esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model
### Extensional Stiffness  
Due to the constant curvature \(\kappa\), the length of the individual sections \(L_{k}\) can be determined as a function of the length of the centerline of the actuator \(L\) and the direction of bending \(\alpha\)  
\[L_{k}=(1+r_{k}\cos(\alpha-\varphi_{k})\kappa)\cdot L. \tag{16}\]  
The extensional stiffness \(E_{k}\) and the stretch ratio \(\frac{L_{k}}{L_{0}}\) induce an axial force \(F_{m,k}\) of the material of each individual section  
\[F_{m,k}=\left(\frac{L_{k}}{L_{0}}-1\right)\cdot E_{k}. \tag{17}\]  
Figure 2: Multi-chamber actuator with three chambers embedded (**A**) and partitioning for the parameter derivation (**B**).  
Without external forces applied to the actuator except \(F_{k}\), the equilibrium of axial forces at every position along the centerline of the actuator is  
\[\sum_{k=1}^{2j+1}F_{k}-F_{m,k}=0. \tag{18}\]  
Substituting Equations (16)-(18) yields  
\[\sum_{k=1}^{2j+1}F_{k}-\left(\frac{(1+r_{k}\cos(\alpha-\varphi_{k})\kappa) \cdot L}{L_{0}}-1\right)\cdot E_{k}=0. \tag{19}\]  
The regular arrangement of sections in a circle allows the simplification  
\[\sum_{k=1}^{2j}E_{k}\cos(\alpha-\varphi_{k})=0,\text{if }j\geq 3, \tag{20}\]  
which, in combination with \(r_{2j+1}=0\) (Equation (15)), transforms Equation (19) to  
\[L=\left(\frac{\sum_{k=1}^{2j+1}F_{k}}{\sum_{k=1}^{2j+1}E_{k}}+1\right)\cdot L_ {0}. \tag{21}\]  
In the beam model, the extensional stiffness of the actuator \(E\) is required. This parameter relates the axial force \(F\) acting on the actuator to its stretch \(\left(\frac{L_{k}}{L_{0}}-1\right)\). Equation (21) implies that  
\[F=\sum_{k=1}^{2j+1}F_{k}, \tag{22}\] \[E=\sum_{k=1}^{2j+1}E_{k}\overset{\eqref{eq:F_k}}{=}j\cdot\left( E^{I}+E^{II}\right)+E^{III}. \tag{23}\]  
As expected, the extensional stiffness of the actuator \(E\) is equal to the sum of extensional stiffness of its individual sections. In particular, it is independent from the direction of bending \(\alpha\) due to Equation (20).
## 3 DTSPN Intersecting Regions Algorithm
### Stage 2  
The second stage takes the GTSP with mutually exclusive node sets and eliminates any intra-set arcs, leaving a GTSP in \"canonical form.\" Define a problem \((P4)\) that differs from problem \((P3)\) only by the arcs and arc costs. Problem \((P4)\) is a GTSP over the graph \(\mathcal{G}^{4}:=(\mathcal{N}^{4},\mathcal{A}^{4},\mathcal{V}^{4})\) with the corresponding cost vector \(c^{4}\) where \(\mathcal{N}^{4}=\mathcal{N}^{3}\) and \(\mathcal{V}^{4}=\mathcal{V}^{3}\). The arc set \(\mathcal{A}^{4}\) is populated in the following manner. For every \(i,j\) pair of nodes in \(\mathcal{N}^{3}\) for which \(\mathcal{M}(i)\
eq\mathcal{M}(j)\), calculate the lowest cost path from \(i\) to \(j\) over the arc set \(\mathcal{A}_{i,j}\subseteq\mathcal{A}^{3}\). An arc \((k,l)\in\mathcal{A}_{i,j}\) if the following four conditions hold,  
1. \(\mathcal{M}(k)\subseteq\mathcal{M}(i)\cup\mathcal{M}(j)\),
2. \(\mathcal{M}(l)\subseteq\mathcal{M}(i)\cup\mathcal{M}(j)\),
3. if \(\mathcal{M}(l)=\mathcal{M}(i)\) then \(\mathcal{M}(k)\) must also equal \(\mathcal{M}(i)\),  
Figure 3: Example of Problem (P3) and Problem (P4) from Stage 1 and Stage 2 of transformation. (**a**) Problem (P3): Nodes \(S_{2}\) and \(S_{3}\) from (P2) lie in multiple node sets. These nodes are duplicated and the spawned nodes \(S_{2^{\prime}}\) and \(S_{3^{\prime}}\) are placed in node set \(\mathcal{V}_{2}\). Zero cost arcs (dashed arrows) are added connecting \(S_{2}\) to \(S_{2^{\prime}}\) and \(S_{3}\) to \(S_{3^{\prime}}\); (**b**) Problem (P4): The intra-set arc \((5,3^{{}^{\prime}})\) from Problem (P3) is removed.  
