esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
## 5 Numerical Simulations
### ADMM_P Algorithm Properties with Different Anchor Placements
#### 5.4.2 Varying Numbers of Anchors  
Simulations in the last subsection show that a predesigned placement of anchors has a positive influence on localization performance. In this scenario, we evaluated the performance of the ADMM_P algorithm by varying the number of anchors. Taking the simulation results of Figure 7 into consideration, we fixed 10 anchors on the region boundary and randomly deployed the remaining anchors inside the region. Other simulation settings were the same as those in the last subsection. Node distribution is shown in Figure 8a, and the corresponding simulations are shown in Figure 8b, which shows that proper anchor placement and an increase in anchor number both lead to an improvement of localization performance.  
Figure 8: Estimation results of sensor network with 10 fixed anchors, five randomly distributed anchors, and 40 randomly distributed sensors. (**a**) Estimation locations. Red asterisks *: anchor locations; black circles \(\circ\): true sensor locations; blue stars \(\star\): estimation results; (**b**) RMSE comparison.  
Figure 7: Estimation results of a sensor network with 10 fixed anchors and 40 randomly distributed sensors. (**a**) Estimation locations. Red asterisks *: anchor locations; black circles \(\circ\): true sensor locations; blue stars \(\star\): estimation results; (**b**) RMSE comparison.
# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 2 System Model and Tool Analysis
### Reinforcement Learning of Q-Learning  
In reinforcement learning, the agent makes a decision and changes the state of the environment. The environment feeds the benefit back to the agent through the related algorithm so that the agent gradually learns the optimal decision. Among them, in \(Q\)-learning [7], the agent makes decisions through the \(Q\) matrix, which is a value matrix with the matrix table header of state and action. The agent checks its own state, selects actions according to the matrix values corresponding to all actions in the corresponding state column, and reacts to the environment. The environment feeds back the reward matrix and modifies the probability map of the corresponding action by changing the \(Q\) matrix in different states. The corresponding \(Q\) matrix update expression is as follows:  
\[Q(s_{t},a_{t})=(1-a)Q(s_{t},a_{t})+a(r_{t+1}+\gamma max_{a}Q(s_{t+1},a)) \tag{5}\]  
where \(a\) is the learning rate and \(r_{t+1}\) is the reward matrix of the feedback from the environment. Noticing that \(Q\)-learning algorithm can apply for coordinated scheduling strategies in different scenarios and obtain the best balanced operation strategy [8], we utilize it here to achieve greater advantages in solving complex learning models.
# Surface Settlement during Tunneling: Field Observation Analysis
## 3 Results  
Given the above data, we analyzed the influence of the following factors on the surface settlement: depth (Figure 4a), the distance in the plan from the tunnel axis to the monitored facility (Figure 4b), the soil class and type in the tunnel face (Figure 4e), the TBM diameter (Figure 4d), and the loads associated with the buildings and structures affected by the tunneling works (Figure 4c). The authors identified a dependence only on the soil class in the face (soft ground/rock soil). There was little or no dependence between the other parameters and the settlement.  
For all the graphs presented, the correlation coefficient between the settlement and the studied parameter was up to 0.4, which indicated a moderate relationship between the parameters.  
The evaluation of the effect of the pressure balance values, presented in Table 1, on the additional vertical displacements was made calculated to identify regularities. The dependence between the settlement and the pressure balance is shown in Figure 5. The Pearson's correlation coefficient [44] was equal to 0.77 between the earth balance and the benchmark settlement during one monitoring cycle. This means there was a close linear relationship interpreted as a strong correlation. Hence, the coefficient of determination [45] was 0.60. This parameter shows the percentage of sampling explainable by the influence of the studied factors. According to the Chaddock scale [46], the coefficient value indicated an evident relationship between the parameters. Using the Student's t-test [47], the value of the correlation coefficient was recognized as significant in 95% of cases.  
Figure 3: Fragment of the PK113-PK114 plan, including the building and the relative position of the designed tunnel.  
Figure 4: The dependence between the building settlement and various factors. (**a**) The dependence between the maximum building settlement and the tunnel depth; (**b**) the dependence between the maximum building settlement and the distance of the building from the tunnel axis in the plan; (**c**) the dependence between the maximum building settlement and the building height; (**d**) the dependence between the maximum building settlement and the TBM diameter; (**e**) the dependence between the maximum building settlement and the soil type in the tunnel face, where 1 is sand, 2 is loam with some sand, 3 is loam, 4 is clay with some loam, 5 is clay, 6 is marlstone, 7 is limestone with clay interlayers, and 8 is limestone.  
