esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Activity Recognition
### Hierarchical Classification Study
#### 5.2.2 LoD2--Process Steps  
The second level of detail focuses on the activities within the work and idle groups. On the one hand, the model for the work group is designed to handle the casing machine, concrete, drill, and reinforce classes. On the other hand, the model for the idle group is responsible for identifying the secondary process time and downtime activities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Level of Detail (LoD)** & **Parent Node** & **MLP** & **DeepConvLSTM** & **DeepConvBiLSTM** \\ \hline LoD1 & - & 0.83 & 0.84 & 0.83 \\ \hline \multirow{2}{*}{LoD2} & Work & 0.42 & 0.58 & 0.62 \\  & Idle & 0.66 & 0.74 & 0.73 \\ \hline \multirow{4}{*}{LoD3} & Drill & 0.59 & 0.85 & 0.85 \\  & Reinforce & 0.68 & 0.68 & 0.68 \\ \cline{1-1}  & Concrete & 0.20 & 0.24 & 0.24 \\ \cline{1-1}  & Idle & 0.39 & 0.39 & 0.41 \\ \hline \hline \end{tabular}
\end{table}
Table 3: F1 scores for the selected activities.  
Figure 6: LoD1 confusion matrix for the DeepConvBiLSTM.  
The F1 scores for each label and the average F1 score are shown in Table 2, while Figure 7 displays the confusion matrices for the individual models' predictions for the DeepConvBiLSTM. The confusion plots for the MLP and the DeepConvLSTM can be found in Appendix A.  
The models differ noticeably in performance. For example, the baseline MLP model has an average F1 score of 0.42, with significant variations among the individual classes. The class drill has a high F1 score of 0.89, with 99% of the samples labeled as drill being correctly classified as such. However, the model tends to classify samples as drill, leading to low F1 values for the concrete and reinforce classes.  
Both hybrid models show better overall performance, with the average F1 score increasing to 0.58 for the DeepConvLSTM and 0.62 for the DeepConvBiLSTM. The confusion matrices for these models show more entries concentrated on the secondary diagonal compared to the _MLP_ model. The concrete class has an improved accuracy of 87% and 89% for the DeepConvLSTM and the DeepConvBiLSTM. The use of bidirectional LSTM layers leads to an overall improvement in performance, as seen in the increase in F1 scores for all classes, including casing machine and reinforce.  
The second group at this level of detail is the idle group, which encompasses downtime and secondary process related to drill and concrete. The differentiation between downtime and secondary processes activities is addressed more effectively at a different level of detail. The baseline MLP model correctly classifies 70% of the downtime class samples and 63% of the secondary processes class samples, resulting in a mean F1 score of 0.66. Accuracy improves to 75% and 73% for the two hybrid models, and the F1 score rises to approximately 0.73.
# An Interface Pressure Detection Method of Cable Silicone Rubber-XLPE Based on Nonlinear Ultrasound
## 2 Finite Element Simulation
### Analysis of Simulation Results  
Figure 5 shows the time domain diagram of the reflected waves at the silicone rubber-XLPE interface at different pressures. As the pressure increased, the reflected wave amplitude at the silicone rubber-XLPE interface slightly decreased. This is because the actual contact area of the interface increased due to pressure, the ultrasonic transmission was enhanced, and the reflection was weakened. Figure 6 shows the spectrum of the reflected waves at the silicone rubber-XLPE interface at different pressures. Comparing Figure 4b with Figure 6, the second harmonic appears in the reflected wave spectrum of the silicone rubber-XLPE interface. This indicates that a significant nonlinear effect occurred when the ultrasonic wave passed through the silicone rubber-XLPE interface. The fundamental wave amplitude and the second harmonic amplitude in Figure 6 were extracted, and the relationship between the two and the pressure at the silicone rubber-XLPE interface is shown in Figure 7a,b. The ultrasonic relative nonlinearity coefficient was calculated according to Equation (8). The relationship between it and the pressure at the silicone rubber-XLPE interface is shown in Figure 8. As the pressure increased, the fundamental amplitude of the reflected wave at the interface decreased, the second harmonic amplitude increased, and the ultrasonic nonlinearity coefficient increased. The nonlinear coefficient steeply increased at first, and then more slowly.  
Figure 5: Silicone rubber-XLPE interface echo time domain waveform at different pressures.  
Figure 8: Relationship between ultrasonic nonlinearity coefficient and pressure at the silicone rubber-XLP interface.  
