esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Imputation Method
### Iterative Feature Optimization  
The proposed imputation method uses the filtered dataset, \(D_{p_{2}}\), obtained after the preprocessing to enhance the reliability of inferring the remaining missing values. This method addresses missing values on a per-feature basis by identifying the most informative features for each feature \(f_{i}\) and using them to train a tailored regression model. This model learns the relationship between the target feature \(f_{i}\) and the selected informative features, enabling it to predict the missing values in \(f_{i}\). A critical question arises before selecting informative features and constructing the model: which feature should be prioritized first? The proposed approach emphasizes features with the highest number of missing values, ensuring that the imputation process leverages the most available observational data. This is clear in Figure 3. When the features are ranked by the number of missing values, the  
Figure 2: Flowchart of the proposed imputation method.  
third feature ranks highest because it has the maximum number of missing data points. The overall sequence of operations follows an iterative pattern that includes feature selection, regression model construction, missing value imputation, and dataset updating. Figure 3 illustrates the complete iterative process.
## 3 Results and Discussion
### Hardened-State Properties of Mortars
#### 3.2.1 Compressive Strength
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Mechanical property**} \\ \hline \multirow{2}{*}{**Mortar**} & \multicolumn{5}{c}{**Compressive Strength (MPa)**} & \multicolumn{5}{c}{**Flexural Strength (MPa)**} \\ \cline{2-11}  & **7 days** & **CV** & **91 days** & **CV** & **365 days** & **CV** & **7 days** & **CV** & **91 days** & **CV** & **365 days** & **CV** \\ \hline RM & 44.8 & 1.1 & 65.7 & 1.2 & 87.5 & 1.6 & 6.5 & 4.2 & 8.1 & 2.9 & 8.4 & 4.1 \\ M-5/0 & 37.4 & 0.9 & 58.1 & 1.1 & 78.9 & 1.4 & 6.1 & 5.4 & 7.7 & 3.6 & 8.2 & 3.8 \\ M-10/0 & 36.0 & 0.4 & 50.0 & 0.8 & 74.9 & 1.1 & 5.6 & 3.8 & 7.2 & 4.1 & 8.0 & 3.3 \\ M-0/10 & 41.6 & 1.0 & 59.6 & 1.2 & 81.5 & 0.9 & 6.2 & 2.1 & 7.5 & 2.7 & 7.8 & 2.4 \\ M-0/20 & 35.1 & 0.7 & 53.6 & 1.0 & 79.4 & 1.1 & 5.7 & 3.4 & 6.6 & 2.9 & 7.2 & 3.1 \\ M-5/10 & 42.0 & 0.8 & 61.3 & 1.1 & 85.4 & 1.4 & 6.2 & 4.1 & 7.6 & 2.8 & 8.0 & 2.9 \\ M-10/20 & 30.6 & 0.4 & 44.9 & 0.9 & 72.1 & 1.1 & 5.8 & 3.1 & 7.1 & 2.7 & 7.4 & 2.4 \\ \hline \multicolumn{11}{c}{**Quantification of synergistic effects**} \\ \hline \multirow{2}{*}{**Age**} & \multicolumn{5}{c}{**Compressive strength**} \\ \cline{2-11}  & \multicolumn{2}{c}{**\(\Delta\) with RC (\%)**} & \multicolumn{2}{c}{**\(\Delta\) with FA (\%)**} & \multicolumn{2}{c}{**\(\Delta\) with RC \(\times\) FA (\%)**} \\ \hline
7 days & \(-16.6\) & \(-19.8\) & \(-7.1\) & \(-21.7\) & \(-6.4\) & \(-31.8\) \\
91 days & \(-11.5\) & \(-23.9\) & \(-9.2\) & \(-18.4\) & \(-6.6\) & \(-31.6\) \\
365 days & \(-9.8\) & \(-14.3\) & \(-6.8\) & \(-9.2\) & \(-2.4\) & \(-17.5\) \\ \hline \multicolumn{11}{c}{**Flexural strength**} \\ \cline{2-11}  & \multicolumn{2}{c}{**\(\Delta\) with RC (\%)**} & \multicolumn{2}{c}{**\(\Delta\) with FA (\%)**} & \multicolumn{2}{c}{**\(\Delta\) with RC \(\times\) FA (\%)**} \\ \hline
7 days & \(-5.8\) & \(-13.7\) & \(-4.0\) & \(-11.5\) & \(-4.9\) & \(-9.6\) \\
91 days & \(-5.6\) & \(-11.9\) & \(-8.1\) & \(-18.3\) & \(-6.2\) & \(-12.6\) \\
365 days & \(-2.6\) & \(-5.0\) & \(-7.2\) & \(-14.7\) & \(-5.1\) & \(-11.8\) \\ \hline \hline \end{tabular} Note: CV: coefficient of variation in percent (\(\%\)).  
