esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Objective Evaluation  
The following section is divided into four parts. In Section 4.1, two array designs are compared to each other to show the improvement of the spatial aliasing of a logarithmically-spaced array over a linearly-spaced one. In the second Section 4.2, the new stereo system proposed in this study is compared to the state-of-the-art ones, which utilize two microphones. In the third Section 4.3, the WNG constraint and the frequency response are analyzed. Finally, in the last Section 4.4, the angular constraints, as well as the phase of the directivity pattern are investigated.
# Gait Phase Estimation Based on User-Walker Interaction Force
## 2 Method for Asymmetric Gait Phase Estimation
### Architecture  
When a user walks with a walker, its handle supports part of the bodyweight of the user to reduce the physical burden to the lower limbs by transferring force to the upper limbs. The change in vertical position of the center of mass of a user during walking leads to a cyclic change in the interaction force in each gait cycle, as shown in Figure 1. In other words, the interaction force contains information of the gait phases. [PERSON] [22] divided the interaction force into three components: (1) supporting force, which represents the part of the bodyweight that is placed on the walker; (2) slow oscillation caused by the cyclic change in the placement of the center of mass changing with the gait phase; and (3) high-frequency vibration originating from the walker. As the slow oscillation contains the information of the gait phase, we use it for estimation.  
The main tool used for gait phase estimation is the AO proposed by [PERSON] [16]. Nevertheless, the supporting force and high-frequency vibration in the user-walker interaction force degrade the estimation accuracy because the AO is sensitive to noise and variations in the input signal. Therefore, preprocessing is necessary to extract the low-frequency gait oscillations from raw data. As shown in Figure 2, the proposed estimation method consists of two subsystems. (1) A band-limited multiple Fourier linear combiner (BMFLC) [23; 24] model extracts the slow gait oscillation from the raw data of the user-walker interaction force, enabling the AO to reflect the information of the gait phase. (2) Two AOs are used to separately obtain the gait phase information from the two legs. The difference between the slow oscillation and the AO output is used as feedback to tune its frequency and phase. In addition, alternate switching between the two AOs is applied in response to the input signal when the HS is detected. Moreover, an adaptive rule for the AO parameter is applied to reduce the impact of the transient amplitude change on phase estimation.  
Figure 1: Interaction force along vertical direction. Black line represents the raw data of the interaction force, blue line represents the signal after BMFLC, and red line represents the reconstructed signal of the AOs.
## 3 Results and Discussion
### Simulation and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Training Step Size**} & \multicolumn{3}{c}{**300,000**} & \multicolumn{3}{c}{**600,000**} \\ \hline \multicolumn{2}{c}{**Scenes**} & **20** & **100** & **200** & **20** & **100** & **200** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Average transit \\ time (s) \\ \end{tabular} } & PPO+CCMR & \multirow{2}{*}{34.42} & \multirow{2}{*}{34.86} & \multirow{2}{*}{41.81} & \multirow{2}{*}{31.61} & \multirow{2}{*}{31.97} & \multirow{2}{*}{39.61} \\  & algorithm & & & & & & \\ \multicolumn{2}{c}{} & Optimized PPO & \multirow{2}{*}{33.62} & \multirow{2}{*}{33.95} & \multirow{2}{*}{36.51} & \multirow{2}{*}{30.86} & \multirow{2}{*}{31.13} & \multirow{2}{*}{34.08} \\  & algorithm & & & & & & \\ \multicolumn{2}{c}{} & 2.32\% & \multicolumn{2}{c}{2.61\%} & \multirow{2}{*}{12.67\%} & \multirow{2}{*}{2.37\%} & \multirow{2}{*}{2.62\%} & \multirow{2}{*}{13.96\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation average pass time. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Training Step Size**} & \multicolumn{3}{c}{**300,000**} & \multicolumn{3}{c}{**600,000**} \\ \hline
**Scenes** & **20** & **100** & **200** & **20** & **100** & **200** \\ \hline \multicolumn{2}{c}{Traffic ratio (Inner: outer)} & 5:5 & 5:5 & 6:4 & 9:1 & 9:1 & 8:2 \\ \multicolumn{2}{c}{} & Inner & 31.30 & 31.55 & 35.15 & 30.48 & 30.74 & 33.52 \\ \multicolumn{2}{c}{} & outer & 35.94 & 36.35 & 38.55 & 34.26 & 34.62 & 36.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The inner and outer loop traffic schedule. The simulation experiment demonstrates that the optimized PPO algorithm effectively enhances the efficiency of simulation training. However, in the case of 20 and 100 vehicles, the optimization algorithm exhibits marginal improvements in terms of driving efficiency, stability, and safety for autonomous vehicles at unsignalized roundabouts. Conversely, when confronted with a scenario involving 200 vehicles, the optimization algorithm significantly enhances both stability and driving efficiency for autonomous vehicles entering unsignalized roundabouts, albeit with limited safety enhancements.
