esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Comparison of Different Models
### Performance Evaluation Criteria  
The linear and nonlinear models would present inevitable forecasting errors due to the statistical approximations. As each model employs the optimal set of parameters that lead to the smallest error for the training dataset, the forecasting errors can be viewed just from the performances of different models.  
To evaluate the forecasting performance of different models, four error indexes including the mean absolute error (MAE), the mean relative percentage error (MRPE), the root mean squared error (RMSE), and the coefficient of determination (R\({}^{2}\)) are employed. The four indexes are defined as  
\[\text{MAE}=\frac{1}{N}\sum\limits_{i=1}^{N}\lvert U_{i}-\hat{U}_{i}\rvert, \tag{19}\]  
\[\text{MRPE}=\frac{1}{N}\sum\limits_{i=1}^{N}\lvert\frac{U_{i}-\hat{U}_{i}}{U_{ i}}\rvert\times 100\%, \tag{20}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{N}\left(U_{i}-\hat{U}_{i} \right)^{2}}, \tag{21}\]  
\[\text{R}^{2}=1-\frac{\sum\limits_{i=1}^{N}\left(U_{i}-\hat{U}_{i}\right)^{2}}{ \sum\limits_{i=1}^{N}\left(U_{i}-\frac{1}{N}\sum\limits_{i=1}^{N}U_{i}\right)^ {2}}, \tag{22}\]  
where U\({}_{i}\) and \(\hat{U}_{i}\) represent the measured and predicted wind speeds at period \(i\), respectively; and \(N\) is the number of data points involved in the performance evaluation.
## 4 Improved Tight Combination Model
### Convergence Analysis of Combination Error
#### 4.5.2 Cumulative Error Analysis of EKF-Based Positive Feedback  
Regarding the module of EKF, the positioning state error \(e_{t}=x_{t}-\hat{x_{t}}\). According to EFK, linearizing \(f\) at \(\hat{x_{t}}\), that is, Taylor expansion, is used to expand the error to obtain the error linear transfer equation [24].  
\[\begin{array}{ll}\dot{e}_{t}&=\dot{x}_{t}-\dot{\hat{x}}_{t}\\ &=f(x_{t},u_{t})+n_{t}-\dot{\hat{x}}_{t}\\ &=f(\hat{x}_{t},u_{t})+F_{\hat{x}_{t},u_{t}}(x_{t}-\hat{x}_{t})+n_{t}-\dot{ \hat{x}}_{t}\\ &=F_{\hat{x}_{t},u_{t}}(x_{t}-\hat{x}_{t})+n_{t}\\ &=F_{\hat{x}_{t},u_{t}}e_{t}+n_{t}\end{array} \tag{29}\]It can be found from Equation (29) that, when noise is applied, the state estimator cannot be predicted, which leads to the failure of system convergence. When the estimate and the true value of the difference is large, the system matrix \(F\) will have larger deviation. If we use \(F\) to transmit the error, then the error transfer system will form positive feedback, which will eventually cause the filter to diverge.  
Meanwhile, the error between the actual measured value and the estimated observation is \(Z_{t}=y_{t}-\hat{y}_{t}\). With same principle, Taylor expansion is used again for hat \(\hat{x}_{t}\), and the linear transfer equation of error is shown in Equation (28) [25].  
\[\begin{array}{ll}z_{t}&=y_{t}-\hat{y}_{t}\\ &=h(x_{t})+s_{t}-\hat{y}_{t}\\ &=h(\hat{x}_{t})+H_{\hat{x}_{t}}(x_{t}-\hat{x}_{t})+s_{t}-\hat{y}_{t}\\ &=H_{\hat{x}_{t}}e_{t}+s_{t}\end{array} \tag{30}\]  
Figure 4: The accumulated errors of IMU speed and position within 4 s.  
It can be found from the equation that the relationship between the state error and the observation error is approximate. Therefore, when there is a large gap between the observed value and the estimated value, the system matrix \(H\) at this time will also have a large deviation, and the error will be further amplified after the error continues to be transmitted by \(H\).
# Dynamic Controllability of Processes without Surprises
## 8 Implementation and Evaluation
### Results and Discussion  
We report the minimum, maximum and average measured execution times for the various process sizes in Table 3, and the average only in Figure 13. On average, executing Algorithm 1 on a process of size (around) 10 in 10N required 0.13 s; in 20N, 0.83 s; in 30N, 6.29 s; in 40N, 18.94 s; in 50N, 41.00 s.  
