esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Directed Minimum Spanning Tree Algorithm
### Edmonds' Centralized DMST Algorithm  
We provide a brief overview of the algorithm proposed in [19], which presents the core ideas of the super vertex-based approach. The following algorithm produces a DMST for \(G\):  
Lovasz's Shrinking Iteration LSI Input: A directed, weighted graph \(G=(V,E,W)\) and a root node \(r\in V\). Output: Either a new graph \(G^{*}\), or a success flag and a DMST \(H\) of \(G\).  
1. If there is a non-root node of \(G\) with no incoming edges, report a failure. Otherwise, for each non-root node of \(G\), subtract the minimum incoming edge weight from all its incoming edges. Select exactly one incoming zero-weight edge for each non-root node to create a subgraph \(H\) of \(G\) with those edges.
2. Find all cycles of \(H\), and denote them \(H_{1},\ldots,H_{C}\). If \(H\) has no cycles, abort the iteration and return (SUCCESS, H). For \(j=1,\ldots,C\), find the set \(V_{j}\) of nodes reachable by dipaths in \(H\) from \(H_{j}\).
3. Compute the all-pairs shortest path distances in \(G\).
4. For each node \(v\in V\), denote \(d_{j}(v):=\min\{d(v,u):u\in H_{j}\}\). For each \(j=1,\ldots,C\), set \(\beta_{j}:=\min\{d_{j}(v):v\in V(G)\setminus\mathbb{V}_{j}\}\) and \(U_{j}:=\{u\in V_{j}:d_{j}(u)\leq\beta_{j}\}\).
5. Create a minor \(G^{*}\) by contracting each \(U_{j}\) into a super vertex \(U^{*}_{j}\), considering all other vertices of \(G\) as simple super vertices \(V^{*}_{1},\ldots,V^{*}_{k}\). For each vertex \(N^{*}\) of \(G^{*}\), let the edge weights in \(G^{*}\) be: \[W^{*}_{N^{*}U^{*}_{j}} =\min\{W_{vu}:v\in N^{*},u\in U^{*}_{j}\}-\beta_{j}+\min\{d_{j}(u ):u\in U^{*}_{j}\}\] for all \(j=1,\ldots,C\), and \[W^{*}_{N^{*}V^{*}} =\min\{W_{vv^{*}}:v\in N^{*}\}\] for all the simple super vertices \(V^{*}\) of \(G^{*}\).
6. Return \(G^{*}\).  
To summarize these iterations: The minimum-weight incoming edge of each node is selected. That weight is subtracted from the weights of every incoming edge to that node, and one of those edges with new weight 0 is selected for each node to create a subgraph \(H\). If \(H\) is a tree, we are done. Otherwise, we find all cycles of the resulting directed subgraph, then compute APSP, and determine the \(V_{j},U_{j}\), and \(\beta_{j}\), which we use to define a new graph with some nodes of the original \(G\) contracted into super vertices.  
The main result for the DMST problem in [14] states that replacing (a) and (b) of step 2. in [PERSON] DMST Algorithm, taking the new \(H\) obtained at each iteration as \(H_{t+1}\) and \(G^{*}\) as \(G_{t+1}\), leads to no more than \(\lceil\log n\rceil\) shrinking iterations needed before success is reported.
## 6 Managerial and Practical Implications
### Practical Implications  
Sensitivity analysis is proposed to show the abilities and limitations of the proposed IVT2F-TOPSIS method. The results imply the flexibility of IVT2F-TOPSIS in adapting to frequent changes in criteria values. Thus, IVT2F-TOPSIS could be practical for real-time adjustments in decision-making within dynamic and uncertain environments. The complexity of Interval-Valued Type-2 fuzzy sets requires specialized knowledge and may necessitate advanced software tools. Additionally, criteria weights depend on expert opinion; therefore, this situation may be limited in everyday managerial contexts. However, the application of the proposed methodology is not complex and does not require a high level of knowledge.  