# Design of Autonomous Mobile Robot for Cleaning in the Environment with Obstacles
## 4 Methodology  
Two experiments were performed to verify the high-level controller explained in Section 3. A standard PC was used with the Linux system (Ubuntu 20.04) and the robot operating system (ROS) Noetic to collect data and implement the high-level controller. For the cleaning robot, Ubuntu LTS 20.04 with ROS Noetic was installed in the Raspberry Pi. The kinematic equations of the unicycle-like robot, low-level PID controller, odometry equations were installed in Arduino Mega (slave controller of Raspberry Pi). The PC and the cleaning robot were connected under the same Wi-Fi router to establish the ROS master and slave connectivity. The control algorithm in the graphical form is represented in Figure 3. The Figure 4 shows the real-time data exchange between the PC and the robot during the experiments. The high-level control algorithm was implemented in a ROS node written in Python language on the PC. In real-time, the odometry data coming from the robot were directly injected into the control algorithm to calculate the control signals for the robot. The calculated control signals were sent to the robot by the PC. The generated data were also saved by the PC as text files during the experiments. After completing the experiments, the authors used MATLAB software to plot the graphs from the text (txt) files. The control method was not very computationally complex, as it had 52 addition and subtraction operators, 54 multiplication operators, 19 sines, cosine, and hyperbolic tangent functions, and one exponential function. The computational complexity analysis was conducted for one obstacle.
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 4 Results
### Monthly Average Change Analysis  
Figures 9 and 10 provide the overall monthly LST spatial distribution and the difference of LST between 2018 and 2021. The spatial variation of LST can easily determined using the monthly datasets of FY-3D MERSION-II data, and the LSTs were gradually increased from January, reached their peak in June to August, and then they declined throughout Pakistan. The highest mean LSTs during 2018 and 2021 were recorded in June (43.6 \({}^{\circ}\)C), and the lowest in January (14.3 \({}^{\circ}\)C). In terms of sub-regions, the monthly LST variations were followed by annual and seasonal trends. Throughout the monthly analysis, region II contained the highest LST and region V, the lowest; these highest and lowest LST values possibly differed due to the latitudinal change and the geographical land cover types. According to Figure 10, a warming trend were observed in region II throughout the monthly results except for the January (\(-\)0.93) and December (\(-\)0.83) months, which resulted in that the LSTs in 2021 were higher than 2018. However, region V does not follow decreasing or increasing trends; there are many cooling and warming trends observed because of the surface heterogeneity and snow cover. Regarding region IV, there are some events in the monthly analysis that follow the cooling trends compared to the 2018 and 2021, but they are not a priority for discussion. The monthly change analysis was calculated by subtracting the monthly rasters between 2018 and 2021. The MK trends analysis was never applied over a monthly basis due to the limited availability of temporal data.  
Figure 8: Spatial distribution of seasonal mean LSTs between 2018 and 2021 in Pakistan. (**a**) Spring, (**b**) summer, (**c**) winter, (**d**) autumn.