Figure 6: Silicon rubber-XLPE interface-reflected wave spectrum at different pressures.  
Figure 7: Curves of the fundamental and second harmonic amplitudes of reflected waves at the silicone rubber-XLPE interface as a function of pressure. (**a**) Fundamental wave amplitude variation curve. (**b**) Second harmonic amplitude variation curve.
## 4 Discussion
All groups showed an increase in the number of fibroblasts on Day 7 and a decline on Day 14. A decrease in the number of fibroblasts on Day 14 in all groups indicates that the healing phase is at the remodeling stage (Table 7). The remodeling phase can be identified by decreasing proliferation and inflammation, reorganization of the extracellular matrix, and regression of newly formed capillary vessels [51]. The fibroblast count in the DAM group was almost the same as the fibroblast count of the NC or normal group. This result indicated that the administration of alginate-mangosteen rind extract combination was effective to heal the open wound. The treatment group of alginate and mangosteen rind extract of DAM showed significant results at each observation on Days 3, 7, and 14 with 65.0 \(\pm\) 1.4%, 85.0 \(\pm\) 0.7%, and 91.0 \(\pm\) 0.7%, respectively (Table 8). The DAM group showed the highest percentage of collagen density compared to other groups. The diabetic conditions increase the production of ROS/RNS, which decreases the collagen synthesis [52]. An increase in the percentage of collagen density in the DAM indicated an increase in collagen synthesis. Achievement of the wound-healing process that occurred in DAM could be due to compounds possessing antioxidant activity from the combined mangosteen peel. Moreover, [PERSON] et al. (2014) reported that alginate is a good absorbent and gel formation agent that has homeostatic properties [15]. Alginate has been used in several types of wounds, including pressure wounds, diabetic wounds, and venous ulcers such as cavities and multiple bleeding sores [14]. In addition, alginate acts as an antioxidant that can capture free radicals and absorbs wound exudate.
# Interdisciplinary Urban Tunnel Control within Smart Cities
## 3 New Road Tunnel Traffic Control Algorithm
### New Road Urban Tunnel Traffic Control
The second parameter that influences the outcome of the traffic control is the traffic situation in the city, expressed by level of service (LOS). When the traffic situation on a selected location in the city is good, the traffic control in tunnel is not influence at all. However, if the traffic situation in the city worsens, it would be contradictory to even contribute to the worsening by improving situation in the tunnel. It can be expected that under such conditions, the situation in the tunnel worsens as well. The length of the red-light phase will be shorter. In Figure 5 the cumulative cost of congestions and related Level of Service were introduced. The urban road traffic control algorithm works with LOS categorization--LOS 1--green; LOS 2--yellow; LOS 3--red. To obtain valuable on-line data, the system must be connected to GLOMODO (Figure 3), which continuously monitors the LOS, including available short-term predictions. This fulfills the idea of connecting the road urban tunnel with the Smart city system through presented syntactic and semantic interface. In the case of \(LOS=1\) (green), a full interval \(T_{R}\) will be used for urban tunnel control according to occupancy prediction, as it is shown in Figure 13. In case of \(LOS=2\) and \(LOS=3\), the maximum of interval \(T_{R,\ max}\) will be reduced accordingly. STEP 6 of the algorithm (see Figure 12) is based on the Equation (9):  
\[T_{R,max}(t)= \begin{cases}MRT&for\ LOS\ 1\\ 0.8\cdot MRT&for\ LOS\ 2\\ 0.4\cdot MRT&for\ LOS\ 3\end{cases}  \tag{9}\]  
where \(MRT\) denotes the maximum red time and it is a parameter that depends on the particular tunnel. For the case of the tunnel Blanka, the value of parameter \(MRT\) equals to  
Figure 13: Schematic depiction of the road urban tunnel traffic control algorithm. 120 s (which corresponds to the cycle length discussed above). The reaction of the tunnel control system will be thus more sensitive to the surrounding traffic situation. The actual length of the red-light signal, \(T_{R}(t)\), i.e., STEP 7 of algorithm (see Figure 12) is currently computed according to the Equation (10):  
\[T_{R}(t)=k_{i}(t+15)\cdot\frac{T_{R,max}(t)}{(\kappa_{KO}-\kappa_{go,\ min})} \tag{10}\]  
As a next enhancement of the algorithm, nonlinear characteristics will be modeled using a fuzzy inference system mechanism [32; 33], which will be tailored-made for specific tunnel construction and its traffic features.
