esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology Inference and Link Parameter Estimation Based on End-to-End Measurements +
## 3 Topology Inference and Estimation of Link Parameters
### Inferring Physical Routing Trees  
As previously mentioned, the output of the algorithm presented in Section 3.1 is the logical routing tree, which consists of the branching points of the physical topology. However, internal nodes where no branching of traffic occurs (i.e., nodes with a single child) do not appear in the logical tree and long paths with no branches might be represented as a single logical link. Therefore, the logical tree might differ significantly from the underlying physical routing tree. To infer more accurately the physical routing tree, in this work we incorporate into the algorithm information regarding the number of hops needed for reaching each node starting from the source. Assuming that we only have access to the network edge and not to the internal nodes, such information can be readily obtained simply by capturing the employed probes at the destination nodes using a packet analyzer tool and examining their time-to-live (TTL) fields. The idea is that two nodes that are siblings must have the same TTL value. At each iteration, the TTL value of the newly created parent of the selected RNNs is calculated based on the TTL value of its children.  
In more detail, we start by extracting the TTL values corresponding to each destination node from the realized end-to-end measurements. Then, we modify the neighbor selection step as follows. After selecting a pair of RNNs, we check the respective TTL values of the two nodes. If they are equal, we proceed to the next step. Otherwise, we can deduce that the node with the smaller TTL value is located in a path with no branching points. To rectify this, we assign this node as the single child of a newly created parent, with a properly adjusted TTL value (i.e., incremented by one). We continue adding such parent nodes until the new TTL value becomes equal to that of the other RNN. Following that in the next step, we properly set (i.e., increment by one) the TTL value of the node that is designated as the parent of the two siblings (i.e., the two RNNs in the first case, or the head of the no branching path and the remaining RNN in the second case), and we proceed to the rest of the algorithm as before.
## 4 Discussion
### Management of Small Renal Lesions  
Among solid renal lesions, most are RCCs [6]. Among benign lesions, AMLs are the most frequent and are typically present with characteristic US features, suggesting a presumptive diagnosis, differently from OCY [9; 35]. At US, AML typically appears as a small, well-defined rounded mass located within the cortex, homogeneously hyperechoic, and is sometimes associated with posterior shadowing. However, several subtypes of renal AMLs exist, collectively composing a heterogeneous group of neoplasms, with variable clinical and radiological behavior [36; 37]. In a minority of cases, fat-poor AMLs may be iso- or hypoechoic to the renal parenchyma or present inhomogeneous areas (particularly in large AMLs). On the other hand, subtypes of RCC may have a high-fat content and present with AML-like characteristics, representing a diagnostic challenge [7; 9]. For this reason, caution is needed in the work-up of hyperechoic renal lesions [6]. In the general population without risk factors, there is a reasonable probability that a small solid renal lesion exhibiting the typical US features of AMLs is benign [6; 38], whereas lymphomas and metastases are malignant lesions with unique clinical settings, and, in most cases, can be easily diagnosed at imaging [31].  
Given the technical difficulty of characterizing the contrast enhancement of small masses and the difficulty of performing a biopsy, active surveillance is currently the ac  
Figure 8: Forest plot for the direct comparison of RCC and AML stiffness values (m/s) obtained from six studies using SWE [18; 19; 21; 22; 25; 26]. Black squares depict single study estimates with their 95% CIs (black lines). The blue diamond represents the summary estimate and its 95% CI.  
cepted management paradigm for small solid lesions, which can be referred to for imaging follow-up [6].
# MLPs Are All You Need for Human Activity Recognition
## 7 Discussion
### Performance of Sliding Window Parameters  
Each dataset contains a different range of activity lengths and repetition rates. The sliding window length has a significant impact depending on how long the activities are in the dataset. The sliding window's parameters were altered to study its effect on the Mixer performance. The model's parameters were fixed, and the step size was constant instead of using an overlap percentage of the window length to prevent the number of samples from affecting the results. Small window intervals contain insufficient data for the Mixer to learn from and make decisions. On the other hand, if the sliding window interval is large relative to the activities in the window, it allows information from multiple activities to be present in a single sliding window, making it harder for the Mixer to determine which activity the sliding window represents among the multiple activities.  
Figure 4: Normalised confusion matrix of the Daphnet Gait dataset.  
