esa-sceva/satcom-chunk-collection huggingface dataset:
# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
## 4 Experimental Result
### Ablation Study and Component Analysis  
To verify the independent contributions of each module of TransECA-Net, this section designs ablation experiments to compare the following variants:  
1. Baseline CNN: Retains only the original CNN structure (without ECANet or Transformer).
2. CNN + ECANet: Integrates the ECANet module based on CNN.
3. Transformer-only: Uses only the standard Transformer architecture (without CNN or ECANet).
4. TransECA-Net (full model): A hybrid architecture combining CNN-ECANet and Transformer.  
The experiments use the same ISCX dataset and hyperparameter configuration (batch size = 128, epoch = 40).  
Figure 4: Normalized confusion matrix of 12 types of encrypted traffic.  
The results in Table 5 demonstrate that after introducing the ECANet module, the classification accuracy of the baseline CNN model increased by 3.00%, and the recall rate improved by 2.84%. This method effectively validates the feasibility of dynamically adjusting feature channel weights while adding only 0.08M parameters and 0.03 GFLOPs of computational cost. On the other hand, the pure Transformer architecture significantly increased the parameters by 45.6% and computation by 244%, achieving 95.30% accuracy and 94.07% recall under these conditions. While it demonstrates notable global feature modeling capabilities, its computational efficiency remains limited. Finally, the complete TransECA-Net model, through the joint optimization of CNN-ECANet and Transformer, achieved state-of-the-art performance in the benchmark (98.64% accuracy and 98.13% recall) with 6.89 M parameters and 13.45 GFLOPs computational cost. Compared to single architectures, this model shows a 4.75% accuracy improvement over the baseline CNN and a 3.34% gain compared to the pure Transformer, fully validating the architectural advantages of fusing local detailed features with global contextual information.
## 3 Numerical Simulations
### Noise Immunity Testing  
Figure 4 shows the form of the forward simulation model and transceiver. The height of the sampling point is 50 m. The background electrical resistance in the forward simulation model is 50 \(\Omega\cdot\)m, while the electrical resistance in the high-resistance anomaly model is 250 \(\Omega\cdot\)m. The anomaly model has a dimension of 600 m \(\times\) 600 m \(\times\) 350 m, with its top surface located at a depth of 250 m. The fundamental frequency and length of the emitter are 12.5 Hz and 2 km, respectively. Samples are collected between \(10^{-3}\) and 20 ms. In addition, Gaussian random noise at 5%, 10%, 20%, and 30% levels is respectively added to the forward simulation data. Analysis of the inversion results in Figure 5 and the fitting error curves in Figure 6 reveals that gradually increasing the noise level increases the number of false anomalies in the inversion result and the fitting error. At a 5% noise level, the inversion result basically matches the true model. At a 10% or 20% noise level, the inversion result can still distinctly show the location and shape of the anomaly. However, this capability is no longer demonstrated at a 30% noise level. Therefore, in practice, reducing the noise level of the input signal as much as possible can help to obtain more information for the geological structure in the inversion result. The overall data noise level should not exceed 20%.  
Figure 4: Schematic diagrams of the transceiver and the position of the anomaly.  
Figure 3: Optimization paths of the **(a)** GD, **(b)** PSO, and **(c)** PSO-GD algorithms.
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
## 7 Discussion  
### Utilising the Full Range of Manufacturer Speed Curves  
Affinity laws are commonly used to model the behaviour of pumps operating at different speeds [53]. These laws help to predict pump performance for any speed by stating that the flow, head, torque, and power can be represented by linear, quadratic, and cubic functions of the pump's speed ratio \((n_{1}/n_{2})\).  
\[\frac{Q_{1}}{Q_{2}}=\frac{n_{1}}{n_{2}},\,\frac{H_{1}}{H_{2}}=\left(\frac{n_{ 1}}{n_{2}}\right)^{2},\,\frac{M_{1}}{M_{2}}=\left(\frac{n_{1}}{n_{2}}\right)^ {2},\,\frac{P_{1}}{P_{2}}=\left(\frac{n_{1}}{n_{2}}\right)^{3} \tag{18}\]  
where \(n_{1}\) and \(n_{2}\) are two different pump speeds.  