IVT2F-TOPSIS's ability to incorporate diverse perspectives and quantify subjective legal criteria makes it practical for evaluating complex disputes where multiple countries or political entities are involved. Therefore, the IVT2F-TOPSIS approach helps international law practitioners achieve consensus on resolutions. Moreover, traditional methods need extensive expert input. This situation could cause inconsistencies from case to case. On the other hand, IVT2F-TOPSIS could reduce such inconsistencies by serving as a complementary decision-making approach in urgent or sensitive international disputes.
## 5 Results and Discussion
### Sensitivity Analysis with Coolant Flow Rate and Ambient Temperature
Specifically, at the middle of riding, the difference in cell inhomogeneity between the serial and parallel designs is relatively small; however, as the riding progresses towards the end, the serial design exhibits significantly larger variations in SOC and OCV among individual cells, resulting in more than two times the level of cell performance variations compared to the parallel design [40]. 3. For both serial and parallel designs, increasing the coolant flow rate results in lower temperatures for the battery, inverter, and motor, leading to higher overall cooling system efficiency and reduced performance variations among cells. However, these benefits diminish with increasing coolant flow rate. While the parallel design exhibits smaller cell-to-cell variations, as the flow rate increases, the difference between the two designs becomes less significant. 4. For both serial and parallel designs, the component temperatures show a nearly linear relationship with the increasing ambient temperature, and so does the cooling system efficiency. As the ambient temperature rises, the cell-to-cell performance variations become smaller for both designs. Specifically for the serial design, when the ambient temperature is 5 \({}^{\circ}\)C, the temperature and SOC differences between cells are as high as 5.25 \({}^{\circ}\)C and 3%, respectively, indicating the necessity for subsequent cell balancing and a detrimental impact on cell lifetime [18]. The thermal design of the battery module has minimal effect on the power consumption of the electric motorcycle, as the difference between the serial and parallel designs in battery efficiency or overall powertrain efficiency is only 0.01%.
# Efficiency of Algorithms for Computing Influence and Information Spreading on Social Networks
## 2 Methods
### Applications
#### 2.3.2 Betweenness and Cohesion Measures  
Next, we will define a betweenness centrality measure for our model. Betweenness centrality measures the ability of a node to mediate influence between nodes in the network. We let \(b_{\{m\}}\) denote the betweenness centrality [6] of a single node \(m\) and \(b_{M}\), the betweenness centrality for a set of nodes \(M\). \(b_{M}\) is defined as the proportional change in _cohesion_ when we remove the set of nodes \(M\) from the network structure, that is  
\[b_{M}=\frac{B-B_{M}}{B}, \tag{6}\]  
where the cohesion \(B\) of the whole network is defined as the sum  
\[B=\sum_{\begin{subarray}{c}s,t\in V\\ s\
eq t\end{subarray}}C(s,t). \tag{7}\]  
\(B_{M}\) is calculated similarly to \(B\) with the set of nodes \(M\) removed from the network structure as  
\[B_{M}=\sum_{\begin{subarray}{c}s,t\in V\setminus M\\ s\
eq t\end{subarray}}C_{M}(s,t), \tag{8}\]  
where \(C_{M}\) is the influence spreading matrix corresponding to a new graph with the set of nodes \(M\) removed.  
Our measure differs from the classical definition of betweenness centrality [20; 21; 22] where the betweenness centrality of a node is defined by the number of shortest paths that pass through the node divided by the number of all shortest paths between pairwise nodes in the network. By contrast, Equation (6)) considers all possible interactions and paths in the network. This is an important distinction to make as a lot of the structure of the graph is left out when we consider only the edges on the shortest paths from a node. In addition to giving more accurate numerical results, our definition is more general in the sense that it is independent of the network model and spreading mechanism, because Equation (6) is based only on matrices \(B\) and \(B_{M}\) that can be calculated for different spreading or connection mechanisms of the network.
## 8 Results and Discussion
### Static Scenario #1  
The duration of the trajectory is 4 min. After 1 min from the start of the scenario, the signal power level of PRN 31 was attenuated by 10 dB for thirty seconds. Thirty seconds later, the signal power level of PRN 21 was attenuated by 20 dB for thirty seconds, as shown in Figure 7.  
