esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Training Process  
Below, the training process of the FM-ITC-Darea algorithm is analyzed, and its convergence is evaluated. The simulation experiments, running 1000 training epochs on a computer with an AMD Ryzen 5600X six-core 3.7 GHz processor and an NVIDIA 1660S graphics card, take approximately 20 to 30 min. The training process is illustrated in Figure 7.  
For comparison, this paper also considers the scenario without control in the current simulation environment, where the reward value is calculated to be \(-150\), as indicated by the yellow line in Figure 7. From the training of the agent in the FM-ITC-Darea strategy, it can be observed that during the first 200 training episodes, the agent continuously learns from the environment. Due to the randomness of the environment, there are significant oscillations during this period. After sufficient exploration, the agent shows an upward trend after 200 episodes and converges around \(-100\). The final reward of the strategy shows an improvement compared to the no-control scenario.  
To further demonstrate the effectiveness of the strategy, Figure 8 shows the variation of TTS during the training process. Compared to the initial stages of training, the FM-ITC-Darea strategy, after training, reduced the TTS by approximately 9.0%, indicating that the strategy can, overall, improve traffic efficiency.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of agents & 4 \\ Training epochs & 1000 \\ Testing epochs & 10 \\ Simulation steps & 620 \\ Effective simulation duration & 7200 s \\ Experience replay buffer capacity & 4000 \\ Network training batch size & 64 \\ Discount factor & 0.99 \\ Actor learning rate & 0.0003 \\ Critic learning rate & 0.003 \\ Soft update parameter & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter list of FACMAC-based ramp merge integration control methods.  
Figure 7: Reward curves for FM-ITC-Darea training processes.
## 3 Analysis of Edge Method Accuracy
### Edge Angle
However, the simulation results show that when the edge images with the same blurry effect and different angles are used, the MTF results are different in various directions, which may be directly related to the rectangular grid structure of digital image detectors or displays. The physical structure leads to different sampling degradation in different directions. The blurry degradation effect of simulated edge images with different angles is consistent, but the sampling degradation effect is different, so the actual MTF curves are scattered with the change of edge angles. The schematic diagrams of degradation caused by a digital sampling of edge images from different angles are shown in Figure 7. In particular, the conclusion is reasonable speculation, and it is necessary and valuable to further study and verify. The smoothness and convergence of MTF curves in the middle and high frequency ranges in Figure 6a,b,e,f, which adopt ISO-cos projection binning, are obviously better than those in Figure 6c,d,g,h, which adopt OMNI projection binning. It indirectly verifies that the accuracy of the ISO-cos projection binning scheme is better than that of OMNI, which is consistent with the graphical analysis result of Figure 1. The binning principles of ISO-cos and OMNI, both of which adopt fixed OSR = 4, are essentially different: the oversampled bin width of ISO-cos (cos(\(\theta\))/4 (pix)) is always less than or equal to that of OMNI (1/4 (pix)) for arbitrary edge angle. To sum up, the binning adaptability of ISO-cos is better than that of OMNI. OMNI binning aims to reduce the probability of an empty bin for large-angle edge projection, while ISO-cos binning aims to achieve more accurate binning. All in all, the feasibility of multidirectional MTF measurements based on arbitrary angle edges can improve the practical application efficiency of the edge method. For the edges in different angle intervals, we can adopt the ISO-cos projection binning model and a more adaptive OSR model to improve the effectiveness and accuracy of the edge method.
## 2 Study Area  
This research focuses its attention on the analysis of mobility in RMSC, which was created to implement the EDUSI of the municipalities bordering the city of Caceres (Figure 1).  
Caceres is a mid-sized city located in the centre of Extremadura. It is bordered by 21 municipalities that make up the RMSC and borders the province of Badajoz to the south. Thus, there is a clear dynamic of movement, with a daily flow of trips between the smaller municipalities to the city of Caceres, thus giving rise to pendular movements from these surrounding municipalities towards the provincial capital, since this city is the socio-economic, administrative and commercial centre of this territory.  