Performance generally improves with increasing overlap, but as there are more samples to train and test, the computational complexity of training the Mixer also rises. In contrast, little to no overlap significantly reduces the sample size, particularly for larger sliding window sizes, which causes the Mixer to over-fit on the dataset.  
Figures 5-7 illustrate the changes in the Mixer's performance when the sliding window length is changed. In datasets with more extended activities, such as PAMAP2 and Daphnet, larger sliding windows increase the model's capability to learn by providing more information. On the other hand, in the Opportunity dataset, which contains shorter activities, the model's performance decreases with larger window lengths. The sliding window figures indicate that the sliding window has a slight effect on the Mixer's performance, but overall the model is not sensitive to the sliding window length.  
Figure 5: Evaluation of sliding window length on the Opportunity dataset  
Figure 6: Evaluation of sliding window length on the Daphnet Gait dataset
## Article  
Active-Learning Reliability Analysis of Automotive Structures Based on Multi-Software Interaction in the MATLAB Environment  
[PERSON] 1,2,2, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2 and [PERSON] 1,2,*  
1 School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510641, China; [EMAIL_ADDRESS] (J.W.); [EMAIL_ADDRESS] (J.C.); [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (F.L)
2 Guangdong Provincial Key Laboratory of Automotive Engineering, Guangzhou 510641, China
* Correspondence: [EMAIL_ADDRESS]  
The reliability design of automotive structures is characterized by numerous variables and implicit responses. The traditional design of experiments for metamodel construction often requires manual adjustment of model parameters and extensive finite element analysis, resulting in inefficiency. To address these issues, active learning-based reliability methods are effective solutions. This study proposes an active-learning reliability analysis method based on multi-software interaction. Firstly, through secondary development of different software and MATLAB (version 2023a)'s batch processing function, a multi-software interactive reliability analysis method is developed, achieving automation in structural parametric design, finite element analysis and post-processing. This provides a more efficient and convenient platform for the implementation of active learning. Secondly, the polynomial chaos-kriging (PCK) active-learning method is introduced, combining the advantages of polynomial chaos expansion (PCE) and kriging. The PCK method captures the global behavior of the computational model using regression-based PCE and local variations using interpolation-based kriging. This metamodel is constructed with fewer training samples, effectively replacing the real multi-dimensional implicit response relations, thereby improving the efficiency of modeling and reliability analysis. Finally, the specific implementation scheme is detailed. The accuracy and efficiency of the proposed method are verified by a reliability engineering example of body-in-white bending and torsional stiffness.  
reliability analysis; active learning; polynomial chaos-Kriging; multi-software interaction; multi-dimensional implicit response
# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 7 ANN Results with Simulated Degradation on _I-V_ Curves
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Applied Degradation: \(R_{s}^{deg}=1.7R_{s}\) (+70\%)**} \\ \hline \(G\left[\frac{W}{m^{2}}\right]\) & \(T\left[{}^{\circ}\mathbf{C}\right]\) & & \(I_{ph}[\mathbf{A}]\) & \(I_{sat}\left[\mathbf{\mu}\mathbf{A}\right]\) & \(\mathbf{\eta}\left[\mathbf{\cdot}\right]\) & \(R_{s}\left[\mathbf{\Omega}\right]\) & \(R_{sh}\left[\mathbf{\Omega}\right]\) \\ \hline \multirow{4}{*}{755} & \multirow{4}{*}{51} & \(\mathbf{\mathrm{P}}\mathbf{\mathrm{H}}\) & \(3.791\) & \(1.195\) & \(1.293\) & \(0.244\) & \(490\) \\  & & \(\mathbf{\mathrm{P}}\mathbf{\mathrm{deg}}\) & \(3.834\) & \(1.377\) & \(1.295\) & \(0.390\) & \(446\) \\  & & \(\mathbf{\mathrm{\Delta}}\mathbf{\mathrm{p}}_{\%}\) & \(1.16\) & \(15.2\) & \(0.175\) & \(\mathbf{59.6}\) & \(-8.9\) \\ \cline{3-8}  & & \(\mathbf{\mathrm{\Delta}}\mathbf{\mathrm{P}}_{\text{mpp},\%}\) & \(0.9\) & \(1.18\) & \(-1.19\) & \(0.16\) & \(-\mathbf{3.24}\) & \(-0.09\) \\ \hline \multirow{4}{*}{795} & \multirow{4}{*}{26} & \(\mathbf{\mathrm{P}}\mathbf{\mathrm{H}}\) & \(3.907\) & \(0.094\) & \(1.305\) & \(0.232\) & \(511\) \\  & & \(\mathbf{\mathrm{P}}\mathbf{\mathrm{deg}}\) & \(3.963\) & \(0.090\) & \(1.294\) & \(0.367\) & \(449\) \\ \cline{3-8}  & & \(\mathbf{\mathrm{\Delta}}\mathbf{\mathrm{p}}_{\%}\) & \(1.44\) & \(-4.0\) & \(-0.84\) & \(\mathbf{58.6}\) & \(-12.2\) \\ \cline{3-8}  & & \(\mathbf{\mathrm{\Delta}}\mathbf{\mathrm{P}}_{\text{mpp},\%}\) & \(0.9\) & \(1.48\) & \(0.31\) & \(-0.87\) & \(-\mathbf{2.58}\) & \(-0.14\) \\ \hline \end{tabular}
\end{table} 
Table 8: SDM parameters estimated with ANN for \(I\)-\(V\) curves of Figure 16.