The analysis of the data from Table 2 shows that there was no correlation between the rate of tunneling and the building settlement. Consequently, the rate of the face excavation and lining installation did not affect the additional displacement of the surrounding structure.  
The settlements' calculation results were found to be similar to the settlements of our colleagues, whose articles we reviewed.
###### Abstract  
Lamb-wave-based structural health monitoring is widely acknowledged as a reliable method for damage identification, classification, localization and quantification. However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting these waves and extracting useful information about the structure's health is still a major challenge. Deep-learning-based strategy offers a great opportunity to address such challenges as the algorithm can operate directly on raw discrete time-domain signals. Unlike traditional methods, which often require careful feature engineering and preprocessing, deep learning can automatically extract relevant features from the raw data. This paper proposes an autoencoder based on a bidirectional long short-term memory network (Bi-LSTM) with maximal overlap discrete wavelet transform (MODWT), layer to detect the signal anomaly and determine the location of the damage in the composite structure. MODWT decomposes the signal into multiple levels of detail with different frequency resolution, capturing both temporal and spectral features simultaneously. Comparing with vanilla Bi-LSTM, this approach enables the model to greatly enhance its ability to detect and locate structural damage in structures, thereby increasing safety and efficiency.  
structural health monitoring; deep learning; Lamb waves; autoencoder; anomaly detection in Composites.
# A Feedrate Planning Method for the NURBS Curve in CNC
## 2 Feedrate Planning of a NURBS Curve Based on CCC
### Establishment of the CCC Mathematical Model
\[\mathrm{g}(\tau)\ \leq D_{u}(\tau)=\sqrt{2a_{\mathrm{T}}(s_{u}-\tau)+C_{1}^{2} (s_{u})},\ \ s\in[0,s_{u}] \tag{5}\]0 Schematic diagram of the constructing principle of CCC. (**a**) Constraint on the feedrate by the deceleration curve; (**b**) the constructing of the critical deceleration curve. Equation (11) for CCC is derived based on the kinematic constraint, but whether it satisfies the geometric constraint represented by \(C_{1}(s)\) remains to be verified by the following process:  
The difference between \(\mathrm{g}(s)\) and \(C_{1}(s)\) can be obtained from Equation (11) as  
\[\mathrm{g}(s)-C_{1}(s)=\max\left[ \begin{array}{c}\sqrt{\lambda_{\min}-2a_{ \mathrm{T}}s}-C_{1}(s),\\ \sqrt{\kappa_{\min}+2a_{\mathrm{T}}s}-C_{1}(s),\\ f_{\min}-C_{1}(s)\end{array} \right],\ \ s\in[0,s_{c}] \tag{12}\]  
For \(\sqrt{\lambda_{\min}-2a_{\mathrm{T}}s}-C_{1}(s)\) in the above formula, assume there exists an arc length value \(s=s_{d}\) to make it greater than 0, namely  
\[\sqrt{\lambda_{\min}-2a_{\mathrm{T}}s_{d}}>C_{1}(s_{d}) \tag{13}\]  
then the following inequation can be obtained:  
\[\lambda_{\min}>C_{1}^{2}(s_{d})+2a_{\mathrm{T}}s_{d}=\lambda_{d} \tag{14}\]  
which contradicts the setting that \(\lambda_{\min}\) is the minimum, therefore, the \(s_{d}\) does not exist, which means \(\sqrt{\lambda_{\min}-2a_{\mathrm{T}}s}-C_{1}(s)\leq 0\). Similarly, it follows that \(\sqrt{\kappa_{\min}+2a_{\mathrm{T}}s}-C_{1}(s)\leq 0\), and combined with \(f_{\min}-C_{1}(s)\leq 0\), the conclusion \(\mathrm{g}(s)-C_{1}(s)\leq 0\) can be drawn. Therefore, \(\mathrm{g}(s)\) satisfies the geometric constraint represented by \(C_{1}(s)\). So far, the establishment of the CCC mathematical model is completed. The data required in the above constructing process of CCC can be obtained by traversing the NURBS curve segment, in which the accumulated arc length at each point on the NURBS can be acquired by accumulating the chord length [28; 29] because of the absence of an analytical solution. The adaptive step size can be used according to the curvature of the NURBS curve during the traversal to ensure the calculation accuracy of the accumulated arc length. Meanwhile, from the constructing process of CCC shown in Figure 4(a) and its mathematical model (Equation (11)), CCC can be regarded as the fitting of \(C_{1}(s)\) using a combined curve. Therefore, before constructing CCC, the NURBS curve can be divided into sub-curves appropriately to avoid the overly complicated variation of a single segment \(C_{1}(s)\), to improve the \"fitting effect\" of CCC.
