esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Volatile Compounds by GC-MS
T1 presented more sesquiterpenes in its composition, and in higher intensity than the other leaf samples, i.e., \(\alpha\)-Copaene (19), \(\beta\)-Elemene (22), \(\beta\)-Bourbonene (20), Bicyclogermacrene (33), \(\alpha\)-Muurolene (34), \(\gamma\)-Muurolene (29), Alloaromandendrene (28),  
Figure 4: Hierarchical cluster analysis (Heatmap) with top 31 features and groups average of _Schinus tetebinthifolia_ volatiles. Distance: Euclidean. Autoscaling features. Data were normalized. Clustering algorithm: Ward. Numbered according to Table 3.  
Germacrene D (30), Caryophyllene (24), Humulene (27) \(\beta\)-Selinene (31), \(\alpha\)-Selinene (32) and \(\delta\)-Cadinene (37); although the monoterpenes, \(\alpha\)-Pinene (1) and \(\beta\)-Pinene (3) were also more intense in T1 samples (Table S1). These compounds have been found in essential oils of other plants, but studies of the biological activities of the isolated compounds are rare. Therefore, the PASS software predictive result was used to indicate possible properties of these compounds (Table 3). Despite T2-T4 samples being practically superimposed in the PCA (Figure 3), in the heatmap (Figure 4) it is possible to note the correlation of some compounds with each individual tree. T2 presented the sesquiterpenes: \(\alpha\)-Gurjunene (23) and Isoledene (18) in higher intensity, as well as Aromandendrene (26); it presented the highest percentage of the monoterpene 3-Carene (6), as well (Table S2). T3 was characterized by the presence of the sesquiterpenes: \(\alpha\)-Copaene (19), \(\beta\)-Selinene (31), and \(\alpha\)-Selinene (32) (Table S3). Finally, T4 presented the highest percentage of \(\alpha\)-Phellandrene (5) and the sesquiterpenes: Caryophyllene (24) (Table S4). PASS software predictive result also indicates possible properties for these compounds (Table 3). The chemical variations in volatile and non-volatile compounds of _S. treehithifolia_ leaves varied more intensely between individuals and were not strongly affected by seasonal effects in this region of Brazil. Neither PCA nor partial least squares discriminant analysis (PLSDA) plots (Figures S9 and S10 in Supplementary Material) of both volatile and non-volatile components showed any groups formed by months of the year or by seasons. Native trees were selected randomly in this study to represent a realistic variability among individuals and were exposed to the same conditions of rain, light, and temperature during the year due to their geographic proximity. Although the age of the plant, the kind of soil and the availability of nutrients and water are factors that could explain some chemical differences between these individuals, apparently the sex of the tree was the main factor responsible for the variation in composition. T1 had male flowers while the others had female flowers, which indicates that the chemical composition of the leaves may be strongly affected by the sex of the flowers. However, further studies, with a larger number of individuals, could confirm this hypothesis.
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 5 Conclusions  
Our study on mountain roads' sharp turns reveals four distinct driving patterns, with aggressive styles significantly increasing conflict risk. Most notably, the \"speeding + short headway + frequent lane changes\" combination elevates the conflict probability 3.72 times above the average. The LSTM prediction model achieved 83.7% accuracy, outperforming traditional methods and offering practical applications for real-time safety systems. Our acceleration-based risk assessment method provides quantitative evaluations for sharp turns, aligning with international standards while uniquely quantifying radius-speed control relationships. Limitations include the limited sample size (30 drivers), the geographic specificity (two Nanjing Province roads), seasonal restrictions (March-August only), simulation-reality gaps, and data integration challenges.  
Future work will expand data collection across regions and seasons, integrate findings into vehicle warning systems, develop targeted driver intervention strategies, conduct cross-cultural comparisons, and apply deep reinforcement learning to optimize traffic management. This research provides data-driven foundations for mountain road safety improvements, with the potential for significant practical impact when translated into concrete safety measures.  
