esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 First Step: Exact Placement of the Dominant Poles
### Extension to Asymmetric Systems  
Even though the linear system in Equation (3) has been introduced in [1] for symmetric systems, the receptance method has been extended to some asymmetric systems with non-symmetric damping and stiffness matrices in [2] and [4]. In these cases, the linear system in Equation (3) should be slightly adjusted. First of all, matrices \(\mathbf{C}\) and \(\mathbf{K}\) should be decomposed into the sum of the symmetric parts \(\mathbf{C}_{s},\ \mathbf{K}_{s}\in\mathbb{R}^{N\times N}\) and asymmetric ones \(\mathbf{C}_{a},\ \mathbf{K}_{a}\in\mathbb{R}^{N\times N}\), namely:  
\[\mathbf{C}=\mathbf{C}_{s}+\mathbf{C}_{a},\ \ \mathbf{K}=\mathbf{K}_{s}+ \mathbf{K}_{a}.  
This formulation of \(\mathbf{H}(\lambda)\) can be exploited to obtain vector \(\mathbf{t}_{i}\) as in Equation (4) and hence the linear system as in Equation (3). More details can be found in [4; 6; 19].
# Flexural Behavior of Corroded High-Speed Railway Simply Supported Prestressed Concrete Box Grider
## 4 Results and Discussion  
### Experimental Phenomena  
During the loading process, the crack development and failure of the scaled beams were recorded and summarized as follows. Taking specimen L1 as an example, when loaded to 72 kN, small oblique cracks first appeared on the web side in the segment between the loading point and the support. As the load increased, oblique cracks developed, and vertical bending cracks appeared in the middle segment. Then, bending cracks extended from the middle span to both ends. The average crack distance was about 100 mm, which was comparable to the stirrup spacing. As the load kept increasing, oblique cracks and  
Figure 7: Calculated section diagram: (**a**) cross-section; (**b**) strain distribution; (**c**) stress distribution; (**d**) constitutive model for corroded prestressing strand; (**e**) constitutive model for concrete.  
vertical bending cracks continued to develop, and the crack number and width increased. When the load increased to 120 kN, cracks developed on the beam flange plate. Figure 8 shows the crack profile of all the scaled beams.  
Beams with different mass losses exhibited different failure characteristics. For specimens L1-L4 with low mass loss, their non-prestressed tensile reinforcement first yielded and then fractured, and then the flange concrete crushed, which exhibited obvious ductile characteristics. When specimens L5 and L6 were damaged, the strand ruptured with a strong and brittle sound. The failure of these two specimens has obvious brittle characteristics, with the bearing capacity decreasing sharply. The failure mode of the scaled beams, which changes from ductile failure to brittle failure as the mass loss increases, can also be supported by reference [17].  
The experiment results are summarized in Table 4 and Figure 9. Strand corrosion could reduce the flexural behavior of PC beams. When the mass loss was 12.30%, the cracking load, ultimate load, and ductility decreased by 27.8%, 29.9%, and 11.5%, respectively. The cracking load of specimen L4 was higher than that of specimen L3, which may be due to the variability in the concrete properties inside each specimen and between different  
Figure 8: Crack profile: (**a**) specimen L1; (**b**) specimen L2; (**c**) specimen L3; (**d**) specimen L4; (**e**) specimen L5; (**f**) specimen L6; (**g**) crack development sequence.  
specimens [42; 43]. There was an obvious turning point in the ultimate load reduction of the scaled beam. When the mass loss exceeded 7%, the ultimate load would degrade faster.
