esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Disturbance Estimation with Extended State Disturbance Observer  
### Design of Extended State Disturbance Observer  
As demonstrated by Equation (4), the disturbance term \(d\), an integral component of the PMSM system, significantly affects system performance due to its direct influence on the control input. Enhancing system robustness requires effective compensation for these disturbances. Nonetheless, in practical applications, direct measurement of disturbance values is not feasible. Therefore, developing a disturbance observer to acquire disturbance information becomes crucial. In response to this need, this paper proposes the design of an ESDO for disturbance estimation.  
To formulate the system's state equation, we define the state variable \(z_{1}\) as \(\omega_{m}\), which represents the mechanical angular velocity of the system. We also set the output \(n\) equal to \(\omega_{m}\). Based on Equation (4), which describes the system dynamics, we can derive Equation (17) by substituting \(z_{1}\) and \(n\) into the equation. This process involves expressing the system's behavior in terms of the state variable \(z_{1}\) and output \(n\), leading to the formulation of the state equation that captures the system's dynamics accurately, as follows:  
\[\left\{\begin{array}{l}\dot{z}_{1}=az_{1}-d+bu\\ n=z_{1}\end{array}\right.. \tag{17}\]  
Here, the disturbance term \(-d\) is introduced as a new state variable \(z_{2}\). In practical PMSM systems, the disturbance \(d\) changes slowly, allowing us to approximate its first-order derivative as zero. This assumption simplifies the analysis and modeling of the system. With this approximation, we can derive the state-space equation of the system, incorporating both the original state variable and the newly defined disturbance state variable. This leads to a more comprehensive representation of the system's dynamics, which can be expressed as follows:  
\[\left\{\begin{array}{l}\dot{z}_{1}=az_{1}+z_{2}+bu\\ \dot{z}_{2}=0\\ n=z_{1}\end{array}\right.. \tag{18}\]  
Next, we implement an extended state feedback mechanism to address the speed estimation error \(e\), using \(z_{1}\) and \(z_{2}\) as the observed variables. This approach allows for more accurate monitoring and compensation of disturbances. The extended disturbance observer is designed to estimate and correct the errors in real time, ensuring the system's robustness and stability. The detailed design of the extended disturbance observer is as follows:  
\[\left\{\begin{array}{l}e=n-\hat{z}_{1}\\ \dot{\hat{z}}_{1}=az_{1}+\hat{z}_{2}+bu+\frac{\sigma_{1}}{\eta}e\\ \dot{\hat{z}}_{2}=\frac{\sigma_{2}}{\eta^{2}}e\end{array}\right., \tag{19}\]  
where \(\eta>0\), and \(\sigma_{1}\) and \(\sigma_{2}\) are positive real numbers, typically chosen to be very small to ensure a high gain in the observer. By employing this extended state disturbance observer (ESDO), we can achieve convergence where \(\hat{z}_{1}\) accurately tracks \(z_{1}\) and \(\hat{z}_{2}\) accurately tracks\(z_{2}\). This ensures that the observer can effectively estimate and compensate for disturbances in the system.