Nowadays, manufacturers usually supply pump performance curves for individual operating speeds. The simultaneous use of all these curves to predict the actual performance of a pump under variable-speed conditions is a logical approach. The proposed numerical method integrates these different speed curves to create a coherent and detailed model of pump performance. By incorporating data from all available curves, this method enables more accurate predictions of pump behaviour over the entire speed range and uses interpolation to estimate performance at a given speed.
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
## 5 Numerical Simulations
### ADMM_P Algorithm Properties with Different Anchor Placements  
#### 5.4.1 Anchor Placement  
In this scenario, we deployed the anchors at the perimeter of the region. We set \(\sigma_{n}^{2}=0.01\times 100\) and \(\alpha_{\text{NLOS}}=3\). We compared localization accuracy when the anchor placement was random or fixed. In Reference [13], the author proposed that an appropriate anchor-placement design could further improve localization accuracy. Hence, we placed the anchors around the perimeter of the region to avoid getting crowded points, so that the higher dimensional projection of anchors could contain more  
Figure 5: Estimation results with different \(\sigma_{n}^{2}\) for \(N=40,M=10\) sensor network versus iteration number \(t\). Probability of NLOS connections was set to \(P_{NLOS}=95\%\). **(a)** RMSE; **(b)** convergence properties.  
Figure 6: Estimation results with different \(\alpha_{\text{NLOS}}\) for \(N=40,M=10\) sensor network versus iteration number \(t\). Probability of NLOS connections was set to \(P_{NLOS}=95\%\). **(a)** RMSE; **(b)** convergence properties.  
sensor nodes. The corresponding simulation results are shown in Figure 7b, from which we can see that the fixed anchor placement improves the localization performance of the ADMM_P algorithm in the same NLOS environment. Furthermore, in the scenario of fixed anchors, the accuracy of the ADMM_P algorithm is higher than that of the SDPH algorithm, even if the former employs the less anchors.
## 3 Results and Discussion
### Particle Size Analysis of Cellulose Nanofibers  
Figure 4 shows the TEM micrographs (Figure 4a) and size distribution (Figure 4b) of the PL-HUS nanofibers obtained by H\({}_{2}\)SO\({}_{4}\) treatment combined with ultrasound. In agreement with other studies [37; 38] where it was claimed that cellulose nanofibers had a typical elongated shape, whose diameter varied from 10 nm to 100 nm, the length and diameter of the nanofibers in this work were 237 nm \(\pm\) 33 nm and 18 nm \(\pm\) 7 nm, respectively.  
Some authors have discussed the reasons for the effect of ultrasound applied after acidic hydrolysis on the production of smaller nanofiber particle sizes. According to [PERSON] et al. [39], sonication has a crucial impact on the particles in liquid suspension of nanocellulose since the microjets involved in the sonicated suspension can disintegrate the large micron-sized aggregates of nanow whiskers, decreasing the particle size gradually, as can be seen in Figure 4a. Likewise, in their study of nanocellulose preparation from ginkgo seed shells, [PERSON] et al. [40] indicated that ultrasonic treatments can change the particle size due to the sound energy provided by acoustic cavitation. These authors mentioned that the violent collapse of bubbles produces microjets and shock waves on the surfacesof suspended cellulose particles, resulting in the scission effect on micron-sized cellulose particles. This scission effect can break the relatively weak interfaces between the fibers, which are bound together mainly by van der Waals forces and hydrogen bonds.
###### Abstract  
Since physical parameters are much more sensitive than modal parameters, structural parameter identification with an extended Kalman filter (EKF) has received extensive attention in structural health monitoring for civil engineering structures. In this paper, EKF-based parameter identification technique is studied with numerical and experimental approaches. A four-degree-of-freedom (4-DOF) system is simulated and analyzed as an example. Different integration methods are examined and their influence to the final identification results of the structural stiffness and damping is also studied. Furthermore, the effect of different kinds of noise is studied as well. Identification results show that the convergence speed and estimation accuracy under Gaussian noises are better than those under non-Gaussian noises. Finally, experiments with a five-story steel frame are conducted to verify the damage identification capacity of the EKF. The results show that stiffness with different damage degrees can be identified effectively, which indicates that the EKF is capable of being applied for damage identification and health monitoring for civil engineering structures.  
extended Kalman filter; parameter identification; damage detection; Gaussian noise; non-Gaussian noise Article
# Experiment Investigation of Bistable Vibration Energy Harvesting with Random Wave Environment
## 4 Discussion
### Efficiency Evaluation of Vibration Power Generation  
The average electric power represented by the following equation is used to evaluate the power generation of a bistable vibration harvesting system.  
\[W_{aver}=\frac{1}{T}\int\frac{V^{2}}{R}dt=\frac{1}{NR}\sum_{i=1}^{N}V_{i}^{2}. \tag{33}\]  
Here, \(V\) and \(V_{i}\) are measured values of voltage and voltage measurements, \(R\) is load resistance load resistance, and \(T\) and \(N\) are measured sample time and sample points of excitation experiment, respectively. In this study, \(R=1\) K\(\Omega\), \(T=60\) s, \(N=10\),000.  