## 3 The Principle of VMD-DWT-Based Denoising
### The Improved VMD-DWT Denoising Algorithm
Thus, the high-order BLIMF parts considered to be significantly corrupted by noise can be locally excluded. Based on a previous study, it is better to adopt DWT sym5 as the wavelet basis and to set the decomposition level at 6 [24]. This method is different from the traditional direct application of wavelet-like thresholding to the decomposition modes, hard or soft. In practice, the result of soft thresholding shows greater efficacy than the hard thresholding in FBG signals [24]. Soft thresholding is then applied to all BLIMF samples when the samples' extremums exceed the threshold, which means that they correspond to a zero-crossing interval and need to be reduced in a smooth way. Thus, the extremum is reduced by an amount exactly equal to the threshold. Considering the energy in each \(u_{k}\), the improved wavelet thresholding is presented in Equation (8). \[\hat{d}_{l}=f(x)=\left\{ \begin{array}{c}sign(d_{i})(|d_{i}|-\frac{T_{i}}{ \exp{(\frac{|d_{i}|}{T}-1)}}),\ |d_{i}|\geq T_{i}\\ 0,\ |d_{i}|\leq T_{i}\end{array} \right. \tag{8}\]  
Here, \(d_{i}\) presents the \(i_{th}\) thresholding BLIMFs, and the standard deviation of the noise is estimated by a robust estimator leading to more accurate estimates, based on the components' median. \[\sigma=\frac{median(|c_{i}|:i=1,2\ldots N)}{0.675}. \tag{9}\]  
As previously mentioned, the noise contained in the BLIMF samples is not standard distributed with variance energy in each mode. In this sense, the reason for adapting a different thresholding \(T_{i}\) per mode \(i\) will become clear in the sequel for the scale dependence. Mathematically, the described soft thresholding operation yields Equation (9):  
\[T=100\sigma\sqrt{2E_{i}\text{lg}(N)}/\text{lg}(j+1). \tag{10}\]  
Here, the \(E_{i}\) shows the energy of the \(K_{i}\) BLIMFs and can be computed directly based on the variation estimate of the first BLIMF using Equation (11). \[\hat{E}_{k}=\frac{E_{1}^{2}}{\beta}\rho^{-k},\ \text{k}=2,3,4\ldots \tag{11}\]  
where \(E_{1}^{2}\) is the energy of the first BLIMFs, and \(\beta\) and \(\rho\) are parameters for a specific VMD implementation, mainly depending on the number of shifting iterations used. They can be estimated based on the large number of independent noise realizations and their corresponding BLIMFs. In Reference [25], the parameters \(\beta\) and \(\rho\) are 0.719 and 2.01, respectively. Step 4: After performing the synthesis function on each scale separately, finally, the de-noising signal is reconstructed by summing all processed BLIMFs after the denoising methods.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**RMSE (Mean)** & **5 bin** & **8 bin** & **11 bin** & **14 bin** & **17 bin** & **20 bin** & **23 bin** & **26 bin** \\ \hline Bin at the Left & 0.0971 & 0.1375 & 0.1491 & 0.0680 & 0.0742 & 0.3782 & 0.0746 & 0.0678 \\ \hline Bin at the Center & 0.0777 & 0.0680 & 0.0687 & 0.0182 & 0.0189 & 0.0260 & 0.0367 & 0.0484 \\ \hline Bin at the Right & 0.1644 & 0.1100 & 0.1335 & 0.1206 & 0.1737 & 0.2157 & 0.0940 & 0.0531 \\ \hline \end{tabular}
\end{table} 
Table 4: RMSE fitting estimate comparison of LSTM trained on a single bin, tested on neighboring ones, using resolution of 1/3 of the octave band width. \begin{table}
\begin{tabular}{l c c c c c} \hline