## 5 Case Study
### Discussion
The comprehensive evaluation results showed that the influencing factors in this study and Scheme A were the same, and consequently, identical analytical models could be constructed for them; the influencing factors in Scheme B differed from this study, and therefore required the construction of different analytical models. According to the above evaluation, we combined the data of this study and Scheme A to construct analytical models for route 355 and constructed additional analytical models for route 352. The analytical models were verified as follows:  
**Classification indicators** The indicator scores in each analytical model are presented in Table 10. The scores of the analytical models for route 355 lay between those for this study and Scheme A, demonstrating that their classifications did not differ greatly. Both the accuracy and F1 score of the analytical models for route 352 were improved, indicating that the models with adjusted energy consumption classifications performed better. **Feature weights** The factors influencing energy consumption in each analytical model are given in Table 11. In the analytical models for route 355, the vehicle speed, motor speed, and total voltage were the factors influencing energy consumption. The influencing factors were the motor speed and motor temperature in the analytical models for route 352. **Driving indicators** The classification rules of each analytical model are listed in Tables 12 and 13. We summarized the indicators for each analytical model based on the above features. For the analytical models for route 355, eco-driving required a motor speed below 773.5 or a total voltage higher than 549.75 \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analytical Model** & **Item** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{3}{*}{Decision tree} & Route 355 & 0.808 & 0.745 & 0.712 & 0.726 \\  & Route 352 & 0.739 & 0.702 & 0.610 & 0.637 \\ \cline{1-1}  & Route 355 & 0.821 & 0.765 & 0.729 & 0.744 \\ \cline{1-1}  & Route 352 & 0.754 & 0.727 & 0.625 & 0.656 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification indicators of each model in the case study. Figure 11: Feature weights of different schemes in the case study. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Analytical Model** & **Item** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{3}{*}{Decision tree} & This study & 0.821 & 0.741 & 0.719 & 0.723 \\  & Scheme A & 0.800 & 0.728 & 0.698 & 0.709 \\ \cline{1-1}  & Scheme B & 0.723 & 0.576 & 0.497 & 0.489 \\ \hline \multirow{3}{*}{Random forest} & This study & 0.832 & 0.778 & 0.743 & 0.758 \\ \cline{1-1}  & Scheme A & 0.809 & 0.749 & 0.715 & 0.727 \\ \cline{1-1}  & Scheme B & 0.731 & 0.497 & 0.613 & 0.485 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Statistics of classification indicators of various schemes in the case study. We can see from the above evaluation and verification that the classification indicators and driving indicators of the same route differed slightly; the energy consumption classification and feature weights
## 2 Background
### Methods for Implementing Streaming Data Encryption  
In the selection of data encryption methods, stream cipher is a type of symmetric-key encryption algorithm, meaning both encryption and decryption parties use the same key. During encryption, a key stream is generated using a pseudorandom number generator, and this key stream is sequentially encrypted with a plaintext data stream to obtain a ciphertext data stream. In terms of design, the stream cipher method is relatively straightforward, allowing real-time transmission effects during network transmission. It is generally limited in its application to streaming data transmission, such as audio and video, and typical stream cipher algorithms include RC4 and eStream [37; 38]. On the other hand, block cipher is also a type of symmetric-key encryption algorithm. The difference is that block encryption requires the plaintext data to be divided into several equally sized blocks before performing matrix operations on the master key and the first plaintext block to obtain a ciphertext block. It also simultaneously generates the round key needed for the calculation of the next set of plaintext blocks. Due to its relatively complex operational design and higher computing performance requirement, block cipher can be used for streaming data transmissions, but is more suitable for transferring file data. Typical block cipher algorithms include DES and AES [39; 40]. After comparing the advantages and disadvantages of stream cipher and block cipher, this study chooses stream cipher as the encryption method to implement the proposed mechanism in this paper.
## 3 System Model
### Channel State and UAV Energy Consumption  
The orthogonal transmission is employed in the uplink to allow multiple vehicles to simultaneously upload their data to the UAV. The building heights are modeled using Rayleigh distribution. To model the channel state and UAV-to-vehicle links, we consider Rician fading. The LoS and NLoS links have different probabilities of occurrence, which is a function of the environment, density and height of buildings, and the elevation angle between the UAV and vehicles. The geometrical statistics of various environments are offered by the International Telecommunication Union (ITU-R), which determine the density, number, and height of the buildings and other obstacles [48]. The NLoS effect decreases as the elevation angle between the receiver and transmitter increases, and the communication link approaches near LoS. The Rician K-factor that represents the strength of LoS component is a function of the elevation angle and the UAV altitude [49]. The Rician K-factor impacts the maximum transmit power and path loss exponents as given by Equation (9):  
\[\widehat{h}_{j}^{i}=\left(\sqrt{\frac{K}{1+K}}\tilde{h}+\sqrt{\frac{1}{1+K}} \widetilde{h}\right) \tag{9}\]  
where \(\widehat{h}_{j}^{i}\) is the channel-gain experienced by the \(i\)th vehicle in the \(j\)th transmission window considering the Rician K-factor. The term \(\tilde{h}\) implies the LoS component, and the term \(\widetilde{h}\) indicates the fading component.
