esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Response Analyses of a Large-Span Powerhouse Cavern Considering Rock-Structure Interaction
## 4 Numerical Modeling
### Selection of the Seismic Input
The seismic properties can be roughly characterized by design response spectra, which depend on the seismic hazard level as well as geological characteristics of the site. EUROCODE 8 describes the spectral shape distinguishing between horizontal and vertical components of accelerograms. For the horizontal components, the elastic spectral shape is expressed by:  
\[0\leq T\leq T_{\rm B}:Se(T)=\alpha_{g}\cdot S\cdot\eta\cdot F_{0}\cdot\left[ \frac{T}{T_{\rm B}}+\frac{1}{\eta\cdot F_{0}}\left(1-\frac{T}{T_{\rm B}} \right)\right] \tag{1}\]  
\[T_{\rm B}\leq T\leq T_{\rm C}:Se(T)=\alpha_{g}\cdot S\cdot\eta\cdot F_{0} \tag{2}\]  
\[T_{\rm C}\leq T\leq T_{\rm D}:Se(T)=\alpha_{g}\cdot S\cdot\eta\cdot F_{0}\cdot \left[\frac{T_{\rm C}}{T}\right] \tag{3}\]  
\[T_{\rm D}\leq T\leq 4s:Se(T)=\alpha_{g}\cdot S\cdot\eta\cdot F_{0}\cdot \left[\frac{T_{\rm C}T_{\rm D}}{T^{2}}\right] \tag{4}\]  
where \(T\) is the vibration period; \(a_{g}\) is the design ground acceleration on type A site class; \(S\) is the soil factor; \(T_{\rm B}\) and \(T_{\rm C}\) are the limiting periods of the spectrum's plateau; \(T_{\rm D}\) is the lowest period of the constant spectral portion; and \(\eta\) is the damping correction factor, and it is equal to one for 5% viscous damping. For the studied cavern, the parameters of the design spectrum are shown in Table 3, according to the EUROCODE 8. Based on the adequate knowledge of the seismic hazard of the site, identification of a dominant scenario should be also defined through a process called deaggregation [22] ([PERSON], 1991), which aims at estimating the most likely earthquake ground motion records. The primary purpose of deaggregation of the seismic hazard, which in terms of M (magni \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(F_{0}\) & \(a_{g}\) (g) & \(T_{\rm B}\) (s) & \(T_{\rm C}\) (s) & \(T_{\rm D}\) (s) & \(S\) & \(\eta\) \\ \hline
2.462 & 0.134 & 0.088073 & 0.264218 & 2.137759 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of design spectrum of the studied cavern for horizontal components. tude) and R (distance) displays their contributions to the annual exceedance, is to assist in the determination of earthquake events (M and R) that are physically reasonable and most likely to cause the exceedance, and the determined earthquake events are usually referred to as scenario earthquake events. With the deaggregation process carried out, for the maximum PGA with a 10 percent of exceedance probability in 50 years, a scenario for the site with the magnitude-distance pair of M = 5.3 and R = 19 km was determined.
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 3 Data Sources and Quality
### Strategies for Addressing Data Quality Issues
#### 3.5.1 Improving Data Quality at Collection  
A fundamental approach to addressing data quality issues is to improve the initial data collection process. Two key strategies have emerged in this area: the implementation of automated data collection systems and the integration of multiple data sources.  
The advent of new technologies offers promising solutions for reducing data quality issues at their source. Event Data Recorders (EDRs) in the European Union [19] represent a significant advance in automated crash data collection, providing accurate information about crash circumstances without relying on human reporting. Similarly, [PERSON] and [PERSON] [22] advocate for intelligent crash reporting systems to address the frequent misreporting of crucial attributes such as crash location, time, and severity.  
Likewise, the systematic integration of data from multiple sources has proven effective in creating more complete crash records. Sweden's STRADA system exemplifies this approach, as illustrated in Figure 4, by systematically combining various data sources to improve reporting completeness [23].  
Several studies demonstrate the value of this approach:  
* [PERSON] and [PERSON] [14] showed how combining insurance claim data with police and hospital records in Ireland provided a more comprehensive picture of crash incidents.
* [PERSON] et al. [24] improved crash injury identification by linking hospital discharge data with state-level crash reports.
