esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Controllability of Processes without Surprises
## 7 Checking Semi-Dynamic Controllability with STNUs
### Checking Dynamic Controllability  
Researchers have developed several techniques for checking the dynamic controllability of STNUs. The most efficient techniques, e.g., the one presented in [9], are based on deriving implicit constraints from the existing explicit constraints according to some constraint propagation rules.  
Constraint propagation applies the propagation rules until either no new implicit edges can be derived (full propagation), or a negative cycle, in the usual sense of graph theory, is derived. In the first case, the STNU, hence its originating process, is dynamically controllable. Otherwise, a constellation of contradicting constraints generating a negative cycle exists, and the STNU is not dynamically controllable. Applying constraint propagation, it is possible to see, for instance, that no negative cycle can be derived in the STNU in Figure 11, thus it is dynamically controllable.  
So applying a constraint propagation procedure to an STNU on the one hand derives all implicit constraints that hold if the given explicit constraints hold, and on the other hand determines whether the STNU is dynamically controllable. With this observation, and applying the results of Section 5, we show that it is possible to design a procedure for checking semi-dynamic controllability of process models.
## Appendix A Structure of the Teletransmission Frame  
Figure 11 shows the data frame format. The HEADER block contains tags to identify the device type (TOKEN), its version (VERSION), and the frame type (F.TYPE). The header block further includes the frame number (F.NUM) and the current time (TIME). Elements marked as RESERVED constitute additional data stores that can be used in more complex applications.  
The data block (DATA) consists of \(m\) sensor data blocks (SENSOR DATA 1,..., SENSOR DATA m), where \(m~{}\in~{}\{1,\,2\,\ldots,16\}\). Only the data from sensors selected fortransmission by the algorithm are added to the data section. The sensor data block consists of the sensor number (SENS ID), averaged values of the coordinates of the vectors \(a_{i}\) (AVG a\({}_{\text{x}}\), AVG a\({}_{\text{y}}\), AVG a\({}_{\text{z}}\)), \(\omega_{i}\) (AVG \(\omega_{\text{x}}\), AVG \(\omega_{\text{y}}\), AVG \(\omega_{\text{z}}\)), \(m_{i}\) (AVG m\({}_{\text{x}}\), AVG m\({}_{\text{y}}\), AVG m\({}_{\text{z}}\)), and the current quaternion \(q_{i}=[q_{0},q_{1},\ q_{2},q_{3}]\) (LATEST q\({}_{0}\), LATEST q\({}_{1}\), LATEST q\({}_{2}\), LATEST q\({}_{3}\)). Both the averaged values of the vector coordinates and the elements of the current quaternion are single-precision four-byte floating-point numbers (floats). The total length of a single sensor data block is 53 B as shown in Figure 11.
# Incorporating Setup Effects into the Reliability Analysis of Driven Piles
###### Abstract  
Driven-pile setup is referred to a phenomenon in which the bearing capacity of driven piles increases with time after the end of driving (EOD). The setup effect can significantly improve the bearing capacity (ultimate resistance) of driven piles after initial installation, especially the ultimate shaft resistance. Based on the reliability theory and considering the setup effects of driven piles, this article presents an increase factor (\(M_{\text{setup}}\)) for the ultimate resistance of driven piles to modify the reliability index calculation formula. At the same time, the correlation between \(R_{0}\) and \(R_{\text{setup}}\) is comprehensively considered in the reliability index calculation. Next, the uncertainty analysis of load and resistance is conducted to determine the ranges of relevant parameters. Meanwhile, the influence of four critical parameters (factor of safety FOS, the ratio of dead load to live load \(\rho=Q_{\text{D}}/Q_{\text{L}}\), \(M_{\text{setup}}\), the correlation coefficient between \(R_{0}\) and \(R_{\text{setup}}\), and \(\rho_{\text{R0,Rsetup}}\)) on reliability index are analyzed. This parametric study indicates that \(\rho\) has a slight influence on the reliability index. However, the reliability index is significantly influenced by FOS, \(M_{\text{setup}}\), and \(\rho_{\text{R0,Rsetup}}\). Finally, by comparisons with the existing results, it is concluded that the formula proposed in this study is reasonable, and more uncertainties are considered to make the calculated reliability index closer to a practical engineering application. The presented formula clearly expresses the incorporation of the pile setup effect into reliability index calculation, and it is conducive to improving the prediction accuracy of the design capacity of driven piles. Therefore, the reliability analysis of driven piles considering setup effects will present a theoretical basis for the application of driven piles in engineering practice.  