**RNN** & **Bin Number** & **SSE (10\({}^{3}\))** & **RSS (Mean)** & **R-Squared** & **RMSE (Mean)** \\ \hline \multirow{8}{*}{LSTM} & 1 & 30.42 & 3.59 & 0.96 & 0.0227 \\  & 2 & 70.52 & 4.41 & 0.91 & 0.0325 \\  & 3 & 59.89 & 4.89 & 0.94 & 0.0354 \\  & 4 & 110.75 & 5.62 & 0.90 & 0.0483 \\  & 5 & 117.16 & 5.53 & 0.88 & 0.0452 \\  & 6 & 38.58 & 4.55 & 0.91 & 0.0311 \\  & 7 & 17.16 & 2.95 & 0.92 & 0.0191 \\  & 8 & 19.62 & 2.93 & 0.95 & 0.0194 \\  & 9 & 23.79 & 2.18 & 0.98 & **0.0113** \\  & 10 & 245.23 & 1.94 & 0.86 & 0.0176 \\  & 11 & 315.59 & 8.44 & 0.82 & 0.1642 \\  & 12 & 16.87 & 2.13 & **0.99** & 0.0134 \\ \hline \multirow{8}{*}{BiLSTM} & 1 & 38.78 & 3.05 & 0.95 & 0.0278 \\  & 2 & 62.71 & 3.30 & 0.92 & 0.0249 \\  & 3 & 29.26 & 3.46 & 0.97 & 0.0236 \\  & 4 & 36.86 & 3.94 & 0.97 & 0.0268 \\  & 5 & 47.86 & 3.82 & 0.95 & 0.0283 \\  & 6 & 19.48 & 3.19 & 0.95 & 0.0222 \\  & 7 & 10.99 & 1.94 & 0.95 & 0.0145 \\  & 8 & 10.95 & 1.92 & 0.97 & 0.0126 \\  & 9 & 10.47 & 1.55 & 0.99 & 0.0099 \\  & 10 & 36.03 & 1.14 & 0.98 & 0.0097 \\  & 11 & 39.34 & 1.25 & 0.98 & 0.0096 \\  & 12 & 4.25 & 1.57 & **1.00** & **0.0092** \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of different RNN structures with fixed layer sizes, trained on a single bin, covering 1/12 of the octave band width. 12 trained models in total, for neighboring bins. For the last study, we chose the seventh band, which is in the middle of the twelve. The experimental results of the model trained for one frequency band and used to predict the reverberation of the other frequency bands are presented in Table 2. The GRU-based RNN model was not considered in this experimental study because the initial tests showed even worse fitting accuracy and the same trends as for the LSTM and BiLSTM-based RNNs.
## 2 Preliminaries
### Effect of Communication Break  
Given that communication interferences impact interactions among agents, the expected value of coefficients of \(\mathbf{\mathcal{L}}\) could not be kept unchanged. If all agents can obtain the planned reference information with faults when there exist interferences, methods in [5] can be adopted for motion control. However, there might be communication breaks, and the rank of \(\mathbf{\mathcal{L}}\) must meet the requirements for formation keeping. Firstly, we give the primary requirements of healthy \(\mathcal{G}\).  
Lemma 1 (6): _If \(\mathcal{G}\) has a directed spanning forest, then we have \(rank\big{(}\mathbf{\mathcal{L}}\big{)}=N-N_{l}\), where \(N_{l}\) represents the number of distributed networks._  
Proof of Lemma 1 can refer to Remark 2.1 in the literature [6].  
Lemma 2: _If there exist communication interferences and \(rank\big{(}\mathbf{\mathcal{L}}\big{)}<N-N_{l}\), where \(N_{l}\) represents the number of distributed networks, then agents cannot keep the expected formation without extra reference information, such as the measurement._  
Proof.: According to [6], the Laplacian matrix \(\mathbf{\mathcal{L}}\) of \(\mathcal{G}\) can be split as:  
\[\mathbf{\mathcal{L}}=\begin{bmatrix}\mathbf{0}_{N_{l}\times N_{l}}&\mathbf{0}_{N_{l}\times (N-N_{l})}\\ \mathbf{L}_{R}&\mathbf{L}_{F}\end{bmatrix} \tag{5}\]  
Based on (5), we have \(rank\big{(}\mathbf{\mathcal{L}}\big{)}\leq N-N_{l}\). Referring to (3), each row of \(\mathbf{\mathcal{L}}\) must have non-zero elements if we want all agents can obtain sufficient interacted information for formation control. Next, we assume \(rank\big{(}\mathbf{\mathcal{L}}\big{)}<N-N_{l}\), then there must exist at least one row of \(\mathbf{\mathcal{L}}_{1}=\big{[}\mathbf{L}_{R}&\mathbf{L}_{F}\big{]}\) in which all elements equal 0, which contradicts the demand in Lemma 2. Therefore, Lemma 2 is proved. \(\square\)  
Based on Lemma 2, we have \(rank\big{(}\mathbf{\mathcal{L}}\big{)}<N-N_{l}\) if the communication interference (link failure, block, or strike) causes communication breaks, the system will get out of control. Thus, extra strategies should be provided, which will be discussed in Section 3.3.