Conceptualization, Q.Z.; methodology, Q.Z.; software, Q.Z.; validation, Q.Z.; formal analysis, Q.Z.; investigation, Q.Z.; resources, Q.Z.; data curation, Q.Z.; writing--original draft preparation, Q.Z.; writing--review and editing, Q.Z., J.Z., Y.Z. and M.R.; visualization, Q.Z.; supervision, Q.Z. and M.R.; project administration, Q.Z. and M.R.; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data are included in the article.  
The authors want to thank the editor and anonymous reviewers for their valuable suggestions for improving this paper.  
The authors declare no conflicts of interest.
# A Critical Review of Flood Risk Management and the Selection of Suitable Measures
## 3 Risk-Based Flood Management
### Assorting Suitable Measures
#### 3.6.2. Suitable Measures at the River  
There are fewer options that can be taken at the river to interrupt the floods, such as shielding banks from erosion and increasing the conveyance of the channel [66]. The rate of flow conveyance, an inverse function of the attenuation rate in the absence of tributary inflows, depends on river and floodplain roughness; by increasing river roughness, the flow tends to deepen in the river and more easily cause flooding. As floodplain flows are generally shallow, they appear to be sluggish, and so attenuation will increase [59; 71]. Excavation should be easily feasible; that is, no bedrock should be found within the diversion excavation depth. Another important consideration is that either a natural body of water should be available to dispose of the diverted water, or enough gradient should be available to drop the water back into the river without having a significant backwater effect [58].
## 2 Materials and Methods
### Efficient-UNet Model for Multiorgan Segmentation
#### 2.3.2 Decoder  
The decoder section of the Efficient-UNet architecture comprises a series of decoding blocks. Each decoding block incorporates convolutional layers, residual blocks, and activation functions, such as ReLU. These blocks play a crucial role in recovering spatial information and refining the segmentation masks generated by the encoder. To obtain the final segmentation maps, a segmentation head that consists of a \(1~{}\times~{}1\) convolution layer is used. This configuration allows the network to generate probability distributions for each pixel across the five organ classes.
## 2 Mathematical Model and Numerical Method
### Problem Setup  
The calculation settings in this paper are simplified from the deep-water pipeline system on the seafloor. The schematic diagram of the computational model for VIV of two inline cylinders is illustrated in Figure 4. It can be seen in the figure that the whole domain is \(30D+L\) in the streamwise direction and \(10.5D+e\) in the transverse direction. The inflow boundary is \(10D\) upstream and the outflow boundary is \(20D\) downstream, which is consistent with the setup of a vibrating cylinder colliding with a rigid wall in [PERSON] and [PERSON]'s work [16]. The top boundary is located at \(10D\) above the center of the cylinder.  
The boundary conditions of the domain are also provided in Figure 4. The uniform flow under freestream velocity conditions is prescribed at the inflow boundary as \(u=U_{\infty}\) and \(v=0\). Along the outlet, a Neumann-type boundary condition is employed: \(\frac{\partial u}{\partial x}=0\) and \(\frac{\partial v}{\partial x}=0\). The top wall is treated as a free-slip boundary while the bottom wall is as a no-slip boundary. The cylinder surface is treated by the DFD method, with a no-slip boundary condition enforced.  
The cylinder occasionally hits the plane wall, causing the cylinder to bounce back. It is assumed that the bouncing is fully elastic and changes only the vertical velocity of the cylinders. In addition, the bouncing back process is completed in one time step. That is:  
\[V_{\epsilon}=-V_{\epsilon}^{\prime},\;\dot{V}_{\epsilon}=\frac{V_{\epsilon}-V _{\epsilon}^{\prime}}{\Delta t} \tag{9}\]  
Figure 4: Schematic diagram of the computational model.  
where \(V_{c}^{\prime}\) and \(V_{c}\) are the vertical velocities of the cylinder before and after bouncing back and \(V_{c}\) is the acceleration at that time step. With reference to the work of [PERSON] [31] and [PERSON] and [PERSON] [33], the bouncing back is forced when the gap between the cylinder and the plane wall is smaller than \(0.02D\).