* [PERSON] et al. [25] demonstrated the benefits of connecting police and medical records for understanding individual crash characteristics.
* [PERSON] et al. [26] revealed significant discrepancies between law enforcement and medical assessments of injury severity, finding overestimation in 45% to 90% of cases.
# Research on Safe Following Distance on an Expressway Based on Braking Process Analysis
## Modeling
### Analysis and Assessment of Braking Distance
#### 3.1.2 Analysis of the Automobile Braking Process  
In order to establish a reasonable minimum following distance model, it is necessary to have a comprehensive understanding of the braking process [21]. Assuming that the front car, A, suddenly appears in some emergency situation (sudden deceleration and stopping or cargo scattering), the curves of the following car's brake-pedal force, braking deceleration and braking time are as shown in Figure 2, when the driver of the following car enacts emergency braking. In Figure 2, \(F_{p}\) is brake-pedal force and \(j\) is brake deceleration.  
Figure 1: Automobiles in a braking scenario.  
According to Figure 2, the driver needs to finish five periods in the actual braking process, and the specific analysis is as follows.  
1. The driver's reaction time \(t_{1}\): the time required for the driver to move the right foot to the brake pedal after the driver detects a dangerous situation and begins to react.
2. The brake coordination time \(t_{2}\): the time required from starting to step down on the brake pedal to eliminate the brake pedal clearance, clearance of various hinges and bearings and drum brake clearance.
3. The braking force growth time \(t_{3}\). At this stage, the braking force gradually increases from zero to the maximum; that is, the braking deceleration increases from zero to the maximum, and the car moves through variable deceleration.
4. The continuous braking time \(t_{4}\). At this stage, the braking force is basically maintained at a stable value; that is, the brake deceleration remains unchanged, and the car goes through uniform deceleration until stopping.
5. The braking relaxation time \(t_{5}\), that is, the time that is required after stopping until the automatic elimination of braking force.
# On the Association between Dental Implants, Osteoporosis and Bone Modulating Therapy
## 4 Discussion
The results of the present study are in the spirit of recent studies claiming the safety of dental implant installation in osteoporoptic patients in general and in those receiving BMT in particular [32; 33; 34; 35], with success rates of osseointegrated dental implants comparable with rest of the study cohort. Several new approaches for BMT and osseointegration were suggested. One study examined the effects of raoxifene during bone formation around the dental implant in the ovariectomy-induced osteoporotic rat maxilla. Female Sprague-Dawley rats were divided into three groups (n = 18 each): sham-operated (control), ovariectomized (OVX), and ovariectomized and raoxifene-administered (RAL). Eight weeks after ovariectomy, both upper first molars were extracted, and implants were placed 4 weeks post-extraction. It was concluded that Raloxifene administration enhanced the osteogenic genes and protein expression in the bone around the implant [36]. Another study aimed to determine the influence of hydrophilic titanium surfaces on gene expression and bone formation during the osseointegration process in an osteoporoptic model. Their results supported that hydrophilic surfaces in situations of osteoporosis may provide additional benefits in the early stages of osseointegration [37]. A recent review [38] suggests that topographical modifications, improvement in hydrophilicity and the development of controlled-release drug-loading systems to improve cellular adhesion, proliferation and differentiation. Surface modifications, along with drug coating, undoubtedly demonstrate better osseointegration, especially in challenged degenerative conditions, such as osteoporosis, osteoarthritis and osteogenesis imperfeta. Loading substances on modified titanium surfaces may be achieved by mechanisms, such as direct coating, adsorption and incorporating in biodegradable polymers.