## 3 Results  
### Mechanical Testing and Electron Fractography  
Five (5) tensile specimens were retrieved for each cooling method. The results of the tensile tests are summarized in Table 2. It is shown that the WQ sample attained higher yield  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Element (\%)** & **Si** & **Fe** & **Cu** & **Mn** & **Mg** & **Cr** & **Zn** & **Ti** & **Al** \\ \hline
0.4–0.8 & 0.7 & 0.15–0.4 & 0.15 & 0.8–1.2 & 0.04–0.35 & 0.25 & 0.15 & Bal. \\ \hline \end{tabular}
\end{table}
Table 1: The nominal chemical composition of the 6061 alloy extruded profiles [31].  
and tensile stress values (304 and 315 MPa) and an intermediate value of elongation at fracture (12.1%), signifying an acceptable combination of strength and ductility. The AC sample did not reach the desired values for the tensile strength (284 MPa with a minimum value of 300 MPa), the yield strength (241 MPa with a minimum value of 280 MPa), and the elongation (9.6% with a minimum value of 10%). The SC sample exhibited acceptable values of strength and ductility (tensile strength: 310 MPa; yield strength: 298 MPa; elongation: 12.8%), which were intermediate regarding the strength and the highest regarding ductility, among the three conditions. These results are indicative of the positive contribution of the increased cooling rate from homogenization or the solution temperature to both strength and ductility.  
During three-point bending tests, the AC sample was severely fractured at a 22 mm vertical distance (Figure 1a), and it was separated into two pieces. The fracture mode was intergranural, and brittle secondary cracks were developed transverse to the main fracture surface (Figure 1b,c). Electron fractography revealed fine and shallow dimples developed on the grain facets, indicating minor ductility behavior. The prevailing mechanism is microvoid coalescence on the grain boundaries caused by Mg\({}_{2}\)Si precipitation. This behavior characterized the whole samples' thickness, and no difference in fracture mode was observed between the surface and mid-thickness regions. The WQ sample exhibited \"orange peeling\" on the bent region, while the sample on the bending line of the SC sample revealed a thin longitudinal crack parallel to the extrusion direction (Figure 1d,e).
# Conversion of Stearic Acid into Bio-Gasoline over Pd/ZSM-5 Catalysts with Enhanced Accessibility
## 3 Results and Discussion
### Reactions of Stearic Acid  
#### 3.2.1 Effect of the Pd Incorporation  
Initially, tests of stearic acid conversion were performed comparing the parent nanocrystalline n-ZSM-5 and Pd/n-ZSM-5 catalysts to ascertain the role played by the addition of the metal. As illustrated in Figure 4, almost complete conversion of stearic acid into hydrocarbons was attained over both catalysts, showing the high decarboxylation activity of these materials under the employed operation conditions (T = 325 \({}^{\circ}\)C, 6 bar of N\({}_{2}\)). Regarding the hydrocarbon distribution (Figure 5), the main products obtained were gases and gasoline range compounds. The main components in the gases are C\({}_{3}\) and C\({}_{4}\) olefins, proceeding from end-chain cracking reactions of the carbocations species formed by stearic acid decarboxylation. On the other hand, two pathways can be envisaged to explain the formation of gasoline range hydrocarbons: i) random cracking reactions at any position in the chain of those carbocations, and ii) oligomerization/cyclization/aromaticization (OCA pathway) of the light olefins formed by severe cracking reactions. Both end-chain cracking and OCA pathways are known to occur on the acid sites of the ZSM-5 zeolite [37; 38; 39]. Interestingly, the addition of Pd to the n-ZSM-5 support results in the production of less gaseous hydrocarbons and more gasoline. Moreover, the carbon atom distribution obtained over the Pd-containing catalyst exhibited a more regular profile, whereas the selectivity towards gasoline-range hydrocarbons increased from 65.5% over the n-ZSM-5 (32) sample up to 77.5% when using the Pd/n-ZSM-5 (32) catalyst. This positive finding can be assigned to a promotion effect of the OCA pathway provoked by the Pd species. In this way, Pd is known to present activity in the oligomerization and polymerization of olefins [40; 41; 42; 43], which would be in agreement with its participation also in olefin oligomerization reactions. Moreover, Pd exhibits hydrogen transfer activity, which could favor the aromatization step in the OCA pathway [44; 45; 46; 47].  