## 3 The Synchronous Machine
### Basic Mechanical Equations  
The angular acceleration of the rotor relates to the net torque as  
\[\frac{\mathrm{d}}{\mathrm{d}t}\omega_{m}=\frac{1}{J}(T_{m}-T_{e}), \tag{73}\]  
where \(J\) is the rotor moment of inertia, \(T_{m}\) is the mechanical torque, and \(T_{e}\) is the electric torque. In addition, the relations between torques and powers are given by  
\[\begin{split} p_{m}&=T_{m}\omega_{m}=\frac{2}{ \mathrm{poles}}T_{m}\omega,\\ p_{e}&=T_{e}\omega_{m}=\frac{2}{\mathrm{poles}}T_{e }\omega,\end{split} \tag{74}\]  
where \(p_{m}\) is the mechanical power accelerating the rotor, \(p_{e}\) is the electric power decelerating the rotor.  
Equations (72) and (73) yield  
\[\frac{\mathrm{d}}{\mathrm{d}t}\omega=\frac{\mathrm{poles}}{2J}(T_{m}-T_{e}), \tag{75}\]  
and combination of (74) and (75) results in  
\[\frac{\mathrm{d}}{\mathrm{d}t}\omega=\left(\frac{\mathrm{poles}}{2}\right)^{2 }\frac{1}{J\omega}(p_{m}-p_{e})=K\frac{\omega_{s}}{\omega}(p_{m}-p_{e}), \tag{76}\]  
where \(K=\left(\frac{\mathrm{poles}}{2}\right)^{2}\frac{1}{J\omega_{s}}\). Under the approximation \(\omega\approx\omega_{s}\), this is the classic swing equation. However, in this section we do not use this approximation, and work directly with the exact expression in (75). In addition, we assume that the mechanical torque is governed by a droop mechanism,  
\[T_{m}=\frac{\mathrm{poles}}{2\omega_{s}}\left(3P_{ref}-\frac{1}{D}(\omega- \omega_{s})\right), \tag{77}\]  
where \(P_{ref}\) is the reference power (defined with respect to a single phase), and \(D\) is the droop control damping factor. Note that this equation assumes a simplified linear relation between frequency and torque, and ignores the dynamics of the mechanical system. Substituting (77) into (75) results in  
\[\frac{\mathrm{d}}{\mathrm{d}t}\omega=K\left(3P_{ref}-\frac{2}{\mathrm{poles}} \omega_{s}T_{e}-\frac{1}{D}(\omega-\omega_{s})\right). \tag{78}\]  
This equation defines the dynamics of the frequency \(\omega\), with respect to the electric torque \(T_{e}\).
## 4 Statistical Optimization and Model Validation
### Errors and Uncertainties
#### 4.4.2 Numerical Error  
The model under investigation is sufficiently complex, such that the governing partial differential equations require approximation of the numerical solutions. These approximations provide an additional source of error. Given a stable, consistent, and robust numerical scheme, the five primary sources of errors in computational physics solutions are included but not limited to insufficient spatial discretization, insufficient temporal discretization, insufficient iterative convergence, computer round-off, and computer programming [56]. Although identifying and quantifying errors from these various sources are the main focus in verification of the numerical scheme, we only investigate the discretization error that originates from a certain choice of the meshing size. Following Reference [55], we take a heuristic approach to quantify this error, in that we make a comparison between different mesh spacing and make use of a generalized Richardson extrapolation to estimate the error. The Richardson extrapolation takes the following form:  
\[f_{k}=\bar{f}+g_{p}h_{k}^{\hat{p}}+\mathcal{O}\Big{(}h_{k}^{\hat{p}+1}\Big{)}, \tag{23}\]  
where \(f_{k}\) denotes the exact solution to the discrete equation on a mesh with spacing \(h_{k}\) (known), \(\bar{f}\) stands for the exact solution to the original PDE (unknown). \(g_{p}\) is the error term coefficient and \(\hat{p}\) indicates the observed order of accuracy. Here, we seek the first order error. Thus, the unknowns, that is, \(\bar{f}\) and \(g_{p}\) can be easily determined via a least square method for the numerical solutions obtained by varying the mesh spacing.
## 3 Numerical Examples
### Numerical Example 1
The results of the comparison between Timoshenko-Vlasov and the traditional beam theory are listed in Table 2 (\(L=3\) m), Table 3 (\(L=10\) m), and Table 4 (\(L=50\) m) in Appendix C. Furthermore, the impact of variations in the axial load on the lowest natural vibration frequency was predicted using Timoshenko-Vlasov beam theory and is shown in Figure 3. The natural frequency becomes zero when the beam undergoes elastic buckling. The corresponding axial loads \(P\) for different beam lengths (\(L\)) and mode numbers (\(n\)) are listed in Table 2.  