Here, \(W_{aver}\) is calculated in Equation (33) for the measurement result of the vibration experiment shown in Figures 7-15 which are considered in the previous section, and the result is summarized in Figure 17.  
In Figure 17, the blue graph shows \(W_{aver}\) when the periodic signal is vibrated, and the red graph shows \(W_{aver}\) when the random signal is combined with the periodic signal. As shown in Figure 17, when the frequency of the periodic signal increases, the generation of the power generation tends to increase, but, in the case of the joint vibration with the random signal and the periodic signal of 0.4 Hz and 0.5 Hz, the stochastic resonance of bistable vibration is generated, and it can be confirmed that the obtained \(W_{aver}\) is higher than other single stable motion states.  
For more detailed comparison, the result obtained by adding the Waver obtained by the excitation of the random signal and the periodic signal separately, when compared with the Waver, in the case of the periodic signal of 0.4 Hz, the sum of the waves obtained by the separate excitation was \(4.72+8.72=13.44\) mW, and the vibration was 15.95 mW, and the difference between them was 18.68%.  
In the case of the periodic signal of 0.5 Hz, the sum of the waves obtained by the separate excitation was \(4.72+2.04=6.76\) mW, and the joint vibration was 21.01 mW, and the difference between them was 210.80%.  
Figure 17: Results of comparing average electrical power. (The blue graph is the result of vibration with a periodic signal. The red graph is the result of vibration with random and periodic signals).
## 2 Two-Stage Time-Domain Equalization Method
### Frame Synchronization Using Zadoff-Chu Sequences  
In order to achieve accurate frame synchronization, a Zadoff-Chu (ZC) sequence is embedded at the beginning of each transmission frame when the required signal is acquired for the experiment. The ZC sequence has constant amplitude and zero cycle autocorrelation, making it very effective in synchronization tasks in wireless communications.  
\[z[n]=\begin{cases}\exp\left(-j\frac{mn(n+1)}{N}\right),&\text{for $N$ odd}\\ \exp\left(-j\frac{mn^{2}}{N}\right),&\text{for $N$ even}\end{cases},\quad 0\leq n<N \tag{9}\]  
Here, \(z[n]\) represents the \(n\)-th sample of the ZC sequence, \(N\) is the sequence length, and \(r\) is the root index of the sequence, which must be coprime with \(N\) to ensure ideal correlation properties. The exponential term creates a phase progression that results in a constant-modulus complex-valued sequence. The choice between \(n(n+1)\) and \(n^{2}\) depends on whether \(N\) is odd or even.  
At the receiver side, we apply a sliding cross-correlation between the incoming signal and the known reference ZC sequence. The frame boundary is identified by the location of the correlation peak, providing precise alignment for subsequent equalization. This synchronization step is implemented in LabVIEW and forms the basis for all downstream processing stages.
## 4 Performance Analysis of QoS Metrics
### Latency Analysis of Zigbee with Multi-Hop AES Encrypted Communication
The RTL of one-hop communication with and without encryption at 10 bytes/packet is 50% and 186.27% less than that of the corresponding two-hop communications and with 60 bytes/packet, it is 319.58% and 517.43% less than that of two-hop with and without AES communication, respectively. Therefore, even though the increased number of hops increases the reliability in terms of link quality and delivery ratio and also increases the transmission range, it also significantly increases the latency of the communication. Therefore, deploying more router nodes increases the reliability, PDR, and transmission range, but affects the latency performance drastically. Thus, multi-hop communication is not suitable for real-time applications where a delay is very critical. We can also analyze that the encryption also worsens the latency performances if not as worse as the increase of the number of hops. The 128-bit AES encryption in Zigbee increases the latency by 153.52% and 134.87% for 10 bytes/packet and 60 bytes/packet, respectively, for one hop and the increment is 32.84% and 59.35%, respectively, for two-hop. Thus, as the number of hops increases the effect of encryption on latency lessens. This is an interesting fact that in a larger network with a higher number of hops, the encryption by itself will less affect the latency in comparison to the single-hop communication. These are the trade-offs of the higher number of hops, data encryption, and the latency that need to be considered while designing a Zigbee network for any indoor or outdoor applications. Figure 11: RTL measurement setup for latency analysis. Figure 12: Zigbee round trip latency with variations of baud rates and packets size for (**a**) encrypted and (**b**) unencrypted communication.
