esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 A Case Study
### Dual Hesitant Fuzzy TOPSIS Approach for MAGDM  
Neglecting the proportional figures, the major steps involved in the traditional TOPSIS approach are:  
Step 1\({}^{{}^{\prime}}\): DMs provide evaluation matrices \(R_{k}=\left[\left\langle\mu_{ij^{\prime}}^{k}\
u_{ij}^{k}\right\rangle\right] _{m\times n};k=1,2,0..,l\) with the traditional IFNs.  
Step 2\({}^{{}^{\prime}}\): Compute the overall evaluation information matrix \(R^{{}^{\prime}}=\left[d_{ij}\right]_{m\times n};d_{ij}\left(i=1,2,0...,m,j=1,2,0...,n\right)\) is a DHFE.  
Step 3\({}^{{}^{\prime}}\): The positive ideal solution (PIS) of alternatives is defined as follows:  
\[R^{+}=\left\{d_{1}^{+},d_{2}^{+},0...,d_{n}^{+}\right\}, \tag{17}\]  
where  
\[d_{j}^{+}=\left\{\begin{array}{ll}\left\langle\max_{1\leq i\leq m}\{d_{ij} \}\right\rangle&\text{for the benefit criteria }c_{j};j=1,2,0...,n\\ \\ \left\langle\min_{1\leq i\leq m}\{d_{ij}\}\right\rangle&\text{for the cost criteria }c_{j};j=1,2,0...,n\end{array}\right.\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & \multicolumn{3}{c}{\(c_{1}\)} & \multicolumn{3}{c}{\(c_{2}\)} & \multicolumn{3}{c}{\(c_{3}\)} \\ \hline  & **Score** & **Deviation** & **Rank** & **Score** & **Deviation** & **Rank** & **Score** & **Deviation** & **Rank** \\ \hline \(x_{1}\) & \(-0.1975\) & \(0.76712\) & 4 & 0.3 & \(0.27982\) & 1 & \(0.18\) & \(0.29120\) & 1 \\ \(x_{2}\) & \(0.1525\) & \(0.17734\) & 2 & \(0.04\) & \(0.51284\) & 2 & \(-0.065\) & \(0.60361\) & 4 \\ \(x_{3}\) & \(0.43\) & \(0.38412\) & 1 & \(-0.07\) & \(0.68162\) & 3 & \(0.0275\) & \(0.44595\) & 3 \\ \(x_{4}\) & \(0.08\) & \(0.33615\) & 3 & \(-0.09\) & \(0.58720\) & 4 & \(0.0525\) & \(0.40850\) & 2 \\ \hline \end{tabular}
\end{table}
Table 6: Score and deviation values of PDHFEs in matrix R.  
Similarly, the negative ideal solution (NIS) of alternatives is defined as follows:  
\[R^{-}=\left\{d_{1}^{-},d_{2}^{-},0...,d_{n}^{-}\right\}, \tag{18}\]  
where  
\[d_{j}^{-}=\left\{\begin{array}{ll}\left\langle\min_{1\leq i\leq m}\{d_{ij}\} \right\rangle&\text{ for the benefit criteria }c_{j};j=1,2,0...,n\\ \\ \left\langle\max_{1\leq i\leq m}\{d_{ij}\}\right\rangle&\text{ for the cost criteria }c_{j};j=1,2,0...,n\\ \end{array}\right.\]  
Step \(4^{{}^{\prime}}:\) Based on Definition 4, measure the separation of each alternative \(x_{i}\) from the PIS as follows:  
\[s_{i}^{+}=d_{dhh}\left(R^{+},R_{i}\right),i=1,2,0...,m, \tag{19}\]  
where \(R_{i}=\{d_{i1},d_{i2},0...,d_{in}\}\).  
Similarly, measure the separation of each alternative \(x_{i}\) from the NIS as follows:  
\[s_{i}^{-}=d_{dhh}\left(R^{-},R_{i}\right),i=1,2,0...,m, \tag{20}\]  
where \(R_{i}=\{d_{i1},d_{i2},0...,d_{in}\}\).  
Step \(5^{{}^{\prime}}:\) Obtain the closeness coefficient of each alternative \(x_{i}\) with respect to the PIS and NIS as:  
\[cl(x_{i})=\frac{s_{i}^{-}}{s_{i}^{+}+s_{i}^{-}};i=1,2,0...,m. \tag{21}\]  
Step \(6^{{}^{\prime}}\): Classify all alternatives on the basis of their closeness coefficient. The greater the closeness coefficient \(cl(x_{i})\), the more acceptable the alternative \(x_{i}\).
