esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Wear Behavior
These matrix-rich parts occurred during the preform manufacturing process or the liquid pressure infiltration process, but, as a result of this wear phenomenon, it was judged that it does not significantly affect the metal matrix composite overall wear behavior. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**At. \%** & **C** & **O** & **Mg** & **Al** & **Si** & **Mo** & **Cr** & **Mn** & **Fe** \\ \hline A & 5.44 & 43.73 & 0 & 0 & 1.01 & 0.31 & 1.97 & 0.65 & 46.88 \\ B & 23.26 & 32.22 & 1.15 & 19.77 & 11.00 & 0 & 0 & 12.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SEM-EDS compositional analysis of the A and B areas of Figure 5c. Figure 6: SEM micrographs of AISI 52100 wear surface with magnifications of (**a**) \(\times\)50, (**b**) \(\times\)150, and (**c**) \(\times\)1000 (**A,B**: EDS analysis area; yellow dotted circles: adhesive wear area; yellow arrows: wear fragments). Figure 7: SEM micrographs of TiC/ AISI 1020 wear surface with (**a**) \(\times\)50, (**b**) \(\times\)150, and (**c**) \(\times\)1500 magnifications (yellow dotted circles: matrix rich area). Figure 8 shows the microstructure of the wear surface of the H13 counterpart pin material. As shown in Figure 8, after the wear test of AISI 52100 and TiC/AISI 1020, an adhesive wear on the pin surface was observed. In the case of the H13 pin tested with a metal matrix composite, there was severe adhesive wear. This indicates that TiC/AISI 1020 with high hardness has superior wear resistance than that of the H13 pin. After the wear tests, the pins were compared. The TiC/AISI 1020 metal matrix test pin had more wear than the AISI 52100 sample pin, indicating that the metal matrix material has better wear resistance than the AISI 52100 bearing steel. Table 4 displays the wear width and wear depth of AISI 52100 and the TiC/AISI 1020 composite after the pin-on-disc tests. In the case of the metal matrix composite, the wear width was 682 \(\upmu\)m, whereas in the case of AISI 52100, the wear width was 2937 \(\upmu\)m. The wear width of AISI 52100 was about three times wider than that of TiC/AISI 1020. The hardness of the heat-treated composite was 76.64 HRC, which was much higher than that of AISI 52100 (62.42 HRC), and the higher the hardness yield, the better the wear resistance. The wear depth of the AISI 52100 bearing material and composite was also measured and compared. Profile measurements show that the wear depth of the TiC/AISI 1020 composite (0.5 \(\upmu\)m) was much smaller than that of the AISI 52100 bearing steel (3 \(\upmu\)m).
## 4 Potential IIoT Applications
### Disaster and Crisis Management Applications  
As far as natural disasters or large-scale unexpected events occur, terrestrial networks may be overloaded or totally devastated. During these urgent situations, NTNs have the ability to provide more effective services compared to traditional infrastructures. More specifically, NTNs can successfully sustain applications, including video surveillance and structural monitoring, as well as facilitate structural monitoring and protection of critical infrastructures, e.g., dams, bridges, power stations, nuclear reactors, etc. [77]. Additionally, NTNs can strengthen security and access controls with respect to border control, flood warning, earthquake detection, early warning for possible fire disasters, weather and environmental monitoring, coastline and pipeline surveillance, and trafficability of maritime routes, e.g., detection of icebergs, and synthetic aperture radar applications. Besides the above, emergency networks via circular flight tracks of HAPs and LAPs can be realized to continuously observe regional hotspots and hazard areas or provide emergency vehicle guidance and inform rescue services [78].  
Figure 4: Various types of specialized sensors and industrial applications for the NTN-based IIoT ecosystem for both the massive machine type communications (mMTC) -based delay-tolerant and ultra-reliable and low-latency communications (URLLC) -based delay-sensitive scenarios.
## 1 Introduction
### Importance of Energy Efficiency and Sustainability  
The relationship between energy efficiency and fault detection in wind turbine blades is well established. [PERSON] et al. [23] demonstrated that predictive maintenance could enhance energy efficiency by reducing downtime and preventing catastrophic failures. [PERSON] et al. [24] used statistical data from offshore wind farms to show that fault detection could improve energy efficiency, using methods like Fuzzy Fault Tree Analysis to assess risks.  