\end{table} 
Table 5: Mechanical properties and quantification of the synergistic effects of the mortars. Figure 4: Results of (**a**) compressive strength and (**b**) strength activity index between 7 and 365 days of curing.
# An Artificial Plant Community Algorithm for Collision-Free Multi-Robot Aggregation
## 5 An Artificial Plant Community Algorithm
### APC-Based REI  
The APC algorithm is based on the growth process of a real plant community, which searches for optimal solutions through the heuristic learning of a swarm of plant individuals. For engineering applications, some suggestions for APC-based REI have been proposed, as detailed below.  
First, the APC algorithm can be deployed as a local computing method. This refers to running the APC algorithm directly on edge robots or other terminal devices, which enables the data processing and convergence computation of the APC algorithm to be completed locally on the edge robots without the need for network transmission of data. The advantage of this approach is its high efficiency and real-time performance, making it suitable for scenarios with extremely high real-time requirements, such as fire monitoring.  
Second, the APC algorithm can also be deployed as an edge computing method. This involves deploying the APC algorithm on edge computing devices in the user's local area network environment, such as edge boxes or edge routers. The advantages of this approach include fast data processing speed, reduced transmission time of data in the network, and reduced network burden and bandwidth pressure. The APC algorithm uses simple and/or/not operators and is thus suitable for the embedded platforms on edge computing devices.  
Third, the APC algorithm can also be deployed as a cloud computing method. In this approach, the APC algorithm is run on the cloud servers for remote access through the Internet, where the cloud servers run the APC algorithm for scheduling and convergence. The associated advantages are strong computing power and ensuring the security and privacy of data. Hence, the APC algorithm may perform better in dynamic aggregation and collision environments, and each edge robot can join or leave the REI system at any time. However, the APC calculation process may result in certain delays and higher costs. If the network is interrupted, the system may not function properly.  
Finally, the APC algorithm can also be deployed in a hybrid manner, using a combination of the three methods described above. The advantages of this approach include easy management and expansion, as well as the ability to dynamically adjust the computing resources according to needs; however, it increases the complexity of the APC system. The artificial plant community is randomly distributed on all edge robots or edge servers, and searches for feasible solutions to the collision-free multi-robot aggregation problem.
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 4 Location and Installation of the Measuring Devices
### Bespoke Network
#### 4.2.1 Automatic Weather Stations  
The city's UCZ is defined using maps, city planning maps, and field work. In the horizontal scale, two predominant situations are identified, both of roughness class 7: the first, zH 10 m and HUZ collective block (closed or open), with a footprint of 0.80 km in radius; and the second, zH 4 m and HUZ single family house (detached, semi-detached or terraced), of 0.32 km in radius. The distribution of 25 AWSs (numbered from \"1\" to \"25\") was optimized, minimizing the superposition of the footprints, in order to achieve an efficient coverage of the city (Figure 4). As for the vertical scale, the proposal was to place the devices on the public streetlamps, at a height of five meters. In the two scales, special attention was paid to the uniformity in the distribution of the placements.