The daily movements of the population living in the RMSC of the study area (mobility between the municipalities that compose it and around the city of Caceres), are facilitated by the structure generated by its important network of communication routes. In this sense, the study area has a favourable situation in terms of road transport, mainly due to the presence of three high-capacity roads: A-66, A-58 and A-5. The A-66 motorway runs from north to south and divides the study area in half, connecting to the north with Plascencia, some 80 km away, and to the south with the capital of the autonomous community (Merida), some 75 km away. For its part, the A-58 motorway serves as a connection between the cities of Caceres and Trujillo (separated by a distance of approximately 45 km). The A-58 in turn serves as the backbone to connect the municipalities located in the eastern part of the network with the city of Caceres and the A-5 motorway. The A-5 connects, via the aforementioned A-58, with the central area of the peninsula and, in particular, with the city of Madrid (approximately 250 km). Similarly, it is necessary to mention the network of national and regional roads that link the most dynamic municipalities of the RMSC with the city of Caceres (which is the centre of development of the neighbouringmunicipalities). In addition to this, there is the road network of the Caceres Provincial Council, which branches off to the connections with networks of higher capacity and allows interconnections between municipalities of smaller population size.  
In contrast, at present, the railway connection is limited to the service provided by the Madrid-Caceres-Badajoz line, which connects the study area with Merida and Badajoz to the south, and with Plascencia, Talavera de la Reina and Madrid to the northeast, highlighting the future incorporation of the Madrid-Extremadura high-speed line, which is currently completing the process of adapting the tracks for the circulation of high-speed trains. However, there is no effective railway connection between the municipalities in the study area and the city of Caceres.
_J. Sens. Actuator Netw._**2013**, \(2\), 354-387; doi:10.3390/jsan2020354  
_Journal of Sensor and Actuator Networks_  
**ISSN 2224-2708**  
www.mdpi.com/journal/jsan  
_Article_  
**Design Issues in Constructing Chain Oriented Logical Topology for Wireless Sensor Networks and a Solution**  
**[PERSON]**  
School of Computing and Mathematics, Charles Sturt University, NSW 2650, Australia;  
E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +61-2-6933-4725  
_Received: 24 December 2012; in revised form: 22 March 2013 / Accepted: 6 June 2013 / Published: 20 June 2013_  
**Abstract:** An optimised logical topology facilitates the sensors of a network to communicate with each other with little overheads, lower energy consumption, and reduced latency. It also lengthens the lifetime of the network, provides scalability, and increases reliability in communications. However, designing an optimal logical topology for wireless sensor networks requires considering numerous factors. In this paper, we elaborately discuss these design issues and challenges. We also present a chain oriented logical topology, which offers solutions to those design issues. The proposed logical topology provides not only a communication abstraction, but also node management and resource management. The performance of the proposed topology is compared with other topologies with respect to energy consumptions, latency, and lifetime of the network.  
**Keywords:** wireless sensor network; chain oriented network; multiple chain; logical topology; topology management; communication abstraction
## 4 Results
### Mesh Sensitivity Analysis
#### 4.1.1 Dynamic Mean Pressure and Cell Size  
The mean dynamic pressure is defined as the total pressure subtracted from the hydrostatic pressure given by the poll height. The results at the impact point \(r/D_{i}=0.00\) are presented in Figure 5a, which shows the relationship between the number of cells in the jet cross-section for all four different meshes.  
Figure 4: Mesh independence analysis. (**a**) Mesh 1, (**b**) Mesh 2, (**c**) Mesh 3, (**d**) Mesh 4.  
The analysis of Figure 5 shows that the mean dynamic pressure values produced by the twoPhaseEulerFoam solver are independent of the mesh resolution when the mesh consists of more than 24 cells in the jet diameter. In contrast, the interFoam solver exhibited mesh dependence, with the mean dynamic pressure increasing as the mesh was refined. When comparing the numerical results with the experimental data collected at the same location under identical conditions, it was found that the mean dynamic pressure obtained from the twoPhaseEulerFoam solver closely matched the experimental data for meshes with over 24 cells in the jet diameter, outperforming the results from interFoam. The interFoam solver, however, overestimated the pressures at the stagnation point.  
