esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation
## 2 Multiple Network Learning with Low-Rank Representation
### Multiple Network Learning
\begin{array}{l}0\,,\,x_{i}<0 \\ w_{i}x_{i}\,,\,x_{i}\geq 0\end{array} \right..\] (8) In computational networks, given an input or set of inputs, the activation function of a neuron defines the output of that neuron with these inputs. In the scenario of the deep neural network,activation functions project \(x_{i}^{\gamma}\) to a higher level hidden representation step by step with a sequence of non-linear mappings, which can be defined as \[(x_{i})^{0}\xrightarrow[W]{\mathcal{R}}(x_{i})^{1}\xrightarrow[W]{\mathcal{R}}...\xrightarrow[W]{\mathcal{R}}(x_{i})^{l}\] (9) where \(l\) is the number of layers, and \(\mathcal{R}\) is the mapping function from input to estimated output. To optimize the weighted matrix \(W\), which contains the mapping parameters, we use a back-propagation strategy. For each echo of this process, the weighted matrix is updated by \(\Delta W\), which is defined by \[\Delta W=-\eta\frac{\partial E}{\partial W}.\] (10) \(\eta\) is the learning rate, and we can define \[\frac{\partial E}{\partial W}=(y_{i}-\mathcal{R}(x_{i}))(x_{i})^{T}.\] (11) In this way, we try to train a model that minimizes the differences between the groundtruth \(y_{i}\) and the estimated output \(\mathcal{R}(x_{i})\). The back-propagation strategy can be modeled by \[(x_{i})^{0}\xleftarrow{\mathcal{R}}(x_{i})^{1}\xleftarrow{\mathcal{R}}... \xleftarrow{\mathcal{R}}(x_{i})^{l}.\] (12)
* Recursive Neural Networks (RNN). RNNs process a structured input with the same set of weights recursively. In this way, we can traverse the given structure into topological order and obtain a structured output or a scalar prediction on it. Different from CNNs, nodes in RNNs are integrated into parents with a weight matrix. This matrix is shared across the whole network. Besides, a non-linearity such as activation functions mentioned above is used. Taking tanh as an example, if \(x_{i}\) and \(x_{j}\) are \(n\)-dimensional features of nodes, their parent must be an \(n\)-dimensional feature, too. It can be computed by \[p_{i,j}=tanh(W[x_{i};x_{j}])\] (13) where \(W\) is a learned \(n\times 2n\) weight matrix, which is usually computed with Stochastic Gradient Descent (SGD). The gradients are calculated using back-propagation through structure (BPTS). BPTS is a variant of the aforementioned back-propagation through time for RNNs. In the proposed MNF-LRR, by combining Equations (7), (11), and (13), Equation (2) can be rewritten as  
\[\alpha\sum_{i}^{n}\parallel x_{i}-W_{ae}x_{i}\parallel^{2}+\beta\sum_{i}^{n} \parallel x_{i}-W_{cnn}x_{i}\parallel^{2}+\gamma\sum_{i}^{n}\parallel x_{i}-W _{rnn}x_{i}\parallel^{2} \tag{14}\]  
where \(W_{ae}\), \(W_{cnn}\), and \(W_{rnn}\) are weighting parameters learned by autoencoders, CNNs, and RNNs, respectively. \(\alpha\), \(\beta\), and \(\gamma\) are switches to turn on or off the corresponding neural networks. In this way, we can compute multi-modal feature representation.