Figure 8 shows the estimated Doppler frequency from the ST and VT modules versus the reference. The shaded area in the figure denotes the period of power attenuation. ST was able to maintain the lock on the carrier frequency of PRN 31. However, the estimated Doppler frequency was noisier when compared to VT. On the contrary, ST lost track of the carrier frequency of PRN 21 during the power attenuation and failed to re-lock into the signal after the signal level returned to its normal value.  
Figure 8: Doppler shift for scenario #1. PRN 21 (**left**), and PRN 31 (**right**).  
Figure 7: The signal power level for scenario #1.  
In contrast, VT was able to maintain lock-on the signal throughout the whole scenario. Table 4 shows the root mean square error (RMSE) values of the positioning solution during the duration of power attenuation. As depicted from the table, vector-based receiver outperformed scalar-based receiver in terms of positioning accuracy. The loss of carrier frequency tracking of PRN 21 in the ST loop is reflected in an erroneous position and velocity solution. In contrast, the vector-based receiver was able to provide a good navigation solution even at a signal attenuation level of 20 dB.
# Nonlinear Model Predictive Control with Terminal Cost for Autonomous Vehicles Trajectory Follow
## 3 Controller Design and Numerical Simulation Verification
### Algorithm Numerical Verification
#### 3.2.3 Control Closed-Loop Simulation  
In the stability test of ordinary vehicles, the double-line shift condition is a test section with a high frequency of use. At present, there are also many scholars who use double line shifting as a test of the trajectory tracking ability of unmanned vehicles [11]. We refer to the double line shifting formula in the paper [11] to obtain the reference trajectory and reference heading angle, as shown in Figure 5 below. In this experiment, the control horizon is set as 15, and the predictive horizon is set as 20. Figure 5 verifies the above conclusion in Section 3.2.2. When the control horizon and the prediction horizon are relatively close, the results calculated by NMPC without terminal cost will affect the path tracking accuracy.  
In Figure 6, the NMPC solution with terminal cost is more accurate. In the process of path tracking, the NMPC solver with terminal cost produces a maximum error of 0.02 m, while the path tracking error of the NMPC solver without terminal cost is 0.06 m. Maximum error in the NMPC-based path tracking scenario was reduced by 30% in the simulation. In order to distinguish the tracking accuracy of the two different path tracking methods significantly, the value of error bound in Figure 6 is set to 0.02 m.  
The output of proposed NMPC-WTC path tracking method can be seen in Figures 7 and 8.  
\begin{table}
\begin{tabular}{c c} \hline
**The Value of Terminal Cost Coefficient (\(\alpha\))** & **Solution Time** \\ \hline
1 (without terminal cost) & 0.030 s \\
2 & 0.032 s \\
10 & 0.043 s \\
100 & 0.058 s \\
500 & 0.061 s \\ \hline \end{tabular}
\end{table}
Table 2: The solution time of NMPC.  
Figure 5: Path tracking in double line shifting scenario.  
Figure 6: Path tracking error in double line shifting scenario.
# Heavy Metal Ion Detection Platforms Based on a Glutathione Probe: A Mini Review
## 3 Heavy Metal Ion Detection Platforms Using Glutathione
### HMI Detection Combined with Electrochemical Techniques
#### 3.2.2 Working-Electrode-Based Platforms
In addition, a practical sample of a wastewater-certified reference material (ERM@-CA713) was tested. In this application, Pb\({}^{2+}\) was specifically detected as expected. Because the selected reference material contained Cr, Cu, Mn, Ni, and so on, which were at concentrations not lower than Pb\({}^{2+}\), and the detected concentration of Pb\({}^{2+}\) was in agreement with the certified metal value, the selectivity of this HMI detection technique was verified. Although the LOD was not so outstanding, these modified electrodes could be stored for weeks without obvious activity decline, showing a good potential towards commercialization. In principle, the ready-made or commercial electronic devices, such as GCE and FET, are advantageous in transferring the HMI concentration information to an electrical signal. Although electrochemical detection of HMIs is not as popular as optical detection, electrochemical sensing platforms also have merits, in that they are more convenient, compact, and cheaper to implement.
## 5 Results and Discussion
### Analysis of the Thermal Performance of the Storage Tank during Discharge with a c-h Ratio of 0
#### 5.2.4 Characteristics of the Internal Flow Field  
The internal flow field characteristics of E4, E5, and E6 during discharge at a flow rate of 0.3 m/s are shown in Figure 16.  