## 2 Related Work
### Combining Strategies of Classifiers  
Given an ensemble learning system that contains \(L\) classifiers and the dataset \(X~{}=~{}\{x_{n}\}\), \(n~{}=~{}1,2,\ldots,N,X\in\mathbb{R}^{D\times N}\), that includes \(C\) categories \(\{c_{1},c_{2},\ldots,c_{C}\}\). For classification problems, if the prediction output of \(h_{i}\) for sample \(x\) is represented by a one-hot coding, then the prediction output can be expressed as a \(C\)-dimensional vector \(\left(h_{i}^{1}(x),h_{i}^{2}(x),\ldots,h_{i}^{C}(x)\right)\), where \(h_{i}^{j}(x)\in\{0,1\}\) denotes the output of \(h_{i}\) on the class label \(c_{j}\). If \(h_{i}\) predicts the sample \(x\) as the class \(c_{j}\), set \(h_{i}^{j}(x)~{}=~{}1\); otherwise, \(h_{i}^{j}(x)~{}=~{}0\).  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notations** & **Description** \\ \hline \(h_{l}\) & The \(l\)th classifier (\(L\) = size of ensemble classifier) \\ \(\mathbf{h}\) & Ensemble classifier, \(\mathbf{h}~{}=~{}[h_{l}],l~{}=~{}1,2,\ldots,L\) \\ \(D\) & The number of dimensions (features) of dataset \\ \(N\) & The number of samples of dataset \\ \(C\) & The categories of dataset \\ \(h_{i}(x)\) & The predicted output of classifier \(h_{i}\) on sample \(x\) \\ \(\mathbf{w}\) & The weight vector of an ensemble classifier \\ \(\cdot\ast\) & The multiplication operation of the corresponding elements in the same dimension vector \\ \(\oplus\) & The XOR operation \\ \(H_{1}(x)\) & The prediction output of ensemble learning by the majority voting method \\ \(H_{2}(x,\mathbf{w})\) & The prediction output of ensemble learning by the weighted voting method \\ \(Div_{1}(\mathbf{h})\) & Diversity measures based on consideration of accuracy \\ \(Div_{2}(\mathbf{h},\mathbf{w})\) & Diversity measures based on avoiding overfitting \\ \(K\) & The number of subsets on a dataset using K-fold cross-validation method \\ \(M\) & The number of clusters on a dataset using K-means clustering method \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and definitions.  
A common classifier combination strategy is the relative majority voting method. The prediction output of ensemble learning \(H_{1}(x)\) can be obtained as follows:  
\[H_{1}(x)\ =\ c_{\underset{j}{\mathrm{argmax}}\sum_{i=1}^{L}u_{i}^{j}(x)} \tag{1}\]  
Another classifier combination strategy is the weighted voting method. The prediction output of ensemble learning \(H_{2}(x,\mathbf{w})\) can be obtained as follows:  
\[H_{2}(x,\mathbf{w})\ =\ c_{\underset{j}{\mathrm{argmax}}\sum_{i=1}^{L}w_{i}^{j}(x)} \tag{2}\]  
where \(\mathbf{w}\ =\ (w_{1},w_{2},\ldots,w_{L})\), \(\sum_{i=1}^{L}w_{i}\ =\ 1,w_{i}\in[0,1]\).
# Analysis of Macroscopic Traffic Network Impacted by Structural Damage to Bridges from Earthquakes
## 3 Model Development
This principle has been accepted as a sound and simple behavioral principle to describe route choice over alternate routes due to congested traffic conditions [38]. To solve the equilibrium problem, the SOLA method in EMME4 has many advantages, including (1) traffic does not need to be explicitly stored, so there are not many data storage requirements; (2) it is parallelized with new multiprocessor and shared memory architectures; and (3) when fully fused, a quasi-probability path flow--a unique link/turn class flow--is created and proportionality is observed. To model the Pohang area, Daegu Metropolitan Area Network and Daegu Metropolitan Area Origin/Destination Data distributed by the National Transportation Database Center were used to create a network around Pohang, Gyeongsangbuk-do (Figure 2). In the network model, initial speed and VDF (volume delay function) parameters for each link were modified to represent actual traffic volume on the road. In the event of earthquakes, some lanes will be closed to general purpose vehicles in order to accommodate emergency vehicles. Additionally, speed limit will be imposed to mitigate structural damages to bridges. Therefore, these conditions were included in this study. Three different structural damage levels to bridges were considered in this study: minor damage, moderate damage, and major damage. Based on this parameter, lanes in use and speed limit were varied to replicate scenarios caused by damage to bridges from earthquakes. For comparison purposes, no damage scenario was included. In this paper, it is assumed that in minor damage scenario, approximately 30% of lanes would be closed and speed limit would be lowered to approximately 70% of the speed limit of \"No Damage\" scenario. Additionally, in more significant damage scenarios, \"Moderate Damage\" and \"Major Damage\", approximately 60% and 80% of lanes, would be closed and speed limit would be lowered to approximately 30% and 20% of \"No Damage\" scenario, respectively. Table 3 displays lanes in use and speed limit information for each scenario.