## 5 Parametric Study: Changing Effects of Length and Split Gaps  
The length and split gap are the most important property for metamaterial performance. Because of the split gap and length, the inductance and capacitance are altered. The metamaterial's resonance frequencies are affected by the modification of the split gaps. The split gap in SRR has the most influence on the proposed metamaterial unit cell's response. Due to the relationship between capacitance and resonance frequency, the change in capacitance value impacts the resonance of the unit cell. The split gaps g1 and g3 are modified on the proposed unit cell for analyzing the resonance results. The split gap g1 is designed in the outer SRR and g3 is developed in the inner SRR. In this study, the lengths of these two split gaps are varied once at a time, whereas the lengths of the other split gap remain fixed. The effects of changing the split gaps are shown in Figure 7. The split gap g1 has an effect on the resonance at 5.6 GHz, 8.93 GHz, and 11.5 GHz, which is shown in Figure 7a. As the split gap g1 decreases from higher gap length to lower gap length, the capacitance increases steadily; this causes the resonance frequency to trend toward the lower values by following Equation (11). The impact of changing the split gap g3 in the inner SRR is shown in Figure 7b. The modification of capacitance value due to the change in g3 causes a minor resonance to exist around 2.4 GHz, though the variation of the resonance frequency is lesser when compared to the effect of the change of g1. The split gap g3 in the inner SRR shows its impact around the resonances within 7 GHz and 14 GHz. As a result, the split gaps play an important role in regulating the modulator's capacitance, and the resonance frequency may be controlled by modifying the gap length. The effect  
Figure 6: (**a**) Equivalent circuit model of metamaterial unit cell and (**b**) transmission coefficients S21 from CST and ADS simulator.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Capacitor** & **Value (pF)** & **Inductor** & **Value (nH)** & **Capacitor** & **Value (pF)** & **Inductor** & **Value (nH)** \\ \hline C1 & 0.2 & L1 & 0.5 & C6 & 1.45 & L6 & 1.61 \\ C2 & 1.5 & L2 & 0.258 & C7 & 0.775 & L7 & 0.85 \\ C3 & 0.85 & L3 & 1.33 & C8 & 0.76 & L8 & 0.408 \\ C4 & 1.8 & L4 & 4.42 & C9 & 4.405 & & \\ C5 & 1.02 & L5 & 1.5 & & & \\ \hline \end{tabular}
\end{table}
Table 3: Circuit components and values of the components.  
of changing the length of the metamaterial design is also observed in this study. I1 and I2 represent the length of the outer and inner SRR copper parts of the design, respectively. The length modification strongly affects the inductance and influences the resonance of the proposed metamaterial. The resonance shifting results based on the modification of the length of the outer and inner SRR are shown in Figure 7c,d, where the lengths are tweaked until we obtained the desired resonances.
# A Novel Nonlinear Magnetic Equivalent Circuit Model for Magnetic Flux Leakage System
###### Abstract  
To ensure efficient inspection using the magnetic flux leakage (MFL) method, generating a flux density near the saturation level within the tested material is essential. This requirement brings high flux density conditions in the system's pole regions. Hence, leakage flux within the slot is excessively triggered, leading to distortion of the defect signal. In this context, the system dimensions stand out as one of the most significant factors affecting the mentioned flux distributions. Therefore, various alternative solutions with different system dimensions arise in the design process of the MFL system. This study proposes a magnetic equivalent circuit (MEC) model to achieve optimal system design. The proposed MEC model is designed considering the nonlinear behavior of the material, leakage flux, and fringing effects. Verification results demonstrate that the MEC model consistently tracks the finite element analysis (FEA) results in calculating the flux densities. Furthermore, the relative errors in the flux density calculations of the tested material are at a maximum level of 10.2% and an average of 5.2% compared to the FEA. These findings indicate that the proposed MEC model can be effectively utilized in rapid prototyping and optimization procedures of MFL system design by providing fast solutions with reasonable accuracy.  
Keywords: nondestructive testing: magnetic flux leakage; magnetic equivalent circuit; finite element analysis; nonlinear solution +
Footnote †: journal: Applied
# Behaviour Analysis of Reinforced Soil Retaining Wall According to Laboratory Scale Test
###### Abstract  
Reinforced soil retaining wall are ground structures that can be readily seen all around us. The development of reinforcements to these walls and their demand have increased rapidly. These walls are advantageous because they can be used not only in simple construction compared with reinforced concrete retaining walls but also when the height of the wall needs to be higher. However, unlike reinforced concrete retaining walls, in which the walls are integrated and resist the earth pressure on the back, the block-type reinforced earth retaining wall method secures its structural stability by frictional force between the buried land and reinforcements. A phenomenon in which a block is cracked or dropped owing to deformation has been frequently reported. In particular, this phenomenon is concentrated at the curved parts of a reinforced soil retaining wall and is mainly known as a stress concentration. However, to date, the design of reinforced soil retaining walls has been limited by the two-dimensional plane strain condition and has not considered the characteristics of the curved part. There is a lack of research on curved part. Therefore, this research determines the behavioural characteristics of curved-part reinforced soil retaining walls with regard to the shape (convex or concave) and angle (60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\), and 150\({}^{\circ}\)). The displacement generated in the straight part and the curved part was analysed through an Laboratory Scale Test. The results showed that the horizontal displacement of the curved part increases as a convex angle becomes smaller, and the horizontal displacement of the curved part decreases as a concave angle becomes smaller. At the center (D and H have the same length, but H represents the height and D represents the separation distance from the center of the curved part) of the convex curve, the horizontal displacement of the 0.5 D section decreased to 13.8%; it decreased to 41.0% in the 1.0 D section. For concave angles, it was revealed that the horizontal displacement from the center 0.0 D to the 0.5 D section of the curved part increased by 25%, and from the 1.0 D section, by 75%. It was confirmed that the displacement difference was largely based on the value of 0.5 D. It was judged that this can be used as basic data for the design and construction guidelines for reinforced soil retaining wall of reinforced soil retaining walls.  