## 4 Results and Discussions
### The Results Obtained by the Two Algorithms in Case 1
Loss found by the OOA and WOA comes from the initial purpose of conducting this paper. Besides reaching the minimum value of active power loss in the grid, the two algorithms are also executed to investigate their performance. As presented in Figure 4, the difference between the LST. Loss values achieved by the OOA and WOA are only 0.0001 kW, which is very small and can be ignored. However, reaching a global solution is one of the most important considerations while applying any meta-heuristic algorithm to solve an optimization problem. In this regard, only the WOA offers this ability while the OOA is trapped in the local optima. Moreover, while taking a look at the remaining criteria, the differences are noticeable. The difference in the positions of WTs found by the OOA and WOA, as presented in Table 1, can be explained as follows: the total power output of WTs found by the OOA is only 2005 kW while that of the WOA is up to 2262 kW. Clearly, the WOA can offer a large capacity of WTs connected to the grid. Due to the large capacity of WTs resulting from the WOA, finding the same positions for placing the two WTs and achieving a similar value of LST. Loss, as found by the OOA, is impossible. In terms of the voltage profile after placing WTs in the grid shown in Figure 5, the difference between the OOA and WOA is not really noticeable. However, when compared to the Base case where WTs are not connected, the node voltage profile improvement is unneglectable.
# Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems
## 3 Outage Probability Analysis
### Outage Probability \(Op_{1}\)  
The outage event of \(D_{1}\), \(OP_{1}\), occurs if \(R\) or \(D_{1}\) fails to decode \(x_{1}\), which can be formulated as  
\[\begin{split} OP_{1}&=1-Pr(\gamma_{R}^{1}>\gamma_{ 1},\gamma_{D1}^{1}>\gamma_{1})\\ &\stackrel{{(a)}}{{=}}1-Pr(|h_{w}|^{2}>\frac{\tau_{ 1}}{\rho_{s}d^{-w}})\times Pr(|h_{1}|^{2}>\frac{\beta_{1}}{\rho_{R}}),\end{split} \tag{4}\]  
where \((a)\) stems from the independence between \(h_{w}\) and \(h_{1}\), \(\gamma_{1}=2^{R_{1}}-1\) with \(R_{1}\) as the target data rate of \(x_{1}\), \(\tau_{1}=\gamma_{1}/(a_{1}-a_{2}\gamma_{1})\) under condition that \(a_{1}>a_{2}\gamma_{1}\) or \(a_{1}>\gamma_{1}/(1+\gamma_{1})\), and similarly \(\beta_{1}=\gamma_{1}/(b_{1}-b_{2}\gamma_{1})\) under condition that \(b_{1}>b_{2}\gamma_{1}\) or \(b_{1}>\gamma_{1}/(1+\gamma_{1})\). With the aid of CDFs in (1) and (3), we obtain a closed-form expression of \(OP_{1}\) as in (5).  
\[\begin{split} OP_{1}=1-e^{\frac{-\tau_{1}}{\rho_{s}d^{-w}}} \Bigg{(}1-wG_{1,2}^{11}\Bigg{(}\frac{1}{\lambda}(\frac{\beta_{1}}{\rho_{R}h_{ r}})^{\frac{1}{r}}\big{|}\begin{array}{c}1\\ 1,0\end{array}\Bigg{)}-\frac{1-w}{\Gamma(a)}G_{1,2}^{11}\Bigg{(}\frac{1}{b^{c }}(\frac{\beta_{1}}{\rho_{R}h_{r}})^{\frac{5}{r}}\big{|}\begin{array}{c}1\\ a,0\end{array}\Bigg{)}\Bigg{)}.\end{split} \tag{5}\]
# NESO-Based Path Following Control for
## 3 Control Strategy Design
### Stability Analysis of the Control Strategy  
A diagram of the designed safe control strategy for the path following of underactuated hovercrafts is shown in Figure 2b. First, a NESO is proposed to approximate the unknown total uncertainties of underactuated hovercrafts. Then, a safe yaw control law based on the NESO is initially proposed by introducing an auxiliary variable and using the backstepping sliding mode control technologies to improve the maneuverability and safety of underactuated hovercrafts.  
The stability of the proposed closed-loop control system is proven as follows.  