## 2 Methods and Materials
### Non-Destructive Testing (NDT) Techniques
COLMAP showed the best overall performance across multiple datasets, especially when using exhaustive matching. It consistently managed to orient a high number of images and kept RMS reprojection errors low, highlighting its reliability in both image orientation and dense reconstruction. OpenMVG, particularly when combined with OpenMVS for dense reconstruction, also performed well, achieving similar reprojection accuracy and completeness, especially on \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **COLMAP** & **OpenMVG** & **AliceVision** \\ \hline Key point extraction & SIFT [69] & SIFT [69] & SIFT [69] \\  & & AKAZE [70] & AKAZE [70] \\ \hline Key point matching & Exhaustive, Sequential, & & \\  & Vocabulary Tree [71] & Brute Force, ANN [73] & ANN [73] \\  & Spatial [72] & Cascade Hashing [74] & Cascade Hashing [74] \\  & Transitive [72] & & \\ \hline Geometric verification & 4 points homography [75] & 4 points homography [75] & 4 points homography [75] \\  & 5 points relative pose [76] & 5 points relative pose [76] & 5 points relative pose [76] \\  & 7–8 points F-matrix [75] & 7–8 points F-matrix [75] & 7–8 points F-matrix [75] \\  & & & \\ \hline Incremental bundle (image & P3P [78] & P3P [78] & PnP [78] \\  & + DLT, EPnP [79] & + DLT, EPnP [79] & + DLT \\  & & & \\ \hline Global bundle adjustment & CERES [80] & CERES [80] & CERES [80] \\ \hline Dense point cloud generation & Patch-based stereo [81] & Patch-based stereo [82] & Semi Global Matching [83] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Architecture and key features of open-source image-based 3D reconstruction pipelines analysed in [68]. datasets like 3 DOMcity. This makes OpenMVG + OpenMVS a strong option for tasks focused on dense reconstruction. AliceVision showed effective results in certain scenarios but had some difficulty delivering consistent performance across diverse datasets. In some cases, it oriented fewer images and had higher reprojection errors compared to COLMAP and OpenMVG. While AliceVision works well in specific situations, it currently lacks the flexibility and robustness that COLMAP and OpenMVG provide, which makes it less suitable for more complex or large-scale projects.
## 3 CFD Modelling as Digital Twin
### Fluid Equations
#### 3.2.3 Energy Equations  
The energy equations are based on the interaction of parameters such as internal energy per unit mass (\(e\)), which is the product of the temperature (\(T\)) and the specific heat of the fluid at constant volume (\(C_{V}\)). Equation (6) shows the representative energy conservation for fluids.  
\[\frac{\partial(\rho e)}{\partial t}+\
abla\cdot(\rho e\mathbf{u})=\
abla \cdot\mathbf{q}+p(\
abla\cdot\mathbf{u})+S_{T} \tag{6}\]  
where the following are expressed:* The first term of the equation corresponds to the internal energy change rate per unit volume concerning time.
* The second term corresponds to the flow of internal energy across the surface of a finite volume moving with a velocity that is defined by the velocity vector \(\mathbf{u}\).
* The third term corresponds to the heat flow (\(\mathbf{q}\)) into and out of a finite volume due to thermal conductivity.
* The term \(p(\
abla\cdot\mathbf{u})\) represents the contribution of work energy from the fluid pressure due to density variation.
* The term \(S_{T}\) corresponds to the energy generation due to the external or internal reactions in a finite volume.
# Kinematic Analysis and Verification of a New 5-DOF Parallel Mechanism
## 2 Design and Verification of the Scheme
### Scheme Improvement and Singularity Analysis  
#### 2.4.1 Scheme Improvement  
Based on the singular defect caused by the same shape of fixed platform and moving platform in the preliminary scheme, the moving platform is changed from pentagon to quadrilateral. The mechanism sketch is shown in Figure 5.  