reinforced soil retaining wall; curved-part; horizontal displacement; laboratory scale test +
Footnote †: journal: Applied Sciences
# Reliability Analysis of Hybrid Laser INS Under Multi-Mode Failure Conditions
## 4 Reliability Analysis Methods  
To address the uncertainty issues in the parameters of the mixed inertial navigation structure, this paper conducts an uncertainty analysis based on the failure probability reliability analysis model of the mixed inertial navigation system. First, a global sensitivity analysis method [33] based on failure probability is employed to analyze the reliability model focusing on navigation function failure. This approach yields global sensitivity indicators for each parameter concerning the system's functional failure probability, allowing for the ranking of parameters based on their importance in influencing functional failure. Additionally, the Sobol sequence sampling method is used to simulate the dual random uncertainties in the outer frame structure parameters and investigate the uncertainty impact of these parameters on the probability of structural strength failure of the outer frame assembly. The reliability analysis workflow is illustrated in Figure 12.
# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 5 Experimental Results
### Performance of ML Algorithms Using ACO-Selected Subset of Features
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classifiers** & **Accuracy** & **AUC** & **Recall** & **Precision** & **F1-Score** & **Kappa** & **MCC** \\ \hline DT & 89.50 & 89.47 & 92.11 & 87.93 & 89.85 & 78.98 & 79.32 \\ RF & 95.73 & 99.17 & 96.72 & 94.93 & 95.77 & 91.46 & 91.56 \\
**ET** & **97.70** & **99.55** & **98.37** & **97.15** & **97.73** & **95.41** & **95.47** \\ Adaboost & 91.47 & 97.24 & 92.49 & 90.99 & 91.57 & 82.95 & 83.27 \\ XGB & 95.08 & 99.03 & 96.41 & 94.18 & 95.14 & 90.17 & 90.45 \\ LR & 89.50 & 94.32 & 92.48 & 87.50 & 89.81 & 78.99 & 79.36 \\ KNN & 89.66 & 95.67 & 98.70 & 83.86 & 90.59 & 79.34 & 80.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of ML classifiers using a selected subset of ACO features. Figure 5: ET classifier confusion matrix. Figure 6: ROC curve of the ET classifier for predicting KD. Figure 7: ROC curve of ML classifiers in predicting KD. (**a**) Random forest. (**b**) XGB.
## 3 Results and Discussion
### Effect of Growing Media  
We performed one-way ANOVA analysis for testing H2. For this test, we reorganized, merged, and rearranged the experiment results of T1, T1 DS, and T1 DC. We created new data tables by using collected values of growth parameters and yields from the three different growing media. The sample size of T1 was normalized to three crops by dividing all measured values by 5. An ANOVA test was performed to determine the influence of the three different growing media. As for the results, there were no statistically significant differences in the growth parameters. Thus, H2 for the growth parameters is not true. However, the analysis results for the yields (after 96 days) of the three groups show that the effects of growing media on yields were statistically significant because the \(p\)-values for the total yield, commercial yield, total fruits, and commercial fruits were less than 0.05. Thus, H2 is true only for the yield.