driven piles; bearing capacity; setup; reliability; correlation coefficient +
Footnote †: journal: _sciences_
# Intelligent Traffic Management in Next-Generation Networks
## 4 Research Challenges and Future Directions
Additionally, the explosion of smart devices and the surge in traffic will need different requirements for their performance. Therefore, the ML concepts can help to find more behaviors and better slices even with a single network infrastructure. **Knowledge transfer in multiple SDN controllers**: The model trained by some SDN controllers can capture common features with other SDN controllers (e.g., domain). This means the data can be transferable between the domain and hence can provide a more general model. For this purpose, transfer learning can be used in order to transfer the knowledge between the different SDN controllers. It helps the model to avoid being trained from scratch, thus accelerating the model convergence and solving the problem of insufficient training data [152]. **Privacy**: In recent years, privacy has been one of the most important concerns in the network domain. ML models, especially DL models, may benefit from training data augmentation. However, the end-users or clients refuse to provide their data due to the risk of data misuse or inspection by external devices. To solve this issue and guarantee that training data remains on its owners, a distributed learning technique like FL can be a promising solution. It enables the operators to benefit through the data of clients without sacrificing their privacy. In this context, FL can be the appropriate approach for network traffic management, especially with SDN architecture, as it reduces the data exchange between the data plane and SDN controller. Therefore, it guarantees data confidentiality, minimizes costs of data communication, and relieves the burden on the SDN controller. Additionally, it can be used with network slicing for user prediction for each slice to enable the local data training without the need of sharing the data between the slices.
# The Shear Behavior of the Curved Interface in Polyurethane-Concrete Composite Structures
## 3 Results and Discussion
### Comparison of Shear Behavior in Curved and Planar Composite Specimens
#### 3.1.4 Peak Displacements of Curved and Planar Specimens with Varying Polyurethane Densities  
The variations in peak displacement corresponding to peak shear strength for curved and planar composite specimens with changing polyurethane density are shown in Figure 9.  
Figure 8: Shear modulus of curved and planar composite specimens with different polyurethane densities.  
Figure 7: Shear strength of curved and planar specimens with different polyurethane densities.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Interface Type**} & \multicolumn{6}{c}{**Polyurethane Density (g/cm\({}^{3}\))**} \\ \cline{2-7}  & **0.25** & **0.33** & **0.42** & **0.51** & **0.58** & **0.66** \\ \hline Curved interface & 1.255 & 1.521 & 1.772 & 1.981 & 2.120 & 2.245 \\ Planar interface & 0.906 & 1.092 & 1.280 & 1.427 & 1.544 & 1.627 \\ Shear strength ratio & 1.39 & 1.39 & 1.38 & 1.39 & 1.37 & 1.38 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Shear strength comparison of curved and planar specimens with different polyurethane densities (MPa).  
It can be seen that the peak displacement of both curved and planar specimens generally decreases linearly with increasing polyurethane density. The peak displacement of curved specimens is significantly greater than that of planar specimens, indicating that curved interfaces are less susceptible to shear failure compared to planar interfaces. Consequently, the engineering structures with curved contact surfaces exhibit higher stability under shear loads. Additionally, it is observed that the rate of the decrease in peak displacement for curved specimens diminishes as the polyurethane density increases, whereas the rate of decrease for planar specimens increases with higher polyurethane density.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 4 City-Wide Deployments
### Infrastructure Deployment in the City of Bristol
#### 4.2.1 Core Tier  
The core DC of Bristol infrastructure is deployed at the University of Bristol High-Performance Network Group Data Centre (HPN-DC). More in detail, this DC hosts two compute servers configured as core NFVI, which are interconnected via fiber to the other two pilot locations in _We-The-Curious_ (WTC-DC) and _MShed Museum_ (M-DC).