Due to the different installation positions of the obstacles, the vortex influence range in Type c is larger. In Type e and Type f, where the porous obstacles are near the bottom, the vortex influence range is smaller, and the streamlines above the obstacles are uniform with stable flow rates. When the obstacle is farther from the outlet, more water is agitated, leading to greater disruption of stratification.  
Installing the porous obstacle near the bottom effectively limits the mixing range of the water, making it more suitable for storage tanks discharging with different c-h ratios in practical applications.  
When water flows out, it can cause uneven flow velocity distribution within the internal flow field, thereby disrupting stratification. In Type f, porous obstacles are installed at both the top and bottom outlets, which twice hinder the internal fluid disturbances, improving the agitation effects caused by hot water flowing out at the outlet. As can be seen from Figure 16, the streamlined distribution of Type f is significantly more uniform than that of Type b. The two porous obstacles further stabilize the fluid flow in the internal flow field, which further helps to reduce the thickness of the thermocline and maintain the thermocline.  
Figure 16: Internal streamline diagram at a flow velocity of 0.3 m/s for c–h ratio 0: (**a**) porous obstacle in the middle; (**b**) porous obstacle near the bottom; (**c**) porous obstacles near both the bottom and top.  
This section analyzes the energy storage characteristics of storage tanks with different structures. In Section 4.1, by comparing the stratification effects of E1, E2, and E3, it is concluded that porous obstacles are superior to orifice plate obstacles in promoting thermal stratification performance. In Section 4.2, the stratification effects of E4, E5, and E6 are discussed, leading to the determination of the optimal installation position for porous obstacles.
## 4 Proposed Algorithm  
### Opportunistic Routing (OR)  
OR broadcasts a data packet to a set of relay candidates that is overheard by the neighboring nodes, whereas in traditional routing a node is (pre)-selected for each transmission. Then, relay candidates that are part of the forwarders list and who have successfully acknowledged the data packet, run a protocol called coordination protocol between themselves, to choose the best relay candidate to onward the data packet. In other words, OR is abstractly comprised of these three steps:  
Step 1: Broadcast a data packet to the relay candidates (this will prepare the forwarder list).  
Step 2: Select the best relay by using a coordination protocol among the nodes in the forwarder list.  
Step 3: Forward the data packet to the selected relay node.  
Considering an example shown in Figure 2, where the source node S sends a packet to the destination node D, through nodes R1, R2, R3, R4, and R5. First, S broadcasts a packet. The relay nodes R1, R2, and R3 might become the forwarder nodes. Further, if R2 is chosen as a potential forwarder, then R4 and R5 might become relay nodes. Similarly, if R5 is the forwarder node, then it forwards the data packets to the destination node D.  
Opportunistic Routing Derived the Following Rewards:  
* _The escalation in reliability_. By using this routing strategy, the reliability of WSN increased significantly, as this protocol transmitted the data packet through any possible link rather than any pre-decided link. Therefore, this routing protocol provided additional links that could act as back up links and thus reduced the chances of transmission failure.
* _The escalation in transmission range_. With this routing protocol, the broadcast nature of the wireless medium provided an upsurge in the transmission range, as all links irrespective of their location and quality of data packets were received. Hence, the data transmission could reach the farthest relay node successfully.  
Figure 2: Depiction of opportunistic routing in wireless sensor networks (WSN).
## 4 Discussion
### Contribution of the Study  
The study provided a theoretical and practical contribution to the existing base of knowledge. Two main domains reflecting these findings are: (i) multidimensional area fusing engineering and sports issues, and (ii) improving the training process in punching techniques in combat sports.  
Real-time data synchronization that combines multimodal sensors and camera fusion is not a common approach because of numerous constraining factors in data acquisition. Nevertheless, one-sided observation, from the point of view of the technical improvement of measuring instruments, does not lead to practical progress. However, unconventional engineering solutions combined with feedback from sports experts gained through application in a standardized environment result in multi-party improvements such as measurement, instrumentation, and enhancement of estimation techniques in the field of sports. It is of utmost importance that the novel solutions are reliable, easy to use, and inexpensive, offering real-time feedback in measuring, controlling, and monitoring athlete performance.  