For data obtained far from the stagnation point, \(r/D_{i}=0.69\) (Figure 5b), both solvers showed mesh independence when more than 12 cells were considered in the jet diameter. The numerical results for \(r/D_{i}>0.50\) displayed a good correlation with experimental results, even with coarser meshes (D12), when using the twoPhaseEulerFoam solver. However, the interFoam-VoF solver continued to overestimate the mean dynamic pressures.
# Automatic Extraction of Material Defect Size by Infrared Image Sequence
## 4 Data Post-Processing
### Primary Detection of Defects
#### 4.3.2 Analysis of Robustness  
To assess the stability and reliability of the proposed automatic method, 100 independent experiments were performed. The results are shown in the scatter diagram Figure 20a,b by uniform selection and random selection, respectively. From Figure 20a, we can see that Frame 164 has a relative frequency of 100% to extract defect #1, and Frame 182 (56%), Frame 183 (40%), and Frame 186 (4%) are chosen for defect #2 using uniform selection. Figure 20b shows that using random selection, the suitable frames for #1 and # 2 are (164, 173) and (173, 193), respectively. The frames from the random selection scheme are more fluctuated than those from the uniform selection scheme, as expected. In either case, the frame selected for extracting defect #1 is ahead of those selected for extracting defect #2. This situation is consistent with Equation (7).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Defect Number**} & \multicolumn{3}{c}{**Uniform Selection**} & \multicolumn{3}{c}{**Random Selection**} \\ \cline{2-7}  & **Frame** & **Defect Pixel** & **Relative Error** & **Frame** & **Defect Pixel** & **Relative Error** \\ \hline \#1 & 164 & 3404.7 & 0.94\% & 166 & 3384.2 & 0.33\% \\ \hline \#2 & 183 & 3420.8 & 2.44\% & 186 & 3430.5 & 2.73\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of defect parameters using multiple sensitive regions.  
Figure 20: The scatter diagram of the experiment conducted 100 times: (**a**) The scatter diagram of the experiment conducted 100 times by uniform selection; (**b**) The scatter diagram of the experiment conducted 100 times by random selection.  
Figure 21 shows the discrete distribution map of the relative deviation of the extracted defect area from the actual size. The symbols \"O\" and \"\(\Delta\)\" in Figure 21 represent defects #1 and #2, respectively. By uniform selection, defect #1 can be accurately detected with 0.42% relative error and the error of #2 is within 2.5%, as shown in Figure 21. From Figure 21, the errors of defect #1 are all within 1.1% and those of #2 are within 3.2%. All in all, the MSDSRM with multiple sensitive regions is very useful for selecting an appropriate thermogram for extracting defects at different depths.
## 2 Finite Element Analysis
### No Load Analysis  
FEA was conducted to increase the accuracy of the analysis. When the rotor of the PMSM rotates under the no-load condition, back EMF is induced in the stator winding. THD is an important factor in electrical equipment and power systems. The THD can be obtained by adding the harmonic components to the fundamental wave components of the voltage or current [18]. A higher THD increases the core loss in electric machines, which can reduce the efficiency and generate excessive heat. The calculated THD of the back EMF of the initial model is 3.519%.
## 5 Comparative Experiments and Discussion
### Discussion of Influencing Factors  
Quantitative suggestions on the influencing factors and the performance of the Mod edge method are summarized as follows:  
(1) It is reasonable to measure multidirectional MTF by using edges with different angles. For [0, 45] deg edge images, the MTF curves decrease with the increase of edge angle, but the consistency of MTF curves obtained by [0.2, 14.5] deg edge in the whole frequency ranges is obviously higher than that of other angle intervals.  