Machine learning, particularly artificial intelligence, has emerged as a crucial tool for fault detection, as exemplified by [PERSON] et al. [25], who analyzed wind turbines using SCADA data. They implemented a Gaussian mixture model, yielding accurate results in predicting and locating faults. [PERSON] et al. [26] proposed, based on data obtained from a SCADA system, a method to differentiate false alarms from real alarms in the detectionof faults in wind turbines. To do this, they first use multivariate clustering to obtain appropriate subsets of wind turbines, and then use an autoregressive neural network. Finally, the residuals between the median values of the cluster and the target values are used to calculate the level of anomaly. [PERSON] et al. [27] emphasized the importance of reducing maintenance and operational costs for wind energy system reliability, employing a state monitoring system as an effective tool for wind turbine blade maintenance.  
Sustainability considerations are essential in analyzing any renewable energy conversion system. [PERSON] et al. [25] focused on sustainability in their study, emphasizing the need for fault-free equipment to achieve optimal results in energy transformation. Their evaluation of various phenomena damaging wind turbine blades concluded that maintenance and fault detection could increase blade lifespan, contributing to savings in greenhouse gas emissions. [PERSON] et al. [28] proposed vibration analysis methods to enhance sustainability by determining blade failures, including erosion, cracking, and mass imbalance, thereby improving blade performance and reducing maintenance costs while minimizing energy conversion efficiency. [PERSON] et al. [29] determined that monitoring offshore wind turbine blades is crucial for power generation due to substantial structural loads, utilizing non-destructive testing methods, and proposed using UAVs for pinpointing failure locations.
# Pediatric Endoscopy, Update 2020
## 6 Ercp
### Anatomic Differences Unique to Children  
The passage of the duodenoscope into the duodenal region is similar to the endoscopy in adults. Sometimes, the higher position of the pylorus can be problematic. As most children will be under general anesthesia, the intubation of the instrument into the esophagus requires extra care and attention. Any application of force should be avoided. If the insertion into the esophagus is difficult, the instrument should be advanced with help of a laryngoscope and a Magill forceps. The working space in the duodenal region in newborns and infants is restricted because of the smaller size of the duodenum. This, together with the steep angle between the bile duct and the duodenum, renders the procedure more challenging than in adults. Due to the difficult anatomy, an unintentional contrast injection into the pancreatic duct can occur; a guide wire insertion first is highly advisable.
# YOLOv5-Based Electric Scooter Crackdown Platform
## 4 Implementation of Proposed System
### Training Performance of Customized YOLOv5s Model
#### 4.3.1 Training Performance Analysis of RIDE Model  
The RIDE model was configured with a batch size of 32 and trained for 250 epochs. Among a total of 1256 datasets, 877 were used for training, 252 for validation, and 127 for testing, resulting in a split ratio of approximately 7:2:1. The training process was then performed accordingly.  
Figure 5 shows the confusion matrix for the RIDE model, which comprises three classes: Non-Ride, Ride, and Background. The matrix indicates that both Non-Ride and Ride classes demonstrate exceptionally high prediction accuracy. Notably, the Background class shows no detection errors, resulting in no recorded values for that class.  
Table 1 presents the precision values for each RIDE model class, calculated from the confusion matrix. Precision for the RIDE model was computed as described in (1). As shown in the table, all classes achieve a precision of 1.0, indicating consistently accurate predictions and reflecting the high reliability and performance of the RIDE model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Precision** \\ \hline Non-Ride & 1.0 \\ Ride & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Precision for each class of RIDE model.  
Figure 5: Confusion matrix of RIDE model.  
The F1 Score, defined as the harmonic mean of precision and recall, is a key metric for evaluating overall model performance. A higher F1 Score signifies better model accuracy. The F1 Score is formulated as shown in (3).  
\[\text{F1 Score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{3}\]  
where the F1 Score reflects the balance between precision and recall, with values ranging from 0.0 to 1.0. This metric is particularly useful for imbalanced datasets because it is designed to maintain an equilibrium between precision and recall, preventing significant disparities between the two. Consequently, it serves as a key metric for performance evaluation.  
Figure 6 displays the F1 Score results of the RIDE model. As shown, when the F1 Score reaches a confidence value of 0.822, all classes achieve a performance score of 1. This finding suggests that setting the RIDE model's confidence threshold to 0.822 can optimize detection performance in real-world scenarios.  