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 3 Challenges and Problem Statement  
The Challenges and Difficulties of WSNs in fault finding are focused with the following points:  
* The WSNs have very minimal resources at every sensor node. The classifiers are used to identify the faults, as they are computed by a simple calculation.
* The location of sensor nodes is hazardous and dangerous environments.
* The fault-finding method mentioned is prompt and quick to reject any loss. This fault-finding method must recognize the faulty data by concerning the correct data, and then the sensor nodes are replaced.
# A Lightweight Uav Swarm Detection Method Integrated Attention Mechanism
## 3 Materials and Methods
### Neck Network  
The three feature layers obtained by CSPDarkNet are sent to the neck network for enhanced feature extraction and feature fusion. The neck network of the UAV swarm detection model is constructed based on the Path Aggregation Network (PANet) [36]. The input feature map is resized through a convolution layer and then fused through up- and down-sampling operations. The specific network structure is shown in Figure 4.  
Figure 3: Diagrammatic sketch of the SE module.  
Figure 2: (a) Original CSPLayer structure (b) CSPLayer structure with SE module.  
In order to improve the detection performance of the model for UAVs, a Convolutional Block Attention Module (CBAM) [7] is firstly applied to the three feature maps obtained by the backbone network, and then sent to the neck network for feature fusion. The CBAM is also applied after each up-sampling and down-sampling operation in the PANet. CBAM is a simple and effective attention module for feedforward convolutional neural networks. It combines the two dimensions of channel and spatial features. When the feature map is input, it first goes through the Channel Attention Module (CAM) and then Spatial Attention Module (SAM). The calculation formula is (2):  
\[\begin{cases}F_{c}=M_{c}(F)\otimes F\\ F_{a}=M_{s}(F_{c})\otimes F_{c}^{\prime}\end{cases} \tag{2}\]  
As shown in Figure 5, in the CAM, for the input feature map \(F\in\mathbb{R}^{C\times H\times W}\), first, Global Average Pooling (GAP) and Global Maximum Pooling (GMP) operations are performed based on the width and height of the input feature map, and then they are processed by a shared neural network Multilayer Perceptron (MLP), respectively. The two processed results are added together, and a one-dimensional channel attention vector \(M_{c}\in\mathbb{R}^{C\times 1\times 1}\) is obtained through the Sigmoid function. Finally, the feature map \(F_{c}\) with channel weights is generated by multiplying the channel attention vector \(M_{c}\) and the feature map \(F\). In the SAM module, for the feature map \(F_{c}\) obtained by CAM, a pooling operation is performed, and then the \(7\times 7\) convolution and Sigmoid function to obtain a two-dimensional spatial attention vector \(M_{s}\in\mathbb{R}^{1\times H\times W}\). Finally, \(F_{c}\) and \(M_{s}\) are multiplied to obtain the final feature map \(F_{a}\).  
Figure 4: The structure of the neck network.