4. if \(\mathcal{M}(k)=\mathcal{M}(j)\) then \(\mathcal{M}(l)\) must also equal \(\mathcal{M}(j)\).  
If the shortest path has finite cost, add the arc \((i,j)\) to the arc set \(\mathcal{A}^{4}\), and set the corresponding arc cost \(c^{4}_{i,j}\) equal to the shortest path cost. If no feasible path exists, then the arc \((i,j)\) will not be part of \(\mathcal{A}^{4}\). The problem defined on \(\mathcal{G}^{4}\) is now in the GTSP canonical form with mutually exclusive node sets and no intra-set arcs. See Figure 3 for an example of Problem (P4). The following theorem from [17] establishes the correctness of the transformation in Stage 2.  
**Theorem 3.2** ([PERSON] and [PERSON] [17]).: _Given an optimal solution, \(y^{*}\), to \((P4)\), we can construct the optimal solution, \(x^{*}\), to \((P3)\)._
# Modelling and Simulation of Traditional Craft Actions
## 3 Simulation Elements
### Objects
#### 3.5.1 Tools  
The shapes of tools or tool parts are designed to afford specific functionalities. In [61; 62; 63], tools are classified according to their use in (a) cut, (b) pinch, or grip, (c) drive, (d) strike, and (e) be struck. In Table 3, we propose an association of tool classes with tool shapes and mechanisms and the action type they are used in. The rationale for this classification is described below.  
The characteristic of cutting tools is their tapered edges. Cutting tools employ the wedge simple machine to concentrate a force at the tapered edge and its sides. When the pressure built at the edge exceeds the yield point of the material, it breaks. The lateral forces of the wedge push apart the material further increasing the tension at the cut. Tools that are struck such as chisels follow the same principle of operation. Scissors are cutting tools that use two wedges in combination with a wheel and axle.  
Sticking tools have flat faces and convert dynamic energy into momentum and eventually force. They are used to either deliver force directly on a workpiece or strike another tool.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tool Type** & **Shape** & **Indicative Tools** & **Action Class** & **Mechanism** \\ \hline \multirow{2}{*}{Cutting} & Tapered edge & Blades & Subtract & \\ \cline{2-5}  & Jagged edges & Saws & Subtract & \\ \hline \multirow{2}{*}{Struck} & Tapered edge & Chisels, Gouges & Subtract & \\ \hline \multirow{2}{*}{Gripping} & \multirow{2}{*}{Pair of jaws} & Tweezers, Pliers, & Move, Interlock, & \\  & & Wrenches & Subtract & \\ \hline \multirow{2}{*}{Striking} & \multirow{2}{*}{Flat face} & Hammers & Subtract, Form, & \\  & & & Interlock & \\ \hline \multirow{2}{*}{Driving} & Tip & Screwdrivers & Interlock & \\  & & Profile & Wrenches & Interlock & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Association of tool types with their shape characteristics and actions.  
Gripping tools are double-handed and have a pair of jaws. To grip, their shape is changed making their jaws come closer and concentrate the force from their handles to their jaws. The pressure and friction stabilise the workpiece within the grip. Tweezers tools rely on the elasticity of the material they are made. Pliers are made using a wheel and axle and two levers. For wrenches and vices, additionally, use a screw simple machine.  
Driving tools are used in conjunction with screws and use torque to drive them. They are distinguished into screwdrivers and wrenches, which are characterised by a tip or a profile, respectively. Screwdrivers are inserted into the drive recess of the screw head, applying torque through the centre axis of the screw. Wrenches have a compatible profile that fits around the external flats of the (hexagonal) screw head, enabling the application of torque from the outside edges.  
Overviews of the mechanical analysis of these tools can be found in [64; 65].
###### Abstract  
In this study, a genetic algorithm was used to calculate the scheduled waiting time according to the train operation frequency of heterogeneous trains operating on one track. The acquired data were then used to determine the appropriate subsidiary track at which high-speed trains can load or release cargo away from low-speed trains. A metaheuristic genetic algorithm was applied and implemented using Javascript/Query. Six cases were investigated, which provided values of subsidiary track that vary according to the operation frequencies of different types of trains, and solutions were derived through 100 simulations using a stochastic method. The analysis results showed that the train overtaking frequency was the highest at the third intermediate station within the simulation, suggesting that this particular station requires a subsidiary track, even if the operating frequency of each train differs across the entire track considered in this study. The results of this study are expected to facilitate objective and practical planning during railway construction.  
subsidiary track; scheduled waiting time; genetic algorithm; heterogeneous train pattern; train operation frequency +
Footnote †: journal: Medical  
around the world are constructed to provide train services required for infrastructure, and as part of this effort, the allocation and utilization of railway capacity is more important than ever [2]. However, if the goal is to install a subsidiary track at every station to improve the train operation service, the project cost will increase and the feasibility of improving the operation will reduce.  