## 2 Experimental Layout  
The diode-pumped Kerr-lens mode-locked solid-state oscillator was usually considered difficult to realize due to the low brightness of diode lasers. Measures should be taken to initiate effective soft-aperture Kerr-lens mode-locking in the laser crystal. In reference [8] the authors employed a polarized single mode fiber laser as the pump source and realized the shortest pulses of 32 fs in a KLM Yb:CaGdAlO\({}_{4}\) oscillator. However, fiber lasers have lower efficiency and higher cost than laser diodes. Therefore, we utilized a fiber-coupled laser diode (Jenoptik, Jena, Germany; JOLD-7.5-BAFC-105) with a small core diameter of 50 \(\upmu\)m and a numerical aperture of 0.22 as the pump source. Next, a tight focusing laser cavity was built to enhance the laser intensity in the Yb:GYSO crystal. The overall experimental layout is depicted in Figure 1. The 976 nm output from the fiber-coupled laser diode was reimaged into the crystal by a telescope with a magnification of 1:0.8. The focusing beam waist in the crystal was estimated to be about 20 \(\upmu\)m. The laser cavity consisted of a pair of concave mirrors with radii of curvature (ROC) of 75 mm. Both surfaces were coated for high transmission at 976 nm (\(R<2\%\)) and high reflection at 1020-1200 nm (\(R>99.9\%\)). The laser medium used in the experiment was a 3 mm long, 5 at.% Yb\({}^{3+}\)-doped GYSO crystal with a \(6\times 5\) mm\({}^{2}\)cross-section. The main absorption peaks at room temperature were at 900, 918, 950, and 976 nm where the highest one at 976 nm belongs to the zero phonon line of Yb\({}^{3+}\). The uncoated crystal was placed at Brewster's angle to minimize the surface reflection. The crystal was wrapped with an indium foil and mounted on a water-cooled copper heat sink kept at 14 \({}^{\circ}\)C. Intracavity chirp compensation was realized by a pair of SF6 prisms whose tip-to-tip distance was set as 267 mm. A plane mirror with a transmission of 0.8% in the range of 1020-1200 nm was used as the output coupler (OC). The small coupling rate was employed to increase the intracavity laser intensity for stronger Kerr nonlinearity at the expense of smaller output power. A plane high reflection mirror was placed on a translation stage as the end mirror. The total cavity length was about 1.27 m, corresponding to a repetition rate of 118 MHz. By this cavity design, the laser beam waist in the crystal was calculated to be about 19.1 \(\upmu\)m \(\times\) 17.1 \(\upmu\)m. The good overlap between the cavity mode and the pump mode is a prerequisite for effective Kerr-lens mode-locking.  
Figure 1: Layout of the Kerr-lens mode-locked Yb:GYSO oscillator. LD: laser diode; M1 and M2: concave mirrors with ROC of 75 mm; P1 and P2: SF6 prisms; HR: high reflection mirror; OC: output coupling mirror of 0.8% transmittance.
# Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission
## 3 Energy Efficiency Optimization of Secure Multicast Transmission
### Minorize-Maximize Method  
We note that function \(R_{\mathrm{mc}}\left(\mathbf{\Lambda}\right)-R_{\mathrm{ev,ub}}\left(\mathbf{ \Lambda}\right)\) in (23) is the subtraction of two concave functions. The function is defined as the difference of convex functions (d.c.), which can not be solved by convex optimization directly. Via using the MM method, we adopt an iterative algorithm to deal with it [40; 41]. The basic idea is to approximate \(R_{\mathrm{ev,ub}}\left(\mathbf{\Lambda}\right)\) by the first-order Taylor series expansion and transform it into a linear function. We can solve the subproblem with further algorithm and get the next iteration. The iterative subproblem is as follows,  
\[\mathbf{\Lambda}^{\left(\ell+1\right)}=\underset{\mathbf{\Lambda}}{\arg \max} \quad W\frac{R_{\mathrm{mc}}\left(\mathbf{\Lambda}\right)-\mathrm{tr} \left\{\left(\frac{\partial}{\partial\mathbf{\Lambda}}R_{\mathrm{ev,ub}}\left( \mathbf{\Lambda}^{\left(\ell\right)}\right)\right)^{T}\mathbf{\Lambda}\right\}}{ P(\mathbf{\Lambda})},\] \[\mathrm{s.t.} \quad\mathrm{tr}\left\{\mathbf{\Lambda}\right\}\leq P_{\mathrm{ max}},\ \mathbf{\Lambda}\succeq\mathbf{0},\ \mathbf{\Lambda}\ \mathrm{diagonal}, \tag{28}\]  
where \(\ell\) denotes the iteration index. Note that the gradient of \(R_{\mathrm{ev,ub}}\left(\mathbf{\Lambda}\right)\) over \(\mathbf{\Lambda}\) can be proved to be a diagonal matrix. We can get its \(k\)th diagonal element as follows,\[\left[\frac{\partial}{\partial\mathbf{\Lambda}}R_{\text{ev,ub}}\left(\mathbf{\Lambda}^{ (\ell)}\right)\right]_{k,k}=\sum_{i=1}^{N_{k}}\frac{\left[\mathbf{\Omega}_{\text{ ev}}\right]_{i,k}}{1+\sum_{j=1}^{M}\left[\mathbf{\Omega}_{\text{ev}}\right]_{i,j}\left[\mathbf{ \Lambda}^{(\ell)}\right]_{j,j}}, \tag{29}\]  
where \(\left\{\mathbf{\Lambda}^{(\ell)}\right\}_{\ell=0}^{\infty}\) is by the MM method in (28), which can converge to a locally optimal solution of the original problem in (20) [42]. Therefore, our next step is to solve the subproblem of each iteration.