# Dual-Neighborhood Tabu Search for Computing Stable Extensions in Abstract Argumentation Frameworks
## 5 Experiment
### Calibration  
In this section, preliminary experiments are conducted to determine the key parameter values of the DNTS algorithm. For the parametric experiments, 30 instances were randomly generated for testing. The number of arguments ranged from 500 to 4000, and the number of edges ranged from 1000 to 250,000. By evaluating a large number of parameter combinations, 64 sets of parameters were finally selected for precise efficacy testing. To ensure reliable results, five tests were conducted for each parameter set. The best, worst, and average running times in seconds were then computed from these test results to evaluate the performance. We tested the following parameters:  
_DisturbPeriod_: perturbation periods are divided into two types: dynamic and static periods. Dynamic perturbation periods include \(|A|\) and \(14|A|\), where \(|A|\) signifies the total number of arguments in the instance, and \(14|A|\) is the setting of the restart parameter in the Haywood algorithm. Although this parameter hardly ever triggers the mechanism it is set for, it was shown to have the most significant effect in the HAYWOOD algorithm; hence, this experiment opts for an equivalent parameter as the perturbation period for testing. The static perturbation periods are 2000 and 8000. _DisturbLevel_: the perturbation levels are set to four levels: 0.01, 0.0025, 0.00125, and 0.001. For example, when the perturbation level is 0.01 and the number of global arguments is 4000, 40 out of the 4000 arguments are perturbed every time the perturbation condition is met. _tl_: the tabu length is set to include four combinations \(tl1=4,tl2=2\); \(tl1=4,tl2=4\); \(tl1=6,tl2=4\), and \(tl=6,tl2=6\). For example, if the number of mislabeled arguments is 20 and \(tl1=4,tl2=4\), the tabu length is set to one-quarter of the number of arguments, i.e., 5. Note that this experiment does not guarantee the optimal values of the parameters, and the optimal scheme may vary from one benchmark to another. The analysis of data from Table 1 reveals that the DNTS algorithm demonstrates relative stability in its best values. However, it exhibits significant fluctuations in its worst values. Notably, the average values tend to be closer to the best values in the majority of cases. Among the four sets of perturbation period settings, the \(14|A|\) setting performs the worst. In the four sets of perturbation level settings, we find that long perturbation periods need to be matched with large perturbation levels, and short perturbation periods need to be matched with small perturbation levels. The tabu length effect is more randomized and can show good or bad results with different parameters. Based on these observations, we screened a set of more stable and efficient parameters for subsequent comparison experiments. DNTS: \(DisturbPeriod=|A|,DisturbLevel=0.00125,tl1=4,tl2=4\)
## 3 Results and Discussion
### Pore Size Distribution of Mortars  
It has been shown that pores larger than 0.1 um in diameter in cement-based materials will adversely affect mechanical properties and impermeability, and they are considered harmful pores [31]. Therefore, this section will focus on the proportion of pores larger than 0.1 um in diameter in mortars.  
Figure 10 illustrates the pore size distribution of the mortar after 28 days of standard curing. The proportion of harmful pores for CCM0, CCM1 and CCM2 was 36.89%, 30.44% and 33.04%, respectively. Compared to CCM0, the proportion of harmful pores of CCM1 and CCM2 declined by 17.5% and 10.4%, respectively. This indicates that the proportion of harmful pores was reduced, and the compactness was improved by adding microcapsules to the mortar. This is due to the fact that mortar is formed by mixing two materials of different particle sizes, cement and sand, with water. After formation, a certain number of pores exist in the mortar. By adding microcapsules to the mortar during the preparation process, some pores and microdefects in the mortar can be filled, at which point the internal structure of the mortar becomes more compact and the proportion of its harmful pores is reduced [32].  
Figure 11 shows the pore size distribution of the pre-damaged mortar after 14 days of self-healing. The proportion of harmful pores was 68.17%, 49.15% and 45.54% for CCM0, CCM1 and CCM2, respectively. It was found that the mortar with microcapsules had significantly lower proportion of harmful pores after self-healing. This is because the pre-load causes internal cracking of the mortar, while the microcracks encounter the microcapsules during the extension process. The shell materials of microcapsules are mainly thermoplastic materials (ceresine wax, nano-CaCO\({}_{3}\)/ceresine wax), which are easy to rupture under the stress, and the released E-44 epoxy resin cures in contact with 2-ethyl-4-methylimidazole to generate healing products that fill the microcracks and reduce the proportion of harmful pores in the mortar.  
Figure 10: Pore size distribution of mortars.
