esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulations Results
### Time-Frequency Spectrum and Space Spectrum  
Firstly, the cross terms extraction in time-frequency analysis was analyzed. The simulation parameters were as follows. The array number was 4, and the array spacing was half of a wavelength. The incidence angles of strong interference and weak signal were 30 and 60, respectively. The auxiliary polarization angles were 45\({}^{\circ}\) and 20\({}^{\circ}\). The polarization phase differences were 0 degrees and 180 degrees. The extraction cross terms parameter was 0.01. The jamming-to-signal ratio (JSR) of strong interference was 7 dB. The decision threshold of the estimated result was 5 degrees. The normalized frequency of strong interference was 0.2 to 0.4. The weak signal normalized frequency was 0 to 0.2. The environment SNR was \(-4\) dB. The time-frequency analysis algorithm was WVD. The snapshot number was 512.  
The time-frequency spectrum of single-channel single-polarization is shown in Figure 3. The time-frequency spectrum after cross terms extraction is shown in Figure 4. It was impossible to obtain the weak nonstationary signal auto terms in the unprocessed time-frequency spectrum. There are two main reasons for this difficulty. First, from the point of view of position distribution, because the signal distribution is relatively concentrated, it is not easy to distinguish in the background of noise. Second, from the perspective of energy size, when the SNR is low, the signal energy is lower than the time-frequency spectrum noise bottom, and the weak signal auto terms are submerged in the noise, further aggravating the difficulty of resolving and extracting the weak signal auto terms. In Figure 4, the middle part of the red dotted line represents the cross terms. The T-F cross terms of the strong and weak nonstationary signals can be obtained by extracting cross terms processing. There were some noisy T-F points, but the cross terms' T-F points were clearly visible.  
The weak nonstationary signal DOA was estimated on the basis of cross terms, and the results are shown in Figure 5. It was almost difficult for A1\(\sim\)A6 to distinguish and estimate the weak signal, while A7 and A8 could obtain it. Among the weak signal results estimated by the algorithms in Figure 5, Algorithms 7 and 8 have the highest credibility, and they are also the basis for further estimation of DOA by the algorithm in this paper. The final estimation results of the algorithm in this paper refer to the results of Algorithms 7 and 8. Figure 5 shows the spatial spectrum of a single DOA estimate, which is only a qualitative analysis. The specific quantitative analysis of the algorithm proposed in this paper (Algorithm 9) is given by the following simulation analysis. The estimated success rate and specific accuracy were given by the following simulation.  
Figure 4: The time-frequency spectrum after extracting the cross terms.  
Figure 3: Unprocessed time-frequency spectrum.
# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 3 Methodology
### Heterogeneous Ensemble Strategy
The base models can include a variety of machine learning algorithms, such as random forest, XGBoost, LightGBM, CatBoost, and neural networks. The diversity of these models is a critical factor in enhancing the effectiveness of stacking, as it allows the meta-learner to capture complementary patterns and improve the overall predictive performance. In regard to the implementation of heterogeneous ensemble stacking, multiple base learners are first trained on the original training data. These base models can be of the same or different types, depending on the problem and dataset. Next, the trained base models are used to generate predictions, based on either the original training data or an independent validation set. These predictions are then treated as meta-factors and used to train the meta-learner, which learns the optimal way to combine the base model outputs. In practical applications, all base models make predictions based on new data, and these predictions are fed into the meta-learner to produce the final prediction. This approach effectively combines the strengths of multiple models, aiming to achieve higher predictive accuracy and better generalization capability.
## 4 Verification and Validation
### Validation of the Stability Improvement  
As previously noted, the AWI can improve the stability. However, the effects of introducing the AWI on the thresholds of material parameters or the fluid viscosity, and the effects of the number of iterations on the value of these parameters, are less understood. We study these effects in five typical cases in this section.