## 3 Aim, Materials, and Methods
### Mixed Transport Scheduling Model under Complex Disasters
#### 4.4.1 Transportation Route Planning  
In the face of sudden disasters or emergencies, emergency management departments encounter significant challenges in efficiently and expeditiously transporting emergency materials to affected areas. By leveraging Internet of Things technologies such as radio frequency identification (RFID) and the global positioning system (GPS), real-time deployment vehicle resources in and around the affected areas can be collected and integrated into a central database. Through network technology, real-time information including vehicle resources, material demand, and the status of affected areas can seamlessly connect with the transportation planning system to ensure the optimal selection of material transport routes. Furthermore, advanced technologies like cloud computing and data mining can be employed for comprehensive intelligent analysis on acquired vehicle resources, human resources, and material resources. For instance, historical data analysis enables the prediction of the required types and quantities of materials in the upcoming hours while facilitating reasonable resource allocation accordingly. Simultaneously, by analyzing  
Figure 5: Mixed transport scheduling model under complex disasters. Source: Own processing according to the literature.  
vehicle driving trajectories along with speed data, scheduling strategies can be optimized to ensure efficient and secure material transport processes. Based on these considerations combined with the transportation planning system's capabilities, an optimal route for emergency material transport can be formulated that accounts for real-time traffic conditions as well as road conditions while addressing supply urgency and disaster-stricken area needs--thereby ensuring the prompt delivery of emergency supplies to support effective disaster relief efforts.
## 3 Results and Discussion
### Titratable Acidity  
The test apples were ripened in a cold store and then stored in room conditions. The titratable acidity values of the fruit that were treated with a magnetic field compared to that of the control samples are shown in Figures 7 and 8. In this study, differences in titratable acidity were noted between the test (MF-treated) and control samples. The content of titratable acid affects the taste of apples [35]. Less acidity with minor changes may delay changes in the texture of the apple flavour. In the presented study, the apples that were treated with MF had up to 4.4% less titratable acid than untreated samples. A study by [PERSON] et al. [36] reported that titratable acid changed slightly in freshly cut cantaloupes and successfully delayed the postharvest maturation process [36]. These data indicated that treating apples with a magnetic field of 2 mT for 15 min can prolong the life of the proper flavour of apples. The differences between the titratable acidity of the treated and untreated samples were significant and could have been caused by a different metabolic rate, which consumes organic acids and leads to a decrease in acidity [37; 38; 39; 40].  
The difference between the titratable acidity of the studied varieties was not significant. The titratable acidity of the apples did not change much after four months of storage. However, research [41; 42; 43] has shown a marked difference in the acidity of apples after storage and a decline in the malic acid content of apples after six months of storage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Storage Condition** & **Equation (y)** & **R** \\ \hline Cold Storage & \(-0.403\)x + 12.865 & 0.98 \\ Room Storage MF & \(-0.371\)x + 12.69 & 0.97 \\ Control & \(-0.381\)x + 12.51 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Regression equations describing the variations in TSS between the different storage conditions of the Idared variety.  
Figure 7: Effects of MF on the TA of Idared apples after 120 days of cold storage. For each experiment, apples were stored in room conditions for 14 days. Statistically significant differences are marked by different letters. Differences between average values that are marked with the same letters (a,b) are not statistically significant. The data were analysed separately for each experiment.  
Apple cultivars have been shown to have significant differences in TSS and acidity [33]. The values of the TSS/TA ratio in the studied fruit are presented in Table 3. The TSS/acid ratio is a major quality parameter and is also essential for predicting the maximum shelf life of fruit [44; 45]. The average TSS/acid ratio for three experiments was recorded as being 3.06 for the Idared variety and 2.94 for Ligol. The MF treatment increased the value of this ratio by up to 5% compared to the control group. The duration of storage had also a significant effect on the TSS/acid ratio of the apples and caused the value of this parameter to gradually decrease by up to 18%. Ligol apples experienced a decrease in the value of this ratio by 2.58.