In the past, the waiting time of trains in a train network was used mainly in the research of delay transfer for late and following trains. However, a second category of waiting time, called the scheduled waiting time, is calculated for railway networks. The scheduled waiting time, like delay, occurs in the train scheduling process and does not affect the operation of other trains [3]. The present study explicitly analyzed and derived the effects of heterogeneous train operation frequency and the existence or absence of a subsidiary track on the weighted scheduled waiting time for a train schedule where overtaking and evacuation occur when heterogeneous trains are operated on the same track. To analyze the location of the subsidiary track with the scheduled waiting time, a metaheuristics algorithm was applied. The results of this study can be used to facilitate objective and practical planning during railway construction.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 4 Reconfiguration Algorithm and Proposed Extension
The second phase of DEN2 DE involves selecting the best label per node for load redistribution, based on the criteria defined in the algorithm itself (such as the number of hops, distance, losses, available load, etc.). In this example, nodes with only one label do not need to apply this phase. However, nodes 4 and 5 will need to select which label they will use, as it will later determine how they will redistribute the load within the SG. The third phase of the algorithm focuses on the redistribution of loads between the nodes. This phase assumes that all nodes have selected a label, i.e., a route to reach the root node. As a result, a logical topology will be formed over the physical topology of the SG, potentially leaving certain links unused. This enables optimal power distribution within the SG, as illustrated in Figure 6b. In summary, DEN2 DE first explores the whole network topology (assigning labels during the process), then it defines the order to follow for resource distribution (this order is represented by the selected label) and, finally, it reconfigures the network based on that order. In this work, several ML and DL models were evaluated to support the DEN2 DE algorithm during its second phase. More specifically, these models provided additional information to enhance the selection of labels (and, hence, the order of resource distribution), as they were chosen considering the prediction of potential faults that may arise in the SG, which eventually improved the reconfiguration process as a whole. Figure 6: Example of load redistribution in an existing SG. (**a**) Suboptimal energy demanding scenario. (**b**) Optimal energy demanding scenario. Figure 7: Operation of the DEN2 DE algorithm. It is important to note that although we evaluated these models exclusively with DEN2 DE, both our dataset and our analysis of AI techniques could also provide a foundation for analyzing other reconfiguration algorithms in SG networks. Ultimately, the techniques examined in our study offer supplementary insights into fault prediction, and it is at the discretion of the specific reconfiguration mechanism to incorporate this information to enhance its procedure.
## 5 Conclusions  
We developed a new automated artifact removal algorithm (AMARA) based on MARA and integrated the beneficial features of ABAMAR. The aim was to achieve long-term stability in the reconstruction. It relies on the fixed artifact free signal parts predefined by an accelerometer. The main advantages are the automated nature, the adaptive threshold, and the reproducibility of the results. In our validation, AMARA identified artifacts comparable to human scorers. Compared to MARA, AMARA detected more of the artifacts, which were specific to movements but more parameters had to be predefined. However, the same parameters can be used for several recordings and processing speed per measurement is much higher than with MARA. Compared to the movement detection of ABAMAR, AMARA detected more movements based on the accelerometer data. In summary, AMARA outperformed MARA in terms of sensitivity, false positive detections, and could time-effectively be applied to a large amount of long-term NIRS data.  
For future work concerning the presented algorithm and the approach to use accelerometer data in order to improve the NIRS signal quality, we suggest three steps: (1) sensitivity and specificity analysis should be conducted for all three algorithms (MARA, AMARA, ABAMAR), (2) further work should be directed to make AMARA more data-adaptive, _i.e._, methods should be identified and selected that determine the optimal parameter values based on optimization routines. These optimized parameter values should then be tested with independent data sets, (3) and finally, finding novel strategies to include acceleration data into the NIRS post-processing routines would be advantageous in order to improve the NIRS data quality. Improving the NIRS signal quality is an important aspect to promote the usage of NIRS devices in biomedical applications, like intensive-care units or experimental brain research.