## 4 Results and Discussion
### Additional Earth Pressure  
The measured earth pressure at points 1, 5, 9 and 13 are displayed in Figure 9, and the final distributions of earth pressure around tunnels before and after excavation are plotted in Figure 10. As can be seen from Figure 9, the vertical earth pressure at monitoring points L1, L9, R1 and R9 gradually decreased with the excavation of the foundation pit. When the excavation was completed, the vertical earth pressure was reduced to the minimum value of 58.27 kPa and 55.21 kPa at crowns, and of 83.47 kPa and 91.75 kPa at inverts of the left and right tunnels. The horizontal earth pressure at monitoring point 13 underwent the same trend as the vertical earth pressure at monitoring point 1 for different stages. Figure 10 displays the fact that, during the excavation and unloading of the foundation pit, the earth pressure around the tunnels is roughly gour  
Figure 8: Tunnel deformation responses for different excavation stages.  
Figure 7: Tunnel displacement with construction simulation stage.  
distributed. The measured earth pressure values at monitoring points 1, 5, 9 and 13 were utilized to derive the pressure ratios before and after the excavation of the foundation pit, which are summarized in Table 2. The reductions in the earth pressure at crowns of the left and right tunnels are 68.06% and 70.22%, while those at the inverts are 63.69% and 59.44%, respectively. Moreover, the reductions in earth pressure at the tunnel side walls are smaller than these at the crowns and inverts of the tunnels. This confirms a larger stress relief magnitude at the tunnels' crowns and inverts than that at side walls. Additionally, the reduction in earth pressure at the tunnel side walls that are away from the foundation pit is slightly larger than the other side wall, near the foundation pit.  
Figure 10: Variations of earth pressure around tunnel before and after excavation (unit: kPa).  
Figure 9: The earth pressure curve for different construction stages.
## 1 Introduction  
As the most common solid malignancy diagnosed in men, prostate cancer was the second most common cause of cancer death in 2016 [1; 2]. However, advances in radiation oncology have led to the development of effective therapies for clinically localized prostate cancer. Stereotactic body radiation therapy (SBRT) is delivered in large fractions of highly conformal radiation therapy. It is designed to deliver a very precise type of external radiation treatment and to provide optimized results while minimizing side effects. SBRT is a safe and effective treatment for clinically localized prostate cancer. Compared with surgery, it is an outpatient procedure that does not require general anesthesia or hospitalization, and it can be completed within four to five sessions. There are diverse benefits: no pain; minimal invasiveness; little or no recovery time; only five treatments instead of 40, the latter being typical of other forms of radiation therapy; few or reduced side effects; and speed, convenience, and effectiveness [3]. Such treatments are believed to be radiobiologically more effective in treating prostate cancers than the conventional low-dose fractions commonly used for prostate cancer radiotherapy [4; 5]. In addition, SBRT treatment of localized prostate cancer has been reported as safe, and it offers excellent local control, as determined by freedom from biochemical recurrence (BCR) in patients presenting with low- and intermediate-risk cancers [6; 7; 8].  
Risk assessments tools have been developed for predicting surgical and radiation therapy outcomes following standard prostate cancer treatment (i.e., D'Amico [9], Kattan nomogram [10], the University of California, San Francisco Cancer of the Prostate Risk Assessment (CAPRA) score [11], and the Candiolo classifier [12]). Although these risk assessments have been developed for predicting outcomes, these assessments have not been applied for use in patients treated with SBRT.  
SBRT has been proven effective for the treatment of low- and intermediate-risk prostate cancer patients, who are the dominant recipients of SBRT treatment. However, there is yet to be a classifier that can be applied to predict BCR of patients treated with SBRT.  