**Theorem 3**: _Consider the underactuated nonlinear model (9) with uncertainties (8) and the turning SLC (7). The closed-loop control system consisting of the NESO (16), the auxiliary system (31), the virtual control input (29), the yaw control law (38), and the surge control law (43) renders all error signals of the closed-loop path following system are bounded under the above assumptions and the designed parameters \(\varsigma\), \(\Delta_{min}\), \(\Delta_{max}\), \(\gamma_{\Delta}\), \(\beta_{i}(i=1,2,3)\), \(\alpha\), \(k_{\psi}\), \(k_{\zeta}\), \(k_{a}\), \(\ell\), \(k_{r}\), \(k_{u}\), \(k_{s}\) and \(h\)._  
**Proof.** Considering the candidate Lyapunov function \(V=V_{o}+V_{r}+V_{u}\), yields  
\[\dot{V} \leq-\kappa V_{o}(\mathbf{E}_{o})^{\frac{\gamma+a-1}{\gamma}}- \bigg{(}k_{\psi}+h\Big{(}k_{s}^{2}+k_{s}\Big{)}-\frac{k_{\zeta}}{2\varepsilon _{1}}-\frac{k_{s}}{2\varepsilon_{2}}-\frac{1}{2}\bigg{)}\psi_{e}^{2}-\bigg{(}k _{a}-\frac{\varepsilon_{1}k_{\zeta}}{2}-\frac{1}{2}\bigg{)}\zeta^{2} \tag{47}\] \[-\bigg{(}h(k_{s}+1)-\frac{1}{2\varepsilon_{3}}-\frac{1}{2}\bigg{)} r_{e}^{2}-\bigg{(}k_{u}-\frac{1}{2\varepsilon_{4}}\bigg{)}u_{e}^{2}+\bigg{(} \frac{\varepsilon_{2}k_{s}}{2}+\frac{\varepsilon_{3}}{2}\bigg{)}b_{dr}+\frac{ \varepsilon_{4}b_{du}}{2}\] \[\leq-\kappa V_{o}(\mathbf{E}_{o})^{\frac{\gamma+a-1}{\gamma}}- \kappa_{c}\|\mathbf{E}_{c}\|+C\leq-F(V)+C\]  
where \(\kappa_{c}=\min\{\kappa_{r},\kappa_{u}\}\), \(C=(\varepsilon_{2}k_{s}/2+\varepsilon_{3}/2)b_{dr}+\varepsilon_{4}b_{du}/2\), and \(F(V)=\kappa V_{o}(\mathbf{E}_{o})^{\frac{\gamma+a-1}{\gamma}}+\kappa_{c}\| \mathbf{E}_{c}\|\geq 0\), then all error signals of the closed-loop system have been proven to be bounded. \(\Box\)  
**Remark 7**: _To analyze the stability of underactuated hovercrafts in the sway direction, consider the total uncertainty and its derivative is bounded such that \(|d_{v}|\leq\mathcal{B}_{12}\) and \(\left|\dot{d}_{v}(t)\right|\leq\mathcal{B}_{22}\) described in Assumption 2 together with the motion Equation (9). Therefore, the sway velocity \(v\) of underactuated hovercrafts is bounded._
## 3 Overview of the Research Framework  
The proposed framework consists of three main stages. (i) Data acquisition and preprocessing: Vibration signals are collected from bearings under different operational conditions as the input data for the prediction model. The data are preprocessed through steps such as noise reduction and feature extraction to extract relevant features for subsequent analysis. (ii) Transformer-attention based RUL prediction: The preprocessed data are fed into the hybrid transformer-attention model. Transformer is employed to identify historical data points with similar degradation patterns with the current bearing condition, the attention mechanism assigns varying importance to these reference data points, and the most essential features can be extracted for the bearing's remaining useful life prediction. The combined approach enables a more refined and accurate RUL prediction. (iii) LSP parameter optimization: Based on the predicted RUL, the optimal laser shock peening parameters are determined based on the specific wear characteristics of each bearing. The selected parameters are applied to the bearing, which is also validated with FEA results. Figure 2 shows the overview of research framework.