Figure 4: Singular position of preliminary scheme.  
Figure 5: Mechanism schematic diagram of the improved scheme.  
In the improved scheme, the fixed platform also uses five mobile pairs as input and presents a positive pentagonal symmetrical distribution in space, while the moving platform uses a rectangular structure.
## 3 Proposed ATMALS-GSK Algorithm
### Atmals-Gsk
#### 3.2.6 Overall Operation of ATMALS-GSK
One striking result is observed in F21, where GSK shows a mean of 193 with a high deviation of 50.7, while ATMALS-GSK consistently scores 100 across all runs with zero variance, reflecting a perfectly stable and optimal outcome. Likewise, in Function F25, ATMALS-GSK closely matches the best performance of GSK but with remarkably improved stability (STD = 0.132 compared to GSK's 20.5), indicating better reliability in difficult scenarios. In some functions such as F17 and F22, while ATMALS-GSK does not always outperform GSK in all statistics, it still offers highly competitive or comparable results with a favorable trade-off between accuracy and stability. This suggests that the adaptive and learning-based mechanisms integrated into ATMALS-GSK enhance its generalization ability across a broad spectrum of optimization landscapes. Figure 10 shows the adaptive value of \(K\) across iterations for functions F1 (as a unimodal function), F4 (as a multimodal function), F11 (as a hybrid function), and F21 (as a composition function), along with the average value over all functions. Figure 11 illustrates the variations of all parameters of the ATMALS-GSK algorithm over iterations during the execution of the algorithm. Additionally, Figure 12 illustrates the average convergence behavior of the original GSK algorithm and the proposed ATMALS-GSK algorithm across various CEC 2017 functions, with a problem dimensionality of \(D=10\). The results, averaged over 51 independent runs, demonstrate the optimization efficiency of both algorithms in terms of their function error value over iterations. As shown, ATMALS-GSK consistently outperforms the original GSK algorithm by achieving faster convergence rates and lower final error values across most test functions.
# CFD and Energy Loss Model Analysis of High-Speed Centrifugal Pump with Low Specific Speed
## 3 Results and Discussion  
### Comparison of Simulation Results with Experimental Data  
The open-mechanical test bed was modified from Refs. [18, 19], as shown in Figure 4. The system includes the water storage tank, the flow valve, the electric control valve, the turbine flowmeter, the surge tank, the data acquisition system, and the high-speed centrifugal pump. The uncertainty is listed in Table 2.  
Figure 4: Comparison between numerical simulation results and experimental data. (**a**) Graph of system; (**b**) image of system.  
The performance test of the centrifugal water pump was carried out at the design speed of 7500 rpm. The flowrate was changed by adjusting the opening of the electric control valve and the speed of the motor. The flowrate, power, head, and efficiency under different water flow conditions were obtained by the data acquisition system, and the results are shown in Table 3. As the flowrate in the experiment increases, the pump head decreases faster and faster, and the efficiency firstly increases and then decreases after reaching 35.9% at \(Q\) = 4 m\({}^{3}\cdot\)h\({}^{-1}\). As for the pump with the low specific speed that was designed by the empirical correlation method, the flowrate corresponding to the highest efficiency is usually greater than the design flowrate. As shown in Figure 5, the predictions by the empirical correlation method have large deviations with the experimental values. The ELM/CFD method significantly improves the prediction accuracy. At \(Q\) = 4 m\({}^{3}\cdot\)h\({}^{-1}\), when the efficiency is the highest, the deviations of the pump head and the efficiency are both less than 2%, and under the design flow are both less than 4%. Moreover, within the range of 50% to 185% of the design flow, the deviations of the head and the efficiency are within 2% and 5%, respectively. The comparison indicates that the ELM/CFD method is accurate and reliable in predicting the performance of the high-speed centrifugal pumps with the low specific speed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Uncertainty/\%** & **Measurement Span** \\ \hline Pressure/MPa & \(\pm\)0.25 & 0–1.5 \\ Power/kW & \(\pm\)0.5 & 0–10 \\ Flowrate/m\({}^{3}\cdot\)h\({}^{-1}\) & \(\pm\)0.5 & 0.5–10 \\ Head/m & \(\pm\)0.35 & \\ Efficiency/\% & \(\pm\)1.17 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental uncertainty.  
Figure 5: Comparison between numerical simulation results and experimental data.