Therefore, we developed a prostate clinical outlook (PCO) classifier application that provides information on predicted BCR, specifically for patients electing to undergo prostate cancer treatment with SBRT. We have also proposed a prostate clinical outlook visualization system (PCOVS) based on the PCO classifier application [13].
## 8 Conclusions  
Emergency rescue scenarios are in complex climatic and terrain environments, characterized by the complexity of the rescue equipment network system, the diversity of intelligence data modalities, the limited system survey boundaries, and the lack of algorithmic support for situational analysis, and UAVs face a variety of complex uncertainties in rescue scenarios, for example, communication link failure and malicious attacks [33]. For this reason, this paper designs a system architecture with elastic arithmetic for the mission requirements, introduces 5G network and MEC technology to provide UAVs with network support for the separation of the control and user plane, expands the system survey boundary, and improves the intelligence detection capability. Second, the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed, which has a substantial improvement over SOA, PSO, and DSA in terms of convergence accuracy, speed and stability. Finally, based on the GLSOAG-driven system resource optimization, the effectiveness and reliability of the resource optimization method is verified through simulation experiments, which proves that the system designed in this paper has a certain degree of feasibility, and that the proposed resource optimization method can effectively support various scales of computational tasks in emergency rescue scenarios and enhance the effectiveness of emergency rescue tasks.  
**Author Contributions:** Conceptualization, S.H. and M.W.; methodology, S.H. and J.Z.; software, J.D. and J.Z.; validation, D.L., J.Z. and S.H.; formal analysis, S.H.; investigation, S.H. and J.Z.; resources, M.W.; data curation, S.H.; writing--original draft preparation, S.H. and J.Z.; writing--review and editing, S.H.; visualization, J.Z. and D.L.; supervision, J.Z. and J.D.; project administration, J.D.; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Natural Science Foundation of China (grant number: 62173340)  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** The authors sincerely thank the editor, the associate editor, and two reviewers for their constructive comments that led to a substantial improvement in this paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Materials and Methods
### Signals Involved in the System
#### 3.1.3 The Chirp Signals  
They represent the second and the most important component of the sonic watermark because they are used to embed the ENF information. Chirp signals have the frequency variable in time. The frequency variation could follow linear or nonlinear laws. Linear chirp (i.e., chirp signals with linear variable frequency) are used in the system proposed in this paper and are very well detailed in [23]. These signals are used in many applications such as radar, echolocation, and remote sensing of cosmic ray induced air showers because they can be detected in harsh SNR conditions. The mathematical expression of a chirp signal centered around the time origin (i.e., \(t=0\)) can be written as:  
\[c_{s}(t)=\text{rect}\Big{(}\frac{t}{T_{s}}\Big{)}\cos\Big{(}2\pi f_{\text{M}}t -\pi s\text{\text{\
## 3 Results Analysis and Discussion
### The Effect of Reinforcement Depth on Soil Behavior  
Within the scope of the experimental study, the soils were equipped with 1, 2, and 3 times the width of the foundation under the foundation plate. To examine the effect of the reinforced soil layer depth on the bearing capacity, the determined experimental groups were evaluated. Determining the optimum reinforcement depth in soils is an important parameter to design and build a more economical structure. As the depth of the reinforced soil from the foundation base increased, the ultimate bearing capacity of the soil increased has also been reported before by other researchers such as [PERSON] et al. [62], [PERSON] et al. [63]. In the model loading tests performed at Dr = 30%, the ultimate bearing capacity increased proportionally as the reinforcement depth increased. At Dr = 50%, changing the depth of reinforcement from B to 2B resulted in significant increases in bearing capacity, but when changed from 2B to 3B, the rate of increase in bearing capacity decreased. When compared according to fiber types, as the reinforcement depth increases, the increase in ultimate bearing capacity was highest in glass fiber-reinforced tests and least in macromesh-reinforced tests. In model tests with Dr = 50% Fl = 24 mm Fc = 2% macromesh fiber reinforced and Dr = 50% Fl = 24 mm Fc = 2% basalt + macromesh fiber-reinforced tests, there was no increase in ultimate bearing capacity when the reinforcement depth was increased from 2B to 3B. Therefore, at Dr = 50%, it is considered sufficient to reinforce the 2B depth from the foundation base. It has been observed that the soil under strip footing and the ideal reinforcement depth are between 2B and 3B according to the varying relative density values [63; 64].