# A Comparative Study of Ensemble Models for Predicting Road Traffic Congestion
## 5 Experimental Results and Discussion
### Models' Misclassification Cost
Table 8 showed 42 and 3557 correctly classified, with 3290 incorrectly classified. The results of the cost computation showed that the AdaBoost (DT) ensemble model obtained the lowest cost of misclassification of 24 when compared to the AdaBoost (SVM) ensemble model, which obtained the highest cost of misclassification of 3290, as shown in Table 9 and Figure 5. This was computed using Equations (8) and (9) above. A road traffic congestion prediction model was developed using ensemble methods. The study results showed that the AdaBoost (DT) ensemble prediction model achieved an accuracy of 99.7% and a prediction misclassification cost of 24, which is lower when compared to the other models. The model obtained the best results in terms of precision, recall, and f1-score metrics. It is hoped that the constructed model would reduce the high number of road traffic collisions and traffic congestion with other interventions already in place. AdaBoost proved to handle the dataset; thus, its performance was as good as expected. This confirmed the suitability of AdaBoost for this kind of dataset and problem  
Figure 5: The model cost results: cost of RF, DT, SVM, bagging, AdaBoosting, and stacking as shown in the x-axis: Model 1—RF, Model 2—DT, Model 3—SVM, Model 4—Bagging (RF), Model 5—Bagging (DT), Model 6—Bagging (SVM), Model 7—AdaBoosting (RF), Model 8—AdaBoosting (DT), Model 9—AdaBoosting (SVM), Model 10—Stacking (LR-[RF, DT and SVM]). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Ensemble Model** & **Cost** \\ \hline Model 1 & RF & 252 \\ \hline Model 2 & DT & 98 \\ \hline Model 3 & SVM & 180 \\ \hline Model 4 & Bagging (RF) & 323 \\ \hline Model 5 & Bagging (DT) & 313 \\ \hline Model 6 & Bagging (SVM) & 190 \\ \hline Model 7 & AdaBoost (RF) & 252 \\ \hline Model 8 & AdaBoost (DT) & 24 \\ \hline Model 9 & AdaBoost (SVM) & 3290 \\ \hline Model 10 & LR [Stacking (RF, DT and SVM)] & 43 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of cost results. domain. However, the dataset used did not include weather and road collision data. This could be why models performed poorly compared to AdaBoost (DT).
# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 8 Discussion and Future Work  
Sink mobility as an emerging technology is widely used in various types of WSNs. One important issue which needs to be addressed is moving path scheduling which has a significant effect on the performance of the mobile agent. Much research has been done to explore efficient data gathering methods for mobile agents, and the moving schema is generally divided into three types, including the controlled moving schema, the uncontrolled moving schema, and the random moving schema. Different moving schema are applied to different applications. In the controlled moving schema, we hope the traveling path is as short as possible to decrease the network latency and reduce the loss  
Figure 11: Overflowing data of the network.  
Figure 12: Comparison of whether to adopt ACO.  
package rate. Meanwhile, the shortest traveling path means that the mobile agent may not approach nodes and cause extra energy consumption. Meanwhile, the predictability of the mobile agent greatly enhances the reliability of the network because nodes can have the exact location of the mobile agent and the routing can be scheduled in advance. The schema of the uncontrolled moving and random moving are mainly used in applications such as endangered animal detecting. The mobile agent is usually placed on the animals and moves with the animals, which may result in network isolation.  
The traveling path planning should be determined by the specific application which demands low energy consumption or low network latency and package loss rate. In our proposed HM-ACPOSO algorithm, we also take energy balancing into consideration. The communication range of each CHs can be adjusted dynamically according to their residual energy so that the weak CHs will be protected using a close communication distance. Additionally, more mobile agents can also be adopted to collect data cooperatively to reduce energy consumption and network latency at the same time, if the budget allows for this.  
Our future work will mainly focus on multiple mobile agents cooperative work which can further enhance the performance of the network. Multiple mobile agents cooperative work faces challenges as follows: Firstly, mobile agents need to communicate with each other to collect data cooperatively. How can the mobile agents communicate with each other efficiently? Then we must determine how to schedule the traveling path of each agent, which is much harder than for multiple agents than for a single mobile agent network. Finally, as the mobile agent number increases, the control messages among the network increase exponentially. How can we decrease the control messages to simply the network? These problems must be solved.