# Stable Throughput Analysis of Heterogeneous Hybrid FSO/RF Networks with Cognitive Radio Capability
## 2 System Models
### Queue Stability  
Let the number of packets stored in the buffer at transmitter \(j\) be \(Q_{j}\). This buffer receives one packet at an average arrival rate of \(\lambda_{j}\). Let \(\mu_{j,n}\) represent the average service rate when the transmitter \(j\) simultaneously services \(n\) packets. The packets stored in \(Q_{j}\) increase by 1 at a rate of \(\lambda_{j}\) or decrease by \(n\) at a rate of \(\mu_{j,n}\). We model the buffer of hybrid FSO/RF as a discrete-time \(M/M^{[B]}/1\) queue, where buck services \(B=2\)[28; 29]. Let \(\pi_{l}\) denote the steady-state probability that buffers \(j\) stores \(l\) packet and \(N\) is a maximum number of simultaneous services packets. In this case, \(\pi_{n}\) can be expressed as follows.  
\[\pi_{0}\lambda_{j}=\sum_{n=1}^{N}\pi_{n}\mu_{j,n}+\pi_{1}\mu_{j,2}+\pi_{1}\mu_ {j,3}+2\pi_{2}\mu_{j,3}, \tag{6}\]  
\[\pi_{l}\lambda_{j}=\sum_{n=1}^{N}\pi_{l+n}\mu_{j,n}. \tag{7}\]  
The average service rate consists of the scenarios where the communication pair of the borrowed frequency is idle (\(Q_{k}=0\)) and when it is active (\(Q_{k}\
eq 0\)). Let the probability of successfully delivering \(n\) packets when \(Q_{k}=0\) and \(Q_{k}\
eq 0\) be \(v_{j,n,0}\) and \(v_{j,n,1}\), respectively. In this case, the average service rate can be expressed as follows.  
\[\mu_{j,n}=\text{Pr}(Q_{k}=0)v_{j,n,0}+\text{Pr}(Q_{k}\
eq 0)v_{j,n,1}, \tag{8}\]where \(\Pr(A)\) denotes the probability of state \(A\).  
For queues where the arrival and service processes are jointly strictly stationary and ergodic, [PERSON] theorem [30] states that the queue at each transmitting node is stable if and only if the average arrival rate is strictly less than the average service rate. Based on [PERSON] theorem, let us define that a queue is stable if \(\Pr(Q_{k}=0)>0\). Moreover, a system is said to be stable if and only if all the queues are stable.  
Our goal is to obtain the stable throughput of the proposed COG and ECO and compare them with that of REF. We investigate how much throughput improvement occurs through the proposed cooperative method, which depends on various network and physical layers parameters. Among the parameters, we mainly focus on the effect of the turbulent FSO channel conditions.
## 3 Results: Verification of the Proposed Methodology
### Application Outputs by Industry
#### 3.2.4 Comparison of Companies by Industry
In summary, the application of the created methodology provides companies with the following benefits:  
* It will make it possible to determine the level of readiness of the area of internal logistics in the company, including sub-areas and their interconnection. * Compares the individual main dimensions with detailed analysis, identifying the weakest points in any dimension, and obtaining the worst-rated sub-areas. * Determination of strong and weak areas of internal logistics in the company. * Possible repeated use of the evaluation tool and subsequent comparison. * Verification of the established strategy for the implementation of Industry 4.0. * Structure and detailed breakdown of the internal logistics area in the company with the determination of a set of indicators that affect the level. * Awareness of the potential of internal logistics within the Industry 4.0 concept and the possible implementation of principles in this area. * Identification of the situation in companies regarding Industry 4.0, whether they are familiar with the principles of Industry 4.0, whether companies can respond to these challenges, under what conditions, and at what time. In general, whether companies in the Czech Republic are ready for Industry 4.0.