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 3 Longitudinal Controller Design  
Figure 2 displays the schematic diagram for the longitudinal motion control. It mainly includes acceleration/brake mode switching logic, fuzzy PID controller, and brake controller. The acceleration/braking mode switching logic judges the driving/braking state of the vehicle according to the expected acceleration obtained by the upper controller, and it realizes the switching between acceleration and deceleration by adding the switching logic of the acceleration tolerance interval to avoid frequent acceleration and deceleration switching, which affects the comfort and stability of speed control. The fuzzy PID controller calculates the expected acceleration according to the deviation between the expected speed and the actual feedback speed, adaptively adjusts the PID control parameters through the established fuzzy rule table, and finally inputs the motor torque. The brake controller is converted into the brake oil pressure according to the difference between the expected speed and the actual speed. The longitudinal motion controller designed in this section considers only the deviation from the expected speed and does not consider the influence of lateral motion for the time being.
# Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions
## 2 Methodology
### Probability Distributions
#### 2.1.5 Fatigue Lifetime (FL3)  
This distribution is also known as the Birnbaum-Saunders distribution [33].  
\[f(x)=\frac{1+\left(x-\gamma\right)\cdot\beta}{2\cdot\alpha\cdot\sqrt{\left(x- \gamma\right)^{3}\cdot\beta}}\cdot\frac{\exp\biggl{(}-\frac{\left(\beta\cdot \left(x-\gamma\right)-1\right)^{2}}{2\cdot\left(x-\gamma\right)\cdot a^{2} \cdot\beta}\biggr{)}}{\sqrt{2\cdot\pi}} \tag{17}\]  
\[F(x)=1-\int\limits_{\gamma}^{x}\frac{1+\left(x-\gamma\right)\cdot\beta}{2\cdot \alpha\cdot\sqrt{\left(x-\gamma\right)^{3}\cdot\beta}}\cdot\frac{\exp\biggl{(} -\frac{\left(\beta\cdot\left(x-\gamma\right)-1\right)^{2}}{2\cdot\left(x- \gamma\right)\cdot a^{2}\cdot\beta}\biggr{)}}{\sqrt{2\cdot\pi}}dx \tag{18}\]  
\[F(x)=1-\frac{1}{2}\cdot\left(1+erf\left(\frac{\beta\cdot\left(x-\gamma\right) -1}{\alpha\cdot\sqrt{2\cdot\left(x-\gamma\right)\cdot\beta}}\right)\right) \tag{19}\]  
\[x(p)=\gamma+\frac{\alpha\cdot\sqrt{qnorm\left(p,0,1\right)^{2}}\cdot\sqrt{a^{2 }\cdot qnorm\left(p,0,1\right)^{2}+4}+\alpha^{2}\cdot qnorm\left(p,0,1\right) ^{2}+2}{2\cdot\beta},\text{ if }p\leq 0.5 \tag{20}\]  
\[x(p)=\gamma+\frac{-\alpha\cdot\sqrt{qnorm\left(p,0,1\right)^{2}}\cdot\sqrt{a^{ 2}\cdot qnorm\left(p,0,1\right)^{2}+4}+\alpha^{2}\cdot qnorm\left(p,0,1\right) ^{2}+2}{2\cdot\beta},\text{ if }p>0.5 \tag{21}\]  
where \(\alpha,\beta,\gamma\) are the shape, the scale and the position parameters; \(\alpha,\beta>0\); \(x\) can take any values in the range \(\gamma<x<\infty\).
## 4 Experimental Verification
### Model Verification Results  
To validate the effectiveness of the MHL model, a multi-frequency signal \(v(t)=50\)\(-19.2\sin(30\pi t)-10.7\sin(80\pi t)-10.7\sin(150\pi t)-10.7\sin(200\pi t)\), which contains frequency components of 15 Hz, 40 Hz, 75 Hz, and 100 Hz, is excited into the FSM. The modeling results presented in Figure 11 indicate that the output of the rate-dependent model is consistent with the actual data, with an NME of 2.66% and an NRMSE of 0.85%. In this frequency range, the rate-dependent model can effectively describe the system dynamics, while the output of the linear dynamic term is close to 1, which is almost ineffective.  