The nonlinear increase in the execution times is explained by the repeated execution of the dynamic controllability checking procedure, called at line 11 of Algorithm 1 for each STP in a process. It is known that checking dynamic controllability for STNUs is \(O(N^{4})\), where \(N\) is the number of STNU nodes [18]. Nevertheless, the proposed procedure is executed only during the design phase of a process, and has no impact on the run time performance of a process execution.  
The results gathered from the experimental evaluation indicate that the required computation times are acceptable for a design time execution. Interestingly, these results are such despite the fact that Algorithm 1 introduces into the STNU new constraints to solve the STPs, and repeatedly executes a dynamic controllability checking procedure. We conclude that the proposed approach is applicable for most practical applications that require checking at design time for semi-dynamic controllability of process models.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Subset** & **Min STNU Nodes** & **Max STNU Nodes** & **UBCs/LBCs** & **Min Splits** & **Max Splits** \\ \hline
10N & 25 & 29 & 2 & 1 & 3 \\
20N & 45 & 51 & 4 & 1 & 4 \\
30N & 67 & 79 & 6 & 2 & 7 \\
40N & 91 & 107 & 8 & 4 & 12 \\
50N & 113 & 129 & 10 & 5 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of the sets of processes used for the experimental evaluation. Each set contains 10 randomly generated processes.
# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 1 Introduction
Video monitoring has a strong real-time capability and can offer the intuitive perception of external force intrusion and other field conditions. It can timely detect abnormal conditions of transmission channels, such as external force damage. However, it requires manual monitoring and manual analysis for multi-directional distance monitoring of transmission channels. Further, it cannot accurately obtain real-time data and timely collection of spatial status data of conductors [16]. * For distance monitoring of tree barriers, commonly used methods include ranging with an airborne laser scanning device, setting up radar or laser ranging device, processing aerial inspection video, building a tree line model to predict distance, and binocular vision image ranging [17]. These methods improve the efficiency of distance measurement of tree barriers but also have some problems [18]. For example, 3D modeling requires regular updating and reconstruction, and the workload is significant. The tree growth cycle model construction is complex, while the stereo matching of binocular vision ranging is complicated [19]. Moreover, these tree barrier ranging methods have high costs and cannot provide real-time dynamic data of transmission lines. * The traditional multi-directional distance monitoring method for a transmission channel cannot provide real-time analysis and processing, abnormal alarm, and trend warning [20]. Therefore, this paper proposes a tower tilt analysis method based on a monocular vision image. It builds an edge computing layer linking the back-end cloud service platform and front-end monitoring sensor to address the real-time data problem, uses the tower to share monocular vision data, develops a ranging algorithm for non-fixed objects, selects the profile and features of the collected information, combines it with the pole tower tilt model to calculate and analyze the tilt, and finally determines the attitude of the transmission tower. This method has the advantages of real-time reliability and simple operation in tower tilt detection, significantly reducing the inspection intensity of staff and enabling timely understanding of the tower tilt degree to ensure the safe and stable operation of the transmission lines.
## 3 Results and Discussion
The good correlation between the theoretical \(F_{crit}\) and experimental \(F_{exp}\) results validate the proposed simplified approach and gives confidence in the presented methodology. Moreover, the theoretical results obtained by using the simplified approach \(F_{crit}\) were compared with the results of the complex model of [PERSON] et al. [31; 32] obtaining a difference smaller than 1% in all the cases studied in this work. The proposed methodology is able not only to predict self-loosening under certain working conditions but it can also be used to understand how each variable affects the screwed joint behavior against self-loosening. From Equation (8) it can be deducted that a high preload is recommended to avoid screw loosening. This conclusion agrees with [25; 40]. Friction coefficients of the screw head and thread contacts \(\mu_{h}\) and \(\mu_{t}\) are also demonstrated to highly affect the behavior against self-loosening. Thus, for a given preload, a high friction coefficient is recommended to avoid screw loosening according to the presented analytical tool and concurring with [21; 22]. There are some other works where friction coefficient is reduced in the screwed joint contact by means of a coating or lubrication obtaining a better behavior against self-loosening [41; 42]. This seems to disagree with the last statement. Nevertheless, in these works different friction coefficients are studied for the same tightening torque, rather than same preload. Hence, the results are not comparable since friction coefficient also affects the preload obtained [26; 43]. Evidently, a higher tightening torque will lead to a higher preload and, therefore, to a better response against self-loosening. The magnitude of the transverse load supported by the screw head also plays an important role in self-loosening [6; 9; 16; 24]. From (8) it can be concluded that a robust design where the screw head suffers the less transverse load possible is desired since the transverse slippage is the cause of rotation, preload loss and, therefore, the screw loosening. Finally, thread parameters affect the behavior of screwed joints against self-loosening as well. On one hand, a small thread pitch helps to avoid self-loosening. According to [PERSON] et al., screws with coarse threads would require a smaller loosening torque than those with fine threads [32]. This statement also agrees with [33; 44]. On the other hand, the half angle of the thread profile \(\alpha\) is recommended to be as high as possible according to [32]. From Equation (8), it can be concluded that a high angle of thread profile will lead to a higher frictional force in the threads, improving the screw-loosening response.