# Deep Subspace Clustering with Block Diagonal Constraint
## 4 Conclusions  
We propose a Deep Subspace Clustering with Block Diagonal Constraint (DSC-BDC) model, in which the coefficient representation matrix is loosely block diagonally constrained by means of a separation strategy. Furthermore, for solving the joint optimization problem, the algorithm alternately updates the auto-encoder module, representation matrix parameters and matrix decomposition module parameters. On the other side, by using the block diagonal constraint, we can learn a better representation matrix from the self-expression coefficient to obtain more stable and excellent clustering performance. From the experiments on four benchmark image datasets, we can conclude that our model is effective and promising. In future work, we will extend our model to deep multi-view subspace clustering. We assume that there is consistent information among different views of multi-view data and that each view has view-specific information [21, 24, 45]. We impose a separation strategy on multi-view data, so that each view shares consistent structural information on the premise of maintaining its view-specific information. At the same time, the block diagonal constraint is constrained to the shared information (matrix) to improve the clustering performance, and we use the network to learn the variables of the separation strategy module instead of solving the variables through the alternating iterative algorithm.  
Conceptualization, Y.S. and J.L.; methodology, J.L. and Y.H.; software, J.L.; validation, J.L., Y.S. and Y.H.; formal analysis, J.L.; investigation, J.L.; resources, Y.S. and Y.H.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, Y.S.; visualization, J.L.; supervision, Y.H.; project administration, Y.S.; and funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 4: Convergence curve of the proposed algorithm on the four databases.  
**Acknowledgments:** This research was supported by National Natural Science Foundation of China under Grants 61772048, 61632006, 61672071, U1811463 and U19B2039 and in part by Beijing Talents Project (2017A24).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Datum Transfer  
### Systematic Coordinate System  
The results of ground-mounted stability testing of optical loads in space have a serious impact on their in-orbit imaging quality. Currently, commonly used geometric measurements to assess truss deformation mainly include instruments such as a theodolite, laser tracker, and industrial photogrammetry [26]. Figure 2 shows a spatial coordinate measurement system composed of each instrument.  
As shown in Figure 2, the coordinate system is established as follows: The M1&Instruments holder coordinate system \(O_{B}\) (\(O_{B}\)-\(X_{B}\)\(Y_{B}\)\(Z_{B}\)), the origin of the coordinate system \(O_{B}\) located at the intersection of three mutually perpendicular planes. The \(Z_{B}\) is the direction of the optical axis, the \(Y_{B}\) axis is the direction of the line connecting M1 and M3, and the \(X_{B}\) is determined by means of a right-handed coordinate system. In the datum platform coordinate system \(O_{O}\) (\(O_{O}\)-\(X_{O}\)\(Y_{O}\)\(Z_{O}\)), the origin of the coordinate system \(O_{O}\) is located at the intersection of the three mutually perpendicular planes, and the direction of the coordinate axes is the direction normal to the three planes. The M2 holder coordinate system \(O_{S}\) (\(O_{S}\)-\(X_{S}\)\(Y_{S}\)\(Z_{S}\)) is established in a similar way to the datum platform coordinate system. The angular relationship between the M1&Instruments holder and the M2 holder can be described by the rotation angles \(\alpha\), \(\beta\), and \(\gamma\) of the \(O_{B}\) and \(O_{S}\) systems with respect to the \(O_{O}\) system. If the manufacturing error and installation error of the cubic prism are ignored, the coordinate system \(O_{\text{CO}}\) (\(O_{\text{CO}}\)-\(X_{\text{CO}}\)\(Y_{\text{CO}}\)\(Z_{\text{CO}}\)) of cubic prism 1 is parallel to the \(O_{O}\) system, and the coordinate system \(O_{\text{CB}}\) (\(O_{\text{CB}}\)-\(X_{\text{CB}}\)\(Y_{\text{CB}}\)\(Z_{\text{CB}}\)) of cubic prism 2 is parallel to the \(O_{B}\) system. The \(X_{\text{CS}}\), \(Y_{\text{CS}}\), and \(Z_{\text{CS}}\) vectors in the coordinate system \(O_{\text{CS}}\) (\(O_{\text{CS}}\)-\(X_{\text{CS}}\)\(Y_{\text{CS}}\)\(Z_{\text{CS}}\)) of the cubic prism 3 can be represented relative to the \(O_{S}\) by the cosine matrix \(M_{\text{CS}\)-\(S\).