## 3 Results and Discussion
### Evaluation Metrics  
In the object detection tasks, two widely used performance metrics are the average precision (AP) and F1 score. At the training process, a detector compares the predicted bounding boxes with the ground truth bounding boxes according to the intersection over union (IOU) at each iteration to update its parameters. Generally, a 0.5 IOU ratio for each prediction at the training stage is aimed for. This means that if the network predicts an object with a bounding box that overlaps with the ground truth box by at least 50%, it is considered as a true prediction. When the localization is a matter for a computer vision task, this ratio could be set higher. In this research, the value remained as 0.5, and it was expected to detect the objects at least with this ratio at the evaluation phase. Therefore, this ratio was used for calculating the performance metrics.  
The F1 score evaluation metric is used to understand the success rate by calculating the precision and recall rate. The precision is the ratio of the actual matches of all objects that are detected as matches and the recall is the ratio of the number of objects that are detected correctly to the number of all ground truth samples. Neither the recall rate nor precision rate is individually enough to measure the performance of the framework; therefore, the harmonic mean of them, which is the F1 score, was also calculated. By defining the true positive (TP) as truly detected objects, the false negative (FN) as non-detected objects, and the false positive (FP) as falsely detected objects, the precision, recall and F1 score was calculated as:  
\[Precision=\frac{TP}{TP+FP}, \tag{8}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{9}\]  
\[F1\ score=2*\frac{Precision*Recall}{Precision+Recall}. \tag{10}\]  
Additionally, 12 different metrics were used to measure the characteristics and performance of the object detection algorithms with the COCO metric API (Table 2). Unless defined otherwise, the average precision (AP) and average recall (AR) weree calculated by averaging over 10 different IOU ranging from 0.5 to 0.95 with 0.05 intervals. Besides, the values where IOU is 0.5 and 0.75 were calculated for AP. AP is the average precision calculation according to all categories and IOU values. In this research, there was only one detection category, which is airplane. AR is the maximum number of detections per image, averaged over categories and IoUs. These calculations were also checked by interpreting the bounding box areas. According to COCO, objects with a size smaller than \(32^{2}\) pixels are defined as small, between \(32^{2}\) and \(96^{2}\) as medium, and more than \(96^{2}\) pixels as large. The metric calculations were performed according to all scale levels and for separate scales [47].
## 4 Discussion
### Duty Cycle  
As the local 10g-SAR compliance assessment with the BRs and DRLs refers to the maximum values averaged over a period of any six minutes, one of the key parameters of the WIT system is the duty cycle of EMR emissions. This is defined as the fraction of a period in which a system is active and corresponds to the emitted power averaged over time. Typical values of the duty cycle in Wi-Fi 2G/Bluetooth applications ranged from 0.02 (2%) to 0.95 (95%) and depended on the protocol/standard used for data transmission in packets, the data transmission rate, and the type of transmission (such as file or voice transfer, music or video streaming, etc.) [34]. SAR values are proportional to the time-averaged power of the affecting EMR and therefore to the duty cycle. For example, in particular applications, they will be 50 times, 4 times, and 5% lower than the values discussed in this study (obtained for continuous EMR, with a duty cycle of 1.0), for data transmissions with duty cycles of 0.02, 0.25, and 0.95 respectively.
