuracy of the CARS-SPA algorithm based on the extraction of sensitive bands of the spectrum, which showed strong feasibility for predicting maize LNC  
Figure 9: CARS method screening process of characteristic wavelength. (**a**) The change rule of sample variable quantity under different sampling times; (**b**) Variation of RMSECV under different sampling times; (**c**) Change trend of regression coefficient of each wavelength with sampling times. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spectral Wavelength/nm** & **Pretreatment Method** & \(\mathbf{R_{p}}^{2}\) & **RMSEP** \\ \hline \multirow{4}{*}{400–1000} & Original spectrum & 0.80 & 0.19 \\ \cline{2-4}  & First derivative & 0.84 & 0.17 \\ \cline{2-4}  & S-G & 0.72 & 0.23 \\ \cline{2-4}  & SNV & 0.80 & 0.19 \\ \cline{2-4}  & Normalization & 0.75 & 0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of partial least squares regression (PLSR) model effects of pretreatment methods. using spectral information and could well invert the intrinsic relationship between spectral information and LNC. Hence, selecting feature wavelengths is significant for predicting corn LNC.
## 3 Analysis of the Dual-Peak Resonance Restructured by Thermal Strains
### Analysis of Type A SMC-FBG
#### 3.1.2. Wavelength Shift \(\Delta\lambda_{sT}\) Caused by the Single-Layer Coating  
The factual elongations of inner layer and FBG are satisfied by the restrictive conditions of Equations (7a) and (7b)  
\[\sigma_{s1z}A_{1} =\sigma_{s2z}A_{2} \tag{7a}\] \[\alpha_{1}\Delta T\frac{L_{1}}{2}+\frac{\sigma_{s1z}}{E_{1}} \frac{L_{1}}{2} =\alpha_{2}\Delta T\frac{L_{2}}{2}-\frac{\sigma_{s2z}}{E_{2}} \frac{L_{2}}{2} \tag{7b}\]  
where, \(\sigma_{siz}\) (\(i=1,2\)) represents the thermal stress of No. \(i\) layer of coating induced by the single-layer coating. Then, the thermal strain \(\varepsilon_{s1z}\) is obtained by Equation (8).  
\[\varepsilon_{s1z}=\frac{\sigma_{s1z}}{E_{1}}=\frac{E_{2}A_{2}(\alpha_{2}- \alpha_{1})}{E_{1}A_{1}+E_{2}A_{2}}\Delta T \tag{8}\]  
The corresponding wavelength shift \(\Delta\lambda_{sT}\) is expressed using Equation (9).  
\[\Delta\lambda_{sT}=\lambda_{B}(\alpha+\xi)\Delta T+\lambda_{B}(1- p_{\rm e})\varepsilon_{s1z}\] \[=\lambda_{B}*\Delta T\Big{[}(\alpha+\xi)+(1-p_{\rm e})*\frac{E_{2 }A_{2}(\alpha_{2}-\alpha_{1})}{E_{1}A_{1}+E_{2}A_{2}}\Big{]} \tag{9}\]where \(K_{sT}\) is the temperature sensitivity of SMC-FBG with the single-layer coating.  
\[K_{sT}=\lambda_{B}*\left[(\alpha+\zeta)+(1-p_{\mathrm{e}})*\frac{E_{2}A_{2}( \alpha_{2}-\alpha_{1})}{E_{1}A_{1}+E_{2}A_{2}}\right] \tag{10}\]
# A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
## 3 Discussion
Science should provide appropriate recommendations for implementation and action. In order to sensitize managers to the topic, more publications of use cases from both science and real-world applications are required. As a starting point for further research, existing examples of ML in supply chain management could be analyzed and evaluated against the background of SCRM in order to illustrate the cross-functional added value of ML applications. Another starting point for future research is an empirical review of the improvement of SCRM by ML, which is still pending. Drawing from the above discussion, we propose:  
**Proposition 3**. : _Companies need more guidance on how to integrate ML into SCRM._  
Several supply chain areas can benefit simultaneously from the results of the ML deployment. The use cases of the CEP companies in Section 2.3. show that the integration of ML not only reduces transport and delivery risks, but also intensifies the customer relationship through an improved information supply. By integrating ML into SCRM, the company can strengthen its position vis-a-vis the customer, since it knows its strengths and weaknesses better. Thus, the cooperation between the individual actors in the supply chain can be improved at the same time. The integration of ML into SCRM can also be an important lever in price negotiations with customers or insurance companies. It can also help to set oneself apart from competitors [4]. ML-based results can help communicating supply risks with organizational decision-makers. Informing decision-makers early on about supply chain risk levels has implications for manager cognition and how they adapt decision-making strategies based on risk knowledge [98]. According to [PERSON] et al. [99], this is an important component of behavioral SCRM. ML consequently supports decision-making and mitigates problems related to managers' cognition and potential biases. From these findings we derive the following proposition:  
**Proposition 4**. : _The integration of ML into SCRM can have a positive effect on other supply chain functions and business units._  
Finally, it should be noted that the integration of ML into the SCRM also requires a critical examination. For example, it must be considered that ML-based risk management systems require high initial investments [85]. In addition to the IT infrastructure, the storage of data (capacity) and the tracing of failure-causes are expensive [28]. There are also legal concerns about the massive collection or use of data for risk management [72]. Thus, new legal regulations for the use of AI/Big Data could follow in the future, which have to be considered in the process phases of risk management.