# Emerging Trends in Hybrid Wireless Communication and Data Management for the Industry 4.0
## 5 Hierarchical and Multi-Tier Communication and Data Management for the Industry 4.0
### Hierarchical and Multi-Tier Network Architecture
Thanks to the information reported by the LMs, the Orchestrator has a global vision of the whole network. The Orchestrator then has the information required and the ability to adapt the configuration of the network based on the current operation conditions. For example, under changes in the configuration of the industrial plant or in the production system, the Orchestrator can reallocate frequency bands to cells implementing licensed technologies based on the new load conditions or the new communication requirements. It could also establish new interworking policies to control interferences between different cells working in the unlicensed spectrum. The Orchestrator also decides to which cell a new device is attached to considering the communication capabilities of the device, the communication requirements of the application, and the current operating conditions of each cell. Finally, end-devices can also participate in the execution of some management functions such as Power Control or Scheduling, among others. The management entity in charge of some management functions will depend on the particular communications requirements of the nodes connected to a cell. This fact will also influence the type of interactions between the LM of the cell and the Orchestrator. LMs of cells that support communication links with loose latency requirements can delegate some of their management functions to the Orchestrator. For these cells, a closer coordination between different cells could be achieved. For example, radio resource allocation of cells sharing the same unlicensed spectrum could be performed by the Orchestrator to better coordinate the use of resources and avoid intersystem interference if nodes supported in the cell do not present high requirements in terms of latency. However, management decisions performed by LMs based on local information are preferred for applications with ultra-highly demanding latency requirements. In this context, cells are organized in different tiers depending on the communication requirements of the industrial application they support, as illustrated in Figure 5. The different requirements in terms of latency and reliability of the application supported by a cell also affects the exact locations where data should be stored and replicated. For example, in time-critical applications, the lower the data access latency bound is, the closer to the destination the data should be replicated. Figure 5: Local Manager (LM)–Orchestrator interaction at different tiers of the management architecture.
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 2 Materials and Methods
### Heart Rate Extraction
#### 2.4.2 Remote PPG Signal Extraction  
The remote PPG signal was then extracted from the video frames using the detected face ROIs. First, for the NIR camera, the remote PPG signal was simply calculated as the spatial mean of the pixels inside the face ROI over time. Here, we must consider that there is spatial variation in the phase of the remote PPG signal across the skin [23], which could affect the spatial mean. Here, we used a Fourier-spectrum-based heart rate extraction (see Section 2.4.3), which limits the effects of the phase information. Additionally, [PERSON] et al. [23] note that local skin pixels can have counter-phase PPG signals, which would be problematic when averaging. In our work, the area of skin a single pixel covers is much larger, which makes it unlikely that we can observe or mitigate these effects. For the RGB camera, the model-based Plane-Orthogonal-to-Skin (POS) method [5] was used to extract the remote PPG signal from the R, G, and B color channels in the face ROI.  
Regularly, the POS method uses the spatial means of the R, G, and B channels inside the face ROI to extract the remote PPG signal. However, in the hospital environment where lighting is less controlled, overexposure of pixels could occur, which distorts the pulsatile changes in color due to blood flow. To limit the influence of overexposed pixels on the channel means, we proposed assigning weights to each pixel based on the frequency of overexposure in the last 0.5 s (16 frames). The weight of each pixel is calculated as \(1-\frac{n}{16}\), where \(n\) is the number of times that pixel was overexposed in the last 16 frames. The weights were updated for each new frame and then used to calculate the weighted spatial means of the R,G, and B channels.
# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
## 5 System Evaluation  
We used our simulation tool for two objectives: verification of the analytic model expressed in Section 3, and evaluation of the formation control algorithm proposed in Section 4. In this section, we address the simulation design of the multi-UAV-based network infrastructure. We implemented the simulation using Python language and ns-3 library. Figure 5 overviews the simulation design and the ns-3 internal functions used, and the following subsections describe the design of the simulation in detail.  
Figure 5: Simulation diagram of the multi-UAV based network infrastructure. GCS: Ground Control Station.
# Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT-2 SAR Images and CNNs
## 4 Methodology
### Incorporation of Bathymetry  
Ice forms as lakes begin to cool down. Air temperature, wind speed, lake size, and lake depth drive processes that lead to ice growth [37]. Lake surface area is a variable that relates the time to reach isothermal conditions as the surface heat flux is directly related to the lake surface area. Lake depth also contributes to cooling and freezing time as the deeper a lake is the longer it generally takes to cool [38]. Given the links between freeze-up and bathymetry, we experimented adding bathymetry as an auxiliary data source to boost the CNN performance. To avoid learning unphysical correlations that could be enforced if detailed bathymetry information is included, the bathymetry is classified into different levels. Zonal bathymetry is created based on classification thresholds proposed by [PERSON] et al. [39]. Considering the depth thresholds that describe each zone, instead of using bathymetry values, we use assigned values of 1 to 4 for Lake Erie and 1 to 5 for Lake Ontario (Figure 4). [PERSON] et al. [39] identified five bathymetric thresholds for the Great Lakes that are related to processes such as littoral energy, aquatic vegetation extent, stratification limits, and light extinction. These zones are identified as: coastal margin zone (0-3 m depth zone), shallow near-shore (3-5 m), deep near shore (5-15 m for Erie; 5-30 for Lake Ontario), shallow off-shore (15-30 m; Lake Erie only), deep off-shore (30-100m), and profundal off-shore (>100 m; Lake Ontario only). These bathymetry maps coincide with visual patterns we have observed with ice formation in these lakes, in that the ice normally forms first in the western basin of Lake Erie, and near the shoreline in Lake Ontario.
## 4 Results and Discussion
### Parametric Analysis
#### 4.3.5 Effect of Solar Tank Temperature  
Tables 7 and 8 present the results obtained for the variation of the solar fraction with the lower and upper temperatures of the solar tank that defines the operating of the absorption chiller with solar heat. The absorption chiller will operate with water from the solar accumulator tank when the upper temperature of the storage tank is between these limits and it is possible to completely cover the cold demand. Concerning the upper temperature, the value of 90 \({}^{\circ}\)C used in the basic design seems reasonable; a higher value of top solar tank temperature would improve the solar coverage somewhat, but it should be taken into account that the limit of 95 \({}^{\circ}\)C, imposed by the absorption chiller, cannot be exceeded. In fact, tank temperature affects, as well, the inlet temperature of the generator, since the hot water directly supplies the chiller generator. As for the lower temperature, the advantage of using values as small as possible is clear. This is because more solar heat can be used and the efficiency of the collector can be improved.
# A Comparison of Electrical Breakdown Models for Polyethylene Nanocomposites
###### Abstract  
The development of direct current high-voltage power cables requires insulating materials having excellent electrically insulation properties. Experiments show that appropriate nanodoping can improve the breakdown strength of polyethylene (PE) nanocomposites. Research indicates that traps, free volumes, and molecular displacement are key factors affecting the breakdown strength. This study comprehensively considered the space charge transport, electron energy gain, and molecular chain long-distance movement during the electrical breakdown process. In addition, we established three simulation models focusing on the electric field distortion due to space charges captured by traps, the energy gain of mobile electrons in free volumes, the free volume expansion caused by long-distance movement of molecular chains under the Coulomb force, and the energy gained by the electrons moving in the enlarged free volumes. The three simulation models considered the electrical breakdown modulated by space charges, with a maximum electric field criterion and a maximum electron energy criterion, and the electrical breakdown modulated by the molecular displacement (EBMD), with a maximum electron energy criterion. These three models were utilized to simulate the breakdown strength dependent on the nanofiller content of PE nanocomposites. The simulation results of the EBMD model coincided best with the experimental results. It was revealed that the breakdown electric field of PE nanodielectrics is improved synergistically by both the strong trapping effect of traps and the strong binding effect of molecular chains in the interfacial regions.  