## 6 Localization Systems
### Indoor Localization Systems
#### 6.1.1 Wi-Fi Localization  
The Wi-Fi system is primarily used to provide networking capabilities and connection to the internet to different devices. Initially, Wi-Fi presented a reception range of about 100 m, which has now increased to about 1 km (optimized for IoT services). Current smartphones and laptops widely support Wi-Fi and most portable devices, making it an ideal choice for indoor localization, even though the existing Wi-Fi networks are, to date, generally used for data communication (i.e., to maximize data throughput and network coverage) rather than localization purposes. Therefore, further algorithms are required to improve their localization feature. Additionally, interference in the ISM band (i.e., the radio bands internationally reserved for specific Industrial, Scientific, and Medical applications) has been shown to affect localization accuracy [179]. Recent attempts have tried to resolve this problem by implementing deep learning-based Wi-Fi systems that achieve an acceptable accuracy of one to two meters [180; 181].
## 4 Experiments
### Experimental Results
#### 4.3.1 Comparison of BUSI Dataset  
The performance of our MSLUnet network, compared with other models on the BUSI dataset, is detailed in Table 2. Optimal experimental results are highlighted in bold in the table. As shown in Table 2, despite the limited data in the BUSI dataset, MSLUnet outperforms other segmentation models in metrics like MIoU, Dice, Recall, and Specificity, achieving optimal results. Moreover, this paper's method demonstrates a 0.7% improvement in the Dice coefficient and a 0.3% enhancement in the mean intersection over union (mIoU) compared to the state-of-the-art LM-Net [28].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Train** & **Test** & **Input Image** \\ \hline BUSI [39] & 517 & 130 & 256 \(\times\) 256 \\ Kvasir-SEG [40] & 800 & 200 & 256 \(\times\) 256 \\ ISIC 2018 [41] & 2075 & 518 & 256 \(\times\) 256 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information on the three medical image segmentation datasets used.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Architecture** & \begin{tabular}{c} **mIoU** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Dice** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Specificity** \\ **(\%)** \\ \end{tabular} \\ \hline UNet [8] & 77.1 & 75.2 & 84.2 & 87.7 & 70.4 \\ UNet++ [9] & 78.3 & 75.8 & 83.1 & **91.7** & 70.8 \\ Attention\_Unet [11] & 76.6 & 72.8 & 83.5 & 87.7 & 69.9 \\ ResUnet++ [25] & 78.6 & 77.0 & 87.5 & 86.6 & 72.6 \\ SwinUnet [24] & 78.5 & 77.4 & 87.9 & 89.7 & 72.7 \\ ConvUNeXt [15] & 77.5 & 73.2 & 82.8 & 90.6 & 70.1 \\ CMUNeXt [43] & 78.3 & 75.0 & 87.3 & 85.9 & 72.5 \\ AAU-Net [44] & 78.7 & 78.9 & 87.6 & 86.3 & 72.9 \\ ULite [29] & 76.4 & 72.9 & 82.2 & 89.3 & 69.2 \\ LM-Net [28] & 79.0 & 79.1 & 87.9 & 86.5 & 73.2 \\ MSLUnet (our) & **79.3** & **79.8** & **88.3** & 88.7 & **73.7** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Presents a comparison of experimental results between MSLUnet and other segmentation models on the BUSI dataset.  
Further visual comparative analysis is provided in Figure 5, which illustrates the visual segmentation results of various networks on the BUSI dataset. Segmenting breast lesions is particularly challenging due to similar grayscale values, variable tumor morphology, and indistinct boundaries, especially in irregularly shaped malignant tumors. The visual results indicate that most networks perform well with single-shaped breast tumors. However, the UNet network struggles with accurately delineating irregularly shaped lesions, often misclassifying dispersed regions as background. Several other models occasionally omit parts of the lesions; the MSLUnet network consistently demonstrates superior segmentation accuracy compared to all other methods evaluated.