Figure 3: TEM micrographs of the Pd/h-ZSM-5 samples. (**a**) Pd/n-ZSM-5 (32); (**b**) Pd/h-ZSM-5 (33); (**c**) Pd/h-ZSM-5 (51); (**d**) Pd/h-ZSM-5 (122).
## 6 Research Areas
### Autonomous Navigation  
[PERSON] et al. [112] define intelligence as \"the ability to discover knowledge and use it\" and autonomy as the \"ability to describe its own purpose without instructions\". Navigating a robot autonomously on a construction site for inspection has been a prime focus of research for many researchers [89; 198; 200]. Technologies have been developed to make the robot aware of its surroundings. Autonomous navigation of robots can be divided into three stages--localization, planning, and navigation.
# Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
## 4 Discussion
The results suggest that further pathways, to overcome some weaknesses shown, will aim to: (i) increase the EUNIS habitat categories to be mapped (i.e., grasslands, shrubs, wetlands, and coastal area); (ii) amplify the geographical extent to a wider area (i.e., from national to continental scale), obtain homogeneous classification within single biome unit (i.e., Mediterranean Basin); (iii) test different semi-supervised machine learning algorithms (e.g., Convolutional Neural Network, [118]) to obtain a more suitable habitat classification; and, (iv) also consider C-band Synthetic Aperture Radar (SAR) satellite data as candidate predictors for habitat classification purposes (e.g., forest canopy structure [119]).
# Robust Image Embedded Watermarking Using DCT and Listless SPIHT
## 3 Proposed Scheme  
The block diagram of the proposed scheme is shown in Figure 1. The uniqueness of the scheme is that the key is generated randomly during embedding process instead of using a known key for embedding in comparison to other methods. The generated key is required to send to the receiver for watermark extraction.  
The embedding and extraction technique of the proposed method is based on combining the CRT technique in the DCT domain. The listless SPIHT compression scheme is used to generate an embedded compressed bit string which can be used for storage and/or transmission. This method added the flexibility to compress losslessly or lossy by setting a target bit rate. In addition, better security and better robustness is achieved during the extraction phase. We have presented the detailed algorithm for embedding and extraction in the following subsections.
# Uncertainty Theory-Based Resilience Analysis for LEO Satellite Communication Systems
## 3 Belief Resilience Measure
### Belief Instantaneous Availability  
Instantaneous availability is the probability that a system or component is performing its required function (i.e., required performance) at a given point in time [17], and it can reflect the overall situation of the system or component. We apply this parameter as the basis for the resilience measure. Considering that the system performance parameters are affected by aleatory and epistemic uncertainties, the belief instantaneous availability is defined based on the uncertainty theory [14; 15] as follows.  
**Definition 8**.: _Belief instantaneous availability is the chance that the system performance margin \(m(t)\) is greater than 0 at time \(t\), i.e.,_  
\[A_{B}(t)=\mathrm{Ch}\{m(t)>0\}, \tag{6}\]  
_where_  
\[m(t)=\begin{cases}p_{th}-p(t),&\text{if $p(t)$ is STB}\\ p(t)-p_{th},&\text{if $p(t)$ is LTB},\\ \min(p_{th,U}-p(t),p(t)-p_{th,L}),\text{if $p(t)$ is NTB}\end{cases} \tag{7}\]  
_where \(STB\), \(LTB\), and \(NTB\), respectively, are the smaller-the-better, the larger-the-better, and the nominal-the-better parameters, and \(m(t)\)[18] describes the distance between a performance parameter \(p(t)\) and its failure threshold \(p_{th}\) at time \(t\). If \(m(t)<0\), a failure occurs, and \(m(t)\geq 0\) means the system is within the available zone that can be operated at time \(t\)._  
If a performance parameter is mainly with aleatory uncertainties, the performance margin degenerates to a random variable. Let \(A_{B}^{(P)}(t)\) denote the belief instantaneous availability under the probability theory and we have  
\[A_{B}(t)=A_{B}^{(P)}(t)=\Pr\{m(t)>0\}. \tag{8}\]If a performance parameter is mainly with epistemic uncertainties, the performance margin degenerates to an uncertain variable. Let \(A_{B}^{(U)}(t)\) denote the belief instantaneous availability under the uncertainty theory and we have  
\[A_{B}(t)=A_{B}^{(U)}(t)=\mathcal{M}\{m(t)>0\}. \tag{9}\]
## 3 Results and Discussion
### Response Surface Methodology (RSM)  
RSM was utilized to develop predictive empirical models in order to evaluate the bruise susceptibility for a wide range of impact cases [15,22].  