(2) The MTF measurement accuracy is higher when the ideal optimal or suboptimal OSR calculated based on \(1/[k\cdot\tan(\theta)]\) (\(k\in\)Z\({}_{+}\)) is adopted, and the minimum computational cost is realized. However, when the calculated OSR is too high (>10) or too low (<2), the actual OSR can be corrected to 8 or 2 to balance the computational cost and MTF measurement accuracy.  
(3) The extracted ROI-H should be increased as much as possible to contain more edge information which can improve the MTF accuracy in middle and high frequency ranges. In practice, the ROI-H ratio of extracted ROI should be above 0.5 at least. On this basis, the ROI-W can be appropriately increased to ensure MTF stability in low frequency ranges.  
(4) It is necessary to add contrast verification to ensure the determined edge contrast is greater than 0.1, thus ensuring the accuracy and effectiveness of the edge method. Although contrast has little impact on the edge method, it is recommended to give priority to high-contrast edge images when other conditions are consistent.  
(5) It is necessary to add SNR verification to ensure the determined edge SNR is greater than 10 dB, thus ensuring the effectiveness of the edge method. When the SNR is lower than 10 dB, appropriate denoising should be done first. It is suggested that only the gentle regions on both sides of the edge should be denoised to keep the original features near the edge most.
# The Role of Trees in Sand Dune Rehabilitation: Insights from Global Experiences
## 4 Discussion
### The Effects of Afforestation on Sand Dunes
#### 4.5.3 Coastal Dune Dynamics and Management  
We also observed a special case for the planting of vegetation types on coastal dunes. Here, the objectives range from cultivating crops to the stabilization of mobile sand dunes [128]. From a historical perspective, afforestation was used to stabilize these dunes and protect nearby cities from wind erosion and salt spray [102; 104]. However, some authors point out that introducing exotic trees has also led to a disruption in the natural dynamic of dunes, as it reduced wind, altered sand transport, and impacted the recovery of dunes after storms [104]. On their end, these shifts led to a retreat of the coastline and a reduction in the resilience of dunes. In this context, some authors are not very enthusiastic about the afforestation of these sand dunes. For example, [PERSON] [129] maintains that the stabilization of dunes is not always necessary. If stabilization works are needed, only local plants should be used, with a focus on pioneers, not trees.  
We found another good example for this situation in Israel's coastal dunes, where a diverse range of sand species (psammophilic) are present alongside endemic ones [123]. As such, the British Mandate of Palestine introduced _Acacia saligna_, an Australian species, in the stabilization of dunes. As time passed, this species has neutralized and even invaded in an aggressive manner the coastal sandy habitats [124]. Overall, this species has changed this ecosystem entirely, as it created shade, altered soil chemistry, and modified the composition of plant and animal communities [125; 126; 127]. This example is essential in emphasizing the unintended ecological costs of afforestation, especially in the context of using non-native species.
# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 2 Literature Review
### Theoretical Framework  
This study develops a theoretical framework for AI and IoT integration in cold chain logistics by combining the Resource-Based View (RBV) and Supply Chain Resilience Theory. RBV focuses on the strategic advantage offered by valuable, rare, inimitable, and non-substitutable (VRIN) resources [11], framing AI and IoT as strategic assets that enhance forecasting accuracy and operational efficiency. In cold chain logistics, these technologies meet VRIN criteria through advanced analytics and real-time monitoring capabilities, making them difficult for competitors to replicate [4].  
Supply Chain Resilience Theory, on the other hand, highlights the importance of adaptability and responsiveness in dynamic environments [12]. AI and IoT support resilience by enabling real-time responses to environmental fluctuations and demand shifts, thereby reducing waste and mitigating spoilage risks. Together, these technologies strengthen a cold chain's ability to handle disruptions, aligning with resilience theory's focus on responsiveness and adaptability.  
Figure 1 visually represents this integrated framework, illustrating how AI and IoT serve as both strategic resources (RBV) and resilience enablers (resilience theory), thus achieving dual goals of efficiency and adaptability in cold chain operations.  