The Timoshenko-Vlasov model generally aligns with traditional results for bending modes, especially in long beams. However, it shows substantial deviations in torsional frequencies. As illustrated in Figure 3, the natural frequency decreases with an increasing axial load and reaches zero at the point of elastic buckling. The corresponding critical axial loads, listed in Table 2, vary significantly with the beam length (\(L\)) and mode number (\(n\)). Traditional beam theories assume decoupled axial, bending, and torsional vibrations, which limits their accuracy. In contrast, the Timoshenko-Vlasov model accounts for the coupled nature of these vibrations, capturing effects such as warping and geometric irregularities, which are particularly important in thin-walled beams. These discrepancies are more pronounced in short beams due to the greater influence of shear deformation and warping. Although the differences reduce as the beam length increases, significant errors in the torsional behavior remain, underscoring the need for advanced models like the Timoshenko-Vlasov to achieve accurate predictions in complex structural analyses.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 9 Discussion
### Deployment Challenges  
The deployment of the proposed wireless network must consider several practical challenges. One of the key factors is ensuring a stable power supply for remote modules. As demonstrated in the proposed design, all active well sites will be connected  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Cost Factor** & **Wi-Fi (IEEE 802.11)** & **Trusted Wireless (Mesh)** \\ \hline Initial investment & Moderate & Higher \\ \hline Installation complexity & Simple to moderate & Moderate to complex (mesh structure) \\ \hline Network scalability & Good & Excellent (mesh support) \\ \hline Maintenance complexity & Moderate & Lower (self-healing) \\ \hline Long-term reliability & Good & High \\ \hline Cost & Lower (widely available components & Higher (specialized hardware and \\  & and no licensing fees) & potential licensing costs) \\ \hline \hline \end{tabular}
\end{table}
Table 16: Comparative Analysis of Wireless Network Solutions.  
to the main power line, ensuring continuous operation. However, to mitigate potential blackouts, backup battery systems are recommended to maintain network functionality during outages.  
Another consideration is network interference. Given that the water station is located 30 km south of the city of Zintan [2] in an uninhabited area, the presence of external wireless networks is unlikely, reducing the risk of interference from neighboring networks.  
Finally, the long-term maintenance of the network will depend on operational conditions and the collaboration between the water station management and the automation system design team. Regular maintenance activities may include periodic antenna realignment, firmware updates, and hardware replacements. These efforts will be crucial to sustaining optimal network performance over time.
# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 3 System Model
### Problem Modeling
If a user request is successfully readjusted, all its virtual network functions must be deployed after readjustment: \[\sum_{k}x_{i,j,k}(t)=1,w_{i}(t)=1,\forall vnf_{j}\in V_{i},\forall n_{k}\in N _{s}\] (20)
9. If a user request is successfully readjusted, after VNF readjustment, all affected virtual links are also correspondingly remapped: \[\sum_{(m,n)}z_{i,j,k}^{m,n}(t)=1,w_{i}(t)=1,\forall e_{j,k}\in E_{i},\forall l _{m,n}(t)\in L(t)\] (21)  
In summary, the in-orbit service orchestration problem of maximizing operator revenue in satellite networks with unchanged underlying available resources can be modeled as an optimization problem under specific constraints, as shown in the following formula:  
\[\begin{split}& OP:\max P=R+C\\ & s.t. (\ref{eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq: eq:  
acquires current network state information, including the remaining computational resources (CPU, memory, and storage) of satellite node \(n_{s,i}\), available bandwidth \(B_{i,j}\) of physical link \(l_{i,j}(t)\), link connection state matrix \(M(t)\), and time-varying link delay \(d_{i,j}(t)\). The orchestrator then processes the request according to specified service requirements within the time slot. Each time slot \([t_{k},t_{k+1}]\) is subdivided into consecutive SFC deployment intervals based on orchestration requirements. For consecutive intervals, we define the current network resource state \(s_{t}\), VNF deployment and the virtual link mapping scheme \(\pi_{t}\), and the post-deployment resource state \(s_{t+1}\) (the initial state for subsequent intervals). This establishes the dynamic orchestration process as \((s_{t},\pi_{t})\to s_{t+1}\). Notably, computational efficiency significantly impacts system performance due to network topology dynamics. Extended calculation periods may span multiple time slots, potentially invalidating initial topology and link characteristic assumptions. Therefore, deployment algorithms must incorporate topology transition considerations to ensure request completion before topological changes occur. Service function chain readjustment becomes necessary when underlying physical network topology changes render existing virtual-to-physical link mappings ineffective. The predictable nature of satellite orbital movements, determined by ephemeris data, enables the forecasting of future network topology configurations. In LEO satellite network orchestration scenarios, service quality degradation due to satellite movement triggers readjustment procedures to maintain quality standards. This predictability facilitates strategic deployment approaches: placing VNFs on relatively stable nodes to maintain service quality consistency and minimize necessary readjustments within the specified timeframes. Additionally, when network service providers prioritize profit maximization, the strategic readjustment of deployed chains based on profit potential, particularly during near-saturation conditions, can optimize global revenue generation.