In this study, prediction models were developed utilizing the RSM approach to predict bruise susceptibility for each impact material, where bruise susceptibility (\(\gamma\)) is a function of drop height (\(X_{1}\)), and of packaging size (\(X_{2}\)), as follows.  
\[Y_{Rigid}=0.000009190+0.000004697X_{1} \tag{1}\]  
\[Y_{Rubber}=0.00001311+0.000001725X_{1}-0.00000659X_{2} \tag{2}\]  
\[Y_{Cartboard}=0.000002683+0.000009768X_{1}+0.000000733X_{2} \tag{3}\]  
\[Y_{ABS}=0.000007801+0.000007100X_{1}-0.000000798X_{2} \tag{4}\]  
Results are illustrated in 3D surface plots as shown in Figure 12a,b for \(0.22^{\prime\prime}\) and \(2.1^{\prime\prime}\) packaging sizes, respectively. According to surface plots, predicted bruise susceptibility values match the simulated values. For packaging size of \(0.22^{\prime\prime}\) (Figure 12a), low bruises are reported for corrugated cardboard at low drop heights. Similarly, for \(2.1^{\prime\prime}\) packaging, rubber material outperformed at different impact height ranges.  
Coefficient of determination values (\(R^{2}\)) were obtained from response surfaces as follows: 0.93 for rigid, 0.99 for rubber, 0.87 for cardboard, and 0.84 for ABS. Although the coefficient of determination values indicates that the predicted empirical model is reasonable, further evaluation is needed. Therefore, simulated bruise susceptibility (BS) values of the FEA model were compared with predicted BS values of RSM. Relative error (%) between simulated and predicted BS values are shown in Tables 4 and 5. It is worth noting that absolute error was calculated instead of relative error in instances where simulated bruise susceptibility equals zero due to the inability to divide by zero. Relative error values ranged from 1% to 24%, which is considered acceptable given the current complexity of bruise volume measurement for fresh produce [22]. Similar error ranges have been reported in the literature, where error values ranged from 7% to 26% for a prediction model for apple damage [25].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Predicted BS** & **Simulated BS** & **Relative Error (\%)** \\ \hline \(1.04\times 10^{-5}\) & \(9.99\times 10^{-6}\) & 4 \\ \hline \(1.15\times 10^{-5}\) & \(1.21\times 10^{-5}\) & 5 \\ \hline \(1.39\times 10^{-5}\) & \(1.37\times 10^{-5}\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative error between simulated and predicted BS values for rigid impact platform.  
Figure 12: Response surface diagram for packaging size (**a**) \(0.22^{\prime\prime}\) and (**b**) \(2.1^{\prime\prime}\).  
Therefore, the developed response surface method and prediction models can be regarded as reasonable and satisfactory.
###### Abstract  
Facilitated by rapid development of the data-intensive techniques together with communication and sensing technology, we can take advantage of smart card data collected through Automatic Fare Collection (AFC) systems to establish connections between public transit and urban spatial structure. In this paper, with a case study on Shenzhen metro system in China, we investigate the agglomeration pattern of passenger flow among subway stations. Specifically, leveraging inbound and outbound passenger flows at subway stations, we propose a clustering refinement approach based on cluster member stability among multiple clusterings produced by isomorphic or heterogeneous clusterers. Furthermore, we validate and elaborate five clusters of subway stations in terms of regional functionality and urban planning by comparing station clusters with reference to government planning policies and regulations of Shenzhen city. Additionally, outlier stations with ambiguous functionalities are detected using proposed clustering refinement framework.  
clustering; cluster ensemble; smart card data; AFC data; passenger flow; urban spatial structure