# Evolutionary Algorithms for Optimization Sequence of Cut in the Laser Cutting Path Problem
## 4 Overview of Application of ABRKGA to LCPP
### An Eulerian Heuristic to Generate Improved Individuals for LCPP
To consider the quality of the solutions (objective function value), the second point of analysis of the results, Tables 6-10 reveal the best and average solutions of each approach and the \"GAP\" column that presents a comparison between the potentials (solution quality and computational time) of each strategy. For the two values incorporated in the \"GAP\" column, if the \"fitness\" or \"time (%)\" values are positive, it indicates a percentage gain. This improvement can be either in terms of the average solution quality or the execution speed of \(e\)-BRKGA compared to BRKGA or vice versa, depending on the sign. For example, consider the \"albano\" instance, the first row in Table 10. The \"fitness (%)\" value in the \"GAP connected\" column between the approaches is 7.68%, and regarding the \"time (%)\" column, it is \(-1.66\%\). These numbers mean that the value of the \"fitness\" column for the \(e\)-BRKGA average multiplied by (1 + 7.68%) is approximately equal to the average \"fitness\" of the BRKGA. Furthermore, the \"time (%)\" column value for the \"GAP connected\" implies that the BRKGA executed in a time that, when multiplied by (1 + 1.66%), is approximately equal to the time spent by \(e\)-BRKGA. A positive GAP percentage \(\alpha\) represents gains to \(e\)-BRKGA in the proportion \(\alpha\). Otherwise, the improvement \(\alpha\) is reached by the BRKGA approach. From Table 10, it is possible to verify that the BRKGA achieved a better average in the quality of results in 17 instances (negative percentages), tied in 2, and lost in 6 when considering the group of instances (C). For this analysis, it is essential to observe that among the six instances where e-BRKGA had a better average fitness, five have the highest number of nodes and edges in the input graph: \"albano\", \"blaz3\", \"inst_26 pol\", \"shapes4\", and \"trousers\"; see Table 3.

Notice that \(e\)-BRKGA found, on average, a 7.68% improvement, with an execution time 1.66% higher, meaning 5.04 s slower than BRKGA, considering the \"albano\" instance. A more significant gain occurs with the \"inst_26 pol\" instance, reaching a 10.69% improve
## Abbreviations \begin{tabular}{l l} ABC & Artificial Bee Colony \\ AEC & Average Energy Cost \\ ASA & Average Sensitivity Area \\ CHIM & Convex Hull of Individual Minima \\ C-RNPP & Constrained Relay Node Placement Problem \\ EA & Evolutionary Algorithm \\ FA & Firefly Algorithm \\ GA & Genetic Algorithm \\ GSA & Gravitational Search Algorithm \\ MO & Multi-Objective \\ MO-ABC & Multi-Objective Artificial Bee Colony \\ MOEA/D & Multi-Objective Evolutionary Algorithm Based on Decomposition \\ MO-FA & Multi-Objective Firefly Algorithm \\ MO-GSA & Multi-Objective Gravitational Search Algorithm \\ MO-VNS & Multi-Objective Variable Neighborhood Search \\ NBI & Normal Boundary Intersection \\ NP & Non-deterministic Polynomial-time \\ NR & Network Reliability \\ NSGA-II & Non-dominated Sorting Genetic Algorithm-II \\ PSO & Particle Swarm Optimization \\ QoS & Quality of Service \\ RN & Relay Node \\ RNPP & Relay Node Placement Problem \\ SIA & Swarm Intelligence Algorithm \\ SPEA2 & Strength Pareto Evolutionary Algorithm 2 \\ TA & Trajectory Algorithm \\ T-WSN & Traditional Wireless Sensor Network \\ ST & Single-Tiered \\ ST-WSN & Single-Tiered Wireless Sensor Network \\ TT-WSN & Two-Tiered Wireless Sensor Network \\ WSN & Wireless Sensor Network \\ \end{tabular}
## 4 MSIMP Based on EMOEAs
### Traditional EMOEAs  
EMOEAs are a class of multi-objective evolutionary algorithms that solve for three or more optimization objective functions and are expensive to evaluate, where \"expensive\" refers to the large amount of computational resources and time consumed for each evaluation of the objective functions [64; 65]. In the field of satellite planning, MSIMP often considers multiple optimization objectives, and the calculation of the fitness values of potential mission planning schemes consumes large amounts of resources. EMOEAs can provide an effective solution for MSIMP via complex multi-objective optimization through their powerful optimization and search capabilities. Table 2 provides a detailed list of classical EMOEAs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