## 3 Numerical Scheme
### Horizontal Polarisation  
Equation (22) is now discretised as:  
\[\mathbf{L}\mathbf{u}^{n}=-\mathbf{\mu}_{t}\mathbf{J}_{\mathbf{u}}^{n}\mathbf{f}_{\mathbf{u }}^{n} \tag{54a}\] \[\mathbf{J}_{\mathbf{u}}^{n}=\left[\begin{array}{c}\mathbf{I}_{\mathbf{N-1}}\end{array} \right]\mathbf{j}_{F}^{n}\left[\begin{array}{c}\mathbf{J}_{B}^{n}\end{array} \right] \tag{54b}\]  
\[\mathbf{L}\] is defined in Equation ( 46 ). \[\mathbf{f}_{\mathbf{u}}^{n}\] is a column vector containing the friction force information: \[\mathbf{f}_{\mathbf{u}}^{n}=\left[\mathbf{f}_{\mathbf{w}}^{n}\right]_{+}\odot\left[ \begin{array}{c}\vdots\\ \varphi_{N}\left(\mathbf{v}_{\mathbf{rel},N}^{n}\right)\\ \vdots\\ \hline\dfrac{\varphi_{F}\left(\mathbf{v}_{\mathbf{rel},F}^{n}\right)}{\varphi_{B} \left(\mathbf{v}_{\mathbf{rel},B}^{n}\right)}\end{array}\right] \tag{55a}\] \[\mathbf{v}_{\mathbf{rel},B}^{n}=\mathbf{h}\delta_{t}\cdot\left(\mathbf{j}_{F}^{n}\mathbf{ \mathbf{T}}^{n}\right)-\delta_{t}\cdot\mathbf{u}_{B}^{n} \tag{55d}\]  
where \(\varphi_{N}\), \(\varphi_{F}\) and \(\varphi_{B}\) are defined in Section 2.5. One can define a vector relative velocity:  
\[\mathbf{v}_{\mathbf{rel}}^{n}=\left[\left(\mathbf{v}_{\mathbf{rel},N}^{n}\right)^{ \mathsf{T}}|\mathbf{v}_{\mathbf{rel},F}^{n}|\mathbf{v}_{\mathbf{rel},B}^{n}\right]^{ \mathsf{T}} \tag{56}\]  
Finally, a matrix equation describes the evolution of the horizontal displacements \(u_{F}^{n}\) and \(u_{B}^{n}\) of the finger and bow, respectively:  
\[\mathbf{M}_{FB}\delta_{tt}\mathbf{u}_{FB}^{n}=\mathbf{K}_{FB}\mathbf{\mu}_{t}\mathbf{u}_{FB}^{ n}-\mathbf{\lambda}_{FB}\delta_{t}\cdot\mathbf{u}_{FB}^{n}+\mathbf{f}_{\mathbf{ext}\;u, FB}^{n} \tag{57a}\] \[\mathbf{K}_{FB}=\left[\begin{array}{cc}\mathcal{K}_{F}&0\\ 0&0\end{array}\right]\] (57b) \[\mathbf{\lambda}_{FB}=\left[\begin{array}{cc}\lambda_{F}&0\\ 0&\lambda_{B}\end{array}\right]\] (57c) \[\mathbf{u}_{FB}^{n}=\left[\begin{array}{c}u_{F}^{n}\\ u_{B}^{n}\end{array}\right]\] (57d) \[\mathbf{f}_{\mathbf{u}FB}^{n}=\left[\mathbf{f}_{\mathbf{w}FB}^{n}\right]_{+}\odot \left[\begin{array}{c}\varphi_{F}(\mathbf{v}_{\mathbf{rel},F}^{n})\\ \varphi_{B}(\mathbf{v}_{\mathbf{rel},B}^{n})\end{array}\right]\] (57e) \[\mathbf{f}_{\mathbf{ext}\;u,FB}^{n}=\left[\begin{array}{c}0\\ f_{\mathbf{ext}\;u,B}^{n}\end{array}\right] \tag{57f}\]where the 2-point averaging operator \(\mu_{t}\). is used as a means to avoid restricting the stability limit of this scheme any further; this aspect will be developed in Section 3.5.4.