In addition, mAP is calculated based on the area under the precision-recall (PR) curve, representing the average precision (AP). mAP50 considers a detection correct only when the intersection over union (IoU)--which measures how closely a predicted bounding box aligns with the actual object--is at least 0.5. By comparison, mAP50-95 evaluates IoU thresholds from 0.5 to 0.95 in increments, calculating the average across these thresholds for a more comprehensive assessment of the model's performance. The RIDE model exhibits outstanding results, achieving mAP50 = 99.475 and mAP50-95 = 99.5, indicating consistently high accuracy across a range of IoU criteria.
## 2 Feasibility Analysis and Modeling of Energy Storage in Transmission Network Expansion Planning
### Constraints
\begin{array}{l}-n_{line,ij}^{o}p_{ij}^{\max}\leq P_{ij,t,s,Y}^{o}\leq n _{line,ij}^{o}p_{ij}^{\max}\\ -n_{line,ij}^{y}p_{ij}^{\max}\leq p_{ij,t,s,Y}^{b}\leq n_{line,ij}^{y}p_{ij}^{ \max}\\ -x_{line,ij,Y}^{p}p_{ij}^{\max}\leq P_{ij,t,s,Y}^{p}\leq x_{line,ij,Y}^{p}p_{ij }^{\max}\end{array} \right. \tag{22}\]  
where \(P_{ij}^{\max}\) is the maximum transmission power of a single line between nodes \(i\) and \(j\). (6) Conventional Generator and Renewable Energy Output Constraints  
\[-\omega_{d,g}\Delta t\leq P_{gen,g,t,s,Y}-P_{gen,g,t-1,s,Y}\leq\omega_{u,g}\Delta t \tag{23}\]  
\[P_{gen,g}^{\min}\leq P_{gen,g,t,s,Y}\leq P_{gen,g}^{\max} \tag{24}\]  
\[0\leq P_{\mathrm{wind},j,t,s,Y}\leq P_{\mathrm{wind},j,t,s,Y}^{f} \tag{25}\]  
\[0\leq P_{\mathrm{solar},k,t,s,Y}\leq P_{\mathrm{solar},k,t,s,Y}^{f} \tag{26}\]  
where: \(\omega_{d,g}\) and \(\omega_{u,g}\) are the ramp-up and ramp-down rates of conventional generator \(g\). \(P_{gen,g,t,s,Y}\) is the active power output of a conventional generator \(g\) in scenario \(s\) during period \(t\) of year \(Y\). \(P_{gen,g}^{\max}\) and \(p_{gen,g}^{\min}\) are the minimum and maximum output limits of conventional generator \(g\). \(\Delta t\) is the time interval. (7) Energy Storage Operation Constraints  
\[0\leq p_{c,i,t,s,Y}\leq P_{gs,i,g}x_{c,i,t,s,Y} \tag{27}\]  
\[0\leq p_{d,i,t,s,Y}\leq P_{gs,i,g}x_{d,i,t,s,Y} \tag{28}\]  
\[v_{c,i,t,s,Y}+v_{d,i,t,s,Y}\leq v_{ess,i,Y} \tag{29}\]  
\[SOC_{\min}\cdot E_{ess,i,Y}\leq e_{i,t,s,Y}\leq SOC_{\max}\cdot E_{ess,i,Y} \tag{30}\]  
\[e_{i,t+1,s,Y}-e_{i,t,s,Y}=p_{c,i,t,s,Y}\Delta t\cdot\eta_{ess}^{+}-p_{d,i,t,s,Y }\Delta t/\eta_{ess}^{-} \tag{31}\]  
\[e_{i,0,s,Y}=e_{i,T_{24},s,Y} \tag{32}\]  
where \(P_{c,i,t,s,Y}\) and \(P_{d,i,t,s,Y}\) represent the charging and discharging power of the energy storage device at the node \((i)\) in the scenario \((s)\) during the period \((t)\) of the year \((Y)\); \(e_{i,t,s,Y}\) represents the capacity of the energy storage device at the node \(i\) in scenario \(s\) during period \(t\) of year \(Y\); \(v_{c,i,t,s,Y}\) and \(v_{d,i,t,s,Y}\) represent the charging and discharging status of the energy storage device at the node \(i\) in scenario \(s\) during period \(t\) of year \(Y\), which are binary variables, where \(1\) indicates charging/discharging and \(0\) indicates no action of the energy storage device; \(\eta_{ess}^{+}\) and \(\eta_{ess}^{-}\) represent the charging efficiency and discharging efficiency of the energy storage device, respectively; the last constraint is the energy balance constraint of the energy storage the since the technical verification of this planning scheme is for a typical day, so \(T_{24}=24\), which means the energy balance constraint of the energy storage is a daily balance constraint.