## 3 Novel Vehicle Terminal Based on Acoustic Communication Technology
### Control Flow  
Due to the fact that the communication mode of developed vehicle terminal includes two ways, such as GPRS and acoustic communication (as shown in Figure 7), the corresponding control flow can be realized in different ways under different circumstances. Specifically, two situations are included as following.  
1. Users use the integrated APP with the acoustic wave communication function to send instructions. Users can use the mobile phone to send the instructions of unlocking, locking, double flashing, whistling, and others. The operation instructions can be controlled in two ways: (1) using the 4G network or the 3G network of mobile phone, the instructions are sent to the platform server. After the server receives the corresponding instruction, it sends the corresponding vehicle terminal through the GPRS channel. When the GPRS module of the vehicle terminal receives the message, it parses the message protocol and judges whether the instruction is valid by comparing the code. If valid, it indicates that the certification is successful and the certification success mark is given, otherwise the fail result will be sent to the server by GPRS, and then remind users and platform managers; and (2) mobile phone speakers emit acoustic information with TOTP password. After receiving acoustic wave information from microphones, the vehicle terminal decodes the acoustic wave information, and judges whether the instruction is valid by comparing the password. If valid, it indicates that the certification is successful and the certification success mark is given, otherwise there is no operation, and uses need to place an order again. When the mark number of successfully certifiating is 1 or more, by using I/O interface, the processor can realize the action control of the corresponding vehicle according to instruction information.
2. When the user in the underground garage with weak GPRS signal strength, users can use the integrated APP with acoustic wave communication function to send instructions. Users use the mobile phone to send the instructions of unlocking, locking, double flashing, whistling, and others. Specifically, mobile phone speakers emit acoustic information with the TOTP password. After receiving acoustic wave information from microphones, the vehicle terminal decodes the acoustic wave information, and judges whether the instruction is valid by comparing the password. If valid, the certification is successful, repeat the operation process of the latter part in the first situation.
## 1 Introduction
To the best of our knowledge, only a few studies have investigated PC post-stroke using non-linear methods [22; 37; 38]. However, a significant gap remains in the literature regarding the application of non-linear measures to functional postural tasks, such as gait initiation (GI). GI is considered an optimal task subphase for detecting PC dysfunction [15; 39; 40] and for studying how the central nervous system (CNS) employs stable and efficient strategies to manage the inherent instability of bipedal posture [41; 42]. For post-stroke individuals, the feedforward phase of GI is particularly challenging, often leading to compensatory strategies that compromise movement stability and efficiency [17]. Despite its importance, most research has predominantly focused on APAs, with limited attention paid to earlier adjustments, such as EPAs, which are equally crucial for preparing the body for movement [41]. Furthermore, studies frequently prioritize CoP dynamics [22; 37; 38] while overlooking joint angle variability, which provides valuable insights into segmental coordination and adaptive strategies [43; 44]. Given the complexity of PC, combining linear and non-linear analyses provides complementary insights: CoP dynamics reflect the overall stability, while joint angle variability reveals the neuromuscular mechanisms underlying segmental coordination and postural adjustments [45; 46]. This integrated approach is crucial for post-stroke populations, where stability and adaptability are often disrupted [17]. While previous research has primarily relied on linear analyses to assess PC, this study aims to advance the field by integrating both linear and non-linear approaches to examine postural complexity and variability during the feedforward phase of GI (\(-600\) ms to \(+50\) ms). By analyzing CoP dynamics alongside joint amplitude variations, this research provides a more comprehensive understanding of how post-stroke individuals adapt their postural strategies. These findings contribute to a deeper insight into PC dysfunctions and may support the development of rehabilitation interventions aimed at enhancing both stability and movement efficiency.