## 4 Power Management for the Energy Router
### Design of the Fuzzy Logic Controller
#### 4.1.2 The Establishment of Fuzzy Logic Rules  
After completing the design of membership functions, fuzzy logic rules should be established according to the basic experience [35].  
The specific charging or discharging criterions of the battery are:  
(1) When the DG output is lower than the load demand, i.e., \(\Delta P<0\), and \(SOC\) is lower than \(SOC_{\text{avr}}\), i.e., \(\Delta SOC<0\), which means \(SOC\) is closer to \(SOC_{\text{min}}\) than to \(SOC_{\text{max}}\), the battery should work in shallow discharge state;  
(2) When the DG output is lower than the load demand, i.e., \(\Delta P<0\), and \(SOC\) is greater than \(SOC_{\text{avr}}\), i.e., \(\Delta SOC>0\), which means \(SOC\) is closer to \(SOC_{\text{max}}\) than to \(SOC_{\text{min}}\), the battery should work in deep discharge state;  
(3) When the DG output is lower than the load demand, i.e., \(\Delta P<0\), and \(SOC\) is equal to \(SOC_{\text{avr}}\), i.e., \(\Delta SOC=0\), the battery can either work in shallow or deep discharge state according to the specific value of \(\Delta P\);  
If \(\Delta P\) is closer to \(-3.75\) kW than to 0 kW, the battery should work in deep discharge state, otherwise the battery will work in shallow discharge state;  
(4) When the DG output is greater than the load demand, i.e., \(\Delta P>0\), and \(SOC\) is lower than \(SOC_{\text{avr}}\), i.e., \(\Delta SOC<0\), the battery should work in deep charge state;  
(5) When the DG output is greater than the load demand, i.e., \(\Delta P>0\), and \(SOC\) is greater than \(SOC_{\text{avr}}\), i.e., \(\Delta SOC>0\), the battery should work in shallow charge state.  
(6) When the DG output is greater than the load demand, i.e., \(\Delta P>0\), and \(SOC\) is equal to \(SOC_{\text{avr}}\), i.e., \(\Delta SOC=0\), the battery can work in either shallow or deep charge state according to the specific value of \(\Delta P\).  
Figure 7: Membership functions of the input and output: (**a**) Membership function of \(\Delta P\); (**b**) Membership function of \(\Delta SOC\); (**c**) Membership function of \(P_{\text{bat}}\).  
If \(\Delta P\) is closer to 3.75 kW than to 0 kW, the battery should work in deep charge state, otherwise the battery will work in shallow charge state.  
(7) When the DG output is equal to the load demand, i.e., \(\Delta P=0\), the battery does not need to charge or discharge no matter \(SOC\) is greater or lower than \(SOC_{\rm avr}\).  
Corresponding to above charging or discharging criterions, the fuzzy logic rules are designed as shown in Table 2.  
Then the set value of the charging or discharging power of the battery can be obtained by the centroid method, which is a kind of defuzzification methods, as shown in Equation (16).  
\[P_{\rm bat}=\frac{\sum_{i=1}^{25}x_{i}\mu_{C}(x_{i})}{\sum_{i=1}^{25}\mu_{C}( x_{i})} \tag{16}\]  
where \(\mu_{c}(x_{i})\) is the membership function of \(P_{\rm bat}\), \(x_{i}\) is the value in the fuzzy domain of \(P_{\rm bat}\).