## 4 The Proposed PUL-DUCG Model
### Knowledge Acquisition and Representation  
In this stage, the PULSs are used to acquire the knowledge parameters of PUL-DUCG (e.g., \(r_{n,l}\), \(b_{ij}\) and \(a_{nk;ij}\)). Here, we use the knowledge parameter \(b_{ij}\) as an example to illustrate the knowledge acquisition process. Suppose that the parameter \(b_{ij}\) is evaluated by \(l\) experts \(\{e_{1},e_{2},\ldots,e_{l}\}\). Each expert is assigned a weight \(\lambda_{k}\) with \(\sum\limits_{k=1}^{l}\lambda_{k}=1\) because of their different backgrounds and experience. Let \(\widetilde{B}^{k}=\left[\widetilde{b}_{ij}^{\ \ k}\right]_{p\times q}\) be the knowledge assessment matrix of the \(k\)th expert, where \(\widetilde{b}^{k}_{ij}=\left\langle\left[s^{k}_{\theta_{ij}^{\prime}}s^{k}_{ ij}\right],\left(u^{k}_{ij},v^{k}_{ij}\right)\right\rangle\) is the linguistic evaluation for the \(j\)th state of the root event \(B_{i}\). Next, the proposed knowledge acquisition is explained in detail.  
**Step 1:** Construct the collective knowledge assessment matrix \(\widetilde{B}\).  
By using the PULWA operator, all individual knowledge assessment matrixes \(\widetilde{B}^{k}\) (\(k=1,2,\ldots,l\)) can be aggregated to obtain the collective knowledge assessment matrix \(\widetilde{B}=\left[\widetilde{b}_{ij}\right]_{p\times q}\), where  
\[\widetilde{b}_{ij}=PULWA\left(\widetilde{b}^{1}_{ij},\widetilde{b}^{2}_{ij}, \ldots,\widetilde{b}^{l}_{ij}\right)=\mathop{\oplus}\limits_{k=1}^{l} \lambda_{k}\widetilde{b}^{k}_{ij}. \tag{12}\]  
**Step 2:** Obtain the first-order cut-set expression of the target event.  
By using Equation (8), the expression of any child event is the combination of parent events and connection events. Then, the expression could be simplified by removing unrelated events from the expression based on the DUCG.  
For example, as shown in Figure 2, the child events \(X_{2}\) and \(X_{3}\) are expressed as:  
\[X_{2}=F_{2;1}B_{1}+F_{2;3}X_{3} \tag{13}\]  
\[X_{3}=F_{3;4}B_{4}+F_{3;2}X_{2} \tag{14}\]  
Using Equations (13) and (14), the expression \(X_{2}\) is expanded as:  
\[X_{2}=F_{2;1}B_{1}+F_{2;3}(F_{3;4}B_{4}+F_{3;2}X_{2})=F_{2;1}B_{1}+F_{2;3}F_{3 ;4}B_{4}+F_{2;3}F_{3;2}X_{2} \tag{15}\]  
A duplicated variable \(X_{2}\) appears on the right-hand side of the equation, so \(X_{2}\) can be removed. When \(X_{2}\) is deleted, it will disappear as a parent event of \(X_{3}\). Finally, \(X_{3}\) has only one parent event \(B_{4}\). Then, \(r_{3}=r_{3;4}+r_{3;2}\) changes into \(r_{3}^{\{2\}}=r_{3;4}\), where the superscript  
Figure 2: Example of a typical PUL-DUCG model.  