## 4 Experimental Verification and Analysis
### Rigid Transformation from Calibration Plate Coordinate System to Robot Coordinate System
According to the four-point calibration method, the calibration is performed for every 10 mm height, and some calibration results are shown in Table 3.  
Figure 8: Image processing results of the calibration plate before and after distortion correction: (**a**) before distortion correction and (**b**) after distortion correction. According to the principle of the four-point calibration method [31; 32]. The rigid transformation matrix of the image coordinate system and the robot coordinate system at each height is obtained, as shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Height (mm)**} & \multirow{2}{*}{**Corner Position**} & \multicolumn{2}{c}{**Robot Coordinate System (mm)**} & \multicolumn{2}{c}{**Image Coordinate System (Pixel)**} \\ \cline{3-6}  & & \(\mathbf{X_{R}}\) & \(\mathbf{Y_{R}}\) & \(\mathbf{X_{C}}\) & \(\mathbf{Y_{C}}\) \\ \hline \multirow{6}{*}{15} & A & \(-1449.7\) & 1585.93 & 1284.804 & 432.0025 \\ \cline{2-6}  & B & 549.17 & 1604.26 & 1290.65 & 3296.744 \\ \cline{2-6}  & C & 536.96 & 2804.99 & 3015.627 & 3293.224 \\ \cline{2-6}  & D & \(-1462.22\) & 2785.86 & 3009.781 & 428.4819 \\ \hline \multirow{6}{*}{45} & A & \(-1414.15\) & 1530.26 & 1191.118 & 470.0939 \\ \cline{2-6}  & B & 584.55 & 1561.77 & 1218.606 & 3365.488 \\ \cline{2-6}  & C & 564.22 & 2762.3 & 2958.563 & 3348.97 \\ \cline{2-6}  & D & \(-1434.56\) & 2729.52 & 2931.075 & 453.5752 \\ \hline \multirow{6}{*}{75} & A & \(-1417.77\) & 1522.52 & 1165.935 & 453.0028 \\ \cline{2-6}  & B & 581.1 & 1536.81 & 1170.013 & 3373.189 \\ \cline{2-6}  & C & 571.3 & 2737.35 & 2925.071 & 3370.738 \\ \cline{2-6}  & D & \(-1427.86\) & 2722.06 & 2920.992 & 450.5518 \\ \hline \multirow{6}{*}{105} & A & \(-1429.65\) & 1529.60 & 1163.014 & 423.642 \\ \cline{2-6}  & B & 569.51 & 1530.36 & 145.789 & 3368.993 \\ \cline{1-1} \cline{2-6}  & C & 568.25 & 2730.58 & 2916.097 & 3379.346 \\ \cline{1-1} \cline{2-6}  & D & \(-1431.17\) & 2729.40 & 2933.322 & 433.995 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Four-point calibration results. Figure 9: Four-point calibration method.
## 3 Numerical Methodology
### Cutting Performance Evaluation Index  
The cutting forces and Mechanical Specific Energy (MSE) are essential characteristics of rock breaking process. The axial force represents the ability of the cutter to penetrate into the formation axially. The smaller the axial force is, the stronger the capacity to penetrate into the formation, and the lower the required weight on bit (WOB). Similarly, the tangential force characterizes the difficulty of rock breaking. The smaller the tangential force is, the easier the rock is broken, and the smaller the torque required by the bit. Meanwhile, the MSE, which represents the energy consumed by breaking per unit volume of rock, can quantitatively represent the rock breaking efficiency, and its formula is  
\[MSE=\frac{W}{V_{cut}}=\frac{F_{h}d}{S_{p}d}=\frac{F_{h}}{S_{p}} \tag{6}\]  
where \(d\) is the cutting stroke, the projection of cutting area \(S_{p}=S_{l}\cos\alpha^{\prime}_{l}+S_{r}\cos\alpha^{\prime}_{r}\) (when both working faces contact with the rock) or \(S_{p}=S_{l/r}\cos\alpha^{\prime}_{l/r}\)(when only the working face on one-side contacts with the rock), \(S_{l/r}\) and \(\alpha^{\prime}_{l/r}\) are the cutting area and the actual back-rake angle of the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rock** & **Elastic** & **Poisson’s** & **Tensile** & **Shear** & **Compressive** & **Friction** \\
**Sample** & **Modulus/GPa** & **Ratio** & **Strength/MPa** & **Strength/MPa** & **Strength/MPa** & **angle/\({}^{o}\)** \\ \hline Wusheng & & & & & & \\ sandstone & 11.54 & 0.062 & 4.346 & 13.56 & 67.548 & 38.03 \\ Beibei & 31.2 & 0.171 & 6.758 & 17.72 & 105.951 & 43.62 \\ limestone & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of rock material [16; 19].  