## 4 Conclusions  
In this study, high-quality single crystals of Tb\({}^{3+}\)-doped Lu\({}_{2.5}\)Y\({}_{0.5}\)(Al\({}_{2.5}\)Ga\({}_{2.5}\))O\({}_{12}\) were successfully grown and systematically evaluated for their optical and scintillation proper  
Figure 8: Scintillation decay curves of Tb-doped LuYAGG single-crystal samples.  
\begin{table}
\begin{tabular}{l c c} \hline
**Samples** & **\(\mathbf{\tau_{1}}\) (ms)** & **\(\mathbf{\tau_{2}}\) (ms)** \\ \hline LuYAGG:Tb 0.5 mol\(\%\) & 0.9 & 3.6 \\ LuYAGG:Tb 1 mol\(\%\) & 0.9 & 3.3 \\ LuYAGG:Tb 5 mol\(\%\) & 0.4 & 2.8 \\ LuYAGG:Tb 10 mol\(\%\) & 0.5 & 2.8 \\ \hline \end{tabular}
\end{table}
Table 3: Scintillation decay time constants for Tb\({}^{3+}\)-doped LuYAGG single crystals.  
ties. All samples exhibited excellent phase purity, high transmittance in the visible range, and sharp emission features characteristic of Tb\({}^{3+}\). The optimal scintillation performance was observed at 5 mol% doping, with a light yield exceeding 60,000 ph/MeV and favorable decay characteristics. These findings highlight the potential of Tb\({}^{3+}\)-doped LuYAGG as a promising green-emitting scintillator material for radiation detection and imaging applications. Further improvements may be achieved through co-doping or host matrix engineering to enhance energy transfer and suppress quenching effects.  
Conceptualization, P.K. and W.C.; Methodology, P.L., T.K. ([PERSON]), Y.E. and K.W.; Software, K.W.; Validation, E.K., T.K. ([PERSON]), D.N. and N.K.; Formal analysis, P.L. and T.K. ([PERSON]); Data curation, P.K.; Writing--original draft, P.L.; Writing--review & editing, P.K.; Visualization, P.L. and P.K.; Supervision, T.Y.; Project ad-ministration, P.K.; Funding acquisition, T.Y., P.L. and P.K. All authors have read and agreed to the published version of the manuscript.  
This work is supported by MEXT Grant-in-Aid for Scientific Research A (22H00309), Scientific Research B (22H03872 and 24K03197), Exploratory Research (22K18997), and the Cooperative Research Project of Research Center for Biomedical Engineering. In addition, the authors would like to express their sincere gratitude to the Physics Program and the Scientific Research Equipment Center at Muban Chombueng Rajabhat University (MCRU) for providing essential facilities and technical assistance. This work is financially supported by the Thailand Science Research and Innovation (TSRI) Basic Research Fund: Fiscal Year 2025 (Project Number 209968) of Muban Chombueng Rajabhat University. Appreciation is also extended to the Department of Physics at King Mongkut's University of Technology Thonburi (KMUT) and the Faculty of Engineering at Kaestasart University (KU) for their valuable support throughout this study.  
Not applicable.  
Data for the results presented in this article are not publicly available.  
The authors declare no conflict of interest.
## 3 Results
### Composite Clinical Score Analysis  
To bridge the quantitative performance evaluation with clinical applicability, two composite metrics are computed as defined above: the overall model score (Equation (20)) and the clinical utility score (Equation (25)). These scores integrate standard statistical measures with clinically relevant priorities, providing a single performance index that reflects both the model's discriminative power and its potential impact in a clinical setting.  
As reported in Table 1, the macro-averaged sensitivity (i.e., recall), PPV, and F1 score for Dataset B are 0.7769, 0.7919, and 0.7770, respectively. The remaining metrics required for the composite scores were computed directly from the confusion matrix. Specifically, a macro-averaged specificity is of approximately 0.9478 and a macro-averaged negative predictive value (NPV) of about 0.9487.  