index [2] indicates event \(X_{2}\). Finally, the first-order cut-set expression of the target event \(X_{2}\) can be expressed as:  
\[\begin{split} X_{2}&=F_{2;1}B_{1}+F_{2;2}F_{34}^{\{2 \}}B_{4}\\ &=(r_{2;1}/r_{2})A_{2;1}B_{1}+(r_{2;3}/r_{2})A_{2;3}\Big{(}r_{3;4 }/r_{3}^{\{2\}}\Big{)}A_{3;4}B_{4}\\ &=(r_{2;1}/r_{2})A_{2;1}B_{1}+(r_{2;3}/r_{2})A_{2;3}A_{3;4}B_{4} \end{split} \tag{16}\]
# AI-Enabled Compact and Efficient Dynamic Light Scattering System for Precise Microparticle Sizing
## 1 Introduction
By leveraging AI, it is possible to automate and enhance the accuracy of microparticle characterization while reducing costs and operational complexity [21; 22; 23]. However, the integration of AI into microparticle size detection remains underexplored, leaving a critical gap in the development of innovative, accessible solutions. Here, we present AI-DLS, a low-cost, efficient framework combining DLS signal acquisition with AI-driven modeling for accurate and scalable microparticle size characterization. The hardware system features a compact design comprising a helium-neon laser, precise optical components, and a high-frequency data acquisition system, achieving significant reductions in size (0.08 m\({}^{2}\)), weight (5 kg), and cost (USD 1000). Signal quality was enhanced using advanced preprocessing techniques, such as Kalman filtering, to minimize noise and improve reliability. Deep learning (DL) models, including deep neural network (DNN), one-dimensional convolutional neural network (1D-CNN) [24], and 2D-CNN with Gramian angular field (GAF) transformations, were employed to analyze ACFs derived from scattered light signals. Among these, the 2D-CNN with fused GAF inputs delivered the best performance, achieving a mean absolute error (MAE) of 10 nm and an accuracy of 0.99. The AI-DLS system demonstrated comparable stability and accuracy to traditional instruments while reducing test time by 80%. This integrated hardware-AI solution addresses the limitations of conventional DLS systems, offering a cost-effective, precise, and deployable tool for microparticle size characterization across diverse research and industrial applications.
# Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm
## 2 Methodology
### Classical Tomographic Model  
The commonly used tomographic approaches are based on a predetermined cuboid-shaped region, which is a fixed region selected to approximately cover the complete distribution of GNSS signals for all epochs. Two spatial discretization models are adopted for the regional division. As shown in Figure 1, one is the voxel-based model (top panel) and another is the node-based model (bottom panel). The voxel-based model uses voxels to discretize the tomographic region and only within the yellow-shaded voxels do GNSS signals actually pass. Conversely, as mentioned before, the empty spaces in the same tomographic model will still contain many voxels (the non-yellow-shaded boxes), which should not be used in the tomographic solution. Moreover, SWV parameters must still be estimated in these voxels by the horizontal/smoothing constraint [24; 25; 26; 27] for the tomographic modeling to be resolved at the expense of reduced accuracy.  
The same problem exists when using a node-based model, which uses nodes at different tomographic planes (different heights) to parameterize the tomographic region. This approach also includes many problematic nodes (white circles in the bottom panel in Figure 1) without surrounding GNSS signals. However, in this instance, only the yellow nodes (yellow circles in the bottom panel) are needed in the tomographic solution. However, the layout of nodes is more flexible than that of voxels since the voxels in the tomographic region need to adjoin each other, but the nodes in the tomographic  
Figure 1: Classical Tomographic Models; (**a**) Voxel-based tomographic model; (**b**) Node-based tomographic model; the voxels (yellow cube) or nodes (yellow solid dots) crossed by or near GNSS signals (red lines) are highlighted in yellow; the black frames represent the tomographic segmentation area.  
region do not have this constraint. Therefore, the node-based tomographic region can be improved easily to match the real observation region (i.e., the coverage areas of the GNSS signals).
## 3 Results  
### Effects of Various Soil Properties on Displacements  
#### 3.1.1 Water Content  
Figure 5 shows the effects of various water contents on tunnel displacements induced by both normal and reverse faults. Water contents of 2%, 9%, and 12% in reverse faults were labeled as M2R, M9R, and M12R, and in normal faults were labeled as M2N, M9N, and M12N. The results show that the tunnel, after a 15 mm displacement by the vertical component, had lost its strength, and tunnel displacement reached 31 mm, which is higher than in the reverse fault with a water content of 9% (M9R). Another important aspect is the major differences (almost 34%) between M2R and M12R, revealing how the existence of water in the field could reduce displacements and damages to tunnels. The same curves in the normal and reverse faults for M9N and M12N show that the percentages of water content at an optimum water content (OMC = 13.9%) exhibited less differences in tunnel displacements. The findings on normal and reverse faults revealed that displacements decline with increasing soil water content (before reaching the OMC level). This aspect can also be found in previous studies [31; 32]. Another reason for the decreasing displacements was found by [PERSON], which is that soil cohesion increases with the rise in water content, causing the shear forces to increase, thus giving the soil more strength [33].