A detailed review of Table 2 reveals that classical EMOEAs are designed to address multi-objective optimization problems with high computational costs. These algorithms reduce the computational burden of objective function evaluations by applying surrogate models (such as Gaussian processes and Kriging models) and optimization strategies (such as trust-region methods and expected improvement functions). For instance, EEMOEA [66] emphasizes the use of \(\varepsilon\)-dominance measures to maintain solution diversity, with a moderate computational cost and response time. Both the SAEA [67] and KO [68] utilize surrogate models (such as Gaussian processes and Kriging models) to predict objective values, whereas the SAEA is characterized by a low computational cost and fast response while KO has a moderate computational cost and slower response. The SAEA significantly reduces the computational cost of objective function evaluations. ASMO [69] and the ESEA [70] integrate multiple surrogate models (such as Gaussian processes, neural networks, and decision trees), resulting in a moderate computational cost with moderate response time and low computational cost with fast response, respectively. The TR-MOEA [71], EGO [72], and ESAEA [73] improve search efficiency, with the TR-MOEA and EGO offering a moderate computational cost/moderate response time and low computational cost/fast response, respectively, while the ESAEA enhances search efficiency and prediction accuracy through a combination of multiple surrogate models. The SA-CMA-ES [74] leverages a covariance matrix adaptation strategy to explore the search space, achieving a moderate computational cost and response time, and excels in complex search spaces. By analyzing these classical EMOEAs, valuable insights can be gained, particularly in the application of surrogate models, integration of optimization strategies, and enhancement of search efficiency. These insights can be applied to design more efficient SA-EMOEAs, providing more effective solutions to high-computational-cost multi-objective optimization problems.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 6 Structured Background Models  
Structured background models, on the basis of a-priori knowledge, assume a specific structure of the background. In the case of the spectral data, this assumption generally arises from the physical principles of the observed data. We can presume that a pixel spectrum is a mixture of the pure spectral signatures (endmembers) of the objects or materials found on the Earth's surface. If we assume the linearity in the spectra mixing, we could also claim that a pixel spectrum actually lies in the subspace spanned by the vectors (spectra) of the unique materials found in the hyperspectral scene. Subspace models, cluster or mixture-based models, and representation-based models are the most prominent techniques for anomaly detection in hyperspectral images for remote sensing applications that utilize structured background models. Nevertheless, all listed techniques generally employ a linear mixture model (LMM) that incorporates additive noise [10]:  
\[\begin{array}{l}\underline{X}=\sum_{i=1}^{L}\delta_{i}\cdot\underline{u}_{i }+\underline{W}\\ \delta_{i}\geq 0,\sum_{i=1}^{L}\delta_{i}=1\end{array}. \tag{33}\]  
where \(\underline{W}\) denotes the model fit error (the spectrum fraction that is not modeled as a mixture or noise), \(\underline{u}_{i}\) represents the \(i\)-th endmember, background eigen or basis vector, cluster or segment centroid, \(L\) is their total number, and \(\delta_{i}\), \(i=1,\dots,\ L\) are their respective abundances. The linear subspace model can be derived from LMM if the abundance constraints in (33) are relaxed [42].  
reconstruction vector \(\underline{r}\) as the detection statistic, which is sometimes called distance from the feature space [82]:  
\[D_{DFFS}(\underline{x}|\mathcal{B})=\|\underline{r}\|^{2}=\underline{x}^{T} \cdot\underline{Q}\cdot\underline{x}=\underline{x}^{T}\cdot(\underline{P}^{ \perp})^{T}\cdot(\underline{P}^{\perp})\cdot\underline{x}\begin{array}{c}>\\ <\\ \end{array}\lambda \tag{38}\]  
The performance of the OSP techniques primarily depends on the quality of the background reconstruction. There should not be leakage from the target space to the background subspace [10], as it will surely impair the detection performance. Both PCA and SVD techniques are susceptible to that issue, so removing target-like pixels from the input data is beneficial before applying the PCA or SVD. That problem motivated the development of techniques that are more robust and less sensitive to an outlier presence; these are presented in the section on representation-based models.