Using these values, and an independently computed AUC of 0.87, the overall model score is calculated by equally weighting AUC, sensitivity, and specificity:  
\[\text{Score}(m_{\theta})\approx\frac{0.87+0.7769+0.9478}{3}\approx 0.8649\quad(8 6.5\%).\]  
Similarly, the clinical utility score is computed by equally weighting the PPV, NPV, and F1 score:  
\[\text{Clinical Utility}\approx\frac{0.7919+0.9487+0.7770}{3}\approx 0.8392\quad(8 3.9\%).\]  
These composite scores, an overall model score of approximately 86.5% and a clinical utility score of roughly 83.9%, demonstrate that the model not only achieves strong statistical performance but also meets key clinical requirements. In other words, the model's high global discriminative ability is well aligned with its capability to provide clinically actionable insights, thereby supporting its potential as a reliable decision support tool.  
In the context of knee osteoarthritis grading, concrete clinical requirements refer to criteria that ensure the model's outputs are directly useful for patient care and decision making. These include (1) high sensitivity for detecting severe and healthy cases to minimize missed diagnoses and unnecessary interventions, (2) high specificity to avoid over-diagnosis, (3) strong positive and negative predictive values to support reliable clinical triage, and (4) robust performance in distinguishing clinically actionable endpoints (such as differentiating between grades that mandate intervention versus those that do not). The weighting schemes and composite scores used in this study are explicitly designed to align with these requirements, prioritizing the accurate identification of cases with significant clinical implications.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 4 Deep Learning Models
### Gated Recurrent Unit (GRU)  
In RNNs, the GRU is a variant that is simpler than the LSTM network while still effectively addressing long-term dependencies and the vanishing gradient problem. The GRU retains key components of the gating mechanism--specifically, the update gate and the reset gate, which regulate the flow of information. However, it omits the memory cell found in LSTM networks. Compared to LSTM, the GRU has fewer parameters, resulting in improved computational efficiency. In many cases, it achieves comparable, or even superior, performance on various tasks. As a lightweight alternative to LSTM networks, the GRU simplifies the gating mechanism, thereby reducing the computational overhead, making it slightly more efficient than LSTM. However, as the sequence length increases,  
Figure 6: LSTM model diagram.  
the computational cost of the GRU also increases rapidly, limiting its scalability in long sequence modeling tasks. Its structural model is shown in Figure 7.  
Update Gate: The purpose of the update gate is to determine the extent to which the hidden state at the current time step should retain information from the previous time step. The formula is as follows:  
\[z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}]). \tag{13}\]  
Reset Gate: The reset gate determines to what extent the hidden state from the previous time step should be disregarded. The formula is as follows:  
\[r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}]). \tag{14}\]  
[PERSON] et al. were the first to apply GRUs to traffic flow prediction and demonstrated through experiments that the GRU-based approach outperformed the ARIMA model in short-term traffic flow forecasting [25]. [PERSON] et al. systematically evaluated three variants of the GRU in RNNs by preserving the structure and reducing the parameters in the update and reset gates. This approach achieved reduced computational costs while maintaining the performance [26]. [PERSON] et al. proposed a Gated Feedback RNN (GF-RNN) method, which extends the conventional approach of stacking multiple recurrent layers by introducing a gating mechanism. This method utilizes GRUs to address the vanishing gradient problem. By incorporating gating mechanisms between recurrent layers, each layer independently controls the flow and feedback of information, thereby enhancing the network's ability to handle long-term dependencies and improving the overall performance [27].
## 3 Results and Discussion
### Metrics of Evaluation  
To evaluate the performance of the model, we used RMSE, MSE, and MAE metrics. The mathematical representation of these metrics is depicted in Equations (11)-(13). RMSE calculates the difference between all predicted data points and the actual data point, then compute the mean of these square errors and finally calculate the square root of the mean values. The MSE calculates the mean disparity between the actual and model output values. The MAE calculates the mean absolute difference between the actual and predicted values.  
\[RMSE = \sqrt{\frac{1}{n}\sum_{1}^{n}(y-\hat{y})^{2}} \tag{11}\]  
\[MSE = \frac{1}{n}\sum_{1}^{n}(y-\hat{y})^{2} \tag{12}\] \[\text{MAE = \frac{1}{n}\sum_{1}^{n}|y-\hat{y}|} \tag{13}\]