## 3 The Statistical Regression Adaptive Kalman Filtering Algorithm
### The Analysis of the Convergence of the Proposed Algorithm
#### 4.1.2 Case 2: Underwater Lake Trial  
In Case 2, the trajectory comparison results for SRAKF, UKF, and CKF are shown in Figures 11 and 12. The latitude and longitude errors are shown in Figures 13 and 14, respectively. The RMSE of position for SRAKF, UKF, and CKF are shown in Table 4.  
Figure 14: Longitude errors for the underwater lake trial.  
Figure 12: Longitude positions for the underwater lake trial.  
Figure 13: Latitude errors for the underwater lake trial.  
From Figures 11 and 12, it can be concluded that the estimation accuracy in terms of the position for SRAKF is superior to that of UKF and CKF. In Table 4, by employing the proposed SRAKF, the RMSE\({}_{\text{La}}\) for SRAKF is 2.0964 \(\times\) 10\({}^{-6}\) deg, which is lower than UKF and CKF. In Figure 14, the SRAKF method has the lowest longitude errors and the RMSE\({}_{\text{Lo}}\) for SRAKF is 6.6276 \(\times\) 10\({}^{-4}\) deg, which is the lowest compared to UKF and CKF.  
From the experimental results, it is obvious that the proposed algorithm is superior to other algorithms. The errors change continuously along with time during positioning. The bigger errors are caused if measurement errors are not updated in real time. The one-step predicted covariance becomes inaccurate due to measurement errors. It is essential to update both the measurement error and the one-step predicted covariance. However, the UKF and CKF errors mainly include two factors: sigma points and the measurement errors are not updated in real time. The truncation errors are likely eliminated since the localization linearization is not used in the proposed SRAKF. Moreover, the real-time update is performed for the measurement error and one-step predicted covariance by employing SRAKF. SRAKF has the best performance in terms of of state estimation.  
When the sensors loaded in the AUV are affected by larger noises, SRAKF can still make an adjustment for \(\mathbf{P}_{k|k-1}\) and \(\mathbf{R}_{k}\) adaptively. Hence, accurate information for \(\mathbf{P}_{k|k-1}\) and \(\mathbf{R}_{k}\) can make the algorithm more stable. However, UKF and CKF cannot deal with larger noise because the large noise measurement has a great impact on calculating \(\mathbf{P}_{zz}\) and \(\mathbf{P}_{xz}\). \(\mathbf{P}_{zz}\) and \(\mathbf{P}_{xz}\) also influence the Kalman gain. SRAKF performs better than UKF and CKF because it can estimate the optimal solutions of \(\mathbf{P}_{k|k-1}\) and \(\mathbf{R}_{k}\) in each EM iteration. Meanwhile, \(\mathbf{X}_{k|k}\) and \(\mathbf{P}_{k|k}\) are updated by using the accurate \(\mathbf{P}_{k|k-1}\) and \(\mathbf{R}_{k}\), so the accuracy and the robustness of SRAKF are greatly improved.
# A Compact Dual-Band Microstrip Patch Antenna for C- and X- and Ku-Band Applications +
## 4 Conclusions  
In conclusion, the pentagonal-shaped dual-band antenna's exceptional versatility enables effective operation on 7.6 GHz and 12 GHz frequencies, making it highly suitable for many communication applications in C-, X-, and Ku-band spectra. The pentagonal-shaped dual-band antenna's versatility in supporting multiple frequency bands eliminates the need for separate antennas or complex systems, streamlining communication setups and reducing costs. Moreover, the proposed antenna has an acceptable gain of 8 dB at its operating frequency of 12 GHz, which makes it useful in high-gain applications. The pentagonal-shaped dual-band antenna, with its practicality, adaptability, dual-band capability, unique shape providing omnidirectional radiation patterns, cost-effectiveness, and versatility, proves to be an ideal choice for industries reliant on efficient and high-performing communication systems, particularly in the domains of satellite and mobile communication, offering reliable signal transmission in all directions.  
Conceptualization, M.Z. and M.M.T.; methodology, M.M.T.; software, M.M.T.; validation, M.Z.; writing--original draft preparation, M.M.T.; writing--review and editing, Y.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data can be obtained from the corresponding author on request.  
The authors declare no conflict of interest.