## 3 Mathematical Modeling
### Multi-Objective Model for FJSP-LS
#### 3.2.4. Maximum and Total Sublot Flowtime (\(Z_{2}\) and \(Z_{3}\))  
The entry time (\(e_{s,j}\)) to the shop floor of a sublot of a job is the time the setup of its first operation begins if the setup is attached. If the setup of the first operation is detached, \(e_{s,j}\) is the time at which the actual processing of the first operation begins as setup can be completed before the raw material is admitted to the shop floor. The constraints in Equations (22)-(25) are used to set the value of this variable. The departure time of the sublot (\(d_{s,j}\)) is simply the completion time of the last operation of the sublot as enforced by the constraint in Equation (26).  
The flowtime of sublot \(s\) of job \(j\), denoted as \(f_{s,j}\), is the interval between the time the sublot enters the shop floor to the time its last operation is finished. Its value is set by the constraint in Equation (27). The constraint in Equation (28) along with the objective function will enforce \(f_{max}\) to assume the maximum flowtime of all the sublots (\(\max_{\forall(s,j)}f_{s,j}\)). The total flowtime of all the sublots is calculated by the constraint in Equation (29). The objective function terms \(Z_{2}\) and \(Z_{3}\) are the values of \(f_{max}\) and \(f_{total}\) as shown in Equations (30) and (31), respectively. The minimization of flowtime can lead to stable or uniform utilization of resources and a rapid turn-around of jobs, and it is particularly important in real-life situations where reducing inventory or holding cost is of primary concern [42].  
\[e_{s,j}\geq c_{1,s,j}-b_{s,j}\cdot\mathrm{T}_{1,j,m}-S_{1,j,m}^{*}\cdot A_{1,j} -\Omega\cdot(1-x_{1,m,1,s,j});\ \ \forall(s,j,m) \tag{22}\]  
\[e_{s,j}\leq c_{1,s,j}-b_{s,j}\cdot\mathrm{T}_{1,j,m}-S_{1,j,m}^{*}\cdot A_{1,j }+\Omega\cdot(1-x_{1,m,1,s,j});\ \ \forall(s,j,m) \tag{23}\]  
\[e_{s,j}\geq c_{1,s,j}-b_{s,j}\cdot\mathrm{T}_{1,j,m}-S_{1,j,m,o^{\prime},j^{ \prime}}\cdot A_{1,j}-2\Omega\cdot(1-x_{r,m,1,s,j}-y_{r-1,m,o^{\prime},j^{ \prime}});\] \[\forall(s,j,r,m)|r>1 \tag{24}\]  
\[e_{s,j}\leq c_{1,s,j}-b_{s,j}\cdot\mathrm{T}_{1,j,m}-S_{1,j,m,o^{\prime},j^{ \prime}}\cdot A_{1,j}+2\Omega\cdot(1-x_{r,m,1,s,j}-y_{r-1,m,o^{\prime},j^{ \prime}});\] \[\forall(s,j,r,m)|r>1 \tag{25}\]  
\[d_{s,j}=c_{O_{j},s,j};\ \ \forall(s,j) \tag{26}\]  
\[f_{s,j}=d_{s,j}-e_{s,j};\ \ \forall(s,j) \tag{27}\]  
\[f_{max}\geq f_{s,j};\ \ \forall(s,j) \tag{28}\]  
\[f_{total}=\sum_{j=1}^{l}\sum_{s=1}^{S_{j}}f_{s,j} \tag{29}\]  
\[Z_{2}=f_{max} \tag{30}\]\[Z_{3}=f_{total} \tag{31}\]
## 3 Results and Discussion
### Combination  
Based on the above findings, the alteration in wall velocity due to changes in zoned coating remains consistent along the axial direction. However, the introduction of pins primarily results in an increased wall velocity near the inlet surface. Consequently, from a theoretical perspective, the integration of zoned coating with disturbed fluid can potentially enhance the axial uniformity of wall velocity. For the high coated zone (the second part) with partition ratio X = 0.6-0.8, four sets of symmetrical or alternating pins are introduced, with respective heights set at 0.3 mm. The results are illustrated in Figure 11.  