Left Section (RBV): Highlights the VRIN attributes of AI and IoT, emphasizing their role in creating a Strategic & Competitive Advantage by reducing waste and enhancing efficiency in a sustainable logistics system.  
Right Section (Resilience Theory): Focuses on elements of Adaptability, Responsiveness, and Predictive Capability that enable proactive risk management, reducing spoilage and strengthening resilience.  
Center (Integrated Framework): Merges insights from both theories, positioning AI and IoT as dual assets that support a sustainable, adaptive cold chain system aligned with economic (cost efficiency) and environmental (waste reduction) goals.  
By integrating RBV and Supply Chain Resilience Theory, this study proposes a theoretical framework where AI and IoT jointly serve as both strategic resources and resilience enablers. The dual theoretical lens illustrates that AI and IoT not only provide a competitive advantage by reducing waste but also enhance supply chain resilience by facilitating adaptive responses to demand and environmental changes. In doing so, the framework supports a sustainable, efficient, and adaptable cold chain system, aligning technological innovation with both economic and environmental goals.
## 3 Results
### Classification of Baseline and Painful States  
We investigated the classification of baseline and painful states using an RF model, employing 12 physiological features as input data. Before the classification analyses, we computed the PFI to identify the most influential features for distinguishing between the two states. The features were ranked in descending order of importance (Figure S1). The top-ranked features were SCR, SCL, and RESP, followed by BVP, FT, and HR, all of which had positive mean PFI values. Notably, the SCR demonstrated greater significance than the other measures. However, the remaining six features yielded negative mean PFI values, indicating their limited utility.  
Subsequently, we conducted classification experiments using only subsets of the top-ranked features identified by the PFI analysis. We compared subsets ranging from two to  
Figure 3: Boxplots of valence, arousal, SCL, and SCR across three pain intensity groups (* \(p\) < 0.05, **_p_ < 0.01, ***_p_ < 0.001).  
five features while monitoring changes in the F1 score (Figure S2). Combining the top-three features (SCR, SCL, and RESP) yielded the highest F1 score of 0.83. Similar F1 values were observed across three-, four-, and five-feature subsets. The disparity between the training and test F1 scores was approximately 0.03, suggesting a consistent performance and minimal overfitting. Table 7 summarizes the evaluation metrics derived from the classification model using these three key features, all exceeding 0.80. This underscores that even a minimal set of three physiological measures can effectively distinguish between the baseline and painful states. Figure S3 presents the corresponding confusion matrix, which indicates that the majority of the samples were accurately classified. These findings illustrate that the selected physiological markers, particularly SCR, SCL, and RESP, effectively capture the transitions between the baseline and painful states.
## 2 Model and Method  
### Model of Vertical Vehicle-Track-Bridge  
Bridge deformation has a significant impact especially on the vertical dynamic behavior of vehicles [3]; therefore, in this paper, the vehicle-rail-bridge coupling vertical model was chosen for analysis. The vertical vehicle-track-bridge coupling system comprises three parts: vehicle, track structure, and bridge, which were modeled separately. The vehicle, traveling at speed V, was regarded as a multi-rigid system consisting of a vehicle body, two bogies, four-wheel pairs, and two suspension systems [48; 49]; the rail was regarded as a finite-length Euler beam, and the Ritz method was used to solve the differential equations of rail vibration [50]; the track structure-bridge was regarded as a beam unit; based on the principle of the invariant value of total potential energy of an elastic system, the coupled dynamics model of track structure was established [35; 51]; vehicle and rail were coupled on the basis of the nonlinear Hertz elastic contact theory [5; 8]; the rail and track structure were connected by fasteners, and the fastener system was simulated by spring dampers. The established nonlinear vehicle-track-bridge vertically coupled dynamics model is shown in Figure 1. The explicit-implicit mixed numerical integration mode was used for numerical calculations; the vehicle-track model was analyzed by a novel explicit two-step integration method, which is fast and nonlinearly adaptable; the track-bridge model was solved by the Newmark-\(\beta\) method to ensure the computational stability of the finite element model of the track structure [52].