## 3 Results
### Absorbed Dose Distributions  
The following section presents a visual example of the activity biodistribution simulated in this study, along with the corresponding \(AD\) distribution maps. Figure 6 shows the activity biodistribution for the case with an \(LS\) of 20% along with the corresponding \(AD\) distributions obtained from the \(MC\) simulation and several of the previously discussed convolution methods, specifically SVOX_L, Lung_296, and Lung_L. It should be noted that the \(AD\) distributions for Lung_296 and Lung_L do not show \(AD\) in the liver region. This is due to the assumption that the activity image was cropped to include only the lungs. This cropping was performed in order to prevent unrealistic contributions to \(AD\) in the lungs from activity in the hepatic region. The images demonstrate that the \(MC\)\(AD\) distribution shows higher \(AD\) per voxel compared to other voxel-based methods, even under more realistic physical assumptions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lung Shunt** & **Lung\_296 (\%)** & **Lung\_221 (\%)** & **Lung\_L (\%)** \\ \hline
10\% & \(-63.8\) & \(-51.5\) & \(-38.1\) \\
20\% & \(-62.3\) & \(-49.5\) & \(-35.9\) \\
30\% & \(-62.1\) & \(-49.2\) & \(-35.5\) \\
40\% & \(-62.0\) & \(-49.1\) & \(-35.4\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative Difference for the SVOX with the new lung tissue kernel with respect to the \(MC\) computed according to Equation (6).  
Figure 5: Correlation plot of the lungs’ \(\overline{AD}\), obtained from \(MC\) simulations with the Reference phantom (on the x-axis), compared with those obtained using the new lung VSV kernel (referred to as Lung_296), those obtained after a global correction based on the mean lung density of the Reference phantom (Lung_221), and those obtained after correcting for tissue heterogeneities according to Equation (3) (Lung_L, using 0.296 g/cm\({}^{3}\) as the uniform tissue density). For comparison, the SVOX_ST data (already presented in Figure 4) are also included. For each data series, each point in the plot corresponds to an increasing \(LS\) value (10%, 20%, 30%, and 40%), with a line representing the linear interpolation of each dataset, provided as a qualitative visual guide only.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lung Shunt** & **\(MC\)** & **Lung\_296** & **Lung\_221** & **Lung\_L** \\ \hline
10\% & \(11.9\) & \(4.3\) & \(5.8\) & \(7.3\) \\
20\% & \(23.0\) & \(8.7\) & \(11.6\) & \(14.7\) \\
30\% & \(34.3\) & \(13.0\) & \(17.4\) & \(22.1\) \\
40\% & \(45.6\) & \(17.3\) & \(23.2\) & \(29.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Lungs’ \(\overline{AD}\) for the \(MC\) simulation and the SVOX with the new lung’s VSV kernel, as visually reported in Figure 5. All the results are reported in Gy per GBq of administered activity.
## 2 Materials and Methods
### Bulk Wave Techniques
#### 2.3.3 Air-Coupled through-Transmission  
The 300 kHz single element flat air-coupled transducers (produced at Ultrasound Research Institute, KTU, Kaunas, Lithuania) were used for the investigation of the bonding quality in the case of air coupled through-transmission investigations. The active diameter of the air-coupled transducers is 14 mm. The nearfield distance for this transducer is calculated to be 43 mm. The distance between two transducers is kept at twice the near field distance (86 mm) and the plate is placed in the middle at the bonded region. The through-transmission setup is shown in Figure 4. Moreover, 10 periods rectangular burst signal with 750 V amplitude is used as the excitation signal. The received signal is pre-amplified by 50 dB static gain and 40 dB dynamic gain. The received signal is then averaged 64 times for each scanning point for noise reduction. The scanner is moved with a step of 0.5 mm and scanned along the bonded region with the defect.
## 2 Materials and Methods
### Methods
#### 2.3.5 Scenario Design  
The main thought behind modeling scenario design is to control whether the input variables to the model change or remain constant. It is commonly used to decompose the magnitude of the impact of different factors on geographical phenomena [9; 34].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Cropland** & **Forestland** & **Grassland** & **Urban Areas** & **Bare Areas** \\ \hline \(\epsilon_{\max}\) & 0.604 & 0.774 & 0.380 & 0.202 & 0.301 \\ \hline \hline \end{tabular} In this table, \(\epsilon_{\max}\) is the maximum light use efficiency under ideal conditions (g C/M).  
\end{table}
Table 2: \(\epsilon_{\max}\) selected for different land cover types in the ARNWC (unit: g C/M).  