## 5 Control Algorithm of USV Swarm  
### USV Control Algorithm  
In this study, since the velocity of ocean currents is learned and utilized as a controlling factor for small USVs forming a swarm, it is imperative to design a control algorithm capable of appropriately responding to external information. Consequently, the controller implemented for the small USV was structured using ANN-PID [9,17,18].  
The three inputs depicted in Figure 9 consist of the error value, error integral value, and error differential value, serving as the foundation for the PID control algorithm. These inputs are fed into a nonlinear hyperbolic tangent function. The hyperbolic tangent function and the signal sum(t) inputted to the function are represented as shown in Equation (12).  
Figure 8: Ocean current prediction data result.  
Figure 7: Ocean current measurement data.  
In this equation, \(ref(t)\) is the desired target, and \(m(t)\) is the current measured value.  
\[\begin{array}{c}f(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}\\ \text{sum}(t)=K_{p}(t)e_{p}(t)+K_{i}(t)e_{i}(t)+K_{d}(t)e_{d}(t)\\ e_{p}(t)=ref(t)-m(t),e_{i}(t)=\int e_{p}(t),e_{d}(t)=\frac{d}{dt}e_{p}(t)\\ k_{p}(t+1)=k_{p}(t)-\eta_{p}e_{p}(t)e_{p}(t)*\frac{e^{2}\text{sum}}{(1+e^{2 \text{sum}})^{2}}*\frac{m(t)-m(t-1)}{u(t)-u(t-1)}\\ k_{i}(t+1)=k_{i}(t)-\eta_{i}e_{i}(t)e_{i}(t)*\frac{4e^{2\text{sum}}}{(1+e^{2 \text{sum}})^{2}}*\frac{m(t)-m(t-1)}{u(t)-u(t-1)}\\ k_{d}(t+1)=k_{d}(t)-\eta_{d}e_{d}(t)e_{d}(t)*\frac{4e^{2\text{sum}}}{(1+e^{2 \text{sum}})^{2}}*\frac{m(t)-m(t-1)}{u(t)-u(t-1)}\\ \end{array} \tag{12}\]  
\[\frac{\partial m}{\partial u}=\frac{\Delta m}{\Delta u}=\frac{m(t)-m(t-1)}{u (t)-u(t-1)} \tag{13}\]  
Equation (13) represents the ultimate equation used to compute the gain of the ANN-PID. Once the error has been sufficiently minimized and the control performance falls within a certain range ensuring system stability, the algorithm is halted, and the calculated gain value is retained.
# Prototype Design and Performance Tests of Beijing Astronaut Robot
## 2 The Mechanical Structure and Test System Design of the Robot
### Structural Design and Key Technologies  
The Beijing Astronaut Robot consists of three identical 4-DOF arms, three end effectors, and a torso, as shown in Figure 2.  
The robot mimics the grasping of a chameleon, and its motion and operation mechanism is composed of three identical arms with end effectors, and has 15 degrees of freedom in total. Each arm consists of a wrist with three rotational degrees of freedom and an elbow with one rotational degree of freedom, a total of 3 \(\times\) 4 = 12 degrees of freedom of motion. The end effectors have 3 \(\times\) 1 = 3 degrees of freedom. In order to identify the environmental changes and determine its own state, the robot is equipped with a monocular vision camera, 6-dimensional force/torque and other sensors, like encoders in every joint, as shown in Figure 2. The overall scheme of the robot is shown in Figure 3.  
Figure 3: DOF (degree of freedom) configuration of the Beijing Astronaut Robot.  
Figure 2: Overall structure of the robot.  
It is researched that the motion of the chameleon in a complex environment is very stable, this feature coincides with the robot's mobile demand [12]. Therefore, the design of our robot's bionic motion and operating mechanism draws on the characteristics of a chameleon. More specifically, the design of the robot arms' degrees of freedom and motion pattern draws on the skeletal structure of a chameleon [10; 13]. The bionic robot arm and the end effector of the Beijing Astronaut Robot are shown in Figure 4.  