# Dynamic Response of Dual-Disk Rotor System with Uncertainties Based on Chebyshev Convex Method
## 4 Numerical Analysis of the Rotor Dynamic Response Based on CCM
### Single Parameter Uncertainty
The resonance peaks under different uncertain parameters are calculated and recorded in Table 3. Comparing the error caused by the parameter uncertainty at the peak of the first-order critical speed and the second-order critical speed under the same uncertainty, it can be seen that the error at the second order is significantly greater than that at the first order. This demonstrates that the peaks in the second-order critical speed range are more sensitive to parameters than the first-order peaks. And compared with the results of CMCS, CCM can have a good interval wrapping effect. Meanwhile, in order to verify the computational efficiency of CCM, the calculation  
Figure 8: Deflection of the dual-disk rotor with stiffness uncertainty. Figure 9: Deflection of the dual-disk rotor with mass uncertainty. of the mass uncertainty response results is taken as an example, and the computer time consumption of the two methods at the same calculation environment is given in Table 4. Obviously, it can be seen that the calculation time of Chebyshev method based on CCM is much shorter than that of CMCS and the computing efficiency is significantly improved.
# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 2 Datasets and Methods  
### Datasets  
#### 2.1.1 Data Descriptions  
This paper uses three large-scale and challenging HSI datasets as benchmarks: the AeroRIT scene [43], The Data Fusion Contest 2018 (DFC2018) dataset and Xiongan New Area Matiwan Village (hereinafter referred to as Xiongan) dataset [44].  
The AeroRIT scene is a HSI of the Rochester Institute of Technology's university campus captured by the Headwall Micro E sensor. The sensor captures a total of 372 spectral bands. We use HSI of a total of 51 bands obtained by sampling every tenth band from 400 nm to 900 nm. This dataset has a ground sampling distance (GSD) of 0.4 m/px, resulting in a 1973 \(\times\) 3975 px image. This dataset is marked with five types of ground objects, as shown in Figure 1. This dataset has problems, such as small target recognition, effects of glint, and shadows. Although there are few types of ground objects, accurate classification is still challenging.  
The DFC2018 hyperspectral data were acquired over Central Houston, Texas, USA, using an ITRES-CASI 1500 airborne sensor. It covers a 380-1050 nm spectral range over 48 contiguous bands at 1 m GSD, resulting in a 1202 \(\times\) 4768 px image. This dataset has a total of 20 types of labels and contains many fine-grained objects. The number of artificial turn, water, and unpaved parking lots classes is too small to be partitioned into the training set and the test set at the same time. The method adopted in this paper cannot evaluate the performance of these three classes, therefore, these three classes are merged into the unspecified class. After processing the labels, there are 17 types of ground objects left, as shown in Figure 2.  
The Xiongan dataset is a HSI of Matiwan Village in Xiongan New Area of China, which is acquired using the visible and near-infrared imaging spectrometer. The spectral range is 400-1000 nm with 256 bands, and the spatial resolution is 0.5 m, resulting in a 1580 \(\times\) 3750 px image. This dataset has a number of fine-grained objects, which are mainly croplands. For the same reason as DFC2018 dataset, we merged the acacia and sparse  
Figure 1: The AeroRIT scene. (**a**) RGB image; (**b**) Ground-truth classification map.