polymer nanocomposites; traps; DC breakdown; energy gain; molecular motion; free volume +
Footnote †: journal: Applied Sciences
## 1 Introduction
### CNN-Based Fusion Methods  
[PERSON] et al. [19] proposed the first CNN-based IVIF method. This method uses Siamese CNN to obtain the weight map of infrared and visible light pixel activity information; uses image pyramids to fuse images at multiple scales; and uses a strategy based on local similarity to adjust the decomposition coefficients of the fusion mode automatically. Inspired by this, researchers have successively proposed a variety of CNN-based methods. [PERSON] et al. proposed an end-to-end unsupervised CNN-based model called VIF-Net [20]. This method introduced a dual-channel dense network to achieve feature extraction and realized feature fusion by directly connecting deep features. [PERSON] et al. introduced an IVIF method--RXDNFuse [21]--using an aggregated residual dense network in the literature. This network combines the network structure advantages of ResNeXt [22] and DenseNet [23], effectively avoiding the limitations of manually designed fusion rules. Considering that lighting factors are important for the effects they have on image fusion quality, [PERSON] et al. designed a light perception-based progressive image fusion network PIAFusion [24]. A light perception subnetwork is used to calculate light distribution and probability; in addition, a progressive feature extractor is developed, which contains a cross-modal differential perception fusion module and a mid-way fusion strategy to extract and fuse complementary information in multi-modal images adaptively. [PERSON] et al. proposed a semantic-aware real-time infrared and visible image fusion network, SeAFusion [25]. This method is the first framework to integrate high-level vision tasks with image fusion. By introducing a semantic loss and a gradient residual dense block, SeAFusion significantly enhances the semantic information in the fused images, thereby improving visual quality and detail preservation while substantially boosting performance in high-level vision tasks. Additionally, it achieves efficient real-time processing. [PERSON] et al. proposed an unsupervised end-to-end image fusion network, U2 Fusion [26]. This method addresses the multi-modal image fusion problem by automatically evaluating the importance of corresponding source images to determine the adaptive degree of information retention.  
Although these methods have adopted some strategies to replace the ground truth label, they have greatly improved the quality of fused images. However, due to the absence of a standard ground truth label, in the image fusion task, the potential performance of CNNs still cannot be maximally presented.
# Evaluation of Ultrasonic Bonding Strength with Optoacoustic Methods
## 5 Finite Element Analysis
### Results  
Figure 20 illustrates the relation between the bonded area and the resonant frequency at each vibration mode obtained by FEA. The results show that the resonant frequency goes up as the bonded area increases. The relation between them is exponential. The bonded area increased as the resonant frequency increased, similar to the experimental results indicated in Figures 12 and 17. This fact supports the claim that the resonant frequency of the bonded part increases with the bonded area. The vibration modes and the resonant frequency analyzed by FEA are illustrated in Figure 21. The colors of the pattern presented in Figure 21 represent the relative magnitude of the displacement vector. The red means the larger displacement, and the blue means the smaller displacement. Figure 21a,b,e,f,h showed the bending vibration modes. The nodes of vibration were seen as a blue area. Figure 21c,d,g illustrated the torsional vibration modes. These vibration modes are consistent with the experimental result observed in the ESPI measurement, except for the eighth vibration mode. The seventh mode shown in Figure 21g coincides with the eighth mode observed in ESPI (Figure 16h). The resonant frequency obtained by FEA was plotted against the experimental result in ESPI in Figure 22. The result indicates that the FEA result is correlated with the ESPI result, and the vibration mode can be visualized using ESPI.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Density (Kg/mm\({}^{3}\))** & **Young’s Modulus (GPa)** & **Poison’s Ration** \\ \hline A6061-T6 & 2.7 \(\times\) 10\({}^{-6}\) & 68.9 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of A6061-T6.  
Figure 19: Enlargement of bonded area.  
Figure 21: Composite displacements in the x, y, z directions. (**a**) First mode: 118.03 Hz, (**b**) Second mode: 701.60 Hz, (**c**) Third mode: 1426.74 Hz, (**d**) Fourth mode: 1659.86 Hz, (**e**) Fifth mode: 1994.54 Hz, (**f**) Sixth mode: 4002.46 Hz, (**g**) Seventh mode: 4019.39 Hz, (**h**) Eighth mode: 6747.37 Hz.  
Figure 20: Relation between first resonant frequency and bonded area.  
Figure 22: Resonance detected by ESPI vs. resonance by FEA.