In order to make the robot end effector grasp the handrail and obtain sufficient force and torque to prevent the occurrence of sliding or rotation [1], we designed a claw driven by a screw (as shown in Figure 5). The rotation of the screw driven by a motor is converted into the opening and closing of the claw [14; 15]. When the lead angle of the screw is smaller than the self-locking angle, the transmission mode has self-locking property, that is, the sliders can only be driven by the rotation of the screw, but the screw cannot be driven by stressing the sliders. Therefore, even if the motor's power is cut off, the gripper can still grasp the handrail to ensure the safe motion of the robot.
# Methacrylate Polymer Monoliths for Separation Applications
## 2 Methacrylate Polymer Monoliths
### Methacrylate Monolith Preparation Methods and Compounds
In this context, mono-vinyl refers to the presence of a single C=C group on the aromatic ring in the crosslinker. Another methacrylate polymerization reaction, which can be utilized is the living polymerization technique. Living polymerization refers to polymerization reactions where the polymer chains grow at more or less the same speed, thereby giving a polymer mixture of similar chain lengths. This is achieved because in, such reactions, termination and chain reactions do not occur and, therefore, the addition of each new monomer unit results in a re-activation of the chain growth. The reaction exists as an equilibrium state composed of an active and a dormant species, whichever species is favored by the equilibrium determines whether the reaction proceeds or stops. These types of polymerizations (Atom Transfer Radical Polymerization (ATRP) and Reversible Addition Fragmentation Transfer (RAFT)) can be used to graft chemical moieties on to substrates [77; 78; 79]. The advantage of such techniques is that they produce polymer surfaces that can be further grafted or functionalized. There are a number of methacrylate-based monomers that have been used in polymerization reactions to produce monolithic stationary phases. The most common cross-linking agents in polymer monoliths are ethylene glycol dimethacrylate (**12**, Figure 4) and ethylene glycol diacrylate. Figure 3: Chemical structures of commonly used porogenic solvents, (**5**) cyclohexanol; (**6**) dodecanol; (**7**) 1,4 butane-diol; (**8**) toluene; (**9**) poly ethylene glycol; (**10**) dimethyl sulfoxide; (**11**) Tetrahydrofuran.
# Analyzing Zone-Based Registration under 2-Step Paging in Mobile Communication Network
## 3 Analysis of ZBR under 2-Step Paging  
In this section, let us analyze the performance of 1 ZR when 2-step paging is adopted by using a Markov chain model. A Markov chain is defined as a process that the conditional distribution of any future state \(X_{n+1}\), given that the past states \(X_{0}\), \(X_{1}\),..., \(X_{n-1}\), and the present state \(X_{n}\), are independent of the past states and depend only on the present state \(X_{n}\)[20]. In this study, since the next cell depends only on the present cell, we can use a Markov chain model to analyze the performance. To evaluate the total signaling cost on radio channels, we define the following notations:  
\(U\): location registration cost for one registration  
\(C_{R}\): location registration cost per unit time  
\(V\): paging cost for one cell  
\(C_{P}\): paging cost per unit time  
\(T_{c}\): interarrival time of calls (r.v., \(T_{c}\sim\text{Exp}[\lambda_{c}]\), \(E(T_{c})=1/\lambda_{c}\))  
\(T_{m}\): time spent in a cell (r.v., \(E(T_{m})=1/\lambda_{m}\))  
\(f_{m}^{*}(s)\): Laplace-Stieltjes transform for \(T_{m}\) (=\(\int_{t=0}^{\infty}e^{-st}f_{m}(t)dt\).  
\(R_{m}\): remaining time from the call occurrence to the time the UE moves out of the cell (r.v.).