# Study on the Seismic Response of Shield Tunnel Structures with the Preload Loss of Bolts
## 2 Numerical Simulation  
### Soil-Shield Tunnel Model  
A 3D model of the soil-tunnel is established, as shown in Figure 1a. The dimensions of the strata are 30 m (width) \(\times\) 30 m (height) \(\times\) 7.5 m (length). The tunnel is buried at a depth of 12 m. The outer boundary distance from the tunnel is twice the tunnel diameter to effectively avoid the boundary effects [26; 27]. The shield tunnel dimensions are depicted in Figure 1b, with an outer diameter of 6 m, an inner diameter of 5.4 m, a segment thickness of 0.3 m, and a ring width of 1.5 m. A single tunnel ring is composed of six segments, including three standard segments (A1/A2/A3), two adjacent segments (CP/BP), and one key segment (KP). The segments of each ring are connected with 12 pieces of grade 8.8 circumferential bolts. Adjacent rings are connected via 10 pieces of grade 8.8 longitudinal bolts, as shown in Figure 1c. Five rings of the shield tunnel have been selected for the study. The segments between adjacent rings are staggered with a deviation angle of 72\({}^{\circ}\), as shown in Figure 1d.  
Figure 1: Stratum–structure model. (**a**) Stratum–structure model. (**b**) Layout of segment rings of tunnel. (**c**) Connection bolts at the joint of shield tunnel segment. (**d**) Refined model of lining ring.
# Study on the Optimization of Pile Length of Micropiles in Soil Landslides
## 2 Methods
### Optimization of Micropiles Length Based on Numerical Simulation
#### 2.4.2 Optimize Process Analysis
PLmin is taken as the construction length if there are no special requirements, i.e., PLmin is the sum of the bearing segment pile length and the embedded segment pile length. The embedded segment pile length is generally taken as one third of the full pile length and not less than 4 m in construction design. After determining PLmin, rebuild the structure model, assign parameters, and calculate the safety factor. If the safety factor is still less than the allowable safety factor [Fs], proceed to the next step. Otherwise, it gives an Error Prompt 2: The minimum pile length already meets safety requirements, and no optimization is needed. Please check if the input parameters are reasonable. (Note: Error Prompt 2 is triggered when the safety factor meets the requirements after modeling and calculating with the design calculated pile length PLmin, indicating that the minimum pile length already meets the safety requirements and there is no room for pile length optimization. However, it is also possible that there are problems with the numerical simulation of the strata parameters, leading to a risky numerical simulation calculation.) Figure 5: Flowchart of the optimization algorithm for micropile length. S8: Set the optimization calculation accuracy \(\varepsilon\). S9: Set the pile length \(\text{PL}=(\text{PL}_{\text{max}}+\text{PL}_{\text{min}})/2\), rebuild the structure model again, and calculate the safety factor. If the safety factor is less than the allowable safety factor, then set \(\text{PL}_{\text{min}}=\text{PL}\); if not, set \(\text{PL}_{\text{max}}=\text{PL}\). S10: Compare the difference between \(\text{PL}_{\text{max}}\) and \(\text{PL}_{\text{min}}\). If it is not greater than the set accuracy \(\varepsilon\), output the optimization results. Otherwise, return to S9. Continue the optimization process until the desired level of calculation accuracy is achieved, and subsequently present the optimized results as output.
# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 3 Cotton Boll Disease Detection Algorithm and Improvement
### Improved Cotton Boll Disease Detection Algorithm Model Framework
#### 3.3.2 Introduce PConv to Optimize the C3k2 Module  
In YOLOv11, the C3k2 module enhances feature extraction and network efficiency via an optimized convolution kernel and cross-stage connections. However, its multi-branch convolutions introduce substantial computational overhead. This manifests primarily in two aspects: (1) substantial computational redundancy exists across feature map channels, and (2) frequent full-channel convolution operations incur high memory access costs. These factors form critical bottlenecks limiting C3k2's computational efficiency. To address these efficiency issues arising from channel redundancy and frequent memory access, the Partial Convolution (PConv) [13] is integrated into the C3k2 module, forming the C3k2_PConv module. PConv effectively reduces redundant computation and memory accesses by performing dense convolution only on a subset of input feature map channels [14]. Its structure is shown in Figure 7.  