Figure 4: Finite element model of the cutter-rock system: (**a**) Axonometric view; (**b**) Right view.  
working faces on the left/right side of convex edge respectively. \(S_{l/r}\) is be obtained according to the infinitesimal method.  
In the process of rock breaking, the forces on the cutters belong to impact loads, whose amplitude affects the cutter's life. Here, the cutting forces are statistically processed. The average value is considered as an index to evaluate the cutting performance, and the standard deviation is adopted to assess the cutting stability.
# RoPT: Route-Planning Model with Transformer
## 5 Experiments  
### Datasets  
The experiments are conducted on real-world trajectory datasets from two cities, Porto [2] and Chengdu, with their statistical characteristics summarized in Table 2. The former is denoted as the Porto Dataset, which is an open dataset of 18 GB, recording taxi trajectories between 7 January to June 2013. The latter is denoted as the Chengdu dataset with a size of 60 GB, which is composed of taxi trajectories from 1 November to 30 November 2016. The Chengdu dataset only contains GPS coordinates without road segment information; the road network and the corresponding adjacent graph of Chengdu is constructed from OpenStreet-Map [30] for matching GPS locations to road sections [31]. Here, the sparsity of the dataset is calculated as the ratio of the number of nodes to the number of edges, which means a node equally appears on how many edges.  
Preprocessing of datasets: First, discontinuous routes are eliminated. If the time between two consecutive points in a route is bigger than 30 s, then the route is divided into two parts. Secondly, Leuven MapMatching based on a Hidden Markov Model (HMM) is used to map GPS trajectory points to the actual road network obtained from OpenStreetMap.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Chengdu** & **Porto** \\ \hline number of nodes & 3041 & 5330 \\ number of edges & 4354 & 11,491 \\ number of trajectories & 5,887,769 & 1,258,165 \\ sparsity & 0.698 & 0.463 \\ mean trajectory length & 23.09 & 40.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of datasets.
## 2 Literature Review
### Existing Approaches in Unsupervised PdM  
Several studies have successfully demonstrated the use of unsupervised learning for equipment monitoring. For instance, [PERSON] et al. [17] validated the robustness of autoencoder-based models in detecting early-stage anomalies under limited labeled data [34; 35]. [PERSON] et al. [22] introduced a hybrid framework combining LSTM networks with GMM clustering, showing improved accuracy in RUL prediction.  
Recent work has explored neuro-fuzzy and observer-based approaches for unsupervised fault diagnosis in wind turbines. For instance, [PERSON] et al. (2024) introduce a robust diagnosis method that uses a Multiple-Output ANFIS (MANFIS) to derive Takagi-Sugeno models, coupled with a bank of zonotopic state estimators for residual generation [36]. In a related study, [PERSON] et al. (2023) present a hybrid FDI approach in which an ANFIS identifies quasi-Linear Parameter Varying (qLPV) models of the turbine, and a bank of qLPV zonotopic observers then detects sensor and actuator faults--notably, this method is trained only on fault-free data [37]. Another recent work ([PERSON] et al., 2022) employs an ANFIS to obtain a convex Takagi-Sugeno interval model of the turbine and designs convex state observers; fault diagnosis is performed by generating residuals from these observers, again using only healthy data for training [38]. These studies demonstrate that advanced neuro-fuzzy identification and interval/zonotopic observer methods can effectively classify or isolate unlabeled faults in wind turbine systems.  
Hybrid models that combine unsupervised and supervised components, such as Autoencoder-GMM for classification followed by LSTM for RUL estimation, have shown promising results in capturing both spatial and temporal degradation patterns [39, 40, 41, 42, 43, 44]. Attention mechanisms further enhance these models by prioritizing critical features in time-series data [8, 45, 46, 47].  
To summarize, it is worth emphasizing that all the literature mentioned and the elaborations presented by [PERSON] et al. (2022) [48], [PERSON] et al. (2021) [49], and [PERSON] and [PERSON] (2024) [50] underscore the growing trend of applying deep learning and unsupervised techniques in predictive maintenance, particularly in scenarios with limited labeled data.
## 2 The System's Design, Architecture, and Operating Principles
Only 20% of the signal power is used to extract the DS information by using the same receiver structure mentioned in Stage 4. The remaining 80% of the signal power is used for the re-modulation of the US by OOK, and its structure is mentioned in Stage 4. Using the Saving Array Waveguide Grating (AWG), an internal feed fiber, and RSOA is a merit of this work's design compared to related studies that utilized cross-seeding techniques [22, 26, 27].