## 4 Results
### Runtime Performance and Model Complexity  
The results presented in Table 4 demonstrate the trade-offs between model complexity and segmentation accuracy for different backbone configurations within the proposed AEA-GAN framework. The MobileNetV3-only variant achieved the highest FPS (38) and the lowest computational complexity (36.4 GFLOPs) with 17.8 million parameters, making it the most lightweight but also the least accurate, with an mIOU of 85.21%. In contrast, the EfficientNetB7-only variant produced improved accuracy (mIOU of 87.38%) but at the cost of significantly higher computational overhead--143.5 GFLOPs, 60.2 million parameters, and a reduced inference speed of 20 FPS.  
According to the data in Table 5, the proposed hybrid configuration balances the strengths of both networks, delivering the best segmentation performance (mIOU of 89.46%) with a moderate model size (39.5 million parameters) and acceptable inference speed of 26 FPS. All experiments were conducted on an NVIDIA T4 GPU, ensuring consistent and realistic performance evaluation. These results confirm the hybrid AEA-GAN's suitability for real-time or near-real-time deployment in ADAS applications, offering an optimal balance between accuracy and efficiency.
## 3 Experimental Results
### Results of IEEE 33-Bus System
#### 3.1.2 Power Losses  
Power losses in each line of the radial system are calculated in different scenarios and the results are presented in Table 6. The results show that the lowest power loss is obtained by using the DGs; here, the optimum power factor is determined in addition to the optimal location and capacity.  
In Scenario IV, the optimum placements, sizes, and power factors of the DGs are specified and they are settled in the radial system. According to the results, it is observed that the total reactive power loss decreased by 93.13% and the total active power loss decreased by 94.42%. The active power loss of each bus in all scenarios is compared in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & **Active Power** & **Loss Reduction** & **Reactive Power** & **Loss Reduction** \\  & **Loss (kW)** & **(\%)** & **Loss (kVAR)** & **(\%)** \\ \hline Base Case & 210.98 & & \(143.14\) & \\ \hline Scenario I & 72.79 & 65.50 & 50.73 & 64.56 \\ \hline Scenario II & 139.41 & 33.92 & 95.01 & 33.62 \\ \hline Scenario III & 12.6571 & 94.00 & 10.37 & 92.75 \\ \hline Scenario IV & 11.7752 & 94.42 & 9.83 & 93.13 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Power losses of the IEEE 33-bus system in different scenarios.  
Figure 6: Voltage profile of the IEEE 33-bus system in different power factor.
# Power Stability Analysis and Evaluation Criteria of Dual-Infeed HVDC with LCC-HVDC and VSC-HVDC
## 3 Power Stability Analysis of Hybrid Dual-Infeed HVDC Systems  
### Concept of Maximum Power Curve  
According to the analysis in Section 2.2, once the selected independent variables \(\gamma_{1}\) and \(I_{\text{d1}}\) are determined, the remaining operating state variables will also be determined. Therefore, the active power \(P_{\text{d1}}\) of LCC-HVDC can be represented by these two independent variables.  
\[P_{\text{d1}}=f(\gamma_{1},I_{\text{d1}}) \tag{20}\]  
When \(\gamma_{1}=\gamma_{\text{IN}}=\gamma_{\text{min}}\), the \(P_{\text{d1}}\)-\(I_{\text{d1}}\) curve is defined as the maximum power curve of LCC-HVDC [12; 20]. Next, the shape of the \(P_{\text{d1}}\)-\(I_{\text{d1}}\) curve will be explained.  