In this study, we treat the impact of land cover change as a joint effort of land cover type conversion (abrupt change) and vegetation growth enhancement (gradual change). On this basis, two sets of five modeling scenarios were designed to analyze the impact of land cover change on vegetation carbon sources/sinks in the ARNWC (Table 3). Among them, the first group was used to simulate the impact of land cover change (including scenarios L1 and L2). Where scenario L1 only allows land cover type to change while keeping other factors consistent at the 2001 level, this scenario assesses the direct impact of land cover type conversion on vegetation carbon source/sink but fails to account for the impact of vegetation growth enhancement. Thereby, in order to assess the impact of vegetation growth enhancement, we use NDVI and EVI as indicators of natural vegetation growth and designed scenario L2, which allows for changes in land cover type, NDVI, and EVI with constant climate factors. This scenario assesses the combined effect of land cover type conversion and vegetation growth enhancement on vegetation carbon source/sink (i.e., the effect of land cover change). The second group contains three scenarios (LC1, LC2, and LC3), where LC1 is used to model annual NEP changes in the study area in a real scenario, which allows all model input variables to change. The results of scenarios LC2 and LC3 can reveal the direct and interactive effects of land cover type conversion and climate change, respectively, where scenario LC2 allows all factors except NDVI and EVI to change, while scenario LC3 allows only NDVI and EVI to change, and all other factors remain unchanged. Given that the modeling scenario design method generally has both direct and indirect strategies to assess the impact of land cover change, the difference in results between scenarios LC1 and LC2/LC3 can further demonstrate the reliability of the research findings.
# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
## 4 Discussion
### Limitations and Future Works  
Despite the promising results achieved in this study, several limitations remain. First, the Pheno4D dataset used consists of tomato seedling point clouds collected under greenhouse conditions, which may limit the model's generalization to more complex field environments and constrain its segmentation performance to the seedling stage. Second, the current method relies on supervised learning, which requires manually annotated data--a process that is labor-intensive and time-consuming, particularly for large-scale datasets. Additionally, although TSINet performs well in segmenting stems and leaves, it may still encounter challenges when dealing with occlusion, noise, or highly overlapping plant structures.  
Future work will focus on extending TSINet to field-grown tomato plants under more complex environmental conditions, including drought, high temperatures, and other stress scenarios, and to plants at later developmental stages. We will also explore the integration of semi-supervised and self-supervised learning strategies to reduce the dependency on large annotated datasets. Furthermore, the incorporation of domain adaptation techniques and the development of more advanced feature aggregation modules are expected to enhance the network's robustness and generalizability. Lastly, real-time deployment and efficiency optimization will be pursued to enable high-throughput, accurate, and non-destructive measurement of tomato phenotypes, thereby facilitating its practical application in precision agriculture and contributing to the advancement of intelligent agriculture.
## 3 Results and Discussions  
A comparative parametric study has been carried out to investigate the effect of turbine efficiency, pump efficiency, Pinch evaporator, and Pinch condenser on the energy, exergetic, and economic indicators of the SORC operating with different working fluids. The simulation was developed in MATLAB(r). The main reference values for the SORC system using the three working fluids are shown in Table 5.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Value** & **Units** & **Reference** \\ \hline Isentropic efficiency turbines & 80 & \(\%\) & [59] \\ Isentropic efficiency pump & 75 & \(\%\) & [59] \\ Cooling water temperature (1 AT) & 50 & \({}^{\circ}\)C & \\ Pinch Point condenser (ITC3) & 15 & \({}^{\circ}\)C & \\ Pinch Point evaporator (ITC2) & 35 & \({}^{\circ}\)C & \\ Pressure Ratio B1 & 2.5 & & \\ Pressure Ratio B2 & 30 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters considered for the proposed configuration.  
The main physical and chemical properties of the process streams are presented in Table 6 for the Acetone, and in the Appendix A are presented the thermodynamic properties for the toluene (Table A1) and cyclohexane (Table A2). Their cost rates and leveled cost per unit of exergy using Acetone as the working fluid can be observed. Because the exergy for some SORC system streams is the same, the changes presented in the cost rates for these streams are due to the different values taken by the specific exergy cost.
## 4 Simulation and Evaluation
### Performance Evaluation  
In our simulation, Bernoulli loss process model is used to simulate link characteristics [20]. Each node periodically transmits packet to the sink. If the sink does not receive any data, it launches a requirement for retransmission. The node number \(N\) is set from 100 to 1000 for different window \(k\). Performance metric of energy efficiency and packet success rate are collected.