Figure 6: Network structure of the EMA (efficient multi-scale attention) mechanism module.  
Figure 7: PConv module structure diagram. Note: c is the number of channels in the input feature map; c\({}_{p}\) is the number ofchannels participating in the convolution; * is the convolution operation; h, w are the height and width dimensions of the feature map.  
C3k2_PConv utilizes the PConv design from FasterNet, replacing the Bottleneck in the feature extraction module with a Faster Block as the main gradient flow branch. This reduces the model's floating-point numbers and computational load. PConv improves computational efficiency by reducing redundant computations and memory accesses, enabling the C3k2_PConv module to maintain efficient feature extraction while reducing computational costs. By replacing some standard convolutional layers in YOLOv11 with the C3k2_PConv module, the model's floating-point computational load and memory usage are reduced, optimizing the utilization of computing resources and enhancing the model's detection capability in complex scenarios. Its structure is shown in Figure 8.
## 5 Mirror Facet Alignment Method
### Three-Rotation Alignment Method
Based on the same simplification, the adjustment values of \(d_{\text{A}}\), \(d_{\text{B}}\), and \(d_{\text{C}}\) of bolt 1, 2, and 3, respectively, in the three-rotation alignment process can be given by:  
\[\left\{ \begin{array}{l}d_{\text{C}}=\frac{\theta_{\text{C}}}{|\theta_{\text{ C}}|}\|\mathbf{C}_{\text{t}}-\mathbf{C}_{\text{AL}}\|\\ d_{\text{B}}=\frac{\theta_{\text{B}}}{|\theta_{\text{B}}|}\|\mathbf{B}_{\text{ t}}-\mathbf{B}_{\text{AL}}\|\\ d_{\text{A}}=\frac{\theta_{\text{A}}}{|\theta_{\text{A}}|}\|\mathbf{A}_{\text{ t}}-\mathbf{A}_{\text{AL}}\|\end{array} \right. \tag{23}\]  
where \(\theta_{\text{C}}/|\theta_{\text{C}}|\), \(\theta_{\text{B}}/|\theta_{\text{B}}|\), and \(\theta_{\text{A}}/|\theta_{\text{A}}|\) are sign coefficients for determining the direction of adjustment of bolts 3, 2, and 1, respectively. If the sign coefficient is equal to 1, the bolt is adjusted downward whereas if the sign coefficient is equal to \(-1\), the bolt is adjusted upward. Since \(\theta_{\text{C}}/|\theta_{\text{C}}|=1\), the bolt adjustment is downward (i.e., \(L_{\text{C}}^{AL}<L_{\text{C}}^{\text{t}}\)). Furthermore, according to the specific pitch of the bolt (threads/mm), the adjustment value of the bolt can be output as the rotational turns of the nut. A flowchart of the three-rotation alignment method is shown in Figure 7. The inputs are the known parameters including the coordinates of feature points T\({}_{1}\)-T\({}_{3}\), ball-hinge centers A\(\sim\)C, and ideal facet parameters of the mirror unit (\(\mathbf{N}_{0}\) and \(\mathbf{Q}\)). These parameters are taken from the technical drawing of the dish concentrator. In the measurements, the spatial coordinates of three feature points on the mirror unit can be determined by the photogrammetry method, which is the only measurement process in the three-rotation alignment method. Finally, the adjustment value of bolts 1, 2, and 3 can be determined by the mathematical model (Section 3.2) and three-rotation alignment model (Section 5.1). Theoretically, the requirement of only a single alignment of the mirror unit can eliminate the axis vector error and avoid repeated adjustments so that the process is simple and convenient. In addition, the spatial coordinates of three feature points can be measured in real-time by the two visual measurement methods [33; 34] to realize real-time monitoring and guidance of the mirror facet alignment. This could be extremely helpful in improving the accuracy and efficiency of mirror installation. Figure 6: The movement of bolt 3 during facet alignment of the mirror unit: (**a**) Schematic drawing showing the geometric parameters of bolt 3 and their movements and (**b**) Diagram showing the maximum deflection angle \(\beta_{\max}=2\beta_{1}\).