Substitute Equation (3) into Equation (1), and we can obtain:  
\[P_{\text{d1}}=V_{\text{d1}}I_{\text{d1}}=aI_{\text{d1}}^{2}+bI_{\text{d1}} \tag{21}\]  
where:  
\[\left\{\begin{array}{c}a=-K_{\text{b}}\frac{3X_{\text{t1}}}{\pi}\\ b=K_{\text{b}}\frac{3\sqrt{2}V_{\text{t1}}}{\pi\tau_{1}}\cos\gamma_{1}\end{array}\right. \tag{22}\]  
Therefore, the \(P_{\text{d1}}\)-\(I_{\text{d1}}\) curve approximates a downward opening parabola, as shown in Figure 2. Point N is the rated operating point, and point M is the maximum available power point of LCC-HVDC.
# Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection
## 4 Proposed Framework
### Stage1: Input (Dataset and Features)  
Two datasets were used to detect fake Instagram accounts, which are described in more detail below, along with an X dataset to enhance the analysis further and improve the accuracy of the detection process.
## 3 Principles and Thermographic Data Analysis  
### Principles  
Remembering that there are three methods of heat transfer (conduction, convection, and radiation) an infrared camera is able to record the amount of radiated heat from an object. In fact, the heat transfer in any material is affected by the presence of subsurface flaws or any other change in material thermal properties. Localized energy differences on the surface of the test object are caused by the changes in heat flow and can be measured using an infrared detector.  
The relation between emitted radiation and surface temperature is given by the Stefan-Boltzmann equation  
\[E=\epsilon\sigma T^{4} \tag{1}\]  
where \(\epsilon\) is the radiation (W/m\({}^{2}\)), \(T\) is the absolute temperature (K), \(\sigma\) is the Stefan-Boltzmann constant (5.67 \(\times\) 10\({}^{-8}\) Wm\({}^{-2}\)K\({}^{-4}\)) and \(\epsilon\) is the emissivity.  
Moreover, the relation between the wavelength of the maximum radiation intensity \(\lambda\) (um) and the temperature is  
\[\lambda=\frac{b}{T} \tag{2}\]  
where \(b\) is the Wien displacement constant (2897 umK).  
For temperatures close to room temperature, the energy is in the infrared region of the electromagnetic spectrum. In particular, the infrared radiation is the region of the electromagnetic spectrum between visible light and microwaves, containing radiation with wavelengths ranging from 0.75 to 10 um.  
Temperature distributions are obtained by data processing from the measured infrared radiation and then recorded in the form of isotherm plots or thermograms. The electronic sensor of the instrument records the value of energy stored by every single pixel and generate an image, in greyscale represented by black and white shades or in a combination of levels of false colours, of the observed object. An example is shown in Figure 5; it should be noted that the colours of the image are used only to give a representation of the pixels' temperatures.
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 3 Preliminaries and Model Definitions
### Hash Function  
A secure one-way hash function is defined with the following properties [27]:  
1. [leftmargin=1.5 cm]
2. Given a input message \(x\) of arbitrary length, the message digest of a fixed length output \(h(x)\) can be calculated accordingly.
3. Given \(y\), it is difficult to calculate the value of \(x=h^{-1}(y)\).
4. Given \(x\), it is computationally infeasible to find \(x^{\prime}\
eq x\) such that \(h(x^{\prime})=h(x)\).
## 2 The Theory of Multiwavelets and the Symmetric Lifting Schemes
### The Symmetric Lifting Scheme  
Symmetry is an important property for multiwavelets. It could ensure the filter banks possess linear phase or generalized linear phase, which avoids the reconstruction error. However, the algorithm described above could not ensure the symmetry of multiwavelet lifting scheme. To guarantee the symmetry, a \"symmetric selection\" method is adopted to select the translation k of multiwavelet functions. Suppose the multiscaling functions \(\phi_{1},\phi_{2}\) and multiwavelets \(\psi_{1},\psi_{2}\) are symmetric or anti-symmetric about the points \(a_{\phi,1},a_{\phi 2},a_{\psi 1},a_{\psi 2}\) respectively. Thus, the \"symmetric selection\" is shown as below [25].  
Take \(\psi_{1}\) for example:  
\[\begin{array}{l}a_{\psi_{1}}-(a_{\phi_{1}}+k_{\phi_{1},j,1})=(a_{\phi_{1}}+k _{\phi_{1},j,2})-a_{\psi_{1}}\\ a_{\psi_{1}}-(a_{\psi_{2}}+k_{\psi_{2},j,1})=(a_{\psi_{2}}+k_{\psi_{2},j,2})-a _{\psi_{1}}\end{array} \tag{13}\]  
where \(i=1,2,j=1,2,\quad,k\in Z\).  