## 3 Results and Discussion
### Relaxation Characteristics of the Three Asphalt Mixtures
#### 3.3.2 Fitting Analysis of Relaxation Modulus of the Three Asphalt Mixtures  
The stress relaxation is an important property to evaluate the viscoelasticity and the resistance to temperature cracking of the asphalt mixtures. To better characterize the relaxation performance of the three asphalt mixtures, the Burgers model and the generalized Maxwell model are used to fit the curves of the relaxation moduli \(E(t)\) of the three types of asphalt mixtures with time, as shown in Figure 6. The expressions of the Burgers model used to fit the relaxation curves and the generalized Maxwell model are shown in Equation (14) and Equation (15), respectively [43; 44; 45; 46]. \[E(t)=G_{1}e^{-t/p_{1}}+G_{2}e^{-t/p_{2}} \tag{14}\]  
where \(G_{1}\) and \(G_{2}\) are the relaxation strengths; \(p_{1}\) and \(p_{2}\) are the relaxation times used to characterize the time required for the asphalt mixture to release stress. \[E(t)=E_{e}+\sum_{j=1}^{n}E_{j}e^{-t/p_{j}} \tag{15}\]  
where \(E_{e}\) is the equilibrium modulus, \(E_{j}\) and \(\rho_{j}\) are the relaxation strengths and the relaxation times, respectively. The relaxation times \(\rho_{j}\) are fixed to the constants, \(\rho_{j}=2\times 10^{(0.3)}\) (j = 1, 2, \(\cdots\), 12). The 1 stopt software is used to fit the relaxation modulus of the three asphalt mixtures based on Equations (14) and (15). The fitting results of the model parameters are shown in Tables 8-11. It can  
Figure 6: Relaxation moduli at different temperatures: (**a**) AM; (**b**) DBFAM; (**c**) SBSAM. be observed from their correlation coefficients R\({}^{2}\) that the fitting curves of the generalized Maxwell model and the Burgers model with four parameters show good agreement with the calculated data. This means that both the generalized Maxwell model and the Burgers model with four parameters can better characterize the relaxation properties of the three asphalt mixtures. However, it can also be seen from the correlation coefficient that the generalized Maxwell model is superior to the Burgers model with four parameters in describing the relaxation modulus trend of three asphalt mixtures with the loading time at different temperatures. The parameters obtained by fitting the generalized Maxwell model can reflect the relaxation properties of the asphalt mixtures, which can provide more reliable material performance parameters for the finite element simulation calculation of asphalt mixtures. Just as the parameters of the generalized Kelvin model, the fitting parameters of the generalized Maxwell model are also difficult to characterize the variation of the relaxation properties of the three asphalt mixtures with the test temperatures. However, the parameters obtained by the Burgers model with four parameters can better reflect the changes in the relaxation properties of three asphalt mixtures with the test temperatures.
# Root Cause Tracing Using Equipment Process Accuracy Evaluation for Looper in Hot Rolling
## 2 EPAE Model of Looper
### Working Principle of the Looper  
In the finishing rolling unit, the rolling process is usually carried out in the order of steel biting, continuous rolling formation, continuous rolling tension establishment, stable continuous rolling, and steel throwing. The looper control can be divided into three main stages: from looping to strip tension formation (entry process), looper small tension continuous rolling (steady-state process), and exit process [17].  
The entry process mainly refers to the short period of time from the head of the strip being bitten by the roller until the strip establishes tension between the frames, which is about 1 s [18]. In the entire continuous rolling process, this period of time is very short. As shown in Figure 4, there are two important positions in the process: mechanical zero position \(\alpha_{0}\) and working zero position \(\alpha_{ref}\), which directly affect the quality and performance of the product during the rolling process.  
The steady-state process refers to maintaining a slight tension during the rolling process, so that the rolled piece can maintain a stable shape and movement between the rollers without deformation or other problems caused by excessive tension [19]. This is usually achieved through a highly closed loop control system to ensure that the force and tension exerted on the rolled piece during the rolling process are effectively controlled. The existence of this stage helps improve the stability of the rolling and the quality of the finished product.  
Figure 4: Starting process of the looper.  
Figure 3: Schematic diagram of looper structure.  