Figure 11: Prediction results of the MHL model under the multi-frequency input signal.  
Figure 10: Open-loop frequency–response curve of FSM system.  
Figure 12 displays the prediction results of the MHL model within the range of 500 to 4000 Hz. It can be seen that the output of the MHL model is in good agreement with the actual response data, and due to the influence of system dynamics, the amplitude of the output signal gradually decreases as the frequency increases. Figure 13 shows the hysteresis curves at different frequencies. It can be observed that the FSM exhibits dynamic hysteresis behavior with increasing frequency, and the shape of the hysteresis curves gradually changes. From Figures 12 and 13, it can be concluded that the MHL model can better describe the dynamic characteristics of the FSM within the range of 4000 Hz, demonstrating the effectiveness and feasibility of the proposed modeling method. Table 3 provides the quantitative results by listing the NME and NRMSE values.
# Applications of Machine Learning in National Territory Spatial Planning
## 2 Materials and Methods
In the future, we should further strengthen the integration of machine learning, food security, and agricultural development to better respond to the major challenges of the new environment caused by global disasters such as climate change, global warming, the COVID-19 pandemic, and the Russia-Ukraine conflict. Methodological Innovation and the System Development of Machine Learning and National Territorial Spatial Planning Integration  
The rapid development of new technologies has promoted the emergence of the \"Smart Society\", and the rise of the smart society has provided a new environment and new requirements for current territorial spatial planning and governance [14]. In recent years, scholars have used artificial intelligence, machine learning, and other territorial spatial computing technology methods to dynamically simulate and evaluate regional space structure [15], urban growth [16], land use change [17], transportation networks [18], and landscape ecology [19], providing support for planning decision-making [20; 21]. At present, considerable exploration has been carried out with many practices at the technical level on territorial spatial planning. However, due to a lack of multi-source data and methods, there is less comprehensive thinking and systematic research on the application of new technologies in territorial spatial planning. Informatization is the core power of empowerment to national territorial spatial planning; however, the current level of technological progress needed not only includes new information and communication technology but also the integrated application of innovative technology [22]. In the papers included in our Special Issue, three teams use machine learning technology to research different fields in national territorial spatial planning from different perspectives, involving land carrying capacity, house price prediction, and the identification of urbanized and non-urbanized areas. [PERSON] et al. (contribution 3) develop a multi-dimensional evaluation index system for land comprehensive carrying capacity (LCCC) using a normal cloud model, which brings the fuzziness and randomness of the evaluation index and its results into consideration. [PERSON] et al. (contribution 5) use the more transparent supervised regularized regression technique and integrate it with the eigenvector spatial filter (ESF) approach to more accurately account for spatial auto-correlation and enhance the prediction accuracy of house prices whilst also improving the explanation ability needed for mass appraisal exercises. Machine learning technologies also promote the development of the dynamic simulation of urban space. For example, [PERSON] et al. (contribution 6) present a standardized and automatic approach aimed at analyzing the performance of clustering methods to identify if an area is urban.
# A Brief Review on Micro-Implants and Their Use in Orthodontics and Dentofacial Orthopaedics
## 3 Results
### Design  
The researchers tried to improve the design of the orthodontic micro-implants to increase torsional strength, stability and reduce bone damage during insertion. An objective function stability quotient (SQ) was built and solved by Korean researchers [10] started from the thread height and pitch of AbsoAnchor SH1312-7 micro-implant (Dentos Inc., Daegu, Korea) as parameters. 3D finite element simulation, torque test, and clinical test led to the creation of four models with optimized thread design and better performance, which indicated that their optimization methodology could be used when designing OMI threads.  
The peak insertion torque value is another parameter that influences the OMI's stability. This depends on the manufacturer [11]; the study showed no correlation between the diameter of six different types of self-drilling micro-implants and torque values. Insertion speed did not affect significantly the peak torque values, but the 6 mm OMIs proved to have significantly higher torque values than the 8 and 10 mm ones. Using a screwdriver for limiting the torque or pre-drilling the cortical bone to reduce insertion torque could be a good choice.