Suppose \(B_{\phi_{1}},B_{\phi_{2}},B_{\psi_{1}},B_{\psi_{2}}=\pm 1\) (1 means symmetry and -1 means anti-symmetry) stand for the symmetry of multiple scaling functions and multiwavelets respectively. \(M(\boldsymbol{\phi},k,n)=\int\boldsymbol{\phi}(x+k)x^{*}dx\) and \(M(\boldsymbol{\Psi},k,n)=\int\boldsymbol{\Psi}(x+k)x^{*}dx\) are substituted to Equation (12):  
\[M=\begin{bmatrix}M(\phi_{i},k_{\phi_{i,j,1}},p)&M(\phi_{i},k_{\phi_{i,j,2}},p )&\cdots&M(\psi_{2},k_{\psi_{2},j,1},p)&M(\psi  
Let \(M_{B}=MB\), \(C=[c_{\psi_{1},\phi_{1},1},\cdots,c_{\psi_{1},\psi_{2},1},\cdots]^{\mathrm{T}}\) and \(M_{\psi}=[M(\psi_{i},0,p),M(\psi_{i},0,p+1),\cdots M(\psi_{i},0,p^{\prime}-1)]^{ \mathrm{T}}\), then:  
\[M_{B}C=M_{\psi} \tag{14}\]  
The solutions of Equation (13) are the coefficients that are used to lift \(\psi_{1}\). The lifting of \(\psi_{2}\) is similar to \(\psi_{1}\) except adopting \(\phi_{1}\) and \(\phi_{2}\) only. The lifting coefficients are substituted into the \"lifting coefficients equation\". Then the equation is performed z-transform and the presentation of the lifting scheme is obtained as follows:  
\[G_{new}(z)=T(z^{2})(G(z)+S(z^{2})H(z)) \tag{15}\]  
New symmetric biorthogonal multiwavelets are constructed with the help of the lifting matrices \(\mathbf{T}(z)\) and \(\mathbf{S}(z)\). Symmetric condition and vanishing moment condition are supplemented to the equation set \(M_{B}C=M_{\psi}\). The solution of the equation set is the lifting coefficients. The lifting matrices \(\mathbf{T}(z)\) and \(\mathbf{S}(z)\) are obtained through using z-transform. Finally, substituting the \(\mathbf{T}(z)\) and \(\mathbf{S}(z)\) into the lifting equation, a new multiwavelet is constructed successfully.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 6 Results and Discussion
### Packet Delivery Ratio Comparison between D2D and MC
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Testbed Metrics and Results**} & \multicolumn{2}{c}{**Cumulative End-to-End Delay**} & \multicolumn{2}{c}{**Percentage of Packet Delivery Ratio**} & \multirow{2}{*}{**Percentage of Traffic Offload**} \\ \cline{2-2} \cline{5-6}  & **Without PU** & **With PU** & **Without PU** & **With PU** & \\ \hline Route 1 (highest priority) & 0.1063 s & 0.1501 s & 44.57\% & 42.15\% & \(\sim\)75\% \\ \hline Route 2 & 0.1176 s & 0.1653 s & 23.4\% & 32.3\% & \(\sim\)50\% \\ \hline Route 3 (lowest Priority) & 0.1298 s & 0.1792 s & 12.13\% & 11.6\% & \(\sim\)25\% \\ \hline Total (Routes 1, 2, and 3) & - & - & 89.36\% & 75.0\% & - \\ \hline Backbone route (without PU) & 0.079 s & & 89.8\% & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A summary comparison of the results gathered in this investigation  
Figure 8: Comparison of the packet delivery ratio between D2D and backbone routes.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 5 Simulation Study
### Experimental Setup  
The topology is generated using 30 sky nodes, including three attacker nodes, twenty-six sender nodes, and one sink node, assuming that up to 10% of nodes can act as malicious nodes in a typical network environment. According to the existing literature, the as  
Figure 5: Simulation workflow.  
sumption of 10% attacker nodes is feasible for the simulation experiments and SMTrust evaluation. For a typical IoT application today, for example, smart home or smart hospital, a network of 20 to 30 nodes is common [25; 27]. However, this number could arguably increase in future, according to the predictions. Attacker nodes are positioned randomly among the normal nodes to demonstrate a real-use case scenario where attacker nodes can intrude on the internal smart environment network.