The exit process is a key step in the finishing rolling process. It refers to adjusting the position of the rolling mill roll sleeve so that it gradually decreases to the minimum value and finally ends the rolling process [20]. At this stage, the rolling mill gradually lowers the position of the roller sleeve and reduces the tension, finally achieving a smooth end of rolling. This process requires careful control to ensure that the shape and size of the final rolled piece meet product specifications.
## 4 Result
### SNR-Based Evaluation of Detection Performance  
To complement the visual observations, a quantitative analysis was performed using SNR for each processed image.The SNR provides an objective metric for evaluating how well a defect can be distinguished from the surrounding background. This approach enables direct comparisons between different conditions, including processing method (PCA vs RPCA), impact energy, and sample positioning.  
Table 1 and the bar chart on Figure 11 summarise the SNR values for all tested CFRP samples across both upper and lower configurations.  
The results presented in the bar chart offer an overview of SNR (dB) values obtained through both RPCA and PCA processing for a range of CFRP samples subjected to various impact energies and configurations. A prominent trend observed in the data is that samples placed in the upper position, closer to the transducer, generally exhibit higher SNR values, regardless of the image processing method or sample type. This highlights the significant effect of sample positioning and transducer proximity on defect detectability.  
As impact energy increases, there is a clear tendency for the SNR values to rise as well, especially in the thin samples. Both RPCA and PCA methods achieve their highest SNR value for the thin-12 J and thick-20 J samples. For these samples, the lower position yields a higher SNR than the upper position.  
In comparing the two image processing approaches on the LVT data set, it becomes evident that RPCA and PCA deliver comparable SNR values in most cases. An examination of the principal components further reveals that PC1 most often corresponds to the highest SNR, particularly for samples with large or clear defects. In cases where the defect is less distinct, the best SNR may be achieved using PC2 or PC3, indicating that relevant defect information can be captured in higher-order components when the variance associated with the defect is not dominant.  
There are several notable anomalies in the dataset. In particular, the Thick-4 J sample, both in upper and lower positions, shows an SNR marked as \"not detected\" for both RPCA and PCA. This absence of a detectable signal is not a result of image processing limitations but rather reflects the physical reality of the sample. Validation using both PT and PAUT inspection confirmed that the thick-4 J sample did not sustain any measurable damage during impact testing. \"not detected\" outcomes are also seen in some lower-positioned thin and thick samples. This may be attributed to several factors, such as the defect not being in close proximity to the transducer or an imperfect match between the transducer excitation frequency and the sample's local defect resonance (LDR) frequency. Both factors can diminish the thermal response associated with a defect, making detection more challenging.
## 3 The Banach Space of Continuous Periodic Functions and Particular Operators \(L\) and \(N\)  
### Definitions of Spaces \(X\) and \(Y\) and the Operators \(L\) and \(N\)  
We consider that the spaces \(X\) and \(Y\) are coincident and defined as the Banach space of continuous and \(\omega\)-periodic functions. More precisely:  