With the addition of pins, the permeation velocity near the inlet is further augmented, while that in the second part is reduced. Meanwhile, the lowest permeability velocity in the first part experiences a slight increase. Based on relevant working conditions of all pins, it is observed that, compared to the initial working conditions without pins, the curves of the condition of adding pins changes around the point, 80% away from the inlet surface, with an increased permeation velocity in front of the point and a decreased permeation velocity behind.  
Upon comparing the differences in Figure 11c, it is observed that the effect of symmetrical arrangement is slightly better than that of the alternate arrangement when only one pair of pins is placed at the rear end of the zoned porous medium. Notably, when X = 0.6, combining porosity partitioning with pins yields the most significant effect, re  
Figure 11: (**a,b**) Curve of axial permeation velocity distribution, and (**c**) variance corresponding to zoned washcoat and pins arranged alternately and symmetrically.  
sulting in an approximately 75% enhancement in homogeneity compared to the initial working condition.  
While the use of pins significantly improves flow uniformity and enhances mixing, it also leads to an increase in pressure drop across the channel. This trade-off between improved flow distribution and higher energy consumption should be carefully considered in the design of such systems.
# Double-Constrained Consensus Clustering with Application to Online Anti-Counterfeiting
## 4 Experimenting with UCI Datasets
### Design and Preparation
The _F measure_ is the harmonic mean of precision and recall:  
\[F=2\frac{P\times R}{P+R} \tag{9}\]  
with  
\[P=\frac{TP}{TP+FP}\,\ \ R=\frac{TP}{TP+FN} \tag{10}\]  
where _TP_, _FP_, and _FN_ are, respectively, the number of true positives (i.e., two objects of the same class assigned to the same cluster), false positives (i.e., two objects of different classes assigned to the same cluster), and false negatives (i.e., two objects of the same class assigned to different clusters). \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Dataset** & **Objects** & **Attributes** & **Classes** & **Must-Links** & **Cannot-Links** & **Must-Links** & **Cannot-Links** \\  & & & & **in the Cover** & **in the Cover** & **(Total No.)** & **(Total No.)** \\ \hline GI & 214 & 10 & 6 & 208 & 208 & 5921 & 16,870 \\ S & 210 & 7 & 3 & 207 & 207 & 7245 & 14,700 \\ UKM & 258 & 5 & 4 & 254 & 282 & 9460 & 23,693 \\ VC & 310 & 6 & 3 & 307 & 340 & 17,895 & 30,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Features and constraints of the four UCI datasets: Glass Identification (GI), Seeds (S), User Knowledge Modeling (UKM), and Vertebral Column (VC).
# Dual-Dimensional Gaussian Splatting Integrating 2D and 3D Gaussians for Surface Reconstruction
## 3 Methods
### Dual-Dimensional Gaussian Modeling  
Figure 1 shows the overall flow of Dual-Dimensional Gaussian Splatting (DDGS), which combines 2D and 3D Gaussians. To implement this, we first augment the homogeneous transformation matrix with the w-axis to define the properties of a Gaussian in world space. A Gaussian is characterized by its learnable parameters, including its center \(\mathbf{\mu}\), tangent vectors \(\mathbf{t}_{u}\), \(\mathbf{t}_{v}\), \(\mathbf{t}_{w}\), and scaling factors \(s_{u}\), \(s_{v}\), \(s_{w}\). The tangent vectors are mutually orthogonal and form a \(3\times 3\) rotation matrix \(\mathbf{R}\), which represents the Gaussian's orientation. Likewise, the scaling factors define the size and variance of the Gaussian and constitute the diagonal of a \(3\times 3\) scaling matrix \(\mathbf{S}\). The Gaussian is classified as a 3D Gaussian if all three scaling factors are nonzero, whereas it is considered a 2D Gaussian if exactly one of the scaling factors is zero. Finally, the homogeneous transformation matrix \(\mathbf{H}^{*}\), which incorporates the additional w-axis compared to 2 DGS, is defined as follows:  
\[\mathbf{H}^{*}=\begin{bmatrix}s_{u}\mathbf{t}_{u}&s_{v}\mathbf{t}_{v}&s_{w} \mathbf{t}_{w}&\mathbf{\mu}\\ 0&0&0&1\end{bmatrix}=\begin{bmatrix}\mathbf{RS}&\mathbf{\mu}\\ 0&1\end{bmatrix}. \tag{3}\]  
This matrix enables the transformation of a Gaussian from local space to world space via \(\mathbf{H}^{*}(u,v,w,1)^{T}\).