## 5 Performance Evaluation and Analysis
### Key Performance Indicators (KPIs)
#### 5.1.1 Probability of Resource Collision (PRC)  
It is to be noted that all vehicles in the simulation operate in the unmanaged Mode of 5G-NR-V2X Mode 2. Figure 5 shows the PRC under an aperiodic traffic environment. The graph compares the SEAC reinforcement learning technique with traditional methods like SB-SPS and DS.  
At the beginning, all the techniques show a similar PRC at low channel busy ratios (CBRs), indicating comparable effectiveness in low-traffic scenarios. However, as the CBR increases, traditional methods like SB-SPS and DS quickly experience a sharp rise in the PRC, leading to higher collision rates. In contrast, the SEAC, particularly in the 10-step simulation, maintains a lower PRC even at higher CBR levels. The SEAC model with 10 steps refers to the reinforcement learning model using a 10-step lookahead strategy. This approach allows the model to make more informed decisions by considering the potential outcomes of actions over the next 10 time steps, optimizing the resource allocation and collision avoidance.  
The model considers the potential outcomes and rewards over the next 10 steps before updating its policy. This allows the SEAC model to make more informed decisions by anticipating the future impact of its actions. The cumulative reward is calculated over N steps, which helps in reducing variance and provides a smoother learning process. This approach balances the immediate rewards and the long-term benefits, making the learning process more stable and robust. This demonstrates SEAC's superior ability to manage resources and prevent collisions under increasing channel congestion, offering a more resilient solution in high-density traffic scenarios.
## 4 Experimental Results
### Single-Hop Scenario  
As mentioned before, this scenario consists of a single-hop link between a sensor node N1 and the gateway node GW. It should be remembered that N1 is also running a parallel processing task.  
The data in Figure 5 correspond to the measurements obtained with the RDC of the devices set at 10%, which is a typical reference value in WSNs. Specifically, the two upper graphs represent the average latency and throughput of the link for the different PTDC values. Moreover, the average wait time of this task is also shown in the last graph of the figure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Priority Levels (1–4)**} \\ \hline
**Tasks** & **PS1** & **PS2** & **PS3** \\ \hline Communication tasks & 3 & 3 & 2 \\ Processing task & 2 & 3 & 3 \\ Synchronization task & 4 & 4 & 4 \\ Auxiliary tasks & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Priority levels of N1 node tasks for the different priority schemes.  
The most noteworthy feature of the two upper graphs is the fact that with priority schemes PS1 and PS2 the communication performance remains stable even if the PTDC increases. This occurs for the three task types evaluated. However, in the PS3 scheme, the communication tasks have a lower priority than the processing one. In this case, the latency increases and the throughput decreases as the PTDC gets higher. Moreover, the longer the \(T_{ON}\) time of the processing task, the greater the magnitude of this effect. Finally, in the last graph, we can observe an opposite effect in the wait time of the processing task. With PS3--communications having a lower priority than the processing task-- these times are very low and stable regardless of the PTDC but, with the other priority schemes, the wait time increases very significantly. In this case, the task type, i.e., the different execution times \(T_{ON}\), also causes large differences in the results.  
In Figure 6, the latency, the throughput and the average processing task wait time are again represented, but in this case the PTDC is set at 5% and is the RDC of the devices what varies.  
In this way, we can see the effect of the RDC on the communication performance, which is quite intuitive to understand. However, it is worth noting the effect that this communication parameter has on the wait time of the processing task, even when it has a very low duty cycle (5%). This can be seen in the lower graph, where we can observe that, for PS1 and PS2 (when the priority of the processing task is lower or equal to that of the communications), the wait time of the processing task increases with the RDC. In this case, again, the type of task causes great differences in the results.  
Figure 5: Single-hop scenario with a fixed RDC of 10%.