\[X=Y=\Big{\{}\mathbf{x}=(x_{1},x_{2},x_{3},x_{4})^{T}\in C( \mathbb{R};\mathbb{R}^{4}):\] \[x_{i}(t+\omega)=x_{i}(t),\quad i=\overline{1,4}\quad\|\mathbf{x }\|:=\sum_{i=1}^{4}\max_{t\in[0,\omega]}|x_{i}(t)|<\infty\Big{\}}. \tag{20}\]  
The operators \(L\) and \(N\) from \(X\) to \(Y\) are defined as follows:  
\[L(\mathbf{x}^{T}) =\bigg{(}\frac{d\mathbf{x}}{dt}\bigg{)}^{T}, \tag{21}\] \[N(\mathbf{x}^{T}) =\bigg{(}b(t)r_{S}(\exp(x_{1}),\exp(x_{2}))\bigg{[}1-\frac{\exp( x_{1})+\exp(x_{2})}{\theta}\bigg{]}-\beta(t)\exp(x_{4})-h(t)\] \[\quad+g(t)\exp(x_{2}-x_{1}),\quad b(t)r_{I}(\exp(x_{1}),\exp(x_{2 }))\bigg{[}1-\frac{\exp(x_{1})+\exp(x_{2})}{\theta}\bigg{]}\] \[\quad+\beta(t)\exp(x_{1}+x_{4}-x_{2})-(\alpha+h+g)(t),\] \[a(t)(1+\exp(x_{4}-x_{3}))\bigg{[}1-\frac{\exp(x_{3})+\exp(x_{4})} {K(\exp(x_{1})+\exp(x_{2}))}\bigg{]}-\gamma(t)\exp(x_{2})-(\mu+c)(t),\] \[\gamma(t)\exp(x_{2}+x_{3}-x_{4})-(\mu+c)(t)\bigg{)}^{T}. \tag{22}\]  
Moreover, we consider \(\Omega\subset X\) the open ball of \(X\) centered at \(0\in X\) and radius \(\sigma\), i.e., \(\Omega\) is the following set:  
\[\Omega=\Big{\{}\mathbf{x}\in X\ :\ \|\mathbf{x}\|<\sigma,\quad\sigma\in \mathbb{R}^{+}\Big{\}}. \tag{23}\]  
Particularly, in some of the results given below, we fix \(\sigma\) by defining it in terms of the coefficients.
# Position Tracking Control of 4-DOF Underwater Robot Leg Using Deep Learning
## 3 Overviews of 4-DOF Underwater Robot Leg  
As a test platform for applying and evaluating the hybrid control method, we constructed a 4-DOF underwater robot leg modeled after the robot in the scenario concept. The robot was designed for swimming underwater, incorporating 4-DOF per leg. Figure 2 illustrates the robot leg under position tracking control. The water tank and the cradle were designed for mobility, enabling experiments in both air and water environments. Figure 3 shows the joint configuration of the robot leg, and Table 1 provides its specifications. To reduce weight and prevent corrosion underwater, we chose Onyx as the frame material, which is a micro carbon fiber-filled nylon designed for three-dimensional (3D) printers manufactured by Markforged Co. (Waltham, MA, USA) The water-proof motor selected is Robotis' XW540-T140 model, and its specifications are detailed in Table 2.  
Figure 4 shows the control system of the robot leg. The UP-squared board was selected as the main controller. After generating the reference trajectory on the board, it is transmitted to the OpenRB-150 communication module via UART communication. After that, when the communication module sends a command to the Dynamixel communication bridge using TTL communication, the bridge switches to RS485 communication and transmits the command to each motor at a frequency of 500 Hz.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \multirow{4}{*}{Weight} & \(m_{1}=0.4623\) kg \\  & \(m_{2}=0.232\) kg \\  & \(m_{3}=0.2863\) kg \\  & \(m_{4}=0.1794\) kg \\  & \(l_{1}=59.5\) mm \\ \multirow{2}{*}{Dimensions} & \(l_{2}=87\) mm \\  & \(l_{3}=180\) mm \\  & \(l_{4}=230\) mm \\ \multirow{2}{*}{D.O.F.} & \multirow{2}{*}{4} \\ Sensors & Encoder 4 ea. \\ \multirow{2}{*}{Matin Controller} & Onyx (micro carbon fiber) \\ \multirow{2}{*}{Matin Controller} & UP Squared board \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of the robot leg.  
Figure 4: Control system diagram.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \multirow{4}{*}{Weight} & \(10\sim 14.8\) V \\  & Stall Torque & 6.9 Nm \\ \multirow{4}{*}{Stall Current} & 4.9 mA \\ \multirow{4}{*}{Weight} & \(165\) g \\ \multirow{2}{*}{Gear Ratio} & \(145.6:1\) \\ \multirow{2}{*}{Operating Temperature} & \(-5\sim 80\)\({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 2: Specifications of XW540-T140, water-proof motor.