The principle of punch performance investigated in this study is shared by all combat sports. That makes the results of the study surpass the scope of karate itself in regard to the developmental phase of the punch. The significance of the impact as the most important stage of the execution cannot be denied. Nevertheless, the initiation of the punch, including both the changing curve of kinematic and temporal parameters, reveals the origins of the RP. In other words, potential deviations or preferable patterns of execution arise in the developmental phase of the technique and determine the final outcome. High-level sports achievements depend on overcoming small and hard-to-detect differences. Due to that, the accuracy and precision of the measurement devices that are insightfully applied in a proper setup are essential. Changing the structure of the punch in different modalities reveals how coaches can influence the improvement of the technique. That is accomplished only by carefully planning specific training tasks based on different goals.
## 2 Numerical Simulation Model
### Numerical Monitoring
#### 2.3.2 Damage Value  
The damage value (\(D\)) of particles was defined as the ratio of the number of broken bonds to the number of initial bonds. The value of \(D\) varies from 0 to 1. In this study, the coordinate number (\(CN\)) of particle packing is about 9.04, which defines the total average  
\begin{table}
\begin{tabular}{c c c} \hline
**Micro Parameters** & **Macro Parameters from Experiments** & **Macro Parameters Predicted by Simulation** \\ \hline Cement effective modulus \(\overline{E}^{*}\) = 5.7 GPa & \(E\) = 23 GPa & \(E\) = 23 GPa \\ \hline Ratio of cement normal to shear stiffness \(\overline{k}_{s}/\overline{k}_{n}\) = 0.2 & \(v\) = 0.19 & \(v\) = 0.19 \\ \hline Tensile strength \(\sigma_{t}\) = 28 MPa & \(UCS\) = 108 MPa & \(UCS\) = 106 MPa \\ \hline Cohesion strength \(c\) = 50 MPa & \(BTS\) = 6.5 MPa & \(BTS\) = 6.5 MPa \\ \hline Friction angle \(\varphi\) = 15\({}^{\circ}\) & & \\ \hline Moment contribution coefficient \(\beta\) = 0.5 & & \\ \hline Bond\(-\)radius multiplier \(\lambda\) = 1 & & \\ \hline \end{tabular}
\end{table}
Table 1: Micro and macro mechanical properties of Beishan granite.  
Figure 4: Numerical model for (**a**) uniaxial compression test and (**b**) Brazilian disc test.  
number of initial bonds for all the particles. If \(D<0.3\), it means one or two bonds of a particle are broken, and this particle will be classified into an elastic zone or main cracked zone. If \(0.3<D<1\), it means most of the bonds of a particle are broken, but this particle is still bonded with other particles, which will be classified into a plastic zone or microcrack zone. \(D\) is equal to 1 for complete damage, which will be classified into a crushed zone.
## 4 Recent Advancements in Applications
### Road Inventory and City Asset Management  
Mapping and monitoring road assets is a critical task primarily managed by various stakeholders, including municipalities, local governments, and contractors [133]. MMSs have proven to be highly efficient tools for optimizing this process. To achieve the objectives of road asset management, car-based MMS platforms are commonly utilized. These systems are equipped with cameras and LiDAR sensors to collect comprehensive data about the surrounding environment, including road surfaces, signage, and infrastructure. The captured images and LiDAR point clouds enable detailed analysis of road conditions, such as surface smoothness, and facilitate the detection of flaws like cracks, potholes, and other anomalies. This data-driven approach enhances the accuracy and efficiency of road maintenance and planning, ultimately contributing to safer and more sustainable urban infrastructure.
## 5 Experiment and Results Description
### Experimental Environment  
The experiments were conducted with the deep-learning framework PyTorch. To ensure that the network was universally applicable, a PC with a common configuration (a 2.3 GHz Core i5 CPU with 8 GB of memory and a NVidia GeForce GTX 1050 Ti graphics card) was used for training. During training, to obtain the optimal model when the model converges without overfitting due to the small amount of data, the loss on the validation set cannot be controlled to be too small. Due to the limitations of the dataset and the experimental environment, the batch size was set to 1 during training.