# Double-Consensus Based Distributed Optimal Energy Management for Multiple Energy Hubs
## 4 Application Examples
### Comparison with Centralized Algorithm  
This case study focuses on verifying the effectiveness of the proposed DDC algorithm by comparing it to the centralized algorithm [30]. The power and heat load demands for each power and heat load are initialized at 150 kW and 140 kW, respectively. By running the DDC algorithm, the estimated electricity and heat Lagrangian multipliers, mismatches and energy inputs are shown in Figure 4. After the algorithm converges, it can be observed that the electricity multipliers converge to a common value, \(\lambda_{p}~{}=~{}27.0354\) cents/kWh, and the heat multipliers converge to another value, \(\lambda_{h}~{}=~{}19.3652\) cents/kWh; meanwhile, the electricity and heat mismatches converge to zero, which means that the power and heat supply-demand equality constraints are satisfied. In addition, the final electricity and gas inputs of each hub are within their corresponding inequality constraints, as shown in Table 2. Therefore, the optimization goal has been fulfilled. Furthermore, the performance of the proposed DDC algorithm is compared with a centralized algorithm. The simulation results provided by the two approaches are shown in Table 2. It can be seen that all of the solutions are very similar, which also verifies the effectiveness of the proposed algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**DDC Algorithm**} & \multicolumn{2}{c}{**Centralized Algorithm**} \\ \cline{2-5}  & \(\mathbf{E^{\mathbf{c}}}\) & \(\mathbf{E^{\mathbf{s}}}\) & \(\mathbf{E^{\mathbf{c}}}\) & \(\mathbf{E^{\mathbf{s}}}\) \\ \hline Hub1 & 62.6477 & 168.2670 & 62.7895 & 168.2715 \\ \hline Hub2 & 87.7216 & 233.4601 & 87.7325 & 233.4569 \\ \hline Hub3 & 80.7525 & 135.3102 & 80.7861 & 135.3133 \\ \hline Hub4 & 135.3545 & 159.9385 & 135.3687 & 159.9256 \\ \hline Hub5 & 59.7517 & 326.7947 & 59.7336 & 326.8010 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with centralized algorithm.  
Figure 4: Comparison with centralized algorithm: (**a**) the electricity mismatch; (**b**) the heat mismatch; (**c**) the electricity multiplier; (**d**) the heat multiplier; (**e**) electricity input; (**f**) gas input.
## 2 Materials and Methods
### MP-SAWS Modeling  
The study of MP-SAWS involves multiple complex physical fields and unique structures, making the calculation of the associated physical properties complex. For accurate and detailed modeling and simulation work, finite element simulation is a commonly used method. During the finite element simulation process, the program divides the physical model into numerous refined grids and converts continuous solids into discrete matrix forms for efficient computer processing while maintaining a high degree of accuracy. This study utilized COMSOL Multiphysics 5.6 finite element analysis software for simulation work. Through modular modeling, the operational modes of SAWS can be analyzed step by step.  
Figure 2a illustrates the complete sensor device with a single period extracted from the SAWS. The sensor utilizes a two-channel structure with two sets of SAW resonators to obtain sensing data. Performing post-calibration can enhance accuracy and eliminate spatial residuals. By selecting parallel directions of the IDT, the propagation of SAW can be simplified and considered a 2D model. Figure 2b displays the parameters of the 2D finite element model. The upper layer consists of aluminum electrodes, while the lower layer is a piezoelectric material composed of a 128\({}^{\circ}\) Y-X LiNbO\({}_{3}\) substrate. \(\lambda\) represents the wavelength of resonance, a represents the electrode width, b represents the distance between adjacent electrodes, D represents the unit of one period, P represents the half-wavelength, h\({}_{AL}\) represents IDT's thickness, and h\({}_{LN}\) represents the LiNbO\({}_{3}\) substrate's thickness. All parameters are indicated in Figure 2b.  
Figure 3a represents the simulation model of the MP-SAWS in COMSOL, designed with 64 periods. The vibration mode during resonance is presented in Figure 3b. Figure 3c displays  
Figure 2: The model and material of MP-SAWS. (**a**) Choosing the periodic unit of MP-SAWS for modeling. (**b**) A 2D finite element simulation diagram and parameter definition.
###### Abstract  
When creating a simulation model to assess the performance of buildings, there is usually a lack of feedback information. Only in the case of measurements of a real building is a direct comparison of the measured values and simulated results possible. Parameter data related to users' behavior or other events can also be obtained. Their evaluated frequency, magnitude and duration, along with boundary conditions, are crucial for the results. It is clear that none of them can be predicted very accurately. Most of them, however, are needed for computer modeling. In this paper we analyzed the well-defined TRNSYS simulation model of offices equipped with radiant ceiling panels for heating and cooling. The model was based on real case offices and was validated based on measurements for 1 year. The analysis included simulations in order to define what effect the parameters related mainly to users have on the energy use and the indoor air temperatures. The study confirmed that specific human activities influence the annual energy use to a relatively small degree and that their effects often counteract. It also confirmed the even more important fact that although small, these activities can influence the thermal comfort of users. It is believed that despite the fact that this research was based on an analysis of offices equipped with radiant ceiling panels, most of the results could be applied generally.  
measurements; modeling; simulation; validation; analysis; energy use; temperature +
Footnote †: journal: Applied Sciences